Statistical communications in infectious diseases最新文献

{"title":"Is The Age-Period-Cohort Model Well Suited to an Epidemic Context? The Case of the French BSE Epidemic","authors":"C. Sala, B. Durand, D. Costagliola, C. Ducrot, D. Calavas","doi":"10.1515/1948-4690.1047","DOIUrl":"https://doi.org/10.1515/1948-4690.1047","url":null,"abstract":"Abstract Although Age-Period-Cohort (APC) models are routinely used in time-trend analyses of chronic diseases, few examples of their application to epidemic diseases are available. APC analyses of the French bovine spongiform encephalopathy (BSE) epidemic revealed an unexpected period effect, which was attributed to the design of the study, in connection with low BSE prevalence and a short surveillance period. The aim of our study was to evaluate the relevance of this hypothesis, the behaviour of the APC model in an epidemic context (e.g. the BSE epidemic) and the impact of including the period effect in an APC model on the estimate of birth cohort effects. We simulated datasets mimicking the French BSE epidemic and its variable pattern, as well as duration and the surveillance time period, and analysed them with a categorical APC model. Results showed a period effect in 44% of analysed datasets, although no period effect had been introduced in the data simulation process. This type of artefactual period effect was detected when a sudden change in cohort prevalence occurred over a short period of time. Additionally, in the context of BSE, including a period effect in the model may dramatically affect the estimation of cohort prevalence, depending on epidemic pattern and, as expected, duration and the surveillance time period according to the year in which highly infected birth cohorts are detected as BSE cases.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86298419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HIV Sexual Networks: The Montreal Experience","authors":"B. Brenner, E. Moodie","doi":"10.1515/1948-4690.1039","DOIUrl":"https://doi.org/10.1515/1948-4690.1039","url":null,"abstract":"While highly active antiretroviral therapy (HAART) has transformed HIV/AIDS to a chronic, treatable disease in Canada, HIV incidence continues to rise among male-sex-male (MSM) populations. Montreal, Canada, is a unique environment for a comprehensive surveillance study on HIV transmission dynamics. Phylogenetic strategies show that half of all new MSM infections in Montreal may arise through onward transmission by individuals who are in primary HIV infection (PHI) (< 6 months post-infection) and often unaware of their HIV status. Large cluster networks, wherein one infection leads to 5-31 onward transmissions, constitute the fastest-growing sub-epidemic, representing 25% and 39% of genotyped incident infections in 2005 and 2009, respectively. This has disturbing implications in light of the introduction of non-B subtype and drug-resistant sub-epidemics. Biological and behavioural correlates of cluster membership are being investigated to establish risk determinants implicated in the onward transmission of the MSM epidemic. Our findings underscore the opportunities and challenges in implementing new testing and tailored prevention paradigms for different MSM populations.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85849845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximum a Posteriori Estimation in Dynamical Models of Primary HIV Infection","authors":"J. Drylewicz, D. Commenges, R. Thiébaut","doi":"10.1515/1948-4690.1040","DOIUrl":"https://doi.org/10.1515/1948-4690.1040","url":null,"abstract":"Dynamical models based on ordinary differential equations (ODE) are increasingly used to model viral infections such as HIV. This kind of model is based on biological knowledge and takes into account complex non-linear interactions between markers. The estimation of such models is made difficult by the lack of analytical solutions and several methods based on Bayesian or frequentist approaches have been proposed. However, because of identifiability issues, in a frequentist approach some parameters have to be fixed to values taken from the literature. In this paper we propose a Maximum A Posteriori (MAP) approach to estimate all the parameters of ODE models, allowing prior knowledge on biological parameters to be taken into account. The MAP approach has two main advantages: the computation time can be fast (relative to the full Bayesian approach) and it is straightforward to incorporate complex prior information. We applied this method to an original model of primary HIV infection taking into account several biological hypotheses for the HIV-immune system interaction. Parameters were estimated using HIV RNA load and CD4 count measurements of 710 patients from the Concerted Action on SeroConversion to AIDS and Death in Europe (CASCADE) Collaboration.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80367629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical Considerations in Determining HIV Incidence from Changes in HIV Prevalence","authors":"R. Brookmeyer, J. Konikoff","doi":"10.2202/1948-4690.1044","DOIUrl":"https://doi.org/10.2202/1948-4690.1044","url":null,"abstract":"The development of methods for estimating HIV incidence is critical for tracking the epidemic and for designing, targeting and evaluating HIV prevention efforts. One method for estimating incidence is based on changes in HIV prevalence. That method is attracting increased attention because national population-based HIV prevalence surveys, such as Demographic and Health Surveys, are being conducted throughout the world. Here, we consider some statistical issues associated with estimating HIV incidence from two population-based HIV prevalence surveys conducted at two different points in time. We show that the incidence estimator depends on the relative survival rate. We evaluate the sensitivity of estimates to incorrect assumptions about the relative survival rate, and show that small errors in the relative survival can, in some situations, create large biases in HIV incidence. We determine sample sizes of prevalence surveys to estimate incidence with precision and show how the sample sizes depend on baseline