Healthcare costs and cost determinants of minor ailments: A population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Estimating the cost of providing healthcare services for minor ailments (MA) is essential to address the impact of newly legislated pharmacist prescribing on the burden these conditions place on healthcare budgets. OBJECTIVES: This study aims to quantify the healthcare costs associated with minor ailment management in Ontario, describe patient characteristics by cost burden, and identify predictors of high-cost encounters. METHODS: This study employed a population-based retrospective cohort design, utilizing linked health administrative data from 2011 to 2019. The cost of care, in 2019 Canadian dollars, for up to 30 days after seeing a physician for MA was estimated from the perspective of a public payer. It included expenses from inpatient or physician visits, prescribed medication and emergency department visits. A gamma regression model with a logarithmic link was used to evaluate the impact of age, sex, income, residence, and a history of comorbidities on total cost. RESULTS: Over 34 million cases of MA were identified during the study period, with musculoskeletal sprains and strains accounting for 23.7 % of these cases. This ailment was also the costliest ($15.8 billion). Inpatient and physician care were the most expensive services. Higher costs were associated with older population groups, males, individuals with lower incomes, those living in urban spaces, and those with comorbidities. CONCLUSION: Hospital and physician-based care pose a substantial financial burden to the Ontario government. An understanding of how the determinants of cost and care pathways influence health budgets is essential to inform decisions on more efficient yet equally effective strategies, such as pharmacist prescribing for MA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".