Oyana Tonny J, Matthews-Juarez Patricia, Cormier Stephania A, Xu Xiaoran, Juarez Paul D
Research Center on Health Disparities, Equity & the Exposome, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Pediatrics, Infectious Disease and Microbiology, Immunology & Biochemistry, University of Tennessee Health Science Center, Le Bonheur Children's Medical Center, Memphis, TN 36163, USA.
Int J Environ Res Public Health. 2015 Dec 22;13(1):ijerph13010013. doi: 10.3390/ijerph13010013.
We have conducted a study to assess the role of environment on the burden of maternal morbidities and mortalities among women using an external exposome approach for the purpose of developing targeted public health interventions to decrease disparities.
We identified counties in the 48 contiguous USA where observed low birthweight (LBW) rates were higher than expected during a five-year study period. The identification was conducted using a retrospective space-time analysis scan for statistically significant clusters with high or low rates by a Discrete Poisson Model.
We observed statistically significant associations of LBW rate with a set of predictive variables. However, in one of the two spatiotemporal models we discovered LBW to be associated with five predictive variables (teen birth rate, adult obesity, uninsured adults, physically unhealthy days, and percent of adults who smoke) in two counties situated in Alabama after adjusting for location changes. Counties with higher than expected LBW rates were similarly associated with two environmental variables (ozone and fine particulate matter).
The county-level predictive measures of LBW offer new insights into spatiotemporal patterns relative to key contributory factors. An external framework provides a promising place-based approach for identifying "hotspots" with implications for designing targeted interventions and control measures to reduce and eliminate health disparities.
我们开展了一项研究,采用外部暴露组方法评估环境对女性孕产妇发病和死亡负担的作用,旨在制定有针对性的公共卫生干预措施以减少差异。
我们确定了美国48个相邻州中在五年研究期间观察到低出生体重(LBW)率高于预期的县。通过离散泊松模型进行回顾性时空分析扫描,以识别具有高或低发生率的统计学显著集群。
我们观察到低出生体重率与一组预测变量之间存在统计学显著关联。然而,在两个时空模型中的一个中,我们发现,在调整位置变化后,阿拉巴马州的两个县的低出生体重与五个预测变量(青少年出生率、成人肥胖率、未参保成年人、身体不健康天数以及吸烟成年人百分比)相关。低出生体重率高于预期的县同样与两个环境变量(臭氧和细颗粒物)相关。
县级低出生体重预测指标为与关键促成因素相关的时空模式提供了新见解。一个外部框架为识别“热点地区”提供了一种有前景的基于地点的方法,这对设计有针对性的干预措施和控制措施以减少和消除健康差异具有启示意义。