No abstract

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.

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.