ArticleStatistics in medicine2018
Estimating population effects of vaccination using large, routinely collected data.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Financial incentives for COVID-19 vaccines in a rural low-resource setting: a cluster-randomized trial.Nature medicine · 2023Trial
- Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention.American journal of epidemiology · 2021Trial
- Temporal Association Between COVID-19 Vaccination and HBsAg Loss in Chronic Hepatitis B Infection: An Interrupted Time Series Analysis.Alimentary pharmacology & therapeutics · 2026Article
- Quasi-experimental methods for pharmacoepidemiology: difference-in-differences and synthetic control methods with case studies for vaccine evaluation.American journal of epidemiology · 2024Article
- Toward evaluation of disseminated effects of medications for opioid use disorder within provider-based clusters using routinely-collected health data.Statistics in medicine · 2022Article
- Estimating Population-Level Effects of the Acellular Pertussis Vaccine Using Routinely Collected Immunization Data.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
- Article
- Implications of a complexity perspective for systematic reviews and guideline development in health decision making.BMJ global health · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Vaccination in populations can have several kinds of effects. Establishing that vaccination produces population-level effects beyond the direct effects in the vaccinated individuals can have important consequences for public health policy. Formal methods have been developed for study designs and analysis that can estimate the different effects of vaccination. However, implementing field studies to evaluate the different effects of vaccination can be expensive, of limited generalizability, or unethical. It would be advantageous to use routinely collected data to estimate the different effects of vaccination. We consider how different types of data are needed to estimate different effects of vaccination. The examples include rotavirus vaccination of young children, influenza vaccination of elderly adults, and a targeted influenza vaccination campaign in schools. Directions for future research are discussed. Copyright © 2017 John Wiley & Sons, Ltd.
Indexed as
Identifiers
What OpenQuestion holds
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.