What racial disparities exist in the prevalence of perinatal bipolar disorder in California?
Bibliographic record
Abstract
Purpose: Mental health conditions are the leading cause of preventable maternal mortality and morbidity, yet few investigations have examined perinatal bipolar disorders. This study sought to examine racial differences in the odds of having a bipolar disorder diagnosis in perinatal women across self-reported racial groups in a large sample in California, USA. Method: This cross-sectional study uses data from 3,831,593 women who had singleton live births in California, USA from 2011 to 2019 existing in a linked dataset which included hospital discharge records and birth certificates. International Classification of Diseases codes were used to identify women with a bipolar disorder diagnosis code on the hospital discharge record. Medical charts and birth certificate data was used to extract information on clinical and demographic covariate characteristics. Multivariable logistic regression was used to estimate the odds of having a bipolar disorder diagnosis across different self-reported racial groups. Results: We identified 19,262 women with bipolar disorder diagnoses. Differences in the presence of a bipolar disorder diagnosis emerged by self-reported race. In the fully adjusted model, Multiracial (selection of two races self-reported) women, compared to single-race White women had the highest odds of having a bipolar disorder diagnosis. Further examination of the all-inclusive Multiracial category revealed differences across subgroups where White/Black, White/American Indian Alaskan Native, and Black/American Indian Alaskan Native women had increased odds for bipolar disorder compared to single race White women. Conclusions: Differences in bipolar disorder diagnoses exist across racial categories and when compared to White women, Multiracial women had the highest odds of bipolar disorder and thus represent a perinatal population of focus for future intervention studies. The increased burden of mental health problems among Multiracial women is consistent with recent research that employs disaggregated race data. More studies of Multiracial women are needed to determine how self-reported racial categories are related to increased risk for perinatal bipolar disorder.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".