Background On 20 September 2022, the Ugandan Ministry of Health declared an outbreak of Ebola disease caused by Sudan ebolavirus.Methods From 6 October 2022 to 10 January 2023, Centers for Disease Control and Prevention (CDC) staff conducted public health assessments at five US ports of entry for travellers identified as having been in Uganda in the past 21 days. CDC also recommended that state, local and territorial health departments ('health departments') conduct post-arrival monitoring of these travellers. CDC provided traveller contact information, daily to 58 health departments, and collected health department data regarding monitoring outcomes.Results Among 11 583 travellers screened, 132 (1%) required additional assessment due to potential exposures or symptoms of concern. Fifty-three (91%) health departments reported receiving traveller data from CDC for 10 114 (87%) travellers, of whom 8499 (84%) were contacted for monitoring, 1547 (15%) could not be contacted and 68 (1%) had no reported outcomes. No travellers with high-risk exposures or Ebola disease were identified.Conclusion Entry risk assessment and post-arrival monitoring of travellers are resource-intensive activities that had low demonstrated yield during this and previous outbreaks. The efficiency of future responses could be improved by incorporating an assessment of risk of importation of disease, accounting for individual travellers' potential for exposure, and expanded use of methods that reduce burden to federal agencies, health departments, and travellers.

Background Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. Materials and methods We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Results Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). Conclusions The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

In 2021, the US government undertook Operation Allies Welcome, in which evacuees from Afghanistan arrived at 2 US ports of entry in Virginia and Pennsylvania. Because of the rapid evacuation process, the US government granted evacuees an exemption to a Centers for Disease Control and Prevention (CDC) requirement in place at that time-namely, that air passengers present a negative SARS-CoV-2 viral test result or documentation of recovery from COVID-19 before they boarded international flights bound for the United States. This study describes cases of SARS-CoV-2 infection detected among 65 068 evacuees who arrived at the 2 ports of entry in August and September 2021. Because evacuees were a population at increased risk for infection with diseases of public health concern, CDC staff helped coordinate on-site and on-arrival testing, visually observed evacuees for signs and symptoms of communicable disease, and referred evacuees for further evaluation and treatment as needed. CDC staff used antigen or nucleic acid amplification tests at the ports of entry to evaluate evacuees aged >= 2 years without documentation of recent SARS-CoV-2 infection. CDC staff isolated evacuees with confirmed SARS-CoV-2 infection and quarantined their close contacts, consistent with CDC guidance at the time, before evacuees rejoined the repatriation process. Of 65 068 evacuees, 214 (0.3%) were confirmed as having SARS-CoV-2 infection after port-of-entry testing. Cases of measles, varicella, pertussis, tuberculosis, hepatitis A, malaria, leishmaniasis, and diarrheal illness were also identified. Although the percentage of SARS-CoV-2 infection was low in this evacuated population, communicable disease detection at US ports of entry, along with vaccination efforts, was an important part of a multilayered approach to mitigate the transmission of disease in congregate housing facilities and into US communities.

In the early years of the COVID-19 pandemic, unprecedented public health measures were designed and implemented to mitigate the spread of SARS-CoV-2. On January 26, 2021, US Centers for Disease Control and Prevention (CDC) staff began daily audits of documents of arriving passengers at 18 US international ports of entry to ensure documentation of either a negative predeparture antigen or nucleic acid amplification test result for SARS-CoV-2 or recent recovery from COVID-19. This case study briefly describes the results of those audits. The CDC found a very low rate of issues overall. Of the 483,251 passengers selected for audit, 2,142 (0.44%) had issues with their COVID-19 test documentation and 1,182 (0.24%) provided documentation of recovery from COVID-19 rather than a negative test result. The low rate of issues noted during traveler audits indicated airlines were largely compliant with the order. However, the burden of SARS-CoV-2 infections within the United States was high during much of this period, which suggests that implementing a predeparture testing requirement earlier in the pandemic might have had more impact on spread. Digital solutions could reduce the burden of similar interventions in the future on airlines, public health authorities, and other partners.

