Data collection

The study population included all incident hospitalisations with an acute infection caused by Streptococcus, Staphylococcus, Enterobacteriaceae or other Gram-negative bacteria identified in 2016 from the PMSI.

The PMSI database covers all hospitalisations (also referred to as stays) in French publicly funded and private hospitals. Because this database is used for reimbursement purposes, each hospitalisation description is standardised, following the Technical Agency for Hospital Information (ATIH) recommendations. It contains ICD-10-coded hospital diagnoses, medical procedures performed during each stay and individual data such as age, sex and geographic area of residence. Diagnoses are coded as primary diagnosis (PD: condition requiring hospitalisation), related diagnosis (RD: adds information to PD) and significant associated diagnosis (SAD: complications and co-morbidities potentially affecting the course or cost of hospitalisation). Anonymised PMSI data from 2016 were extracted from the French National Health Data System (SNDS) that records every individual's demographic information, healthcare encounters and drug reimbursements [Reference Tuppin21]. We restricted the study to metropolitan France (henceforth referred to as France), excluding overseas territories, and representing 96% of its population in 2016, and hospitalisations in short-stay institutions (medicine, surgery, obstetrics).

Stays with admission between 1 January and 31 December 2016 were selected, when the PD, RD or SAD contained at least one specific infection code. Only stays longer than 1 day were considered as hospitalisation. To identify stays with acute infections, lists of ICD-10 codes corresponding to infections, microorganisms and resistance markers were established in collaboration with infectious diseases and PMSI coding specialists (Supplementary Tables S1 and S2). Codes only corresponding to colonisation with AMR were excluded from these lists. Most data available in the literature focuses on the first infection episode in a given year; in order to provide comparable data, only the first infectious episode in patients hospitalised within 2016 was selected. Because of the structure of the database and possible missing bacterial data, the first infectious episode was defined as the first infection that occurred at an anatomical site, also referred to as ‘incident infection’.

Selection and recoding algorithm, and extrapolation

Using PMSI to estimate the incidence of AMR among hospitalised patients proved challenging. Three major constraints of the database were identified.

The first one is related to database structure. Indeed, in the database, diagnoses are not linked: infections are not linked with their causal bacteria or bacteria with their resistance status, except for some specific codes such as ‘J13’ (pneumonia due to S. pneumoniae) or ‘U82.10’ (MRSA) (Table S1). Therefore, linking an infection to its aetiological microorganism and its AMR status was not possible when several infections, pathogens and/or resistance statuses were coded for a given stay. Consequently, stays with several infection sites were excluded, except for blood infections and medical device infections. Indeed, stays with blood infection and another site recorded were categorised as infection of the recorded site. Likewise, medical device infection was favoured when associated with an infection at the same site, e.g. ‘T82.6’ (infection due to cardiac valve prosthesis) and ‘I33.0’ (infective endocarditis).

The second limitation is related to French coding practices and its clinical perspective. Indeed, in France, coding rules imply that AMR status is notified only when resistance implies changing usual clinical management. Consequently, natural resistance or resistance that does not result in modification of conventional management or therapeutic difficulties would not be recorded.

Finally, some stays with infection might not be associated with an infecting microorganism. No microorganism code may indicate a lack of microbiological testing (i.e. the physician treated the infection empirically); or it may indicate negative results of samplings, part of which may be secondary to effective antibiotic therapy administered before sampling.

From the first 2016 stay with one infection, two groups were defined: (M+) stays, with at least one microorganism coded; and (M −), with no microorganism coded (Fig. 1). For M+, to minimise the number of excluded stays with multiple bacteria codes and only one specific AMR code recorded, resistance was attributed to the most relevant bacteria coded, excluding the other bacterial codes (e.g. methicillin resistance was assigned to S. aureus, ESBL resistance to Enterobacteriaceae or other Gram-negative bacteria and vancomycin resistance to Enterococcus). If attribution was not possible, stays with several microorganisms were excluded. In addition, to minimise inappropriate AMR categorisation (e.g. natural resistance), non-concordant microorganism–resistance pairs were recoded to the nearest most relevant (e.g. penicillin-resistant Enterobacteriaceae as ESBL, and vancomycin-resistant Streptococcus as Enterococcus). Relevant microorganism–resistance pairs are listed in Supplementary Table S2. Finally, when assignment to a given microorganism was not possible because of several resistance codes or impossible recoding, AMR status was retained but resistance was classified as ‘unknown’.

Fig. 1.

Flowchart of patient selection, group constitution and extrapolationa – France, 2016.

aGroup M− missing resistance status was extrapolated from group M+ susceptible and resistant bacteria, according to sex, age and site of infection and the variable described, listed in Table 2. Because of insufficient sample size, 452 stays could not be extrapolated. bThe first infectious episode in patients hospitalized within 2016 was defined as the first infection that occurred at an anatomical site.

