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Record W2626093817 · doi:10.1377/hlthaff.2017.0416

Health Spending By State 1991–2014: Measuring Per Capita Spending By Payers And Programs

2017· article· en· W2626093817 on OpenAlexaff
David Lassman, Andrea M. Sisko, Aaron Catlin, Mary Carol Barron, Joseph Benson, Gigi A. Cuckler, Micah Hartman, Anne B. Martin, Lekha Whittle

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsPer capitaMedicaidHealth spendingHealth careConsumption (sociology)BusinessResidenceConsumer spendingState (computer science)Demographic economicsHealth insuranceBaseline (sea)RecessionHealth policyPublic economicsEconomicsEconomic growthEnvironmental healthMedicinePolitical sciencePopulation

Abstract

fetched live from OpenAlex

As the US health sector evolves and changes, it is informative to estimate and analyze health spending trends at the state level. These estimates, which provide information about consumption of health care by residents of a state, serve as a baseline for state and national-level policy discussions. This study examines per capita health spending by state of residence and per enrollee spending for the three largest payers (Medicare, Medicaid, and private health insurance) through 2014. Moreover, it discusses in detail the impacts of the Affordable Care Act implementation and the most recent economic recession and recovery on health spending at the state level. According to this analysis, these factors affected overall annual growth in state health spending and the payers and programs that paid for that care. They did not, however, substantially change state rankings based on per capita spending levels over the period.

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)0.003
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.080
GPT teacher head0.303
Teacher spread0.223 · 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 designNot applicable
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

Citations64
Published2017
Admission routes1
Has abstractyes

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