Health and Quality of Life Outcomes最新文献

Background: Chronic Obstructive Pulmonary Disease (COPD) presents a significant health and economic challenge in low- and middle-income countries (LMICs) in Asia, where healthcare resources are often limited. Self-management programs (SMPs) offer a potential solution by empowering patients to manage their condition and reduce healthcare costs. However, there needs to be more consolidated evidence on the effectiveness of these programs in LMICs, and their success may depend on specific contextual factors.

Methods: This systematic review analyzed studies on COPD self-management interventions in Asian LMICs, focusing on outcomes such as health-related quality of life (HRQoL), exacerbation rates, healthcare utilization, and out-of-pocket (OOP) costs. Two databases- PubMed and Scopus were systematically searched following PRISMA guidelines, and data were extracted and analyzed to identify the effectiveness of these programs and the challenges encountered during implementation.

Results: The findings indicate that self-management programs in LMICs can improve HRQoL, reduce exacerbation rates, and lower OOP costs in specific contexts. However, the effectiveness of these programs is influenced by local healthcare infrastructure, digital and health literacy, and economic barriers. Digital interventions tended to be more beneficial in urban areas with better infrastructure, while programs led by community health workers were more effective in rural settings.

Conclusion: Self-management interventions show promise for managing COPD in resource-limited settings, but context-specific adaptations are essential. Flexible program designs tailored to local conditions and strengthened collaborations among healthcare providers and policymakers are crucial for sustainable implementation and scaling.

Background: Existing research has identified health‑related quality of life (HRQoL) is influenced by a multitude of factors among children and adolescents. However, there has been relatively limited exploration of the multidimensional predictive factors (individual characteristics, health risk behaviors, and negative life events) that contribute to HRQoL. This study aimed to develop a nomogram to predict the HRQoL in children and adolescents.

Methods: A total of 12,145 children and adolescents were surveyed using stratified cluster sampling method, randomly divided into a training set (n = 8503) and a validation set (n = 3642). Logistic regression, lasso regression, and random forest models were combined to identify the most significant predictors of HRQoL. A nomogram was constructed using multivariate logistic regression. The receiver operating characteristic curve, k-fold cross-validation, decision curve analysis (DCA), and internal validation were used to assess the accuracy, discrimination, and generalization of the nomogram.

Results: Non-suicidal self-injury, academic burnout, parental abuse, stress, bullying victimization, healthy diet, and sleep were found to be significant predictors of HRQoL. The area under the curve (AUC) of the training set was 0.765, whereas that of the validation data was 0.775. The k-fold cross-validation (k = 10) revealed good discrimination in internal validation (mean AUC = 0.771). The nomogram had good clinical use since the DCA covered a large threshold probability: 5%-89% (in the training set) and 4%-81% (in the validation set).

Conclusions: The nomogram prediction model constructed in this study can provide a reference for predicting HRQoL in children and adolescents.

Background: Arthritis is a prevalent chronic disease substantially impacting patients' quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often struggle with complex interactions, and existing machine learning (ML) approaches frequently lack the interpretability needed to guide clinical decisions. This study integrates a comprehensive, explainable machine learning (XAI) workflow to identify and interpret key QoL-related predictors of arthritis status in a large national cohort.

Methods: Data were obtained from 15,011 participants aged > 45 years in the 2020 China Health and Retirement Longitudinal Study (CHARLS). We initially selected 55 potential QoL-related predictors spanning demographic, functional, pain, psychosocial, and lifestyle domains. Feature engineering was performed to create aggregate scores, indicators, and binned variables. Missing data were handled using imputation combined with missing indicator variables. A LightGBM-based feature selection process identified 68 key predictors. Nine ML models (including Logistic Regression, RandomForest, GradientBoosting, LightGBM, CatBoost, XGBoost, DecisionTree, NaiveBayes, KNN) were developed using SMOTE-resampled training data, with hyperparameters optimized via Optuna targeting recall. Performance was evaluated on a held-out test set using Area Under the ROC Curve (AUC), Average Precision (AP), Recall, Specificity, Precison, and F1-score. SHapley Additive exPlanations (SHAP) analysis was applied to the best-performing model (GradientBoosting) for interpretation.

