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Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

ABSTRACT: INTRODUCTION:The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modelling techniques and will be used to project dementia prevalence. METHODS AND ANALYSIS:The derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers. ETHICS AND DISSEMINATION:Research ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov NCT03155815, pre-results.

SUBMITTER: Fisher S

PROVIDER: S-EPMC5665213 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

Fisher Stacey S Hsu Amy A Mojaverian Nassim N Taljaard Monica M Huyer Gregory G Manuel Douglas G DG Tanuseputro Peter P

BMJ open 20171024 10

<h4>Introduction</h4>The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable mod ...[more]

PMID: 29070641

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Project description:INTRODUCTION:Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. METHODS AND ANALYSIS:The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. ETHICS AND DISSEMINATION:This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov NCT02267447.

Project description:BackgroundPremature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.MethodsPreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007-2008, 2009-2010, and 2011-2012) from the development cohort (CCHS cycles 2000-2001, 2003-2004, and 2005-2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke's R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell's concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.DiscussionUsing routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

Project description:BackgroundAvailability and accessibility of nutritious foods can vary according to the food outlets present within a neighbourhood or community. There is increasing evidence that community food environments influence food choice, diet and the risk of diet-related chronic disease, however contemporary community food environments assessments (e.g. unhealthy fast food outlets versus healthy supermarkets or fruit and vegetable shops) may be too simplistic to accurately summarise the complexities of their impacts on food choice. This study protocol describes the development of the Food Outlets Dietary Risk (FODR) assessment tool for use by local government in Perth, Western Australia.MethodsSimilar to food safety risk assessment, the FODR assessment tool rates the potential harmful public health nutrition impact of food outlets by identifying and characterising the issues, and assessing the risk of exposure. Scores are attributed to six public health nutrition attributes: 1) availability of nutrient-poor foods; 2) availability of nutritious foods; 3) acceptability and appeal; 4) accessibility; 5) type of business operation; and 6) complex food outlet considerations. Food retail outlets are then classified as having a low, medium, high or very high dietary risk based on their total score.DiscussionA local government administered tool to rate the public health nutrition risk of food outlets requires data which can be collected during routine assessments or sourced from the internet. The ongoing categorical classification of foods available within food outlets as either unhealthy or nutritious will require nutrition scientists' input. An objective risk assessment of the dietary impact of food retail outlets can guide local government planning, policies and interventions to create supportive community food environments. It is intended that locally relevant data can be sourced throughout Australia and in other countries to apply the local context to the FODR assessment tool. Utility and acceptability of the tool will be tested, and consultation with environmental health officers and public health practitioners will inform future iterations.

Project description:Background and purposeStroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity.MethodsData were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests.ResultsThe predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53).ConclusionsDementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Project description:IntroductionOlder adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning.ObjectiveTo predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers.DesignOpen cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013.ParticipantsThe derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013.Main outcome measuresPredicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario.Statistical analysisProportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data.Ethics and disseminationResearch ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals.Trial registration numberNCT02779309, Pre-results.

Dataset Information

Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

Publications

Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

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