BACKGROUND Studies evaluating the association between prenatal ultrasounds and autism spectrum disorder (ASD) have largely produced negative results. Concern remains due to the rising identification of children with ASD and ultrasound use. OBJECTIVE To evaluate the association between prenatal ultrasound use and ASD. METHODS We used data from the Study to Explore Early Development, a multisite case-control study of preschool-aged children with ASD implemented during 2007-2012. We recruited cases from children receiving developmental disability services and randomly selected population controls from birth records. ASD case status was based on in-person standardised assessments. We stratified analyses by pre-existing maternal medical conditions and pregnancy complications associated with increased ultrasound use (ultrasound indications) and used logistic regression to model case status by increasing ultrasound counts. For pregnancies with medical record data on ultrasound timing, we conducted supplementary tests to model associations by trimester of exposure. RESULTS Among 1524 singleton pregnancies, ultrasound indications were more common for ASD cases than controls; respectively, for each group, no indications were reported for 45.1% and 54.2% of pregnancies, while ≥2 indications were reported for 26.1% and 18.4% of pregnancies. The percentage of pregnancies with multiple ultrasounds varied by case status and the presence of ultrasound indications. However, stratified regression models showed no association between increasing ultrasound counts and case status, either for pregnancies without (aOR 1.01, 95% CI 0.92, 1.11) or with ultrasound indications (aOR 1.01, 95% CI 0.95, 1.08). Trimester-specific analyses using medical record data showed no association in any individual trimester. CONCLUSIONS We found no evidence that prenatal ultrasound use increases ASD risk. Study strengths included gold-standard assessments for ASD case classification, comparison of cases with controls, and a stratified sample to account for conditions associated both with increased prenatal ultrasound use and ASD.

Objectives. To describe trends in the number of air travelers categorized as infectious with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2; the virus that causes COVID-19) in the context of total US COVID-19 vaccinations administered, and overall case counts of SARS-CoV-2 in the United States. Methods. We searched the Quarantine Activity Reporting System (QARS) database for travelers with inbound international or domestic air travel, a positive SARS-CoV-2 lab result, and a surveillance categorization of SARS-CoV-2 infection reported during January 2020 to December 2021. Travelers were categorized as infectious during travel if they had arrival dates from 2 days before to 10 days after symptom onset or a positive viral test. Results. We identified 80 715 persons meeting our inclusion criteria; 67 445 persons (83.6%) had at least 1 symptom reported. Of 67 445 symptomatic passengers, 43 884 (65.1%) reported an initial symptom onset date after their flight arrival date. The number of infectious travelers mirrored the overall number of US SARS-CoV-2 cases. Conclusions. Most travelers in the study were asymptomatic during travel, and therefore unknowingly traveled while infectious. During periods of high community transmission, it is important for travelers to stay up to date with COVID-19 vaccinations and consider wearing a high-quality mask to decrease the risk of transmission. (Am J Public Health. 2023;113(8):904–908. https://doi.org/10.2105/AJPH.2023.307325 )

On August 29, 2021, the United States government oversaw the emergent establishment of Operation Allies Welcome (OAW), led by the U.S. Department of Homeland Security (DHS) and implemented by the U.S. Department of Defense (DoD) and U.S. Department of State (DoS), to safely resettle U.S. citizens and Afghan nationals from Afghanistan to the United States. Evacuees were temporarily housed at several overseas locations in Europe and Asia* before being transported via military and charter flights through two U.S. international airports, and onward to eight U.S. military bases,† with hotel A used for isolation and quarantine of persons with or exposed to certain infectious diseases.§ On August 30, CDC issued an Epi-X notice encouraging public health officials to maintain vigilance for measles among Afghan evacuees because of an ongoing measles outbreak in Afghanistan (25,988 clinical cases reported nationwide during January-November 2021) (1) and low routine measles vaccination coverage (66% and 43% for the first and second doses, respectively, in 2020) (2).

