Evaluating the impact of COVID-19 on medication adherence and health care utilization among individuals with psychotic disorders who are prescribed long-acting injectables (LAIs) or clozapine: A population-based study in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Ongoing psychiatric follow-up and medication adherence improve outcomes for patients with psychotic disorders. Due to COVID-19, outpatient care may have been disrupted, impacting healthcare utilization. METHODS: A retrospective population-wide study was conducted for adults in Manitoba, Canada. Medication adherence and healthcare utilization were examined from 2019 to 2021. The presence of a diagnosed psychotic disorder was identified in the five years before the index date in each year. The LAI and clozapine cohorts consisted of those who received at least two prescriptions in each year 180 days before the March 20th index date. The change in adherence was measured using the average Medication Possession Ratio. Healthcare utilization rates were compared using Generalized Estimating Equation models. RESULTS: There were no significant differences between LAI and clozapine discontinuation rates before and during the pandemic. In the LAI cohort, general practitioner visits decreased significantly (-3.5 %, p = 0.039) across four quarters of 2021 versus 2019. All-cause hospitalizations decreased by 16.8 % in 2020 versus 2019 (p = 0.0055), while psychiatric hospitalizations decreased by 18.7 % across four quarters in 2020 (p = 0.0052) and 13.7 % in 2021 (p = 0.0425), versus 2019 in the LAI cohort. There was a significant transition to virtual care during the first wave of COVID-19 (71 % in clozapine, 51 % in LAI cohorts). Trends in total outpatient visits and non-psychiatric hospitalizations remained stable. CONCLUSION: COVID-19 had no substantial impact on LAI and clozapine discontinuation rates for patients previously adherent. Outpatient care remained stable, with a significant proportion of visits being done virtually at the outset of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".