Literature DB >> 7476469

The feasibility and accuracy of anonymized record linkage to estimate shared clientele among three health and social service agencies.

E Jamieson1, J Roberts, G Browne.   

Abstract

This study was designed to test the effectiveness of three computerized methods of record linkage, using minimal identifiers. The goal of the linkage was to determine the proportion of persons receiving Public Health Nursing Services (n = 5,749) who also receive income assistance from the region (n = 38,800) or the province (n = 16,741). The first linkage method was based on full agreement of a number of variables (deterministic matching) and the second was based on the likelihood of the match (probabilistic matching). The third method combined information from methods 1 and 2. Method 3 proved to be the most effective linkage technique, with a specificity of 0.98 and sensitivity of 0.94. The linkage showed that only 17% of the Public Health clientele received income assistance from the region or province, much lower than expected. Factors which have probably led to an underestimate are discussed.

Entities:  

Mesh:

Year:  1995        PMID: 7476469

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  4 in total

1.  Practical introduction to record linkage for injury research.

Authors:  D E Clark
Journal:  Inj Prev       Date:  2004-06       Impact factor: 2.399

2.  Record linkage: making the most out of errors in linking variables.

Authors:  M Tromp; J B Reitsma; A C J Ravelli; N Méray; G J Bonsel
Journal:  AMIA Annu Symp Proc       Date:  2006

3.  Using two on-going HIV studies to obtain clinical data from before, during and after pregnancy for HIV-positive women.

Authors:  Susie E Huntington; Loveleen K Bansi; Claire Thorne; Jane Anderson; Marie-Louise Newell; Graham P Taylor; Deenan Pillay; Teresa Hill; Pat A Tookey; Caroline A Sabin
Journal:  BMC Med Res Methodol       Date:  2012-07-28       Impact factor: 4.615

4.  The SAIL databank: linking multiple health and social care datasets.

Authors:  Ronan A Lyons; Kerina H Jones; Gareth John; Caroline J Brooks; Jean-Philippe Verplancke; David V Ford; Ginevra Brown; Ken Leake
Journal:  BMC Med Inform Decis Mak       Date:  2009-01-16       Impact factor: 2.796

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.