ABSTRACT
Autism spectrum disorder (ASD) affects more than 80,000 children under the age of 18 in Algeria, making it a major public health problem. It is characterized by communication abnormalities, restricted and stereotyped behaviours and resistance to change. To date, scientific publications on autism in Algeria are very rare. This study proposes to report the clinical and paraclinical profiles of ASD children or young adults in an Algerian population, as well as the prenatal, perinatal and postnatal factors associated with ASD. We conducted an ambidirectional cohort study (retrospective and prospective) on 186 persons (143 boys and 43 girls) with a diagnosis of ASD who ranged in chronological age from 3 to 25 years (mean = 7 years 8 months; standard deviation = 3 years 9 months). Data were collected from medical records and patients interviews. The ASD diagnosis was carried out according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, revised, to the Diagnostic and Statistical Manual of Mental Disorders‐5th Ed (DSM‐5) criteria, the Childhood Autism Rating Scale (CARS), Autism Diagnostic Interview‐Revised and Autism Diagnostic Observation Schedule. Insomnia (36.6%) and attention‐deficit/hyperactivity disorder (13%) were the main comorbidities associated with autism. Most of the children (63.4%) were treated following the Treatment and Education of Autistic and Related Communication Handicapped Children. The rate of prenatal, perinatal and postnatal risk factors was registered among the ASD population. The clinical features and comorbidities of autism present among the study group were similar to findings in individuals with ASD in other parts of the world.
Keywords: ambidirectional study, autism, epidemiology
This study is the first to report clinical and paraclinical profiles of autism spectrum disorder (ASD) in Algeria, as well as the prenatal, perinatal and postnatal factors. Findings were similar to other countries. The study highlights the need for increased awareness and training to improve early recognition of ASD in Algeria.
1. Introduction
Autism spectrum disorder (ASD) is a group of complex developmental disorders characterized by deficits in social communication and restricted repetitive patterns of behaviour. The severity of these symptoms varies for each patient. ASD begins in early childhood and affects around 1% of the worldwide population (Myers et al. 2019) Multiple factors have been identified as causes of ASD, such as environmental factors and genetic factors (Gardener et al. 2011).
The current situation of ASD and its prevalence in African countries remain poorly investigated and require further research. In North Africa, and particularly in Algeria, there is a significant lack of accurate data on the prevalence of autism. This absence of information limits the ability of health authorities and healthcare professionals to implement adapted policies for screening, diagnosis and care.
The prevalence of ASD among children with developmental disorders in Egypt and Tunisia was documented as 33.6% and 11.5%, respectively (Bakare and Munir 2011; Seif Eldin et al. 2008).
In Algeria, very few studies have been conducted on ASD. The prevalence of ASD during the period of 1997–2003 was rated at 38% of the total diagnoses among developmental disorders established in daytime hospitalization of Algiers (OuldTaleb 2006).
In 2018, Bekkou reported the epidemiological profile of ASD in the eastern region of Algeria (Bekkou et al. 2018). The Ministry of Health Population and Hospital Reform reported the same year that the evolution of ASD increased to be 1 case in 150 births and that more than 4000 out of 80,000 autistic children have been taken care of in health facilities nationwide (Algeria Press Service 2019). Therefore, research on ASD in Algeria remains limited. Official statistics are scarce, and awareness of ASD among healthcare professionals and the general population is still evolving (Expertise France 2019). This study aims to fill this gap by providing data on the prevalence of comorbidities in ASD, diagnostic practices and challenges faced in Algeria, thereby contributing to the advancement of knowledge on ASD in under‐studied regions.
The aim of the current study is (1) to describe the clinical and paraclinical profiles of ASD individuals in an Algerian population and (2) to investigate the association between prenatal and perinatal factors and the development of ASD in this population.
2. Methods
2.1. Type of Study
The present study is an ambidirectional cohort study conducted on a clinical population of children, adolescents and two adults with ASD and their parents.
Retrospective study data were collected from autistic persons' medical records from 2013 to 2018.
Prospective data were collected from medical records and parents' interviews, from January 2019 until January 2021.
2.2. Participants
A total of 186 individuals s diagnosed with ASD were enrolled from four different facilities in Algiers. Two are health facilities: Child and Adolescent Psychiatry Hospital of Drid Hocine (Algiers) and Neurology Service of the Specialized Hospital Establishment Pr Abdelkader Boukhroufa, Algiers. The two remaining establishments were the specialized educational and teaching institution ‘El RAFAHIYA LI TAWEHOUD’ for children in special need and the Association of the Williams–Beuren syndrome, Algiers.
The diagnosis of autism for all the persons included in the study was established by highly trained physicians (psychiatrists, paediatricians and neurologists) and non‐physicians (psychologists and speech therapists) using the DSM‐IV‐TR or DSM‐V (Diagnostic and Statistical Manual of Mental Disorders Fourth Edition Revised and Fifth Edition) clinical criteria. In addition to the clinical diagnosis, different diagnostic tools were used for distinguishing ASD from other developmental disorders and rating the autism symptom severity. Professionals either used a single diagnosis tool or a combination of two. Some others used a screening tool along with a diagnosis before making the final diagnosis.
