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. 2021 Sep;75(9):843-853.
doi: 10.1136/jech-2020-214797. Epub 2021 Jun 24.

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

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

Stacey Fisher et al. J Epidemiol Community Health. 2021 Sep.

Abstract

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.

Keywords: dementia; disease modeling; epidemiology; public health.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Calibration plots for the full model in validation data; mean predicted 5-year risk of dementia versus observed dementia incidence for (A) men and (B) women, by decile of predicted risk. Histograms display the relative distribution of predicted risk in the population.

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