Variables Associated with Self-Prediction of Psychopharmacological Treatment Adherence in Acute and Chronic Pain Patients

David A Fishbain, Daniel Bruns, John Mark Disorbio, John E Lewis, Jinrun Gao

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Objectives: 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.Design: 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.Setting: 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.

Original languageEnglish
Pages (from-to)508-519
Number of pages12
JournalPain Practice
Volume10
Issue number6
DOIs
StatePublished - Nov 1 2010

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Acute Pain
Chronic Pain
Therapeutics
Rehabilitation
Logistic Models

Keywords

  • Acute pain
  • Acute pain patients
  • Adherence
  • Battery for Health Improvement (BHI 2)
  • Chronic pain
  • Chronic pain patients
  • Community pain-free controls
  • Compliance
  • Litigation
  • Nonadherence
  • Noncompliance
  • Personal injury
  • Psychopharmacological adherence
  • Worker's compensation

ASJC Scopus subject areas

  • Anesthesiology and Pain Medicine

Cite this

Variables Associated with Self-Prediction of Psychopharmacological Treatment Adherence in Acute and Chronic Pain Patients. / Fishbain, David A; Bruns, Daniel; Disorbio, John Mark; Lewis, John E; Gao, Jinrun.

In: Pain Practice, Vol. 10, No. 6, 01.11.2010, p. 508-519.

Research output: Contribution to journalArticle

Fishbain, David A ; Bruns, Daniel ; Disorbio, John Mark ; Lewis, John E ; Gao, Jinrun. / Variables Associated with Self-Prediction of Psychopharmacological Treatment Adherence in Acute and Chronic Pain Patients. In: Pain Practice. 2010 ; Vol. 10, No. 6. pp. 508-519.
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N2 - Objectives: 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.Design: 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.Setting: 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.

AB - Objectives: 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.Design: 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.Setting: 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.

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