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Background and Aims: Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture–recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug‐related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'. Methods: Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. Results: We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74–0.94%) of 15–64‐year‐olds, which is similar to a published estimate based on capture–recapture analysis. Conclusions: Our modelling approach estimates prevalence from drug‐related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture–recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture–recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach. [ABSTRACT FROM AUTHOR]

Background and Aims: Given the costs of alcohol to society, it is important to evaluate whether local alcohol licensing decisions can mitigate the effects of alcohol misuse. Robust natural experiment evaluations of the impact of individual licensing decisions could potentially inform and improve local decision‐making. We aimed to assess whether alcohol licensing decisions could be evaluated at small spatial scale by using a causal inference framework. Design Three natural experiments. Setting and participants: Three English local areas of 1000–15 000 people each. Intervention and comparator: The case study interventions were (i) the closure of a nightclub following reviews; (ii) closure of a restaurant/nightclub following reviews and (iii) implementation of new local licensing guidance (LLG). Trends in outcomes were compared with synthetic counterfactuals created using Bayesian structural time–series. Measurements Time–series data were obtained on emergency department admissions, ambulance call‐outs and alcohol‐related crime at the Lower or Middle Super Output geographical aggregation level. Findings Closure of the nightclub led to temporary 4‐month reductions in antisocial behaviour (−18%; 95% credible interval − 37%, −4%), with no change in other outcomes. Closure of the restaurant/nightclub did not lead to measurable changes in outcomes. The new licensing guidance led to small reductions in drunk and disorderly behaviour (nine of a predicted 21 events averted), and the unplanned end of the LLG coincided with an increase in domestic violence of two incidents per month. Conclusions: The impact of local alcohol policy, even at the level of individual premises, can be evaluated using a causal inference framework. Local government actions such as closure or restriction of alcohol venues and alcohol licensing may have a positive impact on health and crime in the immediate surrounding area. [ABSTRACT FROM AUTHOR]

Summary: In England, 160 000 individuals were estimated to be chronically infected with hepatitis C virus (HCV) in 2005 and the burden of severe HCV‐related liver disease has increased steadily for the past 15 years. Direct‐acting antiviral treatments can clear infection in most patients, motivating HCV elimination targets. However, the current burden of HCV is unknown and new methods are required to monitor progress. We employed a Bayesian back‐calculation approach, combining data on severe HCV‐related liver disease and disease progression, to reconstruct historical HCV incidence and estimate current prevalence in England. We explicitly modelled infections occurring in people who inject drugs, the key risk group, allowing information on the size of this population and surveillance data on HCV prevalence to inform recent incidence. We estimated that there were 143 000 chronic infections in 2015 (95% credible interval 123 000‐161 000), with 34% and 54% in those with recent and past injecting drug use, respectively. Following the planned scale‐up of new treatments, chronic infections were predicted to fall to 113 400 (94 900‐132 400) by the end of 2018 and to 89 500 (71 300‐108 600) by the end of 2020. Numbers developing severe HCV‐related liver disease were predicted to fall by at least 24% from 2015 to 2020. Thus, we describe a coherent framework to monitor progress using routinely collected data, which can be extended to incorporate additional data sources. Planned treatment scale‐up is likely to achieve 2020 WHO targets for HCV morbidity, but substantial efforts will be required to ensure that HCV testing and patient engagement are sufficiently high. People who inject drugs are at high risk of acquiring hepatitis C infection, many of whom will develop cirrhosis and severe liver disease. We use surveillance data to model this process, estimating the number of HCV‐infected people in England and predicted impact of new treatments. We estimate 143,000 people had chronic infection in 2015 and predict that this will fall to around 90,000 in 2020. [ABSTRACT FROM AUTHOR]

