Multidimensional health-transition patterns among a middle-aged and older population

Multidimensional health heterogeneity of Chinese older adults and its determinants

Background

Nowadays, the "Healthy China" and "Actively Addressing Population Aging" are two important national strategies in China. Promoting high-quality development of demand-driven older adults health services is an important way to achieve these strategies. From the perspective of active ageing, assessing the health status of older adults from multiple dimensions becomes crucial as it helps identify their specific health service needs, intervention measures, and health policies tailored to this population.

Methods

Data were derived from the China Health and Retirement Longitudinal Study (CHARLS) wave 4 (2018). A total of 4190 older adults (aged ≥60 years) were included as the analysis sample. Latent class analysis was performed to categorize older adults based on 6 health indicators, including Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), doctor diagnosed chronic diseases, depressive symptoms, cognitive function, and social participation. Multinomial logistic model was used to explore determinants associated with the various patterns of multidimensional health of older adults.

Results

The multidimensional health of older people was classified into three latent classes: Relatively Healthy (Class 1, n = 2806, 66.97%), Highly Depressed and Relatively Health Risk (Class 2, n = 1189, 28.38%), and Functional Impairment (Class 3, n = 195, 4.65%). Gender, age, education, marital status, number of children, alcohol consumption, physical activity, savings, residence, air quality satisfaction, and medical service satisfaction had significant effects on the attribution of all multidimensional health latent classes.

Conclusion

Heterogeneous and multidimensional health classes exist in China's older population, and these classes are influenced by a variety of factors and to varying degrees. Policymakers and healthcare providers can use these evidence to further address the diverse needs of older adults and improve older-care health services, ultimately achieving the goal of Active Ageing and Healthy China.

Modeling of correlated cognitive function and functional disability outcomes with bounded and missing data in a longitudinal aging study

Longitudinal studies of correlated cognitive and disability outcomes among older adults are characterized by missing data due to death or loss to follow-up from deteriorating health conditions. The Mini-Mental State Examination (MMSE) score for assessing cognitive function ranges from a minimum of 0 (floor) to a maximum of 30 (ceiling). To study the risk factors of cognitive function and functional disability, we propose a shared parameter model to handle missingness, correlation between outcomes, and the floor and ceiling effects of the MMSE measurements. The shared random effects in the proposed model handle missingness (either missing at random or missing not at random) and correlation between these outcomes, while the Tobit distribution handles the floor and ceiling effects of the MMSE measurements. We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and a simulation study. By ignoring the MMSE floor and ceiling effects in the analyses of the CLHLS, the association of systolic blood pressure with cognitive function was not significant and the association of age with cognitive function was lower by 16.6% (from -6.237 to -5.201). By ignoring the MMSE floor and ceiling effects in the simulation study, the relative bias in the estimated association of female gender with cognitive function was 43 times higher (from -0.01 to -0.44). The estimated associations obtained with data missing at random were smaller than those with data missing not at random, demonstrating how the missing data mechanism affects the analytic results. Our work underscores the importance of proper model specification in longitudinal analysis of correlated outcomes subject to missingness and bounded values.

Multidimensional trajectories of multimorbidity, functional status, cognitive performance, and depressive symptoms among diverse groups of older adults

Background

Inter-relationships between multimorbidity and geriatric syndromes are poorly understood. This study assesses heterogeneity in joint trajectories of somatic disease, functional status, cognitive performance, and depressive symptomatology.

Methods

We analyzed 16 years of longitudinal data from the Health and Retirement Study (HRS, 1998-2016) for n = 11,565 older adults (≥65 years) in the United States. Group-based mixture modeling identified latent clusters of older adults following similar joint trajectories across domains.

Results

We identified four distinct multidimensional trajectory groups: (1) Minimal Impairment with Low Multimorbidity (32.7% of the sample; mean = 0.60 conditions at age 65, 2.1 conditions at age 90) had limited deterioration; (2) Minimal Impairment with High Multimorbidity (32.9%; mean = 2.3 conditions at age 65, 4.0 at age 90) had minimal deterioration; (3) Multidomain Impairment with Intermediate Multimorbidity (19.9%; mean = 1.3 conditions at age 65, 2.7 at age 90) had moderate depressive symptomatology and functional impariments with worsening cognitive performance; (4) Multidomain Impairment with High Multimorbidity (14.1%; mean = 3.3 conditions at age 65; 4.7 at age 90) had substantial functional limitation and high depressive symptomatology with worsening cognitive performance. Black and Hispanic race/ethnicity, lower wealth, lower education, male sex, and smoking history were significantly associated with membership in the two Multidomain Impairment classes.

