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Individual behavioral response to the spreading of an epidemic plays a crucial role in the progression of the epidemic itself. The risk perception induces individuals to adopt a protective behavior, as for instance reducing their social contacts, adopting more restrictive hygienic measures or undergoing prophylaxis procedures. In this paper, starting with a previously developed lattice-gas SIR model, we construct a coupled behavior-disease model for influenza spreading with spontaneous behavioral changes. The focus is on self-initiated behavioral changes that alter the susceptibility to the disease, without altering the contact patterns among individuals. Three different mechanisms of awareness spreading are analyzed: the local spreading due to the presence in the neighborhood of infective individuals; the global spreading due to the news published by the mass media and to educational campaigns implemented at institutional level; the local spreading occurring through the “thought contagion” among aware and unaware individuals. The peculiarity of the present approach is that the awareness spreading model is calibrated on available data on awareness and concern of the population about the risk of contagion. In particular, the model is validated against the A(H1N1) epidemic outbreak in Italy during the season, by making use of the awareness data gathered by the behavioral risk factor surveillance system (PASSI). We find that, increasing the accordance between the simulated awareness spreading and the PASSI data on risk perception, the agreement between simulated and experimental epidemiological data improves as well. Furthermore, we show that, within our model, the primary mechanism to reproduce a realistic evolution of the awareness during an epidemic, is the one due to globally available information. This result highlights how crucial is the role of mass media and educational campaigns in influencing the epidemic spreading of infectious diseases. [ABSTRACT FROM AUTHOR]

The objectives of this paper are to predict life years lost associated with obesity-related diseases (ORDs) for U.S. non-smoking adults, and to examine the relationship between those ORDs and mortality. Data from the National Health Interview Survey, 1997–2000, were used. We employed mixed proportional hazard models to estimate the association between those ORDs and mortality and used simulations to project life years lost associated with the ORDs. We found that obesity-attributable comorbidities are associated with large decreases in life years and increases in mortality rates. The life years lost associated with ORDs is more marked for younger adults than older adults, for blacks than whites, for males than females, and for the more obese than the less obese. Using U.S. non-smoking adults aged 40 to 49 years as an example to illustrate percentage of the life years lost associated with ORDs, we found that the mean life years lost associated with ORDs for U.S. non-smoking black males aged 40 to 49 years with a body mass index above 40 kg/m2 was 5.43 years, which translates to a 7.5% reduction in total life years. White males of the same age range and same degree of obesity lost 5.23 life years on average – a 6.8% reduction in total life years, followed by black females (5.04 years, a 6.5% reduction in life years), and white females (4.7 years, a 5.8% reduction in life years). Overall, ORDs increased chances of dying and lessened life years by 0.2 to 11.7 years depending on gender, race, BMI classification, and age. [ABSTRACT FROM AUTHOR]

In the absence of reliable systems for registering rare types of vital events large surveys are required to measure changes in their rates. However some events such as maternal deaths are widely known about in the community. This study examined the utility of asking respondents about events in their neighbourhood as an efficient method for measuring relative rates of rare health events such as maternal and infant deaths. A survey was conducted in the health and demographic surveillance system (HDSS) in Matlab, Bangladesh, which includes two areas with different health care regimes. Adult women were asked about any maternal deaths; multiple births; infant deaths, live births and some other events they knew of in a small specified area around their home. Agreement between HDSS records and survey responses was moderate or better (kappa≥0.44) for all the events and greatest for maternal deaths (kappa = 0.77) with 84% being reported. Most events were more likely to be reported if they were recent (p<0.05). Infant mortality rate in one area was 0.56 times that in the other which was well reflected by the ratio of survey results (0.53). Simulations were used to study the ability of the method to detect differences in maternal mortality ratio. These suggested that a sample size around 5000 would give 80% power to detect a 50% decrease from a baseline of 183 which compared well with an estimated sample size around 10 times larger using the direct sisterhood method. The findings suggest that the Neighbourhood Method has potential for monitoring relative differences between areas or changes over time in the rates of rare demographic events, requiring considerably smaller sample sizes than traditional methods. This raises the possibility for interventions to demonstrate real effects on outcomes such as maternal deaths where previously this was only feasible by indirect methods. [ABSTRACT FROM AUTHOR]

Background: The Child and Adolescent Twin Study in Sweden (CATSS) is an on-going, large population-based longitudinal twin study. We aimed (1) to investigate the reliability of two different versions (125-items and 238-items) of Cloninger's Temperament and Character Inventory (TCI) used in the CATSS and the validity of extracting the short version from the long version, (2) to compare these personality dimensions between twins and adolescents from the general population, and (3) to investigate the genetic structure of Cloninger's model. Method: Reliability and correlation analyses were conducted for both TCI versions, 2,714 CATSS-twins were compared to 631 adolescents from the general population, and the genetic structure was investigated through univariate genetic analyses, using a model-fitting approach with structural equation-modeling techniques based on same-sex twin pairs from the CATSS (423 monozygotic and 408 dizygotic pairs). Results: The TCI scores from the short and long versions showed comparable reliability coefficients and were strongly correlated. Twins scored about half a standard deviation higher in the character scales. Three of the four temperament dimensions (Novelty Seeking, Harm Avoidance, and Persistence) had strong genetic and non-shared environmental effects, while Reward Dependence and the three character dimensions had moderate genetic effects, and both shared and non-shared environmental effects. Conclusions: Twins showed higher scores in character dimensions compared to adolescents from the general population. At least among adolescents there is a shared environmental influence for all of the character dimensions, but only for one of the temperament dimensions (i.e., Reward Dependence). This specific finding regarding the existence of shared environmental factors behind the character dimensions in adolescence, together with earlier findings showing a small shared environmental effects on character among young adults and no shared environmental effects on character among adults, suggest that there is a shift in type of environmental influence from adolescence to adulthood regarding character. [ABSTRACT FROM AUTHOR]

Sampling techniques such as Respondent-Driven Sampling (RDS) are widely used in epidemiology to sample “hidden” populations, such that properties of the network can be deduced from the sample. We consider how similar techniques can be designed that allow the discovery of the structure, especially the community structure, of networks. Our method involves collecting samples of a network by random walks and reconstructing the network by probabilistically coalescing vertices, using vertex attributes to determine the probabilities. Even though our method can only approximately reconstruct a part of the original network, it can recover its community structure relatively well. Moreover, it can find the key vertices which, when immunized, can effectively reduce the spread of an infection through the original network. [ABSTRACT FROM AUTHOR]