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CME Article . 2020 Series . Number 5

Burden and Patterns of Multimorbidity

Impact on Disablement in Older Adults

Jacob, Mini E. MD, PhD; Ni, Pengsheng MD; Driver, Jane MD, MPH; Leritz, Elizabeth PhD; Leveille, Suzanne G. PhD; Jette, Alan M. PT, PhD, MPH; Bean, Jonathan F. MD, MPH

Author Information
American Journal of Physical Medicine & Rehabilitation: May 2020 - Volume 99 - Issue 5 - p 359-365
doi: 10.1097/PHM.0000000000001388

Abstract

What Is Known

  • Older adults with multimorbidity have a higher burden of disability.

What Is New

  • Disability burden is related to the number of chronic conditions, rather than any specific pattern of multimorbidity that is common among older adults.

Multimorbidity, defined as the co-occurrence of at least two chronic conditions,1 is present in about 77% of older adults in the United States.2 Despite this high prevalence, and the considerable implications for individuals as well as society,3 research on the incremental impact of multimorbidity (as opposed to individual chronic conditions) on health outcomes has lagged behind.4 One of the most important effects of multimorbidity is its impact on disability, with current literature suggesting that specific patterns5,6 or the overall burden7,8 may be responsible for the accelerated disablement in multimorbid individuals. However, the causal pathway from multimorbidity to disability and the relative impact of disease burden on various domains of disablement is unclear. It is possible that chronic disease primarily affects a set of peripheral neuromuscular attributes important for mobility. It is also possible that the effect is at least partly mediated by brain pathology and cognitive changes, which may lead to a greater effect on participation in life activities than what is expected from peripheral effects. Unraveling this is important for developing effective rehabilitation prescriptions for older adults at risk for disability.

The World Health Organization’s International Classification of Functioning, the primary conceptual model for clinical practice and research on disability across the world, defines three disablement domains—body functions and structure, activity, and participation.9 The Boston Rehabilitative Impairment Study of the Elderly (Boston RISE), which has measures in all these domains in a population of older adults with mobility limitations, provides a unique opportunity to examine the influence of multimorbidity on the disablement process. In this study, the aim was to examine the effect of the burden and patterns of multimorbidity on different disablement domains among Boston RISE participants. It was hypothesized that participants with greater burden, as well as specific patterns of multimorbidity identified from the data, would have greater deficits in body functions and structure, worse activity limitations, and poorer participation.

MATERIALS AND METHODS

The Boston RISE is a longitudinal cohort study of 430 primary care patients 65 yrs or older from nine large primary care practices located across the greater Boston area, recruited between December 2009 and January 2012. The primary aim of the study was to identify rehabilitative impairments (neuromuscular impairments targeted in rehabilitative care) that are most responsible for mobility decline and disability progression among older adults. Study methods have been published.10 Targeted recruitment was used to approximate ethnic/racial representation of older adults residing within a 10-mile radius of the study clinic. Methods were approved by the Spaulding Rehabilitation Hospital Institutional Review Board and written consent was obtained from all participants. Eligibility included difficulty or task modification with walking one-half mile and/or climbing one flight of stairs and the absence of moderate or severe dementia (Mini-Mental State Examination score <18) or severe mobility limitation (Short Physical Performance Battery [SPPB] score <4). Baseline assessments were conducted over two visits; the second was conducted within 2 wks of the first. This study was a secondary analysis of cross-sectional data from Boston RISE participants at their baseline and conforms to all Strengthening the Reporting of Observational Studies in Epidemiology guidelines and reports the required information accordingly (see Supplemental Checklist, Supplemental Digital Content 1, http://links.lww.com/PHM/A946).

Multimorbidity

Participants were asked: “Has a doctor or health professional ever told you that you have this condition?” and responded “yes,” “no,” or “do not know” to a list of 13 chronic conditions—diabetes, high blood pressure, heart disease, cancer, lung disease, kidney disease, stomach or liver disease, anemia or other blood disease, arthritis (osteoarthritis or rheumatoid arthritis), depression, neurologic disorders, osteoporosis, and back pain. Back pain, a patient-reported condition, was included in this list to identify persons with chronic back pain that had warranted clinical consultation. Boston RISE participants are likely to have consulted their primary care provider for chronic back pain as all of them were recruited from primary care practices and had access to healthcare if needed. Participants were considered to have the condition if they responded “yes” to the item and not to have the condition if they said “no.” Participants who said “do not know” were considered to have missing data. Multimorbidity burden was described using a multimorbidity score, a simple count of the number of conditions that the participant reported out of the 13 listed conditions. Multimorbidity patterns were identified using latent class analysis (LCA).

