Association Between Chronic Medical Conditions and Acute Perinatal Psychiatric Health-Care Encounters Among Migrants: A Population-Based Cohort Study
Bibliographic record
Abstract
Objectives:To examine the relationship between prepregnancy chronic medical conditions (CMCs) and the risk of acute perinatal psychiatric health-care encounters (i.e., psychiatric emergency department visits, hospitalizations) among refugees, nonrefugee immigrants, and long-term residents in Ontario.Methods:We conducted a population-based study of 15- to 49-year-old refugees (N = 29,189), nonrefugee immigrants (N = 187,430), and long-term residents (N = 641,385) with and without CMC in Ontario, Canada, with a singleton live birth in 2005 to 2015 and no treatment for mental illness in the 2 years before pregnancy. Modified Poisson regression was used to estimate the relative risk of a psychiatric emergency department visit or hospitalization from conception until 1 year postpartum among women with versus without CMC, stratified by migrant status. An unstratified model with an interaction term between CMC and migrant status was used to test for multiplicativity of effects.Results:The association between CMC and risk of a psychiatric emergency department visit or hospitalization was stronger among refugees (adjusted relative risk [aRR] = 1.87; 95% confidence interval [CI], 1.36 to 2.58) compared to long-term residents (aRR = 1.39; 95% CI, 1.30 to 1.48; interaction P = 0.047). The strength of the association was no different in nonrefugee immigrants (aRR = 1.26; 95% CI, 1.05 to 1.51) compared to long-term residents (interaction P = 0.45).Conclusion:Our study identifies refugee women with CMC as a high-risk group for acute psychiatric health care in the perinatal period. Preventive psychosocial interventions may be warranted to enhance supportive resources for all women with CMC and, in particular refugee women, to reduce the risk of acute psychiatric health care in the perinatal period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".