Health Characteristics Associated With Gaining and Losing Private and Public Health Insurance
Bibliographic record
Abstract
BACKGROUND: Millions of Americans lack or lose health insurance annually, yet how health characteristics predict insurance acquisition and loss remains unclear. OBJECTIVE: To examine associations of health characteristics with acquisition and loss of private and public health insurance. RESEARCH DESIGN AND PARTICIPANTS: Prospective observational analysis of 2000 to 2007 Medical Expenditure Panel Survey data for persons aged 18 to 63 on entry, enrolled for 2 years. We modeled year 2 private and public insurance gain and loss. DEPENDENT VARIABLES: year 2 insurance status [none (reference), any private insurance, or public insurance] among those uninsured in year 1 (N=13,022), and retaining or losing coverage in year 2 among those privately or publicly insured in year 1 (N=47,239). INDEPENDENT VARIABLES: age, sex, race/ethnicity, education, income, region, urbanity, health status, health conditions, year 1 health expenditures, year 1 and 2 employment status, and (in secondary analyses) skepticism toward medical care and insurance. RESULTS: In adjusted analyses, lower income and education were associated with not gaining and with losing private insurance. Poorer health status was associated with public insurance gain. Smoking and being overweight were associated with not gaining private insurance, and smoking with losing private coverage. Secondary analyses adjusting for medical skepticism yielded similar findings. CONCLUSIONS: Social disadvantage and poorer health status are associated with gaining public insurance, whereas social advantage, not smoking, and not being overweight are associated with gaining private insurance, even when adjusting for attitudes toward medical care. Private insurers seem to benefit from relatively low health risk selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".