563607 research-article2014
JAGXXX10.1177/0733464814563607Journal of Applied GerontologyNg et al.
Article
Assessment of an Expanded Functional Disability Scale for Older Adults With Diabetes
Journal of Applied Gerontology 1–20 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464814563607 jag.sagepub.com
Xinyi Ng1, Charlene C. Quinn1, Mehmet Burcu1, and Donna Harrington1
Abstract Although prior literature has shown the plausibility of combining the Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) items to form an expanded scale for measuring the degree of functional decline, this has not been shown in older adults with diabetes who are disproportionately affected by functional disability. Using the 2009 Medicare Current Beneficiary Survey data, we evaluated the factor structure of the pooled ADL and IADL items. Based on our study comprising 2,158 community-dwelling older adults (≥65 years) with diabetes, the unidimensional model exhibited good fit. Despite well-fitting indices, high correlations were observed between the latent constructs (>.70) of the multi-factor models, suggesting a lack of discriminant validity. These findings provide empirical support for a combined scale that can comprehensively and efficiently characterize the extent of functional disability in older adults with diabetes for research, risk adjustment, and evaluation in patientcentered medical homes.
Manuscript received: May 20, 2014; final revision received: August 29, 2014; accepted: November 8, 2014. 1University
of Maryland, Baltimore, USA
Corresponding Author: Xinyi Ng, Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore, 220 Arch Street, 12th Level, Baltimore, MD 21201, USA. Email: [email protected]
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Keywords diabetes, functional disability, confirmatory factor analysis, Medicare
Diabetes mellitus is a highly prevalent chronic disease in the United States, affecting about 27% of the population aged 65 years and over (Centers for Disease Control and Prevention, 2011), and about 34% of the older adults in nursing homes (Coxe, Lennertz, & McCullough, 2013). Functional disability disproportionately affects older adults with diabetes, where approximately 30% to 55% of them experience some form of limitation in their usual activities or mobility (Bruce, Davis, & Davis, 2005; Chiu & Wray, 2011; Kalyani, Saudek, Brancati, & Selvin, 2010). The care and management of the elderly with diabetes is further complicated by the heterogeneous spectrum of functional disability, where some individuals might be physically and cognitively robust whereas others are fraught with multiple comorbidities (American Diabetes Association [ADA], 2014; Blaum, Ofstedal, Langa, & Wray, 2003; Durso, 2006; Sinclair et al., 2012). Recent epidemiologic studies have consistently found that diabetes is associated with disability, which affects quality of life and is a major risk factor for loss of independence, falls, injuries, infections, and institutionalization (ADA, 2008, 2013; Chau et al., 2011; Chiu & Wray, 2011; Salas, Bubolz, & Caro, 2000; Volpato, Maraldi, & Fellin, 2010). The primary concern of older adults with diabetes was also demonstrated to be disease-related complications, such as amputation and blindness, resulting in disabilities (Quandt et al., 2013). These findings highlight the need to effectively identify and assess the extent of functional disability among older adults with diabetes for both clinical research and screening purposes. In current practice, the Activities of Daily Living (ADL; Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963) and Instrumental Activities of Daily Living (IADL; Rosow & Breslau, 1966) are well-established scales conventionally used to ascertain functional disability. The ADL scale measures activities that are essential for self-care, whereas the IADL scale measures activities that are necessary for independent adaptation to living within the environment (Spector, Katz, Murphy, & Fulton, 1987). Psychometric properties of the ADL and IADL scales, such as test–retest reliability and internal consistency, have been previously evaluated and well established (Ivanova et al., 2013). Although both scales are well accepted and widely used for assessing different domains of functional disability, previously published research has suggested the plausibility of combining the ADL and IADL items to form an expanded scale, which is potentially a more sensitive assessment than the stand-alone scales to measure a wider range of functional decline (Fortinsky, Garcia, Joseph Sheehan, Madigan, & TullaiMcGuinness, 2003; Spector & Fleishman, 1998; Spector et al., 1987). This
