ArticleApplied health economics and health policy2022
Estimating the Health Effects of Expansions in Health Expenditure in Indonesia: A Dynamic Panel Data Approach.
Article in Applied health economics and health policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Does increasing overall health expenditure reduce inequality in under 5 mortality rates between provinces in Indonesia?Health policy and planning · 2026Article
- Machine learning outperforms serum creatinine for early risk stratification of hepatorenal syndrome: a prospective single-center validation.Hepatology international · 2026Article
- Analysis of Relevant Factors Influencing the Development of Pharmacist Human Resources in China: Based on the Time Series Vector Auto-Regression Model.Risk management and healthcare policy · 2025Article
- Health care expenses impact on the disability-adjusted life years in non-communicable diseases in the European Union.Frontiers in public health · 2024Article
- Economic evaluation of self-help group interventions for health in LMICs: a scoping review.Health policy and planning · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe marginal productivity of a country's healthcare system refers to the health gains produced per unit change in the level of spending. In budget-constrained settings, this metric reflects the opportunity cost, in terms of health gains forgone, of committing additional or existing resources to alternative uses within the healthcare system. It can therefore assist in evidence-based decisions on whether different interventions represent good value for money.
objectiveThe aim of this paper was to estimate the marginal productivity of the Indonesian healthcare system using subnational data, and to use this to inform health opportunity costs in the country.
methodsWe define a dynamic health production function to model the stream of effects of current and prior public health spending decisions on population under-five mortality. To estimate the model, we use data from the 33 Indonesian provinces for the 2004-2012 period. The estimated elasticity is then translated into gains in terms of cost per DALY (disability-adjusted life-year) averted. We use dynamic panel data methods to address potential endogeneity issues in the model.
resultsOur base-case estimates suggest that a 1% expansion in the level of health spending reduces under-five mortality by 0.38% (95% CI 0.00-0.76), which translates into a cost of averting one DALY of $235 (2019 US$).
conclusionWith Indonesia aiming for universal health coverage, our results support these efforts by highlighting the associated benefits resulting from increases in public health expenditure and have the potential to inform the decision-making process about a suitable locally relevant cost-effectiveness threshold.
Indexed as
Identifiers
35997895What 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.