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利用产妇候产之家对利比里亚农村妇女村庄进行地理定位的方案。

Protocol for geolocating rural villages of women in Liberia utilizing a maternity waiting home.

作者信息

James K H, Perosky J E, McLean K, Nyanplu A, Moyer C A, Lori J R

机构信息

Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA.

University of Michigan School of Nursing, 400 N. Ingalls, Room 3237, Ann Arbor, USA.

出版信息

BMC Res Notes. 2019 Apr 2;12(1):196. doi: 10.1186/s13104-019-4224-1.

Abstract

OBJECTIVE

Geospatial data are used by health systems and researchers to understand disease burdens, trace outbreaks, and allocate resources, however, there are few well-documented protocols for collecting and analyzing geographic information systems data in rural areas of low- and middle-income countries. Even with the proliferation of spatial technologies such as Open Street Map and Google Maps, basic geographic data-such as village locations-are not widely available in many countries in sub-Saharan Africa. The purpose of this paper is to report a step-wise protocol, using geographic information system techniques and tools, developed to collect and analyze the type of spatial data necessary to calculate the distance between rural villages and maternity waiting homes located near rural primary healthcare facilities in Bong County, Liberia.

RESULTS

Using a step-wise approach incorporating local healthcare provider knowledge, intensive field work, and spatial technologies such as Open Street Map and Google Maps for village geospatial data collection and verification, we identified village locations of 93.7% of the women who accessed the five maternity waiting homes in our study from 2012 to 2016.

摘要

目的

卫生系统和研究人员利用地理空间数据来了解疾病负担、追踪疫情并分配资源,然而,在低收入和中等收入国家的农村地区,很少有记录完备的收集和分析地理信息系统数据的方案。即便诸如开放街道地图和谷歌地图等空间技术不断普及,但在撒哈拉以南非洲的许多国家,基本的地理数据(如村庄位置)仍未广泛可得。本文旨在报告一种逐步方案,该方案运用地理信息系统技术和工具,用于收集和分析计算利比里亚邦县农村村庄与位于农村初级医疗设施附近的产妇候产之家之间距离所需的空间数据类型。

结果

通过采用一种逐步方法,将当地医疗服务提供者的知识、密集的实地工作以及诸如开放街道地图和谷歌地图等空间技术用于村庄地理空间数据的收集和核实,我们确定了2012年至2016年期间在我们研究中使用五个产妇候产之家的93.7%的妇女所在的村庄位置。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3bc/6444816/98b179c690a9/13104_2019_4224_Fig1_HTML.jpg

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