Antipsychotic Use and Risk of Breast Cancer in Women With Severe Mental Illness: Replication of a Nationwide Nested Case–Control Database Study
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Breast cancer is more prevalent in women with severe mental illness than in the general population, and use of prolactin-increasing antipsychotics may be a contributing factor. STUDY DESIGN: A nested case-control study was conducted using the Swedish nationwide registers (inpatient/outpatient care, sickness absence, disability pension, prescribed drugs, cancers). All women aged 18-85 years with schizophrenia/schizoaffective/other nonaffective psychotic disorder/bipolar disorder and breast cancer (cases) were matched for age, primary psychiatric diagnosis, and disease duration with five women without cancer (controls). The association between cumulative exposure to prolactin-increasing/prolactin-sparing antipsychotics and breast cancer was analyzed using conditional logistic regression, adjusted for comorbidities and co-medications. STUDY RESULTS: Among 132 061 women, 1642 (1.24%) developed breast cancer between 2010 and 2021, at a mean age of 63.3 ± 11.8 years. Compared with 8173 matched controls, the odds of breast cancer increased in women with prior exposure to prolactin-increasing antipsychotics for 1-4 years (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI] = 1.03-1.41), and for ≥ 5 years (aOR = 1.47, 95%CI = 1.26-1.71). There were no increased or decreased odds of breast cancer with exposure to prolactin-sparing antipsychotics of either 1-4 years (aOR = 1.17, 95%CI = 0.98-1.40) or ≥5 years (aOR = 0.99, 95%CI = 0.78-1.26). The results were consistent across all sensitivity analyses (ie, according to different age groups, cancer types, and primary psychiatric diagnosis). CONCLUSIONS: Although causality remains uncertain, exposure to prolactin-elevating antipsychotics for ≥ 1 year was associated with increased odds of breast cancer in women with severe mental illness. When prescribing antipsychotics, a shared decision-making process should consider individual risk factors for breast cancer.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".