|Year : 2021 | Volume
| Issue : 4 | Page : 424-431
Socioeconomic and demographic profile of autism spectrum disorder
Shivaji Marella, Samiksha Sahu, Swaleha Mujawar, Daniel Saldanha, Suprakash Chaudhury
Department of Psychiatry, Dr. D. Y. Patil Medical College, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India
|Date of Submission||07-May-2018|
|Date of Decision||20-Jul-2020|
|Date of Acceptance||27-Jul-2020|
|Date of Web Publication||17-Jun-2021|
Department of Psychiatry, Dr. D. Y. Patil Medical College, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune . 411 018, Maharashtra
Source of Support: None, Conflict of Interest: None
Background: Autism spectrum disorder (ASD) is a multifactorial disorder resulting from genetic and nongenetic risk factors and their interaction. There is a paucity of data on the socioeconomic and demographic factors underlying ASD from India. Aim: The aim of this study was to evaluate the socioeconomic and demographic profile of ASD. Materials and Methods: Six hundred patients with behavioral complaints reporting to either psychiatric or pediatric outpatient departments were screened and validated as per Autism questionnaire (Childhood Autism Rating Scale [CARS]). Included patients were evaluated on socioeconomic and demographic scale the appropriate Child Behavior Checklist (CBCL). Results: Twenty-eight children out of 600 (4.66%) were found to have ASD. The average age at which parents first noticed symptoms was 16 months (range: 9–24 months). The mean age at first consultation for ASD symptoms was delayed for girls. A highly significant association between intelligence quotient (IQ) and a diagnosis of ASD was seen. The mean IQ of ASD patients (93.2 n = 28) was significantly lesser than either psychiatric diagnoses or no diagnosis. Even though all patients had an IQ >70, there were still 9/28 patients with a level 3 severity of ASD. A highly significant association between ASD diagnosis and CARS scores was seen. Out of the documented 26 presenting complaints, 17 were social impairment related and 9 related to repetitive patterns of behaviors. The clinical findings from the CBCL conform to a previously developed autism profile for CBCL. Conclusion: Children are taking longer than recommended for optimal outcome to receive a diagnosis. Girls were brought for consultation with the pediatrician later than the boys. Male preponderance in ASD with M: F ratio of 6:1 was highly significant. ASD was found higher in MSES and HSES families. Living in urban areas predicted higher severity. IQ was lesser than for other conditions in ASD.
Keywords: Autism spectrum disorder, demographic factors, socioeconomic factors
|How to cite this article:|
Marella S, Sahu S, Mujawar S, Saldanha D, Chaudhury S. Socioeconomic and demographic profile of autism spectrum disorder. Med J DY Patil Vidyapeeth 2021;14:424-31
|How to cite this URL:|
Marella S, Sahu S, Mujawar S, Saldanha D, Chaudhury S. Socioeconomic and demographic profile of autism spectrum disorder. Med J DY Patil Vidyapeeth [serial online] 2021 [cited 2021 Aug 4];14:424-31. Available from: https://www.mjdrdypv.org/text.asp?2021/14/4/424/318698
| Introduction|| |
Autism spectrum disorder (ASD) is characterized by varying degrees of impairments in social communication (SC) and interaction (SI) along with restricted and repetitive patterns of behavior (RRB). It is frequently associated with intellectual impairment, language impairment, medical, genetic or environmental factors, behavioral illness, neurodevelopment disorders, and with mental disorders. Prevalence rates vary from 0.1 to 0.6 – >1% of population. The annual incidence has been steadily rising over the last three decades. Factors underlying this growing incidence of ASD include environmental toxins, infection burden, and changes in socioeconomic status and significantly due to changing criteria for the diagnosis of ASD.,, Evaluation of a patient is best done as early as possible with validated scales now available for children as young as 10–12 months. Early diagnosis and multimodal treatment plan is associated with better outcomes. The search for causation and pathophysiological mechanisms of ASD is ongoing. Researchers explore genetic,, environmental,, immune and infective,,, neurobiological avenues. There are large multicentric longitudinal trials such as Infant Brain Imaging Study that have made significant headway into the realm of predictive biomarkers.
