Healthcare Utilization and Costs for Musculoskeletal Disorders in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the magnitude and costs of ambulatory primary care, specialist physician care, and hospital service use for musculoskeletal disorders (MSDs) in Canada's largest province, Ontario. METHODS: Administrative health databases were analyzed for fiscal year 2013-2014 for adults aged ≥ 18 years, including data on physician services, emergency department (ED) visits, and hospitalizations. International Classification of Diseases diagnostic codes were used to identify MSD services. A validated algorithm was used to estimate direct medical costs. Person-visit rates and numbers of persons and visits were tabulated by care setting, age, sex, and physician specialty. Data were examined for all MSDs combined, as well as for specific diagnostic groupings. RESULTS: Overall, 3.1 million adult Ontarians (28.5%) made over 8 million outpatient physician visits associated with MSDs. These included 5.6 million primary care visits. MSDs accounted for 560,000 (12.3%) of all adult ED visits. Total costs for MSD-related care were $1.6 billion, with 12.6% of costs attributed to primary care, 9.2% to specialist care, 8.6% to ED care, 8.5% to day surgery, and 61.2% associated with inpatient hospitalizations. Costs due to arthritis accounted for 40% of total MSD care costs ($639 million). MSD-related imaging costs were $169 million, yielding a total cost estimate of $1.8 billion for MSDs overall. CONCLUSION: MSDs place a significant and costly burden on the healthcare system. Health system planning needs to consider the large and escalating demand for care to reduce both the individual and population burden.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| 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".