Medical event profiling of COPD patients.
OBJECTIVE
This analysis proposes appropriate descriptive and analytical statistical techniques to profile medical events requiring GP consultation using the example of chronic obstructive pulmonary disease (COPD).


METHODS
Consultation patterns were examined for 1807 cases with a diagnosis compatible with COPD in 1998 and a group of controls matched by sex, age and practice from the General Practice Research Database, a nationally representative UK primary care database. Consulting patterns by Read code chapter and chapter subdivision were examined using cluster analysis, logistic regression and classification and regression trees (CART).


RESULTS
CART and multivariate logistic regression analyses suggested that COPD patients were more likely to consult with pulmonary circulatory disease (multivariate OR: 7.46 (95% confidence intervals: 2.05, 20.01) ), non-COPD respiratory diseases (2.77 (2.28, 3.37)), mycoses (2.0 (1.43, 2.71)), 'symptoms or ill-defined conditions' (1.95 (1.68, 2.29)), or other forms of heart disease (1.84 (1.92, 2.64)), and less likely to consult with hypertensive diseases (0.73 (0.57, 0.96)). Regression also showed positive associations with digestive system diseases (OR: 1.31 (1.02, 1.68)) and negative associations with 'other viral or chlamydial disease' (0.16 (0.03, 0.88)). A borderline significance reduced risk for cancers was seen in univariate logistic regression analyses. Cluster analyses were not useful in discriminating between cases and controls.


CONCLUSIONS
These analyses provide information about the natural history of COPD and could be used to help interpret or detect adverse drug reactions if repeated before and after introduction of a treatment. COPD can be considered a multicomponent disease with more frequent comorbidities than age- and gender-matched individuals without COPD.
Hansell
A L
AL
Department of Epidemiology and Public Health, Imperial College London, UK. a.hansell@imperial.ac.uk
Lam
K A
KA
Richardson
S
S
Visick
G
G
Soriano
J B
JB
D000368:Aged; D016022:Case-Control Studies; D016000:Cluster Analysis; D015897:Comorbidity; D016208:Databases, Factual; D005194:Family Practice / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D017891:Pharmacoepidemiology; D029424:Pulmonary Disease, Chronic Obstructive / Q000145:classification* / Q000453:epidemiology; D012017:Referral and Consultation; D006113:United Kingdom / Q000453:epidemiology