Last data update: Nov 04, 2024. (Total: 48056 publications since 2009)
Records 1-2 (of 2 Records) |
Query Trace: Lin XM[original query] |
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Deployment of the National Notifiable Diseases Surveillance System during the 2022-23 mpox outbreak in the United States-Opportunities and challenges with case notifications during public health emergencies
Rainey JJ , Lin XM , Murphy S , Velazquez-Kronen R , Do T , Hughes C , Harris AM , Maitland A , Gundlapalli AV . PLoS One 2024 19 (4) e0300175 Timely case notifications following the introduction of an uncommon pathogen, such as mpox, are critical for understanding disease transmission and for developing and implementing effective mitigation strategies. When Massachusetts public health officials notified the Centers for Disease Control and Prevention (CDC) about a confirmed orthopoxvirus case on May 17, 2023, which was later confirmed as mpox at CDC, mpox was not a nationally notifiable disease. Because existing processes for new data collections through the National Notifiable Disease Surveillance System were not well suited for implementation during emergency responses at the time of the mpox outbreak, several interim notification approaches were established to capture case data. These interim approaches were successful in generating daily case counts, monitoring disease transmission, and identifying high-risk populations. However, the approaches also required several data collection approvals by the federal government and the Council for State and Territorial Epidemiologists, the use of four different case report forms, and the establishment of complex data management and validation processes involving data element mapping and record-level de-duplication steps. We summarize lessons learned from these interim approaches to inform and improve case notifications during future outbreaks. These lessons reinforce CDC's Data Modernization Initiative to work in close collaboration with state, territorial, and local public health departments to strengthen case-based surveillance prior to the next public health emergency. |
Constructing state and national estimates of vaccination rates from immunization information systems
Raghunathan T , Kirtland K , Li J , White K , Murthy B , Lin XM , Harris L , Gibbs-Scharf L , Zell E . J Surv Stat Methodol 2023 11 (3) 688-712 Immunization Information Systems are confidential computerized population-based systems that collect data from vaccination providers on individual vaccinations administered along with limited patient-level characteristics. Through a data use agreement, Centers for Disease Control and Prevention obtains the individual-level data and aggregates the number of vaccinations for geographical statistical areas defined by the US Census Bureau (counties or equivalent statistical entities) for each vaccine included in system. Currently, 599 counties, covering 11 states, collect and report data using a uniform protocol. We combine these data with inter-decennial population counts from the Population Estimates Program in the US Census Bureau and several covariates from a variety of sources to develop model-based estimates for each of the 3,142 counties in 50 states and the District of Columbia and then aggregate to the state and national levels. We use a hierarchical Bayesian model and Markov Chain Monte Carlo methods to obtain draws from the posterior predictive distribution of the vaccination rates. We use posterior predictive checks and cross-validation to assess the goodness of fit and to validate the models. We also compare the model-based estimates to direct estimates from the National Immunization Surveys. © 2023 The Author(s). Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. |
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