Data mining of tuberculosis patient data using multiple correspondence analysis.
The aim of this study was to demonstrate the epidemiological use of multiple correspondence analysis (MCA), as applied to tuberculosis (TB) data from North East London. Data for TB notifications in North East London primary care trusts (PCTs) between the years 2002 and 2007 were used. TB notification data were entered for MCA allowing display of graphical data output (n=4947); MCA analyses were performed on the whole dataset, by PCT, and by year of notification. Graphical MCA output displayed variance of data categories; clustering of variable categories in MCA output signified association. Clustering patterns in MCA output demonstrated different associations by year of notification, within PCTs and between PCTs. MCA is a useful technique for displaying association of variable categories used in TB epidemiology. Results suggest that MCA could be a useful tool in informing commissioning of TB services.
Rennie
T W
TW
North East London Tuberculosis Commissioning Unit, Newham Primary Care Trust, London, UK. timothy.rennie@pharmacy.ac.uk
Roberts
W
W
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003695:Delivery of Health Care; D005006:Ethnicity; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008131:London / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012307:Risk Factors; D013997:Time Factors; D014376:Tuberculosis / Q000175:diagnosis* / Q000453:epidemiology