key: cord-0910302-6oi8s315
authors: Bilal, Usama; Mullachery, Pricila H; Schnake-Mahl, Alina; Rollins, Heather; McCulley, Edwin; Kolker, Jennifer; Barber, Sharrelle; Diez Roux, Ana V
title: Heterogeneity in Spatial Inequities in COVID-19 Vaccination across 16 Large US Cities
date: 2022-04-22
journal: Am J Epidemiol
DOI: 10.1093/aje/kwac076
sha: 5f4570544091c6bc491d510bb41d39e89a7eb77c
doc_id: 910302
cord_uid: 6oi8s315

Differences in vaccination coverage can perpetuate COVID-19 disparities. We explored the association between neighborhood-level social vulnerability and COVID-19 vaccination coverage in 16 large US cities from the beginning of the vaccination campaign in December 2020 through September 2021. We calculated the proportion of fully vaccinated adults in 866 zip code tabulation areas (ZCTA) of 16 large US cities: Long Beach, Los Angeles, Oakland, San Diego, San Francisco, and San Jose (CA); Chicago (IL); Indianapolis (IN); Minneapolis (MN); New York City (NY); Philadelphia (PA); and Austin, Dallas, Fort Worth, Houston, and San Antonio (TX). We computed absolute and relative total and Social Vulnerability Index(SVI)-related inequities by city. COVID-19 vaccination coverage was 0.75 times (95% CI 0.69-0.81) or 16% (95% CI 12.1-20.2%) percentage points lower in neighborhoods with the highest social vulnerability as compared to those with the lowest. These inequities were heterogeneous, with cities in the West region generally displaying narrower inequities in both the absolute and relative scales. The SVI domains of socioeconomic status and household composition & disability showed the strongest associations with vaccination coverage. Inequities in COVID-19 vaccinations hamper efforts to achieve health equity, as they mirror and could lead to even wider inequities in other COVID-19 outcomes.

vaccinations hamper efforts to achieve health equity, as they mirror and could lead to even wider inequities in other COVID-19 outcomes.

Through December 2021, the COVID-19 pandemic has taken the lives of over 800,000 people in the US. The burden of COVID-19 has been disproportionate among minoritized populations and persons of lower socioeconomic position. We previously reported higher positivity ratios, incidence and mortality rates in areas of high social vulnerability (1) , and these patterns have been replicated in other settings worldwide (2) (3) (4) .

In December 2020, the first two vaccines against SARS-CoV-2 infection were authorized by the FDA. A vaccination roll-out started nationwide, with different prioritization schedules by jurisdiction, but that generally focused first on older adults, healthcare workers, and long-term care facility residents and staff (5) . By April 19 th , 2021, all states had opened vaccine eligibility to all adults aged 16 or above. These vaccines have proven highly efficacious(6, 7) and effective(8), and represent one of the key tools to address the ongoing pandemic.

In the context of higher incidence and mortality rates among low socioeconomic status and minoritized populations, calls for prioritizing these populations emerged (9-11). Early recommendations on vaccine allocation proposed the targeting of neighborhoods with high social vulnerability(11). A modeling study reported that a strategy of geographic targeting of high-risk neighborhoods would result in lower overall COVID-19 mortality as compared to age-based strategies alone (9). However, vaccination policies targeting high-risk neighborhoods have not been widespread, and reports of inequities in vaccination across counties and population subgroups have quickly emerged (12) (13) (14) .

Characterizing social and spatial inequities in cities is critical to developing appropriate interventions and policies to increase COVID-19 vaccination among underserved populations, helping control the pandemic, and reducing health disparities. This is especially important in large cities where social inequalities are more prevalent (15) , and whose high-vulnerability neighborhoods tend to bear the highest overall COVID-19 mortality burden (9). Therefore, the aim of this study was to characterize spatial and social inequities in vaccination coverage in 16 large US cities and examine heterogeneities in spatial and social inequities across cities. We hypothesized that neighborhoods with higher levels of social vulnerability would have lower vaccination coverage, and that the magnitude of inequalities would vary widely by city.

We obtained data from the BCHC COVID-19 Health Inequities in Cities Dashboard (16) 

the summary score. To make coefficients comparable across cities, we re-scaled the SVI so that it ranges from 0 to 1 in each city. We also used, for sensitivity analysis, a scaled version of the SVI that ranges from 0 to 1 across the whole sample. Last, we created quintiles both for the cityspecific and the whole sample versions of the SVI. A higher value of the SVI signifies higher social vulnerability, either overall or by domain.

We described basic neighborhood-level and city-level characteristics and graphically examined the relationship between the SVI and vaccination coverage using scatterplots with smoothed lowess lines and by computing vaccination coverage by quintile of the SVI. We then computed indicators of total relative and absolute inequities by estimating the ratio and difference between the top (defined as the neighborhood at the 90 th percentile of vaccination) vs bottom neighborhoods (10 th percentile).

