背景:卡梅伦县,一个低收入的南德克萨斯-墨西哥边境县,其特点是严重的健康差距,在大流行开始时,德克萨斯州一直是COVID-19死亡率最高的县之一。德克萨斯州各县COVID-19负担的差异表明,需要有效的干预措施来满足当地卫生部门及其社区的具体需求。公开可用的COVID-19监测数据不够及时或粒度,无法提供此类有针对性的干预措施。卡梅伦的机构与学术合作使用了新颖的地理信息科学方法来产生颗粒状的COVID-19监测数据。这些数据用于战略性地针对布朗斯维尔市(COB)的名为“地面靴子”(BOG)的教育外展干预措施。
目的:本研究旨在评估空间针对性社区干预对每日COVID-19测试计数的影响。
方法:COB和UTHealthHouston之间的机构与学术合作导致在人口普查区一级每周创建COVID-19流行病学报告。这些报告指导了普查区的选择,以便在2020年4月21日至6月8日之间提供有针对性的BOG。目标BOG区域和干预日期的记录保存,以及每个人口普查区域的COVID-19每日检测计数,为干预评估提供数据。使用中断时间序列设计来评估目标BOG前后2周对COVID-19测试计数的影响。分段泊松回归分析用于量化BOG前后COVID-19每日测试计数趋势之间的斜率(持续)和截距(即时)变化。为了进行比较,对未收到目标BOG的COB区域进行了其他分析。
结果:在干预期间,48个COB人口普查区中的18个收到了目标BOG。其中,在5个区域中观察到BOG每日测试计数前后斜率的显着变化,其中80%(n=4)具有正斜率变化。与干预前2周观察到的测试趋势相比,阳性斜率变化意味着目标BOG后2周的每日COVID-19测试计数显着增加。在对没有收到目标BOG的30个人口普查区域的额外分析中,在10个区域观察到显著的坡度变化,其中仅在20%(n=2)中观察到正斜率变化。总之,我们发现BOG目标区域的每日COVID-19测试计数斜率变化大多为阳性,而非目标管道的每日COVID-19测试计数斜率变化大多为阴性。
结论:对空间针对性社区干预措施进行评估是必要的,以加强当地应急准备这一重要方法的证据基础。本报告重点介绍了学术机构合作如何建立和评估实时,有针对性的干预措施,为小社区提供精准的公共卫生。
BACKGROUND: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named \"Boots on the Ground\" (BOG) in the City of Brownsville (COB).
OBJECTIVE: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts.
METHODS: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological
reports at the census tract level. These
reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes.
RESULTS: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes.
CONCLUSIONS: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.