Tusting Lucy S, Rek John C, Arinaitwe Emmanuel, Staedke Sarah G, Kamya Moses R, Bottomley Christian, Johnston Deborah, Lines Jo, Dorsey Grant, Lindsay Steve W
Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom; Infectious Disease Research Collaboration, Mulago Hospital Complex, Kampala, Uganda; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom; School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda; Medical Research Council Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Economics, School of Oriental and African Studies, London, United Kingdom; Department of Medicine, University of California at San Francisco, San Francisco, California; School of Biological and Biomedical Sciences, Durham University, Durham, United Kingdom
Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom; Infectious Disease Research Collaboration, Mulago Hospital Complex, Kampala, Uganda; Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom; School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda; Medical Research Council Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom; Department of Economics, School of Oriental and African Studies, London, United Kingdom; Department of Medicine, University of California at San Francisco, San Francisco, California; School of Biological and Biomedical Sciences, Durham University, Durham, United Kingdom.
Am J Trop Med Hyg. 2016 Mar;94(3):650-8. doi: 10.4269/ajtmh.15-0554. Epub 2016 Jan 25.
Socioeconomic position (SEP) is an important risk factor for malaria, but there is no consensus on how to measure SEP in malaria studies. We evaluated the relative strength of four indicators of SEP in predicting malaria risk in Nagongera, Uganda. A total of 318 children resident in 100 households were followed for 36 months to measure parasite prevalence routinely every 3 months and malaria incidence by passive case detection. Household SEP was determined using: 1) two wealth indices, 2) income, 3) occupation, and 4) education. Wealth Index I (reference) included only asset ownership variables. Wealth Index II additionally included food security and house construction variables, which may directly affect malaria. In multivariate analysis, only Wealth Index II and income were associated with the human biting rate, only Wealth Indices I and II were associated with parasite prevalence, and only caregiver's education was associated with malaria incidence. This is the first evaluation of metrics beyond wealth and consumption indices for measuring the association between SEP and malaria. The wealth index still predicted malaria risk after excluding variables directly associated with malaria, but the strength of association was lower. In this setting, wealth indices, income, and education were stronger predictors of socioeconomic differences in malaria risk than occupation.
社会经济地位（SEP）是疟疾的一个重要风险因素，但在疟疾研究中如何衡量SEP尚无共识。我们评估了乌干达纳贡埃拉地区SEP的四个指标在预测疟疾风险方面的相对强度。对居住在100户家庭中的318名儿童进行了为期36个月的跟踪，每3个月定期测量寄生虫感染率，并通过被动病例检测确定疟疾发病率。家庭SEP通过以下方式确定：1）两个财富指数，2）收入，3）职业，4）教育程度。财富指数I（参照）仅包括资产所有权变量。财富指数II还包括粮食安全和房屋建设变量，这些变量可能直接影响疟疾。在多变量分析中，只有财富指数II和收入与按蚊叮咬率相关，只有财富指数I和II与寄生虫感染率相关，只有照顾者的教育程度与疟疾发病率相关。这是首次对除财富和消费指数之外的指标进行评估，以衡量SEP与疟疾之间的关联。在排除与疟疾直接相关的变量后，财富指数仍能预测疟疾风险，但关联强度较低。在这种情况下，财富指数、收入和教育程度比职业更能有力地预测疟疾风险的社会经济差异。
