Principal component analysis of socioeconomic factors and their association with malaria and arbovirus risk in Tanzania: a sensitivity analysis
Principal component analysis of socioeconomic factors and their association with malaria and arbovirus risk in Tanzania: a sensitivity analysis
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DOI：
10.1136/jech-2017-209119
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发表时间：
2017-11-01
影响因子：
6.3
通讯作者：
Kulkarni, Manisha A.
中科院分区：
文献类型：
--
作者：
Homenauth, Esha;Kajeguka, Debora;Kulkarni, Manisha A.
主成分分析（PCA）通常用于创建社会经济代理，以调查财富对疾病状况的独立影响。创建这些代理的指导方针和方法得到了很好的描述和验证。人口和健康调查、世界健康调查和生活水平衡量调查是使用主成分分析来创建财富指数的大型数据集的例子，特别是在低收入和中等收入国家，由于缺乏可靠的收入和支出数据，量化财富与疾病的关联是有问题的。然而，这种方法的应用较小的调查数据集，特别是在农村LMIC设置，是不太严格的studyed.In本文中，我们的目的是突出一些这些问题，通过调查的关联派生财富指数使用PCA的媒介传播的疾病感染的风险在坦桑尼亚重点疟疾和关键虫媒病毒（即登革热和基孔肯雅热）。我们证明，与结合所有社会经济变量的指数相比，由社会经济指标子集组成的指数提供了家庭财富的方法缺陷最少的表示。这些结果表明，选择的社会经济指标包括在财富代理可以影响家庭在整体财富等级的相对位置，随后的强度的疾病协会。因此，这可能会影响未来的资源规划活动，并且在使用基于社区层面调查数据的PCA衍生财富指数的调查人员中应该考虑到这一点，以影响农村低收入国家环境中的项目或政策决策。
Principal component analysis (PCA) is frequently adopted for creating socioeconomic proxies in order to investigate the independent effects of wealth on disease status. The guidelines and methods for the creation of these proxies are well described and validated. The Demographic and Health Survey, World Health Survey and the Living Standards Measurement Survey are examples of large data sets that use PCA to create wealth indices particularly in low and middle-income countries (LMIC), where quantifying wealth-disease associations is problematic due to the unavailability of reliable income and expenditure data. However, the application of this method to smaller survey data sets, especially in rural LMIC settings, is less rigorously studied.In this paper, we aimed to highlight some of these issues by investigating the association of derived wealth indices using PCA on risk of vector-borne disease infection in Tanzania focusing on malaria and key arboviruses (ie, dengue and chikungunya). We demonstrated that indices consisting of subsets of socioeconomic indicators provided the least methodologically flawed representations of household wealth compared with an index that combined all socioeconomic variables. These results suggest that the choice of the socioeconomic indicators included in a wealth proxy can influence the relative position of households in the overall wealth hierarchy, and subsequently the strength of disease associations. This can, therefore, influence future resource planning activities and should be considered among investigators who use a PCA-derived wealth index based on community-level survey data to influence programme or policy decisions in rural LMIC settings.