Impact of the COVID-19 Pandemic on Attendance at the 18-month-old Developmental Screening Visit
Bibliographic record
Abstract
Context: The COVID-19 pandemic disrupted primary care services but its impact on the care of young children remains unclear. The 18-month-old visit is a crucial preventive care visit where primary care providers conduct screening to detect potential developmental delays and areas of concern. Objectives: 1) To determine changes in attendance at the 18-month-old well child visit between pre-COVID and COVID eras; 2) To determine whether these changes differ by health equity stratifiers (sex, rurality, neighbourhood income, neighbourhood material deprivation, and ethnic concentration). Study Design and Analysis: Longitudinal cohort study. We used an interrupted time-series approach and fitted a segmented linear regression model. We estimated the level-change and slope change of preventive visit rates, comparing pre-COVID to COVID months and adjusting for seasonality and trends over time. Setting or Dataset: Electronic Medical Record data from primary care clinics in the University of Toronto Practice Based-Research Network (UTOPIAN) Data Safe Haven (Ontario, Canada). Population Studied: Children 17 to 24 months old. Intervention: The pre-COVID era was defined as March 2015-February 2020 and COVID era March 2020- March 2022. Outcome Measures: The 18-month-old enhanced developmental preventive care visit (in-person or virtual) was identified using billing data from UTOPIAN. Health equity stratifiers were determined using Statistics Canada data and postal codes. Results: Of the 29,942 children in the cohort, 51% were male. Proportions of children across income quintiles were approximately equal (highest to lowest: 22%, 20%, 18%, 17%, 23%). Virtual visit rates were 0% pre-COVID and 16%, 10%, and 3% in 2020, 2021, and 2022, respectively. In the pre-COVID era, there was a small positive trend of 18-month visit rates increasing over time (ß = 0.04, 95% CI: 0.02- 0.05). At the start of the pandemic, there was a level change in the rate of 18-month visits (ß = -1.10; 95% CI: -2.10 to -0.10). A marked drop in visits was observed for the months of March and April 2020. However, there was no significant difference in trends for the 18-month visits in the pre-COVID and COVID-era and no evidence of moderation by health equity stratifiers. Conclusion: Despite a drop in the 18-month visits at the onset of the pandemic, the overall trend in visits was not statistically different between the pre-COVID and COVID eras. Health equity stratifiers did not moderate the association.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".