Can we more precisely classify exposure to antenatal depression and anxiety in multivariable prediction models of pregnancy and birth outcomes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are highly prevalent within the perinatal period and have been associated with myriad adverse pregnancy and birth outcomes. In this study, we sought to investigate whether population-based data can be used to build complex, longitudinal mental health histories that improve our ability to predict adverse pregnancy and birth outcomes. METHODS: Using population-based, administrative datasets, we examined individual-level mental health services use of all birth parents who delivered a live infant in British Columbia, Canada between April 1, 2000, and December 31, 2013, and who were registered with the provincial Medical Services Plan for over 100 days per year from 10-years preconception to 1-year postpartum. We operationalized variables to proxy severity, persistence, and frequency of depression/anxiety from preconception through pregnancy, then constructed predictive regression models for postpartum depression/anxiety and preterm birth. RESULTS: Predictive modeling of postpartum depression/anxiety and preterm birth revealed better predictions and stronger performance with inclusion of a more detailed preconception mental health history. Incorporating dichotomous indicators for depression/anxiety across preconception markedly improved predictive power and model fit. Our detailed measures of mental health service use predicted postpartum depression/anxiety much better than preterm birth. Variables characterizing use of outpatient psychiatry care and outpatient visit frequency within the first five years preconception were most useful in predicting postpartum depression/anxiety and preterm birth, respectively. CONCLUSION: We report a feasible method for developing and applying more nuanced definitions of depression/anxiety within population-based data. By accounting for differing profiles of mental health treatment, mental health history, and current mental health, we can better control for severity of underlying conditions and thus better understand more complex associations between antenatal mental health and adverse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".