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3. Capturing COVID-19–Like Symptoms at Scale Using Banner Ads on an Online News Platform: Pilot Survey Study

Author

Dixon, Brian E, Mukherjee, Sumit, Wiensch, Ashley, Gray, Mary L, Ferres, Juan M Lavista, and Grannis, Shaun J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundIdentifying new COVID-19 cases is challenging. Not every suspected case undergoes testing, because testing kits and other equipment are limited in many parts of the world. Yet populations increasingly use the internet to manage both home and work life during the pandemic, giving researchers mediated connections to millions of people sheltering in place. ObjectiveThe goal of this study was to assess the feasibility of using an online news platform to recruit volunteers willing to report COVID-19–like symptoms and behaviors. MethodsAn online epidemiologic survey captured COVID-19–related symptoms and behaviors from individuals recruited through banner ads offered through Microsoft News. Respondents indicated whether they were experiencing symptoms, whether they received COVID-19 testing, and whether they traveled outside of their local area. ResultsA total of 87,322 respondents completed the survey across a 3-week span at the end of April 2020, with 54.3% of the responses from the United States and 32.0% from Japan. Of the total respondents, 19,631 (22.3%) reported at least one symptom associated with COVID-19. Nearly two-fifths of these respondents (39.1%) reported more than one COVID-19–like symptom. Individuals who reported being tested for COVID-19 were significantly more likely to report symptoms (47.7% vs 21.5%; P

Published
2021
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4. Leadership Perspectives on Implementing Health Information Exchange: Qualitative Study in a Tertiary Veterans Affairs Medical Center

Author

Dixon, Brian E, Luckhurst, Cherie, and Haggstrom, David A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundThe US Department of Veterans Affairs (VA) seeks to achieve interoperability with other organizations, including non-VA community and regional health information exchanges (HIEs). ObjectiveThis study aims to understand the perspectives of leaders involved in implementing information exchange between VA and non-VA providers via a community HIE. MethodsWe interviewed operational, clinical, and information technology leaders at one VA facility and its community HIE partner. Respondents discussed their experiences with VA-HIE, including barriers and facilitators to implementation, and the associated impact on health care providers. Transcribed interviews were coded and analyzed using immersion-crystallization methods. ResultsVA and community HIE leaders found training to be a key factor when implementing VA-HIE and worked cooperatively to provide several styles and locations of training. During recruitment, a high-touch approach was successfully used to enroll patients and overcome their resistance to opting in. Discussion with leaders revealed the high levels of complexity navigated by VA providers and staff to send and retrieve information. Part of the complexity stemmed from the interconnected web of information systems and human teams necessary to implement VA-HIE information sharing. These interrelationships must be effectively managed to guide organizational decision making. ConclusionsOrganizational leaders perceived information sharing to be of essential value in delivering high-quality, coordinated health care. The VA continues to increase access to outside care through the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act. Along with this increase in non-VA medical care, there is a need for greater information sharing between VA and non-VA health care organizations. Insights by leaders into barriers and facilitators to VA-HIE can be applied by other national and regional networks that seek to achieve interoperability across health care delivery systems.

Published
2021
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5. Future of Pandemic Prevention and Response CCC Workshop Report

Author

Danks, David, Mihalcea, Rada, Siek, Katie, Singh, Mona, Dixon, Brian, and Griffin, Haley

Subjects
Computer Science - Computers and Society
Abstract

This report summarizes the discussions and conclusions of a 2-day multidisciplinary workshop that brought together researchers and practitioners in healthcare, computer science, and social sciences to explore what lessons were learned and what actions, primarily in research, could be taken. One consistent observation was that there is significant merit in thinking not only about pandemic situations, but also about peacetime advances, as many healthcare networks and communities are now in a perpetual state of crisis. Attendees discussed how the COVID-19 pandemic amplified gaps in our health and computing systems, and how current and future computing technologies could fill these gaps and improve the trajectory of the next pandemic. Three major computing themes emerged from the workshop: models, data, and infrastructure. Computational models are extremely important during pandemics, from anticipating supply needs of hospitals, to determining the care capacity of hospital and social service providers, to projecting the spread of the disease. Accurate, reliable models can save lives, and inform community leaders on policy decisions. Health system users require accurate, reliable data to achieve success when applying models. This requires data and measurement standardization across health care organizations, modernizing the data infrastructure, and methods for ensuring data remains private while shared for model development, validation, and application. Finally, many health care systems lack the data, compute, and communication infrastructures required to build models on their data, use those models in ordinary operations, or even to reliably access their data. Robust and timely computing research has the potential to better support healthcare works to save lives in times of crisis (e.g., pandemics) and today during relative peacetime.

