Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Ave, Boston, MA, USA.
Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA.
Int J Health Geogr. 2020 Dec 5;19(1):56. doi: 10.1186/s12942-020-00250-0.
Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre's Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala.
We successfully used Epicentre's Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h.
In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population-the unhoused, street dwellers or people living in vehicles.
代表性的家庭调查方法需要最新的抽样框架和样本设计，以尽量减少现场工作的时间和成本，尤其是在中低收入国家。传统的方法，如多阶段聚类抽样、随机游走或空间抽样，可能繁琐、昂贵或不准确，导致众所周知的偏差。然而，一种新的工具，Epicentre 的 Geo-Sampler 程序，允许对结构进行简单的随机抽样，这可以消除其中一些偏差。我们描述了在危地马拉的两个地点使用 Geo-Sampler 选择具有代表性的人群样本进行肾脏疾病调查的研究设计过程、经验和教训。
我们成功地使用 Epicentre 的 Geo-sampler 工具在两个半城市危地马拉社区中抽取了 650 个结构。总体而言，82%的抽样结构是住宅，可以进行招募。经过 30 分钟的培训，就可以由一个人进行样本选择。从样本选择到制作现场地图的过程大约需要 40 小时。
结合我们的设计方案，Epicentre Geo-Sampler 工具为我们在半城市危地马拉的环境中选择代表性人群样本进行患病率调查提供了一种可行、快速且低成本的替代方案。该工具在树木覆盖较重或每栋建筑居住单元较多的密集城市环境中效果可能不佳。同样，虽然该方法是向前迈进的一步，可以将非传统的居住安排（永久性或临时居住在企业、宗教机构或其他结构中的人）包括在内，但它并没有考虑到人群中一些最边缘化和脆弱的人——无家可归者、街头居民或居住在车辆中的人。
