A Framework for Developing and Assessing Custom Case Definitions: A Demonstration Applied to Opioid Overdose in Maryland.

CONTEXT: Public health epidemiologists monitor data sources for disease outbreaks and other events of public health concern, but manual review of records to identify cases of interest is slow and labor-intensive and may not reflect evolving data practices. To automatically identify cases from electronic data sources, epidemiologists must use "case definitions" or formal logic that captures the criteria used to identify a record as a case of interest. OBJECTIVE: To establish a methodology for development and evaluation of case definitions. A logical evaluation framework to approach case definitions will allow jurisdictions the flexibility to implement a case definition tailored to their goals and available data. DESIGN: Case definition development is explained as a process with multiple logical components combining free-text and categorical data fields. The process is illustrated with the development of a case definition to identify emergency medical services (EMS) call records related to opioid overdoses in Maryland. SETTING: The Maryland Department of Health (MDH) installation of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE), which began capturing EMS call records in ESSENCE in 2019 to improve statewide coverage of all-hazards health issues. RESULTS: We describe a case definition evaluation framework and demonstrate its application through development of an opioid overdose case definition to be used in MDH ESSENCE. We show the iterative process of development, from defining how a case can be identified conceptually to examining each component of the conceptual definition and then exploring how to capture that component using available data. CONCLUSION: We present a framework for developing and qualitatively assessing case definitions and demonstrate an application of the framework to identifying opioid overdose incidents from MDH EMS data. We discuss guidelines to support jurisdictions in applying this framework to their own data and public health challenges to improve local surveillance capability.

COVID-19 Vaccine Provider Access and Vaccination Coverage Among Children Aged 5-11 Years - United States, November 2021-January 2022.

On October 29, 2021, the Pfizer-BioNTech pediatric COVID-19 vaccine received Emergency Use Authorization for children aged 5-11 years in the United States.† For a successful immunization program, both access to and uptake of the vaccine are needed. Fifteen million doses were initially made available to pediatric providers to ensure the broadest possible access for the estimated 28 million eligible children aged 5-11 years, especially those in high social vulnerability index (SVI)§ communities. Initial supply was strategically distributed to maximize vaccination opportunities for U.S. children aged 5-11 years. COVID-19 vaccination coverage among persons aged 12-17 years has lagged (1), and vaccine confidence has been identified as a concern among parents and caregivers (2). Therefore, COVID-19 provider access and early vaccination coverage among children aged 5-11 years in high and low SVI communities were examined during November 1, 2021-January 18, 2022. As of November 29, 2021 (4 weeks after program launch), 38,732 providers were enrolled, and 92% of U.S. children aged 5-11 years lived within 5 miles of an active provider. As of January 18, 2022 (11 weeks after program launch), 39,786 providers had administered 13.3 million doses. First dose coverage at 4 weeks after launch was 15.0% (10.5% and 17.5% in high and low SVI areas, respectively; rate ratio [RR] = 0.68; 95% CI = 0.60-0.78), and at 11 weeks was 27.7% (21.2% and 29.0% in high and low SVI areas, respectively; RR = 0.76; 95% CI = 0.68-0.84). Overall series completion at 11 weeks after launch was 19.1% (13.7% and 21.7% in high and low SVI areas, respectively; RR = 0.67; 95% CI = 0.58-0.77). Pharmacies administered 46.4% of doses to this age group, including 48.7% of doses in high SVI areas and 44.4% in low SVI areas. Although COVID-19 vaccination coverage rates were low, particularly in high SVI areas, first dose coverage improved over time. Additional outreach is critical, especially in high SVI areas, to improve vaccine confidence and increase coverage rates among children aged 5-11 years.

The neurophysiological bases of EEG and EEG measurement: a review for the rest of us.

A thorough understanding of the EEG signal and its measurement is necessary to produce high quality data and to draw accurate conclusions from those data. However, publications that discuss relevant topics are written for divergent audiences with specific levels of expertise: explanations are either at an abstract level that leaves readers with a fuzzy understanding of the electrophysiology involved, or are at a technical level that requires mastery of the relevant physics to understand. A clear, comprehensive review of the origin and measurement of EEG that bridges these high and low levels of explanation fills a critical gap in the literature and is necessary for promoting better research practices and peer review. The present paper addresses the neurophysiological source of EEG, propagation of the EEG signal, technical aspects of EEG measurement, and implications for interpretation of EEG data.

Using a high-dimensional graph of semantic space to model relationships among words.

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).