Payment method 1: salary

Salaried providers receive a fixed revenue for working over a given time period. This payment method is typically used to pay providers who are employees of a PCO. Payers may find the fixed salary payment scheme attractive for several reasons. One is that a fixed salary ensures budget security for the payer because it shifts financial risk to providers. Another reason is that the payment method is easy to administer as it only requires information about the employed providers. A fixed salary, however, also has some unattractive features. As providers are not paid for their activities, they are (in the short run) not financially incentivized to exert more than the minimum required effort to avert being laid off. Generally, PCPs may provide less effort by, for example, avoiding costly patients, reducing the frequency of services, or referring patients to other providers. In the longer run, the prospect of receiving a promotion and pay rise as well as the fear of being laid off may financially incentivize them to increase effort beyond the minimum required level. The focus for the provider then becomes the criteria used by employers to enable progression up the career ladder. These criteria can create career incentives that are related to objectively or subjectively assessed performance (Prendergast, Reference Prendergast1999).

Payment method 2: capitation

Providers paid by capitation receive a fixed payment per enlisted patient for a given time period. The payment is independent of the amount of provided care. However, it may be risk adjusted such that patients with characteristics that indicate they are more costly to treat are linked to a higher payment. As the size of the served patient population is typically stable due to patient enrolment with a PCO, this payment method gives payers budget security by shifting financial risk to providers. Capitation payments financially incentivize providers to attract and keep patients on their lists when facing competition, thereby potentially increasing access to care and also improving those aspects of quality that patients can observe. However, if competition for patients is low, providers have less of a financial incentive to exert effort above the minimum requirement. An exception to this lack of financial incentive may be preventive care that reduces providers’ required effort in the longer run.

As with any fixed payment, capitation payments incentivize the providers to minimize their costs of providing care, which may lead them to provide less care than is necessary when quality is difficult to observe (Hennig-Schmidt, Selten & Wiesen, Reference Hennig-Schmidt, Selten and Wiesen2011; Brosig-Koch et al., Reference Brosig-Koch2016; Oxholm et al., Reference Oxholm2019). Capitation may also lead providers to only enlist patients whom they expect to be less costly to treat compared to the capitation payment itself (“cream-skimming” behaviour). This behaviour may increase inequality in patients’ access to care. Such selection issues may be alleviated by regulating patients’ access to providers’ lists or by risk-adjusting the payments based on patients’ expected treatment costs. Risk adjustment shifts some of the financial risk away from the provider to the payer and thereby weakens the providers’ incentives to reduce costs by exerting cream-skimming behaviour. The risk adjustment may also send a signal to providers about patient prioritization (Oxholm et al., Reference Oxholm2019), but there is no direct financial incentive to prioritize already enlisted high-need patients.

Payment method 3: FFS

PCPs can receive a fee for each service they provide. This payment method requires providers to report and bill for the number of activities (e.g. consultations, visits, procedures, episodes). The advantage of FFS over salary and capitation is that providers have a financial incentive to deliver care themselves rather than referring patients to other providers (Hennig-Schmidt, Selten & Wiesen, Reference Hennig-Schmidt, Selten and Wiesen2011; Krasnik et al., Reference Krasnik1990; Brosig-Koch et al., Reference Brosig-Koch2016). A disadvantage of FFS is that providers may be reluctant to provide services that are not covered by the fee schedule, unless some services are bundled together. Another concern for the payer is that they bear most of the financial risks, especially when fees are unregulated. Consequently, FFS increases the risk of overtreatment and supplier-induced demand (Di Guida, Gyrd-Hansen & Oxholm, Reference Oxholm2019; Longden, Hall & Van Gool, Reference Longden, Hall and Van Gool2018), gaming of the system and budget deficits for payers. To minimize these risks some payers may choose to closely monitor service patterns through utilization reviews or to set caps on the payments to providers through controls on fees that can be charged (Wickizer & Lessler, Reference Wickizer and Lessler2002). The fees may be set by either the payers or the PCPs themselves or through negotiations. In cases where PCPs set the fees, they may choose to price discriminate, i.e. charge payers different fees based on patient characteristics. In cases where the payers are the patients themselves, the fee is an out-of-pocket (OOP) cost, and higher fees may reduce demand and thereby have implications for equity in access to care.

Payment method 4: P4P

P4P has become a popular way of remunerating PCPs (Cashin et al., Reference Cashin2014). This payment method links payments directly to performance measures. The performance measures may be based either on completion of processes (e.g. recording blood sugar levels) or on achieving specific intermediate health outcomes for patients (e.g. whether blood sugar levels are in accordance with clinical guidelines). The schemes require an administrative system that measures providers’ performance (which may rely on digital health infrastructure (see Box 3.1.1)). Depending on the performance measures in question, such administrative requirements may be costly. An advantage of P4P is, however, that providers receive feedback on their performance, which may improve the quality of care over and above the effect of the financial incentives (Kolstad, Reference Kolstad2013; Godager, Hennig-Schmidt & Iverson, Reference Godager, Hennig-Schmidt and Iversen2016).

P4P may be designed in different ways, which creates different incentives for the provider (as discussed later in the chapter). These design differences may explain why evidence on the impact of P4P on the provision of care is mixed. The mixed evidence, however, may also be a result of weak evaluation designs of existing studies (see, for example, reviews by Van Herck et al., Reference Van Herck2010; Jia et al., Reference Jia2021; Eijkenaar et al., Reference Eijkenaar2013; Diaconu et al., Reference Diaconu2021; Zaresani & Scott, Reference Zaresani2021; Scott, Liu & Yong, Reference Scott, Liu and Yong2018). Despite the different designs of P4P schemes, they also share several common features that make them attractive to payers. P4P schemes (as opposed to FFS schemes) incentivize providers to only deliver care defined as high quality. For example, by rewarding providers for only one blood sugar level measurement per diabetes patient per time period, as opposed to a FFS scheme rewarding providers for all such measurements independently of the number of previous measurements and the recipient. This focus on what constitutes high-quality care may also reduce heterogeneity in care across providers.

A disadvantage of P4P is that payers may find it difficult to predict providers’ performance and thereby risk facing overrun budgets. Payers may also end up paying for performance that is only weakly related to the individual provider’s efforts. Providers may also have financial incentives to treat patients who are close to meeting or have already met the performance requirements (cream-skimming behaviour), thus minimizing provider effort (Gravelle, Sutton & Ma, Reference Gravelle, Sutton and Ma2010). P4P may thereby increase inequality in patients’ access to care in some cases (Oxholm, Di Guida & Gyrd-Hansen, Reference Oxholm and Gyrd-Hansen2021). By risk-adjusting the performance requirements, payers could potentially alleviate such issues. Similar to the case of FFS, multitasking issues may also arise under P4P if focus is diverted towards more profitable care (Doran et al., Reference Doran2011). Under P4P, providers also have an incentive to game the system to secure higher payments (Gravelle, Sutton & Ma, Reference Gravelle, Sutton and Ma2010; Kalk, Paul & Grabosch, Reference Kalk, Paul and Grabosch2010; Wilding et al., Reference Wilding2022). There may also be spillovers of P4P to other non-incentivized areas of care. For example, resource-constrained providers may choose to focus on incentivized treatments at the expense of non-incentivized treatments (Singh et al., Reference Singh2020), whereas non-incentivized care which is complementary to incentivized care may be positively affected by P4P.

Payment method 5: blended payments

To balance the incentives of the pure payment methods, payments are often blended in primary care (Robinson, Reference Robinson2001). Blended payments are attractive because they enable payers to both incentivize providers to deliver care (e.g. using FFS and P4P) while limiting their spending (e.g. using salary and capitation). Theoretically, it has been shown that mixed payment methods are superior to pure payment methods (Ellis & McGuire, Reference Ellis and McGuire1986). However, in practice, the complexity of these methods may impose high transaction costs and difficulties in predicting behaviour. Empirical evidence on providers’ response to blended payment methods is limited. The results of a laboratory experiment with medical students show that the introduction of a mix of FFS and capitation leads to greater health benefits from care compared to the pure payment methods (Brosig‐Koch et al., Reference Brosig‐Koch, Hehenkamp and Kokot2017). Other studies, using real-world data, show that a change in provider payments from pure capitation to a mix of capitation and FFS increases PCPs’ activity levels and reduces their referral rates (e.g. Krasnik et al., Reference Krasnik1990; Flierman & Groenewegen, Reference Flierman and Groenewegen1992). P4P schemes are also often blended onto other payment models, which accounts for the majority of funding. The blend that best achieves payers’ different goals is, however, difficult to determine in a real-world setting and is likely context dependent.

Background to policy challenge

Many policy-makers believe that a strong primary care system can help reduce the growth or level of health care expenditures of more specialized hospital care. There exists a large literature showing strong associations between the strength (e.g. number of PCPs) of primary care and patients’ health outcomes (Starfield, Shi & Macinko, Reference Starfield, Shi and Macinko2005). Some of this literature also finds associations between a strong primary care system and lower total health expenditures (Baicker & Chandra, Reference Baicker and Chandra2004; Starfield & Shi, Reference Starfield and Shi2002) and fewer inpatient and emergency hospitalizations (Chen et al., Reference Chen2010; Jacobs et al., Reference Jacobs2020; Fortney et al., Reference Fortney2005; Lippi Bruni, Mammi & Ugolini, Reference Lippi Bruni, Mammi and Ugolini2016; Whittaker et al., Reference Whittaker2016; Morciano et al., Reference Morciano2020; Dolton & Pathania, Reference Dolton and Pathania2016). However, if primary care is expanding and the number of specialists and hospitals remain the same, then total health care expenditures will increase overall at the system level (Lau et al., Reference Lau2021; Miller et al., Reference Miller1999).

PCPs can potentially play an important role in improving efficiency by acting as gatekeepers to more expensive specialized care and by providing preventive care. Primary care can also be a substitute for or complement to more specialized care (Lau et al., Reference Lau2021). Primary care can act as a direct substitute when the same tasks can be provided with equal effectiveness by PCPs and specialists (Van Hoof et al., Reference Van Hoof2019). This substitution can, for example, include general health checks, skin checks, minor surgical procedures, antenatal care, care for less severe mental health conditions, child health (in the USA paediatricians act as PCPs for children whereas GPs take on this role in other countries), and monitoring patients with chronic diseases. In cases of substitution, the policy challenge is to ensure that the same quality of care is provided by the least costly health professional, which often is the PCP.

PCPs are usually paid less (Cheng et al., 2012; Leigh et al., Reference Leigh2010) and face lower fixed costs of providing care than specialists. From the patient’s perspective, it may also be less costly to access primary care in terms of travel time and time off work to visit a PCP (or a community pharmacist (see Box 3.1.2)) closer to home than to receive more specialized care in a more distant hospital. Lower costs of accessing care are especially relevant in rural areas where the distance to health care providers is a key issue. However, lower unit costs in primary care do not always translate into lower overall health care expenditure. As expenditure is a function of both unit costs as well as the volume of care, effects on expenditure also depend on the volume of care provided, which could increase in primary care if PCPs, for example, spend more time with patients or see more patients. A systematic review comparing consultations by PCPs to other specialists found that though outcomes were similar and waiting times were lower, costs were generally higher when treating patients in primary care compared to secondary care (Van Hoof et al., Reference Van Hoof2019). Despite the possibility of higher costs of treating patients in primary care, these may be partly offset by lower volume of referrals if the number of patients treated in primary care increases. The most cost-effective setting will, however, differ depending on the health condition and context.

In cases where primary care acts as a complement to specialized care, the policy challenge is to ensure the optimal mix of and collaboration between PCPs and other specialists for each patient. This challenge is related to integrated care for patients with multimorbidities and complex conditions (see Chapter 3.5) and is also related to referrals between PCPs and specialists. More generally, complementarity is about how PCPs and other health professionals with different skill sets can work to achieve the most cost-effective care for each patient. There will be much variation in the most cost-effective mix of PCPs and other specialists across different health conditions, with patients with multimorbidities and chronic diseases more likely to require care from providers across a range of specialties. In these cases, the impact of primary care on reducing the costs of specialized care is much less obvious, though it is believed that generalists, such as PCPs, will have a more holistic view of the patient’s health and circumstances.

The role of primary care payment models in reducing avoidable secondary care

Primary care payment models influence avoidable secondary care through their effects on PCP referrals to secondary, more specialized care (Forrest, Reference Forrest2003). Gatekeeping is a system where a referral from a PCP is required before a patient can visit a specialist. If gatekeeping does not exist then patients can access specialists directly, thereby reducing the impact of primary care payment models. Patients’ direct access could result in specialists seeing patients who do not “need” specialized care, such that specialists are not using their more costly time and training to its maximum benefit. In the longer term there would then be a higher demand for specialists and a misallocation of resources between primary and secondary care, which may also lead to higher health care expenditures overall. If gatekeeping does exist, then different primary care payment models can be used to influence the probability of a referral. More specifically, payment models can provide financial incentives to provide additional care (such as under FFS and P4P) and so reduce referrals, or share the financial risk of care with secondary care providers in vertically integrated systems which have pooled primary and secondary care budgets.

Capitation payment is one payment method which may influence referrals to more specialized care. However, its effect on referrals depends crucially on whether the capitated budget is pooled (also known as a “global” budget) across primary and secondary care, or only covers primary care (“single” budget). Single budget capitation may be paid to the PCO or directly to the PCP based on the number of enrolled patients and covers only the care provided by the PCPs themselves. In this situation, PCPs have an incentive to minimize their own treatment costs, which can be achieved by increasing referrals (Iversen & Lurås, Reference Iversen and Lurås2000). They may also minimize their own costs by choosing to spend less time with each patient and be less focused on coordination of care compared to under FFS and P4P payments. These financial incentives are similar to those present under salaried payment (see Table 3.1.1).

Empirical studies comparing single budget capitation payment to other payment models such as FFS and P4P have confirmed these hypotheses. With FFS payments, PCPs are paid on the basis of volume. PCPs therefore have a financial incentive to do more themselves, such as more follow-up visits. Referrals to specialists may therefore be delayed and be less likely under FFS compared to under (single budget) capitation or salary. Krasnik and colleagues (Reference Krasnik1990) found in Denmark that introducing FFS on top of single budget capitation substantially reduced GPs’ referrals to specialists and hospitals, while these GPs increased the number of face-to-face consultations and the number of diagnostic services undertaken themselves. The net effect on costs and health outcomes was not examined. Flierman and Groenewegen (Reference Flierman and Groenewegen1992) also reported increased activity under FFS mixed with capitation compared to capitation alone. The introduction of capitation in addition to FFS in Norway resulted in more referrals (Iversen & Lurås, Reference Iversen and Lurås2000), but in Canada there was no impact on hospital utilization when physicians switched from FFS to capitation payments plus an incentive to reduce hospitalization (Hutchison et al., Reference Hutchison1996).

Similar to FFS, P4P may encourage PCPs to provide more care themselves than under a fixed payment scheme such as capitation and salary. This increased activity may reduce referrals to hospital. Several studies have examined the spillover effects of P4P on secondary care in the context of the Quality and Outcomes Framework (QOF) (see Box 3.1.3), which is a large-scale P4P scheme added to an existing capitation payment model for PCPs in the United Kingdom (Doran et al., Reference Doran2006). Grigoroglou and colleagues (Reference Grigoroglou2020) found a very small reduction in avoidable hospitalizations for incentivized health conditions, while Harrison and colleagues (Reference Harrison2014) found a larger reduction in avoidable hospitalizations. Gunn et al. (Reference Gunn2021) found a substantial reduction in all-cause, diabetes and cardiovascular disease hospital admissions and emergency admissions for patients with diabetes if QOF targets were attained.

For some treatments, however, the Framework led to an increase in referrals. Gutacker and colleagues (Reference Gutacker2015) found that it led to an increase in psychiatric hospital admissions for people with serious mental illness. This finding suggests that in some circumstances P4P may lead to the identification of unmet need or incentivize more appropriate care. P4P can also directly reward more referrals if this is thought to improve health outcomes (Mcmanus et al., Reference McManus2021). A local P4P scheme in the United Kingdom found that emergency hospital admissions fell when GPs were directly rewarded for reducing them, but health outcomes were not examined (Khedmati Morasae et al., Reference Khedmati Morasae2021).

Even though there may be exceptions as described above, capitation under a single budget is generally found to increase the referral rate compared to FFS and P4P schemes. This financial incentive to refer patients under a single budget capitation-based system may be strengthened further if there is competition for patients. Iversen and Ma (Reference Iversen and Ma2011) show that incentives to refer under capitation payments are higher for PCPs facing more competition, because they may be more likely to respond to patients’ requests to be referred to prevent the patient from moving to another PCP.

The second main form of capitation is global capitation payment, which is where there is a “global” or “pooled” budget that includes total spending on primary and secondary care (and potentially other services). This budget may be held by a practice or a medical group or within a vertically integrated insurance system. Global capitation payments thereby provide a total health care budget based on the number of patients in the population or enrolled with an insurer, which is typically risk adjusted to account for patient complexity. An example is GP Fundholding in the United Kingdom or the use of capitated budgets in Health Maintenance Organizations and Accountable Care Organizations (ACOs) in the USA. In these cases, as under the first type of capitation payment (single budget), there is a financial incentive to minimize costs as providers can keep the savings. But in cases of a global (pooled) budget, this cost minimization includes keeping the savings that would have otherwise been used to pay for hospital care, which is why this payment method is also known as a shared-savings model. Several studies have shown that referrals by PCPs are lower with this type of global capitation payment (Davidson et al., Reference Davidson1992; Lurie et al., Reference Lurie1992; Dusheiko et al., Reference Dusheiko2006).

In ACOs, the global budget has also been combined with P4P. In these models, medical groups can share in any savings made from the global budget as long as they meet a minimum quality threshold (Chernew et al., Reference Chernew2011). Systematic reviews find positive evidence including reductions in spending and no deterioration of outcomes (Cattel & Eijkenaar, Reference Cattel and Eijkenaar2020; Kaufman et al., Reference Kaufman2017). Global budgets may also involve pooling of budgets across different sectors of care. For example, this approach has been tried for health and social care budgets in the United Kingdom, though it was found to have no impact on the utilization of hospitals, but there was some evidence of increased utilization for the most complex patients (Stokes et al., Reference Stokes2019). In this way global budgets that merge primary, secondary care, and social care budgets aim to help support integrated care across different settings (see Chapter 3.5).

A derivative of capitation payments are bundled payments. Bundled payments have been discussed as a way to incentivize integrated care across different sectors and settings for specific groups of patients defined by disease or diagnosis (Tsiachristas et al., Reference Tsiachristas2013; Stokes et al., Reference Stokes2018). Instead of the fixed capitation payment covering all care for each enrolled patient, fixed bundled payments are disease specific. They are designed as a single payment for all care required by a patient in the same episode or multiple services provided across different providers or sectors. Often, multiple FFS payments across a hospital episode may be “bundled” into a single payment (e.g. a diagnosis-related group). Alternatively, a single payment may be used to cover the care of patients in hospital and after they are discharged if they have a chronic disease. Similar to capitation payment, bundled payments provide incentives to reduce costs through providers being able to retain savings.

In the USA’s Medicare, bundled payments have been aimed at hospitals which become responsible for the care of patients up to 90 days after discharge. Though the role of primary care in these schemes is unclear, these schemes have some effects on reducing Medicare spending (Joynt Maddox et al., Reference Joynt Maddox2018; Barnett et al., Reference Barnett2019). In the Netherlands, bundled payments in primary care increased total health care expenditures, especially for those with multimorbidities. Medication and spending on medical specialists increased, while spending on primary care did not. The Dutch PCPs may have increased referrals because they were unable to keep savings from the reduction in use of specialists or because unmet needs were identified. In this context the “bundle” may have been too narrow by only including primary care, such that PCPs were incentivized to reduce only their own costs (Karimi et al., Reference Karimi2021; Stokes et al., Reference Stokes2018).

Until now, our focus has been on whether capitation (single or pooled budgets) may reduce avoidable hospitalizations, also in comparison to other payment models such as P4P and FFS. Adding P4P to FFS may also impact patients’ use of the secondary care sector. The effect on referrals depends on the precise performance measure used in P4P. If for a specific condition referral is a part of improving health outcomes, then PCPs’ referrals to specialists may increase. Several studies have examined the impact of adding other types of P4P to FFS. For example, Chen and colleagues (Reference Chen2017) found no impact of P4P on hospitalizations for hepatitis in Taiwan, where this payment was added onto FFS payment. In Canada, Lavergne and colleagues (Reference Lavergne2018) examined the introduction of an annual payment for guideline-based care plans on top of existing FFS for patients with diabetes, hypertension or chronic obstructive pulmonary disease (COPD). There were no changes in total hospitalizations, though for hypertension hospitalizations for stroke and acute myocardial infarction fell. The addition of P4P to existing FFS payment was also examined in the USA by Rosenthal et al. (Reference Rosenthal2016). They found that inpatient admissions fell in two out of the three examined states. Chen and colleagues (Reference Chen2010) found that after three years of P4P added to FFS payment, patients with diabetes were less likely to be hospitalized. There also exists a specific form of P4P directly incentivizing better coordination of care, called pay for coordination (P4C) (Stokes et al., Reference Stokes2018; Tsiachristas et al., Reference Tsiachristas2013). Under P4C additional payments are made to improve integration with other providers through information sharing. Less evidence exists on the effect of these types of payments on referrals and the use of secondary care.

In summary, there is evidence that primary care payment models can provide financial incentives that influence referrals to more specialized care depending on payment design. Single budget capitation models that only cover PCPs’ care can increase referrals. Generally, FFS and P4P encourage PCPs to undertake more care themselves and so are likely to reduce the use of secondary care compared to other forms of payment, though for P4P this depends on whether referrals are appropriate and lead to improved health outcomes. Global budget capitation payments that pool primary and secondary care funding can also reduce hospitalizations and encourage integration of care across settings. Though we have not examined the quality of the above evidence, many studies across a range of settings and countries had consistent findings. We could find little evidence, however, on how increases or reductions in referrals influence health outcomes and so this question remains an important avenue for further research.

Background to the policy challenge

A recent focus on quality of care and avoiding low-value care has increased the interest in ensuring that primary care payment models support the provision of high-value health care. In most health care systems, there is a difference between the actual care delivered by PCPs and the care recommended in evidence-based clinical guidelines. This difference is particularly present in LMICs (Das & Hammer, Reference Das2014). If patients do not receive high-quality primary care, it may reduce their quality of life and increase their need for more acute and specialized treatment, which is expensive for payers. As many PCPs face an ageing patient population with more complex care needs, following clinical guidelines is becoming even more important. A key policy challenge is therefore to improve the quality of primary care. This challenge is a part of a larger policy agenda on creating value-based health care systems (Smith et al., Reference Smith2021; Porter, Reference Porter2010). Policy-makers may use different interventions to try to improve the quality of primary care. One of these interventions is paying PCPs for their performance.

How can primary care payment models be used to improve quality?

The key payment method used to improve quality of care is P4P. Such schemes are characterized by linking PCPs’ payments directly to their attainment of quality standards. Even though P4P schemes are based on a quite simple concept, they can be designed and implemented in many different ways and settings and can become complex. These design and implementation choices require careful consideration as they may affect PCPs’ provision of care (Mehrotra, Sorbero & Damberg, Reference Mehrotra, Sorbero and Damberg2010; Eijkenaar, Reference Eijkenaar2013). In the following text, we highlight some of the key features to consider for P4P schemes that aim to improve the quality of primary care.

One key feature is the chosen performance indicators. The indicators should ideally reflect patients’ outcomes from care. However, patients’ outcomes may be difficult to measure and depend on factors unrelated to PCPs’ care. Consequently, the schemes instead often include structure and process indicators (Donabedian, Reference Donabedian1988). An example of a structure indicator could be “whether PCPs have a register of their patients’ smoking status”, whereas an example of a process indicator could be “whether patients with COPD were given a spirometry test”. The schemes may also include indicators related to changes in clinical measures that are more closely related to health outcomes, such as the reduction of blood pressure or blood sugar for diabetes. The different types of indicators to a different degree affect PCPs’ ability to control their own performance, potentially affecting their response to the payment scheme (Oxholm, Kristensen & Sutton, Reference Oxholm, Kristensen and Sutton2018).

As all aspects of quality of care cannot be captured by a single performance indicator, payers therefore need to consider the number of indicators to incentivize. On one hand, incentivizing only a few indicators could introduce multitasking issues and myopia because providers may choose to focus only on certain aspects of care (Eggleston, Reference Eggleston2005; Holmstrom & Milgrom, Reference Holmstrom and Milgrom1991). On the other hand, an increase in the number of incentivized indicators may increase the administrative burden and the complexity of the scheme (e.g. the United Kingdom QOF initially listed 146 performance indicators (see Box 3.1.3)). PCPs who operate in small PCOs may find the administrative burden of P4P schemes, including information technology infrastructure, particularly challenging (see Box 3.1.1). Therefore, P4P schemes should strike a balance between these two conflicting concerns.

Providers’ responses to P4P may depend on the basis for their reward – more specifically, the performance measure and the payment function that trigger the payment (Eijkenaar, Reference Eijkenaar2013). The performance measure may be based on their absolute performance (one time point), their improved performance (two time points), or their relative performance (comparison to other providers at one point in time or over two time points). An example of an absolute performance measure could be “the share of patients with COPD who received a spirometry test in a given year”, whereas an improvement in performance could be “the change in the share of patients with COPD who received a spirometry test in a given year compared to one year ago”. An example of a relative performance could be “the share of patients with COPD who received a spirometry test in a given year compared to the share provided across all PCPs”.

Each of the different types of performance measure comes with both advantages and disadvantages from the payer’s perspective. While rewards based on absolute performance may be perceived as transparent, this approach rewards previously achieved performance. In contrast, payments based on improvements reward only performance that has not previously been achieved. The issue with the measure of improvement is that it gives PCPs a financial incentive to lower performance in one year to be able to improve performance in the subsequent year. Paying based on relative performance (such as tournament-based payments) ensures that the reward adapts to general time trends in performance across PCPs. PCPs may, however, then be rewarded without having changed their behaviour, simply by performing better than others. The relative reward may therefore discourage collaborations between PCPs, unless it is paid at a “team” level such as a medical group.

The payment function determines the level of performance that triggers a reward. The function is often linear (flat rate) or non-linear based on thresholds. Flat-rate payments reward PCPs the same amount for each unit increase in their performance measure. For example, PCPs are rewarded for “each COPD patient that has received a spirometry test in a given year”. This payment function is simple and incentivizes continuous increases in the performance measure. However, it does not provide a clear overall performance target for the PCPs to reach. An alternative is to use threshold-based payment functions, which are characterized by lower or upper limits on the performance payments. One example is target-based payments, where a payment is made only if a certain performance target is met. For example, PCPs are only rewarded if “90% of patients with COPD receive a spirometry test in a given year”. Target-based payment schemes set a clear goal for the PCPs to reach, but only focus on the top end of the performance distribution and may thereby fail to incentivize PCPs who either cannot reach the target or have already reached it. Setting multiple targets may solve this issue by setting obtainable goals for those at the lower end of the performance distribution (Eijkenaar, Reference Eijkenaar2013; Mehrotra, Sorbero & Damberg, Reference Mehrotra, Sorbero and Damberg2010).

The basis of reward may also affect patients’ access to care. Providers may choose to prioritize patients where the costs of triggering a performance payment is low, to the detriment of patients where the cost of improving performance are higher, such as those with more complex conditions (Oxholm, Di Guida & Gyrd-Hansen, Reference Oxholm and Gyrd-Hansen2021). A solution to this unintended consequence of P4P could be to risk-adjust the performance requirements, similar to capitation payment. Another factor that may affect patients’ access to care is whether P4P affects patients’ OOP spending. In many LMICs patients pay OOP for their care. P4P may influence these payments if, for example, providers are incentivized to stimulate patients’ service use to improve their performance (Anselmi, Binyaruka & Borghi, Reference Anselmi, Binyaruka and Borghi2017). In such cases, P4P may make care more accessible to lower socioeconomic patient groups.

The size of the payments may also affect PCPs’ responses to the scheme (Eijkenaar, Reference Eijkenaar2013; Mehrotra, Sorbero & Damberg, Reference Mehrotra, Sorbero and Damberg2010; Zaresani & Scott, Reference Zaresani2021). The payment should be sufficient to cover the costs of changing behaviour, but not of a magnitude that creates budget deficits or removes focus from non-incentivized tasks. The costs of achieving a certain level of performance differ across indicators, PCOs and populations, which must be taken into account when setting payment rates.

The salience of the payment also influences the effectiveness of the scheme. A separate bonus may have more of an impact than if the same payment were delivered as part of usual revenues. The time lag between PCPs’ effort and payment may also affect their response to the scheme, with long lags being demotivating for PCPs (e.g. McMahon, Muula & De Allegri, Reference McMahon, Muula and De Allegri2018). Such delays may in some cases be due to auditing of the performance data. Auditing is an important task for reducing the risk of gaming under P4P, which has been found to be present in both HICs and LMICs (e.g. Gravelle, Sutton & Ma, Reference Gravelle, Sutton and Ma2010; Kalk, Paul & Grabosch, Reference Kalk, Paul and Grabosch2010; Wilding et al., Reference Wilding2022). The payers should therefore try to strike a balance between reducing payment delays and ensuring data accuracy.

Payments made to the medical group rather than the individual PCP may decrease the saliency of the scheme as the medical group may decide not to use the payment to increase the personal income of PCPs but for some other purpose. Earmarking the reward for specific quality-enhancing initiatives instead of to PCPs’ personal income could, however, also put focus on continuous quality improvement. In cases where the reward is given as personal income to the PCPs, the payers may choose to allocate payments in a way that reduces the strength of the incentives, such as sharing the payments equally rather than according to relative performance. This approach incentivizes collaboration among PCPs, but may also create so-called free-riding problems (Prendergast, Reference Prendergast1999), where some PCPs receiving the bonus do not contribute their fair share to the joint achievement.

The saliency of the scheme is also affected by feedback to PCPs on their performance. Some electronic health record systems allow for real-time feedback to PCPs or may automatically prompt PCPs to undertake certain actions based on clinical guidelines, which may improve the quality of their care beyond the financial incentives. The feedback on performance may also be made publicly available. As reputation among colleagues and patients may be important to the PCPs, public access to this information could potentially incentivize PCPs to improve their performance further (Prang et al., Reference Prang2021).

Evidence of the consequences of P4P for primary care

The majority of the many literature reviews on the effectiveness of P4P schemes in health care conclude that the effect of P4P on quality of care is mixed and limited (see, for example, the reviews by Van Herck et al., Reference Van Herck2010; Eijkenaar et al., Reference Eijkenaar2013; Scott et al., Reference Scott2011). This conclusion also holds when only considering schemes implemented in LMICs (Diaconu et al., Reference Diaconu2021; Singh et al., Reference Singh2020). Whether care is provided in the primary or secondary health care sector may, however, affect the effectiveness of P4P. Some reviews state that the effect of P4P on the quality of care tend to be more positive when targeted at primary care rather than hospitals (Eijkenaar et al., Reference Eijkenaar2013). A recent systematic review of studies based only on outpatient health care finds that P4P slightly improves the quality of incentivized care (Jia et al., Reference Jia2021).

When assessing the consequences of P4P for primary care, most evidence has come from one of the most comprehensive schemes, the United Kingdom’s QOF (see Box 3.1.3). Studies conclude that in the first years of the QOF the performance on several incentivized indicators; for example, related to diabetes and asthma, improved at a faster rate than before the scheme (Gillam, Siriwardena & Steel, Reference Gillam and Siriwardena2012; Roland & Guthrie, Reference Roland and Guthrie2016; Campbell et al., Reference Campbell2009). In fact, many practices received maximum performance payment in the first year of the scheme (Roland & Guthrie, Reference Roland and Guthrie2016). In the longer run the GPs’ performance on the incentivized indicators generally remained at a high level but without further improvements (Guthrie & Tang, Reference Guthrie and Tang2016). Studies also find indications of the scheme leading to relative reductions in some emergency hospital admissions (Harrison et al., Reference Harrison2014), but no effects on mortality rates (Ryan et al., Reference Ryan2016; Kontopantelis et al., Reference Kontopantelis2015).

