Approximately 70% of cancer cases occur in people aged 50 + years. The probability of developing invasive cancer was about 6% for people aged 50–59 years, 12% for those aged 60–69 years, and 30% for those aged 70 + years [1]. Owing to a marked development of medical technology and therapies, individuals with cancer show a significant increase in the 4-year survival rate [2] and are routinely living beyond their late 60 s [3]. However, many older cancer survivors face another challenge, namely, functional decline [4, 5], either at a normative rate or exacerbated by the cancer. Prior work suggests that functional status is a key determinant of patients’ cancer treatment plans and treatment tolerance [6, 7]. Understanding how function changes and how risk or protective factors impact rate of functional decline are important in understanding the overall quality of life for older adults living with or recovering from cancer.
Disablement Process Models. Disability is a complex process where functional decline and recovery are dynamic, often involving an interplay of many factors. The World Health Organization provides the International Classification of Functioning, Disability, and Health (ICF) that defines functioning as a “dynamic interaction between a person’s health condition, environmental factors, and personal factors” [8]. A related model that informed the ICF is the Disablement Process model [9], which remains a particularly important framework, in part because this model offers more specific details that inform testable variables and hypotheses. The Disablement Process Model describes a general temporal process starting from pathology that leads to structural abnormalities in bodily/organ systems, progression to functional limitations in physical actions, and finally disability (i.e., the inability to independently complete activities of daily living [ADLs]). The pathway also represents crucial intervening stages [10, 11] and contributing factors, including physical, cognitive, psychological, and environmental factors.
From the Disablement Process lens, cancer may be the pathology that triggers disablement, or may be a factor that exacerbates existing changes, potentially accelerating the slope of functional decline [7]. Associations between cancer and functional limitations might support it as both an initiator and a contributing factor for functional decline. Individuals with a cancer diagnosis have a high prevalence rate of impaired functional status [12, 13]. Likewise, different patterns of functional trajectories are identified before and after a cancer diagnosis, suggesting that cancer may influence functional changes [7].
Clinical observations find significant heterogeneity of function among cancer survivors not accounted for by age [14]. A seminal qualitative study [15] and subsequent quantitative studies [16,17,18] suggest that cancer survivors show a relatively stable functional status at early stages of cancer, which may rapidly decline if cancer advances. Alternatively, a large population-based study showed that comorbid conditions rather than cancer diagnosis were associated with impairment in activities of daily living [19].
Notably, studies of cancer and functional limitations are mainly based on Western populations. Whether, and to what extent there are associations between cancer and functional changes have not been formally examined longitudinally in a Chinese older adults population. It is important to investigate, as China’s population is rapidly growing and aging, with high incidence of cancer [20].
We address three main research questions. First, what is the trajectory of functional limitations among participants with and without cancer diagnosis over 4 years? We hypothesized that cancer survivor would have higher levels and more rapidly increasing functional difficulties. Second, how do functional trajectories change before versus after the cancer diagnosis? Despite some mixed findings in prior work, we hypothesized that functional decline would be exacerbated after cancer diagnosis. Third, how are time-varying and time-invariant contributing factors associated with functional trajectories among cancer survivors? Due to limited longitudinal findings on functional change in the context of cancer, we had no specific hypotheses. Instead, we explored associations between demographic variables and key contributing factors and with levels and slopes of functional limitations over time.
Design
Sample/Participants
There were 7,452 participants (mean age = 59.06 years, SD = 8.94 years) recruited and followed up over 3 waves of data collection spanning 4 years (2011, 2013, to 2015; waves 1–3) from the China Health and Retirement Study (CHARLS) [21]. In this sample, 139 participants (mean age = 57.81 years, SD = 9.43 years) self-reported a cancer diagnosis during the 4-year period and were alive at wave 3 data collection.
Data collection
The original CHARLS was a sister study of the Health and Retirement Study (HRS) in the U.S. with aims to understand Chinese community-dwelling adults’ social, economic, and health status using a nationally representative sample of Chinese adults aged 45 years and older with multistage probability sampling methods. Data were collected via one-to-one interviews by trained interviewers or healthcare professionals to increase the response rate. The overall response rate was 80.51% in the first wave. A detailed description of CHARLS data collection methods has been published elsewhere [21].
Measures
Outcome variable. Functional limitations were assessed via self-reported difficulty with seven tasks (1 = yes, 0 = no), including walking 100 m, climbing stairs, chair stand, stooping/crouching/kneeling, lifting 11 pounds, extending arms up, and picking up a coin (range 0–7). Scores were summed such that higher scores indicated greater limitations. Cronbach’s alphas for each wave were 0.79, 0.82, and 0.82, respectively.
