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Original Article

May 11, 2019


Analysis of the severity of occupational injuries in the mining industry using a Bayesian network
Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, Asghar Nikravesh
Epidemiol Health. 2019;41:e2019017.

Original Article

April 23, 2019


Risk factors contributing to the incidence and mortality of acute childhood poisoning in emergency department patients in Iran: a hospital-based case-control study
Hamideh Feiz Disfani, Mostafa Kamandi, Seyed Mohammad Mousavi, Sayyed Majid Sadrzadeh, Roohie Farzaneh, Najme Doolabi, Kazem Rahmani
Epidemiol Health. 2019;41:e2019016.

Original Article

April 21, 2019


Risk factors associated with the recent cholera outbreak in Yemen: a case-control study
Fekri Dureab, Albrecht Jahn, Johannes Krisam, Asma Dureab, Omer Zain, Sameh Al-Awlaqi, Olaf Müller
Epidemiol Health. 2019;41:e2019015.

Original Article

April 20, 2019


Assessment of the risk factors associated with multidrug-resistant tuberculosis in Sudan: a case-control study
Adel Hussein Elduma, Mohammad Ali Mansournia, Abbas Rahimi Foroushani, Hamdan Mustafa Hamdan Ali, Asrar M A/Salam Elegail, Asma Elsony, Kourosh Holakouie-Naieni
Epidemiol Health. 2019;41:e2019014.

Methods

April 8, 2019


Network meta-analysis: application and practice using R software
Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee, Gerta Rücker
Epidemiol Health. 2019;41:e2019013.

