Objective: This study examined the construct validity, and improved the test reliability and the estimation accuracy for the correlation between domains of the WHOQOL-BREF using multidimensional Rasch analysis. Method: A total of 13,083 adults were administered the 28-item WHOQOL-BREF Taiwan version, which consists of 4 subscales (domains). The multidimensional form of the partial credit model was used to examine the fit of the 4 subscales. For comparison, each subscale individually was also fitted to the unidimensional partial credit model. Standard item fit statistics and analysis of differential item functioning (DIF) were used to check model-data fit. Results: After excluding 2 overall items and deleting 7 DIF items, the remaining items of each subscale in the WHOQOL-BREF constituted a single construct. The test reliabilities and correlations between domains obtained from the multidimensional approach, (0.82-0.86) and (0.79-0.89), respectively, were much higher than those obtained from the unidimensional approach, (0.67-0.75) and (0.53-0.65), respectively. Conclusion: The 19-item WHOQOL-BREF measures more succinct latent traits than the original design. The multidimensional approach yields not only more accurate estimates for the correlation between domains but also substantially higher reliabilities, than the standard unidimensional approach.
Background An aging society incurs great losses due to fall-related injuries and mortalities. The foreseeable increased burden of fall-related injury among older people requires a regular nationwide study on the fall epidemic and prevention strategies. Methods The fall epidemic was examined using data from three consecutive waves of the National Health Interview Survey (2005, 2009, and 2013). Common explanatory variables across these surveys included sociodemographic factors (age, sex, and difficulty in performing activities of daily living (ADL) or instrumental ADL), biological factors (vision, comorbidities, urinary incontinence, and depressive symptoms), and behavioral risk factors (sleeping pill use, and frequency of exercise). After the univariate and bivariate analyses, the prevalence of falls was investigated using multiple linear regression models adjusted for age group, sex, and year of survey. A multivariate logistic regression model for falls with adjustments for these common explanatory variables was established across three waves of surveys. The effect of fall prevention programs was examined with the effect size in terms of age-specific and sex-specific prevalence of falls and fall-related hospitalization rates during 2005 and 2009. Results For each survey, there were consecutively 2722; 2900; and 3200 respondents with a mean age of 75.1, 75.6, and 76.4 years, respectively. The multiple linear regression model yielded a negative association between the prevalence of falls and year of survey. Several sociodemographic and biological factors, including female sex, difficulty in performing one basic ADL, difficulty in performing two or more instrumental ADLs, unclear vision, comorbidities, urinary incontinence, and depressive symptoms, were significantly associated with falls. In contrast to the universal positive effect on the prevalence of falls among older adults, the effect size of fall-related hospitalization rates revealed a 2% relative risk reduction only for those aged 65–74 years, but deteriorated for those aged 75–84 (− 10.9%). Conclusion Although the decline in fall prevalence over time supports existing fall intervention strategies in Taiwan, the differential prevention effect and identification of risk factors in older people suggest the necessity of adjusting fall prevention programs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.