Evidence map›Paper›PMID 41387630›Full record

ArticleHealth economics review2025

Selecting risk adjusters with penalized regression and expert judgment: evidence from Colombia.

Camilo Arias

Abstract read
In one paragraph

Article in Health economics review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Camilo AriasUniversidad de La Sabana, Chía, Colombia. camilo.arias@unisabana.edu.co.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk adjustment formulas are essential in health insurance markets, as they mitigate risk selection incentives by aligning revenues with expected healthcare expenses based on enrollee characteristics. However, current formulas can underpredict spending for certain groups, leading to under-compensation for insurers and potentially affecting fairness, quality, and access to care. Many countries are exploring the addition of new variables to improve accuracy, but this can also weaken incentives for cost control. This paper illustrates a methodology approach to risk adjuster selection by using a penalized regression framework that explicitly incorporates the potential downsides of including specific variables. Drawing on a large dataset of over 10 million Colombian health insurance enrollees, we combine statistical estimation with expert assessment of each variable's susceptibility to gaming to construct a specification that limits gaming and maintains predictive accuracy.

Indexed as

Health Insurance MarketsRisk adjustmentVariable selection

Identifiers

PMID41387630
PMCPMC12817417

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.