Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates

Rader, Benjamin; Astley, Christina M; Sy, Karla Therese L; Sewalk, Kara; Hswen, Yulin; Brownstein, John S.; Kraemer, Moritz U. G.

doi:10.1093/jtm/taaa076

Cited by 142 publications

(127 citation statements)

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“…State population density was included as a potential confounder given the association between population structure and SARS-CoV-2 transmission 31 , 32 as well as the association between urban versus rural regions and face mask usage 8 . Percent non-white was included as a confounder due to the relationship with epidemiological indicators of SARS-CoV-2 33 and uptake of non-pharmaceutical interventions 34 . A linear weekly time trend was also modeled.…”

Section: Methodsmentioning

confidence: 99%

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

Rader

White

et al. 2020

Preprint

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Introduction: Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, research is limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. Methods: Serial cross-sectional surveys were administered June 3 through July 31, 2020 via web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N=378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. Results: We find an upward trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Segmented regression analysis of mask wearing found no statistical change following mandates, however the positive trend of increased mask wearing over time was preserved. Conclusion: Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.

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Section: Methodsmentioning

confidence: 99%

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

Rader

White

et al. 2020

Preprint

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“…Many experts in the field of health disparities have authored viewpoints suggesting factors which may contribute to risk of infection with or adverse outcomes from SARS-CoV-2, including language barriers, lower health literacy, higher population density, household overcrowding, reliance on public transit, overrepresentation among essential workers, lack of paid sick leave, inability to work from home, and limited physical and financial access to healthcare. 18,[21][22][23][24][25][26] However, despite widespread interest in this topic, [3][4][5][6] relatively few studies have reported data examining robust socioeconomic concepts in relationship to SARS-CoV-2 outcomes. Many COVID-19 surveillance studies on this topic do not present individual data, but aggregated outcomes over large and diverse geographic areas (e.g.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Cromer

Lakhani

Wexler

et al. 2020

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Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

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“… 7–9 Reduced geographic access to SARS-CoV-2 testing sites is associated with socio-demographic factors that are linked to poor structural access to care and health outcomes. 10 In Aracaju, COVID-19 pandemic has exposed historical inequalities and poor communities have shown limited testing resources and higher fatality rates from COVID-19 compared with communities with better living conditions.…”

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confidence: 99%