Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches

Effect of depression combined with cognitive impairment on dependency risk in rural older adults: analysis of data from the China health and retirement longitudinal study (CHARLS 2020)

BACKGROUND

This study aims to investigate the relationship between cognitive impairment, depressive symptoms, and Activities of Daily Living (ADL)/ Instrumental Activities of Daily Living (IADL) dependency among older people living in rural areas.

METHODS

We analyzed data from the China Health and Aging Longitudinal Study 2020, focusing on the rural older adults over 60. We identified four groups and constructed three regression models (models 1-3) to investigate the relationship between cognitive impairment or depression and dependency.

RESULTS

Based on a logistic regression model, older adults living in rural areas face a sequentially increased risk of dependence on ADL if they in group 2 (OR = 1.33, 95% CI = 1.09-1.63), group 3 (OR = 1.76, 95% CI = 1.43-2.17), or group 4 (OR = 2.38, 95% CI = 1.95-2.91), when compared to a reference group 1. Group 2 (OR = 2.44, 95% CI = 2.03 to 2.93) was more likely to be at risk of IADL dependence than Group 3 (OR = 1.99, 95% CI = 1.64 to 2.43).

CONCLUSIONS

This study highlights the substantial impact of depression and cognitive impairment on ADL/IADL dependence among rural older adults, with an especially heightened risk when both disorders are present simultaneously. Moreover, factors such as gender, age, inpatient services, outpatient services, and self-rated health are strongly associated with functional dependence in this population.

Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data

Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model's ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations.

The impact of long-term care insurance pilot on the mental health of older adults: Quasi-experimental evidence from China

The Chinese government launched pilot programs for a long-term care insurance system in response to the ongoing increase in the aging population. This study uses the difference-in-differences (DID) model to analyze the impact of long-term care insurance on older adults' mental health based on China Health and Retirement Longitudinal Study (CHARLS) four-period panel data from 2011 to 2018. This study found that long-term care insurance reduced Center for Epidemiological Studies Depression Scale (CES-D) scores among older adults by 1.059 points. Moreover, there was an improvement of 0.181 and 0.870 points in mental status and scenario memory scores, respectively. The impact of the long-term care insurance pilot program on improving the mental health of older adults was more pronounced, especially for those with chronic diseases or disabilities as well as those living in rural and western regions. This study also revealed that long-term care insurance enhances mental health by reducing medical expenses and increasing daily companionship and social interaction. Therefore, a pilot study of long-term care insurance showed a significant improvement in the mental health of older adults. To provide a comprehensive care service system for older adults, the government should expand the scope of the pilot program and increase the accessibility of mental health services for older adults.