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Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality

BACKGROUND: Prevention efforts are informed by the numbers of deaths or cases of disease caused by specific risk factors, but these are challenging to estimate in a population. Fortunately, an increasing number of jurisdictions have increasingly rich individual-level, population-based data linking e...

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Autores principales: Tanuseputro, Peter, Perez, Richard, Rosella, Laura, Wilson, Kumanan, Bennett, Carol, Tuna, Meltem, Hennessy, Deirdre, Manson, Heather, Manuel, Douglas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339639/
https://www.ncbi.nlm.nih.gov/pubmed/25717287
http://dx.doi.org/10.1186/s12963-015-0039-z
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author Tanuseputro, Peter
Perez, Richard
Rosella, Laura
Wilson, Kumanan
Bennett, Carol
Tuna, Meltem
Hennessy, Deirdre
Manson, Heather
Manuel, Douglas
author_facet Tanuseputro, Peter
Perez, Richard
Rosella, Laura
Wilson, Kumanan
Bennett, Carol
Tuna, Meltem
Hennessy, Deirdre
Manson, Heather
Manuel, Douglas
author_sort Tanuseputro, Peter
collection PubMed
description BACKGROUND: Prevention efforts are informed by the numbers of deaths or cases of disease caused by specific risk factors, but these are challenging to estimate in a population. Fortunately, an increasing number of jurisdictions have increasingly rich individual-level, population-based data linking exposures and outcomes. These linkages enable multivariable approaches to risk assessment. We demonstrate how this approach can estimate the population burden of risk factors and illustrate its advantages over often-used population-attributable fraction methods. METHODS: We obtained risk factor information for 78,597 individuals from a series of population-based health surveys. Each respondent was linked to death registry (568,997 person-years of follow-up, 6,399 deaths).Two methods were used to obtain population-attributable fractions. First, the mortality rate difference between the entire population and the population of non-smokers was divided by the total mortality rate. Second, often-used attributable fraction formulas were used to combine summary measures of smoking prevalence with relative risks of death for select diseases. The respective fractions were then multiplied to summary measures of mortality to obtain smoking-attributable mortality. Alternatively, for our multivariable approach, we created algorithms for risk of death, predicted by health behaviors and various covariates (age, sex, socioeconomic position, etc.). The burden of smoking was determined by comparing the predicted mortality of the current population with that of a counterfactual population where smoking is eliminated. RESULTS: Our multivariable algorithms accurately predicted an individual’s risk of death based on their health behaviors and other variables in the models. These algorithms estimated that 23.7% of all deaths can be attributed to smoking in Ontario. This is higher than the 20.0% estimated using population-attributable risk methods that considered only select diseases and lower than the 35.4% estimated from population-attributable risk methods that examine the excess burden of all deaths due to smoking. CONCLUSIONS: The multivariable algorithms presented have several advantages, including: controlling for confounders, accounting for complexities in the relationship between multiple exposures and covariates, using consistent definitions of exposure, and using specific measures of risk derived internally from the study population. We propose the wider use of multivariable risk assessment approach as an alternative to population-attributable fraction methods.
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spelling pubmed-43396392015-02-26 Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality Tanuseputro, Peter Perez, Richard Rosella, Laura Wilson, Kumanan Bennett, Carol Tuna, Meltem Hennessy, Deirdre Manson, Heather Manuel, Douglas Popul Health Metr Research BACKGROUND: Prevention efforts are informed by the numbers of deaths or cases of disease caused by specific risk factors, but these are challenging to estimate in a population. Fortunately, an increasing number of jurisdictions have increasingly rich individual-level, population-based data linking exposures and outcomes. These linkages enable multivariable approaches to risk assessment. We demonstrate how this approach can estimate the population burden of risk factors and illustrate its advantages over often-used population-attributable fraction methods. METHODS: We obtained risk factor information for 78,597 individuals from a series of population-based health surveys. Each respondent was linked to death registry (568,997 person-years of follow-up, 6,399 deaths).Two methods were used to obtain population-attributable fractions. First, the mortality rate difference between the entire population and the population of non-smokers was divided by the total mortality rate. Second, often-used attributable fraction formulas were used to combine summary measures of smoking prevalence with relative risks of death for select diseases. The respective fractions were then multiplied to summary measures of mortality to obtain smoking-attributable mortality. Alternatively, for our multivariable approach, we created algorithms for risk of death, predicted by health behaviors and various covariates (age, sex, socioeconomic position, etc.). The burden of smoking was determined by comparing the predicted mortality of the current population with that of a counterfactual population where smoking is eliminated. RESULTS: Our multivariable algorithms accurately predicted an individual’s risk of death based on their health behaviors and other variables in the models. These algorithms estimated that 23.7% of all deaths can be attributed to smoking in Ontario. This is higher than the 20.0% estimated using population-attributable risk methods that considered only select diseases and lower than the 35.4% estimated from population-attributable risk methods that examine the excess burden of all deaths due to smoking. CONCLUSIONS: The multivariable algorithms presented have several advantages, including: controlling for confounders, accounting for complexities in the relationship between multiple exposures and covariates, using consistent definitions of exposure, and using specific measures of risk derived internally from the study population. We propose the wider use of multivariable risk assessment approach as an alternative to population-attributable fraction methods. BioMed Central 2015-02-19 /pmc/articles/PMC4339639/ /pubmed/25717287 http://dx.doi.org/10.1186/s12963-015-0039-z Text en © Tanuseputro et al.; licensee BioMed Central. 2015 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 work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Tanuseputro, Peter
Perez, Richard
Rosella, Laura
Wilson, Kumanan
Bennett, Carol
Tuna, Meltem
Hennessy, Deirdre
Manson, Heather
Manuel, Douglas
Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title_full Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title_fullStr Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title_full_unstemmed Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title_short Improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
title_sort improving the estimation of the burden of risk factors: an illustrative comparison of methods to measure smoking-attributable mortality
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339639/
https://www.ncbi.nlm.nih.gov/pubmed/25717287
http://dx.doi.org/10.1186/s12963-015-0039-z
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