Advancing methods for evaluating enteric vaccines across the development pipeline Restricted; Files Only
Garcia Quesada, Maria (Spring 2025)
Abstract
Diarrhea is a leading cause of morbidity and mortality globally, particularly among children <5 years of age. Several vaccines against important enteric pathogens, such as Shigella and norovirus, are in the pipeline. The path to licensure and distribution of these vaccines hinges on our ability to properly prioritize and evaluate them. The overarching goal of this dissertation was to develop methods for improving the evaluation of investigational enteric vaccines and estimate potential direct and indirect effects of those vaccines.
AIM 1: Hybrid studies may be used to estimate incidence of infectious diseases by enrolling cases in medical facilities, estimating population denominators in the community, and calculating incidence using some adjustment for healthcare seeking behaviors. We conducted a simulation to evaluate approaches for healthcare seeking adjustment and variance estimation in hybrid studies, and applied our approach to the Enterics for Global Health (EFGH) study of Shigella incidence. We found propensity for healthcare seeking scores can be used to accurately adjust for healthcare seeking behaviors. M-estimation may estimate appropriate variance with reasonable precision, but with significant implementation challenges, making the non-parametric bootstrap an appealing alternative.
AIM 2: Shigella vaccine candidates are approaching phase III trials, but primary trial endpoints have not been agreed-upon. Enteropathogens frequently cause subclinical infections, so when there are co-infections with Shigella during diarrhea, etiologic attribution may be biased, which may bias vaccine efficacy (VE) estimates. We conducted a simulation to identify Shigella vaccine trial endpoints that minimize misclassification of true Shigella-attributable diarrhea episodes and bias in the observed VE. We found that using a Shigella quantity cutoff can be adjusted to result in the least bias, particularly for moderate-to-severe diarrhea, but bias may vary by setting. Using a Shigella quantity cutoff was also most efficient for reducing trial sample size to detect VE with sufficient statistical power.
AIM 3: Norovirus is the leading cause of diarrheal disease globally and investigational vaccines may have important indirect effects on symptoms and viral shedding. We used a mathematical model of norovirus transmission and immunity to estimate potential population-level impact of a norovirus vaccination program in the US, parametrized using challenge trial data of a norovirus vaccine. We found that, even with a low VE against infection, a norovirus vaccine that reduces symptoms has the potential to substantially reduce norovirus morbidity and mortality. A pediatric vaccine may avert more cases and outpatient visits while conferring indirect protection to adults, while an adult vaccine may avert more hospitalizations and deaths with protection largely limited to adults.
The results of this dissertation will be crucial to prioritizing enteric vaccines, estimating the sample size of a vaccine trial, defining appropriate trial endpoints, and considering potential indirect effects of a vaccine for licensure. Given the high burden of infectious diarrhea, particularly among children in low resource settings, this dissertation has the potential to have an important impact on global public health.
Table of Contents
Chapter 1 – Introduction 1
Overarching goal and specific aims 4
Chapter 2 – Comparing existing and novel methods for estimating etiology-specific diarrheal disease incidence using hybrid study data 5
Background 7
Methods 9
Enterics for Global Health (EFGH) Shigella Surveillance Study Data 9
Simulated Hybrid Study Data 11
Healthcare seeking adjustment 12
Adjusted incidence and variance 14
Results 21
Simulation results 21
EFGH Results 24
Discussion 26
Tables 32
Figures 35
Appendix 2-1: Illustration of advantage of propensity score model 37
Appendix 2-2: Variable selection for EFGH propensity score model 39
Appendix 2-3: Supplementary Tables and Figures 42
Chapter 3 – Identifying optimal endpoint definitions using quantitative PCR to minimize outcome misclassification in upcoming Shigella vaccine trials 48
Background 50
Methods 51
Data source 51
Estimating simulation parameters 52
Vaccine assumptions and scenarios 54
Simulation of vaccine trial 54
Trial analysis 56
Results 57
Endpoint sensitivity and specificity 57
Observed vs true VE 58
Trial sample size 59
Sensitivity analysis of vaccine effects 60
Discussion 61
Tables 66
Figures 67
Appendix 3-1: Supplementary Tables and Figures 71
Chapter 4 – Estimating the potential population-level impact a norovirus vaccine using mathematical modeling 78
Background 80
Methods 81
Analysis of HIL-214 challenge trial 81
Compartmental model structure 83
Force of infection 85
Model fitting 86
Model scenarios and outcomes 86
Uncertainty around fitted parameters and model outcomes 87
Impact of distinct vaccine effects 87
Results 88
HIL-214 challenge trial 88
Model fitting 88
Outcomes averted by pediatric vaccination 89
Outcomes averted by adult vaccination 90
NNV 90
Impact of distinct vaccine effects 91
Discussion 92
Tables 98
Figures 103
Appendix 4-1: Supplemental Methods 107
Appendix 4-2: Supplementary Tables & Figures 109
Chapter 5 – Summary of Results and Conclusions 115
Chapter 6 – References 120
About this Dissertation
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File download under embargo until 22 May 2027 | 2025-04-11 10:48:03 -0400 | File download under embargo until 22 May 2027 |
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