Evidence map›Paper›PMID 37543994›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2024

Using machine learning to estimate health spillover effects.

Bruno Wichmann, Roberta Moreira Wichmann

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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 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
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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

2 authors.

Bruno WichmannDepartment of Resource Economics and Environmental Sociology, College of Natural and Applied Sciences, University of Alberta, 503 General Services Building, Edmonton, T6G-2H1, AB, Canada. bwichmann@ualberta.ca.ORCID http://orcid.org/0000-0003-1104-0035
Roberta Moreira WichmannWorld Bank, SCES Trecho 03, Lote 05, Ed. Polo 8, S/N, Brasília, DF, CEP 70200-003, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effects during the COVID-19 pandemic. The pandemic forced hospitals to develop new protocols to offer intensive care to both COVID and non-COVID patients. Our results show that the need to care for COVID patients affects health outcomes of non-COVID patients. Controlling for a number of confounders, we find that mortality rates and length of stay of non-COVID ICU patients increase when hospitals simultaneously offer intensive care to both types of patients. Policy simulations suggest that an increase in the number of ICU beds can counter morbidity spillover, but it is unlikely to be a feasible approach to counter mortality spillover.

Indexed as

COVID-19Intensive Care UnitsMachine LearningSARS-CoV-2BrazilHospital MortalityHumansLength of StayPandemicsBrazilCOVID-19 pandemicIntensive care unitsMachine learningNon-COVID-19 patientsSpillover effects

Identifiers

PMID37543994

What OpenQuestion holds

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Registered trials

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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.