Screening tools used were the Modified‐Checklist for Autism in Toddlers (M‐CHAT) (Baron‐Cohen et al. 1992); diagnosis tools used were the Childhood Autism Rating Scale (CARS) (Saemundsen et al. 2007), Evaluation of autistic behaviour (ECA) (Sauvage 1984), Autism Behavior Checklist (ABC) (Krug et al. 1980), Autism Diagnostic Observation Schedule (ADOS) (Lord et al. 2010), Autism Diagnostic Interview‐Revised (ADI‐R) (Lord et al. 1994) and the Behavioral Summarized Evaluation (BSE) (Barthelemy et al. 1990).
2.3. Therapy Approach
ASD affected individuals recruited were treated following different psychological approaches:
Therapy of exchange and development (TED) (Lelord et al. 1978).
Applied behaviour analysis (ABA) (Lovaas 1960).
Treatment and Education of Autistic and Related Communication Handicapped Children (TEACCH) (Schopler 1997).
The Picture Exchange Communication System (PECS) (Bondy and Frost 1994) is an intervention on ABA.
2.4. Inclusion and Exclusion Criteria
2.4.1. Inclusion Criteria
All individuals included in the study had a diagnosis of ASD according to clinical criteria of the Diagnostic and Statistical Manual of Mental Disorders using the DSM‐IV‐TR or DSM‐V, depending on the diagnostic period. The children included in the study were aged at least 3 years.
One of the methodological challenges of this study lies in the use of the DSM‐IV‐TR and DSM‐5 criteria. To avoid inconsistencies in diagnoses, we applied a systematic conversion of old diagnoses (DSM‐IV‐TR) into DSM‐5 equivalents using recommendations established by previous studies (Volkmar and McPartland 2014). This conversion was performed based on rigorous clinical criteria and by cross‐checking evaluation results with the ADOS‐2 and ADI‐R, which are recognized as international standards in ASD diagnosis.
This approach allows for the harmonization of diagnostic classifications and ensures continuity between patients diagnosed before and after 2018. Moreover, we considered the specific differences introduced by the DSM‐5, particularly the removal of subcategories and the new conceptualization of the disorder in severity levels. Among the 80 retrospective cases, 67 (83.8%) met DSM‐5 diagnostic criteria directly based on standardized tools and available clinical data. The remaining 13 cases (16.3%) initially showed partial compatibility but were reviewed in detail by clinicians to assess whether they fulfilled DSM‐5 core domains, including social communication deficits and restricted repetitive behaviours. Following this clinical reassessment, all 13 cases were confirmed as meeting DSM‐5 criteria for ASD and were therefore included in the final analysis.
The use of multiple diagnostic tools in this study aims to ensure a comprehensive and accurate assessment of ASD. Each tool used (M‐CHAT, CARS, ECA, ABC, ADOS, ADI‐R and BSE) targets specific aspects of the disorder, allowing for a finer analysis of patient profiles. This combined approach is consistent with international recommendations that advocate for the use of multiple instruments to increase the validity and reliability of diagnoses (Lord et al. 2012).
Several of the tools used in this study were adapted and validated for Arabic‐speaking populations, including in Algeria. The M‐CHAT‐R/F, CARS‐2 and ECA were administered in their Modern Standard Arabic (MSA) or French versions, depending on the caregiver's language preference (Mneimneh et al. 2016; Alboali et al. 2020; El Fakir et al. 2021). To date, no validated Algerian Arabic (Darja) versions of these instruments exist. However, minor verbal adaptations were occasionally made during administration to ensure caregiver comprehension, especially for low‐literacy populations. These modifications were limited to phrasing and did not alter the conceptual content of the items.
The ADOS and ADI‐R, on the other hand, are internationally standardized behavioural tools recognized for their language independence, making them suitable for different populations (Lord et al. 2018). The study also included trained professionals to ensure that test administration adhered to international standards.
The diagnostic tools selected in this study were chosen to complement each other rather than overlap. Each tool evaluates a specific dimension of ASD:
The M‐CHAT is an early screening tool for young children.
The CARS and ECA allow for an in‐depth behavioural assessment of ASD.
The ADOS and ADI‐R are global references for precise clinical diagnosis.
The ABC and BSE provide additional elements to better characterize cognitive and behavioural profiles.
The integration of these tools enables a more holistic analysis and reduces the risk of bias related to the use of a single instrument. This multi‐evaluation approach is recommended for studies involving heterogeneous populations and ensures better comparability of results (Huerta and Lord 2012).
2.4.2. Exclusion Criteria
We excluded children with severe or profound intellectual disability in whom a definitive diagnosis of autism could not be made.
