Introduction: Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder whose prevalence is constantly increasing. Objective: The aim of the study was to investigate the association between ASD and socioeconomic and demographic factors of parents of children/adolescents in Northern Minas Gerais, Brazil. Methods: A case-control study was carried out, consisting of 1,134 children/adolescents, 248 with ASD and 886 without ASD. A semi-structured questionnaire was used and multiple logistic regression was performed. Results: Children/adolescents with ASD are more likely to be male (OR: 3.91; 95% CI: 2.67–5.68), children of mothers aged ≥25 years (OR: 2.15; 95% CI: 1.50–3.09), who worked outside the home during pregnancy (OR: 1.52; 95% CI: 1.04–2.24) and that, at the time of the interview, they were not inserted in the labor market (OR: 3.17; 95% CI: 2.44–5.65), white (OR: 1.49; 95% CI: 1.01–2.22), and who performed prenatal care in private institutions (OR: 1.97; 95% CI: 1.38–2.80). Conclusion: The socioeconomic and demographic factors associated with ASD are important for the diagnosis and, consequently, for the increase in the number of reported cases. Thus, public policies are needed to allow equal access to the diagnosis and treatment of this disorder.

Introdução: O Transtorno do Espectro Autista (TEA) é um transtorno do neurodesenvolvolvimento de início precoce, cuja prevalência está em constante aumento.Objetivo: investigar a associação entre TEA e fatores socioeconômicos e demográficos de pais de crianças/adolescentes no Norte de Minas Gerais, Brasil.Métodos: foi realizado um estudo caso-controle, composto por 1,134 crianças/adolescentes, 248 com TEA e 886 sem TEA. Foi utilizado um questionário semiestruturado e realizada regressão logística múltipla.Resultados: crianças/adolescentes com TEA são mais propensos a ser do sexo masculino (OR: 3,91; IC 95%: 2,67–5,68), filhos de mães com idade ≥25 anos (OR: 2,15; IC 95%: 1,50–3,09), que trabalhavam fora de casa durante a gravidez (OR: 1,52; IC 95%: 1,04–2,24) e que, no momento da entrevista, não estavam inseridas no mercado de trabalho (OR: 3,17; IC 95%: 2,44–5,65), brancas (OR: 1,49; IC 95%: 1,01–2,22) e que realizaram pré-natal em instituições privadas (OR: 1,97; IC 95%: 1,38–2,80).Conclusão: os fatores socioeconómicos e demográficos associados ao TEA são importantes para o diagnóstico e, consequentemente, para o aumento do número de casos notificados. Assim, são necessárias políticas públicas que possibilitem acesso igualitário ao diagnóstico e tratamento desse transtorno.

KeywordsTranstorno do espectro autista, Fatores demográficos, Fatores socioeconómicos, Estudo de caso-controlo

The prevalence of autism spectrum disorder (ASD) has steadily increased [1]. In the 1960s, there were an estimated four to five cases of infantile autism per 10,000 births [2]; in 2009, this number increased substantially, becoming 60–70 cases per 10,000 births [3]. A study carried out in the USA with eight-year-old children, in 2010, estimated the prevalence of one case of ASD for every 68 [4]; however, in 2014, this number changed to 1 case of ASD for 59 children [5], and in 2016, the prevalence increased again, considering a case for 54 children [6]. A study carried out with data from 2019 to 2020 revealed that the prevalence of ASD in the USA is one autistic in every 30 children between 3 and 17 years old in that country [7].

Changes in diagnostic practices or greater knowledge about ASD are factors that stand out in its epidemiology; however, they do not fully explain the increased prevalence of this disorder [8]. Genetic factors play an important role in the etiology of ASD; however, studies in this area show that the agreement rate in monozygotic twins is incomplete [9], which shows the association of non-genetic factors in their etiology [10]. However, there are gaps regarding the influence of potentially modifiable external factors [11], such as lifestyle, socioeconomic, and demographic conditions. These factors may contribute directly or indirectly to the diagnosis of this disorder, and it is important to carry out investigations on this topic.

Studies have indicated an inverse relationship to what is normally observed for other health conditions, with a tendency to an increase in the prevalence of ASD among people at the high socioeconomic level [12, 13]. However, others pointed to the low socioeconomic level [14], and still a study that did not find this relationship [15]. Socioeconomic and demographic factors have been assessed by education, age, parental occupation, family income, ethnicity/racial groups, gender, socioeconomic status, and family size. However, the results found about socioeconomic and demographic factors and ASD are conflicting, with the main studies being carried out predominantly in high-income countries. Considering the scarcity of studies developed in Latin America and the family’s social and economic impact, caused by the presence of ASD, this study aimed to investigate the association between ASD and socioeconomic factors and demographics in children and adolescents in Northern Minas Gerais – Brazil.

This study was carried out in the city of Montes Claros, located in the north of Minas Gerais – Brazil, a medium-sized municipality with approximately 400 thousand inhabitants. This is an excerpt from a case-control study, entitled “Autism Spectrum Disorder in Montes Claros: A Case-Control Study,” which investigated the possible associations between ASD and pre-factors, perinatal, and postnatal. The methodology of this study has been described in detail in previously published works [16, 17].

