Journal of School of Public Health and

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Background and Aim: Logistic regression is an analytic tool widely used in medical and epidemiologic research. In many studies, we face data sets in which some of the data are not recorded. A simple way to deal with such "missing data" is to simply ignore the subjects with missing observations, and perform the analysis on cases for which complete data are available.
Materials and Methods: We consider methods for analyzing logistic regression models with complete data recorded for some covariates (Z) but missing data for other covariates (X). When data on X are Missing At Random (MAR), we present a likelihood approach for the observed data that allows the analysis as if the data were complete.
Results: By this approach, estimation of parameters is done using both Maximum Likelihood and Bayesian methods through the Markov Chain Monte Carlo numerical computation scheme and the results are compared. The illustrative example considered in this article involves data from lung auscultations as part of a Health Survey in Tehran.
Conclusion: In comparing different methods, Bayesian estimates using the model described in this study are much closer to those generated by analysis of the full data by the standard model.

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  Background and Aim: A ssociations between air pollution and morbidity have been reported in several studies. Due to limited publications in the literature for Iran, this study aimed to determine the association between air pollution and hospital admissions of respiratory disease patients in Tabriz, Iran.

  Materials and Methods: The methodology used in this study was case -crossover and the artificial neural network model. The variables of the model included air quality, hospital admission and air pollutants. Daily hospital admission data were collected from five hospitals in Tabriz, Iran based on the International Classification of Diseases (ICD-10) , air quality data including NO₂, SO₂, CO, PM₁₀ and O₃ from the six fixed online air quality monitoring stations, and the daily mean temperature and relative humidity data for the same period from the East Azerbaijan Meteorological Bureau.

  Results : P articulate matter with a median aerometric diameter <10 μm (PM₁₀) was found to be the most important pollutant affecting respiratory hospital admissions. The ANNs data showed that the most important causes of hospital admissions were for COPD NO₂, NO and CO, for respiratory infections PM_10, and for asthma PM₁₀, O₃ and CO. The highest associations were observed between hospital admissions due to COPD and asthma in females and those due to respiratory infections in males. The elderly (individuals over 65 years old) were at the highest risk.

  Conclusion: The results show a significant relation between air pollutants and respiratory hospital admissions in Tabriz, Iran. The importance and necessity of enforcement of existing regulations and enacting laws to prevent and control the adverse health effects of air pollution are confirmed.

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Background and Aim: Air pollution has been widely established as an important risk factor for heart and respiratory diseases and mortality. The aim of this study was to compare the relationships between short-term exposure to air pollutants and hospital admissions, cardiovascular and respiratory deaths and total deaths in Ahvaz and Shahrekord, Iran.
 Materials and Methods: In this ecological and time-series study data were collected on hospital admissions, cardiovascular and respiratory deaths and total deaths between 2012 and 2018. For data analysis Quasi-Poisson regression combined with linear distributed lag models were used and adjusted for trend, seasonality, temperature, relative humidity, weekdays and holidays.
Results: Data analysis showed that in Ahvaz there were statistically significant direct correlations between PM₁₀ exposure and respiratory admissions, PM_2.5 exposure and total deaths and cardiovascular admissions, O₃ exposure and total deaths, and CO exposure and cardiovascular admissions. As regards Shahrekord, there were statistically significant direct correlations between PM₁₀ exposure and respiratory deaths, PM₁₀ exposure and cardiovascular deaths, PM_2.5 exposure and cardiovascular and respiratory admissions and respiratory deaths, O₃ exposure and total deaths, and CO exposure and respiratory deaths.
Conclusion: It seems there are statistically significant relationships between air pollution and hospital admissions and deaths in Ahvaz and, to a lesser extent, in Shahrekord.