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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]

Published evidence suggests that aspects of trial design lead to biased intervention effect estimates, but findings from different studies are inconsistent. This study combined data from 7 meta-epidemiologic studies and removed overlaps to derive a final data set of 234 unique meta-analyses containing 1973 trials. Outcome measures were classified as "mortality," "other objective," "or sub-jective," and Bayesian hierarchical models were used to estimate associations of trial characteristics with average bias and between-trial heterogeneity. Intervention effect estimates seemed to be ex-aggerated in trials with inadequate or unclear (vs. adequate) random-sequence generation (ratio of odds ratios, 0.89 [95% cred-ible interval {Crl}, 0.82 to 0.96]) and with inadequate or unclear (vs. adequate) allocation concealment (ratio of odds ratios, 0.93 [Crl, 0.87 to 0.99]). Lack of or unclear double-blinding (vs. double-blinding) was associated with an average of 13% exaggeration of intervention effects (ratio of odds ratios, 0.87 [Crl, 0.79 to 0.96]), and between-trial heterogeneity was increased for such studies (SD increase in heterogeneity, 0.14 [Crl, 0.02 to 0.30]). For each char-acteristic, average bias and increases in between-trial heterogeneity were driven primarily by trials with subjective outcomes, with little evidence of bias in trials with objective and mortality outcomes. This study is limited by incomplete trial reporting, and findings may be confounded by other study design characteristics. Bias associated with study design characteristics may lead to exaggeration of in-tervention effect estimates and increases in between-trial heteroge-neity in trials reporting subjectively assessed outcomes. [ABSTRACT FROM AUTHOR]

The tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a Bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution. [ABSTRACT FROM AUTHOR]