Design and Interpretation of Vaccine Field Studies

Me, Halloran; Im, Longini; Struchiner, Cláudio J.

doi:10.1093/oxfordjournals.epirev.a017990

Cited by 168 publications

(146 citation statements)

References 68 publications

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Section: Vaccinationmentioning

confidence: 99%

“…At the individual level, vaccines may reduce infectiousness or colonization, or protect against infection (Halloran et al, 2009). At the population level, the reduction in transmission due to widespread vaccination has indirect protective effects (i.e., herd immunity) for unvaccinated animals as well as for vaccinated animals (Halloran et al, 2009). The epidemiologic measure of protection induced by vaccination is known as vaccine efficacy (VE) and is expressed as a measure of relative risk (RR) in the vaccinated group compared with the unvaccinated group (Halloran et al, 1999), VE ¼ 1 À RR.…”

Section: Vaccinationmentioning

confidence: 99%

“…At the population level, the reduction in transmission due to widespread vaccination has indirect protective effects (i.e., herd immunity) for unvaccinated animals as well as for vaccinated animals (Halloran et al, 2009). The epidemiologic measure of protection induced by vaccination is known as vaccine efficacy (VE) and is expressed as a measure of relative risk (RR) in the vaccinated group compared with the unvaccinated group (Halloran et al, 1999), VE ¼ 1 À RR. We may estimate different measures of VE to capture the multidimensional effect of vaccines (e.g., VE col is vaccine efficacy for colonization and VE I is vaccine efficacy for infectiousness).…”

Section: Vaccinationmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical Modeling of the Transmission and Control of Foodborne Pathogens and Antimicrobial Resistance at Preharvest

Lanzas

Gröhn

2011

Foodborne Pathogens and Disease

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Foodborne diseases are a significant health-care and economic burden. Most foodborne pathogens are enteric pathogens harbored in the gastrointestinal tract of farm animals. Understanding the transmission of foodborne pathogens and the dissemination of antimicrobial resistance at the farm level is necessary to design effective control strategies at preharvest. Mathematical models improve our understanding of pathogen dynamics by providing a theoretical framework in which factors affecting transmission and control of the pathogens can be explicitly considered. In this review, we aim to present the principles underlying the mathematical modeling of foodborne pathogens and antimicrobial resistance at the farm level to a broader audience.

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Section: Vaccinationmentioning

confidence: 99%

Section: Vaccinationmentioning

confidence: 99%

Section: Vaccinationmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical Modeling of the Transmission and Control of Foodborne Pathogens and Antimicrobial Resistance at Preharvest

Lanzas

Gröhn

2011

Foodborne Pathogens and Disease

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“…(Manski, 1993) In the infectious context, for example, causal inference based on assignment of individual units to treatments is biased by inter-unit dependencies that have been well-described in the vaccine efficacy literature (Halloran, Longini, & Struchiner, 1999). Just as an unvaccinated person experiences reduced risk of infection when another person in the same household is vaccinated, a low-income person experiences reduced risk of suffering the negative consequences of poverty when sharing a household with a high-income person (Krieger, Chen, & Selby, 1999).…”

Section: Limitations Of Randomized Social Interventionsmentioning

confidence: 99%

Causal inference from randomized trials in social epidemiology

Kaufman

Poole

2003

Social Science & Medicine

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Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology. r

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“…Specifically, we use a causal framework to describe the typical problem of confounding in VE studies and describe three approaches that have been used to address the problem: a) the use of negative control outcomes in a cohort or case-control study, b) the use of a laboratory testnegative control group in a study of all patients tested for the infection of interest, and c) the use of an ecological time-trend design to measure indirect and overall causal effects (5). Our running example is the problem of estimating influenza VE, a recently controversial area that illustrates how concerns about bias in the evidence base arose, how these approaches to reduce or detect bias are used, and how well they address the issues we are raising.…”

Section: Introductionmentioning

confidence: 99%

Observational studies and the difficult quest for causality: lessons from vaccine effectiveness and impact studies

2016

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Although randomized placebo-controlled trials (RCT) are critical to establish efficacy of vaccines at the time of licensure, important remaining questions about vaccine effectiveness (VE) -used here to include individual-level measures and population-wide impact of vaccine programs --can only be answered once the vaccine is in use, from observational studies. However, such studies are inherently at risk for bias. Using a causal framework and illustrating with examples we review newer approaches to detecting and avoiding confounding and selection bias in three major classes of observational study design: cohort, case-control, and ecological studies. Studies of influenza VE, especially in seniors, are an excellent demonstration of the challenges of detecting and reducing such bias, and so we use influenza VE as a running example. We take a fresh look at the time-trend studies often dismissed as "ecological." Such designs are the only observational study design that can measure the overall effect of a vaccination program (indirect (herd) as well as direct effects), and are in fact already an important part of the evidence base for several vaccines currently in use. Despite the great strides towards more robust observational study designs, challenges lie ahead for evaluating best practices for achieving robust unbiased results from observational studies. This is critical for evaluation of national and global vaccine program effectiveness.

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Design and Interpretation of Vaccine Field Studies

Cited by 168 publications

References 68 publications

Mathematical Modeling of the Transmission and Control of Foodborne Pathogens and Antimicrobial Resistance at Preharvest

Mathematical Modeling of the Transmission and Control of Foodborne Pathogens and Antimicrobial Resistance at Preharvest

Causal inference from randomized trials in social epidemiology

Observational studies and the difficult quest for causality: lessons from vaccine effectiveness and impact studies

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