治疗负担的纵向轨迹:美国中西部患有多种慢性疾病的成年人的前瞻性调查研究。

David T Eton, Roger T Anderson, Jennifer L St Sauver, Elizabeth A Rogers, Mark Linzer, Minji K Lee
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引用次数: 4

摘要

目的:确定多重慢性疾病(MCC)患者的治疗负担是否存在不同的纵向模式,如果存在,探索可能揭示潜在干预途径的预测因子。方法:我们分析了来自美国明尼苏达州东南部396名患有MCC的成年人的前瞻性邮寄调查研究数据。参与者在基线、6个月、12个月和24个月完成了治疗负担、患者治疗体验和自我管理(PETS)以及健康相关和心理社会概念的有效测量。潜在类别生长混合模型(LCGM)确定了pet的两个综合指数得分:工作量和影响的治疗负担轨迹。使用多变量逻辑回归来识别轨迹的独立预测因子。结果:LCGM支持pet工作量的2级模型,包括一组持续高工作量(N = 69)和一组持续低工作量(N = 311)。pet影响支持一个3级模型,包括持续高影响组(N = 62),持续低影响组(N = 278),以及随时间增加的影响组(N = 51)。逻辑回归分析显示,随着时间的推移,以下因素与持续高或增加的治疗负担模式相关:较低的健康素养,较低的自我效能,与他人的人际关系挑战更多,以及较差的主观身心健康报告(均p < 0.05)。结论:MCC患者的治疗负担存在不同的纵向模式。提高健康素养、增强自我效能感和减轻负面社会互动的影响可能有助于减轻治疗负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States.

Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States.

Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States.

Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States.

Objectives: Determine whether there are different longitudinal patterns of treatment burden in people living with multiple chronic conditions (MCC) and, if so, explore predictors that might reveal potential routes of intervention.

Methods: We analyzed data from a prospective mailed survey study of 396 adults living with MCC in southeastern Minnesota, USA. Participants completed a measure of treatment burden, the Patient Experience with Treatment and Self-management (PETS), and valid measures of health-related and psycho-social concepts at baseline, 6, 12, and 24 months. Latent class growth mixture modeling (LCGM) determined trajectories of treatment burden in two summary index scores of the PETS: Workload and Impact. Multivariable logistic regressions were used to identify independent predictors of the trajectories.

Results: LCGM supported a 2-class model for PETS Workload, including a group of consistently high workload (N = 69) and a group of consistently low workload (N = 311) over time. A 3-class model was supported for PETS Impact, including groups of consistently high impact (N = 62), consistently low impact (N = 278), and increasing impact (N = 51) over time. Logistic regression analyses showed that the following factors were associated with patterns of consistently high or increasing treatment burden over time: lower health literacy, lower self-efficacy, more interpersonal challenges with others, and worse subjective reports of physical and mental health (all p < .05).

Conclusions: Different longitudinal patterns of treatment burden exist among people with MCC. Raising health literacy, enhancing self-efficacy, and lessening the effects of negative social interactions might help reduce treatment burden.

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