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Summary Background A target to eliminate HIV transmission in England by 2030 was set in early 2019. This study aimed to estimate trends from 2013 to 2019 in HIV prevalence, particularly the number of people living with undiagnosed HIV, by exposure group, ethnicity, gender, age group, and region. These estimates are essential to monitor progress towards elimination. Methods A Bayesian synthesis of evidence from multiple surveillance, demographic, and survey datasets relevant to HIV in England was used to estimate trends in the number of people living with HIV, the proportion of people unaware of their HIV infection, and the corresponding prevalence of undiagnosed HIV. All estimates were stratified by exposure group, ethnicity, gender, age group (15–34, 35–44, 45–59, or 60–74 years), region (London, or outside of London) and year (2013–19). Findings The total number of people living with HIV aged 15–74 years in England increased from 83 500 (95% credible interval 80 200–89 600) in 2013 to 92 800 (91 000–95 600) in 2019. The proportion diagnosed steadily increased from 86% (80–90%) to 94% (91–95%) during the same time period, corresponding to a halving in the number of undiagnosed infections from 11 600 (8300–17 700) to 5900 (4400–8700) and in undiagnosed prevalence from 0·29 (0·21–0·44) to 0·14 (0·11–0·21) per 1000 population. Similar steep declines were estimated in all subgroups of gay, bisexual, and other men who have sex with men and in most subgroups of Black African heterosexuals. The pace of reduction was less pronounced for heterosexuals in other ethnic groups and people who inject drugs, particularly outside London; however, undiagnosed prevalence in these groups has remained very low. Interpretation The UNAIDS target of diagnosing 90% of people living with HIV by 2020 was reached by 2016 in England, with the country on track to achieve the new target of 95% diagnosed by 2025. Reductions in transmission and undiagnosed prevalence have corresponded to large scale-up of testing in key populations and early diagnosis and treatment. Additional and intensified prevention measures are required to eliminate transmission of HIV among the communities that have experienced slower declines than other subgroups, despite having very low prevalences of HIV. Funding UK Medical Research Council and Public Health England.

Background and Aims Indirect estimation methods are required for estimating the size of populations where only a portion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used but have been criticised 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 about additional fDRPs observed outside of 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. Findings 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.

Peer reviewed: True, Funder: NIHR Health Protection Unit in Behavioural Science and Evaluation, When comparing the risk of a post-infection binary outcome, for example, hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection. Typically, the infection time is unknown and positive test time used as a proxy for it. Positive test time may also be used when assessing how risk of the outcome changes over calendar time. We show that if time from infection to positive test is correlated with the outcome, the risk conditional on positive test time is a function of the trajectory of infection incidence. Hence, a risk ratio adjusted for positive test time can be quite different from the risk ratio adjusted for infection time. We propose a simple sensitivity analysis that indicates how risk ratios adjusted for positive test time and infection time may differ. This involves adjusting for a shifted positive test time, shifted to make the difference between it and infection time uncorrelated with the outcome. We illustrate this method by reanalysing published results on the relative risk of hospitalisation following infection with the Alpha versus pre-existing variants of SARS-CoV-2. Results indicate the relative risk adjusted for infection time may be lower than that adjusted for positive test time.

