The Impact of the COVID-19 Pandemic on the Frequency of Primary Care Visits for Substance Use in Ontario, Canada
Bibliographic record
Abstract
Context: The COVID-19 pandemic has led to increased stress and anxiety as well as maladaptive coping strategies including substance use. Objective: This study aims to assess the impact of the COVID-19 pandemic on the frequency of primary care visits for substance use including tobacco, alcohol, and other drug use among primary care patients in Ontario, Canada. Study Design and Analysis: We conducted a longitudinal cohort study of family medicine patients from March 14, 2019, to March 13, 2021. We used generalized linear models to compare the frequency of substance-use related fee and diagnostic codes pre-pandemic and during the pandemic. Logistic regression was used to model the binary outcome of whether a substance-use related visit took place in either period. Setting or Dataset: Patient data was obtained from the University of Toronto Practice-Based Research Network (UTOPIAN) database with over four hundred contributing family physicians. Population Studied: The study population included 234,730 and 240,044 adult family medicine outpatients in the pre-pandemic and pandemic periods, respectively. Intervention/Instrument: The study looked at the impact of the COVID-19 pandemic on the frequency of visits for substance use; March 14-2019-March 13, 2020 was defined as the pre-pandemic period while March 14, 2020-March 13, 2021 was defined as the pandemic period. Outcome Measures: We used two types of measures to identify patients presenting to family physicians with a substance use-related issue: (1) Ontario Health Insurance Plan (OHIP) service fee codes for primary care visits related to substance use and (2) OHIP diagnostic codes for primary care visits related to substance use. Results: Relative to the pre-pandemic period, there were decreases during the pandemic in the proportion of patients who had a primary care visit for tobacco-use related reasons from 1411 to 419 per 100,000 patients, and alcohol-use related reasons from 314 to 274 per 100,000 patients. However, the proportion of patients having a primary care visit for other drug-use related reasons increased from 618 to 717 per 100,000 patients. Conclusions: Substance-use related primary care visits were found to have decreased in the first year of the COVID-19 pandemic relative to the year before. Our results, interpreted in a context of increased substance use prevalence during the COVID-19 pandemic, likely represent an unmet need for patients with substance use disorders during the pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".