The National Population Health Survey’s assessment of depression risk factor associations: a simulation study assessing vulnerability to bias
Bibliographic record
Abstract
BACKGROUND: In Canada, the major source of longitudinal information on major depression epidemiology has been the National Population Health Survey (NPHS). However, the timing of NPHS interviews may raise concerns about the quality of its estimates. Specifically, the NPHS interview assesses major depressive episodes (MDE) in the year before an interview, whereas the interviews are conducted 2 years apart. The objective of this study was to determine whether this aspect of the NPHS can be expected to introduce bias into longitudinal estimates of risk factor associations. METHODS: A simulation model was used to represent the underlying epidemiology and the expected results of a study adopting the NPHS approach to assessment of MDE. The model was used to explore the extent of the resulting distortion of estimates across a range of underlying hazard ratios. RESULTS: The simulations indicated that the timing and coverage of depression interviews in the NPHS would not introduce substantial bias. The model suggested that incidence would be underestimated as a result of episodes being missed, but that this would not substantially distort estimates of association. CONCLUSION: The timing of interviews in the NPHS is not expected to cause biased relative risk estimates. NPHS estimates may, of course, be influenced by other sources of bias.
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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 | 0.033 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".