Trends in osteoporosis care patterns during the COVID-19 pandemic in Alberta, Canada
Bibliographic record
Abstract
PURPOSE/INTRODUCTION: The objective of this study was to describe osteoporosis-related care patterns during the coronavirus disease 2019 (COVID-19) pandemic in Alberta, Canada, relative to the 3-year preceding. METHODS: A repeated cross-sectional study design encompassing 3-month periods of continuous administrative health data between March 15, 2017, and September 14, 2020, described osteoporosis-related healthcare resource utilization (HCRU) and treatment patterns. Outcomes included patients with osteoporosis-related healthcare encounters, physician visits, diagnostic and laboratory test volumes, and treatment initiations and disruptions. The percent change between outcomes was calculated, averaged across the control periods (2017-2019), relative to the COVID-19 periods (2020). RESULTS: Relative to the average control March to June period, all HCRU declined during the corresponding COVID-19 period. There was a reduction of 14% in patients with osteoporosis healthcare encounters, 13% in general practitioner visits, 9% in specialist practitioner visits, 47% in bone mineral density tests, and 13% in vitamin D tests. Treatment initiations declined 43%, 26%, and 35% for oral bisphosphonates, intravenous bisphosphonates, and denosumab, respectively. Slight increases were observed in the proportion of patients with treatment disruptions. In the subsequent June to September period, HCRU either returned to or surpassed pre-pandemic levels, when including telehealth visits accounting for 33-45% of healthcare encounters during the COVID periods. Oral bisphosphonate treatment initiations remained lower than pre-pandemic levels. CONCLUSIONS: This study demonstrates the COVID-19 pandemic and corresponding public health lockdowns further heightened the "crisis" around the known gap in osteoporosis care and altered the provision of care (e.g., use of telehealth and initiation of treatment). Osteoporosis has a known substantial care and management disparity, which has been classified as a crisis. The COVID-19 pandemic created additional burden on osteoporosis patient care with healthcare encounters, physician visits, diagnostic and laboratory tests, and treatment initiations all declining during the initial pandemic period, relative to previous years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".