The role of drug and alcohol use and the risk of motor vehicle crashes in Shiraz,Iran, 2014: A case–crossover study

Background: Traffic accidents and traffic-related injuries and mortality have become a major public health concern in Iran. This study aimed to examine the role of drug and alcohol use in motor vehicle accidents in Iran.

Methods: This case–crossover study was conducted on 441 drivers who survived a road traffic crash and were taken to the emergency department of Shahid Rajaee trauma hospital in Shiraz, southern Iran. Data were collected using checklists that included demographic characteristics and drug and alcohol use prior to driving. Alcohol and drug use was identified through self-report, and cannabis, morphine, and methamphetamine urine tests were used to confirm drug abuse among drivers.

Results: In total 17.9% of drivers reported using drugs (cannabis, opium, or metamphetamine) and 8.84% of drivers reported consuming alcohol prior to the collision. The crude odds ratios (ORs) for having a crash for opium, cannabis, and metamphetamine were 1.94 (95% interval confidence [CI], 1.11–3.38), 2.37 (95% CI, 1.03–5.42), 5.5 (95% CI, 1.21–24.81), respectively, and for all drugs was 3.83 (95% CI, 2.28–6.43). The OR for alcohol was 3.5 (95% CI, 1.73–7.06) based on self-report.

Conclusion: Drug and alcohol use are increasing the risk of traffic crashes in Iran. Risk-reducing programs must be designed and implemented.     

Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making