title: Harmonisation of variables names prior to conducting statistical analyses with multiple datasets: an automated approach.
abstract: BACKGROUND
  Data requirements by governments, donors and the international community to measure health and development achievements have increased in the last decade. Datasets produced in surveys conducted in several countries and years are often combined to analyse time trends and geographical patterns of demographic and health related indicators. However, since not all datasets have the same structure, variables definitions and codes, they have to be harmonised prior to submitting them to the statistical analyses. Manually searching, renaming and recoding variables are extremely tedious and prone to errors tasks, overall when the number of datasets and variables are large. This article presents an automated approach to harmonise variables names across several datasets, which optimises the search of variables, minimises manual inputs and reduces the risk of error.
  
  
  RESULTS
  Three consecutive algorithms are applied iteratively to search for each variable of interest for the analyses in all datasets. The first search (A) captures particular cases that could not be solved in an automated way in the search iterations; the second search (B) is run if search A produced no hits and identifies variables the labels of which contain certain key terms defined by the user. If this search produces no hits, a third one (C) is run to retrieve variables which have been identified in other surveys, as an illustration. For each variable of interest, the outputs of these engines can be (O1) a single best matching variable is found, (O2) more than one matching variable is found or (O3) not matching variables are found. Output O2 is solved by user judgement. Examples using four variables are presented showing that the searches have a 100% sensitivity and specificity after a second iteration.
  
  
  CONCLUSIONS
  Efficient and tested automated algorithms should be used to support the harmonisation process needed to analyse multiple datasets. This is especially relevant when the numbers of datasets or variables to be included are large.
authors: Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4051, Switzerland. x.bosch@unibas.ch
authors: Xavier
authors: X
authors: Bosch-Capblanch
references: Lancet. 2005 Oct 22-28;366(9495):1460-6
references: 16243091
references: Lancet. 2008 Apr 12;371(9620):1247-58
references: 18406859
references: Lancet. 2009 Jun 20;373(9681):2113-24
references: 19541038
references: Lancet. 2010 Jun 5;375(9730):2032-44
references: 20569843