It was decided to calculate the sample size for the case study and independent control, and it estimated odds ratio of 1,9 [18], with a probability of 0.18 of exposure between controls; a study power of 80% was established, with a significance level of 0.05 and four controls per case. To mitigate possible losses, the sample size was increased by 10%, and deff = 1.5 was adopted to correct the design effect. The required sample size was defined in 213 cases and 852 controls.

The case group was composed of children/adolescents, aged between 2 and 15 years, who attended the Associação Norte Mineira de Apoio ao Autista (ANDA) and in eight clinics with public and private assistance. Children with a medical report and whose mothers answered the question of the data collection instrument as positive with ASD were considered “Does your child have a diagnosis of ASD?”. The diagnosis of ASD was confirmed by a team of health professionals specializing in ASD, who were based on the diagnostic criteria for ASD proposed by the Diagnostic and Statistical Manual of Mental Disorders.

The control group was made up of neurotypical children/adolescents, with no signs of ASD, who studied in the same schools and had the same age group as the cases in the ratio of four to one. The presence of signs of ASD was considered an exclusion criterion, and screening was performed by the Modified Checklist for Autism in Toddlers (M-CHAT) [19]. It is noteworthy that the M-CHAT was chosen because it is an instrument with a simple format, easy to use and quick to apply. As it is used to screen children aged 18–24 months, mothers were instructed to answer it considering their children in this age group. It is noteworthy that the persistence of specific signs of ASD with increasing age is expected when appropriate interventions are not carried out. After this procedure, 120 children with ASD signs were identified and referred for a better diagnostic investigation.

For data collection, a semi-structured instrument developed from a literature review and reviewed by a multi-professional team was used. This was pre-tested and, after adjustments, applied to the mothers of both groups, by a previously trained team at a place and time defined by the participants. The socioeconomic and demographic variables assessed were divided into four groups: data from the child/adolescents, the family, the father, and the mother, and are described and categorized in Table 1.

Table 1.

Socioeconomic and demographic characteristics of children in the case and control groups, Montes Claros, Minas Gerais, Brazil, 2016

