The impact of the COVID-19 on childhood growth amongst children under 6 years old A longitudinal cohort study in Ontario, Canada
Bibliographic record
Abstract
ObjectiveThere is evidence that the COVID-19 pandemic has had an impact on childhood growth and obesity prevalence; however, the extent to which it has affected young children (<6 years old) living in Canada is unclear. Examining the impact of the COVID-19 pandemic on childhood growth among young children is important to understanding the potential enduring health and developmental repercussions. In this study, we sought to determine: 1) the association between the COVID-19 pandemic and the rate of change in the standardized body mass index (zBMI); 2) the association between the COVID-19 pandemic and the mean zBMI; and 3) whether the association between the COVID-19 pandemic and the rate of change in zBMI differed by zBMI quantiles.MethodsThis was a longitudinal cohort study that used Ontario electronic medical records data from the practiced-based research network UTOPIAN. The population was children <6 years old (N=22,307) who had had at least one primary care visit between March 10th, 2018 – March 11th, 2022. The main exposure was the COVID-19 era (March 11, 2020-March 11, 2022). The outcome of interest was zBMI. Piecewise and linear mixed effects models with knots at the onset of COVID and 1 year into-COVID and quantile regression models were used to test the association between exposures and outcomes adjusted for rurality, racialized and newcomer index, income quintile and Material Resources Index.ResultsThe cohort comprised of 83,269 visits from 22,307 unique children. The majority of visits (67.9%) occurred before COVID. Among the children, there was an even sex distribution, a high prevalence of urban residency, and a higher diversity in terms of racialized and newcomer populations. Overall, 17.6% of the children were classified as overweight or affected by obesity. The mean zBMI pre- and during COVID were -0.122 (SD: 1.300) and -0.394 (SD: 1.330), respectively. The piecewise linear mixed effects model revealed a pre-COVID annual increase inzBMI (0.009 SD units per year on average (95% CI: 0.001, 0.017), which did not change during COVID (-0.004; 95% CI:-0.019, 0.011). The linear mixed effects model found evidence of a relationship between the COVID-19 era and an increase in mean zBMI (0.158 SD units, 95% CI: 0.256, 0.291). In all models, rural, more diverse, and more material resourced populations were associated with a decrease in the rate of change in zBMI. In the quantile regression analysis, zBMI quantiles did not show evidence of an association with the COVID-19 era.ConclusionOur analysis revealed that while the pre-COVID rate of change in zBMI was increasing, there was no evidence of an association between the COVID-19 era and the rate of change in zBMI. However, the COVID era was associated with an overall increase in mean zBMI. Further, there was no evidence of an association between the COVID-19 era and rate of change in zBMI differing by zBMI quantile. Our findings highlight an important ongoing public health emergency, overweight and obesity, that has persisted through the pandemic. This epidemic is far from seeing an end like the COVID pandemic. Effective primary care and public health interventions are required that would ideally address the multiple environmental and socio- cultural dimensions of childhood growth
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".