Journal
INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE
Volume 21, Issue 5, Pages 517-522Publisher
INT UNION AGAINST TUBERCULOSIS LUNG DISEASE (I U A T L D)
DOI: 10.5588/ijtld.16.0588
Keywords
TB; diagnostic accuracy; positive predictive value; secondary data; non-tuberculous mycobacterial infection
Categories
Funding
- Canadian Institutes of Health Research, Ottawa, ON, Canada (Banting and Best Doctoral Award)
- BC Lung Association Vancouver, BC
- Quebec Respiratory Health Network Training Program, Montreal, QC, USA
Ask authors/readers for more resources
BACKGROUND: An increasing number of studies are using health administrative databases for tuberculosis (TB) research. However, there are limitations to using such databases for identifying patients with TB. OBJECTIVE: To summarise validated methods for identifying TB in health administrative databases. METHODS: We conducted a systematic literature search in two databases (Ovid Medline and Embase, January 1980 January 2016). We limited the search to diagnostic accuracy studies assessing algorithms derived from drug prescription, International Classification of Diseases (ICD) diagnostic code and/or laboratory data for identifying patients with TB in health administrative databases. RESULTS: The search identified 2413 unique citations. Of the 40 full-text articles reviewed, we included 14 in our review. Algorithms and diagnostic accuracy outcomes to identify TB varied widely across studies, with positive predictive value ranging from 1.3% to 100% and sensitivity ranging from 20% to 100%. CONCLUSIONS: Diagnostic accuracy measures of algorithms using out-patient, in-patient and/or laboratory data to identify patients with TB in health administrative databases vary widely across studies. Use solely of ICD diagnostic codes to identify TB, particularly when using out-patient records, is likely to lead to incorrect estimates of case numbers, given the current limitations of ICD systems in coding TB.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available