IMPROVING MEDICAID ENROLLMENT AND POPULATION HEALTH:THREE PAPERS FOR STATES.
Bibliographic record
Abstract
This dissertation consists of three research studies designed to assist states with improving the health of their populations. Due to states’ greatly-increased responsibilities for promoting, providing, and regulating their residents’ health insurance; their continuing responsibilities for public health promotion, prevention, and surveillance; and their growing understanding of the relationship between individual health and the well-being of communities, many states now see themselves as stewards of their populations’ health. Yet, states struggle to ensure that eligible individuals receive insurance. Plus, they lack robust systems to monitor the health of their populations. In addition, they face ongoing fiscal and staffing challenges that will make it difficult to satisfy these responsibilities in the foreseeable future. The first two studies utilize quantitative and data visualization techniques to describe state-level Medicaid and Children’s Health Insurance Program (CHIP) enrollment patterns and dynamics between 2000 and 2011 for the purposes of identifying policies and procedures to expedite eligibility determinations, renewals, and transfers and thereby improve program participation. Specifically, study one utilizes state administrative data and dummy variables representing eligibility policies and procedures to estimate the relationship between unemployment and enrollment during a period of significant economic and policy change. It finds that the Medicaid participation rate increases with the unemployment rate and with large expansions of eligibility criteria, such as an expansion to childless adults like that authorized under the Affordable Care Act (ACA). Study two, the first to demonstrate Medicaid enrollment seasonality, draws from a robust set of state-level administrative data to analyze month-to-month changes by eligibility category. The four eligibility categories—children, parents, aged, and disabled— show distinct and consistent enrollment patterns. Insights into these patterns can inform outreach efforts, as well as the development of eligibility policies and management strategies for preventing backlogs. The third study, which received Fulbright program support, draws lessons and recommendations for states for monitoring population health from a case study of population health monitoring in the Canadian province of Saskatchewan, a federal substate that shares many socioeconomic characteristics with its American counterparts and which has provided universal health insurance for over fifty years.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".