VariablesCase, n = 248, n (%)Control, n = 886, n (%)Gross (95% CI)p value**
Children’s data 
 Sex    0.000 
  Male 201 (81.0) 449 (50.7) 4.16 (2.95–5.87)  
  Female 47 (19.0) 437 (49.3) 1.00  
 Age group    0.132 
  11–15 years 38 (15.3) 128 (14.4) 0.93 (0.61–1.41)  
  6–10 years 89 (35.9) 380 (42.9) 0.73 (0.54–0.99)  
  2–5 years 121 (48.8) 378 (42.7) 1.00  
 School type    0.660 
  Do not study 14 (5.7) 66 (7.4) 0.72 (0.40–1.32)  
  Toilet 67 (27.0) 247 (27.9) 0.90 (0.66–1.24)  
  Public/philanthropic 167 (67.3) 573 (64.7) 1.00  
 Birth order    0.023 
  Firstborn 138 (57.0) 422 (48.7) 1.40 (1.05–1.86)  
  Not firstborn 104 (43.0) 444 (51.3) 1.00  
Family data 
 Socioeconomic class    0.320 
  A or B 149 (60.1) 493 (56.2) 0.83 (0.42–1.65)  
  C 87 (35.1) 351 (40.0) 0.68 (0.34–1.37)  
  D or E 12 (4.8) 33 (3.8) 1.00  
 Family income***    0.014 
  >6 minimum wages 55 (22.2) 134 (15.1) 1.62 (1.10–2.38)  
  2–6 minimum wages 97 (39.1) 373 (42.1) 1.03 (0.75–1.41)  
  <2 minimum wages 96 (38.7) 379 (42.8) 1.00  
 Type of residence    0.082 
  Own home 181 (73.0) 612 (69.4) 0.99 (0.70–1.42)  
  Assigned 14 (5.6) 91 (10.3) 0.52 (2.27–0.99)  
  Leased 53 (21.4) 179 (20.3) 1.00  
 Family size (number of members)    0.005 
  ≤3 88 (35.5) 234 (26.4) 1.53 (1.14–2.07)  
  >3 160 (64.5) 652 (73.6) 1.00  
Father’s details 
 Age (childbirth)    0.086 
  ≥35 years 152 (61.3) 595 (67.2) 1.29 (0.97–1.73)  
  <35 years 96 (38.7) 291 (32.8) 1.00  
 Education (current)    0.000 
  University education 72 (30.3) 162 (19.1) 2.11 (1.40–3.16)  
  High school 113 (47.5) 433 (51.2) 1.24 (0.86–1.77)  
  Elementary school 53 (22.3) 252 (29.7) 1.00  
 Occupational class (current)    0.827 
  Entrepreneur/employer/professional/others 110 (46.6) 394 (47.4) 0.97 (0.97–1.29)  
  Public/private worker 126 (53.4) 437 (52.6) 1.00  
Mother’s data 
 Age (childbirth)    0.008 
  ≥35 years 54 (21.8) 156 (17.6) 2.27 (1.46–3.54)  
  25–34 years 148 (59.7) 437 (49.3) 2.15 (1.50–3.09)  
  <25 years 54 (21.8) 293 (33.1) 1.00  
 Education    0.030 
  University education 109 (44.0) 291 (32.9) 1.62 (1.05–2.52)  
  High school 106 (42.7) 450 (50.9) 1.02 (0.66–1.58)  
  Elementary school 33 (13.3) 143 (16.2) 1.00  
 Worked during pregnancy (away from home)    0.050 
  Yes 166 (68.6) 540 (61.7) 1.36 (1.00–1.84)  
  Not 76 (31.4) 335 (38.3) 1.00  
 Occupational class (current)    0.000 
  Housewife 96 (38.9) 222 (25.2) 2.11 (1.52–2.93)  
  Employer/professional/others 60 (24.3) 214 (24.3) 1.37 (0.95–1.97)  
  Public/private worker 91 (36.8) 444 (50.5) 1.00  
 Had help with housework during pregnancy    0.958 
  Yes 153 (61.9) 543 (62.1) 1.01 (0.75–1.35)  
  Not 94 (38.1) 331 (37.9) 1.00  
 Marital status in pregnancy    0.367 
  Single/widow/separated/divorced 38 (15.4) 154 (17.8) 0.84 (0.57–1.23)  
  Married/stable relationship 209 (84.6) 709 (82.2) 1.00  
 Mother skin color (self-reported)    0.001 
  White 66 (26.6) 149 (16.8) 1.79 (1.29–2.50)  
  Not white 182 (73.4) 737 (83.2) 1.00  
 Prenatal location    0.015 
  Private (health plan and health insurance) 149 (60.1) 366 (41.7) 2.10 (1.58–2.80)  
  Public (SUS****) 99 (39.9) 511 (58.3) 1.00  
VariablesCase, n = 248, n (%)Control, n = 886, n (%)Gross (95% CI)p value**
Children’s data 
 Sex    0.000 
  Male 201 (81.0) 449 (50.7) 4.16 (2.95–5.87)  
  Female 47 (19.0) 437 (49.3) 1.00  
 Age group    0.132 
  11–15 years 38 (15.3) 128 (14.4) 0.93 (0.61–1.41)  
  6–10 years 89 (35.9) 380 (42.9) 0.73 (0.54–0.99)  
  2–5 years 121 (48.8) 378 (42.7) 1.00  
 School type    0.660 
  Do not study 14 (5.7) 66 (7.4) 0.72 (0.40–1.32)  
  Toilet 67 (27.0) 247 (27.9) 0.90 (0.66–1.24)  
  Public/philanthropic 167 (67.3) 573 (64.7) 1.00  
 Birth order    0.023 
  Firstborn 138 (57.0) 422 (48.7) 1.40 (1.05–1.86)  
  Not firstborn 104 (43.0) 444 (51.3) 1.00  
Family data 
 Socioeconomic class    0.320 
  A or B 149 (60.1) 493 (56.2) 0.83 (0.42–1.65)  
  C 87 (35.1) 351 (40.0) 0.68 (0.34–1.37)  
  D or E 12 (4.8) 33 (3.8) 1.00  
 Family income***    0.014 
  >6 minimum wages 55 (22.2) 134 (15.1) 1.62 (1.10–2.38)  
  2–6 minimum wages 97 (39.1) 373 (42.1) 1.03 (0.75–1.41)  
  <2 minimum wages 96 (38.7) 379 (42.8) 1.00  
 Type of residence    0.082 
  Own home 181 (73.0) 612 (69.4) 0.99 (0.70–1.42)  
  Assigned 14 (5.6) 91 (10.3) 0.52 (2.27–0.99)  
  Leased 53 (21.4) 179 (20.3) 1.00  
 Family size (number of members)    0.005 
  ≤3 88 (35.5) 234 (26.4) 1.53 (1.14–2.07)  
  >3 160 (64.5) 652 (73.6) 1.00  
Father’s details 
 Age (childbirth)    0.086 
  ≥35 years 152 (61.3) 595 (67.2) 1.29 (0.97–1.73)  
  <35 years 96 (38.7) 291 (32.8) 1.00  
 Education (current)    0.000 
  University education 72 (30.3) 162 (19.1) 2.11 (1.40–3.16)  
  High school 113 (47.5) 433 (51.2) 1.24 (0.86–1.77)  
  Elementary school 53 (22.3) 252 (29.7) 1.00  
 Occupational class (current)    0.827 
  Entrepreneur/employer/professional/others 110 (46.6) 394 (47.4) 0.97 (0.97–1.29)  
  Public/private worker 126 (53.4) 437 (52.6) 1.00  
Mother’s data 
 Age (childbirth)    0.008 
  ≥35 years 54 (21.8) 156 (17.6) 2.27 (1.46–3.54)  
  25–34 years 148 (59.7) 437 (49.3) 2.15 (1.50–3.09)  
  <25 years 54 (21.8) 293 (33.1) 1.00  
 Education    0.030 
  University education 109 (44.0) 291 (32.9) 1.62 (1.05–2.52)  
  High school 106 (42.7) 450 (50.9) 1.02 (0.66–1.58)  
  Elementary school 33 (13.3) 143 (16.2) 1.00  
 Worked during pregnancy (away from home)    0.050 
  Yes 166 (68.6) 540 (61.7) 1.36 (1.00–1.84)  
  Not 76 (31.4) 335 (38.3) 1.00  
 Occupational class (current)    0.000 
  Housewife 96 (38.9) 222 (25.2) 2.11 (1.52–2.93)  
  Employer/professional/others 60 (24.3) 214 (24.3) 1.37 (0.95–1.97)  
  Public/private worker 91 (36.8) 444 (50.5) 1.00  
 Had help with housework during pregnancy    0.958 
  Yes 153 (61.9) 543 (62.1) 1.01 (0.75–1.35)  
  Not 94 (38.1) 331 (37.9) 1.00  
 Marital status in pregnancy    0.367 
  Single/widow/separated/divorced 38 (15.4) 154 (17.8) 0.84 (0.57–1.23)  
  Married/stable relationship 209 (84.6) 709 (82.2) 1.00  
 Mother skin color (self-reported)    0.001 
  White 66 (26.6) 149 (16.8) 1.79 (1.29–2.50)  
  Not white 182 (73.4) 737 (83.2) 1.00  
 Prenatal location    0.015 
  Private (health plan and health insurance) 149 (60.1) 366 (41.7) 2.10 (1.58–2.80)  
  Public (SUS****) 99 (39.9) 511 (58.3) 1.00  

