Antipsychotics and other risk factors for mortality among people with schizophrenia during an extreme heat event: a population-based case-control study
Bibliographic record
Abstract
Schizophrenia is associated with increased mortality during extreme heat events (EHEs), including the unprecedented 2021 Western North America EHE in British Columbia, Canada. We sought to examine the association between mortality during the 2021 EHE and multiple risk factors among people with schizophrenia, with a focus on antipsychotic medications. We identified all individuals with schizophrenia in British Columbia using an administrative chronic disease registry and linked them with antipsychotic dispensations for the 30 days prior to the EHE. We compared 137 individuals who died during the EHE with 57,394 who survived using multiple logistic regression. Analyses also included age, sex, income assistance, comorbidities, and indicators of schizophrenia severity. Dispensation of any antipsychotic was associated with increased EHE mortality [OR 2.43, 95% CI 1.52, 4.01], which was driven by haloperidol, clozapine, and zuclopenthixol. The risk was increased for dispensation of two or more antipsychotics in combination [OR 4.05, 95% CI 2.41, 6.98]. Other variables associated with EHE mortality included being on income assistance, age, sex, having a mental health-related emergency department visit, and time since disease registry entry. Being dispensed an antipsychotic prior to the EHE was associated with increased odds of mortality among people with schizophrenia. Some antipsychotics had larger effects, and risk increased with combination therapy. There were also significant non-pharmaceutical risk factors. People with schizophrenia are at-risk during EHEs due to multiple overlapping factors, including antipsychotic medications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".