Scaling up child development centres in Bangladesh

Khan, Naila Zaman; Sultana, Ronald G.; Ahmed, Farid Uddin; Shilpi, Asma Begum; Sultana, Nasrin

doi:10.1111/cch.12530

Cited by 17 publications

(18 citation statements)

References 12 publications

(15 reference statements)

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“…A range of interventions for children with or at risk of developmental disabilities are well described in the literature, 8 , 9 , 17 , 29 and include primary prevention aimed at reducing the incidence of developmental disabilities, secondary prevention through early detection of disabilities at a time when the brain is still very sensitive to interventions and change, and tertiary prevention through comprehensive community-based rehabilitation programmes (now termed community-based inclusive development), as exemplified by some early childhood development initiatives in high-burden LMICs such as Bangladesh and India. 30 , 31 Some disabilities might be less amenable than others to prevention. For example, the GBD study did not investigate the causes of ASD and ADHD, which are often attributed to complex interactions between genetic and environmental factors.…”

Section: Discussionmentioning

confidence: 99%

Developmental disabilities among children younger than 5 years in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Olusanya¹,

Davis²,

Wertlieb³

et al. 2018

The Lancet Global Health

411

286

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SummaryBackgroundThe Sustainable Development Goals (SDGs) mandate systematic monitoring of the health and wellbeing of all children to achieve optimal early childhood development. However, global epidemiological data on children with developmental disabilities are scarce. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 provides a comprehensive assessment of prevalence and years lived with disability (YLDs) for development disabilities among children younger than 5 years in 195 countries and territories from 1990 to 2016.MethodsWe estimated prevalence and YLDs for epilepsy, intellectual disability, hearing loss, vision loss, autism spectrum disorder, and attention deficit hyperactivity disorder. YLDs were estimated as the product of the prevalence estimate and the disability weight for each mutually exclusive disorder, corrected for comorbidity. We used DisMod-MR 2.1, a Bayesian meta-regression tool, on a pool of primary data derived from systematic reviews of the literature, health surveys, hospital and claims databases, cohort studies, and disease-specific registries.FindingsGlobally, 52·9 million (95% uncertainty interval [UI] 48·7–57·3; or 8·4% [7·7–9·1]) children younger than 5 years (54% males) had developmental disabilities in 2016 compared with 53·0 million (49·0–57·1; or 8·9% [8·2–9·5]) in 1990. About 95% of these children lived in low-income and middle-income countries. YLDs among these children increased from 3·8 million (95% UI 2·8–4·9) in 1990 to 3·9 million (2·9–5·2) in 2016. These disabilities accounted for 13·3% of the 29·3 million YLDs for all health conditions among children younger than 5 years in 2016. Vision loss was the most prevalent disability, followed by hearing loss, intellectual disability, and autism spectrum disorder. However, intellectual disability was the largest contributor to YLDs in both 1990 and 2016. Although the prevalence of developmental disabilities among children younger than 5 years decreased in all countries (except for North America) between 1990 and 2016, the number of children with developmental disabilities increased significantly in sub-Saharan Africa (71·3%) and in North Africa and the Middle East (7·6%). South Asia had the highest prevalence of children with developmental disabilities in 2016 and North America had the lowest.InterpretationThe global burden of developmental disabilities has not significantly improved since 1990, suggesting inadequate global attention on the developmental potential of children who survived childhood as a result of child survival programmes, particularly in sub-Saharan Africa and south Asia. The SDGs provide a framework for policy and action to address the needs of children with or at risk of developmental disabilities, particularly in resource-poor countries.FundingThe Bill & Melinda Gates Foundation.

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Section: Discussionmentioning

confidence: 99%

Developmental disabilities among children younger than 5 years in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Olusanya¹,

Davis²,

Wertlieb³

et al. 2018

The Lancet Global Health

411

286

View full text Add to dashboard Cite

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“…Based on the raters’ previous annotation patterns and their associated models, the child would receive a predicted risk probability of developmental delay or ASD and a clinical team nearby could then be alerted, as appropriate. Since 2008, Dr Khan and her team have assisted the government to establish multidisciplinary Child Development Centers in tertiary hospitals across Bangladesh [26]. Fifteen such Child Development Centers are currently operational, whose chief mandate is to diagnose and provide appropriate management for a range of neurodevelopmental disorders including autism.…”

Section: Discussionmentioning

confidence: 99%

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study

Tariq¹,

Fleming²,

Schwartz³

et al. 2019

J Med Internet Res

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Background Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children’s “risk scores” for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features. Objective Using videos of Bangladeshi children collected from Dhaka Shishu Children’s Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. Methods Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers’ process aligns with clinical intuition. Results Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental delays. Conclusions These results show promise for using a mobile video-based and machine learning–directed approach for early and remote detection of autism in Bangladeshi children. This strategy could provide important resources for developmental health in developing countries with few clinical resources for diagnosis, helping children get access to care at an early age. Future research aimed at extending the application of this approach to identify a range of other conditions and determine the population-level burden of developmental disabilities and impairments will be of high value.

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“…Caseload of ASDs in clinical settings also shows a similar disparity by family socioeconomic status. In the 15 Child Development Centers established within government tertiary hospitals serving the lowest income populations across the country [Khan et al, 2018], there was a rising caseload of ASDs in their General Developmental Assessment Clinic by family income, that is, within low (6%), low middle (15%), middle (34%), and high income (44%) families [Khan et al, 2018, p. 24]. These disparities in prevalence in epidemiological surveys and incidence in clinical settings by urban/rural residence and by wealth quintile pose questions regarding the antecedents or risk factors leading to ASDs in different populations within the same country, even with sociopolitical, geographical, and cultural similarities, and where the professional skills, scales, tools, and procedures applied for the assessment and diagnosis of ASD were the same.…”

Section: Naila Z Khan Clinical Neurosciences Center Bangladesh Protimentioning

confidence: 99%

Gaps in Current Autism Research: The Thoughts of the Autism Research Editorial Board and Associate Editors

et al. 2019

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Scaling up child development centres in Bangladesh

Abstract: Background Child Development Centres (CDCs) have been established within government medical

Cited by 17 publications

References 12 publications

Developmental disabilities among children younger than 5 years in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Developmental disabilities among children younger than 5 years in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study

Gaps in Current Autism Research: The Thoughts of the Autism Research Editorial Board and Associate Editors

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