Totals may vary depending on the variables that have not been answered (missing).

**χ2 test.

***Current minimum wage: R $ 880.00 in 2014.

****Unified Health System.

Frequency distributions of all variables were constructed, according to the case and control groups. To assess the association between ASD and the other variables, the χ2 test was used, and those variables that presented a descriptive level (p value) less than 0.20 were selected for multiple analyses. In the multiple analysis, the logistic regression model was adopted with a stepwise procedure, whose association magnitude between the outcome and the independent variables was estimated by the odds ratio, with respective 95% confidence intervals. Adjustment variables were used: parity, smoking, and family with ASD. In assessing the quality of fit of the model, the Hosmer-Lemeshow test and the pseudo-R2 Nagelkerke statistic were adopted. Statistical analyses were performed using the statistical software Statistical Package for the Social Sciences – SPSS version 23.0 (IBM, Chicago, IL, USA). This study was approved by the Research Ethics Committee (CEP) of the State University of Montes Claros (opinion nr 534.000/14), and all those responsible for the children/adolescents signed the Free and Informed Consent Form.

The final sample consisted of 1,134 individuals, of whom 248 were in the case-study group and 886 were in the control group. Of these, 81.0% and 50.7% of the case and control groups, respectively, were male, with a significant difference (p < 0.001).

A similar mean age was observed between the groups: 6.4 years (SD = 3.6) in the case group and 6.6 years (SD = 3.4) in the control group (p = 0.521). The distribution of the age group was also similar between the groups (p = 0.132), and in the total sample surveyed (cases and controls), 44.0% belonged to the age group of 2 to 5 years, 41.4% were aged between 6 and 10 years, and 14.6% were older than 10 years. The groups were also similar in terms of social class (p = 0.115) and the type of school they attended (p = 0.660).

In the bivariate analysis, positive and significant associations with ASD were confirmed for the following characteristics: sex of the child, birth order, family income, family size, education of the parents, maternal age, the mother who worked during pregnancy, occupational class, maternal skin color, and prenatal care (Table 1). In the multiple analysis, it was observed that children and adolescents with ASD remained more likely to be male, being children of mothers aged 25 years or over whose professional occupation was outside the home during pregnancy and who, at the time of the interview, were housewives, businesswomen, or self-employed professionals who declared themselves to be white and who performed prenatal care in private institutions (Table 2).

Table 2.

Adjusted odds ratio, with respective 95% confidence intervals, for ASD, according to socioeconomic and demographic characteristics of families, Montes Claros, Minas Gerais, Brazil, 2016

