The City of Brownsville was made vulnerable to the COVID-19 pandemic due to high rates of obesity and diabetes, high rates of poverty, and adverse social determinants of health. To address the unique challenges faced by the community, Brownsville’s COVID-19 response brought together the skills of academia with the local understanding and health expertise of the city’s public health department to craft a pandemic response that addressed the specific needs and unique challenges of the residents. This article explores the response partnerships formed and the data-driven, community-oriented campaigns that were designed by the Brownsville Public Health Department. The collaborative partnership of the COVID-19 working group and the innovative dissemination strategies designed by the health department provided an effective method of disease mitigation among the city’s most vulnerable residents. The article demonstrates the impact of the response campaigns by including a resident’s perspective on the impact of the response, specifically how the health department tailored their efforts to meet the needs of the Brownsville community.
BACKGROUND The COVID-19 pandemic uncovered the dearth of resources and experience to respond effectively in local health departments, particularly in smaller communities. Publicly available surveillance data, the key information for local health departments, was not sufficiently timely or granular for targeted interventions. The City of Brownsville (COB) is located in a low-income south Texas border county plagued with severe health disparities. The COB, public health department shared local COVID-19 surveillance data weekly with academic partners that produced near real-time weekly geospatial maps of these data. Census tract level case maps were used to strategically target an educational outreach intervention named “Boots on the Ground” (BOG), and application of novel statistical methods were used to evaluate its impact. OBJECTIVE To evaluate the slope (sustained) and intercept (immediate) change in COVID-19 daily test counts 2 weeks pre and post BOG delivery. METHODS Using an interrupted time series design we evaluated the COB census tracts that received targeted BOG between April 21-June 8, 2020. A piece-wise Poisson regression analysis was used to quantify the sustained and immediate change between pre and post BOG COVID-19 daily test count trends. A sensitivity analysis of tracts that did not receive targeted BOG was conducted for comparison purposes. RESULTS During the intervention period, 18 of 48 COB census tracts received targeted BOG. Among these census tracts, significant difference in the slope coefficients from pre- and post-BOG daily test counts was observed in 5 tracts, with 2 tracts having a significant difference in the intercept. Additionally, 80% (4/5) of the significant slope changes showed an increase in pre- and post-slopes. This means the testing trend two weeks post BOG had a sustained increase from the trend two weeks pre-BOG. In the sensitivity analysis of the 30 census tracts not receiving BOG, the opposite was observed. In these tracts, 80% (8/10) of those with significant slope changes had a decrease in the pre- and post-BOG COVID-19 testing slopes. CONCLUSIONS Targeting and evaluation of public health interventions is necessary and possible, particularly in small communities. This report highlights how collaboration between a school of public health and a local health department established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.
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