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A Pilot Project Using Pediatricians as Initial Diagnosticians in Multidisciplinary Autism Evaluations for Young Children
Abstract
Objectives:
Wait times for autism spectrum disorder (ASD) evaluations are long, thereby delaying access to ASD-specific services. We asked how our traditional care model (requiring all patients to see psychologists for ASD diagnostic decisions) compared to an alternative model that better utilizes the available clinicians, including initial evaluation by speech, audiology, and pediatrics (trained in Level 2 autism screening tools). Pediatricians could diagnose immediately if certain about diagnosis but could refer uncertain cases to psychology. Accuracy and time to diagnosis, charges, and parent satisfaction were our main outcome measures.
Methods:
Data were gathered through record extraction (n = 244) and parent questionnaire (n = 57). We compared time to diagnosis, charges, and parent satisfaction between traditional and alternative models. Agreement between pediatrician and psychologist diagnoses was examined for a subset (n = 18).
Results:
The alternative model's time to diagnosis was 44% faster (85 vs 152 d) and 33% less costly overall. Diagnostic agreement was 93% for children with ASD diagnoses and 100% for children without ASD diagnoses. Pediatricians expressed higher diagnostic certainty about children with higher levels of ASD symptoms. Parents reported no differences in high satisfaction with experiences, family-centered care, and shared decision making.
Conclusion:
Efficient use of available clinicians with additional training in Level 2 autism screening resulted in improvements in time to diagnosis and reduced charges for families. Coordination of multidisciplinary teams makes this possible, with strategic sequencing of patients through workflow. Flexibility was key to not only allowing pediatricians to refer uncertain cases to psychology for diagnosis but also allowing for diagnosis by a pediatrician when symptomatic presentation clearly met diagnostic criteria.
... 23 Various strategies to improve access to diagnostic assessment for children with ASD have been explored. 24,25 Several studies have examined the role of the primary care pediatrician (PCP) in assessing these children, 26,27 along with training and capacity-building strategies-such as case-based learning, 28,29 didactic lectures or workshops, 24,27,30 and shadowing 31 -for PCPs to perform diagnostic evaluation for ASD. ...
... 23 Various strategies to improve access to diagnostic assessment for children with ASD have been explored. 24,25 Several studies have examined the role of the primary care pediatrician (PCP) in assessing these children, 26,27 along with training and capacity-building strategies-such as case-based learning, 28,29 didactic lectures or workshops, 24,27,30 and shadowing 31 -for PCPs to perform diagnostic evaluation for ASD. ...
... 31 Finally, studies show a high level of diagnostic agreement between PCPs and specialists. 24,27 In response to the mounting need for access to timely diagnostic evaluation for children identified as at high risk of autism, the authors developed a model for integrating the diagnostic evaluation for autism in the primary care setting with the aim of expediting the diagnostic evaluation of children suspected to have autism thereby providing an opportunity for early interventions and improved outcomes. In this study, we report on this model, which involves training the PCP to serve as the primary diagnostician. ...
Background and Objectives
Children with autism spectrum disorder (ASD) continue to experience significant delays in diagnosis and interventions. One of the main factors contributing to this delay is a shortage of developmental-behavioral specialists. Diagnostic evaluation of ASD by primary care pediatricians (PCPs) has been shown to be reliable and to decrease the interval from first concern to diagnosis. In this paper, we present the results of a primary care ASD diagnosis program in which the PCP serves as the primary diagnostician and leverages the infrastructure of the primary care medical home to support the child and family during the pre- and post-diagnostic periods, along with data on parental satisfaction with this model.
Methods
Retrospective data from a cohort of patients evaluated through this program were analyzed to determine the mean age at diagnosis and interval from referral for evaluation to diagnosis. We used survey methodology to obtain data from parents regarding their satisfaction with the process.
Results
Data from 8 of 20 children evaluated from April 2021 through May 2022 showed a median age of diagnosis of 34.5 months compared to the national average of 49 months. Mean interval from referral for evaluation to diagnosis was 3.5 months. Parental survey responses indicated high satisfaction.
Conclusions
This model was successful in shortening the interval from referral to diagnosis resulting in significant decrease of age at diagnosis compared with the national average. Widespread implementation could improve access to timely diagnostic services and improve outcomes for children with ASD.
... Examples of such initiatives include programs such as START-ED (Warren et al., 2009), ECHO Autism (Bellesheim et al., 2020;Mazurek et al., 2017), and ECHO Autism STAT (Mazurek et al., 2019) that train PCPs to use standardized screening or diagnostic tools and employ diagnostic decision-making frameworks (Guan et al., 2022). Most evidence from these and related initiatives suggests that, in the majority of cases, PCP-provided diagnoses were consistent with the diagnostic decision of a multidisciplinary team or independent evaluation using standardized tools (Ahlers et al., 2019;Guan et al., 2022;Mcclure et al., 2010;Sohl et al., 2023). However, findings from one capacity-building initiative indicated that pediatricians may misclassify individuals without ongoing support and training (Warren et al., 2009). ...
... Evaluation of the effectiveness of these training initiatives to date has been based on pilot studies; thus, more research in this area is needed. In addition to improved provider self-efficacy and diagnostic accuracy, outcomes from studies of physician training indicate an approximate 50% reduction in time to diagnosis (Ahlers et al., 2019;Harrison et al., 2017). The reduction in time from first concern to diagnosis does more than just alleviate pressure on the current diagnostic system. ...
... The lag between research to clinical practice in health sciences is approximately 17 years to reach routine clinical practice (Morris et al., 2011). Though research on diagnostic training initiatives was conducted in community settings (Ahlers et al., 2019;Swanson et al., 2014), the present findings suggest that these initiatives have not yet been widely implemented or disseminated and have had limited reach to everyday clinical practice (as of 2019). ...
The capacity of the workforce lags behind the current demand for timely autism diagnostic assessment. Primary care providers (PCPs) are well-positioned to diagnose autism at earlier ages than providers from other disciplines. Thus, bolstering PCPs’ diagnostic capabilities has been the focus of many recent capacity-building initiatives. Using data from the National Survey of Children’s Health, this study aimed to evaluate whether diagnosis of autism in primary care has changed over time and whether diagnosis in primary care relates to age at autism diagnosis. Results indicated that the likelihood of being diagnosed with autism by a PCP decreased by about 2% with every passing year from 2004 to 2019 when controlling for demographic characteristics. PCPs diagnosed children approximately 1 year earlier than non-PCPs (e.g., psychiatrists and psychologists), which supports the critical role PCPs can play in timely diagnosis. Further research is needed to understand why the proportion of children diagnosed by PCPs has decreased over time; however, these findings suggest that capacity-building initiatives have not yet reached community practice. Future research should focus on the dissemination and implementation of training initiatives in community-based primary care practices.
Lay abstract
The current demand for autism diagnostic services exceeds the ability of the workforce to assess and diagnose children in a timely manner. One solution may be to equip primary care providers (PCPs) with the tools and expertise needed to diagnose autism within their practice. PCPs are often trusted professionals who have many touchpoints with children during early development, in which they can identify early signs of autism. Recent initiatives have focused on bolstering PCPs’ diagnostic capabilities; however, no studies have examined how the rates of autism diagnosis in primary care have changed over time. We aimed to evaluate whether autism diagnosis in primary care has changed over time and how diagnosis in primary care relates to a child’s age at the time of diagnosis. We found that the likelihood of a child being diagnosed by a PCP decreased by about 2% with every passing year from 2004 to 2019 when accounting for demographic characteristics. In our sample, PCPs diagnosed children approximately 1 year earlier than non-PCPs (e.g., psychologists and psychiatrists). Further research is needed to understand why the proportion of children diagnosed by PCPs decreases over time. However, this decrease suggests more work is needed to get capacity-building initiatives into community primary care practice. Though we must continue to find effective ways to build community PCPs’ ability to diagnose autism, the present findings support the crucial role PCPs can play in early autism diagnosis.
