Immunization Coverage and Medicaid Managed Care in New Mexico: A Multimethod Assessment
Bibliographic record
Abstract
BACKGROUND: We wanted to examine the association between Medicaid managed care (MMC) and changing immunization coverage in New Mexico, a predominantly rural, poor, and multiethnic state. METHODS: As part of a multimethod assessment of MMC, we studied trends in quantitative data from the National Immunization Survey (NIS) using temporal plots, Fisher's exact test, and the Cochran-Armitage trend test. To help explain changes in immunization rates in relation to MMC, we analyzed qualitative data gathered through ethnographic observations at safety net institutions: income support (welfare) offices, community health centers, hospital emergency departments, private physicians' offices, mental health institutions, managed care organizations, and agencies of state government. RESULTS: Immunization coverage decreased significantly after implementation of MMC, from 80% in 1996 to 73% in 2001 for the 4:3:1 vaccination series (Fisher's exact test, P = .031). New Mexico dropped in rank among states from 30th for this vaccination series in 1996 to 50th in 2001. A significant decreasing trend (Cochran-Armitage P = .025) in coverage occurred between 1996 and 2001. Findings from the ethnographic study revealed conditions that might have contributed to decreased immunization coverage: (1) reduced funding for immunizations at public health clinics, and difficulties in gaining access to MMC providers; (2) informal referrals from managed care organizations and contracting physicians to community health centers and state-run public health clinics; and (3) increased workloads and delays at community health centers, linked partly to these informal referrals for immunizations. CONCLUSIONS: Medicaid reform in New Mexico did not improve immunization coverage, which declined significantly to among the lowest in the nation. Reduced funding for public health clinics and informal referrals may have contributed to this decline. These observations show how unanticipated and adverse consequences can result from policy interventions in complex insurance systems.
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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 | 0.002 | 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) | 0.000 | 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".