Evaluation of a methodology for a collaborative multiple source surveillance network for autism spectrum disorders--Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2002.
Autism spectrum disorders (ASDs) encompass a spectrum of conditions, including autistic disorder; pervasive developmental disorders, not otherwise specified (PDD-NOS); and Asperger disorder. Impairments associated with ASDs can range from mild to severe. In 2000, in response to increasing public heath concern regarding ASDs, CDC established the Autism and Developmental Disabilities Monitoring (ADDM) Network. The primary objective of this ongoing surveillance system is to track the prevalence and characteristics of ASDs in the United States. ADDM data are useful to understand the prevalence of ASDs and have implications for improved identification, health and education service planning, and intervention for children with ASDs. Because complete, valid, timely, and representative prevalence estimates are essential to inform public health responses to ASDs, evaluating the effectiveness and efficiency of the ADDM methodology is needed to determine how well these methods meet the network's objective.
The ADDM Network is a multiple-source, population-based, active system for monitoring ASDs and other developmental disabilities. In 2002, data were collected from 14 collaborative sites. This report describes an evaluation conducted using guidelines established by CDC for evaluating public health surveillance systems and is based on examination of the following characteristics of the ADDM Network surveillance system: simplicity, flexibility, data quality, acceptability, representativeness, sensitivity, predictive value positive (PVP), timeliness, stability, data confidentiality and security, and sources of variability.
Using multiple sources for case ascertainment strengthens the system's representativeness, sensitivity, and flexibility, and the clinician review process aims to bolster PVP. Sensitivity and PVP are difficult to measure, but the ADDM methodology provides the best possible estimate currently available of prevalence of ASDs without conducting complete population screening and diagnostic clinical case confirmation. Although the system is dependent on the quality and availability of information in evaluation records, extensive quality control and data cleaning protocols and missing records assessments ensure the most accurate reflection of the records reviewed. Maintaining timeliness remains a challenge with this complex methodology, and continuous effort is needed to improve timeliness and simplicity without sacrificing data quality. The most difficult influences to assess are the effects of changes in diagnostic and treatment practices, service provision, and community awareness. Information sharing through education and outreach with site-specific stakeholders is the best mechanism for understanding the current climate in the community with respect to changes in service provision and public policy related to ASDs, which can affect prevalence estimates.
These evaluation results and descriptions can be used to help interpret the ADDM Network 2002 surveillance year data and can serve as a model for other public health surveillance systems, especially those designed to monitor the prevalence of complex disorders.
"Therefore, careful data screening procedures are needed to ensure that the data can reflect actual patterns in the schools. Some of these concerns have been studied in other health surveillance research areas -. The purpose of this study was to systematically evaluate different data screening procedures on school level estimates of fitness outcomes collected from local schools spread throughout the US. "
[Show abstract][Hide abstract] ABSTRACT: Background: There has been a great interest in tracking health-related fitness across the United States. The NFL PLAY 60 FITNESSGRAM Partnership Project (NFL P60FGPP) is a large participatory research network that involves the surveillance of fitness among more than 1000 schools spread throughout the country. Fitness data are collected by school staff and therefore these data can vary in quality and representativeness. Therefore, careful screening procedures are needed to ensure that the data can reflect actual patterns in the schools. This study examined the impact of different data screening procedures on outcomes of aerobic fitness (AF) collected from the NFL P60FGPP. Methods: Data were compiled from 149,101 youth from 504 schools and were processed using the established age-and gender-specific AF FITNESSGRAM health-related standards. Data were subjected to three different screening procedures (based on grade size and boy-to-girl ratio per grade). Linear models were computed to obtain unadjusted and adjusted (for age, BMI-Z, and socio-economic status) estimates of % youth in the Healthy Fitness Zone (HFZ) in order to determine if, 1) there were differences in % in the HFZ and 2) if differences could be explained by changes in the representativeness of the sample due to the different data screening procedures. Results: Depending on the screening procedure used, the final sample ranged from 96,999 (no screening) to 46,572 youth (most stringent criteria). The proportion of youth achieving appropriate levels of AF ranged from 56% to 61% with unscreened data resulting in consistently lower percentages of youth achieving the standard (P < 0.05). Overall, these differences were not explained by possible changes in demographic characteristics as the result of applying different screening criteria. Conclusions: The findings demonstrate the importance of establishing appropriate screening procedures that maximize sample size while also ensuring generalizability of the findings.
Open Journal of Preventive Medicine 11/2014; 4(4):876-886. DOI:10.4236/ojpm.2014.411099
[Show abstract][Hide abstract] ABSTRACT: Recent advances in studies of Autism Spectrum Disorders (ASD) has uncovered many new candidate genes and continues to do so at an accelerated pace. To address the genetic complexity of ASD, we have developed AutDB (http://www.mindspec.org/autdb.html), a publicly available web-portal for on-going collection, manual annotation and visualization of genes linked to the disorder. We present a disease-driven database model in AutDB where all genes connected to ASD are collected and classified according to their genetic variation: candidates identified from genetic association studies, rare single gene mutations and genes linked to syndromic autism. Gene entries are richly annotated for their relevance to autism, along with an in-depth view of their molecular functions. The content of AutDB originates entirely from the published scientific literature and is organized to optimize its use by the research community. The main focus of this resource is to provide an up-to-date, annotated list of ASD candidate genes in the form of reference dataset for interrogating molecular mechanisms underlying the disorder. Our model for consolidated knowledge representation in genetically complex disorders could be replicated to study other such disorders.
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