Health insurance mediation of the Mexican American non-Hispanic white disparity on early breast cancer diagnosis
Bibliographic record
Abstract
We examined health insurance mediation of the Mexican American (MA) non-Hispanic white (NHW) disparity on early breast cancer diagnosis. Based on social capital and barrio advantage theories, we hypothesized a 3-way ethnicity by poverty by health insurance interaction, that is, that 2-way poverty by health insurance interaction effects would differ between ethnic groups. We secondarily analyzed registry data for 303 MA and 3,611 NHW women diagnosed with breast cancer between 1996 and 2000 who were originally followed until 2011. Predictors of early, node negative (NN) disease at diagnosis were analyzed. Socioeconomic data were obtained from the 2000 census to categorize neighborhood poverty: high (30% or more of the census tract households were poor), middle (5% to 29% poor) and low (less than 5% poor). Barrios were neighborhoods where 50% or more of the residents were MA. Primary health insurers were Medicaid, Medicare, private or none. MA women were 13% less likely to be diagnosed early with NN disease (RR = 0.87), but this MA-NHW disparity was completely mediated by the main and interacting effects of health insurance. Advantages of health insurance were largest in low poverty neighborhoods among NHW women (RR = 1.20) while among MA women they were, paradoxically, largest in high poverty, MA barrios (RR = 1.45). Advantages of being privately insured were observed for all. Medicare seemed additionally instrumental for NHW women and Medicaid for MA women. These findings are consistent with the theory that more facilitative social and economic capital is available to MA women in barrios and to NHW women in more affluent neighborhoods. It is there that each respective group of women is probably best able to absorb the indirect and direct, but uncovered, costs of breast cancer screening and diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".