P4P’s moderate impact on quality of care could be explained by poor evaluation designs, but also by the schemes being designed and implemented in many different ways and settings. For example, small sizes of payments have been highlighted as an explanation for providers’ lack of response. However, a recent meta-regression analysis finds only weak evidence of a positive association between payment size and the effectiveness of P4P schemes (Zaresani & Scott, Reference Zaresani2021). In fact, the analysis finds only a few design features to be associated with the effectiveness of P4P. This conclusion should, however, be interpreted with caution as P4P studies often lack a detailed description of the design features of the interventions. More evidence is therefore needed on the impact of different designs of P4P on the quality of primary care.

P4P may not only impact quality of care but also access to care. The evidence on how P4P affects access to care is limited. Access issues are especially present in LMICs (Diaconu et al., Reference Diaconu2021). Some studies based on LMICs suggest that P4P may improve access to care (Singh et al., Reference Singh2020). This finding may partly be explained by providers doing more outreach work under P4P. For example, a study evaluating a P4P scheme in Rwanda suggests that community outreach may play a role in improving quality of child and maternal care services under P4P (Basinga et al., Reference Basinga2011). Several studies based on LMICs also mention the importance of availability of skilled personnel, drugs and equipment to improve patients’ access to care under P4P (Singh et al., Reference Singh2020). P4P may, however, in itself also have a positive effect on the availability of these resources and thereby improve patients’ access to care. Another factor that may affect patients’ access to care is OOP payments. A recent review of P4P based on LMICs, however, finds no clear changes in user fees under P4P (Diaconu et al., Reference Diaconu2021).

The payer–hospital relationship

Health systems differ widely in their number of payers and hospitals. At one extreme, in a health system in which everybody pays the full cost of care themselves, there are as many payers as there are patients. At the other extreme, patients pay nothing out of pocket (OOP) but instead a single payer, usually a government body, takes responsibility for paying for care. Between these extremes, other health systems feature multiple payers, such as insurance companies, sickness funds or regional health authorities. On the hospital side, some small countries have only a single hospital; in other countries there are many hospitals, sometimes located quite close to each other and, perhaps, in competition with one another. In due course, we shall argue that the number of payers, in particular, and, to a lesser extent, the number of hospitals, are key factors in the choice of payment method.

The choice of payment method is also determined by what payers and hospitals wish to achieve and the system context. To help shape the discussion that follows, we start with a stylized health system with a representative payer and a representative hospital, each with their own set of interests and objectives. We use a principal–agent set-up to describe their relationship, in which the principal (here, the payer) uses a contract (the payment method) to encourage the agent (the hospital) to work in the principal’s interest.

This principal–agent relationship is likely to be imperfect, for two main reasons. First, even if the payer and hospital share some common objectives, they will not coincide exactly. The payer and hospital may attach different weights to those objectives that they have in common, and there might be some objectives that they do not share. The payer uses the contract to better align the interests of the hospital with its own objectives, offering the hospital some form of reward (usually financial) if the hospital does what the payer wants.

Second, the hospital will enjoy informational advantages over the payer: the skills of the payer (priority-setting, pricing, benchmarking) are normally quite different from those of the hospital (operational management and service delivery). As such, the hospital will normally be better placed than the payer to know which combination of inputs (skills, technology) is most likely to lead to the best outcome for an individual patient, and the average and marginal cost to the hospital of treating the patient in this way. But the hospital might exploit informational advantages to further its own interests, both during contract negotiation and in contract delivery (Mühlbacher, Amelung & Juhnke, Reference Mühlbacher, Amelung and Juhnke2018; Jiang, Pang & Savin, Reference Jiang, Pang and Savin2020). During negotiations, the hospital might be able to secure a more generous contract by quoting a higher price than if both parties were equally informed about precisely what work was involved and the marginal cost of that work. If the hospital exploits this advantage, it would mean the payer ends up paying more than it needs to get the job done. Alternatively, or additionally, the hospital might fail to fulfil its contractual obligations in full, perhaps by not delivering contracted services to a professional standard, or perhaps, discharging patients before they have recovered sufficiently to be able to cope at home. To guard against such possibilities, the payer might include specific incentives in payment method design, notably to safeguard quality, as we shall discuss later, and may need to monitor delivery and threaten sanctions if delivery is not to the desired standard.

Payer objectives: how they affect payment model design

From the payer´s perspective, there are four key issues to consider when designing how to pay hospitals: the number and type of patients to pay for, the quality of care, how much to pay hospitals, and how much effort the payer has to put into managing the payment method(s).

Sufficient income to cover costs

The main reason that the interests of payer and hospital differ is that provision of health care is not costless. The hospital has to employ a variety of labour and capital resources in order to deliver care, incurring various costs in the process. Consequently, the hospital requires a stream of revenue to ensure that it can function. The hospital will be concerned that the money received from the payer is at least sufficient to cover these costs. The more that the hospital is driven to make its income from the payer exceed its costs – be that by increasing the former or by reducing the latter – the greater the weight it will place on this element in its objective function.

A manageable model

It also takes effort to manage the contract with the payer – directly (in terms of the hospital’s administrative burden) and indirectly (in terms of how stretching the contract’s terms are on the hospital’s staff). Like the payer, the hospital might prefer a simpler payment model, with minimal reporting requirements. This preference will be driven partly by a desire to reduce its administrative burden, but a simpler model might also provide greater scope for the hospital to increase the surplus of its revenue over its costs, by exploiting its information advantages. For this reason, the payer might not choose to adopt the simplest payment model.

Payment methods for hospitals: four key types

Hospital payment methods can be categorized into four archetypal groups. In this section we set out the key features of each, summarized in Table 3.2.1, allowing us then to discuss the factors influencing which model payers are likely to adopt.

Table 3.2.1Key types of hospital payment method

a

Table 3.2.1a Long description

The table has 5 columns: Criterion, Line-item budgeting, Fee-for-service (F F S), Block contracts, and Activity-based funding (A B F). It reads as follows. Row 1: Description of patients. Line-item budgeting: Not applicable. F F S: Individual patients. Block contracts: By cohort, such as department, hospital, locality, region or demographic (e.g. frail and elderly patients with multiple chronic conditions). A B F: D R G for inpatients and other classifications for outpatients, mental health care, community health care.

Row 2: Price setting. Line-item budgeting: Expected cost of resource type. F F S: Price list for each service or payment per day. Block contracts: Price is out-turn of negotiated total contract value divided by cohort covered. A B F: Prospective price set by payer so as to incentivize hospitals (for example, by yardstick or actual competition).

Row 3: Volume of activity. Line-item budgeting: Not specified. F F S: Hospitals face no restrictions on volume. Block contracts: Expectations on hospital - normally including activity levels per cohort - set out in contract. A B F: In the simplest form of unit pricing, no restrictions of volume. In more complex forms of bundled pricing, implicit constraints on volume.

b

Table 3.2.1b Long description

The table has 5 columns: Criterion, Line-item budgeting, Fee-for-service (F F S), Block contracts, and Activity-based funding (A B F). It reads as follows. Row 4: Quality. Line-item budgeting: Not specified. F F S: Not specified, but implied that more services indicate higher quality. Block contracts: May be set out in contract. A B F: May be included in payment function.

Row 5: Administrative complexity or effort. Line-item budgeting: Low complexity: need method to link costs to be funded (staff, medicines, machines, etc.) to hospital role. F F S: High complexity: need for itemized bills, may involve fee schedule to reflect costs of different items, and monitoring systems to avoid overservicing and ensure payment integrity. Block contracts: Moderate complexity: need to develop systems to ensure adequate services are provided to meet needs of patient cohorts and to ensure efficiency. A B F: Highest complexity: need to implement D R G classification, develop tariff or prices (unit and/or more complex) and monitor systems for payment integrity (for example, code, count and cost units of activity).

DRG: diagnosis-related group.

Source: Authors.

Line-item budgeting

Under line-item budgeting hospitals are funded according to the type and amount of resource inputs used in delivering care. Budget lines tend to be itemized in fairly crude terms as inputs, such as medical and nursing staff, medicines, meals, laundry, materials, equipment, energy, estate and management costs. The payer, usually a government agency, sets the budget in advance, often built up by categories of inputs (e.g. approval to employ a specific number of nurses for a given amount of money) so that the hospital has clarity about the money it will receive to pay for these items.

This was the form of funding across the former Soviet Union in which central government determined the quantity of resources available to each hospital and fixed wages and prices (Mikesell & Mullins, Reference Mikesell and Mullins2001; Street & Haycock, Reference Street and Haycock1999; Moreno-Serra & Wagstaff, Reference Moreno-Serra and Wagstaff2010).

FFS

FFS is particularly common in health systems in which patients (or their insurers) pay for care themselves, with hospitals issuing bills for the care and treatment provided. Under FFS, hospitals receive a fee for each medical service provided to each patient, these being itemized on the bill. The services will include diagnostic tests, operative procedures, medications and a daily “hotel” charge to cover such costs as laundry, cleaning, meals, energy and general overheads. Hospitals in some health systems use a simple billing arrangement, loading everything onto the daily charge, with hospitals receiving daily payments rather than a price for each itemized service.

Block contracts

The key idea of the block contract funding model is that the amount to be paid is determined in advance. As a minimum, block contracts stipulate the delivery timescale, the total amount of money to be paid, and the cohort (that is, type and number) of patients to be treated. There might also be some specification of quality. Sometimes the block contract will take the form of a capitation payment model covering a particular patient population, with the amount reflecting expectations about utilization among that cohort. The capitation payment (or price per patient) is simply the total amount of money specified in the contract divided by the number of patients in the cohort. The payer and hospital are likely to enter into several contracts, differentiated according to how cohorts are described. The cohort might reflect the hospital department with primary responsibility for delivering the particular type of care, the hospital’s catchment area, the wider geographical region the hospital serves, or some indication of the type of patient in the cohort (e.g. frail and elderly patients with multiple chronic conditions). Sometimes a hospital might enter into a single contract for an especially large patient cohort, this special case being referred to as a “global budget”. Where capitation payments rely on the clinical characteristics of enrollees, the payer has to be wary of gaming risks where the funded entity artificially inflates diagnoses (Geruso & Layton, Reference Geruso and Layton2020; Porter & Kaplan, Reference Porter and Kaplan2016; Chernew et al., Reference Chernew2021).

A block contract can be used in a variety of different circumstances, with different effects. For instance, block contracts might be the historical form of the payer–hospital relationship, with contracts determined by history or past negotiations. Here payers put a high value on their effort in determining and monitoring contracts, with less emphasis on quantity or quality. Negotiation-based block contracts might involve an expected quantity to set the value of the contract, but with little scope for variation from the total spend if volume exceeds expectations. If so, activity-related cost risk sits with the hospital, not the payer. So this form of block contract might be preferred by payers with tight budget constraints. Because of their relative simplicity, block contracts are often used for services which cannot be described particularly well, such as where an activity measure has not yet been developed (e.g. mental health services); it is a unique service (e.g. highly specialized care); or the service would be unviable under other methods of payment, but the payer wants to ensure the service continues (e.g. small rural hospitals necessary for access). With this form of block contract, the activity-related cost risk sits with the payer.

Activity-based funding

The fourth payment type is activity-based funding, which is sometimes referred to as case mix funding, DRG funding or the prospective payment system.

In terms of how patients are described, activity-based funding can be considered a half-way house between FFS and block contracts. Under FFS, a personalized bill is drawn up for each individual patient. Under block contracts, the contract is usually specified at cohort – often departmental – level, implying that patients are described in fairly large and heterogeneous groups in the contract. Under activity-based funding, patients admitted to hospital are usually described using DRGs for patients, though other DRG-like classifications might be used for activity conducted in emergency departments, outpatient or other (e.g. mental health, community) settings. DRGs were first developed by Fetter et al. (Reference Fetter1980) as a means to identify groups of admitted patients with similar diagnoses who were expected to have similar resource requirements during their hospital stay. This design principle has guided the development of DRG systems around the world (e.g. Busse et al., Reference Busse2011; Kimberly & de Pouvourville, Reference Kimberly1993). The design of the DRG classification aims to maximize within-group homogeneity but, because there is a limited number of groups in the DRG classification, inevitably each group is still somewhat heterogeneous. Consequently, activity-based funding entails a less precise description of patient specificity than under FFS (where effectively each patient is their own group) but a more precise description than under block contracts.

Key factor 1: number of payers

The first and most important factor is the number of payers in the health system. The fewer payers there are, the more power they are able to exercise in negotiations with hospitals. In health systems with no third-party payers, each patient is a payer, with minimal bargaining power. In such circumstances, patients are price-takers, having to pay what hospitals ask, with FFS being the typical payment model. If the health system features third-party payers in the form of insurance companies or sickness funds, FFS is also common, but these payers may be able to exert collective influence on hospitals in the form of a fee schedule in order to contain overall payments. But, even so, hospitals enjoy considerable power in deciding what volume of services to provide to each insured individual.

In other health systems, notably where health care is funded via taxation, third-party payers often take the form of government agencies with responsibility for specific populations, perhaps defined on the basis of geography, income or age. These payers receive a share of national tax funds to fulfil these responsibilities, this share usually calculated using a capitation formula reflecting the size and composition of the population. If there are only a few hospitals in their area, agencies may prefer to negotiate block contracts with hospitals (because of their lower administrative burden) which will likely reflect the volume (number) and type (composition) of hospital patients covered by the contract. As there are just two parties to each contract, the hospital may be able to exploit its information advantage to secure favourable terms.

Some health systems feature just a single payer. This shifts the balance of bargaining power from hospitals to the single payer, who is able to exploit its monopsony purchasing power and force hospitals into being “price-takers” rather than “price-makers” by imposing either line-item budgeting or activity-based funding payment models.

Key factor 2: number of hospitals

The second factor influencing the choice of payment model is the number of hospitals to which payments are to be made. If there were a very large number of hospitals, all providing similar services and competing with one another to attract fully informed patients, FFS would be an efficient choice. This competitive environment would ensure that high-quality services were provided and fees were kept low. But this is not the customary environment that prevails in the hospital sector because of the asymmetry in information between hospitals and their patients. Rather, it is much more common for hospitals to face little competition, instead enjoying a degree of local monopoly power. A hospital might even have national monopoly power, in smaller countries or if they are the only providers of specialized care. The greater its monopoly power, the more ability a hospital has to set its own fees, thus making the FFS model less attractive to payers.

The other three payment models impose more financial control over hospitals than the FFS model, and line-item budgeting and block contract can be applied irrespective of the number of hospitals in the health system. However, under activity-based funding prices are set by comparing costs across hospitals, so there needs to be a sufficient number of hospitals from which the payer collects comparative data. As with FFS, the fewer the hospitals, the greater the scope for a hospital to influence prices advantageously, because its own data will have greater influence on the price it faces. To limit this influence, payers implementing activity-based funding may set prices using comparative cost or pricing information from hospitals in other jurisdictions.

Key factor 3: relative importance of payer objectives

The third determinant of the choice of payment model is the relative weight that payers attach to their different objectives, in particular: controlling hospital costs, volume of activity and quality of service.

Key factor 4: administrative effort

The final relevant factor is the effort involved in managing the payment methods (Baxter et al., Reference Baxter2015). Payers and hospitals need to optimize the amount of effort involved. Payment arrangements are not frictionless: both payers and hospitals need to exert effort in agreeing and managing the contractual relationship between the two parties. For hospitals, this will require ensuring that internal structures of the hospital make it easy to address the exogenous incentives – control of the mix of services used in treating a patient typically rests in the hands of the medical staff and so the job of hospital senior management includes ensuring that hospital doctors recognize the hospital’s funding constraints (Young & Saltman, Reference Young and Saltman1985).

Payers also need to exert effort to ensure that the funding design delivers on the objectives that they have prioritized. The more complex the funding model, the more effort is expended by both hospitals and payers in managing and monitoring it. Payers, especially, have to be wary of making the funding model overly complex. The more complex the model, the more difficult it will be for hospitals to understand the nature of the incentives at play and the more likely they are to ignore the incentives or respond to what they perceive the incentives to be (Abeler & Jäger, Reference Abeler and Jäger2015). Moreover, if the incentives are unstable – for example if prices vary widely from year to year – hospitals may decide that the effort in responding to a given year’s incentives is not worth the benefit, and so they might ignore the incentive.

Line-item budgeting involves minimal administrative complexity, both in terms of how the budget is specified and in how spending is monitored. As such, this payment method is likely to be preferred in places where information systems are poorly developed. Activity-based funding is the most complex of the payment methods, requiring implementation and maintenance of a classification system to describe patients, a pricing schedule and a monitoring system to verify the integrity of payments made by payers to hospitals. FFS and block contracts lie between these extremes.

The importance of management and policy skills can be seen in the results of the implementation of activity-based funding in central and eastern Europe, and central Asia. In their study of the system-wide impacts of hospital payment reform, Moreno-Serra and Wagstaff (Reference Moreno-Serra and Wagstaff2010) found that, contrary to theory, total health expenditure increased following implementation of activity-based funding. Moreno-Serra and Wagstaff hypothesized that this may have been due to DRG coding-creep. The potential for coding-creep should have been foreseen and its effect mitigated through a variety of strategies such as assuming a DRG effect of a particular size and factoring that into the price in advance, or through rigorous auditing of coding. The impact of coding-creep could also be ameliorated by imposing lagged soft expenditure caps (Hahn, Reference Hahn2014; Holahan & Zukerman, Reference Holahan and Zukerman1993).

The process of transition: what is involved?

The transition process is often a long one, as the preconditions are developed (Annear et al., Reference Annear2018), or the right policy window appears. Improved provision of comparative information to providers – to allow them to see where they stand on efficiency and other variables – is often part of a capacity-building phase.

A common next step is that this comparative information is used to inform budget setting – benchmarking here is an input into the budget negotiating process or used to inform the block contract. For example, in their review of the development of activity-based funding in 12 countries in Europe, Busse and Quentin (Reference Busse, Quentin and Busse2011) showed that in half the countries the original use of the DRG classification system was simply descriptive or to inform budget allocation. Bredenkamp, Bales and Kahur (Reference Bredenkamp, Bales and Kahur2020) drew a similar conclusion in their review of the transition to activity-based funding in nine countries. Indeed, the possibility for such benchmarking was one of the drivers of the initial development of DRGs (Fetter, Brand & Gamache, Reference Fetter and Brand1991).

Transitioning: key questions and trends

Payers normally will have objectives about how the health system transitions from the old funding system to the new. These objectives might be about the pace of the transition. Over how many years is the phase-in? What proportion of funding is on the old versus new basis? And what should be the ordering of the transition – for example, starting with patients having elective procedures, or particular classes of patients or particular types of costs (Duckett, Reference Duckett, Annear and Huntington2015)? Because there will be winners and losers from any transition between funding systems, the payer’s objectives about the pace and phasing will often also factor in how to manage these gains and losses (Short & Goldfarb, Reference Short and Goldfarb1987).

The introduction of new funding systems also often provides the opportunity to revisit the nature of the relationship between the payer and the hospital–- what types of controls might be kept or changed? How much autonomy will hospitals have in the new environment? What constraints might there be on hospital innovation? Introducing new funding systems may even be driven by a desire to change that relationship.

Payers might try to pursue multiple objectives simultaneously, although this will require a more sophisticated design pursuant to the Tinbergen Rule, that multiple objectives require multiple instruments (Tinbergen, Reference Tinbergen1952). For example, one of the early implementations in 1993 of activity-based funding, in Victoria, Australia, had three objectives: to reduce total hospital expenditure; to improve the efficiency of hospitals; and to increase the number of patients treated and reduce elective procedure waiting lists (Duckett, Reference Duckett1995). Activity-based funding in Victoria replaced a system of negotiated block contracts. The stimulus for the introduction was a significant budget cut (15% over three years), effectively a tightening of the budget constraint, and a recognition that a reduction in the budget of that magnitude would, under a block contract system, probably result in a significant reduction in the quantity of services provided (and hence access). An activity-based funding model for acute inpatient activity was implemented – with hospital-specific activity caps but a potential to share in a capped allocation of extra funds for extra activity (Street & Duckett, Reference Street and Duckett1996) – with all other hospital services paid on a block contract in the absence of robust activity measures for those services.

Preconditions for transitioning from simpler to more complex methods

In view of the considerations set out in this section, it is common in practice to see a mix of funding methods used within any given health system, thereby moderating the extreme effects of any single method. “Transition” then means altering the mix of methods, rather than shifting wholesale from one to another. Yet regardless of whether transition is from one method to another or is a shift in the mix of methods from one to another, there are four preconditions for shifting from simpler funding methods (line-item budgeting, block contracts) to more complex ones (FFS, activity-based funding):

1. 1. The development of robust risk-adjustment measures, generally based on an already established version of the DRG classification; but the larger the jurisdiction, the more likely an internationally imported version will be adapted to reflect local clinical practice.

2. 2. The implementation of information systems – notably coding systems and grouping software. This is required to ensure that hospital managers have the appropriate information to identify where there is scope for performance improvements.

3. 3. The development of administrative skills in the payer to design and manage the new system. This is a nontrivial task as poorly designed payment systems may not be seen as legitimate and are unlikely to gain acceptance; or may fail to anticipate the full range of perverse provider responses, including gaming. Poor ongoing management of a new funding system may not incorporate appropriate monitoring and interventions to sanction poor performance and again undermine confidence in the system, or may not incorporate adaptation for new technology.

4. 4. The development of management skills in hospitals. A change of budget processes will also require a different set of skills – perhaps from managing negotiations with the payer to managing efficiencies within the organization more tightly.

The scarcer that administrative and management skills are, and the more challenging the development of risk-adjustment measures and information systems is, the more likely it is that the funding model or mix of models will gravitate towards simpler forms (Baxter et al., Reference Baxter2015).

P4P: what it is

Health care funding contracts are often “incomplete” in the sense that they are not able to specify fully what is to be provided, especially in terms of quality, and this carries an inherent risk that providers might skimp on quality to reduce costs. In addition to evidence-based governance strategies to encourage quality improvement (Donabedian, Reference Donabedian2002; Langley et al., Reference Langley2009; Tello, Barbazza & Waddell, Reference Tello and Waddell2020), payers mitigate this risk by incorporating into payment design additional payments against additional metrics. Importantly, P4P is described here as a supplement: the basic stream of revenue needs to be adequate for all hospitals and the P4P supplement is an add-on, to reward superior performance on the relevant dimension (Cattel, Eijkenaar & Schut, Reference Cattel, Eijkenaar and Schut2020; Cattel & Eijkenaar, Reference Cattel and Eijkenaar2020). There is now an extensive literature about P4P, and an array of potential P4P designs (Cromwell et al., Reference Cromwell2011; Conrad, Reference Conrad2015), with the net impact of a specific implementation the result of the interplay of several factors including gaming, target and reward setting, and health increasing and health decreasing substitutions (Friedman & Scheffler, Reference Friedman, Scheffler and Scheffler2016). Perverse responses – including gaming (Carter, Newhouse & Relles, Reference Carter, Newhouse and Relles1990; Rosenberg, Fryback & Katz, Reference Rosenberg, Fryback and Katz2000; Bevan & Hood, Reference Bevan and Hood2006; Steinbusch et al., Reference Steinbusch2007; Mannion & Braithwaite, Reference Mannion and Braithwaite2012; Georgescu & Hartmann, Reference Georgescu and Hartmann2013) – can be mitigated with regulatory strategies including monitoring, and penalties.

Difficulties with P4P

Although intuitively and theoretically sound, in practice P4P implementations – especially those which focus on changing a single provider type (hospital or primary care) or specialty – have generally not lived up to the expectations and rhetoric (Mendelson et al., Reference Mendelson2017; Scott, Liu & Yong, Reference Scott, Liu and Yong2018; Cattel, Eijkenaar & Schut, Reference Cattel, Eijkenaar and Schut2020; Cattel & Eijkenaar, Reference Cattel and Eijkenaar2020; Singh et al., Reference Singh2021). Song’s summary of the state of evidence about “accountable care” in the USA can equally apply to P4P:

Unfortunately, P4P, especially when poorly designed, may crowd out intrinsic professional motivation and can create perverse incentives as evidenced in both the mid-Staffordshire scandal in England (Francis, Reference Francis2013; Powell, Reference Powell2019; Smith & Chambers, Reference Smith and Chambers2019; Entwistle & Doering, Reference Entwistle and Doering2024) and the Bundaberg Hospital scandal in Queensland, Australia (van der Weyden, Reference van der Weyden2005; Thomas, Reference Thomas2007; Casali & Day, Reference Casali and Day2010; Edwards, Lawrence & Ashkanasy, Reference Edwards, Lawrence, Ashkanasy, Ashkanasy, Härtel and Zerbe2016), where organizational targets and incentives for budget control or additional activity created environments where safety concerns were ignored. However, well-designed performance monitoring and P4P incentives can help to ensure that organizations pay appropriate attention to their processes and outcomes (Conrad & Perry, Reference Conrad and Perry2009; Conrad, Reference Conrad2015; Young & Conrad, Reference Young and Conrad2007; Smith et al., Reference Smith2009), particularly as they impact on the most vulnerable.

The evidence suggests, therefore, that careful consideration has to be given to how to incorporate P4P arrangements within the hospital payment model. The cost of including a P4P component may be modest, especially where the cost of the ostensible reward is transferred from other streams of the hospital revenue. In these circumstances, P4P simply leads to a slightly different distribution of a capped budget. P4P incentives serve a political and rhetorical purpose signalling that the payer values the metric rewarded and directing hospitals – who could be expected to respond to incentives at the margin – to pay attention to this metric too.

From block contracts to activity-based funding

Until 2002, acute-care services and mental health, community and ambulance services were paid for with block contracts. From 2003, activity-based funding began to be phased in to gradually replace block contracts for acute hospital care. It began on a small scale, with just 15 prices for elective surgery (and just 0.2% of commissioners’ budgets) during a time of increases in NHS funding. It did so to address growing waiting lists and a so-called postcode lottery in access to care (Martin et al., Reference Martin2003). Activity-based funding expanded steadily until by 2012 it had grown to 3000 prices for 1400 DRGs, covering 34% of payers’ budgets. It grew further from 2013 following an expansion in the scope for payment to follow patients’ choices; to 9000 prices for 2800 DRGs and 40% of commissioners’ budgets. It included P4P elements linked to quality.

As it expanded, activity-based funding for acute care promoted productivity in hospitals (Farrar et al., Reference Farrar2009). Its P4P schemes improved quality (Meacock, Kristensen & Sutton, Reference Meacock, Kristensen and Sutton2014). It also helped amass rigorous data on activity, costs and outcomes. Set against this, it rewarded unilateral interest and not collaboration (Cooper et al., Reference Cooper2011; Bevan & Skellern, Reference Bevan and Skellern2011). It hampered management of expenditure and of unwarranted variation in activity (Mannion & Street, Reference Mannion and Street2009) and it was burdensome to administer (Marini & Street, Reference Street2007). In addition, the mix of activity-based funding for hospitals with block contracts for community, mental health and ambulance services frustrated better integration of health care (Bevan & Janus, Reference Bevan and Janus2011).

Changes in the number of payers

Until 2005 each hospital entered into contracts with around two dozen payers, then termed district health authorities. Tax funding was allocated to authorities according to estimates of the health needs for the population of each district. From 2006 the landscape for payers changed from district health authorities to over 300 local, primary care-led clinical commissioning groups. Hospitals then entered into thousands of contracts with these payers. As these contracts moved from block contract to activity-based funding, the system’s demands on management skills and information systems increased.

At its peak in 2015, 88% by value of contracts between hospitals and payers used activity-based funding. By 2018 that had reduced to 58%. There were two drivers for this reduction: administrative burden and budget. In a typical year in this period, hospitals issued some 600 000 monthly invoices to payers. Almost 200 000 of these were for less than £1000 as hospitals and payers argued about month-end bills. A shared desire by some local hospitals and payers to reduce this administrative effort saw many move reimbursement away from activity-based funding.

A sustained period of austerity in United Kingdom government spending followed the 2008 global financial crisis (Ford, Reference Ford2013). This reduced the rate of annual funding growth to the NHS to less than half its long-run average (Walshe & Smith, Reference Walshe and Smith2015; Charlesworth & Bloor, Reference Charlesworth and Bloor2018). By 2015 this manifested itself in a lack of hospital capacity and in large and growing hospital deficits, as prices for DRGs were scaled down by more than 10% below average unit costs. As a consequence, the incentives for head-to-head competition between hospitals declined, as did the desire of payers and hospitals to contract using activity-based funding.

Increasing complexity in activity-based funding

Initially, as activity-based funding was expanded to support patient choice and head-to-head competition between hospitals, it became more complex (Marshall, Charlesworth & Hurst, Reference Marshall, Charlesworth and Hurst2014). As well as the number of DRGs attracting activity-based prices expanding greatly, four types of financial incentives for quality were added to the payment system. The first enabled hospitals to reclaim funding held back by payers (of a few percentage points of their budgets) when they achieved improvements in process quality (e.g. ensuring hospital staff had winter flu vaccinations) and patterns of care, especially preventive care (e.g. screening infants for malnutrition). The second rewarded outcome quality, with hospitals paid a higher price for “best practice” (meaning high-quality and cost-effective) care, with the goal of reducing unexplained variation in hospitals’ clinical quality in areas such as adult stroke care and children’s diabetes care. The third enabled payers to claw back activity-based funding (of a few percentage points of their budgets) if hospitals did not meet standards for quality on metrics such as hospital-acquired infections, waiting times for elective care or emergency admissions for non-elective care. And the fourth gave hospitals top-up payments, either for extra cost incurred (e.g. on long staying or very complex patients) or for exceeding activity thresholds in certain areas (e.g. faster emergency care, more elective care). Later versions of these top-up payments included risk-sharing mechanisms, to help ensure spending did not exceed payers’ funding allocations.

Even where head-to-head competition between hospitals was not possible (or became less desirable), activity-based funding was used to underpin comparative competition. This is because prices were based on reported costs. Each year hospitals submitted to NHS England data on the cost of each unit of activity for each DRG. Subject to adjustments, the average unit cost for each DRG was the activity-based price for that DRG (Street & Maynard, Reference Street2007). A hospital whose cost lay below the activity-based price it received earned a surplus on each unit of that activity; one whose cost lay above was incentivized to reduce its unit costs.

Replacing competition with collaboration as the main driver of improvement

To incentivize increased activity, activity-based funding introduced unit prices for individual DRGs. As activity-based funding expanded to support patient choice, prices continued to be unitary to ensure the right type of head-to-head competition between hospitals. Because in reality English hospitals faced little competition, they had influence over the prices they faced. Competition between hospitals then had two countervailing effects on quality. On the one hand, it encouraged hospitals to improve quality for a given price in order to attract patients and funding. On the other hand, higher quality may be costly to provide and is hard for patients and payers to judge. So by reducing profitability, competition discouraged hospitals from spending on (hard-to-judge) quality, in order to keep costs and (easy-to-judge) prices down. The overall impact of competition on quality is the net of these two effects. To ensure patient choice drove quality up rather than prices down, activity-based funding prevented price competition by fixing unit prices above average unit costs.

As the impact of government austerity on NHS funding was felt, hospitals lacked capacity and financial deficits grew. With little incentive for hospitals to compete, there was little head-to-head competition between them. Collaboration and not competition became the principal driver of improvement in hospital performance (Allen et al., Reference Allen2017; Alderwick & Ham, Reference Alderwick and Ham2016). The 2019 NHS Long Term Plan cemented this, foreshadowing multilateral, integrated local care systems to replace bilateral commissioner-provider relationships, the better to promote integrated care (NHS England, 2019). To support this multilateralism, the Long Term Plan ushered in population-based funding and a return to block contracts to replace activity-based funding.