VariablesORa (95% CI)
Children’s data 
 Sex 
  Male 3.91 (2.67–5.68) 
  Female 1.00 
Mother’s data 
 Age (childbirth) 
  ≥35 years 2.27 (1.46–3.54) 
  25–34 years 2.15 (1.50–3.09) 
  <25 years 1.00 
 Worked during pregnancy (away from home) 
  Yes 1.52 (1.04–2.24) 
  Not 1.00 
 Occupational class (currently) 
  Housewife 3.17 (2.44–5.65) 
  Employer/professional/others 1.78 (1.17–2.72) 
  Public/private worker 1.00 
 Mother skin color (self-reported) 
  White 1.49 (1.01–2.22) 
  Not white 1.0 
 Prenatal location 
  Private (health plan and health insurance) 1.97 (1.38–2.80) 
  Public (SUS) 1.00 
VariablesORa (95% CI)
Children’s data 
 Sex 
  Male 3.91 (2.67–5.68) 
  Female 1.00 
Mother’s data 
 Age (childbirth) 
  ≥35 years 2.27 (1.46–3.54) 
  25–34 years 2.15 (1.50–3.09) 
  <25 years 1.00 
 Worked during pregnancy (away from home) 
  Yes 1.52 (1.04–2.24) 
  Not 1.00 
 Occupational class (currently) 
  Housewife 3.17 (2.44–5.65) 
  Employer/professional/others 1.78 (1.17–2.72) 
  Public/private worker 1.00 
 Mother skin color (self-reported) 
  White 1.49 (1.01–2.22) 
  Not white 1.0 
 Prenatal location 
  Private (health plan and health insurance) 1.97 (1.38–2.80) 
  Public (SUS) 1.00 

Hosmer-Lemeshow test; p value = 0.344; pseudo R2 Nagelkerke = 0.228; model adjusted by: parity, smoking, prematurity, family with ASD.

ORa, adjusted odds ratio; CI, confidence interval.

In recent years, interest in studies exploring the impact of socioeconomic factors on health status, including ASD, has steadily increased. This is mainly related to the fact that this disorder is among the top ten causes of disability worldwide in children between 5 and 9 years of age and the constant increase in its prevalence [2, 3, 5].

In this study, it was observed that children and adolescents with ASD are more likely to be male, children of mothers aged 25 years or over, who had occupational activity during pregnancy, and who were not formally inserted at the time of the interview. In the labor market, who declared themselves to be white and who performed prenatal care in private institutions? It was found that some of these factors facilitate access to health services, being decisive for the diagnosis of ASD and, consequently, for the increase in the number of reported cases.

It was confirmed that children and adolescents with ASD had an approximately four-fold chance of being male, as well as in studies conducted with other ethnically different populations [20‒22]. The specific factors responsible for the higher prevalence of men with ASD are still unclear.

One possible explanation is that exposure to fetal testosterone affects brain development and behavior. Between the eighth and twenty-fourth weeks of gestation, there is an increase in the level of this androgen, which plays an important role in cerebral masculinization. There is evidence of a relationship between cognitive traits present in ASD and amniotic fetal testosterone level, which is inversely associated with frequency of eye contact at 12 months, vocabulary development at 18 and 24 months, quality of social relations at 48 months, and empathy at 48 and 96 months and is positively associated with interests restricted to 48 months. Therefore, it appears that this androgen plays a role in the development of ASD and its greater prevalence in males.

However, in clinical samples, it was also observed that people with ASD are more likely to be the children of mothers aged 25 years or older. Similar results were found in other studies that pointed out that the prevalence of ASD increases in children of older mothers [23‒25]. In another study conducted with this same population, it was found that the magnitude of the association was greater when both parents were old [16]. Several explanations have been reported to justify the possible relationship of ASD with the increase in the age of the parents, among them the new mutation [26], the epigenetic [10], and complications in pregnancy and/or childbirth, more evident in women with advanced age [27‒29]. This finding suggests the hypothesis that advanced maternal age may be directly related to the development and diagnosis of ASD.

In this study, parents’ education was not associated with ASD, which is a divergent result from previous studies that identified higher education among parents of children with ASD [30, 31]. Mazurek et al. [32] pointed out that less education combined with the advanced age of parents are factors significantly associated with delayed diagnosis of ASD, and Fujiwara [33] identified suspicion of children with ASD and less maternal education.

It was observed that children and adolescents with ASD are more likely to be the children of mothers who worked outside the home during pregnancy and who, at the time of the interview, were not part of the formal labor market, being a housewife/liberal professional. These results suggest changes in the employability profile of mothers who, given the greater demands of child care with ASD, may abandon formal work. These findings are in line with other studies [34], which pointed out that, after having their child diagnosed with ASD, some mothers renounced their professional careers to be their child’s main caregiver, while parents were more committed to the profession outside the home. Despite the great individual variations, people with ASD often require from families extensive and permanent care periods [35], requiring a family adjustment to adapt to the new demands faced with an ASD diagnosis [34].

Among family adjustments, the option for a reduced number of family members also stands out, as this study found that, in families with children and adolescents with ASD, the number of members is less than or equal to three, suggesting that the couple had only one child, which has ASD. This fact may be directly related to the child’s birth order with the diagnosis of ASD, in which bivariate analyzes showed that this disorder is more common in firstborn children. However, after adjustments, the birth order lost statistical significance. Results diverged from those of other studies, in which the fact of being the firstborn remained significantly associated with ASD, even after adjustments [36].

Intentionally, in this study, we sought to seek similar groups in terms of social class since the people in the control group attended the same type of school as those in the case group. However, factors related to social class had a significantly higher percentage in the families of children and adolescents with ASD, such as higher family income, mothers of white color, and those who had prenatal care in paid health services. Skin color and prenatal care remained significant even after adjustments.

