Showing 7 results for Poisson
N Zare, M Sayadi, E Rezaeyan Fard, H Ghaem,
Volume 6, Issue 1 (6-2010)
Abstract
Background & objectives: statistical modeling explicates the observed changes in data by means of mathematics equations. In cases that dependent variable is count, Poisson model is applied. If Poisson model is not applicable in a specific situation, it is better to apply the generalized Poisson model. So, our emphasis in this study is to notice the data structure, introducing the generalized Poisson regression model and its application in estimates of effective factors coefficients on the number of children and comparing it with Poisson regression model results.
Methods: Besides introducing Poisson regression model, we introduced its application in fertility data analysis. A sample of 1019 women in rural areas of Fars was selected by cross sectional and stratified sampling methods. The number of children of family was determined as a count response variable for model validation.
Results: The sample mean and sample variance of the response variable Y, the number of children, are respectively 4.3 and 8.3 (over-dispersion). Log-likelihood was -1950.93 for Poisson regression and -1946.93 for generalized Poisson regression model.
Conclusions: The results revealed that this data have over-dispersion. According to selection criteria, the suitable model for this data analysis was generalized Poisson regression model. It can estimate effective factors coefficients on the number of children exactly.
F Amani, A Kazemnejad, R Habibi, E Hajizadeh,
Volume 7, Issue 1 (6-2011)
Abstract
Background & Objectives: Changing the pattern of mortality gives important perspective of health determinants.
The aim of this study is to detect location and time of mortality pattern change in country using statistical change
point method during 1971-2009 Years.
Methods: We assume for years before and after 0 k ,
t y has a Poisson distribution with means 0
l and 1
l ,
respectively. We used several methods for estimation change point in real data by assume Poisson model.
Results: Using two simulated and real data analysis showed that the change point has been occurred in year
1993 and this confirmed by all methods.
Conclusion: Our findings have shown that the change pattern of mortality trend in Iran is related to improvement
of health indicators and decreasing mortality rate in Iran.
J Yazdani Cherati , E Ahmadi Baseri , M Saki, S Etemadinejad,
Volume 9, Issue 4 (3-2014)
Abstract
Background & Objectives: Tuberculosis (TB) is one of the major infectious diseases in Iran and has pulmonary and extra-pulmonary manifestations. Considering the differences in the distribution of the cases across different regions, we decided to study the geographical distribution, epidemiologic characteristics, and disease pattern in Lorestan.
Methods: This ecologic (descriptive analytical) survey was done in Lorestan between 2002 and 2008. The data was collected from the Health Department of Lorestan University of Medical Sciences and included the history of 1481 patients suffering from TB. The study variables were sex, disease type, residential location, age, and year. The data were analyzed using statistical package SAS 9.2 and descriptive and inferential statistics were applied.
Results: From 1481 registered patients 58.4% were male and 41.6% were female among which 68.74% and 29.98% lived in urban and rural areas and 1.28% were nomads. The mean age of the patients was 41.87. The highest and lowest incidence rates were observed in Khoram Abad (19.38 per 100000) and Azna (7.04 per 100000), respectively. Using Poisson regression, it was observed that the effects of age structure and residency on the incidence rate were significant.
Conclusion: The percentage of nomads was identified as the most important demographic factor in the incidence rate of TB in Lorestan. Allocation of better resources and appropriate training can be effective in controlling and preventing the disease.
S Sharifi, M Karami, N Esmailnasab, Gh Rooshanaei, Farsan,
Volume 12, Issue 4 (2-2017)
Abstract
Background and Objectives: Cardiac diseases are a major cause of death in Iran. The number of deaths from cardiac diseases can be reduced through controlling air pollution. The aim of this study was to determine the relationship between increased air pollution and mortality from respiratory and cardiac diseases in Tehran.
Methods: The average daily concentrations of five pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and particulate matter less than 10 microns (PM10) were collected from 8 stations in Tehran, Iran. Then, their effects on the number of daily deaths due to cardiovascular and respiratory diseases were calculated using time series and Poisson GLARMA model (generalized linear autoregressive moving average). The climatic elements such as mean, maximum, and minimum temperature and daily humidity were considered as confounding factors.
Results: After adjustment for potential confounding variables of the final model of the pollutants, the mean daily ozone level (P = 0.02) and particulate matters less than 10 microns (P <0.001) had a significant correlation with the number of daily deaths.
Conclusion: According to the results of this study that addressed the relationship between air pollutants and death using new statistical methods, it is necessary to take more effective measures to control ozone and particulate matters less than 10 microns to reduce the mortality of heart and respiratory diseases in Tehran.
A Bagheri, Hb Razeghi Nasrabad , M Saadati,
Volume 13, Issue 2 (9-2017)
Abstract
Background and Objectives: Changes in ideals and aspirations of childbearing are important factors in fertility behavior. Nowadays, fertility rate reduction below the replacement level and decreased childbearing ideals are the most common fertility challenges in Iran. So, with the decrease in the fertility rate, it is necessary to be aware of the ideal number of children and its determinants in order to adopt suitable population policies contexts. The main objective of this study was to investigate factors affecting the ideal number of children using Poisson regression model.
Methods: In 2012, 389 ever married women aged 15-49 in Semnan Province were selected using two-phase stratified random sampling method and studied through applying a structured questionnaire. To model the ideal number of children by Poisson regression model, marriage duration has taken as offset and the number of children, job status, education level, marriage type, and resident place were considered as predictors. The model was fitted with SPSS software version 22.
Results: All predictors in this study had significant effects on ideal number of children in Semnan (p-value <0.05). Women’s ideal number of children who had 2 or fewer children, were employed, and had university education with consanguineous and rural marriage was higher than those who had 3 and more children, were unemployed, and had elementary and secondary education with inter-family and urban marriage.
Conclusion: To model the ideal number of children, since it is discrete and count, a Poisson regression model is more efficient as compared to linear regression model.
S Ghorbani Gholiabad , M Sadeghifar, R Ghorbani Gholiabad , O Hamidi,
Volume 14, Issue 1 (6-2018)
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.
F Amini, A Abadi, M Namdari, Z Ghorbani, S Azimi,
Volume 16, Issue 2 (8-2020)
Abstract
Background and Objectives: Cancer is a complex disease with a lengthy and expensive course of treatment that causes many problems for the community. Knowledge of oral cancer plays an important role in early diagnosis. The aim of this study was to determine the level of knowledge about the symptoms and risk factors of oral cancer and assess the related factors.
Methods: In this study, 671 parents of primary school children were randomly selected from primary schools in four districts of Tehran. The participants were asked to answer questions related to demographic characteristics and knowledge of the risk factors and symptoms of oral cancer. Data analysis was done using Poisson regression model and multi-level Poisson regression model using SPSS and STATA software. The AICI Akaike Information Criterion (AIC) was applied to evaluate the models.
Results: The mean score of knowledge was 3.7 with a standard deviation of 6.7. Among the studied variables, female gender, advanced age, a higher SES score, and a higher welfare index had positive effects on oral cancer knowledge (P <0.05).
Conclusion: The results of this study showed that demographic, social and economic factors of parents were effective on oral cancer. It can be statistically concluded that a multilevel Poisson regression model is more suitable for analyzing this data.