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combined ADL-IADL scale can offer several practical advantages to clinicians and researchers. For one, it could provide clinicians with a more comprehensive assessment of the extent of functional disability that would aid in the tailoring of treatment regimens. In addition, in the context of research practice, a single scale confers the analytic advantage of avoiding problems with collinearity that typically occur when multiple, highly correlated scales are concurrently used. To date, studies addressing the dimensionality of functional disability have yielded inconsistent findings in the general older adult population. Although a body of evidence suggests that the ADL and IADL can be combined into a unidimensional scale (Fortinsky et al., 2003; Kempen & Suurmeijer, 1990; Spector & Fleishman, 1998; Spector et al., 1987) (i.e., the ADL and IADL measure the same underlying factor or construct), others argue that functional disability is multidimensional and multiple scales are needed to provide a functional disability profile for patients (Clark, Stump, & Wolinsky, 1997; Thomas, Rockwood, & McDowell, 1998). Furthermore, the IADL scale has also been hypothesized to comprise two dimensions—the physical IADLs and the cognitive IADLs (Ng, Niti, Chiam, & Kua, 2006; Thomas et al., 1998), such that the combined ADL and IADL items could potentially represent three separate constructs accounting for the full spectrum of functional disability. Clinical guidelines highlight the importance of recognizing and addressing functional disability among older adults with diabetes, for whom treatment goals should be tailored according to the degree and complexity of functional disability (ADA, 2014; A. F. Brown, Mangione, Saliba, & Sarkisian, 2003; Durso, 2006; Sue Kirkman et al., 2012). Consequently, there is a need to appropriately characterize functional disability within this population. Coupled with the debate over the dimensionality of functional disability measures, this study seeks to evaluate the dimensionality and to examine the factor structure of the pooled items from the individual ADL and IADL scales. This is the first study that examines whether the ADL and IADL items can be combined into a singular and unidimensional scale for an enhanced and practical assessment of functional disability in older adults with diabetes. It is particularly important to assess the unidimensionality of functional disability within the diabetes subpopulation of older adults as they have unique or varying needs from the general older adult population.
Method Data Source This cross-sectional study analyzed data from the 2009 Medicare Current Beneficiary Survey (MCBS), which is a nationally representative survey of
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Medicare beneficiaries conducted by the Centers for Medicare and Medicaid Services (CMS; 2013). The survey dataset contains comprehensive information regarding individual demographic characteristics, health and functional status, and person-level health service utilization and expenditure. Survey responses were also supplemented and linked to administrative data on Medicare enrollment and all Medicare Part A (hospital insurance), Part B (medical insurance), and Part D (prescription drug coverage) claims records. This study was reviewed and approved by the University of Maryland Baltimore Institutional Review Board.
Sample The study sample was selected according to the following inclusion criteria: (a) beneficiaries aged 65 years and older, (b) resided in the community, and (c) diagnosed with diabetes. Presence of diabetes was ascertained using both self-report and claims information. We identified presence of diabetes from claims using an algorithm adapted from the CMS for the Chronic Condition Data Warehouse (Hebert et al., 1999; Chronic Conditions Data Warehouse, 2014), where diabetes was considered present if a beneficiary has either (a) one or more inpatient hospital or home health care claims or (b) at least two outpatient hospital or physician claims with the following International Classification of Disease, Ninth revision (ICD-9) codes: 250.xx, 357.2, 362.01, 362.02, 366.41. Diabetes was also considered present if a beneficiary self-reported having diabetes with a positive response to the survey question, “Has a doctor ever told you that you had any type of diabetes, including: sugar diabetes, high blood sugar, (borderline diabetes, pre-diabetes, or pregnancy-related diabetes/borderline diabetes, or pre-diabetes)?”
Measures To ascertain the presence of limitation in the ADL, beneficiaries in the MCBS were presented with a series of questions asking “Because of a health or physical problem, do you have any difficulty in (1) bathing or showering, (2) dressing, (3) eating, (4) getting in or out of bed or chairs, (5) walking, and (6) using the toilet?” The IADL scale measures higher order tasks that require greater neuropsychological organization than the basic ADL, and are necessary for independent living (Ng et al., 2006; Spector et al., 1987). To ascertain limitations in IADL, beneficiaries were asked whether they have difficulty in (a) using the telephone, (b) doing light housework, (c) doing heavy housework, (d) preparing meals, (e) shopping, and (f) managing money.
