BACKGROUND Lack of accurate data on the distribution of sub-national populations in low- and middle-income countries impairs planning, monitoring, and evaluation of interventions. Novel, low-cost methods to develop unbiased survey sampling frames at sub-national, sub-provincial, and even sub-district levels are urgently needed. This article details our experience using remote satellite imagery to develop a provincial-level representative community survey sampling frame to evaluate the effects of a 7-year health system intervention in Sofala Province, Mozambique. METHODS Mozambique's most recent census was conducted in 2007, and no data are readily available to generate enumeration areas for representative health survey sampling frames. To remedy this, we partnered with the Humanitarian OpenStreetMap Team to digitize every building in Sofala and Manica provinces (685,189 Sofala; 925,713 Manica) using up-to-date remote satellite imagery, with final results deposited in the open-source OpenStreetMap database. We then created a probability proportional to size sampling frame by overlaying a grid of 2.106 km resolution (0.02 decimal degrees) across each province, and calculating the number of buildings within each grid square. Squares containing buildings were used as our primary sampling unit with replacement. Study teams navigated to the geographic center of each selected square using geographic positioning system coordinates, and then conducted a standard "random walk" procedure to select 20 households for each time a given square was selected. Based on sample size calculations, we targeted a minimum of 1500 households in each province. We selected 88 grids within each province to reach 1760 households, anticipating ongoing conflict and transport issues could preclude the inclusion of some clusters. RESULTS Civil conflict issues forced the exclusion of 8 of 31 subdistricts in Sofala and 15 of 39 subdistricts in Manica. Using Android tablets, Open Data Kit software, and a remote RedCap data capture system, our final sample included 1549 households in Sofala (4669 adults; 4766 children; 33 missing age) and 1538 households in Manica (4422 adults; 4898 children; 33 missing age). CONCLUSIONS Other implementation or evaluation teams may consider employing similar methods to track population distributions for health systems planning or the development of representative sampling frames using remote satellite imagery.

中文翻译：

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使用莫桑比克的开源远程卫星图像开发具有代表性的社区健康调查抽样框架。

背景技术关于低收入和中等收入国家次国家人口分布的准确数据的缺乏，妨碍了干预措施的计划，监测和评估。迫切需要新颖，低成本的方法来开发国家以下，省以下甚至县一级的无偏差调查抽样框架。本文详细介绍了我们使用远程卫星图像开发省级代表性社区调查抽样框架以评估莫桑比克索法拉省7年卫生系统干预措施的效果的经验。方法莫桑比克最近一次人口普查是在2007年进行的，尚无数据可用于生成代表性健康调查抽样框的枚举区域。为了解决这个问题，我们与人道主义OpenStreetMap团队合作，使用最新的远程卫星图像数字化了索法拉和马尼卡省（685,189索法拉； 925,713马尼卡）的每栋建筑物，并将最终结果存储在开源OpenStreetMap数据库中。然后，我们通过在每个省上覆盖一个2.106 km分辨率（0.02十进制度度）的网格并计算每个网格正方形内的建筑物数，来创建与大小采样框成比例的概率。包含建筑物的正方形用作替换的主要抽样单位。研究小组使用地理定位系统坐标导航到每个选定广场的地理中心，然后进行标准的“随机游走”程序，每次选择给定广场时都选择20个家庭。根据样本量计算，我们针对每个省的至少1500户家庭。我们在每个省内选择了88个网格，以覆盖1760户家庭，因为他们预计持续的冲突和交通问题可能会排除某些集群。结果内乱问题迫使沙发区31个街道中的8个街道和马尼卡39个街道中的15个街道被排除在外。使用Android平板电脑，Open Data Kit软件和远程RedCap数据捕获系统，我们的最终样本包括索法拉州的1549户家庭（4669名成人； 4766名儿童； 33岁的失踪年龄）；马尼卡的1538户家庭（4422名成人； 4898名儿童； 33名失踪的居民）年龄）。结论其他实施或评估小组可能会考虑采用类似方法来跟踪人口分布，以进行卫生系统规划或使用远程卫星图像开发代表性采样框。