Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings: Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation: Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding: Bill & Melinda Gates Foundation

The performance assessment for the dMEWMA in capturing changes in simple linear profiles

In traditional quality control, the quality of product is typically modeled as the univariate or multivariate distribution of the quality parameters. In more recent applications, the quality is modeled using the relationship between a response and independent variable. This paper investigates the performance of multivariate version of the double dMEWMA statistics in detecting changes in step shift in the intercept, slope, and error-variance of simple linear quality profiles. The statistical performance of the dMEWMA chart is estimated and compared versus three different charting techniques includeing the Hotelling T2, EWMA/R and dEWMA3. For all the compared charts, the average run length (ARL) under wide range of shift levels are estimated in order to draw beneficial conclusions. IEOM Society International

Ridge penalization-based generalized linear model (GzLM) for predicting risky-driving index

Road traffic crashes remain one of the major causes of preventable death and injury worldwide. Human behavior is considered one of the main factors leading to such tragic losses. In this paper, we analyze the responses of an online survey questionnaire and identify the variables that are most likely to be correlated with individual driving behavior of drivers. Weights are allocated to nine risky-driving behaviors considered in the survey based on self-reported frequency of the driving behaviors the participants were involved in at the time of a recent traffic crash. Initially, weighted individual self-rated risky-driving behaviors are used to estimate the risky-driving index (RDI) for individual drivers. RDI is defined as a quantitative measure of a driver's risky-driving propensities based on basic profile and driving history. Finally, a standardized model for predicting a driver's RDI is proposed using Ridge penalization-based generalized linear regression with a standard error of estimate equal to 0.713. According to the model, female drivers have lower RDI compared to male drivers. Also, younger drivers have higher RDI than older drivers. Lastly, hours driven per day have more positive impact on RDI than the number of accidents or the driving experience of a driver. IEOM Society International

Usage of Non-Linear Regression for Modeling the Behavior of Motor Vehicle Crash Fatality (MVF) Rate

Data analysis for vehicular crash counts is essential for transportation and traffic management systems (TTMS) to develop practical and innovative road safety interventions. The crash trend analysis, in particular, is the most popular technique for extracting an underlying trend or pattern of behavior in crash data. The recent years have seen a growing concern in the State of Qatar of the consequences of motor vehicle crashes (MVCs) and their associated fatalities (MVFs) on the economy, society, and the performance of the whole road network. This paper reports on the results of using nonlinear regression for crash trend analysis highlighting the substantial enhancement of road safety level in the State of Qatar during the period between 2003 and 2015. One of the critical findings of the study is the notable decline in the increasing tendency of both the MVF/100,000 population and the MVF/100,000 car over the last thirteen years in the State of Qatar. The matter that makes this finding worthy of comment is that it occurs over the period in which the State of Qatar is witnessing a significant growth in the population density and traffic volume. Several valuable contributions and recommendations were drawn and reported. � IEOM Society International.ATTADAMOUNE MICRO - FINANCE;EATON Powering Business world wide;informs;LINDO SYSTEMS INC;SIEMENSScopu

Penalized Conway-Maxwell-Poisson regression for modelling dispersed discrete data: The case study of motor vehicle crash frequency

Statistical modelling of road crashes has been of extreme interest to researchers over the last decades. Such models are necessary for the investigation of the opportunities for road safety improvement. The motor vehicle crash frequency (MVC-F) is probably the most important count of road crashes. In practice, like many of other discrete variables, this count is often diagnosed with over- or underdispersion, i.e. the variance is greater or less than the mean. The traditional regression models, especially those based on the Poisson distribution, are inefficient in modelling dispersed count data. On the contrary, the Conway-Maxwell-Poisson (COM-Poisson) distribution has been proven powerful in modelling count data with a wide range of dispersion. In crash data modelling, many situations may give rise to collinearity between contributory crash factors. Under this situation, the maximum likelihood estimates of the coefficients of the COM-Poisson GLM become increasingly unreliable as the collinearity among the model predictors increases. This paper addresses this issue and proposes a penalized likelihood scheme to be used with the COM-Poisson GLM regression for improving its prediction performance. For better GLM regression output, we suggest implementing the penalized COM-Poisson GLM regression under a K- fold cross-validation framework. A real-world crash example is provided, showing the performance of the penalized COM-Poisson GLM regression compared to the Poisson and the classical COM-Poisson GLM regressions. - 2019 Elsevier LtdScopu

Analyzing the impact of human characteristics on the comprehensibility of road traffic signs