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The available responses for all items in the ADL and IADL scales consisted of “yes,” “no,” “doesn’t do,” “not ascertained,” “don’t know,” or “refused.” Responses of “not ascertained,” “don’t know,” or “refused” were deemed as missing. When a beneficiary indicated “doesn’t do,” a follow-up question was asked to determine if they did not do the particular activity because of health problems. If they indicated yes to the follow-up question, they were recoded as having limitation for that particular activity. In contrast, those who indicated “doesn’t do” in the original question and “no” to the follow-up question would be recoded as having a missing response because we cannot determine with certainty if they did not perform that activity due to health constraints or their lifestyles do not include engagement in that particular activity (e.g., some older persons might not need to prepare their own meals on a regular basis). In this way, the final responses to every item on both scales were dichotomous (1 = yes, 0 = no), with allowances for missing responses.
Data Analysis Descriptive analyses of the baseline characteristics were first conducted. Individuals who had missing responses on all 12 questions were excluded from the analysis. Internal consistency reliability of the ADL, IADL, and combined ADL-IADL scales was assessed using Cronbach’s alpha. A Cronbach’s alpha value between .70 and .95 is generally acceptable (Tavakol & Dennick, 2011). Concurrent validity of the combined ADL-IADL scale was further assessed by examining the proportion of beneficiaries with selfreported comorbidities at different score ranges along the ADL-IADL scale. It was hypothesized that a higher score on the ADL-IADL scale would reflect a higher burden of comorbidities. These analyses were conducted using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA) or STATA/MP version 12 (StataCorp, 2011). Confirmatory factor analyses (CFAs) were conducted using Mplus version 7.1 (Muthén & Muthén, 1998-2012) with weighted least squares with mean- and variance-adjusted estimation (WLSMV) for dichotomous data (Schmitt, 2011) to examine the factor structure of the pooled items from the ADL and IADL. CFA is appropriate in this context as the dimensionalities of these items from the two scales have been explored in previous studies (Clark et al., 1997; Fortinsky et al., 2003; Kempen & Suurmeijer, 1990; Ng et al., 2006; Spector & Fleishman, 1998; Spector et al., 1987; Thomas et al., 1998). Hence, instead of using an exploratory factor analysis to derive the factor structure for a new set of variables, our research interest lies in investigating whether existing established dimensionalities apply to a different population, older adults with diabetes.
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Based on prior research as stated above, three competing models were tested: (a) a one-factor model combining all ADL and IADL items into one factor, (b) a two-factor model with the ADL items on one factor and the IADL items on a second factor, and (c) a three-factor model with the ADL items, the physical IADLs, and the cognitive IADLs each forming separate factors. The three competing models were evaluated and compared with multiple fit indices, including the chi-square goodness-of-fit index, comparative fit index (CFI), Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). A model has good or acceptable fit when it has a nonsignificant chi-square test, CFI and TLI above 0.95 (Hu & Bentler, 1999; Kline, 2011), and RMSEA of .06 or less (Hu & Bentler, 1999). In addition, both unstandardized and standardized coefficients were examined; ideally, the standardized coefficients should be close to or greater than 0.70 (Kline, 2011).
Results After excluding 10 observations with missing responses on all 12 items, the final study sample comprised 2,158 elderly Medicare beneficiaries who resided in the community and had diabetes. Table 1 includes baseline sociodemographic characteristics, self-reported comorbidities, and self-rated health status of the study sample. More than half of the beneficiaries were between 65 and 80 years old, married, and had a high school education or less. The beneficiaries were also predominantly white (81.4%) and reported household income of US$50,000 or less (86.7%). The proportions of beneficiaries who indicated difficulties in any of the 12 functional activities are presented in Table 2. For the ADL items, the largest proportion of beneficiaries (34.5%) with diabetes faced difficulties in walking. Between 10% and 20% of the study population indicated difficulties with each of bathing/showering, dressing, and getting in and out of bed/chairs. Less than 10% reported difficulties with using the toilet or eating. With regard to IADL items, a substantial proportion of the diabetic elderly (40.4%) had difficulties with doing heavy housework. In contrast, approximately 1 in 10 beneficiaries reported problems using the telephone or managing money.
