Ng Yue-Harn, Litvinovich Igor, Leyva Yuridia, Ford C Graham, Zhu Yiliang, Kendall Kellee, Croswell Emilee, Puttarajappa Chethan M, Dew Mary Amanda, Shapiro Ron, Unruh Mark L, Myaskovsky Larissa
Division of Nephrology, Department of Internal Medicine, University of New Mexico, Albuquerque, NM.
Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA.
Transplant Direct. 2021 Dec 13;8(1):e1256. doi: 10.1097/TXD.0000000000001256. eCollection 2022 Jan.
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.
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.
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.
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).
药物依从性的障碍可能与其他依从性领域的障碍不同。在本研究中，我们评估了肾移植前因素与肾移植后3个不同领域的不依从行为之间的关联。
我们进行了一项前瞻性队列研究，在肾移植初始评估时（基线——社会人口统计学、病情相关、卫生系统和患者相关心理社会因素中的不依从预测因素）以及肾移植后约6个月（依从性结果：药物治疗、医疗随访和生活方式行为）对患者进行访谈。在我们机构接受肾移植并进行了约6个月随访访谈的所有患者均纳入研究。我们使用从健康习惯调查得出的连续综合指标评估3个不同领域的不依从情况。我们建立了多元线性和逻辑回归模型，并对基线特征进行调整，以预测依从性结果。
我们纳入了173名参与者。黑人种族（依从性得分的平均差异：-0.72；95%置信区间[CI]，-1.12至-0.32）和较高收入（平均差异：-0.34；95%CI，-0.67至-0.02）预示着较低的药物依从性。种族歧视经历预示着较低的依从性（优势比，0.31；95%CI，0.12 - 0.76），而具有内控点预示着对医疗随访有更好的依从性（优势比，1.46；95%CI，1.06 - 2.03）。在生活方式领域，较高的教育水平（平均差异：0.75；95%CI，0.21 - 1.29）和较低的体重指数（平均差异：-0.08；95%CI，-0.13至-0.03）预示着对饮食建议有更好的依从性，但没有危险因素预示着运动依从性。
不同的不依从行为可能源于不同的动机和危险因素（例如，因经历种族歧视而不就诊）。因此，依从性干预应针对高危人群进行个体化（例如，对医务人员进行减少偏见培训以提高患者就诊依从性）。
