Medication, Healthcare Follow-up, and Lifestyle Nonadherence: Do They Share the Same Risk Factors?

Yue-Harn Ng; Igor Litvinovich; Yuridia Leyva; C Graham Ford; Yiliang Zhu; Kellee Kendall; Emilee Croswell; Chethan M Puttarajappa; Mary Amanda Dew; Ron Shapiro; Mark L Unruh; Larissa Myaskovsky

doi:10.1097/TXD.0000000000001256

Medication, Healthcare Follow-up, and Lifestyle Nonadherence: Do They Share the Same Risk Factors?

Transplant Direct. 2021 Dec 13;8(1):e1256. doi: 10.1097/TXD.0000000000001256. eCollection 2022 Jan.

Authors

Yue-Harn Ng^{1

2}, Igor Litvinovich¹, Yuridia Leyva³, C Graham Ford³, Yiliang Zhu⁴, Kellee Kendall⁵, Emilee Croswell⁶, Chethan M Puttarajappa⁶, Mary Amanda Dew⁷, Ron Shapiro⁸, Mark L Unruh¹, Larissa Myaskovsky^{1

3}

Affiliations

¹ Division of Nephrology, Department of Internal Medicine, University of New Mexico, Albuquerque, NM.
² Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA.
³ Center for the Healthcare Equity in Kidney Disease (CHEK-D), University of New Mexico Health Science Center, Albuquerque, NM.
⁴ Division of Epidemiology, Biostatistics and Preventive Medicine, Department of Internal Medicine, University of New Mexico, Albuquerque, NM.
⁵ Highmark Health, Pittsburgh, PA.
⁶ Department of Medicine, School of Medicine, University of Pittsburgh, PA.
⁷ Department of Psychiatry, School of Medicine, University of Pittsburgh, PA.
⁸ Mount Sinai Recanati/Miller Transplantation Institute, Icahn School of Medicine.

Abstract

Barriers to medication adherence may differ from barriers in other domains of adherence. In this study, we assessed the association between pre-kidney transplantation (KT) factors with nonadherent behaviors in 3 different domains post-KT.

Methods: We conducted a prospective cohort study with patient interviews at initial KT evaluation (baseline-nonadherence predictors in sociodemographic, condition-related, health system, and patient-related psychosocial factors) and at ≈6 mo post-KT (adherence outcomes: medications, healthcare follow-up, and lifestyle behavior). All patients who underwent KT at our institution and had ≈6-mo follow-up interview were included in the study. We assessed nonadherence in 3 different domains using continuous composite measures derived from the Health Habit Survey. We built multiple linear and logistic regression models, adjusting for baseline characteristics, to predict adherence outcomes.

Results: We included 173 participants. Black race (mean difference in adherence score: -0.72; 95% confidence interval [CI], -1.12 to -0.32) and higher income (mean difference: -0.34; 95% CI, -0.67 to -0.02) predicted lower medication adherence. Experience of racial discrimination predicted lower adherence (odds ratio, 0.31; 95% CI, 0.12-0.76) and having internal locus of control predicted better adherence (odds ratio, 1.46; 95% CI, 1.06-2.03) to healthcare follow-up. In the lifestyle domain, higher education (mean difference: 0.75; 95% CI, 0.21-1.29) and lower body mass index (mean difference: -0.08; 95% CI, -0.13 to -0.03) predicted better adherence to dietary recommendations, but no risk factors predicted exercise adherence.

Conclusions: Different nonadherence behaviors may stem from different motivation and risk factors (eg, clinic nonattendance due to experiencing racial discrimination). Thus adherence intervention should be individualized to target at-risk population (eg, bias reduction training for medical staff to improve patient adherence to clinic visit).

Abstract

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