Yu et al. [12] and Kelly et al. [13] pointed out that the increase in families’ socioeconomic status was associated, proportionally, with the risk of ASD in their children. However, other studies have pointed out that ASD is associated with lower socioeconomic status [11, 14], suggesting that such associations mainly reflect a bias in the detection of cases, with an artificially increased prevalence, as some results differ according to the source used to measure the prevalence. In the present study, a diversified data source was used: public, private schools, associations, and public and private clinics, which mitigated possible verification/selection biases.

As for family income, Dodds et al. [37] reinforced that it is not considered a cause for ASD, so this variable possibly reflects the role of potentially correlated cultural and educational factors. They add that access to the health system can interfere with obtaining a diagnosis more easily and, not properly, with the chances of developing ASD. Therefore, this variable deserves attention even though it is not considered a direct cause for ASD, as it interferes with the diagnostic process, allowing it to be earlier in families with higher income and later in those with low income, with little access to diagnostic and treatment services. Thus, where the health system is not universal, there are consequences for the prognosis of the child with this disorder.

It was found that children/adolescents with ASD had twice the chance of their mothers having performed prenatal care in the private network or health plan/health plan. Results were different from those found by King and Bearman [38], who pointed out the fact that individuals whose prenatal care was paid had a reduced risk for diagnosing ASD. Because this variable is related to socioeconomic status, the association found in this study may be due to these families having easier access to diagnostic and treatment services for ASD. It is worth mentioning that, in the region where the study was conducted, there are clear disparities related to the ease of accessing health services and scarce public policies for detecting and monitoring people with this disorder.

The data obtained in this study also showed that there is an increased chance of children and adolescents with ASD being children of mothers who declared themselves to be white. Similar results from other studies that identified a higher prevalence of ASD in white children [39] pointed out that fathers were more likely to be black or Asian and mothers to be of Hispanic origin. These results lead to the question of whether non-white skin color is a protective factor against ASD or whether socioeconomic issues and access to health services contribute to underdiagnosis in these populations.

The limitations of this study are the use, in data collection, of a self-reported semi-structured questionnaire of the mothers, which can be related to a possible memory bias. To mitigate this factor, it was requested, at the time of the interview, that the prenatal card and vaccine booklet confirm the information, and there was consistency between the documents and the mothers’ report. Another limiting factor was the impossibility of confirming the criteria used to diagnose and classify the ASD degrees of individuals in the case group since the diagnosis was made by different professionals; however, by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), all diagnosed individuals are part of the independent sub-classification spectrum.

It is worth mentioning that this study is a pioneer in Latin America; it provided a robust sample with an approximate proportion of four controls per case, a selection of controls representative of the general population, with a screening of children with signs of ASD. Another relevant factor, in the case of a study that investigated socioeconomic conditions, was the fact that the individuals in the group were captured in private and public clinics, which enabled a diversified sample.

This study protocol was reviewed and approved by the Research Ethics Committee (CEP) of the State University of Montes Claros with approval number 534.000/14. Written informed consent was obtained from all those responsible for the children/adolescents who signed the “Free and Informed Consent Form.”

The authors have no conflicts of interest to declare.

The study was funded by the Research Support Foundation of the State of Minas Gerais (FAPEMIG).

Laura Vicuña Santos Bandeira, Ionara Aparecida Mendes Cezar, Steffany Lara Nunes Oliveira, Ana Júlia Soares Oliveira, Victor Bruno da Silva, and Maria Silveira Nunes participated in the preparation of the instrument and data collection, interpretation of data, and writing of the article. Fernanda Alves Maia and Marise Fagundes Silveira participated in the conception, design, analysis, and interpretation of data and final review of the article. Luiz Fernando de Rezende participated in data analysis, writing, and review of the article.

All data generated or analyzed during this study are included in this article and its supplementary material files. Further enquiries can be directed to the corresponding author.

