Imperial College London
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ProfessorChristopherMillett

Faculty of Medicine, School of Public Health

Professor of Public Health
 
 
 
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Contact

 

c.millett Website

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Coeli:2021:10.1186/s12911-021-01550-6,
author = {Coeli, CM and Saraceni, V and Mota, Medeiros Jr P and Santos, HPDS and Guillen, LCT and Alves, LGSB and Hone, T and Millett, C and Trajman, A and Durovni, B},
doi = {10.1186/s12911-021-01550-6},
journal = {BMC Medical Informatics and Decision Making},
pages = {1--13},
title = {Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil},
url = {http://dx.doi.org/10.1186/s12911-021-01550-6},
volume = {21},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundLinking Brazilian databases demands the development of algorithms and processes to deal with various challenges including the large size of the databases, the low number and poor quality of personal identifiers available to be compared (national security number not mandatory), and some characteristics of Brazilian names that make the linkage process prone to errors. This study aims to describe and evaluate the quality of the processes used to create an individual-linked database for data-intensive research on the impacts on health indicators of the expansion of primary care in Rio de Janeiro City, Brazil.MethodsWe created an individual-level dataset linking social benefits recipients, primary health care, hospital admission and mortality data. The databases were pre-processed, and we adopted a multiple approach strategy combining deterministic and probabilistic record linkage techniques, and an extensive clerical review of the potential matches. Relying on manual review as the gold standard, we estimated the false match (false-positive) proportion of each approach (deterministic, probabilistic, clerical review) and the missed match proportion (false-negative) of the clerical review approach. To assess the sensitivity (recall) to identifying social benefits recipients’ deaths, we used their vital status registered on the primary care database as the gold standard.ResultsIn all linkage processes, the deterministic approach identified most of the matches. However, the proportion of matches identified in each approach varied. The false match proportion was around 1% or less in almost all approaches. The missed match proportion in the clerical review approach of all linkage processes were under 3%. We estimated a recall of 93.6% (95% CI 92.8–94.3) for the linkage between social benefits recipients and mortality data.ConclusionThe adoption of a linkage strategy combining pre-processing routines, deterministic, and probabilistic strategies, as well as
AU  - Coeli,CM
AU  - Saraceni,V
AU  - Mota,Medeiros Jr P
AU  - Santos,HPDS
AU  - Guillen,LCT
AU  - Alves,LGSB
AU  - Hone,T
AU  - Millett,C
AU  - Trajman,A
AU  - Durovni,B
DO  - 10.1186/s12911-021-01550-6
EP  - 13
PY  - 2021///
SN  - 1472-6947
SP  - 1
TI  - Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil
T2  - BMC Medical Informatics and Decision Making
UR  - http://dx.doi.org/10.1186/s12911-021-01550-6
UR  - https://link.springer.com/article/10.1186/s12911-021-01550-6
UR  - http://hdl.handle.net/10044/1/89829
VL  - 21
ER  -
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