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Objective: Early recognition of autism is important, but diagnosis age varies among children. Recent studies have aimed to identify factors affecting age of diagnosis and several studies have attempted to explore geographic variation in age at diagnosis of autism. However, there is a lack of research examining geographic variations with multiple models to find whether geographic differences can be explained by risk factors such as socioeconomic status and differences in child characteristics. This study aimed to address this gap of knowledge by comparing age at diagnosis of autism between the group of people living in the center of the province and the group of people living in the rest of the province, considering potential medical and socioeconomic confounders. Method: The study population consisted of 50 autistic children born in East Azerbaijan Province between 2004 and 2016. Initially, univariate testing by ANOVA was performed to identify family and individual factors contributing to differences in age at autism diagnosis. Following this, the association between living in the center of the province and age at diagnosis in univariate and multivariate analyses was examined. Results: Results from the initial univariate analysis indicate a significant association between living in the center of province and early diagnosis. However, inclusion of possible confounders in multiple model illustrates that these geographical disparities in age at diagnosis can be explained by differences in socioeconomic and medical status. Conclusion: Although geographic variation in age at diagnosis of autism was observed, analyses show that differences in individual and family-level factors may contribute to geographic differences. In this study, most of the observed variation was accounted for by family-level factors rather than geographic policies. Findings prove that multiple strategies are required to identify targeted interventions and strategies.

Introduction: Adverse effects of air pollution on human health has been identified as a major concern worldwide. We conducted this study to investigate the association between prenatal exposure to ambient air pollution and Low Birth Weight (LBW) and preterm among newborns in the megacity of Tehran. Materials and methods: This cross-sectional study was carried out on 1605 newborns. Data on the residential locations of mothers were collected from birth certificates. To estimate the prenatal exposure to air pollutants, data of 21 air quality monitoring stations in Tehran were used. The Man-Whitney test was used to estimate the association between exposure to ambient air pollution and LBW and preterm. Results: The mean birth weight in our analyses was 3117 g. A significant association was found between maternal exposure to particulate matter less than 2.5 µm (PM2.5) in the first trimester and incidence of preterm (p=0.011) and LBW (p=0.003) in newborns. Also, a close association was observed between exposure to Carbon Monoxide (CO) in the first and second trimesters and LBW (p=0.002, p=0.015). There were no statistically significant associations in LBW in the case of Particulate Matter less than 10 µm (PM10), Sulfur Dioxide (SO2), and Ozone (O3). Conclusion: The results revealed that the higher risk for LBW was related to ambient PM2.5 and CO. Since many factors may affect LBW, and the pathogenic mechanisms of the effect of air pollution on LBW have not been completely elucidated, the findings should be interpreted with caution and further studies need to be conducted on this issue considering the large sample size