Internal Consistency and Concurrent Validity of the Combined ADL-IADL Scales The Cronbach’s alpha for the ADL (.798), IADL (.834), and combined ADLIADL (.890) scales displayed good internal consistency reliability. As the scores on the combined ADL-IADL scale increased, the proportion of beneficiaries with comorbidities increased correspondingly (Table 3). In particular,
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Ng et al. Table 1. Baseline Characteristics of Beneficiaries With Diabetes (N = 2,158). All beneficiaries (N = 2,158) Characteristics Age 65-69 70-74 75-79 80-84 85+ Gender Female Male Race (missing = 10) White African American Others Marital status (missing = 2) Single Married Education (missing = 8) Less than high school High school or vocational diploma Some college College degree or higher Income (missing = 1) US$50,000 Number of comorbiditiesa 0-1 2-3 4+ Self-rated health status (missing = 5) Excellent Very good Good Fair Poor
n
%
459 491 462 412 334
21.3 22.8 21.4 19.1 15.5
1,096 1,062
50.8 49.2
1,748 235 165
81.4 10.9 7.7
1,014 1,142
47.0 53.0
589 813 332 416
27.4 37.8 15.4 19.4
1,079 792 286
50.0 36.7 13.3
325 1,051 782
15.1 48.7 36.2
181 548 770 471 183
8.4 25.5 35.8 21.9 8.5
Data Source. Medicare Current Beneficiary Survey (2009). Note. Ten observations were dropped as they had missing values on all items of the Activities of Living scale and the Instrumental Activities of Living scale. aComorbidity categories are self-reported and include cardiac disease, hypertension, cerebrovascular disease (CVD), respiratory disease, cancer, arthritis, neurological conditions, psychiatric disorder, and osteoporosis or bone fracture.
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Table 2. Proportion of Beneficiaries With Difficulties in ADL or IADL (N = 2,158). Beneficiaries with difficulty (N = 2,158) ADL/IADL items
n
%
ADL scale Do you have any difficulty in bathing or showering? Yes 332 15.4 No 1,825 84.5 NA/missing 1 0.1 Do you have any difficulty in dressing? Yes 232 10.8 No 1,926 89.2 NA/missing — — Do you have any difficulty in eating? Yes 49 2.3 No 2,109 97.7 NA/missing — — Do you have any difficulty getting in or out of bed or chairs? Yes 369 17.1 No 1,783 82.6 NA/missing 6 0.3 Do you have any difficulty in walking? Yes 744 34.5 No 1,414 65.5 NA/missing — — Do you have any difficulty in using the toilet? Yes 164 7.6 No 1,994 92.4 NA/missing — — IADL scale Do you have any difficulty in doing light housework (such as washing dishes, straightening up, or light cleaning)? Yes 349 16.2 No 1,725 79.9 NA/missing 84 3.9 Do you have any difficulty in doing heavy housework (such as scrubbing floors or washing windows)? Yes 871 40.4 No 1,092 50.6 NA/missing 195 9.0 Do you have any difficulty in preparing (your/his/her) own meals? Yes 280 13.0 No 1,771 82.0 NA/missing 107 5.0 Do you have any difficulty in shopping for personal items (such as toilet items or medicines)? Yes 408 18.9 No 1,720 79.7 NA/missing 30 1.4 (continued)
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Ng et al. Table 2. (continued) Beneficiaries with difficulty (N = 2,158) ADL/IADL items
%
n
Do you have any difficulty in using the telephone? Yes 172 No 1,979 NA/missing 7 Do you have any difficulty in managing money (such as keeping track of expenses or paying bills)? Yes 203 No 1,886 NA/missing 69
8.0 91.7 0.3 9.4 87.4 3.2
Data Source. Medicare Current Beneficiary Survey (2009). Note. Available responses were “yes,” “no,” “doesn’t do,” “not ascertained,” “don’t know,” or “refused.” If the latter three are selected, responses were treated as missing. If “doesn’t do” is indicated, a followup question was asked to determine whether they did not do the particular activity because of health problems. Recoding: If they indicated yes to the follow-up question, they would be recoded as “yes.” If they indicated no to the follow-up question, they would be recoded as missing. Ten observations were dropped as they were missing on all items of the ADL and IADL. ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living; NA = not applicable.