2.5. Study Design
We conducted a retrospective study based on collecting information from medical records of children who had visited the facilities cited before the year of 2018. A prospective study was conducted on other children during the period of 2019–2021, in which information was collected from both medical records and an interview with the parents or caregivers of the children (see Figure 1).
FIGURE 1.
Diagram representing the study design followed for the study.
2.6. Data Collection
Based on several examples from the literature, we established a questionnaire addressed to the parents and caregivers comprising three sections (Data S1). The first section was related to personal data of the children and their parents including age, gender and demographic details. We also collected information on the presence or absence of consanguinity and a history of psychological disorders.
The second section of the questionnaire included two parts: The first part concerned clinical information: age of first diagnosis, ASD screening and assessment tool, ASD severity along with the presence of genetic pathologies or syndromes linked to syndromic ASD and behaviour therapy taken. In the second part, we looked at the presence of comorbidities associated with autism, such as neurological disorders (sleep disorder, epilepsy and visual disorders), developmental or intellectual disability, psychiatric disorders such as attention‐deficit/hyperactivity disorder (ADHD) and digestive disorders. Furthermore, electroencephalography (EEG), brain imaging and m agnetic resonance imaging (MRI) were also registered.
In order to investigate the perinatal and prenatal factors, we devoted the third part of the questionnaire to collect information on the medical condition of the mother regarding pregnancy: thyroid disorders, polycystic ovary syndrome, high blood pressure, diabetes, medical drug consumption, maternal age at labour and complications during and after delivery of the child with an ASD.
Questionnaires were filled with information from the children's medical records for the retrospective study and from medical records and parent's interview for the prospective study.
2.7. Statistical Methods
The statistical analysis was done using Statistical Package for Social Sciences software for Windows, Release 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. USA). A one‐way analysis of variance (ANOVA) was used to determine whether there was a relation of dependence between the maternal age and the severity of the disorder using R software.
2.8. Ethical Considerations
This work has been carried out with respect to confidentiality and anonymity during the processing of the children's data. Informal consent from parents or legal guardians of participants for the prospective study was obtained after the nature of the procedures had been fully explained. All participants have provided written consent and the present study was approved by the local ethics committee: ‘Algerian National Committee for the Evaluation and Programming of University Research’.
3. Results
Eighty medical records were registered from the retrospective study, and 106 persons were enrolled in the prospective study. The number of cases registered each year is represented in Figure 2.
FIGURE 2.
Number of cases registered each year in the retrospective (a) and prospective (b) studies.
3.1. Socio‐Demographic Characteristics
3.1.1. The Distribution of the Participants According to Their Age
Age of children or young adults from the retrospective study ranged from 3 years to 25 years old, with a mean (± standard deviation) of 7.3 ± 3.2 years. The mean age of 7.5 ± 4.7 years for the females was not statistically different from that of 7.2 ± 2.6 years for the males according to students T test (p = 0.823) (Table 1a).
TABLE 1.
Age of participants, statistical parameters.
| 1a: Retrospective study | |||
|---|---|---|---|
| Age | Female | Male | Total |
| Number | 19 | 61 | 80 |
| Mean | 7.5 | 7.2 | 7.3 |
| Standard deviation | 4.7 | 2.6 | 3.2 |
| Median | 6.0 | 7.0 | 7.0 |
| 1st quartile | 5.0 | 6.0 | 5.0 |
| 3rd quartile | 8.0 | 8.0 | 8.0 |
| Minimum | 3.0 | 3.0 | 3.0 |
| Maximum | 25.0 | 16.0 | 25.0 |
| 1b: Prospective study | |||
|---|---|---|---|
| Age | Female | Male | Total |
| Number | 24 | 82 | 106 |
| Mean | 8.5 | 8.0 | 8.1 |
| Standard deviation | 4.8 | 4.2 | 4.3 |
| Median | 7.5 | 7.0 | 7.0 |
| 1st quartile | 5.3 | 5.0 | 5.0 |
| 3rd quartile | 10.5 | 9.8 | 10.0 |
| Minimum | 3.0 | 3.0 | 3.0 |
| Maximum | 20.0 | 22.0 | 22.0 |
The mean age for the children or young adults in the prospective study was 8.1 ± 4.4 years with a range of 3–22 years. The mean age of 8.5 ± 4.8 years for the females was not different from that of the males: 8.0 ± 4.2 years (p = 0.647) (see Table 1b and Figure 3).
FIGURE 3.
Distribution of ages in the retrospective (a) and prospective (b) studies.
3.1.2. The Distribution of the Participants According to Their Gender
Retrospective study registered 61 males (76.3%) and 19 females (23.8%).
Male predominance was also observed in the prospective study with 82 males (77.4%) and 24 females (22.6%). Overall, there were 143 males and 43 females, giving a male/female ratio of 3.3 (143/43). Statistical analysis showed no significant difference between both studies.
3.1.3. Origin of Participants
Participants included in the present study were from all Algerian provinces (Table 2). Statistical analysis showed no significant difference between both studies.