Results: Several models achieved strong predictive performance, with GradientBoosting yielding the highest AUC (0.767, 95% CI: 0.752-0.782) and high AP (0.678, 95% CI: 0.655-0.702). SHAP analysis identified multi-site pain burden (particularly knee/leg pain and pain location count), age, self-rated health, sleep quality, functional limitations (ADL counts/scores), and negative affect as the most influential predictors driving arthritis status prediction.

Conclusions: This study successfully applied an XAI pipeline to identify and rank key QoL-related factors predictive of arthritis status in a large Chinese cohort, achieving robust model performance. Pain burden, age, subjective health, sleep, functional status, and psychological well-being are critical determinants. These interpretable findings can inform risk stratification and guide targeted interventions focusing on these key areas to potentially improve arthritis management.

Purpose: Different health-related quality of life scales are suitable for various populations and scenarios; hence, the selection of measurement tools must be based on research objectives and focal points. To facilitate the choice between EQ-5D-5L and SF-6Dv1 in clinical and economic research of myopic patients undergoing corneal refractive surgery, the study aims to assess and compare EQ-5D-5L and SF-6Dv1 among the patients in China.

Methods: A total of 411 participants with a mean age of 25.6 years were recruited. Information collected including demographic characteristics, EQ-5D-5L and SF-6Dv1. The ceiling effect was evaluated by calculating the proportion of participants who reported the best possible health states. Agreement between EQ-5D-5L and SF-6Dv1 health utility scores (HUSs) was tested using intraclass correlation coefficient (ICC) and Bland-Altman plot. Convergent validity of EQ-5D-5L and SF-6Dv1 HUSs was evaluated using the Spearman correlation coefficient (r) with VF-14 as a calibration standard.

Results: Ceiling effects were 75.2% and 40.4% for EQ-5D-5L and SF-6Dv1, respectively. ICC between the two HUSs was 0.611 (average-measure). Bland-Altman plots showed that the agreement varied across different HUS segments. Correlation between EQ-5D-5L HUS and VF-14 (r = 0.363) was higher than that between SF-6Dv1 HUS and VF-14 (r = 0.226). EQ-5D-5L and SF-6Dv1 HUSs can only distinguish one clinical condition (i.e., chronic pain symptom); but they can identify health differences across all self-reported known-groups. EQ-5D-5L HUS had greater sensitivity; but SF-6Dv1 HUS was more responsive at follow-ups.

Conclusions: EQ-5D-5L and SF-6Dv1 HUS cannot be used interchangeably in myopic patients undergoing corneal refractive surgery. It appears that EQ-5D-5L HUS performs better in terms of convergent validity and sensitivity; but SF-6Dv1 HUS is more responsive.

In this study, we propose several methods to account for reporting heterogeneity in self-reported data coming from Visual Analogue Scales (VAS) using corresponding VAS-based anchoring vignettes. Though widely used as a measurement tool in many disciplines, VAS may suffer from reporting heterogeneity. Such reporting heterogeneity and potential solutions to solve this problem in the context of VAS measures have not yet been addressed in the literature. Using VAS-based anchoring vignettes and standard vignettes assumptions, we show how double-index models can be used to address reporting heterogeneity in VAS. We then apply our methods to real data assessing reporting heterogeneity in VAS-measured Quality of Life (QoL) among students in Switzerland. We show that the findings of previous studies showing positive associations between being a female and QoL might be entirely driven by reporting heterogeneity.

Background: Generic preference-based instruments, such as the Short Form 6-Dimensions (SF-6D) and EuroQol 5-Dimensions (EQ-5D), can generate utility scores that facilitate the estimation of health-related quality of life (HRQoL) which is commonly used in cost-utility analysis. This study investigated the associations between utility scores and potential socio-demographic factors in Chinese patients with dialysis using quantile regression.

Methods: Patients were recruited in a multicenter survey conducted between November 2023 and January 2024 for dialysis patients in China. Patient responses to the SF-6D version 2 (SF-6Dv2) instruments were used to calculate utility scores. The relationships between utility scores and potential socio-demographic factors were examined using both ordinary least squares (OLS) and quantile regression models. The Wald test was employed to test the differences in coefficients across quantiles in quantile regression. Model performance was assessed using 5-fold cross-validation.