Background: \ud The SARS-CoV-2 delta (B.1.617.2) variant was first detected in England in March, 2021. It has since rapidly become the predominant lineage, owing to high transmissibility. It is suspected that the delta variant is associated with more severe disease than the previously dominant alpha (B.1.1.7) variant. We aimed to characterise the severity of the delta variant compared with the alpha variant by determining the relative risk of hospital attendance outcomes.\ud \ud Methods: \ud This cohort study was done among all patients with COVID-19 in England between March 29 and May 23, 2021, who were identified as being infected with either the alpha or delta SARS-CoV-2 variant through whole-genome sequencing. Individual-level data on these patients were linked to routine health-care datasets on vaccination, emergency care attendance, hospital admission, and mortality (data from Public Health England's Second Generation Surveillance System and COVID-19-associated deaths dataset; the National Immunisation Management System; and NHS Digital Secondary Uses Services and Emergency Care Data Set). The risk for hospital admission and emergency care attendance were compared between patients with sequencing-confirmed delta and alpha variants for the whole cohort and by vaccination status subgroups. Stratified Cox regression was used to adjust for age, sex, ethnicity, deprivation, recent international travel, area of residence, calendar week, and vaccination status.\ud \ud Findings: \ud Individual-level data on 43 338 COVID-19-positive patients (8682 with the delta variant, 34 656 with the alpha variant; median age 31 years [IQR 17–43]) were included in our analysis. 196 (2·3%) patients with the delta variant versus 764 (2·2%) patients with the alpha variant were admitted to hospital within 14 days after the specimen was taken (adjusted hazard ratio [HR] 2·26 [95% CI 1·32–3·89]). 498 (5·7%) patients with the delta variant versus 1448 (4·2%) patients with the alpha variant were admitted to hospital or attended emergency care within 14 days (adjusted HR 1·45 [1·08–1·95]). Most patients were unvaccinated (32 078 [74·0%] across both groups). The HRs for vaccinated patients with the delta variant versus the alpha variant (adjusted HR for hospital admission 1·94 [95% CI 0·47–8·05] and for hospital admission or emergency care attendance 1·58 [0·69–3·61]) were similar to the HRs for unvaccinated patients (2·32 [1·29–4·16] and 1·43 [1·04–1·97]; p=0·82 for both) but the precision for the vaccinated subgroup was low.\ud \ud Interpretation: \ud This large national study found a higher hospital admission or emergency care attendance risk for patients with COVID-19 infected with the delta variant compared with the alpha variant. Results suggest that outbreaks of the delta variant in unvaccinated populations might lead to a greater burden on health-care services than the alpha variant.\ud \ud Funding: \ud Medical Research Council; UK Research and Innovation; Department of Health and Social Care; and National Institute for Health Research.

Background: Personalised or stratified medicine has played an increasingly important role in improving bio-medical care in recent years. A Bayesian joint modelling approach to dynamic prediction of HIV progression and mortality allows such individualised predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival.Methods: Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression, as measured by CD4 counts, and survival, to obtain individualised dynamic predictions of both processes that were updated at each visit time in the follow-up period. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging.Results: A total of 254 subjects were observed in the dataset with a median age of 30 years (interquartile range, IQR, 26–38). The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7 -39 months. The median baseline CD4 count was 129 cells/mm3 (IQR 61–247 cells/mm3). From the joint model with highest posterior weight, subjects whose functional status was working were significantly associated with a higher baseline CD4 count (β = 1.86; 95% CI: 0.65 3.04) whereas subjects who were bedridden were significantly associated with a lower baseline CD4 count (estimated effect β = -3.54; 95% CI: -5.65, -1.39), compared to ambulatory patients. A unit increase in weight of the individual increased the mean square root CD4 measurement by 0.06. The estimates of the association structure parameters from all three models considered indicated that the HIV mortality hazard at any time point is associated with the current underlying value of the CD4 count at the same time point. The model with highest posterior weight also had a time-dependent slope, indicating that HIV mortality is also associated with the rate of change in CD4 count. From both the model-averaged predictions and the highest posterior weight model alone, an increase in CD4 count was predicted at different visit times from the dynamic predictions. It was also found that there was an increase in the width of prediction intervals as time progressed.Conclusions: Functional status, weight and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements. For this particular dataset, model averaging the dynamic predictions resulted in only one of the hypothesised association structures having non-zero weight at the majority of time points for each individual. The predictions were therefore similar whether we averaged them over models or derived them from the highest posterior weight model alone. We also observed that the parameter estimates in the both the CD4 count and survival sub-models showed slight variability between the postulated association structures.