Disablement Domains

Body Functions and Structures

Leg strength was measured in each leg by determining the one repetition maximum for each leg with a Keiser pneumatic leg press machine using a previously published protocol.11 The maximum value observed on either side was recorded as the peak leg strength. This was normalized for body weight. Peak power was recorded as the highest recorded power out of five trials performed with each leg at 40% and 70% of the one repetition maximum. Peak leg velocity was calculated by dividing peak power by the graphically displayed force at peak power recorded during the testing. Trunk extensor muscle endurance was measured with the participant lying prone on a specialized plinth positioned 45 degrees from vertical with feet fixed in position on a footplate and the body supported below the waist by the table.12 The participant maintained their trunk in a neutral position within the sagittal plane in line with their pelvis and legs for as long as possible up to 2.5 mins with arms across the chest. The time for which the position was maintained was recorded in seconds. Knee and ankle range of motion (ROM) were measured with a goniometer.13 Ankle ROM was considered to be impaired if there was inability to dorsiflex past 90 degrees or plantar flex past 110 degrees in either leg. Foot sensation was measured over the dorsum of the big toes using the Semmes-Weinstein monofilament test using the standard clinical 10 g and 1.4 g monofilaments.14 Foot sensation was considered to be impaired if the participant could not perceive three out of four touches from both the monofilaments on both sides. Both impaired ankle ROM and sensory loss were dichotomized as being present or absent.

Activities

Performance measures, as well as self-report, were used to assess the activities domain. The SPPB includes three performance measures: progressive standing balance, usual pace walking speed, and a five-repetition chair stand test. For the standing balance test, participants were asked to maintain their balance for 10 secs with their legs in side-by-side, semitandem, and full tandem positions. For walking speed, participants walked at their usual pace over a 4-m walking course; time was recorded and the best performance of two walks was considered for calculation of walk speed. In the chair stand test, participants were asked to stand up and down five times, as quickly as they could with their hands folded across their chest. Time to complete the test was recorded. Using established cut points, scores for each component (0 to 4) were summed to create a total score between 0 and 12, with higher scores indicating better performance. Gait speed was also considered as a distinct outcome variable, apart from the SPPB. Walking ability was measured using the 400-m walk test, during which participants walked as fast as they could to cover the distance. Scores in two subdomains of the Late Life Function Instrument were also considered—Advanced Lower Extremity Function (activities that involve a high level of physical ability and endurance, such as walking several blocks) and Basic Lower Extremity Function (activities primarily involving standing, stooping, and short walking activities).

Participation

Participation was assessed using the Late-Life Disability Instrument, which measures both frequency of participation and limitations in capability to perform 16 life tasks. Frequency dimension questions ask, “How often do you” do a specific task, whereas limitation dimension questions ask, “To what extent do you feel limited” in doing the task. Scores in the two dimensions were considered as distinct measures.

Confounders

Age, sex, and race were self-reported. Weight and height were measured using standardized techniques and body mass index was calculated. Physical activity was assessed using the Physical Activity Scale for the Elderly questionnaire.15 Pain was assessed using two subscales of the Brief Pain Inventory (BPI).16 The four-item Brief Pain Inventory pain severity subscale measures global chronic pain severity (pain lasting more than 2 wks), and the seven-item pain interference subscale measures pain interference with daily activities. The Mini-Mental Status Examination (MMSE) scores were used as a measure of global cognition.17

Statistical Analysis

For this analysis, five participants who did not have complete data on chronic conditions (as they responded that they did not know whether they have a certain condition) were excluded, resulting in a sample size of 425. The multimorbidity among participants is described in terms of burden (multimorbidity score) as well as patterns. Predominant patterns of multimorbidity in this population were identified using LCA. LCA enables the grouping of individuals into mutually exclusive groups or latent classes based on their answers to a set of categorical indicator variables. In LCA models, variation of the observed indicator variables (presence or absence of 13 chronic conditions in this case) is modeled as a function of membership in unobserved latent classes (chronic disease patterns, in this scenario). Increasingly complex models (with more latent classes) were developed and compared using sample size adjusted Bayesian information criteria. A final model was selected based on low Bayesian information criteria and clinical interpretability of the latent classes. Subsequently, class membership probabilities were computed, and participants were assigned class membership based on highest computed probability. Characteristics of the sample in terms of multimorbidity score and multimorbidity patterns were described using means and percentages. Linear and logistic regression models examined relationship between multimorbidity burden (score) and disablement variables, as well as latent class membership (patterns) and disablement variables. To allow for comparison of effect sizes, regression models using standardized outcome measures were built. Disablement measures that were continuous were standardized by subtracting the mean and dividing by the standard deviation. To allow for fair comparison between multimorbidity score and multimorbidity LCA class, the multimorbidity score was categorized into three groups (0–2, 3–5, and ≥6) and repeated the regression analyses. All regression models adjusted for age, sex, race, body mass index, Physical Activity Scale for the Elderly scores, pain scores, and MMSE scores. As there were missing values in some of the disablement domain measures, a sensitivity analysis was performed using multiple imputed datasets and the results were compared with complete case analysis. LCA was performed using MPlus and other analyses were conducted using SAS version 9.3 (SAS Institute, Inc, Cary, NC).

