Emergency department utilization and hospital admissions for ambulatory care sensitive conditions among people seeking a primary care provider during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Primary care attachment improves health care access and health outcomes, but many Canadians are unattached, seeking a provider via provincial wait-lists. This Nova Scotia-wide cohort study compares emergency department utilization and hospital admission associated with insufficient primary care management among patients on and off a provincial primary care wait-list, before and during the first waves of the COVID-19 pandemic. METHODS: We linked wait-list and Nova Scotian administrative health data to describe people on and off wait-list, by quarter, between Jan. 1, 2017, and Dec. 24, 2020. We quantified emergency department utilization and ambulatory care sensitive condition (ACSC) hospital admission rates by wait-list status from physician claims and hospital admission data. We compared relative differences during the COVID-19 first and second waves with the previous year. RESULTS: During the study period, 100 867 people in Nova Scotia (10.1% of the provincial population) were on the wait-list. Those on the wait-list had higher emergency department utilization and ACSC hospital admission. Emergency department utilization was higher overall for individuals aged 65 years and older, and females; lowest during the first 2 COVID-19 waves; and differed more by wait-list status for those younger than 65 years. Emergency department contacts and ACSC hospital admissions decreased during the COVID-19 pandemic relative to the previous year, and for emergency department utilization, this difference was more pronounced for those on the wait-list. INTERPRETATION: People in Nova Scotia seeking primary care attachment via the provincial wait-list use hospital-based services more frequently than those not on the wait-list. Although both groups have had lower utilization during COVID-19, existing challenges to primary care access for those actively seeking a provider were further exacerbated during the initial waves of the pandemic. The degree to which forgone services produces downstream health burden remains in question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".