Objective To evaluate the relation between diagnosis of covid-19 with SARS-CoV-2 variant B.1.1.7 (also known as variant of concern 202012/01) and the risk of hospital admission compared with diagnosis with wild-type SARS-CoV-2 variants. Design Retrospective cohort analysis. Setting Community based SARS-CoV-2 testing in England, individually linked with hospital admission data. Participants 839 278 patients with laboratory confirmed covid-19, of whom 36 233 had been admitted to hospital within 14 days, tested between 23 November 2020 and 31 January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF), a proxy test for the B.1.1.7 variant. Patient data were stratified by age, sex, ethnicity, deprivation, region of residence, and date of positive test. Main outcome measures Hospital admission between one and 14 days after the first positive SARS-CoV-2 test. Results 27 710 (4.7%) of 592 409 patients with SGTF variants and 8523 (3.5%) of 246 869 patients without SGTF variants had been admitted to hospital within one to 14 days. The stratum adjusted hazard ratio of hospital admission was 1.52 (95% confidence interval 1.47 to 1.57) for patients with covid-19 infected with SGTF variants, compared with those infected with non-SGTF variants. The effect was modified by age (P Conclusions The results suggest that the risk of hospital admission is higher for people infected with the B.1.1.7 variant compared with wild-type SARS-CoV-2, likely reflecting a more severe disease. The higher severity may be specific to adults older than 30 years.

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements may be evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomised two-arm trials with a binary outcome. We show that “ring” recruitment strategies, prioritising participants at imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomisation, discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine. [ABSTRACT FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection. Methods: An observational cohort study is performed, using data on all RT-PCR confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively. Findings: Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission and increased with age. Care home residents aged 65+ are at increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective effect on mortality risk and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors. Interpretation: Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time. Funding: This work has been funded by the Medical Research Council (De Angelis, Jackson, Presanis: Unit programme number MC UU 00002/11; Kunzmann: Unit programme number MC_UU_00002/10); and the UKRI-MRC COVID-19 Rapid Call (Presanis, De Angelis, grant no MC_PC_19074). Declaration of Interests: None to declare.

Background Increased incidence of invasive pneumococcal disease (IPD) attributable to non-vaccine serotypes (NVT) has been reported in several countries following introduction of PCV7 and PCV13 vaccines, concurrently with a reduction in vaccine-type IPD. Such serotype replacement has, importantly, emerged in England, offsetting the benefit of PCV introduction. We scrutinise most recent findings to assess if the estimated increase in NVT disease might result from surveillance artefacts. Methods Using IPD surveillance for 2000-2018, we estimate the impact of PCV7 and PCV13 introduction on age-serotype-specific incidence rates through a synthetic control regression model, building counterfactuals by combining age-specific incidences reported for pathogens unaffected by PCVs. Results Following the introduction of PCV7 and PCV13 (pre-2006 vs post-2011), total IPD incidence declined by 57% and by 76% in children younger than 5. PCV7-IPD decreased by 93% in all age groups, whereas PCV13-IPD declined by 68% since PCV13 was introduced. Importantly, NVT-IPD increased by 43% after PCV7, with non-significant statistical increases in most age groups. Conclusions Through appropriate statistical modelling, we disentangled the impact of vaccine and improved surveillance on the changes in IPD incidence rates. By controlling for the confounding effects of improved surveillance, we are able to estimate a lower serotype replacement. Key messages Pneumococcal vaccine has been beneficial despite serotype replacement. Adequate statistical methods are needed to disentangle the two phenomena.

Background Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Methods Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. Results The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates for R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. Conclusions This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

Influenza viruses may cause severe human infections leading to hospitalization or death. Linear regression models were fitted to population-based data on hospitalizations and deaths. Surveillance data on influenza virus activity permitted inference on influenza-associated hospitalizations and deaths. The ratios of these estimates were used as a potential indicator of severity. Influenza was associated with 431 (95% CrI: 358–503) respiratory deaths and 12,700 (95% CrI: 11,700–13,700) respiratory hospitalizations per year. Majority of the excess deaths occurred in persons ≥65 y of age. The ratios of deaths to hospitalizations in adults ≥65 y were significantly higher for influenza A(H1N1) and A(H1N1)pdm09 compared to A(H3N2) and B. Substantial disease burden associated with influenza viruses were estimated in Hong Kong particularly among children and elderly in 1998–2013. Infections with influenza A(H1N1) was suggested to be more serious than A(H3N2) in older adults.