To describe relative and absolute SVI-related inequities we estimated the Relative Index of Inequality (RII) and the Slope Index of Inequality (SII). These indices represent the ratio or difference in vaccination coverage between the top (most vulnerable) and bottom (least vulnerable) parts of the SVI distribution, while accounting for the distribution across the full range of social vulnerability. We estimated the indices using a linear model at the neighborhood level, stratified by city, with log(vaccination coverage) (for the RII) or vaccination coverage (for the SII) as the outcomes, and the SVI as the main exposure. The SVI coefficient (exponentiated in the case of the RII) and associated 95% confidence intervals (CI) represent the RII or SII for the city. We adjusted these models by the % of the population of each neighborhood aged 18-44, 45-64, and 65 or above, but also show results unadjusted by age. We also run these models using the four SVI domains instead of the summary SVI. To address potential differences in the

distribution of the SVI across cities we repeated the same models using the SVI scaled to the whole sample instead to each city separately.

To examine heterogeneity in the RII and SII, we used a multilevel linear model of neighborhoods nested in cities, with a random intercept for city and introduced the SVI as both a fixed and random coefficient. To test whether there was variability in these inequities we compared models with and without the SVI random slope using the likelihood ratio test. To assess whether there were geographical differences in these inequities, and how much of the variability in inequities was explained by geography, we added an interaction between the SVI and census region (using South as the reference, as it was the region with the largest number of observations).

All analyses were conducted using R v4.1 (R Foundation, Vienna, Austria). This analysis was approved by the Drexel University Institutional Review Board under number 2102008373.

We included a total of 866 neighborhoods (ZCTAs) in 16 We found wide heterogeneity in the levels of vaccination between neighborhoods within cities ( Table 2) Results were relatively similar in models unadjusted for age (Web Figure 4 ; Spearman correlation coefficient between adjusted and unadjusted RIIs=0.65), with generally narrower inequities after age-adjustment. Models using the city-specific rescaled SVI vs the whole sample rescaled SVI showed virtually unchanged results (Web Figure 5 ; Spearman correlation coefficient between city-specific and nationwide SVI RIIs=0.99). We also tested whether there were regional differences in coverage, and found that, compared to the Southern US, cities in the West had a 27% narrower RII (exponentiated interaction coefficient=1.27, 95% CI 1.11 to 1.46), and an SII that was 12.7% percentage units closer to the null (interaction coefficient=12.7, 95% CI 5.6 to 19.8). Adding region and an interaction with the SVI explained 92% and 73% of the variability in inequities (Web Table 2 ). Table 4 ).

We documented wide spatial inequities in COVID-19 vaccination through September 2021 in 16 large US cities. We found negative and heterogeneous associations between social vulnerability and vaccination coverage for all cities. Overall, coverage in areas of the highest social vulnerability was 0.75 times lower than the coverage in areas of the lowest social vulnerability, or 16% percentage points lower. However, these disparities were regionally heterogeneous, as cities in the West region tended to have narrower inequities compared to cities in other regions.

We also observed that the social vulnerability domains of socioeconomic status and household composition & disability were more strongly associated with vaccination coverage, as compared to the domains of minority status & language and housing type & transportation.

Findings from this study mirror other reports examining inequities in COVID-19 positivity, incidence, and/or mortality by neighborhood (1, 4) . This is highly problematic from an (19)) and older adults (9) are more likely to be white A limitation of our study is that we rely on aggregated surveillance data, which may not be complete. While we used data on neighborhood of residence, to our knowledge the data for

Philadelphia, Chicago and New York City does not include residents who were vaccinated outside of their respective cities, while residents vaccinated outside their states of residence are not captured as vaccinated in the data we used. While we have no data on who was vaccinated outside their cities or states, considering the barriers outlined above, we speculate that individuals of high SES may be more likely to be vaccinated outside their cities, so this would bias our estimates of inequities towards the null. ZCTAs are very imperfect proxies for neighborhoods, but they represent a practical way to collect data during a public health emergency. Heterogeneity in the SVI (and its components) within zip codes may have led to underestimation of inequities. We also lacked longitudinal data for some of the cities included in our study, so we could not assess trends in these inequities; this type of data may be useful to evaluate some of the interventions outlined above. Last, given the cross-sectional, descriptive, and ecological nature of our study, caution should be used in drawing causal inferences or conclusions at the individual-level. For example, our adjustment for age was crude and indirect, adjusting for the age distribution of each neighborhood, as we had no data on vaccination by neighborhood and age. We also lacked data on coverage by neighborhood and race/ethnicity, so we could not explicitly examine disparities by population subgroup.

In summary, we found wide but heterogeneous spatial inequities in COVID-19 vaccination in 16 US cities, with areas of high social vulnerability having the lowest vaccination coverage. While we cannot infer a causal relationship between social vulnerability and vaccination coverage, the combination of these patterns with disproportionate impact of COVID-19 in these same racial segregation in US cities, along with systematic disinvestment in poor and non-white neighborhoods(48), continues to affect health in these neighborhoods. Certainly, we need to learn from this pandemic experience in order to develop better strategies to improve efforts to deliver vaccines equitably in the future (41) . Careful evaluation of the well-intentioned efforts many cities made to improve equity in vaccine access is needed. More generally however, the pandemic and our response to it has made it abundantly clear that addressing structural factors linked to income inequality, racism, and segregation will be fundamental to promoting population health and health equity across all health conditions. In October of 2020, well before any vaccines were approved for emergency use, a committee of the National Academies issued a consensus report with recommendations for vaccine distribution(11 

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