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Costs after incident breast cancer diagnosis among high-deductible health plan members.

2019· article· en· W2980846478 on OpenAlexaff
J. Frank Wharam, Jamie Wallace, Christine Y. Lu, Anita K. Wagner, Stephen Soumerai, Craig C. Earle, Larissa Nekhlyudov, Dennis Ross‐Degnan, Fang Zhang

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineDeductibleBreast cancerHealth careProxy (statistics)Health planCancerDemographyFamily medicineInternal medicineActuarial science

Abstract

fetched live from OpenAlex

120 Background: High-deductible health plans (HDHP) are associated with breast cancer treatment delays of up to 10 months, but their impact on health outcomes is unknown. We hypothesized that, compared with women in generous plans, HDHP members would present with more advanced disease and thus experience higher total costs of early care. Methods: We studied 2004-2014 claims data from a large US health insurer. We included women aged 25-64 who were in traditional low-deductible (≤$500) health plans for 1 baseline year then experienced either an employer-mandated switch to HDHPs (≥$1000) for up to 4 or years or an employer-mandated continuation in low deductible plans. We defined the HDHP switch date as the index date. We then restricted to women who developed incident breast cancer after the index date. Using baseline characteristics, we closely matched HDHP members with incident breast cancer to contemporaneous women with incident breast cancer who remained in low-deductible plans. We measured total costs of all health care services in the 60 days after incident breast cancer diagnosis as a proxy for the intensity of incident breast cancer care. We used negative binomial regression adjusted for baseline characteristics to compare total 60-day costs among HDHP and control members. We also subset analyses to low-income women. Results: We included 1514 HDHP members and 9283 matched controls. 60-day costs after incident breast cancer diagnosis were $24,151 (95% CI: $22,766, $25,535) among HDHP members and $22,474 ($21,952, $22,996) among controls, an absolute difference of $1677 ($197, $3156) and a relative difference of 7.5% (8.1%, 14.1%). Low-income HDHP members had corresponding absolute and relative differences of $2653 ($368, $4939) and 12.5% (1.5%, 23.5). Conclusions: HDHP members with incident breast cancer had 7.5% higher health care costs in the 60 days after incident breast cancer than women with more generous coverage, a finding driven 12.5% higher costs among low-income HDHP members. Results raise concerns that delays in breast cancer care among HDHP members are associated with more advanced disease and adverse outcomes.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.380
Teacher spread0.305 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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