Data-Driven Approach to Design an Efficient Mass Vaccination and Public Health Monitoring Informatics Platform

2025 Annual Symposium On Demand

Presentation Time: 03:15 PM - 03:27 PM

Abstract Keywords: Public Health, Documentation Burden, Clinical Decision Support, Real-World Evidence Generation, Global Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics

Documenting clients, screenings and vaccinations administered is of particular importance during mass vaccination, since information regarding uptake is critical for monitoring adverse effects and vaccine efficacy. This is especially important when a newly-developed vaccine is being dispensed or when multiple doses of vaccine are needed per person. Despite these needs, there is no uniform or integrated system for effective vaccine data collection. In this paper, we describe and analyze five types of electronic technologies for registration and screening in vaccination clinics. We contrast their functionalities, usability and operations performance based on time-motion studies and service data collected during actual influenza vaccination campaigns. We evaluate their dispensing performance under an optimal dispensing clinic design. Our analysis shows that each of these electronic technologies can improve overall throughput by 16% to 45%. Based on our findings, we design a prototypical registration and screening system with integrated information flow that can be used for dispensing, monitoring and assessing mass vaccination. The system connects to the local Immunization Information System and electronic medical record systems. The design is flexible and adaptable for different types of medical countermeasures, and is suitable for regional public health departments.

Speaker:
Eva Lee, PhD
Georgia Institute of Technology

Author:
YiFan Liu, PhD - Georgia Institute of Technlogy;

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Making SCD Warriors Visible: A Public Health Visualization Project

2025 Annual Symposium On Demand

Presentation Time: 03:27 PM - 03:39 PM

Abstract Keywords: Public Health, User-centered Design Methods, Information Visualization, Population Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics

There are 7.74 million individuals living with sickle cell disease (SCD) globally, making it the most inherited blood disorder. Public health seeks to measure the epidemiology of SCD as it occurs in nations and among populations that are under-resourced and at-risk. Despite screening efforts deployed around the world, many patients affected by SCD (aka SCD warriors) are lost-to-follow-up as they age into adulthood. In the Indiana SCD Data Collection program, data are linked across multiple clinical and public health sources to create a comprehensive dataset on people living with SCD in the U.S. state. The program further deployed user-centered design approaches to create a usable dashboard that visualizes data on the populations living with SCD. The dashboard makes SCD warriors visible to clinicians, public health, and community organizations that serve patients and their families.

Speaker:
Brian Dixon, MPA, PhD
Regenstrief Institute

Authors:
Brian Dixon, MPA, PhD - Regenstrief Institute; Muchiri Elia Wandai, PhD, MS - Regenstrief Institute; Elisabeth Reese, MD - Indiana University School of Medicine; Mengyu Di, MSPH - Regenstrief Institute; Gerard Hills, M.D. - Regenstrief Institute; Jennifer Gatz, PhD - Regenstrief Institute; Amanda Okolo, MPH - Innovative Hematology; Brandon Hardesty, MD - Innovative Hematology;

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An early-phase local model for pandemics using public health data: application to the COVID-19 pandemic

2025 Annual Symposium On Demand

Presentation Time: 03:39 PM - 03:51 PM

Abstract Keywords: Infectious Diseases and Epidemiology, Public Health, Data Mining
Primary Track: Foundations
Programmatic Theme: Public Health Informatics

The emergence of novel infectious pathogens challenges early-phase modeling of disease transmission due to limited, low-quality data and an incomplete understanding of the pathogen. Additionally, regional variations in outbreaks necessitate models that incorporate local dynamics. We present an early-phase local model that leverages constrained public health data, primarily infection counts and aggregated regional characteristics, to study disease transmission dynamics. To address data limitations and potential model misspecifications, we incorporate a quasi-likelihood approach with a flexible error term. Furthermore, we introduce an online estimator that enables real-time data updates, supported by an iterative algorithm for parameter estimation. We applied this method to early COVID-19 data, analyzing infection counts and county-level risk factors from more than 800 U.S. counties to predict disease spread and assess the impact of social behavior, demographics, and vaccination coverage on disease transmission. This framework improves early outbreak analysis and informs local pandemic response under suboptimal data conditions.

