Iranian Journal of

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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.

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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.