A joint analysis of influenza-associated hospitalizations and mortality in Hong Kong, 1998–2013

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HIV prevalence and undiagnosed infection among a community sample of gay and bisexual men in Scotland, 2005-2011: implications for HIV testing policy and prevention

<b>Objective</b><p></p> To examine HIV prevalence, HIV testing behaviour, undiagnosed infection and risk factors for HIV positivity among a community sample of gay men in Scotland.<p></p> <b>Methods</b><p></p> Cross-sectional survey of gay and bisexual men attending commercial gay venues in Glasgow and Edinburgh, Scotland with voluntary anonymous HIV testing of oral fluid samples in 2011. A response rate of 65.2% was achieved (1515 participants).<p></p> <b>Results</b><p></p> HIV prevalence (4.8%, 95% confidence interval, CI 3.8% to 6.2%) remained stable compared to previous survey years (2005 and 2008) and the proportion of undiagnosed infection among HIV-positive men (25.4%) remained similar to that recorded in 2008. Half of the participants who provided an oral fluid sample stated that they had had an HIV test in the previous 12 months; this proportion is significantly higher when compared to previous study years (50.7% versus 33.8% in 2005, p<0.001). Older age (>25 years) was associated with HIV positivity (1.8% in those <25 versus 6.4% in older ages group) as was a sexually transmitted infection (STI) diagnosis within the previous 12 months (adjusted odds ratio 2.13, 95% CI 1.09–4.14). There was no significant association between age and having an STI or age and any of the sexual behaviours recorded.<p></p> <b>Conclusion</b><p></p> HIV transmission continues to occur among gay and bisexual men in Scotland. Despite evidence of recent testing within the previous six months, suggesting a willingness to test, the current opt-out policy may have reached its limit with regards to maximising HIV test uptake. Novel strategies are required to improve regular testing opportunities and more frequent testing as there are implications for the use of other biomedical HIV interventions.<p></p&gt

Estimation of HIV burden through Bayesian evidence synthesis

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

Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009.

HIV 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

Bridging the data gaps in the epidemiology of hepatitis C virus infection in Malaysia using multi-parameter evidence synthesis

BACKGROUND: Collecting adequate information on key epidemiological indicators is a prerequisite to informing a public health response to reduce the impact of hepatitis C virus (HCV) infection in Malaysia. Our goal was to overcome the acute data shortage typical of low/middle income countries using statistical modelling to estimate the national HCV prevalence and the distribution over transmission pathways as of the end of 2009. METHODS: Multi-parameter evidence synthesis methods were applied to combine all available relevant data sources - both direct and indirect - that inform the epidemiological parameters of interest. RESULTS: An estimated 454,000 (95% credible interval [CrI]: 392,000 to 535,000) HCV antibody-positive individuals were living in Malaysia in 2009; this represents 2.5% (95% CrI: 2.2-3.0%) of the population aged 15-64 years. Among males of Malay ethnicity, for 77% (95% CrI: 69-85%) the route of probable transmission was active or a previous history of injecting drugs. The corresponding proportions were smaller for male Chinese and Indian/other ethnic groups (40% and 71%, respectively). The estimated prevalence in females of all ethnicities was 1% (95% CrI: 0.6 to 1.4%); 92% (95% CrI: 88 to 95%) of infections were attributable to non-drug injecting routes of transmission. CONCLUSIONS: The prevalent number of persons living with HCV infection in Malaysia is estimated to be very high. Low/middle income countries often lack a comprehensive evidence base; however, evidence synthesis methods can assist in filling the data gaps required for the development of effective policy to address the future public health and economic burden due to HCV. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-014-0564-6) contains supplementary material, which is available to authorized users

Assessing the causal effect of binary interventions from observational panel data with few treated units

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

Conflict diagnostics in directed acyclic graphs, with applications in bayesian evidence synthesis

Complex stochastic models represented by directed acyclic graphs (DAGs) are increasingly employed to synthesise multiple, imperfect and disparate sources of evidence, to estimate quantities that are difficult to measure directly. The various data sources are dependent on shared parameters and hence have the potential to conflict with each other, as well as with the model. In a Bayesian framework, the model consists of three components: the prior distribution, the assumed form of the likelihood and structural assumptions. Any of these components may be incompatible with the observed data. The detection and quantification of such conflict and of data sources that are inconsistent with each other is therefore a crucial component of the model criticism process. We first review Bayesian model criticism, with a focus on conflict detection, before describing a general diagnostic for detecting and quantifying conflict between the evidence in different partitions of a DAG. The diagnostic is a p-value based on splitting the information contributing to inference about a "separator" node or group of nodes into two independent groups and testing whether the two groups result in the same inference about the separator node(s). We illustrate the method with three comprehensive examples: an evidence synthesis to estimate HIV prevalence; an evidence synthesis to estimate influenza case-severity; and a hierarchical growth model for rat weights.This work was supported by the Medical Research Council [Unit Programme Numbers U105260566 and U105260557

Projections of the current and future disease burden of hepatitis C virus infection in Malaysia

The prevalence of hepatitis C virus (HCV) infection in Malaysia has been estimated at 2.5% of the adult population. Our objective, satisfying one of the directives of the WHO Framework for Global Action on Viral Hepatitis, was to forecast the HCV disease burden in Malaysia using modelling methods.An age-structured multi-state Markov model was developed to simulate the natural history of HCV infection. We tested three historical incidence scenarios that would give rise to the estimated prevalence in 2009, and calculated the incidence of cirrhosis, end-stage liver disease, and death, and disability-adjusted life-years (DALYs) under each scenario, to the year 2039. In the baseline scenario, current antiviral treatment levels were extended from 2014 to the end of the simulation period. To estimate the disease burden averted under current sustained virological response rates and treatment levels, the baseline scenario was compared to a counterfactual scenario in which no past or future treatment is assumed.In the baseline scenario, the projected disease burden for the year 2039 is 94,900 DALYs/year (95% credible interval (CrI): 77,100 to 124,500), with 2,002 (95% CrI: 1340 to 3040) and 540 (95% CrI: 251 to 1,030) individuals predicted to develop decompensated cirrhosis and hepatocellular carcinoma, respectively, in that year. Although current treatment practice is estimated to avert a cumulative total of 2,200 deaths from DC or HCC, a cumulative total of 63,900 HCV-related deaths is projected by 2039.The HCV-related disease burden is already high and is forecast to rise steeply over the coming decades under current levels of antiviral treatment. Increased governmental resources to improve HCV screening and treatment rates and to reduce transmission are essential to address the high projected HCV disease burden in Malaysia

Estimating age-stratified influenza-associated invasive pneumococcal disease in England: a time-series model based on population surveillance data

BACKGROUND:Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups. METHODS AND FINDINGS:We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t-1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%-28.2%, and 6.07%, CI 2.83%-9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%-3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%-3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates. CONCLUSIONS:Our estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups

Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009-2011

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