Has ‘lifetime prevalence’ reached the end of its life? An examination of the concept
Bibliographic record
Abstract
Many cross-sectional surveys in psychiatric epidemiology report estimates of lifetime prevalence, and the results consistently show a declining trend with age for such disorders as depression and anxiety. In a closed cohort with no mortality, lifetime prevalence should increase or remain constant with age. For mortality to account for declining lifetime prevalence, mortality rates in those with a disorder must exceed those without a disorder by a sufficient extent that more cases would be removed from the prevalence pool than are added by new cases, and this is unlikely to occur across most of the age range. We argue that the decline in lifetime prevalence with age cannot be explained by period or cohort effects or be due to a survivor effect, and are likely due to a variety of other factors, such as study design, forgetting, or reframing. Further, because lifetime prevalence is insensitive to changes in treatment effectiveness or demand for services, it is a parameter that should be dropped from the lexicon of psychiatric epidemiology.
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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 | 0.038 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".