Road traffic injury mortality and morbidity by country development status, 2011-2017.

PURPOSE: This research examined road traffic injury mortality and morbidity disparities across of country development status, and discussed the possibility of reducing country disparities by various actions to accelerate the pace of achieving Sustainable Development Goals target 3.6 - to halve the number of global deaths and injuries from road traffic accidents by 2020. METHODS: Data for road traffic mortality, morbidity, and socio-demographic index (SDI) were extracted by country from the estimates of the Global Burden of Disease study, and the implementation of the three types of national actions (legislation, prioritized vehicle safety standards, and trauma-related post-crash care service) were extracted from the Global Status Report on Road Safety by World Health Organization. We fitted joinpoint regression analysis to identify and quantify the significant rate changes from 2011 to 2017. RESULTS: Age-adjusted road traffic mortality decreased substantially for all the five SDI categories from 2011 to 2017 (by 7.52%-16.08%). Age-adjusted road traffic mortality decreased significantly as SDI increased in the study time period, while age-adjusted morbidity generally increased as SDI increased. Subgroup analysis by road user yielded similar results, but with two major differences during the study period of 2011 to 2017: (1) pedestrians in the high SDI countries experienced the lowest mortality (1.68-1.90 per 100,000 population) and morbidity (110.45-112.72 per 100,000 population for incidence and 487.48-491.24 per 100,000 population for prevalence), and (2) motor vehicle occupants in the high SDI countries had the lowest mortality (4.07-4.50 per 100,000 population) but the highest morbidity (428.74-467.78 per 100,000 population for incidence and 1025.70-1116.60 per 100,000 population for prevalence). Implementation of the three types of national actions remained nearly unchanged in all five SDI categories from 2011 to 2017 and was consistently stronger in the higher SDI countries than in the lower SDI countries. Lower income nations comprise the heaviest burden of global road traffic injuries and deaths. CONCLUSION: Global road traffic deaths would decrease substantially if the large mortality disparities across country development status were reduced through full implementation of proven national actions including legislation and law enforcement, prioritized vehicle safety standards and trauma-related post-crash care services.

A new method for the discovery of essential proteins.

BACKGROUND: Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value. METHOD: In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality. CONCLUSIONS: We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.