Cancer diagnosis. The CHARLS survey asked participants to self-report any cancer diagnosis by a physician at each measurement occasion (wave). We recoded a between-person binary indicator for cancer diagnosis at each wave (1 = yes, 0 = no). We also coded a time-varying cancer diagnosis timing variable (i.e., at the within-person level, the change from having no cancer = 0, to having cancer = 1).
Time to/from diagnosis. We centered each person at time 0 on the measurement wave where cancer diagnoses were first reported. Negative time scores indicate occasions prior to cancer diagnosis (pre-diagnosis), and positive scores indicate post-diagnosis occasions. The time to/from diagnosis variable for participants first reporting diagnosis at wave 1 were therefore coded as 0, 1, 2, whereas the those first reporting diagnosis at wave 2 and 3 were coded as -1, 0, 1, and -2, -1, 0, respectively.
Contributing factors. Four sets of contributing factors for disability were assessed as, depressive symptoms (psychological factor), pain and falls (physical factor), self-reported memory problems (cognitive factor), and social contact, and availability of support (environmental). All measures are described below. Except for pain (which was only measured in wave 2), all variables were measured over 3 waves, and were time-varying predictors. To align person-level differences in time-varying predictors to person-level differences in functional trajectories, observations were summarized across each phase (pre-diagnosis, onset, and post-diagnosis). For example, repeated scores of subjective memory problems obtained prior to cancer diagnosis were averaged to obtain a person-level pre-diagnosis memory score predicting pre-diagnosis functional change, the memory score obtained at cancer diagnosis onset was used as the predictor of the intercept, and repeated scores obtained after diagnosis were averaged to a person-level score predicting post-diagnosis functional change (for the binary measures on falls, contacts, and participation in social activities, we used the maximum rather than the average). Phase-specific parsing made it possible to accommodate the time-varying nature of moderating factors within the multiphase modeling framework [10].
Depressive symptoms were assessed using 10-items of Center for Epidemiologic Studies Depression scale (CESD-10, range = 0–24) [22], with a higher score indicating more depressive symptoms. Cronbach’s alphas of CESD-10 for each wave were 0.81, 0.76, and 0.80, respectively. Pain was assessed using the question “Do you feel any pain? (1 = none, 2 = a little, 3 = some, 4 = quite a bit, and 5 = a lot)” in wave 2. Falls was assessed using the question “Have you fallen down in the last two years? (1 = yes, 0 = no).” Self-reported memory problems were assessed using the question “How would you rate your memory at the present time (1 = excellent to 5 = poor)?”, coded with a higher score indicating poorer self-rated memory. Social contact was measured by any weekly contact with children, including in-person meet, email, and phone or text (1 = yes, 0 = no). Availability of support was measured by number of people living in the same household (range = 1–16), and participation in any social groups or activities (1 = yes, 0 = no).
Demographic covariates. Demographic variables were assessed at baseline and grand mean-centered for participant age (in years), sex (0 = female; 1 = male), education (0 = none, 1 = less than lower secondary, 2 = upper secondary and vocational training, 3 = tertiary). Marital status was coded as a time-varying continuous variable to accommodate changes in status over time (1 = married, 3 = partnered, 4 = separated, 5 = divorced, 7 = widowed, and 8 = never married).
Data analysis
Preliminary analysis. Descriptive statistics at baseline were assessed for all participants, and separately for those with and without cancer, with comparisons made using t-test or chi-square statistics. We further examined functional limitations over time by fitting an empty model (specifically, multilevel growth curve model) with linear time as the only predictor.
Research question 1. To examine functional trajectories for those participants with and without cancer, we used an ordinary growth curve model with functional limitations as the outcome (Model 1). In the level-1 within-person model, we specified functional limitations as:
$${\text{Functional limitations}}_{ti}={\pi }_{0i}+{\pi }_{1i}({\mathrm{Time}}_{ti})+{\varepsilon }_{ti}$$
(1)
where functional limitations for person i at time t was a function of an intercept \({(\pi }_{0i},\) baseline functional limitations), linear time (\({\pi }_{1i},\) within-person association between time and functional limitations), and the within-person residual, \({\varepsilon }_{ti}\), whose variance was \({\sigma }_{\varepsilon }^{2}\) and assumed to be homogeneous across persons. In the level-2 model, the individual specific intercepts and slopes were specified as:
$$\begin{array}{c}{\pi }_{0i}={\beta }_{00}+{\beta }_{01}({\mathrm{Diagnosis}}_{i})+{\upsilon }_{0i}\\ {\pi }_{1i}={\beta }_{10}+{\beta }_{11}({\mathrm{Diagnosis}}_{i})\end{array}$$
(2)
where βs were sample-level parameters. The person-specific intercept, \({\pi }_{0i}\), and the person-specific slope, \({\pi }_{1i}\), from Eq.(1) were each modeled as a function of time-invariant and between-person cancer diagnosisi (1 = participants had cancer and 0 = participants without cancer), while controlling for demographic variables (not shown in Eq. 2). \({\upsilon }_{0i}\) were between-person differences in the intercept with a variance, \({\sigma }_{\upsilon }^{2}\).
