研究县级以下地区的部分社会人口特征,以进行公共卫生监测。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-11-01 DOI:10.1186/s12963-024-00352-y
D Aaron Vinson, Angela K Werner
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引用次数: 0

摘要

背景:在县一级绘制与环境健康危害相关的健康结果图,可简化该县人口所经历的风险。美国疾病控制和预防中心的国家环境公共健康跟踪计划已开发出汇总人口普查区的县级地理分区,以便显示稳定、最小压制的数据。这有助于突出环境健康结果和风险数据的更多地方差异。但是,我们希望了解所使用的汇总方法是否将社会人口统计学上相似或不相似的地区相互汇总在一起。本分析试图探讨追踪计划所确定的暴露于环境健康危害的风险可能增加的特定人群的分布是否在这些县以下地理区域中得以保留,并以人口普查区作为创建这些地理区域的基础:计算五个州的每个县以下地理区域的三个社会人口特征(65 岁及以上人口、少数种族和族裔人口以及贫困线以下人口)的平均值,并将其归入五个分组。确定并比较了每个分县地域和人口普查区的分组差异:各普查区和两个汇总的县以下地域的社会人口特征相似。在某些情况下,人口较少的普查区或人口高度倾斜的普查区(例如,65 岁及以上人口比例非常高)与不同的普查区合并在一起,以满足跟踪计划的合并方法的要求。根据社会人口变量和汇总水平的不同,研究区域内有 2.41%-6.59%的普查区存在这种情况:跟踪计划的分县汇总方法汇总了具有相似特征的普查区。与分辨率较高的县级数据相比,这两个新的县级以下地理区域可作为卫生官员和决策者的潜在选择,利用分辨率更高的健康结果和环境危害数据制定有针对性的干预措施。
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Examining select sociodemographic characteristics of sub-county geographies for public health surveillance.

Background: Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them.

Methods: Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract.

Results: The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program's aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level.

Conclusions: The Tracking Program's sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

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