The impact of changes to heroin supply on blood-borne virus notifications and injecting related harms in New South Wales, Australia

BACKGROUND: In early 2001 Australia experienced a sudden and unexpected disruption to heroin availability, know as the 'heroin shortage'. This 'shortage has been linked to a decrease in needle and syringe output and therefore possibly a reduction in injecting drug use. We aimed to examine changes, if any, in blood-borne viral infections and presentations for injecting related problems related to injecting drug use following the reduction heroin availability in Australia, in the context of widespread harm reduction measures. METHODS: Time series analysis of State level databases on HIV, hepatitis B, hepatitis C notifications and hospital and emergency department data. Examination of changes in HIV, hepatitis B, hepatitis C notifications and hospital and emergency department admissions for injection-related problems following the onset of the heroin shortage; non-parametric curve-fitting of number of hepatitis C notifications among those aged 15–19 years. RESULTS: There were no changes observed in hospital visits for injection-related problems. There was no change related to the onset heroin shortage in the number of hepatitis C notifications among persons aged 15–19 years, but HCV notifications have subsequently decreased in this group. No change occurred in HIV and hepatitis B notifications. CONCLUSION: A marked reduction in heroin supply resulted in no increase in injection-related harm at the community level. However, a delayed decrease in HCV notifications among young people may be related. These changes occurred in a setting with widespread, publicly funded harm reduction initiatives

Estimating the size and dynamics of an injecting drug user population and implications for health service coverage: comparison of indirect prevalence estimation methods

Aims (i) To compare indirect estimation methods to obtain mean injecting drug use (IDU) prevalence for a confined geographic location; and (ii) to use these estimates to calculate IDU and injection coverage of a medically supervised injecting facility. Design Multiple indirect prevalence estimation methods. Setting Kings Cross, Sydney, Australia. Participants IDUs residing in Kings Cross area postcodes recorded in surveillance data of the Sydney Medically Supervised Injecting Centre (MSIC) between November 2001 and October 2002. Measurements Two closed and one open capture–recapture (CRC) models (Poisson regression, truncated Poisson and Jolly–Seber, respectively) were fitted to the observed data. Multiplier estimates were derived from opioid overdose mortality data and a cross-sectional survey of needle and syringe programme attendees. MSIC client injection frequency and the number of needles and syringes distributed in the study area were used to estimate injection prevalence and injection coverage. Findings From three convergent estimates, the mean estimated size of the IDU population aged 15–54 years was 1103 (range 877–1288), yielding a population prevalence of 3.6% (2.9–4.3%). Mean IDU coverage was 70.7% (range 59.1–86.7%) and the mean adjusted injection coverage was 8.8% (range 7.3–10.8%). Approximately 11.3% of the total IDU population were estimated to be new entrants to the population per month. Conclusions Credible local area IDU prevalence estimates using MSIC surveillance data were obtained. MSIC appears to achieve high coverage of the local IDU population, although only an estimated one in 10 injections occurs at MSIC. Future prevalence estimation efforts should incorporate open models to capture the dynamic nature of IDU populationsAims (i) To compare indirect estimation methods to obtain mean injecting drug use (IDU) prevalence for a confined geographic location; and (ii) to use these estimates to calculate IDU and injection coverage of a medically supervised injecting facility. Design Multiple indirect prevalence estimation methods. Setting Kings Cross, Sydney, Australia. Participants IDUs residing in Kings Cross area postcodes recorded in surveillance data of the Sydney Medically Supervised Injecting Centre (MSIC) between November 2001 and October 2002. Measurements Two closed and one open capture–recapture (CRC) models (Poisson regression, truncated Poisson and Jolly–Seber, respectively) were fitted to the observed data. Multiplier estimates were derived from opioid overdose mortality data and a cross-sectional survey of needle and syringe programme attendees. MSIC client injection frequency and the number of needles and syringes distributed in the study area were used to estimate injection prevalence and injection coverage. Findings From three convergent estimates, the mean estimated size of the IDU population aged 15–54 years was 1103 (range 877–1288), yielding a population prevalence of 3.6% (2.9–4.3%). Mean IDU coverage was 70.7% (range 59.1–86.7%) and the mean adjusted injection coverage was 8.8% (range 7.3–10.8%). Approximately 11.3% of the total IDU population were estimated to be new entrants to the population per month. Conclusions Credible local area IDU prevalence estimates using MSIC surveillance data were obtained. MSIC appears to achieve high coverage of the local IDU population, although only an estimated one in 10 injections occurs at MSIC. Future prevalence estimation efforts should incorporate open models to capture the dynamic nature of IDU population