RESULTS

The overall means and percentages of baseline measures among Boston RISE participants, as well as distribution of measures based on multimorbidity burden and multimorbidity classes, are shown in Table 1. The mean age of the participants in this study was 76.5 yrs, 67.5% were women, and 17.4% were non-White. LCA identified three different multimorbidity classes in this sample, with each class representing a general pattern in comorbid conditions at the individual level in this population. The prevalence of each chronic condition in the three groups is shown in Figure 1. Class 1 was a low-multimorbidity group with a low prevalence of all chronic conditions compared with class 2 and class 3. Most class 1 participants had hypertension (78%) and an average of 3.2 chronic conditions. Class 2 participants had an average of four chronic conditions, with a predominance in musculoskeletal conditions (arthritis, 80%; back pain, 60%; osteoporosis, 39%) compared with class 1 and class 3, but vascular disease burden in this class was lower than in the other classes. Class 3 was a high multimorbidity group with an average of 6.6 conditions. This group had a higher prevalence multiple chronic conditions, including vascular, musculoskeletal, and metabolic (hypertension, 91%; arthritis, 81%; back pain, 78%; heart disease, 52%; diabetes, 41%; and cancer, 43%) when compared with the other two classes.

TABLE 1
TABLE 1:
Baseline characteristics according to multimorbidity pattern (latent class) and multimorbidity score (N = 425 older adults, Boston RISE, 2009–2012)
FIGURE 1
FIGURE 1:
Prevalence of chronic conditions across multimorbidity latent classes among Boston RISE participants.

Results of multivariable regression testing association between multimorbidity scores and disablement measures are displayed in Table 2. Results of regression after imputation of missing data (leg strength, 10.1%; leg velocity, 11.5%; leg power, 11.5%; trunk extensor endurance, 5.9%; knee flexion ROM, 1.2%; and ankle ROM impairment, 1.8%) were not different from the results of complete case analysis; hence, the results of complete case analysis are presented. Results using multimorbidity score as a categorical variable (three categories: 0–2, 3–5, and ≥6) produced results similar to that of the continuous measure; the results for the continuous variable are presented here. For every one-point higher multimorbidity score, participants had significantly lower values of leg strength, leg power, trunk extensor endurance, SPPB score, gait speed, and all four Late Life Function and Disability Instrument subscores. Multimorbidity score was not independently associated with leg velocity, ROM, sensory loss, or 400-m walk time. The standardized coefficients in Table 2 demonstrate some differences in the strength of relationships between multimorbidity burden and different disablement domains. A one-point higher multimorbidity score was associated with a decrease of 0.06 SD units in the body structures and functions domain, 0.07 to 0.10 SD unit decrease in the activities domain score, and a 0.07 to 0.09 units decrease in the participation domain score. There were no significant associations between multimorbidity patterns (latent class membership) and disablement outcomes (Tables 3 and 4).

TABLE 2
TABLE 2:
Association between multimorbidity score and disability domains in Boston RISE participants
TABLE 3
TABLE 3:
Association between multimorbidity pattern (latent class) and body function domain in Boston RISE participants
TABLE 4
TABLE 4:
Association between multimorbidity pattern (latent class) and activities/participation domains in Boston RISE participants

DISCUSSION

In this study of the association between multimorbidity and International Classification of Functioning disablement domains, it was found that multimorbidity burden was a significant predictor of worse performance in all three disablement domains. However, an association between multimorbidity patterns and disability measures was not found. This indicates that a simple count of major chronic conditions may be more reflective of an individual’s disablement status than the spectrum of conditions that are present. This has important implications to clinicians providing care for older adults with multimorbidity—it identifies the chronic disease count as a simple tool that could be used to identify those at risk for impending mobility disability. Such a tool could be easily implemented in geriatric specialty clinics and primary care practices, helping to streamline the implementation of universal preventive rehabilitation for older adults at risk for mobility disability.

