Neurodevelopmental disorders in children aged 2-9 years: Population-based burden estimates across five regions in India.

BACKGROUND: Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. METHODS AND FINDINGS: We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. CONCLUSIONS: The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions.

INCLEN Diagnostic Tool for Neuromotor Impairments (INDT-NMI) for primary care physician: development and validation.

OBJECTIVE: To develop and validate a diagnostic tool for use by primary care physicians for diagnosing neuro-motor impairment among 2-9 year old children in primary care settings. STUDY DESIGN: Modified Delphi technique involving national (n=49) and international (n=6) experts was used for development of INDT-NMI. The tool was then validated through a cross sectional study. SETTING: Neurology specialty clinics of three tertiary care pediatric centers in New Delhi, India. PARTICIPANTS: 454 children aged 2-9 years [mean (SD) age: 60.4 (23.7) mo], selected through systematic random sampling, underwent assessment for identification and classification of neuromotor impairments (NMI). INTERVENTION: All study subjects were first administered INDT-NMI (candidate test) by a trained physician followed by expert assessment for NMI and other neurodevelopment disorders (NDD) by team of two pediatric neurologists (Gold standard). RESULTS: According to expert evaluation, 171 (37.8%) children had neuromotor impairments. There were four categories of subjects: NMI alone (n=66); NMI+other NDDs (n=105); Other NDDs without NMI (n=225) and 'Normal' group (n=58). Using expert evaluation as gold standard, overall sensitivity of the INDT-NMI was 75.4% and specificity was 86.8%. INDT-NMI helped graduate physicians to correctly classify 86.6% (112/129) children with NMI into different types (cerebral palsy, neuromotor diseases and other NMI). Graduate physicians assigned 40 children (8.8%) as 'indeterminate', 38 (95%) of whom had either NDD and/or NMI and thus merited referral. Misclassification of NMI occurred in those with mild changes in muscle tone, dystonia, or ataxia and associated NDDs. CONCLUSIONS: Graduate primary care physicians with a structured short training can administer the new tool and diagnose NMI in 2-9 year old children with high validity. INDT-NMI requires further evaluation in actual primary care settings.

INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD): development and validation.

OBJECTIVE: To develop and validate INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD). DESIGN: Diagnostic test evaluation by cross sectional design. SETTING: Four tertiary pediatric neurology centers in Delhi and Thiruvanthapuram, India. METHODS: Children aged 2-9 years were enrolled in the study. INDT-ASD and Childhood Autism Rating Scale (CARS) were administered in a randomly decided sequence by trained psychologist, followed by an expert evaluation by DSM-IV TR diagnostic criteria (gold standard). MAIN OUTCOME MEASURES: Psychometric parameters of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency. RESULTS: 154 children (110 boys, mean age 64.2 mo) were enrolled. The overall diagnostic accuracy (AUC=0.97, 95% CI 0.93, 0.99; P<0.001) and validity (sensitivity 98%, specificity 95%, positive predictive value 91%, negative predictive value 99%) of INDT-ASD for Autism spectrum disorder were high, taking expert diagnosis using DSM-IV-TR as gold standard. The concordance rate between the INDT-ASD and expert diagnosis for 'ASD group' was 82.52% [Cohen's k=0.89; 95% CI (0.82, 0.97); P=0.001]. The internal consistency of INDT-ASD was 0.96. The convergent validity with CARS (r = 0.73, P= 0.001) and divergent validity with Binet-Kamat Test of intelligence (r = -0.37; P=0.004) were significantly high. INDT-ASD has a 4-factor structure explaining 85.3% of the variance. CONCLUSIONS: INDT-ASD has high diagnostic accuracy, adequate content validity, good internal consistency high criterion validity and high to moderate convergent validity and 4-factor construct validity for diagnosis of Autistm spectrum disorder.

INCLEN Diagnostic Tool for Neuromotor Impairments (INDT-NMI) for Primary Care Physician: Development and Validation.

Objectives: To develop and validate a diagnostic tool for use by primary care physicians for diagnosing neuro-motor impairment among 2-9 year old children in primary care settings. Study design: Modified Delphi technique involving national (n=49) and international (n=6) experts was used for development of INDT-NMI. The tool was then validated through a cross sectional study. Setting: Neurology specialty clinics of three tertiary care pediatric centers in New Delhi, India. Participants: 454 children aged 2-9 years [mean (SD) age: 60.4 (23.7) mo], selected through systematic random sampling, underwent assessment for identification and classification of neuromotor impairments (NMI). Intervention: All study subjects were first administered INDTNMI (candidate test) by a trained physician followed by expert assessment for NMI and other neurodevelopment disorders (NDD) by team of two pediatric neurologists (Gold standard). Results: According to expert evaluation, 171 (37.8%) children had neuromotor impairments. There were four categories of subjects: NMI alone (n=66); NMI+other NDDs (n=105); Other NDDs without NMI (n=225) and ‘Normal’ group (n=58). Using expert evaluation as gold standard, overall sensitivity of the INDTNMI was 75.4% and specificity was 86.8%. INDT-NMI helped graduate physicians to correctly classify 86.6% (112/129) children with NMI into different types (cerebral palsy, neuromotor diseases and other NMI). Graduate physicians assigned 40 children (8.8%) as ‘indeterminate’, 38 (95%) of whom had either NDD and/or NMI and thus merited referral. Misclassification of NMI occurred in those with mild changes in muscle tone, dystonia, or ataxia and associated NDDs. Conclusion: Graduate primary care physicians with a structured short training can administer the new tool and diagnose NMI in 2-9 year old children with high validity. INDT-NMI requires further evaluation in actual primary care settings.

INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD): Development and Validation.

Objective: To develop and validate INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD). Design: Diagnostic test evaluation by cross sectional design Setting: Four tertiary pediatric neurology centers in Delhi and Thiruvanthapuram, India. Methods: Children aged 2-9 years were enrolled in the study. INDT-ASD and Childhood Autism Rating Scale (CARS) were administered in a randomly decided sequence by trained psychologist, followed by an expert evaluation by DSM-IV TR diagnostic criteria (gold standard). Main outcome measures: Psychometric parameters of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency. Results: 154 children (110 boys, mean age 64.2 mo) were enrolled. The overall diagnostic accuracy (AUC=0.97, 95% CI 0.93, 0.99; P<0.001) and validity (sensitivity 98%, specificity 95%, positive predictive value 91%, negative predictive value 99%) of INDT-ASD for Autism spectrum disorder were high, taking expert diagnosis using DSM-IV-TR as gold standard. The concordance rate between the INDT-ASD and expert diagnosis for ‘ASD group’ was 82.52% [Cohen’s κ=0.89; 95% CI (0.82, 0.97); P=0.001]. The internal consistency of INDT-ASD was 0.96. The convergent validity with CARS (r = 0.73, P= 0.001) and divergent validity with Binet-Kamat Test of intelligence (r = -0.37; P=0.004) were significantly high. INDT-ASD has a 4-factor structure explaining 85.3% of the variance. Conclusion: INDT-ASD has high diagnostic accuracy, adequate content validity, good internal consistency high criterion validity and high to moderate convergent validity and 4-factor construct validity for diagnosis of Autistm spectrum disorder.