prevalence, the relative survival rate, and the population HIV incidence rate. We find that even if the relative survival rate were known exactly, there are situations where prohibitively large prevalence surveys would be required to produce reliable incidence estimates. These situations can occur either when the baseline prevalence is large, the relative survival rate is near 1, or the population incidence is small. Because information on the relative survival rate may be limited or not specific to the population under study, we suggest an approach to empirically estimate this critical parameter by augmenting population-based prevalence surveys with a mortality follow-up sub-study. We determine sample sizes of the prevalence surveys and mortality sub-studies for this augmented design and provide the necessary R code (version 2.13.0) for sample size determinations. We conclude that caution should be exercised when solely relying on changes in prevalence as the method for determining HIV incidence because of the method's sensitivity to mortality assumptions and the very large sample size requirements in some settings.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90118778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using HIV Diagnostic Data to Estimate HIV Incidence: Method and Simulation","authors":"P. Yan, Fan Zhang, H. Wand","doi":"10.2202/1948-4690.1011","DOIUrl":"https://doi.org/10.2202/1948-4690.1011","url":null,"abstract":"We propose a new approach to estimate the number of new infections with the human immunodeficiency virus (HIV), by integrating the back-calculation method based on HIV diagnostic data with proportions of recent infections among newly diagnosed individuals. This is done by establishing an explicit link between the distribution of time-since-infection given being tested and the distribution of time-to-testing given being infected. The trend in the proportions of recent infections identifies the time-to-testing distribution, which would have not been identifiable based on HIV surveillance data alone, and makes back-calculation possible. The integration of the proportions of recent infections among newly diagnosed HIV into the model allows a probabilistic interpretation of the estimated proportions of recent infections based on the results of laboratory tests, in terms of the estimated distribution of the time-since-infection given being tested.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81291384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Sequential Phase 2b Trial Design for Evaluating Vaccine Efficacy and Immune Correlates for Multiple HIV Vaccine Regimens.","authors":"Peter B Gilbert,&nbsp;Douglas Grove,&nbsp;Erin Gabriel,&nbsp;Ying Huang,&nbsp;Glenda Gray,&nbsp;Scott M Hammer,&nbsp;Susan P Buchbinder,&nbsp;James Kublin,&nbsp;Lawrence Corey,&nbsp;Steven G Self","doi":"10.2202/1948-4690.1037","DOIUrl":"https://doi.org/10.2202/1948-4690.1037","url":null,"abstract":"<p><p>Five preventative HIV vaccine efficacy trials have been conducted over the last 12 years, all of which evaluated vaccine efficacy (VE) to prevent HIV infection for a single vaccine regimen versus placebo. Now that one of these trials has supported partial VE of a prime-boost vaccine regimen, there is interest in conducting efficacy trials that simultaneously evaluate multiple prime-boost vaccine regimens against a shared placebo group in the same geographic region, for accelerating the pace of vaccine development. This article proposes such a design, which has main objectives (1) to evaluate VE of each regimen versus placebo against HIV exposures occurring near the time of the immunizations; (2) to evaluate durability of VE for each vaccine regimen showing reliable evidence for positive VE; (3) to expeditiously evaluate the immune correlates of protection if any vaccine regimen shows reliable evidence for positive VE; and (4) to compare VE among the vaccine regimens. The design uses sequential monitoring for the events of vaccine harm, non-efficacy, and high efficacy, selected to weed out poor vaccines as rapidly as possible while guarding against prematurely weeding out a vaccine that does not confer efficacy until most of the immunizations are received. The evaluation of the design shows that testing multiple vaccine regimens is important for providing a well-powered assessment of the correlation of vaccine-induced immune responses with HIV infection, and is critically important for providing a reasonably powered assessment of the value of identified correlates as surrogate endpoints for HIV infection.</p>","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1948-4690.1037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31073396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sample Size for a Binomial Proportion with Autocorrelation.","authors":"Amalia S Magaret,&nbsp;Jeffrey Stanaway","doi":"10.2202/1948-4690.1036","DOIUrl":"https://doi.org/10.2202/1948-4690.1036","url":null,"abstract":"<p><p>A flexible sample size computation is desired for a binomial outcome consisting of repeated binary measures with autocorrelation over time. This type of outcome is common in viral shedding studies, in which each individual's outcome is a proportion: the number of samples on which virus is detected out of number of samples assessed. Autocorrelation between proximal samples occurs in some conditions such as herpes infection, in which reactivation is episodic. We determine a sample size computation that accounts for: (1) participant-level differences in outcome frequency, (2) autocorrelation in time between samples, and (3) varying number of samples per participant. In addition, we develop a computation appropriate for crossover designs that accounts for the dependence of the investigational treatment effect on the pretreatment detection frequency. The computations are validated through comparison with real and simulated data, and sensitivity to misspecification of parameter values is examined graphically.