Aims To estimate the number of people who have ever injected drugs (defined here as PWID) living in Scotland in 2009 who have been infected with the hepatitis C virus (HCV) and to quantify and characterize the population remaining undiagnosed. Methods Information from routine surveillance ( n = 22 616) and survey data ( n = 2511) was combined using a multiparameter evidence synthesis approach to estimate the size of the PWID population, HCV antibody prevalence and the proportion of HCV antibody prevalent cases who have been diagnosed, in subgroups defined by recency of injecting (in the last year or not), age (15-34 and 35-64 years), gender and region of residence (Greater Glasgow and Clyde and the rest of Scotland). Results HCV antibody-prevalence among PWID in Scotland during 2009 was estimated to be 57% [95% CI=52−61%], corresponding to 46 657 [95% credible interval (CI) = 33 812-66 803] prevalent cases. Of these, 27 434 (95% CI = 14 636-47 564) were undiagnosed, representing 59% [95% CI=43−71%] of prevalent cases. Among the undiagnosed, 83% (95% CI = 75-89%) were PWID who had not injected in the last year and 71% (95% CI = 58-85%) were aged 35-64 years. Conclusions The number of undiagnosed hepatitis C virus-infected cases in Scotland appears to be particularly high among those who have injected drugs more than 1 year ago and are more than 35 years old. [ABSTRACT FROM AUTHOR]

Aims To investigate childhood influences on onset of injection drug use. Design Matched case-control study. Setting Edinburgh, Scotland. Participants A total of 432 individuals presenting at a community health facility with injection drug use and 432 age- and sex-matched non-injecting controls recruited through the same facility. Measurements Main exposures considered were family structure and experience of public care, carer substance use, physical and sexual victimization and conduct problems, all measured at personal interview. The outcome was history of adult injection drug use recorded in medical records corroborated at personal interview. Findings Compared to two-parent families all other family structures were associated with increased risk of injection drug use, the greatest increased risk being associated with public care. Violence, criminality and financial problems in the family were also associated with increased risk, as were all types of carer substance use. The greatest increased risk was associated with markers of early conduct problems, particularly school exclusion and childhood contact with the criminal justice system. In multivariable analyses the strongest risk factors for later injecting were always having lived with a relative or family friend (not always a parent) and in care/adopted/foster home at any point [odds ratio ( OR) = 2.66, 95% confidence interval ( CI): 1.02-6.92 and OR = 2.17, 95% CI: 0.91-5.17, respectively], experienced violence from parent or carer ( OR = 2.06, 95% CI: 1.26, 3.38) and early evidence of conduct problems [ever excluded from school ( OR = 2.73, 95% CI: 1.68, 4.45); childhood criminality (ever arrested by police pre-adult OR = 3.05, 95% CI: 1.90, 4.89, ever been in borstal/young offenders/list D school OR = 4.70, 95% CI: 2.02, 10.94)]. After adjustment for family structure and conduct problems, sexual victimization was associated weakly with injecting onset ( OR = 1.29, 95% CI: 0.76-2.19). More than 70% of injection drug use onset appeared attributable to the risk factors identified. Conclusions Injection drug use in adults is associated strongly with prior childhood adversity, in particular not living with both parents and early conduct problems. Prevention initiatives should also consider these risk factors. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims To investigate whether opiate substitution therapy (OST) and needle and syringe programmes (NSP) can reduce hepatitis C virus (HCV) transmission among injecting drug users (IDUs). Design Meta-analysis and pooled analysis, with logistic regression allowing adjustment for gender, injecting duration, crack injecting and homelessness. Setting Six UK sites (Birmingham, Bristol, Glasgow, Leeds, London and Wales), community recruitment. Participants A total of 2986 IDUs surveyed during 2001-09. Measurement Questionnaire responses were used to define intervention categories for OST (on OST or not) and high NSP coverage (≥100% versus <100% needles per injection). The primary outcome was new HCV infection, measured as antibody seroconversion at follow-up or HCV antibody-negative/RNA-positive result in cross-sectional surveys. Findings Preliminary meta-analysis showed little evidence of heterogeneity between the studies on the effects of OST ( I2 = 48%, P = 0.09) and NSP ( I2 = 0%, P = 0.75), allowing data pooling. The analysis of both interventions included 919 subjects with 40 new HCV infections. Both receiving OST and high NSP coverage were associated with a reduction in new HCV infection [adjusted odds ratios (AORs) = 0.41, 95% confidence interval (CI): 0.21-0.82 and 0.48, 95% CI: 0.25-0.93, respectively]. Full harm reduction (on OST plus high NSP coverage) reduced the odds of new HCV infection by nearly 80% (AOR = 0.21, 95% CI: 0.08-0.52). Full harm reduction was associated with a reduction in self-reported needle sharing by 48% (AOR 0.52, 95% CI: 0.32-0.83) and mean injecting frequency by 20.8 injections per month (95% CI: −27.3 to −14.4). Conclusions There is good evidence that uptake of opiate substitution therapy and high coverage of needle and syringe programmes can substantially reduce the risk of hepatitis C virus transmission among injecting drug users. Research is now required on whether the scaling-up of intervention exposure can reduce and limit hepatitis C virus prevalence in this population. [ABSTRACT FROM AUTHOR]

