No CrossRef data available.
Article contents
An online diagnosis for mild behavioral impairment diagnosis: a tool for low and middle-income countries?
Published online by Cambridge University Press: 24 January 2022
Abstract
An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
Information
- Type
- Commentary
- Information
- International Psychogeriatrics , Volume 35 , Special Issue 11: Issue Theme: Neuropsychiatric Symptoms of Mild and Major Neurocognitive Disorders , November 2023 , pp. 601 - 603
- Copyright
- © International Psychogeriatric Association 2022
References
GBD 2019 Dementia Forecasting Collaborators (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. S2468-2667(21)00249-8. Advance online publication. https://doi.org/10.1016/S2468-2667(21)00249-8
Google Scholar
Cummings, J., Lee, G., Zhong, K., Fonseca, J. and Taghva, K. (2021). Alzheimer’s disease drug development pipeline: 2021. Alzheimers Dement (N Y), 7, e12179.10.1002/trc2.12179CrossRefGoogle ScholarPubMed
Dubois, B. et al. (2014). Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurology, 13, 614–629.10.1016/S1474-4422(14)70090-0CrossRefGoogle ScholarPubMed
Dubois, B., Padovani, A., Scheltens, P., Rossi, A. and Dell'Agnello, G. (2016). Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges. Journal of Alzheimer’s Disease, 49, 617–631.CrossRefGoogle ScholarPubMed
Ferri, C. P. and Oliveira, D. (2019). Harmonization of epidemiological studies on dementia in Latin America why does it matter? Dementia & Neuropsychologia, 13, 363–366.CrossRefGoogle ScholarPubMed
Ibanez, A. et al. (2021). The multi-partner consortium to expand dementia research in Latin America (ReDLat): driving multicentric research and implementation science. Frontiers in Neurology, 12, 631722.CrossRefGoogle ScholarPubMed
Ismail, Z. et al. (2017). The mild behavioral impairment checklist (MBI-C): a rating scale for neuropsychiatric symptoms in Pre-Dementia populations. Journal of Alzheimer’s Disease, 56, 929–938.10.3233/JAD-160979CrossRefGoogle ScholarPubMed
Ismail, Z. et al. (2022). Psychosis in alzheimer disease - mechanisms, genetics and therapeutic opportunities. Nature Reviews Neurology, 23, 170.Google Scholar
Ismail, Z. et al. (2016). Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment. Alzheimer’s & Dementia, 12, 195–202.CrossRefGoogle ScholarPubMed
Kassam, F. et al. (2022). Cognitive profile of people with mild behavioral impairment in Brain Health Registry participants. International Psychogeriatrics.CrossRefGoogle ScholarPubMed
Livingston, G. et al. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet, 396, 413–446.10.1016/S0140-6736(20)30367-6CrossRefGoogle ScholarPubMed
Magklara, E., Stephan, B. C. M. and Robinson, L. (2019). Current approaches to dementia screening and case finding in low- and middle-income countries: research update and recommendations. International Journal of Geriatric Psychiatry, 34, 3–7.CrossRefGoogle ScholarPubMed
Mortby, M. E. et al. (2018). Dementia clinical trial implications of mild behavioral impairment. International Psychogeriatrics, 30, 171–175.CrossRefGoogle ScholarPubMed
Parra, M. A. et al. (2018). Dementia in Latin America: assessing the present and envisioning the future. Neurology, 90, 222–231.CrossRefGoogle ScholarPubMed
Piovezan, R. D., Oliveira, D., Arias, N., Acosta, D., Prince, M. J. and Ferri, C. P. (2020). Mortality rates and mortality risk factors in older adults with dementia from low- and middle-Income countries: the 10/66 dementia research group Population-Based cohort study. Journal of Alzheimer’s Disease, 75, 581–593.10.3233/JAD-200078CrossRefGoogle Scholar
Robinson, L., Tang, E. and Taylor, J. P. (2015). Dementia: timely diagnosis and early intervention. BMJ, 350, h3029–h3029.CrossRefGoogle ScholarPubMed
Sexton, C., Snyder, H. M., Chandrasekaran, L., Worley, S. and Carrillo, M. C. (2021). Expanding representation of low and middle income countries in global dementia research: commentary from the Alzheimer’s association. Frontiers in Neurology, 12, 633777.10.3389/fneur.2021.633777CrossRefGoogle ScholarPubMed
Tsoy, E. et al. (2021). Global perspectives on brief cognitive assessments for dementia diagnosis. Journal of Alzheimer’s Disease, 82, 1001–1013.CrossRefGoogle ScholarPubMed
Weizenbaum, E. L., Fulford, D., Torous, J., Pinsky, E., Kolachalama, V. B. and Cronin-Golomb, A. (2021). Smartphone-based neuropsychological assessment in Parkinson’s disease: feasibility, validity, and contextually driven variability in cognition. Journal of the International Neuropsychological Society, 1–13. DOI 10.1017/S1355617721000503.Google ScholarPubMed
Wolters, F. J. et al. (2020). Twenty-seven-year time trends in dementia incidence in Europe and the United States: the Alzheimer cohorts consortium. Neurology, 95, e519–e531.CrossRefGoogle ScholarPubMed
You have
Access
Dementia is a global health problem, affecting over 50 million persons worldwide. It is predicted to triple in the next 30 years, mainly to population growth and ageing (Collaborators, 2022). The increase in prevalence will be especially relevant in Low and Middle-Income countries (LMICs) like sub-Saharan Africa and Latin America (Livingston et al., Reference Livingston2020). LMIC are usually underrepresented in dementia studies (Sexton et al., Reference Sexton, Snyder, Chandrasekaran, Worley and Carrillo2021), despite the fact they report a higher mortality rate due to dementia than high-income countries (HIC) (Piovezan et al., Reference Piovezan, Oliveira, Arias, Acosta, Prince and Ferri2020). Over 80 million people live with dementia in LMIC compared to 40 million in HIC (Wolters et al., Reference Wolters2020).
