Association between multimorbidity and memory-related diseases among middle-aged and older adults: Evidence from the China Health and Retirement Longitudinal Study

Chen, Chen; Zhao, Yihao; Su, Binbin; Wu, Yu; Zhong, Panliang; Zheng, Xiaoying

doi:10.3389/fpubh.2023.1115207

Cited by 2 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, there has been a burgeoning interest in investigating the intricate relationship between Activities of Daily Living (ADL) and memory-related diseases (MDs), largely fueled by the burgeoning olderadults population (1). This subject matter has attracted substantial attention within the domains of medical and public health research (2)(3)(4). MDs, encompassing Alzheimer's Disease (5), Parkinson's Disease Dementia (6), Frontotemporal Dementia (7), Vascular Dementia (8), Huntington's Disease (9), and Amyotrophic Lateral Sclerosis (10), impose formidable challenges on individuals, families, and healthcare systems due to their profound impact on cognitive faculties and overall well-being.…”

Section: Introductionmentioning

confidence: 99%

Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

He,

Wang,

Wang

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

IntroductionMemory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs.BackgroundMDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs.MethodsData from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.ResultsPopulation characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson’s correlation analysis demonstrated a robust negative association (r = −0.188, p < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant (B = −0.002, β = −0.142, t = −14.393, 95% CI = −0.002, −0.001, p = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI’s role in enhancing predictive abilities, with the DT model demonstrating superior performance.ConclusionThis study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.

show abstract