Epidemiology and Health

Original Articles

Demographic and epidemiological characteristics of scorpion envenomation and daily forecasting of scorpion sting counts in Touggourt, Algeria: Kaouthar Boubekeur, Mohamed L’Hadj, Schehrazad Selmane; Epidemiol Health. 2020;42:e2020050. Published online July 6, 2020; DOI: https://doi.org/10.4178/epih.e2020050

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Abstract PDF Supplementary Material: Abstract
OBJECTIVES
This study was conducted to provide better insights into the demographic and epidemiological characteristics of scorpion envenomation in an endemic area in Algeria and to identify the model that best predicted daily scorpion sting counts.
METHODS
Daily sting data from January 1, 2013 to August 31, 2016 were extracted from questionnaires designed to elicit information on scorpion stings from the two emergency medical service providers in Touggourt, Algeria. Count regression models were applied to the daily sting data.
RESULTS
A total of 4,712 scorpion sting cases were documented, of which 70% occurred in people aged between 10 years and 49 years. The male-to-female ratio was 1.3. The upper and lower limbs were the most common locations of scorpion stings (90.4% of cases). Most stings (92.8%) were mild. The percent of people stung inside dwellings was 68.8%. The hourly distribution of stings showed a peak between 10:00 a.m. and 11:00 a.m. The daily number of stings ranged from 0 to 24. The occurrence of stings was highest on Sundays. The incidence of scorpion stings increased sharply in the summer. The mean annual incidence rate was 542 cases per 100,000 inhabitants. The fitted count regression models showed that a negative binomial hurdle model was appropriate for forecasting daily stings in terms of temperature and relative humidity, and the fitted data agreed considerably with the actual data.
CONCLUSIONS
This study showed that daily scorpion sting data provided meaningful insights; and the negative binomial Hurdle model was preferable for predicting daily scorpion sting counts.; Summary

Effects of human and organizational deficiencies on workers’ safety behavior at a mining site in Iran: Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid Kalatpour, Ali Reza Soltanian, Maryam SeyedTabib; Epidemiol Health. 2018;40:e2018019. Published online May 18, 2018; DOI: https://doi.org/10.4178/epih.e2018019

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Abstract

OBJECTIVES
Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents.

METHODS

In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers’ unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies.

RESULTS

Organizational deficiencies had a direct positive effect on workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.23). The effect of unsafe supervision on workers’ violations and workers’ errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers’ unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers’ unsafe acts.

CONCLUSIONS

Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

Summary

Citations

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Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors: Shiva Borzouei, Ali Reza Soltanian; Epidemiol Health. 2018;40:e2018007. Published online March 10, 2018; DOI: https://doi.org/10.4178/epih.e2018007

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287 Download
13 Web of Science
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Abstract PDF

Abstract

OBJECTIVES
To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.

METHODS

This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.

RESULTS

Variables found to be significant at a level of p<0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.

CONCLUSIONS

In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Summary

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