Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.

BACKGROUND: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. OBJECTIVE: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. METHODS: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. RESULTS: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs. CONCLUSION: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.

Prevalence and risk factors of falls among community-dwelling older people: results from three consecutive waves of the national health interview survey in Taiwan.

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.