Evidence-based knowledge of infectious disease burden, including prevalence, incidence, severity and transmission, in different population strata and locations, and possibly in real time, is crucial to the planning and evaluation of public health policies. Direct observation of a disease process is rarely possible. However, latent characteristics of an epidemic and its evolution can often be inferred from the synthesis of indirect information from various routine data sources, as well as expert opinion. The simultaneous synthesis of multiple data sources, often conveniently carried out in a Bayesian framework, poses a number of statistical and computational challenges: the heterogeneity in type, relevance and granularity of the data, together with selection and informative observation biases, lead to complex probabilistic models that are difficult to build and fit, and challenging to criticize. Using motivating case studies of influenza, this chapter illustrates the cycle of model development and criticism in the context of Bayesian evidence synthesis, highlighting the challenges of complex model building, computationally efficient inference, and conflicting evidence.

IntroductionHepatitis C virus (HCV) is the second largest contributor to liver disease in the UK, with injecting drug use as the main risk factor among the estimated 200 000 people currently infected. Despite effective prevention interventions, chronic HCV prevalence remains around 40% among people who inject drugs (PWID). New direct-acting antiviral (DAA) HCV therapies combine high cure rates (>90%) and short treatment duration (8 to 12 weeks). Theoretical mathematical modelling evidence suggests HCV treatment scale-up can prevent transmission and substantially reduce HCV prevalence/incidence among PWID. Our primary aim is to generate empirical evidence on the effectiveness of HCV ‘Treatment as Prevention’ (TasP) in PWID.Methods and analysisWe plan to establish a natural experiment with Tayside, Scotland, as a single intervention site where HCV care pathways are being expanded (including specialist drug treatment clinics, needle and syringe programmes (NSPs), pharmacies and prison) and HCV treatment for PWID is being rapidly scaled-up. Other sites in Scotland and England will act as potential controls. Over 2 years from 2017/2018, at least 500 PWID will be treated in Tayside, which simulation studies project will reduce chronic HCV prevalence among PWID by 62% (from 26% to 10%) and HCV incidence will fall by approximately 2/3 (from 4.2 per 100 person-years (p100py) to 1.4 p100py). Treatment response and re-infection rates will be monitored. We will conduct focus groups and interviews with service providers and patients that accept and decline treatment to identify barriers and facilitators in implementing TasP. We will conduct longitudinal interviews with up to 40 PWID to assess whether successful HCV treatment alters their perspectives on and engagement with drug treatment and recovery. Trained peer researchers will be involved in data collection and dissemination. The primary outcome – chronic HCV prevalence in PWID – is measured using information from the Needle Exchange Surveillance Initiative survey in Scotland and the Unlinked Anonymous Monitoring Programme in England, conducted at least four times before and three times during and after the intervention. We will adapt Bayesian synthetic control methods (specifically the Causal Impact Method) to generate the cumulative impact of the intervention on chronic HCV prevalence and incidence. We will use a dynamic HCV transmission and economic model to evaluate the cost-effectiveness of the HCV TasP intervention, and to estimate the contribution of the scale-up in HCV treatment to observe changes in HCV prevalence. Through the qualitative data we will systematically explore key mechanisms of TasP real world implementation from provider and patient perspectives to develop a manual for scaling up HCV treatment in other settings. We will compare qualitative accounts of drug treatment and recovery with a ‘virtual cohort’ of PWID linking information on HCV treatment with Scottish Drug treatment databases to test whether DAA treatment improves drug treatment outcomes.Ethics and disseminationExtending HCV community care pathways is covered by ethics (ERADICATE C,ISRCTN27564683, Super DOT C Trial clinicaltrials.gov:NCT02706223). Ethical approval for extra data collection from patients including health utilities and qualitative interviews has been granted (REC ref: 18/ES/0128) and ISCRCTN registration has been completed (ISRCTN72038467). Our findings will have direct National Health Service and patient relevance; informing prioritisation given to early HCV treatment for PWID. We will present findings to practitioners and policymakers, and support design of an evaluation of HCV TasP in England.

