Predictors of Voluntary Disenrollment From Medicare Managed Care
Bibliographic record
Abstract
BACKGROUND: Prior research on selection bias in Medicare plans has demonstrated favorable enrollment of healthier beneficiaries, resulting in plan overpayment. However, total selection bias depends not only on who enrolls, but also on who disenrolls. Few studies examine selectivity in disenrollment; it is unclear how those who leave plans differ from those who remain. OBJECTIVE: The examination of health status and plan characteristics as potential predictors of voluntary disenrollment from Medicare managed care. RESEARCH DESIGN: Baseline data on health of Medicare managed care enrollees are from the 1998 Medicare Health Outcomes Survey, merged with data on enrollment status and plan characteristics. Beneficiary voluntary disenrollment, versus continuous enrollment, 24 months after completing the survey was modeled as a function of perceived health in 1998 and plan characteristics. The sample included 109,882 community-dwelling elderly. RESULTS: Between 1998 and 2000, 24% of Medicare managed care enrollees voluntarily disenrolled from plans. Poor perceived physical and mental health significantly increased the odds of voluntary disenrollment. Odds of disenrollment were higher for members of plans that increased premiums and had low market share between 1998 and 2000. Conversely, gaining drug coverage in a plan between 1998 and 2000 lowered the odds of disenrollment (relative to no coverage). CONCLUSION: Medicare plans experience favorable selection bias partly because sicker members are likelier to disenroll. Plan-level policies that influence market share and benefits, particularly pharmaceutical coverage, also have important effects on disenrollment, regardless of health effects. Understanding both individual and plan influences on disenrollment is critical to benefit coverage and disenrollment restriction ("lock in") policies.
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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) | 0.002 | 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".