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http://dx.doi.org/10.3961/jpmph.20.256

Social Determinants of COVID-19 in Massachusetts, United States: An Ecological Study  

Hawkins, Devan (Instructor of Public Health, Public Health Program, School of Arts and Sciences, MCPHS University)
Publication Information
Journal of Preventive Medicine and Public Health / v.53, no.4, 2020 , pp. 220-227 More about this Journal
Abstract
Objectives: The aim of this study was to assess how different social determinants of health (SDoH) may be related to variability in coronavirus disease 2019 (COVID-19) rates in cities and towns in Massachusetts (MA). Methods: Data about the total number of cases, tests, and rates of COVID-19 as of June 10, 2020 were obtained for cities and towns in MA. The data on COVID-19 were matched with data on various SDoH variables at the city and town level from the American Community Survey. These variables included information about income, poverty, employment, renting, and insurance coverage. We compared COVID-19 rates according to these SDoH variables. Results: There were clear gradients in the rates of COVID-19 according to SDoH variables. Communities with more poverty, lower income, lower insurance coverage, more unemployment, and a higher percentage of the workforce employed in essential services, including healthcare, had higher rates of COVID-19. Most of these differences were not accounted for by different rates of testing in these cities and towns. Conclusions: SDoH variables may explain some of the variability in the risk of COVID-19 across cities and towns in MA. Data about SDoH should be part of the standard surveillance for COVID-19. Efforts should be made to address social factors that may be putting communities at an elevated risk.
Keywords
Covid-19; Social determinants; Inequality; United States;
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1 CDC COVID-19 Response Team. Geographic differences in COVID-19 cases, deaths, and incidence - United States, February 12-April 7, 2020. MMWR Morb Mortal Wkly Rep 2020;69(15): 465-471.   DOI
2 Garg S, Kim L, Whitaker M, O'Halloran A, Cummings C, Holstein R, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019 - COVID-NET, 14 States, March 1-30, 2020. MMWR Morb Mortal Wkly Rep 2020;69(15):458-464.   DOI
3 NYC Health. Age-adjusted rates of lab confirmed COVID-19 nonhospitalized cases, estimated non-fatal hospitalized cases, and patients known to have died 100 000 by race/ethnicity group as of April 16, 2020 [cited 2020 Jun 1]. Available from: https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-deaths-race-ethnicity-04162020-1.pdf.
4 Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020;323(13): 1239-1242.   DOI
5 Hawkins D. Differential occupational risk for COVID-19 and other infection exposure according to race and ethnicity. Am J Ind Med 2020. doi: https://doi.org/10.1002/ajim.23145.
6 Tsai J, Wilson M. COVID-19: a potential public health problem for homeless populations. Lancet Public Health 2020;5(4): e186-e187.   DOI
7 Baggett TP, Keyes H, Sporn N, Gaeta JM. COVID-19 outbreak at a large homeless shelter in Boston: implications for universal testing. MedRxiv 2020. doi: https://doi.org/10.1101/2020.04.12.20059618.
8 Krieger N, Fee E. Social class: the missing link in U.S. health data. Int J Health Serv 1994;24(1):25-44.   DOI
9 Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures--the public health disparities geocoding project. Am J Public Health 2003;93(10):1655-1671.   DOI
10 Wadhera RK, Wadhera P, Gaba P, Figueroa JF, Joynt Maddox KE, Yeh RW, et al. Variation in COVID-19 hospitalizations and deaths across New York City boroughs. JAMA 2020;323(21): 2192-2195.   DOI
11 Chen JT, Krieger N. Revealing the unequal burden of COVID-19 by income, race/ethnicity, and household crowding: US county vs ZIP code analyses. Harvard Center for Population and Development Studies Working Paper Series, Volume 19, Number 1; 2020 Apr 21 [cited 2020 Jun 1]. Available from: https://tinyurl.com/ya44we2r.
12 Gohil SK, Datta R, Cao C, Phelan MJ, Nguyen V, Rowther AA, et al. Impact of hospital population case-mix, including poverty, on hospital all-cause and infection-related 30-day readmission rates. Clin Infect Dis 2015;61(8):1235-1243.   DOI
13 Chen JT, Waterman PD, Krieger N. COVID-19 and the unequal surge in mortality rates in Massachusetts, by city/town and ZIP code measures of poverty, household crowding, race/ethnicity, and racialized economic segregation. Harvard Center for Population and Development Studies Working Paper Series, Volume 19, Number 2; 2020 May 9 [cited 2020 Jun 1]. Available from: https://www.hsph.harvard.edu/population-development/research/working-papers/harvard-pop-centerworking-paper-series/.
14 UMass Donahue Institute. Massachusetts population estimates program [cited 2020 Jul 27]. Available from: http://www.donahue.umassp.edu/business-groups/economic-public-policy-research/massachusetts-population-estimates-program/population-projections.
15 Massachusetts Department of Public Health COVID-19 Dashboard. Dashboard of public health indicators [cited 2020 Jun 18]. Available from: https://www.mass.gov/doc/covid-19-dashboard-june-18-2020/download.
16 Holtgrave DR, Crosby RA. Social capital, poverty, and income inequality as predictors of gonorrhoea, syphilis, chlamydia and AIDS case rates in the United States. Sex Transm Infect 2003;79(1):62-64.   DOI
17 Barr RG, Diez-Roux AV, Knirsch CA, Pablos-Mendez A. Neighborhood poverty and the resurgence of tuberculosis in New York City, 1984-1992. Am J Public Health 2001;91(9):1487-1493.   DOI
18 Wang J, Zhou M, Liu F. Reasons for healthcare workers becoming infected with novel coronavirus disease 2019 (COVID-19) in China. J Hosp Infect 2020;105(1):100-101.   DOI
19 Burke RM, Midgley CM, Dratch A, Fenstersheib M, Haupt T, Holshue M, et al. Active monitoring of persons exposed to patients with confirmed COVID-19 - United States, January-February 2020. MMWR Morb Mortal Wkly Rep 2020;69(9):245-246.   DOI
20 Ran L, Chen X, Wang Y, Wu W, Zhang L, Tan X. Risk factors of healthcare workers with corona virus disease 2019: a retrospective cohort study in a designated hospital of Wuhan in China. Clin Infect Dis 2020;ciaa287.
21 Barbieri T, Basso G, Scicchitano S. Italian workers at risk during the COVID-19 epidemic. SSRN 2020. doi: http://dx.doi.org/10. 2139/ssrn.3572065.
22 Koh D. Occupational risks for COVID-19 infection. Occup Med (Lond) 2020;70(1):3-5.   DOI
23 United States Department of Labor. Unemployment insurance weekly claims data [cited 2020 May 2]. Available from https://oui.doleta.gov/press/2020/043020.pdf.
24 Swasey B. 'Atlas of inequality' shows income segregation around Boston; 2019 Mar 12 [cited 2020 May 2]. Available from: https://www.wbur.org/bostonomix/2019/03/12/boston-place-inequality-mit-media-lab.
25 Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med 2015;128:316-326.   DOI
26 Chokshi DA. Income, poverty, and health inequality. JAMA 2018;319(13):1312-1313.   DOI
27 Schmitt-Grohe S, Teoh H, Uribe M. COVID-19: testing inequality in New York City. NBER Working Paper No. w27019 [cited 2020 Jun 18]. Available from: https://ssrn.com/abstract= 3580577.