Knowledge of the severity of an influenza outbreak is crucial for informing and monitoring appropriate public health responses, both during and after an epidemic. However, case-fatality, case-intensive care admission and case-hospitalisation risks are difficult to measure directly. Bayesian evidence synthesis methods have previously been employed to combine fragmented, under-ascertained and biased surveillance data coherently and consistently, to estimate case-severity risks in the first two waves of the 2009 A/H1N1 influenza pandemic experienced in England. We present in detail the complex probabilistic model underlying this evidence synthesis, and extend the analysis to also estimate severity in the third wave of the pandemic strain during the 2010/2011 influenza season. We adapt the model to account for changes in the surveillance data available over the three waves. We consider two approaches: (a) a two-stage approach using posterior distributions from the model for the first two waves to inform priors for the third wave model; and (b) a one-stage approach modelling all three waves simultaneously. Both approaches result in the same key conclusions: (1) that the age-distribution of the case-severity risks is "u"-shaped, with children and older adults having the highest severity; (2) that the age-distribution of the infection attack rate changes over waves, school-age children being most affected in the first two waves and the attack rate in adults over 25 increasing from the second to third waves; and (3) that when averaged over all age groups, case-severity appears to increase over the three waves. The extent to which the final conclusion is driven by the change in age-distribution of those infected over time is subject to discussion., Published in at http://dx.doi.org/10.1214/14-AOAS775 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when joining submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. The approach also enables splitting of large joint models into smaller submodels, allowing inference for the original joint model to be conducted via our multi-stage algorithm. We motivate and demonstrate our approach through two examples: joining components of an evidence synthesis of A/H1N1 influenza, and splitting a large ecology model.

Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Objective: Preexposure prophylaxis (PrEP) is a promising intervention to help end the HIV epidemic among men who have sex with men (MSM) in the Netherlands. We aimed to assess the impact of PrEP on HIV prevalence in this population and to determine the levels of PrEP coverage necessary for HIV elimination. Design and methods: We developed a mathematical model of HIV transmission in a population stratified by sexual risk behavior with universal antiretroviral treatment (ART) and daily PrEP use depending on an individual's risk behavior. We computed the effective reproduction number, HIV prevalence, ART and PrEP coverage for increasing ART and PrEP uptake levels, and examined how these were affected by PrEP effectiveness and duration of PrEP use. Results: At current levels of ART coverage of 80%, PrEP effectiveness of 86% and PrEP duration of 5 years, HIV elimination required 82% PrEP coverage in the highest risk group (12 000 MSM with more than 18 partners per year). If ART coverage increased by 9%, the elimination threshold was at 70% PrEP coverage. For shorter PrEP duration and lower effectiveness elimination prospects were less favorable. For the same number of PrEP users distributed among two groups with highest risk behavior, prevalence dropped from the current 8 to 4.6%. Conclusion: PrEP for HIV prevention among MSM could, in principle, eliminate HIV from this population in the Netherlands. The highest impact of PrEP on prevalence was predicted when ART and PrEP coverage increased simultaneously and PrEP was used by the highest risk individuals.

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.

Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.

In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.

Highlights • Health decision making increasingly uses models and data from multiple sources. • Inference on model parameters using a multiplicity of data sources is challenging. • Key challenges include more thoughtful model specification and criticism. • Addressing these problems rigorously will require better use of existing tools. • Challenges in epidemic models may motivate new statistical methods., Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.

BACKGROUND: Hard-to-reach population subgroups are typically investigated using convenience sampling, which may give biased estimates. Combining information from such surveys, a probability survey and clinic surveillance, can potentially minimize the bias. We developed a methodology to estimate the prevalence of undiagnosed HIV infection among men who have sex with men (MSM) in England and Wales aged 16-44 years in 2003, making fuller use of the available data than earlier work. METHODS: We performed a synthesis of three data sources: genitourinary medicine clinic surveillance (11?380 tests), a venue-based convenience survey including anonymous HIV testing (3702 MSM) and a general population sexual behaviour survey (134 MSM). A logistic regression model to predict undiagnosed infection was fitted to the convenience survey data and then applied to the MSMs in the population survey to estimate the prevalence of undiagnosed infection in the general MSM population. This estimate was corrected for selection biases in the convenience survey using clinic surveillance data. A sensitivity analysis addressed uncertainty in our assumptions. RESULTS: The estimated prevalence of undiagnosed HIV in MSM was 2.4% [95% confidence interval (95% CI 1.7-3.0%)], and between 1.6% (95% CI 1.1-2.0%) and 3.3% (95% CI 2.4-4.1%) depending on assumptions; corresponding to 5500 (3390-7180), 3610 (2180-4740) and 7570 (4790-9840) men, and undiagnosed fractions of 33, 24 and 40%, respectively. CONCLUSIONS: Our estimates are consistent with earlier work that did not make full use of data sources. Reconciling data from multiple sources, including probability-, clinic- and venue-based convenience samples can reduce bias in estimates. This methodology could be applied in other settings to take full advantage of multiple imperfect data sources.

