Identifying Influential Variables on Health Expenditure of the Organisation for Economic Co-Operation and Development (OECD) Countries.

CHAID decision tree Health expenditure Regression

Journal

Iranian journal of public health
ISSN: 2251-6093
Titre abrégé: Iran J Public Health
Pays: Iran
ID NLM: 7505531

Informations de publication

Date de publication:
Aug 2024
Historique:
received: 10 11 2023
accepted: 16 02 2024
medline: 17 10 2024
pubmed: 17 10 2024
entrez: 17 10 2024
Statut: ppublish

Résumé

Health expenditures of countries have an increasing trend in general and identifying variables affecting health expenditure is an important step toward budget planning for financial sustainability. This study aimed to examine the health expenditure of the Organisation for Economic Co-operation and Development (OECD) countries and identify influential variables. The data for the years 2000-2018 of OECD countries' current health expenditure (% of GDP) and economic, demographic, and health variables, considered to affect the health expenditure, to include in the analysis were extracted using the World Bank database (World Bank 2021). Data analys using Chi-Squared Automatic Interaction Detection (CHAID) decision tree technique. Fifteen variables in economic, demographic, and health categories are selected to build the CHAID decision tree. As a result of CHAID analysis, five variables are identified as influential on current health expenditure, which are gross domestic product per capita, life expectancy at birth, death rate, out-of-pocket expenditure, and fertility rate. Thirty-seven OECD countries are classified into eleven groups by the decision rules in terms of the current health expenditure. The high value of the correlation coefficient between the predicted values and the actual values of health expenditure of countries indicates good prediction performance. Moreover, the regression models built using the identified influential variables as explanatory variables give good forecast accuracy. As an effective tool, the CHAID decision tree technique provides a rule-based model in the form of a tree with nodes and branches, illustrating the splitting process graphically with identified variables and their cut-off points for classification and prediction.

Sections du résumé

Background UNASSIGNED
Health expenditures of countries have an increasing trend in general and identifying variables affecting health expenditure is an important step toward budget planning for financial sustainability. This study aimed to examine the health expenditure of the Organisation for Economic Co-operation and Development (OECD) countries and identify influential variables.
Methods UNASSIGNED
The data for the years 2000-2018 of OECD countries' current health expenditure (% of GDP) and economic, demographic, and health variables, considered to affect the health expenditure, to include in the analysis were extracted using the World Bank database (World Bank 2021). Data analys using Chi-Squared Automatic Interaction Detection (CHAID) decision tree technique. Fifteen variables in economic, demographic, and health categories are selected to build the CHAID decision tree.
Results UNASSIGNED
As a result of CHAID analysis, five variables are identified as influential on current health expenditure, which are gross domestic product per capita, life expectancy at birth, death rate, out-of-pocket expenditure, and fertility rate. Thirty-seven OECD countries are classified into eleven groups by the decision rules in terms of the current health expenditure. The high value of the correlation coefficient between the predicted values and the actual values of health expenditure of countries indicates good prediction performance. Moreover, the regression models built using the identified influential variables as explanatory variables give good forecast accuracy.
Conclusion UNASSIGNED
As an effective tool, the CHAID decision tree technique provides a rule-based model in the form of a tree with nodes and branches, illustrating the splitting process graphically with identified variables and their cut-off points for classification and prediction.

Identifiants

pubmed: 39415867
doi: 10.18502/ijph.v53i8.16290
pii: IJPH-53-1847
pmc: PMC11475169
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1847-1857

Informations de copyright

Copyright© 2024 Issever et al. Published by Tehran University of Medical Sciences.

Auteurs

Tugce Issever (T)

Department of Industrial Engineering, Institute of Pure and Applied Sciences, Marmara University, Goztepe Campus, Istanbul, Turkey.

Bahar Sennaroglu (B)

Department of Industrial Engineering, Faculty of Engineering, Marmara University, Maltepe Campus, Istanbul, Turkey.

Cem Cagri Donmez (CC)

Department of Industrial Engineering, Faculty of Engineering, Marmara University, Maltepe Campus, Istanbul, Turkey.

Adnan Corum (A)

Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Bahcesehir University, Besiktas South Campus, Istanbul, Turkey.

Classifications MeSH