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Record W2918295236 · doi:10.1377/hlthaff.2018.05026

Vulnerable And Less Vulnerable Women In High-Deductible Health Plans Experienced Delayed Breast Cancer Care

2019· article· en· W2918295236 on OpenAlexaff
J. Frank Wharam, Fang Zhang, Jamie Wallace, Christine Y. Lu, Craig C. Earle, Stephen B. Soumerai, Larissa Nekhlyudov, Dennis Ross‐Degnan

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsEsri (Canada)
FundersNational Cancer Institute
KeywordsDeductibleBreast cancerMedicineHealth careLow incomeHealth insuranceDemographyFamily medicineCancerInternal medicineActuarial science

Abstract

fetched live from OpenAlex

The effects of high-deductible health plans (HDHPs) on breast cancer diagnosis and treatment among vulnerable populations are unknown. We examined time to first breast cancer diagnostic testing, diagnosis, and chemotherapy among a group of women whose employers switched their insurance coverage from health plans with low deductibles ($500 or less) to plans with high deductibles ($1,000 or more) between 2004 and 2014. Primary subgroups of interest comprised 54,403 low-income and 76,776 high-income women continuously enrolled in low-deductible plans for a year and then up to four years in HDHPs. Matched controls had contemporaneous low-deductible enrollment. Low-income women in HDHPs experienced relative delays of 1.6 months to first breast imaging, 2.7 months to first biopsy, 6.6 months to incident early-stage breast cancer diagnosis, and 8.7 months to first chemotherapy. High-income HDHP members had shorter delays that did not differ significantly from those of their low-income counterparts. HDHP members living in metropolitan, nonmetropolitan, predominantly white, and predominantly nonwhite areas also experienced delayed breast cancer care. Policies may be needed to reduce out-of-pocket spending obligations for breast cancer care.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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 designQualitative
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

Citations70
Published2019
Admission routes1
Has abstractyes

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