Peripartum Mental Illness in Mothers With Multiple Sclerosis and Other Chronic Diseases in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Peripartum mood and anxiety disorders constitute the most frequent form of maternal morbidity in the general population, but little is known about peripartum mental illness in mothers with multiple sclerosis (MS). We compared the incidence and prevalence of peripartum mental illness among mothers with MS, epilepsy, inflammatory bowel disease (IBD), and diabetes and women without these conditions. METHODS: Using linked population-based administrative health data from ON, Canada, we conducted a cohort study of mothers with MS, epilepsy, IBD, and diabetes and without these diseases (comparators) who had a live birth with index dates, defined as 1 year before conception, between 2002 and 2017. Using validated definitions, we estimated the incidence and prevalence of mental illness (any, depression, anxiety, bipolar disorder, psychosis, substance use, suicide attempt) during the prenatal (PN) period (from conception to birth) and 3 years postpartum. We compared incidence and prevalence estimates between cohorts using simple incidence ratios (IRs) and prevalence ratios with 95% CIs and using Poisson regression models adjusting for confounders. RESULTS: We included 894,852 mothers (1,745 with MS; 5,954 with epilepsy; 4,924 with IBD; 13,002 with diabetes; 869,227 comparators). At conception, the mean (SD) maternal age was 28.6 (5.7) years. Any incident mental illness affected 8.4% of mothers with MS prenatally and 14.2% during the first postpartum year; depression and anxiety were the most common incident disorders. The first postpartum year was a higher risk period than the PN period (any mental illness IR 1.27; 95% CI 1.08-1.50). After adjustment, mothers with MS had an increased incidence of any mental illness during the PN (IR 1.26; 95% CI 1.11-1.44) and postpartum (IR 1.33; 95% CI 1.20-1.47, first postpartum year) periods than comparator mothers. Similarly, mothers with MS had an increased incidence of all specific mental illnesses except suicide attempt during the PN period vs comparator mothers. Any prevalent mental illness affected 42% of mothers with MS prenatally and 50.3% in the first postpartum year. DISCUSSION: Mothers with MS had an elevated incidence and prevalence of peripartum mental illness compared with comparator mothers, although residual confounding cannot be excluded. These findings emphasize the need for preventive interventions and early treatment of mental illness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".