ASD is characterized by spinal cord injury deficits and RRBs. The diagnosis requires the presence of all three components of persistent deficits in SC and SI, namely deficits in social-emotional reciprocity, deficits in nonverbal communicative behaviors, and deficits in developing maintaining and understanding relationships. In addition, any two out of four components of restricted RRB interests and activities, namely stereotyped or repetitive motor movements; use of objects or speech; insistence on sameness, inflexible adherence to routines, or ritualized patterns of verbal nonverbal behavior; highly restricted fixated interests which are abnormal in focus or intensity; hyper- or hypo-reactivity to sensory input; or unusual interest in sensory aspects of the environment. To aid in the rapid and accurate diagnosis of ASD, a number of validated standardized questionnaires are available. Through psychological and psychopharmacological research, a number of therapeutic interventions have been developed. The goals of these treatments are to reduce disruptive behaviors and promote learning in language acquisition, communication, and self-help. The key is early and intensive intervention with regularly updated goals and strategies. A review of literature in 2011 found only 41 papers published in India. The majority of research pertained to clinical findings of ASD. It is only recently that community-based data are being generated. While there are extensive data available on the sociodemographic factors underlying ASD globally, minimal data are available from institutions across India. Keeping in mind the paucity of information in the prevalence of ASD in institutionally documented research in India, a study was conceptualized to evaluate the demographic and clinical profile of patients of ASD.
| Materials and Methods|| |
The observational, prospective, and cross-sectional study was carried out at a tertiary care hospital attached to a medical college. Ethical clearance was obtained from the Institutional Ethics Committee. Written informed consent was obtained from parent of patients enrolled for the study.
Six hundred patients with behavioral problems reporting to a tertiary care center were taken into the study after a sample size was determined based on NIMHANS data of 6 ASD cases in all cause admissions to child psychiatry unit in a 1-year period. Utilizing Fisher's formula (Z2P [1 − P]/D2), a sample size of 402 was arrived at with 1% permissible error. However, this sample size would lead to an expected 17 cases of ASD. Keeping a minimum desired 25 cases in mind for a good analysis of ASD sociodemographic and clinical features, a sample size of 591–600 was arrived at.
- Patients in the age group 2 years to 12 years
- Having behavioral complaints.
- Children younger than 2 years and older than 12 years
- Children with active medical condition
- Patients unwilling to undergo the complete study protocol.
Sociodemographic and clinical profile questionnaire
This included questions about sociodemographic details, source of referral, age at initial recognition of symptoms, age at first medical consultation, previous treatment, family history, and birth and developmental history.
Kuppuswamy's Socioeconomic Scale
This widely used scale classifies the study populations into higher, upper middle, lower middle, upper lower, and lower socioeconomic status (LSES). For the present study, corrections were applied for the year in which patient was examined according to the standard prescribed procedure.
The Childhood Autism Rating Scale
The Childhood Autism Rating Scale (CARS) is a widely used screening and diagnostic tool for ASD. It has been shown to be valid and to have good correlation with other screening tests along with good internal 7 consistency. CARS aids the screening, diagnosis and categorization of (for Diagnostic and Statistical Manual of Mental Disorders IV [DSM IV] and ICD10) ASD.,
The Child Behavior Checklist
It is a scale with proven psychometric properties that is easily completed by parents. The child behavior checklist (CBCL) is available separately for the 1½ years to 5 years' age group and the 5–18 years' age group. It can be used as a screening device, for eliciting comorbid illnesses as well as for making differential diagnoses.
Six hundred patients with behavioral complaints reporting to either psychiatric or pediatric outpatient departments were screened. Based on outcome of screening, the patients received appropriate diagnosis of ASD based on DSM-5 criteria and validated as per the CARS. Included patients were evaluated on sociodemographic and clinical questionnaire and the appropriate CBCL. A detailed record of the pediatric evaluation along with all investigations was maintained.