Published
2024

7. Factors Related to User Ratings and User Downloads of Mobile Apps for Maternal and Infant Health: Cross-Sectional Study

Author

Biviji, Rizwana, Vest, Joshua R, Dixon, Brian E, Cullen, Theresa, and Harle, Christopher A

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMobile health apps related to maternal and infant health (MIH) are prevalent and frequently used. Some of these apps are extremely popular and have been downloaded over 5 million times. However, the understanding of user behavior and user adoption of these apps based on consumer preferences for different app features and categories is limited. ObjectiveThis study aimed to examine the relationship between MIH app characteristics and users’ perceived satisfaction and intent to use. MethodsThe associations between app characteristics, ratings, and downloads were assessed in a sample of MIH apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. Multivariable linear regression was used to assess the relationship between app characteristics and user ratings, and ordinal logistic regression was used to assess the relationship between app characteristics and user downloads. ResultsThe analyses of user ratings and downloads included 421 and 213 apps, respectively. The average user rating was 3.79 out of 5. Compared with the Apple App Store, the Google Play Store was associated with high user ratings (beta=.33; P=.005). Apps with higher standardized user ratings (beta=.80; P

Published
2020
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11. Real World Performance of the 21st Century Cures Act Population Level Application Programming Interface

Author

Jones, James R, Gottlieb, Daniel, McMurry, Andrew J, Atreja, Ashish, Desai, Pankaja M, Dixon, Brian E, Payne, Philip RO, Saldanha, Anil J, Shankar, Prabhu, Solad, Yauheni, Wilcox, Adam B, Ali, Momeena S, Kang, Eugene, Kirchner, Lyndsey, Martin, Andrew M, Sprouse, Elizabeth, Taylor, David, Terry, Michael, Ignatov, Vladimir, Network, the SMART Cumulus, and Mandl, Kenneth D

Subjects
Information and Computing Sciences, Health Services and Systems, Health Sciences, Patient Safety, Networking and Information Technology R&D (NITRD), Data Science, Generic health relevance, Good Health and Well Being
Abstract

OBJECTIVE: To evaluate the real-world performance in delivering patient data on populations, of the SMART/HL7 Bulk FHIR Access API, required in Electronic Health Records (EHRs) under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at five healthcare sites from April to September 2023, including four hospitals using EHRs certified for interoperability, and one Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across six types of FHIR resources. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8,000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1,555-2,500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12,000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Published
2023

15. Variance estimation in pseudo-expected estimating equations for missing data

Author

Bakoyannis, Giorgos, Mpofu, Philani B., Broyles, Andrea, and Dixon, Brian B.

Subjects
Statistics - Methodology
Abstract

Missing data is a common challenge in biomedical research. This fact, along with growing dataset volumes of the modern era, make the issue of computationally-efficient analysis with missing data of crucial practical importance. A general computationally-efficient estimation framework for dealing with missing data is the pseudo-expected estimating equations (PEEE) approach. The method is applicable with any parametric model for which estimation involves the solution of a set of estimating equations, such as likelihood score equations. A key limitation of the PEEE approach is that there is currently no closed-form variance estimator, and variance estimation requires the computationally burdensome bootstrap method. In this work, we address the gap and provide a closed-form variance estimator whose computation can be significantly faster than a bootstrap approach. Our variance estimator is shown to be consistent even with auxiliary variables and under misspecified models for the incomplete variables. Simulation studies show that our variance estimator performs well and that its computation can be over 50 times faster than the bootstrap. The computational efficiency gain from our proposed variance estimator is crucial with large datasets or when the main analysis method is computationally intensive. Finally, the PEEE approach along with our variance estimator are used to analyze incomplete electronic health record data of patients with traumatic brain injury.

Published
2022

16. Respiratory syncytial virus (RSV) vaccine effectiveness against RSV-associated hospitalisations and emergency department encounters among adults aged 60 years and older in the USA, October, 2023, to March, 2024: a test-negative design analysis

Author

Payne, Amanda B, Watts, Janet A, Mitchell, Patrick K, Dascomb, Kristin, Irving, Stephanie A, Klein, Nicola P, Grannis, Shaun J, Ong, Toan C, Ball, Sarah W, DeSilva, Malini B, Natarajan, Karthik, Sheffield, Tamara, Bride, Daniel, Arndorfer, Julie, Naleway, Allison L, Koppolu, Padma, Fireman, Bruce, Zerbo, Ousseny, Timbol, Julius, Goddard, Kristin, Dixon, Brian E, Fadel, William F, Rogerson, Colin, Allen, Katie S, Rao, Suchitra, Mayer, David, Barron, Michelle, Reese, Sarah E, Rowley, Elizabeth A K, Najdowski, Morgan, Ciesla, Allison Avrich, Mak, Josephine, Reeves, Emily L, Akinsete, Omobosola O, McEvoy, Charlene E, Essien, Inih J, Tenforde, Mark W, Fleming-Dutra, Katherine E, and Link-Gelles, Ruth

