Evidence map›Paper›PMID 38223316›Full record

ArticleHealth affairs scholar2023

Continuous Medicaid coverage during the COVID-19 public health emergency reduced churning, but did not eliminate it.

Daniel B Nelson, Anna L Goldman, Fang Zhang, Hao Yu

Abstract read
In one paragraph

Article in Health affairs scholar, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 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

4 authors.

Daniel B NelsonDepartment of Population Medicine, Harvard Medical School, Boston, MA 02215, United States.ORCID 0000-0001-9249-4439
Anna L GoldmanDepartment of Medicine, Boston University School of Medicine, Boston, MA 02118, United States.
Fang ZhangDepartment of Population Medicine, Harvard Medical School, Boston, MA 02215, United States.ORCID 0000-0002-8282-8738
Hao YuDepartment of Population Medicine, Harvard Medical School, Boston, MA 02215, United States.

Funding

Impact of the National Health Service Corps on Health DisparitiesR01MD013736 · NIMHD · HARVARD PILGRIM HEALTH CARE, INC. · PI YU, HAO · 2020 to 2023
$1.7M
Medicaid Accountable Care Organizations (ACO) and Quality of Care for Adults with Serious Mental Illness (SMI)K23MH128639 · NIMH · BOSTON MEDICAL CENTER · PI GOLDMAN, ANNA · 2022 to 2025
$758k
NIMHD NIH HHS R01 MD013736NIMH NIH HHS K23 MH128639
6 · The paper itself

Abstract

Preserving insurance coverage in the wake of pandemic-related job loss was a priority in early 2020. To this end, the Families First Coronavirus Response Act implemented a continuous coverage policy in Medicaid to shore up access to health insurance. Prior to the pandemic, Medicaid enrollees experienced frequent coverage disruptions, known as "churning." The effect of the continuous coverage policy on churning during the COVID-19 public health emergency (PHE) is unknown. We performed a difference-in-differences analysis of nonelderly Medicaid enrollees using longitudinal national survey data to compare a 2019-2020 cohort exposed to the policy with a control cohort in 2018-2019. We found that the policy led to reduced transitions to uninsurance among adults, although not among children. The policy prevented over 300 000 transitions to uninsurance each month. However, disenrollment from Medicaid persisted at a low rate, despite the continuous coverage policy. As the PHE unwinds, policymakers should consider long-term continuous coverage policies to minimize churning in Medicaid.

Indexed as

COVID-19 public health emergencyinsurance churninginsurance coverageMedicaid

Identifiers

PMID38223316
PMCPMC10786332

What OpenQuestion holds

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LicenceCC BY-NC
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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.