Perinatal Mental Illness and Risk of Incident Autoimmune Disease: A Population-Based Propensity-Score Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated elevated risk for autoimmune disease associated with perinatal mental illness, but the extent to which this risk is specific to mental illness arising perinatally, and not mental illness generally, is unknown. Our objective was to compare the risk of autoimmune disease in women with mental illness arising within the perinatal period to (1) women with mental illness arising outside the perinatal period and (2) women who did not develop mental illness. METHODS: We conducted a population-based matched cohort study of women aged 15-49 years with no history of mental illness or autoimmune disease in Ontario, Canada, 1998-2018. The exposed, 60,701 women with mental illness arising between conception and 365 days postpartum were propensity score-matched to (1) 264,864 women with mental illness arising non-perinatally and (2) 469,164 women who did not develop mental illness. Hazard ratios (HR) for autoimmune disease were generated using Cox proportional hazards models. RESULTS: The incidence of autoimmune disease was similar among women with mental illness arising perinatally compared to those with mental illness arising non-perinatally (138.4 vs 140.7 per 100,000 person-years; HR 0.98, 95% CI 0.92-1.05), and elevated among women with mental illness arising perinatally compared to those who did not develop mental illness (138.4 vs 88.9 per 100,000 person-years; HR 1.54, 95% CI 1.44-1.64). The HR for the latter comparison was more pronounced for autoimmune disease with brain-reactive antibodies than other autoimmune disease. CONCLUSION: Perinatal mental illness is associated with increased risk of autoimmune disease that is no different than that of mental illness arising non-perinatally. Women with mental illness, regardless of the timing of onset, could benefit from early detection of autoimmune disease.
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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 | 0.000 |
| 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 | 0.000 |
| 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".