RT Journal Article SR Electronic T1 Development and validation of a predictive algorithm for risk of dementia in the community setting JF Journal of Epidemiology and Community Health JO J Epidemiol Community Health FD BMJ Publishing Group Ltd SP 843 OP 853 DO 10.1136/jech-2020-214797 VO 75 IS 9 A1 Fisher, Stacey A1 Manuel, Douglas G A1 Hsu, Amy T A1 Bennett, Carol A1 Tuna, Meltem A1 Bader Eddeen, Anan A1 Sequeira, Yulric A1 Jessri, Mahsa A1 Taljaard, Monica A1 Anderson, Geoffrey M A1 Tanuseputro, Peter YR 2021 UL http://jech.bmj.com/content/75/9/843.abstract AB Background Most 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.Methods The 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.Results Among 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.Conclusions This 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.Data were linked using unique encoded identifiers and analysed at ICES. The data set from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the data set publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www.ices.on.ca/DAS. The full data set creation plan and underlying analytical code are available from the authors upon request, understanding that the programmes may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.