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. 2023 Aug 31:20:E76.
doi: 10.5888/pcd20.230025.

Linking Local-Level Chronic Disease and Social Vulnerability Measures to Inform Planning Efforts: A COPD Example

Susan A Carlson  1   2 , Kathleen B Watson  1 , Sarah Rockhill  3 , Yan Wang  1 , Magdalena M Pankowska  4 , Kurt J Greenlund  1
Affiliations

Linking Local-Level Chronic Disease and Social Vulnerability Measures to Inform Planning Efforts: A COPD Example

Susan A Carlson et al. Prev Chronic Dis. .

Abstract

Introduction: Data are publicly available to identify geographic differences in health outcomes, including chronic obstructive pulmonary disease (COPD), and social vulnerability; however, examples of combining data across sources to understand disease burden in the context of community vulnerability are lacking.

Methods: We merged county and census tract model-based estimates of COPD prevalence from PLACES (www.cdc.gov/PLACES) with social vulnerability measures from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry Social Vulnerability Index (https://www.atsdr.cdc.gov/placeandhealth/svi), including 4 themes (socioeconomic, household composition and disability, minority status and language, and housing type and transportation), and the overall Social Vulnerability Index (SVI). We used the merged data set to create vulnerability profiles by COPD prevalence, explore joint geographic patterns, and calculate COPD population estimates by vulnerability levels.

Results: Counties and census tracts with high COPD prevalence (quartile 4) had high median vulnerability rankings (range: 0-1) for 2 themes: socioeconomic (county, 0.81; tract, 0.77) and household composition and disability (county, 0.75; tract, 0.81). Concordant high COPD prevalence and vulnerability for these themes were clustered along the Ohio and lower Mississippi rivers. The estimated number of adults with COPD residing in counties with high vulnerability was 2.5 million (tract: 4.7 million) for the socioeconomic theme and 2.3 million (tract: 5.0 million) for the household composition and disability theme (high overall SVI: county, 4.5 million; tract, 4.7 million).

Conclusion: Data from 2 publicly available tools can be combined, analyzed, and visualized to jointly examine local COPD estimates and social vulnerability. These analyses can be replicated with other measures to expand the use of these cross-cutting tools for public health planning.

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Figures

Figure 1
Figure 1
Prevalence of chronic obstructive pulmonary disease (COPD) by categories for the 4 vulnerability themes and the overall social vulnerability index (SVI) at the (a) county and (b) census tract level. Categories were based on quartiles of rankings and categorized into low (quartile 1), moderate (quartiles 2 and 3), and high (quartile 4). All pairwise differences in median county- and tract-level COPD prevalence were significantly different (P < .001) for the overall SVI and for all themes, except for the comparison of county-level moderate and high vulnerability for the Housing type and Transportation theme. Median and population weighted COPD prevalence estimates are shown in the figures and the estimated number of adults with COPD (in millions) are provided in the figure labels.
Figure 2
Figure 2
County- and census tract–level model-based prevalence estimates of chronic obstructive pulmonary disease, US, 2018. Maps were classified into 4 classes using quartiles (county: quartile 1 [3.5%–7.3%], quartile 2 [7.4%–8.9%], quartile 3 [9.0%–10.6%], quartile 4 [10.7%–19.7%]; census tract: quartile 1 [1.1%–5.3%], quartile 2 [5.4%–6.9%], quartile 3 [7.0%–8.9%], quartile 4 [9.0%–26.7%]). Source: PLACES: Local Data for Better Health, County Data 2022 release (www.cdc.gov/places).
Figure 3
Figure 3
Areas of concordance (low vulnerability and low prevalence; high vulnerability and high prevalence) and discordance (low vulnerability and high prevalence; high vulnerability and low prevalence) between categories of chronic obstructive pulmonary disease prevalence and social vulnerability, US, 2018. Prevalence estimates and rankings were each categorized as low when in quartile 1 and high when in quartile 4. Concordance and discordance are mapped by county and census tract by 4 vulnerability themes: socioeconomic, household composition and disability, minority status and language, and housing type and transportation, and by the overall social vulnerability index. Source: PLACES: Local Data for Better Health (2020 data release), www.cdc.gov/places. 2018 CDC/ATSDR SVI (2020 data release) (https://www.atsdr.cdc.gov/placeandhealth/svi).
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References

    1. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2030: social determinants of health. Accessed January 5, 2023. https://health.gov/healthypeople/objectives-and-data/social-determinants...
    1. Bazemore AW, Cottrell EK, Gold R, Hughes LS, Phillips RL, Angier H, et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc 2016;23(2):407–412. 10.1093/jamia/ocv088 - DOI - PMC - PubMed
    1. National Academies of Sciences, Engineering, and Medicine. Integrating social care into the delivery of health care: moving upstream to improve the nation’s health. Washington, DC: National Academies Press, 2019. - PubMed
    1. World Health Organization, Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on social determinants of health; 2008. - PubMed
    1. Greenlund KJ, Lu H, Wang Y, Matthews KA, LeClercq JM, Lee B, et al. PLACES: Local data for better health. Prev Chronic Dis 2022;19:E31. 10.5888/pcd19.210459 - DOI - PMC - PubMed