Table 3. Proportion of Beneficiaries With Comorbidities, Stratified by Scores on the Combined ADL-IADL Scale. Scale score on the combined ADL-IADL
0 (%)
1-4 (%)
5-8 (%)
9-12 (%)
Heart Hypertension CVD Cancer Arthritis Neurological Psychiatric Bone Respiratory
43.7 80.3 9.6 17.6 51.8 3.8 8.3 15.0 14.2
61.1 86.4 17.0 26.0 72.7 9.5 15.9 21.7 24.0
63.4 88.6 26.4 24.0 78.1 22.0 27.2 31.2 36.2
73.1 87.5 46.2 27.9 84.6 52.9 57.7 55.8 73.1
Data Source. Medicare Current Beneficiary Survey (2009). Note. Comorbidity categories are self-reported and include cardiac disease, hypertension, CVD, respiratory disease, cancer, arthritis, neurological conditions, psychiatric disorder, and osteoporosis or bone fracture. ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living; CVD = cerebrovascular disease.
comparing a combined ADL-IADL score of 9–12 to 0, we observed a fivefold or more increase in cerebrovascular disease, neurological, psychiatric, and respiratory conditions.
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Confirmatory Factor Analysis Estimated coefficients and model fit indices for the one-factor, two-factor, and three-factor models are presented in Tables 4 and 5, respectively. The unidimensional model is illustrated in Figure 1. Standardized coefficients for nine items in the one-factor model are 0.70 or greater; three items have coefficients approaching 0.70: eating (0.696), getting in or out of bed or chairs (0.668), and using the telephone (0.637) (see Table 4). Similarly, most of the standardized coefficients in the two-factor and three-factor models were at least 0.70 (see Table 4). All three models had significant chi-square goodness-of-fit values (see Table 5). The three-factor model had a RMSEA value that was less than .06, and the RMSEA for the one-factor and two-factor models were above .06 but below .08. Incremental fit indices for all three models were excellent, with CFI and TLI values above 0.95. Because all three models exhibited reasonable fit based on the CFI, TLI, and RMSEA indices, no modifications were made to avoid increasing the complexity of the models. In the two-factor model, we observed a very strong correlation of .898 between the ADL and IADL factors. Likewise, the correlations among the latent constructs in the three-factor model were also strong to very strong: ADL with physical IADL (r = .910), physical IADL with cognitive IADL (r = .908), and cognitive IADL with ADL (r = .745). When we excluded beneficiaries reporting pre-diabetes, which represented only 3% of the study population, the findings did not change (see the online appendix).
