Journal article
Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses
BMC public health, Vol.22(1), pp.747-747
04/14/2022
DOI: 10.1186/s12889-022-13168-y
PMCID: PMC9008430
PMID: 35421958
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.
Details
- Title: Subtitle
- Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses
- Creators
- Charisse Madlock-Brown - University of Tennessee Health Science CenterKen Wilkens - National Institute of Diabetes and Digestive and Kidney DiseasesNicole Weiskopf - Oregon Health & Science UniversityNina Cesare - Boston UniversitySharmodeep Bhattacharyya - Oregon State UniversityNaomi O. Riches - University of UtahJuan Espinoza - Children's Hospital of Los AngelesDavid Dorr - Oregon Health & Science UniversityKerry Goetz - National Eye InstituteJimmy Phuong - Seattle UniversityAnupam Sule - Trinity Health Oakland HospitalHadi Kharrazi - Johns Hopkins UniversityFeifan Liu - University of Massachusetts Chan Medical SchoolCindy Lemon - University of Tennessee Health Science CenterWilliam G. Adams - Boston Medical Center
- Resource Type
- Journal article
- Publication Details
- BMC public health, Vol.22(1), pp.747-747
- DOI
- 10.1186/s12889-022-13168-y
- PMID
- 35421958
- PMCID
- PMC9008430
- NLM abbreviation
- BMC Public Health
- ISSN
- 1471-2458
- eISSN
- 1471-2458
- Publisher
- Springer Nature
- Number of pages
- 13
- Grant note
- 5T32DK110966-04 / National Institute of Diabetes and Digestive and Kidney Diseases Ruth L. Kirschstein National Research Service Award of the National Institutes of Health NIH/NCATS 1UL1TR001430-01 / Boston University Clinical Translational Science Institute NIH/NCATS U24TR002306 / National Center for Data to Health U24TR002306-04S3 / National COVID Cohort Collaborative [NIH/NCATS] NIH/NCATS UL1TR001855 / Southern California and Translational Institute
- Language
- English
- Date published
- 04/14/2022
- Academic Unit
- Nursing
- Record Identifier
- 9984446740402771
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