Subscriber continuity in health insurance plans: factors associated with re-enrollment and coverage changes
Bibliographic record
Abstract
Aims: This study examined the extent to which Marketplace health insurance subscribers re-enroll a second year. Among re-enrollees, we sought to examine movement to more and less generous insurance plans (based on actuarial value), and the extent to which adverse selection, adverse retention, and aging in place are evident from re-enrollment choices made. Methods: Re-enrollment from 2015 to 2016 and 2016 to 2017 and movement to more and less generous insurance plans was examined using enrollment and insurance claims data from two US Federally-facilitated Marketplace insurance carriers operating in the state of New Mexico for 2015–2017. Insurance plans are assigned to metal levels based on estimated plan actuarial value: Bronze (60%), Silver (70%), and Gold (80%). Odds ratios (ORs) and 95% confidence intervals (OR CI) were estimated using logistic regressions for subscribers with base-year healthcare utilization. ORs were estimated for (1) re-enrollment in the year following the base year, and (2) movement to a higher or lower actuarial value plan. Results: Approximately 50% of subscribers re-enrolled with the same carrier for 2016 and 60% for 2017. Being enrolled 12 months was the strongest predictor for second year re-enrollment. Older individuals were more likely to re-enroll. Re-enrollment was lower for the insurance carrier with higher second year premium changes. Chronic condition utilization characteristics were positively associated with re-enrollment. Approximately 12% of Bronze subscribers moved to Silver or Gold, and had higher utilization after re-enrollment. Among Silver subscribers, 6% moved to Gold and 6% to Bronze. Approximately 37% of Gold subscribers moved to Silver or Bronze. Discussion: Re-enrollment was similar to published non-group insurance rates. Adverse selection and aging in place were observed. Evidence was weak for adverse retention. Some coverage change choices were rational, while others suggest subscribers may have difficulty making insurance choice decisions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".