Results: A total of 378 patients were included. Age, education level, having a loan due to illness, currently working, monthly income > 8000 RMB and number of comorbidities were associated with utility scores. The quantile regression coefficients and Wald test suggested that the size of the associations between the utility scores and factors varied along with the utility score distribution. Quantile regression yielded more accurate fitted and predicted values compared to OLS regression.

Conclusion: Quantile regression offers a valuable complement in analyzing factors associated with utility scores among Chinese dialysis patients. For policymakers, differentiated nonclinical strategies may be needed to improve HRQoL across varying health states within this population.

Background: Health-related quality of life (HRQoL) is increasingly recognized as a critical indicator of well-being in early childhood, yet its associations with 24-hour movement behaviors-physical activity, screen time, and sleep-remain underexplored in Asian populations. This study aims to examine the association between adherence to the 24-hour movement guidelines (24-MG) and health-related quality of life (HRQOL) among preschoolers residing in major urban areas of Singapore, Japan, and China.

Methods: A cross-sectional survey was conducted among 6,634 children aged 3-6 years across Singapore (n = 3,672), Japan (n = 760), and China (n = 2,202). Movement behaviors were assessed using the validated SMALLQ^® tool, and HRQoL was evaluated using the Pediatric Quality of Life Inventory (PedsQL™). Logistic regression models were applied to determine the associations between different patterns of 24-h MG adherence and HRQoL, adjusting for demographic variables.

Results: Full adherence to all three 24-h MG components was significantly associated with higher physical and psychosocial HRQoL scores in Singapore and China. In Japan, adherence to physical activity guidelines alone is associated with higher HRQoL. Notably, Chinese children had the highest adherence rates across all individual and combined movement behaviors. Conversely, Japanese children had the lowest rates of full adherence and were more likely to fall short of all guidelines.

Conclusions: This study provides novel evidence from three urban Asian contexts that adherence to the 24-hour movement guidelines positively correlates with HRQoL in preschool-aged children. The findings highlight the importance of integrated movement behavior frameworks and support the development of culturally tailored public health policies to improve early childhood well-being.

Background: Australia has a well-established health technology assessment process and there is extensive use of generic health related quality of life (HRQoL) instruments in evidence presented to it. However, there are gaps in tools and evidence available to support evaluation of paediatric health. The aim of this paper is to produce an Australian EQ-5D-Y-3L (Y-3L) value set.

Methods: The methods follow the international Y-3L valuation protocol, but with an expanded design. Data were collected using Composite Time Trade Off (cTTO) and Discrete Choice Experiment (DCE) data from two independent samples of adult members of the Australian general public. In total, 52 Y-3L health states, assigned into four blocks of 14 health states each containing health state 33333, were valued using cTTO. cTTO data were collected via videoconferencing interview and each respondent valued 14 health states. Mean observed cTTO values were adjusted for censoring at -1 using a Tobit model. For the DCE component, 150 latent scale DCE choice pairs were collected via an online survey with each participant completing 15 pairs. DCE data were modelled using a garbage class mixed logit model. Two approaches to anchor DCE data to the Quality Adjusted Life Years (QALYs) scale were explored: anchoring on the value for the worst health state (33333); and mapping DCE data onto the mean cTTO values using all 52 health states. Two evaluation criteria were used to select the final value set: (1) coefficient significance and logical consistency; (2) prediction accuracy of the mean observed cTTO values.

Results: In total, 268 individuals participated in the cTTO interviews, and 1002 completed the DCE. The linear mapping without intercept performed best and was selected as the final value set. Health state values ranged between 0.142 and 1. The relative importance of domains by level 3 coefficients (ordered from most to least important) was: pain/discomfort, then feeling worried, sad or unhappy, usual activities, looking after myself, and mobility.

Conclusion: This study reports an Australian value set for the Y-3L, which enables the calculation of QALYs for use in the economic evaluation of paediatric interventions and can support evidence development and decision making.