Mental Disorders Among Mothers of Children Born Preterm: A Population-Based Cohort Study in Canada
Bibliographic record
Abstract
BackgroundOur aim was to examine the association between preterm delivery and incident maternal mental disorders using a population-based cohort of mothers in Canada.MethodsRetrospective matched cohort study using Manitoba Centre for Health Policy (MCHP) administrative data in Manitoba. Mothers who delivered preterm babies (<37 weeks gestational age) between 1998 and 2013 were matched 1:5 to mothers of term babies using socio-demographic variables. Primary outcome was any incident mental disorder within 5 years of delivery defined as any of (a) mood and anxiety disorders, (b) psychotic disorders, (c) substance use disorders, and (d) suicide or suicide attempts. Multivariable Poisson regression model was used to estimate the 5-year adjusted incidence rate ratios (IRRs).ResultsMothers of preterm children (N = 5,361) had similar incidence rates of any mental disorder (17.4% vs. 16.6%, IRR = 0.99, 95% CI, 0.91 to 1.07) compared to mothers of term children (N = 24,932). Mothers of term children had a higher rate of any mental disorder in the first year while mothers of preterm children had higher rates from 2 to 5 years. Being the mother of a child born <28 week (IRR = 1.5, 95% CI, 1.14 to 2.04), but not 28–33 weeks (IRR = 1.03, 95% CI, 0.86 to 1.19) or 34–36 weeks (IRR = 0.96, 95% CI, 0.88 to 1.05), was associated with any mental disorder.InterpretationMothers of preterm and term children had similar rates of incident mental disorders within 5-years post-delivery. Extreme prematurity was a risk factor for any mental disorder. Targeted screening and support of this latter group may be beneficial.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".