Impact of the COVID-19 Pandemic and Control Measures on Screening and Diagnoses of Type 2 Diabetes in British Columbia
Bibliographic record
Abstract
INTRODUCTION: In British Columbia (BC), Canada, COVID-19 and associated control measures impacted routine care for patients with diabetes. Some of these measures may have impacted timely screening and diagnosis of type 2 diabetes. We assessed the impact of control measures on screening and diagnosis of type 2 diabetes in BC. METHODS: We used data from the BC COVID-19 Cohort, which includes COVID-19 and healthcare administrative data on all residents of BC. We assessed and compared screening (≥40 yrs) and diagnosis (≥18 yrs) of diabetes among the adult population during the pandemic period (1 April 2020-31 December 2022), with 1 January 2016-31 March 2020 used as a historical reference period. We used interrupted time series with generalized additive models to evaluate the impact of policy measures on screening and diagnoses trends. RESULTS: We observed an initial decline in the mean number of screenings and diagnoses. In the third post-policy phase (January 2022-December 2022), there was a 4.8% (-5.1, 15.4) increase in screenings while after an initial reduction in diabetes diagnoses, we observed a significant increase of 31.6% (17.8, 46.6) in the third post-policy phase. Further stratification by age and sex showed the entire increase in diagnoses trends was driven by younger females with a 56.4% (25.1, 92.9) and 58.7% (38.2, 81.3) increment in diagnoses in the 18-29 and 40-49 age groups, respectively. CONCLUSIONS: The initial reduced number of screenings and diagnoses followed by the significant upward trend in diabetes diagnoses in the later post-policy phase have important clinical and public health implications. Further research is needed to understand the post-pandemic increase in diabetes among females.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".