Evidence map›Paper›PMID 38437234›Full record

ArticlePloS one2024

Delving into public-expenditure elasticity: Evidence from a National Health Service acute-care hospital network.

Micaela Comendeiro-Maaløe, Manuel Ridao-Lopez, Enrique Bernal-Delgado, Andreu Sansó-Rosselló

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Micaela Comendeiro-MaaløeData Science for Health Services and Policy Research, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain.ORCID 0000-0002-1479-8418
Manuel Ridao-LopezNetwork for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Instituto de Salud Carlos III, Madrid, Spain.
Enrique Bernal-DelgadoData Science for Health Services and Policy Research, Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain.ORCID 0000-0002-0961-3298
Andreu Sansó-RossellóDepartment of Applied Economics, University of the Balearic Islands, Palma, Spain.ORCID 0000-0002-2845-8932

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe sustainability of public hospital financing in Spain is a recurring issue, given its representativeness in annual public healthcare budgets which must adapt to the macroeconomic challenges that influence the evolution of spending. Knowing whether the responsiveness of hospital expenditure to its determinants (need, utilisation, and quasi-prices) varies according to the type of hospital could help better design strategies aimed at optimising performance.

methodsUsing SARIMAX models, we dynamically assess unique nationwide monthly activity data over a 14-year period from 274 acute-care hospitals in the Spanish National Health Service network, clustering these providers according to the average severity of the episodes treated.

resultsAll groups showed seasonal patterns and increasing trends in the evolution of expenditure. The fourth quartile of hospitals, treating the most severe episodes and accounting for more than 50% of expenditure, is the most sensitive to quasi-price factors, particularly the number of beds per hospital. Meanwhile, the first quartile of hospitals, which treat the least severe episodes and account for 10% of expenditure, is most sensitive to quantity factors, for which expenditure showed an elasticity above one, while factors of production were not affected.

conclusionsBelonging to one or another cluster of hospitals means that the determinants of expenditure have a different impact and intensity. The system should focus on these differences in order to optimally modulate expenditure not only according to the needs of the population, but also according to the macroeconomic situation, while leaving hospitals room for manoeuvre in case of unforeseen events. The findings suggest strengthening a network of smaller hospitals (Group 1)-closer to their reference population, focused on managing and responding to chronicity and stabilising acute events-prior to transfer to tertiary hospitals (Group 4)-larger but appropriately sized, specialising in solving acute and complex health problems-when needed.

Indexed as

Health ExpendituresState MedicineElasticityHospitals, PublicTertiary Care Centers

Identifiers

PMID38437234
PMCPMC10911587

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.