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Record W4205706002 · doi:10.1377/hlthaff.2021.01150

The ACA Medicaid Expansion And Perinatal Insurance, Health Care Use, And Health Outcomes: A Systematic Review

2022· review· en· W4205706002 on OpenAlexaff
Meghan Bellerose, Lauren Collin, Jamie R. Daw

Bibliographic record

VenueHealth Affairs · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMedicaidMedicineAttendanceEthnic groupPrenatal careHealth careFamily medicineHealth insurancePregnancyEnvironmental healthDemographyPopulation

Abstract

fetched live from OpenAlex

The Affordable Care Act (ACA) Medicaid expansion increased Medicaid eligibility for low-income adults regardless of their pregnancy or parental status. Variation in states' adoption of this expansion created a natural experiment to study the effects of expanding public insurance on insurance coverage, health care use, and health outcomes during preconception, pregnancy, and postpartum. We conducted a systematic review of relevant literature on this topic, analyzing twenty-four studies published between January 2014 and April 2021. We found that the ACA Medicaid expansion increased preconception and postpartum Medicaid coverage with corresponding declines in uninsurance, private insurance coverage, and insurance churn. There was limited evidence that Medicaid expansion increased perinatal health care use or improved infant birth outcomes overall, although some studies reported reduced racial and ethnic disparities in rates of prenatal and postpartum visit attendance, maternal mortality, low birthweight, and preterm births. Stronger data collection on preconception and postpartum outcomes with sufficient sample sizes to stratify by race and ethnicity is needed to assess the full impact of the ACA and emerging Medicaid policy changes, such as the postpartum Medicaid extension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.147
GPT teacher head0.372
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations80
Published2022
Admission routes1
Has abstractyes

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