Accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in Detecting Autism and Other Developmental Disorders in Community Clinics

This study determined the accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15–36 months) who had M-CHAT performed in 2006–2011. Overall sensitivities for detecting ASD and all DD were poor but better in the 21 to 21 months) and a useful screening tool for all DD. © 2017, Springer Science+Business Media, LLC

Effectiveness of Using Artificial Intelligence for Early Child Development Screening

This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results

Mapping national information and communication technology (ICT) infrastructure to the requirements of potential digital health interventions in low- and middle-income countries

Background Digital health can support health care in low- and middle-income countries (LMICs) by overcoming problems of distance, poor infrastructure and the need to provide community practitioners with specialist support. We used five RESPIRE countries as exemplars (Bangladesh, India, Indonesia, Malaysia, Pakistan) to identify the digital health solutions that are valuable in their local setting, worked together with local clinicians and researchers to explore digital health policy, electricity/ICT infrastructure, and socio-cultural factors influencing users’ ability to access, adopt and utilise digital health. Methods We adopted the Joanna Briggs Institute’s scoping review protocol and followed the Cochrane Rapid Review method to accelerate the review process, using the Implementation and Operation of Mobile Health projects framework and The Extended Technology Acceptance Model of Mobile Telephony to categorise the results. We conducted the review in four stages: (1) establishing value, (2) identifying digital health policy, (3) searching for evidence of infrastructure, design, and end-user adoption, (4) local input to interpret relevance and adoption factors. We used open-source national/international statistics such as the World Health Organization, International Telecommunication Union, Groupe Speciale Mobile, and local news/articles/government statistics to scope the current status, and systematically searched five databases for locally relevant exemplars. Results We found 118 studies (2015-2021) and 114 supplementary online news articles and national statistics. Digital health policy was available in all countries, but scarce skilled labour, lack of legislation/interoperability support, and interrupted electricity and internet services were limitations. Older patients, women and those living in rural areas were least likely to have access to ICT infrastructure. Renewable energy has potential in enabling digital health care. Low usage mobile data and voice service packages are relatively affordable options for mHealth in the five countries. Conclusions Effective implementation of digital health technologies requires a supportive policy, stable electricity infrastructures, affordable mobile internet service, and good understanding of the socio-economic context in order to tailor the intervention such that it functional, accessible, feasible, user-friendly and trusted by the target users. We suggest a checklist of contextual factors that developers of digital health initiatives in LMICs should consider at an early stage in the development process

Global respiratory syncytial virus–related infant community deaths

Background Respiratory syncytial virus (RSV) is a leading cause of pediatric death, with >99% of mortality occurring in low- and lower middle-income countries. At least half of RSV-related deaths are estimated to occur in the community, but clinical characteristics of this group of children remain poorly characterized. Methods The RSV Global Online Mortality Database (RSV GOLD), a global registry of under-5 children who have died with RSV-related illness, describes clinical characteristics of children dying of RSV through global data sharing. RSV GOLD acts as a collaborative platform for global deaths, including community mortality studies described in this supplement. We aimed to compare the age distribution of infant deaths <6 months occurring in the community with in-hospital. Results We studied 829 RSV-related deaths <1 year of age from 38 developing countries, including 166 community deaths from 12 countries. There were 629 deaths that occurred <6 months, of which 156 (25%) occurred in the community. Among infants who died before 6 months of age, median age at death in the community (1.5 months; IQR: 0.8−3.3) was lower than in-hospital (2.4 months; IQR: 1.5−4.0; P < .0001). The proportion of neonatal deaths was higher in the community (29%, 46/156) than in-hospital (12%, 57/473, P < 0.0001). Conclusions We observed that children in the community die at a younger age. We expect that maternal vaccination or immunoprophylaxis against RSV will have a larger impact on RSV-related mortality in the community than in-hospital. This case series of RSV-related community deaths, made possible through global data sharing, allowed us to assess the potential impact of future RSV vaccines

Topical Review: Mind Your Language-Translation Matters (A Narrative Review of Translation Challenges)

10.1093/jpepsy/jsw036JOURNAL OF PEDIATRIC PSYCHOLOGY41101110-111

Accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in Detecting Autism and Other Developmental Disorders in Community Clinics

This study determined the accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15–36 months) who had M-CHAT performed in 2006–2011. Overall sensitivities for detecting ASD and all DD were poor but better in the 21 to 21 months) and a useful screening tool for all DD