527 Background: We initiated a nationwide Virtual Tumor Board (VTB) program for pancreatic cancer (PC) patients (pts). The VTB consists of oncology experts and serves as an advisory panel by providing information on treatment (Tx) options based on a comprehensive review of patients’ oncologic history. Personalized Tx options and their rationales are provided and outcomes tracked in a prospective registry (XCELSIOR). Methods: PC pts who participated in XCELSIOR shared access to their full medical records, which were collected, processed, and abstracted. The panel reviewed cases asynchronously through an interactive platform followed by a VTB which was held weekly through videoconferencing. Tx options were summarized into a written report and provided to patients and their physicians. Outcomes and quality of life are tracked longitudinally through an IRB-approved 21CFR11 compliant observational registry (XCELSIOR). Results: From 9/2020 to 8/2021, the VTB reviewed 79 unique cases; 56% were male; median age at diagnosis was 66 (50-87). At the time of VTB, 68 (87%) had metastatic disease and 8 (10%) had locally advanced disease. Median prior therapy lines was 2 (0-9), with 26 (35%), 24 (32%), 6 (8%), and 19 (25%) pts having received 1, 2, 3 and 4+ lines of therapy, respectively. Median time from diagnosis for pts presenting after 1, 2, and 3+ lines of prior Tx was 9.5, 11, and 17.5 months, respectively. First-line Tx was FOLFIRINOX in 40 (53%) pts and gemcitabine/nab-paclitaxel in 22 (29%) pts. At the time of VTB, 32 (37%) of patients had stable disease, 23 (26%) had disease progression, 18 (21%) had recently started a new Tx, 7 (8%) were responding to Tx, 3 (3%) had stable disease on imaging but rising CA 19-9, and 4 (4%) were others. Prior to VTB, 69 (87%) pts had molecular profiling results available. Collectively the VTB provided 375 Tx and diagnostic (NGS, imaging, etc.) options with a median of 4 (1-12) options per patient. As of 9/8/2021, 87 VTB reports were provided. Of 25 instances of ‘no Tx decision’, 10 (40%) are deceased, 10 (40%) are stable, and 5 (10%) had other reasons. Of the 25 people who started a subsequent Tx, 14 (56%) were identified by the VTB. These included 9 (64%) FDA-approved, 3 (21%) off-label, and 2 (14%) on-trial Tx. Tx not identified by the VTB included 3 (33%) FDA-approved, 2 (22%) off-label, 2 (22%) on-trial, and 2 (22%) local Tx. Conclusions: We present our experience of utilizing a platform for patients to receive a virtual tumor board review and utilize an IRB-approved registry as a learning system. Early data indicate successes in identifying treatment and clinical trial opportunities. Future steps include streamlining communication with primary oncologists and enhancing access to treatments. NCT03793088.

The heterogeneous nature of children with symptoms of autism spectrum disorder (ASD) makes it difficult to identify risk factors and effective treatment options. We sought to identify behavioral and developmental features that best define the heterogeneity and homogeneity in 2-5-year-old children classified with ASD and subthreshold ASD characteristics. Children were enrolled in a multisite case-control study of ASD. Detailed behavioral and developmental data were gathered by maternal telephone interview, parent-administered questionnaires, child cognitive evaluation, and ASD diagnostic measures. Participants with a positive ASD screen score or prior ASD diagnosis were referred for comprehensive evaluation. Children in the ASD group met study criteria based on this evaluation; children who did not meet study criteria were categorized as having subthreshold ASD characteristics. There were 1480 children classified as ASD (81.6% boys) and 594 children classified as having subthreshold ASD characteristics (70.2% boys) in the sample. Factors associated with dysregulation (e.g., aggression, anxiety/depression, sleep problems) followed by developmental abilities (e.g., expressive and receptive language skills) most contributed to heterogeneity in both groups of children. Atypical sensory response contributed to homogeneity in children classified as ASD but not those with subthreshold characteristics. These findings suggest that dysregulation and developmental abilities are clinical features that can impact functioning in children with ASD and other DD, and that documenting these features in pediatric records may help meet the needs of the individual child. Sensory dysfunction could be considered a core feature of ASD and thus used to inform more targeted screening, evaluation, treatment, and research efforts. LAY SUMMARY: The diverse nature of autism spectrum disorder (ASD) makes it difficult to find risk factors and treatment options. We identified the most dissimilar and most similar symptom(s) in children classified as ASD and as having subthreshold ASD characteristics. Factors associated with dysregulation and developmental abilities contributed to diversity in both groups of children. Sensory dysfunction was the most common symptom in children with ASD but not those with subthreshold characteristics. Findings can inform clinical practice and research.

Autism spectrum disorder (ASD) is a condition characterized by impaired social communication as well as restricted and repetitive behaviors. It is considered a neurodevelopmental disorder because it is associated with neurologic changes that may begin in prenatal or early postnatal life, alters the typical pattern of child development, and produces chronic signs and symptoms that usually manifest in early childhood and have potential long-term consequences. In past decades, autism was conceptualized as a strictly defined set of behaviors, usually accompanied by intellectual impairment. Today, it is recognized as a spectrum, ranging from mild to severe, in which behaviors vary substantially and the majority of children who fall on the spectrum have average to above average intellectual ability. Here, the authors discuss the risk factors for ASD, its epidemiology, common concurrent conditions, evaluation, diagnosis, treatments, and outcomes.