Background and Objectives One component of the rationale for lifetime exclusion of men who have sex with men (MSM) from blood donation in the UK is the probable reduction in the risk of transfusion-transmitted HIV; this exclusion has recently been questioned. Materials and Methods Data about HIV in blood donors and MSM were analysed to estimate the risk of infectious donations entering the blood supply under different scenarios of donor selection criteria (and donor compliance) for MSM and a heterosexual group with increased risk of HIV. Results In 2005–2007, a change from lifetime exclusion of MSM to 5-year deferral or no deferral increased the point estimate of HIV risk by between 0·4% and 7·4% depending on compliance with the deferral (range −4% to 15%) and 26·5% (range 18% to 43%) respectively. A change from a 12-month deferral of the high-risk heterosexual group to lifetime exclusion reduced the estimated risk by about 7·2% (range 6% to 9%). Each point estimate was within the probable range of risk under the current criteria. Conclusion If prevalence is the only factor affected by a reduced deferral, then the increased risk of HIV is probably negligible. However, the impact of a change depends on compliance; if this stays the same or worsens, the risk is expected to increase because of more incident infections in MSM who donate blood. The risk of transfusion-transmitted HIV could probably be reduced further by improving compliance with any exclusion, particularly after recent risk behaviours.

Objectives: To determine limitations and strengths of three methodologies developed to estimate HIV prevalence and the number of people living with HIV/AIDS (PLWHA). Methods: The UNAIDS/WHO Workbook method; the Multiparameter Evidence Synthesis (MPES) adopted by the Health Protection Agency; and the UNAIDS/WHO Estimation and Projection Package (EPP) and Spectrum method were used and their applicability and feasibility were assessed. All methods estimate the number infected in mutually exclusive risk groups among 15-70-year-olds. Results: Using data from the Netherlands, the Workbook method estimated 23 969 PLWHA as of January 2008. MPES estimated 21 444 PLWHA, with a 95% credible interval (Crl) of 17204-28 694. Adult HIV prevalence was estimated at 0.2% (95% Crl 0.15-0.24%) and 40% (95% Crl 25-55%) were undiagnosed. Spectrum applied gender-specific mortality, resulting in a projected estimate of 19115 PLWHA. Conclusion: Although outcomes differed between the methods, they broadly concurred. An advantage of MPES is that the proportion diagnosed can be estimated by risk group, which is important for policy guidance. However, before MPES can be used on a larger scale, it should be made more easily applicable. If the aim is not only to obtain annual estimates, but also short-term projections, then EPP and Spectrum are more suitable. Research into developing and refining analytical tools, which make use of all available information, is recommended, especially HIV diagnosed cases, as this information is becoming routinely collected in most countries with concentrated HIV epidemics.

SummaryInferential approaches based on the synthesis of diverse sources of evidence are increasingly employed in epidemiology as a means of exploiting all available information, perhaps from studies of differing designs. The application of the synthesis of evidence to real world problems generally leads to the formulation of probability models which are highly complex and for which there is a clear need for a well-defined iterative process of model criticism. This process should include an appraisal of model fit and the detection of inconsistent or conflicting evidence. The latter is especially relevant as, typically, multiple sources of data provide information on the same parameter. Detected conflicts need then to be resolved. We present a case-study of the detection and resolution of conflicting evidence, using as an illustration the estimation of the prevalence of human immunodeficiency virus (HIV) infection in England and Wales. We employ a Bayesian model to synthesize routine surveillance and survey data. The population aged 15–44 years is divided into mutually exclusive exposure groups. In each group g, we simultaneously estimate the proportion of the total population belonging to the group (ρ≫), the proportion of individuals infected with HIV (π≫) and the proportion of HIV positive individuals who are diagnosed (δ≫). The total number of HIV infections, both diagnosed and undiagnosed, is then estimated as a function of the parameters ρ≫, π≫ and δ≫. Model fit is assessed by examining the posterior mean deviance. Identification of the data items to which the model exhibits a lack of fit leads to the detection of conflicting evidence, one example of which is a conflict between census data and survey data over the size of the female Sub-Saharan African born population. This conflict arises from a naive interpretation of the representativeness of the survey data and is resolved by using two approaches: exclusion of data and expansion of the model to accommodate the bias.

