DEFINING DIABETES MELLITUS USING ADMINISTRATIVE CLAIMS DATA
Author(s)
Saunders-Hastings P1, Burrell T2, Srichaikul J1, Lee T3, Ahima O4, Chada K4, Wong HL4, Shoaibi A5
1Gevity Consulting Inc., Ottawa, ON, Canada, 2IBM Watson Health, Cambridge, MA, USA, 3Epi Excellence LLC, Garnet Valley, PA, USA, 4Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA, 5US Food and Drug Administration, Silver Spring, MD, USA
OBJECTIVES The Center for Biologics Evaluation and Research Biologics Effectiveness and Safety (BEST) Initiative sought to develop administrative claims-based definitions, referred to as algorithms, for populations of interest. These algorithms will be used in epidemiologic studies to evaluate biologics’ safety. The objective of this study was to develop algorithms for diabetes mellitus (DM) as a study covariate, study population, and outcome. METHODS Authors conducted a literature review using PubMed. Findings were leveraged to develop initial algorithms that were mapped from the International Classification of Diseases, Ninth Revision, Clinical Modification to the Tenth Revision via forward–backward mapping using General Equivalence Mappings. Clinical subject matter experts reviewed draft algorithms. Each algorithm was characterized in the IBM® MarketScan® Research Databases via the IBM MarketScan Treatment Pathways tool or ad hoc programming. Descriptive statistics of patients identified by each algorithm were generated for 2014–2017. RESULTS The annual number of individuals who met the general (any) DM “Covariate” criteria ranged from 76.8 to 78.8 individuals/1,000 enrolled/year, with a higher proportion of males than females. Among a cohort of 71,039,547 patients, 109,342 (0.2%) and 1,141,553 (1.6%) met the “Population” criteria for type 1 DM (T1DM) and type II DM (T2DM), respectively. Males represented 52.6% and 51.1% of those with T1DM and T2DM, respectively. Results from individual queries of codes in the “Outcome” iteration suggest that the number of individuals identified by T2DM codes was highest (3,500,744), followed by T1DM (354,579), other diabetes (270,337), and secondary diabetes (109,150). CONCLUSIONS Forward–backward mapping was used to develop three new algorithms for DM. Though not validated, these algorithms were applied to generate statistics on the frequency of reporting for each iteration between 2014 and 2017. This effort should support subsequent epidemiologic studies on the safety and effectiveness of biologics among an at-risk population in the United States.
Conference/Value in Health Info
2020-05, ISPOR 2020, Orlando, FL, USA
Value in Health, Volume 23, Issue 5, S1 (May 2020)
Code
PDB66
Topic
Epidemiology & Public Health
Topic Subcategory
Disease Classification & Coding, Safety & Pharmacoepidemiology
Disease
Diabetes/Endocrine/Metabolic Disorders