Real-time geospatial analysis identifies gaps in COVID-19 vaccination in a minority population.

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.

Risk factors for severity of COVID-19 in hospital patients age 18-29 years.

BACKGROUND: Since February 2020, over 2.5 million Texans have been diagnosed with COVID-19, and 20% are young adults at risk for SARS-CoV-2 exposure at work, academic, and social settings. This study investigated demographic and clinical risk factors for severe disease and readmission among young adults 18-29 years old, who were diagnosed at a hospital encounter in Houston, Texas, USA. METHODS AND FINDINGS: A retrospective registry-based chart review was conducted investigating demographic and clinical risk factors for severe COVID-19 among patients aged 18-29 with positive SARS-CoV-2 tests within a large metropolitan healthcare system in Houston, Texas, USA. In the cohort of 1,853 young adult patients diagnosed with COVID-19 infection at a hospital encounter, including 226 pregnant women, 1,438 (78%) scored 0 on the Charlson Comorbidity Index, and 833 (45%) were obese (≥30 kg/m2). Within 30 days of their diagnostic encounter, 316 (17%) patients were diagnosed with pneumonia, 148 (8%) received other severe disease diagnoses, and 268 (14%) returned to the hospital after being discharged home. In multivariable logistic regression analyses, increasing age (adjusted odds ratio [aOR] 1.1, 95% confidence interval [CI] 1.1-1.2, p<0.001), male gender (aOR 1.8, 95% CI 1.2-2.7, p = 0.002), Hispanic ethnicity (aOR 1.9, 95% CI 1.2-3.1, p = 0.01), obesity (3.1, 95% CI 1.9-5.1, p<0.001), asthma history (aOR 2.3, 95% CI 1.3-4.0, p = 0.003), congestive heart failure (aOR 6.0, 95% CI 1.5-25.1, p = 0.01), cerebrovascular disease (aOR 4.9, 95% CI 1.7-14.7, p = 0.004), and diabetes (aOR 3.4, 95% CI 1.9-6.2, p<0.001) were predictive of severe disease diagnoses within 30 days. Non-Hispanic Black race (aOR 1.6, 95% CI 1.0-2.4, p = 0.04), obesity (aOR 1.7, 95% CI 1.0-2.9, p = 0.046), asthma history (aOR 1.7, 95% CI 1.0-2.7, p = 0.03), myocardial infarction history (aOR 6.2, 95% CI 1.7-23.3, p = 0.01), and household exposure (aOR 1.5, 95% CI 1.1-2.2, p = 0.02) were predictive of 30-day readmission. CONCLUSIONS: This investigation demonstrated the significant risk of severe disease and readmission among young adult populations, especially marginalized communities and people with comorbidities, including obesity, asthma, cardiovascular disease, and diabetes. Health authorities must emphasize COVID-19 awareness and prevention in young adults and continue investigating risk factors for severe disease, readmission and long-term sequalae.