Three definitions of informal payments

A comprehensive review of the literature in 2013 identified 61 studies that defined informal payments in health care, and suggested that there were three distinct and original definitions (Cherecheş et al., Reference Cherecheş2013). The first was proposed by Lewis (Reference Lewis1999) who defined informal payments as:

The second definition was introduced by Belli, Gotsadze and Shahriari (Reference Belli, Gotsadze and Shahriari2004) who explicitly positioned informal payments within the broader category of OOP payments. Specifically, they noted that informal payments are:

Finally, a now widely used definition of informal payments was developed by Gaal and colleagues (Reference Gaal2006) which captures the full spectrum of informal payments globally and underscores the importance of entitlements as the defining feature of informal payments that distinguishes them from other payments in the health sector. Thus, according to this definition, an informal payment is:

Therefore, in order to define and measure informal payments in the health sector, we need to understand the services that individuals are entitled to within the publicly funded, statutory health programmes.

Motivation: donation, FFS, tax avoidance

The question of motivation for the payment is an important consideration because, at one end of the spectrum, gift-giving that is common cultural practice may be a nuisance for some but may not in fact represent a policy problem in a jurisdiction. However, at the other end of the spectrum, an informal payment that is motivated by pressure on patients may have adverse impacts on intermediate and final health system objectives including efficiency, quality, equity in access, equity in finance, financial protection and health outcomes (see Box 3.3.1). Gaal and colleagues (Reference Gaal2006) delineate the two dominant motivations of informal payments as donation and FFS. The former is a “benign informal payment”, often called a “gratitude payment” because it is given voluntarily, is considered a form of appreciation, and is not given with the intention of directly impacting the services being provided. The latter is an “under-the-table” payment motivated by either an individual’s desire to receive safe, timely or good quality care, or to ensure or facilitate access to care, thereby potentially impacting health outcomes. Due to the lack of transparency, an under-the-table payment also limits the health system’s ability to protect individuals financially (e.g. by means of exemption mechanisms) and to ensure funds are reinvested into health systems in a way that is aligned with health system goals (Gaal et al., Reference Gaal2006).

Beyond these two major categories of motivation for informal payments – donation and FFS – there is a third which falls in between: a form of patient–doctor collusion with the primary aim of tax avoidance. All three of these exchanges have negative impacts on the health system, notwithstanding the benign intentions of straightforward gift-giving. For example, all can lead to reduced levels of trust in the doctor–patient relationship which may in turn lead to underutilization of health services. If patients anticipate additional fees for care that they are entitled to, they might be disincentivized to seek physician services. Also, there may be spillover effects on other parts of the health system (Currie, Lin & Meng, Reference Currie, Lin and Meng2013), such as incentivizing physicians to provide more care, including potentially unnecessary care, to patients who pay informally.

There is limited, and often contradictory, evidence to support one motivation or the other (Gaal et al., Reference Gaal2006). A systematic review of 31 studies (globally) found the same number of studies that reported the purpose of an informal payment as an expression of gratitude (10 studies) as those that reported the purpose as consistent with the FFS or fee-for-commodity hypothesis (10 studies) (Stepurko et al., Reference Stepurko2010). Others reported that the reasons for informal payments were to gain: better quality of care (10 studies); access to care (four studies); faster access (six studies); and psychological comfort (four studies). That same review found that nine studies reported the patients as initiating the payment (as an expression of thanks) and a further nine studies found the providers initiated it (as a required payment for service). Empirical literature on the motivation behind informal payments is sparse, and we look first at the theoretical drivers of informal payments.

Cognitive behaviour theory

Gaal and McKee (Reference Gaal and McKee2004) developed a theoretical framework drawing on cognitive behaviour theory to explains the causes of informal payments. They posit that informal payments are a “reaction by dissatisfied patients and physicians to shortage – a manifestation of deteriorating organization/system performance”. They extend Hirschman’s (Reference Hirschman1970) theory of “exit, voice and loyalty” as reactions to failures in organizations or states, by adding a new option/reaction which they call “inxit” (or a “informal/internal exit”) whereby dissatisfied patients and providers do not leave the system (exit), nor express their disappointment by complaining to bring about change in the system (voice), but rather they “seek to change its activities, not through open complaints, but using informal methods, such as payment or connections”.

The Gaal and McKee (Reference Gaal and McKee2004) cognitive behavioural model helps to explain why informal payments may arise under economic conditions of shortage. Specifically, when there is no option to leave the system (exit), as in Semashko systems with no private options, and there are no mechanisms for complaints to be heard (voice), then “inxit” as manifested by informal payments is the only possible reaction by patients to deteriorating health system performance. Thus, the policy solutions to address informal payments must simultaneously address the problem of shortage (e.g. by increasing investment in the system and health worker salaries) and expanding the formal options available to patients and providers to express their concerns with poor quality care; for example, through performance measurement and (enhanced) complaints procedures, or possibly with additional options to exit the public system (though this may exacerbate inequities).

Institutional theory

Additional explanations of the drivers of informal payments draw on institutional theory, considering both formal institutional shortcomings as well as informal institutions such as unwritten conventions and practices. Some of these institutional shortcomings include corruption in political systems and public programmes, lack of trust in government, and lack of government oversight over public programmes (Allin, Davaki & Mossialos, Reference Allin, Davaki and Mossialos2006; Pourtaleb et al., Reference Pourtaleb2020). Moreover, informal payments are expected to arise when “norms, practices and values of the informal institutions are not in symmetry with the formal rules of the game (i.e. laws and regulations)” (Williams & Horodnic, Reference Horodnic and Polese2017).

Recently, Binyaruka and colleagues (Reference Binyaruka2021) developed a supply-side theoretical framework which conceptualizes informal payments by placing the notion of concentrated power (which is similar to the issues arising from information asymmetry that affords more power to physicians than to both payers and patients) as a means to extract rent from various institutions and relationships. In Binyaruka et al.’s (Reference Binyaruka2021) model, individuals who have more power are more likely to obtain informal payments. Moreover, these individuals are more likely to bypass any transparency and anticorruption mechanisms because they have the power to game the system without consequences (Binyaruka et al., Reference Binyaruka2021). Thus, informal payments can arise in contexts where there is inadequate oversight in place to prevent abuse, suggesting a need for anticorruption measures and supervision along with awareness campaigns to address information asymmetry. The level of informal payments in a jurisdiction will relate to the health system and broader institutional context, including the extent of resource shortages and trust in government to address aspects of deteriorating health system performance, as well as the level of power held by medical professionals and health organizations.

Economic factors

Economic factors broadly concern shortages, or a scarcity of resources in the publicly financed health system. Shortages – or a mismatch between the health care needs and expectations of the population and the supply and accessibility of care – are seen to be a necessary but not sufficient condition for informal payments to arise (Gaal & McKee, Reference Gaal and McKee2004). Data from Transparency International’s Global Corruption Barometer (Fig. 3.3.1) show that informal payments are present mostly in LMICs and that there is a correlation between health spending as a share of national income and prevalence of informal payments. (This correlation is also depicted in Fig. 3.3.4.)

Several studies have demonstrated the link between economic conditions and informal payments. For example, Tambor and colleagues (Reference Tambor2013) grouped 35 European countries into three categories drawing on a Health Consumer Powerhouse survey: no informal payments reported; some incidence of informal payments reported; and widespread informal payments reported. They found that countries with widespread informal payments had significantly lower public health expenditure as a share of total health spending.Footnote ¹ Also, a cross-sectional analysis of Eurobarometer data from 28 European countries found a higher likelihood of patients reporting having made an informal payment in health care for countries with lower levels of health expenditure, either as a percentage of GDP or per capita (Williams & Horodnic, Reference Williams and Horodnic2018).

Sociocultural factors

Sociocultural reasons generally refer to a nationally embedded culture of tipping or custom of expressing gratitude through payments or gifts. Though there is probably a role played by cultural norms in explaining the origin and persistence of informal payments in many countries, and this appears as a theme in recent systematic reviews (Pourtaleb et al., Reference Pourtaleb2020), such explanations are insufficient to explain the majority of informal payments. For instance, the inability to identify a clear beginning and end to many clinical encounters, such as with chronic diseases management and primary care, make it difficult to separate gifts as an expression of gratitude from gifts/payments with a different motivation (e.g. to gain higher quality care/better access) (Gaal, Jakab & Shishkin, Reference Gaal, Jakab, Shishkin, Kutzin, Cashin and Jakab2010). Also, the fact that informal payments often take the form of in-kind contributions to the health care intervention itself, such as medical supplies or medicines, suggests that informal payments may not be a simple expression of gratitude in these contexts.

Factors related to governance and institutions

View from the literature

A systematic review of 38 studies (globally) reported the variation in prevalence of informal payments (the percentage of patient encounters that had any type of informal payment) ranged from 2% to 80%, with higher prevalence in the inpatient sector than the outpatient (Khodamoradi et al., Reference Khodamoradi2018). Another systematic review of 33 studies (globally) also found informal payments were more common in inpatient services, surgery/delivery services (Amiri et al., Reference Amiri2019). A scoping review of the Western Balkans included 24 studies and found the highest prevalence of informal payments in the region was in Albania (19–91%), compared to 7–70% in KosovoFootnote ², 7–23% in Serbia, and 5–22% in Bosnia and Herzegovina (Buch Mejsner & Eklund Karlsson, Reference Buch Mejsner and Eklund Karlsson2017). Another systematic review and meta-analysis of 15 studies in Iran estimated that the prevalence of informal payments was approximately 35%, with the main reasons provided as appreciating the staff, and in response to requests made by hospital staff (Mirabedini et al., Reference Mirabedini2017).

Measuring informal payments using cross-sectional survey data

As there is (most often) a lack of recording of informal payments in any state accounting or information system, national and international surveys are the main source of data on informal payments. Nevertheless, due to the often illicit nature of the practice and subsequent underreporting, these surveys might also underestimate the true prevalence. Another challenge of measurement relates to the methods followed, as different studies may adopt different definitions and approaches that make international comparisons challenging and not easily interpretable (Cohen, Reference Cohen2012).

In response to this, we compiled a list of cross-sectional surveys that measure informal payments. The list in Table 3.3.1 describes how the question of experience concerning the practice has been approached by various organizations in different regional settings. Further, we draw on the Global Corruption Barometer by Transparency International in this section because it utilizes a uniform survey design to compare the prevalence of informal payments across regions. The surveys measure patients’ experience of paying physicians informally for care they are otherwise entitled to. This includes information on the public health sector in Latin America and the Caribbean, the Middle East and north Africa, Africa, Asia and the Pacific, and the EU. We used the most recent data available for each region, which meant that the time of collection varied across the global sample – EU (2021), Asia and the Pacific (2020), Latin America and the Caribbean (2019), Middle East and north Africa (2019), Africa (2019). Globally, 11% of patients made an informal payment in the 12 months prior to the survey. Fig. 3.3.1 illustrates the variation in prevalence of informal payments across regions.

Table 3.3.1List of cross-sectional surveys on informal payments

a

Table 3.3.1a Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 1: Special Eurobarometer (245, 291, 325, 374, 397). Question: Thinking about these contacts in the past 12 months, has anyone in (YOUR COUNTRY) asked you, or expected you, to pay a bribe for his or her services? Type of service: The health care sector. Possible responses: Binary response (Yes; No). Regions: European Union. Countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden Source: European Commission (2005; 2007; 2009; 2011; 2014).

Row 2: Special Eurobarometer (397, 470, 502, 523, 534). Question: Apart from official fees did you have to give an extra payment or a valuable gift to a nurse or a doctor, or make a donation to the hospital? Type of service: Public health care practitioner such as a G P (general practitioner) or a public health care institution such as a public hospital in the past 12 months. Possible responses: Binary response (Yes; No). Regions: European Union. Countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden. Source: European Commission (2014; 2017; 2020; 2022; 2023).

b

Table 3.3.1b Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 3: Special Eurobarometer (470, 502, 523, 534). Question: Thinking about these contacts in the past 12 months has anyone in (YOUR COUNTRY) asked you or expected you to give a gift, favour or extra money for his or her services? Type of service: The health care sector. Possible responses: Binary response (Yes; No). Regions: European Union. Countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden. Source: European Commission (2017; 2020; 2022; 2023).

Row 4: Afrobarometer (Rounds 5, 6, 7, 8). Question: And how often, if ever, did you have to pay a bribe, give a gift, or do a favour for a health worker or clinic or hospital staff in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Africa. Countries: Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d’Ivoire, Egypt, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe. Source: Afrobarometer (2015; 2016; 2019; 2022).

c

Table 3.3.1c Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 5: L I T S (Round 1). Question: In your opinion, how often is it necessary for people like you to have to make unofficial payments or gifts in these situations? Type of service: Receive medical treatment in the public health system? Possible responses: 5-item Likert scale (Never; Always). Regions: Eastern Europe, central Asia. Countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, North Macedonia, Poland, Romania, the Russian Federation, Serbia, Slovak Republic, Slovenia, Tajikistan, Türkiye, Turkmenistan, Ukraine, Uzbekistan. Source: E B R D (2006).

Row 6: L I T S (Round 2 and 3). Question: Did you or any member of your household make an unofficial payment or gift when using these services over the past 12 months? Type of service: Medical treatment in the public health system. Possible responses: Binary response (Yes; No). Regions: Eastern Europe, central Asia. Countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Egypt, Estonia, Georgia, Greece, Hungary, Jordan, Kazakhstan, Kosovo3, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Romania, the Russian Federation, Serbia, Slovakia, Slovenia, Tajikistan, Tunisia, Türkiye Turkmenistan, Ukraine, Uzbekistan. Source: E B R D (2011; 2016).

d

Table 3.3.1d Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 7: L I T S (Round 3). Question: Have you encountered any [payments required for services that should be free] with your local public health clinic or hospital during the past 12 months? Type of service: Public health clinic or hospital. Possible responses: Binary response (Yes; No) Regions: Eastern Europe, central Asia, western Europe. Countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Egypt, Estonia, Georgia, Germany, Greece, Hungary, Italy, Jordan, Kazakhstan, Kosovo4, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Romania, the Russian Federation, Serbia, Slovakia, Slovenia, Tajikistan, Tunisia, Türkiye, Turkmenistan, Ukraine, Uzbekistan. Source: E B R D (2016).

Row 8: Global Corruption Barometer: Africa (2019). Question: In the past 12 months have you had contact with a public clinic or hospital? How often did you have to pay a bribe, give a gift, or do a favour for a health worker, clinic or hospital staff in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Africa. Countries: Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d’Ivoire, Democratic Republic of the Congo, Eswatini, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe. Source: Transparency International (2019a).

e

Table 3.3.1e Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 9: Global Corruption Barometer: Latin America (2019). Type of service: In the past 12 months have you had contact with a public clinic or hospital? How often did you have to pay a bribe, give a gift, or do a favour for a health worker or clinic or hospital staff in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Latin America, Caribbean. Countries: Argentina, Bahamas, Barbados, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Guatemala, Guyana, Honduras, Jamaica, Mexico, Panama, Peru, Trinidad and Tobago, Venezuela. Source: Transparency International (2019b).

Row 10: Global Corruption Barometer: MENA (2019). Question: In the past 12 months have you had contact with a public clinic or hospital? How often did you have to pay a bribe, give a gift, or do a favour for a health worker or clinic or hospital staff in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Middle East, north Africa. Countries: Jordan, Lebanon, Morocco, Palestine, Sudan, Tunisia. Source: Transparency International (2019c).

Row 11: Global Corruption Barometer: MENA (2019). Question: In the past 12 months have you used wasta in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Middle East, north Africa. Countries: Jordan, Lebanon, Palestine. Source: Transparency International (2019c).

f

Table 3.3.1f Long description

The table has 7 columns: Survey and round, Question on incidence rate, Type of service or service provider specified, Possible responses, Regions included in the sample, Countries covered, and Source. It reads as follows. Row 12: Global Corruption Barometer: Asia (2020). Question: And how often, if ever, did you have to pay a bribe, give a gift, or do a favour for a health worker or clinic or hospital staff in order to get the medical care you needed? Type of service: Public clinic or hospital. Possible responses: Categorical response (Never; Once or twice; A few times; Often). Regions: Asia. Countries: Bangladesh, Cambodia, China, India, Indonesia, Japan, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Philippines, Republic of Korea, Sri Lanka, Taiwan, Thailand, Vietnam. Source: Transparency International (2020).

Row 13: Global Corruption Barometer: European Union (2021). Question: And how often, if ever, did you have to pay a bribe, give a gift, or do a favour for a health worker or clinic or hospital staff in order to get the medical care you needed? Regions: European Union. Countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, France, Finland, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Hungary, Sweden. Source: Transparency International (2021).

Note: The datasets listed in the table above are all publicly available and were screened to identify the relevant questions.

Fig. 3.3.1

Prevalence of informal payments in selected countries, 2019–2021

Source: Transparency International (2019a; 2019b; 2019c; 2020; 2021).

There is significant variation between regions globally. In the EU 6.9% of patients paid informally, compared to 10.9% in Latin America and the Caribbean, 11.6% in the Asia–Pacific region, 14.2% in sub-Saharan Africa and 16.6% in the Middle East and north Africa.

Importantly, informal payments also vary within regions. Fig. 3.3.2 illustrates the share of patients in the EU who paid in 2019. While the EU scores the lowest on this indicator across the globe, in parts of eastern Europe over 22% of patients have had to pay informally when using the publicly financed health care system (Table 3.3.2).

Fig. 3.3.2

Prevalence of informal payments in the EU, 2021

Source: Transparency International (2021).

A similar trend is observed in other geographical contexts. For example, Fig. 3.3.3 shows that in the Asia–Pacific region, the prevalence varies between 1% of patients in the Maldives to 26% in China (Table 3.3.3).

Fig. 3.3.3

Prevalence of informal payments in the Asia–Pacific region, 2020

Source: Transparency International (2020).

Cross-sectional survey data: associations between informal payments and levels of health finance, OOP payments and physician density

Below, we explore the association between the share of patients who have made an informal health payment and health financing levels, OOP payments and physician density. These reflect the financial sustainability and resourcefulness of the health care institutions and allow us to compare settings which have different types of health system organization. Public spending on health care affects scarcity of resources in the system, potentially leading to increased competition between patients for access to treatments. On the supply side, low expenditures are associated with low physician pay which could increase the demand for informal payments as a subsidy.

Fig. 3.3.4 visualizes a modest negative association between GDP spending on health and informal payments across the world. As countries spend a larger share of their GDP on health care, the prevalence of informal payments goes down. The relationships differ by region, with countries in the Latin America and Caribbean region and Europe displaying more statistically and economically negative associations when compared with other regions.

Fig. 3.3.4

Share of GDP spent on health care versus percentage of patients who paid informally in the past 12 months, 2019–2021

Note: A simple linear regression between the share of GDP spent on health care and percentage of patients offering informal payments shows a negative relationship (y = −1.47x + 0.207) with an R-squared value of 0.12, suggesting minimal association between these two variables.

Figure 3.3.4 Long description

The y-axis represents Respondents who paid a bribe in percentage, from 0 to 50, while the x-axis represents Health spending as a percentage of G D P, from 2.5 percent to 10.0 percent. The plots are categorised into W H O regions: Africa, Americas, Eastern Mediterranean Europe, South East Asia, and Western Pacific. The best-fit line runs from about (2.0, 18) to (12, 3). The equation for the line is given as y equals 0.207 minus 1.47 times x, where R-squared equals 0.12.

Sources: Transparency International (2019a; 2019b; 2019c; 2020; 2021) and WHO (2023a).

Next, we consider the relationship between formal OOP payments and informal payments. Unfortunately, it is not possible to distinguish between the two types of formal OOP payments – direct payments for costs incurred outside of the publicly financed or other third-party insurance coverage programme, and cost-sharing, or user charges to cover part of the costs of care within the public or private insurance coverage programme. There may be theoretical arguments for a negative association, for example if private options allow health care providers to supplement their income in formal, legitimate ways, and allow patients to express their dissatisfaction with the publicly financed health system by opting out and paying privately. However, Fig. 3.3.5, which illustrates this relationship between the two payment types on a global level, shows an overall positive association between the two: higher levels of OOP payments are associated with a higher prevalence of informal payments.

Fig. 3.3.5

OOP payments as share of total health spending versus percentage of patients who have paid informally in the past 12 months, 2019–2021

Note: A simple linear regression between household OOP spending and percentage of patients offering informal payments shows a positive relationship (y = 0.00263x + 0.0295) with an R-squared value of 0.21, suggesting modest positive association between these two variables.

Figure 3.3.5 Long description

The y-axis represents Respondents who paid a bribe in percentage, from 0 to 50, while the x-axis represents Household out-of-pocket spending as a percentage of current health spending, from 0 to 60. The plots are categorised into W H O regions: Africa, Americas, Eastern Mediterranean Europe, South East Asia, and Western Pacific. The best-fit line runs from about (4, 5) to (75, 22). The equation for the line is given as y equals 0.0295 plus 0.00263 times x, where R-squared equals 0.21.

Sources: Transparency International (2019a; 2019b; 2019c; 2020; 2021) and WHO (2023b).

Absolute health system constraints or resource shortages may also contribute to informal payments. A commonly used measure of the extent of resource shortages in a health system is the density of physicians per 1000 people. Our measure here includes both primary and specialized physicians. The fitted regression line in Fig. 3.3.6 illustrates the negative association between physician density and prevalence of informal payments. In accordance with theories of market supply and demand, patients in systems with better availability of physicians would on average be less likely to experience informal payments. Where physician density is high, shortages of doctor appointments/visits are less likely, and so informal payments are not needed to bridge the gap between the current market price and equilibrium price. Greece, which we discuss in more detail in our case study, is one of the outliers. Although physician density in the country is relatively high at around 5.5 doctors per 1000 people, the corresponding prevalence of informal payments is around 13%.

Fig. 3.3.6

Physician density versus percentage of patients who have paid informally in the past 12 months, 2014–2021

Note: A simple linear regression between physician density care and percentage of patients offering informal payments shows a negative relationship (y = −0.00182x + 0.15) with an R-squared value of 0.11, suggesting minimal association between these two variables.

Figure 3.3.6 Long description

The y-axis represents Respondents who paid a bribe in percentage, from 0 to 50, while the x-axis represents Physicians per 10,000 people, from 0 to 60. The plots are categorised into W H O regions: Africa, Americas, Eastern Mediterranean Europe, South East Asia, and Western Pacific. The best-fit line runs from about (0, 15) to (70, 2). The equation for the line is given as y equals 0.149 minus 0.0018 times x, where R-squared equals 0.11.

Sources: Transparency International (2019a; 2019b; 2019c; 2020; 2021) and World Bank (2023).

Prevalence of informal payments: summary

The global prevalence of informal payments is characterized by wide variation, notably in terms of both within-region and across-region variation. In addition, we explored three health systems indicators that have been shown to be significantly associated with informal payments: level of spend on health care, level of OOP payments and physician supply – and we provide some support for these associations on a global scale. Importantly, there are major data gaps in the comparable data on both the prevalence and frequency of informal payments but also the institutional and health system factors that may contribute to these payments. The continued use of comparable national surveys and reporting by organizations such as Transparency International is vital to support scholarly research and routine monitoring and tracking of the trends in informal payments over time.

Financing arrangements in the Greek health system: a brief overview

Health financing in Greece is mixed, funded by both public and private expenditure. Public funding accounts for almost 60% of total health expenditure and consists of resources from taxation and social insurance, with each of them having almost equal contribution. Formal OOP payments make up more than a third of total health expenditure, a much higher share than the EU average of 22% (OECD & EU, 2020). In terms of funding, the Greek health system is historically considered as one of the most privatized among EU countries, with OOP payments being the main source of private funding and a major health financing mechanism (Abel-Smith et al., Reference Abel-Smith1994; Kyriopoulos, Nikolosi & Mossialos, Reference Kyriopoulos, Nikoloski and Mossialos2019; Mossialos, Allin & Davaki, Reference Mossialos, Allin and Davaki2005). At the same time, private health insurance is underdeveloped due to several economic, social, cultural and market-related aspects (Davaki & Mossialos, Reference Davaki and Mossialos2005; Economou, Reference Economou, Sagan and Thomson2016). Greece’s funding mix can be mainly attributed to the chronic weaknesses of the public sector, such as long waiting lists, low levels of satisfaction and responsiveness, weak primary care and undersupply of some types of services (Kanavos & Souliotis, Reference Kanavos, Souliotis and Meghir2017; Mossialos, Allin & Davaki, Reference Mossialos, Allin and Davaki2005).

The scale of informal payments in Greece

A significant proportion of household health expenditure arises from informal payments, which are traditionally regarded as one the main idiosyncrasies and policy challenges in Greece’s health financing system. Informal transactions are generally widespread in both hospital and outpatient care and across public and private services (Economou et al., Reference Economou2017). Using data from 2012 and some extrapolation assumptions, Souliotis and colleagues (Reference Souliotis2016) estimated that informal health payments amount to €1.5 billion, accounting for 28% of household health expenditure in Greece. Findings for inpatient services demonstrate that informal payments accounted for 20% of household payments for inpatient care in 2005 (Siskou et al., Reference Siskou2008), while a more recent survey revealed that the respective figure was 13% (Grigorakis et al., Reference Grigorakis2016).

Evidence from survey data reveals that informal payments were incurred in almost one third of admissions to public hospitals and in 36% of visits to private practitioners (Souliotis et al., Reference Souliotis2016). In addition, they were made in 13.5% of visits to private hospitals and 19% of appointments to private diagnostic centres. These findings are generally in line with those of an earlier study, which showed that 36% of patients admitted to public hospitals reported informal payments. In addition, patients who underwent a surgical procedure were more than twice as likely to incur informal payments (Liaropoulos et al., Reference Liaropoulos2008). Another study, of a sample of women who received obstetric care in public hospitals, revealed that more than seven out of 10 women faced informal payments (Kaitelidou et al., Reference Kaitelidou2013). Using data from a convenience sample, a more recent study showed that informal transactions were undertaken in 63% of health care incidents, the rate being greater for the utilization of oncology and surgical and emergency care (Giannouchos et al., Reference Giannouchos2020). Informal payments were reported in more than 43% of a sample of 217 admissions to private hospitals for surgical care (Grigorakis et al., Reference Grigorakis2016). Evidence from patients who visited contracted physicians also reveals that 47% of them made informal payments to get prescriptions (Kyriklidis et al., Reference Kyriklidis2016).

Explaining informal payments in Greece

Informal payments have traditionally served as a complement to inadequate public funding and can be largely attributed to the fragmented and patchy design of health coverage (Mossialos & Allin, Reference Mossialos and Allin2005) and the inefficient pricing and doctor reimbursement systems, while also reflecting the lack of effective control mechanisms (Economou et al., Reference Economou2017).

In a recent qualitative report, key informants identified both direct and indirect motives for the incidence of informal payments in Greece (WHO Regional Office for Europe, 2018). Doctors’ demand for additional payment, better access to high-quality care, provider choice, fear of getting inadequate care and adherence to the common practice were considered as the key reasons for the extensive informal transactions within the Greek health system. However, earlier studies report mixed findings. A strand of the evidence shows that patients tend to incur informal payments in order to skip the long waiting lists in Greek health system and facilitate access to care (Economou & Giorno, Reference Economou2009). This is also confirmed by quantitative evidence. According to survey data, more than 70% of patients who incurred informal payments did so to facilitate access and reduce waiting times. The corresponding figure was greater among respondents who visited public hospitals, private practitioners and private diagnostic centres, exceeding 80% (Souliotis et al., Reference Souliotis2016). The findings on the importance of access to health care as the key explanation for the incidence of informal payments contradict earlier evidence, which reveal that fear of receiving suboptimal care was the most common reason for paying informally in Greek public hospitals (Liaropoulos et al., Reference Liaropoulos2008). Almost nine out of 10 Greek patients who incurred under-the-table payments in public services did so following a doctor’s request, rather than offering them as a gratuity (Souliotis et al., Reference Souliotis2016). This is consistent with some earlier findings, showing that the primary reason for making such payments was doctor demands, with a smaller share of patients offering them as an expression of gratitude to health providers (Davaki & Mossialos, Reference Davaki and Mossialos2005; Kaitelidou et al., Reference Kaitelidou2013; Liaropoulos et al., Reference Liaropoulos2008).

Impact of informal payments on the Greek health system

Informal payments have detrimental effects on Greece’s health system performance, with their prevalence partly explaining the high rates of dissatisfaction with the public health services (Kyriopoulos & Mossialos, Reference Kyriopoulos, Mossialos and Immergut2021). Their high frequency does not merely reflect luxury preferences or cultural idiosyncrasies. Rather, informal transactions are an established practice and widespread behaviour, with a large share of doctors demanding such payments to facilitate access to care (Souliotis et al., Reference Souliotis2016). This practice directly compromises access to care, with those who cannot afford such payments facing additional barriers reflected in different quality of care or longer waiting lists (Mossialos, Allin & Davaki, Reference Mossialos, Allin and Davaki2005).

There is conflicting evidence on the link between financial status and the probability of informal payments in Greece. A strand of the literature has shown that socioeconomic characteristics are not associated with the incidence and intensity of informal payments (Horodnic et al., Reference Horodnic and Polese2017; Liaropoulos et al., Reference Liaropoulos2008), whereas a more recent study found that the better-off tend to make larger informal payments (Giannouchos et al., Reference Giannouchos2020). This discrepancy is potentially explained by the decreasing capacity to pay during the Greek economic crisis, especially among poorer households. But even if the worse-off pay less than their richer counterparts, their payments correspond to a higher share of their household income (Economou & Giorno, Reference Economou2009). In this context, informal payments further exacerbate the regressivity of an already regressive funding mix, disproportionately burden poorer households and compromise equity in financing and financial protection (Economou et al., Reference Economou2017; Kyriopoulos, Nikolosi & Mossialos, Reference Kyriopoulos, Nikoloski and Mossialos2021). For example, recent qualitative findings shows that Greek households may even cover their informal expenses for health care through credit (WHO Regional Office for Europe, 2018). Evidence from self-reported data demonstrates that informal payments had a large impact on living standards for 56% of people with poor financial status and for almost a third of their better-off counterparts (Souliotis et al., Reference Souliotis2016).

Patient pathways are currently guided by an informal referral process, given the lack of a well-functioning referral system and explicit official clinical guidelines. This system is largely driven and reinforced by informal payments, exacerbating inefficiency as a result of provider incentives for supplier-induced demand and suboptimal use of existing resources (WHO Regional Office for Europe, 2018).

Policy responses to informal payments

In Greece, there have been efforts to address the persistence of informal payments and minimize their adverse impacts. There have been multiple measures proposed for short-term implementation and impact: (i) focusing on the legal prohibition along with active enforcement of preventive regulations; (ii) increasing doctors’ salaries; (iii) providing information to raise awareness and increase transparency; (iv) introducing channels of voice and public opinion; and (v) formalizing informal payments. Apart from the short-term measures, there are several long-term policies for reducing fragmentation, inefficiencies and structural weaknesses in financing and delivery of care, which can help reduce the incidence and intensity of informal payments (Kyriopoulos & Mossialos, Reference Kyriopoulos, Mossialos and Immergut2021). For example, a list of such measures includes changes in provider payment and pay for performance schemes, reduced fragmentation of pooling, reform of primary care and the referral system, better care coordination, and expansion of the benefits package. To date these have had limited effects, due in part to the external environment: the financial crisis of 2008 and subsequent austerity measures, and the global COVID-19 pandemic have further challenged efforts to reform and strengthen the health system in Greece and to make a dent in informal payments. In addition, there are endemic and deep-rooted vested interests that often block the required reforms (Mossialos, Allin & Davaki, Reference Mossialos, Allin and Davaki2005; Kyriopoulos & Mossialos, Reference Kyriopoulos, Mossialos and Immergut2021). Some of the doctors’ associations are powerful interest groups with strong incentives to maintain the status quo, which allows them to secure higher income and autonomy. In addition, although politicians rhetorically emphasize the importance of addressing informal payments, they have not yet devised a comprehensive strategy for doing so. The reason for this possibly relates to the additional fiscal space needed for increasing public spending for health care and the political commitment required for the introduction of comprehensive auditing mechanisms.

