On February 18, 2022, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).In 2021, a national emergency* for children's mental health was declared by several pediatric health organizations, and the U.S. Surgeon General released an advisory † on mental health among youths. These actions resulted from ongoing concerns about children's mental health in the United States, which was exacerbated by the COVID-19 pandemic (1,2). During March-October 2020, among all emergency department (ED) visits, the proportion of mental health-related visits increased by 24% among U.S. children aged 5-11 years and 31% among adolescents aged 12-17 years, compared with 2019 (2). CDC examined changes in U.S. pediatric ED visits for overall mental health conditions (MHCs) and ED visits associated with specific MHCs (depression; anxiety; disruptive behavioral and impulse-control disorders; attention-deficit/hyperactivity disorder; trauma and stressor-related disorders; bipolar disorders; eating disorders; tic disorders; and obsessive-compulsive disorders [OCD]) during 2019 through January 2022 among children and adolescents aged 0-17 years, overall and by sex and age. After declines in weekly visits associated with MHCs among those aged 0-17 years during 2020, weekly numbers of ED visits for MHCs overall and for specific MHCs varied by age and sex during 2021 and January 2022, when compared with corresponding weeks in 2019. Among adolescent females aged 12-17 years, weekly visits increased for two of nine MHCs during 2020 (eating disorders and tic disorders), for four of nine MHCs during 2021 (depression, eating disorders, tic disorders, and OCD), and for five of nine MHCs during January 2022 (anxiety, trauma and stressor-related disorders, eating disorders, tic disorders, and OCD), and overall MHC visits during January 2022, compared with 2019. Early identification and expanded evidence-based prevention and intervention strategies are critical to improving children's and adolescents' * https://www.aap.org/en/advocacy/child-and-adolescent-healthy-mentaldevelopment/aap-aacap-cha-declaration-of-a-national-emergency-inchild-and-adolescent-mental-health/ † https://www.hhs.gov/sites/default/files/surgeon-general-youth-mental-healthadvisory.pdf
is a collaboration among CDC, local, and state health departments, and federal, academic, and private sector partners. https://www.cdc.gov/nssp/index.html † To reduce artifactual impact from changes in reporting patterns, analyses were restricted to facilities with a coefficient of variation ≤40 and average weekly informative discharge diagnosis ≥75% complete with consistent discharge diagnosis code formatting throughout 2019-2022. Visits from 1,674 facilities from 41 states were eligible to be included in the study. All facilities from three counties in California (El Dorado, Plumas, and Yosemite),
IMPORTANCEThe COVID-19 pandemic has negatively affected adult mental health (MH), with racial and ethnic minoritized groups disproportionately affected.OBJECTIVE To examine changes in adult MH-related emergency department (ED) visits into the Delta variant pandemic period and identify changes and inequities in these visits before and during COVID-19 case surges.
Objective: This session will present the impacts of enhancements made to National Syndromic Surveillance Program (NSSP) BioSense Platform Onboarding in 2017 from the perspective of CDC and public health jurisdictions.Introduction: In 2017, the National Syndromic Surveillance Program (NSSP) continued to expand as a national scope data source with over 6,500 facilities registered on the BioSense Platform, including 4,000 active, 1,800 onboarding, and 700 planned or inactive facilities. 2,086 of the active facilities are Emergency Departments across 49 sites in 41 states. The growth of data available in NSSP has been driven by continued enhancements to tools and processes used by the NSSP Onboarding Team. These enhancements help to rapidly integrate new healthcare facilities and onboard new public health sites in support of American Hospital Association (AHA) Emergency Department (ED) representativeness goals. Furthermore, with these improvements to the onboarding process, including the Master Facility Table update process and automated data validation reporting, NSSP has broadened stakeholder participation in the onboarding process.Description: This panel presentation will focus on the impact of the enhancements to NSSP Onboarding processes and tools that are the key enablers for NSSP to gather a site and nationally representative data source for event detection and novel surveillance. Panelists include Mr. Travis Mayo, NSSP Onboarding Manager, who will present the key enablers to accelerating NSSP Onboarding including enhancements to the management of the Master Facility Table (MFT), tailoring of the Engage, Connect, Validate, and Operate methodology, and the introduction of automated data validation reports. Building on the enablers presented by Mr. Mayo, Mr. Michael Coletta, will present on NSSP priorities and initiatives to optimize program efficiency in support of onboarding new sites and continuing to onboard facilities in support of national objectives for ED representatives. Mrs. Sophia Crossen will present the impact of NSSP changes in Kansas onboarding and surveillance initiatives. Mrs. Kirsten Oliver, will demonstrate how NSSP onboarding has impacted syndromic surveillance activities in West Virginia.With the need to always be looking ahead, each panelist will draw on their experiences in 2017, including their perspective on opportunities in 2018 to continue to enhance NSSP onboarding. These perspectives will serve as a basis for launching into questions and discussions from the audience to collect NSSP onboarding experiences in 2017 and ideas for continued enhancement in 2018.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The round table will present the improvements implemented by NSSP Onboarding and discuss the following:- What strengths and weaknesses have the enhancements surfaced in onboarding processes- How have the enhancements impacted local onboarding initiatives and priorities- How have the enhancements changed the roles of key players in the onboarding process
