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JMIR Public Health Surveill ; 7(8): e29205, 2021 08 05.
Article in English | MEDLINE | ID: covidwho-2141332


BACKGROUND: Previous studies have shown that various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at the county or zip code level. OBJECTIVE: This analysis was carried out at a granular spatial resolution of census tracts to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of a Mexican American population living in Cameron County, Texas on the border of the United States and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups. METHODS: We included all reported COVID-19-positive cases confirmed by reverse transcription-polymerase chain reaction testing between March 18 (first case reported) and December 16, 2020, in Cameron County, Texas. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal negative binomial model to investigate the COVID-19 incidence rate in relation to census tract demographics and SDOH obtained from the American Community Survey. Moreover, we investigated the impact of local mitigation policy on COVID-19 by creating the binary variable "shelter-in-place." The analysis was performed on all COVID-19-confirmed cases and age-stratified subgroups. RESULTS: Our analysis revealed that the relative incidence risk (RR) of COVID-19 was higher among census tracts with a higher percentage of single-parent households (RR=1.016, 95% posterior credible intervals [CIs] 1.005, 1.027) and a higher percentage of the population with limited English proficiency (RR=1.015, 95% CI 1.003, 1.028). Lower RR was associated with lower income (RR=0.972, 95% CI 0.953, 0.993) and the percentage of the population younger than 18 years (RR=0.976, 95% CI 0.959, 0.993). The most significant association was related to the "shelter-in-place" variable, where the incidence risk of COVID-19 was reduced by over 50%, comparing the time periods when the policy was present versus absent (RR=0.506, 95% CI 0.454, 0.563). Moreover, age-stratified analyses identified different significant contributing factors and a varying magnitude of the "shelter-in-place" effect. CONCLUSIONS: In our study, SDOH including social environment and local emergency measures were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and a high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments in COVID-19 control, mitigation, and implementation of vaccine strategies.

COVID-19/epidemiology , Hispanic or Latino , Social Determinants of Health , Adolescent , Adult , Aged , Aged, 80 and over , Censuses , Female , Health Equity , Humans , Incidence , Male , Mexico/ethnology , Middle Aged , Minority Groups , Physical Distancing , SARS-CoV-2 , Socioeconomic Factors , Spatial Analysis , Texas/epidemiology , United States , Vulnerable Populations , Young Adult
Sci Rep ; 11(1): 18117, 2021 09 13.
Article in English | MEDLINE | ID: covidwho-1406408


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.

COVID-19 Vaccines/immunology , COVID-19/immunology , Immunization Programs/statistics & numerical data , SARS-CoV-2/immunology , Vaccination Coverage/statistics & numerical data , Vaccination/statistics & numerical data , COVID-19/prevention & control , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Geography , Hispanic or Latino/statistics & numerical data , Humans , Immunization Programs/methods , Mexico/ethnology , Minority Groups/statistics & numerical data , Minority Health/statistics & numerical data , SARS-CoV-2/physiology , Socioeconomic Factors , Texas/ethnology , Vaccination/methods , Vaccination Coverage/methods , Vulnerable Populations/ethnology , Vulnerable Populations/statistics & numerical data