Collective impact of chronic medical conditions and poverty on perinatal mental illness: population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Chronic medical conditions (CMCs) and poverty commonly co-occur and, while both have been shown to independently increase the risk of perinatal mental illness, their collective impact has not been examined. METHODS: This population-based study included 853 433 Ontario (Canada) women with a singleton live birth and no recent mental healthcare. CMCs were identified using validated algorithms and disease registries, and poverty was ascertained using neighbourhood income quintile. Perinatal mental illness was defined as a healthcare encounter for a mental health or substance use disorder in pregnancy or the first year postpartum. Modified Poisson regression was used to test the independent impacts of CMC and poverty on perinatal mental illness risk, adjusted for covariates, and additive interaction between the two exposures was assessed using the relative excess risk due to interaction (RERI) and synergy index (SI). RESULTS: CMC and poverty were each independently associated with increased risk of perinatal mental illness (CMC vs no CMC exposure: 19.8% vs 15.6%, adjusted relative risk (aRR) 1.21, 95% CI (CI) 1.20 to 1.23; poverty vs no poverty exposure: 16.7% vs 15.5%, aRR 1.06, 95% CI 1.05 to 1.07). However, measures of additive interaction for the collective impact of both exposures on perinatal mental illness risk were not statistically significant (RERI 0.02, 95% CI -0.01 to 0.06; SI 1.09, 95% CI 0.95 to 1.24). CONCLUSION: CMC and poverty are independent risk factors for perinatal mental illness and should be assessed as part of a comprehensive management programme that includes prevention strategies and effective screening and treatment pathways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".