Conclusions

There is substantial heterogeneity in combined trajectories of interrelated health domains in late life. Membership in the two most impaired classes was more likely for minoritized older adults.

Differences in Health Behavior Profiles of Adolescents in Urban and Rural Areas in a Korean City

Through a latent class analysis approach, we can classify individuals and identify subgroups according to health behavior patterns, and find evidence for the development of customized intervention programs to target high-risk groups. Our study aimed to explore differences in latent classes of health behaviors in adolescents by region (urban vs. rural areas) in a Korean city. This cross-sectional secondary analysis utilized data collected from all first graders’ student health checkups in middle school and high school in a city of the largest island in Korea in 2016 (n = 1807). Health behavior indicators included both healthy (consuming breakfast regularly, consuming vegetables daily, consuming milk daily, consuming fast food on a limited basis, engaging in vigorous physical activities, brushing teeth, and practicing hand hygiene) and unhealthy (drinking, smoking, and overusing the internet) behaviors. Nutritional and diet behaviors were important factors for classifying healthy and unhealthy adolescents in both regions. Approximately 11% of rural students belonged to the risky group, which was characterized by a high level of drinking alcohol and smoking. These results suggest that when developing health policies for adolescents, customized policy-making and education based on the targeted groups’ behavioral patterns could be more effective than a uniform approach.

Joint trajectories of cognition and gait speed in Mexican American and European American older adults: The San Antonio longitudinal study of aging

OBJECTIVES

Cognitive decline and gait speed slowing are independent predictors of disability and mortality. While both factors increase in prevalence with advancing age, little is known about their combined patterns of change. The study goal was to identify joint trajectories of cognition and gait speed within an aging bi-ethnic cohort of Mexican Americans and European Americans.

METHODS/DESIGN

Participants included 182 Mexican Americans and 188 European Americans, ages 65 to 74, who were followed over a mean of 9.5 years. Cognition was assessed with the mini-mental state examination and gait speed was examined with a timed 10-ft walk. Joint trajectory classes of cognition and gait speed were identified with latent growth mixture modeling. Odd-ratios assessed predictors for trajectory classes.

RESULTS

Three latent trajectory classes were identified: (a) relatively stable cognition and gait (termed stable cognition and gait class, 65.4%); (b) deteriorating cognition and gait (termed cognitive and physical vulnerability class, 22.2%); (c) stable cognition and deteriorating gait (termed physical vulnerability class, 12.4%). The odds of classification in the cognitive and physical vulnerability class vs stable cognition and gait class was associated with Mexican American ethnicity (OR = 3.771, P = .016), age (OR = 1.186, P = .017), income (OR = 0.828, P = .029), education (OR = 0.703, P < .001), and diabetes (OR = 4.547, P = .010). The odds of classification in the physical vulnerability class was associated with female sex (OR = 6.481, P = .004) and body mass index (OR = 1.118, P = .025).

CONCLUSIONS

The trajectories of cognition and gait speed were generally parallel, suggesting the two domains may act synergistically to shape important health outcomes. Socioeconomic disparities and Mexican American ethnicity independently conferred risk for accelerated decline.

Psychological characteristics and stress differentiate between high from low health trajectories in later life: a machine learning analysis

Objective: This study set out to empirically identify joint health trajectories in individuals of advanced age. Predictors of subgroup allocation were investigated to identify the impact of psychological characteristics, stress, and socio-demographic variables on more favorable aging trajectories.Method: The sample consisted of N = 334 older adults (M_AGE=68.31 years; SD = 9.71). Clustered health trajectories were identified using a longitudinal variant of k-means and were based on health and satisfaction with life. Random forests with conditional interference were computed to examine predictive capabilities. Key predictors included psychological resilience resources, exposure to childhood adversities, and chronic stress. Data was collected via a survey, at two different time points one year apart.Results: Two different clustered health trajectories were identified: A 'constant high health' (low number of health-related symptoms, 65.6%) and a 'maintaining low health' profile (high number of symptoms, 34.4%). Over the one-year study period, both symptom profiles remained stable. Random forest analyses showed chronic stress to be the most important predictor in the interaction with other risk and also buffering factors.Conclusion: This study provides empirical evidence for two stable health trajectories in later life over one year. These results highlight the importance of chronic stress, but also psychological resilience resources in predicting aging trajectories.