Research question 2. Among participants with cancer, we aimed to examine whether levels and slopes of functional limitations post-diagnosis differed from pre-diagnoses. To help examine this aim, we applied multiphase growth curve models (Model 2), which can model and compare the slopes of change in functional limitations before versus after the diagnosis. Model 2 had predictors of time to/from diagnosisit, time-varying cancer diagnosisit (1 = had cancer and 0 = no cancer), and the interaction between time to/from diagnosisit × cancer diagnosisit, while controlling for demographic variables. The level-1 within-person multiphase growth model was specified as:
$${\text{Functional limitations}}_{ti}={\pi }_{0i}+ {\pi }_{1i}(\mathrm{Time to}/\mathrm{from }{\mathrm{diagnosis}}_{ti})\hspace{0.17em}+\hspace{0.17em}{\pi }_{2i}({\mathrm{Diagnosis}}_{ti})\hspace{0.17em}+\hspace{0.17em}{\pi }_{3i}({\mathrm{Diagnosis}}_{ti}\hspace{0.17em}\times \hspace{0.17em}\mathrm{Time to}/\mathrm{from }{\mathrm{diagnosis}}_{ti})+{\varepsilon }_{ti}$$
(3)
where functional limitations for person i at time t was a function of an intercept \({(\pi }_{0i},\) functional limitations at the first report of cancer diagnosis), an individual specific slope parameter (\({\pi }_{1i},\) linear rate of cancer diagnosis-related change in functional limitations before cancer diagnosis, when diagnosisti = 0), an individual specific parameter (\({\pi }_{2i},\) discrete differences in level of functional limitations between pre- and post-cancer diagnosis), a second individual specific slope parameter (\({\pi }_{3i},\) differences in the linear rate of cancer diagnosis-related change in functional limitations between the pre and post-diagnosis phases), and the within-person residual, \({\varepsilon }_{ti}\), whose variance was \({\sigma }_{\varepsilon }^{2}\) and assumed to be homogeneous across persons. In the level-2 model, the individual specific intercepts and slopes were specified as:
$$\begin{array}{c}{\pi }_{0i}={\beta }_{00}+{\beta }_{01}({\mathrm{Age}}_{i})\hspace{0.17em}+\hspace{0.17em}{\beta }_{02}({\mathrm{Male}}_{i})\hspace{0.17em}+\hspace{0.17em}{\beta }_{03}({\mathrm{Education}}_{i})\hspace{0.17em}+\hspace{0.17em}{\beta }_{04}({\mathrm{Marital status}}_{i})+{\upsilon }_{0i}\\ {\pi }_{1i}={\beta }_{10}\\ \begin{array}{c}{\pi }_{2i}={\beta }_{20}\\ {\pi }_{3i}={\beta }_{30}\end{array}\end{array}.$$
(4)
where βs were sample-level parameters, representing the mean intercept (\({\beta }_{00}\)) and mean slopes (\({\beta }_{10}, {\beta }_{20}, {\beta }_{30}\)) of the functional limitation trajectory pooling over all participants with cancer in the sample. The person-specific intercept, \({\pi }_{0i}\), from Eq. (3) was further modeled as a function of participant age (\({\beta }_{01}\)), being male (\({\beta }_{02}\)), education level (\({\beta }_{03}\)), and marital status (\({\beta }_{04}\)). \({\upsilon }_{0i}\) were unexplained between-person differences in the intercept with a variance, \({\sigma }_{\upsilon }^{2}\), representing the degree of individual variability around the mean intercept.
Research questions 3. We applied the full multiphase growth curve model (Model 3) by adding covariates to Eq. (4) of Model 2. More specifically, we explored whether contributing factors had effects on levels (as main effects, Model 3.1) and slopes by fitting additional interaction terms between the disability contributing factors × time to/from diagnosisit × cancer diagnosisit (Model 3.2). We trimmed non-significant variables, one at a time, to achieve model parsimony. All results were reported using unstandardized coefficients β with standard error (se). All analyses were performed using SAS (version 9.4), and statistical significance was considered at p < 0.05 level (2-tailed).
