Evidence map›Paper›PMID 31797425›Full record

ArticleStatistics in medicine2020

Ecological inference for infectious disease data, with application to vaccination strategies.

Leigh H Fisher, Jon Wakefield

Abstract read
In one paragraph

Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Leigh H FisherVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.ORCID 0000-0001-5228-1455
Jon WakefieldDepartment of Biostatistics, University of Washington, Seattle, Washington.

Funding

SPATIO-TEMPORAL EPIDEMIOLOGY: METHODS AND APPLICATIONSR01CA095994 · NCI · UNIVERSITY OF WASHINGTON · PI WAKEFIELD, JONATHAN C · 2005 to 2017
$1.6M
NCI NIH HHS R01 CA095994NCI NIH HHS R01CA095994
6 · The paper itself

Abstract

Disease surveillance systems provide a rich source of data regarding infectious diseases, aggregated across geographical regions. The analysis of such ecological data is fraught with difficulties, and, unless care and suitable data summaries are available, will lead to biased estimates of individual-level parameters. We consider using surveillance data to study the impacts of vaccination. To catalog the problems of ecological inference, we start with an individual-level model, which contains familiar parameters, and derive an ecologically consistent model for infectious diseases in partially vaccinated populations. We compare with other popular model classes and highlight deficiencies. We explore the properties of the new model through simulation and demonstrate that, under standard assumptions, the ecological model provides less biased estimates. We then fit the new model to data collected on measles outbreaks in Germany from 2005-2007.

Indexed as

BiasCommunicable DiseasesComputer SimulationEpidemiologic MethodsHumansPublic Health SurveillanceRegression AnalysisRisk AssessmentVaccinationcount dataecological biastime seriesvaccine coverage

Identifiers

PMID31797425
PMCPMC11016350

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.