TABLE 2.
Number of ASD cases according to their origin, from retrospective and prospective study. Chi‐square test 2 × 3 (fusion of 2 last cells: p = 0.16).
|
Retrospective N = 80 |
Prospective N = 106 |
|||
|---|---|---|---|---|
| Geographic region of origin | n | % | n | % |
| Northern region | 74 | 92.4 | 89 | 84.0 |
| Eastern region | 2 | 2.5 | 9 | 8.5 |
| Southern region | 3 | 3.8 | 7 | 6.6 |
| Western region | 1 | 1.3 | 1 | 0.9 |
3.1.4. Consanguinity
In the retrospective study, consanguinity was reported for six different children (7.5%) with third‐degree consanguineous parents. Among the 106 ASD children of the prospective study, a total of 15 (14.2%) had third‐degree relation parents and two (1.9%) distantly related parents. Statistical analysis showed no significant difference between both studies (chi‐square, p = 0.08).
3.1.5. History of Psychological Disorders in the Family
We defined psychiatric history as the presence of a psychiatric diagnosis among children's parents or their maternal/paternal aunts and uncles.
In the retrospective study we report that 12 children (15%) had an affected relative. Nine children had a history of psychiatric disorders from the paternal side and 3 children from the maternal side. Autism was reported in 5 of these cases (4 children from paternal and one from maternal side), and the remaining 7 children were reported as being affected by intellectual disability from the maternal and paternal side (5 children from paternal and two from maternal side).
Out of the prospective cases, 10 children (9.4%) had at least a family member diagnosed with a mental disorder, 5 of whom were paternal relatives and 5 were maternal relatives, all with intellectual disability, and two children had a father diagnosed with depression.
3.2. Clinical Data Results
3.2.1. Age of Diagnosis
We defined the age of diagnosis for children as the chronological age of the child, the year of their entry into the facility for an ASD diagnosis.
In the retrospective study, no child was diagnosed as having autism before the age of 3 years. About 30% of children were diagnosed before the age of 4 years, making the mean age of ASD diagnosis for the retrospective study 5.6 ± 2.1 years. The oldest child at diagnosis was aged 13 years (see Table 3a).
TABLE 3.
Age at diagnosis, statistical parameters.
| 3a: Retrospective study | |||
|---|---|---|---|
| Age diagnosis | Female | Male | Total |
| Number | 9 | 35 | 44 |
| Mean | 6.8 | 5.3 | 5.6 |
| Standard deviation | 2.9 | 1.7 | 2.1 |
| Median | 5.0 | 5.0 | 5.0 |
| 1st quartile | 5.0 | 4.0 | 4.0 |
| 3rd quartile | 7.0 | 6.0 | 6.3 |
| Minimum | 4.0 | 3.0 | 3.0 |
| Maximum | 13.0 | 10.0 | 13.0 |
| 3b: Prospective study | |||
|---|---|---|---|
| Age diagnosis | Female | Male | Total |
| Number | 24 | 73 | 97 |
| Mean | 5.5 | 4.0 | 4.4 |
| Standard deviation | 3.5 | 2.5 | 2.8 |
| Median | 4.0 | 3.0 | 4.0 |
| 1st quartile | 3.0 | 2.0 | 2.5 |
| 3rd quartile | 5.5 | 5.0 | 5.0 |
| Minimum | 2.0 | 1.0 | 1.0 |
| Maximum | 14.0 | 13.0 | 14.0 |
From the prospective study, about 70% of children were diagnosed before the age of 4 years; the mean age of diagnosis was 4.0 ± 2.5 years. This mean is significantly different from that of the retrospective study (Student's t‐test, p = 0.0086) (see Table 3b).
3.2.2. Diagnosis Assessment Tool
Most practitioners administered CARS for diagnosing and rating ASD in the retrospective study, in 60% (n = 48) of the population studied. ECA was registered in 21.3% (n = 17). CARS was combined with M‐CHAT for 4 children (5%) and with BSE for one child (1.3%). ADOS and ADI‐R were used for diagnosing one child (1.3%).
Most children from the prospective study were administered the combination of three tools: CARS, M‐CHAT and BSE; this combination has been used for the diagnosis and the rating of 40% of children (n = 30). CARS and M‐CHAT were both used for 29.3% of the population (n = 22). CARS alone has been used for 16% of the children (n = 12).
One child was diagnosed clinically by DSM IV‐RT, and this child had Asperger syndrome.
DSM‐V was used in all retrospective cases (n = 80; 100%) and in 99.1% of the prospective cases (n = 105); the remaining case was diagnosed using DSM‐IV‐RT; this case was diagnosed prior to 2013.
The screening tools M‐CHAT and the combination of ADI‐R/ADOS were the least used screening tools with a rate of 5.7% (n = 6) use for M‐CHAT and 3.8% (n = 4) for ADI‐R/ADOS.
