ArticleEpidemiology (Cambridge, Mass.)2014
Estimating the per-exposure effect of infectious disease interventions.
Article in Epidemiology (Cambridge, Mass.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Accounting for local incidence when estimating rotavirus vaccine efficacy among countries: a pooled analysis of monovalent rotavirus vaccine trials.American journal of epidemiology · 2025Article
- Over- and under-estimation of vaccine effectiveness.BMC medical research methodology · 2025Article
- Analysing vaccine efficacy evaluated in phase 3 clinical trials carried out during outbreaks.Infectious Disease Modelling · 2024Article
- Randomization for the susceptibility effect of an infectious disease intervention.Journal of mathematical biology · 2022Article
- A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study.BMC medical research methodology · 2022Article
- SARS-CoV-2 breakthrough infections in vaccinated individuals: measurement, causes and impact.Nature reviews. Immunology · 2022Review
- Force of infection: a determinant of vaccine efficacy?NPJ vaccines · 2021Review
- Identification of causal intervention effects under contagion.Journal of causal inference · 2021Article
- Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia.International journal of environmental research and public health · 2020Article
- Heterogeneous susceptibility to rotavirus infection and gastroenteritis in two birth cohort studies: Parameter estimation and epidemiological implications.PLoS computational biology · 2019Article
- Article
- Impact of stochastically generated heterogeneity in hazard rates on individually randomized vaccine efficacy trials.Clinical trials (London, England) · 2018Article
- Risk ratios for contagious outcomes.Journal of the Royal Society, Interface · 2018Article
- Simulations for designing and interpreting intervention trials in infectious diseases.BMC medicine · 2017Article
- Article
- A Dynamic Transmission Model to Evaluate the Effectiveness of Infection Control Strategies.Open forum infectious diseases · 2017Article
- Clinical trials: The mathematics of falling vaccine efficacy with rising disease incidence.Vaccine · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.