Development and validation of a predictive algorithm for risk of dementia in the community setting

• Published in: Journal of epidemiology and community health (1979) 2021-09, Vol.75 (9), p.843-853
• Main Authors: Fisher, Stacey, Manuel, Douglas G, Hsu, Amy T, Bennett, Carol, Tuna, Meltem, Bader Eddeen, Anan, Sequeira, Yulric, Jessri, Mahsa, Taljaard, Monica, Anderson, Geoffrey M, Tanuseputro, Peter
• Format: Article
• Language: English
• Subjects: Algorithms; Ambulatory care; Datasets; Decision making; Dementia; Dementia disorders; Diabetes mellitus; Ethnicity; Health insurance; Health surveys; Hospitals; Hypertension; Multilingualism; Noncitizens; Original Research; Planning; Regression analysis; Risk assessment; Risk factors; Risk taking; Sociodemographics; Statistical analysis; Variables
• ISSN: 0143-005X; 1470-2738
• DOI: 10.1136/jech-2020-214797

BackgroundMost dementia algorithms are unsuitable for population-level assessment and planning as they are designed for use in the clinical setting. A predictive risk algorithm to estimate 5-year dementia risk in the community setting was developed.MethodsThe Dementia Population Risk Tool (DemPoRT) was derived using Ontario respondents to the Canadian Community Health Survey (survey years 2001 to 2012). Five-year incidence of physician-diagnosed dementia was ascertained by individual linkage to administrative healthcare databases and using a validated case ascertainment definition with follow-up to March 2017. Sex-specific proportional hazards regression models considering competing risk of death were developed using self-reported risk factors including information on socio-demographic characteristics, general and chronic health conditions, health behaviours and physical function.ResultsAmong 75 460 respondents included in the combined derivation and validation cohorts, there were 8448 cases of incident dementia in 348 677 person-years of follow-up (5-year cumulative incidence, men: 0.044, 95% CI: 0.042 to 0.047; women: 0.057, 95% CI: 0.055 to 0.060). The final full models each include 90 df (65 main effects and 25 interactions) and 28 predictors (8 continuous). The DemPoRT algorithm is discriminating (C-statistic in validation data: men 0.83 (95% CI: 0.81 to 0.85); women 0.83 (95% CI: 0.81 to 0.85)) and well-calibrated in a wide range of subgroups including behavioural risk exposure categories, socio-demographic groups and by diabetes and hypertension status.ConclusionsThis algorithm will support the development and evaluation of population-level dementia prevention strategies, support decision-making for population health and can be used by individuals or their clinicians for individual risk assessment.