Health-related quality of life profiles in adolescents and young adults with chronic conditions

被引:1
|
作者
Wang, Suwei [1 ]
Arizmendi, Cara J. [1 ]
Blalock, Dan V. [2 ,3 ]
Chen, Dandan [1 ]
Lin, Li [1 ]
Thissen, David [4 ]
Huang, I-Chan [5 ]
DeWalt, Darren A. [6 ]
Reeve, Bryce B. [1 ,7 ]
机构
[1] Duke Univ Sch Med, Ctr Hlth Measurement, Dept Populat Hlth Sci, DUMC 104023, 215 Morris St, Suite 230, Durham, NC 27701 USA
[2] Duke Univ Sch Med, Dept Psychiat & Behav Sci, Durham, NC USA
[3] Durham Vet Affairs Med Ctr, Hlth Serv Res & Dev, Durham, NC USA
[4] Univ N Carolina, Dept Psychol & Neurosci, Chapel Hill, NC USA
[5] St Jude Childrens Res Hosp, Dept Epidemiol & Canc Control, Memphis, TN USA
[6] Univ N Carolina, Sch Med, Dept Med, Chapel Hill, NC USA
[7] Duke Univ Sch Med, Dept Pediat, Durham, NC 27710 USA
基金
美国国家卫生研究院;
关键词
AYA; HRQOL; LPA; Chronic conditions; PROMIS; LATENT CLASS ANALYSIS; CANCER SURVIVORS; CHRONIC ILLNESS; CHRONIC PAIN; CHILDREN; DISEASE; NUMBER; MODELS; IMPACT; HRQOL;
D O I
10.1007/s11136-023-03463-5
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
PurposeTo assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions.MethodsAYAs (N = 872) aged 14-20 years completed NIH's Patient-Reported Outcomes Measurement Information System(& REG;) (PROMIS & REG;) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores. The optimal number of profiles was determined by model fit statistics, likelihood ratio test, and entropy. Multinomial logistic regression models were used to examine how LPA's HRQOL profile membership was associated with patient demographic and chronic conditions. The model prediction accuracy on profile membership was evaluated using Huberty's I index with a threshold of 0.35 for good effect.ResultsA 4-profile LPA model was selected. A total of 161 (18.5%), 256 (29.4%), 364 (41.7%), and 91 (10.4%) AYAs were classified into Minimal, Mild, Moderate, and Severe HRQOL Impact profiles. AYAs in each profile had distinctive mean scores with over a half standard deviation (5-points in PROMIS T-scores) of difference between profiles across most HRQOL domains. AYAs who were female or had conditions such as mental health condition, hypertension, and self-reported chronic pain were more likely to be in the Severe HRQOL Impact profile. The Huberty's I index was 0.36.ConclusionsApproximately half of AYAs with a chronic condition experience moderate to severe HRQOL impact. The availability of risk prediction models for HRQOL impact will help to identify AYAs who are in greatest need of closer clinical care follow-up.
引用
收藏
页码:3171 / 3183
页数:13
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