Trends in activities of daily living disability among Chinese older adults from 1998 to 2018: an age-period-cohort analysis

Increasing Rate of Hospitalization for Inflammatory Bowel Disease Is an Age-Related Effect: A Canadian Population Study

INTRODUCTION

To understand trends in the risk of all-cause hospitalization for individuals with inflammatory bowel disease, we explored age, period, and cohort effects in Canada.

METHODS

Repeated cross-sectional survey data from the 2005-2014 Canadian Community Health Survey linked to the Discharge Abstract Database to capture the all-cause hospitalization within 3 years of entry into the study for eligible individuals. Random-effects 2-level models estimated fixed effects for age and random effects for time periods and birth cohorts on the risk of all-cause hospitalization within 3 years entry into the study.

RESULTS

An estimated 197,000 individuals were eligible for study inclusion. From this, an estimated 70,140 all-cause hospitalizations occurred within 3 years postentry into the study. The risk of hospitalization within 3 years increased with age and across birth cohorts, with older cohorts experiencing greater risks of hospitalization. A small temporal effect was identified for both inflammatory bowel disease groups. Within birth cohorts, the risk of hospitalization increased across ages for Crohn's disease, but in individuals with ulcerative colitis, the risk decreased across ages, except for the 2 oldest birth cohorts.

DISCUSSION

These data support the hypothesis that age effects are primarily responsible for increased risk of hospitalizations. As the prevalence of IBD continues to rise and age distribution of Canadians shifts toward an older-aged population, increasing the allocation of healthcare resources to prevent age-related risks of hospitalizations would be beneficial to reduce hospital burdens.

Cohort trends in intrinsic capacity in England and China

To understand how the health of older adults today compares to that of previous generations, we estimated intrinsic capacity and subdomains of cognitive, locomotor, sensory, psychological and vitality capacities in participants of the English Longitudinal Study of Ageing and the China Health and Retirement Longitudinal Study. Applying multilevel growth curve models, we found that more recent cohorts entered older ages with higher levels of capacity, while subsequent age-related declines were somewhat compressed compared to earlier cohorts. Trends were most evident for the cognitive, locomotor and vitality capacities. Improvements were large, with the greatest gains being in the most recent cohorts. For example, a 68-year-old participant of the English Longitudinal Study of Ageing born in 1950 had higher capacity than a 62-year-old born 10 years earlier. Trends were similar for men and women and were generally consistent across English and Chinese cohorts. Possible causes include broad societal influences and improvements in medical care.

Age, period, cohort effects in trends of depressive symptoms among middle-aged and older Chinese adults

Objectives

To investigate the effects of age, period, and cohort on the trends of depression; and to examine the influence of these three temporal effects on residential disparities in depression.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) during 2011 to 2020, involving 77,703 respondents aged 45 years old and above. The measurement of depressive symptoms was the score of 10-question version of the Center for Epidemiologic Studies Depression Scale (CES-D 10). The hierarchical age-period-cohort cross-classified random effects models were conducted to examine trends in depressive symptoms related to age, period and cohort.

Results

CES-D scores increased with age and slightly decreased at older age. The cohort trends mostly increased except for a downward trend among those born in 1950s. As for the period effect, CES-D scores decreased gradually from 2011 to 2013 followed by a upward trend. Rural residents were associated with higher level of depression than those live in urban area. These residence gaps in depression enlarged before the age of 80, and then narrowed. The urban-rural disparities in CES-D scores gradually diminished across cohorts, while the corresponding period-based change in urban-rural gaps was not significant.

Conclusion

When age, period, cohort factors are considered, the age effects on depression dominated, and the period and cohort variations were relatively small. The residence disparities in depression reduced with successive cohorts, more attention should be paid to the worsening depression condition of younger cohorts in urban areas.

Is 70 the new 60? A longitudinal analysis of cohort trends inintrinsic capacity in England and China

To understand how the health of older adults today compares to that of previous generations, we estimated intrinsic capacity and subdomains of cognitive, locomotor, sensory, psychological and vitality capacities in participants of the English Longitudinal Study on Ageing (ELSA) and the China Health and Retirement Longitudinal Study (CHARLS). We applied multilevel growth curve models to examine change over time and cohort trends. We found that more recent cohorts entered older ages with higher levels of capacity, and their subsequent age-related declines were somewhat compressed compared to earlier cohorts. These improvements in capacity were large, with the greatest gains being in the most recent cohorts. For example, a 68-year-old ELSA participant born in 1950 had higher capacity than a 62-year-old born just 10 years earlier. Trends were similar for men and women, and findings were generally consistent across English and Chinese cohorts.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study

Background

Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial.

Methods

We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted.

Results

During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model.

Conclusion

Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.