Who pays for R&D?

The pharmaceutical industry funds the development, commercialization and manufacture of medicines. The public sector regulates the industry and incentivizes the development and production of medicines it typically neglects. It also pays for most of the initial research, which is what we deal with first.

Investment in R&D: does it produce the medicines we need?

As the primary proponent and cost carrier of medicines’ commercialization and manufacture, the pharmaceutical industry largely determines which medicine candidates enter the development pipeline. Private R&D investment choices have shown that expected profits and market growth are more important factors in pharmaceutical company decisions than unmet societal needs (Kalindjian et al., Reference Kalindjian2022). Risk of failure does not seem to be a prominent determinant of medicine development decisions, as particularly risky domains like oncology have experienced an important investment surge in recent years (Huang & Nambudiri, Reference Huang and Nambudiri2020).

As of late, the pharmaceutical sector has experienced a fall in numbers of new medicines brought to market, as medicine development has become increasingly specialized, complex and lengthy with the advent of biologicals and biotechnology-based gene and cell therapies (also known as ATMPs) (Hay et al., Reference Hay2014). R&D investment is increasingly concentrated in areas protected from competition, such as medicines for rare or neglected diseases and ATMPs. The area of oncology has seen a particular peak in authorizations over the last decade, with close to 50% of the global pipeline focused on oncology and immunology (Shimmings, Reference Shimmings2017). While cancer is a major public health issue, other chronic diseases such as cardiovascular conditions which cause millions of deaths each year have experienced a decline in investment and medicine development (Fordyce et al., Reference Fordyce2015). Moreover, evidence suggests at least one in five new cancer medicines do not bring any improvement to patients in terms of quality of life, overall survival, or safety benefits (Davis et al., Reference Davis2017; Salas-Vega, Iliopoulos & Mossialos, Reference Salas-Vega, Iliopoulos and Mossialos2017; Vokinger et al., Reference Vokinger2020; Reference Vokinger2022). Neglected and antimicrobial-resistant diseases are another area of public health concern, which is underrepresented in medicine development efforts, likely due to the limited scope for returns on investment (see chapters 3.9 and 3.10). Overall, R&D efforts do not appear to correlate with global disease burden (Cottingham, Kalbaugh & Fisher, Reference Cottingham, Kalbaugh and Fisher2014). Relatedly, incentives originally introduced to propel the development of medicines to cater for the unmet needs of rare disease patients have also had unexpected effects, oversteering investment in this area and contributing to the emergence of “niche” blockbuster products (Kumar Kakkar & Dahiya, Reference Kumar Kakkar and Dahiya2014). The Pharmaceutical Strategy for Europe and ongoing revisions of the EU’s general pharmaceutical legislation aim to address and correct some of these possible misincentives in the European context (European Commission, 2023a; 2023b).

Even in areas with substantial R&D investment like cancer, there is a disproportionate focus on projects of shorter duration, such as late-stage cancer, due to the immediacy of financial rewards and the limited profitability stemming from fixed patent duration (Budish, Roin & Williams, Reference Budish, Roin and Williams2015). The system’s reliance on patents as one of the primary incentives of innovation actively influences industry practices and hampers medicine innovation. In particular, it has created a market for new medicines which form part of the same therapeutic class as existing medicines (known as “me-too” medicines) and provide limited or no added clinical benefits due to their closely related molecular structures and modes of action. Me-too medicines build on existing knowledge and are hence faster to develop and commercialize. In addition, being subject to conventional intellectual property protection, they can represent profitable ventures for medicine developers once patents for other members of the same class expire (Gagne, Reference Gagne2011). These and similar approaches adopted by industry to extend the market protection and profitability of their products impede the introduction of generic alternatives which would improve the affordability of medicines for patients.

Research duplication and waste represent other important sources of costs, which can be challenging to fully ascertain. Evidence suggests that up to 80% of clinical trials produce redundant findings and approximately 50% never publish full reports of performed research and results (Glasziou & Chalmers, Reference Glasziou and Chalmers2016). The failure to disclose full records and clinical effectiveness information from clinical trials is accompanied by a reporting bias towards positive clinical outcomes (Mitra-Majumdar & Kesselheim, Reference Mitra-Majumdar and Kesselheim2022). As a result, a substantial proportion of medical research funding risks being misspent as research efforts are duplicated even when they have demonstrably yielded meaningless results. In addition, there are ethical implications of failing to report negative results of clinical trials. If these results were reported, it could prevent and protect future trial participants and patients from receiving therapies which are unlikely to generate clinical benefits (Nygaard, Reference Nygaard2017). Additionally, without full disclosure of all clinical trial results, public payers risk overpaying for medicines if they lack and cannot avail themselves of the full picture of clinical trial outcomes when entering pricing negotiations (Vogler, Reference Vogler2022a).

To enable fast access to medicines addressing unmet patient needs, the EMA provides manufacturers with the option of obtaining early scientific advice and accelerated assessments. This was systematized in its PRIME scheme, a voluntary scheme providing guidance on data and clinical trial requirements to participating medicines at early stages of development (EMA, n.d. c). Approaches such as this one are more permissive with regards to the standards of evidence required than traditional marketing authorization procedures and have shown to reduce average approval time by a few years (Panteli & Edwards, Reference Panteli and Edwards2018). This may result in efficiency gains in the R&D process leading up to authorization, although safety and efficacy should continuously be monitored as well as missing evidence substantiated to comply with post-marketing requirements. Concerns have been raised, however, that accelerated assessments may expose patients to safety risks and progressively lower evidentiary requirements if used inadequately, instead of effectively managing unmet needs (Panteli & Edwards, Reference Panteli and Edwards2018). Box 3.4.5 describes an additional way to accelerate the development of medicines to meet medical needs currently uncatered for, by repurposing existing medicines.

How much money is spent on R&D?

Despite fewer new products being commercialized, the sector has experienced continued increases in investment with substantial incentives provided by public and private entities to spur innovation (Huang & Nambudiri, Reference Huang and Nambudiri2020). The pharmaceutical industry is characterized by elevated fixed R&D and marketing costs and a low marginal production cost. As explained above, it takes substantial investment to discover and launch a new medicine, while the cost of producing an extra unit is usually comparatively small (Cutler, Reference Cutler2020; Rennane, Baker & Mulcahy, Reference Rennane, Baker and Mulcahy2021). Several studies have deployed different methodologies and sources to attempt cost estimations of the R&D process, which range between US$ 314 million and US$ 2.8 billion for the development of new molecular entities (DiMasi, Grabowski & Hansen, Reference DiMasi, Grabowski and Hansen2016; Wouters, McKee & Lutyen, Reference Wouters, McKee and Luyten2020). Other publications report even broader ranges. Rennane and colleagues detected gaps of US$ 113 million to US$ 6 billion accounting for all new medicines, including those developed in specific cost-intensive therapeutic areas such as Alzheimer’s disease (Rennane, Baker & Mulcahy, Reference Rennane, Baker and Mulcahy2021). Similarly, a 2016 report by the United Nations Secretary-General’s High-Level Panel on Access to Medicines (2016) described estimates ranging between US$ 100 million to US$ 4.2 billion.

The costliest of the R&D stages are phase III clinical trials, with USA estimates suggesting that the cost of a pivotal trial alone lies somewhere between US$ 12 to US$ 53 million depending on therapeutic area studied. In the same study, cost estimates for phase II trials and phase I trials also varied importantly by therapeutic area being examined, with ranges of US$ 7 to US$ 20 million and US$ 1 to US$ 7 million, respectively (Sertkaya et al., Reference Sertkaya2016). Figures in a similar ballpark were reported by other USA-based investigations (Moore et al., Reference Moore2018; Reference Moore2020). The question remains whether pharmaceutical companies’ overall R&D investments are truly reflective of the high pharmaceutical prices charged at the end-users’ side. A few initiatives have been pressing for more transparency in this regard. For example, DNDi, the public–private partnership working towards treatment solutions for neglected diseases, has pledged to disclose the R&D costs of its products (DNDi, n.d. a; n.d. b; 2022), which as some evidence suggests, have been lower than those reported by pharmaceutical companies (Junod Moser et al., Reference Junod Moser2023; Moon, Reference Moon2017; Moon, Bermudez & ’t Hoen, Reference Moon, Bermudez and Hoen2012).

Pharmaceutical companies argue that the elevated failure rate and high costs incurred in the early stages of a medicine’s life-cycle necessitate the observed high prices of newly marketed products. However, the industry’s resistance to disclosing the full costs of medicine development undermines this argument and has prompted multiple investigations into the true costs of the R&D pipeline. Moreover, this argument fails to account for the substantial public investment in basic research as stated above. Going forward, enhancing the system’s transparency is key and could potentially support R&D priority-setting based primarily on societal needs rather than commercial viability (Panteli & Edwards, Reference Panteli and Edwards2018).

In this first half of the chapter, we have looked at the financing of medicines’ R&D. In the next half, we turn to look at how their purchase is funded and some of the processes involved.

Who pays for the purchase of medicines?

Generally, pharmaceutical expenditure is covered by a mix of public and private funds. Solidarity-based health systems, which represent the most common model in Europe, leverage public funds to ensure access to essential health services, including essential medicines, for their populations. Primarily, this entails coverage via social health insurance models or a national health service, which rely on diverse revenue streams, including forms of taxation, employment and social security contributions (see Chapter 1.1). On average, 58% of pharmaceuticals are funded by government/compulsory schemes (39% are paid OOP and 3% through voluntary health insurance (VHI) schemes (see Chapter 1.3)) across Organisation for Economic Co-operation and Development countries, although there is considerable cross-country variation; at the two ends of the spectrum, over 80% of retail pharmaceuticals are funded publicly in France (83%), Ireland (82%), Germany (81%) and Croatia (80%) and fewer than 40% in Poland (35%), Bulgaria (24%) and Chile (20%) (OECD, 2023).

Although publicly funded and organized systems are designed to provide universal access, financial protection does not necessarily cover all pharmaceuticals, and patients are typically required to cover a part of the expenses for reimbursed (i.e. publicly funded) medicines in the community (i.e. outpatient sector). In countries with more limited progress towards universal health coverage, most medicines (in both outpatient and hospital sectors) are to be paid fully OOP by the patients. This may lead to a high financial burden for patients and their carers and can even result in catastrophic spending.

There are different types of co-payments (e.g. fixed prescription fee, percentage co-payments of the medicine price, and deductibles), with variable effects on the amount paid by the patients, and by the public payer (see Chapter 2.4). In solidarity-based health systems, vulnerable population groups may be fully or partially exempt from co-payments for outpatient reimbursable medicines (Panteli et al., Reference Panteli2016; Vogler, Zimmermann & Haasis, Reference Vogler, Zimmermann and Haasis2018; Vogler, Haasis & Zimmermann, Reference Vogler, Haasis and Zimmermann2021; WHO Regional Office for Europe, 2018). Evidence shows that patients in low- and middle-income countries (LMICs) are more frequently exposed to high OOP payments for pharmaceuticals with over 90% of the 150 million individuals incurring catastrophic health expenditure globally residing in these countries (WHO & IBRD, 2020). Even in the World Health Organization (WHO) European Region, estimates from 24 countriesFootnote ¹comprising 18 HICs and six middle-income countries) suggest that up to 9% of households are impoverished and unable to pay for basic needs due to health spending, although large differences are observed among EU countries (estimates ranging between 0.3% to 5.9%) and non-EU countries (estimates ranging between 3.6% to 9.0%) (Thomson, Cylus & Evetovits, Reference Thomson, Cylus and Evetovits2019). Importantly, OOP payments for medicines are a key driver for this catastrophic and impoverishing health spending in Europe (Thomson, Cylus & Evetovits, Reference Thomson, Cylus and Evetovits2019; WHO & IBRD, 2020).

Purchasing medicines: what is covered?

Funding medicines implies compensating the actors in the supply chain for their services related to the product: the pharmaceutical manufacturer for the development, production and launch of the product; importers, wholesalers and other distributors for their logistics services; and pharmacies for services around dispensing the medicine. Usually, in price-regulated settings of solidarity-based systems, these services are remunerated through the different components of the medicine price: the ex-factory price to reward the manufacturer, the wholesale markup for the distributor, and the pharmacy markup for the pharmacy.Footnote ² The costs attributable to medicines’ distribution can be considerable, but are beyond the scope of this chapter. The following paragraphs discuss which medicines are reimbursed by public payers, and how this is determined.

How much money is spent on the purchase of medicines?

Pharmaceutical expenditure is the product of the volume of medicines consumed and their price (Fig. 3.4.1). For comparative purposes, pharmaceutical expenditure is usually expressed in one of three ways: per capita, as a share of national gross domestic product (GDP), or as a component of total health spending. Per capita annual spending for outpatient pharmaceuticals in the EU was estimated at €462 in 2020, with considerable variation detected across Member States (OECD & EU, 2022). At €660 per capita, Germany was the highest spender, while Denmark lay well below the EU average at €258 per capita (see Panteli et al. (Reference Panteli2016) on the relevant context for these figures). In the wider European Region, disparities in spending are even more extreme. Adjusted for purchasing power parity, current expenditure on medical goods ranged from €1281 per capita in Germany to €221 per capita in Moldova in 2020 (WHO, 2020). Lastly, if measured as a share of GDP, pharmaceutical expenditure usually corresponds to current health expenditure (as a share of GDP) times the share of health expenditure spent on outpatient pharmaceuticals, and in the EU ranged from 0.6% (of GDP) in Luxembourg to 2.87% (of GDP) in Greece in 2020 (OECD, n.d.; Panteli et al., Reference Panteli2016). As explained above, there is a lack of data on the costs incurred from hospital pharmaceuticals in health systems and sometimes also from private households, making it challenging to paint a comprehensive picture of total pharmaceutical expenditure. Generally, the share of pharmaceutical expenditure out of total health expenditure tends to correlate negatively with national income level. This is observed in many LMICs, where spending on pharmaceuticals can reach up to 60% of health care spending (Milne & Kaitin, Reference Milne and Kaitin2019). One reason for this is that HICs have a stronger price negotiating power.

At the EU level, 29% of outpatient medicines were financed by private households in 2020 (OECD, 2022), while public expenditure accounted for 70% and VHI for 1% of total expenditure on outpatient pharmaceuticals. The shares of public and private expenditure vary significantly across different Member States, with particularly high public shares observed in Cyprus (85%), Germany (82%) and France (82%) and low shares in Bulgaria (24%), Poland (35%), and Latvia (41%). About half of what is spent OOP by patients in the EU went towards medical goods (including outpatient pharmaceuticals and therapeutic appliances) in 2020 (OECD, n.d.). The comprehensiveness of the benefit basket, the level of prices as well as the type of co-payments impact actual patient expenditure.

Fig. 3.4.1

Pharmaceutical expenditures are composed of the volume of medicines consumed times their prices

OTC: over the counter; VAT: value-added tax; VHI: voluntary health insurance.

Figure 3.4.1 Long description

The first graphic explains the costs when prescriptions are covered. The individual pharmaceuticals include Ex-factory, Wholesale, Retail, and VAT, which together form the Cost-sharing and Reimbursement price. Only the reimbursement price is multiplied by the Consumption volume, which results in the (Measured) public expenditure. This equals the Rebates (average X percent). The second graphic explains the costs when medications are O T C and not covered. The individual pharmaceuticals include Ex-factory, Wholesale, Retail, and VAT, which together form the Direct payment and V H I. This is multiplied by the Consumption volume to get the (Measured) private expenditure.

Source: Adapted from Busse & Panteli (2019).

How are purchase prices determined?

Payers employ a range of mechanisms to determine the prices they are willing and able to pay for pharmaceuticals, against the backdrop of balancing necessary considerations of cost containment with the need to retain a healthy R&D pipeline (Box 3.4.6). Approaches include value-based pricing, managed-entry agreements, external reference pricing, internal price referencing and centralized procurement. We explain each of these briefly below.

While most countries only regulate prices for reimbursed medicines, some (e.g. Lithuania, Türkiye) statutorily determine (or negotiate) the prices of all medicines (Vogler, Zimmermann & Haasis, Reference Vogler, Zimmermann and Haasis2018). The rationale for this scope of price regulation is to protect patients from excessive prices even if they pay for their medicines OOP.

National variations in purchase price

The mix of different mechanisms employed by countries along with factors such as purchasing power and market size contribute to a considerable price variation across health systems globally. For example, studies examining the costs of new hepatitis C medicines based on direct-acting antivirals sofosbuvir (Sovaldi®) and ledipasvir/sofosbuvir (see the Value-based pricing section) show considerable variation in originator prices across countries (e.g. the median list price for a 12-week treatment course was of US$ 40 502 in 2020 with a range from US$ 10 730 in Argentina to US$  91 461 in Italy (Barber et al., Reference Barber2020).

Evidence suggests that national income levels correlate negatively with prices – at least with official list ones. Although evidence on medicine prices in LMICs remains limited, over the past few years higher prices in LMICs compared to HICs have been documented for the same treatments (Moye-Holz & Vogler, Reference Vogler2022). This pattern is not limited to territories outside Europe, as similar discrepancies have been demonstrated between EU Member States of different income levels (Moye-Holz & Vogler, Reference Vogler2022; Vogler, Vitry & Babar. Reference Vogler, Vitry and Babar2016). This appears to be linked to lower health care budgets and less power to negotiate discounts with manufacturers, who are attracted to markets in wealthy countries amenable to purchasing expensive specialty medicines, such as oncology medicines. Evidence from Latin American countries suggests ineffective or lack of pricing regulation in these countries may result in higher prices in comparison to prices paid in HICs for these products (Moye-Holz & Vogler, Reference Vogler2022).

International price comparisons face a number of methodological challenges (Panteli et al., Reference Panteli2016), not least in terms of meaningfulness of results due to the widespread use of confidential discounts. One of the few studies that has published confidential price data (for cancer medicines) highlighted differences of up to 58% in discounted prices across 15 European countries (van Harten et al., Reference van Harten2016). Countries with large markets such as Italy and Spain are more likely to be granted higher discounts for medicines than other countries, for instance in central and eastern Europe (Vogler, Reference Vogler2021a). Furthermore, there is no explicit and generally accepted definition of what constitutes an “affordable” or a “fair” price for a medicine; this means that while comparisons can highlight differences, they cannot automatically guide policy decisions.

Definition of integrated care

Using Struckmann and colleagues’ (Reference Struckmann2018) definition, integrated care can be understood as:

This definition encompasses both a system and a patient perspective, where integrated services are those that are experienced by the patient as coordinated (Goodwin, Reference Goodwin2016).

The goal of care integration can be achieved through multiple means – of which payments, the focus of this chapter, are one. According to Poku, Kagan and Yehia (Reference Poku, Kagan and Yehia2019), there is an inverse relationship between care that is truly integrated and the need for care coordination: the more integrated care services are, the less need there is for coordination. The delivery of integrated care may, but need not, involve the formation of new vertically integrated organizations and it is important to note that organizational integration does not automatically solve the need for internal coordination (Williamson, Reference Williamson2002).

The role of payment models

In the same way that health systems may require reform to meet the changing needs of the population, there is a need for new payment methods that better align with current policy goals (Leijten et al., Reference Leijten2018; Struckmann et al., Reference Struckmann2017). This chapter discusses payment models that can support the delivery of integrated care by non-integrated organizations, as well as models intended to incentivize the delivery of integrated care by vertically integrated organizations. The current ways of paying for primary and secondary care (Box 3.5.1) do not fit well with the provision of integrated care. For example, fee-for-service (FFS) and prospective payments based on diagnosis-related groups (DRGs) are based on activity, and primarily incentivize a high throughput of discrete units of care, rather than a long-term, well-coordinated, holistic care effort that ensures quality and cost–effectiveness for the chronically ill and multimorbid across the spectrum of care (Busse & Mays, Reference Busse, Mays, Nolte and McKee2008).

The redesign of payment models to enable more efficient care for patients with one or more chronic diseases requires providers to recognize the interdependency of care delivered across multiple sectors. Although this goal cannot be met by changes to the payment system alone, it is clear that most existing payment models are designed with a single sector in mind, and do not encourage providers to better coordinate their care or consider the implications of care in one sector on the expected cost and benefits of care in other sectors (Mason et al., Reference Mason2015). Although more than 100 integrated care programmes for people with multimorbidity have been implemented in Europe, these challenges are rarely addressed by new innovative payment methods (Struckmann et al., Reference Struckmann2017). However, such methods do exist and are worthy of consideration, as we shall show in what follows.

We begin by outlining a set of criteria by which we can benchmark payment models with respect to their ability to meet the policy goal of greater care integration across sectors; we provide a comparative example to illustrate the need for system change. We then use the criteria to examine traditional provider payment models and their failure to meet the health system challenges of today. Next, we consider the options available for policy-makers looking for new payment models that can encourage integrated care, and the models’ potential to incentivize integrated care, in theory and in practice, including three case studies, from Germany, the Netherlands and the USA, respectively. Finally, we draw policy lessons from the foregoing and highlight where the evidence base still needs further strengthening.

FFS and DRG payments

FFS payments are mostly used in primary or ambulatory care and involve paying for each service unit provided. The fee level usually relates to the type and complexity of the provided service. This generally gives providers the financial incentive to provide as many reimbursable services as possible (Busse & Mays, Reference Busse, Mays, Nolte and McKee2008; Gosden et al., Reference Gosden2000). Therefore, FFS systems may stimulate inappropriate or unnecessary use of services and offer little or no incentive for (macro level) expenditure control (Ellis & Miller, Reference Ellis, Miller and Heggenhougen2008). Particularly for patients living with multiple chronic conditions, this may result in having to make more doctor visits with a greater number of doctors, which could also have repercussions on the quality of the care they receive (Rijken et al., 2017).

DRG-based case payments are frequently used in hospitals. If based on average costs, they provide strong incentives for efficiency (Shleifer, Reference Shleifer1985), but bring the same challenges as FFS payments and equally come with the risk of disregarding patient needs and appropriateness of care (Busse et al., Reference Busse2011).

With regard to care integration, neither FFS nor DRG-based payments offer providers incentives to coordinate care or consider in which setting care will be provided with greater efficiency as this could lower the number of reimbursable services/episodes (Struckmann et al., Reference Struckmann2017).

Global budgets and capitation payments

Global budgets and capitation payments involve paying providers a fixed amount to provide services to patients for a particular time, regardless of the volume of services provided to individual patients. Both payment methods are administratively simple and control macro level expenditure well but they create few financial incentives for providing care proactively and therefore do not incentivize figuring in the expected impact of care on future care needs. Providers may respond by providing as little care as possible to each patient (care-skimping) as the providers bear the financial risk. This in turn may create the potential for undersupply of services, lower quality of service provided, increased referrals and the adverse selection of low-risk patients (Busse & Mays, Reference Busse, Mays, Nolte and McKee2008; Ellis & Miller, Reference Ellis, Miller and Heggenhougen2008; Nolte & Knai, Reference Nolte and Knai2015). The latter particularly threatens access to appropriate care for people with multiple chronic diseases.

Traditional payment models’ inadequacy: summary

In practice, most countries have implemented combinations of these payment methods (blended payments) based on different types of information (about the service, the provider or the patient) in order to mitigate the adverse incentives of the methods individually and create a more balanced set of incentives.

Generally, these payment mechanisms do not sustain a LTC effort, are focused on a single specialty or sector, and do not recognize the interdependency of care delivered across the care spectrum. This particularly affects the appropriateness and quality of care for patients with multiple chronic diseases. In terms of factoring in the impact of care on future needs and therefore costs, only capitation and global budgets give some incentive for reducing future care needs, but only in the same sector and within the current budget period. Providers paid by FFS and DRG could even be seen to benefit financially from maintaining or increasing patients’ future care needs, although altruistic provider motives would be expected to limit the extent of perverse responses to these incentives.

In addition to incentivizing the goals for integrating care, payment models should also support, or at least not work against, other policy goals such as access to care, equality and transparency/accountability, and avoidance of unintended consequences. One of the important benefits of basing payments on provider activity has been that it gives purchasers and regulators access to clear information about activity in the health sectors. If new payment models move away from being activity based, it is important to ensure that information of similar richness and accuracy can still be obtained – a point to bear in mind, when considering new, alternative models.

P4C

At the most basic level, existing payment methods can be adjusted to promote better coordination of care with a payment type known as P4C (Tsiachristas et al., Reference Tsiachristas2013). The adjustment can be made by: (i) adjusting the care provider’s budget to cover; for example, the cost of employing a case manager tasked with ensuring coordinated care; (ii) increasing capitation payments for each registered patient with a provider that employs case managers or multidisciplinary teams; or (iii) providing additional fees for coordination activities such as case review and participation in meetings. Combinations of the three are also possible.

Bundled payments

Bundled payments is the term used for payments covering more than one episode of care for the same patient. As shown by Stokes and colleagues (Reference Stokes2018), what is included in the care bundle can vary greatly, from payments covering all episodes of care for a given condition from a single provider to payments covering all care for a patient from multiple providers.

The effect of a bundled payment on care integration depends on the exact scope of the payment in terms of the target population, time perspective, how many sectors are included, how many providers are covered, the number of diseases/activities covered for the population, supporting levers in the form of quality measurement, the proportion of income affected and whether there is additional financial integration or risk sharing in place (Stokes et al., Reference Stokes2018). Fig. 3.5.1 gives an example of how three existing bundled payment systems vary on these dimensions, two from the Netherlands (for diabetes and elderly care, respectively) and one from the USA (Medicare BCPI).

Fig. 3.5.1

Three bundled payment schemes and how they vary on key dimensions

Source: Stokes et al. (2018).

In general, the broader the scope of the payment, the greater the incentive for integration of care but also the greater the financial risk transferred to the provider (Struckmann et al., Reference Struckmann2017). Therefore, the introduction of a bundled payment model must always consider the existing provision structure and the availability of suitable contractors to accept the financial risks involved with the introduction of bundled payments.

When the payment bundle covers more than one provider, the contractual arrangement required increases in complexity as the mechanism necessitates financial integration between the two or more providers who become jointly responsible for the care of a patient. Such arrangements are much harder to implement than P4C because they require changes that go beyond simply modifying the payment system and imply a transfer of financial risk from the payer(s) to the provider(s). The multiple providers may be from within the same sector (e.g. primary care) or across sectors (e.g. primary and secondary care providers). They may choose to form a new integrated care provision organization, often referred to as an accountable care organization (ACO). But even without forming a new organization, multiple providers can assume joint financial responsibility.

A review by Mason and colleagues (Reference Mason2015) identified eight types of financial integration, including: (i) transfer payments and cross-charging, where one provider either makes contributions to or pays fees to compensate the other entity; (ii) pooled funds, where multiple providers contribute to a common fund that can be spent on shared patients; and (iii) structural integration, where a new entity is formed with a single management team.

The difference to the add-on payment methods previously discussed is that financial integration offers a greater incentive to providers to recognize their joint impact on patient outcomes, by creating a financial interdependency between providers across sectors and assuming a mutual financial risk.

Theoretically, the broadest bundled payment would cover all care provided to all patients living in an area over a defined period of time. However, this type of payment would be usually called a broad capitation payment or a population-based budget. This implies that the question of how to pay providers and how to assure coordination of care is simply transferred from the purchaser to another organization, which then becomes the payer for individual providers (Struckmann et al., Reference Struckmann2017). In this situation, purchasers arguably have less capacity to act as strategic purchasers and influence service provision without supporting collection of information about activity and quality, and care provision under such a payment bundle becomes opaque for the payer and the public.

Shared-savings model

One of the most common models of financial integration designed to promote integrated care is shared-savings. In this model, participating providers agree to cooperate in a network and to collaborate with the aim of achieving joint savings for the care provided to participating patients. If such savings are made, a share is distributed to providers. Of course, the providers, patients and services eligible for participation in the programme have to be explicitly defined before the start of the programme (Hayen et al., Reference Hayen2015). Within the model, each individual provider continues to be paid according to the established payment system, but all costs for patients participating in the integrated care programme are registered and retrospectively compared to previous figures or a benchmark set to ensure that random fluctuations are not rewarded or punished. The payer is thus able to determine if any savings have been made, a share of which can then be shared among the collaborating providers.

Usually, shared-savings models require a new organizational structure to coordinate the roles of the participating providers, (re)distribute savings between them, help with the coordination of care, and facilitate the development of joint clinical pathways or joint electronic medical records. The (re)distribution mechanism is very important as it may well determine the success of the programme and the extent to which all providers gain from the reorganization of care (Struckmann et al., Reference Struckmann2017). Payers will likely want to specify reporting requirements about activity and quality to ensure transparency.

Ensuring quality of care

Finally, payment systems can be adjusted to incorporate incentives for quality. This is often called pay for performance (P4P) or pay for quality (P4Q) and can be based on structures, processes and/or outcomes (Donabedian, Reference Donabedian1988; McManus et al., Reference McManus2021). P4P can be used to achieve agreed quality targets, for which payment to providers (professionals or institutions) is modified upwards or downwards. Within a provider network, target achievements can be recorded electronically and compared among participating providers, for example, with respect to patient experience (Struckmann et al., Reference Struckmann2017).

Measuring and incentivizing quality of care is particularly important when payments allow providers to interpret patients’ care needs broadly, as is the case with shared-savings models, bundled payments and capitation payments made to integrated care structures. This is because broader payments provide larger incentives and room for providers to reduce costs – and, in the absence of adequate mechanisms to monitor and reward quality, providers may attempt to cut costs by reducing the provision of services, disregarding patient needs and providing lower quality care.

Table 3.5.1 also provides examples of these monitoring and rewarding mechanisms. For example, providers could be given penalties or bonuses for having appropriate structures in place for the care of people with chronic diseases (e.g. employing personnel with special training in multimorbidity, case managers) calculated as a percentage of their usual income. Furthermore, bonuses could be given for achieved outcomes of care (e.g. lower than average rates of mortality or hospital admissions, high patient satisfaction). Lastly, providers could receive a bonus if care is delivered in line with recommended treatment pathways and guidelines (e.g. if they perform a biannual polypharmacy review) (Struckmann et al., Reference Struckmann2017).

Designing appropriate P4P programmes is a complicated task because of the many issues surrounding the measurement of quality (e.g. which indicators to use and how), the definition of targets (e.g. absolute targets or relative targets), the level of the payment adjustment (e.g. individuals, groups, institutions), the form of the incentive (bonus or penalty), the use of risk adjustment and so on (Eijkenaar, Reference Eijkenaar2013).

Case study 1: Bundled payment scheme for chronic diseases, Netherlands

A pilot of this bundled payment scheme with a focus on diabetes care began in 2007 and the scheme was fully operational from 2010. The scheme is voluntary, and allows health care providers to form care groups consisting of multiple providers that take joint clinical and financial responsibility for the patients covered by the scheme; for example, patients with diabetes, COPD or vascular disease. Participating care groups receive a fixed payment to cover disease-specific care, while other care is still reimbursed separately. The amount is set through negotiation between the payer and the care group. The group participants can then either provide care for patients themselves or subcontract to other providers (Llano, Reference Llano2013). The aim of the scheme is to improve the quality of care, and the contracts specify the care process and outcome quality standards of care that providers must adhere to under the scheme.

An evaluation by the Dutch National Institute for Public Health and the Environment (Struijs et al., Reference Struijs2012) found modest improvements in most process indicators of quality, including foot examinations, kidney function testing and cholesterol testing, while numbers of eye examination declined. Nevertheless, the variation in the quality of care between care groups was considerable. It should be noted, however, that the analysis did not use a control group, and it is therefore possible that the improvements in process quality would have occurred in the absence of the payment change regardless. We have not been able to locate stronger or more recent empirical evidence on the impact of the Dutch bundled payments on the quality of care.

