title: Association between sitagliptin adherence and self-monitoring of blood glucose.
abstract: BACKGROUND
  We evaluated the association between self-monitoring of blood glucose (SMBG) use and sitagliptin or sitagliptin/metformin (SSMT) adherence. SSMT was chosen as these medications have little risk of hypoglycemia and are believed to not require SMBG data for titration.
  
  
  METHODS
  This was an observational study using data extracted from a large United States insurance claims database (i3 InVision™ Data Mart, Ingenix, Inc.). Data were extracted on noninsulin-using patients initiating SSMT for each 12-month period pre- and post-SSMT initiation. Logistic regression was used to assess the relationship between SMBG use and the likelihood of being medication adherent (defined as a medication possession ratio of ≥75%) while controlling for covariates.
  
  
  RESULTS
  This analysis included 7,306 patients (57.6% male; mean age 54.2 years). Mean pre-SSMT hemoglobin A1c (HbA1c) was 8.0%. In the post-SSMT initiation period, 58% of patients were adherent with SSMT. Older age, male gender, prior use of oral diabetes medication, and lower HbA1c were associated with improved SSMT adherence. SMBG use was associated with improved adherence [odds ratio (OR) ranged from 1.198 to 1.338; p < .05] compared with patients with no SMBG use pre- or post-SSMT initiation. For patients who began SMBG after starting SSMT, greater SMBG use was associated with better adherence (OR 1.449 for higher vs 1.246 for lower strip use; p < .05).
  
  
  CONCLUSIONS
  This study demonstrated that SMBG is associated with improved SSMT adherence. This relationship is strengthened with greater SMBG use.
authors: Johnson & Johnson, New Brunswick, New Jersey, USA.
authors: Somesh
authors: S
authors: Nigam
authors: Naunihal S
authors: NS
authors: Virdi
authors: Mehmet
authors: M
authors: Daskiran
authors: Chris M
authors: CM
authors: Kozma
authors: Andrew
authors: A
authors: Paris
authors: William M
authors: WM
authors: Dickson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D015190:Blood Glucose Self-Monitoring; D002648:Child; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy*; D004359:Drug Therapy, Combination; D005260:Female; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced; D007004:Hypoglycemic Agents / Q000009:adverse effects / Q000627:therapeutic use*; D016015:Logistic Models; D008297:Male; D055118:Medication Adherence; D008687:Metformin / Q000627:therapeutic use; D008875:Middle Aged; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011719:Pyrazines / Q000009:adverse effects / Q000627:therapeutic use*; D012307:Risk Factors; D000068900:Sitagliptin Phosphate; D013997:Time Factors; D016896:Treatment Outcome; D014230:Triazoles / Q000009:adverse effects / Q000627:therapeutic use*; D014481:United States; D055815:Young Adult
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D007004:Hypoglycemic Agents; D011719:Pyrazines; D014230:Triazoles; D008687:Metformin; D000068900:Sitagliptin Phosphate