Impact of marginalization on characteristics and healthcare utilization among people with substance use disorder in Ontario, Canada, before and during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To describe and compare the characteristics of people with SUD and their use of healthcare services in two ways: 1) across varying levels of marginalization and 2) before and during the pandemic. METHODS: We conducted a population-based cross-sectional study using administrative data from Ontario, Canada. We included individuals age 16+ with a recorded diagnosis of SUD between June 2018-2019 (pre-pandemic) and June 2021-2022 (during-pandemic). Baseline sociodemographic and clinical characteristics and use of healthcare services were enumerated across the five quintiles of the Ontario Marginalization Index. RESULTS: 259,497 pre-pandemic and 276,459 during-pandemic people with SUD were identified. Over 40% belonged to the two highest marginalization quintiles (Q4/Q5). Most had an outpatient visit with similar percentages across quintiles, however the number of visits increased with increasing marginalization (pre-pandemic: mean 8.5 visits in Q1 vs 13.0 visits in Q5; during-pandemic: mean 9.5 in Q1 vs 13.4 in Q5). There was no consistent pattern in percent of people who sought alcohol-related outpatient care, however more marginalized people sought drug-related outpatient care (pre-pandemic: 19.1% in Q1 vs 31.7% in Q5; during-pandemic: 18.7% in Q1 vs 32.5% in Q5). Almost half of people with SUD had an emergency department (ED) visit, of which more belonged to higher marginalization quintiles (pre-pandemic: 43.5% in Q1 vs 49.8% in Q5; during-pandemic: 41.4% in Q1 vs 49.3% in Q5). CONCLUSIONS: SUD prevalence and most health service utilization remained similar from pre- to during-pandemic. Increasing marginalization was associated with increased use of healthcare among people with SUD. Future research should aim to further explore the complex relationship between marginalization and substance use.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".