Search

Your search keyword '"Woodworth, Kate R."' showing total 7 results
Start Over Author "Woodworth, Kate R." Publication Year Range This year
7 results on '"Woodworth, Kate R."'

2. Outcomes up to age 36 months after congenital Zika virus infection—U.S. states

Author

Neelam, Varsha, Woodworth, Kate R., Chang, Daniel J., Roth, Nicole M., Reynolds, Megan R., Akosa, Amanda, Carr, Christopher P., Anderson, Kayla N., Mulkey, Sarah B., DeBiasi, Roberta L., Biddle, Cara, Lee, Ellen H., Elmore, Amanda L., Scotland, Sarah J., Sowunmi, Similoluwa, Longcore, Nicole D., Ahmed, Muhammad, Langlois, Peter H., Khuwaja, Salma, Browne, Shea Elizabeth, Lind, Leah, Shim, Kyoo, Gosciminski, Michael, Blumenfeld, Rachel, Khuntia, Shreya, Halai, Umme-Aiman, Locklear, Autumn, Chan, Mary, Willabus, Teri’, Tonzel, Julius, Marzec, Natalie S., Barreto, Nianest Alers, Sanchez, Connie, Fornoff, Jane, Hale, Shelby, Nance, Amy, Iguchi, Lisa, Adibhatla, Sowmya N., Potts, Emily, Schiffman, Elizabeth, Raman, Devin, McDonald, Morgan F., Stricklin, Brandi, Ludwig, Elizabeth, Denson, Lindsay, Contreras, Dianna, Romitti, Paul A., Ferrell, Emily, Marx, Meghan, Signs, Kimberly, Cook, Amie, Leedom, Vinita Oberoi, Beauregard, Suzann, Orantes, Lucia C., Cronquist, Laura, Roush, Lesley, Godfred-Cato, Shana, Gilboa, Suzanne M., Meaney-Delman, Dana, Honein, Margaret A., Moore, Cynthia A., and Tong, Van T.

Published
2024
Full Text
View/download PDF

3. Frequency of Children Diagnosed with Perinatal Hepatitis C, United States, 2018-2020

Author

Newton, Suzanne M., Woodworth, Kate R., Chang, Daniel, Sizemore, Lindsey, Wingate, Heather, Pinckney, Leah, Osinski, Anthony, Orkis, Lauren, Reynolds, Bethany D., Carpentieri, Cynthia, Halai, Umme-Aiman, Lyu, Caleb, Longcore, Nicole, Thomas, Nadia, Wills, Aprielle, Akosa, Amanda, Olsen, Emily O'Malley, Panagiotakopoulos, Lakshmi, Thompson, Nicola D., Gilboa, Suzanne M., and Tong, Van T.

Subjects
United States. Centers for Disease Control and Prevention -- Analysis, Viral antibodies -- Analysis, Hepatitis C virus -- Analysis, Infants -- Analysis, Infection -- Prevention, RNA -- Analysis, Disease transmission -- Prevention, Public health -- Analysis, Antibodies -- Analysis, Health
Abstract

Hepatitis C virus (HCV) can be transmitted perinatally (1). Rates of acute HCV infection have increased recently (2), but few children perinatally exposed to HCV are tested and referred to [...]

Published
2024

4. Pregnancy and infant outcomes following SARS-CoV-2 infection in pregnancy during delta variant predominance – Surveillance for Emerging Threats to Pregnant People and Infants

Author

Reeves, Emily L., Neelam, Varsha, Carlson, Jeffrey M., Olsen, Emily O., Fox, Charise J., Woodworth, Kate R., Nestoridi, Eirini, Mobley, Evan, Montero Castro, Shaila, Dzimira, Paula, Sokale, Ayomide, Sizemore, Lindsey, Hall, Aron J., Ellington, Sascha, Cohn, Amanda, Gilboa, Suzanne M., and Tong, Van T.

Published
2024
Full Text
View/download PDF

7. Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data.

Author

Newton, Suzanne M., Distler, Samantha, Woodworth, Kate R., Chang, Daniel, Roth, Nicole M., Board, Amy, Hutcherson, Hailee, Cragan, Janet D., Gilboa, Suzanne M., and Tong, Van T.

Abstract

Background: The Surveillance for Emerging Threats to Pregnant People and Infants Network (SET‐NET) collects data abstracted from medical records and birth defects registries on pregnant people and their infants to understand outcomes associated with prenatal exposures. We developed an automated process to categorize possible birth defects for prenatal COVID‐19, hepatitis C, and syphilis surveillance. By employing keyword searches, fuzzy matching, natural language processing (NLP), and machine learning (ML), we aimed to decrease the number of cases needing manual clinician review. Methods: SET‐NET captures International Classification of Diseases, 10th Revision, Clinical Modification (ICD‐10‐CM) codes and free text describing birth defects. For unstructured data, we used keyword searches, and then conducted fuzzy matching with a cut‐off match score of ≥90%. Finally, we employed NLP and ML by testing three predictive models to categorize birth defect data. Results: As of June 2023, 8326 observations containing data on possible birth defects were submitted to SET‐NET. The majority (n = 6758 [81%]) were matched to an ICD‐10‐CM code and 1568 (19%) were unable to be matched. Through keyword searches and fuzzy matching, we categorized 1387/1568 possible birth defects. Of the remaining 181 unmatched observations, we correctly categorized 144 (80%) using a predictive model. Conclusions: Using automated approaches allowed for categorization of 99.6% of reported possible birth defects, which helps detect possible patterns requiring further investigation. Without employing these analytic approaches, manual review would have been needed for 1568 observations. These methods can be employed to quickly and accurately sift through data to inform public health responses. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results