Socio-demographic determinants of coinfections by HIV, hepatitis B and hepatitis C viruses in central Italian prisoners

BACKGROUND: The coinfections HIV/HCV/HBV are an important health issue in penitentiary communities. The aim of the study was to examine HIV, HBV and HCV coinfections determinants amongst prisoners in the jails of Southern Lazio (Central Italy), in the period 1995-2000. METHODS: Diagnosis of seropositivities for HIV, HBV and HCV was made using ELISA method. A multiple logistic regression analysis was conducted to verify the influence of socio-demographic factors on the HIV/HBV/HCV coinfections. RESULTS: HIV/HCV, HBV/HCV and HIV/HBV coinfections were detected in 42 (4%), 203 (17.9%) and 31 (2.9%) inmates, respectively. These coinfections are significantly associated with the status of drug addiction (OR = 16.02; p = 0.012; OR = 4.15; p < 0.001; OR = 23.57; p = 0.002), smoking habits (OR = 3.73; p = 0.033; OR = 1.42; p = 0.088; OR = 4.25; p = 0.053) and Italian nationality (OR = 7.05; p = 0.009; OR = 2.31; p < 0.001; OR = 4.61; p = 0.04). CONCLUSION: The prevalence of HIV, HBV and HCV seropositivity in jails suggests that information and education programs for inmates could be useful to reduce the spread of such infections

Predicting Urban Innovation from the Workforce Mobility Network in US

While great emphasis has been placed on the role of social interactions as driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among US metropolitan areas. We found that node centrality computed on this network accounts for most of the variability observed in cities' innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time