PROBLEM/CONDITION:Autism spectrum disorder (ASD).PERIOD COVERED:2016.DESCRIPTION OF SYSTEM:The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network, a subset of the overall ADDM Network, is an active surveillance program that estimates ASD prevalence and monitors early identification of ASD among children aged 4 years. Children included in surveillance year 2016 were born in 2012 and had a parent or guardian who lived in the surveillance area in Arizona, Colorado, Missouri, New Jersey, North Carolina, or Wisconsin, at any time during 2016. Children were identified from records of community sources including general pediatric health clinics, special education programs, and early intervention programs. Data from comprehensive evaluations performed by community professionals were abstracted and reviewed by trained clinicians using a standardized ASD surveillance case definition with criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).RESULTS:In 2016, the overall ASD prevalence was 15.6 per 1,000 (one in 64) children aged 4 years for Early ADDM Network sites. Prevalence varied from 8.8 per 1,000 in Missouri to 25.3 per 1,000 in New Jersey. At every site, prevalence was higher among boys than among girls, with an overall male-to-female prevalence ratio of 3.5 (95% confidence interval [CI] = 3.1-4.1). Prevalence of ASD between non-Hispanic white (white) and non-Hispanic black (black) children was similar at each site (overall prevalence ratio: 0.9; 95% CI = 0.8-1.1). The prevalence of ASD using DSM-5 criteria was lower than the prevalence using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria at one of four sites that used criteria from both editions. Among sites where ≥60% of children aged 4 years had information about intellectual disability (intelligence quotient ≤70 or examiner's statement of intellectual disability documented in an evaluation), 53% of children with ASD had co-occurring intellectual disability. Of all children aged 4 years with ASD, 84% had a first evaluation at age ≤36 months and 71% of children who met the surveillance case definition had a previous ASD diagnosis from a community provider. Median age at first evaluation and diagnosis for this age group was 26 months and 33 months, respectively. Cumulative incidence of autism diagnoses received by age 48 months was higher for children aged 4 years than for those aged 8 years identified in Early ADDM Network surveillance areas in 2016.INTERPRETATION:In 2016, the overall prevalence of ASD in the Early ADDM Network using DSM-5 criteria (15.6 per 1,000 children aged 4 years) was higher than the 2014 estimate using DSM-5 criteria (14.1 per 1,000). Children born in 2012 had a higher cumulative incidence of ASD diagnoses by age 48 months compared with children born in 2008, which indicates more early identification of ASD in the younger group. The disparity in ASD prevalence has decreased between white and black children. Prevalence of co-occurring intellectual disability was higher than in 2014, suggesting children with intellectual disability continue to be identified at younger ages. More children received evaluations by age 36 months in 2016 than in 2014, which is consistent with Healthy People 2020 goals. Median age at earliest ASD diagnosis has not changed considerably since 2014.PUBLIC HEALTH ACTION:More children aged 4 years with ASD are being evaluated by age 36 months and diagnosed by age 48 months, but there is still room for improvement in early identification. Timely evaluation of children by community providers as soon as developmental concerns have been identified might result in earlier ASD diagnoses, earlier receipt of evidence-based interventions, and improved developmental outcomes.

Problem/Condition Autism spectrum disorder (ASD). Period Covered 2016. Description of System The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Results For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (40% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively). Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months). Interpretation The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children. Public Health Action These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.

