Search

Background: The study of well-being is becoming a priority in social sciences. The Day Reconstruction Method (DRM) was developed to assess affective states. The aim of the present study was to validate an abbreviated version of the DRM designed for administration in population studies, and to assess its test-retest properties. Principal Findings: 1560 adults from Jodhpur (India) were interviewed using an abbreviated version of the DRM, and a week later they were re-interviewed using the original long version of the DRM, after which the abbreviated version of the DRM was compared with the original version. A regression model considering interaction terms was employed to analyse the impact of sociodemographic characteristics on net affect. Test-retest reliability was assessed, and found to be moderate. Positive affect showed more test-retest reliability than negative affect, while net affect had more temporal stability than U-index. The affect of sets A, B, and C, taken together, had a moderate predictive ability compared with the affect obtained using the full version of the DRM: AUC = 0.67 for positive affect; 0.66 for net affect; 0.61 for negative affect; and 0.60 for the U-index. Household income, gender, and setting all had a significant impact on net affect. Conclusions: Net affect and positive affect showed moderate temporal stability, whereas negative affect and the U-index showed fair temporal stability. Evaluating the affective state using the abbreviated version of the DRM provides a profile of the population similar to that of the full version. The results provide considerable support for using the short version of the DRM as an instrument to measure subjective well-being in large population surveys, This paper uses data from World Health Organization (WHO) Study on global AGEing and adult health (SAGE). WHO’s SAGE is supported by the United States National Institute on Aging through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-01) and through a research grant (R01- AG034479). The paper was supported by the Instituto de Salud Carlos III-FIS research grant number PS09/00295, the Spanish Ministry of Science and Innovation: ACI-Promociona (ACI2009-1010), and the Centro de Investigación Biomédica Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III

ObjectivesThis paper examines the ethnic and gender differentials in high blood pressure (HBP), diabetes, coronary heart disease (CHD), arthritis and asthma among older people in Malaysia, and how these diseases along with other factors affect self-rated health. Differentials in the prevalence of non-communicable diseases among older people are examined in the context of socio-cultural perspectives in multi-ethnic Malaysia.MethodsData for this paper are obtained from the 2004 Malaysian Population and Family Survey. The survey covered a nationally representative sample of 3,406 persons aged 50 and over, comprising three main ethnic groups (Malays, Chinese and Indians) and all other indigenous groups. Bivariate analyses and hierarchical logistic regression were used in the analyses.ResultsArthritis was the most common non-communicable disease (NCD), followed by HBP, diabetes, asthma and CHD. Older females were more likely than males to have arthritis and HBP, but males were more likely to have asthma. Diabetes and CHD were most prevalent among Indians, while arthritis and HBP were most prevalent among the Indigenous groups. Older people were more likely to report poor health if they suffered from NCD, especially CHD. Controlling for socio-economic, health and lifestyle factors, Chinese were least likely to report poor health, whereas Indians and Indigenous people were more likely to do so. Chinese that had HBP were more likely to report poor health compared to other ethnic groups with the same disease. Among those with arthritis, Indians were more likely to report poor health.ConclusionPerceived health status and prevalence of arthritis, HBP, diabetes, asthma and CHD varied widely across ethnic groups. Promotion of healthy lifestyle, early detection and timely intervention of NCDs affecting different ethnic groups and gender with socio-cultural orientations would go a long way in alleviating the debilitating effects of the common NCDs among older people.

Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies--we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure--future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.

Objectives: Increasing walking and cycling, and reducing motorised transport, are health and environmental priorities. This paper examines levels and trends in the use of different commute modes in England and Wales, both overall and with respect to small-area deprivation. It also investigates whether commute modal share can serve as a proxy for travel behaviour more generally. Methods: 23.7 million adult commuters reported their usual main mode of travelling to work in the 2011 census in England and Wales; similar data were available for 1971–2001. Indices of Multiple Deprivation were used to characterise socio-economic patterning. The National Travel Survey (2002–2010) was used to examine correlations between commute modal share and modal share of total travel time. These correlations were calculated across 150 non-overlapping populations defined by region, year band and income. Results: Among commuters in 2011, 67.1% used private motorised transport as their usual main commute mode (−1.8 percentage-point change since 2001); 17.8% used public transport (+1.8% change); 10.9% walked (−0.1% change); and 3.1% cycled (+0.1% change). Walking and, to a marginal extent, cycling were more common among those from deprived areas, but these gradients had flattened over the previous decade to the point of having essentially disappeared for cycling. In the National Travel Survey, commute modal share and total modal share were reasonably highly correlated for private motorised transport (r = 0.94), public transport (r = 0.96), walking (r = 0.88 excluding London) and cycling (r = 0.77). Conclusions: England and Wales remain car-dependent, but the trends are slightly more encouraging. Unlike many health behaviours, it is more common for socio-economically disadvantaged groups to commute using physically active modes. This association is, however, weakening and may soon reverse for cycling. At a population level, commute modal share provides a reasonable proxy for broader travel patterns, enhancing the value of the census in characterising background trends and evaluating interventions.

