https://doi.org/10.4081/gh.2023.1153
A spatiotemporal analysis of the social determinants of health for COVID-19
Submitted: 18 September 2022
Accepted: 24 January 2023
Published: 25 May 2023
Accepted: 24 January 2023
Abstract Views: 1275
PDF: 635
HTML: 16
HTML: 16
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.
Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, Li J, Zand R, 2021. Racial, Economic, and Health Inequity and COVID-19 Infection in the United States. J Racial Ethn Health Disparities 8:732-42.
DOI: https://doi.org/10.1007/s40615-020-00833-4
Adhikari S, Pantaleo N, Feldman J, Ogedegbe O, Thorpe L, Troxel A, 2020. Assessment of Community-Level Disparities in Coronavirus Disease 2019 (COVID-19) Infections and Deaths in Large US Metropolitan Areas. JAMA Netw Open 3:e2016938.
DOI: https://doi.org/10.1001/jamanetworkopen.2020.16938
Albrecht D, 2022. Vaccination, politics, and COVID-19 impacts. BMC Public Health 22:317.
DOI: https://doi.org/10.1186/s12889-021-12432-x
Bernal J, Andrews N, Gower C, Phil D, Gallagher E, Simmons R, Thelwall S, Stowe J, Tessier E, Groves N, Debrera G, Myers R, Campbell C, Amirthalingam G, Edmunds M, Zambon M, Brown K, Hopkins S, Chand M, Ramsay M, 2021. Effectiveness of COVID-19 vaccines against the B.1.617.2 (Delta) Variant. N Engl J Med 7:585-94.
DOI: https://doi.org/10.1056/NEJMoa2108891
Brunsdon C, Charlton M, Harris P, 2012. Living with collinearity in local regression models. In Proceedings of the Tenth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 6. Available from: https://www.geos.ed.ac.uk/~gisteac/proceedingsonline/GISRUK2012/Papers/presentation-9.pdf
Centers for Disease Control, 2021. About COVID-19. Last updated January 24. Available form: https://www.cdc.gov/coronavirus/2019-ncov/your-health/about-covid-19.html
Chen V, Park K, Sun F, Yang T, 2021. Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties. PLoS One 17. DOI: https://doi.org/10.1371/journal.pone.0265673
DOI: https://doi.org/10.1371/journal.pone.0265673
Dragano N, Rupprecht C, Dortmann O, Schieder M, Wahrendorf M, 2020. Higher risk of COVID-19 hospitalization for unemployed: an analysis of 1,298,416 health insured individuals in Germany. medRxiv https://doi.org/10.1101/2020.06.17.20133918
DOI: https://doi.org/10.1101/2020.06.17.20133918
Fielding-Miller R, Sundaram M, Brouwer K, 2020. Social determinants of COVID-19 mortality at the country level. PLoS One, 15. DOI: https://doi.org/10.1371/journal.pone.0240151
DOI: https://doi.org/10.1101/2020.05.03.20089698
Fotheringham, A S, Brunsdon C, Charlton M, 2003. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, West Sussex: Wiley.
Garber J. 2021. One in three COVID-19 deaths may be linked to health insurance gaps. Lown Institute. March 22, 2021. Available from: https://lowninstitute.org/one-in-three-covid-19-deaths-linked-to-gaps-in-health-insurance-coverage/
Golestaneh L, Neugarten J, Fisher M, Billett H, Reyes Gil M, Johns T, Yunes M, Mokrzycki M, Coco M, Norris K, Perez H, Scott S, Kim R, Bellin E, 2020. The association of race and COVID-19 mortality. E Clin Med 25:100455.
DOI: https://doi.org/10.1016/j.eclinm.2020.100455
Gray II D, Anyane-Yeboa A, Balzora S, Issaka R, May F, 2020. COVID-19 and the other pandemic: populations made vulnerable by systemic inequity. Nat Rev Gastroenterol Hepatol 17:520-22.
DOI: https://doi.org/10.1038/s41575-020-0330-8
Hawkins R, Charles E, Mehaffey J, 2020. Socio-economic status and COVID-19 -related cases and fatalities. Public Health 189:129-34.
DOI: https://doi.org/10.1016/j.puhe.2020.09.016
Hooper M, Napoles A, Perez-Stable E, 2020. COVID-19 and Racial/Ethnic Disparities. JAMA 323:2466 67.