Discussion Assessing and quantifying functional disability among older adults with diabetes is extremely important in the management of diabetes because many diabetes-related complications are associated with functional limitations (ADA, 2014; Durso, 2006; Sinclair et al., 2012). For example, nerve problems arising from diabetes may limit mobility, and uncontrolled diabetes can interfere with cognition and affects the ability to manage money (Chiu & Wray, 2011). The ADL and IADL scales are traditionally used as separate scales, and to the best of our knowledge, this is the first study examining the factor structure of the ADL and IADL items among a large representative sample of older adults with diabetes living in the community. Our incremental fit indices (CFI and TLI) and RMSEA values were satisfactory for the one-factor model, indicating that the ADL and IADL can be combined to form a single measure of functional disability for older adults
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1.000 (0.000); 0.905 (0.011) 0.989 (0.013); 0.895 (0.012) 0.770 (0.042); 0.696 (0.038) 0.738 (0.017); 0.668 (0.013) 0.957 (0.017); 0.866 (0.013) 0.959 (0.019); 0.867 (0.016) 0.704 (0.035); 0.637 (0.031) 0.901 (0.021); 0.815 (0.017) 1.051 (0.013); 0.951 (0.008) 0.987 (0.017); 0.893 (0.012) 1.039 (0.014); 0.939 (0.009) 1.034 (0.014); 0.935 (0.008)
One-factor 1.000 (0.000); 0.930 (0.010) 0.987 (0.015); 0.918 (0.012) 0.762 (0.041); 0.709 (0.038) 0.743 (0.017); 0.691 (0.014) 0.966 (0.017); 0.898 (0.013) 0.952 (0.019); 0.885 (0.016)
ADL
1.000 (0.000); 0.654 (0.032) 1.274 (0.061); 0.833 (0.017) 1.479 (0.072); 0.966 (0.007) 1.398 (0.071); 0.9914 (0.012) 1.457 (0.071); 0.952 (0.009) 1.454 (0.071); 0.950 (0.008)
IADL 1.000 (0.000); 0.929 (0.010) 0.988 (0.015); 0.918 (0.012) 0.763 (0.041); 0.709 (0.038) 0.744 (0.017); 0.692 (0.014) 0.966 (0.017); 0.897 (0.012) 0.953 (0.019); 0.885 (0.016)
ADL
1.000 (0.000); 0.726 (0.033) 1.297 (0.064); 0.942 (0.023)
IADL cognitive
Three-factor
1.000 (0.000); 0.963 (0.007) 0.944 (0.014); 0.910 (0.012) 0.985 (0.011); 0.948 (0.009) 0.982 (0.010); 0.946 (0.008)
IADL physical
Data Source. Medicare Current Beneficiary Survey (2009). Note. A total of 2,158 observations were included; 10 observations were excluded as they were missing on all items. The weighted least squares with mean and varianceadjusted estimator was used to derive unstandardized and standardized coefficients. The standardized coefficients reported are the STDYX coefficients from MPlus. All p values were highly significant at
JAGXXX10.1177/0733464814563607Journal of Applied GerontologyNg et al.
Article
Assessment of an Expanded Functional Disability Scale for Older Adults With Diabetes
Journal of Applied Gerontology 1–20 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464814563607 jag.sagepub.com
Xinyi Ng1, Charlene C. Quinn1, Mehmet Burcu1, and Donna Harrington1
Abstract Although prior literature has shown the plausibility of combining the Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) items to form an expanded scale for measuring the degree of functional decline, this has not been shown in older adults with diabetes who are disproportionately affected by functional disability. Using the 2009 Medicare Current Beneficiary Survey data, we evaluated the factor structure of the pooled ADL and IADL items. Based on our study comprising 2,158 community-dwelling older adults (≥65 years) with diabetes, the unidimensional model exhibited good fit. Despite well-fitting indices, high correlations were observed between the latent constructs (>.70) of the multi-factor models, suggesting a lack of discriminant validity. These findings provide empirical support for a combined scale that can comprehensively and efficiently characterize the extent of functional disability in older adults with diabetes for research, risk adjustment, and evaluation in patientcentered medical homes.
