Guy Thomas

Development of Diabetes in Chinese With the Metabolic Syndrome A 6-year prospective study

OBJECTIVE—We investigated the association of the metabolic syndrome with new-onset diabetes in the Hong Kong Cardiovascular Risk Factor Prevalence Study cohort. RESEARCH DESIGN AND METHODS—We followed up on 1,679 subjects without diabetes at baseline. Those with a previous diagnosis of diabetes or those who were receiving drug treatment were considered to be diabetic. The remaining subjects underwent a 75-g oral glucose tolerance test (OGTT). Diabetes was defined by plasma glucose ≥7.0 mmol/l with fasting and/or ≥11.1 mmol/l at 2 h. RESULTS—The prevalences of the metabolic syndrome at baseline were 14.5 and 11.4%, respectively, according to U.S. National Cholesterol Education Program (NCEP) and International Diabetes Federation (IDF) criteria. After a median of 6.4 years, there were 66 and 54 new cases of diabetes in men and women, respectively. The metabolic syndrome at baseline predicted incident diabetes. Hazard ratios (HRs) for the NCEP and IDF definitions of the syndrome were 4.1 [95% CI 2.8–6.0] and 3.5 [2.3–5.2], respectively. HRs for fasting plasma glucose (FPG) ≥6.1 or 5.6 mmol/l were 6.9 [4.1–11.5] and 4.1 [2.8–6.0], respectively. The NCEP and IDF criteria had 41.9 and 31.7% sensitivity and 87.5 and 90.2% specificity, respectively. Their positive predictive values were low, ∼20%, but their negative predictive values were ∼95%. CONCLUSIONS—The metabolic syndrome, particularly its component, elevated FPG, predicts diabetes in Chinese. An individual without the metabolic syndrome is unlikely to develop diabetes, but one who has it should practice therapeutic lifestyle changes and have periodic FPG measurements to detect new-onset diabetes.

Real-time Estimates in Early Detection of SARS

A statistical method can be used for early monitoring of the effect of disease control measures.

Investigating transmission in a two-wave epidemic of Chikungunya fever, Réunion Island.

An epidemic of Chikungunya fever, a mosquito-borne viral disease, spectacularly swept through Réunion Island (population 780,000) in 2005-2006. There were 3,000 cases in a first wave (March-June 2005) and more than 250,000 cases in a second (December 2005-April 2006). Adapting newly developed epidemiological tools to vector-borne diseases, we show that despite this massive difference in magnitude, the transmission potential as measured by the number of secondary cases per index case (or reproduction number), remained similar during the two consecutive waves. The best estimate for the initial reproduction number R(0) was 3.7, with a possible range from 2 to 11 depending on incubation duration and lifespan of the mosquito. We conclude that an increase in virulence between the two seasons was not necessary to explain the change in magnitude of the epidemics, and that the attack rate may be well over 50% in Chikungunya fever epidemics in the absence of intervention.

S. pneumoniae transmission according to inclusion in conjugate vaccines: Bayesian analysis of a longitudinal follow-up in schools

BackgroundRecent trends of pneumococcal colonization in the United States, following the introduction of conjugate vaccination, indicate that non-vaccine serotypes tend to replace vaccine serotypes. The eventual extent of this replacement is however unknown and depends on serotype-specific carriage and transmission characteristics.MethodsHere, some of these characteristics were estimated for vaccine and non-vaccine serotypes from the follow-up of 4,488 schoolchildren in France in 2000. A Bayesian approach using Markov chain Monte Carlo data augmentation techniques was used for estimation.ResultsVaccine and non-vaccine serotypes were found to have similar characteristics: the mean duration of carriage was 23 days (95% credible interval (CI): 21, 25 days) for vaccine serotypes and 22 days (95% CI: 20, 24 days) for non-vaccine serotypes; within a school of size 100, the Secondary Attack Rate was 1.1% (95% CI: 1.0%, 1.2%) for both vaccine and non-vaccine serotypes.ConclusionThis study supports that, in 3–6 years old children, no competitive advantage exists for vaccine serotypes compared to non-vaccine serotypes. This is an argument in favour of important serotype replacement. It would be important to validate the result for infants, who are known to be the main reservoir in maintaining transmission. Overall reduction in pathogenicity should also be taken into account in forecasting the future burden of pneumococcal colonization in vaccinated populations.