DOI: https://doi.org/10.1001/jama.2020.8598
Hsu P, Hayes-Bautista D, 2021. The epidemiology of diversity: COVID-19 case rate patterns in California. J Immigr Minor Health 23:857-62.
DOI: https://doi.org/10.1007/s10903-021-01159-x
Islam N, Lacey B, Shabnam S, Erzurumluoglu A, Dambha-Miller H, Chowell G, Kawachi I, Marmot M, 2021. Social inequality and the syndemic of chronic disease and COVID-19: County-level analysis in the USA. J Epidemiol Community Health 75:496-500.
DOI: https://doi.org/10.1136/jech-2020-215626
Kaiser Family Foundation. 2021. KFF COVID-19 Vaccine Monitor: October 2021. Last updated October 2021. https://www.kff.org/coronavirus-covid-19/poll-finding/kff-covid-19-vaccine-monitor-october-2021/
Mandavilli A. The coronavirus has infected more than half of Americans, the C.D.C. reports. New York Times, April 27, 2022. Available from: https://www.nytimes.com/2022/04/26/health/coronavirus-antibodies-americans-cdc.html
MIT, 2020. Election Data and Science Lab; U.S. President 1976-2020; https://electionlab.mit.edu/data
Mollalo A, Vahedi B, Rivera K, 2020. GIS-based Spatial Modeling of COVID-19 Incidence Rate in the Continental United States. Sci Total Environ 728:138884.
DOI: https://doi.org/10.1016/j.scitotenv.2020.138884
Mollalo A, Tatar M, 2021. Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States. Int J Environ Res Public Health 18:9488.
DOI: https://doi.org/10.3390/ijerph18189488
Mollalo A, Rivera K, Vahabi N, 2021. Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States. Sustain Cities Soc 67:102738.
DOI: https://doi.org/10.1016/j.scs.2021.102738
Mouliou D, Kotsiou O, Gourgoulianis K, 2021. Estimates of COVID-19 risk factorsamong social strata and predictors for a vulnerability to the infection. Int J Environ Res Public Health 18:8701.
DOI: https://doi.org/10.3390/ijerph18168701
Neumann-Podczaska A, Al-Saad S, Wieczorowska-Tobis K, 2020. COVID-19 - clinical picture in the elderly population: A qualitative systematic review. Aging Dis 11:998-1008.
DOI: https://doi.org/10.14336/AD.2020.0620
New York Times. 2021. COVID-19 Tracker. Distributed by GitHub. Available from: https://github.com/nytimes/covid-19-data/blob/master/us-counties.csv
Paul A, Englert P, Varga M, 2021. Socio-economic disparities and COVID-19 in the USA. J Phys Complex 2:035017.
DOI: https://doi.org/10.1088/2632-072X/ac0fc7
Quan D, Wong L, Shallal A, Madan R, Hamdan A, Ahdi H, Daneshvar A, Mahajan M, Nasereldin M, Van Harn M, Opara I, Zervos M, 2021. Impact of race and socioeconomic status on outcomes in patients hospitalized with COVID-19. J Gen Intern Med 36:1302-09.
DOI: https://doi.org/10.1007/s11606-020-06527-1
Ruiz J. Cuomo says New York followed federal guidelines when sending coronavirus patients to nursing homes. CNN, May 24 2020. Available from: https://edition.cnn.com/2020/05/23/politics/cuomo-new-york-nursing-homes-coronavirus-patients/index.html
Shmueli L, 2021. Predicting intention to receive COVID-19 vaccine among the general population using the health belief model and the theory of planned behavior model. BMC Public Health 21:804.
DOI: https://doi.org/10.1186/s12889-021-10816-7
Singu S, Acharya A, Challagundla K, Byrareddy, S, 2020. Impact of social determinants of health on the emerging COVID-19 pandemic in the United States. Front Public Health 8. DOI: https://doi.org/10.3389/fpubh.2020.00406
DOI: https://doi.org/10.3389/fpubh.2020.00406
Sy K, White L, Nichols B, 2021. Population density and basic reproductive number of COVID-19 across United States counties. PLoS One, 16. DOI: https://doi.org/10.1371/journal.pone.0249271
DOI: https://doi.org/10.1371/journal.pone.0249271
U.S. Census Bureau, 2021. 2020 Census; generated by Claire Bonzani; using data.census.gov; https://data.census.gov
U.S. Census Bureau, 2021. TIGER/Line Shapefiles; https://www.census.gov/cgi-bin/geo/shapefiles/index.php
USDA, 2021. Economic research service, unemployment and median household income and education tables; https://www.ers.usda.gov/data-products/county-level-data-sets/
Viswanath K, Bekalu M, Dhawan D, Pinnamaneni R, Lang J, and McLoud R, 2021. Individual and social determinants of COVID-19 vaccine uptake. BMC Public Health 21:818.