Given the above limitation and the large number of stays with missing bacteria codes, we first analysed stays with complete information, i.e. those with an infection associated with an identified microorganism, whether resistant or susceptible. Extrapolation was then performed on the M − group, assuming that these stays were similar to group M+, conditional on gender, age and infection site stratum (classified as below). Thus, microorganism and AMR distributions were extrapolated from group M+, according to those parameters. For descriptive purposes, resistance was also extrapolated according to some patients’ and stay recorded characteristics: Charlson comorbidity index, infection as PD, length of stay, surgical procedure during the stay and in-hospital death. Consequently, some minor resistance-distribution differences could be noted, reflecting marginal differences in distributions of these variables between the M+ and M− groups. When <10 M+ stays were found for a given set of gender, age class and infection site, extrapolation was not performed and the corresponding observations were excluded. 95% CI were estimated for extrapolated number of stays and resistance rates. Moreover, the distributions of the different categories within a variable were calculated from the estimated number of stays. Since confidence interval were defined independently for each class, it was thus not relevant to estimate confidence interval for distributions.

Variables

Patients were characterised by gender, age (<28 days, 28 days–5 years, 6–15, 16–35, 36–65, 66–80, >80) and Charlson comorbidity index (0, 1–2, 3 or >4) [Reference Charlson22].

Stays were characterised by their duration (1–7, 7–14 and >14 days), characteristic of infection (PD or not), surgical procedure during the stay (yes or no) and in-hospital mortality. Surgical procedures were identified by their codes on the French common classification of medical procedures [23]. In-hospital mortality was recorded if the patient died during hospitalisation, regardless of the cause of death.

The following variables were used to describe the infections: site and bacterial species involved, AMR status and resistance type. Infection sites were stratified into 13 categories (Table S1): (1) urinary and genital tracts, (2) medical device associated, (3) skin and soft tissues, (4) lower respiratory tract, (5) abdomen and digestive tract, (6) bacteraemia and sepsis (henceforth blood infection), (7) infection during pregnancy, (8) bone and joint, (9) newborn infection, (10) heart and mediastinum, (11) ear, nose and throat, (12) eye, (13) nervous system. Medical device infections included foreign body infections and those associated with diagnostic procedures. Bacterial species were classified as Staphylococcus, Streptococcus, Enterobacteriaceae or other Gram-negative bacteria and by species. Resistance was categorised as to (1) penicillin, (2) methicillin, (3) extended-spectrum β-lactamase (ESBL), (4) other β-lactams resistance mechanisms, (5) vancomycin and related, (6) quinolones, (7) multiple antibiotics, (8) highly drug-resistant, (9) MDRB, (10) other or unspecified or (11) unknown (as explained above). In the French coding system, a MDRB code means that ‘multidrug-resistant’ is written in the bacteriology reports; conversely ‘resistant to multiple antibiotics’ is used when the bacteria is resistant to several antibiotics but multidrug-resistant is not written. It was assumed that MDRB refers to a bacterium resistant to at least three classes of antibiotic to which it is normally sensitive. Some specific resistances cannot currently be extracted from the PMSI, for example, resistance to third-generation cephalosporins is coded within group [Reference Davies and Davies4]. Microorganisms were considered susceptible to antibiotics when no resistance was coded (i.e. there was indeed no resistance marker detected, or if present, the resistance was not clinically significant and did not require a modification of conventional management of infection).

Analyses

To validate M+ selection and reclassification, S. aureus, Enterobacteriaceae and Streptococcus estimated resistance rates were compared with those available from 2016 EARS-net data for France [11]. Rates were estimated from M+ blood infections (primary and secondary) and meningitis (Enterobacteriaceae and S. pneumoniae only) in order to be compatible with EARS-net data. EARS-net ESBL-pE rates were estimated from rates provided for third-generation cephalosporin-resistant isolates [11, 24]. The same validation was conducted on the extrapolated population.

The overall population was described after extrapolation. The characteristics of patients (gender, age and Charlson index), stays (infection as PD, duration, surgery during the stay, in-hospital mortality) and infections (site) were studied. For each infection site, resistance rates were estimated. Microorganism distribution in the population, associated resistance rates and frequencies of the most frequent bacterium–resistance pairs were studied. Because of the very large size of the sample, and the wide variety of hospitalisations and infections, no statistical test was used to compare the two groups of patients having resistant or susceptible bacterial infections.

Finally, incidences of total AMRB and of the most frequent bacterium–resistance pairs were estimated, by dividing the number of hospitalisations with AMRB infection by the total number of days of hospitalisations in short-stay institutions (medicine, surgery, obstetrics), in metropolitan France in 2016 [25]. They were expressed as the number of cases per 1000 patient-days.

Statistical analyses were computed with SAS Enterprise Guide (v 7.13 software, SAS Institute Inc., Cary, NC, USA). The study and data extraction were approved by the French Data Protection Agency (CNIL, approval DE-2016–176).