Anne M. Presanis

The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis

Marc Lipsitch and colleagues use complementary data from two US cities, Milwaukee and New York City, to assess the severity of pandemic (H1N1) 2009 influenza in the United States.

Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London

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.

Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis

Objective To assess the impact of the 2009 A/H1N1 influenza pandemic in England during the two waves of activity up to end of February 2010 by estimating the probabilities of cases leading to severe events and the proportion of the population infected. Design A Bayesian evidence synthesis of all available relevant surveillance data in England to estimate severity of the pandemic. Data sources All available surveillance systems relevant to the pandemic 2009 A/H1N1 influenza outbreak in England from June 2009 to February 2010. Pre-existing influenza surveillance systems, including estimated numbers of symptomatic cases based on consultations to the health service for influenza-like illness and cross sectional population serological surveys, as well as systems set up in response to the pandemic, including follow-up of laboratory confirmed cases up to end of June 2009 (FF100 and Fluzone databases), retrospective and prospective follow-up of confirmed hospitalised cases, and reported deaths associated with pandemic 2009 A/H1N1 influenza. Main outcome measures Age specific and wave specific probabilities of infection and symptomatic infection resulting in hospitalisation, intensive care admission, and death, as well as infection attack rates (both symptomatic and total). The probabilities of intensive care admission and death given hospitalisation over time are also estimated to evaluate potential changes in severity across waves. Results In the summer wave of A/H1N1 influenza, 0.54% (95% credible interval 0.33% to 0.82%) of the estimated 606 100 (419 300 to 886 300) symptomatic cases were hospitalised, 0.05% (0.03% to 0.08%) entered intensive care, and 0.015% (0.010% to 0.022%) died. These correspond to 3200 (2300 to 4700) hospital admissions, 310 (200 to 480) intensive care admissions, and 90 (80 to 110) deaths in the summer wave. In the second wave, 0.55% (0.28% to 0.89%) of the 1 352 000 (829 900 to 2 806 000) estimated symptomatic cases were hospitalised, 0.10% (0.05% to 0.16%) were admitted to intensive care, and 0.025% (0.013% to 0.040%) died. These correspond to 7500 (5900 to 9700) hospitalisations, 1340 (1030 to 1790) admissions to intensive care, and 240 (310 to 380) deaths. Just over a third (35% (26% to 45%)) of infections were estimated to be symptomatic. The estimated probabilities of infections resulting in severe events were therefore 0.19% (0.12% to 0.29%), 0.02% (0.01% to 0.03%), and 0.005% (0.004% to 0.008%) in the summer wave for hospitalisation, intensive care admission, and death respectively. The corresponding second wave probabilities are 0.19% (0.10% to 0.32%), 0.03% (0.02% to 0.06%), and 0.009% (0.004% to 0.014%). An estimated 30% (20% to 43%) of hospitalisations were detected in surveillance systems in the summer, compared with 20% (15% to 25%) in the second wave. Across the two waves, a mid-estimate of 11.2% (7.4% to 18.9%) of the population of England were infected, rising to 29.5% (16.9% to 64.1%) in 5-14 year olds. Sensitivity analyses to the evidence included suggest this infection attack rate could be as low as 5.9% (4.2% to 8.7%) or as high as 28.4% (26.0% to 30.8%). In terms of the probability that an infection leads to death in the second wave, these correspond, respectively, to a high estimate of 0.017% (0.011% to 0.024%) and a low estimate of 0.0027% (0.0024% to 0.0031%). Conclusions This study suggests a mild pandemic, characterised by case and infection severity ratios increasing between waves. Results suggest low ascertainment rates, highlighting the importance of systems enabling early robust estimation of severity, to inform optimal public health responses, particularly in light of the apparent resurgence of the 2009 A/H1N1 strain in the 2010-11 influenza season.

The severity of pandemic H1N1 influenza in the United States, April -- July 2009.

BACKGROUND Accurate measures of the severity of pandemic influenza A/H1N1 (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely. METHODS AND FINDINGS We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data: medically attended cases in Milwaukee or self-reported influenza-like illness in New York, were used to estimate ratios of symptomatic cases:hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic cases that died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated sCFR of 0.045% (95% credible interval, CI 0.020%-0.090%), sCIR of 0.222% (0.105%-0.425%), and sCHR of 1.37% (0.68%-2.52%). Using self-reported ILI, we obtained estimates approximately 6-9 times lower. sCFR was highest in the 18-64 age group, and sCIR and sCHR highest in the 18-64 or 0-4 age group depending on the approach. CONCLUSIONS These estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with greatest impact in young children and non-elderly adults. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the proportion infected or symptomatic were lower.

National estimate of HIV prevalence in the Netherlands: comparison and applicability of different estimation tools.

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 (CrI) of 17 204–28 694. Adult HIV prevalence was estimated at 0.2% (95% CrI 0.15–0.24%) and 40% (95% CrI 25–55%) were undiagnosed. Spectrum applied gender-specific mortality, resulting in a projected estimate of 19 115 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.

Insights into the rise in HIV infections, 2001 to 2008: a Bayesian synthesis of prevalence evidence.

Objective:To estimate trends in prevalence of HIV infection, undiagnosed and total, among adults aged 15–44 years in England and Wales since 2001. Design:Multiple surveillance systems and survey data are available to inform different aspects of the HIV epidemic in England and Wales. To coherently and consistently combine this information to estimate trends in HIV prevalence, we apply a multiparameter evidence synthesis in a Bayesian statistical framework. Methods:The study population is stratified by exposure group and region of residence. We synthesize data from behavioural and community surveys, unlinked anonymous seroprevalence surveys, and an annual survey of individuals with diagnosed HIV infection. Prevalence estimates are given with 95% credible intervals. Results:The estimated number of prevalent HIV infections in 15–44-year-olds has increased from 32 400 (29 600–35 900) in 2001 to 54 500 (50 500–59 100) in 2008, corresponding to an estimated prevalence of 1.5 per 1000 (1.4–1.7) rising to 2.4 per 1000 (2.3–2.6) in 2008. A rise in prevalence of diagnosed infection contributes substantially to the increase. There is no evidence of a statistically significant decrease in the prevalence of undiagnosed infection. The proportion of infections that are diagnosed has therefore also increased. Conclusion:Although the increase in the proportion of infections that are diagnosed is encouraging, the rise in HIV prevalence and lack of evidence of a decrease in prevalence of undiagnosed infection suggest that diagnosis rates are not high enough to reduce the pool of individuals unaware of their infection and that new infections must be occurring.

Four key challenges in infectious disease modelling using data from multiple sources.

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.

An evidence synthesis approach to estimating the incidence of seasonal influenza in the Netherlands

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.

Estimating the number of people with hepatitis C virus who have ever injected drugs and have yet to be diagnosed: an evidence synthesis approach for Scotland

Abstract 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.

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

SUMMARY 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.