ArticlePloS one2019
Estimation of vaccination coverage from electronic healthcare records; methods performance evaluation - A contribution of the ADVANCE-project.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines, using existing electronic healthcare record (eHR) databases in Europe. Part of the data in such sources is missing due to incomplete follow-up hampering the accurate estimation of vaccination coverage. We compared different methods for coverage estimation from eHR databases; naïve period prevalence, complete case period prevalence, period prevalence adjusted for follow-up time, Kaplan-Meier (KM) analysis and (adjusted) inverse probability weighing (IPW).
methodsWe created simulation scenarios with different proportions of completeness of follow-up. Both completeness independent and dependent from vaccination date and status were considered. The root mean squared error (RMSE) and relative difference between the estimated and true coverage were used to assess the performance of the different methods for each of the scenarios. We included data examples on the vaccination coverage of human papilloma virus and pertussis component containing vaccines from the Spanish BIFAP database.
resultsUnder completeness independent from vaccination date or status, several methods provided estimates with bias close to zero. However, when dependence between completeness of follow-up and vaccination date or status was present, all methods generated biased estimates. The IPW/CDF methods were generally the least biased. Preference for a specific method should be based on the type of censoring and type of dependence between completeness of follow-up and vaccination. Additional insights into these aspects, might be gained by applying several methods.
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.