Trends in missed paediatric preventive primary care visits during the COVID-19 pandemic using routinely collected electronic medical records in Ontario, Canada (2015–2022)
Bibliographic record
Abstract
BACKGROUND: Well child visits (WCV) are fundamental to preventive primary care. We examined trends in WCV attendance during the COVID-19 pandemic and characterised variation by patient and provider characteristics. METHODS: Deidentified electronic medical records from two academic practice-based research networks in Ontario were used to create age-specific cohorts of children under age six attending WCVs from 2015 to 2022. Patients' residential postal codes were linked to neighbourhood-level measures to estimate socioeconomic status. Monthly visit rates were modelled using segmented linear regression with autoregressive residuals. Changes associated with COVID-19 were assessed using level change and trend change of monthly visit rates. FINDINGS: For the 53 256 included children, WCV attendance increased from 2015 to 2020 for cohorts aged 15 months and younger and was stable for 18-month, 2-3-year and 4-6-year visits. The COVID-19 pandemic was associated with decreased WCV attendance in all ages except ages 1-2 weeks, 1 month, 12 months, 15 months and 18 months, in whom attendance was unchanged. The rate of change in WCV attendance rates pre-COVID-19 compared with post-COVID-19 was unchanged, with the exception of increased rate of change for the 1-2 weeks and 2-3 years old cohorts. Lower attendance rates were observed in children residing in neighbourhoods with the highest material deprivation, rural regions and those whose family physicians were men or older than 65 years. INTERPRETATION: Prepandemic gains in WCV attendance were stable or improved after the initial reductions observed at the pandemic onset, suggesting that WCVs were prioritised by family physicians and families. Targeted strategies are needed to improve WCV attendance for vulnerable groups.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".