Appropriate statistical analysis was carried out using open source R software. Analysis of the association between demographic and socioeconomic variables and severity of ASD was done using Pearson Chi-square test. ANOVA was used to analyze the association of age and intelligence quotient (IQ) with the three classes. Bonferroni correction was applied.
| Results|| |
The study sample comprised 600 children and adolescents, with an average age of 7.14 years. The range of age was 2–12 years. Psychiatric diagnoses were present in 234 (39%) of the patients. On the CARS, the mean score was 17.03 (median score = 16.00) [Table 1]. A total of 28 individuals met the DSM 5 diagnostic criteria for ASD. The average age of ASD patients was 8.78 years. The range of age was 6–12 years. The average age at which symptoms were first recognized was 16 (male = 15, female = 16) months. However, the average age of first medical consultation was 35 (male = 33.2; female = 45) months. The average age of first diagnosis was 49 months. Thus, the average period between first recognition of symptoms and first diagnosis was 33 months. The average CARS score was 33.23. None of the ASD patients had any congenital malformations. All ASD patients had an IQ of >70. [Table 2] gives the association of age with severity of ASD. The association of sex with diagnostic groups and with severity of ASD is shown in [Table 3] and [Table 4], respectively. The association of place of residence (urban vs. rural) with diagnostic groups and with severity of ASD is given in [Table 5] and [Table 6], respectively.
|Table 1: Sociodemographic characteristics of all patients in the study and patients with autism spectrum disorder|
Click here to view
|Table 5: Association of place of residence (urban vs. rural) with diagnostic group|
Click here to view
|Table 6: Association of place of residence (urban/rural) with Severity of autism spectrum disorder|
Click here to view
IQ data were only available for 102 patients including ASD (n = 28), other psychiatric disorders (n = 52) excluding intellectual disability (n = 7), and no diagnosis (n = 15). Among the ASD patients, two patients (7%) had an IQ in the 120–129 range, 1 (4%) had IQ in the 110–119 range, 17 (61%) patients had an IQ in the 90–109 range, while four patients (14%) each were in 80–89 and70–79 range. None of the ASD group was intellectually disabled. The mean IQ of ASD children (range: – 70–125) was significantly less (P: 000046) than the children with psychiatric diagnosis other than intellectual disability (range: 80–135). The mean of ASD children is also significantly less than the mean (range: 94–135) of normal children (P = 0.00015).
| Discussion|| |
This study carried out in a tertiary care hospital attached to a medical college was aimed to assess the sociodemographic profile of the ASD patients. In the present study, 28 children out of 600 (4.66%) were found to have ASD. This is lower than the figure of 10.2% (200 out of 1957) in a study conducted at NIMHANS but higher than 1.6% (46 out of 2942) found in a study from PGIMER. The average age at which parents first noticed symptoms was 16 months. The ASD patients in our study then spent an average of 19 months before their first consultation for ASD at the mean age of 35 months. They received a diagnosis, on an average, a further 14 months later at the mean age of diagnosis of 49 months. This is a very long wait at each stage occurring in spite of the efforts of professionals. The Indian Pediatric Association suggests that a screening examination should be done at the age of 18 and then at 24 months. This would reduce the time from first symptom noticed to first consultation and diagnosis. An Indian study found that parents first noticed symptoms at 26 months, waited 32 months for the first consultation, and received a diagnosis a further 24 months thereafter. Symptoms were noted 6–10 months later than in the West. This was because the parents thought of their child as “good child” who was not demanding and kept quiet. The study found that ASD children who first visited the hospital with medical complaints were seen earlier but diagnosed later than children who presented with ASD-related complaints. An American study found that only a small minority of patients were diagnosed before 3 years. One-third to half of the patients were diagnosed after 6 years of age. Less severe ASD symptoms were associated with a later age at diagnosis. An Indian study found that all parents recognized symptoms before 3 years of age but that the mean age of consultation was 67 months which was lower than in the present study. The mean age at symptom recognition was 24 months and the mean age at first consultation was 37 months.
Reasons for the patients in this study being above 6 years of age along with 6 years being the most common age could include school commencement at this age which brings with it a significant challenge for the child to adapt [Table 1]. This is also a stage at which teachers and other students might notice otherwise subtle signs. All the ASD children in this study had an IQ higher than 70 which means that the more severely symptomatic ASD + ID children were not a part of the study sample. The less severe cases are seen at later ages.
[Table 2] shows that there was no association between age at examination of the patient and severity of the patients' symptoms. This contradicts the understanding, that with increasing age, there is a decrease in the number and severity of symptoms. This is both due to the neurodevelopmental nature of ASD as well as the fact that with greater age patients are more likely to have received appropriate interventions which are known to improve the symptoms.
