Evidence map›Paper›PMID 30808566›Full record

ArticleVaccine2019

Redefining vaccination coverage and timeliness measures using electronic immunization registry data in low- and middle-income countries.

Samantha B Dolan, Emily Carnahan, Jessica C Shearer, Emily N Beylerian, Jenny Thompson, Skye S Gilbert, Laurie Werner, Tove K Ryman

Abstract read
In one paragraph

Article in Vaccine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 3 pooled it
–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

41 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Samantha B DolanDolan Consulting LLC; PATH, Seattle, USA; Department of Global Health, University of Washington, Seattle, USA. Electronic address: sdolan11@uw.edu.
Emily CarnahanPATH, Seattle, USA.
Jessica C ShearerPATH, Seattle, USA.
Emily N BeylerianPATH, Seattle, USA.
Jenny ThompsonPATH, Seattle, USA.
Skye S GilbertPATH, Seattle, USA.
Laurie WernerPATH, Seattle, USA.
Tove K RymanBill and Melinda Gates Foundation, Seattle, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccine coverage is routinely used as a performance indicator for immunization programs both at local and global levels. For many national immunization programs, there are challenges with accurately estimating vaccination coverage based on available data sources, however an increasing number of low- and middle-income countries (LMICs) have begun implementing electronic immunization registries to replace health facilities' paper-based tools and aggregate reporting systems. These systems allow for more efficient capture and use of routinely reported individual-level data that can be used to calculate dose-specific and cohort vaccination coverage, replacing the commonly used aggregate routine health information system data. With these individual-level data immunization programs have the opportunity to redefine performance measures to enhance programmatic decision-making at all levels of the health system. In this commentary, we discuss how measures for assessing vaccination status and program performance can be redefined and recalculated using these data when generated at the health facility level and the implications of the use and availability of electronic individual-level data.

Indexed as

Developing CountriesElectronic Health RecordsImmunization ProgramsVaccination CoverageHumansProgram EvaluationPublic Health SurveillanceRegistriesVaccinationVaccinesVaccinesElectronic immunization registryImmunizationIndividual-level dataMeasuresRoutine health information systemVaccine

Identifiers

PMID30808566
PMCPMC6420680

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.