Chronic respiratory disease disparity between American Indian/Alaska Native and white populations, 2011-2018.


Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
28 07 2021
Historique:
received: 03 11 2020
accepted: 21 07 2021
entrez: 29 7 2021
pubmed: 30 7 2021
medline: 6 8 2021
Statut: epublish

Résumé

American Indian/Alaska Native (AI/AN) populations have been disproportionately affected by chronic respiratory diseases for reasons incompletely understood. Past research into disease disparity using population-based surveys mostly focused on state-specific factors. The present study investigates the independent contributions of AI/AN racial status and other socioeconomic/demographic variables to chronic respiratory disease disparity in an 11-state region with historically high AI/AN representation. Using data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning years 2011-2018, this work provides an updated assessment of disease disparity and potential determinants of respiratory health in AI/AN populations. This cross-sectional study used data from the BRFSS survey, 2011-2018. The study population included AI/AN and non-Hispanic white individuals resident in 11 states with increased proportion of AI/AN individuals. The yearly number of respondents averaged 75,029 (62878-87,350) which included approximately 5% AI/AN respondents (4.5-6.3%). We compared the yearly adjusted prevalence for chronic respiratory disease, where disease status was defined by self-reported history of having asthma and/or chronic obstructive pulmonary disease (COPD). Multivariable logistic regression was performed to determine if being AI/AN was independently associated with chronic respiratory disease. Covariates included demographic (age, sex), socioeconomic (marital status, education level, annual household income), and behavioral (smoking, weight morbidity) variables. The AI/AN population consistently displayed higher adjusted prevalence of chronic respiratory disease compared to the non-Hispanic white population. However, the AI/AN race/ethnicity characteristic was not independently associated with chronic respiratory disease (OR, 0.93; 95% CI, 0.79-1.10 in 2017). In contrast, indicators of low socioeconomic status such as annual household income of <$10,000 (OR, 2.02; 95% CI, 1.64-2.49 in 2017) and having less than high school education (OR, 1.37; 95% CI, 1.16-1.63 in 2017) were positively associated with disease. These trends persisted for all years analyzed. This study highlighted that AI/AN socioeconomic burdens are key determinants of chronic respiratory disease, in addition to well-established risk factors such as smoking and weight morbidity. Disease disparity experienced by the AI/AN population is therefore likely a symptom of disproportionate socioeconomic challenges they face. Further promotion of public health and social service efforts may be able to improve AI/AN health and decrease this disease disparity.

Sections du résumé

BACKGROUND
American Indian/Alaska Native (AI/AN) populations have been disproportionately affected by chronic respiratory diseases for reasons incompletely understood. Past research into disease disparity using population-based surveys mostly focused on state-specific factors. The present study investigates the independent contributions of AI/AN racial status and other socioeconomic/demographic variables to chronic respiratory disease disparity in an 11-state region with historically high AI/AN representation. Using data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning years 2011-2018, this work provides an updated assessment of disease disparity and potential determinants of respiratory health in AI/AN populations.
METHODS
This cross-sectional study used data from the BRFSS survey, 2011-2018. The study population included AI/AN and non-Hispanic white individuals resident in 11 states with increased proportion of AI/AN individuals. The yearly number of respondents averaged 75,029 (62878-87,350) which included approximately 5% AI/AN respondents (4.5-6.3%). We compared the yearly adjusted prevalence for chronic respiratory disease, where disease status was defined by self-reported history of having asthma and/or chronic obstructive pulmonary disease (COPD). Multivariable logistic regression was performed to determine if being AI/AN was independently associated with chronic respiratory disease. Covariates included demographic (age, sex), socioeconomic (marital status, education level, annual household income), and behavioral (smoking, weight morbidity) variables.
RESULTS
The AI/AN population consistently displayed higher adjusted prevalence of chronic respiratory disease compared to the non-Hispanic white population. However, the AI/AN race/ethnicity characteristic was not independently associated with chronic respiratory disease (OR, 0.93; 95% CI, 0.79-1.10 in 2017). In contrast, indicators of low socioeconomic status such as annual household income of <$10,000 (OR, 2.02; 95% CI, 1.64-2.49 in 2017) and having less than high school education (OR, 1.37; 95% CI, 1.16-1.63 in 2017) were positively associated with disease. These trends persisted for all years analyzed.
CONCLUSIONS
This study highlighted that AI/AN socioeconomic burdens are key determinants of chronic respiratory disease, in addition to well-established risk factors such as smoking and weight morbidity. Disease disparity experienced by the AI/AN population is therefore likely a symptom of disproportionate socioeconomic challenges they face. Further promotion of public health and social service efforts may be able to improve AI/AN health and decrease this disease disparity.

Identifiants

pubmed: 34320979
doi: 10.1186/s12889-021-11528-8
pii: 10.1186/s12889-021-11528-8
pmc: PMC8317382
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1466

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM103841
Pays : United States

Informations de copyright

© 2021. The Author(s).

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Auteurs

Kimberly G Laffey (KG)

Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA. laffeyk@health.missouri.edu.
Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA. laffeyk@health.missouri.edu.

Alfreda D Nelson (AD)

Department of Surgery, School of Medicine, University of Missouri, Columbia, MO, USA.

Matthew J Laffey (MJ)

Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.

Quynh Nguyen (Q)

Institute for Data Science and Informatics, University of Missouri, Columbia, MO, USA.
Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO, USA.

Lincoln R Sheets (LR)

Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, USA.

Adam G Schrum (AG)

Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.
Department of Surgery, School of Medicine, University of Missouri, Columbia, MO, USA.
Department of Biomedical, Biological, and Chemical Engineering, College of Engineering, University of Missouri, Columbia, MO, USA.

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Classifications MeSH