ObjectiveTo compare and contrast two ESSENCE syndrome definition query methods and establish best practices for syndrome definition creation.IntroductionThe Kansas Syndromic Surveillance Program (KSSP) utilizes the ESSENCE v.1.20 program provided by the National Syndromic Surveillance Program to view and analyze Kansas Emergency Department (ED) data.Methods that allow an ESSENCE user to query both the Discharge Diagnosis (DD) and Chief Complaint (CC) fields simultaneously allow for more specific and accurate syndromic surveillance definitions. As ESSENCE use increases, two common methodologies have been developed for querying the data in this way.The first is a query of the field named “CC and DD.” The CC and DD field contains a concatenation of the parsed patient chief complaint and the discharge diagnosis. The discharge diagnosis consists of the last non-null value for that patient visit ID and the chief complaint parsed is the first non-null chief complaint value for that patient visit ID that is parsed by the ESSENCE platform. For this comparison, this method shall be called the CCDD method.The second method involves a query of the fields named, “Chief Complaint History” and “Discharge Diagnosis History.” While the first requires only one field be queried, this method queries the CC History and DD History fields, combines the resulting data and de-duplicates this final data set by the C_BioSense_ID. Chief Complaint History is a list of all chief complaint values related to a singular ED visit, and Discharge Diagnosis History is the same concept, except involving all Discharge Diagnosis values. For this comparison, this method shall be called the CCDDHX method.While both methods are based on the same query concept, each method can yield different results.MethodsA program was created in R Studio to analyze a user-provided query.Simple queries were randomly generated. Twenty randomly generated queries were run through the R Studio program and disparities between data sets were recorded. All KSSP production facility ED visits during the month of August 2017 were analyzed.Secondly, three queries actively utilized in KSSP practice were run through the program. These queries were Firework-Related Injuries, Frostbite and Cold Exposure, and Rabies Exposure. The queries were run on all KSSP production facility ED visits, and coincided with the timeline of relevant exposures.ResultsIn the random query trials, an average of 5.4% of the cases captured using the CCDD field method were unique and not captured by the same query in the CCDDHX method. Using the CCDDHX method, an average of 6.1% of the cases captured were unique and not captured by the CCDD method.When using the program to compare syndromes from actively utilized KSSP practice, the disparity between the two methods was much lower.Firework-Related InjuriesDuring the time period queried, the CCDD method returned 171 cases and the CCDDHX method returned 169 cases. All CCDDHX method cases were captured by the CCDD method. The CCDD method returned 2 cases not captured by the CCDDHX method. These two cases were confirmed as true positive firework-related injury cases.Frostbite and Cold ExposureDuring the time period queried, CCDD method returned 328 cases and the CCDDHX method returned 344 cases. The CCDDHX method captured 16 cases that the CCDD method did not. The CCDD method did not capture any additional cases when compared to the CCDDHX method. After review, 10 (62.5%) of these 16 cases not captured by the CCDD method were true positive cases.Rabies ExposureDuring the time period queried, the CCDD method returned 474 cases and the CCDDHX method returned 473 cases. The CCDDHX method captured 7 cases that the CCDD method did not. The CCDD method returned 8 cases not captured by the CCDDHX method. After review, the 7 unique cases captured in the CCDDHX method contained 3 (42.9%) true positive cases and 3 (37.5%) of the 8 cases not captured by the CCDDHX method were true positives.ConclusionsThe twenty random queries showed a disparity between methods. When utilizing the same program to analyze three actively utilized KSSP definitions, both methods yielded similar results with a much smaller disparity. The CCDDHX method inherently requires more steps and requires more queries to be run through ESSENCE, making the method less timely and more difficult to share. Despite these downsides, CCDDHX will capture cases that appear throughout the history of field updates.Further variance between methods is likely due to the CCDD field utilizing the ESSENCE-processed CC while the CCDDHX field utilizes the CC verbatim as produced by the ED facility. This allows the CCDD method to tap into the powerful spelling correction and abbreviation-parsing steps that ESSENCE employs, but incorrect machine corrections and replacements, while rare, can negatively affect syndrome definition performance.The greater disparity in methods for the random queries may be due to the short (3 letter) text portion of the queries. Short segments are more likely to be found in multiple words than text of actual queries. Utilizing larger randomly generated text segments may resolve this and is a planned next step for this research.Our next step is to share the R Studio program to allow further replication. The Kansas Syndromic Surveillance Program is also continuing similar research to ensure that best practices are being met.
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