Manuscript received: May 20, 2014; final revision received: August 29, 2014; accepted: November 8, 2014. 1University
of Maryland, Baltimore, USA
Corresponding Author: Xinyi Ng, Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore, 220 Arch Street, 12th Level, Baltimore, MD 21201, USA. Email: [email protected]
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Keywords diabetes, functional disability, confirmatory factor analysis, Medicare
Diabetes mellitus is a highly prevalent chronic disease in the United States, affecting about 27% of the population aged 65 years and over (Centers for Disease Control and Prevention, 2011), and about 34% of the older adults in nursing homes (Coxe, Lennertz, & McCullough, 2013). Functional disability disproportionately affects older adults with diabetes, where approximately 30% to 55% of them experience some form of limitation in their usual activities or mobility (Bruce, Davis, & Davis, 2005; Chiu & Wray, 2011; Kalyani, Saudek, Brancati, & Selvin, 2010). The care and management of the elderly with diabetes is further complicated by the heterogeneous spectrum of functional disability, where some individuals might be physically and cognitively robust whereas others are fraught with multiple comorbidities (American Diabetes Association [ADA], 2014; Blaum, Ofstedal, Langa, & Wray, 2003; Durso, 2006; Sinclair et al., 2012). Recent epidemiologic studies have consistently found that diabetes is associated with disability, which affects quality of life and is a major risk factor for loss of independence, falls, injuries, infections, and institutionalization (ADA, 2008, 2013; Chau et al., 2011; Chiu & Wray, 2011; Salas, Bubolz, & Caro, 2000; Volpato, Maraldi, & Fellin, 2010). The primary concern of older adults with diabetes was also demonstrated to be disease-related complications, such as amputation and blindness, resulting in disabilities (Quandt et al., 2013). These findings highlight the need to effectively identify and assess the extent of functional disability among older adults with diabetes for both clinical research and screening purposes. In current practice, the Activities of Daily Living (ADL; Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963) and Instrumental Activities of Daily Living (IADL; Rosow & Breslau, 1966) are well-established scales conventionally used to ascertain functional disability. The ADL scale measures activities that are essential for self-care, whereas the IADL scale measures activities that are necessary for independent adaptation to living within the environment (Spector, Katz, Murphy, & Fulton, 1987). Psychometric properties of the ADL and IADL scales, such as test–retest reliability and internal consistency, have been previously evaluated and well established (Ivanova et al., 2013). Although both scales are well accepted and widely used for assessing different domains of functional disability, previously published research has suggested the plausibility of combining the ADL and IADL items to form an expanded scale, which is potentially a more sensitive assessment than the stand-alone scales to measure a wider range of functional decline (Fortinsky, Garcia, Joseph Sheehan, Madigan, & TullaiMcGuinness, 2003; Spector & Fleishman, 1998; Spector et al., 1987). This
Downloaded from jag.sagepub.com at Karolinska Institutets Universitetsbibliotek on November 15, 2015
Ng et al.
3
combined ADL-IADL scale can offer several practical advantages to clinicians and researchers. For one, it could provide clinicians with a more comprehensive assessment of the extent of functional disability that would aid in the tailoring of treatment regimens. In addition, in the context of research practice, a single scale confers the analytic advantage of avoiding problems with collinearity that typically occur when multiple, highly correlated scales are concurrently used. To date, studies addressing the dimensionality of functional disability have yielded inconsistent findings in the general older adult population. Although a body of evidence suggests that the ADL and IADL can be combined into a unidimensional scale (Fortinsky et al., 2003; Kempen & Suurmeijer, 1990; Spector & Fleishman, 1998; Spector et al., 1987) (i.e., the ADL and IADL measure the same underlying factor or construct), others argue that functional disability is multidimensional and multiple scales are needed to provide a functional disability profile for patients (Clark, Stump, & Wolinsky, 1997; Thomas, Rockwood, & McDowell, 1998). Furthermore, the IADL scale has also been hypothesized to comprise two dimensions—the physical IADLs and the cognitive IADLs (Ng, Niti, Chiam, & Kua, 2006; Thomas et al., 1998), such that the combined ADL and IADL items could potentially represent three separate constructs accounting for the full spectrum of functional disability. Clinical guidelines highlight the importance of recognizing and addressing functional disability among older adults with diabetes, for whom treatment goals should be tailored according to the degree and complexity of functional disability (ADA, 2014; A. F. Brown, Mangione, Saliba, & Sarkisian, 2003; Durso, 2006; Sue Kirkman et al., 2012). Consequently, there is a need to appropriately characterize functional disability within this population. Coupled with the debate over the dimensionality of functional disability measures, this study seeks to evaluate the dimensionality and to examine the factor structure of the pooled items from the individual ADL and IADL scales. This is the first study that examines whether the ADL and IADL items can be combined into a singular and unidimensional scale for an enhanced and practical assessment of functional disability in older adults with diabetes. It is particularly important to assess the unidimensionality of functional disability within the diabetes subpopulation of older adults as they have unique or varying needs from the general older adult population.
