Description of an incidence-based model for Assessing comorbidity patterns in disease natural history
BMJ Open 2016;6: e012105. doi:10.1136/ bmjopen-2016-012105
Victor A Kiri1,2 victor.kiri at fvjkconsult.com
Received 1 April 2016 Revised 16 June 2016 Accepted 5 July 2016
Background: Patients with a chronic disease often suffer from other diseases called comorbidities, which can be important factors in the assessment of risks associated with the disease and its management. However, comorbidities can pose important methodological issues because factors such as time, age, duration and the disease can influence their impact on the risk of interest.
Methods: To identify comorbidities of a chronic disease, it is common practice to construct 2 separate cohorts of patients—a set with the disease and another as a random sample of patients free of the disease—and compare the event rates for each candidate’s comorbidity over a specific period between the 2, while accounting for factors which may confound the results. We describe an incidence-based alternative approach that exploits the longitudinal properties of observational databases to track incident event rates along the natural history of the chronic disease. We illustrate it in a retrospective cohort of patients with chronic obstructive pulmonary disease (COPD) aged 50 and above—each patient with COPD was matched with another without COPD on certain confounding factors.
Results: We obtained 24 079 matched pairs. We found that chronic conditions such as
- lung cancer,
- fracture and
were more common in patients with COPD. We also found evidence of time-varying associations.
Conclusions: Our findings in COPD suggest that time is an important factor and comorbidity studies which are based on information in a single fixed period (such as first year postdiagnosis of COPD) are more likely to report spurious associations.