Rural-Urban Differences in Age at Autism Diagnosis: A Multiple Model Analysis

Nima  Ghahari; Fatemeh  Yousefian; Saeed  Behzadi; Amin  Jalilzadeh

doi:10.18502/ijps.v17i3.9729

Nima Ghahari Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Fatemeh Yousefian Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Saeed Behzadi Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Amin Jalilzadeh Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

DOI: https://doi.org/10.18502/ijps.v17i3.9729

Keywords: Autism Spectrum Disorder; Early Diagnosis; Health Services Accessibility; Health Status Disparities; Multivariate Analysis; Symptom Assessment; Socioeconomic Factors

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.