Among ASD patients, there was a male:female ratio of 6:1 in this study. Three previous studies showed somewhat lower ratios of 3.6:1.33, 4:1.61, and 4:1.27.,, In the present study, the male preponderance was highly statistically significant. There was a higher M:F predominance in ASD as compared to other psychiatric diagnoses and the no diagnosis group. There was, however, no difference between the other psychiatric diagnoses and no diagnosis groups [Table 3]. The finding was not in keeping with previous studies in spite of this study having found cases of neurodevelopmental disorders such as attention-deficit/hyperactivity disorder, ID, conduct disorder, and social (pragmatic) communication disorder (SPCD). The high M: F ratio in ASD is sought to be explained by the extreme male brain hypothesis, which states that females have a greater tendency for empathy whereas males have a greater tendency to systemize. This is related with the hypothesis that higher prenatal exposure to fetal testosterone is related to presence of higher autistic traits in children with ASD. In this study, there was no relationship between severity of ASD and sex which is in agreement with previous studies.,
Interaction with IQ and sex has been studied. This study found that the M:F ratio dropped as IQ decreased and increased as IQ increased. It was found that females were lesser in high IQ ASD patients. The study suggested there may be an underdiagnosis of girls with ASD because girls are better at SI. It also suggests that some RRBs are misattributed as normal behavior for girls, for example, repeated playing with the same doll over and over again. The quiet girl may be wrongly considered just a “good girl.” Girls are reared differently to boys, and this may be contributory. This possibility of underdiagnosis and delayed diagnosis is also raised by this study. In the present study, we see that boys and girls have similar age at first symptoms noticed by parents – 15 and 16 months, respectively. However, the mean age at first consultation for ASD symptoms was very delayed for girls. Whereas boys were shown first at a mean age of 33 months, girls received their first consult only at an average of 45 months. The girls and boys, however, had a similar age at diagnosis overall of 49.5 and 49 months, respectively. The delay in first consultation can have a significant detrimental effect on the long-term outcome of the case as early and intensive intervention is crucial for good long-term outcome in ASD.,,
In current study, there was a highly significant association between socioeconomic status and a diagnosis of ASD when compared to both other psychiatric disorder sand the no diagnosis group. There was a preponderance of Middle socioeconomic status (MSES) families of children who had ASD. There was a disproportionate representation of High socioeconomic status (HSES) families as well. This finding is in agreement with Daley's study. A northwest Indian study found that there was a higher prevalence in the LSES population. A study from NIMHANS posits that there is lower ASD in LSES because of low awareness, increased tolerance for symptoms and greater stressors. They also raise the possibility of tools used being more sensitive for the urban middle class. A Swedish study found that ASD was higher in the LSES and in children of manual laborers. However, a USA study found that ASD was higher in families with income >$90,000 versus those with income <$30,000. While the US has a private healthcare system which is backed by insurance paid for by the individual, Sweden has a governmental decentralized universal health-care system. This raises the possibility that the type of health-care delivery system has an effect on the diagnosis of ASD. Another NIMHANS study found that 55.7% of patients belonged to the MSES, which, although lesser than the 78% found in this study, still suggests an MSES preponderance. A study suggests that due to the environment in which they grow up LSES children have greater mood and behavioral problems such as overactivity, irritability, low frustration tolerance, agitation, and labile mood. This may decrease the diagnosis of ASD as those symptoms may not receive a priority in the face of more pressing complaints. Another Indian study found that there was a greater chance of ASD in higher and middle socioeconomic status with an odds ratio of 7.16.
[Table 1] shows that 90% (542) patients were referred to the psychiatry department by pediatrics department or outside doctors and 10% came directly due to parental concerns regarding the child. Among ASD patients, 26 (92.9%) were referred and 2 (7.1%) came directly. These two patients had the highest age at diagnosis of 74 and 78 months as they had never previously received a psychiatric or pediatric evaluation for ASD. They also had the longest interval between first appearing symptoms and diagnosis. They had lower severity. It must, however, be noted that only 7.1% of ASD patients and 11.7% of all patients came directly to the psychiatrist. This indicates a need for greater public education about child mental health issues. Among the no diagnosis group, 91.3% of patients are referred for reasons such as no abnormalities on clinical and laboratory tests, stomach pain with no cause, excessive crying, and being uncooperative. Daley found that only 6/37 patients in the study went to a tertiary institute for a second opinion. A recent study found that 55.7% of patients came referred to NIMHANS whereas 44.3% came directly. They found that 90.5% of patients received their first diagnosis at the institute. Only 66.2% of patients received a professional consultation prior to reaching NIMHANS. In contrast, the present study finds that 10.5% of patients received their first diagnosis at the institute and 89.5% had a prior diagnosis.
