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Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data.
Shi, Fanghui; Zhang, Jiajia; Yang, Xueying; Sun, Xiaowen; Li, Zhenlong; Weissman, Sharon; Olatosi, Bankole; Li, Xiaoming.
Afiliação
  • Shi F; South Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA. FSHI@email.sc.edu.
  • Zhang J; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA. FSHI@email.sc.edu.
  • Yang X; University of South Carolina Big Data Health Science Center, 915 Greene Street, Columbia, SC, 29208, USA. FSHI@email.sc.edu.
  • Sun X; South Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
  • Li Z; University of South Carolina Big Data Health Science Center, 915 Greene Street, Columbia, SC, 29208, USA.
  • Weissman S; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Olatosi B; South Carolina SmartState Center for Healthcare Quality, Columbia, SC, USA.
  • Li X; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
BMC Public Health ; 23(1): 2135, 2023 10 31.
Article em En | MEDLINE | ID: mdl-37907874
ABSTRACT

BACKGROUND:

COVID-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.

METHODS:

Using 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.

RESULTS:

As 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 (ß = -0.94, 95%CI -1.80, -0.08), while such associations reversed in the later phase (ß = 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 (ß -1.19 and -0.42; p-value is < 0.05 for both).

CONCLUSIONS:

County-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.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 4_TD Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: BMC Public Health Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 4_TD Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: BMC Public Health Ano de publicação: 2023 Tipo de documento: Article