Charisse Madlock-Brown1,2, Ken Wilkens3, Nicole Weiskopf4, Nina Cesare5, Sharmodeep Bhattacharyya6, Naomi O Riches7, Juan Espinoza8, David Dorr4, Kerry Goetz9, Jimmy Phuong10,11, Anupam Sule12, Hadi Kharrazi13, Feifan Liu14, Cindy Lemon15, William G Adams16. 1. Health Informatics and Information Management, University of Tennessee Health Science Center, 66 North Pauline St. rm 221, Memphis, TN, 38163, USA. cmadlock@uthsc.edu. 2. Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA. cmadlock@uthsc.edu. 3. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA. 4. Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. 5. Biostatistics and Epidemiology Data Analytics Center, Boston University, Boston, MA, USA. 6. Department of Statistics, Oregon State University, Corvallis, OR, USA. 7. Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT, USA. 8. Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA. 9. National Eye Institute, Bethesda, MD, USA. 10. University of Washington Research Information Technologies, Seattle, WA, USA. 11. Harborview Injury Prevention Research Center, Seattle, WA, USA. 12. Internal Medicine, St Joseph Mercy Oakland Hospital, Pontiac, MI, USA. 13. Johns Hopkins School of Public Health, Baltimore, MD, USA. 14. Chan Medical School, University of Massachusetts, Worcester, MA, USA. 15. Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA. 16. Boston Medical Center/Boston University School of Medicine, Boston, MA, USA.
Abstract
BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
Authors: Saman Khalatbari-Soltani; Robert C Cumming; Cyrille Delpierre; Michelle Kelly-Irving Journal: J Epidemiol Community Health Date: 2020-05-08 Impact factor: 3.710
Authors: Charisse Madlock-Brown; Ken Wilkens; Nicole Weiskopf; Nina Cesare; Sharmodeep Bhattacharyya; Naomi O Riches; Juan Espinoza; David Dorr; Kerry Goetz; Jimmy Phuong; Anupam Sule; Hadi Kharrazi; Feifan Liu; Cindy Lemon; William G Adams Journal: BMC Public Health Date: 2022-06-24 Impact factor: 4.135
Authors: Emily R Pfaff; Charisse Madlock-Brown; John M Baratta; Abhishek Bhatia; Hannah Davis; Andrew Girvin; Elaine Hill; Liz Kelly; Kristin Kostka; Johanna Loomba; Julie A McMurry; Rachel Wong; Tellen D Bennett; Richard Moffitt; Christopher G Chute; Melissa Haendel Journal: medRxiv Date: 2022-09-02
Authors: Oliver Ibarrondo; Maíra Aguiar; Nico Stollenwerk; Rubén Blasco-Aguado; Igor Larrañaga; Joseba Bidaurrazaga; Carlo Delfin S Estadilla; Javier Mar Journal: Int J Environ Res Public Health Date: 2022-10-05 Impact factor: 4.614