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Development and validation of a predictive algorithm for risk of dementia in the community setting
  1. Stacey Fisher1,2,3,
  2. Douglas G Manuel1,2,3,4,5,
  3. Amy T Hsu1,2,3,5,
  4. Carol Bennett1,2,
  5. Meltem Tuna1,2,
  6. Anan Bader Eddeen1,2,
  7. Yulric Sequeira1,3,
  8. Mahsa Jessri1,2,4,
  9. Monica Taljaard1,3,
  10. Geoffrey M Anderson6,7,
  11. Peter Tanuseputro1,2,5,8
  1. 1 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
  2. 2 Populations & Public Health, ICES, Ottawa, Ontario, Canada
  3. 3 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
  4. 4 Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
  5. 5 Centre for Individualized Health, Bruyere Research Institute, Ottawa, Ontario, Canada
  6. 6 Cardiovascular Research, ICES, Toronto, Ontario, Canada
  7. 7 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  8. 8 Department of Medicine, University of Ottawa, Ottawa, ON, Canada
  1. Correspondence to Dr Stacey Fisher, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; stacey.fisher{at}utoronto.ca

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.

  • public health
  • epidemiology
  • dementia
  • disease modeling

Data availability statement

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.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Data availability statement

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.

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Footnotes

  • Twitter @StaceyFisher_, @amytmhsu

  • Contributors SF was responsible for the study design, protocol development, data analysis, interpretation of the results and drafting and revision of the manuscript. DGM and PT were responsible for conception of the project, grant application and contributed to the study design, protocol development and result interpretation. MTu, ABE and MTa contributed to the study design, protocol development and result interpretation and provided data/statistical support. CB, MJ and ATH contributed to the design of the study, protocol development and result interpretation. YS provided statistical support and is primarily responsible for the online web calculator, visualisation tool and application programming interface. GA contributed to result interpretation. All authors provided critical reviews of the manuscript.

  • Funding The results reported herein correspond to specific aims of grant MOP 142237 to Douglas G Manuel from the Canadian Institutes of Health Research (CIHR). This study was supported by ICES, formerly known as the Institute of Clinical Evaluative Sciences, which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by CIHR, ICES or the Ontario MOHLTC is intended or should be inferred.

  • Competing interests None declared.

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