... No study randomised participants to intervention and comparison groups and less than half (15 of 33) used any inferential statistics to test the hypothesis that the redesign intervention was associated with a change in waiting time. Only five studies provided any information about the characteristics of the groups being compared and therefore about confounding variables, (Ahlers et al., 2019;Camden et al., 2013;Creen et al., 2021;Hine et al., 2020;Horton & Hall, 2008) and there was uncertainty about how waiting was defined or measured (i.e. the validity and reliability of outcome measures) in 14 studies (42%). A total of 29 studies did not fulfil the criterion of collecting data over the same period of time, as they used historical comparison groups, and of the four studies using concurrent comparison groups, (Clemente et al., 2006;Creen et al., 2021;Harrison et al., 2017;Horton & Hall, 2008), two were from a separate service that could not be considered the same population (Clemente et al., 2006;Horton & Hall, 2008). ...
... Austin et al. delivered by specialists (usually medical practitioners). Eleven studies were identified ( Table 3) that evaluated interventions in which a service usually provided by a specialist was provided by other members of a healthcare team to increase treatment capacity (Ahlers et al., 2019;Belthur et al., 2003;Choueiri et al., 2021;Creen et al., 2021;Harrison et al., 2017;Hine et al., 2020;Hurtubise et al., 2017;Mathai, 2007;McClure et al., 2010;Mir et al., 2016;Williams-Arya et al., 2019). This category included five autism services (Ahlers et al., 2019;Choueiri et al., 2021;Hine et al., 2020;McClure et al., 2010;Williams-Arya et al., 2019) and two behavioural assessment clinics (Creen et al., 2021;Harrison et al., 2017) in which assessment roles were distributed to either trained allied health professionals or a multi-disciplinary team, reducing dependence on specialist roles such as a psychologist or paediatrician. ...
... Eleven studies were identified ( Table 3) that evaluated interventions in which a service usually provided by a specialist was provided by other members of a healthcare team to increase treatment capacity (Ahlers et al., 2019;Belthur et al., 2003;Choueiri et al., 2021;Creen et al., 2021;Harrison et al., 2017;Hine et al., 2020;Hurtubise et al., 2017;Mathai, 2007;McClure et al., 2010;Mir et al., 2016;Williams-Arya et al., 2019). This category included five autism services (Ahlers et al., 2019;Choueiri et al., 2021;Hine et al., 2020;McClure et al., 2010;Williams-Arya et al., 2019) and two behavioural assessment clinics (Creen et al., 2021;Harrison et al., 2017) in which assessment roles were distributed to either trained allied health professionals or a multi-disciplinary team, reducing dependence on specialist roles such as a psychologist or paediatrician. Three studies (two general orthopaedic services and one cerebral palsy clinic) involved physiotherapists doing work previously done by medical specialists (Belthur et al., 2003;Hurtubise et al., 2017;Mir et al., 2016). ...
Despite well‐documented benefits of rehabilitation and therapy services for children with disabilities, long waiting lists to access these services are common. There is a growing body of evidence, primarily from mixed or adult services, demonstrating that waiting times can be reduced through strategies that target wasteful processes and support services to keep up with demand. However, providers of rehabilitation and therapy services for children face additional complexities related to the long‐term nature of many developmental conditions and the need to consider timing of interventions with developmental milestones and education transition points. This review aimed to synthesise available evidence on service redesign strategies in reducing waiting time for paediatric therapy services. We conducted a systematic review of studies conducted in outpatient paediatric rehabilitation or therapy settings, including physical and mental health services, evaluating a service redesign intervention and presenting comparative data on time to access care. Two reviewers independently applied inclusion criteria, assessed risk of bias and extracted data. Findings were analysed descriptively and the certainty of evidence was synthesised according to criteria for health service research. From 1934 studies identified, 33 met the criteria for inclusion. Interventions were categorised as rapid response strategies, process efficiency interventions or substitution strategies (using alternative providers in place of medical specialists). Reductions in waiting time were reported in 30 studies. Evidence is limited by study designs with high risk of bias, but this is mitigated by consistency of findings and large effect sizes. There is moderate‐certainty evidence that service redesign strategies similar to those used in adult populations can be applied in paediatric rehabilitation and therapy settings to reduce waiting time.
... Only 1 study reported the race/ethnicity of the patients, with 75% of them being White. 24 Most studies did not report how patients were referred or selected; only 2 studies mentioned that patients were young children with a query of ASD within the PCPs' own practices or had a positive autism screen. 21,26 None of the studies provided details on what additional information was available to the PCPs, such as the patient's functional level or previous language assessments. ...
... Training Content Four of 6 programs included training in administering a standardized ASD screening tool, either the Modified Checklist for Autism in Toddlers or the Screening Tool for Autism in Toddlers and Young Children (STAT). 21,23,24,26 Two of the 6 programs provided training in administering and scoring the Autism Diagnostic Observation Schedule, an interactive assessment of autism signs. 22,24 Two of the 4 studies required providers to submit video-recorded sessions of themselves interacting with a child to assess their reliability in administering the STAT. ...
... 21,23,24,26 Two of the 6 programs provided training in administering and scoring the Autism Diagnostic Observation Schedule, an interactive assessment of autism signs. 22,24 Two of the 4 studies required providers to submit video-recorded sessions of themselves interacting with a child to assess their reliability in administering the STAT. 21,23 Four of 6 programs provided training on how to establish a diagnosis and how to communicate the diagnosis to patients and families. ...
Objectives:
Training primary care providers to provide diagnostic assessments for autism spectrum disorder (ASD) decreases wait times and improves diagnostic access. Outcomes related to the quality of these assessments and the impacts on system capacity have not been systematically examined. This systematic review identifies and summarizes published studies that included ASD diagnostic training for primary care providers (PCPs) and aims to guide future training and evaluation methods.
Methods:
Systematic searches of electronic databases, reference lists, and journals identified 6 studies that met 3 inclusion criteria: training for PCPs, community setting, and training outcome(s) reported. These studies were critically reviewed to characterize (1) study design, (2) training model, and (3) outcomes.
Results:
All studies were either pre-post design or nonrandomized trials with a relatively small number of participants. There was considerable heterogeneity among studies regarding the training provided and the program evaluation process. The most evaluated outcomes were access to autism diagnosis and accuracy of diagnosis.
Conclusion:
Training PCPs to make ASD diagnoses can yield high diagnostic agreement with specialty teams' assessments and reduce diagnostic wait times. Current data are limited by small sample size, poor to fair quality study methodology, and heterogenous study designs and outcome evaluations. Evidence is insufficient to draw conclusions about the overall effects of training PCPs for ASD diagnostic assessments. Since further research is still needed, this review highlights which outcomes are relevant to consider when evaluating the quality of ASD assessments across the continuum of approaches.
... Some studies commented on private assessment practices being perceived as superior to publicly funded assessments and also noting that the associated shorter wait times were also desirable (Ho et al. 2014;Hurt et al. 2019). Two studies in this review compared parental perceptions between different diagnostic models (Ahlers et al. 2019;Reese et al. 2013). One study looked at parental perceptions of a model where assessment for ASD was done first by general pediatricians with only uncertain cases being referred for further assessment by psychologists compared with a model where all cases referred for psychologist ASD assessment. ...
... One study looked at parental perceptions of a model where assessment for ASD was done first by general pediatricians with only uncertain cases being referred for further assessment by psychologists compared with a model where all cases referred for psychologist ASD assessment. It found no significant differences in parental satisfaction, perceptions of family-centered care, or shared decision-making between the two models of assessment (Ahlers et al. 2019). The other study compared the process of completing diagnostic tools including the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observational Schedule (Module 1) in-person versus using teleconferencing technology. ...
... However, further study is needed to examine acceptability of different assessment methods and diagnostician characteristics (i.e., multi-disciplinary team versus solo practitioner, generalist versus sub-specialist). In our review, two studies comparing different diagnostic models examined parent acceptability (Ahlers et al. 2019;Reese et al. 2013). Future studies examining the accuracy and efficiency of different assessors and assessment methods should also consider parent acceptability as this has the potential to affect acceptance of the diagnosis and sets the tone for future interactions with healthcare and support services. ...
The purpose of this review was to identify the quantity, breadth, and methodological characteristics of literature examining parent perspectives of autism spectrum disorder diagnosis, synthesize key research findings, and highlight gaps in the current literature. A systematic search was conducted for the period January 1994–February 2020. One hundred and twenty-two articles underwent data extraction. The majority of studies took place in Europe and North America in high-income countries. Over half of the studies used qualitative methodology. Four key components of the diagnostic experience were identified: journey to assessment, assessment process, delivery of the diagnosis and feedback session, and provision of information, resources, and support. Themes of parental emotions and parental satisfaction with the diagnostic process were also found.
