key: cord-1022557-h74nfkhx
authors: Bauer, Cici; Zhang, Kehe; Lee, Miryoung; Jones, Michelle; Rodriguez, Arturo; de la Cerda, Isela; Reininger, Belinda; Fisher-Hoch, Susan P.; McCormick, Joseph B.
title: Real-time geospatial analysis identifies gaps in COVID-19 vaccination in a minority population
date: 2021-09-13
journal: Sci Rep
DOI: 10.1038/s41598-021-97416-y
sha: f1e1998a6feddcf436ca761e2376d9e037246b2f
doc_id: 1022557
cord_uid: h74nfkhx

COVID-19 vaccination is being rapidly rolled out in the US and many other countries, and it is crucial to provide fast and accurate assessment of vaccination coverage and vaccination gaps to make strategic adjustments promoting vaccine coverage. We reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population living in South Texas on the US-Mexico Border, to inform vaccination campaign strategies. We developed 4 rank-based approaches to evaluate the vaccination gap at the census tract level, which considered both population vulnerability and vaccination priority and eligibility. We identified areas with the highest vaccination gaps using different assessment approaches. Real-time geospatial analysis to identify vaccination gaps is critical to rapidly increase vaccination uptake, and to reach herd immunity in the vulnerable and the vaccine hesitant groups. Our results assisted the City of Brownsville Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and deploying services to areas identified as high vaccination gap. The analyses and responses can be adopted in other locations.

Study region and population. The minority population in the COB has been disproportionally affected by COVID-19, with cumulative COVID-19 case rate of 9.13%, case fatality rate (COVID-19 death among COVID-19 cases) of 3.42% and mortality rate (COVID-19 death among total population) of 0.31%, between March 19th, 2020 (first case reported from COB) and March 13th, 2021, reported from Cameron County at the time of this analysis 6 . All three rates were much higher than the national cumulative case rate of 8.9%, case fatality rate of 1.82% and overall mortality rate of 0.16% 7 . This population is also among the most vulnerable populations, with the social vulnerability index (SVI) for Cameron County ranked among the lowest 5% in the US (i.e., SVI rank 96.7%) 8 , those aged 65 years and older, and/or those aged 16 years and older with at least one medical condition with increased COVID-19 risk (Phase 1B). COVID-19 vaccination data in this report were obtained from the Texas Immunization Registry database (ImmTrac2) and encompass all those vaccinated in the city clinics since February 5th through March 13th, 2021. These data were used to assess vaccination coverage up to March 13th during the initial phase. We calculated the COVID-19 vaccination coverage as the population that has received at least one dose of vaccination per 10,000 population, for each census tract within the study region and during this initial rollout phase.

We followed the COVID-19 Vaccination Planning to assess local COVID-19 vaccination gaps 9 . We first ranked the census tracts by their vaccination priority from the lowest to the highest, and then ranked the census tracts by their actual vaccinations coverage rates also from the lowest to the highest. The difference between the two ranks was then used to quantify the vaccination gap.

To assess the COVID-19 vaccination priority, different metrics can be used, and each may focus on a different aspect of COVID-19, such as the impact, social vulnerability, or priority population eligible for COVID-19 vaccination. Here we considered and compared the following four priority assessments. In Approach 1, we used census tract cumulative COVID-19 case rate reported above, and areas with high rate were considered priority. Approach 2 used SVI, and so areas with higher SVI were ranked as higher priority. Approach 3 used the census tract percentage of population aged 65 and older, so that areas with higher percentage of elderly were designated as higher priority. This approach accounted for the vaccination eligibility in the initial rollout phase, as the higher priority was given to the elderly. Finally in Approach 4, we used the COVID-19 community vulnerability index (CCVI) created by Surgo Ventures 10 . CCVI covered seven themes of COVID-19 risk factors including socioeconomic status, work environment, ethnicity and healthcare system. We averaged the theme-specific scores to obtain an overall CCVI composite score, which ranged from 0 to 1 with 0 the lowest vulnerability and 1 the highest. The overall composite score was used to rank the census tracts for vaccine priority.