Among ASD patients in the present study, 82% came from the urban area and 18% from the rural area. There was no significant association between diagnosis of ASD and place of residence. A study from 4 metropolitan cities found a higher prevalence in urban areas. A study from Northwest India found a higher prevalence among the rural population along with LSES. [Table 6] shows that there was a significant association between severity and living in the urban area. Urban patients had a higher severity level 2 and 3 (92.3% and 100%) whereas rural patients had higher level 1 severity (66.7%) than urban patients. The higher severity in the urban population may reflect the disproportionate number of patients coming from the surrounding urban area. In addition, there may be a problem of distance to be traveled by rural patients. There are greater facilities available for patients in urban areas along with a greater awareness of ASD and its manifestations. Studies about risk factors have found that residence in capital cities and large metropolitan cities along with their suburbs caused a higher risk profile for ASD.,
There was a highly significant association between IQ and a diagnosis of ASD [Table 7]. The mean IQ of ASD patients (93.2; n = 28) was significantly lesser than both mean IQ in other psychiatric conditions (106.58; n = 52 excluding 7 ID patients) and no diagnosis (110; n = 15). The lack of association between IQ and severity [Table 8] could be because there were no patients with comorbid ID in this study. A Swedish study found that out of 55 ASD patients, 30 had an IQ <50, 14 had an IQ in the 50–70 range, and 11 had an IQ >70. They found only four patients with an IQ above 100. Reduced cognitive ability is a feature of ASD. In most cases of ASD with comorbid ID, the ID is moderate to severe. Separating severe ID from ASD is difficult. ASD clusters in families but ID does not. There are greater RRBs in cases of ASD + ID, and IQ is a predictor for functional outcome with lower IQ predicting adverse outcome. Another study found that IQ varies with severity and has a relationship with symptomatology. Decreasing IQ predicts social disconnection, decreasing empathy but increased RRBs, and sensory seeking. Increasing IQ predicts special abilities, sound sensitivity, and unusual fears. A study from the USA found that the mean verbal IQ in ASD patients was 68.4 ± 29 and mean nonverbal IQ was 83.3 ± 26.6. In the present study, there was a lower IQ associated with ASD. Even though all patients had an IQ >70, there were still 9/28 patients with a level 3 severity of ASD. An Indian study found the mean IQ among ASD patients was 66.62.
The mean CARS score for the overall sample is 17.03, with a median of 16. The highest mean CARS score (33.2) was seen in ASD as expected [Table 9]. A study found that there were 3 factors to CARS: SI impairment, negative emotionality, and distorted sensory response. Chlebowski et al. recommended a cutoff of 25.5 which was adopted for this study. The present study found that all ASD patients scored higher than 25.5. None of the patients screened were misdiagnosed by CARS at the recommended cutoff. There were no false positives or false-negatives. It is to be noted, however, that SPCD, schizophrenia, and ID had high scores on CARS (though <25.5). This is probably because these conditions also have a neurodevelopmental basis and share some common signs and symptoms. Among the individual questions of CARS abnormal responses were seen in 100% of cases relating to people, imitation, emotional response, change adaptation, and nonverbal communication. The question regarding general clinical impression lends value to the clinician's impression. All but one patient had abnormalities in body use impairments and verbal communication questions.
|Table 7: Association between intelligence quotient and psychiatric diagnosis|
Click here to view
|Table 8: Association of intelligence quotient with severity of autism spectrum disorder|
Click here to view
|Table 9: Mean Childhood Autism Rating Scale score of all patients screened|
Click here to view
| Conclusion|| |
ASD was diagnosed in 4.66% of the patients screened in this study. Children are taking longer than recommended for optimal outcome to receive a diagnosis. Girls were brought for consultation with the pediatrician later than boys. There is a male preponderance in ASD. ASD was found higher in MSES and HSES families. Living in urban areas predicted higher severity. IQ was lesser than for other conditions (excluding ID) in ASD. CARS is a good screening and diagnostic instrument. Further research is essential on a wider scale of sociodemographic variables and clinical structured interviews.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). 5th
ed.. Washington, DC: American Psychiatric Association; 2013.
