Literature DB >> 19784456

Accuracy of probabilistic record linkage applied to health databases: systematic review.

Daniele Pinto da Silveira1, Elizabeth Artmann.   

Abstract

OBJECTIVE: To analyze both national and international literature on validity of record linkage procedure of health databases focusing on quality assessment of results.
METHODS: A systematic review of cohort, case-control, and cross-sectional studies that evaluated quality of probabilistic record linkage of health databases was conducted. Cochrane methodology of systematic reviews was used. The following databases were widely searched: Medline, LILACS, Scopus, SciELO and Scirus. A time filter was not applied and articles were searched in the following languages: Portuguese, Spanish, French and English.
RESULTS: Summary measures of the quality of probabilistic record linkage were sensitivity, specificity, and positive predictive value. There were identified 202 studies, and after applying the inclusion criteria, a total of 33 articles were reviewed. Only six had complete data on the summary measures of interest. The main limitations were: no reviewer to evaluate titles and abstracts; and no blinding of the article's authors in the review process. Most scientific publications in this field were from the United States, United Kingdom, and New Zealand. Overall, the accuracy of probabilistic record linkage of databases ranged from 74% to 98% sensitivity and 99% to 100% specificity.
CONCLUSIONS: Probabilistic record linkage of health databases has notably been characterized by high sensitivity and greater flexibility of the procedure's sensitivity, indicating concern with data accuracy. The positive predictive value in studies shows a high proportion of truly positive record pairs. The quality assessment of these procedures has been proved essential for validating the results obtained in these studies, and can also contribute to improve large health databases available in Brazil.

Mesh:

Year:  2009        PMID: 19784456     DOI: 10.1590/s0034-89102009005000060

Source DB:  PubMed          Journal:  Rev Saude Publica        ISSN: 0034-8910            Impact factor:   2.106


  21 in total

1.  A machine learning approach to create blocking criteria for record linkage.

Authors:  Phan H Giang
Journal:  Health Care Manag Sci       Date:  2014-04-29

2.  Incorporating the Last Four Digits of Social Security Numbers Substantially Improves Linking Patient Data from De-identified Hospital Claims Databases.

Authors:  James M Naessens; Sue L Visscher; Stephanie M Peterson; Kristi M Swanson; Matthew G Johnson; Parvez A Rahman; Joe Schindler; Mark Sonneborn; Donald E Fry; Michael Pine
Journal:  Health Serv Res       Date:  2015-06-15       Impact factor: 3.402

3.  Transnational Record Linkage for Tuberculosis Surveillance and Program Evaluation.

Authors:  Kaylynn Aiona; Phillip Lowenthal; John A Painter; Randall Reves; Jennifer Flood; Matthew Parker; Yunxin Fu; Kirsten Wall; Nicholas D Walter
Journal:  Public Health Rep       Date:  2015 Sep-Oct       Impact factor: 2.792

4.  Data linkage: a powerful research tool with potential problems.

Authors:  Megan A Bohensky; Damien Jolley; Vijaya Sundararajan; Sue Evans; David V Pilcher; Ian Scott; Caroline A Brand
Journal:  BMC Health Serv Res       Date:  2010-12-22       Impact factor: 2.655

5.  Accuracy of Probabilistic Linkage Using the Enhanced Matching System for Public Health and Epidemiological Studies.

Authors:  Robert W Aldridge; Kunju Shaji; Andrew C Hayward; Ibrahim Abubakar
Journal:  PLoS One       Date:  2015-08-24       Impact factor: 3.240

6.  Embracing the Sparse, Noisy, and Interrelated Aspects of Patient Demographics for use in Clinical Medical Record Linkage.

Authors:  Stephen M Ash; King Ip-Lin
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2015-03-25

7.  Probabilistic data linkage: a case study of comparative effectiveness in COPD.

Authors:  Christopher M Blanchette; Mitch Dekoven; Ajita P De; Melissa Roberts
Journal:  Drugs Context       Date:  2013-10-31

8.  Application of Capture-Recapture for Fine-tuning Uncertainties About National Maternal Mortality Estimates.

Authors:  Bahareh Yazdizadeh; Kazem Mohammad; Saharnaz Nedjat; Nasrin Changizi; Arash Azemikhah; Nahid Jafari; Laleh Radpoyan; Reza Majdzadeh
Journal:  Int J Prev Med       Date:  2014-05

9.  Study protocol of the ESUB-MG cluster randomized trial: a pragmatic trial assessing the implementation of urine drug screening in general practice for buprenorphine maintained patients.

Authors: 
Journal:  BMC Fam Pract       Date:  2016-03-01       Impact factor: 2.497

10.  Is medical perspective on clinical governance practices associated with clinical units' performance and mortality? A cross-sectional study through a record-linkage procedure.

Authors:  Guido Sarchielli; Giovanni De Plato; Mario Cavalli; Stefano Albertini; Ilaria Nonni; Lucia Bencivenni; Arianna Montali; Antonio Ventura; Francesca Montali
Journal:  SAGE Open Med       Date:  2016-07-22
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