The Water-associated Disease Index (WADI) was developed to identify and visualize vulnerability to different water-associated diseases by integrating a range of social and biophysical determinants in map format. In this study vulnerability is used to encompass conditions of exposure, susceptibility, and differential coping capacity to a water-associated health hazard. By assessing these conditions, the tool is designed to provide stakeholders with an integrated and long-term understanding of subnational vulnerabilities to water-associated disease and contribute to intervention strategies to reduce the burden of illness. The objective of this paper is to describe and validate the WADI tool by applying it to dengue. A systemic ecohealth framework that considers links between people, the environment and health was applied to identify secondary datasets, populating the index with components including climate conditions, land cover, education status and water use practices. Data were aggregated to create composite indicators of exposure and of susceptibility in a Geographic Information System (GIS). These indicators were weighted by their contribution to dengue vulnerability, and the output consisted of an overall index visualized in map format. The WADI was validated in this Malaysia case study, demonstrating a significant association with dengue rates at a sub-national level, and illustrating a range of factors that drive vulnerability to the disease within the country. The index output indicated high vulnerability to dengue in urban areas, especially in the capital Kuala Lumpur and surrounding region. However, in other regions, vulnerability to dengue varied throughout the year due to the influence of seasonal climate conditions, such as monsoon patterns. The WADI tool complements early warning models for water-associated disease by providing upstream information for planning prevention and control approaches, which increasingly require a comprehensive and geographically broad understanding of vulnerability for implementation.

Recent studies suggest that obesity may be “contagious” between individuals in social networks. Social contagion (influence), however, may not be identifiable using traditional statistical approaches because they cannot distinguish contagion from homophily (the propensity for individuals to select friends who are similar to themselves) or from shared environmental influences. In this paper, we apply the stochastic actor-based model (SABM) framework developed by Snijders and colleagues to data on adolescent body mass index (BMI), screen time, and playing active sports. Our primary hypothesis was that social influences on adolescent body size and related behaviors are independent of friend selection. Employing the SABM, we simultaneously modeled network dynamics (friendship selection based on homophily and structural characteristics of the network) and social influence. We focused on the 2 largest schools in the National Longitudinal Study of Adolescent Health (Add Health) and held the school environment constant by examining the 2 school networks separately (N = 624 and 1151). Results show support in both schools for homophily on BMI, but also for social influence on BMI. There was no evidence of homophily on screen time in either school, while only one of the schools showed homophily on playing active sports. There was, however, evidence of social influence on screen time in one of the schools, and playing active sports in both schools. These results suggest that both homophily and social influence are important in understanding patterns of adolescent obesity. Intervention efforts should take into consideration peers’ influence on one another, rather than treating “high risk” adolescents in isolation.

The dynamics of infectious diseases that are spread through direct contact have been proven to depend on the strength of community structure or modularity within the underlying network. It has been recently shown that weighted networks with similar modularity values may exhibit different mixing styles regarding the number of connections among communities and their respective weights. However, the effect of mixing style on epidemic behavior was still unclear. In this paper, we simulate the spread of disease within networks with different mixing styles: a dense-weak style (i.e., many edges among the communities with small weights) and a sparse-strong style (i.e., a few edges among the communities with large weights). Simulation results show that, with the same modularity: 1) the mixing style significantly influences the epidemic size, speed, pattern and immunization strategy; 2) the increase of the number of communities amplifies the effect of the mixing style; 3) when the mixing style changes from sparse-strong to dense-weak, there is a 'saturation point', after which the epidemic size and pattern become stable. We also provide a mean-field solution of the epidemic threshold and size on weighted community networks with arbitrary external and internal degree distribution. The solution explains the effect of the second moment of the degree distribution, and a symmetric effect of internal and external connections (incl. degree distribution and weight). Our study has both potential significance for designing more accurate metrics for the community structure and exploring diffusion dynamics on metapopulation networks.

