Impact of the COVID-19 Pandemic on the Incidence and Composition of Urolithiasis in New Brunswick, Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic affected the presentation and management of many diseases. Lifestyle and dietary habit changes caused by social limitations and quarantine may alter the incidence of chronic diseases such as urolithiasis. This epidemiology study aimed to investigate the impact of the COVID-19 pandemic on the incidence of kidney stones in the Canadian province of New Brunswick. Methods: Reports of 3253 kidney stone analysis from 1 January 2021, to 31 December 2023, were retrospectively reviewed from the laboratory information system. All stone compositions were analysed by Fourier transforminfrared spectrometry technology. Incident rates were compared with 3838 urolithiasis reports from 1 October 2016, to 30 September 2019, stratified by different ages, sexes and the geographic regional health authority (RHA) zones. Results: The overall incidence of urolithiasis in New Brunswick significantly dropped from 148 (95% confidence interval (CI) 139–157) per 100,000 person-years in 2016–2019 to 115 (95% CI 110–120) per 100,000 person-years in the period of 2021–2023 (χ2: 94.167, p < 0.01, risk ratio: 0.78). The decrease occurred in both sexes, most age groups and the most densely populated RHA zones (Zones 1 and 2; p < 0.01). However, geographic differences on the impact of COVID-19 on prevalence were observed. Similar to that of the pre-COVID-19 period, calcium oxalate monohydrate remained as the predominant (61.27%) kidney stone type in 2021–2023. Struvite stone decreased from 2.19% to 0.86%. Conclusions: The incidence rate of urolithiasis in New Brunswick was significantly lower in the COVID-19 pandemic than in the pre-COVID-19 period. However, we found no significant change in kidney stone compositions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".