Early empirical evidence suggested that expenditure increased in the short run for patients covered by the scheme (Mohnen, Baan & Struijs, Reference Mohnen, Baan and Struijs2015) and a recent study (Karimi et al., Reference Karimi, Tsiachristas, Looman, Stokes, Galen and Mölken M2021) confirmed that this was also the result in the long run. According to a recent evidence scan (Struijs et al., Reference Struijs2020) the Dutch bundled payment scheme seems to be an outlier in this respect. Summarizing evidence on 11 bundled payment models, the authors found modest savings or modest reductions in spending growth reported in 20 of 32 studies; 18 of the studies reported quality improvements.

Case study 2: Medicare Shared Savings Program, USA

The Medicare Shared Savings Program is a voluntary scheme that enables health care providers (e.g. hospitals and/or primary care physicians (PCPs)) to form ACOs which take population responsibility for the quality, costs and care of their patient population (CMS, 2021). Launched in 2012, the scheme offers participating ACOs a menu of risk-sharing contracts between Medicare and the ACO. ACOs taking on larger financial risk may also receive greater shares of the savings. A one-sided option is the most popular, where only savings – not losses – are shared; this option was taken up by 99% of the participating ACOs in 2015 (McWilliams et al., Reference McWilliams2018). The participating providers are paid by FFS. A prerequisite for accessing the shared savings is that the quality of care meets agreed performance standards. In addition, only savings above a certain level, known as the minimum savings rate, qualify for sharing. As pointed out by Pope and Kautter (Reference Pope and Kautter2012), the minimum savings requirement can be seen as an attempt by Medicare to distinguish random variation in expenditure from expenditure reductions due to the ACO’s “true” cost-control effort.

An evaluation by McWilliams and colleagues (Reference McWilliams2016) after the first year of the programme found that in the 32 participating ACOs spending per beneficiary was about 1% lower for beneficiaries enrolled in an ACO participating in the Medicare Shared Savings Program. ACOs with higher baseline spending generated larger savings, but there was no difference in the savings generated by ACOs whose providers integrated financially, compared with those that did not. In a follow-up study, McWilliams et al. (Reference McWilliams2018) found that shared-savings contracts entered into by groups of PCPs were on average associated with greater savings, and that the savings were higher the longer the groups had been participating in the scheme. The spending reductions generated by physician ACOs in 2015 amounted to US$ 256 million in net savings to Medicare, the payer. Hospital-integrated ACOs, which have weaker incentives to generate savings due to the implications for other revenue streams not covered by the ACO contract, did not on average generate statistically significant spending reductions, and the reductions that were generated were offset by bonus payments. Subsequently, concern about whether savings had been generated by providers selecting patients with lower risk was not supported by empirical evidence (McWilliams et al., Reference McWilliams2020).

Case study 3: Gesundes Kinzigtal, Germany

An example of a European-type ACO is provided by the Gesundes Kinzigtal (“Healthy Kinzigtal”) programme in Germany (Hildebrandt, Schulte & Stunder, Reference Hildebrandt, Schulte and Stunder2012). The programme started in 2005 and targets the entire population of the city of Kinzigtal, regardless of disease or age. The guiding principle of the programme is the triple aim concept – improving patients’ experience of care, improving population health and reducing per capita costs of care – and the main elements of the programme comprise self-management support, prevention, patient-centred care and an electronic networking system (Struckmann, Boerma & van Ginneken, Reference Struckmann, Boerma and van Ginneken2015).

The Gesundes Kinzigtal programme also aims to improve the financial margin for the purchaser, which in the German context are sickness funds, and in this specific case two sickness funds: AOK and LKK. The programme aims to generate savings within the Kinzigtal region as compared to German “standardized” costs, which are average costs across all sickness funds, and a reference period prior to the intervention. These standardized costs are calculated in the German risk adjustment mechanism, which allocates money from a central allocation pool to purchasers. Since 2009, allocations for each individual are based on age, sex and marginal expenditures for one of 80 diseases as coded the previous year (Buchner, Goepffarth & Wasem, Reference Buchner, Goepffarth and Wasem2013). The contracts between Gesundes Kinzigtal GmbH (i.e. the company) and the two sickness funds are based on the virtual budget of each fund’s total allocation from the central allocation pool – the budget is “virtual” because the money is not actually passed through to providers, who continue to receive their reimbursements from the sickness funds as usual. The financial result is measured by the total expenditure for the insured Kinzigtal population both within and outside partner institutions compared to the allocation from the pool. If the sickness fund spends less than it receives, the gain is shared between the fund and Gesundes Kinzigtal (Busse & Stahl, Reference Busse and Stahl2014).

Over the years the programme has managed to achieve savings. By 2010 the per capita expenditures of an LKK policy holder in the Gesundes Kinzigtal programme relative to those in the control group had decreased about 16.9% since 2005 (Hildebrandt, Schulte & Stunder, Reference Hildebrandt, Schulte and Stunder2012). An internal evaluation of the AOK and Gesundes Kinzigtal over the period 2006 to 2013 found that the programme had led to a net annual saving for the sickness funds of close to 3% (after sharing the 6.5% surplus difference with Gesundes Kinzigtal). In 2012, the relative cost reduction that could be allocated to the activities of the programme amounted to around 7.9%.

LTC: its services

LTC comprises a range of services aimed at slowing down and managing the decline in functional capacity among beneficiaries with a high degree of long-term dependency (Colombo et al., Reference Colombo2011; OECD, 2018). LTC services are provided to dependent people, such as older adults (aged more than 65 years) or people with physical or mental disabilities suffering from chronic conditions with functional or cognitive limitations over an extended period.Footnote ² LTC services cover various forms of care. In this chapter, we include: (i) personal care and help with ADL, such as eating, bathing and toileting; and (ii) social care, which deals with assistance services that enable a person to live independently and help them with IADL, such as shopping, laundry, cooking, not putting oneself at risk (e.g. getting lost), or forgetting to turn off an electric device (Gori, Fernandez & Wittenberg, Reference Gori, Fernandez and Wittenberg2016; OECD, 2018). We include cash benefits to buy the aforementioned services (i.e. monetary benefits for people needing ADL and/or IADL), as these payments are taken as a proxy for a paid transaction. However, we exclude medical or nursing LTC, such as wound dressing, administering medication, health counselling, palliative care, pain relief and medical diagnosis with relation to a long-term condition, as this type of service is usually funded by health systems.

LTC: its settings and caregivers

The severity of beneficiaries’ dependency determines not only the type and number of hours and services, but also the setting where LTC is provided.

Day care centres

In day care centres, beneficiaries receive several hours of care on a daily basis, particularly preventive care. The centres include nursing and rehabilitation services such as physiotherapy, occupational therapy and mental health support, provided by skilled and unskilled caregivers. These formal caregivers are part of community care although working in an institution (the day care centre), as beneficiaries live in their own homes.

Nursing/care homes

Recipients of care in nursing/care homes are those who require LTC due to physical or mental disability/dementia, but are not in need of complex medical care. Caregivers include skilled and unskilled caregivers (such as nurses and allied health professionals). Patients admitted to nursing homes for a short period following an acute-care hospitalization to recover before being able to return to their homes are excluded from this analysis. We also exclude geriatric/LTC wards in hospitals, because the care provided in these wards is medical and the costs are typically captured in health expenditure data. Sheltered housing is also absent in our analysis, as this type of institution is designed for independent residents. Residents of sheltered housing who receive help with ADL are considered community-based care beneficiaries and are mentioned under “community care”.

Such, then, are the conceptual underpinnings and context for our analysis of the use of payment mechanisms in LTC markets, but first we need to see why caution is needed, if thinking simply to replicate the way they function in health care markets.

LTC versus health care: type of care

While in health care the aim is to cure or improve a patient’s condition, in LTC it is about improving quality of life, where conditions are either longstanding or permanent (Bakx, Reference Bakx2015). There is also the question of the duration of an “episode of care” and the subsequent distributions of costs. Health costs are usually unpredicted and are very high for very few people, and the demand for care is usually not known in advance. For LTC, once an individual becomes disabled, costs are usually permanent and likely to increase gradually, though the duration of care varies. While only a small fraction of the population is responsible for most expenditures in health markets, funds in LTC are dispersed over a broader percentage of those aged more than 65 years (Berk & Monheit, Reference Berk and Monheit2001; Mitchell, Reference 512Mitchell2019; Zuvekas & Cohen, Reference Zuvekas and Cohen2007). In LTC it can be challenging to predict longer-term expenditures because it is difficult to foresee: (i) the probability of need (Costa-Font, Courbage & Swartz, Reference Costa-Font, Courbage and Swartz2015); (ii) the duration of care; and (iii) the future costs of the LTC services (Barr, Reference Barr2010). For LTC it is challenging to define a clear episode of care that can be costed and priced in advance. Thus, prospective, activity-based payments are difficult to apply, and diagnosis-related group (DRG) payments are rare in LTC (Cots et al., Reference Cots and Busse2011) (see Chapter 3.2 for more on DRGs).

LTC versus health care: type of provider

While health care is provided by highly skilled professionals, most LTC is provided by low-paid, low skilled (low or untrained) formal caregivers or informal (i.e. unpaid) caregivers (see Chapter 2.5). Informal caregivers typically face high opportunity costs in terms of time spent providing care and income lost due to reduced participation in the formal labour market, as well as a loss of personal time. To compensate informal caregivers, some countries offer incentives, such as tax exemptions, work leave days or other allowances. These have important impacts on caregivers’ well-being, income, willingness to provide LTC and the interplay between formal and informal care. However, the incentives given do not compensate for the full cost of care, and there is no formal contractual relation between provider/informal caregiver and the beneficiary. Implicit contracts are mainly based on altruism or exchange motivation that might be influenced by the level and availability of supply of formal care (Costa-Font, Jiménez-Martín & Vilaplana-Prieto, Reference 509Costa-Font, Jiménez-Martín and Vilaplana-Prieto2022). Controlling for or incentivizing quality of informal care remains a challenge (OECD, 2020b).

LTC versus health care: level of fragmentation

Compared to health care markets, LTC markets are highly fragmented, with various sources of funds coexisting, and many payer agencies responsible for the different settings and types of care (see Box 3.6.1). Inadequate (re)allocation of funds may create incentives for cost shifting by LTC payers, resulting in attempts to shift unattractive (i.e. labour-intensive or high-risk) beneficiaries and costs to other care settings or payers. Shifting beneficiaries to less optimal care settings is exacerbated when different payers are responsible for different types of care settings. In the Netherlands, for example, regional care offices pay for institutional and intensive home care (ADL), while community IADL care is paid for by local government. Since local government funds are not earmarked for LTC, those with greater budgetary pressures tend not to invest in preventive, less intensive care and instead have an incentive to shift beneficiaries to more intensive care settings paid for by the regional care offices (Alders & Schut, Reference Alders and Schut2021). Beneficiaries may not only forgo IADL care, which can result in increased dependency levels, but they might end up receiving unsuitable (more intensive) care. Shifting beneficiaries to more intensive care settings may thus increase expenditures without added benefit. The undertreatment of patients, and the accompanying increase in their levels of dependency, in order to move them to other care settings and payers, has also been reported in Italy, Spain (Arlotti & Aguilar-Hendrickson, Reference Arlotti2018) and Portugal (Lopes, Mateus & Hernández-Quevedo, Reference Lopes and Mateus2018).

Two payment mechanisms used to mitigate this type of cost shifting and to promote continuity of care are bundled payments and pay for performance (P4P) (see Chapter 3.1). In health care markets, their effects have been modest (Eckhardt et al., Reference Eckhardt and Busse2019). The experience with these payments for LTC is less extensive, and outcomes are even less pronounced. The ability to reduce excess demand or total expenditures in LTC is lower than in health care, when each care setting is funded by a different payer (Kattenberg & Bakx, Reference Kattenberg and Bakx2021). To mitigate cost shifting related to LTC’s fragmentation of payers, it would be more effective to combine policy tools such as merging budgets, integrating providers or integrating LTC within health payers, rather than rely on payments as a single solution (McClellan et al., Reference McClellan2017). Experiences from England and Sweden show that pooling budgets for health care and LTC for older adults reduced duplication and fragmentation of care, improved coordination of care and collaboration between the sectors. However, no clear evidence of an improvement of quality of care was found (Hultberg et al., Reference Hultberg2005). In addition, in the short term, there was no evidence of reduced costs. In the Pooling and allocation of funds section, we describe in more detail countries’ LTC payer agencies, and how pooled funds are redistributed among payers to improve equity in financing.

LTC versus health care: quality of care

Concerns about how to incentivize high-quality care is core to the design of payments for LTC providers (Barber et al., Reference Barber2021), while those paying for health care are balancing a greater range of payment objectives (see chapters 3.1 and 3.2). In the context of LTC, poor quality care by formal (paid) providers may exist in the form of neglect, overmedicalization or provision of services in the wrong setting; for example, not providing enough hours of care at home or treating a non-severely disabled person in a nursing home (National Academies of Sciences, Engineering, and Medicine, 2022). In addition to P4P and bundled payments, already mentioned, pay for coordination, pay for quality (P4Q) and shared-savings models have also been implemented to promote quality of care with mixed results, and without evidence for positive effects in the long run (Wieczorek et al., Reference Wieczorek2022). One of the main challenges of applying value-based payments is the difficulty in defining, measuring and collecting data on quality in LTC. It is also clear that payments and their impacts are context specific, and financial incentives should be developed and tailored to each local setting (Struckmann et al., Reference Struckmann2017).

Overmedicalization is more common in nursing homes than in home care, and is a further instance of service distortion, as institutionalized beneficiaries of LTC consume more or inappropriate medication, particularly opioids and psychiatric drugs, compared to those non-institutionalized (Fog et al., Reference 510Fog2017; García-Gollarte et al., 2014; Jensen-Dahm et al., Reference Jensen-Dahm2015). Keeping beneficiaries at home might reduce the phenomenon of overmedicalization, as it is less prevalent in this setting. One way to ensure more beneficiaries are cared for at home may be to set higher or more accessible benefits for home care than for institutional care.

LTC versus health care: moral hazard and excess demand

In health economics, moral hazard is a phenomenon where, in theory, patients consume more services or technologies because they are insured and do not see real prices (see Chapter 2.4) (Stone, Reference Stone2011; van de Ven & Ellis, Reference van de Ven, Ellis, Newhouse and Cuyler2000); but in LTC this is different since beneficiaries are not typically insured and much of their care is unremunerated. Yet, moral hazard can still occur in LTC when publicly paid-for beneficiaries create an excess demand for: (i) LTC at an earlier stage of disability; (ii) more hours of care than needed; or (iii) more costly services, such as nursing homes (without real need), or replacing informal care with formal care too soon, thus potentially increasing public expenditures (Bakx et al., Reference Bakx2015a; Konetzka, Reference Konetzka2006).

Countries typically reduce the risk of this market failure by imposing cost-sharing or limiting eligibility for benefits, but this raises risk of creating barriers to accessing basic LTC (Bakx et al., Reference Bakx2015a). A better strategy to mitigate excess demand is setting clear eligibility criteria at the national level, based on need (Bakx, Douven & Schut, Reference Bakx, Douven and Schut2021; Waitzberg et al., Reference Waitzberg2020b). For example, the Netherlands publicly funds both home care and nursing home care in similar scope. Beneficiaries are allocated to the setting (home or nursing home) and intensity of care (number of hours) with clear eligibility criteria based on level of need. There is little room for choice of care setting, and thus for preferring institutional care over home care (Bakx et al., Reference Bakx, Douven and Schut2020). In Spain too, needs tests based on severity of IADL and ADL limitation allocate beneficiaries into one of four tiers of care, leaving little room for excess demand (Costa-Font, Jiménez-Martín & Vilaplana-Prieto, Reference 509Costa-Font, Jiménez-Martín and Vilaplana-Prieto2022). Eligibility should be frequently reassessed with needs tests, to allocate patients to the proper care setting. Box 3.6.2 contains a summary of LTC market failures and policy options.

Collectors and payer agencies

Payer agencies receive public LTC funds from national or local collectors of funds and commission or purchase LTC services from providers on behalf of beneficiaries (Busse, Schreyögg & Stargadt, Reference Busse, Schreyögg and Stargardt2017). Alternatively, payer agencies provide cash benefits to beneficiaries (see Fig. 3.6.1). In countries with multiple payers, payer agencies can be either local governments or health/LTC insurance plans, while countries with a single payer task the central government itself or another agency (such as a national insurance institute) as the payer agency. As such, in single-payer systems, the agency that collects funds, usually the central government, is also the payer agency since it purchases services from providers and/or transfers cash benefits to beneficiaries directly. Such agencies thus have an important role in purchasing LTC services and ensuring their supply for those in need, according to eligibility, in an equal and efficient way (Thomson, Foubister & Mossialos, Reference Thomson, Foubister and Mossialos2009). Frequently, countries have different payers for the different LTC settings, i.e. a payer responsible for institutional care, and another payer for community care (as described in Box 3.6.1).

How are allocation decisions made?

Similar to mechanisms for allocating pooled funds to health care payers, allocation mechanisms for LTC should be based on predicted need of LTC, through a risk-adjusted capitation formula to ensure equity in financing (van de Ven & Ellis, Reference van de Ven, Ellis, Newhouse and Cuyler2000). Yet, contrary to health care, in LTC, multiple payer agencies are each responsible for different care settings, and may receive funds from different sources, and through different allocation mechanisms. LTC funds can be allocated to multiple payer agencies based on government decisions, automatic updates of past budgets or past use, or the predicted needs of the population they serve calculated via a risk/needs-based allocation formula. The more the distribution of funds is done based on objective and transparent criteria that reflect LTC needs, the higher the equity of funding. However, it is important to note that distribution of funds according to a needs-based formula alone does not ensure equity of distribution to a certain population if each region or payer agency commissions a different set of services for populations with the same need, or does so according to different eligibility criteria. Therefore, eligibility criteria should be set at the national level (Waitzberg et.al, Reference Waitzberg2020b).

In systems with single-payer agencies, funds do not need to be pooled and allocated. This is the case in Israel, where the National Insurance Institute is a single collector of funds and is a single payer for home-based LTC public provision (Asiskovitch, Reference Asiskovitch2013).

In a study that analysed 25 LTC systems in 17 countries, only seven systems had a single payer, with the central government also functioning as the payer agency (Austria, Czechia and Italy for cash benefits; Croatia and Cyprus for institutional and community care; and Israel, as mentioned in the previous paragraph). Out of the remaining 18 systems, eight distributed funds according to past budgets, government decisions or a general needs formula for public services (Waitzberg et al., Reference Waitzberg2020b). The other 10 countries did so based on a specific risk/needs-based formula for LTC, including Austria (for in-kind benefits), Czechia (for 15% of its budget), England, France, Germany, Lithuania, the Netherlands (for institutional care and community intensive ADL care), Norway, Portugal and Sweden (Table 3.6.1). Norway and Sweden applied demographic risk adjusters such as marital status to consider the existence of alternative informal care. They also used information about spoken language and residence in sparsely populated areas to reflect special caregiver needs. A few countries considered further risk adjusters such as disability, dependency level or chronic diseases (England, the Netherlands) and/or previous years’ expenditures (Austria, France, Norway). France further considered socioeconomic risk adjusters such as the number of allowance claimants and income of older people in the area overseen by the respective local authority. In England up to 2020, needs-based allocation formulae accounted for differences in wages across areas and to recognize differences in costs driven by factors outside the control of payers (local authorities) and providers.

Table 3.6.1Risk adjusters used in needs-based formulae for allocating pooled funds, 2018

Table 3.6.1 Long description

The table has 4 main columns: Demographic (age, gender, marital status, household composition), Socioeconomic (income, education, allowance claimants), Health or disability condition (number of A D L limitations, chronic conditions, level of help needed), and L T C costs (previous year, and expected). A cross is marked against the countries under the columns where applicable. It reads as follows. Austria (in kind): L T C costs. Czechia (15 percent): Demographic; Health or disability condition. France: Demographic; Socioeconomic. Germany: L T C costs. Lithuania: Demographic. Netherlands (institutional): Health or disability condition. Netherlands (community): Demographic; Socioeconomic; Health or disability condition. Norway: Demographic; Socioeconomic; L T C costs. Sweden: Demographic. United Kingdom (England): Demographic; Socioeconomic; Health or disability condition; L T C costs.

ADL: activities of daily living; LTC: long-term care.

Source: Waitzberg et al. (Reference Waitzberg2020b).

Compared to allocation formulae for health care, those used for LTC are simpler (Bryndová, Hroboň & Tulejová, Reference Bryndová, Hroboň and Tulejová2019; Cylus et al., Reference Cylus2018; van de Ven et al., Reference van de Ven2007). Maybe, due to the nature of LTC markets, where age and other demographic characteristics are strong predictors of need, these adjusters are good enough to allocate funds in an equitable manner. Future studies could test this hypothesis. LTC funds could also be adjusted retrospectively to account for unpredicted expenditures. That is even more important for LTC than for health care, because LTC is a social service and, as already discussed, can be challenging to insure.

With the funds now allocated to the payer agencies, we turn to the downward yellow arrow on the right hand side of the triangle in Fig. 3.6.1: the purchase of services from the providers of LTC – in particular, the payment methods used and their associated incentives.

Payments to institutional care (nursing or care homes)

In nursing homes, beneficiaries may receive some specialized services, such as pain and psychiatric medications, rehabilitation and other allied health services (physiotherapy, speech and occupational therapy, diet and pharmacy counselling), and receive accommodation services for each overnight stay. Like hospitals (see chapter 3.2, for more on paying for hospitals), nursing homes have fixed costs such as infrastructure and personnel and, to a lesser extent, variable costs, such as consumables and payments to independent professionals. Variable costs in nursing homes, however, are proportionally small compared to hospitals, making it easier to work out a daily LTC tariff (per diem).

Community care

The key cost in community care is the labour of its formal caregivers. As in institutional care, most are low-skilled (see Chapter 2.5). In many countries, they are paid based on the number of hours they work. Elsewhere, caregivers are paid a global monthly wage or a fraction, corresponding to the worked hours. Usually, salaries are accompanied by quality norms and constant measurements. Current wages for personal home care in many countries are the minimum wage per hour and sometimes even lower, primarily due to unpaid time (i.e. unpaid travel time and “on-call” hours) (OECD, 2020b; Cravo Oliveira Hashiguchi & Llena-Nozal, Reference Cravo Oliveira Hashiguchi and Llena-Nozal2020). Since the publicly financed hours are often not enough to cover the needs of the LTC beneficiary, the caregiver might receive payments from two payers: one being the public payer, and the other being the beneficiary or their family as private funding directly paid to the caregiver to ensure the beneficiary receives the number of hours of care they need.

The caregiver’s characteristics and the way a home care beneficiary receives the payment (in kind or in cash) can impact the level of expenditure, regardless of whether the care is formal or informal. In Germany, for example, LTC beneficiaries are free to choose between cash or in-kind benefits for home care. In-kind benefits are more than double the amount of cash benefits for the same care level assessed. Cash benefits are mostly chosen when the caregiver is an informal one, usually a relative, while in-kind benefits are the preferred choice for those contracting a formal caregiver (OECD, 2020b).

Informal caregivers can be rewarded by receiving allowances (cash benefits) or non-monetary benefits (extra care leave or paid leave from their regular work to care for relatives, tax benefits, respite care services and more, to incentivize informal caregiving). Both methods increase the willingness of informal carers to provide LTC and may promote home care instead of nursing home care (Brimblecombe et.al, Reference Brimblecombe2018). Notably, in countries where the employer is not reimbursed for the paid sick leave or paid leave that an employee uses to help care for a relative, the costs of such legislation shift to the private/commercial sector. In some countries, such as Germany, LTC beneficiaries receiving cash benefits can use this money to pay their informal caregivers (Blümel et al., Reference Blümel2020). The monetary and non-monetary benefits usually do not cover the full costs of care, such as foregone working hours, and are rather simply an acknowledgement that providing care involves costs (OECD, 2020b). For example, in Spain, caregiving allowances are unconditional (paid directly to the caregiver’s bank account) but are lower than the minimum wage (Costa-Font, Jiménez-Martín & Vilaplana-Prieto, Reference 509Costa-Font, Jiménez-Martín and Vilaplana-Prieto2022). High benefits may disincentivize carers to participate in the formal labour market, particularly for low-skilled workers and especially if they are means tested (Colombo et al., Reference Colombo2011). In Spain, caregiving allowances incentivized informal care, while the effects of public funding of home care on informal care was mixed, with no clear pattern of substitution or complementarity of informal and formal home care (Costa-Font, Jiménez-Martín & Vilaplana-Prieto, Reference 509Costa-Font, Jiménez-Martín and Vilaplana-Prieto2022).

The role of financing arrangements

As organization and funding arrangements within a health system are built up over time, the nature of the health financing system has practical implications for horizontal priority-setting. Horizontal priority-setting assumes that there is some form of collective financing mechanism (general taxation/social insurance/employer scheme) where utilization is independent of funding contribution. Moving towards a collective financing mechanism requires a health financing transition. The transition involves a rise in total health spending per person financed through pooled resources, accompanied by a gradual decrease in OOP spending (Fan & Savedoff, Reference Fan and Savedoff2014). However, this delineation does not always exist in practice and most systems use some mixture of funding mechanisms. These funding mechanisms often affect social goals and can pose challenges to ensuring that prioritized services are provided. For example, certain types of financing arrangements can influence provider or patient behaviour by stimulating or suppressing demand for specific health services (e.g. curative services) or for services that may be of priority to specific population groups (e.g. the wealthy). They may also impact the size of the revenue base. To illustrate these issues, we consider challenges to horizontal priority-setting with three types of internal funding mechanisms that coexist with collective financing: private insurance, complementary insurance and user charges.

In many countries that have collective health financing schemes, individuals can choose to also have voluntary (private) health insurance (see Chapter 1.3). For example, the United Kingdom has private health insurance alongside collectively financed health care; individuals can use private insurance to replace collective coverage with insurance premiums set by the private provider based on the individual’s risk. Individuals choose private insurance if the benefits/services covered under private insurance are perceived as greater than those from the collective HBP given the cost of the premium. In this case, the collective HBP may receive less political support from wealthy individuals who prefer and obtain private insurance. To garner political support for the collective HBP, policy-makers may be forced to design packages that are attractive to wealthy individuals, which can result in trading off equity and limiting coverage for interventions that are deemed to represent the best value for money.

The existence of a large(r) private insurance sector (such as in the USA) can also have profound consequences for the horizontal priority-setting process. The existence of an opt-out option from the public system can mean significantly lower revenues for the collective HBP, limiting the services that can be provided and the population who can be covered. Private insurance tends to attract wealthier individuals, who are generally healthier and whose needs may not be covered by the collective HBP. Thus, the budget available for a collective HBP through horizontal priority-setting is intrinsically linked to the benefits being offered.

Alternative arrangements to private insurance are supplementary and complementary health insurance alongside a collective HBP. For example, in many Latin American and Caribbean countries, private health insurance is a secondary source of coverage for some of the population. Its role is supplementary, i.e. covering health goods and services not covered in the basic HBP in Belize, Ecuador, Guyana, Peru, Suriname and Uruguay. However, in others, such as Argentina, Brazil, Chile, Honduras and Peru, its role is complementary, i.e. covering cost-sharing for health goods and services covered in the basic HBP (Lorenzoni et al., Reference Lorenzoni2019). In this scenario, establishing a comprehensive package of services that is affordable and accessible to all may be challenged by a need to offer services which are not covered by supplementary or complementary health insurance or a package that predominantly meets the needs of those who are unable to afford private health insurance. The sustainability of such an offering may also be in question if the services covered by the collective HBP are those that are likely to result in catastrophic health expenditure.

A third type of funding mechanism may influence the success of a comprehensive collective HBP: user charges (see Chapter 2.4). User charges are OOP payments for services or contributions towards the cost of a service. Within a collective HBP, they can be used to try to regulate demand for health care and generate revenue for the health system. If user charges are implemented within the HBP without exemptions in place, they result in catastrophic payments for individuals who cannot afford the charges, exacerbating inequity within the health system. On the other hand, the removal of user charges or widespread exemptions may result in decreased revenue for the health system, limiting the ability to provide a wider range of services or cover a large population.

In many countries, the provision of public health interventions, mental health services and diagnosis and treatment of communicable disease has been vertically arranged, with corresponding vertical allocations of funding for these specific areas. This arrangement offers little flexibility for allocations across areas and can present significant challenges in incorporating these services within a more integrated priority-setting exercise. In LMICs, the vertical arrangement of certain services, particularly those for communicable diseases, are due to their reliance on external funding. External funding from bilateral, multilateral and philanthropic organizations have typically been targeted to specific disease areas or priority groups. Examples include the Global Fund, Gavi, the United States President’s Emergency Plan for AIDS Relief (PEPFAR) and the Bill & Melinda Gates Foundation (Atun, Bennett & Duran, Reference Atun, Bennett and Duran2008).

Furthermore, the level and availability of external funding may dictate which health issues are placed high on national health agendas as well as the types of interventions that are selected to address them. Donors with disease-specific priorities may limit the extent to which health systems can leverage economies of scope in the delivery of services through a shared platform (horizontally). Such arrangements also pose a challenge for setting a cost–effectiveness threshold that reflects the budget constraint across horizontal interventions. Cost–effectiveness thresholds set by national policy-makers will reflect the limited domestic budget. Interventions selected according to these cost–effectiveness criteria must be highly cost-effective (reflecting the narrow domestic budget), but the limited budget may also narrow the scope of services covered.

The role of procurement arrangements

Low-income and lower-middle-income countries are estimated to spend around US$ 50 billion per year on global health products (Silverman et al., Reference Silverman2019, based on estimates from Rosen et al., Reference Rosen2017). Despite this, access to essential medicines and health products in these countries remains insufficient, leaving individuals and families to access them in the private sector from OOP payments (Ewen et al., Reference Ewen2017). Health product procurement in low-income countries is also highly dependent on donors or privately purchased in lower-middle-income countries. Moving towards a horizontally set HBP implies fundamental changes to how health products and medicines are financed, transitioning away from OOP payments towards pooled financing and procurement.

In implementing a package of services that will expand access and coverage, countries need to procure a wide range of products to meet the needs of horizontally set benefits plans. In doing so, countries must justify the selection of products that meet the goals of wider access to services and coverage within their budgets. Health technology assessments and cost–effectiveness analyses can play a useful role in product selection, but not all health-improving products will be locally cost-effective within highly constrained government budgets (Wirtz et al., Reference Wirtz2017). Furthermore, problems may be compounded by transition away from donor aid as income levels rise. Public sector spending in LMICs accounts for around 50% of expenditure on health products. Global health institutions and nongovernmental organizations, however, account for a large share of this public sector funding (Silverman et al., Reference Silverman2019). These funding mechanisms are not guaranteed to persist, and their support is usually based on countries becoming self-reliant over time (Wirtz et al., Reference Wirtz2017). As support from donors wanes, countries will not only have to make up for their share but raise additional resources to expand services and products covered under the benefits plan.

Some countries have come together to use pooled procurement systems to aggregate demand for pharmaceutical products. Pooled procurement, sometimes also called group purchasing or group contracting, combines decentralized decision processes on required volumes with centrally negotiated prices (Dubois, Lefouili & Straub, Reference Dubois, Lefouili and Straub2021; Nemzoff, Chalkidou & Over, Reference Nemzoff, Chalkidou and Over2019). For example, the Organization of Eastern Caribbean States Pharmaceutical Procurement Scheme, established in the 1980s, aggregates demand for products on the essential medicines list of 10 Caribbean states. Similarly, the Pan American Health Organization Revolving Fund (for vaccines, syringes and related supplies) and the Strategic Fund (for vector control medicines, diagnostic kits and related equipment) aggregate demand across several Latin American and Caribbean countries. This approach has also been adopted by global health institutions such as the Global Fund, UNITAID and Gavi, who in some shape or form have adopted product “market-shaping” strategies aimed at lowering prices through volume guarantees, combining demand across countries or ensuring continuous supply by contracting multiple suppliers. Countries no longer eligible for or reliant on such organizations and arrangements for health products may be faced with higher prices for these products due to their limited negotiating power and smaller-scale purchases. Apart from facing higher transaction costs due to lower volumes, unorganized demand may also deter suppliers from low-volume markets and/or from offering preferential pricing arrangements. This lack of organization from the demand side may also discourage investment in development of new products or manufacturing capacity (Silverman et al., Reference Silverman2019). To be fit for purpose, procurement processes in a horizontally set plan will need to optimize for quality, price, supply security and efficiency. Many LMICs have fragmented supply chains with purchasing, distribution and delivery processes vertically arranged across funders or programme/disease areas. Silverman and colleagues (Reference Silverman2019) provide the example of Nigeria, which has parallel funding, purchasing and distribution systems by commodity area, for example essential medicines, maternal and child health and HIV, tuberculosis (TB) and malaria. These duplications prevent the organization of demand and obstruct planning and delivery of a unified HBP. Further inefficiencies in procurement processes due to institutional and administrative challenges such as difficult tendering procedures or delayed payments to suppliers may result in increased prices due to the higher transaction cost associated with operating in such markets.

