ABSTRACT
HIV and hepatitis C virus (HCV) infection rates among persons, who use drugs, have risen during the US overdose crisis. We elicited patient perspectives about these interconnected infections to identify the areas of misinformation that might prevent appropriate management. We used in-depth interviews and thematic analysis of coded data collected from patients (N = 24) at detox and from key informants (N = 10). Seventy-one per cent reported injecting drugs. We found that patient narratives included misinformation about HIV and HCV transmission, natural history and treatment. Some participants thought that activities such as sharing drinkware or food with persons with HIV could lead to infection, while others believed that mainly men who have sex with men were at risk. Despite significant improvements in treatment, some participants still believed that HIV was a fatal condition, while others noted that treatment was only necessary at later stages. Some participants thought that HCV was a common, mild infection that might not need immediate attention, and others stated that individuals who were actively using drugs were ineligible for treatment. The current study exposes a considerable level of misinformation about HIV prevention and about the importance and benefits of HCV therapy. Educational interventions are necessary to counter misinformation identified.
ABSTRACT
OBJECTIVE: To develop and validate a prediction model for postdischarge opioid use in patients undergoing cesarean birth. METHODS: We conducted a prospective cohort study of patients undergoing cesarean birth. Patients were enrolled postoperatively, and they completed pain and opioid use questionnaires 14 days after cesarean birth. Clinical data were abstracted from the electronic health record (EHR). Participants were prescribed 30 tablets of hydrocodone 5 mg-acetaminophen 325 mg at discharge and were queried about postdischarge opioid use. The primary outcome was total morphine milligram equivalents used. We constructed three proportional odds predictive models of postdischarge opioid use: a full model with 34 predictors available before hospital discharge, an EHR model that excluded questionnaire data, and a reduced model. The reduced model used forward selection to sequentially add predictors until 90% of the full model performance was achieved. Predictors were ranked a priori based on data from the literature and prior research. Predictive accuracy was estimated using discrimination (concordance index). RESULTS: Between 2019 and 2020, 459 participants were enrolled and 279 filled the standardized study prescription. Of the 398 with outcome measurements, participants used a median of eight tablets (interquartile range 1-18 tablets) after discharge, 23.5% used no opioids, and 23.0% used all opioids. Each of the models demonstrated high accuracy predicting postdischarge opioid use (concordance index range 0.74-0.76 for all models). We selected the reduced model as our final model given its similar model performance with the fewest number of predictors, all obtained from the EHR (inpatient opioid use, tobacco use, and depression or anxiety). CONCLUSION: A model with three predictors readily found in the EHR-inpatient opioid use, tobacco use, and depression or anxiety-accurately estimated postdischarge opioid use. This represents an opportunity for individualizing opioid prescriptions after cesarean birth.