3.2.3. ASD Rating
The ASD scaling tests allowed us to divide the studied population into three groups according to the ASD symptoms severity: mild autism, moderate autism and severe autism.
According to retrospective results, 27 children (33.8%) (21 males [26.3%] and 6 females [7.5%]) out of 80 were ranked as having mild autism, 27 (33.8%) (20 males [25%] and 7 females [8.8%]) as having moderate autism, and 26 (32.5%) (20 males (25%) and 6 females (7.5%)) as having severe autism.
In the prospective study, 25 children (23.6%) (21males (19.8%) and 4 females (3.8%)) out of 106 were ranked as having mild autism, 41 (38.6%) (27 males (25.5%) and 14 females (13.2%)) as having moderate autism, and 40 (37.7%) (34 males [32%] and 6 females [5.6%]) as having severe autism. For ASD rating, in both studies, there was no significant difference (p = 0.12 [mild autism]; p = 0.50 [moderate autism]; p = 0.46 [severe autism]) (Figure 4). Statistical analysis showed no significant difference between both studies (chi‐square, p = 0.31).
FIGURE 4.
Autism severity distribution in the retrospective (a) and prospective (b) studies.
3.2.4. Syndromic ASD
Among 80 medical records, only one case was reported having syndromic autism, that is, phenylketonuria.
Four different pathologies were identified in the ASD children enrolled in the prospective study. One child had phenylketonuria, one had San Filippo syndrome, another had Prader–Willi syndrome, and one child had tuberous sclerosis complex. None of the children from both studies had fragile X syndrome.
3.2.5. Comorbidities
The diagnosis of comorbidities was reported with children's parents, clinical neuropsychiatric observation, DSM‐IV‐R and DSM‐V criteria. Psycho‐Educational Profile Third Edition was used in some cases to evaluate the general cognitive development of children and detect any intellectual deficiency.
In the retrospective study, insomnia and intestinal disorders (constipation) were the main comorbidities registered among the studied cases, with a rate of 28.8% for insomnia and 8.8% for intestinal disorders. ADHD and epilepsy were present in 12 and 5 children, respectively, 15% and 6.3%. Visual disorders and intellectual disability were the least registered comorbidities, with a presence rate of 2.5% and 1.3% each, respectively.
In the prospective study, insomnia (n = 45, 42.5%) and visual disorders (n = 20, 18.9%) were the main comorbidities associated with autism. Intestinal disorders (constipation) were present in 9.4% (n = 10), and ADHD was registered for 12 children (11.3%). Only 5 children (4.7%) had epilepsy or seizures, and none was diabetic.
Not all children were subject to an intellectual quotient. Four children had mental retardation among 20 evaluated.
Comorbidities rates reported in both studies are represented in Table 4. There was a significant difference in the comorbidities between the two studies (p < 0.001).
TABLE 4.
Comorbidities registered in the retrospective and prospective study. Chi‐square test, p < 0.001.
|
Retrospective N = 80 |
Prospective N = 106 |
|||
|---|---|---|---|---|
| Comorbidities | n | % | n | % |
| Intellectual disability | 1 | 1.3 | 11 | 10.4 |
| Epilepsy | 5 | 6.3 | 5 | 4.7 |
| Insomnia | 23 | 28.8 | 45 | 42.5 |
| ADHD | 12 | 15.0 | 12 | 11.3 |
| Strabismus | 0 | 0.0 | 10 | 9.4 |
| Myopia | 2 | 2.5 | 10 | 9.4 |
| Intestinal disorders | 7 | 8.8 | 10 | 9.4 |
| No comorbidities | 30 | 37.5 | 3 | 3.0 |
3.2.6. ASD Behaviour Therapy Approach
Table 5 shows the distribution of each therapy approach used in both studies. There was a significant difference in the therapy approaches in the two studies (p < 0.001). The TEACCH method had a very similar distribution in the two studies. On the other hand, children with no therapy approach increased from 1% to 16% between the retrospective and the prospective study. Finally, the TED approach represented 21% of all approaches in the retrospective study and was absent in the prospective study.
TABLE 5.
Therapy approaches used in both studies. Chi‐square test (fusion of the last 5 cells): p < 0.001.
| Retrospective | Prospective | |||
|---|---|---|---|---|
| N = 80 | N = 106 | |||
| Applied therapeutic approach | N | % | N | % |
| TEACCH | 50 | 62.5 | 68 | 64.2 |
| No approach | 1 | 1.3 | 17 | 16.0 |
| TED | 17 | 21.3 | 0 | 0.0 |
| TEACCH + ABA + PECS | 7 | 8.9 | 8 | 7.5 |
| TEACCH + ABA | 1 | 1.3 | 6 | 5.7 |
| ABA | 1 | 1.3 | 3 | 2.8 |
| TEACCH + PECS | 2 | 2.5 | 1 | 0.94 |
| PECS | 0 | 0.0 | 2 | 1.9 |
| ABA + PECS | 1 | 1.3 | 1 | 0.9 |
3.3. Paraclinical Data Results
3.3.1. Neurological Tests
In the retrospective study, EEG was performed for 51 children; 8 children's EEG was pathological, evoking irregular rhythms and/or the presence of epileptic patterns. MRI was clear for 23 children and evoked abnormalities for 3 different children: porencephaly, demyelinating lesions and hydrocephalus. The remaining 54 children had no MRI done.
