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Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data
BACKGROUND: Coal workers' pneumoconiosis (CWP) is an occupational lung disease due to inhalation of coal dust. We estimated mortality from CWP and other pneumoconioses among Medicare beneficiaries. METHODS: We used the 5% Medicare Limited Claims Data Set, 2011–2014, to identify patients diagnos...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9305938/ https://www.ncbi.nlm.nih.gov/pubmed/35133653 http://dx.doi.org/10.1002/ajim.23330 |
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author | Paul, Rajib Adeyemi, Oluwaseun Arif, Ahmed A. |
author_facet | Paul, Rajib Adeyemi, Oluwaseun Arif, Ahmed A. |
author_sort | Paul, Rajib |
collection | PubMed |
description | BACKGROUND: Coal workers' pneumoconiosis (CWP) is an occupational lung disease due to inhalation of coal dust. We estimated mortality from CWP and other pneumoconioses among Medicare beneficiaries. METHODS: We used the 5% Medicare Limited Claims Data Set, 2011–2014, to identify patients diagnosed with ICD‐9‐CM 500 (CWP) through 505 (Asbestosis, Pneumoconiosis due to other silica or silicates, Pneumoconiosis due to other inorganic dust, Pneumonopathy due to inhalation of other dust, and Pneumoconiosis, unspecified) codes. We applied binary regression models with spatial random effects to determine the association between CWP and mortality. Our inferences are based on Bayesian spatial hierarchical models, and model fitting was performed using Integrated Nested Laplace Approximation (INLA) algorithm in R/RStudio software. RESULTS: The median age of the sample was 76 years. In a sample of 8531 Medicare beneficiaries, 2568 died. Medicare beneficiaries with CWP had 25% higher odds of death (adjusted OR: 1.25, 95% CI: 1.07, 1.46) than those with other types of pneumoconiosis. The number of comorbid conditions elevated the odds of death by 10% (adjusted OR: 1.10, 95% CI: 1.09, 1.10). CONCLUSION: CWP increases the likelihood of death among Medicare beneficiaries. Healthcare professionals should make concerted efforts to monitor patients with CWP to prevent premature mortality. |
format | Online Article Text |
id | pubmed-9305938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93059382022-07-28 Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data Paul, Rajib Adeyemi, Oluwaseun Arif, Ahmed A. Am J Ind Med Brief Report BACKGROUND: Coal workers' pneumoconiosis (CWP) is an occupational lung disease due to inhalation of coal dust. We estimated mortality from CWP and other pneumoconioses among Medicare beneficiaries. METHODS: We used the 5% Medicare Limited Claims Data Set, 2011–2014, to identify patients diagnosed with ICD‐9‐CM 500 (CWP) through 505 (Asbestosis, Pneumoconiosis due to other silica or silicates, Pneumoconiosis due to other inorganic dust, Pneumonopathy due to inhalation of other dust, and Pneumoconiosis, unspecified) codes. We applied binary regression models with spatial random effects to determine the association between CWP and mortality. Our inferences are based on Bayesian spatial hierarchical models, and model fitting was performed using Integrated Nested Laplace Approximation (INLA) algorithm in R/RStudio software. RESULTS: The median age of the sample was 76 years. In a sample of 8531 Medicare beneficiaries, 2568 died. Medicare beneficiaries with CWP had 25% higher odds of death (adjusted OR: 1.25, 95% CI: 1.07, 1.46) than those with other types of pneumoconiosis. The number of comorbid conditions elevated the odds of death by 10% (adjusted OR: 1.10, 95% CI: 1.09, 1.10). CONCLUSION: CWP increases the likelihood of death among Medicare beneficiaries. Healthcare professionals should make concerted efforts to monitor patients with CWP to prevent premature mortality. John Wiley and Sons Inc. 2022-02-08 2022-04 /pmc/articles/PMC9305938/ /pubmed/35133653 http://dx.doi.org/10.1002/ajim.23330 Text en © 2022 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals LLC https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Brief Report Paul, Rajib Adeyemi, Oluwaseun Arif, Ahmed A. Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title | Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title_full | Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title_fullStr | Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title_full_unstemmed | Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title_short | Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
title_sort | estimating mortality from coal workers' pneumoconiosis among medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9305938/ https://www.ncbi.nlm.nih.gov/pubmed/35133653 http://dx.doi.org/10.1002/ajim.23330 |
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