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Evaluation of record linkage of two large administrative databases in a middle income country: stillbirths and notifications of dengue during pregnancy in Brazil

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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Title
Evaluation of record linkage of two large administrative databases in a middle income country: stillbirths and notifications of dengue during pregnancy in Brazil
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0506-5
Pubmed ID
Authors

Enny S Paixão, Katie Harron, Kleydson Andrade, Maria Glória Teixeira, Rosemeire L. Fiaccone, Maria da Conceição N. Costa, Laura C. Rodrigues

Abstract

Due to the increasing availability of individual-level information across different electronic datasets, record linkage has become an efficient and important research tool. High quality linkage is essential for producing robust results. The objective of this study was to describe the process of preparing and linking national Brazilian datasets, and to compare the accuracy of different linkage methods for assessing the risk of stillbirth due to dengue in pregnancy. We linked mothers and stillbirths in two routinely collected datasets from Brazil for 2009-2010: for dengue in pregnancy, notifications of infectious diseases (SINAN); for stillbirths, mortality (SIM). Since there was no unique identifier, we used probabilistic linkage based on maternal name, age and municipality. We compared two probabilistic approaches, each with two thresholds: 1) a bespoke linkage algorithm; 2) a standard linkage software widely used in Brazil (ReclinkIII), and used manual review to identify further links. Sensitivity and positive predictive value (PPV) were estimated using a subset of gold-standard data created through manual review. We examined the characteristics of false-matches and missed-matches to identify any sources of bias. From records of 678,999 dengue cases and 62,373 stillbirths, the gold-standard linkage identified 191 cases. The bespoke linkage algorithm with a conservative threshold produced 131 links, with sensitivity = 64.4% (68 missed-matches) and PPV = 92.5% (8 false-matches). Manual review of uncertain links identified an additional 37 links, increasing sensitivity to 83.7%. The bespoke algorithm with a relaxed threshold identified 132 true matches (sensitivity = 69.1%), but introduced 61 false-matches (PPV = 68.4%). ReclinkIII produced lower sensitivity and PPV than the bespoke linkage algorithm. Linkage error was not associated with any recorded study variables. Despite a lack of unique identifiers for linking mothers and stillbirths, we demonstrate a high standard of linkage of large routine databases from a middle income country. Probabilistic linkage and manual review were essential for accurately identifying cases for a case-control study, but this approach may not be feasible for larger databases or for linkage of more common outcomes.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 21%
Researcher 15 20%
Student > Bachelor 8 11%
Student > Ph. D. Student 7 9%
Student > Doctoral Student 6 8%
Other 14 19%
Unknown 9 12%
Readers by discipline Count As %
Medicine and Dentistry 21 28%
Nursing and Health Professions 10 13%
Social Sciences 6 8%
Agricultural and Biological Sciences 6 8%
Computer Science 5 7%
Other 13 17%
Unknown 14 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2018.
All research outputs
#10,230,080
of 12,813,846 outputs
Outputs from BMC Medical Informatics and Decision Making
#952
of 1,158 outputs
Outputs of similar age
#248,945
of 343,716 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 4 outputs
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