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Showing 4 results for Hamidi
Modeling and Spatial Epidemiology of Diarrhea in Mazandaran Province
M Baaghideh, A Hamidian , Aa Dadashi Roudbari , F Mayvaneh,
Volume 12, Issue 1 (Vol 12, No.1 2016)
Abstract

Background and Objectives: Spatial epidemiology is the description and analysis of geographic variations of diseases with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. Disease mapping includes a set of statistical techniques that lead to providing clean maps based on estimation of the incidence, prevalence and mortality rates for the users to enable them to reliably estimate the distribution of the diseases. In this study, the spatial epidemiology of diarrhea was evaluated in Mazandaran.

Methods: In the present study, statistical methods like the Moran’s I spatial autocorrelation, Anselin local Moran’s I, K function and Ripley index were used to evaluate 4924 cases between 1385-1390, based on certain factors detected in diarrheal diseases.

Results: The results of the global Moran index showed that this disease provided adherence to a cluster pattern in the province. The results of the K function also showed that apart from the western regions of the province, the diarrheal disease followed the above-mentioned cluster pattern. The results of Anselin local Moran and sensitive indicators revealed that the central region of the province, including the cities of Sari, Qaymshmhr, and Babol had the highest prevalence of the disease.

Conclusion: The results showed that the prevalence of syndrome diarrhea follows the pattern cluster and the use of spatial analysis methods in a specific geographic area is appropriate for programs to reduce health risks. And in epidemiological studies, analysis and risk assessment diarrhea syndrome are very important.


Forecasting Frequency of Delivery using Poisson Hidden Markov Models
S Ghorbani Gholiabad , M Sadeghifar, R Ghorbani Gholiabad , O Hamidi,
Volume 14, Issue 1 (Vol 14, No 1, 2018)
Abstract
Background and Objectives: Delivery is one of the most important services in the health systems, and increasing its effectiveness and efficiency are a health priorities. The aim of this study was to forecast the number of deliveries in order to design plans for using all facilities to provide patients with better services.
Methods: The data used in this study were the number of deliveries per month in Hakim Jorjani Hospital, Gorgan, Iran during the years 2010 to 2016. Due to the over-dispersion of the data and non-compliance with a Poisson distribution, the Poisson hidden Markov model was applied to predict the frequency of monthly deliveries. The model parameters were estimated using the maximum likelihood method and expectation maximization algorithm.
Results: The use of the Akaike criteria revealed the frequency of delivery in different months in the hospital followed a Poisson hidden Markov models with three hidden states, and the mean Poisson distribution in each component was 193.74, 236.05, and 272.61 labors, respectively.
Conclusion: The results of this study showed that government’s encouraging policies have had short-term, limited effects on increasing fertility with minimal effects on the results of the two-year forecast.
Predicting the Frequency of Human Brucellosis using Climatic Indices by Three Data Mining Techniques of Radial Basis Function, Multilayer Perceptron and Nearest Neighbor: A Comparative Study
L Tapak, N Shirmohammadi-Khorram , O Hamidi, Z Maryanaji,
Volume 14, Issue 2 (Vol.14, No.2, 2018)
Abstract
Background and Objectives: Identification of statistical models has a great impact on early and accurate detection of outbreaks of infectious diseases and timely warning in health surveillance. This study evaluated and compared the performance of the three data mining techniques in time series prediction of brucellosis.
 
Methods: In this time series, the data of the human brucellosis cases and climatology parameters of Hamadan, west of Iran, were analyzed on a monthly basis from 2004 (March/April) to 2017 (February/March). The data were split into two subsets of train (80%) and test (20%). Three techniques, i.e. radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network methods as well as K Nearest neighbor (KNN), were used in both subsets. The root mean square errors (RMSE), mean absolute errors (MAE), mean absolute relative errors (MARE), determination coefficient (R2) and intra-class correlation coefficient (ICC) were used for performance comparison.
 
Results: Results indicated that RMSE (23.79), MAE (20.65) and MARE (0.25) for MLP were smaller compared to the values of the other two models. The ICC (0.75) and R2 (0.61) values were also better for this model. Thus, the MLP model outperformed the other models in predicting the used data. The most important climatology variable was temperature.
 
Conclusion: MLP can be effectively applied to diagnose the behavior of brucellosis over time. Further research is necessary to detect the most suitable method for predicting the trend of this disease.
 
Probabilistic Carcinogen Risk Assessment from Exposure to Chromium in Drinking Water Resources in Rural Areas of Qazvin Province
Zahra Hamidi, Mehdi Ranjbaran, Fateme Qotbi Nia, Akram Bahojb, Hamid Karyab,
Volume 18, Issue 3 (Vol.18, No.3, Autumn 2022)
Abstract
Background and Objectives: Chromium is a heavy metal that toxic to humans in small concentrations. This study aimed to evaluate the cancer risk of exposure to chromium in drinking water in rural areas of Qazvin province.
Methods: Water sampling was performed according to the standard methods for water and wastewater examination and chromium analysis was performed with ICP-OES. Exposure factors were determined using a validated questionnaire. Finally, the risk assessment of oral and dermal exposure to chromium was performed using the risk assessment technique. Monte Carlo simulation was also used to determine the uncertainty caused by point risk estimation.
Results: The mean concentration of chromium in drinking water was 2.8±5.04 μg/l. The excess lifetime cancer estimated by the Monte Carlo simulation was 30.8 cases per 100,000 in the studied population, indicating 100 cases of cancer in the population living in rural areas of the Qazvin province.
Conclusion: Based on the obtained results, it can be concluded that although the concentration of chromium was lower than the maximum allowed in the national standard (0.05 mg/l), the risk of carcinogenesis was higher than the acceptable risk level of WHO (1 case per 100,000). Also, using the results obtained from the Monte Carlo simulation instead of point estimation provides higher confidence in risk management decisions.

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