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将家庭调查与机构评估相联系:利用马拉维全国代表性数据对地理空间方法的比较。

Linking household surveys and facility assessments: a comparison of geospatial methods using nationally representative data from Malawi.

机构信息

Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.

Malawi Ministry of Health is the institution, Lilongwe, Malawi.

出版信息

Popul Health Metr. 2020 Dec 10;18(1):30. doi: 10.1186/s12963-020-00242-z.

DOI:10.1186/s12963-020-00242-z
PMID:33302989
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7731755/
Abstract

BACKGROUND

Linking facility and household surveys through geographic methods is a popular technique to draw conclusions about the relationship between health services and population health outcomes at local levels. These methods are useful tools for measuring effective coverage and tracking progress towards Universal Health Coverage, but are understudied. This paper compares the appropriateness of several geospatial methods used for linking individuals (within displaced survey cluster locations) to their source of family planning (at undisplaced health facilities) at a national level.

METHODS

In Malawi, geographic methods linked a population health survey, rural clusters from the Woman's Questionnaire of the 2015 Malawi Demographic and Health Survey (MDHS 2015), to Malawi's national health facility census to understand the service environment where women receive family planning services. Individuals from MDHS 2015 clusters were linked to health facilities through four geographic methods: (i) closest facility, (ii) buffer (5 km), (iii) administrative boundary, and (iv) a newly described theoretical catchment area method. Results were compared across metrics to assess the number of unlinked clusters (data lost), the number of linkages per cluster (precision of linkage), and the number of women linked to their last source of modern contraceptive (appropriateness of linkage).

RESULTS

The closest facility and administrative boundary methods linked every cluster to at least one facility, while the 5-km buffer method left 288 clusters (35.3%) unlinked. The theoretical catchment area method linked all but one cluster to at least one facility (99.9% linked). Closest facility, 5-km buffer, administrative boundary, and catchment methods linked clusters to 1.0, 1.4, 21.1, and 3.3 facilities on average, respectively. Overall, the closest facility, 5-km buffer, administrative boundary, and catchment methods appropriately linked 64.8%, 51.9%, 97.5%, and 88.9% of women to their last source of modern contraceptive, respectively.

CONCLUSIONS

Of the methods studied, the theoretical catchment area linking method loses a marginal amount of population data, links clusters to a relatively low number of facilities, and maintains a high level of appropriate linkages. This linking method is demonstrated at scale and can be used to link individuals to qualities of their service environments and better understand the pathways through which interventions impact health.

摘要

背景

通过地理方法将设施调查和家庭调查联系起来,是一种在地方层面上得出卫生服务与人群健康结果之间关系的结论的常用方法。这些方法是衡量有效覆盖范围和跟踪全民健康覆盖进展的有用工具,但研究不足。本文比较了几种在全国范围内将个人(在流离失所的调查群落地点内)与其计划生育来源(在未流离失所的卫生设施)联系起来的地理方法的适宜性。

方法

在马拉维,通过地理方法将一项人口健康调查、2015 年马拉维人口与健康调查(MDHS 2015)中的农村群集与马拉维全国卫生设施普查联系起来,以了解妇女获得计划生育服务的服务环境。通过四种地理方法将 MDHS 2015 群集中的个人与卫生设施联系起来:(i)最近的设施,(ii)缓冲区(5 公里),(iii)行政边界,以及(iv)新描述的理论集水区域方法。通过比较各项指标来评估未联系群集的数量(数据丢失)、每个群集的联系数量(联系的精度)以及与最近的现代避孕来源联系的妇女数量(联系的适宜性)。

结果

最近的设施和行政边界方法将每个群集至少与一个设施联系起来,而 5 公里缓冲区方法留下 288 个(35.3%)群集未联系。理论集水区域方法将所有但一个群集至少与一个设施联系起来(99.9%联系)。最近的设施、5 公里缓冲区、行政边界和集水方法分别平均将群集与 1.0、1.4、21.1 和 3.3 个设施联系起来。总体而言,最近的设施、5 公里缓冲区、行政边界和集水方法分别适当地将 64.8%、51.9%、97.5%和 88.9%的妇女与其最近的现代避孕来源联系起来。

结论

在所研究的方法中,理论集水区域联系方法仅丢失少量人口数据,将群集与相对较少的设施联系起来,并保持高度适当的联系。这种联系方法在规模上得到了验证,可用于将个人与服务环境的质量联系起来,并更好地了解干预措施通过哪些途径影响健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/f540140e416c/12963_2020_242_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/99162d0dff7b/12963_2020_242_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/5b7dfa9ff65b/12963_2020_242_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/f540140e416c/12963_2020_242_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/99162d0dff7b/12963_2020_242_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/5b7dfa9ff65b/12963_2020_242_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e91/7731755/f540140e416c/12963_2020_242_Fig3_HTML.jpg

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