The number of Chinese oldest-old (aged 80+) is growing rapidly and some studies have shown that the health status is unequal among older persons in different regions. However, to the best of our knowledge, no study to date has analyzed health inequalities among the oldest-old in urban and rural areas in China. This study therefore aimed to examine the correlation between health inequalities among the oldest-old in urban and rural areas of China. From the 8th wave of the Chinese Longitudinal Health Longevity Survey (CLHLS), we selected 8124 oldest-old participants who met the requirements of the study. Chi-square tests were used to analyze the distribution characteristics of indicators and a logistic model was performed to determine the factors associated with different self-rated health (SRH). The Fairlie model was adopted to decompose the causes and related contributions to health inequality. Our results found that of the Chinese oldest-old, 46.57% were in good health. Urban residents reported significantly better SRH than rural residents (50.17% vs. 45.13%). Variables associated with good and poor SRH had different distribution characteristics. The logistic model suggested that marital status, alcohol consumption, and annual income were important factors underlying the SRH differences. Our decomposition analysis indicated that 76.64% of the SRH differences were caused by observational factors, and validated that the difference in SRH between urban and rural areas was significantly (P<0.05) associated with exercise status (45.44%), annual income (37.64%), social activity status (3.75%), age (-5.27%), and alcohol consumption (-2.66%). Therefore, socioeconomic status and individual lifestyle status were the main factors underlying the health inequality between urban and rural Chinese oldest-old.

Objectives: The aim of this study was to investigate the factors influencing urban–rural differences in depressive symptoms among old people in China and to measure the contribution of relevant influencing factors. Design: A cross-sectional research. The 2018 data from The Chinese Longitudinal Health Longevity Survey (CLHLS). Setting: Twenty-three provinces in China. Participants: From the 8th CLHLS, 11,245 elderly participants were selected who met the requirements of the study. Measurements: We established binary logistic regression models to explore the main influencing factors of their depressive symptoms and used Fairlie models to analyze the influencing factors of the differences in depressive symptoms between the urban and rural elderly and their contribution. Results: The percentage of depressive symptoms among Chinese older adults was 11.72%, and the results showed that rural older adults (12.41%) had higher rates of depressive symptoms than urban (10.13%). The Fairlie decomposition analysis revealed that 73.96% of the difference in depressive symptoms could be explained, which was primarily associated with differences in annual income (31.51%), education level (28.05%), sleep time ( − 25.67%), self-reported health (24.18%), instrumental activities of daily living dysfunction (20.73%), exercise (17.72%), living status ( − 8.31%), age ( − 3.84%), activities of daily living dysfunction ( − 3.29%), and social activity (2.44%). Conclusions: The prevalence of depressive symptoms was higher in rural than in urban older adults, which was primarily associated with differences in socioeconomic status, personal lifestyle, and health status factors between the urban and rural residents. If these factors were addressed, we could make targeted and precise intervention strategies to improve the mental health of high-risk elderly.

Background: Immune checkpoint inhibitors (ICIs) have elicited durable antitumor responses in multiple types of cancers. However, ICIs could also induce potential toxicities that involve all organs, including renal system. In this study, we aimed to conduct a comprehensive description of the ICIs‐induced renal toxicities and the potential effects of chemotherapy.Methods: We conducted a pharmacovigilance study based on US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database between 01 January 2014 and 30 June 2019. Disproportionality analysis was used to assess the association between ICIs and renal adverse events (AEs), including reporting odds ratio (ROR) and information component (IC). ROR₀₂₅ and IC₀₂₅ are, respectively, 95% confidence interval lower end of ROR and IC. If the value of ROR₀₂₅ exceeding one or IC₀₂₅ higher than zero, then a signal was considered statistically significant.Results: A total of 30,602,758 reports were extracted from the database, with 4578 reports for ICIs‐associated renal AEs. Renal AEs were more frequently reported in anti‐PD‐1/PD‐L1 versus anti‐CTLA‐4 monotherapy group (ROR: 1.75, 95% CI: 1.52–2.01). Similarly, renal AEs were more commonly reported in ICIs polytherapy other than monotherapy group (ROR: 1.18, 95% CI: 1.10–1.27). Notably, ICIs plus chemotherapy strategies reported more renal toxicities compared to sole ICIs regimens (ROR: 1.30, 95% CI: 1.17–1.45), whereas exhibited lower fatality outcome rates. Importantly, acute kidney injury (1139, 24.88%) and renal failure (464, 10.14%) were the top two most commonly reported ICIs‐associated renal AEs, and also observed with the top two highest level of fatality outcome rates.Conclusions: A spectrum of renal AEs was detected in ICIs regimens and could be reinforced by ICIs combination. Compared to sole ICIs regimens, ICIs plus chemotherapy strategy reported more renal toxicities but lower fatality outcome rates. With the increasing popularity of ICIs especially combination strategies, it is vital important for clinicians to guarantee balance between durable clinical effects and potential renal toxicities in latest immunotherapy strategies.