Speaker:
Jing Huang, PhD
University of Pennsylvania

Authors:
Jiasheng Shi, PhD - The Chinese University of Hong Kong, Shenzhen; Jeffrey Morris, PhD - University of Pennsylvania Perelman School of Medicine; David Rubin, MD - University of California;

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Data Modernization in Action: Synthesizing Pioneering Informatics Projects in Public Health and Data Modernization Stories from Public Health Agencies

2025 Annual Symposium On Demand

Presentation Time: 03:51 PM - 04:03 PM

Abstract Keywords: Public Health, Data Modernization, Infectious Diseases and Epidemiology
Primary Track: Applications
Programmatic Theme: Public Health Informatics

With data considered as the ‘oxygen’ of public health, the Data Modernization Initiative (DMI) to enhance the public health data and information infrastructure is critical. The DMI Stories from the Field features data modernization from public health agencies to highlight success/progress. These stories (n=241) were analyzed, with outbreak response, information systems capacity, epidemiology/laboratory capacity being some of the common topics. Total codes (n=199) across DMI stories were organized into 7 themes and the top 3 codes were communication, collaboration and public health agencies. Key takeaways and next steps were identified and validated with expert input across people, product, process and partnership categories and people factor was critical along with funding/sustainability. Ongoing DMI stories and future studies for evaluating impact are recommended. DMI stories are a great option to communicate the projects and impact of DMI to a larger public audience and garner support for this vital endeavor.

Speaker:
Chanhee Kim, PhD
University of Minnesota

Authors:
Chanhee Kim, PhD - University of Minnesota; Aasa Dahlberg Schmit, B.Sc. - HLN Consulting; Sarah Solarz, MPH - Minnesota Department of Health; Sripriya Rajamani, MBBS, MPH, PhD, FAMIA - University of Minnesota;

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Which decisions affect cohort distribution in COVID-19 data analytics?

2025 Annual Symposium On Demand

Presentation Time: 04:03 PM - 04:15 PM

Abstract Keywords: Racial Disparities, Health Equity, Clinical Decision Support, Diversity, Equity, Inclusion, and Accessibility
Primary Track: Foundations

Data analytics has emerged as a crucial tool for understanding the multifaceted impacts of the COVID-19 pandemic. By collecting and analyzing extensive datasets, researchers have gained valuable insights into the virus's transmission, severity, and the effectiveness of public health measures. Yet, many contradictive and non-reproducible results have been published. The significance of cohort representativeness in this context cannot be overstated, as diverse cohorts provide a comprehensive understanding of how various demographic and clinical factors influence COVID-19 outcomes. This study investigates the impact of decision-making processes on cohort diversity, focusing on demographic categories including sex, race, and ethnicity. Results show that decisions made during data preprocessing and cohort construction increase variability in demographic distribution. Specifically, the difference in female representation varied by 0.77% to 2.68%, in Black race from 1.17% to 5.15%, and in Hispanic or Latino ethnicity from 5.84% to 8.21%. It highlights how arbitrary decisions can lead to varying data including changes to data distribution. Seemingly unrelated to demographics factors, including timing and provider selection, significantly influence patient distribution and outcomes, underscoring the necessity for informed, data-driven strategies. The findings emphasize the importance of strategic, evidence-based decision-making to enhance consistency, optimize resource utilization, and effectively serve diverse populations, ultimately contributing to more equitable health outcomes and informed public health policies.

Speaker:
Atefehsadat Haghighathoseini, Doctoral Student
George Mason University

Authors:
Atefehsadat Haghighathoseini, Doctoral Student - George Mason University; Lemba Priscille Ngana, PhD Health Services Research (Knowledge Discovery and Health Informatics Concentration) - George Mason University; Janusz Wojtusiak, PhD - George Mason University; Hua Min, PhD - George Mason University; Nirup M Menon, PhD - College of Business at George Mason University;

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Comparison of Disease Prevalence and Associations Across EHR Sources in the All of Us Research Program

2025 Annual Symposium On Demand

Presentation Time: 04:15 PM - 04:27 PM

Abstract Keywords: Phenomics and Phenome-wide Association Studies, Population Health, Evaluation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

The All of Us Research Program collects electronic health records (EHRs) from participants (PPI) and healthcare provider organizations (HPO). This study compared disease prevalence and genotype-phenotype associations between these sources. PPI participants were younger, predominantly White, female, insured, with lower disease prevalence than HPO, but both exceeded national estimates. Despite prevalence differences, both sources replicated known phenotypic and genetic associations, and also showed moderately concordant in effect sizes, supporting joint analysis with careful harmonization.

Speaker:
Chenjie Zeng, PhD
NIH

Authors:
Bennett Waxse, MD, PhD - NIAID/CNH; Joshua Denny, MD, MS - National Institutes of Health;

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