ArticleInternational journal for equity in health2022
Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya.
Article in International journal for equity in health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Socioeconomic disparities in the postnatal growth of preterm infants: a systematic review.Pediatric research · 2025Pooled it
- Association between ambient temperature exposure and pregnancy outcomes in participants undergoing IVF: a retrospective cohort study.Journal of assisted reproduction and genetics · 2026Article
- Dynamic Associations between Ambient PMEnvironment & health (Washington, D.C.) · 2026Article
- Dynamic associations between ambient air pollution and transitions of biochemical pregnancy, clinical pregnancy, and live birth among women treated with IVF/ICSI.Journal of assisted reproduction and genetics · 2026Article
- Sociodemographic determinants and prevalence of metabolic risk factors and the metabolic syndrome among women aged 20-49 in kirinyaga county, Kenya.Frontiers in epidemiology · 2026Article
- Intent to Receive Human Papillomavirus Vaccination among School-going Pre-teens and Adolescent Girls.International journal of MCH and AIDS · 2025Article
- Catastrophic famine in Gaza: Unprecedented levels of hunger post-October 7th. A real population-based study from the Gaza Strip.PloS one · 2025Article
- Socioeconomic inequalities in food purchasing practices and expenditure patterns: Results from a cross-sectional household survey in western Kenya.Frontiers in public health · 2023Article
- Navigating the local foodscape: qualitative investigation of food retail and dietary preferences in Kisumu and Homa Bay Counties, western Kenya.BMC public health · 2022Article
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7 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
introductionLow household socioeconomic status is associated with unhealthy behaviours including poor diet and adverse health outcomes. Different methods leading to variations in SES classification has the potential to generate spurious research findings or misinform policy. In low and middle-income countries, there are additional complexities in defining household SES, a need for fieldwork to be conducted efficiently, and a dearth of information on how classification could impact estimation of disease risk.
methodsUsing cross-sectional data from 200 households in Kisumu County, Western Kenya, we compared three approaches of classifying households into low, middle, or high SES: fieldworkers (FWs), Community Health Volunteers (CHVs), and a Multiple Correspondence Analysis econometric model (MCA). We estimated the sensitivity, specificity, inter-rater reliability and misclassification of the three methods using MCA as a comparator. We applied an unadjusted generalized linear model to determine prevalence ratios to assess the association of household SES status with a self-reported diagnosis of diabetes or hypertension for one household member.
resultsCompared with MCA, FWs successfully classified 21.7% (95%CI = 14.4%-31.4%) of low SES households, 32.8% (95%CI = 23.2-44.3) of middle SES households, and no high SES households. CHVs successfully classified 22.5% (95%CI = 14.5%-33.1%) of low SES households, 32.8% (95%CI = 23.2%-44.3%) of middle SES households, and no high SES households. The level of agreement in SES classification was similar between FWs and CHVs but poor compared to MCA, particularly for high SES. None of the three methods differed in estimating the risk of hypertension or diabetes.
conclusionsFW and CHV assessments are community-driven methods for SES classification. Compared to MCA, these approaches appeared biased towards low or middle SES households and not sensitive to high household SES. The three methods did not differ in risk estimation for diabetes and hypertension. A mix of approaches and further evaluation to refine SES classification methodology is recommended.
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