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Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study
BACKGROUND: Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996740/ https://www.ncbi.nlm.nih.gov/pubmed/31913130 http://dx.doi.org/10.2196/13018 |
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author | Cepeda, M Soledad Reps, Jenna Kern, David M Stang, Paul |
author_facet | Cepeda, M Soledad Reps, Jenna Kern, David M Stang, Paul |
author_sort | Cepeda, M Soledad |
collection | PubMed |
description | BACKGROUND: Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. OBJECTIVE: This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. METHODS: To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. RESULTS: A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. CONCLUSIONS: Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention. |
format | Online Article Text |
id | pubmed-6996740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-69967402020-02-20 Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study Cepeda, M Soledad Reps, Jenna Kern, David M Stang, Paul JMIR Public Health Surveill Original Paper BACKGROUND: Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. OBJECTIVE: This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. METHODS: To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. RESULTS: A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. CONCLUSIONS: Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention. JMIR Publications 2020-01-08 /pmc/articles/PMC6996740/ /pubmed/31913130 http://dx.doi.org/10.2196/13018 Text en ©M Soledad Cepeda, Jenna Reps, David M Kern, Paul Stang. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 08.01.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Cepeda, M Soledad Reps, Jenna Kern, David M Stang, Paul Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title | Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title_full | Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title_fullStr | Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title_full_unstemmed | Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title_short | Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study |
title_sort | medical conditions predictive of self-reported poor health: retrospective cohort study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6996740/ https://www.ncbi.nlm.nih.gov/pubmed/31913130 http://dx.doi.org/10.2196/13018 |
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