1.
Gal
G
,
Abiri
L
,
Reichenberg
A
,
Gabis
L
,
Gross
R
.
Time trends in reported autism spectrum disorders in Israel, 1986–2005
.
J Autism Dev Disord
.
2012
;
42
(
3
):
428
31
.
2.
Lotter
V
.
Epidemiology of autistic conditions in young children
.
Soc Psychiatry
.
1966
;
1
(
3
):
124
35
.
3.
Autism and Developmental Disabilities Monitoring Network Surveillance Year 2006 Principal Investigators
Centers for Disease Control and Prevention (CDC)
.
Prevalence of autism spectrum disorders: autism and developmental disabilities monitoring network, United States, 2006
.
MMWR Surveill Summ
.
2009
;
58
(
10
):
1
20
.
4.
Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators
Centers for Disease Control and Prevention (CDC)
.
Prevalence of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2010
.
MMWR Surveill Summ
.
2014
;
63
(
2
):
1
21
.
5.
Baio
J
,
Wiggins
L
,
Christensen
DL
,
Maenner
MJ
,
Daniels
J
,
Warren
Z
et al
.
Prevalence of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2014
.
MMWR Surveill Summ
.
2018
;
67
(
6
):
1
23
.
6.
Maenner
MJ
,
Shaw
KA
,
Baio
J
EdS1
Washington
A
,
Patrick
M
et al
.
Prevalence of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2016
.
MMWR Surveill Summ
.
2020
;
69
(
4
):
1
12
.
7.
Li
Q
,
Li
Y
,
Liu
B
,
Chen
Q
,
Xing
X
,
Xu
G
et al
.
Prevalence of autism spectrum disorder among children and adolescents in the United States from 2019 to 2020
.
JAMA Pediatr
.
2022
;
176
(
9
):
943
5
.
8.
Grether
JK
,
Anderson
MC
,
Croen
LA
,
Smith
D
,
Windham
GC
.
Risk of autism and increasing maternal and paternal age in a large North American population
.
Am J Epidemiol
.
2009
;
170
(
9
):
1118
26
.
9.
Hallmayer
J
,
Cleveland
S
,
Torres
A
,
Phillips
J
,
Cohen
B
,
Torigoe
T
et al
.
Genetic heritability and shared environmental factors among twin pairs with autism
.
Arch Gen Psychiatry
.
2011
;
68
(
11
):
1095
102
.
10.
Keil
KP
,
Lein
PJ
.
DNA methylation: a mechanism linking environmental chemical exposures to risk of autism spectrum disorders
.
Environ Epigenet
.
2016
2
1
dvv012
.
11.
DiGuiseppi
CG
,
Daniels
JL
,
Fallin
DM
,
Rosenberg
SA
,
Schieve
LA
,
Thomas
KC
et al
.
Demographic profile of families and children in the Study to Explore Early Development (SEED): case-control study of autism spectrum disorder
.
Disabil Health J
.
2016
;
9
(
3
):
544
51
.
12.
Yu
T
,
Lien
YJ
,
Liang
FW
,
Kuo
PL
.
Parental socioeconomic status and autism spectrum disorder in offspring: a population-based cohort study in Taiwan
.
Am J Epidemiol
.
2021
;
190
(
5
):
807
16
.
13.
Kelly
B
,
Williams
S
,
Collins
S
,
Mushtaq
F
,
Mon-Williams
M
,
Wright
B
et al
.
The association between socioeconomic status and autism diagnosis in the United Kingdom for children aged 5–8 years of age: findings from the Born in Bradford cohort
.
Autism
.
2019
;
23
(
1
):
131
40
.
14.
Silva
EF
.
O impacto financeiro nas famílias que tem diagnóstico de TEA (Transtorno do Espectro Autista) e suas consequências financeiras e econômicas para a sociedade
. In:
Soares
AM
,
org
, editors.
Tópicos especiais em ciências da saúde: teoria, métodos e práticas
Ponta Grossa
AYA Editora
.
2022
. p.
190
201
.
15.
Larsson
HJ
,
Eaton
WW
,
Madsen
KM
,
Vestergaard
M
,
Olesen
AV
,
Agerbo
E
et al
.
Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status
.
Am J Epidemiol
.
2005
;
161
(
10
):
916
28
; discussion 926–8.
16.
Maia
FA
,
Almeida
MTC
,
Alves
MR
,
Bandeira
LVS
,
Silva
VBD
,
Nunes
NF
et al
.
Transtorno do espectro do autismo e idade dos genitores: estudo de caso-controle no Brasil
.
Cad Saúde Pública
.
2018
;
34
(
8
):
e00109917
.
17.
Maia
FA
,
Oliveira
LMM
,
Almeida
MTC
,
Alves
MR
,
Saeger
VSd A
,
Silva
VB
et al
.
Autism spectrum disorder and postnatal factors: a case-control study in Brazil
.
Rev Paul Pediatr
.
2019
;
37
(
4
):
398
405
.
18.
Quinlan
CA
,
McVeigh
KH
,
Driver
CR
,
Govind
P
,
Karpati
A
.
Parental age and autism spectrum disorders among New York City children 0–36 months of age
.
Matern Child Health J
.
2015
;
19
(
8
):
1783
90
.
19.
Pondé
MP
,
Novaes
CM
,
Losapio
MF
.
Frequency of symptoms of attention deficit and hyperactivity disorder in autistic children