... 16,17 Typically, early detection starts in the paediatric primary care setting, which presents many challenges, 16,[20][21][22][23][24][25] such as a predictive shortage of PCPs resulting in a lack of continuity of care. [1][2][3][4] In the United States, PCPs include physicians, nurse practitioners, clinical nurse specialists, or physician assistants, as allowed under state law. Yet, the United States 2010 Patient Protection and Affordable Care Act 18 expanded PCPs to include nonphysician providers (NPPs), such as occupational therapists, to improve healthcare access, [26][27][28][29] possibly decreasing these challenges. ...
... developmental-behavioural paediatricians and psychologists). 3,4 Many PCPs may be unaware that health promotion and prevention are within occupational therapy's scope of practice. 40,75 Emerging literature on overall occupational therapy efficacy and affordability in adult primary care [75][76][77] and emerging grey literature capturing the efficacy of occupational therapy's value in paediatric primary care 78 illustrates the value of occupational therapy for healthcare systems 73 serving population health. ...
Background
Earlier detection of children at risk for neurodevelopmental disorders is critical and has longstanding repercussions if not addressed early enough.
Objectives
To explore the supporting or facilitating characteristics of paediatric primary care models of care for early detection in infants and toddlers at risk for neurodevelopmental disorders, identify practitioners involved, and describe how they align with occupational therapy’s scope of practice.
Methods
A scoping review following the Joanna Briggs Institute framework was used. PubMed Central, Cumulative Index to Nursing & Allied Health Literature, and Scopus databases were searched. The search was conducted between January and February 2022. Inclusion criteria were: children aged 0–3 years old; neurodevelopmental disorders including cerebral palsy (CP) and autism spectrum disorder (ASD); models of care used in the paediatric primary care setting and addressing concepts of timing and plasticity; peer-reviewed literature written in English; published between 2010 and 2022. Study protocol registered at https://doi.org/10.17605/OSF.IO/MD4K5
Results
We identified 1,434 publications, yielding 22 studies that met inclusion criteria. Models of care characteristics included the use of technology, education to parents and staff, funding to utilize innovative models of care, assessment variability, organizational management changes, increased visit length, earlier timeline for neurodevelopmental screening, and collaboration with current office staff or nonphysician practitioners. The top 4 providers were paediatricians, general or family practitioners, nurse/nurse practitioners, and office staff. All studies aligned with occupational therapy health promotion scope of practice and intervention approach yet did not include occupational therapy within the paediatric primary care setting.
Conclusions
No studies included occupational therapy as a healthcare provider that could be used within the paediatric primary care setting. However, all studies demonstrated models of care facilitating characteristics aligning with occupational therapy practice. Models of care facilitating characteristics identified interdisciplinary staff as a major contributor, which can include occupational therapy, to improve early detection within paediatric primary care.
... Of the six studies, there was one quality improvement project (Harrison et al., 2017), one feasibility study (Hine et al., 2020), one pilot study (Mazurek et al., 2019), and three intervention trials (Ahlers et al., 2019;Hine et al., 2018;Swanson et al., 2014). ...
... Swanson et al. (2014) and Hine et al. (2020) have also used the STAT tool for ASD diagnosis of tier-one children in primary care with satisfactory results. Ahlers et al. (2019) found a 44% faster diagnosis rate with less cost associated with primary care ASD diagnosis. Flexibility was key to allowing pediatricians to refer uncertain cases to psychology specialty clinics for diagnosis versus allowing for diagnosis by a pediatrician when symptomatic presentation met diagnostic criteria. ...
Introduction:
This scoping review was performed to collect, examine, and present literature on interventions promoting the diagnosis of Autism Spectrum Disorder (ASD) in U.S. primary health care settings.
Method:
The literature searched was from 2011-2022, in the English language, in PubMed, CINAHL, Psych INFO, Cochrane, and Web of Science, for persons with autism or ASD aged ≤ 18 years.
Results:
Six studies met the search criteria, including a quality improvement project, a feasibility study, a pilot study, and three primary care provider (PCP) intervention trials. Measured outcomes included: accuracy of diagnosis (n = 4), practice change maintenance (n = 3), time to diagnosis (n = 2), specialty clinic appointment wait time (n = 1), PCP comfort making ASD diagnosis (n = 1), and increased ASD diagnosis (n = 1).
Discussion:
Results inform future implementation of PCP ASD diagnosis for the most obvious cases of ASD and research evaluating PCP training, using longitudinal measures of PCP knowledge of ASD and intention to diagnose.
... In the case of autism, such early interventions can improve therapy outcomes. Research has shown that involving pediatricians as initial diagnosticians in multidisciplinary evaluations for young children with ASD [51]. The biomarkers identified by our method could be used alongside other behavioral and brain imaging techniques as a comprehensive early diagnosis workflow. ...
This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.
... 11 Recent guidelines have recommended that children with clear-cut or uncomplicated presentations of ASD can undergo streamlined assessment. 12,13 The increasing prevalence of ASD (1 of 44 children in the US 14 and 1 of 66 children in Canada 15 ) has contributed to lengthy wait times for diagnosis, delaying access to supports. The median clinician-reported wait time from referral to receipt of ASD diagnosis in Canada was previously reported as 7 months 14 ; this has likely lengthened since the COVID-19 pandemic. ...
Importance:
Wait times for autism spectrum disorder (ASD) diagnosis are lengthy because of inadequate supply of specialist teams. General pediatricians may be able to diagnose some cases of ASD, thereby reducing wait times.
Objective:
To determine the accuracy of ASD diagnostic assessments conducted by general pediatricians compared with a multidisciplinary team (MDT).
Design, setting, and participants:
This prospective diagnostic study was conducted in and a specialist assessment center in Toronto, Ontario, Canada, and Ontario general pediatrician practices from June 2016 to March 2020. Children were younger than 5.5 years, referred with a developmental concern, and without an existing ASD diagnosis. Data analysis was performed from October 2021 to February 2022.
Exposures:
The pediatrician and MDT each conducted blinded assessments and recorded a decision as to whether the child had ASD.
Main outcomes and measures:
Main outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A logistic regression was performed to identify factors associated with accurate pediatrician assessment for children with or without an ASD diagnosis.
Results:
Seventeen pediatricians (12 women [71%]) participated in the study and referred 106 children (79 boys [75%]; mean [SD] age, 41.9 [13.3] months). Sixty participants (57%) were from minoritized racial and ethnic groups (eg, Black, Asian, Hispanic, Middle Eastern, and multiracial). Seventy-two participants (68%) received a diagnosis of ASD by the MDT. Sensitivity and specificity of the pediatrician assessments compared with MDT were 0.75 (95% CI, 0.67-0.83) and 0.79 (95% CI, 0.62-0.91), respectively. The PPV of the pediatrician assessments was 0.89 (95% CI, 0.80-0.94) (ie, 89% agreement with the MDT), and NPV was 0.60 (95% CI, 0.49-0.70) (ie, 60% agreement with the MDT). Higher pediatrician certainty (odds ratio [OR], 3.33; 95% CI, 1.71-7.34; P = .001) was associated with increased diagnostic accuracy for children with ASD. Lower accuracy was seen for children with higher Visual Reception subscale developmental skills (OR, 0.93; 95% CI, 0.89-0.97; P = .001), speaking abilities (OR, 0.17; 95% CI, 0.03-0.67; P = .03), and White race (OR, 0.32; 95% CI, 0.10-0.97; P = .04). Age, gender, and Autism Diagnostic Observation Schedule, 2nd Edition composite scores were not significantly associated with the accuracy of assessments. All 7 children with a sibling with ASD received an accurate diagnosis; otherwise, no significant factors were identified for accuracy in children without ASD.
Conclusions and relevance:
This study of concordance of autism assessment between pediatricians and an expert MDT in young children found high accuracy when general pediatricians felt confident and lower accuracy when ruling out ASD. These findings suggest that children with co-occurring delays may be potential candidates for community assessment.
... Ahlers et al found that the involvement of available clinicians with additional training in autism screening resulted in improvements in time to diagnosis and reduced charges for families. 31 Our study revealed that physicians' sex, specialty and hospital level might be associated with their beliefs about ASD management. Female physicians and physicians specialising in paediatrics and psychology were more likely to have correct beliefs about the management of ASD. ...
