Knowledge of mechanisms of infection in vulnerable populations is needed in order to prepare for future outbreaks. Here, using a unique dataset collected during a 2009 outbreak of influenza A(H1N1)pdm09 in a university town, we evaluated mechanisms of infection and identified that an epidemiological model containing partial protection of susceptibles best describes H1N1 dynamics in a rural university environment. We found that the protected group was over 14 times less susceptible to H1N1 infection than unprotected susceptibles. Our estimates show that the basic reproductive rate, R 0, was 5·96 (95% confidence interval 5·83-6·61), and, importantly, R 0 could be decreased to below 1 and similar epidemics could be avoided by increasing the proportion of the initial protected group. Moreover, several weeks into the epidemic, this protected group generated more new infections than the unprotected susceptible group, and thus, such protected groups should be taken into account while studying influenza epidemics in similar settings.
Data are rare on influenza outbreaks spreading through a workplace, but such transmission dynamics would be useful for comparison with the spread of the infection in other settings. We collected and compared infection data from two settings, a workplace and a university campus, during the 2009 pandemic influenza A(H1N1)pdm09 outbreak in a geographically contained community. Trajectories of infection were markedly alike in both settings. This suggests that transmission behaviour was similar in individuals in the two environments, despite the condition that individuals can leave the workplace setting in order to avoid transmission.
In the beginning of fall semester 2009, over 2,000 students contacted the student health service at Washington State University to report symptoms of influenza. The epidemic in Pullman, WA made national news, and many speculated on the severity and extent of the disease spread. Analysis of data from the influenza A(H1N1)pdm09 epidemic in Pullman, WA offers an opportunity to gain insights into characteristics of this rural campus community outbreak. In this study, an individual-based stochastic epidemic simulation model was used with the data to estimate infection parameters and make projections of the number of symptomatic individuals that would result given a variety of plausible scenarios. The parameters that were estimated include the number of individuals initially infected and the basic reproductive ratio (R0). The model was then used to predict the magnitude of infection with vaccination, isolation and quarantine. The results show that the best single intervention strategy is vaccination, and the reduction in infection is greatest when vaccination, isolation and quarantine are used simultaneously.
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registrationClinicaltrials.gov (NCT01038583)
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