Cargando…

Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa

BACKGROUND: Comorbidities are strong predictors of current and future healthcare needs and costs; however, comorbidities are not evenly distributed geographically. A growing need has emerged for comorbidity surveillance that can inform decision-making. Comorbidity-derived risk scores are increasingl...

Descripción completa

Detalles Bibliográficos
Autores principales: Mannie, Cristina, Kharrazi, Hadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667849/
https://www.ncbi.nlm.nih.gov/pubmed/33198704
http://dx.doi.org/10.1186/s12889-020-09771-6
_version_ 1783610392366284800
author Mannie, Cristina
Kharrazi, Hadi
author_facet Mannie, Cristina
Kharrazi, Hadi
author_sort Mannie, Cristina
collection PubMed
description BACKGROUND: Comorbidities are strong predictors of current and future healthcare needs and costs; however, comorbidities are not evenly distributed geographically. A growing need has emerged for comorbidity surveillance that can inform decision-making. Comorbidity-derived risk scores are increasingly being used as valuable measures of individual health to describe and explain disease burden in populations. METHODS: This study assessed the geographical distribution of comorbidity and its associated financial implications among commercially insured individuals in South Africa (SA). A retrospective, cross-sectional analysis was performed comparing the geographical distribution of comorbidities for 2.6 million commercially insured individuals over 2016–2017, stratified by geographical districts in SA. We applied the Johns Hopkins ACG® System across the insurance claims data of a large health plan administrator in SA to measure comorbidity as a risk score for each individual. We aggregated individual risk scores to determine the average risk score per district, also known as the comorbidity index (CMI), to describe the overall disease burden of each district. RESULTS: We observed consistently high CMI scores in districts of the Free State and KwaZulu-Natal provinces for all population groups before and after age adjustment. Some areas exhibited almost 30% higher healthcare utilization after age adjustment. Districts in the Northern Cape and Limpopo provinces had the lowest CMI scores with 40% lower than expected healthcare utilization in some areas after age adjustment. CONCLUSIONS: Our results show underlying disparities in CMI at national, provincial, and district levels. Use of geo-level CMI scores, along with other social data affecting health outcomes, can enable public health departments to improve the management of disease burdens locally and nationally. Our results could also improve the identification of underserved individuals, hence bridging the gap between public health and population health management efforts. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12889-020-09771-6.
format Online
Article
Text
id pubmed-7667849
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-76678492020-11-17 Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa Mannie, Cristina Kharrazi, Hadi BMC Public Health Research Article BACKGROUND: Comorbidities are strong predictors of current and future healthcare needs and costs; however, comorbidities are not evenly distributed geographically. A growing need has emerged for comorbidity surveillance that can inform decision-making. Comorbidity-derived risk scores are increasingly being used as valuable measures of individual health to describe and explain disease burden in populations. METHODS: This study assessed the geographical distribution of comorbidity and its associated financial implications among commercially insured individuals in South Africa (SA). A retrospective, cross-sectional analysis was performed comparing the geographical distribution of comorbidities for 2.6 million commercially insured individuals over 2016–2017, stratified by geographical districts in SA. We applied the Johns Hopkins ACG® System across the insurance claims data of a large health plan administrator in SA to measure comorbidity as a risk score for each individual. We aggregated individual risk scores to determine the average risk score per district, also known as the comorbidity index (CMI), to describe the overall disease burden of each district. RESULTS: We observed consistently high CMI scores in districts of the Free State and KwaZulu-Natal provinces for all population groups before and after age adjustment. Some areas exhibited almost 30% higher healthcare utilization after age adjustment. Districts in the Northern Cape and Limpopo provinces had the lowest CMI scores with 40% lower than expected healthcare utilization in some areas after age adjustment. CONCLUSIONS: Our results show underlying disparities in CMI at national, provincial, and district levels. Use of geo-level CMI scores, along with other social data affecting health outcomes, can enable public health departments to improve the management of disease burdens locally and nationally. Our results could also improve the identification of underserved individuals, hence bridging the gap between public health and population health management efforts. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12889-020-09771-6. BioMed Central 2020-11-16 /pmc/articles/PMC7667849/ /pubmed/33198704 http://dx.doi.org/10.1186/s12889-020-09771-6 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 Article
Mannie, Cristina
Kharrazi, Hadi
Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title_full Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title_fullStr Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title_full_unstemmed Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title_short Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa
title_sort assessing the geographical distribution of comorbidity among commercially insured individuals in south africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667849/
https://www.ncbi.nlm.nih.gov/pubmed/33198704
http://dx.doi.org/10.1186/s12889-020-09771-6
work_keys_str_mv AT manniecristina assessingthegeographicaldistributionofcomorbidityamongcommerciallyinsuredindividualsinsouthafrica
AT kharrazihadi assessingthegeographicaldistributionofcomorbidityamongcommerciallyinsuredindividualsinsouthafrica