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Record W2342938935 · doi:10.1377/hlthaff.2015.1195

Traditional Medicare Versus Private Insurance: How Spending, Volume, And Price Change At Age Sixty-Five

2016· article· en· W2342938935 on OpenAlexaboutno aff
Jacob Wallace, Zirui Song

Bibliographic record

VenueHealth Affairs · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsBeneficiaryHealth and Retirement StudyRegression discontinuity designDemographic economicsPrivate insuranceHealth careQuarter (Canadian coin)Patient Protection and Affordable Care ActActuarial scienceHealth insuranceBusinessMedicineEconomicsFinanceGerontologyEconomic growth

Abstract

fetched live from OpenAlex

To slow the growth of Medicare spending, some policy makers have advocated raising the Medicare eligibility age from the current sixty-five years to sixty-seven years. For the majority of affected adults, this would delay entry into Medicare and increase the time they are covered by private insurance. Despite its policy importance, little is known about how such a change would affect national health care spending, which is the sum of health care spending for all consumers and payers-including governments. We examined how spending differed between Medicare and private insurance using longitudinal data on imaging and procedures for a national cohort of individuals who switched from private insurance to Medicare at age sixty-five. Using a regression discontinuity design, we found that spending fell by $38.56 per beneficiary per quarter-or 32.4 percent-upon entry into Medicare at age sixty-five. In contrast, we found no changes in the volume of services at age sixty-five. For the previously insured, entry into Medicare led to a large drop in spending driven by lower provider prices, which may reflect Medicare's purchasing power as a large insurer. These findings imply that increasing the Medicare eligibility age may raise national health care spending by replacing Medicare coverage with private insurance, which pays higher provider prices than Medicare does.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.127
GPT teacher head0.284
Teacher spread0.156 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2016
Admission routes1
Has abstractyes

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