Impact of COVID-19 Pandemic on Healthcare Utilization in People with Diabetes: A Time-Segmented Longitudinal Study of Alberta’s Tomorrow Project
Bibliographic record
Abstract
OBJECTIVE: The objective is to characterize the impact of COVID-19 on major healthcare for diabetes, including hospitalization, emergency department (ED) visits and primary care visits in Alberta, Canada. METHODS: Participants from Alberta's Tomorrow Project (ATP) with pre-existing diabetes prior to 1 April 2018 were included and followed up to 31 March 2021. A time-segmented regression model was used to characterize the impact of COVID-19 on healthcare utilization after adjusting for seasonality, socio-demographic factors, lifestyle behaviors and comorbidity profile of patients. RESULTS: Among 6099 participants (53.5% females, age at diagnosis 56.1 ± 9.9 y), the overall rate of hospitalization, ED visits and primary care visits was 151.5, 525.9 and 8826.9 per 1000 person-year during the COVID-19 pandemic (up to 31 March 2021), which means they reduced by 12% and 22% and increased by 6%, compared to pre-pandemic rates, respectively. Specifically, the first COVID-19 state of emergency (first wave of the outbreak) was associated with reduced rates of hospitalization, ED visits and primary care visits, by 79.4% (95% CI: 61.3-89.0%), 93.2% (95% CI: 74.6-98.2%) and 65.7% (95% CI: 47.3-77.7%), respectively. During the second state of emergency, healthcare utilization continued to decrease; however, a rebound (increase) of ED visits was observed during the period when the public health state of emergency was relaxed. CONCLUSION: The declared COVID-19 states of emergency had a negative impact on healthcare utilization for people with diabetes, especially for hospital and ED services, which suggests the importance of enhancing the capacity of these two healthcare sectors during future COVID-19-like public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".