DOI: https://doi.org/10.1186/s12889-021-10862-1
Whittle R, Diaz-Artiles A, 2020. An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC Med 18:271.
DOI: https://doi.org/10.1186/s12916-020-01731-6
WHO, 2022. Social determinants of health. Available from: https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1
Wu X, Zhang J, 2021. Exploration of spatial-temporal varying impacts on COVID-19 cumulative cases in texas using geographically weighted regression (GWR). Environ Sci Pollut Res Int 28:43732-46.
DOI: https://doi.org/10.1007/s11356-021-13653-8
How to Cite
Bonzani, C., Scull, P., & Yamamoto, D. (2023). A spatiotemporal analysis of the social determinants of health for COVID-19. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1153
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- William C. Goedel, Seann D. Regan, Basile Chaix, Asa Radix, Sari L. Reisner, Aron C. Janssen, Dustin T. Duncan, Using global positioning system methods to explore mobility patterns and exposure to high HIV prevalence neighbourhoods among transgender women in New York , Geospatial Health: Vol. 14 No. 2 (2019)
- Vicente Y. Belizario, Jr., John Paul Caesar R. delos Trinos, Nestor Lentejas, Allen J. Alonte, Agnes N. Cuayzon, Marian E. Isiderio, Rodel Delgado, Marilou Tejero, Victorio B. Molina, Use of geographic information system as a tool for schistosomiasis surveillance in an endemic Municipality in Eastern Samar, The Philippines , Geospatial Health: Vol. 16 No. 1 (2021)
- Enrique Ibarra-Zapata, Darío Gaytán-Hernández, Verònica Gallegos-García, Claudia Elena González-Acevedo, Thuluz Meza-Menchaca, María Judith Rios-Lugo, Héctor Hernández-Mendoza, Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study , Geospatial Health: Vol. 16 No. 1 (2021)
- Tobias Törnros, Helen Dorn, Markus Reichert, Ulrich Ebner-Priemer, Hans-Joachim Salize, Heike Tost, Andreas Meyer-Lindenberg, Alexander Zipf, A comparison of temporal and location-based sampling strategies for global positioning system-triggered electronic diaries , Geospatial Health: Vol. 11 No. 3 (2016)
- Man Sing Wong, Hung Chak Ho, Agnes Tse, Geospatial context of social and environmental factors associated with health risk during temperature extremes: Review and discussion , Geospatial Health: Vol. 15 No. 1 (2020)
- Isabel Ramos, Juan J. Cubillas, Francisco R. Feito, Tomas Ureña, Spatial analysis and prediction of the flow of patients to public health centres in a middle-sized Spanish city , Geospatial Health: Vol. 11 No. 3 (2016)
- Kamukama Ismail, Gilbert Maiga, Denis Ssebuggwawo, Peter Nabende, Ali Mansourian, Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017 , Geospatial Health: Vol. 15 No. 2 (2020)
- Laura Thompson, Maggie Sugg, Jennifer Runkle, Report-back for geo-referenced environmental data: A case study on personal monitoring of temperature in outdoor workers , Geospatial Health: Vol. 13 No. 1 (2018)
- Askhat Shaltynov, Jorge Rocha , Ulzhan Jamedinova, Ayan Myssayev, Assessment of primary healthcare accessibility and inequality in north-eastern Kazakhstan , Geospatial Health: Vol. 17 No. 1 (2022)
- N. Sogoba, P. Vounatsou, M.M. Bagayoko, S. Doumbia, G. Dolo, L. Gosoniu, S.F. Traore, Y.T. Toure, T. Smith, The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali , Geospatial Health: Vol. 1 No. 2 (2007)
<< < 21 22 23 24 25 26 27 28 29 30 > >>
You may also start an advanced similarity search for this article.