Traffic safety is one of major challenges facing most communities worldwide. To improve traffic safety, regulating traffic movement through markings, signs, channelization, and others is essential. Traffic signs are used either to regulate road user movements by highlighting priorities or to inform road users about traffic regulations and traffic conditions. In general, traffic signs are essential element in road operation. Thus, understanding these signs by road users is of prime importance for efficient and safe traffic operations. This study investigates the comprehensiveness of traffic signs by drivers and the impact of driver characteristics gender, nationality, age, language, and educational level. Clarifying the correlation between the understanding of traffic signs and driver characteristics will yield to recommendations and help in identifying proper countermeasures to enhance the communities' knowledge of traffic signs and consequently improve traffic safety level. The study used an electronic survey as a research instrument for data collection. The results have shown significant impact of some of human characteristics on their ability to recognize the metal-plate and electronic traffic signals. IEOM Society International

Optimizing national resource usage through the integration of University shuttle service with the public rail: The case study in Qatar University

Qatar University is the largest public educational institution in the State of Qatar. The university community has diverse committed faculty who not only teaching but also conducting research studies contributing actively to the needs of the society. Qatar University aims to improve the transportation network inside the campus and reduce the traffic congestions. This work aims to provide the transportation department at Qatar University with a unique network design integrating the rail terminal with the in-campus shuttle system. The new design mainly aims to facilitate the transition of students, faculty members, staffs, and visitors between Qatar University Campus's buildings and Qatar Rail terminal leading to encourage customers to use the Qatar rail service. Consequently, shuttle stations locations are optimized, routes are designed, and sufficient number of buses is specified for each route based on the density in each building inside the campus. The performance of the new network design is evaluated using ProModel� software. IEOM Society International

Control charts for variability monitoring in high-dimensional processes

Monitoring process variability is associated with detecting changes in the covariance matrix of a multivariate normal process. Most monitoring methods estimate the sample covariance matrix and compare it with the in-control covariance matrix that is mostly priori known based on the sufficient historical data. However, when the sample size is smaller than the number of variables, the sample covariance matrix is not applicable to estimate the covariance matrix, since the matrix may not be positive semi-definite. In this paper, we propose a new control chart for monitoring changes in the covariance matrix when the sample size is smaller than the number of variables. The proposed chart is based on the ridge penalized likelihood ratio. It detects general changes, without sparsity assumption, in the covariance matrix efficiently when the sample size is small, while other existing penalized likelihood-based methods are expected to detect only sparse changes in the covariance matrix. The superiority of the proposed chart is demonstrated through an average run length performance to variety in shift patterns. The proposed chart also maintains a low computational complexity. These differentiated properties of the proposed chart were proved through numerous simulation studies and in a real example from the semiconductor industry.This publication was made possible by the NPRP award [ NPRP 05-563-2-142 ] and [ NPRP-7-1040-2-393 ] from the Qatar National Research Fund (a member of The Qatar Foundation).Scopu

Multivariate statistical process control charts based on the approximate sequential ?2 test

Similar to the univariate CUSUM chart, a multivariate CUSUM (MCUSUM) chart can be designed to detect a particular size of the mean shift optimally based on the scheme of a sequential likelihood ratio test for the noncentrality parameter. However, in multivariate case, the probability ratio of a sequential test is intractable mathematically and the test statistic based on the ratio does not have a closed form expression which makes it impractical for real application. We drive an approximate log-likelihood ratio and propose a multivariate statistical process control chart based on a sequential ?2 test to detect a change in the noncentrality parameter. The statistical properties of the proposed test statistic are explored. The average runs length (ARL) performance of the proposed charts is compared with other MCUSUM charts for process mean monitoring. The experimental results reveal that the proposed charts achieve superior, both zero-state and steady-state, ARL performance over a wide range of mean shifts, especially when the dimension of measurements is large.Qatar National Research Fund (QNRF) [grant number NPRP 08-323-2-116]Scopu

Attitudes towards road safety among pre-drivers: The case of Qatar

A preliminary study involving school students aged 12-18 years attempted to test whether there is difference in attitudes towards road safety between Arab and non-Arab respondents. The sampled participants were categorized according to age group, ethnicity and type of school they attend. Responses on their perceived driving ability, basic road safety knowledge and their willingness to participate in a common road safety campaign were analyzed. The results show that Arab students of pre-driving age tend to perceive that they have the skills to drive and showed poorer attitude towards road safety than non-Arab students. Older students of Arab origin and in public schools are more likely not to participate in road safety campaigns as compared to the younger age group of non-Arab origin and in private schools. Attitude change interventions that is appealing to pre-drivers of Arab origin studying in public school could be more effective strategy to raise the road safety awareness in par with the rest of the residents. Carefully designed contents for driving simulators that convey interactive road safety lessons and Variable Message Signs (VMS) can be considered and maximize effectiveness of road safety campaigns. � IEOM Society International.Eaton;INFORMS (Institute for Operations Research and Management Sciences);SiemensScopu