Evidence map›Paper›PMID 40200979›Full record

ArticleHealth affairs scholar2025

Examining selection in Affordable Care Act (ACA) Marketplaces: special enrollment periods.

Saumya Chatrath, Alison A Galbraith, Laura F Garabedian

Abstract read
In one paragraph

Article in Health affairs scholar, 2025. 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

3 authors.

Saumya ChatrathDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.ORCID https://orcid.org/0000-0002-7113-0561
Alison A GalbraithDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.ORCID https://orcid.org/0000-0002-7309-5915
Laura F GarabedianDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.ORCID https://orcid.org/0000-0002-0014-4181

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Affordable Care Act (ACA) Marketplace members who enroll through a special enrollment period (SEP) have significantly higher average monthly spending than members who enroll through the annual open enrollment period (OEP), driven primarily by higher inpatient spending. Methods: Using data from a large national insurer that participated in the federal ACA Marketplace from 2015 to 2016 in 24 US states, we examined differences between SEP and OEP Marketplace enrollees in time from enrollment to inpatient use of predictable and discretionary care (ie, hip and knee replacement), predictable and nondiscretionary care (ie, childbirth), and nonpredictable and nondiscretionary care (ie, acute myocardial infarction and stroke). We examined whether a 2016 policy that increased SEP eligibility verification requirements was associated with changes in utilization. Results: When compared with OEP Marketplace members, SEP members had significantly higher rates of care in all 3 categories. The 2016 policy was not associated with changes in utilization rates. Conclusion: Our results provide evidence that there is adverse selection in the SEP of the ACA Marketplaces. However, since SEP members were more likely to seek care for services that are predictable and nonpredictable, and discretionary and nondiscretionary, the optimal policy response to reduce adverse selection needs to be nuanced and multipronged.

Indexed as

adverse selectionMarketplacespecial enrollment period

Identifiers

PMID40200979
PMCPMC11976056

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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.