Evidence map›Paper›PMID 36909929›Full record

ArticleSSM - population health2023

Improving the evaluation of an integrated healthcare system using entropy balancing: Population health improvements in Gesundes Kinzigtal.

Nicolas Larrain, Oliver Groene

Abstract read
In one paragraph

Article in SSM - population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Nicolas LarrainHamburg Centre for Health Economics (HCHE), University of Hamburg, Hamburg, Germany.
Oliver GroeneOptiMedis AG, Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Evidence of integrated healthcare networks' effect on population health is scarce. Moreover, current designs for evaluating such networks have shortcomings that can result in misleading conclusions. Our paper evaluates Gesundes Kinzigtal, a best-practice integrated healthcare network, using an innovative design that enlightens the discussion about health gains produced by integrated healthcare. Research question: What is the effect of Gesundes Kinzigtal on population health? Methods: We evaluated the effect of the integrated healthcare initiative by performing a quasi-experimental evaluation based on entropy balancing. Integrated network participants were compared to a control group and followed for five years. Claims data from 2004 to 2018 was used. Population health outcomes correspond to survival (Cox hazard ratio, Kaplan-Meier curve), mortality ratio, mean age at the time of death, and years of life lost or gained. Design validity was evaluated by assessing group balance at baseline. Finally, we compared our results to those obtained using a previously published design for evaluating integrated networks. Results: The treatment group was composed of 9083 network participants, compared to an equivalent control group, showing, respectively, a mortality ratio of 5.4% vs 7.5% (p < 0.05), mean age at the time of death of 80.1 vs 80.3 (p > 0.05) and a gain of 0.2 years of life per person for the treatment group (p > 0.05). The Cox hazard ratio (0.72; p < 0.05) and mean survival time (1784 vs 1768 days; p < 0.05) showed better survival for treated participants. Results using the previously published design were more favorable for the treatment group; however, the design excluded participants significantly associated with greater healthcare needs. Discussion: The integrated network had a favorable effect on participants' mortality and survival risk. Previous evaluations based on propensity score matching might overestimate the network's impact on population health by excluding participants with greater healthcare needs.

Indexed as

Entropy balancingImpact evaluationIntegrated healthcarePropensity score matchingQuasi-experiment

Identifiers

PMID36909929
PMCPMC9996350

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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.