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Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study”
Population attributable fraction (PAF) refers to the proportion of all cases with a particular outcome in a population that could be prevented by eliminating a specific exposure. The authors of a recent paper evaluated the prevalence and estimated the PAFs for risk factors of TB among elderly people...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699105/ https://www.ncbi.nlm.nih.gov/pubmed/31422773 http://dx.doi.org/10.1186/s40249-019-0587-8 |
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author | Khosravi, Ahmad Mansournia, Mohammad Ali |
author_facet | Khosravi, Ahmad Mansournia, Mohammad Ali |
author_sort | Khosravi, Ahmad |
collection | PubMed |
description | Population attributable fraction (PAF) refers to the proportion of all cases with a particular outcome in a population that could be prevented by eliminating a specific exposure. The authors of a recent paper evaluated the prevalence and estimated the PAFs for risk factors of TB among elderly people in China [Inf Dis Poverty. 2019;8:7]. Confounding is inevitable in observational studies and Levin’s formula is of limited use in practice for unbiasedly estimating PAF. In a complex survey design, an unbiased estimation of the PAF can be calculated using a sample-weighted version of the Miettinen formula or a sample weighed parametric g-formula. With respect to causal interpretation of PAF in public health setting, computation of PAF is logical and practical when the exposure is amenable to intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-019-0587-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6699105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66991052019-08-26 Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” Khosravi, Ahmad Mansournia, Mohammad Ali Infect Dis Poverty Letter to the Editor Population attributable fraction (PAF) refers to the proportion of all cases with a particular outcome in a population that could be prevented by eliminating a specific exposure. The authors of a recent paper evaluated the prevalence and estimated the PAFs for risk factors of TB among elderly people in China [Inf Dis Poverty. 2019;8:7]. Confounding is inevitable in observational studies and Levin’s formula is of limited use in practice for unbiasedly estimating PAF. In a complex survey design, an unbiased estimation of the PAF can be calculated using a sample-weighted version of the Miettinen formula or a sample weighed parametric g-formula. With respect to causal interpretation of PAF in public health setting, computation of PAF is logical and practical when the exposure is amenable to intervention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-019-0587-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-19 /pmc/articles/PMC6699105/ /pubmed/31422773 http://dx.doi.org/10.1186/s40249-019-0587-8 Text en © The Author(s). 2019 Open AccessThis 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. 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 | Letter to the Editor Khosravi, Ahmad Mansournia, Mohammad Ali Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title | Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title_full | Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title_fullStr | Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title_full_unstemmed | Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title_short | Recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in China: a population based cross-sectional study” |
title_sort | recommendation on unbiased estimation of population attributable fraction calculated in “prevalence and risk factors of active pulmonary tuberculosis among elderly people in china: a population based cross-sectional study” |
topic | Letter to the Editor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699105/ https://www.ncbi.nlm.nih.gov/pubmed/31422773 http://dx.doi.org/10.1186/s40249-019-0587-8 |
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