Pre-employment health lifestyle profiles and actual turnover among newly graduated nurses: A descriptive and prospective longitudinal study

BACKGROUND

Newly graduated nurses' pre-employment health lifestyles play particularly important roles in their smooth adaptation to and retention in clinical nursing; however, the longitudinal relationship between pre-employment health lifestyles and work outcomes, such as turnover, remains underexamined. To identify the health lifestyle profiles of specific populations of interest, recent studies have employed multifaceted approaches using health behaviors and/or statuses.

OBJECTIVES

To identify the pre-employment health lifestyle profiles of newly graduated nurses, and to examine the longitudinal relationships between health lifestyle profiles and actual turnover.

DESIGN

Descriptive and prospective longitudinal study design.

SETTINGS

One tertiary hospital in Seoul, South Korea.

PARTICIPANTS

A total of 464 newly graduated nurses who started work between September 2014 and December 2015.

METHODS

The outcome was actual turnover-whether participants had resigned from the organization and the days they worked up to December 31, 2017. We measured eight health lifestyle variables on the first day of orientation before ward placement (i.e., at baseline): quantity and quality of sleep, eating three meals a day, having a regular diet, alcohol consumption, moderate exercise, depression, and self-rated health. We employed latent class analysis to identify the health lifestyle profiles of new nurses, and used Cox proportional hazards regression to examine the longitudinal relationships between health lifestyle profiles and actual turnover.

RESULTS

We classified newly graduated nurses' pre-employment health lifestyle profiles into two groups: unhealthy lifestyle (15.6%) and discordant (84.4%). Compared with the new nurses in the discordant group, those in the unhealthy lifestyle group had significantly higher probabilities of resigning (HR = 2.38, 95% CIs of HR = 1.62-3.50); this relationship remained significant after adjusting for perceived job stress at six weeks of work (HR = 2.26, 95% CIs of HR = 1.50-3.39).

CONCLUSIONS

This study identified significant differences in the patterns of newly graduated nurses' pre-employment health lifestyles; our analysis showed that classification in the unhealthy lifestyle group was a turnover risk factor. Given that new nurses' health lifestyles affect work outcomes, hospitals should implement organizational and educational initiatives to encourage healthy lifestyles. In considering pre-employment health lifestyle profiles, hospitals should also monitor novice nurses' adaptation and wellness. Nursing education should include strategies to enhance nursing students' own health. Further extensive longitudinal studies should seek to identify the health lifestyle profiles of heterogeneous nurse populations.

Rapidly developing multimorbidity and disability in older adults: does social background matter?

BACKGROUND

Multimorbidity is among the most disabling geriatric conditions. In this study, we explored whether a rapid development of multimorbidity potentiates its impact on the functional independence of older adults, and whether different sociodemographic factors play a role beyond the rate of chronic disease accumulation.

METHODS

A random sample of persons aged ≥60 years (n = 2387) from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was followed over 6 years. The speed of multimorbidity development was estimated as the rate of chronic disease accumulation (linear mixed models) and further dichotomized into the upper versus the three lower rate quartiles. Binomial negative mixed models were used to analyse the association between speed of multimorbidity development and disability (impaired basic and instrumental activities of daily living), expressed as the incidence rate ratio (IRR). The effect of sociodemographic factors, including sex, education, occupation and social network, was investigated.

RESULTS

The risk of new activity impairment was higher among participants who developed multimorbidity faster (IRR 2.4, 95% CI 1.9-3.1) compared with those who accumulated diseases more slowly overtime, even after considering the baseline number of chronic conditions. Only female sex (IRR for women vs. men 1.6, 95% CI 1.2-2.0) and social network (IRR for poor vs. rich social network 1.7, 95% CI 1.3-2.2) showed an effect on disability beyond the rate of chronic disease accumulation.