Background
The incidence of autism spectrum disorders (ASDs) is increasing greatly, with high demands for earlier diagnosis and intervention. However, little is known about ASD knowledge and beliefs among physicians in China.
Methods
A questionnaire survey was used to assess the knowledge and beliefs about the diagnosis and management of ASD and 24 beliefs regarding the treatment and prognostic aspects of ASD among physicians of any medical specialty in China.
Results
A total of 1160 physicians were recruited and surveyed, with an average score of 8.48±2.66 (total of 18 points) for the questionnaire on the main symptoms of ASD and 14.35±3.69 (total of 24 points) for beliefs about ASD. Physicians’ age, sex, specialty and practice years were related to their knowledge of identifying patients with ASD. Physicians specialising in paediatrics/psychology/psychiatry scored higher than those specialising in other specialties both in response to diagnostic questions (8.98±2.63 vs 8.30±2.65, p<0.001) and beliefs about ASD (15.57±3.44 vs 13.97±3.97, p<0.001).
Conclusions
Knowledge and beliefs about ASD symptoms, diagnosis and treatment are insufficient among physicians in China. Education and training programmes on this topic should be enhanced in physicians of all specialties, especially for male physicians who have short years in practice.
... 11 A recent study using pediatricians as initial diagnosticians in a tertiary clinic reported high diagnostic agreement (93% for ASD-positive cases and 100% for ASD-negative cases), a 44% reduction in wait time, and 33% lower costs than their traditional multidisciplinary model. 12 Involvement of consultant pediatricians in ASD diagnosis holds promise to improve diagnostic access; however, relatively little work has explored their perceptions of providing ASD diagnoses. In 2017, Penner et al. 13 published a qualitative study involving in-depth interviews with consultant pediatricians practicing in an urban Ontario setting regarding their perspectives of the ASD diagnostic process. ...
Background:
Consultant pediatricians represent a potential resource for increasing autism spectrum disorder (ASD) diagnostic capacity; however, little is known about how they perceive their roles in ASD diagnosis.
Objective:
The objective of this study was to examine the perspectives of rural consultant pediatricians regarding their perceived roles, facilitators, and barriers in ASD diagnosis.
Methods:
We performed a qualitative study using thematic analysis. Consultant pediatricians from 3 small-sized and medium-sized Ontario communities were recruited. Semistructured interviews were conducted, transcribed, coded, and analyzed.
Results:
Fourteen pediatricians participated in this study. Participants all considered ASD diagnosis to be in their scope of practice. The major theme identified was the process of diagnosing ASD, which occurred in 3 stages: preassessment (gathering information before the first clinic visit), diagnosis, and service access. All these stages are influenced by ecological factors consisting of characteristics of the child, family, individual physician, pediatric group practice, and the broader system of ASD care.
Conclusion:
Consultant pediatricians practicing in nonurban Ontario communities see ASD diagnosis as part of their scope of practice and collaboratively work within groups to address the needs of their communities. Strategies aimed at increasing diagnostic capacity should target salaried group practices and improve the efficiency of assessments through preclinic information gathering.
... The delay in diagnosis is due to several factors, including a lack of effective screening and a shortage of experienced health care professionals (Ahlers et al., 2019;Carbone et al., 2016). There is a pressing need for tools enabling health care professionals in the primary care sector to identify children for referral to ASD specialists (Abbas et al., 2020). ...
Background:
Diagnosing autism spectrum disorder (ASD) is complex and time-consuming. The present work systematically examines the importance of items from the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) in discerning children with and without ASD. Knowledge of the most discriminative features and their underlying concepts may prove valuable for the future training tools that assist clinicians to substantiate or extenuate a suspicion of ASD in nonverbal and minimally verbal children.
Methods:
In two samples of nonverbal (N = 466) and minimally verbal (N = 566) children with ASD (N = 509) and other mental disorders or developmental delays (N = 523), we applied random forests (RFs) to (i) the combination of ADI-R and ADOS data versus (ii) ADOS data alone. We compared the predictive performance of reduced feature models against outcomes provided by models containing all features.
Results:
For nonverbal children, the RF classifier indicated social orientation to be most powerful in differentiating ASD from non-ASD cases. In minimally verbal children, we find language/speech peculiarities in combination with facial/nonverbal expressions and reciprocity to be most distinctive.
Conclusion:
Based on machine learning strategies, we carve out those symptoms of ASD that prove to be central for the differentiation of ASD cases from those with other developmental or mental disorders (high specificity in minimally verbal children). These core concepts ought to be considered in the future training tools for clinicians.
... However, to our knowledge, the above studies are the only available research on the diagnostic utility of the ASRSp 2-5. Despite the lack of research, several recent studies have utilized the ASRSp 2-5 (e.g., Ahlers et al., 2019;Hedley et al., 2015;Ros et al., 2018;Piwowarczyk et al., 2020). ...
Background
Questionnaires are frequently utilized in autism spectrum disorder (ASD) assessment and research, but there is limited evidence regarding their psychometric properties. Only two studies have investigated the diagnostic utility of the Autism Spectrum Rating Scale-2-5 parent report (ASRSp 2-5), and in both of these studies, the use of a gold-standard ASD measure in the diagnostic process is unclear.
Method
The current study investigated the criterion validity of the ASRSp-2-5 in a clinical sample of 238 children (X¯ age = 3.66; autism[AUT] n = 84; non-autism[NOT] n = 154). Children were diagnosed via a comprehensive assessment that included the Autism Diagnostic Observation Schedule-2.
Results
Analyses included t-tests/ANCOVAs, Logistic Regression (with and without covariates), and ROC analyses (with t-scores and predicted probabilities to account for covariates). The ASRSp 2-5 scores intended to be used for diagnosis (Total and DSM-5) were non-significant in all analyses. Higher scores in the NOT group were found for Unusual Behavior and Behavioral Rigidity. However, mean differences were non-significant with inclusion of control variables (IQ and age). Social, Peer Interaction, and Social-Emotional Reciprocity demonstrated the strongest findings. The ASD group had significantly higher scores on these scales, and findings held with covariates included (IQ for Social; age for Peer Interaction). However, odds ratios were small whether or not covariates were included, and AUCs were poor-fair (.69–.70). Sensitivity and specificity could not both be optimized at any cutpoint.
Conclusions
The findings were not as strong or as consistent as expected. Results suggest the ASRSp 2-5 is most useful in ruling out ASD in cases of low scores (<60). When the ASRSp 2-5 is utilized, behavioral, social, and emotional problems associated with other disorders must be considered in interpreting scores. Additional research on this measure is necessary.
... For example, screenings may occur in accessible settings (e.g., home, preschool, child care, other community programs) rather than solely relying on primary care clinicians for screening at well-child visits (see chapters "Screening and Surveillance" and "Developmental Screening in Community-Based Settings"). Also, as some authors highlighted, using an interprofessional team for ASD diagnostic evaluation is faster and more cost-effective than a typical evaluation by a psychologist (Ahlers et al., 2019; see chapters "Interdisciplinary Evaluation of Autism Spectrum Disorder" and "Dell Children's (S)TAAR Model of Early Autism Assessment" for an in-depth review of evaluation). Finally, timely access to services is described in the "Management" and "Transition to Early Schooling for Children with ASD" chapters, which is particularly relevant at this time given that the American Academy of Pediatrics recently shared new recommendations for identifying and supporting children with ASD (Hyman, Levy, Myers, & AAP Council on Children with Disabilities, 2020). ...
In the concluding chapter of the book, a brief review of preceding chapters is provided. The purpose of this chapter is to reflect on common themes throughout the chapters, and to highlight some key areas for future work in interprofessional care coordination (ICC) for autism spectrum disorder. Specifically, some current and future priorities may include (1) person-centered and family-centered care coordination, (2) innovative assessment and service management approaches, and (3) teaming and telehealth models that reduce barriers to accessing coordinated services.
... In both of these models, wait time to the initial developmental assessment was reduced, as was the number of children referred for evaluation by a DBP specialist. 20,21 Effective models have also been developed in which DBP and MH specialists use videoconferencing technology to provide direct care to children and families and education to PCCs to improve outcomes and access to care for children with MH and DB concerns. [22][23][24] This study describes a model in which board-eligible/ certified DBP pediatricians were colocated within a primary care pediatrics Federally Qualified Health Center (FQHC) for the purpose of identification and management of young children with developmental or behavioral concerns. ...
