Computing Theoretical Rates of Part C Eligibility Based on Developmental Delays

Rosenberg, Steven A.; Ellison, Misoo C.; Fast, Bruce; Robinson, Cordelia; Lazar, Radu

doi:10.1007/s10995-012-0982-2

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…This represents about 5% of the sample. This percent of outliers is between the prevalence data of 13% for developmental disabilities and the 1.5–2% estimates for prevalence data of ASD 84,85 . Longitudinal follow up will confirm if around 3% of this sample will have closely related developmental disorders affecting language or developmental delays that do not fulfill full criteria for autism.…”

Section: Proof Of Principle: Anomaly Detection As a Computational Examentioning

confidence: 79%

Neurodevelopmental heterogeneity and computational approaches for understanding autism

et al. 2019

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In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to streamline ASD’s diagnostic methods, to discern similarities and differences from comorbid diagnoses, and to follow developmentally variable outcomes. Both supervised machine learning models for classification outcome and unsupervised approaches to identify new dimensions and subgroups are discussed. We provide an illustrative example of how computational analytic methods and a longitudinal design can improve our inferential ability to detect early dysfunctional behaviors that may or may not reach threshold levels for formal diagnoses. Specifically, an unsupervised machine learning approach of anomaly detection is used to illustrate how community samples may be utilized to investigate early autism risk, multidimensional features, and outcome variables. Because ASD symptoms and challenges are not static within individuals across development, computational approaches present a promising method to elucidate subgroups of etiological contributions to phenotype, alternative developmental courses, interactions with biomedical comorbidities, and to predict potential responses to therapeutic interventions.

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Section: Proof Of Principle: Anomaly Detection As a Computational Examentioning

confidence: 79%

Neurodevelopmental heterogeneity and computational approaches for understanding autism

et al. 2019

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“…For example, Hispanic and Black families are less likely to be referred to high-risk infant follow up programs [6] and Black families are five to eight times less likely to follow up at the 24 month visit in early intervention programs [7]. Disparities in transition-to-home and follow-up for families who have limited English proficiency and/or are minorities or low-income exist and may be reduced by engaging families in the process of re-designing transition of care from NICU-to-home [3,4,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Viewpoints from families for improving transition from NICU-to-home for infants with medical complexity at a safety net hospital: a qualitative study

et al. 2019

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Background: We have limited information on families' experiences during transition and after discharge from the neonatal intensive care unit. Methods: Open-ended semi-structured interviews were conducted with English or Spanish-speaking families enrolled in Medicaid in an urban high-risk infant follow up clinic at a safety-net center, which serves preterm and high-risk term infants. We generated salient themes using inductive-deductive thematic analysis. Results: Twenty-one participants completed the study. The infant's median (IQR) birth weight was 1750 (1305, 2641) grams; 71% were Hispanic and 10% were Black non-Hispanic; 62% reported living in a neighborhood with 3-4th quartile economic hardship. All were classified as having chronic disease per the Pediatric Medical Complexity Algorithm and 67% had medical complexity. A conceptual model was constructed and the analysis revealed major themes describing families' challenges and ideas to support transition centered on the parent-child role and parent self-efficacy. The challenges were: (1) comparison to normal babies, (2) caregiver mental health, (3) need for information. Ideas to support transition included, (1) support systems, (2) interventions using mobile health technology (3) improved communication to the primary care provider and (4) information regarding financial assistance programs. Specific subthemes differed in frequency counts between infants with and without medical complexity. Conclusions: Families often compare their preterm or high-risk infant to their peers and mothers feel great anxiety and stress. However, families often found hope and resilience in peer support and cited that in addition to information needs, interventions using mobile health technology and transition and financial systems could better support families after discharge.

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“…Although developmental delays are relatively common, and the importance of early intervention is well-established (Pinto-Martin, Dunkle, Earls, Fliedner, & Landes, 2005;Ramey & Ramey, 2004), only a small proportion of young children with delays actually receive intervention services. The estimated prevalence of developmental delay is 17%, or one in six, of all children under age 5 (Boyle et al, 2011), and 10% to 12% of children under age 3 (Rosenberg, Ellison, Fast, Robinson, & Lazar, 2013;Sices, 2007); yet the percentage of children in the United States under-availability of programs, narrow eligibility criteria, funding, and poor interagency linkages and coordination (Coulter, Wallace, & Laude, 1993;Halfon et al, 2004;Marshall, Kirby, & Gorski, 2016;Marshall & Mendez, 2014).…”

Section: Introductionmentioning

confidence: 99%

Parents’ Experiences Navigating Intervention Systems for Young Children With Mild Language Delays

Marshall

Adelman

Kesten

et al. 2017

Journal of Early Intervention

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The purpose of this study was to explore the experiences of parents of children with mild language delays who were referred to an Individuals With Disabilities Education Act (IDEA) birth-to-3 or preschool program, were determined not eligible for services, and were referred on to a community-based program for short-term speech-language therapy. Interviews were conducted in the parents’ preferred language (10 Spanish, 10 English). A grounded theory approach was applied to identify emergent themes. Results indicated that the process of recognizing a developmental delay, receiving assessment results, and enrolling in services presents challenges that require parents to demonstrate engagement and advocacy throughout. This study highlights parents’ motivations for seeking out early intervention services and underscores the importance of understanding parents’ experiences navigating developmental service systems. Program planners can partner with parents to address barriers and also capitalize on successes to facilitate timely response to developmental concerns, appropriate referrals, and effective interventions.

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Computing Theoretical Rates of Part C Eligibility Based on Developmental Delays

Cited by 8 publications

References 6 publications

Neurodevelopmental heterogeneity and computational approaches for understanding autism

Neurodevelopmental heterogeneity and computational approaches for understanding autism

Viewpoints from families for improving transition from NICU-to-home for infants with medical complexity at a safety net hospital: a qualitative study

Parents’ Experiences Navigating Intervention Systems for Young Children With Mild Language Delays

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