Only 14 children had undergone a brain x‐ray scanner, and no abnormalities were detected.
In the prospective study, EEG was performed for 71 cases. EEG was pathological in 16 children, evoking abnormalities in favour of irregular rhythm and/or the presence of epileptic patterns.
MRI was clear for 48 children. Eight children had one of the following abnormalities: enlargement of grooves in the frontotemporal region, cerebellar hypoplasia, demyelinating lesions and leucoencephalopathy. Regarding brain scans, 26 children had no abnormalities detected; the remaining 80 children had never done a brain scanning test.
3.3.2. Genetic Tests
Only one child out of 80 in the retrospective records had a karyotyping, and no abnormalities were found.
In the prospective study, 15 children had a karyotyping done and only one of them, who was a male of 10 years old, presented the Robertsonian translocation 45,XY,der(13;14)(q10;q10). Fragile X test was performed for five children, and no mutation was detected in the FMR1 gene.
3.4. Prenatal, Perinatal and Postnatal Factors Associated With ASD
3.4.1. Maternal Age
Maternal age at birth of the affected child was registered in 175 medical records.
There was a slight decrease in the mothers' average age with the degree of autism severity. However, ANOVA analysis revealed that this change was not significant in both studies (p = 0.551).
3.4.2. Medical Condition of the Mother During Pregnancy
No cases of diabetes were reported in the retrospective study. The only medication consumed by mothers during pregnancy was paracetamol, consumed by one mother.
In the prospective study, gestational hypertension was present in only 2 (2.0%) mothers. Lastly, 4 (4.0%) mothers had gestational diabetes (Table 4). All mothers have declared consuming treatments under medical prescription and a medical follow‐up. Insulin was administered to treat gestational diabetes by the concerned cases. One out of 4 hypothyroidism cases consumed levothyroxine as a treatment.
Paracetamol along with antibiotic (amoxicillin) was consumed in 3 cases only. All mothers reported that these two treatments were for tonsillitis.
3.4.3. Complications During and After Labour
Caesarean delivery, gestational age lower than 36 weeks, fetal distress and postpartum haemorrhage were the complications registered. Caesarean delivery was the main prenatal risk recorded at 19.7% in both studies (Table 6).
TABLE 6.
Perinatal factors reported from both studies.
|
Retrospective N = 77 |
Prospective N = 98 |
|||
|---|---|---|---|---|
| Prenatal, perinatal and postnatal factors | n = 26 | % | n = 59 | % |
| Maternal medical condition | ||||
| Thyroid disorders | 3 | 3.9 | 11 | 11.2 |
| PCOS | 1 | 1.3 | 6 | 6.1 |
| Gestational hypertension | 4 | 5.2 | 2 | 2.0 |
| Gestational diabetes | 0 | 0.0 | 4 | 4.0 |
| Perinatal factors | ||||
| Caesarean delivery | 13 | 16.9 | 22 | 22.4 |
| Gestational age ≤ 36 weeks | 2 | 2.6 | 3 | 3.0 |
| Fetal distress | 2 | 2.6 | 7 | 7.1 |
| Postpartum haemorrhage | 1 | 1.3 | 4 | 4.0 |
Abbreviation: PCOS, polycystic ovary syndrome.
4. Discussion
The present study represents a first attempt to report the clinical and paraclinical profiles of an Algerian population with ASD, as well as some prenatal, perinatal and postnatal factors associated with autism spectrum disorder.
Our study used the developmental systems approach to analyse the factors influencing autism in the Algerian population. The theoretical framework of the developmental systems approach (Volkmar and McPartland 2014) supports the hypothesis that ASD results from an interaction between genetic and environmental factors. Our findings contribute to deciphering these influences within the specific context of Algeria. It provides a framework for understanding disparities in access to diagnosis and care according to the socioeconomic context of families. By adopting this approach, we can better interpret the results and propose recommendations adapted to the Algerian context.
We conducted an ambidirectional cohort study by collecting data from both a retrospective study based on medical record treatment and a prospective study based on medical records and parents' caregivers' interviews. The studied variables were designed according to the clinical aspects and probable risk factors of ASD from existing literature. Overall, all the variables desired for the cohort studies were found, which reflects the interest of experts in collecting information regarding autistic children in the facilities where the study was conducted.
We acknowledge that the use of multiple diagnostic tools with different levels of validation and standardization can influence results. However, to minimize these biases, we selected instruments that are either internationally recognized for their robustness (ADOS‐2 and ADI‐R) or validated and adapted into Arabic (M‐CHAT‐R/F, CARS‐2 and ECA). Furthermore, rigorous training of clinicians in administering these tests ensured consistent and reliable application in our study context (Huerta and Lord 2012).