SUMMARYHIV spread in men who have sex with men (MSM) is an increasing problem in Poland. Despite the existence of a surveillance system, there is no direct evidence to allow estimation of HIV prevalence and the proportion undiagnosed in MSM. We extracted data on HIV and the MSM population in Poland, including case-based surveillance data, diagnostic testing prevalence data and behavioural data relating to self-reported prior diagnosis, stratified by age (⩽35, >35 years) and region (Mazowieckie including the capital city of Warsaw; other regions). They were integrated into one model based on a Bayesian evidence synthesis approach. The posterior distributions for HIV prevalence and the undiagnosed fraction were estimated by Markov Chain Monte Carlo methods. To improve the model fit we repeated the analysis, introducing bias parameters to account for potential lack of representativeness in data. By placing additional constraints on bias parameters we obtained precisely identified estimates. This family of models indicates a high undiagnosed fraction [68·3%, 95% credibility interval (CrI) 53·9–76·1] and overall low prevalence (2·3%, 95% CrI 1·4–4·1) of HIV in MSM. Additional data are necessary in order to produce more robust epidemiological estimates. More effort is urgently needed to ensure timely diagnosis of HIV in Poland.

Approaches for the analysis of statistical parametric maps (SPMs) can be crudely grouped into three main categories in which different philosophies are applied to delineate activated regions. These being type I error control thresholding, false discovery rate (FDR) control thresholding and posterior probability thresholding. To better understand the properties of these main approaches, we carried out a simulation study to compare the approaches as they would be used on real data sets. Using default settings, we find that posterior probability thresholding is the most powerful approach, and type I error control thresholding provides the lowest levels of type I error. False discovery rate control thresholding performs in between the other approaches for both these criteria, although for some parameter settings this approach can approximate the performance of posterior probability thresholding. Based on these results, we discuss the relative merits of the three approaches in an attempt to decide upon an optimal approach. We conclude that viewing the problem of delineating areas of activation as a classification problem provides a highly interpretable framework for comparing the methods. Within this framework, we highlight the role of the loss function, which explicitly penalizes the types of errors that may occur in a given analysis.

Objectives To estimate, using Bayesian evidence synthesis, the age-group-specific annual incidence of symptomatic infection with seasonal influenza in the Netherlands over the period 2005–2007. Methods The Netherlands population and age group distribution for 2006 defined the base population. The number of influenza-like illness (ILI) cases was estimated from sentinel surveillance data and adjusted for underascertainment using the estimated proportion of ILI cases that do not consult a general practitioner. The estimated number of symptomatic influenza (SI) cases was based on indirect evidence from the surveillance of ILI cases and the proportions of laboratory-confirmed influenza cases in the 2004/5, 2005/6 and 2006/7 respiratory years. In scenario analysis, the number of SI cases prevented by increasing vaccination uptake within the 65 + age group was estimated. Results The overall symptomatic infection attack rate (SIAR) over the period 2005–2007 was estimated at 2·5% (95% credible interval [CI]: 2·1–3·2%); 410 200 SI cases (95% CI: 338 500–518 600) were estimated to occur annually. Age-group-specific SIARs were estimated for

Planning, implementation and evaluation of public health policies to control the human immunodeficiency virus (HIV) epidemic require regular monitoring of disease burden. This includes the proportion living with HIV, whether diagnosed or not, and the rate of new infections in the general population and in specific risk groups and regions. Estimation of these quantities is not straightforward: data informing them directly are not typically available, but a wealth of indirect information from surveillance systems and ad hoc studies can inform functions of these quantities. In this paper we show how the estimation problem can be successfully solved through a Bayesian evidence synthesis approach, relaxing the focus on "best available" data to which classical methods are typically restricted. This more comprehensive and flexible use of evidence has led to the adoption of our proposed approach as the official method to estimate HIV prevalence in the United Kingdom since 2005., Comment: Published in at http://dx.doi.org/10.1214/13-STS428 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS488 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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.