Census Tract Patterns and Contextual Social Determinants of Health Associated With COVID-19 in a Hispanic Population From South Texas: A Spatiotemporal Perspective.
Census Tract Patterns and Contextual Social Determinants of Health Associated With COVID-19 in a Hispanic Population From South Texas: A Spatiotemporal Perspective.
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DOI：
10.2196/29205
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发表时间：
2021-08-05
影响因子：
8.5
通讯作者：
Reininger B
中科院分区：
文献类型：
--
作者：
Bauer C;Zhang K;Lee M;Fisher-Hoch S;Guajardo E;McCormick J;de la Cerda I;Fernandez ME;Reininger B
先前的研究表明，不同的健康社会决定因素可能是导致县或邮政编码地区少数民族和服务不足人群新冠肺炎发病率和死亡率差异的原因之一。这项分析是在人口普查区域的细粒度空间分辨率下进行的，目的是探索西班牙裔人口与新冠肺炎发病率相关的空间模式和背景SDOH，拉美裔人口主要由居住在美国和墨西哥边境的德克萨斯州卡梅伦县的墨西哥裔美国人组成。我们进行了年龄分层分析，以确定不同的SDOH贡献，并按年龄组量化其影响。我们纳入了德克萨斯州卡梅伦县在2020年3月18日(第一例报告病例)至12月16日期间经逆转录-聚合酶链式反应确认的所有新冠肺炎阳性病例。确诊的新冠肺炎病例按人口普查区域汇总到每周一次。我们采用贝叶斯时空负二项模型调查了新冠肺炎的发病率与美国社区调查获得的人口普查区域、人口统计学和SDOH值之间的关系。此外，我们通过创建二元变量“庇护所原地”，调查了当地缓解政策对新冠肺炎的影响。对所有新冠肺炎确诊病例和年龄分层亚组进行了分析。我们的分析显示，在单亲家庭比例较高(RR=1.016，95%可信区间[CI]1.005，1.027)和英语水平有限的人口比例较高(RR=1.015，95%CI 1.003，1.028)的人口普查区域，新冠肺炎的相对发病风险(RR)较高。低RR与低收入(RR=0.972，95%CI 0.953，0.993)和18岁以下人口比例(RR=0.976，95%CI 0.959，0.993)有关。最显著的关联与“就地避难”变量有关，在该变量中，有政策和没有政策的时间段相比，新冠肺炎的发生风险降低了50%以上(RR=0.506，95%CI 0.454，0.563)。此外，按年龄分层的分析确定了不同的重要因素和不同程度的“就地避难”效应。在我们的研究中，我们确定了包括社会环境和当地应急措施在内的特别危险因素与新冠肺炎在人口普查区域水平上的关系，这些人处于高度贫困的人口中，医疗保健机会有限，慢性病患病率很高。我们的分析结果为设计有效的检测策略提供了关键知识，并协助当地公共卫生部门控制、缓解和实施疫苗战略。
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. 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. 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. 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. 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.
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影响因子：
3.7
作者：
Fielding-Miller RK;Sundaram ME;Brouwer K
通讯作者：
Brouwer K
DOI：
10.3390/ijerph18041495
发表时间：
2021-02-04
影响因子：
--
作者：
Oluyomi AO;Gunter SM;Leining LM;Murray KO;Amos C
通讯作者：
Amos C
影响因子：
3.7
作者：
Fisher-Hoch, Susan P.;Vatcheva, Kristina P.;McCormick, Joseph B.
通讯作者：
McCormick, Joseph B.
影响因子：
4.1
作者：
Watt, Gordon P.;Fisher-Hoch, Susan P.;Thanikachalam, Mohan
通讯作者：
Thanikachalam, Mohan
影响因子：
3.4
作者：
Cordes, Jack;Castro, Marcia C.
通讯作者：
Castro, Marcia C.