Towards a dynamic description of major depression epidemiology
Bibliographic record
Abstract
AIMS: The substantial impact of major depression on population health is widely acknowledged. To date, health system responses to this condition have been largely shaped by observational findings. In the future, health policy decisions will benefit from an increasingly integrated and dynamic understanding of the epidemiology of this condition. Policy decisions can also be supported by the development of decision-support tools that can simulate the impact of alternative policy decisions on population health. Markov models are useful both in epidemiological modelling and in decision analysis. METHODS: In this project, a Markov model describing major depression epidemiology was developed. The model employed a Markov Tunnel in order to depict the dependence of recovery probabilities on episode duration. Transition probabilities, including incidence, recovery and mortality were estimated from Canadian national survey data. RESULTS: Episode incidence was approximately 3% per year. Recovery rates declined exponentially over time. The model predicted point prevalence at slightly less than 1%, agreeing closely with observed prevalence data. CONCLUSIONS: Epidemiological models describing the dynamic relationships between major depression incidence, prevalence, recovery and mortality can help to integrate available epidemiological data. Such models offer an attractive option for support of health policy decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".