Division of Public Health, College of Human Medicine, Michigan State University, 200 E. 1st Street, Flint, MI, 48502, USA.
Dept of Geography, Environment & Spatial Sciences, College of Social Science, Michigan State University, 673 Auditorium Road, Room 115, East Lansing, MI, 48824, USA.
Int J Health Geogr. 2019 Nov 8;18(1):24. doi: 10.1186/s12942-019-0190-z.
Identifying and intervening on health disparities requires representative community public health data. For cities with high vacancy and transient populations, traditional methods of population estimation for refining random samples are not feasible. The aim of this project was to develop a novel method for systematic observations to establish community epidemiologic samples.
We devised a four-step population randomization observation process for Flint, Michigan, USA: (1) Use recent total population data for community areas (i.e., neighborhoods) to establish the proportional sample size for each area, (2) Randomly select street segments of each community area, (3) Deploy raters to conduct observations about habitation for each randomly selected segment, and (4) Complete observations for second and third street segments, depending on vacancy levels. We implemented this systematic observation process on 400 randomly selected street segments. Of these, 130 (32.5%) required assessment of secondary segments due to high vacancy. Among the 130 primary segments, 28 (21.5%) required assessment of tertiary (or more) segments. For 71.5% of the 400 primary street segments, there was consensus among raters on whether the dwelling inhabited or uninhabited.
Houses observed with this method could have easily been considered uninhabited via other methods. This could cause residents of ambiguous dwellings (likely to be the most marginalized residents with highest levels of unmet health needs) to be underrepresented in the resultant sample.
识别和干预健康差异需要有代表性的社区公共卫生数据。对于人口流动性大、空房率高的城市,传统的人口估计方法来细化随机样本是不可行的。本项目旨在开发一种新的系统观察方法,以建立社区流行病学样本。
我们为美国密歇根州弗林特市设计了一个四步人群随机观察过程:(1)使用最近的社区(即邻里)的总人口数据,为每个区域确定比例抽样大小;(2)随机选择每个社区区域的街道段;(3)部署评估员对每个随机选择的路段进行居住情况观察;(4)根据空房率完成第二和第三街道段的观察。我们在 400 个随机选择的街道段上实施了这个系统观察过程。其中,由于高空房率,有 130 个(32.5%)需要评估次要路段。在 130 个主要路段中,有 28 个(21.5%)需要评估第三级(或更多级)路段。在 400 个主要街道段的 71.5%中,评估员对居住或无人居住的居住情况有共识。
通过这种方法观察到的房屋可能很容易被其他方法认为是无人居住的。这可能导致居住在模糊住所的居民(可能是最边缘化的居民,他们的健康需求最未得到满足)在最终样本中代表性不足。