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China’s fast economic growth contributes to the rapid development of its urbanization process, and also renders a series of industrial accidents, which often cause loss of life, damage to property and environment, thus requiring the associated risk analysis and safety control measures to be implemented in advance. However, incompleteness of historical failure data before the occurrence of accidents makes it difficult to use traditional risk analysis approaches such as probabilistic risk analysis in many cases. This paper aims to develop a new methodology capable of assessing regional industrial safety (RIS) in an uncertain environment. A hierarchical structure for modelling the risks influencing RIS is first constructed. The hybrid of evidential reasoning (ER) and Analytical Hierarchy Process (AHP) is then used to assess the risks in a complementary way, in which AHP is hired to evaluate the weight of each risk factor and ER is employed to synthesise the safety evaluations of the investigated region(s) against the risk factors from the bottom to the top level in the hierarchy. The successful application of the hybrid approach in a real case analysis of RIS in several major districts of Beijing (capital of China) demonstrates its feasibility as well as provides risk analysts and safety engineers with useful insights on effective solutions to comprehensive risk assessment of RIS in metropolitan cities. The contribution of this paper is made by the findings on the comparison of risk levels of RIS at different regions against various risk factors so that best practices from the good performer(s) can be used to improve the safety of the others. [ABSTRACT FROM AUTHOR]

Accidents, bad weathers, traffic congestions, etc. led to the uncertainties of travel times in real-life road networks, which greatly affected the quality of individual’s life and the reliability of transportation system. This paper addressed the school bus routing problem in such a stochastic and time-dependent road environment. Firstly, the problem was set based on a single-school configuration, and the students were picked up at their homes, which was in line with the current situation of school bus systems in China. Thus, it could be regarded as an independent problem of school bus route generation in random dynamic networks, which could be solved as a variant of extended Vehicle Routing Problem. However, due to the fluctuation and uncertainty of link travel times, the arrival time at each stop including the destination was varying. Therefore, the selection of optimal path connecting the current service node with the next one was treated as a sub-problem in this study, where the reliability of travel times in the stochastic and time-varying network was highly concerned by such time-rigid commuters. To this end, a Robust Optimal Schedule Times model with a hard time windows constraint was built to generate a most cost-reliable route for school buses. By the use of Robust Optimization method, it was intended to minimize the worst-case total cost which combined the cost of earlier schedule delays with the disutility of travel times. It was also proved that the proposed model could be converted into solving a conventional problem in deterministic dynamic networks for a reduction of computation complexity, which provided the potential of applying to the practical problems. Finally, the validity of the proposed model and its performance evaluation was analyzed through a small-scale computational instance, where all the link travel times in the simulated network were attributed to both time-varying and stochastic. Then, a mathematical programming solver was used to find the exact optimal solution. The results indicated that the model was valid, and the necessity of considering the stochastic and time-dependent nature of transportation networks was also confirmed in the case study. [ABSTRACT FROM AUTHOR]

Introduction: Analysts estimating the costs or cost-effectiveness of health interventions requiring hospitalization often cut corners because they lack data and the costs of undertaking full step-down costing studies are high. They sometimes use the costs taken from a single hospital, sometimes use simple rules of thumb for allocating total hospital costs between general inpatient care and the outpatient department, and sometimes use the average cost of an inpatient bed-day instead of a ward-specific cost. Purpose: In this paper we explore for the first time the extent and the causes of variation in ward-specific costs across hospitals, using data from China. We then use the resulting model to show how ward-specific costs for hospitals outside the data set could be estimated using information on the determinants identified in the paper. Methodology: Ward-specific costs estimated using step-down costing methods from 41 hospitals in 12 provinces of China were used. We used seemingly unrelated regressions to identify the determinants of variability in the ratio of the costs of specific wards to that of the outpatient department, and explain how this can be used to generate ward-specific unit costs. Findings: Ward-specific unit costs varied considerably across hospitals, ranging from 1 to 24 times the unit cost in the outpatient department — average unit costs are not a good proxy for costs at specialty wards in general. The most important sources of variability were the number of staff and the level of capacity utilization. Practice Implications: More careful hospital costing studies are clearly needed. In the meantime, we have shown that in China it is possible to estimate ward-specific unit costs taking into account key determinants of variability in costs across wards. This might well be a better alternative than using simple rules of thumb or using estimates from a single study. [ABSTRACT FROM AUTHOR]

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions. [ABSTRACT FROM AUTHOR]

Objective: The School-based Education Program to Reduce Salt Intake in Children and Their Families study was a cluster randomized control trial among grade five students in 28 primary schools and their families in Changzhi, China. It achieved a significant effect in lowering systolic blood pressure (SBP) in all family adults by 2.3 mmHg and in elderlies (aged > = 60 years) by 9.5 mmHg. The aim of this study was to assess the cost-effectiveness of this salt reduction program. Methods: Costs of the intervention were assessed using an ingredients approach to identify resource use. A trial-based incremental cost-effectiveness ratio (ICER) was estimated based on the observed effectiveness in lowering SBP. A Markov model was used to estimate the long-term cost-effectiveness of the intervention, and then based on population data, extrapolated to a scenario where the program is scaled up nationwide. Findings were presented in terms of an incremental cost per quality-adjusted life year (QALY). The perspective was that of the health sector. Results: The intervention cost Int$19.04 per family and yielded an ICER of Int$2.74 (90% CI: 1.17–12.30) per mmHg reduction of SBP in all participants (combining children and adult participants together) compared with control group. If scaled up nationwide for 10 years and assumed deterioration in treatment effect of 50% over this period, it would reach 165 million families and estimated to avert 42,720 acute myocardial infarction deaths and 107,512 stroke deaths in China. This would represent a gain of 635,816 QALYs over 10-year time frame, translating into Int$1,358 per QALY gained. Conclusion: Based on WHO-CHOICE criteria, our analysis demonstrated that the proposed salt reduction strategy is highly cost-effective, and if scaled up nationwide, the benefits could be substantial. Trial registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Objective: To determine whether the New Cooperative Medical Insurance Scheme (NCMS) is associated with decreased levels of catastrophic health expenditure and reduced impoverishment due to medical expenses in rural households of China. Methods: An analysis of a national representative sample of 38,945 rural households (129,635 people) from the 2008 National Health Service Survey was performed. Logistic regression models used binary indicator of catastrophic health expenditure as dependent variable, with household consumption, demographic characteristics, health insurance schemes, and chronic illness as independent variables. Results: Higher percentage of households experiencing catastrophic health expenditure and medical impoverishment correlates to increased health care need. While the higher socio-economic status households had similar levels of catastrophic health expenditure as compared with the lowest. Households covered by the NCMS had similar levels of catastrophic health expenditure and medical impoverishment as those without health insurance. Conclusion: Despite over 95% of coverage, the NCMS has failed to prevent catastrophic health expenditure and medical impoverishment. An upgrade of benefit packages is needed, and effective cost control mechanisms on the provider side needs to be considered. [ABSTRACT FROM AUTHOR]