Hepatitis B and C in individuals with a history of antipsychotic medication use: A population-based evaluation
Bibliographic record
Abstract
BACKGROUND: A better understanding of links between mental illness and risk of bloodborne infectious disease could inform preventive and therapeutic strategies in individuals with mental illness. METHODS: We performed a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) to estimate the seroprevalence of hepatitis B and C in individuals with and without a prior prescription for antipsychotic medications, and to determine whether differences in seroprevalence could be explained by differential distribution in known infection risk factors. Multivariable logistic regression models were used to examine the association between receipt of antipsychotic medication and HBV and HCV seropositivity. RESULTS: Those who had HBV core antibody had 1.64 (95% CI: 0.89, 3.02) times the odds and those with HCV antibody (anti-HCV) had 3.48 (95% CI: 1.71, 7.09) times the odds of having a prescription for at least one antipsychotic medication compared to those who did not have HBV core antibody or HCV antibody, respectively. While prior antipsychotic receipt was a potent risk marker for HCV seropositivity, risk was explained by adjusting for known bloodborne infection risk factors (adjusted ORs 1.01 [95% CI: 0.50, 2.02] and 1.38 [95% CI: 0.44, 4.36] for HBV and HCV, respectively). CONCLUSIONS: Prior receipt of antipsychotic medications is a strong predictor of HCV (and to a lesser extent HBV) seropositivity. Treatment with antipsychotic medications should be considered as additional risk markers for individuals who may benefit from targeted prevention, screening, and harm reduction interventions for HCV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".