Problem/Condition: Autism spectrum disorder (ASD) is estimated to affect up to 3% of children in the United States. Public health surveillance for ASD among children aged 4 years provides information about trends in prevalence, characteristics of children with ASD, and progress made toward decreasing the age of identification of ASD so that evidence-based interventions can begin as early as possible. Period Covered: 2010, 2012, and 2014. Description of System: The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network is an active surveillance system that provides biennial estimates of the prevalence and characteristics of ASD among children aged 4 years whose parents or guardians lived within designated sites. During surveillance years 2010, 2012, or 2014, data were collected in seven sites: Arizona, Colorado, Missouri, New Jersey, North Carolina, Utah, and Wisconsin. The Early ADDM Network is a subset of the broader ADDM Network (which included 13 total sites over the same period) that has been conducting ASD surveillance among children aged 8 years since 2000. Each Early ADDM site covers a smaller geographic area than the broader ADDM Network. Early ADDM ASD surveillance is conducted in two phases using the same methods and project staff members as the ADDM Network. The first phase consists of reviewing and abstracting data from children's records, including comprehensive evaluations performed by community professionals. Sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, special education records (for children aged >= 3 years) were reviewed for Arizona, Colorado, New Jersey, North Carolina, and Utah, and early intervention records (for children aged 0 to < 3 years) were reviewed for New Jersey, North Carolina, Utah, and Wisconsin; in Wisconsin, early intervention records were reviewed for 2014 only. The second phase involves a review of the abstracted evaluations by trained clinicians using a standardized case definition and method. A child is considered to meet the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism), or Asperger disorder (2010, 2012, and 2014). For 2014 only, prevalence estimates based on surveillance case definitions according to DSM-IV-TR and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) were compared. This report provides estimates of overall ASD prevalence and prevalence by sex and race/ethnicity; characteristics of children aged 4 years with ASD, including age at first developmental evaluation, age at ASD diagnosis, and cognitive function; and trends in ASD prevalence and characteristics among Early ADDM sites with data for all 3 surveillance years (2010, 2012, and 2014), including comparisons with children aged 8 years living in the same geographic area. Analyses of time trends in ASD prevalence are restricted to the three sites that contributed data for all 3 surveillance years with consistent data sources (Arizona, Missouri, and New Jersey). Results: The overall ASD prevalence was 13.4 per 1,000 children aged 4 years in 2010, 15.3 in 2012, and 17.0 in 2014 for Early ADDM sites with data for the specific years. ASD prevalence was determined using a surveillance case definition based on DSM-IV-TR. Within each surveillance year, ASD prevalence among children aged 4 years varied across surveillance sites and was lowest each year for Missouri (8.5, 8.1, and 9.6 per 1,000, for 2010, 2012, and 2014, respectively) and highest each year for New Jersey (19.7, 22.1, and 28.4 per 1,000, for the same years, respectively). Aggregated prevalence estimates were higher for sites that reviewed education and health care records than for sites that reviewed only health care records. Among all participating sites and years, ASD prevalence among children aged 4 years was consistently higher among boys than girls; prevalence ratios ranged from 2.6 (Arizona and Wisconsin in 2010) to 5.2 boys per one girl (Colorado in 2014). In 2010, ASD prevalence was higher among non-Hispanic white children than among Hispanic children in Arizona and non-Hispanic black children in Missouri; no other differences were observed by race/ethnicity. Among four sites with >= 60% data on cognitive test scores (Arizona, New Jersey, North Carolina, and Utah), the frequency of co-occurring intellectual disabilities was significantly higher among children aged 4 years than among those aged 8 years for each site in each surveillance year except Arizona in 2010. The percentage of children with ASD who had a first evaluation by age 36 months ranged from 48.8% in Missouri in 2012 to 88.9% in Wisconsin in 2014. The percentage of children with a previous ASD diagnosis from a community provider varied by site, ranging from 43.0% for Arizona in 2012 to 86.5% for Missouri in 2012. The median age at earliest known ASD diagnosis varied from 28 months in North Carolina in 2014 to 39.0 months in Missouri and Wisconsin in 2012. In 2014, the ASD prevalence based on the DSM-IV-TR case definition was 20% higher than the prevalence based on the DSM-5 (17.0 versus 14.1 per 1,000, respectively). Trends in ASD prevalence and characteristics among children aged 4 years during the study period were assessed for the three sites with data for all 3 years and consistent data sources (Arizona, Missouri, and New Jersey) using the DSM-IV-TR case definition; prevalence was higher in 2014 than in 2010 among children aged 4 years in New Jersey and was stable in Arizona and Missouri. In Missouri, ASD prevalence was higher among children aged 8 years than among children aged 4 years. The percentage of children with ASD who had a comprehensive evaluation by age 36 months was stable in Arizona and Missouri and decreased in New Jersey. In the three sites, no change occurred in the age at earliest known ASD diagnosis during 2010-2014. Interpretation: The findings suggest that ASD prevalence among children aged 4 years was higher in 2014 than in 2010 in one site and remained stable in others. Among children with ASD, the frequency of cognitive impairment was higher among children aged 4 years than among those aged 8 years and suggests that surveillance at age 4 years might more often include children with more severe symptoms or those with co-occurring conditions such as intellectual disability. In the sites with data for all years and consistent data sources, no change in the age at earliest known ASD diagnosis was found, and children received their first developmental evaluation at the same or a later age in 2014 compared with 2010. Delays in the initiation of a first developmental evaluation might adversely affect children by delaying access to treatment and special services that can improve outcomes for children with ASD. Public Health Action: Efforts to increase awareness of ASD and improve the identification of ASD by community providers can facilitate early diagnosis of children with ASD. Heterogeneity of results across sites suggests that community-level differences in evaluation and diagnostic services as well as access to data sources might affect estimates of ASD prevalence and age of identification. Continuing improvements in providing developmental evaluations to children as soon as developmental concerns are identified might result in earlier ASD diagnoses and earlier receipt of services, which might improve developmental outcomes.

