Cargando…

Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2

Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific...

Descripción completa

Detalles Bibliográficos
Autores principales: Chidumwa, Glory, Maposa, Innocent, Kowal, Paul, Micklesfield, Lisa K., Ware, Lisa J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796507/
https://www.ncbi.nlm.nih.gov/pubmed/33466566
http://dx.doi.org/10.3390/ijerph18010359
_version_ 1783634698746986496
author Chidumwa, Glory
Maposa, Innocent
Kowal, Paul
Micklesfield, Lisa K.
Ware, Lisa J.
author_facet Chidumwa, Glory
Maposa, Innocent
Kowal, Paul
Micklesfield, Lisa K.
Ware, Lisa J.
author_sort Chidumwa, Glory
collection PubMed
description Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific geographical variation of hypertension and diabetes. The current analysis used the Study on Global Ageing and Adult Health (SAGE) South Africa Wave 2 (2014/15) data collected from 2761 participants. Of the 2761 adults (median age = 56 years), 641 (23.2%) had high blood pressure on measurement and 338 (12.3%) reported being diagnosed with diabetes. The shared component has distinct spatial patterns with higher values of odds in the eastern districts of Kwa-Zulu Natal and central Gauteng province. The shared component may represent unmeasured health behavior characteristics or the social determinants of health in our population. Our study further showed how a shared component (latent and unmeasured health behavior characteristics or the social determinants of health) is distributed across South Africa among the older adult population. Further research using similar shared joint models may focus on extending these models for multiple diseases with ecological factors and also incorporating sampling weights in the spatial analyses.
format Online
Article
Text
id pubmed-7796507
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77965072021-01-10 Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2 Chidumwa, Glory Maposa, Innocent Kowal, Paul Micklesfield, Lisa K. Ware, Lisa J. Int J Environ Res Public Health Article Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific geographical variation of hypertension and diabetes. The current analysis used the Study on Global Ageing and Adult Health (SAGE) South Africa Wave 2 (2014/15) data collected from 2761 participants. Of the 2761 adults (median age = 56 years), 641 (23.2%) had high blood pressure on measurement and 338 (12.3%) reported being diagnosed with diabetes. The shared component has distinct spatial patterns with higher values of odds in the eastern districts of Kwa-Zulu Natal and central Gauteng province. The shared component may represent unmeasured health behavior characteristics or the social determinants of health in our population. Our study further showed how a shared component (latent and unmeasured health behavior characteristics or the social determinants of health) is distributed across South Africa among the older adult population. Further research using similar shared joint models may focus on extending these models for multiple diseases with ecological factors and also incorporating sampling weights in the spatial analyses. MDPI 2021-01-05 2021-01 /pmc/articles/PMC7796507/ /pubmed/33466566 http://dx.doi.org/10.3390/ijerph18010359 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chidumwa, Glory
Maposa, Innocent
Kowal, Paul
Micklesfield, Lisa K.
Ware, Lisa J.
Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title_full Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title_fullStr Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title_full_unstemmed Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title_short Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2
title_sort bivariate joint spatial modeling to identify shared risk patterns of hypertension and diabetes in south africa: evidence from who sage south africa wave 2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796507/
https://www.ncbi.nlm.nih.gov/pubmed/33466566
http://dx.doi.org/10.3390/ijerph18010359
work_keys_str_mv AT chidumwaglory bivariatejointspatialmodelingtoidentifysharedriskpatternsofhypertensionanddiabetesinsouthafricaevidencefromwhosagesouthafricawave2
AT maposainnocent bivariatejointspatialmodelingtoidentifysharedriskpatternsofhypertensionanddiabetesinsouthafricaevidencefromwhosagesouthafricawave2
AT kowalpaul bivariatejointspatialmodelingtoidentifysharedriskpatternsofhypertensionanddiabetesinsouthafricaevidencefromwhosagesouthafricawave2
AT micklesfieldlisak bivariatejointspatialmodelingtoidentifysharedriskpatternsofhypertensionanddiabetesinsouthafricaevidencefromwhosagesouthafricawave2
AT warelisaj bivariatejointspatialmodelingtoidentifysharedriskpatternsofhypertensionanddiabetesinsouthafricaevidencefromwhosagesouthafricawave2