ArticleThe Lancet regional health. Southeast Asia2024
Impact of health benefit package policy interventions on service utilisation under government-funded health insurance in Punjab, India: analysis of
Article in The Lancet regional health. Southeast Asia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Political determinants of melanoma: Global inequities in skin cancer care.JAAD international · 2026Article
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
- Financial Viability of Private Hospitals Operating Under India's National Health Insurance Scheme Ayushman Bharat Pradhan Mantri-Jan Arogya Yojana (AB PM-JAY).Applied health economics and health policy · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The design of health benefits package (HBP), and its associated payment and pricing system, is central to the performance of government-funded health insurance programmes. We evaluated the impact of revision in HBP within India's Methods: We analysed the data on 1.35 million hospitalisation claims submitted by all the 886 (222 government and 664 private) empanelled hospitals in state of Punjab, from August 2019 to December 2022, to assess the change in utilisation from HBP 1.0 to HBP 2.0. The packages were stratified based on the nature of revision introduced in HBP 2.0, i.e., change in nomenclature, construct, price, or a combination of these. Data from National Health System Cost Database on cost of each of the packages was used to determine the cost-price differential for each package during HBP 1.0 and 2.0 respectively. A dose-response relationship was also evaluated, based on the multiplicity of revision type undertaken, or based on extent of price correction done. Change in the number of monthly claims, and the number of monthly claims per package was computed for each package category using an appropriate seasonal autoregressive integrated moving average (SARIMA) time series model. Findings: Overall, we found that the HBP revision led to a positive impact on utilisation of services. While changes in HBP nomenclature and construct had a positive effect, incorporating price corrections further accentuated the impact. The pricing reforms highly impacted those packages which were originally significantly under-priced. However, we did not find statistically significant dose-response relationship based on extent of price correction. Thirdly, the overall impact of HBP revision was similar in public and private hospitals. Interpretation: Our paper demonstrates the significant positive impact of PM-JAY HBP revisions on utilisation. HBP revisions need to be undertaken with the anticipation of its long-term intended effects. Funding: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).
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.