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. 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. 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.

Between 2006 and 2012 India reported an annual average of 20,474 dengue cases. Although dengue has been notifiable since 1996, regional comparisons suggest that reported numbers substantially underrepresent the full impact of the disease. Adjustment for underreporting from a case study in Madurai district and an expert Delphi panel yielded an annual average of 5,778,406 clinically diagnosed dengue cases between 2006 and 2012, or 282 times the reported number per year. The total direct annual medical cost was US$548 million. Ambulatory settings treated 67% of cases representing 18% of costs, whereas 33% of cases were hospitalized, comprising 82% of costs. Eighty percent of expenditures went to private facilities. Including non-medical and indirect costs based on other dengue-endemic countries raises the economic cost to $1.11 billion, or $0.88 per capita. The economic and disease burden of dengue in India is substantially more than captured by officially reported cases, and increased control measures merit serious consideration.

Emissions of airborne particles from biomass burning are a significant source of black carbon (BC) and brown carbon (BrC) in rural areas of developing countries where biomass is the predominant energy source for cooking and heating. This study explores the molecular composition of organic aerosols from household cooking emissions with a focus on identifying fuel-specific compounds and BrC chromophores. Traditional meals were prepared by a local cook with dung and brushwood-fueled cookstoves in a village in Palwal district, Haryana, India. Cooking was done in a village kitchen while controlling for variables including stove type, fuel moisture, and meal. Fine particulate matter (PM_2.5) emissions were collected on filters, and then analyzed via nanospray desorption electrospray ionization–high-resolution mass spectrometry (nano-DESI-HRMS) and high-performance liquid chromatography–photodiode array–high-resolution mass spectrometry (HPLC-PDA-HRMS) techniques. The nano-DESI-HRMS analysis provided an inventory of numerous compounds present in the particle phase. Although several compounds observed in this study have been previously characterized using gas chromatography methods a majority of the species in the nano-DESI spectra were newly observed biomass burning compounds. Both the stove (chulha or angithi) and the fuel (brushwood or dung) affected the composition of organic aerosols. The geometric mean of the PM_2.5 emission factor and the observed molecular complexity increased in the following order: brushwood–chulha (7.3 ± 1.8 g kg⁻¹ dry fuel, 93 compounds), dung–chulha (21.1 ± 4.2 g kg⁻¹ dry fuel, 212 compounds), and dung–angithi (29.8 ± 11.5 g kg⁻¹ dry fuel, 262 compounds). The mass-normalized absorption coefficient (MAC_bulk) for the organic-solvent extractable material for brushwood PM_2.5 was 3.7 ± 1.5 and 1.9 ± 0.8 m² g⁻¹ at 360 and 405 nm, respectively, which was approximately a factor of two higher than that for dung PM_2.5. The HPLC-PDA-HRMS analysis showed that, regardless of fuel type, the main chromophores were C_xH_yO_z lignin fragments. The main chromophores accounting for the higher MAC_bulk values of brushwood PM_2.5 were C₈H₁₀O₃ (tentatively assigned to syringol), nitrophenols C₈H₉NO₄, and C₁₀H₁₀O₃ (tentatively assigned to methoxycinnamic acid).

The Global Vaccine Action Plan 2011-2020 (GVAP) was developed to realize the ambitions of the Decade of Vaccines - that all individuals and communities enjoy lives free from vaccine-preventable diseases. It included a comprehensive monitoring and evaluation/accountability framework to assess progress towards global targets with recommendations for corrective actions. While many of the GVAP targets are very unlikely to be met by the end of 2020, substantial progress has nevertheless been made, establishing a strong foundation for a successor global immunization strategy, the Immunization Agenda 2030 (IA2030). The Strategic Advisory Group of Experts on immunization has made a series of recommendations to ensure that the lessons learned from GVAP inform the development and implementation of IA2030.

Vandana Gurnani and colleagues report an analysis from the Intensified Mission Indradhanush strategy in India, showing that cross-sectoral participation can contribute to improved vaccination coverage of children at high risk

To develop and validate INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD). Diagnostic test evaluation by cross sectional design Four tertiary pediatric neurology centers in Delhi and Thiruvanthapuram, India. 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). Psychometric parameters of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency. 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. 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.