title: Proximal Predictors of Long-Term Discontinuance with Noninsulin Antihyperglycemic Agents.
abstract: BACKGROUND
  Noninsulin antihyperglycemic agents (NAAs) are the mainstay of treatment for type 2 diabetes, yet persistence in NAA use is suboptimal in many diabetes patients. Most of the research on NAA discontinuance has focused on sociodemographic characteristics and general health status, but such factors are inherently limited in explaining dynamic events such as discontinuance.
  
  
  OBJECTIVE
  To assess the relative importance of static and proximal dynamic factors in explaining long-term NAA discontinuance among Medicare beneficiaries with diabetes.
  
  
  METHODS
  Two sets of probability models were estimated to predict NAA discontinuance as a function of static variables (age, sex, race, original reason for Medicare entitlement, low-income subsidy and dual Medicare/Medicaid eligibility status, and disease burden) and 21 dynamic factors capturing month-by-month changes in drug use, health status, and use of medical services leading up to discontinuance (defined as month 0) and the previous 4 months (designated months -1 to -4) among 71,619 patients with diabetes enrolled in Medicare Part D plans in 2006-2008.
  
  
  RESULTS
  Static variables explained just 1.2% of the variance in probability of NAA discontinuance compared with 14% for all variables combined. Key time-related predictors of NAA discontinuance included discontinuation with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) and statins, hypoglycemia, NAA usage gaps, insulin use, and discharge from hospitals and skilled nursing facilities (SNFs). The strongest significant predictors (P < 0.05) of NAA discontinuance were discontinuation with statins and ACEIs/ARBs in month 0 (predicted probabilities of 37% and 34%, respectively). Other variables that significantly increased the probability of NAA discontinuance by 10% or more were hypoglycemia in month 0 (14%) and month -1 (17%), discontinuance with ACEIs/ARBs in months -1 (15%) and -2 (10%), discontinuance with statins in month -1 (13%), and insulin use in month 0 (12%). Experiencing a previous gap in NAA therapy was associated with higher likelihood of discontinuance if the gap occurred in month -2 (10%) or month -4 (6%), but a gap in therapy in month -1 actually reduced the likelihood of discontinuance by 13%. Discharge from a hospital or SNF was consistently associated with higher probabilities of NAA discontinuance ranging between 4% and 10%, with higher probabilities occurring closer to month 0.
  
  
  CONCLUSIONS
  A cascade of dynamic changes preceding discontinuance with NAA therapy among Medicare Part D enrollees with diabetes was observed between 2006 and 2008. Understanding that lack of persistence in drug use is a dynamic rather than a static phenomenon opens up new avenues for investigating and ultimately improving adherence behavior in the elderly.
  
  
  BACKGROUND
  This study was funded by Merck & Co. Huang and Raipathak are employees of Merck & Co. Brandt reports consultancy and speaker fees from Catapult, Omnicare, RAND, HRSA, CMS, and AGS Beers Criteria. Loh is currently employed at Touro College of Pharmacy. All other authors have no relevant potential conflicts of interest to disclose. Study concept and design were primarily contributed by Stuart, Quinn, and Brandt, along with Shen, Roberto, Hendrick, Huang, and Rajpathak. Shen, Loh, Hendrick, and Kim collected the data, and data interpretation was performed primarily by Stuart, Shen, and Roberto, assisted by Quinn, Brandt, Hendrick, Huang, and Rajpathak. The manuscript was written primarily by Stuart, with assistance from the other authors, and revised by Huang, Rajpathak, and Stuart, with assistance from the other authors.
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Bruce C
authors: BC
authors: Stuart
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Xian
authors: X
authors: Shen
authors: 2 University of Maryland School of Medicine, Baltimore, Maryland.
authors: Charlene C
authors: CC
authors: Quinn
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Nicole
authors: N
authors: Brandt
authors: 3 Pharmaceutical Research and Manufacturers of America, Washington, DC, and University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Pamela
authors: P
authors: Roberto
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: F Ellen
authors: FE
authors: Loh
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Franklin
authors: F
authors: Hendrick
authors: 1 University of Maryland School of Pharmacy, Baltimore, Maryland.
authors: Caroline
authors: C
authors: Kim
authors: 4 Merck Research Labs, Merck & Co., Kenilworth, New Jersey.
authors: Xingyue
authors: X
authors: Huang
authors: 4 Merck Research Labs, Merck & Co., Kenilworth, New Jersey.
authors: Swapnil
authors: S
authors: Rajpathak
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy* / Q000453:epidemiology; D005260:Female; D005544:Forecasting; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D008297:Male; D054524:Medicare Part D / Q000639:trends*; D055118:Medication Adherence; D013997:Time Factors; D014481:United States / Q000453:epidemiology
chemical_list: D007004:Hypoglycemic Agents