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Record W2792927636 · doi:10.1371/journal.pone.0192809

Rotavirus vaccine coverage and factors associated with uptake using linked data: Ontario, Canada

2018· article· en· W2792927636 on OpenAlexafffundabout
Sarah E. Wilson, Hannah Chung, Kevin L. Schwartz, Astrid Guttmann, Shelley L. Deeks, Jeffrey C. Kwong, Natasha S. Crowcroft, Laura Wing, Karen Tu

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsHospital for Sick ChildrenInstitute for Clinical Evaluative SciencesPublic Health OntarioUniversity of Toronto
FundersPublic Health OntarioUniversity of TorontoOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicineConfidence intervalRotavirus vaccineOdds ratioVaccinationLogistic regressionPediatricsRotavirusMedical recordSeries (stratigraphy)DemographyInternal medicineImmunologyDiarrhea

Abstract

fetched live from OpenAlex

BACKGROUND: In August 2011, Ontario, Canada introduced a rotavirus immunization program using Rotarix™ vaccine. No assessments of rotavirus vaccine coverage have been previously conducted in Ontario. METHODS: We assessed vaccine coverage (series initiation and completion) and factors associated with uptake using the Electronic Medical Record Administrative data Linked Database (EMRALD), a collection of family physician electronic medical records (EMR) linked to health administrative data. Series initiation (1 dose) and series completion (2 doses) before and after the program's introduction were calculated. To identify factors associated with series initiation and completion, adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) were calculated using logistic regression. RESULTS: A total of 12,525 children were included. Series completion increased each year of the program (73%, 79% and 84%, respectively). Factors associated with series initiation included high continuity of care (aOR = 2.15; 95%CI, 1.61-2.87), maternal influenza vaccination (aOR = 1.55; 95%CI,1.24-1.93), maternal immmigration to Canada in the last five years (aOR = 1.47; 95% CI, 1.05-2.04), and having no siblings (aOR = 1.62; 95%CI,1.30-2.03). Relative to the first program year, infants were more likely to initiate the series in the second year (aOR = 1.71; 95% CI 1.39-2.10) and third year (aOR = 2.02; 95% CI 1.56-2.61) of the program. Infants receiving care from physicians with large practices were less likely to initiate the series (aOR 0.91; 95%CI, 0.88-0.94, per 100 patients rostered) and less likely to complete the series (aOR 0.94; 95%CI, 0.91-0.97, per 100 patients rostered). Additional associations were identified for series completion. CONCLUSIONS: Family physician delivery achieved moderately high coverage in the program's first three years. This assessment demonstrates the usefulness of EMR data for evaluating vaccine coverage. Important insights into factors associated with initiation or completion (i.e. high continuity of care, smaller roster sizes, rural practice location) suggest areas for research and potential program supports.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.006
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.167
GPT teacher head0.300
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2018
Admission routes3
Has abstractyes

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