Evidence map›Paper›PMID 30921353›Full record

ArticlePloS one2019

Validating linkage of multiple population-based administrative databases in Brazil.

Enny S Paixão, Oona M R Campbell, Laura C Rodrigues, Maria Glória Teixeira, Maria da Conceição N Costa, Elizabeth B Brickley, Katie Harron

Abstract readValidation Study
In one paragraph

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Reflections on modern methods: linkage error bias.International journal of epidemiology · 2019
    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

7 authors.

Enny S PaixãoLondon School of Hygiene and Tropical Medicine, Bloomsbury, London, United Kingdom.ORCID 0000-0002-4797-908X
Oona M R CampbellLondon School of Hygiene and Tropical Medicine, Bloomsbury, London, United Kingdom.
Laura C RodriguesLondon School of Hygiene and Tropical Medicine, Bloomsbury, London, United Kingdom.
Maria Glória TeixeiraInstituto de Saúde Coletiva, Rua Basílio da Gama, s/n.Canela, CEP, Salvador, Bahia, Brazil.
Maria da Conceição N CostaInstituto de Saúde Coletiva, Rua Basílio da Gama, s/n.Canela, CEP, Salvador, Bahia, Brazil.
Elizabeth B BrickleyLondon School of Hygiene and Tropical Medicine, Bloomsbury, London, United Kingdom.ORCID 0000-0003-0280-2288
Katie HarronLondon School of Hygiene and Tropical Medicine, Bloomsbury, London, United Kingdom.ORCID 0000-0002-3418-2856

Funding

Wellcome Trust 103975/Z/14/ZWellcome Trust 205377
6 · The paper itself

Abstract

backgroundLinking routinely-collected data provides an opportunity to measure the effects of exposures that occur before birth on maternal, fetal and infant outcomes. High quality linkage is a prerequisite for producing reliable results, and there are specific challenges in mother-baby linkage. Using population-based administrative databases from Brazil, this study aimed to estimate the accuracy of linkage between maternal deaths and birth outcomes and dengue notifications, and to identify potential sources of bias when assessing the risk of maternal death due to dengue in pregnancy.

methodsWe identified women with dengue during pregnancy in a previously linked dataset of dengue notifications in women who had experienced a live birth or stillbirth during 2007-2012. We then linked this dataset with maternal death records probabilistically using maternal name, age and municipality. We estimated the accuracy of the linkage, and examined the characteristics of false-matches and missed-matches to identify any sources of bias.

resultsOf the 10,259 maternal deaths recorded in 2007-2012, 6717 were linked: 5444 to a live birth record, 1306 to a stillbirth record, and 33 to both a live and stillbirth record. After identifying 2620 missed-matches and 124 false-matches, our estimated sensitivity was 72%, specificity was 88%, and positive predictive value was 98%. Linkage errors were associated with maternal education and self-identified race; women with more than 7 years of education or who self-declared as Caucasian were more likely to link. Dengue status was not associated with linkage error.

conclusionDespite not having unique identifiers to link mothers and birth outcomes, we demonstrated a high standard of linkage, with sensitivity and specificity values comparable to previous literature. Although there were no differences in the characteristics of dengue cases missed or included in our linked dataset, linkage error occurred disproportionally by some social-demographic characteristics, which should be taken into account in future analyses.

Indexed as

Birth CertificatesDatabases, FactualDeath CertificatesMedical Record LinkageAdultBrazilDengueFemaleHumansInfant, NewbornLive BirthMaleMaternal DeathMaternal MortalityPregnancyPregnancy Complications, Infectious

Identifiers

PMID30921353
PMCPMC6438533

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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.