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The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year-old children in multiple US communities. From 2000 to 2016, investigators at ADDM Network sites classified ASD from collected text descriptions of behaviors from medical and educational evaluations which were reviewed and coded by ADDM Network clinicians. It took at least 4 years to publish data from a given surveillance year. In 2018, we developed an alternative case definition utilizing ASD diagnoses or classifications made by community professionals. Using data from surveillance years 2014 and 2016, we compared the new and previous ASD case definitions. Compared with the prevalence based on the previous case definition, the prevalence based on the new case definition was similar for 2014 and slightly lower for 2016. Sex and race/ethnicity prevalence ratios were nearly unchanged. Compared with the previous case definition, the new case definition’s sensitivity was 86% and its positive predictive value was 89%. The new case definition does not require clinical review and collects about half as much data, yielding more timely reporting. It also more directly measures community identification of ASD, thus allowing for more valid comparisons among communities, and reduces resource requirements while retaining measurement properties similar to those of the previous definition.

Long-standing systemic social, economic, and environmental inequities in the United States have put many communities of color (racial and ethnic minority groups) at increased risk for exposure to and infection with SARS-CoV-2, the virus that causes COVID-19, as well as more severe COVID-19-related outcomes (1-3). Because race and ethnicity are missing for a proportion of reported COVID-19 cases, counties with substantial missing information often are excluded from analyses of disparities (4). Thus, as a complement to these case-based analyses, population-based studies can help direct public health interventions. Using data from the 50 states and the District of Columbia (DC), CDC identified counties where five racial and ethnic minority groups (Hispanic or Latino [Hispanic], non-Hispanic Black or African American [Black], non-Hispanic Asian [Asian], non-Hispanic American Indian or Alaska Native [AI/AN], and non-Hispanic Native Hawaiian or other Pacific Islander [NH/PI]) might have experienced high COVID-19 impact during April 1-December 22, 2020. These counties had high 2-week COVID-19 incidences (>100 new cases per 100,000 persons in the total population) and percentages of persons in five racial and ethnic groups that were larger than the national percentages (denoted as "large"). During April 1-14, a total of 359 (11.4%) of 3,142 U.S. counties reported high COVID-19 incidence, including 28.7% of counties with large percentages of Asian persons and 27.9% of counties with large percentages of Black persons. During August 5-18, high COVID-19 incidence was reported by 2,034 (64.7%) counties, including 92.4% of counties with large percentages of Black persons and 74.5% of counties with large percentages of Hispanic persons. During December 9-22, high COVID-19 incidence was reported by 3,114 (99.1%) counties, including >95% of those with large percentages of persons in each of the five racial and ethnic minority groups. The findings of this population-based analysis complement those of case-based analyses. In jurisdictions with substantial missing race and ethnicity information, this method could be applied to smaller geographic areas, to identify communities of color that might be experiencing high potential COVID-19 impact. As areas with high rates of new infection change over time, public health efforts can be tailored to the needs of communities of color as the pandemic evolves and integrated with longer-term plans to improve health equity.