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Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases
BACKGROUND: To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation whi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934839/ https://www.ncbi.nlm.nih.gov/pubmed/29724215 http://dx.doi.org/10.1186/s12913-018-3128-4 |
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author | de Vries, Eline F. Heijink, Richard Struijs, Jeroen N. Baan, Caroline A. |
author_facet | de Vries, Eline F. Heijink, Richard Struijs, Jeroen N. Baan, Caroline A. |
author_sort | de Vries, Eline F. |
collection | PubMed |
description | BACKGROUND: To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. METHODS: We used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models. RESULTS: Regions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies. CONCLUSIONS: The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3128-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5934839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59348392018-05-11 Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases de Vries, Eline F. Heijink, Richard Struijs, Jeroen N. Baan, Caroline A. BMC Health Serv Res Research Article BACKGROUND: To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. METHODS: We used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models. RESULTS: Regions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies. CONCLUSIONS: The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3128-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-03 /pmc/articles/PMC5934839/ /pubmed/29724215 http://dx.doi.org/10.1186/s12913-018-3128-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Article de Vries, Eline F. Heijink, Richard Struijs, Jeroen N. Baan, Caroline A. Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title | Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title_full | Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title_fullStr | Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title_full_unstemmed | Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title_short | Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
title_sort | unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934839/ https://www.ncbi.nlm.nih.gov/pubmed/29724215 http://dx.doi.org/10.1186/s12913-018-3128-4 |
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