Published
2024
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20. The Immune System of Bony Fish

Author

Semple, Shawna L., primary, Heath, George, additional, Rodríguez-Ramos, Tania, additional, Betancourt, Jesus L., additional, and Dixon, Brian, additional

Published
2024
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35. Number needed to vaccinate with a COVID-19 booster to prevent a COVID-19-associated hospitalization during SARS-CoV-2 Omicron BA.1 variant predominance, December 2021–February 2022, VISION Network: a retrospective cohort study

Author

Adams, Katherine, Riddles, John J., Rowley, Elizabeth A.K., Grannis, Shaun J., Gaglani, Manjusha, Fireman, Bruce, Hartmann, Emily, Naleway, Allison L., Stenehjem, Edward, Hughes, Alexandria, Dalton, Alexandra F., Natarajan, Karthik, Dascomb, Kristin, Raiyani, Chandni, Irving, Stephanie A., Sloan-Aagard, Chantel, Kharbanda, Anupam B., DeSilva, Malini B., Dixon, Brian E., Ong, Toan C., Keller, Jean, Dickerson, Monica, Grisel, Nancy, Murthy, Kempapura, Nanez, Juan, Fadel, William F., Ball, Sarah W., Patel, Palak, Arndorfer, Julie, Mamawala, Mufaddal, Valvi, Nimish R., Dunne, Margaret M., Griggs, Eric P., Embi, Peter J., Thompson, Mark G., Link-Gelles, Ruth, and Tenforde, Mark W.

Published
2023
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40. Laboratory-Confirmed COVID-19 Among Adults Hospitalized with COVID-19–Like Illness with Infection-Induced or mRNA Vaccine-Induced SARS-CoV-2 Immunity — Nine States, January–September 2021

Author

Bozio, Catherine H., Grannis, Shaun J., Naleway, Allison L., Ong, Toan C., Butterfield, Kristen A., DeSilva, Malini B., Natarajan, Karthik, Yang, Duck-Hye, Rao, Suchitra, Klein, Nicola P., Irving, Stephanie A., Dixon, Brian E., Dascomb, Kristin, Liao, I-Chia, Reynolds, Sue, McEvoy, Charlene, Han, Jungmi, Reese, Sarah E., Lewis, Ned, Fadel, William F., Grisel, Nancy, Murthy, Kempapura, Ferdinands, Jill, Kharbanda, Anupam B., Mitchell, Patrick K., Goddard, Kristin, Embi, Peter J., Arndorfer, Julie, Raiyani, Chandni, Patel, Palak, Rowley, Elizabeth A., Fireman, Bruce, Valvi, Nimish R., Griggs, Eric P., Levy, Matthew E., Zerbo, Ousseny, Porter, Rachael M., Birch, Rebecca J., Blanton, Lenee, Ball, Sarah W., Steffens, Andrea, Olson, Natalie, Williams, Jeremiah, Dickerson, Monica, McMorrow, Meredith, Schrag, Stephanie J., Verani, Jennifer R., Fry, Alicia M., Azziz-Baumgartner, Eduardo, Barron, Michelle, Gaglani, Manjusha, Thompson, Mark G., and Stenehjem, Edward

Published
2021

41. Effectiveness of 2-Dose Vaccination with mRNA COVID-19 Vaccines Against COVID-19–Associated Hospitalizations Among Immunocompromised Adults — Nine States, January–September 2021

Author

Embi, Peter J., Levy, Matthew E., Naleway, Allison L., Patel, Palak, Gaglani, Manjusha, Natarajan, Karthik, Dascomb, Kristin, Ong, Toan C., Klein, Nicola P., Liao, I-Chia, Grannis, Shaun J., Han, Jungmi, Stenehjem, Edward, Dunne, Margaret M., Lewis, Ned, Irving, Stephanie A., Rao, Suchitra, McEvoy, Charlene, Bozio, Catherine H., Murthy, Kempapura, Dixon, Brian E., Grisel, Nancy, Yang, Duck-Hye, Goddard, Kristin, Kharbanda, Anupam B., Reynolds, Sue, Raiyani, Chandni, Fadel, William F., Arndorfer, Julie, Rowley, Elizabeth A., Fireman, Bruce, Ferdinands, Jill, Valvi, Nimish R., Ball, Sarah W., Zerbo, Ousseny, Griggs, Eric P., Mitchell, Patrick K., Porter, Rachael M., Kiduko, Salome A., Blanton, Lenee, Zhuang, Yan, Steffens, Andrea, Reese, Sarah E., Olson, Natalie, Williams, Jeremiah, Dickerson, Monica, McMorrow, Meredith, Schrag, Stephanie J., Verani, Jennifer R., Fry, Alicia M., Azziz-Baumgartner, Eduardo, Barron, Michelle A., Thompson, Mark G., and DeSilva, Malini B.

Published
2021

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