Neighborhood Vulnerability and COVID-19 Vaccination Coverage in Philadelphia, PA
Tuhina Srivastava, MPH, is a doctoral candidate in Epidemiology in the Graduate Group in Epidemiology and Biostatistics (GGEB) at the Perelman School of Medicine at the University of Pennsylvania. She has utilized both computational and laboratory methodologies to study pressing public health concerns, and her current research focuses on measuring and decreasing health disparities related to infectious diseases and vaccination coverage. She is passionate about increasing vaccine uptake and achieving health equity, especially in vulnerable communities, and has numerous publications in this field.
She received the Neal Nathanson Award for Excellence in Global Public Health in May 2017 upon her Master of Public Health graduation from the University of Pennsylvania. She has worked globally in Botswana studying HIV and cryptococcal meningitis coinfections. She received the Young Investigator Award from the Burroughs Wellcome Fund at the 10th International Conference on Cryptococcus and Cryptococcosis in 2017 for this global research and the President Amy Gutman Leadership Award from University of Pennsylvania. She is also an Associate Fellow at the Center for Public Health Initiatives.
During her doctoral degree, she hopes to continue employing novel approaches to study infectious disease transmission and learn more about science communication, health policy research, and public health advocacy.
During the COVID-19 pandemic, residents of color living in disadvantaged communities in Philadelphia, Pennsylvania had disproportionately higher rates of cases, hospitalizations, and deaths, as well as lower rates of COVID-19 vaccination compared to largely white communities. Given these disparities, our objective was to evaluate differences in neighborhood vulnerability by COVID-19 vaccination coverage at the census tract level in Philadelphia and to compare the performance of two statistical place-based measures of disadvantage (“disadvantage indices”). Neighborhood vulnerability was characterized by the CDC Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). COVID-19 vaccination coverage (percent of population with 1+ vaccine doses) was obtained at the census tract level through OpenDataPhilly. Kruskal-Wallis tests showed a statistically significant difference in the median vulnerability score (p>0.001 for both SVI and CCVI, separately) between census tracts with similar vaccination coverage. Except for census tracts with <20% coverage, which had lower vulnerability scores (<60th percentile), tracts with higher coverage rates had lower overall vulnerability scores for both SVI and CCVI. SVI and CCVI were also highly correlated (Pearson correlation coefficient [95% CI] = 0.94 [0.93-0.95], p<0.001), suggesting similar performance between these indices in characterizing vulnerability. Our finding of lower vaccination rates among census tracts with higher vulnerability suggests that these communities should be prioritized for COVID-19 vaccination outreach. Using disadvantage indices can aid with both routine and emergency public health programming, beyond COVID-19, and can help guide resource allocation to neighborhoods that may have a disproportionately greater need, mitigating existing health disparities.
Keywordsvaccination, COVID-19, health disparities, disadvantage, vulnerability, vaccines, neighborhood
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