Using geographical analysis to identify child health inequality in sub-Saharan Africa

One challenge to achieving Millennium Development Goals was inequitable access to quality health services. In order to achieve the Sustainable Development Goals, interventions need to reach underserved populations. Analyzing health indicators in small geographic units aids the identification of hotspots where coverage lags behind neighboring areas. The purpose of these analyses is to identify areas of low coverage or high need in order to inform effective resource allocation to reduce child health inequity between and within countries. Using data from The Demographic and Health Survey Program surveys conducted in 27 selected African countries between 2010 and 2014, we computed estimates for six child health indicators for subnational regions. We calculated Global Moran’s I statistics and used Local Indicator of Spatial Association analysis to produce a spatial layer showing spatial associations. We created maps to visualize sub-national autocorrelation and spatial clusters. The Global Moran’s I statistic was positive for each indicator (range: 0.41 to 0.68), and statistically significant (p

实现千年发展目标（Millennium Development Goals）所面临的一项核心挑战，是优质卫生服务的获取机会存在不公平性。为实现可持续发展目标（Sustainable Development Goals），健康干预措施需覆盖服务欠缺人群。对小型地理单元内的卫生指标进行分析，有助于识别覆盖率落后于周边区域的热点地区。本类分析的目的在于识别覆盖率偏低或健康需求较高的区域，从而为优化资源配置提供决策依据，以减少国家间及国家内部的儿童健康不公平现象。本研究利用2010年至2014年间在27个选定非洲国家开展的人口与健康调查项目（Demographic and Health Survey Program）数据，为次国家区域计算了6项儿童卫生指标的估计值。我们计算了全局莫兰I（Global Moran’s I）统计量，并采用局部空间关联指标分析方法，生成了展示空间关联关系的空间图层。我们绘制了专题地图以可视化次国家自相关与空间聚类分布。各项指标的全局莫兰I统计量均为正值（取值范围：0.41至0.68），且具有统计学显著性（p