Cargando…
Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya
INTRODUCTION: Low 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-inco...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994881/ https://www.ncbi.nlm.nih.gov/pubmed/35397583 http://dx.doi.org/10.1186/s12939-022-01652-1 |
_version_ | 1784684196702191616 |
---|---|
author | Were, Vincent Foley, Louise Turner-Moss, Eleanor Mogo, Ebele Wadende, Pamela Musuva, Rosemary Obonyo, Charles |
author_facet | Were, Vincent Foley, Louise Turner-Moss, Eleanor Mogo, Ebele Wadende, Pamela Musuva, Rosemary Obonyo, Charles |
author_sort | Were, Vincent |
collection | PubMed |
description | INTRODUCTION: Low 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. METHODS: Using 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. RESULTS: Compared 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. CONCLUSIONS: FW 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-022-01652-1. |
format | Online Article Text |
id | pubmed-8994881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89948812022-04-11 Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya Were, Vincent Foley, Louise Turner-Moss, Eleanor Mogo, Ebele Wadende, Pamela Musuva, Rosemary Obonyo, Charles Int J Equity Health Research INTRODUCTION: Low 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. METHODS: Using 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. RESULTS: Compared 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. CONCLUSIONS: FW 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-022-01652-1. BioMed Central 2022-04-09 /pmc/articles/PMC8994881/ /pubmed/35397583 http://dx.doi.org/10.1186/s12939-022-01652-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Were, Vincent Foley, Louise Turner-Moss, Eleanor Mogo, Ebele Wadende, Pamela Musuva, Rosemary Obonyo, Charles Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title | Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title_full | Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title_fullStr | Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title_full_unstemmed | Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title_short | Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya |
title_sort | comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, kisumu, western kenya |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994881/ https://www.ncbi.nlm.nih.gov/pubmed/35397583 http://dx.doi.org/10.1186/s12939-022-01652-1 |
work_keys_str_mv | AT werevincent comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT foleylouise comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT turnermosseleanor comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT mogoebele comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT wadendepamela comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT musuvarosemary comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya AT obonyocharles comparisonofhouseholdsocioeconomicstatusclassificationmethodsandeffectsonriskestimationlessonsfromanaturalexperimentalstudykisumuwesternkenya |