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Background: The Right Size Roadmap was developed by the Association of Public Health Laboratories and the Centers for Disease Control and Prevention to improve influenza virologic surveillance efficiency. Guidelines were provided to state health departments regarding representativeness and statistical estimates of specimen numbers needed for seasonal influenza situational awareness, rare or novel influenza virus detection, and rare or novel influenza virus investigation. Objective: The aim of this study was to compare Roadmap sampling recommendations with Idaho’s influenza virologic surveillance to determine implementation feasibility. Methods: We calculated the proportion of medically attended influenza-like illness (MA-ILI) from Idaho’s influenza-like illness surveillance among outpatients during October 2008 to May 2014, applied data to Roadmap-provided sample size calculators, and compared calculations with actual numbers of specimens tested for influenza by the Idaho Bureau of Laboratories (IBL). We assessed representativeness among patients’ tested specimens to census estimates by age, sex, and health district residence. Results: Among outpatients surveilled, Idaho’s mean annual proportion of MA-ILI was 2.30% (20,834/905,818) during a 5-year period. Thus, according to Roadmap recommendations, Idaho needs to collect 128 specimens from MA-ILI patients/week for situational awareness, 1496 influenza-positive specimens/week for detection of a rare or novel influenza virus at 0.2% prevalence, and after detection, 478 specimens/week to confirm true prevalence is ≤2% of influenza-positive samples. The mean number of respiratory specimens Idaho tested for influenza/week, excluding the 2009-2010 influenza season, ranged from 6 to 24. Various influenza virus types and subtypes were collected and specimen submission sources were representative in terms of geographic distribution, patient age range and sex, and disease severity. Conclusions: Insufficient numbers of respiratory specimens are submitted to IBL for influenza laboratory testing. Increased specimen submission would facilitate meeting Roadmap sample size recommendations. [JMIR Public Health Surveill 2017;3(3):e57]

The structure and function of microorganisms that live in and on us, the human microbiota, are a tremendous resource. Microbiota may help to explain individual variability in health outcomes and be a source of new biomarkers for environmental exposures and of novel prognostic and diagnostic indicators. The increase in availability of low-cost, high-throughput techniques makes it relatively straightforward to include microbiota assessments in epidemiologic studies. With the recent joint publications of the findings of the Human Microbiome Consortium and related studies, the consequent surge of interest in microbiome research, and remarkable media attention, the time is ripe for epidemiologists to contribute their expertise to and translate results of microbiota research for population health.

Skin, the largest human organ, is a complex and dynamic ecosystem inhabited by a multitude of microorganisms. Host demographics and genetics, human behavior, local and regional environmental characteristics, and transmission events may all potentially drive human skin microbiota variability, resulting in an alteration of microbial community structure. This alteration may have important consequences regarding health and disease outcomes among individuals. More specifically, certain diversity patterns of human microbiota may be predictive or diagnostic of disease. The purpose of this review is to briefly describe the skin microbiota, outline the potential determining factors driving its variability, posit the likelihood of an association between the resulting microbial community structure on the skin with disease outcomes among individuals, and finally, to present some challenges and implications for studying the skin microbiota.

We assessed the dynamics of hand microbial community structure of 34 healthcare workers from a single surgical intensive care unit over a short (3 week) time period, whilst taking into account the technical sources of variability introduced by specimen collection, DNA extraction, and sequencing. Sample collection took place at 3 different time points. Only the sampling collection method appeared to have a significant impact on the observed hand microbial community structure among the healthcare workers. Analysis of samples collected using glove-juice showed a slightly more similar microbial composition within individual hand samples over time than between the hands of different individuals over time. This was not true for samples collected using a swab, where samples from a single individual were no more similar to each other over time than those among other individuals over time, suggesting they were essentially independent. DNA extraction techniques (lysozyme only versus enzyme cocktail) and sequencing (replicate set 1 versus 2) using Ion Torrent Personal Genome Machine, were not influential to the microbial community structures. Glove-juice sample collection may likely be the method of choice in hand hygiene studies in the healthcare setting.