Kopetz PB, Lee ED. Autism worldwide: Prevalence, perception, acceptance, action. J Soc Sci 2012;8:196-201.
Frazier TW, Youngstrom EA, Embacher R, Hardan AY, Constantino JN, Law P, et al
. Demographic and clinical correlates of autism symptom domains and autism spectrum diagnosis. Autism 2014;18:571-82.
Rai D, Lewis G, Lundberg M, Araya R, Svensson A, Dalman C, et al
. Parental socioeconomic status and risk of offspring autism spectrum disorders in a Swedish population-based study. J Am Acad Child Adolesc Psychiatry 2012;51:467-760.
Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, et al
. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 2016;540:423-7.
Ansel A, Rosenzweig JP, Zisman PD, Melamed M, Gesundheit B. Variation in gene expression in autism spectrum disorders: An extensive review of transcriptomic studies. Front Neurosci 2016;10:601.
Rossignol DA, Genuis SJ, Frye RE. Environmental toxicants and autism spectrum disorders: A systematic review. Transl Psychiatry 2014;4:e360.
Rzhetsky A, Bagley SC, Wang K, Lyttle CS, Cook EH Jr., Altman RB, et al
. Environmental and state-level regulatory factors affect the incidence of autism and intellectual disability. PLoS Comput Biol 2014;10:e1003518.
Hsiao EY, McBride SW, Hsien S, Sharon G, Hyde ER, McCue T, et al
. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 2013;155:1451-63.
Zerbo O, Qian Y, Yoshida C, Grether JK, Van de Water J, Croen LA. Maternal infection during pregnancy and autism spectrum disorders. J Autism Dev Disord 2015;45:4015-25.
Mahic M, Mjaaland S, Bøvelstad HM, Gunnes N, Susser E, Bresnahan M, et al
. Maternal immunoreactivity to herpes simplex virus 2 and risk of autism spectrum disorder in male offspring. Imperiale MJ, editor. mSphere 2017;2:e00016-17.
Zatkova M, Bakos J, Hodosy J, Ostatnikova D. Synapse alterations in autism: Review of animal model findings. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2016;160:201-10.
Padmanabhan A, Lynch CJ, Schaer M, Menon V. The default mode network in autism. Biol Psychiatry Cogn Neurosci Neuroimaging 2017;2:476-86.
Sadock BJ, Sadock VA, Ruiz P. Kaplan and Sadock's Comprehensive Textbook of Psychiatry. 10th
ed..Philadelphia: Wolters Kluwer Health; 2017.
Malhotra S, Vikas A. Pervasive developmental disorders: Indian scene. J Indian Assoc Child Adolesc Ment Heal 2005;1:5.
Poovathinal SA, Anitha A, Thomas R, Kaniamattam M, Melempatt N, Anilkumar A, et al
. Prevalence of autism spectrum disorders in a semiurban community in south India. Ann Epidemiol 2016;26:663-500.
Kommu JV, Gayathri K R, Srinath S, Girimaji SC, P Seshadri S, Gopalakrishna G, et al
. Profile of two hundred children with Autism Spectrum Disorder from a tertiary child and adolescent psychiatry centre. Asian J Psychiatr 2017;28:51-6.
Singh T, Sharma S, Nagesh S. Socio-economic status scales updated for 2017. Int J Res Med Sci Int J Res Med Sci 2017;55:3264-7.
Mick K. Diagnosing Autism: Comparison of the Childhood Autism Rating Scale (CARS) and the Autism Diagnostic Observation Schedule (ADOS); 2005. Available from: http://hdl.handle.net/10057/439
. [Last accessed on 2020 Apr 20].
Chlebowski C, Green JA, Barton ML, Fein D. Using the childhood autism rating scale to diagnose autism spectrum disorders. J Autism Dev Disord 2010;40:787-99.
Achenbach TM, Ruffle TM. The child behavior checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatr Rev 2000;21:265-71.
