Objective: To identify predictors of increased adiposity for different measures of adiposity. Design: Prospective cohort study, the Melbourne Collaborative Cohort Study (MCCS), with data at baseline (1990)(1991)(1992)(1993)(1994) and wave 2 (2003-2007). Setting: Participants recruited from the community. Subjects: Australian-born participants (n 5879) aged 40 to 69 years who were not current smokers and who were free from common chronic diseases at recruitment. At baseline and at wave 2, weight and waist circumference were measured; while demographic and lifestyle variables were obtained at baseline via structured interviews. Results: Participants who reported any recreational physical activity at baseline had lower weight and smaller waist circumference at wave 2 than those who did not, particularly for younger participants and for vigorous physical activity. Walking for leisure was not associated, and greater physical activity at work was associated, with greater adiposity measures at wave 2. A diet low in carbohydrates and fibre, but high in fat and protein, predicted greater weight and waist circumference at wave 2. Participants were less likely to have elevated weight or waist circumference at wave 2 if they consumed low to moderate amounts of alcohol. Conclusions: Our findings indicate that promoting vigorous physical activity, encouraging a diet high in carbohydrate and fibre but low in fat and protein, and limiting alcohol intake could be promising approaches for preventing obesity in middle-aged adults. Similar interventions should successfully address the management of both weight and waist circumference, as they were predicted by similar factors. Keywords Weight Waist circumference Obesity Predictors Physical activityUnderstanding the individual-level predictors of weight and waist circumference gain is critical, both to identify individuals most at risk and to allow targeting of the behavioural risk factors likely to contribute the most to overweight and obesity. While consensus is forming around the role of specific dietary elements and markers of physical activity and inactivity associated with increased weight gain (1)(2)(3) , few studies have analysed predictors of increasing waist circumference (4)(5)(6)(7) and the majority of these have focused solely on dietary factors.Waist circumference is a better indicator of excess visceral adiposity (8) , which tends to be more closely associated with metabolic abnormalities and some cancers than overall adiposity as assessed by BMI (3) . Further, there is evidence that waist circumference is increasing at a faster rate than BMI (7,9)

BackgroundIn view of ongoing pandemic threats such as the recent human cases of novel avian influenza A(H7N9) in China, it is important that all countries continue their preparedness efforts. Since 2006, Central American countries have received donor funding and technical assistance from the U.S. Centers for Disease Control and Prevention (CDC) to build and improve their capacity for influenza surveillance and pandemic preparedness. Our objective was to measure changes in pandemic preparedness in this region, and explore factors associated with these changes, using evaluations conducted between 2008 and 2012.MethodsEight Central American countries scored their pandemic preparedness across 12 capabilities in 2008, 2010 and 2012, using a standardized tool developed by CDC. Scores were calculated by country and capability and compared between evaluation years using the Student’s t-test and Wilcoxon Rank Sum test, respectively. Virological data reported to WHO were used to assess changes in testing capacity between evaluation years. Linear regression was used to examine associations between scores, donor funding, technical assistance and WHO reporting.ResultsAll countries improved their pandemic preparedness between 2008 and 2012 and seven made statistically significant gains (p < 0.05). Increases in median scores were observed for all 12 capabilities over the same period and were statistically significant for eight of these (p < 0.05): country planning, communications, routine influenza surveillance, national respiratory disease surveillance, outbreak response, resources for containment, community interventions and health sector response. We found a positive association between preparedness scores and cumulative funding between 2006 and 2011 (R2 = 0.5, p < 0.01). The number of specimens reported to WHO from participating countries increased significantly from 5,551 (2008) to 18,172 (2012) (p < 0.01).ConclusionsCentral America has made significant improvements in influenza pandemic preparedness between 2008 and 2012. U.S. donor funding and technical assistance provided to the region is likely to have contributed to the improvements we observed, although information on other sources of funding and support was unavailable to study. Gains are also likely the result of countries’ response to the 2009 influenza pandemic. Further research is required to determine the degree to which pandemic improvements are sustainable.

The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups.

BackgroundLaboratory testing is a fundamental component of influenza surveillance for detecting novel strains with pandemic potential and informing biannual vaccine strain selection. The United States (U.S.) Centers for Disease Control and Prevention (CDC), under the auspices of its WHO Collaborating Center for Influenza, is one of the major public health agencies which provides support globally to build national capacity for influenza surveillance. Our main objective was to determine if laboratory assessments supported capacity building efforts for improved global influenza surveillance.MethodsIn 2010, 35 national influenza laboratories were assessed in 34 countries, using a standardized tool. Post-assessment, each laboratory received a report with a list of recommendations for improvement. Uptake of recommendations were reviewed 3.2 mean years after the initial assessments and categorized as complete, in-progress, no action or no update. This was a retrospective study; follow-up took place through routine project management rather than at a set time-point post-assessment. WHO data on National Influenza Centre (NIC) designation, External Quality Assessment Project (EQAP) participation and FluNet reporting was used to measure laboratory capacity longitudinally and independently of the assessments. All data was further stratified by World Bank country income category.ResultsAt follow-up, 81 % of 614 recommendations were either complete (350) or in-progress (145) for 32 laboratories (91 % response rate). The number of countries reporting to FluNet and the number of specimens they reported annually increased between 2005, when they were first funded by CDC, and 2010, the assessment year (p < 0.01). Improvements were also seen in EQAP participation and NIC designation over time and more so for low and lower-middle income countries.ConclusionsAssessments using a standardized tool have been beneficial to improving laboratory-based influenza surveillance. Specific recommendations helped countries identify and prioritize areas for improvement. Data from assessments helped CDC focus its technical assistance by country and region. Low and lower-middle income countries made greater improvements in their laboratories compared with upper-middle income countries. Future research could include an analysis of annual funding and technical assistance by country. Our approach serves as an example for capacity building for other diseases.