Evidence map›Paper›PMID 37907874›Full record

ArticleBMC public health2023

Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data.

Fanghui Shi, Jiajia Zhang, Xueying Yang, Xiaowen Sun, Zhenlong Li, Sharon Weissman, Bankole Olatosi, Xiaoming Li

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Fanghui ShiSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA. FSHI@email.sc.edu.
Jiajia ZhangSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
Xueying YangSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
Xiaowen SunSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
Zhenlong LiUniversity of South Carolina Big Data Health Science Center, 915 Greene Street, Columbia, SC, 29208, USA.
Sharon WeissmanUniversity of South Carolina Big Data Health Science Center, 915 Greene Street, Columbia, SC, 29208, USA.
Bankole OlatosiSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
Xiaoming LiSouth Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.

Funding

Multilevel Determinants of Racial and Ethnic Disparities in Maternal Morbidity and Mortality in the Context of COVID-19 PandemicR01AI127203 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI LI, XIAOMING, OLATOSI, BANKOLE · 2017 to 2021
$5.2M
Patterns and predictors of racial/ethnic disparities in HIV care continuum in the SouthR21NR021079 · NINR · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI YANG, XUEYING · 2023 to 2023
$399k
NIAID NIH HHS R01 AI127203NINR NIH HHS R21 NR021079
6 · The paper itself

Abstract

backgroundCOVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic.

methodsUsing SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC.

resultsAs of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (β = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (β = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (β: -1.19 and -0.42; p-value is < 0.05 for both).

conclusionsCounty-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

Indexed as

COVID-19Cost of IllnessCOVID-19 TestingElectronic Health RecordsHumansSouth CarolinaCounty-levelCOVID-19 testingSocioeconomicSouth Carolina

Identifiers

PMID37907874
PMCPMC10617158

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.