Prioritizing risk factors of hazardous material road transportation accidents using the fuzzy AHP method.

BACKGROUND: Annually, large amounts of hazardous materials (hazmat) are transported through the roads and this movement causes various accidents. Identifying the causes of these accidents is a critical issue for all public governments. OBJECTIVES: This study aimed to identify the effective risk factors for hazmat road transport accidents and determine their relative weight using the fuzzy analytical hierarchy process (AHP) method. METHODS: This qualitative study was conducted in 2021 in Iran and included four steps, i.e., the identification (using literature review and semi-structured interview), determination (according to the expert panel opinion), classification, and prioritization of effective factors in hazmat road transportation accidents. To prioritize and determine the relative weight of the effective factors, the fuzzy AHP technique was used. RESULTS: In total, 159 risk factors were identified, which were classified into six factors (including road, transportation management, vehicle, cargo, driver, and weather conditions) and 24 sub-factors. The main factor (greatest relative weight) with the highest priority was the driver (0.181). The road (0.167), cargo (0.166), vehicle (0.169), transportation management (0.161), and weather conditions (0.159) were the next priorities, in that order. CONCLUSION: The results demonstrated that the driver is the most important factor in causing accidents when transporting hazmat by road. The findings of this study might have the potential to decrease the frequency and consequence of accidents caused by the road transport of hazmat.

An intelligent algorithm for assessing patient safety culture and adverse events voluntary reporting using PCA and ANFIS.

BACKGROUND: Patient safety culture (PSC) as a main component of the organizational culture plays a key role in providing safe, effective and economic cares and services in healthcare organizations. PSC provides a way to assist hospitals in order to improve patient safety and prevent medical errors. OBJECTIVE: The present study aimed to measure PSC and healthcare professionals' attitude towards voluntary reporting of adverse events in two hospitals in Iran and to develop a hybrid intelligent approach for modeling PSC grades. METHODS: The Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire and a two-part questionnaire were used for examining the PSC and healthcare professionals' attitude towards voluntary reporting of adverse events, respectively. Principal component analysis (PCA) was applied to extract of the main components in the HSOPSC questionnaire and to construct 12 dimensions of patient safety culture. The overall grade of patient safety culture was modeled using adaptive neuro-fuzzy inference systems (ANFIS) as a classification problem. RESULTS: Almost half of the participants have experienced a medical error and adverse events. The PSC grade was acceptable from the point of view of 55.5% and 50% of participants in hospital No.1 and hospital No.2, respectively. The overall accuracy of ANFIS in modeling overall grades of patient safety culture in both study hospitals was 0.84. Of those individuals gave an acceptable grade on patient safety culture in both study hospitals, more than 50% believed that all medical errors and near misses should be reported. CONCLUSIONS: The ANFIS algorithm was proposed for modeling and predicting of PSC for healthcare organizations. The results confirm the capability of the proposed model to predict patient safety grades in healthcare settings.

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study.

OBJECTIVE: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. DESIGN: Prospective cohort study. SETTING: Tehran Lipid and Glucose Study (TLGS). METHODS: A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. PRIMARY OUTCOME MEASURE: T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication. RESULTS: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. CONCLUSIONS: Our study emphasises the utility of DT for exploring interactions between predictor variables.