The epidemiology of treatment-resistant depression in Manitoba: a retrospective cohort study using administrative health data
Bibliographic record
Abstract
Major depressive disorder (MDD) is a condition that is difficult to treat, as many individuals do not experience complete remission, and many eventually relapse. Individuals who do not respond to antidepressant therapy are defined as having treatment-resistant depression (TRD). The epidemiology of TRD has been studied using commercial insurance claims data in the United States, but no such studies exist for a general population or in Canada. This thesis describes the epidemiology of TRD in Manitoba between 1996 and 2016 and compares the risks for ambulatory visits, emergency department (ED) visits, hospitalizations, and all-cause mortality for individuals with TRD to those with non-treatment-resistant MDD. I used the Anderson-Gill generalization of the extended Cox proportional hazards model to analyze these risks. TRD was defined using the Massachusetts General Hospital Staging Method (MGH-s), where scores of 2.5 and higher correspond to TRD. I identified 169,511 adults living in Manitoba between 1996 and 2016 who were diagnosed with MDD and dispensed at least six weeks of antidepressants from the Manitoba Population Research Data Repository at the Manitoba Centre for Health Policy. By 2016, 18,663 individuals (11.0% of the cohort) met the criteria for TRD. Compared to MGH-s scores of 1, the hazard ratios (HR) for mental health related ED visits for MGH-2.5 and MGH-3 were 3.9 and 5.6 , respectively. The HR for hospitalizations with a primary diagnosis of MDD were 4.9 and 8.3 for MGH-2.5 and MGH-3, respectively. For individuals 10 years below the average age of the cohort, TRD was associated with a three-fold increased risk for all-cause mortality. For individuals 10 years above the average age of the cohort, TRD was associated with a two-fold increased risk for all-cause mortality. These findings show that TRD is an important risk factor for requiring more intensive medical care for mood and anxiety disorders. Increasing hazards associated with increases in MGH-s scores support the hypothesis that TRD follows a severity continuum. As the risk for all-cause mortality was higher for individuals with TRD, further research is needed to determine whether TRD is associated with a higher risk for suicide.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".