Objective:
We developed a colocation "Rapid Developmental Evaluation" (RDE) model for Developmental-Behavioral Pediatrics (DBP) to evaluate young children for developmental concerns raised during routine developmental surveillance and screening in a pediatric primary care Federally Qualified Health Center (FQHC). In this low-income patient population, we anticipated that colocation would improve patient access to DBP and decrease time from referral to first developmental evaluation and therapeutic services.
Methods:
Children were assessed at the FQHC by a DBP pediatrician, who made recommendations for therapeutic services and further diagnostic evaluations. A retrospective chart review over 27 months (N = 151) investigated dates of referral and visit, primary concern, diagnosis, and referral to tertiary DBP center and associated tertiary DBP center dates of service and diagnoses if appropriate. We surveyed primary care clinicians (PCCs) for satisfaction.
Results:
The DBP pediatrician recommended that 51% of children be referred to the tertiary DBP center for further diagnostic evaluation or routine DBP follow-up. Average wait from referral to an RDE visit was 57 days compared with 137.3 days for the tertiary DBP center. Children referred from RDE to the tertiary DBP center completed visits at a higher rate (77%) than those referred from other sites (54%). RDE-recommended therapeutic services were initiated for 73% of children by the tertiary visit. Fidelity of diagnosis between RDE and the tertiary DBP center was high, as was PCC satisfaction.
Conclusion:
Colocation of a DBP pediatrician in an FQHC primary care pediatrics program decreased time to first developmental assessment and referral for early intervention services for an at-risk, low-income patient population.
... However, ASD was initially missed at a substantial rate in those with other language and developmental delays [Pierce et al., 2019], with 23.8% who were later diagnosed at 3-4 years old missed during their initial visit between 12 and 36 months. While there has been limited research attention on the potential for community providers to detect and diagnose ASD in toddlers with stability (as opposed to researchers and university-associated clinics), there is some evidence to support methods that streamline children from primary care into early intervention (EI) services without a full diagnostic evaluation, either through a 2-tiered screening process [Rotholz, Kinsman, Lacy, & Charles, 2017], pediatrician diagnosis (only in high certainty cases), or use of level 2 screening methods in a multidisciplinary team setting [Ahlers et al., 2019]. These studies support the value of implementing level 2 screening and flexible diagnostic processes to improve earlier access to EI services. ...
The purpose of this study was to examine the utility of the Systematic Observation of Red Flags (SORF; Dow et al., 2016) as a level 2 screener for autism spectrum disorder (ASD) in toddlers during a naturalistic video‐recorded home observation. Psychometric properties of the SORF were examined in a sample of 228 toddlers—84 with ASD, 82 with developmental delay (DD), and 62 with typical development (TD). Trained undergraduate research assistants blind to diagnosis rated 22 red flags (RF) of ASD associated with DSM‐5 diagnostic criteria using a 4‐point scale. The following scores were computed: a total score summing all items, domain scores summing social communication and restricted, repetitive behavior items, and number of RF counting items with scores of 2 or 3 indicating clear symptom presence. The performance of the total, domain, and RF scores and individual items were examined. A composite score was formed with six items with the best psychometric performance: poor eye gaze directed to faces, limited showing and pointing, limited coordination of nonverbal communication, less interest in people than objects, repetitive use of objects, and excessive interest in particular objects, actions, or activities. The 6‐item composite provides a brief measure with optimal performance, while the RF may be instrumental for clinicians who are interested in characterizing the range of observed symptoms. The SORF shows promise as a practical alternative to currently available screening methods for implementation by nonexperts with the potential to increase feasibility and reduce common obstacles to access to care. Autism Res 2020, 13: 122–133 . © The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.
Lay Summary
Research suggests that current autism spectrum disorder (ASD) screening tools are not accurate enough to use in routine screening. The Systematic Observation of Red Flags was developed as a practical option for children at high risk for ASD. It can be used with video‐recorded samples of parent–child interactions in the home and by raters who are not experts in ASD. It shows promise in predicting ASD risk in toddlers to determine if a full diagnostic evaluation is necessary.
ECHO ( Extensions for Community Healthcare Outcomes) Autism is a telementoring learning model to increase community capacity for autism-related health care. Seventy-seven pediatric providers (mostly primary care, seeing exclusively Medicaid patient populations) enrolled in 1 year of ECHO Autism Washington. Analysis of self-report surveys showed a significant increase in autism diagnoses made by ECHO providers after 1 year, F(1, 65) = 7.52, P = .008. Providers who attended more sessions reported making more diagnoses, F(2, 613.26), P = .045. Of note, autism diagnoses were not externally validated. The total number of reported barriers reduced, F(2, 61) = 13.5), P < .001, and confidence ratings increased F(2, 60) = 24.21, P < .001. The average number of diagnostic referrals from ECHO providers to the state’s largest autism specialty clinic significantly reduced, t(43) = 4.23, P < .001, with significantly fewer diagnostic referrals made during and after ECHO training compared with a comparison group of 28 non-ECHO providers, t(58.77) = −3.36, P < .001. Overall, 1 year of ECHO Autism Washington participation led to significant changes in autism diagnostic practices.
Importance
Finding effective and scalable solutions to address diagnostic delays and disparities in autism is a public health imperative. Approaches that integrate eye-tracking biomarkers into tiered community-based models of autism evaluation hold promise for addressing this problem.
Objective
To determine whether a battery of eye-tracking biomarkers can reliably differentiate young children with and without autism in a community-referred sample collected during clinical evaluation in the primary care setting and to evaluate whether combining eye-tracking biomarkers with primary care practitioner (PCP) diagnosis and diagnostic certainty is associated with diagnostic outcome.
Design, Setting, and Participants
Early Autism Evaluation (EAE) Hub system PCPs referred a consecutive sample of children to this prospective diagnostic study for blinded eye-tracking index test and follow-up expert evaluation from June 7, 2019, to September 23, 2022. Participants included 146 children (aged 14-48 months) consecutively referred by 7 EAE Hubs. Of 154 children enrolled, 146 provided usable data for at least 1 eye-tracking measure.
Main Outcomes and Measures
The primary outcomes were sensitivity and specificity of a composite eye-tracking (ie, index) test, which was a consolidated measure based on significant eye-tracking indices, compared with reference standard expert clinical autism diagnosis. Secondary outcome measures were sensitivity and specificity of an integrated approach using an index test and PCP diagnosis and certainty.
Results
Among 146 children (mean [SD] age, 2.6 [0.6] years; 104 [71%] male; 21 [14%] Hispanic or Latine and 96 [66%] non-Latine White; 102 [70%] with a reference standard autism diagnosis), 113 (77%) had concordant autism outcomes between the index (composite biomarker) and reference outcomes, with 77.5% sensitivity (95% CI, 68.4%-84.5%) and 77.3% specificity (95% CI, 63.0%-87.2%). When index diagnosis was based on the combination of a composite biomarker, PCP diagnosis, and diagnostic certainty, outcomes were concordant with reference standard for 114 of 127 cases (90%) with a sensitivity of 90.7% (95% CI, 83.3%-95.0%) and a specificity of 86.7% (95% CI, 70.3%-94.7%).
Conclusions and Relevance
In this prospective diagnostic study, a composite eye-tracking biomarker was associated with a best-estimate clinical diagnosis of autism, and an integrated diagnostic model including PCP diagnosis and diagnostic certainty demonstrated improved sensitivity and specificity. These findings suggest that equipping PCPs with a multimethod diagnostic approach has the potential to substantially improve access to timely, accurate diagnosis in local communities.
Objectives
Waiting times in the UK for an autism diagnostic assessment have increased rapidly in the last 5 years. This review explored research (including ‘grey’ literature) to uncover the current evidence base about autism diagnostic pathways and what works best, for whom and in what circumstances, to deliver high quality and timely diagnosis.
Design
We performed a Rapid Realist Review consistent with recognised standards for realist syntheses. We collected 129 grey literature and policy/guidelines and 220 articles from seven databases (January 2011–December 2019). We developed programme theories of how, why and in what contexts an intervention worked, based on cross comparison and synthesis of evidence. The focus was on identifying factors that contributed to a clearly defined intervention (the diagnostic pathway), associated with specific outcomes (high quality and timely), within specific parameters (Autism diagnostic services in Paediatric and Child & Adolescent Mental Health services in the UK). Our Expert Stakeholder Group, including representatives from local parent forums, national advocacy groups and clinicians, was integral to the process.