</p>","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1948-4690.1036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31293877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Modeling of HCV and HIV Co-Infection among Injecting Drug Users in Italy and Spain Using Individual Cross-Sectional Data","authors":"E. Del Fava, Z. Shkedy, Niel Hens, M. Aerts, B. Suligoi, L. Camoni, F. Vallejo, L. Wiessing, M. Kretzschmar","doi":"10.2202/1948-4690.1010","DOIUrl":"https://doi.org/10.2202/1948-4690.1010","url":null,"abstract":"The aim of the analysis presented in this paper is to study co-infection with hepatitis C virus (HCV) and human immunodeficiency virus (HIV) in injecting drug users (IDUs) using a joint modeling approach that makes use of multivariate statistical methods for current status data. Using marginal models, we estimate association measures between HCV and HIV infections at individual level, i.e., odds ratios and correlation coefficients, and we regress them against some risk factors, e.g., the length of the injecting career, the age at first injection, the ever sharing of syringes, and the frequency of current injecting. In addition, we fit random-effects models that take into account the individual heterogeneity in the acquisition of the infections. For our analysis, we use cross-sectional data from two independent serological surveys, one carried out in Italy (IT) in 2005 on 856 subjects, and the other in three Spanish (ES) cities, between 2001 and 2003, on 589 subjects. We found that the infections are positively associated within individuals, e.g., ORIT=2.56 with 95% confidence interval (CI) (1.43, 6.68) and ORES= 2.42, with 95% CI (1.41, 4.30). We found that the odds ratio and the correlation between HCV and HIV infections increase positively with the length of the injecting career. Moreover, they are found to be significantly positive in case IDUs have never shared syringes or report low injecting frequencies. The variance of the individual random effects is positive, e.g., σb2=0.34 (0.14, 0.62), indicating that there is significant individual heterogeneity in the acquisition of the infections. Our results show that a significant association between HCV and HIV infections within IDUs is related to significant individual heterogeneity in the acquisition of the infections. Indeed, the association between these infections in IDUs who report ever sharing syringes is not significant, which can be explained by a higher homogeneity in their behaviors and, therefore, in their acquisition of the infections.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84911597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Approximate Bayesian Computation to Estimate Transmission Rates of Nosocomial Pathogens","authors":"C. Drovandi, A. Pettitt","doi":"10.2202/1948-4690.1025","DOIUrl":"https://doi.org/10.2202/1948-4690.1025","url":null,"abstract":"In this paper, we apply a simulation based approach for estimating transmission rates of nosocomial pathogens. In particular, the objective is to infer the transmission rate between colonised health-care practitioners and uncolonised patients (and vice versa) solely from routinely collected incidence data. The method, using approximate Bayesian computation, is substantially less computer intensive and easier to implement than likelihood-based approaches we refer to here. We find through replacing the likelihood with a comparison of an efficient summary statistic between observed and simulated data that little is lost in the precision of estimated transmission rates. Furthermore, we investigate the impact of incorporating uncertainty in previously fixed parameters on the precision of the estimated transmission rates.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72557566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Modeling of HCV and HIV Infections among Injecting Drug Users in Italy Using Repeated Cross-Sectional Prevalence Data","authors":"E. Del Fava, Adetayo S Kasim, Muhammad Usman, Z. Shkedy, Niel Hens, M. Aerts, K. Bollaerts, Gianpaolo Scalia Tomba, P. Vickerman, A. J. Sutton, L. Wiessing, M. Kretzschmar","doi":"10.2202/1948-4690.1009","DOIUrl":"https://doi.org/10.2202/1948-4690.1009","url":null,"abstract":"During their injecting career, injecting drug users (IDUs) are exposed to some infections, like hepatitis C virus (HCV) infection and human immunodeficiency virus (HIV) infection, due to their injecting behavioral risk factors, such as sharing syringes or other paraphernalia containing infected blood, or sexual behavior risk factors. If we consider that these IDUs might belong to a social network of people where these behavioral risk factors are spread, then HCV and HIV infections might be associated at both the individual and the population level. In this paper, we study the association between HCV and HIV infection at the population level using aggregate data. Our aim is to define a hierarchy of structured models with which the association between HCV and HIV infection at population level and the time trend of prevalence can be investigated. The data analyzed in the paper are “diagnostic testing data,” which consist of repeated cross-sectional prevalence measurements from 1998 to 2006 for HCV and HIV infection, obtained from a sample of 515 drug treatment centers spread among the 20 regions in Italy, where subjects went for a serum diagnostic test. Since we do not have any individual data, it is not possible to relate these prevalence data to socio-demographic or behavioral risk data. Each region defines a cluster with repeated prevalence data for HCV and HIV infection over time. Several modeling approaches, such as generalized linear mixed models (GLMMs) and hierarchical Bayesian models are applied to the data. First, we test different covariance structures for the region-specific random effects in the GLMM context; second, a hierarchical Bayesian model is used to refit the best GLMM in order to obtain the posterior distribution for the parameters of primary interest. We found that the correlation at population level between HCV and HIV is approximately 0.68 and the prevalence of the two infections generally decreased over the years, compared to the situation in 1998.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"43 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77724860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}