Investigating heterogeneity in pneumococcal transmission : A bayesian MCMC approach applied to a follow-up of schools

The analysis of communicable agent transmission from field data is typically hampered by missing data, dependence between individual trajectories, and sometimes by heterogeneity among competing pathogens. Methods based on data augmentation and Markov chain Monte Carlo sampling have been used to analyze such data in small communities (typically households), with little diversity in pathogens. In this article the approach is extended to analyze the transmission of 15 Streptococcus pneumoniae serotypes in schoolchildren, where hundreds of individual trajectories interact and a substantial portion of trajectories are unobserved. For each child, the data were augmented to describe the detailed time course of S. pneumoniae carriage. The Bayesian hierarchical model ensured consistency between observed and augmented data; described the latent dynamics of S. pneumoniae acquisition and clearance; and specified priors. To investigate heterogeneity among serotypes, a clustering step was introduced to select a parsimonious description of transmission characteristics. The joint posterior distribution of parameters, augmented data, and clusters of serotypes was explored by reversible-jump MCMC sampling. The approach made it possible to make inferences simultaneously on the number of clusters of serotypes and on the transmission characteristics of each cluster.

Modelling the epidemic of variant Creutzfeldt-Jakob disease in the UK based on age characteristics: updated, detailed analysis

Incubation period of the new variant Creutzfeldt-Jakob disease (vCJD) from infection to clinical onset and the eventual impact of the disease remain major concerns. Based on i) epidemiological conceptualization of human exposure to BSE contaminated material, ii) exponentially decreasing susceptibility after 15 years of age, and iii) typical incubation period (IP) distributions for time from infection to onset, we have previously estimated mean incubation period and projected number of vCJD cases. In this paper, we investigate the robustness of these estimates with respect to i-iii using the UK’s 113 vCJD cases with clinical onset before December 2000. Mean incubation period was estimated at 16.4 years (95% CI 11.4-24.8), 15.9 years (95% CI 11.4-22.0), 14.1 years (95% CI 10.4-24.2) with the log-normal, Gamma and Weibull distributions respectively. Corresponding predictions for the total size of the epidemic ranged from 183 to 304. Maximal susceptibility to infection between 1.3 and 15.9 years and decreasing by 15% per year of age thereafter yielded the best fit. The shape of the IP distribution did not affect the predictions. In summary, within a set of reasonable assumptions, mean incubation period for vCJD ranged from 15 to 20 years, and the eventual impact of vCJD was a few hundred patients.

Estimating influenza latency and infectious period durations using viral excretion data.

Influenza infection natural history is often described as a progression through four successive stages: Susceptible-Exposed/Latent-Infectious-Removed (SEIR). The duration of each stage determines the average generation time, the time between infection of a case and infection of his/her infector. Recently, several authors have justified somewhat arbitrary choices in stage durations by how close the resulting generation time distribution was to viral excretion over time after infection. Taking this reasoning one step further, we propose that the viral excretion profile over time can be used directly to estimate the required parameters in an SEIR model. In our approach, the latency and infectious period distributions are estimated by minimizing the Kullback-Leibler divergence between the model-based generation time probability density function and the normalized average viral excretion profile. Following this approach, we estimated that the latency and infectious period last respectively 1.6 and 1.0 days on average using excretion profiles from experimental infections. Interestingly, we find that only 5% of cases are infectious for more than 2.9 days. We also discuss the consequences of these estimates for the evaluation of the efficacy of control measures such as isolation or treatment. We estimate that, under a best-case scenario where symptoms appear at the end of the latency period, index cases must be isolated or treated at most within 16h after symptoms onset to avoid 50% of secondary cases. This study provides the first estimates of latency and infectious period for influenza based directly on viral excretion data. It provides additional evidence that isolation or treatment of cases would be effective only if adopted shortly after symptoms onset, and shows that four days of isolation may be enough to avoid most transmissions.

Temporal Variability and Social Heterogeneity in Disease Transmission: The Case of SARS in Hong Kong

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002–2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (±0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.

Deterministic and Stochastic Modeling of Pneumococcal Resistance to Penicillin

ABSTRACT A stochastic compartmental model of the progression of pneumococcal resistance to penicillin G in a human community is built, through intra-individual selection and inter-individual transmission. It is structured by the resistance level of colonizing bacteria and driven by jump intensity functions. The Markov process associated with the model tends to the solution of a deterministic system when the size of the population tends to infinity. The behavior of the stochastic mean sample path is simulated for small population sizes and compared to the solution of the limit deterministic system. For populations over 5,000 individuals, the deterministic solution is a good approximation of the mean stochastic sample path. Both stochastic and deterministic predictions have proved useful to understand resistance selection mechanisms and to evaluate strategies for resistance prevention, such as a reduction in antibiotic consumption or vaccination.

Estimation of Balanced Simultaneous Confidence Sets for SIR Models

Stochastic compartmental (e.g., SIR) models have proven useful for studying the epidemics of childhood diseases while taking into account the variability of the epidemic dynamics. Here, we present a method for estimating balanced simultaneous confidence sets for the mean sample path of a stochastic SIR model, thus providing a simple representation of both the typical behavior and the variability of the epidemic. The confidence sets are estimated by a bootstrap procedure, using asymptotic properties of density dependent jump Markov processes. The method is applied to chickenpox epidemics in France and the coverage probability of the confidence sets is estimated in that context.