Health Plan Switching and Attrition Bias in the RAND Health Insurance Experiment
Bibliographic record
Abstract
One of the most influential “lessons” of the RAND Health Insurance Experiment (HIE) is that cost sharing can reduce hospitalizations by about a quarter, with no effect on health for the average adult. In an earlier paper in this journal, I suggested that a portion of this reduction is due to participants becoming ill and dropping out of the experiment in order to switch to their preexperiment insurance coverage and thus avoid paying the cost-sharing amount. The sixteenfold higher voluntary attrition rate in the cost-sharing arms provides compelling evidence in support of this alternative explanation. Evidence is also provided by the finding in Manning, Duan, Keeler (1993) that the predicted number of hospitalizations among those who dropped out of the coinsurance arms was significantly greater by 34.5 percent than the actual number of hospitalizations, suggesting that participants anticipate hospitalizations and leave the experiment before incurring the cost-sharing payment. Still more evidence is provided by the finding that those (cost-sharing) participants with greater incomes, instead of being more likely to be hospitalized, as greater income usually implies, were less likely to be hospitalized than poor participants. This finding is consistent with their having better preexperiment insurance coverage than poor participants and therefore being more likely to have an incentive to drop out. This inpatient attrition bias makes it dangerous to rely on this lesson of the HIE, because it is not clear that hospitalizations were actually reduced by one-quarter, much less that if such a reduction actually had occurred, there would be no health consequences.
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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 | 0.118 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
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".