Abstract
Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.
Highlights
The United States (US) has been severely affected by SARSCoV-2, but cases in the US are not evenly distributed across the population [1]
Harris County has over 4.7 million people and is one of the most diverse regions in the country, with 44% of the population identifying as Hispanic or Latino, 29% identifying as White, 20% identifying as Black, and 7% identifying as Asian [17]
The overall Social Vulnerability Index (SVI), the socioeconomic status theme, the minority status/language theme, and the housing type/transportation theme were significantly associated with the percent-at-home
Summary
The United States (US) has been severely affected by SARSCoV-2, but cases in the US are not evenly distributed across the population [1]. Emerging studies suggest that social vulnerability and inability to stay at home play a major role in increased cases of SARS-CoV-2 within a community. Social vulnerability is the degree to which a community exhibits social conditions that may affect their ability to prevent serious injury, illness, or loss in the event of a disaster [10]. CDC created the Social Vulnerability Index (SVI) for local public health organizations to assess and prioritize census tracts that may be vulnerable to disasters such as a pandemic [10]. Though CDC’s SVI was not constructed with a pandemic in mind, the SVI can be very useful in identifying vulnerable populations, poverty, and living conditions which may make populations more susceptible to communicable diseases such as SARS-CoV-2 [10]. Disadvantaged communities may be less likely to be able to stay at home because of financial constraints; the inability to work from home compared to higher wage-earners; being a member of the essential workforce; having fewer savings, and having to work or risk losing income [11, 12]
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