This study’s findings also provide fresh insight regarding how chronic disease burden may affect different aspects of the disablement process. Whereas previous research has examined the role of individual diseases or multimorbidity on specific measures like muscle strength,18 gait speed,19 or activities of daily living disability,20 this study compared the relative impact of disease on multiple measures in the three different disablement domains. This is important because, although profoundly disabling neurologic conditions like stroke and dementia are known to affect all domains, it has been less evident whether the burden of multiple chronic conditions among ambulatory community living older adults as in Boston RISE can have a significant impact on certain domains like the participation domain. While their effects on structure and function, and even activities, are known to an extent, their role in participation could be limited, given the potential for adaptation. However, this study’s findings indicate that disease burden has a similar impact on all three domains, with a possible stronger impact on the participation domain. This aligns with findings that older adults with chronic diseases exhibit reduced social participation, beyond what could be caused by locomotor activity limitation.21 This phenomenon could be due to the effects of disease on the brain, communication and sensory limitations caused by disease, or environmental factors perpetuated by the disease conditions.

Further research is needed to confirm the pathways in which disease burden may be leading to participation restrictions. However, the strong impact of disease on participation, greater than what was anticipated from its effect on function, provides evidence for the hypothesis in this study that chronic diseases contribute to disablement through both central and peripheral disease processes. The peripheral processes may contribute to impairments, functional limitations, and participation restriction, but participation may be particularly influenced by brain changes as well, especially those that affect cognitive processes. Adjustment for cognitive status using MMSE scores, which was largely within normal limits across groups, was carried out. However, the MMSE, a global measure of cognitive function, may not be sensitive enough to detect subtle differences in executive function that is important for planning, organizing, and initiating community participation.

Several researchers have attempted to develop latent classes of multimorbidity among older adults, but there has been limited consensus among their findings. Olaya et al.22 identified three latent classes—healthy, metabolic/stroke and cardiorespiratory/mental/arthritis—whereas the three classes in this study distinguished participants into high multimorbidity, low multimorbidity, and a group with a high prevalence of musculoskeletal conditions and less vascular disease. Larsen et al.23 identified seven classes of multimorbidity, whereas Whitson et al.24 identified six. Some identified classes are common between studies; the wide variety in classes identified by different studies indicate that multimorbidity among older adults is very heterogeneous and defies strict classification into groups. The concept of multimorbidity latent class is intuitively attractive in both clinical care and research because of the possibility of using class membership as a measure of global health status and aligning research outcomes as well as clinical care to specific disease patterns. The findings of this study confirm that although it is possible to derive clinically plausible multimorbidity patterns using latent class analyses, these may not be useful at the individual level for detecting risk for poor physical function and mobility. However, a simple count of chronic conditions, irrespective of the pattern, may be a good index for disability risk assessment. This principle is the basis of the cumulative deficit theory of frailty25 and has been utilized effectively in the development of frailty indices that count symptoms, signs, diseases, and disabilities, the sheer number of conditions thereby carrying more weight than the actual nature of the deficits.26

This study’s findings are important for the clinical care of the aging population. Disability and poor physical function have a substantial impact on the quality of life of the individual, increase the risk of hospitalization, prolong hospital stay, and increase healthcare costs. It is important that care providers identify multimorbidity as a strong risk factor for functional loss and institute preventive or rehabilitative interventions. This study’s findings indicate that interventions should address neuromuscular impairments and focus on individuals with a disproportionate burden of comorbidities. Other factors that may be influencing social participation need to be identified and addressed.

Strengths of this study include extensive neuromuscular attribute measures and functional measures and a relatively large sample of older primary care patients. However, disease conditions were self-reported, and duration and treatment of illness were not considered. The design of this study was cross-sectional, so the temporal relationship of the observed associations cannot be confirmed. The study sample included only older patients with known mobility problems and was not population based. Hence, these findings may not be generalizable to diverse populations.

CONCLUSION

Multimorbidity burden, rather than specific patterns of multimorbidity, is associated with poor performance in all three disablement domains—body structure and function, activities, and participation—among older adults at risk for disability. This suggests that multimorbidity counts may be an excellent tool for risk stratification and identification of persons in need of rehabilitation.

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Keywords:

Multimorbidity; Disablement; Older Adults; Mobility; Function

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