Existing financing and procurement arrangements can pose challenges to successfully moving from vertical to horizontal priority-setting. In the next section, we discuss potential ways of addressing these challenges and highlight efforts already being made by international donors to assist countries transitioning from donor funds or those never eligible for funding for; for example, the Global Fund’s Wambo platform for COVID-19 products, or Gavi’s purchasing agreements for transitioning countries.

Reducing fragmentation by integrating financing schemes

In countries with multiple financing streams, fragmentation of financing streams can be reduced by merging existing ones where possible, thus reducing overlaps and generating a wider risk pool, so improving equity (see Chapter 2.1 for more on pooling). Amalgamation of existing schemes requires strong political will and there is potential for resistance from existing beneficiaries of the different schemes. Such resistance may arise if a unified benefits package implies fewer services covered than any of the separate schemes or higher levels of contributions for the same or fewer services. To overcome resistance to the consolidation, sometimes new legislation may be required. Such legislation may place the management of financing of different schemes with a single institution while paving the way for eventual consolidation (Towse, Mills & Tangcharoensathien, Reference Towse and Tangcharoensathien2004). Such an approach was implemented by Thailand when trying to consolidate four existing schemes in a move towards UHC (Box 3.7.1). However, full consolidation has not been achieved and three separate schemes still exist. Despite the extended coverage of Thailand’s UHC scheme, its benefits package is still less generous than those offered by two of the existing schemes – civil servants and social security schemes – making consolidation unlikely to take place soon.

Expanding the fiscal space

Implementing a horizontally set benefits plan often requires an increase in resources. In general, with tax-financed health systems, simply improving tax collection could generate more resources for health. However, such expansions, even if feasible, may not guarantee a larger allocation or more consistent support for health care. One potential solution may be for governments to leverage OOP spending; for example, by enticing those paying OOPs to join a collective scheme for a small premium, through which certain frequently used services or purchased drugs could be accessed at better negotiated prices. Such an approach is an alternative to the more commonly used insurance for health financing approach and facilitates greater efficiency in spending. Health insurance for raising funds has been shown to be unlikely to raise revenues commensurate with the requirements of providing adequate coverage for financial risk protection in LMICs (Barasa et al., Reference Barasa2021; Gheorghe et al., Reference Gheorghe2019). Another option is the creation of new taxes or the earmarking of existing tax sources for health spending (Box 3.7.2). Earmarking “involves separating all or a portion of total revenue – or revenue from a tax or group of taxes – and setting it aside for a designated purpose” (WHO, 2017a). Some obvious candidates include levying or increasing taxes on unhealthy products, such as tobacco and alcohol, or a reduction in subsidies for fossil fuels. The use of this earmarked revenue can be channelled to specific health-related priorities rather than to general health spending. The main advantage of earmarking unhealthy products is that in the short-to-medium term they can be a source of continuous, regular funding that is not affected by budgetary reviews. There is also likely to be political support for tax increases on such products when the proceeds are channelled towards health care (WHO, 2016). Over the longer term, revenues may fall as consumption falls or a black market thrives. Such an approach may also generate small revenues in countries where certain unhealthy behaviours (such as smoking) are not dominant. One of the main criticisms of earmarking revenues is that they limit budgetary flexibility and earmarked funds could potentially have been channelled towards more deserving programmes or projects. In this case, soft earmarking for certain priorities may be an option. For example, Thailand’s Health Promotion Foundation is funded directly through a 2% earmarked tax on alcohol and tobacco. Opponents also highlight the importance of determining public spending based on policy decisions rather than by the amount of revenue raised by an earmarked tax. Cases studies on earmarking tobacco tax revenues in nine countries have shown some evidence that when revenues are channelled directly into an autonomous fund dedicated to public health, such as into the overall pool of funds for a UHC benefits plan, then those revenues successfully add funding for health (WHO, 2017a).

Cooperation between donors and countries

Cooperation between countries and international donors is vital to successfully implementing horizontal priority-setting. Ochalek and colleagues (Reference Ochalek2018) suggest estimating the scale of the health opportunity costs associated with any restrictions and conditions on donor assistance to facilitate a more informed and accountable negotiation between stakeholders, including careful examination of the reasons for restrictions. However, a more pragmatic and sustainable approach may be a change in the role of donors in countries transitioning from donor-dependent to fully domestically funded. Such a role may include supporting interventions that are marginally cost-ineffective, i.e. subsidizing key cost-ineffective interventions to make them cost-effective for a country’s threshold. Such an approach has been shown to be an equitable and sustainable model, especially for middle-income countries (Morton, Arulselvan & Thomas, Reference Morton and Thomas2018). Kanpirom and colleagues (Reference Kanpirom2017) also highlight the importance of not assuming value for money is a static property of interventions. Interventions implemented by a donor that are deemed initially cost-ineffective for a country due to their high start-up costs may become more cost-effective over time due to economies of scale, improvements in efficiency, etc. To ensure sustainability of donor-funded services after eligibility for donor funds ends, both countries and donors should monitor cost–effectiveness over time. This would enable countries to justify continued support for such cost-effective interventions within a horizontally set benefits package.

Donors also have a role alongside national health insurance schemes in ensuring equitable access to services. National schemes often do not prioritize the poorest, sickest or minority groups, resulting in unequal coverage (Schneider, Nakamura & Wu, Reference Schneider, Nakamura and Wu2019). Because of their emphasis on high-priority diseases and conditions by disease burden, national schemes and other UHC programmes may disregard important disease burdens in small populations (Kanpirom et al., Reference Kanpirom2017). For example, poor and marginalized groups living with HIV may not be able enrol in a national scheme as premiums may be unaffordable, or due to the limited regional coverage of the scheme (Global Fund, 2018). Donors supporting countries could focus their conditions or restrictions on requiring access for these groups, or where not feasible within the country’s budget, support such access themselves. Donors such as the Global Fund who invest in specific diseases (HIV, TB and malaria in this case) could pay for the three diseases through insurance funds, as is currently done in Viet Nam, where the Global Fund pays the insurance premiums of people living with HIV through their social health insurance (SHI), with a commitment from the government to absorb these premiums over time under domestic resources. ART and HIV-related care and services became reimbursable under Viet Nam’s SHI in late 2014. Over 70% of people living with HIV were due to receive ARTs via SHI by 2020 (Global Fund, 2020a). In Thailand, through a similar arrangement, migrant populations irrespective of registration status are covered by health insurance (Herberholz, Reference Herberholz2020).

Improving procurement and supply chain management

Improving procurement and supply chain management is critical to making health systems fit for delivering a unified HBP. Such improvements can reduce costs and limit supply shortages (WHO, 2015). Changes can include reducing fragmentation by centralizing procurement and improving data systems to monitor, manage and forecast demand for health products (Seidman & Atun, Reference Seidman and Atun2017). For example, the Nigeria Supply Chain Integration Project was established in 2015 with a mandate to coordinate product supply management across health programmes and funders with the aim of improving cost-efficiencies, reducing waste and minimizing stockouts. The project created and continues to maintain a logistics database on health products and pharmaceuticals across all health programmes in Nigeria, using it to monitor stock levels. The process also involved the creation and coordination of warehousing and transportation systems for health products across local, state and federal levels. Dubois and colleagues (Reference Dubois, Lefouili and Straub2021) further analysed the effect of centralized procurement on drug prices using data from seven LMICs. They found centralized procurement of drugs does lead to lower prices. However, if there are high levels of supply-side concentration, then the extent of price reduction is lower. In some cases, decentralization of financial management and procurement directly to the health facility level has been shown to be effective in improving the availability of medicines and medical equipment in health facilities (Kajuni & Mpenzi, Reference Kajuni and Mpenzi2021).

Any integration of vertical, particularly donor-financed services into a horizontal HBP requires a realignment of procurement arrangements. There are several possible actions for donors to facilitate such integration. For instance, donors can incorporate value-for-money assessments such as those for health technologies into their selection and price negotiation processes. Currently, many donors such as the Global Fund rely mainly on price negotiations based on bulk purchasing. In the longer term using value for money can help countries making the transition away from donor funding avoid the trap of investing in low-cost technologies that may have minimal impact or pay higher prices due to their inability to meet the bulk purchasing requirements for donor-negotiated prices (Kanpirom et al., Reference Kanpirom2017). Gavi, for example, assists countries transitioning away from its funding with access to multiyear supply agreement prices through manufacturer commitments via UNICEF or the Pan American Health Organization (Gavi’s designated procurement agents). However, currently this arrangement only applies to select vaccines, and is time-limited with varying lengths. Keller and Glassman (Reference Keller and Glassman2019) discuss other strategies that Gavi might adopt, including offering incentives to manufacturers to ensure steady vaccine supply, or permitting buy-ins from non-eligible countries. The latter may enable Gavi to globally negotiate tiered pricing agreements for countries, based on local affordability and cost–effectiveness. Delivering on horizontal priority-setting also requires access to safe, effective, good quality and affordable health technologies, medicines and vaccines. WHO (2017b) estimates at least 10% of medicines in LMICs are substandard or falsified, costing approximately US$ 31 billion annually. This is because many developing countries do not have the technical, institutional or financial capacities to regulate health product markets. Donors can facilitate alignment across countries and, where relevant, diseases, through the pooled procurement of products (such as vaccines or antiretrovirals) that meet World Health Organization requirements for international product safety and quality. Such market-shaping activities can benefit not just countries within the pool, but also those outside signalling good quality health products. One such example is the Wambo pilot for transactions not funded by the Global Fund, which was expanded to include COVID-19 products (Global Fund, 2020b).

Within a country, national regulatory authorities play a key role in selecting and approving health products. Thus, ensuring strong country regulatory systems is critical to ensuring countries are using good quality, safe and efficacious health products. Donors, in their efforts to strengthen health systems, could support investments in improving governance, technical competence, monitoring and evaluation capabilities. They could also make on-budget spending a key performance indicator for countries. Finally, donors can facilitate broader information sharing across countries, both within and outside pooled procurement arrangements, so addressing information asymmetry in product quality and safety. Countries, on the other hand, should lead the way in implementing regulatory reforms with donor support, resulting in less reliance on branding as a signal of quality and encourage high-quality generic suppliers (Silverman et al., Reference Silverman2019). They may also leverage models used by international donors such as organizing demand, volume guarantees and pooled procurement on a regional or global basis (Kim & Skordis-Worrall, Reference Kim and Skordis-Worrall2017; Seidman & Atun, Reference Seidman and Atun2017).

Common goods for health and market failures

The financing rationale for pandemic preparedness presented in this chapter is informed by WHO’s Common Goods for Health agenda.Footnote ¹ This defines common goods for health as all population-based functions or interventions that require public financing (supplemented by donor funding in some cases), regardless of whether they are delivered by public or private sector providers, and that fulfil the following conditions:

• Common goods for health contribute to human health and sustained economic progress over the long-term.

• Provision or preservation of common goods for health are subject to specific market failures, typically because they are either public goods (meaning they are non-rival – so the consumption of the good by an individual does not affect the amount available for others – and non-exclusionary – available for all to use (Samuelson, Reference Samuelson1954)), or they have large social externalities.

Despite their importance and the necessity of public financing to support them, common goods for health suffer from both market and collective action failures, and as a result are often underfunded and underprovided for relative to investments in personal, facility-based services (Gaudin et al., Reference Gaudin2019). For many common goods for health expenditures, which can be either capital or recurrent in nature, it is impossible to charge a price (or a fine) to some or all users or consumers of the function. This can discourage private actors from financing common goods for health for fear that others will “free-ride” on their investments, and it limits optimal production. Pandemic-related activities can be usefully classified as common goods for health (Yazbeck & Soucat, Reference Soucat2019). These activities are notable for their market and collective action failures, which lie at the heart of their underinvestment. This means that the predominant source of funding for pandemic preparedness activities will be through public revenues as described later in this chapter.

In the next section we look at how to decide what preparedness activities to finance at both national and global levels, and how to ensure value for money.

Challenge 1: How to decide how much and what to finance?

Deciding what to finance at the country level depends on first assessing the existing pandemic preparedness activities and resources and then identifying the gaps, in the context of total available resources. While the activities for prevention and preparedness are varied and many, guiding questions can be asked of each activity’s cost estimate, such as:

• Which threats are being addressed (e.g. pandemic only, or broader emergency and disaster risks)?

• What stage of the pandemic do activities correspond to: acute, protracted or no one particular stage?

• Do they include animal health along with human health activities?

• Do they include health system-related activities that help to improve health even in times of no pandemic?

• Are they health-only or do they include non-health-related activities?

At the global level, there have only been a few attempts to understand the resource needs for preparedness. What is clear is that greater investment is needed than has occurred traditionally. Pre-COVID-19, the World Bank estimated that global investments in preparedness need to be approximately US$ 4.5 billion per year (World Bank, 2012). Others estimated even larger sums such as US$ 12.3 billion in one-time capital costs, with US$ 13.8 billion yearly recurrent costs (Peters et al., Reference 569Peters2019). Estimates made after the onset of the COVID-19 pandemic by McKinsey and Co. put the figure at many more billions of dollars – US$ 285 billion to US$  430 billion over 10 years, with many of the costs being incurred as start-up capital costs in the first few years, and sustained annual costs over the time frame (Craven et al., Reference Craven2020).

Beyond estimating potential costs, there has also been an effort to track external assistance for common goods for health at the global, regional and country levels (Schaeferhoff et al., Reference Schaeferhoff2019). One study estimated that in 2017 there was US$ 0.48 billion allocated to global functions for pandemic preparedness and an additional US$ 1.36 billion for control of cross-border disease global functions (e.g. information exchange, early warning and response systems, risk assessment). Despite these efforts, there is a clear disconnect between the amounts committed as external assistance and the amounts needed (Yamey et al., Reference Yamey2019).

At the country level, the activities to be financed can be established as part of a pandemic risk management plan. When undertaking such plans, it is first useful to undertake a resource mapping exercise to understand where resources are currently being allocated, so that gaps and areas for improvements can be identified. Tools to conduct such mappings include the World Bank’s Health Security Financing Assessment (HSFA) tool and WHO’s REMAP tool (Osornprasop et al., Reference Osornprasop2020; WHO, 2019b). Once resources have been mapped, these can be integrated into planning for current and future preparedness activities, and also costed. WHO developed a National Action Plan for Health Security (NAPHS) framework, which provides a costing tool and has been implemented in several countries. The NAPHS plans conducted to date show high variation in cost distribution and estimates within NAPHS technical areas (World Bank Group, 2019b).

When using these costing tools, policy-makers must make careful consideration of cost drivers and the extent to which they are assessed in these models. For example, in Nigeria, three different pandemic costing tools were comparatively implemented and it was found that while in reality the high cost of developing and maintaining a strategic stockpile of medical countermeasures was a very large cost driver, this was not included as a category in several of the tools. In a similar costing exercise undertaken in Tanzania, animal health activities, such as animal vaccination, were another potentially large cost driver, but did not always feature in costing exercises (Mghamba et al., Reference Mghamba2018). Therefore, in decision-making when planning for pandemic preparedness, it is important to weigh the benefits and constraints of different tools and frameworks and consider how accurately they will estimate resource needs.

Challenge 2: How to ensure value for money?

To understand what constitutes a good investment in pandemic preparedness, it is necessary to look at the value for money of the different potential activities. The cost–benefit outcomes for pandemic preparedness are usually quite favourable due to the large impacts of catastrophic events compared to the costs of prevention, and the related co-benefits of tackling other health issues such as antimicrobial resistance (AMR) (Jonas, Reference Jonas2013). A key challenge, however, is to understand the probability of the catastrophic events occurring and to achieve the right balance of investment for preparedness without drawing resources from other essential activities. The assessment of value for money of activities for all health emergency preparedness also faces challenges such as high levels of uncertainty, uneven impacts and the differing perspectives (including attitude to risk) represented in the health system (Branch-Elliman, Safdar & Nelson, Reference Branch-Elliman, Safdar and Nelson2021).

Moreover, it is not a one-off decision. Policy-makers need to establish and maintain systems for ongoing assessments of the value for money of prevention and preparedness activities in order to continuously inform financing decisions based on the expected costs, the potential impacts from different risks, and overall budget availability. In addition, other elements besides cost may factor into decision-making. For example, the “rule of rescue” principle (when the perceived health needs of an individual or particular group override all other considerations) or wider societal priorities might lead policy-makers to invest in certain areas of preparedness regardless of their cost–effectiveness. The value-for-money considerations will also need to account for financial considerations of affordability and understanding of where costs are incurred. For example, costs for a large vaccine development programme may be incurred at the global level, while the delivery costs for the subsequent vaccinations would be borne by health system budgets at national or subnational level.

Once resource needs are estimated and value for money is assessed, financing mechanisms need to be established to execute preparedness activities. The next sections provide understanding on how to finance the activities needed for pandemic preparedness.

High-level recommendations

Despite providing separate analyses and recommendations, there is convergence around several global and domestic financing priorities needed for preparedness – especially in the wake of the COVID-19 pandemic. In its report on Improving Pandemic Preparedness by 2025, the Wellcome Trust (2021) summarizes these as follows.

National-level mechanisms

As mentioned above, investments in pandemic preparedness activities are made in the context of competing demands for scarce budget resources. Countries face one or both of the following challenges: (i) fiscal, where they do not have enough resources; and (ii) prioritization, where they do not give high priority to investments in prevention and preparedness. The latter may be for several reasons, depending on preferences, incentives, politics or, as is likely, a mix of all of these.

At the national level, strategies to increase the investment base for pandemic preparedness include generating domestic resources from taxes or levies, introducing incentive mechanisms that increase awareness of the risks of infectious disease outbreaks to businesses, leveraging insurance models and pooling donor resources within specific prevention and preparedness and response funds or institutions (International Working Group on Financing Preparedness, 2017).

The precise form of the financing mechanism used not only depends on the level at which the prevention and preparedness activities reside, but also on the nature of what needs to be financed. Financing for pandemic preparedness can generally be differentiated into three types:

1. 1. upfront capital investments for systems and infrastructure;

2. 2. sustained, long-term financing for ongoing operations and recurrent costs; and

3. 3. rapidly scalable surge financing that is, ideally, strategically identified in advance of an emergency, and able to be quickly deployed (Bill & Melinda Gates Foundation, 2021).

Here, we consider the available financing mechanisms in terms of revenue and the role of domestic resources; external assistance and loans; purchasing/payment mechanisms; incentivizing preparedness; and a variety of options for public financial management systems to enable both flexible and accountable financing arrangements.

Public financial management objective 1: flexibility

Lessons from the Ebola epidemic in west and central Africa in 2014 highlight that flexible (unearmarked), rapidly disbursable funds are critical to meet the ever-changing needs of a public health emergency (World Bank Group, 2019a). The financing-related problems encountered in the epidemic included: delays in disbursement of funds, ineffective use of financing, bureaucratic delays, poor transparency and information sharing, lack of accountability and systems to track funds, low capacity to properly procure and enforce financing management – and all of these issues relate to the mechanics of financing rather than the overall resource envelope (World Bank Group, 2019a).

The need for financing mechanisms that are quick, easy to activate, flexible, and accounted for was seen again in the COVID-19 pandemic (Barroy et al., Reference Barroy2020b). Delayed and ad hoc funding is often at the heart of humanitarian agency concerns about their ability to effectively respond to crises (OECD, 2017). However, as discussed above, projecting how much to budget for preparedness activities in the face of resource constraints can be challenging due to the uncertainty of events (Board on Global Health et al., 2016). Still, the level of agility of fast and flexible spending can be enabled through prenegotiated plans delineating ex post and ex ante financing (Board on Global Health et al., 2016). Some countries may make public financial management adjustments to expand and reprioritize budgetary space to allow funds to flow to preparedness activities (Ndii, Reference Ndii2010).

Additional considerations are particularly necessary in fragile and conflict-affected settings where government capacity for intervention is severely limited. The collective action and consensus building essential for sustainable government action in favour of the commons is a special challenge in countries where societies have been fractured (Adulyanon, Reference Adulyanon2012). At the same time, investing in preparedness activities in these settings has particular urgency, given the potential deficits in the capacity, willingness and ability of the government to ensure provision of these goods. Actions to finance pandemic preparedness in these settings can include: targeted capacity-building efforts; coordination of external agencies’ funding; or, in cases where government structures and processes are too weak, establishment of a substitute or shadow process that can be transitioned to domestic institutions as soon as possible.

In countries where governments have greater capacity to act, programme budgeting offers a way of managing public finances that can allow the flexibility that responding to a pandemic requires. The multisectoral nature of pandemic preparedness means that different parts of the public sector may be responsible for both financing and provision of these activities. From a budgetary perspective, the structure of a country’s budget can place constraints on effectively matching financing with prevention and preparedness objectives (Barroy et al., Reference Barroy2018). For example, rigid line-items can be obstacles to the cross-cutting approaches needed to drive efficiency in organizing, delivering and rapidly deploying prevention and preparedness activities (Sparkes, Kutzin & Earle, Reference Sparkes, Kutzin and Earle2019). Moving away from input-based budgets and towards budgets that are formulated and executed based on programmes can help strengthen the linkages between budget allocations and government priorities, as well as foster the flexibility to quickly adapt budgets in the face of crisis (Barroy et al., Reference Barroy2020a). Australia’s experience of programme budgeting provides an example of how such arrangements can support cross-cutting investments in common goods for health (Australian Government, 2017) (Box 3.8.2).

The flexibility that programme budgeting affords was on display in the COVID-19 response, with New Zealand, Mexico and South Africa all adding a COVID-19 budgetary programme to the health budget where they were then able to redirect programme envelopes for the response (Barroy et al., Reference Barroy2020b).

Other countries have worked around budget inflexibility by introducing exceptional spending measures. These measures, often introduced by executive decree, can allow for rapid budget reallocation, activation of contingency funds, emergency spending authorization, simplified approvals and rapid fund disbursement to subnational levels (WHO, 2022). Having systems and plans in place in advance of a pandemic enables a faster response. This may also include the revision of finance laws to ensure that these sorts of flexibilities are baked into legislation for certain crisis circumstances.

Effectively integrating domestic financing for pandemic preparedness into annual and multiyear budgets requires targeted efforts. Funding for cross-cutting activities (such as laboratories, training, surveillance or information systems) requires pooled resources and coordination among the health sector and other relevant sectors (Sparkes, Kutzin & Earle, Reference Sparkes, Kutzin and Earle2019). This coordination can be achieved through a health sector medium-term expenditure framework (van Eden, Gentry & Gupta, Reference van Eden, Gentry, Gupta, Lam, 570Rodlauer and Modernizing2017), public expenditure reviews (World Bank Group, 2017) or other processes that build on evidence-based, high-quality diagnostics of health challenges and policy effectiveness.

These processes provide clear priorities for spending over time, particularly in the face of both fiscal and political constraints, and involve focusing on strengthening national and subnational institutions to deliver cross-cutting activities. New Zealand, for example, has implemented a budgetary framework that enables cross-agency funding as a way to improve collaboration and reduce the transaction costs of working across agencies (The Treasury, 2015). The framework contains three possible funding models to determine both government and financing arrangements associated with delivering activities across agencies that contribute to shared outcomes: cost recovery charges, wherein one agency buys a service from another agency and recovers costs through a service fee; pooled funding, in which a group of agencies combine funds in order to share costs to achieve a common goal; and centrally determined funding, when ministers determine cross-agency collaboration is needed and then identify funding sources.

Public financial management objective 2: accountability

The expenditure flexibility required for pandemic preparedness must be matched with strong accountability mechanisms. Often opaque country financial management systems and outdated information systems make financial accountability difficult and a persistent problem (World Bank Group, 2019a). This dynamic not only lends itself to corruption and lack of transparency but also to inefficiencies and wasteful spending (Khasiani et al., Reference Khasiani2020). Budget accountability mechanisms include ex post and/or risk-based controls, regular updates to public spending systems, developing performance frameworks, using programme envelopes for expenditure tracking and ensuring that finance authorities take a lead role (Barroy et al., Reference Barroy2020a). For example, both France and China have introduced and strengthened tracking procedures that take into account clearly defined policy goals and targets.

Global and regional mechanisms

Beyond the national-level mechanisms, global and regional mechanisms for pandemic preparedness financing also play important roles. After all, financing for pandemic preparedness represents a vital investment in global solidarity to avoid or mitigate catastrophic pandemic costs. As noted by the Global Preparedness Monitoring Board 2020 Report, “global preparedness is not simply the sum of national preparedness” (Global Monitoring Preparedness Board, 2020). Mechanisms for pathogen tracking, early alert systems, R&D, regulatory capacity-building and harmonization, allocation of countermeasures, stockpiles and supply chains are all necessary at the global and regional level and require dedicated funding. Regional networks and global coordination of funding and response, as well as sharing general best practices around financing preparedness, are all global prevention and preparedness activities. To establish each of these activities, a range of financing mechanisms to fund them should be considered (Global Monitoring Preparedness Board, 2020). Here, then, we consider the potential of pooled resources.

AMR: causes and effects

However, growing AMR is rapidly pushing our existing antibiotic arsenal into obsoletion. AMR is a biologic process whereby bacteria develop mechanisms over time to escape or buffer antibiotics, making the drugs ineffective. Constant overuse and poor regulation over antibiotic consumption in human and agricultural settings has accelerated AMR in recent years. Between 2000 and 2015, global antibiotic consumption increased by 65%. This can largely be attributed to unregulated use in low- and middle-income countries (LMICs) (Klein et al., Reference Klein2018). The World Health Organization’s (WHO’s) Global Antimicrobial Resistance and Use Surveillance (GLASS) Report for 2021 revealed growing resistance rates to key antibiotics used for common infections (WHO, 2021a). For instance, first-line drugs for urinary tract infections including co-trimoxazole and ciprofloxacin are reaching average resistance rates of 50% and 30%, respectively, for common urinary pathogens.

Today, the impacts of AMR are already high, and they are growing rapidly. Currently, more than 700 000 people die worldwide from drug-resistant pathogens annually (Review on Antimicrobial Resistance, 2016).

AMR and its associated mortality disproportionately affect LMICs but high-income countries are also significantly impacted. In the USA, an estimated 35 000 people die from the >2.8 million antibiotic-resistant infections that occur each year (CDC, 2019). Similarly, AMR is responsible for approximately 33 000 deaths annually in the EU (OECD & ECDC, 2019). At the current rate of advancement, AMR is expected to be responsible for 10 million annual deaths globally by 2050, overtaking the annual mortality of cancer (Review on Antimicrobial Resistance, 2016). The cumulative economic cost of growing AMR is predicted to be US$ 100 trillion between 2014 and 2050 (Review on Antimicrobial Resistance, 2016). This equates to an approximate 2–3.5% drop in predicted global gross domestic product (GDP).

A frighteningly thin antibiotics pipeline

In the past, developing new antibiotics appeared to be the easiest mechanism to overcome emerging resistant pathogens. As certain antibiotics became less effective against evolving bacteria, treatment for infections could be supplemented or replaced by newer generations of the same antibiotic or by a new, more effective class of antibiotic. Pharmaceutical companies leveraged scientific breakthroughs and were rewarded with high-value patents. But, due to a combination of financial, regulatory and scientific barriers to continued development of new antibiotics, the focus of R&D has shifted away to other therapeutic areas such as oncology and cardiovascular disease. As a result, the antibiotic development pipeline has dwindled to almost a complete standstill, leading to increasing challenges in the market, and ultimately to poorer health outcomes.

In 1990, there were 18 large-capital pharmaceutical companies actively developing antibiotics, but as of 2020 there were only eight (Butler, Blaskovich & Cooper, Reference Butler, Blaskovich and Cooper2013; AMF, 2020). Of these, only two companies ranked within the top 50 drug companies by sales in the world. Over the 25-year period from 1990 to 2015, there were no approvals of novel antibiotic drugs with unique chemical structures for clinical use (WHO, 2017a) (Fig. 3.9.1). Developers had relied on reformulating existing antibiotic drugs or combining two different classes of antibiotic. This void in discovery and development has meant that the antibiotic pipeline is frighteningly thin relative to the unrelenting advance of AMR.

Fig. 3.9.1

Number of new classes of antibiotics discovered or patented each decade

Figure 3.9.1 Long description

The y-axis represents the Number of antibiotic classes discovered or patented from 0 to 10, while the x-axis represents the Decade from the 1890s to the 2010s. The values are as follows. 1890s: 0. 1900s: 1. 1910s: 0. 1920s: 1. 1930s: 1. 1940s: 7. 1950s: 9. 1960s: 5. 1970s: 5. 1980s: 2. 1990s: 0. 2000s: 0. 2010s: 0.

Sources: Adapted from Pew Charitable Trusts (2016); originally from Silver (2011).

This chapter looks at how to fill this void in the discovery and development of new antibiotics. In particular, we describe the barriers to development and examine the policy and funding tools that can overcome them, in terms of the push and pull incentives available to policy-makers. We outline the various initiatives underway – multilateral, international, national and regulatory. We assess the state of progress in antibiotic development up to 2021 and the performance of the various incentive mechanisms, and suggest other measures that are required.

Barriers in basic research and product trials

Research efforts to understand and identify new molecules for candidate drugs present scientific difficulties. Bacteria, particularly Gram-negative varieties, are highly resilient to recent experimental mechanisms of destruction (WHO, 2017b). In addition, scientific expertise in this area is currently lacking and still recovering from the discovery void that began around 1990 (Silver, Reference Silver2011). Thus, the preclinical stage of antibiotic research is often ominously referred to as the “valley of death” (So et al., Reference So2012). As discussed in Chapter 3.4, basic research has predominantly been tackled by academics funded by the public sector, while clinical trials have been the domain of private pharmaceutical companies, thus leaving a gap in funding and appropriate actors to move from molecular discovery to clinical trials.

It costs roughly US$ 130 million to take a drug candidate through clinical trial phases I to III (Review on Antimicrobial Resistance, 2015) and many drug candidates will be discarded along the way, at a financial loss. Additionally, recruiting patients with acute bacterial infections for antibiotics clinical trials poses logistic challenges due to short treatment windows and lack of rapid point-of-care diagnostic tools to identify potential participants. Post-approval follow-on trials, which build further evidence for effectiveness, amount to an additional US$ 146 million costs on average (Review on Antimicrobial Resistance, 2015a). These expenses and uncertainties are often prohibitively high for small and medium-sized enterprises (SMEs) (Renwick, Brogan & Mossialos, Reference Renwick, Brogan and Mossialos2016). Despite these challenges of economies of scale, SMEs represent over 95% of the share of antibiotics in the current antibiotic pipeline and roughly 70% of the companies active in the antibiotic R&D pipeline have never developed, commercialized or marketed a drug product before.

Barriers in market approval and commercialization

Procedural differences in antibiotic approval processes between drug regulatory agencies can make global licensing time-consuming and expensive (Renwick, Simpkin & Mossialos, Reference Renwick, Simpkin and Mossialos2016). These differences relate to patient selection criteria, definitions of clinical end-points, specification of statistical parameters and rules regarding expedited approvals (Stern et al., Reference Stern2017; and see Chapter 3.4).

