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Record W3021577033 · doi:10.3386/w27120

The Value of Health Insurance during a Crisis: Effects of Medicaid Implementation on Pandemic Influenza Mortality

2020· report· en· W3021577033 on OpenAlexafffund
Karen Clay, Joshua Lewis, Edson Severnini, Xiao Wang

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversité de MontréalMcGill University
FundersCarnegie Mellon UniversityUniversité de MontréalNational Science Foundation
KeywordsMedicaidPandemicInfluenza pandemicCoronavirus disease 2019 (COVID-19)Value (mathematics)Actuarial scienceBusinessMedicineEconomicsEconomic growthHealth careStatisticsInternal medicineDiseaseInfectious disease (medical specialty)Mathematics

Abstract

fetched live from OpenAlex

This paper studies how better access to public health insurance affects infant mortality during pandemics. Our analysis combines cross-state variation in mandated eligibility for Medicaid with two influenza pandemics -the 1957-58 "Asian Flu" Pandemic and the 1968-69 "Hong Kong Flu" Pandemic -that arrived shortly before and after the program's introduction. Exploiting heterogeneity in the underlying severity of these two shocks across counties, we find no relationship between Medicaid eligibility and pandemic infant mortality during the 1957-58 outbreak. In contrast during the 1968-1969 pandemic, which occurred after Medicaid implementation, we find that better access to insurance in high-eligibility states substantially reduced infant mortality. The reductions in pandemic infant mortality are too large to be attributable solely to new Medicaid recipients, suggesting that the expansion in health insurance coverage mitigated disease transmission among the broader population.

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)0.018
metaresearch head score (Gemma)0.006
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 score0.382
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.006
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 science0.0010.000
Research integrity0.0000.002
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 head0.516
GPT teacher head0.687
Teacher spread0.170 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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