Long-term trends in the burden of edentulism in China over three decades: A Joinpoint regression and age-period-cohort analysis based on the global burden of disease study 2019

Background

To investigate secular trends in edentulism incidence, prevalence, and years lived with disability (YLDs) rates in Chinese men and women from 1990 to 2019.

Methods

Data were obtained from the Global Burden of Disease Study 2019. The annual percentage change and average annual percentage change were calculated using Joinpoint regression analysis. The age-period-cohort (APC) analysis estimated the independent age, period, and cohort effects.

Results

From 1990 to 2019, the crude incidence, prevalence, and YLDs of edentulism in the Chinese population increased year by year, while the age-standardized incidence, prevalence, and YLDs decreased, and the latter was higher in women than in men. The APC analysis showed that the age effect increased in men and women from age 20 to 74 and decreased thereafter. The risk of tooth loss increased with age. However, the relationship was not linear. The temporal effect showed a gradual increase; the risk of missing teeth gradually increased with the changing modern living environment. The cohort effect showed a single decreasing trend, with the early birth cohort having a higher risk of tooth loss than the later birth cohort population. The age, period, and cohort effects were consistent for both sexes.

Conclusion

Although the standardized incidence, prevalence, and YLD rate and cohort effect of dentition loss in China are declining, they are still causing a severe burden to China due to the continued aging of the population and the rising period effect. Despite the decreasing trends of the standardized incidence and prevalence of dentition loss and the rate of YLDs, China should develop more effective oral disease prevention and control strategies to reduce the increasing burden of edentulism in the older adult, especially in older women.

The health, work, and retirement study: representing experiences of later life in Aotearoa New Zealand

Older adults represent a large and growing section of Aotearoa New Zealand's population. Longitudinal research on experiences of later life enables understanding of both the capabilities with which people are ageing, and their determinants. The Health, Work, and Retirement (HWR) study has to date conducted eight biennial longitudinal postal surveys of health and well-being with older people (n = 11,601 respondents; 49.4% of Māori descent). Survey data are linked at the individual-level to other modes of data collection, including cognitive assessments, life course history interviews, and national health records. This article describes the HWR study and its potential to support our understanding of ageing in Aotearoa New Zealand. We present an illustrative analysis of data collected to date, using indicators of physical health-related functional ability from n = 10,728 adults aged 55-80 to describe mean trajectories of physical ability with age, by birth cohort and gender. As the original participant cohort recruited in 2006 reach ages 71-86 in 2022, future directions for study include expanding the study's core longitudinal measures to include follow-up assessments of cognitive functioning to understand factors predicting cognitive decline, and linkage to national datasets to identify population-level profiles of risk for conditions such as frailty.

Adverse Childhood Experiences and Trajectories of ADL Disability among Middle-Aged and Older Adults in China: Findings from the CHARLS Cohort Study

OBJECTIVES

There is little evidence on the association between adverse childhood experiences (ACEs) and disability trajectories among middle-aged and older adults. This study aimed to investigate the association between ACEs and activities of daily living (ADL) trajectories over eight years of follow-up and the mediation role of different chronic diseases on this relationship.

DESIGN

Prospective cohort study, eight-year follow-up.

SETTING

China Health and Retirement Longitudinal Study(CHARLS).

PARTICIPANTS

A total of 10651 participants age 45 and over from CHARLS 2011 to 2018 were included in this study.

MEASUREMENTS

Five ACEs exposure groups were formed based on the cumulative ACE scores. A 6-item ADL score was used, including bathing, dressing, eating, getting in/out of bed, using the toilet, and controlling urination, to measure the ADL disability, and the group-based trajectory model (GBTM) was used to identify the ADL disability trajectories. Multinomial logistic regression was performed to investigate the association between ACEs and ADL disability trajectory memberships, and KHB-method was used to estimate the contribution of different chronic diseases on this relationship.

RESULTS

Of the 10651 participants, 9.64% showed a mild-increasing trajectory in terms of change in ADL score during follow-up, followed by the low-mild trajectory (32.00%) and low-low trajectory (58.36%). Compared with those without ACEs exposure, participants who had ≥4 ACEs were associated with an increased risk of being on low-mild trajectory (OR 1.32, 95%CI:1.11-1.57) and mild-increasing trajectory (OR 1.41, 95%CI: 1.06-1.89), respectively. Besides, mediation analysis revealed chronic diseases had a mediation effect in this association, with the largest effect from arthritis or rheumatism (over 60%), followed by digestive system disease (around 14%), respiratory disease (around 12%), and cardio-metabolic disease (around 5%).

CONCLUSION

This study suggested that exposure to ACEs was associated with a higher risk of being worse ADL disability trajectories. Moreover, chronic disease accounts for a meaningful proportion of this association. Further studies are needed to clarify how chronic diseases mediate the association between ACEs and ADL disability trajectories.