In the past few years a number of antibody biomarkers have been developed to distinguish between recent and established Human Immunodeficiency Virus (HIV) infection. Typically, a specific threshold/cut-off of the biomarker is chosen, values below which are indicative of recent infections. Such biomarkers have attracted considerable interest as the basis for incidence estimation using a cross-sectional sample. An estimate of HIV incidence can be obtained from the prevalence of recent infection, as measured in the sample, and knowledge of the time spent in the recent infection state, known as the window period. However, such calculations are based on a number of assumptions concerning the distribution of the window period. We compare two statistical methods for estimating the mean and distribution of a window period using data on repeated measurements of an antibody biomarker from a cohort of HIV seroconverters. The methods account for the interval-censored nature of both the date of seroconversion and the date of crossing a specific threshold. We illustrate the methods using repeated measurements of the Avidity Index (AI) and make recommendations about the choice of threshold for this biomarker so that the resulting window period satisfies the assumptions for incidence estimation. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Back-calculation is a method of obtaining estimates of the number of infections of a disease over time. Data on an endpoint of the disease, together with knowledge of the time from infection to endpoint, allows reconstruction of the incidence of infection. The technique has had much success when applied to the HIV epidemic, using incidence of AIDS diagnoses to inform past HIV infections. In recent years, the period from infection to AIDS has changed considerably due to new regimes of anti-viral therapies. This has led to attempts to use incidence of first positive HIV test as an alternative basis for back-calculation. Developing on earlier work, this paper explores the feasibility of a multi-state formulation of the back-calculation method that models the disease and diagnosis processes and uses HIV diagnoses as an endpoint. Estimation is carried out in a Bayesian framework, which naturally allows incorporation of external information to inform the diagnosis probabilities. The idea is illustrated on data from the HIV epidemic in homosexuals in England and Wales. [ABSTRACT FROM AUTHOR]

The authors reflect on the use of multiple parameter evidence synthesis (MPES) to estimate people who inject drugs (PWID) and drug use. The authors state that diverse information sources can be integrated by MPES into a single coherent model which was said to be similar to systematic reviews. The authors state that MPES was also used for other infections including chlamdyia and toxoplasmosis.

Background and Aims: Given the costs of alcohol to society, it is important to evaluate whether local alcohol licensing decisions can mitigate the effects of alcohol misuse. Robust natural experiment evaluations of the impact of individual licensing decisions could potentially inform and improve local decision-making. We aimed to assess whether alcohol licensing decisions could be evaluated at small spatial scale by using a causal inference framework., Design: Three natural experiments., Setting and Participants: Three English local areas of 1000-15 000 people each., Intervention and Comparator: The case study interventions were (i) the closure of a nightclub following reviews; (ii) closure of a restaurant/nightclub following reviews and (iii) implementation of new local licensing guidance (LLG). Trends in outcomes were compared with synthetic counterfactuals created using Bayesian structural time-series., Measurements: Time-series data were obtained on emergency department admissions, ambulance call-outs and alcohol-related crime at the Lower or Middle Super Output geographical aggregation level., Findings: Closure of the nightclub led to temporary 4-month reductions in antisocial behaviour (-18%; 95% credible interval - 37%, -4%), with no change in other outcomes. Closure of the restaurant/nightclub did not lead to measurable changes in outcomes. The new licensing guidance led to small reductions in drunk and disorderly behaviour (nine of a predicted 21 events averted), and the unplanned end of the LLG coincided with an increase in domestic violence of two incidents per month., Conclusions: The impact of local alcohol policy, even at the level of individual premises, can be evaluated using a causal inference framework. Local government actions such as closure or restriction of alcohol venues and alcohol licensing may have a positive impact on health and crime in the immediate surrounding area., (© 2020 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)