Cargando…

Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors

BACKGROUND: One in four Kenyans aged 18–69 years have raised blood pressure. Despite this high prevalence of hypertension and known association between socioeconomic status and hypertension, there is limited understanding of factors explaining inequalities in raised blood pressure in Kenya. Hence, w...

Descripción completa

Detalles Bibliográficos
Autores principales: Gatimu, Samwel Maina, John, Thomas Wiswa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709247/
https://www.ncbi.nlm.nih.gov/pubmed/33267846
http://dx.doi.org/10.1186/s12939-020-01321-1
_version_ 1783617708033572864
author Gatimu, Samwel Maina
John, Thomas Wiswa
author_facet Gatimu, Samwel Maina
John, Thomas Wiswa
author_sort Gatimu, Samwel Maina
collection PubMed
description BACKGROUND: One in four Kenyans aged 18–69 years have raised blood pressure. Despite this high prevalence of hypertension and known association between socioeconomic status and hypertension, there is limited understanding of factors explaining inequalities in raised blood pressure in Kenya. Hence, we quantified the socioeconomic inequality in hypertension in Kenya and decomposed the determinants contributing to such inequality. METHODS: We used data from the 2015 Kenya STEPwise survey for non-communicable diseases risk factors. We included 4422 respondents aged 18–69 years. We estimated the socioeconomic inequality using the concentration index (C) and decomposed the C using Wagstaff decomposition analysis. RESULTS: The overall concentration index of hypertension in Kenya was − 0.08 (95% CI: − 0.14, − 0.02; p = 0.005), showing socioeconomic inequalities in hypertension disfavouring the poor population. About half (47.1%) of the pro-rich inequalities in hypertension was explained by body mass index while 26.7% by socioeconomic factors (wealth index (10.4%), education (9.3%) and paid employment (7.0%)) and 17.6% by sociodemographic factors (female gender (10.5%), age (4.3%) and marital status (0.6%)). Regional differences explained 7.1% of the estimated inequality with the Central region alone explaining 6.0% of the observed inequality. Our model explained 99.7% of the estimated socioeconomic inequality in hypertension in Kenya with a small non-explained part of the inequality (− 0.0002). CONCLUSION: The present study shows substantial socioeconomic inequalities in hypertension in Kenya, mainly explained by metabolic risk factors (body mass index), individual health behaviours, and socioeconomic factors. Kenya needs gender- and equity-focused interventions to curb the rising burden of hypertension and inequalities in hypertension. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-020-01321-1.
format Online
Article
Text
id pubmed-7709247
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-77092472020-12-02 Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors Gatimu, Samwel Maina John, Thomas Wiswa Int J Equity Health Research BACKGROUND: One in four Kenyans aged 18–69 years have raised blood pressure. Despite this high prevalence of hypertension and known association between socioeconomic status and hypertension, there is limited understanding of factors explaining inequalities in raised blood pressure in Kenya. Hence, we quantified the socioeconomic inequality in hypertension in Kenya and decomposed the determinants contributing to such inequality. METHODS: We used data from the 2015 Kenya STEPwise survey for non-communicable diseases risk factors. We included 4422 respondents aged 18–69 years. We estimated the socioeconomic inequality using the concentration index (C) and decomposed the C using Wagstaff decomposition analysis. RESULTS: The overall concentration index of hypertension in Kenya was − 0.08 (95% CI: − 0.14, − 0.02; p = 0.005), showing socioeconomic inequalities in hypertension disfavouring the poor population. About half (47.1%) of the pro-rich inequalities in hypertension was explained by body mass index while 26.7% by socioeconomic factors (wealth index (10.4%), education (9.3%) and paid employment (7.0%)) and 17.6% by sociodemographic factors (female gender (10.5%), age (4.3%) and marital status (0.6%)). Regional differences explained 7.1% of the estimated inequality with the Central region alone explaining 6.0% of the observed inequality. Our model explained 99.7% of the estimated socioeconomic inequality in hypertension in Kenya with a small non-explained part of the inequality (− 0.0002). CONCLUSION: The present study shows substantial socioeconomic inequalities in hypertension in Kenya, mainly explained by metabolic risk factors (body mass index), individual health behaviours, and socioeconomic factors. Kenya needs gender- and equity-focused interventions to curb the rising burden of hypertension and inequalities in hypertension. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-020-01321-1. BioMed Central 2020-12-02 /pmc/articles/PMC7709247/ /pubmed/33267846 http://dx.doi.org/10.1186/s12939-020-01321-1 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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
Gatimu, Samwel Maina
John, Thomas Wiswa
Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title_full Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title_fullStr Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title_full_unstemmed Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title_short Socioeconomic inequalities in hypertension in Kenya: a decomposition analysis of 2015 Kenya STEPwise survey on non-communicable diseases risk factors
title_sort socioeconomic inequalities in hypertension in kenya: a decomposition analysis of 2015 kenya stepwise survey on non-communicable diseases risk factors
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709247/
https://www.ncbi.nlm.nih.gov/pubmed/33267846
http://dx.doi.org/10.1186/s12939-020-01321-1
work_keys_str_mv AT gatimusamwelmaina socioeconomicinequalitiesinhypertensioninkenyaadecompositionanalysisof2015kenyastepwisesurveyonnoncommunicablediseasesriskfactors
AT johnthomaswiswa socioeconomicinequalitiesinhypertensioninkenyaadecompositionanalysisof2015kenyastepwisesurveyonnoncommunicablediseasesriskfactors