Background Zoonotic diseases account for over 60% of all communicable diseases causing illness in humans and 75% of recently emerging infectious diseases. As limited resources are available for the control and prevention of zoonotic diseases, it is necessary to prioritize diseases in order to direct resources into those with the greatest needs. The selection of criteria for prioritization has traditionally been on the basis of expert opinion; however, details of the methods used to identify criteria from expert opinion often are not published and a full range of criteria may not be captured by expert opinion. Methodology/Principal Findings This study used six focus groups to identify criteria for the prioritization of zoonotic diseases in Canada. Focus groups included people from the public, animal health professionals and human health professionals. A total of 59 criteria were identified for prioritizing zoonotic diseases. Human-related criteria accounted for the highest proportion of criteria identified (55%), followed by animal-related criteria (26%) then pathogen/disease-related criteria (19%). Similarities and differences were observed in the identification and scoring of criteria for disease prioritization between groups; the public groups were strongly influenced by the individual-level of disease burden, the responsibility of the scientific community in disease prioritization and the experiences of recent events while the professional groups were influenced by the societal- and population-level of disease burden and political and public pressure. Conclusions/Significance This was the first study to describe a mixed semi-quantitative and qualitative approach to deriving criteria for disease prioritization. This was also the first study to involve the opinion of the general public regarding disease prioritization. The number of criteria identified highlights the difficulty in prioritizing zoonotic diseases. The method presented in this paper has formulated a comprehensive list of criteria that can be used to inform future disease prioritization studies.

Ruth Loos and colleagues report findings from a meta-analysis of multiple studies examining the extent to which physical activity attenuates effects of a specific gene variant, FTO, on obesity in adults and children. They report a fairly substantial attenuation by physical activity on the effects of this genetic variant on the risk of obesity in adults., Background The FTO gene harbors the strongest known susceptibility locus for obesity. While many individual studies have suggested that physical activity (PA) may attenuate the effect of FTO on obesity risk, other studies have not been able to confirm this interaction. To confirm or refute unambiguously whether PA attenuates the association of FTO with obesity risk, we meta-analyzed data from 45 studies of adults (n = 218,166) and nine studies of children and adolescents (n = 19,268). Methods and Findings All studies identified to have data on the FTO rs9939609 variant (or any proxy [r 2>0.8]) and PA were invited to participate, regardless of ethnicity or age of the participants. PA was standardized by categorizing it into a dichotomous variable (physically inactive versus active) in each study. Overall, 25% of adults and 13% of children were categorized as inactive. Interaction analyses were performed within each study by including the FTO×PA interaction term in an additive model, adjusting for age and sex. Subsequently, random effects meta-analysis was used to pool the interaction terms. In adults, the minor (A−) allele of rs9939609 increased the odds of obesity by 1.23-fold/allele (95% CI 1.20–1.26), but PA attenuated this effect (p interaction = 0.001). More specifically, the minor allele of rs9939609 increased the odds of obesity less in the physically active group (odds ratio = 1.22/allele, 95% CI 1.19–1.25) than in the inactive group (odds ratio = 1.30/allele, 95% CI 1.24–1.36). No such interaction was found in children and adolescents. Conclusions The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of PA in particular in those genetically predisposed to obesity. Please see later in the article for the Editors' Summary, Editors’ Summary Background Two in three Americans are overweight, of whom half are obese, and the trend towards increasing obesity is now seen across developed and developing countries. There has long been interest in understanding the impact of genes and environment when it comes to apportioning responsibility for obesity. Carrying a change in the FTO gene is common (found in three-quarters of Europeans and North Americans) and is associated with a 20%–30% increased risk of obesity. Some overweight or obese individuals may feel that the dice are loaded and there is little point in fighting the fat; it has been reported that those made aware of their genetic susceptibility to obesity may still choose a poor diet. A similar fatalism may occur when overweight and obese people consider physical activity. But disentangling the influence of physical activity on those genetically susceptible to obesity from other factors that might impact weight is not straightforward, as it requires large sample sizes, could be subject to publication bias, and may rely on less than ideal self-reporting methods. Why Was This Study Done? The public health ramifications of understanding the interaction between genetic susceptibility to obesity and physical activity are considerable. Tackling the rising prevalence of obesity will inevitably include interventions principally aimed at changing dietary intake and/or increasing physical activity, but the evidence for these with regards to those genetically susceptible has been lacking to date. The authors of this paper set out to explore the interaction between the commonest genetic susceptibility trait and physical activity using a rigorous meta-analysis of a large number of studies. What Did the Researchers Do and Find? The authors were concerned that a meta-analysis of published studies would be limited both by the data available to them and by possible bias. Instead of this more widely used approach, they took the literature search as their starting point, identified other studies through their collaborators’ network, and then undertook a meta-analysis of all available studies using a new and standardized analysis plan. This entailed an extremely large number of authors mining their data afresh to extract the relevant data points to enable such a meta-analysis. Physical activity was identified in the original studies in many different ways, including by self-report or by using an external measure of activity or heart rate. In order to perform the meta-analysis, participants were labeled as physically active or inactive in each study. For studies that had used a continuous scale, the authors decided that the bottom 20% of the participants were inactive (10% for children and adolescents). Using data from over 218,000 adults, the authors found that carrying a copy of the susceptibility gene increased the odds of obesity by 1.23-fold. But the size of this influence was 27% less in the genetically susceptible adults who were physically active (1.22-fold) compared to those who were physically inactive (1.30-fold). In a smaller study of about 19,000 children, no such effect of physical activity was seen. What Do these Findings Mean? This study demonstrates that people who carry the susceptibility gene for obesity can benefit from physical activity. This should inform health care professionals and the wider public that the view of genetically determined obesity not being amenable to exercise is incorrect and should be challenged. Dissemination, implementation, and ensuring uptake of effective physical activity programs remains a challenge and deserves further consideration. That the researchers treated “physically active” as a yes/no category, and how they categorized individuals, could be criticized, but this was done for pragmatic reasons, as a variety of means of assessing physical activity were used across the studies. It is unlikely that the findings would have changed if the authors had used a different method of defining physically active. Most of the studies included in the meta-analysis looked at one time point only; information about the influence of physical activity on weight changes over time in genetically susceptible individuals is only beginning to emerge. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001116. This study is further discussed in a PLoS Medicine Perspective by Lennert Veerman The US Centers for Disease Control and Prevention provides obesity-related statistics, details of prevention programs, and an overview on public health strategy in the United States A more worldwide view is given by the World Health Organization The UK National Health Service website gives information on physical activity guidelines for different age groups, while similar information can also be found from US sources

