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Background: Autism prevalence has increased considerably, but its etiology is still poorly understood. While there have been suggestions regarding associations between air pollution exposure and neurodevelopmental disorders, several studies have looked at the effect of air pollution exposure on autism. However, the results are inconsistent. The possible role of unknown confounders is mainly blamed for this inconsistency. Methods: To minimize confounding effects, we evaluated the impact of air pollution exposure on autism using a family-based case-control study. Cases were individuals with a diagnosis of autism born between 2009 and 2012 in Isfahan city, Iran. The controls did not have a previous history of autism and were cousins of the case person. The controls were matched with the autistic cases in terms of residential location and age range. For each trimester of pregnancy, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and PM10 exposure were estimated using the inverse distance weighted method. Results: The analysis indicates a significant association between CO exposure and autism in the second trimester (OR = 1.59; p = 0.046, 95% CI: 1.01-2.51) and entire pregnancy (OR = 2.02; p = 0.049, 95% CI: 1.01-2.95). Likewise, exposure to NO2 during the second trimester (OR = 1.17; p = 0.006, 95% CI: 1.04-1.31), third trimester (OR = 1.11; p = 0.046, 95% CI: 1.01-1.24), and entire pregnancy (OR = 1.27; p = 0.007, 95% CI: 1.07-1.51) were found to be associated with increased risk of autism. Conclusions: Overall, our study found higher exposure to CO and NO2, particularly during the second and third trimesters of pregnancy, was significantly associated with a higher risk of autism.
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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.
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INTRODUCTION: Early diagnosis of autism is a critical step for gaining early intervention. The earlier interventions begin, the greater chance to reduce symptoms of autism over the lifespan. Despite the improvement in early diagnosis, age at diagnosis varies by residential locations. In order to improve early screening services, this study aims to identify geographic clusters of early and late diagnosis of autism, in addition, it is aimed to compare cases inside the clusters with the rest of the province on characteristics and socioeconomic factors. MATERIALS AND METHODS: Survey data were collected from 163 autistics born from 1996 to 2011 in Isfahan Province, Iran. As this study found diagnosis of autism occur at an earlier age among children, who on average every 2.5 months increased for each year of age, distance from regression line has been used to determine how early a case was diagnosed compared to other identified cases. After dividing cases into 5 classes based on their distances from the regression line, the ordinal based space-time scan statistic in SaTScan was used to identify geographic areas within specific time periods that have significantly elevated proportions of autistic children who received diagnosis at the earlier or later stages. RESULTS: The space-time analysis identified two geographic areas that age of diagnosis was inconsistent with the overall study area, the first area has an early diagnosis in central part of Isfahan megacity between 1998 and 2006 (P = .001), the second area shows to have a late diagnosis centered by Najafabad from 2010 through 2015 (P = .007). CONCLUSIONS: The result of our spatial analysis can be used to evaluate the performance of diagnosis services and additionally provide information to target specific at-risk population for further interventions.
