10-Year Trends in Healthcare Spending among Patients with Schizophrenia in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Schizophrenia is characterized by high levels of disability often resulting in increased healthcare utilization and spending. With expanding healthcare costs across all healthcare sectors, there is a need to understand how healthcare spending has changed over time. We conducted a population-based study using administrative health data from Alberta, Canada, to describe changes in medical complexity and direct healthcare spending among patients with schizophrenia over a 10-year period. METHODS: A serial cross-sectional study from January 1, 2008, to December 31, 2017, was conducted to determine changes in demographic characteristics, medical complexity, and costs among all adults (18 years or older) with schizophrenia. Total healthcare spending and sector-specific costs attributable to hospitalizations, emergency department visits, practitioner billings, and prescriptions were calculated and compared over time. RESULTS: = 33,176) within the province. There was a marked change in medical complexity with the number of patients living with 3 or more comorbidities increasing from 33.0% to 47.3%. Direct annual healthcare costs increased 2-fold from 321 to 639 million CAD (493 million USD) with a 7-fold increase in medication expenditures over the 10-year time frame. As of 2017, spending on pharmaceutical treatment surpassed hospitalizations as the leading spending category in this population. CONCLUSIONS: Healthcare spending among patients with schizophrenia continues to increase and may be partially attributable to growing rates of multimorbidity within this population. Although promising second-generation antipsychotic medications have entered the market, this has resulted in considerable changes in the distribution of healthcare spending over time. These findings will inform policy discussions around resource allocation and efforts to curb health spending while also improving care for patients with schizophrenia.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".