Changes in Childhood Immunization Rates by County Characteristics in Michigan: 2017–2023
Bibliographic record
Abstract
OBJECTIVE: To describe changes in Michigan's childhood immunization rates by county sociodemographic characteristics during 2017-2023. METHODS: We analyzed county-level data from Michigan's immunization registry. For each quarter from quarter 1 2017 through quarter 1 2023, we calculated the mean completion rate of a childhood immunization composite, an adolescent immunization composite, the male human papillomavirus (HPV) vaccination series, and the female HPV vaccination series. We assessed changes in these rates among all Michigan counties and among county subgroups defined by median household income, rurality, and uninsurance rate. RESULTS: During the study period, the mean completion rate of the childhood and adolescent immunization composite declined from 75.7% to 66.8% and from 80.7% to 74.7%, respectively. The mean completion rate of the male and female HPV vaccination series increased from 35.1% to 42.3% and from 43.4% to 45.2%, respectively. These increases were smaller than the increase predicted by prepandemic trends. In counties with lower income and higher uninsurance, declines in the mean completion rate of both composite measures were greater compared with counties with higher income and lower uninsurance, whereas increases in the mean completion rate of the male HPV vaccination series were smaller. Changes according to rurality were inconsistent. CONCLUSIONS: Routine childhood and adolescent immunizations are declining in Michigan, and increases in HPV vaccination are slowing, particularly in counties with lower income and higher uninsurance rates. Findings suggest progress toward increasing childhood immunizations is stalling. Targeted efforts to increase immunizations in counties with lower income and higher uninsurance rates may be warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".