Algorithm development and the clinical and economic burden of Cushing’s disease in a large US health plan database

Abstract

PurposeThis study aimed to develop an algorithm to identify patients with CD, and quantify the clinical and economic burden that patients with CD face compared to CD-free controls.MethodsA retrospective cohort study of CD patients was conducted in a large US commercial health plan database between 1/1/2007 and 12/31/2011. A control group with no evidence of CD during the same time was matched 1:3 based on demographics. Comorbidity rates were compared using Poisson and health care costs were compared using robust variance estimation.ResultsA case-finding algorithm identified 877 CD patients, who were matched to 2631 CD-free controls. The age and sex distribution of the selected population matched the known epidemiology of CD. CD patients were found to have comorbidity rates that were two to five times higher and health care costs that were four to seven times higher than CD-free controls.ConclusionAn algorithm based on eight pituitary conditions and procedures appeared to identify CD patients in a claims database without a unique diagnosis code. Young CD patients had high rates of comorbidities that are more commonly observed in an older population (e.g., diabetes, hypertension, and cardiovascular disease). Observed health care costs were also high for CD patients compared to CD-free controls, but may have been even higher if the sample had included healthier controls with no health care use as well. Earlier diagnosis, improved surgery success rates, and better treatments may all help to reduce the chronic comorbidity and high health care costs associated with CD.Electronic supplementary materialThe online version of this article (doi:10.1007/s11102-015-0695-9) contains supplementary material, which is available to authorized users.