.
Arq Neuropsiquiatr
.
2010
;
68
(
1
):
103
6
.
20.
Maenner
MJ
,
Shaw
KA
,
Bakian
AV
,
Bilder
DA
,
Durkin
MS
,
Esler
A
et al
.
Prevalence and characteristics of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2018
.
MMWR Surveill Summ
.
2021
;
70
(
11
):
1
16
.
21.
Morales-Hidalgo
P
,
Roigé-Castellví
J
,
Hernández-Martínez
C
,
Voltas
N
,
Canals
J
.
Prevalence and characteristics of autism spectrum disorder among Spanish school-age children
.
J Autism Dev Disord
.
2018
;
48
(
9
):
3176
90
.
22.
Zeidan
J
,
Fombonne
E
,
Scorah
J
,
Ibrahim
A
,
Durkin
MS
,
Saxena
S
et al
.
Global prevalence of autism: a systematic review update
.
Autism Res
.
2022
;
15
(
5
):
778
90
.
23.
Lung
FW
,
Chiang
TL
,
Lin
SJ
,
Lee
MC
,
Shu
BC
.
Advanced maternal age and maternal education disparity in children with autism spectrum disorder
.
Matern Child Health J
.
2018
;
22
(
7
):
941
9
.
24.
Al-Mamari
W
,
Idris
AB
,
Al-Zadjali
AA
,
Jalees
S
,
Murthi
S
,
Al-Jabri
M
et al
.
Parental age and the risk of autism spectrum disorder in Oman: a case-control study
.
Sultan Qaboos Univ Med J
.
2021
;
21
(
3
):
465
71
.
25.
Kim
JY
,
Son
MJ
,
Son
CY
,
Radua
J
,
Eisenhut
M
,
Gressier
F
et al
.
Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence
.
Lancet Psychiatry
.
2019
;
6
(
7
):
590
600
.
26.
Byars
SG
,
Boomsma
JJ
.
Opposite differential risks for autism and schizophrenia based on maternal age, paternal age, and parental age differences
.
Evol Med Public Health
.
2016
;
2016
(
1
):
286
98
.
27.
Pinheiro
RL
,
Areia
AL
,
Mota Pinto
A
,
Donato
H
.
Advanced maternal age: adverse outcomes of pregnancy, a meta-analysis
.
Acta Med Port
.
2019
;
32
(
3
):
219
26
.
28.
Frederiksen
LE
,
Ernst
A
,
Brix
N
,
Braskhøj Lauridsen
LL
,
Roos
L
,
Ramlau-Hansen
CH
et al
.
Risk of adverse pregnancy outcomes at advanced maternal age
.
Obstet Gynecol
.
2018
;
131
(
3
):
457
63
.
29.
Guarga Montori
M
,
Álvarez Martínez
A
,
Luna Álvarez
C
,
Abadía Cuchí
N
,
Mateo Alcalá
P
,
Ruiz-Martínez
S
.
Advanced maternal age and adverse pregnancy outcomes: a cohort study
.
Taiwan J Obstet Gynecol
.
2021
;
60
(
1
):
119
24
.
30.
Lin
Y
,
Wang
G
,
Yang
Y
,
Jin
X
,
Huang
H
,
Zhang
Y
et al
.
Risk factors for ASD: risk factors for autism spectrum disorder in Shanghai, China: a population-based case-control study
.
J Autism Dev Disord
.
2023
;
53
(
8
):
2954
63
.
31.
Lyall
K
,
Song
L
,
Botteron
K
,
Croen
LA
,
Dager
SR
,
Fallin
MD
et al
.
The association between parental age and autism-related outcomes in children at high familial risk for autism
.
Autism Res
.
2020
;
13
(
6
):
998
1010
.
32.
Mazurek
MO
,
Handen
BL
,
Wodka
EL
,
Nowinski
L
,
Butter
E
,
Engelhardt
CR
.
Age at first autism spectrum disorder diagnosis: the role of birth cohort, demographic factors, and clinical features
.
J Dev Behav Pediatr
.
2014
;
35
(
9
):
561
9
.
33.
Fujiwara
T
.
Socioeconomic status and the risk of suspected autism spectrum disorders among 18-month-old toddlers in Japan: a population-based study
.
J Autism Dev Disord
.
2014
;
44
(
6
):
1323
31
.
34.
Favero-Nunes
MA
,
Santos
MA
.
Therapeutic itinerary taken by mothers of children with autistic disorder
.
Psicol Reflex Crit
.
2010
;
23
(
2
):
208
21
.
35.
Gomes
PT
,
Lima
LH
,
Bueno
MK
,
Araújo
LA
,
Souza
NM
.
Autism in Brazil: a systematic review of family challenges and coping strategies
.
J Pediatr
.
2015
;
91
(
2
):
111
21
.
36.
Alvares
GA
,
Licari
MK
,
Stevenson
PG
,
Bebbington
K
,
Cooper
MN
,
Glasson
EJ
et al
.
Investigating associations between birth order and autism diagnostic phenotypes
.
J Child Psychol Psychiatry
.
2021
;
62
(
8
):
961
70
.
37.
Dodds
L
,
Fell
DB
,
Shea
S
,
Armson
BA
,
Allen
AC
,
Bryson
S
.
The role of prenatal, obstetric and neonatal factors in the development of autism
.
J Autism Dev Disord
.
2011
;
41
(
7
):
891
902
.
38.
King
MD
,
Bearman
PS
.
Socioeconomic status and the increased prevalence of autism in California
.
Am Sociol Rev
.
2011
;
76
(
2
):
320
46
.
39.
Anorson
N
,
Male
I
,
Farr
W
,
Memon
A
.
Prevalence of autism in Europe, North America and Oceania, 2000–2020: a systematic review
.
Eur J Public Health
.
2021
31
Suppl 3
786
.