ObjectiveTo compare prevalence estimates obtained by the ADDM cerebral palsy surveillance method to other administrative or diagnostic indications of cerebral palsy.IntroductionCerebral Palsy (CP) is the most common cause of motor disability in children. CP registries often rely on administrative data such as CP diagnoses or International Classification of Diseases (ICD) codes indicative of CP. However, little is known about the validity of these indicators. We calculated sensitivity, specificity, positive and negative predictive values of CP ICD-9 codes and CP diagnoses compared to a “gold standard” CP classification based on detailed medical and education record review.MethodsThis sample includes 50,332 8-year-olds living in four US sites (32 counties in Alabama, 5 counties in Georgia, 10 counties in Wisconsin, and 5 counties in Missouri) in 2006, 2008, and 2010. The Autism and Developmental Disabilities Monitoring (ADDM) Network reviewed medical and education records for these children as part of the US Centers for Disease Control and Prevention population-based surveillance of developmental disabilities. All of these children received special education services or were assigned one or more ICD-9 codes associated with a variety of developmental disabilities by community medical providers. Medical and education records were reviewed by trained staff; if the records contained CP diagnoses or motor findings indicative of CP, detailed clinical information was abstracted for additional review by trained clinicians who determined whether the child met the CP case definition based on all information available. Abstracted records were also reviewed for evidence of known motor disorders or genetic conditions that disqualified a child from being a CP case, such as inborn error of metabolism or muscular dystrophy. Trained clinicians reviewed and excluded children with confirmed disqualifying conditions.We calculated CP prevalence, sensitivity, specificity, and positive and negative predictive values for three different methods used to identify cases, using the ADDM surveillance case identification as the gold standard. These methods include: 1) ICD-9 codes for CP (342–344); 2) a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions, and 3) both ICD-9 codes (342–344) and a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions. In an attempt to avoid requiring record review for method 1, we considered using ICD-9 codes for disqualifying conditions. However, we found that ICD codes for these conditions did not correlate well with disqualifying conditions identified in medical record reviews; therefore disqualifying conditions were not considered for method 1. Methods 2 and 3 did require review of medical records for disqualifying conditions and for a written CP diagnosis, but overall were less extensive than traditional ADDM surveillance methods.In order to determine the impact of different classification criteria on how and which children are captured by surveillance methods, we compared demographic and other characteristics of all children who met the ADDM surveillance case definition. We compared children who would and would not be classified as CP cases using method 3.ResultsOut of the total 50,332 children, 1294 met the ADDM surveillance case definition, 2201 had CP ICD codes (method 1), 1502 had a written CP diagnosis and no disqualifying conditions (method 2), and 1345 had both CP ICD codes and a written diagnosis and no disqualifying conditions (method 3). Each study year, between 32—48% of abstracted children were excluded due to disqualifying conditions found in medical records. The ADDM network gold standard CP prevalence was 3.3 per 1000 in 2006, 3.1 per 1000 in 2008, and 2.9 per 1000 in 2010.For method 1, sensitivity was 90.0%, specificity was 97.4%, positive predictive value was 51.6% and negative predictive value was 99.7%. Method 1 prevalence estimates were 5.3 per 1000 in 2006, 4.6 per 1000 in 2008, and 4.6 per 1000 in 2010. For method 2, sensitivity was 98.1%, specificity was 88.4%, PPV was 84.5% and NPV was 98.4% compared to the ADDM Network definition. Method 2 estimated prevalence was 3.9 per 1000 for 2006, 3.6 per 1000 for 2008, and 3.2 per 1000 for 2010. For method 3, sensitivity was 89.6%, specificity was 99.5%, PPV was 84.3% and NPV was 99.7%. Method 3 estimated prevalence was 3.5 per 1000 for 2006, 3.2 per 1000 for 2008, and 2.8 per 1000 for 2010.Using Pearson’s Chi-Square tests, we compared demographic and other characteristics of ADDM Network CP case children who also met method 3 case definition (n = 1134) and children who met the ADDM Network CP definition but not method 3 case definition (n = 160). Demographic information was not different between these children. ADDM Network CP case children who did not meet method 3 criteria were significantly less likely to require a wheelchair for mobility than children who met method 3 criteria (4.4% versus 27.4%, p < .05).ConclusionsRelying on ICD-9 codes without excluding disqualifying conditions to identify CP cases (method 1) resulted in high sensitivity (90%), but low positive predictive value as well as an overestimated CP prevalence when compared with the ADDM Network method. Use of a written diagnosis and excluding disqualifying conditions (method 2) resulted in very high sensitivity (98%), with fewer false positives but overestimated CP prevalence compared to the ADDM estimate. In contrast, using both CP ICD codes and a written CP diagnosis and excluding disqualifying conditions (method 3) yielded prevalence estimates similar to ADDM Network CP estimates; this approach also had high sensitivity, specificity, and PPV. Methods 2 and 3 still require manual record review, unlike method 1. For method 2, reviewers would need to review all records for CP and disqualifying conditions. Method 3 only requires review of records with CP ICD codes, comprising 4% of all records currently reviewed. Method 3 would fail to capture children without both a written diagnosis and ICD codes; and this approach may be less sensitive for detecting CP among children with less severe motor impairment than using the gold standard.Using ICD codes and written CP diagnoses contained in medical and education records combined with a limited medical record review to identify disqualifying conditions could lower operational costs of CP surveillance while preserving accurate prevalence estimates compared with the more labor-intensive processes currently used. Further evaluation is needed to determine if improvements in efficiency are worth potential trade-offs in the data collected by the system. Of particular importance is whether the approach could capture all the necessary indicators that are important to stakeholders. Additional analyses would also need to evaluate whether the surveillance methods affect other findings, such as previously observed disparities, co-occurring conditions, or CP severity.

