Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May 5:15:981-993.
doi: 10.2147/COPD.S238933. eCollection 2020.

The Association Between Neighborhood Socioeconomic Disadvantage and Chronic Obstructive Pulmonary Disease

Panagis Galiatsatos  1 , Han Woo  1 , Laura M Paulin  2 , Amy Kind  3   4 , Nirupama Putcha  1 , Amanda J Gassett  5 , Christopher B Cooper  6 , Mark T Dransfield  7 , Trisha M Parekh  7 , Gabriela R Oates  8 , R Graham Barr  9 , Alejandro P Comellas  10 , Meilan K Han  11 , Stephen P Peters  12 , Jerry A Krishnan  13 , Wassim W Labaki  11 , Meredith C McCormack  1 , Joel D Kaufman  14 , Nadia N Hansel  1
Affiliations

The Association Between Neighborhood Socioeconomic Disadvantage and Chronic Obstructive Pulmonary Disease

Panagis Galiatsatos et al. Int J Chron Obstruct Pulmon Dis. .

Abstract

Rationale: Individual socioeconomic status has been shown to influence the outcomes of patients with chronic obstructive pulmonary disease (COPD). However, contextual factors may also play a role. The objective of this study is to evaluate the association between neighborhood socioeconomic disadvantage measured by the area deprivation index (ADI) and COPD-related outcomes.

Methods: Residential addresses of SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) subjects with COPD (FEV1/FVC <0.70) at baseline were geocoded and linked to their respective ADI national ranking score at the census block group level. The associations between the ADI and COPD-related outcomes were evaluated by examining the contrast between participants living in the most-disadvantaged (top quintile) to the least-disadvantaged (bottom quintile) neighborhood. Regression models included adjustment for individual-level demographics, socioeconomic variables (personal income, education), exposures (smoking status, packs per year, occupational exposures), clinical characteristics (FEV1% predicted, body mass index) and neighborhood rural status.

Results: A total of 1800 participants were included in the analysis. Participants residing in the most-disadvantaged neighborhoods had 56% higher rate of COPD exacerbation (P<0.001), 98% higher rate of severe COPD exacerbation (P=0.001), a 1.6 point higher CAT score (P<0.001), 3.1 points higher SGRQ (P<0.001), and 24.6 meters less six-minute walk distance (P=0.008) compared with participants who resided in the least disadvantaged neighborhoods.

Conclusion: Participants with COPD who reside in more-disadvantaged neighborhoods had worse COPD outcomes compared to those residing in less-disadvantaged neighborhoods. Neighborhood effects were independent of individual-level socioeconomic factors, suggesting that contextual factors could be used to inform intervention strategies targeting high-risk persons with COPD.

Keywords: COPD; area deprivation index; health disparities.

PubMed Disclaimer

Conflict of interest statement

Dr Laura Paulin reports grants from NIEHS, during the conduct of the study. Dr Nirupama Putcha reports grants from NIH, outside the submitted work. Dr Christopher Cooper reports personal fees from GlaxoSmithKline, outside the submitted work. Dr Mark Dransfield reports grants from NIH, during the conduct of the study. Dr R Graham Barr reports grants from NIH, Foundation for the NIH, grants, non-financial support from COPD Foundation, during the conduct of the study. Dr Alejandro Comellas reports grants from NIH, during the conduct of the study; non-financial support from VIDA, outside the submitted work. Dr Meilan Han reports personal fees from GSK, AZ, BI, Merck, and Mylan, non-financial support from Novartis, grants from Sunovion, outside the submitted work. Professor Stephen Peters reports grants from NIH and NHLBI, outside the submitted work. Dr Jerry Krishnan reports grants from NIH and ResMed, during the conduct of the study. Dr Wassim Labaki reports non-financial support from Pulmonx, outside the submitted work. Dr Meredith McCormack reports grants from NIH/NIMHD and Environmental Protection Agency, during the conduct of the study; personal fees from Celgene, Glaxo Smith Kline, and UpToDate, outside the submitted work. Dr Nadia Hansel reports grants, personal fees from AstraZeneca and GSK, grants from Boehringer Ingelheim and COPD Foundation, and NIH; personal fees from Mylan, outside the submitted work. The authors report no other conflicts of interest in this work.

Figures

Figure 1
Figure 1
Distribution of ADI national rank by covariate levels. For continuous covariates (age, BMI, FEV1% predicted, pack years), the variables were dichotomized in the way it is described in the legends.
Figure 2
Figure 2
Average predicted difference between top and bottom quantile ADI national rank: the most-disadvantaged vs the least-disadvantaged neighborhoods. *Six-minute walk was rescaled by a factor of one-tenth (e.g., the value −2 meters indicates −20 meters). #For exacerbation variables only, Y-axis indicates the rate ratio (e.g., the value of 2 would indicate the rate of exacerbation that is twice as great for the most disadvantaged neighborhood than that for the least disadvantaged). Notes: Y-axis represents the regression coefficient for the most-disadvantaged 20th percentile (with the least-disadvantaged 20th percentile as the reference category) in the fully adjusted regression of outcomes on quintile ADI.
Figure 3
Figure 3
Association of continuous ADI and COPD-related outcomes by rural status. The charts illustrate the interactions between continuous ADI and rural status on their associations with SGRQ, dyspnea, and 6-minute walk distance. The y-axis represents the predicted difference in outcome for one SD increase in ADI based on the fully adjusted regression model adjusting for age, sex, education, income, marital status, BMI, FEV1% predicted, smoking status, pack years, and occupational exposure.
See this image and copyright information in PMC

References

    1. Beckfield J, Olafsdottir S, Bakhtiari E. Health inequalities in global context. Am Behav Sci. 2013;57(8):1014–1039. doi:10.1177/0002764213487343 - DOI - PMC - PubMed
    1. Prescott E, Lange P, Vestbo J. Socioeconomic status, lung function and admission to hospital for COPD: results from the Copenhagen City heart study. Eur Respir J. 1999;13(5):1109–1114. doi:10.1034/j.1399-3003.1999.13e28.x - DOI - PubMed
    1. Durfey SNM, Kind AJH, Buckingham WR, DuGoff EH, Trivedi AN. Neighborhood disadvantage and chronic disease management. Health Serv Res. 2019;54(Suppl 1):206–216. doi:10.1111/1475-6773.13092 - DOI - PMC - PubMed
    1. Sheets L, Petroski GF, Jaddoo J, et al. The effect of neighborhood disadvantage on diabetes prevalence. AMIA Annu Symp Proc. 2017;2017:1547–1553. - PMC - PubMed
    1. Jung D, Kind A, Robert S, Buckingham W, DuGoff E. Linking neighborhood context and health in community-dwelling older adults in the medicare advantage program. J Am Geriatr Soc. 2018;66(6):1158–1164. doi:10.1111/jgs.15366 - DOI - PMC - PubMed

MeSH terms