Is it Really Worse to Have Public Health Insurance Than to Have No Insurance at All? Health Insurance and Adult Health in the United States
Bibliographic record
Abstract
Using prospective cohort data from the 1979 National Longitudinal Survey of Youth, this study examines the extent to which health insurance coverage and the source of that coverage affect adult health. While previous research has shown that privately insured nonelderly individuals enjoy better health outcomes than their uninsured counterparts, the same relationship does not hold for those publicly insured through programs such as Medicaid. Because it is unclear whether this finding reflects a true causal relationship or is in fact due to selection bias on socioeconomic status and health, previous estimates of the contribution of health insurance to inequities in health may have been biased. This study attempts to disentangle these competing hypotheses of causation or selection bias by using fixed effects models with sibling clusters to corroborate--or contradict--the results of a conventional OLS regression. By controlling for unobserved factors shared by siblings, such as parental genetic influences, sibling models estimate health insurance effects that are less affected by selection bias. Findings suggest that, among the US. birth cohorts of 1957 to 1961, the negative relationship between public health insurance and health is not causal, but rather due to prior health and socioeconomic status. Conversely, the lack of health insurance coverage has a strong cumulative negative impact on adult health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".