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Record W3027306572 · doi:10.1002/cncr.32975

Medicare access increases cancer detection and drives down mortality rates

2020· article· en· W3027306572 on OpenAlexaboutno aff
Carrie Printz

Bibliographic record

VenueCancer · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLife expectancyCancerDemographyEpidemiologyPopulationColorectal cancerIncidence (geometry)Mortality rateBreast cancerSurveillance, Epidemiology, and End ResultsCancer registryLung cancerGerontologySurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Having Medicare coverage significantly impacts both the detection of certain cancers as well as life expectancy after diagnosis, researchers from the University of Wisconsin School of Medicine and Public Health found. Their study, published in the Journal of Policy Analysis and Management, analyzed cancer detection and mortality rates at age 65 years, the age at which near-universal Medicare eligibility begins.1 Using vital statistics data and data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program from 2001 to 2015, investigators specifically assessed individuals aged 59 to 71 years. They honed in on breast, colorectal, and lung cancer incidence because screening for these diseases is recommended both before and after age 65 years, and because these tumors are the leading causes of cancer mortality nationwide. The findings indicated that the detection of all 3 cancers rose by 50 diagnoses per 100,000 population at age 65 years, a 10% increase compared with individuals aged 63 and 64 years, with early-stage cancers contributing to much of that surge. At the same time, mortality rates for these cancers among individuals aged 65 years declined by 9 per 100,000 population for women, a 4.5% decrease compared with that for women aged 63 and 64 years. However, the cancer mortality rate did not change significantly for men. Additional findings have demonstrated a pronounced increase in diagnoses among black women. Only 55% of this group was diagnosed with cancer prior to metastases at ages 63 and 64 years compared with 62% of nonblack women. Black women at these ages also had a 22% higher cancer mortality rate compared with nonblack women. Once they became eligible for Medicare at age 65 years, early detection rates among black women increased significantly: up to 62 diagnoses per 100,000 population (15%) compared with 47 per 100,000 population (11%) among nonblack women. Their mortality rates also underwent a large decline at age 65 years, falling to 20 per 100,000 population, or 9%. Although no significant changes in detection or mortality rates were noted to occur in black men, Medicare eligibility does appear to improve health equity among women with cancer, according to investigators. In an effort to demonstrate the validity of their findings, researchers also analyzed data from Canada, where residents of any age have access to public health insurance, therefore experiencing no change in coverage at age 65 years. The authors found that cancer mortality rates were nearly identical for both countries prior to age 65 years. However, at age 65 years in the United States, cancer mortality declined and cancer detection increased whereas individuals residing in Canada experienced no such change. The finding alleviates concerns that life changes at age 65 years, other than Medicare eligibility, could have affected the results of the study, researchers say. The authors hope their findings will inform future policy discussions regarding both Medicare and medical insurance in general.

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)0.019
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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.129
GPT teacher head0.421
Teacher spread0.292 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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