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The future burden of obesity in Canada: a modelling study

OBJECTIVES: We applied the validated Obesity Population Risk Tool (OPoRT) to estimate the future burden of obesity in Canada using baseline risk factors attained through routinely collected survey data. METHODS: OPoRT was developed using logistic regression with sex-specific generalized estimating e...

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Autores principales: O’Neill, Meghan, Kornas, Kathy, Rosella, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900264/
https://www.ncbi.nlm.nih.gov/pubmed/31429040
http://dx.doi.org/10.17269/s41997-019-00251-y
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author O’Neill, Meghan
Kornas, Kathy
Rosella, Laura
author_facet O’Neill, Meghan
Kornas, Kathy
Rosella, Laura
author_sort O’Neill, Meghan
collection PubMed
description OBJECTIVES: We applied the validated Obesity Population Risk Tool (OPoRT) to estimate the future burden of obesity in Canada using baseline risk factors attained through routinely collected survey data. METHODS: OPoRT was developed using logistic regression with sex-specific generalized estimating equations to predict the 10-year prevalence of obesity (outcome BMI ≥ 30.0) among adults 18 and older. The algorithm includes 17 predictive factors, including socio-demographic and health behavioural characteristics. OPoRT demonstrated excellent discrimination (C-statistic ≥ 0.89) and achieved calibration. We applied OPoRT to Canadian Community Health Survey (2013/14) data to predict the future prevalence of obesity in Canada for a variety of population subgroups. RESULTS: The predicted burden of obesity grew from 261 cases per 1000 in 2013/14 to 326 cases per 1000 in 2023/24 corresponding to a total of 8.54 million individuals with obesity. The burden is expected to be higher among males (347 cases per 1000) than females (305 cases per 1000). Individuals aged 35–49 had the highest predicted burden of obesity (374 cases per 1000) and the largest number of predicted cases (2.42 million), while individuals in the ≥ 65 age group had the lowest predicted burden (236 cases per 1000). The number of individuals with obesity per 1000 is highest among those severely food insecure (452 cases per 1000), compared with food secure individuals (324 cases per 1000). CONCLUSIONS: OPoRT can be used to estimate the future population burden of obesity, to identify priority subgroups at an elevated risk. Burden estimates should be reflected in approaches to curb the future burden of obesity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-019-00251-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-69002642019-12-20 The future burden of obesity in Canada: a modelling study O’Neill, Meghan Kornas, Kathy Rosella, Laura Can J Public Health Quantitative Research OBJECTIVES: We applied the validated Obesity Population Risk Tool (OPoRT) to estimate the future burden of obesity in Canada using baseline risk factors attained through routinely collected survey data. METHODS: OPoRT was developed using logistic regression with sex-specific generalized estimating equations to predict the 10-year prevalence of obesity (outcome BMI ≥ 30.0) among adults 18 and older. The algorithm includes 17 predictive factors, including socio-demographic and health behavioural characteristics. OPoRT demonstrated excellent discrimination (C-statistic ≥ 0.89) and achieved calibration. We applied OPoRT to Canadian Community Health Survey (2013/14) data to predict the future prevalence of obesity in Canada for a variety of population subgroups. RESULTS: The predicted burden of obesity grew from 261 cases per 1000 in 2013/14 to 326 cases per 1000 in 2023/24 corresponding to a total of 8.54 million individuals with obesity. The burden is expected to be higher among males (347 cases per 1000) than females (305 cases per 1000). Individuals aged 35–49 had the highest predicted burden of obesity (374 cases per 1000) and the largest number of predicted cases (2.42 million), while individuals in the ≥ 65 age group had the lowest predicted burden (236 cases per 1000). The number of individuals with obesity per 1000 is highest among those severely food insecure (452 cases per 1000), compared with food secure individuals (324 cases per 1000). CONCLUSIONS: OPoRT can be used to estimate the future population burden of obesity, to identify priority subgroups at an elevated risk. Burden estimates should be reflected in approaches to curb the future burden of obesity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-019-00251-y) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-08-19 /pmc/articles/PMC6900264/ /pubmed/31429040 http://dx.doi.org/10.17269/s41997-019-00251-y Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Quantitative Research
O’Neill, Meghan
Kornas, Kathy
Rosella, Laura
The future burden of obesity in Canada: a modelling study
title The future burden of obesity in Canada: a modelling study
title_full The future burden of obesity in Canada: a modelling study
title_fullStr The future burden of obesity in Canada: a modelling study
title_full_unstemmed The future burden of obesity in Canada: a modelling study
title_short The future burden of obesity in Canada: a modelling study
title_sort future burden of obesity in canada: a modelling study
topic Quantitative Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900264/
https://www.ncbi.nlm.nih.gov/pubmed/31429040
http://dx.doi.org/10.17269/s41997-019-00251-y
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