ArticlePakistan journal of medical sciences
Determinants of health expenditure in OECD countries: A decision tree model.
Article in Pakistan journal of medical sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Economic and demographic influences on health expenditures: robust approaches for income and aging effects.Health economics review · 2025Article
- Examining the interconnections among income, food prices, food insecurity, and health expenditure: a multicausality approach.BMC public health · 2025Article
- Article
- Determinants of healthcare expenditures: evidence from Türkiye's healthcare system.Frontiers in public health · 2025Article
- Identifying Influential Variables on Health Expenditure of the Organisation for Economic Co-Operation and Development (OECD) Countries.Iranian journal of public health · 2024Article
- The Longitudinal Association between Multiple Frailty Criteria and Depressive Symptomatology Five Years Later in Older Adults: A Decision Tree Modelling Approach.Journal of clinical medicine · 2024Article
- Determinants of public health expenditure in the EU.PloS one · 2024Article
- The application of artificial intelligence in health financing: a scoping review.Cost effectiveness and resource allocation : C/E · 2023Article
- The Relationship Between Health Expenditure, COJournal of the knowledge economy · 2023Article
- Impact of Environmental Quality on Healthcare Expenditures in Developing Countries: A Panel Data Approach.Healthcare (Basel, Switzerland) · 2022Article
- The relationship between health expenditure indicators and economic growth in OECD countries: A Driscoll-Kraay approach.Frontiers in public health · 2022Article
- Spatial and Temporal Differences in the Health Expenditure Efficiency of China: Reflections Based on the Background of the COVID-19 Pandemic.Frontiers in public health · 2022Article
- Health Policies Based on Patient Satisfaction: A Bibliometric Study.Healthcare (Basel, Switzerland) · 2021Review
- Exploring the Role of Health Expenditure and Maternal Mortality in South Asian Countries: An Approach towards Shaping Better Health Policy.International journal of environmental research and public health · 2021Article
- Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease.International journal of environmental research and public health · 2021Article
- Self-Declared Roma Ethnicity and Health Insurance Expenditures: A Nationwide Cross-Sectional Investigation at the General Medical Practice Level in Hungary.International journal of environmental research and public health · 2020Article
- Future Development Through the Past: Pathology of the Financing Dimension of Iran's Health Transformation Plan.ClinicoEconomics and outcomes research : CEOR · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to identify the major variables in the estimation of health expenditure in OECD member countries with the decision tree method and to categorize the member countries by health expenditure.
methodsThe study population comprised the 2014 data of the 35 OECD countries. In the study, health expenditure as a share of gross domestic product was the dependent variable while gross domestic product per capita, percentage of total population covered by public and private insurance, out-of-pocket health expenditure as percentage of total expenditure on health, age dependency ratio, life expectancy at birth, number of hospitals per million population, number of physicians per 1000 population/head counts, pharmaceutical sales and perceived health status were designated as independent variables. The decision tree model was constructed with the CART algorithm using the Orange data mining software package.
resultsIn the study, GDP per capita, life expectancy at birth, age dependency ratio, number of hospitals and percentage of the population with a bad perceived health status were identified as the major variables in the estimation of health expenditure. OECD countries were categorized in 6 groups according to the decision tree model. According to the CART algorithm used in the model, the classification accuracy rate and the precision of estimation were computed as 80.56% and 81.25%, respectively.
conclusionThe study results revealed that the most important determinant for estimating the share of GDP allocated to health expenditure was GDP per capita. Future studies should be conducted with the inclusion of different variables in the model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.