Dan V Blalock1, Leah L Zullig2, Hayden B Bosworth3, Shannon S Taylor4, Corrine I Voils5. 1. Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, 411 W. Chapel Hill St., Suite 600, Durham, NC 27701, USA; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 40 Duke Medicine Circle, Durham, NC 27710, USA. Electronic address: dvb@duke.edu. 2. Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, 411 W. Chapel Hill St., Suite 600, Durham, NC 27701, USA; Department of Population Health Sciences, Duke University Medical Center, 2200 W. Main St., Durham, NC 27705, USA. Electronic address: leah.zullig@duke.edu. 3. Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System, 411 W. Chapel Hill St., Suite 600, Durham, NC 27701, USA; Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 40 Duke Medicine Circle, Durham, NC 27710, USA; Department of Population Health Sciences, Duke University Medical Center, 2200 W. Main St., Durham, NC 27705, USA; School of Nursing, Duke University Medical Center, 307 Kent Dr., Durham, NC 27710, USA. Electronic address: boswo001@duke.edu. 4. Department of Behavioral, Social, and Population Health Sciences, University of South Carolina School of Medicine, Greenville, 607 Grove Rd, Greenville, SC 29605, USA. 5. William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA; Department of Surgery, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave., Madison, WI 53792, USA. Electronic address: voils@surgery.wisc.edu.
Abstract
OBJECTIVE: Self-report measures of medication nonadherence are frequently adapted to new clinical populations without evidence of validity. We evaluated the predictive validity of a medication nonadherence measure previously validated in patients with hypertension among patients taking cholesterol-reducing medications. METHOD: This secondary analysis involves data from a randomized trial (VA HSR&D IIR 08-297) conducted at the Durham Veterans Affairs Medical Center. At baseline, 6-months, and 12-months, serum cholesterol was obtained and participants (n = 236) completed a 3-item measure of extent of nonadherence to cholesterol-reducing medications. Two cross-lagged panel models with covariates, in addition to growth curve analysis, were used to examine the predictive utility of self-reported nonadherence on concurrent and future cholesterol levels, while accounting for potential reverse-causation. RESULTS: Extent of nonadherence items produced reliable scores across time and fit a single-factor model (CFI = 0.99). Nonadherence, and changes in nonadherence, moderately predicted future cholesterol values, and changes in cholesterol values (7 of 9 longitudinal associations were significant at p < .05; B's ranged from 0.16 to 0.35). Evidence for reverse associations was weaker (3 of 9 longitudinal associations were significant at p < .05; B's ranged from 0.16 to 0.36). CONCLUSION: Analyses support the predictive validity of this medication nonadherence measure over the competing reverse-causation hypothesis. Published by Elsevier Inc.
RCT Entities:
OBJECTIVE: Self-report measures of medication nonadherence are frequently adapted to new clinical populations without evidence of validity. We evaluated the predictive validity of a medication nonadherence measure previously validated in patients with hypertension among patients taking cholesterol-reducing medications. METHOD: This secondary analysis involves data from a randomized trial (VA HSR&D IIR 08-297) conducted at the Durham Veterans Affairs Medical Center. At baseline, 6-months, and 12-months, serum cholesterol was obtained and participants (n = 236) completed a 3-item measure of extent of nonadherence to cholesterol-reducing medications. Two cross-lagged panel models with covariates, in addition to growth curve analysis, were used to examine the predictive utility of self-reported nonadherence on concurrent and future cholesterol levels, while accounting for potential reverse-causation. RESULTS: Extent of nonadherence items produced reliable scores across time and fit a single-factor model (CFI = 0.99). Nonadherence, and changes in nonadherence, moderately predicted future cholesterol values, and changes in cholesterol values (7 of 9 longitudinal associations were significant at p < .05; B's ranged from 0.16 to 0.35). Evidence for reverse associations was weaker (3 of 9 longitudinal associations were significant at p < .05; B's ranged from 0.16 to 0.36). CONCLUSION: Analyses support the predictive validity of this medication nonadherence measure over the competing reverse-causation hypothesis. Published by Elsevier Inc.
Authors: Corrine I Voils; Heather A King; Carolyn T Thorpe; Dan V Blalock; Ian M Kronish; Bryce B Reeve; Colleen Boatright; Ziad F Gellad Journal: Dig Dis Sci Date: 2019-04-29 Impact factor: 3.487
Authors: Yu Heng Kwan; Livia Jia Yi Oo; Dionne Hui Fang Loh; Truls Østbye; Lian Leng Low; Hayden Barry Bosworth; Julian Thumboo; Jie Kie Phang; Si Dun Weng; Dan V Blalock; Eng Hui Chew; Kai Zhen Yap; Corrinne Yong Koon Tan; Sungwon Yoon; Warren Fong Journal: J Med Internet Res Date: 2020-10-08 Impact factor: 5.428
Authors: Yu Heng Kwan; Si Dun Weng; Dionne Hui Fang Loh; Truls Østbye; Lian Leng Low; Hayden Barry Bosworth; Julian Thumboo; Jie Kie Phang; Livia Jia Yi Oo; Dan V Blalock; Eng Hui Chew; Kai Zhen Yap; Corrinne Yong Koon Tan; Sungwon Yoon; Warren Fong Journal: J Med Internet Res Date: 2020-10-09 Impact factor: 5.428