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Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)
BACKGROUND: Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts. METHODS: We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal Nationa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773177/ https://www.ncbi.nlm.nih.gov/pubmed/29346391 http://dx.doi.org/10.1371/journal.pone.0191169 |
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author | Lebenbaum, Michael Espin-Garcia, Osvaldo Li, Yi Rosella, Laura C. |
author_facet | Lebenbaum, Michael Espin-Garcia, Osvaldo Li, Yi Rosella, Laura C. |
author_sort | Lebenbaum, Michael |
collection | PubMed |
description | BACKGROUND: Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts. METHODS: We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ(2)). RESULTS: Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ(2) <20). CONCLUSION: OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity. |
format | Online Article Text |
id | pubmed-5773177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57731772018-01-26 Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) Lebenbaum, Michael Espin-Garcia, Osvaldo Li, Yi Rosella, Laura C. PLoS One Research Article BACKGROUND: Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts. METHODS: We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ(2)). RESULTS: Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ(2) <20). CONCLUSION: OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity. Public Library of Science 2018-01-18 /pmc/articles/PMC5773177/ /pubmed/29346391 http://dx.doi.org/10.1371/journal.pone.0191169 Text en © 2018 Lebenbaum et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lebenbaum, Michael Espin-Garcia, Osvaldo Li, Yi Rosella, Laura C. Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title | Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title_full | Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title_fullStr | Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title_full_unstemmed | Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title_short | Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT) |
title_sort | development and validation of a population based risk algorithm for obesity: the obesity population risk tool (oport) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773177/ https://www.ncbi.nlm.nih.gov/pubmed/29346391 http://dx.doi.org/10.1371/journal.pone.0191169 |
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