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Record W2006850050 · doi:10.1377/hlthaff.2012.1018

Insurance Expansion In Massachusetts Did Not Reduce Access Among Previously Insured Medicare Patients

2013· article· en· W2006850050 on OpenAlexaboutno aff
Karen E. Joynt, David C. Chan, E. John Orav, Ashish K. Jha

Bibliographic record

VenueHealth Affairs · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersNational Institute on AgingAgency for Healthcare Research and Quality
KeywordsSpillover effectMedicineQuarter (Canadian coin)Health insurancePatient Protection and Affordable Care ActHealth careFamily medicineEnvironmental healthDemographyGerontologyEconomics

Abstract

fetched live from OpenAlex

Critics of Massachusetts's health reform, a model for the Affordable Care Act, have argued that insurance expansion probably had a negative spillover effect leading to worse outcomes among already insured patients, such as vulnerable Medicare patients. Using Medicare data from 2004 to 2009, we examined trends in preventable hospitalizations for conditions such as uncontrolled hypertension and diabetes--markers of access to effective primary care--in Massachusetts compared to control states. We found that after Massachusetts's health reform, preventable hospitalization rates for Medicare patients actually decreased more in Massachusetts than in control states (a reduction of 101 admissions per 100,000 patients per quarter compared to a reduction of 83 admissions). Therefore, we found no evidence that Massachusetts's insurance expansion had a deleterious spillover effect on preventable hospitalizations among the previously insured. Our findings should offer some reassurance that it is possible to expand access to uninsured Americans without negatively affecting important clinical outcomes for those who are already insured.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.291
Teacher spread0.245 · 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 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

Citations42
Published2013
Admission routes1
Has abstractyes

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