The COVID-19 vaccination gap was assessed by each of the four approaches. Census tracts were identified as having vaccination gap if the rank of vaccination coverage rates was lower than the rank of priory; otherwise, they were identified as no gap. Among the census tracts with vaccination gaps, we further classified them into high, medium and low gap group by dividing the rank difference to 3 equal size groups. All analyses were performed in R Studio 11 . To create the maps presented in this analysis, we first obtained the GIS shapefiles from 2019 TIGER/ Line shapefiles from the U.S. Census Bureau 12 , and used R package "tmap" to create the choropleth maps 13 . year 2018 (c), percentage of population 65 years old and above (d) and CCVI composite score (e). These maps present somewhat different spatial patterns and identify different sets of census tracts as the priority areas. Figure 2 presents the maps of census tracts identified as no-gap/low/median/high gap. Each panel corresponds to one of the four approaches described above for assessing the gap. Some areas were identified as high gap areas by all four approaches, while some difference was also noticed. We noted some rural areas on the outskirts of the city were identified as high gap area only when using the cumulative case rate to assess the gap. Areas in the city downtown were consistently identified as high gap areas.

The analysis has allowed COB Public Health Department to strategically target areas with high vaccination gaps through their "boots on the ground" campaign, while considering the population characteristics in these areas (e.g., age or primary language spoken at home). For example, COB Public Health Department has developed and implemented a vaccination messaging approach utilizing door-to-door outreach in both English and Spanish languages in identified high gap areas with older populations, with additional messaging via the city's social media platforms and the weekly COVID update targeting high gap areas with higher young populations 14 .

In this analysis, we reported the first phase of COVID-19 vaccination rollout in the COB between February 5th, 2021 and March 13th, 2021. We developed four rank-based approaches to evaluate the vaccination gap, which considered COVID-19 impact, population vulnerability and vaccination eligibility. The rank-based approach, despite being simple, can accommodate the fast-changing dynamics of COVID-19 vaccination. For example, starting March 29th, 2021, vaccination eligibility for COVID-19 vaccination in Texas will include all population aged 16 years and older. Eligibility criteria should be accounted for when assessing vaccination gaps. As COVID-19 vaccine distribution plans evolve, together with COVID-19 herd immunity accumulating, we need a flexible approach such as the ones developed in this analysis to enable rapid vaccine strategy adaptations.

Real-time geospatial analysis to identify vaccination gaps is critical to increase vaccination uptake in the vulnerable and vaccine hesitant groups 15 . Our analyses have assisted the COB Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and the deployment of services to areas with the largest gaps. Along with low education and low income levels, Hispanics showed higher COVID-19 vaccine hesitancy compared with their counterparts 15 . Our real-time geospatial analysis incorporating SVI, for example, would provide the information on vaccine hesitant groups overlapping with high SVI. The effective use of real-time geospatial analysis can assist local public health departments, particularly those with limited resources, to develop effective, as well as cultural and language appropriate strategies to reach communities and increase vaccination uptake. 

Socioeconomic status and prevalence of obesity and diabetes in a Mexican American Community

Undiagnosed diabetes and pre-diabetes in health disparities

Mexican American and South Asian population-based cohorts reveal high prevalence of type 2 diabetes and crucial differences in metabolic phenotypes

County-Level COVID-19 Vaccination Coverage and Social Vulnerability-United States

Census tract patterns and contextual social determinants of health associated with COVID-19 in a hispanic population from South Texas: A spatiotemporal perspective

CDC's Social Vulnerability Index (SVI)

COVID-19 Vaccine Information

Bringing Greater Precision to the COVID-19 Response

RStudio: Integrated Development Environment for R, manual

Census Mapping Files

Tmap: Thematic Maps in R

COVID-19vaccination hesitancy in the United States: A rapid national assessment

The authors declare no competing interests. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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