Current Issue
Volume 41; 2019
Original Article Analysis of the severity of occupational injuries in the mining industry using a Bayesian network
Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, et al. Epidemiol Health. 2019;41:e2019017.
  • Abstract
  • View article
Original Article Risk factors contributing to the incidence and mortality of acute childhood poisoning in emergency department patients in Iran: a hospital-based case-control study
Hamideh Feiz Disfani, Mostafa Kamandi, Seyed Mohammad Mousavi, Sayyed Majid Sadrzadeh, et al. Epidemiol Health. 2019;41:e2019016.
  • Abstract
  • View article
Abstract
OBJECTIVES:
Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.
METHODS:
The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.
RESULTS:
Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents.
CONCLUSIONS:
Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Abstract
OBJECTIVES:
Since poisoning is one of the most important preventable factors contributing to the hospitalization and death of children who present to emergency departments, this study was carried out to investigate the risk factors contributing to the incidence and mortality of acute childhood poisoning.
METHODS:
This hospital-based case-control study included 243 cases and 489 controls, drawn from daily admissions to the emergency departments of the included hospitals according to the inclusion and exclusion criteria.
RESULTS:
Gastrointestinal poisoning was the most common poisoning type, found in 87.7% of subjects, and medications were the most common cause of poisoning (49.8%). Multiple logistic regression analysis showed that a history of poisoning (odds ratio [OR], 10.44; 95% confidence interval [CI], 5.58 to 19.51; p<0.001) and the availability of poisonous substances (OR, 8.88; 95% CI, 5.41 to 14.56; p<0.001) were among the most important predictors of childhood poisoning. Respiratory poisoning (OR, 6.72; 95% CI, 1.40 to 32.07; p<0.05) and the presence of addiction in the family (OR, 4.54; 95% CI, 1.10 to 18.68; p<0.05) were the most important predictors of mortality among children with poisoning.
CONCLUSIONS:
Addiction and the presence of physical or psychological disorders in family members, a history of poisoning, and the availability of poisonous substances were significantly associated with the incidence of childhood poisoning and resultant mortality.
Original Article Risk factors associated with the recent cholera outbreak in Yemen: a case-control study
Fekri Dureab, Albrecht Jahn, Johannes Krisam, Asma Dureab, et al. Epidemiol Health. 2019;41:e2019015.
  • Abstract
  • View article
Original Article Assessment of the risk factors associated with multidrug-resistant tuberculosis in Sudan: a case-control study
Adel Hussein Elduma, Mohammad Ali Mansournia, Abbas Rahimi Foroushani, Hamdan Mustafa Hamdan Ali, et al. Epidemiol Health. 2019;41:e2019014.
  • Abstract
  • View article
Abstract
OBJECTIVES:
The cholera outbreak in Yemen has become the largest in the recent history of cholera records, having reached more than 1.4 million cases since it started in late 2016. This study aimed to identify risk factors for cholera in this outbreak.
METHODS:
A case-control study was conducted in Aden in 2018 to investigate risk factors for cholera in this still-ongoing outbreak. In total, 59 cholera cases and 118 community controls were studied.
RESULTS:
The following risk factors were associated with being a cholera case in the bivariate analysis: a history of travelling and having had visitors from outside Aden Province; eating outside the house; not washing fruit, vegetables, and khat (a local herbal stimulant) before consumption; using common-source water; and not using chlorine or soap in the household. In the multivariate analysis, not washing khat and the use of common-source water remained significant risk factors for being a cholera case.
CONCLUSIONS:
Behavioural factors and unsafe water appear to be the major risk factors in the recent cholera outbreak in Yemen. In order to reduce the risk of cholera, hygiene practices for washing khat and vegetables and the use and accessibility of safe drinking water should be promoted at the community level.
Abstract
OBJECTIVES:
The emergence of multidrug-resistant tuberculosis (MDR-TB) is a major challenge for the global control of tuberculosis (TB). The aim of this study was to determine the risk factors associated with MDR-TB in Sudan.
METHODS:
This case-control study was conducted from May 2017 to February 2019. Patients newly diagnosed with MDR-TB were selected as cases, and controls were selected from TB patients who responded to first-line anti-TB drugs. A questionnaire was designed and used to collect data from study participants. Logistic regression was used to evaluate associations between risk factors and MDR-TB infection. The best multivariate model was selected based on the likelihood ratio test.
RESULTS:
A total of 430 cases and 860 controls were selected for this study. A history of previous TB treatment (adjusted odds ratio [aOR], 54.85; 95% confidence interval [CI], 30.48 to 98.69) was strongly associated with MDR-TB infection. We identified interruption of TB treatment (aOR, 7.62; 95% CI, 3.16 to 18.34), contact with MDR-TB patients (aOR, 5.40; 95% CI, 2.69 to 10.74), lower body weight (aOR, 0.89; 95% CI, 0.87 to 0.91), and water pipe smoking (aOR, 3.23; 95% CI, 1.73 to 6.04) as factors associated with MDR-TB infection.
CONCLUSIONS:
Previous TB treatment and interruption of TB treatment were found to be the main predictors of MDR-TB. Additionally, this study found that contact with MDR-TB patients and water pipe smoking were associated with MDR-TB infection in Sudan. More efforts are required to decrease the rate of treatment interruption, to strengthen patients’ adherence to treatment, and to reduce contact with MDR-TB patients.
Methods Network meta-analysis: application and practice using R software
Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee, Gerta Rücker Epidemiol Health. 2019;41:e2019013.
  • Abstract
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  • Korean summary
  • Supplementary data
Original Article Comparison of estimates and time series stability of Korea Community Health Survey and Korea National Health and Nutrition Examination Survey
Ji Son Ki, Ho Kim Epidemiol Health. 2019;41:e2019012.
  • Abstract
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  • Korean summary
  • Supplementary data
Abstract
The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Abstract
OBJECTIVES:
In South Korea, there are two nationwide health surveys conducted by the Korea Centers for Disease Control and Prevention: the Korea Community Health Survey (KCHS) and Korea National Health and Nutrition Examination Survey (KNHANES). The two surveys are directly comparable, as they have the same target population with some common items, and because both surveys are used in various analyses, identifying the similarities and disparities between the two surveys would promote their appropriate use. Therefore, this study aimed to compare the estimates of six variables in KCHS and eight variables in KNHANES over a six-year period and compare time series stability of region-specific and sex- and age-specific subgroup estimates.
METHODS:
Data from adults aged 19 years or older in the 2010-2015 KCHS and KNHANES were examined to analyze the differences of estimates and 95% confidence interval for self-rated health, current smoking rate, monthly drinking rate, hypertension diagnosis rate, diabetes diagnosis rate, obesity prevalence, hypertension prevalence, and diabetes prevalence. The variables were then clustered into subgroups by city as well as sex and age to assess the time series stability of the estimates based on mean square error.
RESULTS:
With the exception of self-rated health, the estimates taken based on questionnaires, namely current smoking rate, monthly drinking rate, hypertension diagnosis rate, and diabetes diagnosis rate, only differed by less than 1.0%p for both KCHS and KNHANES. However, for KNHANES, estimates taken from physical examination data, namely obesity prevalence, hypertension prevalence, and diabetes prevalence, differed by 1.9-8.4%p, which was greater than the gap in the estimates taken from questionnaires. KCHS had a greater time series stability for subgroup estimates than KNHANES.
CONCLUSIONS:
When using the data from KCHS and KNHANES, the data should be selected and used based on the purpose of analysis and policy and in consideration of the various differences between the two data.

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