Developing appropriate risk messages during challenging situations like public health outbreaks is complicated. The focus of this paper is on how First Nations and Metis people in Manitoba, Canada, responded to the public health management of pandemic H1N1, using a focus group methodology (n = 23 focus groups). Focus group conversations explored participant reactions to messaging regarding the identification of H1N1 virus risk groups, the H1N1 vaccine and how priority groups to receive the vaccine were established. To better contextualize the intentions of public health professionals, key informant interviews (n = 20) were conducted with different health decision makers (e.g., public health officials, people responsible for communications, representatives from some First Nations and Metis self-governing organizations). While risk communication practice has improved, 'one size' messaging campaigns do not work effectively, particularly when communicating about who is most 'at-risk'. Public health agencies need to pay more attention to the specific socio-economic, historical and cultural contexts of First Nations and Metis citizens when planning for, communicating and managing responses associated with pandemic outbreaks to better tailor both the messages and delivery. More attention is needed to directly engage First Nations and Metis communities in the development and dissemination of risk messaging.

BACKGROUND: Influenza epidemics have a substantial impact on human health, by increasing the mortality from pneumonia and influenza, respiratory and circulatory diseases, and all causes. This paper provides estimates of excess mortality rates associated with influenza virus circulation for 7 causes of death and 8 age groups in Portugal during the period of 1980-2004. METHODOLOGY/PRINCIPAL FINDINGS: We compiled monthly mortality time series data by age for all-cause mortality, cerebrovascular diseases, ischemic heart diseases, diseases of the respiratory system, chronic respiratory diseases, pneumonia and influenza. We also used a control outcome, deaths from injuries. Age- and cause-specific baseline mortality was modelled by the ARIMA approach; excess deaths attributable to influenza were calculated by subtracting expected deaths from observed deaths during influenza epidemic periods. Influenza was associated with a seasonal average of 24.7 all-cause excess deaths per 100,000 inhabitants, approximately 90% of which were among seniors over 65 yrs. Excess mortality was 3-6 fold higher during seasons dominated by the A(H3N2) subtype than seasons dominated by A(H1N1)/B. High excess mortality impact was also seen in children under the age of four years. Seasonal excess mortality rates from all the studied causes of death were highly correlated with each other (Pearson correlation range, 0.65 to 0.95, P0.64, P