Emergency department utilization and hospitalizations for ambulatory care sensitive conditions among unattached people actively seeking a primary care provider during the COVID-19 pandemic: a retrospective cohort study
Bibliographic record
Abstract
ABSTRACT Background Primary care (PC) attachment improves healthcare access and prevention and management of chronic conditions. Yet, growing proportions of Canadians are unattached, signing-up on provincial waitlists. Understanding variations in healthcare utilization during COVID-19, and among potentially vulnerable unattached patients, is needed. This study compares emergency department (ED) utilization and hospitalization among those on and off a provincial PC waitlist, during the first two waves of COVID-19. Methods Waitlist and administrative health data were linked to describe persons ever/never on the waitlist between January 1, 2017, and December 24, 2020. ED utilization and ambulatory care sensitive conditions (ACSC) hospitalization rates by current waitlist status were quantified from physician claims and hospitalization data. Relative differences during COVID-19 first and second waves were compared with the previous year. Results During the study period, 100,867 Nova Scotians (10.1%) were on the waitlist. Those on the waitlist had higher ED utilization and ACSC hospitalizations. ED utilization was higher overall for individuals ≥65 years and females; lowest during first two COVID-19 waves; and differed more by waitlist status for those <65 years. ED contacts and ACSC hospitalizations decreased during COVID-19 relative to the previous year, and for ED utilization this difference was more pronounced for those on the waitlist. Interpretation Nova Scotians seeking PC attachment utilize hospital-based services more frequently than those not on the waitlist. Both groups had lower utilization during the COVID-19 pandemic than the year before. The degree to which forgone services produces downstream health burden remains to be seen.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".