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Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis

INTRODUCTION: Healthcare expenditure, a common input used in health systems efficiency analyses is affected by population age structure. However, while age structure is usually considered to adjust health system outputs, health expenditure and other inputs are seldom adjusted. We propose methods for...

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Autores principales: Santos, João Vasco, Martins, Filipa Santos, Pestana, Joana, Souza, Júlio, Freitas, Alberto, Cylus, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926817/
https://www.ncbi.nlm.nih.gov/pubmed/36781709
http://dx.doi.org/10.1186/s13561-023-00421-2
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author Santos, João Vasco
Martins, Filipa Santos
Pestana, Joana
Souza, Júlio
Freitas, Alberto
Cylus, Jonathan
author_facet Santos, João Vasco
Martins, Filipa Santos
Pestana, Joana
Souza, Júlio
Freitas, Alberto
Cylus, Jonathan
author_sort Santos, João Vasco
collection PubMed
description INTRODUCTION: Healthcare expenditure, a common input used in health systems efficiency analyses is affected by population age structure. However, while age structure is usually considered to adjust health system outputs, health expenditure and other inputs are seldom adjusted. We propose methods for adjusting Health Expenditure per Capita (HEpC) for population age structure on health system efficiency analyses and assess the goodness-of-fit, correlation, reliability and disagreement of different approaches. METHODS: We performed a worldwide (188 countries) cross-sectional study of efficiency in 2015, using a stochastic frontier analysis. As single outputs, healthy life expectancy (HALE) at birth and at 65 years-old were considered in different models. We developed five models using as inputs: (1) HEpC (unadjusted); (2) age-adjusted HEpC; (3) HEpC and the proportion of 0–14, 15–64 and 65 + years-old; (4) HEpC and 5-year age-groups; and (5) HEpC ageing index. Akaike and Bayesian information criteria, Spearman’s rank correlation, intraclass correlation coefficient and information-based measure of disagreement were computed. RESULTS: Models 1 and 2 showed the highest correlation (0.981 and 0.986 for HALE at birth and HALE at 65 years-old, respectively) and reliability (0.986 and 0.988) and the lowest disagreement (0.011 and 0.014). Model 2, with age-adjusted HEpC, presented the lowest information criteria values. CONCLUSIONS: Despite different models showing good correlation and reliability and low disagreement, there was important variability when age structure is considered that cannot be disregarded. The age-adjusted HE model provided the best goodness-of-fit and was the closest option to the current standard. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13561-023-00421-2.
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spelling pubmed-99268172023-02-15 Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis Santos, João Vasco Martins, Filipa Santos Pestana, Joana Souza, Júlio Freitas, Alberto Cylus, Jonathan Health Econ Rev Research INTRODUCTION: Healthcare expenditure, a common input used in health systems efficiency analyses is affected by population age structure. However, while age structure is usually considered to adjust health system outputs, health expenditure and other inputs are seldom adjusted. We propose methods for adjusting Health Expenditure per Capita (HEpC) for population age structure on health system efficiency analyses and assess the goodness-of-fit, correlation, reliability and disagreement of different approaches. METHODS: We performed a worldwide (188 countries) cross-sectional study of efficiency in 2015, using a stochastic frontier analysis. As single outputs, healthy life expectancy (HALE) at birth and at 65 years-old were considered in different models. We developed five models using as inputs: (1) HEpC (unadjusted); (2) age-adjusted HEpC; (3) HEpC and the proportion of 0–14, 15–64 and 65 + years-old; (4) HEpC and 5-year age-groups; and (5) HEpC ageing index. Akaike and Bayesian information criteria, Spearman’s rank correlation, intraclass correlation coefficient and information-based measure of disagreement were computed. RESULTS: Models 1 and 2 showed the highest correlation (0.981 and 0.986 for HALE at birth and HALE at 65 years-old, respectively) and reliability (0.986 and 0.988) and the lowest disagreement (0.011 and 0.014). Model 2, with age-adjusted HEpC, presented the lowest information criteria values. CONCLUSIONS: Despite different models showing good correlation and reliability and low disagreement, there was important variability when age structure is considered that cannot be disregarded. The age-adjusted HE model provided the best goodness-of-fit and was the closest option to the current standard. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13561-023-00421-2. Springer Berlin Heidelberg 2023-02-13 /pmc/articles/PMC9926817/ /pubmed/36781709 http://dx.doi.org/10.1186/s13561-023-00421-2 Text en © The Author(s) 2023 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
Santos, João Vasco
Martins, Filipa Santos
Pestana, Joana
Souza, Júlio
Freitas, Alberto
Cylus, Jonathan
Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title_full Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title_fullStr Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title_full_unstemmed Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title_short Should we adjust health expenditure for age structure on health systems efficiency? A worldwide analysis
title_sort should we adjust health expenditure for age structure on health systems efficiency? a worldwide analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926817/
https://www.ncbi.nlm.nih.gov/pubmed/36781709
http://dx.doi.org/10.1186/s13561-023-00421-2
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