Measles-containing vaccination rates in southern Alberta
Bibliographic record
Abstract
BACKGROUND: Southern Alberta is home to many unique homogeneous communities that typically educate their children in private schools. A number of these communities do not promote immunization as a preventive public health measure, although the reasons behind this vary. People within these communities keep themselves somewhat secluded from other populations and thus do not benefit from overall herd immunity. This has led to frequent outbreaks of vaccine-preventable diseases in private schools affiliated with these homogeneous religious communities. OBJECTIVE: To report on low immunization rates of measles, mumps, rubella (MMR) and MMR-varicella in southern Alberta communities and schools and to compare the epidemiology of immunization rates in certain vulnerable communities with those of same-age cohorts in South Zone communities. METHODS: The analysis includes immunization data at the individual level submitted to the provincial immunization repository, Immunization and Adverse Reactions to Immunization, and the Alberta Health Services Meditech module between January 1, 2013, and June 30, 2013. RESULTS: Heterogeneity of immunization status was found among communities and among schools. The status of two year old children up to date on immunizations ranged from 46.6% in Fort Macleod to 71.9% in Oyen, with a mean of 57.3 children in every 100 up to date. By age seven, the mean percentage of immunized children in southern Alberta was 77.6%, ranging from 57.8% in Picture Butte to 94.6% in Oyen. Immunization status among schools ranged from 17% to 100%, with a mean of 89.3% of children fully immunized and a median of 91% immunized. CONCLUSION: There is heterogeneity of immunization uptake for childhood measles-containing vaccine by community and by school in southern Alberta. This study highlights that the location of the school may not align with geographic community as it pertains to immunization rates. Analysis of childhood immunization data at both community and school level is important in understanding the risks of vaccine-preventable illness spread in a given geographic region, such as Alberta South Zone. Data from this study can be used to inform specific interventions required to improve immunization coverage rates in these unique homogeneous cultural communities and their respective schools, and to decrease the risk of measles transmission in Southern Alberta.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".