Background: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. Objective: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. Methods: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing’an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. Results: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm–based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. Conclusions: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD.

In China, In China, depression among the oldest-old (aged 80 + years) is a major public health issue. As the gap in development between urban and rural China widens, the aim of this study was to demonstrated whether there are disparities in the incidence of depressive symptoms between the urban and rural oldest-old (aged 80+) in China and to quantify the contribution of relevant influencing factors. The study evaluated data on 5,116 oldest adults (female, 55.88%; male, 44.12%) from the 2018 Chinese Longitudinal Healthy Longevity Survey. A chi-square test analyzed the distribution characteristics of related indicators among the oldest-old adults in the two areas. Multiple linear regression was applied to exploring the influencing factors of depressive symptoms in the oldest-old (aged 80+) adults in different regions. The Blinder-Oaxaca decomposition method quantified the four categories of influencing factors (demographic characteristics, sociological characteristics, personal lifestyle, personal lifestyle) of the differences in depressive symptoms and estimated their contributions among urban and rural oldest-old (aged 80+) adults. 14.60% of oldest-old (aged 80+) adults reported having depressed symptoms, with rural oldest-old (aged 80+) adults reporting more of these symptoms (15.70%) than urban oldest-old (aged 80+) adults (12.25%). In urban areas, age (90-99: β = -0.583, 95%CI = -1.008 to -0.158), annual income (rich: β = -0.699, 95%CI = -1.368 to -0.029), smoking (yes: β = -0.752, 95%CI = -1.376 to -0.127), exercise (yes: β = -1.447, 95%CI = -1.828 to -1.065), self-rated health status (good: β = -2.994, 95% CI = -3.362 to -2.625) were protective factors against depressive symptoms, and married status (other: β = 1.222, 95%CI = 0.564 to 1.880) were risk factors. In rural areas, education level (1-6: β = -0.374, 95%CI = -0.686 to -0.062), annual income (poor: β = -0.374, 95%CI = -0.721 to -0.026; rich: β = -0.781, 95%CI = -1.115 to -0.447), exercise (yes: β = -0.844, 95%CI = -1.143 to -0.545) and SRH status (good: β = -3.023, 95%CI = -3.274 to -2.772) were protective factors, whereas body mass index (BMI) (< 18.5: β = 0.532, 95%CI = 0.221 to 0.842) and married status (widowed: β = 0.630, 95%CI = 0.283 to 0.978) were risk factors. The Blinder-Oaxaca decomposition showed that 90.47% of the factors, such as age (90-99, 1.90%), living status (living in an institution, -12.35%), annual income (rich, 42.69%), smoking (yes, -5.56%), exercise (yes, 37.45%), and SRH (good, 19.09%) could significantly explain the depressive symptoms differences between the two populations (p < 0.05). The oldest-old (aged 80+) adults in rural areas had a higher prevalence of depressive symptoms than that in urban areas, mostly attributed to variations in income, marital status, physical activity, and health status. These findings can help government agencies design targeted and precise intervention strategies (e.g., health education, improving medical security, and providing sports venues and equipment) to improve mental health and lower the risk of depression among the oldest population.

This study aimed to compare and analyse the differences in smoking prevalence, and knowledge, attitudes, and factors associated with smoking between the rural and urban elderly population in China. In total, 6,966 participants aged 60 and above were included in this study, which assessed their smoking-related knowledge, attitudes, and perceptions toward tobacco control. The Chi-square test and logistic regression model were used for statistical analysis, and the Fairlie model was used for decomposition analysis. The overall prevalence of smoking was 25.6%; the rate was much higher in men than in women (overall: OR = 26.234; urban: OR = 31.260; rural: OR = 23.889). The rate of correct responses to all questions on smoking problems was significantly higher among the urban elderly than the rural elderly. Further, 64.18% of the participants supported printing photos of the health hazards of smoking on the cover of cigarette packs, and the rural elderly were more supportive of this. Moreover, only 36.52% of the participants supported increasing taxation and retail price of cigarettes; the urban elderly showed more support for this. Rules about smoking at home also played an important role, especially for families where smoking was not allowed at home, but with exceptions to the rule; however, this factor was only meaningful in urban families (urban: OR = 0.117). Through the Fairlie decomposition analysis, gender (-1.62%), age (-2.03%), region (13.68%), knowing about e-cigarettes (5.17%), rules about smoking at home (3.95%), and smoking-related knowledge scores (42.85%) were found to be associated with rural-urban disparities. This study focused on the differences in smoking between urban and rural areas in China. Smoking among the urban elderly was significantly less prevalent compared with the rural population. Factors including education, region, and smoking-related knowledge need to be addressed to reduce the gap between urban and rural health hazards in China.