July 13, 2021


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Tags: Autism, Estimating, Maenner, Matthew, prevalence, quickly, Spectrum


Categories: autism

Q&A with Matthew Maenner: Estimating autism prevalence rapidly | Spectrum

Matthew Maenner

Supervision Team Leader, National Center for Birth Defects and Developmental Disorders

Every two years, the Autism and Developmental Disabilities Monitoring (ADDM) Network of the US Centers for Disease Control and Prevention (CDC) measures the prevalence of autism in 8-year-old children in several states.

ADDM clinics scour children’s medical and school records for diagnoses and other information to determine which of them have autism. The researchers used the resulting data to estimate the prevalence of autism for the 11 states that make up the network.

This process takes at least four years to produce results.

For the 2018 surveillance year, the ADDM network introduced a faster method of tracking prevalence. Under the new system, researchers no longer look for signs of autism in children’s school and medical records. Instead, they draw from three sources of data: records of autism diagnoses from clinicians, special education classifications of autism, and hospital billing codes for autism services. The system omits the time-consuming and labor-intensive evaluations of the ADDM clinics.

The new method uses half as much data as the old one, but still delivers similar prevalence results, according to a published comparison of data from 2014 and 2016. The results appeared in the American Journal of Epidemiology in April.

Spectrum spoke to Matthew Maenner, one of the developers of the new system, about the updated methodology and the new insights it could offer. Maenner is an epidemiologist and leader of a surveillance team for the CDC’s National Center on Birth Defects and Developmental Disabilities.

spectrum: What were the problems like that ADDM Network Has Determined The Prevalence Of Autism?

Matt Maenner: The previous approach was first used in 1996 to monitor autism in children in the greater Atlanta, Georgia area. It is designed to identify children who have characteristics of autism but may be assessed by practitioners who do not consistently identify autism. The process was resource intensive and the amount of peer-reviewed records had increased dramatically in recent years.

S: What are the advantages of the new method?

MM: The current and previous methods provide similar results in terms of overall prevalence, gender ratio, racial and ethnic differences, age at diagnosis, and the percentage of children with intellectual disabilities. But the new method simplifies data collection and requires about half as many evaluations and cognitive tests. This improved efficiency has enabled the CDC to support more study centers than would be possible with the previous methods. It also allows the network to report data earlier, expand tracking of early detection, and add a follow-up exam with children as young as 16 years old.

The new method also more directly measures community practices related to autism identification and services. Differences in the prevalence of autism between groups could be interpreted as differences in community-level practices for identifying autism or in the availability of autism services.

S: What compromises are there when switching from the old to the new method?

MM: A compromise arises from the fact that the prevalence estimates no longer include children who have only been identified as autistic by ADDM clinics based on behavioral descriptions in medical and school records. But the estimates will now include all children whose school or medical records show a diagnosis of autism, a special education classification of autism, or a code from the International Classification of Diseases for Autism. In the years 2014 and 2016, which we compared in the newspaper, the “lost” and “gained” children balanced each other out at most locations. However, it is difficult to say with certainty whether the group of children who were not previously identified using the old method was complete, as this method likely did not capture all of the unidentified cases at the ADDM sites. In addition to this uncertainty, evidence suggests that the previous method also undercounted non-white children, who had less access to reviews and services.

All in all, the new method will expand the types of data sources that ADDM sites can use to identify all children with autism compared to the previous method that required written ratings. For example, some websites will now include data from government funded autism services like Medicaid. This could lead to a more complete count of children with autism and would provide additional context on how autism services are used in a community.

S: How has the process of implementing this new method developed?

MM: Our team at the CDC converted the ADDM data system with modern tools, created documentation and training materials, developed quality control processes and trained and supported the employees at the ADDM locations in using the new method. They were successful – and finished on time – and I’m so proud of them. We also appreciate the flexibility and determination of the investigators and staff at the ADDM locations. Data collection was relatively smooth – especially given all the disruption in 2020. Looking back, some sites may have had difficulty completing planned activities using the previous method, but the new approach is likely to be much more resilient to obstacles in physically accessing reviews.

S: What are the next steps to deploy the new case definitions?

MM: The network has collected data for 2018 and we are working on the next prevalence reports. The new method has given us the flexibility to test a cost-effective approach to statewide autism monitoring that provides at least some data for each community in a state. ADDM sites usually only monitor autism in part of a state, but we have been able to adapt our programs and tools to try to make county-level estimates for children of all ages through record-linking. While these estimates are not as detailed as those of regular ADDM activities, they could provide useful information to communities that do not currently have local data on autism.

The network is also collecting data for the next surveillance year – 2020 – which will include the same locations as 2018. It will continue to monitor autism prevalence, track progress in early detection of autism, and describe the health and service needs of adolescents with autism. As the 2018 and 2020 municipalities are the same, it may be possible to observe changes or interruptions in development assessments or services used in 2020.

Quote this article: https://doi.org/10.53053/JBRS7461


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