Juneja M, Mukherjee SB, Sharma S, Jain R, Das B, Sabu P. Evaluation of a parent-based behavioral intervention program for children with autism in a low-resource setting. J Pediatr Neurosci 2012;7:16-8. [Full text]
National Consultation Meeting for Developing IAP Guidelines on Neuro Developmental Disorders under the aegis of IAP Childhood Disability Group and the Committee on Child Development and Neurodevelopmental Disorders, Dalwai S, Ahmed S, Udani V, Mundkur N, Kamath SS, et al
. Consensus statement of the Indian academy of pediatrics on evaluation and management of autism spectrum disorder. Indian Pediatr 2017;54:385-93.
Daley TC. From symptom recognition to diagnosis: Children with autism in urban India. Soc Sci Med 2004;58:1323-35.
Sheldrick RC, Maye MP, Carter AS. Age at first identification of autism spectrum disorder: An analysis of two US surveys. J Am Acad Child Adolesc Psychiatry 2017;56:313-20.
Mayes SD, Calhoun SL. Impact of IQ, age, SES, gender, and race on autistic symptoms. Res Autism Spectr Disord 2011;5:749-57.
Gillberg C, Cederlund M, Lamberg K, Zeijlon L. Brief report: “the autism epidemic”. The registered prevalence of autism in a Swedish urban area. J Autism Dev Disord 2006;36:429-35.
Chakrabarti S, Fombonne E. Pervasive developmental disorders in preschool children. JAMA 2001;285:3093.
Baron-Cohen S, Lombardo MV, Auyeung B, Ashwin E, Chakrabarti B, Knickmeyer R. Why are autism spectrum conditions more prevalent in males? PLoS Biol 2011;9:e1001081.
Frazier TW, Hardan AY. Equivalence of symptom dimensions in females and males with autism. Autism 2017;21:749-59.
Halladay AK, Bishop S, Constantino JN, Daniels AM, Koenig K, Palmer K, et al
. Sex and gender differences in autism spectrum disorder: Summarizing evidence gaps and identifying emerging areas of priority. Mol Autism 2015;6:36.
Naik US. Clinical practice guidelines for practice parameters or childhood autism. IPS Clin Pract Guidel 2008;p 210-37.
Raina SK, Kashyap V, Bhardwaj AK, Kumar D, Chander V. Prevalence of autism spectrum disorders among children (1-10 years of age)-findings of a mid-term report from Northwest India. J Postgrad Med 2015;61:243-6.
] [Full text]
Srinath S, Girimaji SC, Gururaj G, Seshadri S, Subbakrishna DK, Bhola P, et al
. Epidemiological study of child adolescent psychiatric disorders in urban & amp; rural areas of Bangalore, India. Indian J Med Res 2005;122:67-79.
Thomas P, Zahorodny W, Peng B, Kim S, Jani N, Halperin W, et al
. The association of autism diagnosis with socioeconomic status. Autism 2012;16:201-13.
George B, Padman MS, Nair MK, Leena ML, Russell PS. CDC Kerala 12: Sociodemographic factors among children (2–6 y) with autism – A case control study. Indian J Pediatr 2014;81 Suppl 2:S129-32.
Froehlich-Santino W, Londono Tobon A, Cleveland S, Torres A, Phillips J, Cohen B, et al
. Prenatal and perinatal risk factors in a twin study of autism spectrum disorders. J Psychiatr Res 2014;54:100-8.
Gillberg C, Steffenburg S, Schaumann H. Autism-epidemiology: Is autism more common now than 10 years ago? Br J Psychiatry 1991;158:403-9.
Nazeer A, Ghaziuddin M. Autism spectrum disorders: Clinical features and diagnosis. Pediatr Clin North Am 2012;59:19-25, ix.
Huerta M, Bishop SL, Duncan A, Hus V, Lord C. Application of DSM-5 criteria for autism spectrum disorder to three samples of children with DSM-IV diagnoses of pervasive developmental disorders. Am J Psychiatry 2012;169:1056-64.
Singhi P, Malhi P. Clinical and neurodevelopmental profile of young children with autism. Indian Pediatr 2001;38:384-90.
Russell PS, Daniel A, Russell S, Mammen P, Abel JS, Raj LE, et al
. Diagnostic accuracy, reliability and validity of childhood autism rating Scale in India. World J Pediatr 2010;6:141-7.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]