Measure what matters: considerations for outcome measurement of care coordination for children with neurodevelopmental disabilities and medical complexity

Introduction

Care Coordination (CC) is a significant intervention to enhance family's capacity in caring for children with neurodevelopmental disability and medical complexity (NDD-MC). CC assists with integration of medical and behavioral care and services, partnerships with medical and community-based supports, and access to medical, behavioral, and educational supports and services. Although there is some consensus on the principles that characterize optimal CC for children with NDD-MC, challenges remain in measuring and quantifying the impacts of CC related to these principles. Two key challenges include: (1) identification of measures that capture CC impacts from the medical system, care provider, and family perspectives; and (2) recognition of the important community context outside of a hospital or clinical setting.

Methods

This study used a multilevel model variant of the triangulation mixed methods design to assess the impact of a CC project implemented in Alberta, Canada, on family quality of life, resource use, and care integration at the broader environmental and household levels. At the broader environmental level, we used linked administrative data. At the household level we used quantitative pre-post survey datasets, and aggregate findings from qualitative interviews to measure group-level impacts and an embedded multiple-case design to draw comparisons, capture the nuances of children with NDD-MC and their families, and expand on factors driving the high variability in outcome measures. Three theoretical propositions formed the basis of the analytical strategy for our case study evidence to explore factors affecting the high variability in outcome measures.

Discussion

This study expanded on the factors used to measure the outcomes of CC and adds to our understanding of how CC as an intervention impacts resource use, quality of life, and care integration of children with NDD-MC and their families. Given the heterogeneous nature of this population, evaluation studies that account for the variable and multi-level impacts of CC interventions are critical to inform practice, implementation, and policy of CC for children with NDD-MC.

49 Implementation of a Sensory Toolkit in the Emergency Department for Children with Autism Spectrum Disorder

Background

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and restricted, repetitive patterns of behaviour, interests, or activities. Children with ASD frequently visit the emergency department (ED), and present more for primary care-related issues and other non-emergent reasons than children without ASD. Visiting the ED can be challenging and stressful for children with ASD, their caregivers, and the healthcare professionals caring for them. Sensory Toolkits are a collection of sensory items that can help to distract and calm children, including during times of stress. We hypothesized that a Sensory Toolkit will improve the experience of children with ASD and their families in our ED.

Objectives

The primary objective of this study was to evaluate the effect of a Sensory Toolkit on the experience of children with ASD and their families visiting a tertiary-care hospital ED.

Design/Methods

After ethical approval was obtained, a pre-implementation survey was conducted on parents of children with ASD about prior experiences in the ED and their interest in a Sensory Toolkit. A Sensory Toolkit specific to children with ASD was developed through literature review and in consultation with families of children with ASD. The Sensory Toolkit, which included a light wand, squeeze ball, push-pop bubble toy, and other items, was launched in a tertiary-care hospital ED in June 2021 with 50 Toolkits. A post-implementation survey was conducted to explore parents’ experiences of their child using the Sensory Toolkit in the ED and to obtain feedback to help improve the Toolkit.

Results

In the pre-implementation survey, 80% (32/40) of parents felt that a Sensory Toolkit would improve the experience of children with ASD in the ED. Post-implementation, 100% (12/12; response rate ~25% from 50 Toolkits) of parents felt that the Sensory Toolkit was helpful for their child. Parents reported that the Sensory Toolkit was “AMAZING” and “A really great idea, thank you!”. Feedback provided regarding improvements that could be made to the Sensory Toolkit was highly patient-specific and often contradictory. One parent suggested more items to chew on while another parent suggested elimination of the chewable items and one parent loved the light up toy while another suggested to remove it.

Conclusion

The Sensory Toolkit contributes positively to the experience of children with ASD and their families in a tertiary-care hospital ED. This study will help inform improvement of the Sensory Toolkit in the ED, including consideration of more specialized Sensory Toolkits to meet individual patient needs. Given the success in the ED, the Sensory Toolkit could be expanded into other areas of the tertiary-care hospital and beyond.

60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

Introduction/Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain.

Objectives

The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning.

Design/Methods

Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network.

Results

The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys.

Conclusion

To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

Validation of single nucleotide polymorphisms associated with milk production traits in dairy cattle

Single nucleotide polymorphism (SNP) associations with milk production traits found to be significant in different screening experiments, including SNP in genes hypothesized to be in gene pathways affecting milk production, were tested in a validation population to confirm their association. In total, 423 SNP were genotyped across 411 Holstein bulls, and their association with 6 milk production traits--Australian Selection Index (indicating the profitability of an animal's milk production), protein, fat, and milk yields, and protein and fat composition--were tested using single SNP regressions. Seventy-two SNP were significantly associated with one or more of the traits; their effects were in the same direction as in the screening experiment and therefore their association was considered validated. An over-representation of SNP (43 of the 423) on chromosome 20 was observed, including a SNP in the growth hormone receptor gene previously published as having an association with protein composition and protein and milk yields. The association with protein composition was confirmed in this experiment, but not the association with protein and milk yields. A multiple SNP regression analysis for all SNP on chromosome 20 was performed for all 6 traits, which revealed that this mutation was not significantly associated with any of the milk production traits and that at least 2 other quantitative trait loci were present on chromosome 20.

Canadian health surveys, 1950 1997

This paper provides a brief history of Canadian health surveys and a review of health or health-related surveys from the first national household survey in 1950/51 to the present time. Surveys have evolved from collecting information on health care utilization, health status and some risk behaviours to a wider range of health determinants following the 1974 Lalonde report. In addition to the occasional cross-sectional surveys, there are periodic surveys, longitudinal surveys, school-based surveys and surveys based on subgroups in the population or specific topic areas. The survey review is presented in the following four tables: Table 1 summarizes national surveys including such information as the date(s) and frequency of data collection, topic areas, target population, sample size and response rate for each survey; Table 2 provides the same information for provincial surveys; both tables point to Table 3, which supplements the previous information with survey sponsors and contacts; Table 4 provides similar information for commercial surveys. This reference, which will be updated periodically, is intended to act as a source of information and support in the development of new surveys.