An additional challenge is that despite their public health value, antibiotics offer minimal (or even negative) commercial reward compared to other therapeutic areas (So et al., Reference So2011; and see Chapter 3.4). Potential sales volumes are restricted by short treatment durations and hospital stewardship programmes that limit access to novel drugs, both of which are critical practices to thwart the advance of resistance rates. In addition, the large overlap in clinical application of newly patented antibiotics with existing generic alternatives places downward pressure on prices (Renwick, Simpkin & Mossialos, Reference Renwick, Simpkin and Mossialos2016).

A 2003 study calculated that investment in musculoskeletal, neurologic and cancer medications were 11.5, 7.2 and 3.0 times more profitable, respectively, than developing an intravenous antibiotic targeting Gram-positive bacteria (Projan, Reference Projan2003). This type of analysis has not been replicated with more current figures, but large-capital pharmaceutical companies have continued to exit the antibiotic R&D space due to profitability. Most recently, Novartis closed its antibiotic development department in 2018 citing plans to focus on oncology projects (Plackett, Reference Plackett2020). The oncology sector is by far the largest drug market, comprising approximately 20% of global pharmaceutical sales with a total market value of US$ 199.5 billion in 2021 (Business Wire, 2022). In comparison, the total global market value of antibiotics in 2021 was estimated at US$ 47.2 billion (Research and Markets, Reference Research and Markets2023). Furthermore, a 2017 study showed that the median R&D cost per drug, including opportunity cost, for 10 new cancer drugs was US$ 648 million and significantly lower than previously estimated (Prasad & Mailankody, Reference Prasad and Mailankody2017). After a median duration of four years on the market, these cancer drugs had accrued revenues that were almost 10 times the total R&D cost.

Push and pull incentives: overview

Push incentives reduce the cost of researching and developing new antibiotics, while pull mechanisms increase the potential revenue of a successfully marketed antibiotic. Examples of push incentives include research grants, access to shared resources and product development partnerships to split R&D costs (Dutescu & Hillie, Reference Dutescu and Hillie2021). Pull mechanisms include outcome-based rewards that directly increase revenue such as monetary prizes, reimbursement premiums, advanced market commitments to purchase the drug and patent buyouts by governments (Dutescu & Hillie, Reference Dutescu and Hillie2021). If large enough, outcome-based pull rewards can replace the traditional revenue stream generated by the sales volumes of a licensed antibiotic. This concept is referred to as “delinkage” since the antibiotic’s revenue is delinked or uncoupled from its sales, thus removing the commercial incentive to promote the drug’s use (Rex & Outterson, Reference Rex and Outterson2016). Alternatively, pull mechanisms may be legal or regulatory, providing incentives such as accelerated procedures for marketing approval or extensions to the patent period. As of 2015, there were 47 different core incentives available or proposed for antibiotic developers that ranged from simple push or pull mechanisms to complex hybrid models (Table 3.9.1) (Renwick, Brogan & Mossialos, Reference Renwick, Brogan and Mossialos2016).

Table 3.9.1A selection of push and pull mechanisms available or proposed for incentivizing antibiotic R&D

Table 3.9.1 Long description

Push incentive strategies: Supporting open access to research; Grants for scientific personnel; Direct funding; Conditional grants; Funding translational research; Tax incentives; Refundable tax credits; Product development partnership.

Outcome-based pull incentive strategies: End prize; Milestone prize; Pay-for-performance payments; Patent buyout; Payer license; Research tournament; Advanced market commitment; Strategic antibiotic reserve; Service-availability premium.

Lego-regulatory pull incentive strategies: Accelerated assessment and approval; Market exclusivity extensions; Transferable intellectual property rights; Conservation-based market exclusivity; Liability protection; Anti-trust waivers; Sui generis rights; Value-based reimbursement; Targeted approval specifications; Priority review vouchers.

Source: Renwick, Brogan & Mossialos (Reference Renwick, Brogan and Mossialos2016).

Designing a global incentive package for stimulating antibiotic innovation is a complex task with numerous variables. Policy-makers need a methodology for selecting a complete and realistic set of incentives from the surplus of options that takes account of their unique circumstances and contexts. In 2015, the author of this chapter published a conceptual framework to guide policy-makers with this challenge (Fig. 3.9.3) (Renwick, Brogan & Mossialos, Reference Renwick, Brogan and Mossialos2016). The framework breaks the strategy down into three steps or phases. The first phase involves fashioning a core incentive package targeting the economic criteria necessary for rebalancing the market. According to the framework, this core incentive package must:

• improve the profitability of developing and commercializing a novel antibiotic;

• make market participation feasible for SMEs;

• encourage investment by large pharmaceutical companies;

• facilitate cooperation across all stakeholders including patients, academics, policy-makers, regulators and industry.

The second step requires adjusting the core incentive package to address public health goals pertaining to sustainability and patient access to new antibiotics. The final step considers the implementation and operational practicalities that are specific to national context.

Fig. 3.9.3

Framework for developing a holistic incentive package for antibiotic development

NPV: net present value; SME: small and medium-sized enterprise.

Figure 3.9.3 Long description

The infographic is presented as three concentric circles. The inner circle reads: Market factors: Improves N P V; Enables S M E participation; Encourages Big Pharma participation; Facilitates cooperation and synergy. The middle circle reads: Public health goals: Supports antibiotic sustainability; Promotes patient access; Targets high-priority medical need. The outer circle reads: Implementation and operational feasibility.

Source: Simpkin et al. (2017).

Multilateral initiatives

National health systems alone cannot correct the global market for antibiotics or contain the transborder threat of AMR. The international community has come together to create several multilateral initiatives including the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR), the Global Antibiotic Research and Development Partnership (GARDP), the Combating Antibiotic Resistant Bacteria Biopharmaceutical Accelerator (CARB-X), the European and Developing Countries Clinical Trial Partnership (EDCTP), the Global Antimicrobial Resistance Innovation Fund (GAMRIF) and the AMR Action Fund.

EU initiatives

The EU has been a leader in initiating policy action to revitalize the antibiotic market. The key EU organizations fostering antibiotic R&D are the European Commission’s Directorate-General for Research and Innovation (DG-RTD), the Innovative Medicines Initiative (IMI), the InnovFin Infectious Diseases Facility (InnovFin ID) and the European Health Preparedness and Response Authority (HERA).

Initiatives in the USA

There are two key governmental bodies in the USA that run programmes to incentivize antibiotic R&D: the National Institute for Allergy and Infectious Diseases (NIAID) and the Biomedical Advanced Research and Development Authority (BARDA).

United Kingdom initiatives

The United Kingdom Government offers a variety of programmes with push-based funding to support antibiotic R&D, primarily at the basic science and preclinical stages. The National Institute for Health Research’s (NIHR) Biomedical Research Centres and Health Protection Research Units have started a variety of programmes conducting basic science research that is laying the groundwork for antibiotic development (NIHR, 2023).

Regulatory initiatives

Traditional antibiotics

WHO and the Pew Charitable Trust keep close track of the antibacterial agents in clinical development. Between 2014 and 2020, 14 antibiotics had been developed and approved for use by the FDA, EMA or other national drug regulatory agencies (Pew, Reference Pew2021). Of these 14 approvals, 11 occurred in the last four years between 2017 and 2020 (WHO, 2021b). During the same 2014–2020 period, 19 antibiotic drug candidates were discontinued at various stages of development (Pew, Reference Pew2021).

As of March 2021, there were 43 drugs in clinical development: 15 in phase I, 13 in phase II, 13 in phase III and two applying for regulatory approval (Pew, Reference Pew2021) (Fig. 3.9.4). Of these drugs in development, approximately half (26) target a WHO priority pathogen (WHO, 2021b). As with most drug developments, the R&D and market approval process is lengthy. The phase III antibiotics are three to five years from potentially reaching the market. However, the phase I and II antibiotics have development timelines of at least five to 10 years and successful progression to marketing approval is far from certain. Antibiotics in phase I clinical trials have only a 14% likelihood of reaching the market. This means that of the 10 phase I antibiotics targeting resistant Gram-negative bacteria only one or two are likely to succeed.

Fig. 3.9.4

Antibiotic drugs and alternative antibacterial therapies in clinical development

NDA: new drug application; PPL: priority pathogens list; WHO: World Health Organization.

Figure 3.9.4 Long description

The y-axis represents the Number of drugs from 0 to 18, while the x-axis lists the phases. Each section has 3 bars, for Total small molecule antibiotics, W H O P P L small molecule antibiotics, and Alternative antibacterials, respectively. The values are as follows. Phase 1: 15; 14; 8. Phase 2: 13; 4; 13. Phase 3: 13; 6; 4. N D A: 2; 2; 0.

Source: WHO (2021b).

A compounding problem is that most of the pipeline drugs are redevelopments of classic antibiotic compounds or are combination therapies of existing antibiotic molecules.

These types of less original antibiotics are at higher risk of quickly losing effectiveness in clinical practice because of cross-resistance and therefore offer less clinical benefit over existing options. Of the 11 drugs approved from 2017 to 2020, only two drugs represent a novel antibiotic class with new pathogen target (meropenem/vabrobactam and lefamulin) (WHO, 2021b). Furthermore, of the 26 antibiotics in the pipeline targeting a WHO PPL pathogen, only seven (27%) meet at least one criteria for innovation: (i) absence of cross-resistance to existing antibiotics; (ii) new chemical class: (iii) new target; or (iv) new mechanism of action. The recent 2021 WHO analysis concluded that the current antibacterial pipeline is inadequate for the soaring resistance rates. This sentiment was echoed in the Access to Medicine Foundation’s 2021 AMR Benchmark Report, an independent assessment of key industry players across a spectrum of AMR priorities related to R&D, production and manufacturing and appropriate access and stewardship (AMR, 2021).

Alternative antibacterial therapies

A burgeoning portfolio of alternative antibacterial therapies in addition to antibiotics is now emerging and includes antibodies, bacteriophages, immunomodulating agents, microbiome-modulating agents and various peptides (Fig. 3.9.5). These products would likely supplement typical antibiotic regimens as adjunct or preventive therapies. WHO’s pipeline analysis found that there are 27 alternative therapies in development targeting PPL bacteria: eight in phase I, 14 in phase II, four in phase III and one not in a defined clinical trial phase (WHO, 2021b) (Fig. 3.9.4). Most of these alternative therapies are in the early phase of clinical trials and target specific pathogens, namely Staphylococcus aureus and Clostridium difficile. This specificity limits clinical applicability when there is diagnostic uncertainty about the exact pathogen causing infection, which is a common issue in LMICs. Furthermore, a significant challenge will be later-stage clinical trials as well as regulatory hurdles, as regulatory agencies have minimal experience with many of these drugs.

Fig. 3.9.5

Types of alternative antibacterial therapies in development

Figure 3.9.5 Long description

The x-axis notes the numbers from 0 to 10, while the y-axis lists the therapies. The values are as follows. Miscellaneous: 4. Immuno-modulating agents: 2. Microbiome-modulating agents: 8. Bacteriophage and phage-derived enzymes: 4. Antibodies: 9.

Source: WHO (2021b).

Early-stage push incentives

Push incentives, such as grants for researchers and direct project funding, are best used to facilitate the earlier stages of R&D from basic science up to clinical development. Most push funding for antibiotic R&D is directed towards basic antimicrobial science and less so towards clinical development. An estimated 86% of European national-level public funding of antibiotics was in this category (Kelly et al., Reference Kelly2016). The JPIAMR, DG-RTD, CARB-X, United States NIAID, United Kingdom MRC and United Kingdom NIHR preferentially fund antimicrobial basic science and preclinical development. While early-stage push funding of antimicrobial science is integral to the R&D process, there is a need for more late-stage push funding of clinical trials to help translate scientific innovation into marketable products. The overemphasis on early-stage push funding reflects the fact that basic science and preclinical development lends itself to being partitioned into projects requiring smaller individual monetary commitments than clinical trials. In addition, public funders can more easily justify supporting non-profit academic work. Basic science is largely the domain of academia and, as a result, private companies often do not benefit from early-stage push funding. Yet, clinical trials, which are usually operated by private companies, are by far the most expensive aspect of R&D (Review on Antimicrobial Resistance, 2015). SMEs are the most impacted by the lack of late-stage push funding as they often struggle to raise the capital necessary for clinical trials (Renwick, Simpkin & Mossialos, Reference Renwick, Simpkin and Mossialos2016).

As more drug candidates transition to clinical development, early-stage push funding could be pooled and reallocated to late-stage push funding to ensure viable antibiotics make it to the market approval stage. In addition, existing programmes, such as BARDA and the IMI’s COMBACTE, which specifically fund clinical trials could be further expanded. The AMR Action Fund is another promising new initiative that specifically addresses the lack of push funding in the latter half of the clinical development pipeline (i.e. phase II and III). Public and regulatory organizations could bolster this initiative by offering further funding and clinical trial guidance, respectively. Public–private partnerships naturally lend themselves to facilitating early- and late-stage clinical development by pooling investment and expertise from industry and governmental organizations while also dispersing financial risk. Many major incentive programmes such as BARDA, IMI and CARB-X rely on public–private partnerships and have been successful in adding promising drugs to the pipeline.

Lego-regulatory incentives

Lego-regulatory pull incentives, such as those offered by the EMA and FDA, are most effective at facilitating progress through the market approval stage.

Both the EMA and FDA offer several incentives that decrease the approval timeline for antibiotics: regulatory guidance, expedited pathways and conditional market authorization. The primary value of these incentives comes from indirectly increasing the effective patent period of the antibiotic since it reaches the market earlier (Mossialos et al., Reference Mossialos2010). But there is a balance to be struck between speeding up the approval process and ensuring that licensed drugs meet standards for quality, safety and efficacy (Renwick, Simpkin & Mossialos, Reference Renwick, Simpkin and Mossialos2016). It is unlikely that these regulatory processes can be shortened any further without sacrificing regulatory standards. It remains to be seen how these lego-regulatory incentives may be applied to the variety of alternative antibacterial therapies in the pipeline. In addition, many of the pipeline antibiotics are not expected to be high-volume products and therefore adding to their effective patent period does not translate into meaningful revenue. Market exclusivity extensions suffer from this same problem. Therefore, it may be worthwhile to explore alternative incentives that allow priority review (e.g. priority review vouchers) or market exclusivity extensions (e.g. transferable intellectual property rights (TIPR)) to be transferred from an approved antibiotic to another product in the developer’s portfolio that would benefit more from the longer effective patent period (Ferraro, Towse & Mestre-Ferrandiz, Reference Ferraro, Towse and Mestre-Ferrandiz2017). Incentives such as priority review vouchers and TIPR could provide a market incentive to license new antibiotics without requiring upfront government funding. However, it is important to be aware that priority review vouchers and TIPR do not incentivize antibiotic commercialization or access and they could have broader pharmaceutical market consequences (Ferraro, Towse & Mestre-Ferrandiz, Reference Ferraro, Towse and Mestre-Ferrandiz2017; Mossialos et al., Reference Mossialos2010).

Harmonization between the EMA and FDA’s market approval requirements has been a step towards lowering market approval costs and time. However, the EMA and FDA regulatory processes are relatively similar, unlike the Japanese Pharmaceuticals and Medical Devices Agency or the Chinese Food and Drug Administration. Harmonization efforts among these agencies will prove more challenging but could further relieve companies of duplicative regulatory approval costs. Including the Japanese Pharmaceuticals and Medical Devices Agency in TATFAR discussions was a laudable starting point.

The need for outcome-based pull incentives

Push funding and legal or regulatory incentives can drive viable antibiotics to licensing; however, they are weak incentives for the commercialization and distribution of the product. Net profits from sales of an innovative new antibiotic are perceived to be limited for several reasons, especially when compared to therapeutic areas with the highest sales revenues; for example, oncology, immunology and musculoskeletal medicine (EvaluatePharma, 2021). A novel antibiotic would be reserved as a treatment of last resort or may only target a rare resistant pathogen, which restricts potential sales revenue. High product prices are unlikely to compensate for low sales volume because of the considerable overlap in effectiveness between existing antibiotics. Also, future rapid-point-of-care diagnostic tools could cut into the revenue potential for newly marketed antibiotics (Outterson et al., Reference Outterson2015). Therefore, large outcome-based pull incentives are necessary in the absence of a viable market. Pull incentives have the added benefit of potentially allowing SMEs to secure venture capital for clinical trials. However, pull incentives for antibiotics have been mostly absent from current funding initiatives. The only established outcome-based pull incentives currently available are relatively limited advanced market commitments offered by BARDA and GARDP for certain low-volume antibiotics (Simpkin et al., Reference Simpkin2017).

Subscription models

World Health Organization’s (WHO’s) neglected diseases roadmap, 2021–2030

The WHO Road Map for Neglected Tropical Diseases 2021–2030 sets out global targets and milestones for 2030, including 90% reduction in cases requiring interventions, 75% reduction in DALYs lost to neglected diseases, working to eliminate at least one neglected disease in 100 countries, and eradicating two neglected diseases globally. The roadmap highlights research, development and innovation as “fundamental enablers of programmatic progress for all neglected diseases” (WHO, 2021a). To achieve this endeavour, the WHO Council highlights the need for a systematic approach to innovation and the elimination of unacceptable inequities in access to treatment and prevention (WHO, 2021b).

Delinking the costs of R&D from the cost of new therapies: financing incentive mechanisms

The eradication of neglected diseases or the mitigation of their impacts requires greater pharmaceutical innovation. The report on financing and coordination by WHO’s consultative expert working group on R&D (WHO, 2012) highlights that novel financing incentive mechanisms are required to address these challenges. The World Health Assembly passed a resolution in 2006 that highlighted the importance of “incentive mechanisms … addressing the linkage between the cost of R&D and the price of medicines, vaccines, diagnostic kits and other health care products” in addressing unmet needs for neglected diseases (WHO, 2007). The design of financing incentive mechanisms is important to ensure not only sustained investment in R&D but also an efficient targeting of effort and resources towards key areas of need.

In this chapter, we quantify some of the systemic gaps with regards to the distribution of pharmaceutical R&D activity and funding across neglected diseases, and describe some of the underlying reasons for these gaps. We then discuss the financial incentive mechanisms in use since 2007 in terms of pre-discovery push (public–private partnerships, in particular product development partnerships, R&D grants and subsidies, fiscal measures) and post-discovery pull (transferable patent extensions, patent buyouts, priority review vouchers (PRVs), advance market commitments (AMCs), prizes). We examine the evidence for their effectiveness levels, noting their relative advantages and disadvantages. We conclude with insights into the overall state of R&D funding for neglected diseases.Footnote ¹

Sources of funding

The three most common sources of funding were industry, philanthropic and public sources. Between 2007 and 2019, public and philanthropic sources accounted for more than 70% and 20% of total expenditure, respectively, while industry sources accounted for about 10%.

The United States of America (USA) consistently ranks as the top funder, funding on average 65% of total spending between 2007 and 2019. Apart from the EU, which funds on average 3% of the total expenditure, other top funding countries include the United Kingdom and France, which fund on average 6% and 2%, respectively. However, it is important to note that over the same period, about 13% of the total spending came from countries that were not specified.

Fig. 3.10.7 shows total neglected disease R&D spending by type of recipient. On average academics and other research institutions receive the highest amount of investment followed by industry, whereas “other intermediaries”Footnote ² receive the lowest amount of funding.

Fig. 3.10.7

Academics and research institutions receive the biggest proportion of investment

PDP: product development partnership.

Fig. 3.10.7 Long description

The y-axis represents Total investment in U S dollars, from 0 to 4 billion, while the x-axis lists the years. The total values are as follows. 2007: 2.95 billion. 2008 to 2016: Between 3 and 3.5 billion. 2017: 3.6 billion. 2018: 4 billion. 2019: 3.9 billion.

Note: Authors’ analysesData source: Policy Cures Research (2021b).

Inequities and inequalities in R&D activity and funding

When we compare the distributions of pharmaceutical R&D activity and global DALYs across neglected diseases (Fig. 3.10.8), for the diseases at the top of the distribution of DALYs there is a disproportionate concentration of innovation in low-burden diseases, meaning that low-burden diseases receive more than the fair share of R&D activity when we consider their need relative to other diseases (inequity pro-low burden). This inequity is even more pronounced once we consider market launches in LMICs where we see low-burden diseases experience more market launches than their relative need would warrant when compared to high burden diseases (Fig. 3.10.9).

Fig. 3.10.8

Low-burden diseases receive more than their fair share of R&D

DALYs: disability-adjusted life-years; R&D: research and development.

Note: Authors’ analyses.

Sources: DALYs data from IHME (2019); R&D data from IQVIA (2019).

Fig. 3.10.9

LMICs receive few market launches for new pharmaceutical products

DALYs: disability-adjusted life-years; R&D: research and development.

Note: Authors’ analyses.

Sources: DALYs data from IHME (2019); R&D data from IQVIA (2019).

These inequities can be attributed to inequalities in the distribution of R&D funding across neglected diseases and/or the fact that most LMICs lack the resources and research infrastructure to develop their own medicines (Mrazek & Mossialos, Reference Mrazek and Mossialos2003).

The 10 neglected diseases that received the highest R&D investment in US dollars between 2007 and 2019 for new products and technologies to address challenges in public health are shown in Fig. 3.10.10. HIV/AIDS received the highest investment by about US$ 16.9 billion, followed by malaria and TB, with US$ 7.9 billion and US$ 7.8 billion, respectively. Mycetoma, leptospirosis and hepatitis B received the lowest investment, which cumulatively in the last five years (2015 to 2019), was around US$ 1.5 million, US$  9.3 million and US$  16.5 million, respectively (Policy Cures Research, 2021a).

Fig. 3.10.10

HIV/AIDS, malaria and TB receive most investment

B: billion.

Note: Authors’ analysesData source: Policy Cures Research (2021b).

Between 2015 and 2018 the cumulative spending has seen a steady increase, albeit remaining concentrated on HIV/AIDS, malaria and TB. Looking at global spending for R&D activity for neglected diseases, we observe a disproportionate concentration on low-burden neglected diseases.

Fig. 3.10.11 (the concentration curve of global R&D activity (1980–2019) and DALYs in LMICs (2018–2019)) and Fig. 3.10.12 (the concentration curve of market launches in LMICs (1980–2019) and DALYs (2018–2019) in LMICs (2018–2019)) show that low-burden diseases receive more than their fair share of funding given their relative disease burden. With the exception of spending on regulatory, implementation, market and post-market launch activities, this inequity is more pronounced when one considers funding allocated only to LMICs.

Fig. 3.10.11 and 3.10.12

Low-burden diseases received more than their fair share of funding

DALYs: disability-adjusted life-years R&D; research and development.

Note: Authors’ analyses.

Sources: DALYs data from IHME (2019); R&D data from IQVIA (2019).

Reasons for inequities and inequalities

Several mechanisms may explain these inequities in innovation that impact differential access to treatment for neglected diseases to the detriment of health and well-being in LMICs. Concerns around pricing, affordability and the low potential for profit of those treatments in LMICs implies that firms have little incentive to develop innovations for these populations. This is aggravated by HICs giving priority to fund R&D for diseases that impact their own populations and the lack of international coordination in pooling and allocating resources to innovation taking account of health needs across all disease areas and geographies, as well as affordability.

These inequities are also intrinsically related to market failures and the effectiveness of mitigating strategies, including financing and incentive mechanisms.

Product development partnerships (PDPs)

PDPs are the most common type of public–private partnerships for neglected diseases (Aerts et al., Reference Aerts2017; Widdus, Reference Widdus2005). PDPs may exist as voluntary collaborations between private companies and public funders, but may also involve contract-based stakeholders such as academic centres that provide scientific expertise in drug development (Moran, Reference Moran2005; Moran et al., Reference Moran2005; Widdus, Reference Widdus2005). More recently, along with public and private partners (e.g. academia, public funding organizations and pharmaceutical companies) PDPs, and public–private partnerships more broadly, include diverse stakeholders such as regulators, insurers and patient organizations (de Vruehm, de Vlieger & Crommelin, Reference de Vrueh, de Vlieger and Crommelin2019).

In 2017, there were around two dozen PDPs for neglected diseases, most created around 2000 (Borrás, Reference Borrás2017), with most focusing on malaria, HIV/AIDS and tuberculosis (TB). Notable examples are shown in Table 3.10.3 (Munoz et al., Reference Munoz2015; Borrás, Reference Borrás2017).

While focusing on all R&D stages, PDPs are particularly relevant in incentivizing early-stage innovation when firms may not have easy access to capital (Grace & Kyle, Reference Grace and Kyle2009) by financing the large upfront R&D cost through public or private funding, or a combination of both (Chapman et al., Reference Chapman2020; Sloan & Hsieh, Reference Sloan and Hsieh2017).

External financing (mainly through the public sector and multiple philanthropic organizations) and amounting to 16% of total “external” R&D funding for neglected diseases (Chapman et al., Reference Chapman2020) reduces the cost of failure for collaborating partners by sharing the risk of failure early on between them, thus incentivizing private R&D (Moran et al., Reference Moran2005; WHO, 2013). In exchange for funding and research inputs, the developer commits to invest in less profitable products at early R&D stages of basic research and to a reduction in eventual profits. The public sector contributes to the development and/or clinical trial stages that require most of the investment (Moran, Reference Moran2005). In most cases, PDPs channel funding through pharma partners, who own the rights to the product. In return for those rights, the partner commits to ensuring global access to the product at a reasonable price for those in developing countries (NBR, 2014). Notably in some product development schemes intellectual property rights (IPR) are owned by the partnership, so enabling some pricing control once the innovation is developed and thus promoting affordability and access (Buse & Walt, Reference Buse and Walt2000). Box 3.10.1 provides a case study of a PDP and Table 3.10.3 provides selected examples to show PDPs’ common features and differences with regard to IPR.

Table 3.10.3PDPs have increased overall investment in R&D and innovation for neglected diseases

Table 3.10.3 Long description

The table has 8 columns: Name of P D P, About or purpose of P D P, Funding amount, Stakeholders involved, Disease focus, Type of technology, Targeted R and D stage, and Intellectual property rights implications. It reads as follow. Row 1: Drugs for Neglected Disease Initiative (D N D i). About: Collaborative P D P. Non-profit R and D organization established in 2003 to fund and manage R and D development for medicines across over 200 partners. D N D i has already delivered eight field-adapted and affordable treatments for five deadly diseases, and aims to have delivered 25 new treatments by 2028. Funding amount: 615.5 million Euros (raised by end of 2019). Funding source: 57 percent public, 43 percent private. Stakeholders involved: Founding partners: W H O T D R, M S F, Malaysian Ministry of Health, Kenya Medical Research Institute, Indian Council of Medical Research, Oswaldo Cruz Foundation and Institute Pasteur. Donors: 1. Public: Australian Trade and Investment Commission, United Kingdom Department of Health and Social Care, Dutch Ministry of Foreign Affairs, French Development Agency, G I Z, European Commission, etc. 2. Private: B M G F, M S F, Takeda Pharmaceuticals, Wellcome Trust, etc. Disease focus: Visceral Leishmaniasis, Human African trypanosomiasis (sleeping sickness), Chagas, paediatric H I V, filarial infections, mycetoma and hepatitis C. Type of technology: Treatment (drugs). Targeted: R and D stage: Drug discovery, translational research, clinical trials and registration and access. Intellectual property rights implications: Variable per drug D N D i adheres to basic principles stated in its intellectual property rights policy. The overarching theme is that it will ensure that the results of the work carried out under its auspices are disseminated as widely as possible and its products made readily available and affordable in developing countries.

Row 2: Medicines for Malaria Venture (M M V). About: Collaborative P D P. Non-profit product development partnership established in 1999 to fund and manage R and D development and access to antimalarial medicines across around 400 partners in 50 countries (donors and clinical centres in endemic countries). MMV has validated more than 19 new malaria drug targets since 1999. Funding amount: 350 million U S D (raised by the end of 2018); Funding source: More than 60 percent private. Stakeholders involved: Founding partners: Launched with initial seed finance of 4 million U S D from the Government of Switzerland, United Kingdom Department for International Development, the Government of the Netherlands, the World Bank and the Rockefeller Foundation. Clinical centre partners: 50 clinical trial sites in sub-Saharan Africa, South East Asia and South America. Donors: 1. Public: United States government (U S A I D, N I H), Australian Government; Development of Foreign Affairs and Trade, United Kingdom Government (D F I D and D o H), Netherlands Ministry for Development Cooperation, etc. 2. Private: B M G F, Wellcome Trust, G S K, Sanofi, etc. Disease focus: Malaria. Type of technology: Treatment (drugs). Targeted R and D stage: Drug discovery, translational research, clinical trials and registration and access. Intellectual property rights implications: M M V retains rights to any Intellectual property rights that is essential in allowing them to develop and launch drugs for the benefit of M M V target group. Principles include: exclusivity, royalty-free, transferable Intellectual property rights.

Row 3: T B Alliance. About: Collaborative P D P. Non-profit product development partnership of around 38 partners established in 2000 to fund and manage R and D development and access to T B medicines. Funding amount: Around 184 million U S D (raised by end of 2019). Stakeholders involved: Founding partners: Launched with issuance of Cape Town Declaration by around 28 partners, including B M G F, European Commission, M S F, W H O, World Bank, Wellcome Trust, etc. Donors: 1. Public: Australian Aid, United Kingdom F C D O and M R C, U S A I D, Germany Federal Ministry of Education and Research, Netherlands Ministry of Foreign Affairs, Ministry of Foreign Affairs (Republic of Korea), etc. 2. Private: B M G F, The Rockefeller Foundation, E D C T P, Global Health Innovative Technology Fund (Japan), U N I T A I D, etc. Disease focus: Tuberculosis. Type of technology: Treatment (drugs). Targeted R and D stage: Drug discovery, translational research, clinical trials and registration and access. Intellectual property rights implications: T B Alliance’s mission includes an explicit commitment to A A A Mandate that all new products will be adopted, available and affordable to those with T B. No further information on Intellectual property rights implications.

BMGF: Bill & Melinda Gates Foundation; DFID: Department for International Development; DNDi: Drugs for Neglected Disease Initiative; DoH: Department of Health; EDCTP: European and Developing Countries Clinical Trials Partnership; FCDO: Foreign, Commonwealth and Development Office; GIZ: German International Cooperation Society; MMV: Medicines for Malaria Venture; MRC: Medical Research Council; MSF: Médecins sans Frontières; NIH: US National Institute of Health; PDP: product development partnership; TB: tuberculosis; TDR: Special Programme for Research and Training in Tropical Diseases; WHO:World Health Organization.

Sources: DNDi (2004; 2019a; 2019b; 2023); MMV (2018; 2023a; 2023b; 2023c; 2023d); TB Alliance (2019; 2020; 2023).

Despite the wide proliferation of public–private partnerships in recent years, in particular those focused on product development, a systematic review by Aerts et al. (Reference Aerts2017) did not find a single impact evaluation related to them, barring one analysis that compared the cost–effectiveness of the PDP model against AMCs and a hybrid scheme (PDP until phase II trials, followed by AMCs later) in the context of immunizations for malaria. While costs were lowest for the PDP scheme, DALYs averted were also the lowest, rendering PDPs a non-cost-effective approach. Most descriptive evidence, however, suggests that PDPs have led to a substantial increase in investment in R&D and innovation for neglected diseases. Between 2009 and 2013, 57% of new approvals in WHO’s list of essential medicines were developed through PDPs. PDPs have been particularly successful in vaccine development, with more than 400 PDP-led vaccines under development (Breitstein & Spigelman, Reference Breitstein and Spigelman2013); and over half of the vaccines for neglected diseases during the period 2009 to 2013 resulting from PDPs (Cohen, Sturgeon & Cohen, Reference Cohen, Sturgeon and Cohen2014). Despite these promising results, impact evaluations are seriously lacking and consistent data collection and availability of data to analyse cost–effectiveness of PDPs are necessary.

