Wheeler David C, Raman Shyam, Jones Resa M, Schootman Mario, Nelson Erik J
Department of Biostatistics, Virginia Commonwealth University, United States.
Department of Epidemiology and Biostatistics, Indiana University-Bloomington, United States.
Spat Spatiotemporal Epidemiol. 2019 Aug;30:100286. doi: 10.1016/j.sste.2019.100286. Epub 2019 Jul 4.
Lead exposure adversely affects children's health. Exposure in the United States is highest among socioeconomically disadvantaged individuals who disproportionately live in substandard housing. We used Bayesian binomial regression models to estimate a neighborhood deprivation index and its association with elevated blood lead level (EBLL) risk using blood lead level testing data in Maryland census tracts. Our results show the probability of EBLL was spatially structured with high values in Baltimore city and low values in the District of Columbia suburbs and Baltimore suburbs. The association between the neighborhood deprivation index and EBLL risk was statistically significant after accounting for spatial dependence in probability of EBLL. The percent of houses built before 1940, African Americans, and renter occupied housing were the most important variables in the index. Bayesian models provide a flexible one-step approach to modeling risk associated with neighborhood deprivation while accounting for spatially structured and unstructured heterogeneity in risk.
铅暴露会对儿童健康产生不利影响。在美国，社会经济地位不利的人群中铅暴露情况最为严重，这些人不成比例地居住在不符合标准的住房中。我们使用贝叶斯二项回归模型，利用马里兰州人口普查区的血铅水平检测数据，估算邻里贫困指数及其与血铅水平升高（EBLL）风险的关联。我们的结果显示，EBLL的概率在空间上呈现出结构性，巴尔的摩市的值较高，而哥伦比亚特区郊区和巴尔的摩郊区的值较低。在考虑了EBLL概率的空间依赖性之后，邻里贫困指数与EBLL风险之间的关联具有统计学意义。1940年以前建造的房屋比例、非裔美国人以及出租房屋是该指数中最重要的变量。贝叶斯模型提供了一种灵活的一步法，用于在考虑风险中空间结构化和非结构化异质性的同时，对与邻里贫困相关的风险进行建模。
