Sochas Laura
Department of Social Policy, London School of Economics and Political Science, London, UK.
BMJ Glob Health. 2020 Feb 10;4(Suppl 5):e002139. doi: 10.1136/bmjgh-2019-002139. eCollection 2019.
The growing use of Geographic Information Systems (GIS) to link population-level data to health facility data is key for the inclusion of health system environments in analyses of health disparities. However, such approaches commonly focus on just a couple of aspects of the health system environment and only report on the average and independent effect of each dimension.
Using GIS to link Demographic and Health Survey data on births (2008-13/14) to Service Availability and Readiness Assessment data on health facilities (2010) in Zambia, this paper rigorously measures the multiple dimensions of an accessible health system environment. Using multilevel Bayesian methods (multilevel analysis of individual heterogeneity and discriminatory accuracy), it investigates whether multidimensional health system environments defined with reference to both geographic and social location cut across individual-level and community-level heterogeneity to reliably predict facility delivery.
Random intercepts representing different health system environments have an intraclass correlation coefficient of 25%, which demonstrates high levels of discriminatory accuracy. Health system environments with four or more access barriers are particularly likely to predict lower than average access to facility delivery. Including barriers related to geographic location in the non-random part of the model results in a proportional change in variance of 74% relative to only 27% for barriers related to social discrimination.
Health system environments defined as a combination of geographic and social location can effectively distinguish between population groups with high versus low probabilities of access. Barriers related to geographic location appear more important than social discrimination in the context of Zambian maternal healthcare access. Under a progressive universalism approach, resources should be disproportionately invested in the worst health system environments.
越来越多地使用地理信息系统（GIS）将人口层面的数据与卫生设施数据相联系，这对于在健康差异分析中纳入卫生系统环境至关重要。然而，此类方法通常仅关注卫生系统环境的几个方面，并且只报告每个维度的平均和独立影响。
本文利用GIS将赞比亚关于出生的人口与健康调查数据（2008 - 13/14年）与卫生设施的服务可用性和准备情况评估数据（2010年）相联系，严格衡量了可及的卫生系统环境的多个维度。使用多级贝叶斯方法（个体异质性和判别准确性的多级分析），研究了参照地理和社会位置定义的多维卫生系统环境是否跨越个体层面和社区层面的异质性，以可靠地预测机构分娩情况。
代表不同卫生系统环境的随机截距的组内相关系数为25%，这表明具有较高的判别准确性。存在四个或更多获取障碍的卫生系统环境尤其有可能预测到机构分娩的可及性低于平均水平。在模型的非随机部分纳入与地理位置相关的障碍，相对于与社会歧视相关的障碍，方差的比例变化为74%，而后者仅为27%。
定义为地理和社会位置组合的卫生系统环境能够有效区分获取可能性高与低的人群组。在赞比亚产妇获得医疗服务的背景下，与地理位置相关的障碍似乎比社会歧视更为重要。在渐进式普遍主义方法下，应将资源不成比例地投入到最差的卫生系统环境中。
