title: Variables associated with self-prediction of psychopharmacological treatment adherence in acute and chronic pain patients.
abstract: OBJECTIVE
  The objectives of this article were the following: (1) determine risk for self-predicted future psychopharmacological nonadherence in rehabilitation acute pain patients (APPs) and rehabilitation chronic pain patients (CPPs) vs. pain-free community controls and community patients, and (2) determine which variables predict nonadherence.
  
  
  METHODS
  The Battery for Health Improvement 2 was developed utilizing a healthy (pain-free) community sample (N = 1,478), a community patient sample (N = 158), and a rehabilitation patient sample (N = 777) of which 326 were APPs, 341 were CPPs, and 110 were patients without pain. These groups predicted their future psychopharmacological treatment adherence. Risk for nonadherence was calculated for each group utilizing the healthy community sample as the reference group. Nonadherent and adherent APPs and CPPs were compared statistically on variables of interest. Significant variables (P ≤ 0.01) were utilized in APPs' and CPPs' logistic regression models to predict nonadherence.
  
  
  METHODS
  The participants in this article were from a variety of settings.
  
  
  RESULTS
  Of APPs and CPPs, 10.74% and 10.85%, respectively, predicted that they would be nonadherent. Risk for nonadherence was greater in both groups vs. healthy nonpain community subjects and nonhealthy community patients. The predictors for APPs' nonadherence were general resistance to using medications and a tendency to forget physicians' suggestions. For CPPs, the predictors were general resistance to using medications, fear of dependence on prescription medications, and fighting with loved ones. The models classified 90% and 89% of APPs and CPPs (respectively) correctly. However, these were no better than the base rate.
  
  
  CONCLUSIONS
  APPs and CPPs are at greater risk for self-predicted psychopharmacological nonadherence than healthy community subjects and community patients. We cannot as yet predict self-predicted psychopharmacological nonadherence at greater than the base rate. However, the identified variables could be clinically useful.
authors.affiliation: Department of Psychiatry, Miller School of Medicine at University of Miami, Miami, Florida 33136, USA. d.fishbain@miami.edu
authors.forename: David A
authors.initials: DA
authors.lastname: Fishbain
authors.forename: Daniel
authors.initials: D
authors.lastname: Bruns
authors.forename: John Mark
authors.initials: JM
authors.lastname: Disorbio
authors.forename: John E
authors.initials: JE
authors.lastname: Lewis
authors.forename: Jinrun
authors.initials: J
authors.lastname: Gao
mesh_terms: D000328:Adult; D000700:Analgesics / Q000627:therapeutic use; D016009:Chi-Square Distribution; D002908:Chronic Disease; D004358:Drug Therapy / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D055118:Medication Adherence / Q000523:psychology*; D008875:Middle Aged; D010146:Pain / Q000188:drug therapy / Q000523:psychology* / Q000534:rehabilitation; D011237:Predictive Value of Tests; D012111:Residence Characteristics; D018570:Risk Assessment; D016903:Drug Monitoring
chemical_list: D000700:Analgesics