Economic Burden of Depression and Associated Resource Use in Manitoba, Canada
Bibliographic record
Abstract
Objectives To characterize the health-care utilization and economic burden associated with depression in Manitoba, Canada. Methods Patient-level data were retrieved from the Manitoba Centre for Health Policy administrative, clinical, and laboratory databases for the study period of January 1, 1996, through December 31, 2016. Patients were assigned to the depression cohort based on diagnoses recorded in hospitalizations and outpatient physician claims, as well as antidepressant prescription drug claims. A comparison cohort of nondepressed subjects, matched with replacement for age, gender, place of residence (urban vs. rural), and index date, was created. Demographics, comorbidities, intentional self-harm, mortality, health-care utilization, prescription drug utilization, and costs of health-care utilization and social services were compared between depressed patients and matched nondepressed patients, and incidence rate ratios and hazard ratios were reported. Results There were 190,065 patients in the depression cohort and 378,177 patients in the nondepression cohort. Comorbidities were 43% more prevalent among depressed patients. Intentional self-harm, all-cause mortality, and suicide mortality were higher among patients with depression than the nondepression cohort. Health-care utilization—including hospitalizations, physician visits, physician-provided psychotherapy, and prescription drugs—was higher in the depression than the nondepression cohort. Mean health-care utilization costs were 3.5 times higher among depressed patients than nondepressed patients ($10,064 and $2,832, respectively). Similarly, mean social services costs were 3 times higher ($1,522 and $510, respectively). Overall, depression adds a total average cost of $8,244 ( SD = $40,542) per person per year. Conclusions Depression contributes significantly to health burden and per patient costs in Manitoba, Canada. Extrapolation of the results to the entire Canadian health-care system projects an excess of $12 billion annually in health system spending.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".