Linguistic diversity in Algeria—particularly the coexistence of Arabic (Darja and Modern Standard Arabic), French and Berber—did present practical challenges during assessments. These included variations in caregiver comprehension of test items and child responses influenced by code switching or uneven language exposure. To address this, we adapted our approach by allowing flexible administration in the caregiver's dominant language (typically Arabic or French) and by clarifying items orally when needed. Although standardized tools do not fully account for this multilingual context, clinical judgement and cultural familiarity helped ensure reliable interpretation of responses.
Most of the results were similar in the two studies. Only three parameters were statistically different between the two studies: the age of diagnosis, the frequencies of comorbidities and the therapies. Globally, these differences result rather from an evolution of the practices than from methodological biases.
The decrease of the diagnosis age from 5.6 to 4.4 years between the two studies results certainly from an improved awareness of the medical community to ASD during this period. None of the cases reported in our study was diagnosed as having autism before the age of 3 years. It should be noted that, although the majority of parents of children with ASD have reported the age of onset to be prior to 24 months, they did not reach for a medical diagnosis before the age of 3 years. Parental concern emanates when restricted behaviour and language problems turn into a handicap that refrains the development of the child.
Diagnostic strategies registered in the current study were multidisciplinary; this goes in line with the universal recommendations (Mukherjee 2017). Overall, there was a similarity in the process of diagnosis for the prospective and retrospective studies. Although all clinicians from the different facilities followed the DSM criteria for the diagnosis of the disorder, a slight difference in the use of screening assessment tools was registered. A variation in diagnosis tools and in assessment used by clinicians to diagnose an Algerian ASD population was also observed in a previous study (Loumi et al. 2014).
Studies have shown that CARS is a reliable and stable indicator of autism in any child over 2 years of age as well as in adolescents (Geier et al. 2013) and that formal screening is more effective than relying on clinical judgement alone (Hsiao 2014).
The most common comorbidities in ASD children in the two studies were intellectual disability, insomnia and ADHD. Comorbidities found in our study, particularly intellectual disability and strabismus, corroborate with those associated with autism in previous studies (Lai et al. 2014).
The significant difference between the two studies was markedly due to a decrease in the ‘no comorbidity’ group decreasing from 37.5% to 3%, this indicating, similarly to the diagnosis of ASD, an effort to improve the clinical evaluation. These comorbidities can modulate the severity of autistic symptoms and impact the effectiveness of therapeutic interventions. For example, the presence of an anxiety disorder can reinforce social avoidance behaviours, whereas untreated ADHD can exacerbate attention and learning difficulties (Simonoff et al. 2008). These findings emphasize the importance of holistic care that integrates the treatment of comorbidities into intervention plans.
The main difference between the two studies was the absence of the TED approach in the prospective study and the increase of ‘no approach’ rising from 1.3% to 16%. In the prospective study, practitioners (psychologists and speech therapists) stopped using the TED approach; we have no clear explanation for this situation. It may be explained either by a feeling by practitioners or parents of lesser efficiency of TED or just by a change in practice due to different practitioners. Concerning the increase of the ‘no therapeutics’ group, we cannot exclude that this results from incomplete data collection. There is currently no consensus regarding which interventions are most effective for ASD treatment. TEACCH is a relatively common treatment option for individuals with autism (Green et al. 2006). The same findings were registered in our study regarding the widespread use of TEACCH for treating ASD individuals; however, given the fact that TEACCH was not exclusively followed by all the children recruited and the short duration of the intervention in some cases, the efficacy of this approach cannot be concluded.
A male predominance was observed in both studies; the male/female ratio was reported to be 3.2 and 3.4 for both studies, which matched the global estimation of the male/female ratio (Fombonne et al. 2011).
In our study, we observed ASD children or young adults from different regions of Algeria. The capital Algiers was the native province of the majority of ASD cases enrolled, which has probably to do with the availability of specialized structures in the capital. Lack of specialized facilities in other provinces represents a major issue for parents with affected children. It is also worth mentioning that this lack of autism specialized structures in other parts of the country obliges the parents to change their addresses to the capital Algiers for more appropriate intervention for their children.