CONCLUSIONS

Rapidly developing multimorbidity is a negative prognostic factor for disability. However, sociodemographic factors such as sex and social network may determine older adults' reserves of functional ability, helping them to live independently despite the rapid accumulation of chronic conditions.

Socioeconomic stratification and multidimensional health trajectories: evidence of convergence in later old age

OBJECTIVES

This research sought to examine socioeconomic stratification in the joint trajectories of physical, emotional, and cognitive functioning among older Americans and how it differs by age groups.

METHODS

We used data from a nationally representative sample of 9,237 Americans age 65 or older from the Health and Retirement Study, who were observed biennially from 1998 to 2010. Joint trajectories of physical, emotional, and cognitive functioning were characterized using a group-based mixture model. We then applied multinomial logistic regression analysis to evaluate their linkages with socioeconomic status and how the linkages differ by age groups.

RESULTS

We identified four distinct patterns of joint changes in physical, emotional, and cognitive functioning over time. Accounting for 29.3%, 23.5%, 24.5%, and 22.6% of the older Americans, respectively, these trajectory patterns characterized groups of individuals experiencing minimal to severe levels of impairment and deterioration. Lower education, income, and net worth were associated with trajectories featuring greater impairment or more rapid deterioration in these functional dimensions. Disparities based on education, however, attenuated in later old age, whereas health benefits associated with higher income and higher net worth persisted into advanced age.

DISCUSSION

Distinct patterns of joint trajectories of physical, emotional, and cognitive functioning exist in old age. There were significant socioeconomic differences in the joint trajectories, with education-based inequality in health converging in later old age. Further research identifying strategies to alleviate the disproportionate burden of poor multidimensional health trajectories in lower socioeconomic groups is important.

Health status profiles in community-dwelling elderly using self-reported health indicators: a latent class analysis

PURPOSE

Latent class analysis (LCA), a statistical method for identifying latent classes within a population using multiple indicators, has been used to study the heterogeneity of health among the elderly. We aim to identify health status profiles of older adults using LCA and examine the socio-demographic characteristics associated with each profile.

METHODS

A community health survey of residents ≥60 years was conducted in Marine Parade, Singapore. We performed LCA on seven health indicators (number of chronic conditions, activities of daily living (ADL) dependency, pain, depression, cognition, social isolation, and frequency of socialising) to identify distinct classes of health status profiles. Multivariable logistic regression was conducted to examine the socio-demographic characteristics associated with each profile.

RESULTS

Of the 2,444 elderly interviewed, we identified two health status profiles: "Health at risk" (n = 465, 19.0 %), and "Relatively healthy" (n = 1,979, 81.0 %). The "Health at risk" profile was characterised by high probabilities of 3+ chronic conditions (λ = 0.63), at least one basic/instrumental ADL dependency (λ = 0.56), moderate/extreme pain (λ = 0.55), cognitive impairment (λ = 0.29), depressive symptoms (λ = 0.29), social isolation (λ = 0.27), and infrequent socialisation (λ = 0.61). Individuals who were older (65-74, 75-84, and 85+ years), females, of non-Chinese ethnicity (Indian, Malay, and Others), had primary and lower education, and were unemployed/not employed were more likely to be "Health at risk".

CONCLUSION

Using LCA, we identified two distinct health status profiles which accounted for the heterogeneity of the elderly population. Selected socio-demographic characteristics were associated with different profiles and provide implications for the structuring of future public health interventions targeting the older population.

Heterogeneity in healthy aging

For a surprisingly large segment of the older population, chronological age is not a relevant marker for understanding, measuring, or experiencing healthy aging. Using the 2003 Medical Expenditure Panel Survey and the 2004 Health and Retirement Study to examine the proportion of Americans exhibiting five markers of health and the variation in health-related quality of life across each of eight age groups, we find that a significant proportion of older Americans is healthy within every age group beginning at age 51, including among those aged 85+. For example, 48% of those aged 51-54 and 28% of those aged 85+ have excellent or very good self-reported health status; similarly, 89% of those aged 51-54 and 56% of those aged 85+ report no health-based limitations in work or housework. Also, health-related quality of life ranges widely within every age group, yet there is only a comparatively small variation in median quality of life across age groups, suggesting that older Americans today may be experiencing substantially different age-health trajectories than their predecessors. Patterns are similar for medical expenditures. Several policy implications are explored.