Recommendations for universal screening were rapidly adopted. Improved management of preterm deliveries and improved collection, processing, and reporting of culture results may prevent additional cases of early-onset group B streptococcal disease.

Before the introduction of the quadrivalent meningococcal conjugate vaccine, the incidence of meningococcal disease in the United States decreased to a historic low. However, meningococcal disease still causes a substantial burden of disease among all age groups. Future vaccination strategies may include targeting infants and preventing serogroup B meningococcal disease.

Routine screening for group B streptococcus during pregnancy prevents more cases of early-onset disease than the risk-based approach. Recommendations that endorse both strategies as equivalent warrant reconsideration.

Chemical contaminants (e.g. metals, pesticides, pharmaceuticals) are changing ecosystems via effects on wildlife. Indeed, recent work explicitly performed under environmentally realistic conditions reveals that chemical contaminants can have both direct and indirect effects at multiple levels of organization by influencing animal behaviour. Altered behaviour reflects multiple physiological changes and links individual- to population-level processes, thereby representing a sensitive tool for holistically assessing impacts of environmentally relevant contaminant concentrations. Here, we show that even if direct effects of contaminants on behavioural responses are reasonably well documented, there are significant knowledge gaps in understanding both the plasticity (i.e. individual variation) and evolution of contaminant-induced behavioural changes. We explore implications of multi-level processes by developing a conceptual framework that integrates direct and indirect effects on behaviour under environmentally realistic contexts. Our framework illustrates how sublethal behavioural effects of contaminants can be both negative and positive, varying dynamically within the same individuals and populations. This is because linkages within communities will act indirectly to alter and even magnify contaminant-induced effects. Given the increasing pressure on wildlife and ecosystems from chemical pollution, we argue there is a need to incorporate existing knowledge in ecology and evolution to improve ecological hazard and risk assessments.

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with all other racial/ethnic groups included in the total). The gastroschisis case definition was based on the British Pediatric Association Classification of Diseases code (756.71) or the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code for gastroschisis (756.73, or before 10/1/2009, 756.79, with verification to confirm cases of gastroschisis, because the previous code was shared with omphalocele). Gastroschisis cases included live births, fetal deaths, † and elective terminations. § Data were pooled at CDC, and gastroschisis prevalence was calculated for each year, maternal age group, and race/ethnicity. Prevalence was calculated as number of gastroschisis cases among all birth outcomes divided by the total number of live births. The denominators of total number of live births in the same catchment area as the birth defects surveillance program were reported by states or obtained from public use data files. Poisson exact methods were used to calculate 95% CIs for each prevalence estimate. Prevalence ratios were calculated by dividing the prevalence during 2006-2012 by the prevalence during 1995-2005, and CIs for the prevalence ratios were calculated using Poisson regression.Because the comparison of prevalence between the two study periods involved an artificial breakpoint during the 18-year data span and only examined pooled prevalence within those periods, joinpoint regression analysis was used to identify statistically significant changes in the annual prevalence of gastroschisis over the course of the entire study period (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Joinpoint regression initially models annual trend data by fitting a straight line (i.e., zero joinpoints). Then, joinpoints are added, one at a time, and a Monte Carlo permutation test is used to determine the optimal number of joinpoints. Each joinpoint in the final model corresponds to a significant change in the trend, and an AAPC and its 95% CI are calculated to describe how the rate changes within each time interval (3). The estimated overall percent change was calculated by first converting the AAPC to the projected single year change in prevalence and then exponentiating to the number of years studied minus one to estimate the total increase throughout the 18 years. This gives the magnitude of the increase, which

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