Contrary to expectations, our study did not reveal a significant link between consanguinity and ASD. Several hypotheses can explain this observation: (1) a potential protective effect of other genetic or environmental factors in the studied population, (2) a sample that may not be large enough to detect this effect or (3) the need for more in‐depth genetic analysis to explore complex interactions between genes and environment. Further research incorporating genomic analyses would be necessary to better understand these dynamics (Al‐Salehi and Al‐Hifthy 2020). Consanguineous parents have a risk of 3.2% of having an ASD offspring (Mamidala et al. 2015). Belhadj et al. (2006) reported a 39.3% rate of consanguinity in 63 cases of ASD in a Tunisian population, and this is not consistent with our findings because only 21 out of 175 parents (12%) were consanguineous. This rate remains lower than the rate of inbreeding in the Algerian population, which is estimated to be at 38% (Fondation Nationale Pour la Promotion de la Santé et le Développement de la Recherche 2007). Another Tunisian case–control study (comparing 51 ASD cases with 40 controls) reported no significant association between consanguineous marriage and ASD diagnosis (Ben Jemaa et al. 2021). Although we did not identify any studies from Morocco directly examining parental consanguinity as a risk factor for ASD, one Moroccan publication on ASD demographics mentioned the presence of consanguinity descriptively but did not evaluate its statistical relationship with ASD risk (Tazi et al. 2020).
Our findings corroborate with those of Abdel Meguid, where consanguinity was not found to be a possible risk factor for ASD among an Egyptian population (Abdel Meguid et al. 2018). These regional data are consistent with our findings in Algeria, reinforcing the conclusion that consanguinity is not a major independent risk factor for ASD in North African populations.
Age of parents is considered a risk factor for the occurrence of ASD and the degree of severity. In 2010, the state‐of‐the‐art synthesis concluded that the frequency of autistic children increases weakly with the age of the father and mother (risk multiplied by 1.3 for mother over 35 and per 1.4 for father over 40). In our study, we note that there was a slight decrease in the mothers' average age with the degree of autism severity, although it did not reach significance.
Unfortunately, genetic analyses cannot be done in Algeria as a routine test due to the lack of specialized genetic laboratories on one hand and the high price of genetic tests on the other hand.
Several studies have investigated the relationship between prenatal, perinatal and postnatal factors and autism (Kolevzon et al. 2007), and their results showed that advanced maternal age, short gestation age, gestational hypertension and caesarean delivery were associated with increased risk of autism (Sandin et al. 2012). Although a high rate of caesarean delivery was recorded in our ASD population, the results of our study showed that the advanced age of the mother did not necessarily impact the severity of ASD. Gestational hypertension was the most frequent factor associated with ASD. Thus, it was still unclear whether these factors are causal or play a secondary role in the development of autism. Further studies are needed to verify our findings.
5. Conclusion
Our study gives an insight into the clinical and paraclinical profiles of an autistic population in Algeria. Our findings do not necessarily reflect the actual situation of ASD on the national territory. The retrospective study is a valuable method because we found mainly the same results in the retrospective study as in the prospective study. Several differences may be due to the evolution of the recruitment of children.
Clinical features and comorbidities of autism present among the study group were similar to findings in individuals with autism in other parts of the world.
Unfortunately, there is a huge lack of multi‐disciplinary centres in Algeria that can provide high‐quality intervention for autistic individuals. Experts in different fields who have experience in working with autistic children should assemble to establish a diagnosis and treatment consensus and better management of ASD individuals.
Significant efforts should be aimed at raising awareness among parents and caregivers for early diagnosis and therefore early and better intervention.
The findings of this study highlight the need to strengthen awareness and training actions to improve the early recognition of ASD in Algeria. In particular, we recommend:
The implementation of specialized training for healthcare professionals on the diagnosis and management of ASD.
The development of early intervention programmes based on behavioural and educational approaches adapted to the local context.
The promotion of awareness campaigns among the general public to reduce stigma and encourage families to seek early consultation.
The development of public policies dedicated to the social and educational inclusion of children with ASD (Dawson et al. 2010).
The Algerian National Autism Plan (2024–2029), developed through the PROFAS C+ cooperation programme and endorsed by the Council of Ministers in June 2025, defines four strategic axes, including national training for professionals, awareness campaigns and the creation of regional care centres. Our proposed actions are in line with these official priorities, particularly the emphasis on early screening, intersectoral coordination, and family support services (Expertise France 2019; Ministère de la santé Algérie 2023; El Watan 2025).
Author Contributions
Ourida Loumi had full access to all the data in the study and drafted the manuscript. Ourida Loumi and Christian R. Andres designed, contributed and performed the study by statistical analysis, reviewing the literature, writing and revising the manuscript and agreeing to publish the article. All authors have agreed to the published version of the manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1. Supporting Information.
Acknowledgements
We would like to thank all the autistic persons and their parents whose participation made this study possible. We thank Doctors Ameur El Khedoud Wahiba, Lougani, Tabari Mohamed and Meddad Faiza for their assistance in collecting biological samples and/or clinical data.
Loumi, O. , and Andres C.. 2025. “Contribution of an Ambidirectional Cohort Study on the Epidemiology of 186 Autism Spectrum Disorder Cases in an Algerian Population.” International Journal of Developmental Neuroscience 85, no. 5: e70036. 10.1002/jdn.70036.
Funding: The authors received no specific funding for this work.
Data Availability Statement
Data are available on request from the authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1. Supporting Information.
Data Availability Statement
Data are available on request from the authors.