Up to this day, there is no curative or disease-modifying treatment, nor is it expected to have one in the following years, despite significant pharmacological research, including new biological models (Cummings et al., Reference Cummings, Lee, Zhong, Fonseca and Taghva2021). Notwithstanding that, timely diagnosis allows for early and multidisciplinary intervention to delay cognitive deterioration and improve quality of life (Robinson et al., Reference Robinson, Tang and Taylor2015).
Aiming to generate an early diagnosis, the evaluation has evolved from purely clinical to include more complex diagnostic tools, like neuroimaging, neuropsychology, and biomarkers (Dubois et al., Reference Dubois2014). However, in LMIC, due mainly to economic and technological barriers, the diagnosis is bound to remain primarily clinical (Ibanez et al., Reference Ibanez2021).
The clinical description of dementia has been usually based on cognitive impairment and its impact on autonomy. Nevertheless, growing evidence shows behavioral symptoms are preceding and influencing cognitive decline (Ismail et al., Reference Ismail2022).
Aiming to include the behavioral changes into the dementia continuum diagnosis, The Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART) created research diagnostic criteria for mild behavioral impairment (MBI) (Ismail et al., Reference Ismail2016). It is defined as a syndrome characterized by late-onset persistent, change from longstanding patterns of behavior, and it is considered to increase the risk of cognitive decay.
ISTAART has also developed a rating scale for MBI diagnosis, the MBI checklist (MBI-C) (Ismail et al., Reference Ismail2017); it has been validated in community and clinical settings and across preclinical and prodromal disease populations.
The work of Prof Kassam et al. (Reference Kassam2022) provides solid evidence on the feasibility of using unsupervised online assessments to evaluate the presence of MBI, using MBI-C and collecting information from both the patient and the caregiver. As expected, MBI was also associated with cognitive impairments.
The implications of those findings are very relevant for both the clinical and the research aspects of dementia care.
The possibility of delivering a cheap and fast way of identifying at-risk patients tackles one of the main barriers described to a timely diagnosis, including lack of training and shortage of specialized diagnostic services (Dubois et al., Reference Dubois, Padovani, Scheltens, Rossi and Dell'Agnello2016).
Having a cheap diagnosis tool is especially relevant for LMIC, where the availability of well-trained specialists is more limited than in HIC, and it is usually restricted to big cities (Magklara et al., Reference Magklara, Stephan and Robinson2019). There are traditionally no primary health care programs to address diagnosis or timely and adequate referral nor adequate training or support from secondary care, contributing to misdiagnosis/underdiagnosis (Parra et al., Reference Parra2018).
In this context, several strategies have been used to increase the diagnosis in a fast and easy way to implement that provides the untrained medical team quick information on the patient status and who to referral. Digital evaluations have proven to be helpful in that context (Tsoy et al., Reference Tsoy2021). A need for harmonization in the diagnosis procedures in LMIC has also been raised (Ferri and Oliveira, Reference Ferri and Oliveira2019) and having an online diagnosis tool might help in that process.
Regarding research, identifying pre-dementia patients provides an opportunity to explore current and novel medications to manage behavioral symptoms and potentially delay or prevent incident dementia (Mortby et al., Reference Mortby2018). Having a sample of MBI patients to follow over time will provide information on several areas, like cognitive differentiation with Mild Cognitive Impairment, differential impact on caregiver burden, risk of progression to dementia, and many more.
Sadly, several barriers limit the global implementation of an online evaluation of MBI, again especially relevant in the LMIC context, like low access to technology, poverty, and socioeconomic vulnerability (Parra et al., Reference Parra2018). Those barriers impact the groups at a higher risk of dementia and underdiagnosis, like old age and illiterates. This will demand an initial effort to implement but, in the midterm, will generate benefits both in a clinical setting and in public health costs.
The authors had a good sample of 499 participant–informant dyads. However, its sociodemographic characteristics limit the extrapolation of the data to other populations. Therefore, the next steps in the authors' research should replicate the results in different groups and work on ways to implement online MBI in a simple way that could be easily implemented, like an app, as it has been described on other neurodegenerative disorders (Weizenbaum et al., Reference Weizenbaum, Fulford, Torous, Pinsky, Kolachalama and Cronin-Golomb2021). This way, you can also include therapeutics areas in the same device. Also, a population follow-up is needed to confirm the risk of dementia and analyze the individual risk of a cognitive feature.
Online evaluations allow us to gather vast loads of information in a fast, easy to implement, and reliable way. For us clinicians and researchers working in a context with few trained professionals, like LMIC, those evaluations allow us not only to have better research but also: improve the quality and quantity of our diagnoses and treatments, diminish the socioeconomical gap and provide good health for most of our population and not for a selected few.