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Record W2885566006 · doi:10.1377/hlthaff.2018.0124

Impact Of Medicaid Expansion On Coverage And Treatment Of Low-Income Adults With Substance Use Disorders

2018· article· en· W2885566006 on OpenAlexaff
Mark Olfson, Melanie M. Wall, Colleen L. Barry, Christine Mauro, Ramin Mojtabai

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsColumbia College
FundersNational Institute on Drug Abuse
KeywordsMedicaidSubstance useMedicineHealth insurancePsychiatrySubstance abusePsychological interventionEnvironmental healthHealth careGerontologyFamily medicine

Abstract

fetched live from OpenAlex

Extensive undertreatment of substance use disorders has focused attention on whether the expansion of eligibility for Medicaid under the Affordable Care Act (ACA) has promoted increased coverage and treatment of these disorders. We assessed changes in coverage and substance use disorder treatment among low-income adults with the disorders following the 2014 ACA Medicaid expansion, using data for 2008-15 from the National Survey on Drug Use and Health. The percentage of low-income expansion state residents with substance use disorders who were uninsured decreased from 34.4 percent in 2012-13 to 20.4 percent in 2014-15, while the corresponding decrease among residents of nonexpansion states was from 45.2 percent to 38.6 percent. However, there was no corresponding increase in overall substance use disorder treatment in either expansion or nonexpansion states. The differential increase in insurance coverage suggests that Medicaid expansion contributed to insurance gains, but corresponding treatment gains were not observed. Increasing treatment may require the integration of substance use disorder treatment with other medical services and clinical interventions to motivate people to engage in treatment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.283
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations73
Published2018
Admission routes1
Has abstractyes

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