National estimates of seroincidence and seroprevalence for herpes simplex virus type 1 and type 2 among US military adults aged 18 to 29 years.

BACKGROUND: While population-based seroprevalence studies of herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2) are widespread, seroincidence studies are largely limited to select or high-risk populations. The US military offers a potential population to derive national seroincidence rate estimates for young adults (ages 18-29). METHODS: We used banked, longitudinal serum specimens collected in a cohort of 1094 military personnel aged 18 to 30 years who served between 1989 and 2005 to estimate national HSV-1 and HSV-2 seroincidence and seroprevalence for the young, adult military population, weighted according to the US Census. Serum was tested with indirect ELISA (enzyme-linked immunosorbent assay). RESULTS: Estimated national seroincidence rates for the US young, adult military population were 9.1 per 100 person-years (95% confidence interval: 4.6-13.5) for HSV-1 and 6.2 (95% confidence interval: 3.1-9.3) for HSV-2. Female sex and black race were associated with significantly higher HSV-2 seroconversion rates. Our estimated HSV1/2 seroprevalences were comparable to US national data provided by National Health and Nutrition Examination Surveys' serosurveys except for non-Hispanic blacks and Hispanics. CONCLUSION: Although these US 2000 Census-weighted estimates of HSV-1 and HSV-2 seroincidence apply only to young, military adults, they nonetheless supply, to our knowledge, the only national figures that might be used to predict US national HSV1/2 seroincidence in young adults. Thus, we believe that our findings in this military population can be used to inform the planning of HSV-1 and 2 prevention measures in the general, young-adult US population.

Mathematical modeling and the epidemiological research process.

The authors of this paper advocate for the expanded use of mathematical models in epidemiology and provide an overview of the principles of mathematical modeling. Mathematical models can be used throughout the epidemiological research process. Initially they may help to refine study questions by visually expressing complex systems, directing literature searches, and identifying sensitive variables. In the study design phase, models can be used to test sampling strategies, to estimate sample size and power, and to predict outcomes for studies impractical due to time or ethical considerations. Once data are collected, models can assist in the interpretation of results, the exploration of causal pathways, and the combined analysis of data from multiple sources. Finally, models are commonly used in the process of applying research findings to public health practice by estimating population risk, predicting the effects of interventions, and contributing to the evaluation of ongoing programs. Mathematical modeling has the potential to make significant contributions to the field of epidemiology by enhancing the research process, serving as a tool for communicating findings to policymakers, and fostering interdisciplinary collaboration.