The objectives of our study were to (a) report how many children met an autism spectrum disorder (ASD) surveillance definition but had no clinical diagnosis of ASD in health or education records and (b) evaluate differences in demographic, individual, and service factors between children with and without a documented ASD diagnosis. ASD surveillance was conducted in selected areas of Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin. Children were defined as having ASD if sufficient social and behavioral deficits and/or an ASD diagnosis were noted in health and/or education records. Among 4,498 children, 1,135 (25%) had ASD indicators without having an ASD diagnosis. Of those 1,135 children without a documented ASD diagnosis, 628 (55%) were not known to receive ASD services in public school. Factors associated with not having a clinical diagnosis of ASD were non-White race, no intellectual disability, older age at first developmental concern, older age at first developmental evaluation, special education eligibility other than ASD, and need for fewer supports. These results highlight the importance of reducing disparities in the diagnosis of children with ASD characteristics so that appropriate interventions can be promoted across communities. Autism Res 2020, 13: 464-473. © 2019 International Society for AutismResearch,Wiley Periodicals, Inc. LAY SUMMARY: Children who did not have a clinical diagnosis of autism spectrum disorder (ASD) documented in health or education records were more likely to be non-White and have fewer developmental problems than children with a clinical diagnosis of ASD. They were brought to the attention of healthcare providers at older ages and needed fewer supports than children with a clinical diagnosis of ASD. All children with ASD symptoms who meet diagnostic criteria should be given a clinical diagnosis so they can receive treatment specific to their needs.

Background: Developmental disabilities are present in a significant proportion of US children. Surveillance of developmental disabilities is crucial for monitoring population trends, guiding research into risk factors, and informing resource allocation. Objective/Hypothesis: We examined overall prevalence, prevalence by demographic characteristics, and trends over time for cerebral palsy (CP), intellectual disability (ID), moderate to severe hearing loss (MSHL), and blindness. Methods: Data from the 2009-2016 National Health Interview Survey (NHIS) were analyzed for children 3-17 years of age. Question wording was consistent over time except for ID, which changed in 2011 to replace the term "mental retardation." Demographic differences and linear trends (over three time periods) were assessed by Chi-square tests and Wald-F tests. Results: Prevalence estimates per 1000 children ages 3-17 years for CP, ID, MSHL, and blindness were 3.2 (95% CI: 2.7, 3.7), 11.1 (95% CI: 10.2, 12.1), 6.4 (95% CI: 5.6, 7.2), and 1.6 (95% CI: 1.3, 2.0), respectively. Disability prevalence was higher for children with low birthweight and from families of lower parental education, income <= 200% of federal poverty level, and public insurance. Older children had higher ID prevalence; boys had significantly higher CP and ID prevalences. Only ID demonstrated a significantly increased trend over time (p = 0.0002). Conclusions: We provide nationally representative prevalence estimates for four developmental disabilities; recent estimates are comparable to those from records-based studies. Prevalences were stable except for ID, which increased after 2010, coincident with the questionnaire change. A substantial number of US children continue to have these disabilities and service needs. (C) 2019 Elsevier Inc. All rights reserved.

