METHODS: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.
FINDINGS: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.
INTERPRETATION: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.
FUNDING: National Institute on Drug Abuse.
METHODS: A cross-sectional study was conducted in Amman, the capital and largest city of Jordan, using a validated questionnaire. It was distributed to pharmacists working in community and hospital pharmacies using a convenience sampling technique. Descriptive and inferential statistics were performed in this study.
RESULTS: A total of 340 questionnaires distributed, 300 (88%) pharmacists responded. Over 50% of pharmacists claimed that they have sufficient knowledge regarding FDI. Virtually, the overall median (interquartile range) knowledge score was 18 (15-21), approximately 60%. The highest knowledge scores were for alcohol-drug interactions section (66.6%) followed by both common food-drug interactions and the timing of drug intake to food consumption sections with a score of (58.3%) for each, reflecting a suboptimal knowledge of FDIs among the pharmacists.
CONCLUSION: Pharmacists had unsatisfactory knowledge about common FDIs, with no significant difference between hospital and community pharmacists. Therefore, more attention and efforts should be played to improve awareness about potential food-drug interactions.