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Record W3161080656 · doi:10.1101/2021.05.11.21257048

Impact of the COVID-19 pandemic on the provision of routine childhood immunizations in Ontario, Canada

2021· preprint· en· W3161080656 on OpenAlexafffundabout
Pierre‐Philippe Piché‐Renaud, Catherine Ji, Daniel S. Farrar, Jeremy Friedman, Michelle Science, Ian Kitai, Sharon Burey, Mark Feldman, Shaun K. Morris

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsWestern UniversityCentre for Global Health ResearchUniversity of TorontoUniversity Health NetworkSickKids FoundationHospital for Sick Children
FundersHospital for Sick Children
KeywordsMedicinePandemicFamily medicineVaccinationLogistic regressionOdds ratioImmunizationCoronavirus disease 2019 (COVID-19)PostponementDemographyPediatricsDisease

Abstract

fetched live from OpenAlex

ABSTRACT Background The COVID-19 pandemic has a worldwide impact on all health services, including childhood immunizations. In Canada, there is limited data to quantify and characterize this issue. Methods We conducted a descriptive, cross-sectional study by distributing online surveys to physicians across Ontario. The survey included three sections: provider characteristics, impact of COVID-19 on professional practice, and impact of COVID-19 on routine childhood immunization services. Multivariable logistic regression identified factors associated with modification of immunization services. Results A total of 475 respondents answered the survey from May 27 th to July 3 rd 2020, including 189 family physicians and 286 pediatricians. The median proportion of in-person visits reported by physicians before the pandemic was 99% and dropped to 18% during the first wave of the pandemic in Ontario. In total, 175 (44.6%) of the 392 respondents who usually provide vaccination to children acknowledged a negative impact caused by the pandemic on their immunization services, ranging from temporary closure of their practice (n=18; 4.6%) to postponement of vaccines in certain age groups (n=103; 26.3%). Pediatricians were more likely to experience a negative impact on their immunization services compared to family physicians (adjusted odds ratio [aOR]=2.64, 95% CI: 1.48-4.68), as well as early career physicians compared to their more senior colleagues (aOR=2.69, 95% CI: 1.30-5.56), whereas physicians from suburban settings were less impacted than physicians from urban settings (aOR=0.62, 95% CI: 0.39-0.99). The most frequently identified barriers to immunizations during the pandemic were parental concerns around COVID-19 (n=305; 77.8%), lack of personal protective equipment (PPE; n=123; 31.3%) and healthcare workers’ concerns of contracting COVID-19 (n=105; 26.8%). Conclusions COVID-19 has caused substantial modifications to pediatric immunization services across Ontario. Strategies to mitigate barriers to immunizations during the pandemic need to be implemented in order to avoid immunity gaps that could lead to an increase in vaccine preventable diseases. HIGHLIGHTS We have conducted a descriptive, cross-sectional study by distributing online surveys to pediatricians and family physicians across Ontario to assess the impact of the COVID-19 pandemic on their immunization practices. The COVID-19 pandemic has caused a substantial decrease in in-person visits and a related disruption to routine childhood immunization services during the first wave of the pandemic. The main barriers to immunizations during the pandemic included parents’ and healthcare providers’ concerns of contracting COVID-19, and lack of appropriate personal protective equipment (PPE). Solutions to maintain childhood immunizations during the pandemic included assistance in providing PPE to clinical practices, dedicated centers for vaccination, and parental education.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.043
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.037
GPT teacher head0.307
Teacher spread0.270 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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