Results
Based on 45 relevant articles, we identified 7 programme theories that were integral to the process of diagnostic service delivery. Four were related to the clinical pathway: initial recognition of possible autism; referral and triaging; diagnostic model; and providing feedback to parents. Three programme theories were pertinent to all stages of the referral and diagnostic process: working in partnership with families; interagency working; and training, service evaluation and development.
Conclusions
This theory informed review of childhood autism diagnostic pathways identified important aspects that may contribute to efficient, high quality and family-friendly service delivery. The programme theories will be further tested through a national survey of current practice and in-depth longitudinal case studies of exemplar services.
Trial registration number
NCT04422483 .
Recent surveillance efforts indicate that 1 in 54 American children meet the criteria for Autism Spectrum Disorder (ASD), making it the fastest growing neurodevelopmental disorder in the U.S. Despite evidence that ASD can be reliably diagnosed as early as 24 months, the median age at ASD diagnosis in 2016 in the U.S. was 51 months. The CLEAR Autism Diagnostic Evaluation (CADE; Willard & Kroncke, 2019), was developed in response to the need to improve, shorten, and standardize the clinical ASD evaluation process. The CADE is a 33-item rating scale designed to be completed by caregivers and clinicians. The current study was conducted to examine the reliability and validity of the CADE using a sample of 191 individuals who received a private evaluation for ASD. Using the client’s evaluation records, clinicians completed the CADE items. The coefficient alpha was .94, which indicates that the items form a scale that has high internal consistency. The CADE total scores were highly correlated with ADOS scores, with r values ranging from .52—.86, and discriminated between those participants with a diagnosis of ASD and those without (p < .001). Receiver operator characteristic (ROC) curve analyses indicated excellent diagnostic accuracy of the CADE total score (ROC area under the curve = .998). Results suggest that the CADE can be used as an efficient and accurate means of evaluating ASD. Limitations and implications for use of the CADE are discussed.
Working in interprofessional teams is best practice in providing services to individuals with Autism Spectrum Disorder (ASD). In order to provide quality Interprofessional Care (IPC) for individuals with ASD and their families, associated healthcare professionals need a foundation for practice based in interprofessional training. This training can be achieved through Interprofessional Education (IPE). This chapter, which is divided into three sections, focuses on IPE and training with an emphasis on ASD. Section 1, Teamwork in Interprofessional Care and Education, addresses team science, how to effectively work in groups, and the link between IPC and service provision for individuals with ASD. Section 2, The Details of IPE, addresses the who, what, where, when, and why of IPE and outlines its various components. Section 3, A Detailed Example of an IPE Program, discusses the federally funded Leadership Education in Neurodevelopmental and Related Disabilities (LEND) program, including exploring relevant historical information and a specific example of one LEND program. The chapter concludes with a brief conclusion and recommendations.
Background: Previous research has suggested that gender diversity affects everyone in the family, with positive mental health and global outcomes for gender diverse youth reliant on receiving adequate family support and validation. Although the individual mental health, treatment and outcomes for gender diverse youth have received recent research attention, much less is known about a family perspective. Hence, a review of the literature exploring youth gender diversity from a family perspective is warranted.
Aims: To systematically identify, appraise and summarize all published literature primarily exploring gender diversity in young people under the age of 18 years, as well as selected literature pertaining to a family understanding.
Methods: Six electronic databases (CINAHL, MEDLINE, EMBASE, PsycINFO, SCOPUS, Web of Science) were searched for relevant literature pertaining to youth under the age of 18 years.
Results: Research evidence was consistently found to support the beneficial effects of a supportive family system for youth experiencing gender diversity, and a systemic understanding and approach for professionals. Conversely, lack of family support was found to lead to poorer mental health and adverse life outcomes. Few articles explored the experience of siblings under the age of 18 years.
Discussion: This literature review is the first to critically evaluate and summarize all published studies which adopted a family understanding of youth gender diversity. The review highlighted a lack of current research and the need for further targeted research, which utilizes a systemic clinical approach to guide support for gender diverse youth and family members.
Problem/condition:
Autism spectrum disorder (ASD).
Period covered:
2014.
Description of system:
The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence of autism spectrum disorder (ASD) among children aged 8 years whose parents or guardians reside within 11 ADDM sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). ADDM surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by professional service providers in the community. Staff completing record review and abstraction receive extensive training and supervision and are evaluated according to strict reliability standards to certify effective initial training, identify ongoing training needs, and ensure adherence to the prescribed methodology. Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported.
Results:
For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1-29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] <70), 25% were in the borderline range (IQ 71-85), and 44% had IQ scores in the average to above average range (i.e., IQ >85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85).
Interpretation:
Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability.
Public health action:
Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.
Objectives:
To provide an example of a successful, novel statewide effort to increase early identification of young children at risk for autism spectrum disorder (ASD) using a 2-tiered screening process with enhanced quality assessment, interagency policy collaboration and coordination.
Methods:
The South Carolina Act Early Team (SCAET) provided focused collaboration among leaders representing state agencies, universities, health care systems, private organizations, and families to improve quality of life for children with ASD. Specific focus was on implementing policy changes and training to result in earlier identification and home-based behavioral intervention for young children at risk for ASD.
Results:
Policy changes, training, and modified state agency practices were accomplished. Presumptive eligibility, on the basis of a 2-tiered screening process was implemented by BabyNet (South Carolina's Early Intervention Program) in collaboration with the lead agency for developmental disability services. There was a fivefold increase in children eligible for early intensive behavioral intervention without waiting for a diagnosis of ASD, avoiding long waits for diagnostic evaluations. Only 16 children (2.5%) were later found not to have ASD from a comprehensive evaluation.
Conclusions:
Improvements in early identification and intervention are feasible through collaborative policy change. The South Carolina Act Early Team and its key stakeholders committed to improving outcomes for this population used existing tools and methods in new ways to improve early identification of children with ASD and to make available evidence-based intervention services. This example should be replicable in other states with key stakeholders working collaboratively for the benefit of young children with ASD.
Clinical initiatives have aimed to reduce the age at ASD diagnosis in the UK. This study investigated whether the median age at diagnosis in childhood has reduced in recent years, and identified the factors associated with earlier diagnosis in the UK. Data on 2134 children with ASD came from two large family databases. Results showed that the age of ASD diagnosis has not decreased. The median age of diagnosis of all ASDs was 55 months. Factors associated with earlier age of diagnosis were autism diagnosis (compared with other ASD), language regression, language delay, lower socioeconomic status, and greater degree of support required. Effective clinical strategies are needed to identify children with characteristics that have in the past delayed ASD diagnosis.
Objective:
The prevalence of autism spectrum disorder is steadily increasing and placing more demands on already overburdened diagnostic and treatment systems. A thoughtful, systematic reorganization of autism service delivery may reduce delays and better meet the growing need.
Methods:
Two clinical centers in the Autism Intervention Research Network on Physical Health, Cincinnati Children's Hospital Medical Center (CCHMC) and Nationwide Children's Hospital (NCH), undertook a year-long access improvement project to reduce delays to care by using system analysis to identify sources of delay and to target changes by using a set of defined access principles. Although both sites addressed access, they focused on slightly different targets (reducing number of patients with autism spectrum disorders waiting for follow-up appointments at NCH and reducing delay to new diagnosis at CCHMC).
Results:
Both sites achieved dramatic improvements in their complex, multidisciplinary systems. A 94% reduction in number of patients on the waitlist from 99 to 6 patients and a 22% reduction in median delay for a new ongoing care appointment were realized at NCH. A 94% reduction in third next available appointment for new physician visits for children 3 to 5 years old was realized at CCHMC.
Conclusions:
This article demonstrates that 2 different clinical systems improved access to care for autism diagnosis and follow-up care by identifying sources of delay and using targeted changes based on a set of access change principles. With appropriate guidance and data analysis, improvements in access can be made.
