An integrated information system for all-hazards health preparedness and response: New York State Health Emergency Response Data System.

Effective and timely exchange of information among healthcare, state and local public health, and other health emergency response partners is essential to all-hazards emergency preparedness and response. Since fall of 2001, NY State Department of Health has partnered with the healthcare and public health community in New York to implement a statewide Health Emergency Response Data System to meet this need. During this time, it has been used in a wide range of preparedness and response applications including regional and local exercises, surveillance, health facility asset tracking, and response to actual health events. The architecture, design, and implementation model used in the system readily support all-hazards preparedness and response at state, regional, and local levels. It has become one of the most important assets to health emergency response in New York State.     

An implementation story: moving the GAIN from pilot project to statewide use

Similar to implementing an evidence-based practice (EPB), implementing an evidence-based assessment (EBA) is a long, complex process that can take several years to complete. Between 2002 and 2007, the state of Missouri first piloted the Global Appraisal of Individual Needs-Initial (GAIN-I; Dennis et al. 2006) assessment at one state-contracted adolescent substance abuse treatment program and then implemented the GAIN statewide. This case study documents the implementation process through Fixsen and colleagues' (2005) six stages of implementation, from exploration and adoption through sustainability, and outlines challenges and solutions encountered at the external, organizational, and individual staff level. Strengths of this project included recognition that implementation is an ongoing multistep process, strong support by state-level staff, contracting with an external purveyor for ongoing technical assistance at the agency level as well as training of front-line clinicians, and an implementation team comprised of stakeholders at multiple levels.     

Subcategories of Restricted and Repetitive Behaviors in Children with Autism Spectrum Disorders

Research suggests that restricted and repetitive behaviors (RRBs) can be subdivided into Repetitive Sensory Motor (RSM) and Insistence on Sameness (IS) behaviors. However, because the majority of previous studies have used the Autism Diagnostic Interview-Revised (ADI-R), it is not clear whether these subcategories reflect the actual organization of RRBs in ASD. Using data from the Simons Simplex Collection (n = 1,825), we examined the association between scores on the ADI-R and the Repetitive Behavior Scale-Revised. Analyses supported the construct validity of RSM and IS subcategories. As in previous studies, IS behaviors showed no relationship with IQ. These findings support the continued use of RRB subcategories, particularly IS behaviors, as a means of creating more behaviorally homogeneous subgroups of children with ASD.     

A replication of the Autism Diagnostic Observation Schedule (ADOS) revised algorithms

Objective:To replicate the factor structure and predictive validity of revised Autism Diagnostic Observation Schedule algorithms in an independent dataset (N = 1,282).Method:Algorithm revisions were replicated using data from children ages 18 months to 16 years collected at 11 North American sites participating in the Collaborative Programs for Excellence in Autism and the Studies to Advance Autism Research and Treatment.Results:Sensitivities and specificities approximated or exceeded those of the old algorithms except for young children with phrase speech and a clinical diagnosis of pervasive developmental disorders not otherwise specified.Conclusions:Revised algorithms increase comparability between modules and improve the predictive validity of the Autism Diagnostic Observation Schedule forautism cases compared to the original algorithms.