Influence of the COVID-19 pandemic on drug and healthcare utilisation among First Nations with diabetes in Alberta, Canada: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to assess changes in diabetes management and healthcare utilisation among First Nations with diabetes in Alberta before and during the COVID-19 pandemic. DESIGN: This analysis used a retrospective cohort in a case-control design. Individual-level administrative health datasets (1 April 2018 to 31 March 2022) were linked and data were formatted as a segmented interrupted time series. SETTING: This study took place in Alberta, Canada using administrative data. PARTICIPANTS: Adult First Nations and non-First Nations (matched 1:1) with diabetes and living in Alberta were included (n=28 101; 53% female, 47% male). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the change in incidence rate of general practitioner (GP) visits, emergency department (ED) visits, hospitalisations and diabetes-related drug dispenses during-COVID-19 versus pre-COVID-19, quantified using generalised linear regressions. The secondary outcome was to report the reasons for non-drug outcomes pre-COVID-19 and during-COVID-19, based on primary diagnosis International Statistical Classification of Diseases and Related Health Problems codes. RESULTS: Pre-COVID-19, baseline rates of GP visits, ED visits, hospitalisations and drug dispenses were significantly higher among First Nations compared with non-First Nations (rate differences 398.32 (391.97-404.67), 100.58 (98.32-102.84), 14.49 (13.56-15.43), 876.98 (868.72-885.24) per 100 person-years (PY); p<0.0001). This corresponds to rates greater by 33%, 78%, 62% and 45%, respectively (p<0.0001). These disparities generally became greater during COVID-19 (rate differences 437.21 (430.83-443.59), 91.29 (89.28-93.31), 16.90 (16.00-17.80), 1066.55 (1057.74-1075.37) per 100 PY; p<0.0001) and rates higher by 36%, 95%, 86% and 49%, respectively (p<0.0001). Among First Nations during COVID-19, the rate of ED visits and hospitalisations decreased from baseline by 17% and 2%; the rate of GP visits and drug dispenses increased by 0.54% and 15%. Non-First Nations experienced greater comparative drops in GP visits, ED visits and hospitalisations, and a smaller increase in drug dispenses. There were no discernible patterns regarding the secondary outcome. CONCLUSIONS: Healthcare utilisation was substantially elevated among First Nations compared with non-First Nations peoples before and during COVID-19. While the generalisability of our findings to other health systems and populations may be limited, our findings are clinically applicable among First Nations across Alberta in order to help direct public health programming post-COVID-19.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".