A sample of 1047 parents completed an online survey about their experiences and opinions regarding the process of attaining a diagnosis of autism spectrum disorder for their children. The results revealed that parents usually waited a year from when they first had concerns about their child's development before they sought professional help. On average, there was a delay of around 3.5 years from the point at which parents first approached a health professional with their concerns to the confirmation of an autism spectrum disorder diagnosis. Just over half of the parents surveyed were dissatisfied with the diagnostic process as a whole. Several factors predicted parents' overall levels of satisfaction with the diagnostic process, including the time taken to receive a diagnosis, satisfaction with the information provided at diagnosis, the manner of the diagnosing professional, the stress associated with the diagnostic process and satisfaction with post-diagnostic support. Post-diagnosis, the support (if any) that was provided to parents was deemed unsatisfactory, and this was highlighted as an area of particular concern among parents.
© The Author(s) 2015.
Obtaining a diagnosis of autism spectrum disorder (ASD) often includes long delays, multiple diagnoses, and several visits to healthcare professionals. Consequently, parents often report dissatisfaction and frustration with the diagnostic process and their child's diagnosis. One hundred and sixty-six parents/ caregivers of children in Canada with various ASD diagnoses completed a self-report questionnaire related to their experienc-es with the diagnostic process. Parents expressed high levels of satisfaction with their child's current diagnosis, despite delays and concerns related to additional psychological issues identi-fied, particularly anxiety. Difficulties in obtaining a diagnosis may not only have a significant impact on the timing of inter-vention, it can also increase family stress and caregiver burden. As such, it is essential to understand families' experiences in order to help improve the diagnostic process. The degree of heterogeneity of symptom presentation of indi-viduals with autism spectrum disorder (ASD), coupled with potential co-morbid mental health concerns, can complicate the process of obtaining an accurate diagnosis. As a result, individuals with ASD may be undiagnosed, misdiagnosed, or receive numerous diagnoses (Siklos & Kerns, 2007; White, Oswald, Ollendick, & Scahill, 2009). Delays in early identifi-cation have detrimental effects on the child receiving early and appropriate intervention. Consequently, parents are often dissatisfied with the process in obtaining an ASD diagnosis, which can contribute to the stress and caregiver burden that these families often feel (Howlin & Moore, 1997; White et al., 2009). It is therefore important to understand more about how the diagnostic process affects caregivers, and how co-morbid mental health issues may complicate the diagnostic process.
Autism spectrum disorders (ASDs) were once considered lifelong disorders, but recent findings indicate that some children with ASDs no longer meet diagnostic criteria for any ASD and reach normal cognitive function. These children are considered to have achieved "optimal outcomes" (OO). The present study aimed to retrospectively examine group differences in the intervention history of children and adolescents with OO and those with high-functioning autism (HFA).
The current study examined intervention histories in 25 individuals with OO and 34 individuals with HFA (current age, 8-21 years), who did not differ on age, sex, nonverbal intelligence, or family income. Intervention history was collected through detailed parent questionnaires.
Children in the OO group had earlier parental concern, received earlier referrals to specialists, and had earlier and more intensive intervention than those in the HFA group. Substantially more children with OO than HFA received applied behavior analysis (ABA) therapy, although for children who received ABA, the intensity did not differ between the groups. Children in the HFA group were more likely to have received medication, especially antipsychotics and antidepressants. There were no group differences in the percent of children receiving special diets or supplements.
These data suggest that OO individuals generally receive earlier, more intense interventions, and more ABA, whereas HFA individuals receive more pharmacologic treatments. Although the use of retrospective data is a clear limitation to the current study, the substantial differences in the reported provision of early intervention, and ABA in particular, is highly suggestive and should be replicated in prospective studies.
The increased prevalence of autism spectrum disorder and documented benefits of early intensive intervention have created a need for flexible systems for determining eligibility for autism-specific services. This study evaluated the effectiveness of a training program designed to enhance autism spectrum disorder identification and assessment within community pediatric settings across the state. Twenty-seven pediatric providers participated in regional trainings across a 3.5-year period. Trainings provided clinicians with strategies for conducting relatively brief within-practice interactive assessments following positive autism spectrum disorder screenings. Program evaluation was measured approximately 1.5 years following training through (a) clinician self-reports of practice change and (b) blind diagnostic verification of a subset of children assessed. Pediatric providers participating in the training reported significant changes in screening and consultation practices following training, with a reported 85% increase in diagnostic identification of children with autism spectrum disorder within their own practice setting. In addition, substantial agreement (86%-93%) was found between pediatrician diagnostic judgments and independent, comprehensive blinded diagnostic evaluations. Collaborative training methods that allow autism spectrum disorder identification within broader community pediatric settings may help translate enhanced screening initiatives into more effective and efficient diagnosis and treatment.
A previously published randomized clinical trial indicated that a developmental behavioral intervention, the Early Start Denver Model (ESDM), resulted in gains in IQ, language, and adaptive behavior of children with autism spectrum disorder. This report describes a secondary outcome measurement from this trial, EEG activity.
Forty-eight 18- to 30-month-old children with autism spectrum disorder were randomized to receive the ESDM or referral to community intervention for 2 years. After the intervention (age 48 to 77 months), EEG activity (event-related potentials and spectral power) was measured during the presentation of faces versus objects. Age-matched typical children were also assessed.
The ESDM group exhibited greater improvements in autism symptoms, IQ, language, and adaptive and social behaviors than the community intervention group. The ESDM group and typical children showed a shorter Nc latency and increased cortical activation (decreased α power and increased θ power) when viewing faces, whereas the community intervention group showed the opposite pattern (shorter latency event-related potential [ERP] and greater cortical activation when viewing objects). Greater cortical activation while viewing faces was associated with improved social behavior.
This was the first trial to demonstrate that early behavioral intervention is associated with normalized patterns of brain activity, which is associated with improvements in social behavior, in young children with autism spectrum disorder.
An unresolved issue in the field of implementation research is how to conceptualize and evaluate successful implementation. This paper advances the concept of "implementation outcomes" distinct from service system and clinical treatment outcomes. This paper proposes a heuristic, working "taxonomy" of eight conceptually distinct implementation outcomes-acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability-along with their nominal definitions. We propose a two-pronged agenda for research on implementation outcomes. Conceptualizing and measuring implementation outcomes will advance understanding of implementation processes, enhance efficiency in implementation research, and pave the way for studies of the comparative effectiveness of implementation strategies.
The study examined the properties of the Screening Tool for Autism in Two-Year-Olds (STAT) for children under 24 months. The STAT provides a standard context for observing social-communicative behavior in play, imitation, and communication. Seventy-one children received the STAT between 12 and 23 months of age and a follow-up diagnostic evaluation after 24 months. All had an older sibling with an autism spectrum diagnosis (n=59) or had been referred for evaluation for concerns about autism (n=12). Signal detection analysis resulted in a cut score of 2.75 for this sample, which yielded a sensitivity of 0.95, specificity of 0.73, positive predictive value of 0.56, and negative predictive value of 0.97. False positives were highest for the 12- to 13-month-old age group; STAT screening properties were improved when the sample was limited to children 14 months and older. Implications for using the STAT with children under 24 months are discussed.
Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled "Autism: Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.
Objective:
To describe and evaluate the effectiveness of a quality improvement project to decrease wait time to evaluation for children referred to Developmental Behavioral Pediatricians (DBPs).
Methods:
The authors created a Behavioral/Developmental Access Clinic (BDAC) staffed by a general pediatrician (GP) and pediatric psychologist. Clinicians in the BDAC provided comprehensive developmental evaluations for children in a discrete age range (<5 yr old). We describe the establishment of the BDAC along with referrals, diagnoses, and recommended follow-up for patients seen by the GP. We used 2-tailed t tests to compare the mean time with initial evaluation for patients seen in BDAC versus a DBP.
Results:
Sixty-three children were seen in BDAC over 6 months. Referrals from the BDAC included: physical/occupational/speech therapy (71%), psychology (35%), audiology (25%), genetics (14%), and neurology (8%). Five patients (8%) were diagnosed with autism spectrum disorder (ASD). Compared with time to appointment with a DBP (327 d), mean time to developmental assessment was shorter for the 45 patients who accepted earlier appointments in the BDAC (159 d), and for the 18 children seen in the BDAC as new referrals (11 d), p < .001. Follow-up with a DBP was recommended for 9 (50%) of the new patient referrals evaluated in BDAC.