PROBLEM/CONDITION:Autism spectrum disorder (ASD). PERIOD COVERED:2012. DESCRIPTION OF SYSTEM:The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence and characteristics of ASD among children aged 8 years whose parents or guardians reside in 11 ADDM Network sites in the United States (Arkansas, Arizona, Colorado, Georgia, Maryland, Missouri, New Jersey, North Carolina, South Carolina, Utah, and Wisconsin). Surveillance to determine ASD case status is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional service providers in the community. Data sources identified for record review are categorized as either 1) education source type, including developmental evaluations to determine eligibility for special education services or 2) health care source type, including diagnostic and developmental evaluations. The second phase involves the review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors that are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only). In addition, this report describes the proportion of children with ASD with a score consistent with intellectual disability on a standardized intellectual ability test, the age at which the earliest known comprehensive evaluation was performed, the proportion of children with a previous ASD diagnosis, the specific type of ASD diagnosis, and any special education eligibility classification. RESULTS:For 2012, the combined estimated prevalence of ASD among the 11 ADDM Network sites was 14.6 per 1,000 (one in 68) children aged 8 years. Estimated prevalence was significantly higher among boys aged 8 years (23.6 per 1,000) than among girls aged 8 years (5.3 per 1,000). Estimated ASD prevalence was significantly higher among non-Hispanic white children aged 8 years (15.5 per 1,000) compared with non-Hispanic black children (13.2 per 1,000), and Hispanic (10.1 per 1,000) children aged 8 years. Estimated prevalence varied widely among the 11 ADDM Network sites, ranging from 8.2 per 1,000 children aged 8 years (in the area of the Maryland site where only health care records were reviewed) to 24.6 per 1,000 children aged 8 years (in New Jersey, where both education and health care records were reviewed). Estimated prevalence was higher in surveillance sites where education records and health records were reviewed compared with sites where health records only were reviewed (17.1 per 1,000 and 10.7 per 1,000 children aged 8 years, respectively; p<0.05). Among children identified with ASD by the ADDM Network, 82% had a previous ASD diagnosis or educational classification; this did not vary by sex or between non-Hispanic white and non-Hispanic black children. A lower percentage of Hispanic children (78%) had a previous ASD diagnosis or classification compared with non-Hispanic white children (82%) and with non-Hispanic black children (84%). The median age at earliest known comprehensive evaluation was 40 months, and 43% of children had received an earliest known comprehensive evaluation by age 36 months. The percentage of children with an earliest known comprehensive evaluation by age 36 months was similar for boys and girls, but was higher for non-Hispanic white children (45%) compared with non-Hispanic black children (40%) and Hispanic children (39%). INTERPRETATION:Overall estimated ASD prevalence was 14.6 per 1,000 children aged 8 years in the ADDM Network sites in 2012. The higher estimated prevalence among sites that reviewed both education and health records suggests the role of special education systems in providing comprehensive evaluations and services to children with developmental disabilities. Disparities by race/ethnicity in estimated ASD prevalence, particularly for Hispanic children, as well as disparities in the age of earliest comprehensive evaluation and presence of a previous ASD diagnosis or classification, suggest that access to treatment and services might be lacking or delayed for some children. PUBLIC HEALTH ACTION:The ADDM Network will continue to monitor the prevalence and characteristics of ASD among children aged 8 years living in selected sites across the United States. Recommendations from the ADDM Network include enhancing strategies to 1) lower the age of first evaluation of ASD by community providers in accordance with the Healthy People 2020 goal that children with ASD are evaluated by age 36 months and begin receiving community-based support and services by age 48 months; 2) reduce disparities by race/ethnicity in identified ASD prevalence, the age of first comprehensive evaluation, and presence of a previous ASD diagnosis or classification; and 3) assess the effect on ASD prevalence of the revised ASD diagnostic criteria published in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

The Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP) conducts surveillance for ASD based on behaviors documented in children’s records, without dependence on an existing ASD diagnosis. An ASD diagnosis, however, may confer eligibility for services beyond those available without a formal diagnosis. Identification of demographic factors associated with the absence of an ASD diagnosis may suggest areas for improvement in ASD identification. MADDSP data were used to assess the association between race/ethnicity and ASD diagnosis among children meeting surveillance case criteria for ASD.