R&D grants and subsidies

Grants and subsidies are normally in-advance lump sums awarded to fund R&D. They tend to focus on key priority areas identified by funders and cover the initiation of discovery or early stage research. Grants and subsidies constitute the main means to fund R&D adopted by public funders and third parties, contributing to 77% of total R&D funding for neglected diseases (Chapman et al., Reference Chapman2020). They specifically focus on early basic research, preclinical and clinical development stages, but tend to cover a wide range of neglected diseases (WIPO, 2014) and are primarily provided by public and philanthropic sources. This includes, for example, governments that allocate a proportion of their gross domestic product (GDP) such as the USA through the United States National Institute of Health and USAID, or the United Kingdom through the Department for International Development (DFID)Footnote ³ and Medical Research Council (MRC), as well as philanthropic organizations such as the Bill & Melinda Gates Foundation (BMGF) and the Wellcome Trust (Chapman et al., Reference Chapman2016). Grant funds are channelled either directly to researchers and developers or through a conduit (20% of research grants) such as public–private partnerships/PDPs with some private sector participation (Pugatch Consilium, 2017), or through non-PDP intermediaries such as the Global Health Innovative Technology Fund (GHIT) and the European and Developing Countries Clinical Trial Partnership. Non-PDP intermediaries generally have a broader funding base than PDPs, supported by science and technology organizations, aid agencies and philanthropic foundations, making them less vulnerable to changes from one donor funding stream (Table 3.10.4).

Table 3.10.4Selected examples of grants intermediaries

Table 3.10.4 Long description

The table has 8 columns: Name of intermediary, About or purpose of intermediary, Funding amount, Stakeholders involved, Disease focus, Type of technology, Targeted R and D stage, and Intellectual property rights approach. It reads as follows. Row 1: Global Health Innovative Technology Fund (G H I T). About: G H I T fund is a general incorporated association in Japan (P P P), founded in 2012 to facilitate and invest in global partnerships (including P D Ps) for the discovery and development of new health technology for infectious diseases prevalent in developing countries. Funding amount: More than 260 million U S D invested to date. Stakeholders involved: Japanese Government, B M G F and 6 Japanese pharmaceutical companies, and foundations such as the B M G F and Wellcome Trust. Disease focus: Malaria, T B and the 17 W H O defined N T Ds. Type of technology: Drugs, vaccines and diagnostics. Targeted R and D stage: Discovery, preclinical and clinical stages and registration. Intellectual property rights approach: Supports open access innovations through its access policy. When product development partners and/or participants are successfully granted a patent deriving from projects invested by the GHIT Fund, product development partners and/or participants will grant royalty-free licenses to users operating in least developed countries, as categorised by the United Nations classification and low-income countries, categorised by the World Bank classification.

Row 2: European and Developing Countries Clinical Trials Partnership (E D C T P 1 and 2). About: E D C T P is an E U-funded P P P established in 2014 between countries in Europe and sub-Saharan Africa to enhance research capacity, collaboration and accelerate the development of new or improved medical interventions for the identification, treatment and prevention of poverty-related infectious diseases. E D C T P 1 (2003 to 2015). E D C T P 2 (2014 to 2024). Funding amount: Total fund around 752.7 million Euros to date. Stakeholders involved: 14 European and 16 sub-Saharan African countries, supported by the European Union, third parties and participating states. Disease focus: T B, H I V, malaria, diarrhoeal diseases, lower respiratory tract infections, other neglected infectious diseases, including emerging and re-emerging infections of particular relevance in sub-Saharan Africa. Type of technology: Drugs, vaccines and diagnostics. Targeted R and D stage Phase 1 to 4 clinical trials, with an emphasis on phase 2 and 3 trials and access (product-focused implementation research). E D C T P also funds capacity development and international networking. Intellectual property rights approach: No specific information on Intellectual property rights implications was detected.

BMGF: Bill & Melinda Gates Foundation; EDCTP: European and Developing Countries Clinical Trials Partnership; EU: European Union; GHIT: Global Health Innovative Technology Fund; NTDs: neglected tropical disease; PDPs: product development partnership; PPP: public–public partnership; TB: tuberculosis; WHO: World Health Organization.

Sources: GHIT (2013; 2022; 2023a; 2023b), EDCTP (2020; 2022a; 2022b; 2022c).

Evidence of the impact of grants and subsidies on neglected disease-specific R&D is lacking. However, general pharmaceutical industry research suggests that grants and subsidies have a positive effect on R&D investment, and the innovation of new molecules and patent applications (Toole, Reference Toole2012; Azoulay et al., Reference Azoulay2019; Kourouklis, Reference Kourouklis2021). These effects vary across different stages of drug development – in the case of orphan drugs, they are most pronounced in phase I and phase II of the drug development process (Kourouklis, Reference Kourouklis2021).

Fiscal incentives

Fiscal incentives focus on reducing the cost of R&D, raising the long-term rate of return on R&D investments, and include: tax exemptions, tax deferrals, tax allowances and tax credits (OECD, 2016).Footnote ⁴ Some apply to current expenditures, others to capital or revenues. Tax allowance, exemption and deduction consist of reducing the taxable amount by reducing the tax base before the tax liability is computed. With tax credits, in contrast, the tax liability is reduced by subtracting the credit after the liability has been determined. Tax deferrals do not reduce tax liability but defer tax payment. These incentives can be used to target single firms or foster collaboration across firms, with some schemes designed to support SMEs, and they can also be used to target a specific disease area or groups of diseases.

In practice, fiscal incentives for neglected diseases are mostly in the form of tax credits, exemptions and reliefs. Although the R&D phases targeted by fiscal incentives may vary across initiatives, tax incentives for neglected disease R&D have often been considered for encouraging basic and non-clinical research for vaccines or medicinal products (Rao, Reference Rao2011). By reducing the R&D cost, these incentives encourage innovation in areas of high risk and smaller returns on investment – such as neglected diseases. These particularly benefit SMEs that, while financially constrained, are often agile and innovative in neglected disease innovation (BVGH & BIO, 2012), thus increasing the prospects of breakthrough innovation.

There are two main tax incentive initiatives highlighted in literature (Monroe, Greenberg & Basey, 2015; Pugatch Consilium, 2017). These are the United States Neglected Disease Tax Credit Proposal (HR3156) which mandates a transfer of IPR, and the United Kingdom Vaccine Research Relief which is not necessarily limited to a neglected disease focus, but rather extends to orphan drugs and humanitarian driven innovations (Monroe, Greenberg & Basey, 2015; UK Government, 2016; US Congress, 2009a) (Table 3.10.5).

Table 3.10.5Fiscal incentives’ effectiveness in stimulating neglected disease research is unclear

Table 3.10.5 Long description

The table has 6 columns: Fiscal incentive, About the scheme or incentive nature, Eligibility criteria or disease focus, Targeted companies, Benefits claimed, and Intellectual property rights implications. It reads as follows. Row 1: United Kingdom Vaccine Research Relief (V R R). About: Proposed and introduced in 2003, implemented and expired in 2017. In the United Kingdom R and D tax credits were introduced for S M Es in 2000 and extended to large companies from 2002. This is normally done through S M E scheme and Research and Development Expenditure Credits (R D E C) (non-neglected disease specific). V R R was introduced as an additional top-up relief for vaccines research. All companies may deduct a standard 100 percent of their R and D expenditure from their taxable income under S M E scheme and R D E C. While V R R allows 50 percent deduction on top for eligible R and D expenditure. Criteria: H I V/AIDS, T B and Malaria. Expenditures on activities ranging from identification of a chemical entity, or a potential vaccine through completion of Phase 3 clinical trials. Targeted companies: All companies. Large companies claiming VRR can only use the deduction option; they cannot claim payable credits for V R R. Benefits claimed: Number of claims under the V R R scheme remained at about 10 a year with support claimed of less than 5 million pounds each year between 2003 to 2004 and on average around 20 million pounds each year in the period up to 2012 to 2013. Intellectual property rights implications: No information detected on intellectual property rights implications.

Row 2: United States neglected disease tax credit proposal (H R 3156). About: Proposed in 2009, no information detected on introduction or implementation. This proposal was put forward by the biotechnology company (biotech) Genzyme with the sponsorship of Representative Donald Payne and others. 50 percent tax credit for non-clinical expenses for research on neglected disease treatments. Criteria: All N T Ds (see Table 3.10.1) and any infectious disease with unmet need or disproportionately affecting the poor. Qualified research: Only preclinical or basic research. Companies could not use the credit for expenses incurred from clinical trials or regulatory review. This includes expenses for in-house or contracted research activities undertaken for the purpose of non-clinical basic research only. With this specific criteria of discovering information - which is 1. technological in nature, and 2. the application of which is intended to be useful in the development of a new or improved business component of the taxpayer. Targeted companies: All companies. In-house or contracted research services. Benefits claimed: No information detected on implementation. Intellectual property rights implications: Mandates a transfer of intellectual property rights and could only apply for the credit if they have donated a royalty-free licence to a foreign government or a non-profit research organization for the claimed research in that year.

NTD: neglected tropical disease; SME: small and medium-sized enterprise; TB: tuberculosis; VRR: Vaccine Research Relief.

Sources: US Congress (2009a; 2009b), UK Government (2016; 2020) and European Commission (2008).

Given the reduced number of fiscal incentives for neglected diseases, their effectiveness in spurring research for neglected diseases is difficult to ascertain. Rao (Reference Rao2011) argues that tax credits in the United Kingdom did not attract new firms to invest in R&D for neglected diseases and alone are insufficient to increase R&D spending in global health. In contrast, the tax credits created in the Orphan Drug Act (P.L. 97–414) in 1983 have had a significant impact on orphan drug development, leading to 201 new orphan drugs in the market and this trend has been particularly strong in recent years; for example, 18 new drugs gained approval in 2014 (EY, 2015). Yin (Reference Yin2008) shows that the Orphan Drug Act-led income tax credit (equal to 50% of clinical trial expenses) increased the annual flow of clinical trials for drugs for rare diseases by 69%.

Transferable patent extensions

Transferable patent extensions (TPEs) or wildcard patent extensions are a form of supply-side production response intended to encourage R&D for critically needed drugs in less attractive disease areas (Sonderholm, Reference Sonderholm2009). A successful innovator of a new drug is granted a one-time patent extension (ranging from six months to two years in the USA and five years in Europe) that can be applied to one of the innovator’s other drugs or sold to another firm. Such an extension is worthwhile for companies with a drug in their portfolio whose patent is near to expiry, and provide a source of return on investment (Moran, Reference Moran2005; Sonderholm, Reference Sonderholm2009; Seabury & Sood, Reference Seabury and Sood2017). These incentives can be particularly important for attracting companies to undertake R&D for neglected diseases because the return on investment in neglected disease markets remains insufficient due to limited market size and affordability (Seabury & Sood, Reference Seabury and Sood2017). Our review did not yield notable examples of TPEs granted for neglected diseases, but they have been proposed as a potential incentive in the context of developing new antimicrobials to tackle the problem of antimicrobial resistance (AMR) (Spellberg et al., Reference Spellberg2007; Spellberg et al., Reference Spellberg2008; Outterson, Samora & Keller-Cuda, Reference Outterson, Samora and Keller-Cuda2007). Within this context, Outterson and McDonnell (Reference Outterson and McDonnell2016) calculate the potential costs of a 12-month extension and conclude that such an extension would cost the USA’s health system more than US$ 4.8 billion, making such an extension extremely inefficient.

Patent buyouts

A patent buyout is an outcome-based pull mechanism aimed at delinking returns from price and volume-based sales by acquiring the patent directly from the innovator in exchange for a payment (Batista et al., Reference Batista2019). The government offers to buy the rights to the patent to encourage innovation and subsequently places the patent in the public domain for unrestricted access. The scheme may specify criteria ex ante to run a prize-like competition or may engage in ex post identification of a suitable patent candidate (Robinson, Ritchie & Kenny, Reference Robinson, Ritchie and Kenny2020). Patent buyouts add a markup to private market valuations and internalize the social returns to innovation, which in the context of limited market size for neglected diseases, assures innovators a satisfactory return on investment. By abolishing private patent rights, it also fosters access to innovation through lower prices.

Despite the theoretical applicability of buyouts, there is a lack of real-world application and empirical evidence on its effectiveness in the context of neglected diseases. In the context of neglected diseases, patent buyouts have only been proposed within the Medical Innovation Prize Fund (S.2210) as an end-product prize (US Congress, 2007). Theoretical evidence on welfare effects concludes that buyouts rewarding innovators based on observed market outcomes are an effective tool to improve social welfare (Galasso, Mitchell & Virag, Reference Galasso, Mitchell and Virag2016; Radhakrishnan, Reference Radhakrishnan2016). Outterson (Reference Outterson2006) argues that by recovering lost R&D costs, buyouts are a cost-effective method to improve access and health outcomes in LMICs. For example, the lost R&D cost recovery for GlaxoSmithKline’s Cervarix human papillomavirus vaccine is as little as US$ 29.2 million. Therefore, for a patent buyout that would cost as little as US$ 30 million, the medicine could be immediately available for millions in LMICs in its generic form.

Priority review vouchers (PRVs)

The PRV scheme enables firms with drugs approved for certain tropical diseases to apply for a voucher that can be used to obtain priority review for an unrelated drug application, so shortening the regulatory agency review time from a standard average of 10 months to a maximum of 6 months (Ridley, Grabowski & Moe, Reference Ridley, Grabowski and Moe2006; Gaffney, Mezher & Brennan, Reference Gaffney, Mezher and Brennan2019; Aerts et al., Reference Aerts2022). Alternatively, the firm can sell the voucher to another firm.

The innovator retains IPR and prices may be unaffordable if this mechanism is not complemented by differential (tier) pricing, donation or a drug access programme.

At the time of writing, PRV initiatives are primarily offered through the United States FDA’s scheme (FDA, 2020). To be eligible, products need to satisfy certain criteria including: approval by the FDA or the EMA; recognition as a product for serious conditions where there is unmet medical need; adequate and well-controlled trials have established that the drug influences a surrogate end-point and there is clinical superiority to other existing therapies. PRVs mostly focus on late-stage clinical development, registration and post-marketing and delivery stages of the R&D model and are commonly awarded to drugs. For a potential blockbuster drug, the value of PRV has been estimated by some experts to be worth anywhere between US$ 50 million to US$ 500 million (Robertson et al., Reference Robertson2012). As of late 2018, seven PRVs had been awarded for neglected/tropical diseases – in malaria, tuberculosis, leishmaniasis, cholera, onchocerciasis (river blindness) and Chagas disease (US FDA, 2017; Cohen, 2018). Since then, the PRV programmes have expanded substantially, with many more vouchers issued across tropical disease, rare paediatric disease and medical countermeasure indications).

Notable examples include: Coartem, which is now a first-line treatment for malaria across Africa and South-East Asia; benznidazole indicated for Chagas disease (in use as early as the 1970s, approved by FDA in 2017); Krintafel, the first new antimalarial in over 18 years (approved in 2018); and Moxidectin, a treatment for onchocerciasis (approved in 2018) (MDGH, 2018; Cohen, Reference Cohen2018) (Table 3.10.6).

Table 3.10.6PRVs’ impact on R&D is unclear because of a lack of data

Table 3.10.6 Long description

The table has 6 columns: Drug name, Supplier(s), Year P R V granted, Disease focus, How was the voucher redeemed or traded, and Approach to I P R transfer. It reads as follows. Row 1: Coartem (artemether or lumefantrine); Novartis; 2008; Malaria; First P R V so market value of P R V was unknown. In 2011, Novartis redeemed the voucher on a B L A for Ilaris (canakinumab) to treat gouty arthritis. F D A declined the application; Innovator retains I P R. Row 2: Benznidazole; Chemo Research; 2017; Chagas; blank; Innovator retains I P R. Row 3: Krintafel; G S K; 2018; P. vivax malaria; No information found or anonymously redeemed; Innovator retains I P R. Row 4: Moxidectin; Medicines Development for Global health; 2018; Onchocerciasis (river blindness); blank; Innovator retains I P R.

BLA: biologics licence application; FDA: Food & Drug Administration; IPR: intellectual property rights; PRV: priority review voucher.

Sources: FDA (2008; 2017; 2018; 2020) and Wang (Reference Wang2018).

Evidence on the impact of PRVs on R&D is hampered by a lack of data; sample sizes are small due to the low number of vouchers issued. PRVs have been found to not significantly impact clinical trial activity for neglected tropical disease (Aerts et al., Reference Aerts2022; Jain et al., Reference Jain2017) nor for rare paediatric conditions (Hwang et al., Reference Hwang2019) but have been found to increase the likelihood of initiation of large patient trials for paediatric rare disease (Hwang et al., Reference Hwang2019).

Advance market commitments (AMCs)

In AMCs the funder/donor commits to (fully or partially) subsidize the initial purchases of innovative medicines at an agreed price, for an established maximum threshold, to be provided to a specified set of countries. The subsidy is normally only triggered when countries purchase the innovation. The binding agreement is signed before the medicine is developed and contains a full specification of the desired product and use. They also often involve a commitment by the firm to sell at an affordable price (close to marginal cost and below the AMC price) all quantities beyond the agreed subsidization threshold in the agreed countries (Levine, Kremer & Albright, Reference Levine, Kremer and Albright2005).

By “pooling” purchase commitments across many countries the scheme creates a profitable and less risky market for the developer by establishing volume guarantees. By pooling funding from several donors and negotiating a low price it enables LMICs to access the medicine. The risk is shared by the developer and the funder: while in the development phase costs are born entirely by the developer who still bears fully the risks associated with the scientific challenges of R&D, when innovation occurs the funder commits to a level of demand in participating countries, thus minimizing for the developer the risk associated with low demand and reduced affordability in LMICs (including international advocacy for low prices of novel treatments) (Berndt et al., Reference Berndt2007). For the funder/sponsor the scheme is beneficial as the financial commitment only occurs if a medicine with social value targeting key areas of need is developed and launched in the market.

AMC is intended to focus on clinical development, registration and post-marketing and delivery stages of the R&D model (Pugatch Consilium, 2017). At the time of writing, only two AMCs have been implemented. In 2007, BMGF initiated the first AMC pilot programme targeting the development of pneumococcal conjugate vaccine (PCV) (Gavi-run pilot Pneumococcal AMC) and in June 2020, Gavi launched the second AMC, COVID-19 Vaccines AMC (COVAX AMC) to accelerate access to 1.8 billion COVID-19 vaccine doses for 92 lower-income countries (Gavi, 2022). Price details of the most recent Gavi supply agreement are provided in Table 3.10.7.

Table 3.10.7Selected example of advanced market commitments

Table 3.10.7 Long description

The table has 9 columns and reads as follows. Name: Pneumo A M C 2009. About or purpose of A M C: It was launched by donors in 2009 to accelerate the development of vaccines that meet country needs, scale production and accelerate vaccine uptake, while testing the A M C concept for future applications. Funding amount: 1.5 billion U S D to the Pneumococcal A M C (as of January 2021). Stakeholders involved: A M C donors: B M G F, Governments of Canada, Italy, Norway, the Russian Federation and the United Kingdom. Vaccines rolled out in 45 countries across 3 continents by 2015. Manufacturers: G S K, Pfizer and S I I. Eligibility criteria and disease focus: Pneumococcal disease; Target product profile required to be prequalified by W H O. How is A M C awarded?: The A M C is delivered through supply agreements with manufacturers where donors make a financial commitment through Gavi alliance to fully or partially finance the purchase of treatments meeting pre-established criteria at a specified price to be distributed on Gavi countries. Specific supply agreements are available on the Gavi Pneumo A M C. A latest AMC was awarded in June 2020 by UNICEF in its capacity as Gavi’s procurement agency to SII for its recently W H O-prequalified P C V 10 product. S I I received an award of 10 million doses annually (Annual Supply Commitment) from 2020 for a period of 10 years, for a cumulative total of 100 million doses. 5 percent of the A M C funds are allocated to S I I according to the A M C terms and conditions, representing 75 million U S D. The Tail Price for this agreement is 2.00 U S D per dose (for the 5-dose vial presentation) from 2020 onwards, a reduction of over 30 percent from the previous lowest Tail Price of 2.90 U S D per dose. Type of technology: Vaccines. Targeted R and D stage: Focused mainly on late-stage R and D and launch. I P R implications Innovator retains I P R.

^a Gavi: Supply agreements, https://www.gavi.org/investing-gavi/innovative-financing/pneumococcal-amc/manufacturers/supply-agreements

AMC: advance market commitment; BMGF: Bill & Melinda Gates Foundation; IPR: intellectual property rights; R&D: research and development; SII: Serum Institute of India. WHO: World Health Organization

Sources: Gavi (Reference Gavi2020a; Reference Gavi2020b; Reference Gavi2021).

With only two AMCs fully implemented, evidence is either descriptive due to a lack of appropriate counterfactuals or based on simulations. For the PCV AMC, Médecins sans Frontières (MSF) indicated that for over 12 years donors have granted more than US$ 1 billion in subsidies to two vaccine producers, GSK and Pfizer (main producers of the vaccine in the market) (MSF, 2020). Although in principle both manufacturers were obliged to cut the price after a predetermined level of doses was sold, or to license their technology to other manufacturers, prices were only marginally reduced and the transfer of technological know-how was not adequate to expand the manufacturer base into LMICs (MSF, 2020). COVAX AMC has also gathered more than US$ 6 billion in funds from governments, foundations and the private sector (WHO, 2021c). Further evidence suggests that the AMC for PCV has increased vaccination coverage across 60 countries immunizing over 49 million children and saving 14 to 17 million DALYs through to 2015 (Kremer, Levin & Snyder, Reference Kremer, Levin and Snyder2020; BCG, 2015). Both the PCV AMC as well as a vaccine AMC for malaria, TB and HIV/AIDS have been shown to be cost-effective in modelling studies (Berndt et al., Reference Berndt2007; Tasslimi et al., Reference Tasslimi2011).

Prizes

Prizes are rewards for innovations that may focus either on the technical specifications of a product or on the desired outcomes it delivers. Prizes may adopt different models but essentially are a pay for performance scheme where the developer receives a lump sum amount if a certain innovation with social impact is achieved (Hollis, Reference Hollis2005a; Reference Hollis2005b; Love & Hubbard, Reference Love and Hubbard2007), with the risk borne by the developer. Prizes thus incentivize innovations where social marginal benefits outweigh (or are equal to) marginal costs of development, thus promoting an efficient allocation of innovation effort. Some propose that this mechanism be voluntary, and complement the patent system by imposing structure on market rewards defined on the basis of incremental benefits (Hollis, Reference Hollis2005a; Pogge, Reference Pogge2005); for example, the Health Impact Fund prize scheme proposed by Hollis and Pogge (Reference Hollis and Pogge2008). Others recommend that such schemes be compulsory, with companies forgoing IPR so the developed drug could be made available globally, i.e. also in economically deprived populations (Love & Hubbard, Reference Love and Hubbard2009; Syed, Reference Syed2009; Shavell & Van Ypersele, Reference Shavell and Van Ypersele1999).

In the context of neglected diseases, prize schemes have mostly been used to reward innovation in relation to a specific disease; however, there have been some schemes that encompass any innovation for neglected diseases (Pugatch Consilium, 2017).

To date, only five of the 11 prize schemes in health targeted neglected diseases (Monroe, Greenberg & Basey, 2015; Pugatch Consilium, 2017), including the Qualcomm Tricorder XPRIZE (run by the XPRIZE Foundation and sponsored by the Qualcomm Foundation), the Health Impact Fund, the EU vaccine prize led by the European Research Council, the Longitude Prize led by Nesta (a United Kingdom innovation foundation) and a combined initiative proposed by Bangladesh, Barbados, Bolivia and Suriname. With a combined funding purse of roughly US$ 130 million, these prizes mostly focus on basic R&D research, preclinical development and post-marketing and delivery stages of the R&D model. Disease fields included treatments for TB, AMR and vaccine cold chain development, with some prizes allowing companies to maintain IPR and others replacing IPR rights with respective prize awards (Table 3.10.8).

Table 3.10.8Selected examples of prizes

a

Table 3.10.8a Long description

The table has 10 columns: Name, About or purpose of prize, Prize amount, Stakeholders involved, Eligibility criteria and disease focus, How is prize awarded, Who was awarded, Type of technology, Targeted R and D stage, and I P R implication. It reads as follows. Row 1: XPRIZE Foundation (10 million U S D). About: Milestone inducement prize. Non-profit organisation that designs and hosts public competitions intended to encourage technological development to benefit humanity. Many of the prize competitions are not global health or neglected disease-specific. In 2008, the Foundation has recently partnered with the Bill and Melinda Gates Foundation to develop a prize for a better T B diagnostic tool. Stakeholders involved: Prize partners: B M G F; Qualcomm Foundation, A N A, American, Telemedicine Association, Arthur Clarke Center for Human Imagination, Continua Health Alliance, Health 2.0, H I M S S, I E E E, MemsIndustry Group, Point Clear Solutions, Open Photonics Inc., Quest, Shimmer, U S San Diego Clinical and Translational Research Institute, Wireless Life Sciences Alliance and Xamarin. Eligibility criteria: T B; The winning team was required to develop a tricorder device that: 1. accurately diagnose 13 health conditions; 2. capture five real-time health vital signs; 3. provide a compelling consumer experience. How is prize awarded? Any company can enter a competition-style prize, with awards to first and second place among competing teams from various companies. Who was awarded? Pennsylvania-based team, Final Frontier Medical Devices (first place). Taiwan-based finalist, Dynamical Biomarkers Group (second place). Type of technology: Diagnostics. Targeted R&D stage: Clinical testing and registration. The foundation conducts consumer testing at the Altman Clinical and Translational Research Institute. This testing included the same rigour and protocols as a clinical trial but focused on the objectives of the XPRIZE competition. I P R implications: No information found.

Row 2: E U Vaccine Prize (2 million Euros). About: Inducement prize competition launched in 2012 by the European Commission, with more prizes to be offered via the Horizon 2020 Research Funding programme. The prizes intend to accelerate the development of vaccines and solve common issues of vaccine access. Stakeholders involved: Donors: E U Research Council. Eligibility criteria: Cold chain development for vaccines. Competition criteria, includes alternative ways of formulating, preserving or transporting vaccines. How is prize awarded? Competition cash prize with a jury appraising submissions and deciding on winner. No particular approach was prescribed and competitors were invited to convince the jury that their solution could respond best to the competition criteria, including alternative ways of formulating, preserving or transporting vaccines. Who was awarded? CureVac G m b H (2014). Type of technology: Vaccines. Targeted R&D stage: Discovery, preclinical and clinical stages, and registration. I P R implications: Owner maintains I P R.

b

Table 3.10.8b Long description

The table has 10 columns: Name, About or purpose of prize, Prize amount, Stakeholders involved, Eligibility criteria and disease focus, How is prize awarded, Who was awarded, Type of technology, Targeted R and D stage, and I P R implication. It reads as follows. Row 3: Health Impact Fund (proposal; A reasonable minimum required is 6 billion U S D per year): About: A new proposal by Incentives for Global Health to provide a complementary or alternative to the patent system for development of pharmaceutical innovations intended for poor patients. Stakeholders involved: Founding partner: Incentives for Global Health. Potential funding partners: Governments, charitable foundations or international taxes. Eligibility criteria: Pharmaceutical innovations intended for poor patients. How is prize awarded? The product is supplied at a generic price with a potential mark up for increment health gains, and the developer is not rewarded for the R&D element until it can demonstrate that the resulting product has health value for the intended patients. A 10-year fixed payment contingent on development, making I P R available for competing manufacturers, and calculated according to health impact of developed product. Who was awarded? (blank). Type of technology: Drugs. Targeted R&D stage: Discovery, preclinical and clinical stages, and registration. I P R implications: Developer making I P R available for competing manufacturers.

Row 4: Longitude Prize (10 million-pound prize fund): About: An ex ante inducement prize launched in 2014, awarded to the submission considered most impactful and feasible, currently for a competitive A M R innovation. Stakeholders involved: Lead by: Nesta (United Kingdom). Funding partners: Innovate United Kingdom and Biotechnology Industry Research Assistance Council (B I R A C). Supported by: Amazon, B B C, Science Museum, and Marks and Clerk I P services. Eligibility criteria: A M R. Any team of innovators from anywhere in the world that can invent an affordable, accurate, fast and easy-to-use point-of-care diagnostic test for bacterial infections that will allow health professionals worldwide to administer the right antibiotics at the right time (prize rules). How is prize awarded? Competition style prize, with 8 million pounds pay-out to winner, judged by a Prize Advisory Panel. Who was awarded? The first challenge is ongoing with around 50 registered teams. Type of technology: Diagnostics. Targeted R&D stage: n/a. Testing will take place over the six months following the Prize Advisory Panel’s decision. If the Panel judges that the testing confirms that the entry meets the criteria, they will recommend the entry as a potential winner. I P R implications: Developer maintains I P R.

The table has 10 columns: Name, About or purpose of prize, Prize amount, Stakeholders involved, Eligibility criteria and disease focus, How is prize awarded, Who was awarded, Type of technology, Targeted R and D stage, and I P R implication. It reads as follows. Row 5: B B B S open-source dividend (100 million U S D for T B. 250 million U S D for Chagas. 10 percent of donor assistance for health (D A H) to enhance access): About: Proposal by Bangladesh, Barbados, Bolivia and Suriname. Introduced in 2009 and 2015, of an open-source dividend and milestone prizes fund to reward R&D open innovation and access for donor-supported markets. Some of the Bangladesh, Barbados, Bolivia and Suriname proposals include the obligation to grant open, reasonable and non-discriminatory licenses to patent pools (such as the UNITAID patent pool). Stakeholders involved: B B B S, W H O, and Knowledge Ecology International. Eligibility criteria: Chagas disease, T B, H I V/AIDS, and malaria. Information on technical specification information not found. How is prize awarded? B B B S developed a prize fund that is endowed through a committee placed in W H O, which then allocates prizes to neglected diseases in separate disease-specific proposals, each drafted separately. The main objective is to share a percentage of end-product prize money with those who openly, freely share knowledge materials and technology. Example: The 2015 Development of Low-Cost Rapid Diagnostic Test for Tuberculosis Proposal suggests endowing a 100 million U S D for an entrant T B diagnostic that satisfies criteria set by W H O committee. A licensing pool would manage the intellectual property and guarantee that the diagnostic would be made available at an accessible price. Who was awarded? No information found. Type of technology: Drugs, diagnostics and vaccines. Targeted R&D stage: Clinical trials. I P R implications: I P R to be transferred to patent pool.

AMR: antimicrobial resistance; ANA: All Nippon Airways; BBBS: Bangladesh, Barbados, Bolivia and Suriname; BBC: British Broadcasting Corporation; BMGF: Bill & Melinda Gates Foundation; EU: European Union; HIMSS: Healthcare Information and Management Systems Society; IEEE: Institute of Electrical and Electronics Engineers; IP: intellectual property; IPR: intellectual property rights; TB: tuberculosis; WHO: World Health Organization.

Sources: For XPRIZE: XPRIZE Foundation (2020; 2023), Philanthropy News Digest (2008); for EU Vaccine Prize: European Commission (2014); Health impact fund (Incentives for Global Health (2021)); Longitude prize (Longitude Prize, Reference Prizen.d. a; n.d. b; n.d. c); BBBS: WHO (2011; 2015a; 2015b).

Different prize schemes focusing on specific disease areas and sometimes on unmet needs have spurred innovative activity in early stages of R&D. For instance, the Gotham Prize for Cancer research has led to the development of novel approaches to treat cancer (Bernstein, Reference Bernstein2008). Similarly, the Dr Paul Janssen Award, aimed at solving unmet medical needs and rewarding scientists for basic or clinical research, has advanced innovation in the treatment of rare diseases such as mosaicism (Johnson & Johnson, 2020). The Prize4Life organization has accelerated discovery of treatment of amyotrophic lateral sclerosis (ALS) through its ALS Prediction Prize scheme (Leone, Reference Leone2012). While such awards have increased early-stage discovery in critical areas as described above, systematic evidence on the impact of prizes on stimulating R&D for neglected diseases is virtually non-existent.