Conclusion:
The BDAC allowed for earlier developmental assessment of young children, with potential for earlier diagnosis and treatment of developmental disorders, including ASD. Opportunity for initial evaluation in BDAC decreased the number of patients requiring evaluation by DBPs, improving access to this subspecialty in our institution.
Objective:
To describe influences on shared decision making (SDM) between primary care pediatricians and parents of young children with autism spectrum disorder (ASD).
Methods:
We conducted a qualitative study using key informant interviews with 20 pediatricians of varying experience from 10 primary care practices and 20 English speaking parents of young children (ages 2-5 years) with a parent reported diagnosis of ASD. Subjects were recruited through purposive sampling. Interviews were audio taped, transcribed verbatim and analyzed using an integrated approach to data analysis. Differences in coding were resolved by consensus. We achieved thematic saturation and ceased recruitment after 20 interviews were completed within each group.
Results:
Three primary themes emerged: 1) pediatricians and parents reported knowledge gaps by pediatricians about ASD treatments and community resources and ambiguity regarding the pediatrician's role in ASD care; 2) there was little communication between parents and pediatricians about treatment choices; 3) use of CAM treatments created conflict between pediatricians and parents and, as a result, parents may independently pursue treatments, without benefit of discussing safety and efficacy with pediatricians. Despite these barriers, parents desired increased support and guidance from their pediatricians, including for CAM.
Conclusions:
Much work is needed to effectively foster SDM in the context of ASD treatment decisions in primary care, including pediatrician training in ASD to enhance knowledge about evidence-based and novel treatments, clinical practice guidelines and community resources.
Objective:
To determine the prevalence of receipt of early intervention and therapeutic services in children suspected of having possible autism spectrum disorder (ASD) before their diagnostic ASD evaluations.
Methods:
The electronic medical records of all children ≤5 years of age evaluated at a single regional ASD clinic between September 2012 and June 2014 were reviewed. Information regarding type of services, clinical diagnoses, and demographic information was abstracted for each patient.
Results:
Five hundred sixty-one children (mean age = 44 mo [SD, 10 mo]; 80% [N = 450] male; 20% [N = 111] female) completed a diagnostic ASD evaluation. Of these children, 497 (89%) were already receiving early intervention services, and only 64 (11%) were not receiving any services. Receipt of services did not vary based on race, ethnicity, insurance type, or primary language. Children who were already receiving services were more likely to receive an ASD diagnosis (67%, N = 332) than those not receiving services (44% [N = 28]; p ≤ .001).
Conclusion:
Despite concerns that long wait lists for diagnostic ASD evaluations may delay initiation of critical early interventions, our data indicate that most children are receiving early intervention services before their diagnostic ASD evaluations, particularly if an ASD diagnosis is confirmed. This may be attributable to increased awareness among primary care providers and families of the importance of early interventions. Further investigation into access to more intensive interventions (such as applied behavioral analysis) once an ASD diagnosis is established is warranted.
Subspecialty physicians who have expertise in the diagnosis of autism spectrum disorder typically do not have the resources to administer comprehensive diagnostic observational assessments for patients suspected of ASD. The autism mental status exam (AMSE) is a free and brief eight-item observation tool that addresses this practice gap. The AMSE, designed by Child and Adolescent Psychiatrists, Developmental Behavioral Pediatricians and Pediatric Neurologists structures the observation and documentation of signs and symptoms of ASD and yields a score. Excellent sensitivity and specificity was demonstrated in a population of high-risk adults. This protocol now investigates the AMSE's test performance in a population of 45 young children age 18 months to 5 years with suspected ASD or social and communication concerns who are evaluated at an autism research center. Each subject received a developmental evaluation, including the AMSE, performed by a Child and Adolescent Psychiatrist, that was followed by independent standardized assessment using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview-Revised. A Best Estimate Diagnosis protocol used DSM-5 criteria to ascertain a diagnosis of ASD or non-ASD. Receiver operating characteristic curve analysis was used to determine the AMSE cut point with the highest sensitivity and specificity. Findings indicate an optimized sensitivity of 94% and a specificity of 100% for this high prevalence group. Because of its high classification accuracy in this sample of children the AMSE holds promise as a tool that can support both diagnostic decision making and standardize point of care observational assessment of ASD in high risk children. Autism Res 2015. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.
© 2015 International Society for Autism Research, Wiley Periodicals, Inc.
Despite their range and complexity, adherence to Scottish Intercollegiate Guidelines Network guideline for the diagnosis and assessment of autism spectrum disorders (ASD) was shown to be high within child development and specialist diagnostic clinics serving a geographical cohort of children diagnosed under the age of 7 years. A retrospective analysis of comprehensive clinical notes demonstrated that the recommended discretionary use of structured history instruments was increased after medical training (p = 0.003). 56 % (51/90) of children received the diagnosis of ASD at their initial specialist appointment. 51 % underwent the recommended discretionary structured observational instrument. This further assessment was more likely to be required for older children in the reaudited group (p = 0.001). The implications for service capacity planning when delivering best practice recommendations are discussed.
Objective:
This study validates the Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F), a screening tool for low-risk toddlers, and demonstrates improved utility compared with the original M-CHAT.
Methods:
Toddlers (N = 16,071) were screened during 18- and 24-month well-child care visits in metropolitan Atlanta and Connecticut. Parents of toddlers at risk on M-CHAT-R completed follow-up; those who continued to show risk were evaluated.
Results:
The reliability and validity of the M-CHAT-R/F were demonstrated, and optimal scoring was determined by using receiver operating characteristic curves. Children whose total score was ≥ 3 initially and ≥ 2 after follow-up had a 47.5% risk of being diagnosed with autism spectrum disorder (ASD; confidence interval [95% CI]: 0.41-0.54) and a 94.6% risk of any developmental delay or concern (95% CI: 0.92-0.98). Total score was more effective than alternative scores. An algorithm based on 3 risk levels is recommended to maximize clinical utility and to reduce age of diagnosis and onset of early intervention. The M-CHAT-R detects ASD at a higher rate compared with the M-CHAT while also reducing the number of children needing the follow-up. Children in the current study were diagnosed 2 years younger than the national median age of diagnosis.
Conclusions:
The M-CHAT-R/F detects many cases of ASD in toddlers; physicians using the 2-stage screener can be confident that most screen-positive cases warrant evaluation and referral for early intervention. Widespread implementation of universal screening can lower the age of ASD diagnosis by 2 years compared with recent surveillance findings, increasing time available for early intervention.
We assessed the extent to which parents of children with autism spectrum disorder report that they are engaged in shared decision making. We measured the association between shared decision making and (a) satisfaction with care, (b) perceived guidance regarding controversial issues in autism spectrum disorder, and (c) perceived assistance navigating the multitude of treatment options. Surveys assessing primary medical care and decision-making processes were developed on the basis of the U.S. Department of Health and Human Service's Consumer Assessment of Healthcare Providers and Systems survey. In May 2009, after pilot testing, we sent surveys to 203 parents of children from ages 3 to 18 with International Classification of Diseases-9 and parent-confirmed autism spectrum disorder diagnoses. The response rate was 64%. Controlling for key demographic variables, parents of children with autism spectrum disorder reporting higher levels of shared decision making reported significantly greater satisfaction with the overall quality of their child's health care (p ≤ .0001). Parents reporting higher levels of shared decision making were also significantly more likely to report receiving guidance on the many treatment options (p = .0002) and controversial issues related to autism spectrum disorder (p = .0322). In this study, shared decision making was associated with higher parent satisfaction and improved guidance regarding treatments and controversial issues within primary care for children with autism spectrum disorder.
Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
The Pre-Linguistic Autism Diagnostic Observation Schedule (PL-ADOS) is a semistructured observation scale designed for use as a diagnostic tool for children less than 6 years old who are not yet using phrase speech and are suspected of having autism. The PL-ADOS takes approximately 30 minutes to administer and is appropriate for use with this population because of its emphasis on playful interactions and the use of toys designed for young children. Reliability studies indicated that both individual activity ratings and summary ratings could be reliably scored from videotaped assessments by naive raters. Additionally, PL-ADOS scores of nonverbal preschool-aged children referred for clinical diagnosis and classified on the basis of a diagnostic team's clinical judgment, clearly discriminated between autistic and nonautistic developmentally disabled children. The resulting diagnostic algorithm is theoretically linked to diagnostic constructs associated with ICD-10 and DSM-IV criteria for autism.
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