Method Data Source This cross-sectional study analyzed data from the 2009 Medicare Current Beneficiary Survey (MCBS), which is a nationally representative survey of
Downloaded from jag.sagepub.com at Karolinska Institutets Universitetsbibliotek on November 15, 2015
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Journal of Applied Gerontology
Medicare beneficiaries conducted by the Centers for Medicare and Medicaid Services (CMS; 2013). The survey dataset contains comprehensive information regarding individual demographic characteristics, health and functional status, and person-level health service utilization and expenditure. Survey responses were also supplemented and linked to administrative data on Medicare enrollment and all Medicare Part A (hospital insurance), Part B (medical insurance), and Part D (prescription drug coverage) claims records. This study was reviewed and approved by the University of Maryland Baltimore Institutional Review Board.
Sample The study sample was selected according to the following inclusion criteria: (a) beneficiaries aged 65 years and older, (b) resided in the community, and (c) diagnosed with diabetes. Presence of diabetes was ascertained using both self-report and claims information. We identified presence of diabetes from claims using an algorithm adapted from the CMS for the Chronic Condition Data Warehouse (Hebert et al., 1999; Chronic Conditions Data Warehouse, 2014), where diabetes was considered present if a beneficiary has either (a) one or more inpatient hospital or home health care claims or (b) at least two outpatient hospital or physician claims with the following International Classification of Disease, Ninth revision (ICD-9) codes: 250.xx, 357.2, 362.01, 362.02, 366.41. Diabetes was also considered present if a beneficiary self-reported having diabetes with a positive response to the survey question, “Has a doctor ever told you that you had any type of diabetes, including: sugar diabetes, high blood sugar, (borderline diabetes, pre-diabetes, or pregnancy-related diabetes/borderline diabetes, or pre-diabetes)?”
Measures To ascertain the presence of limitation in the ADL, beneficiaries in the MCBS were presented with a series of questions asking “Because of a health or physical problem, do you have any difficulty in (1) bathing or showering, (2) dressing, (3) eating, (4) getting in or out of bed or chairs, (5) walking, and (6) using the toilet?” The IADL scale measures higher order tasks that require greater neuropsychological organization than the basic ADL, and are necessary for independent living (Ng et al., 2006; Spector et al., 1987). To ascertain limitations in IADL, beneficiaries were asked whether they have difficulty in (a) using the telephone, (b) doing light housework, (c) doing heavy housework, (d) preparing meals, (e) shopping, and (f) managing money.
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Ng et al.
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The available responses for all items in the ADL and IADL scales consisted of “yes,” “no,” “doesn’t do,” “not ascertained,” “don’t know,” or “refused.” Responses of “not ascertained,” “don’t know,” or “refused” were deemed as missing. When a beneficiary indicated “doesn’t do,” a follow-up question was asked to determine if they did not do the particular activity because of health problems. If they indicated yes to the follow-up question, they were recoded as having limitation for that particular activity. In contrast, those who indicated “doesn’t do” in the original question and “no” to the follow-up question would be recoded as having a missing response because we cannot determine with certainty if they did not perform that activity due to health constraints or their lifestyles do not include engagement in that particular activity (e.g., some older persons might not need to prepare their own meals on a regular basis). In this way, the final responses to every item on both scales were dichotomous (1 = yes, 0 = no), with allowances for missing responses.
Data Analysis Descriptive analyses of the baseline characteristics were first conducted. Individuals who had missing responses on all 12 questions were excluded from the analysis. Internal consistency reliability of the ADL, IADL, and combined ADL-IADL scales was assessed using Cronbach’s alpha. A Cronbach’s alpha value between .70 and .95 is generally acceptable (Tavakol & Dennick, 2011). Concurrent validity of the combined ADL-IADL scale was further assessed by examining the proportion of beneficiaries with selfreported comorbidities at different score ranges along the ADL-IADL scale. It was hypothesized that a higher score on the ADL-IADL scale would reflect a higher burden of comorbidities. These analyses were conducted using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA) or STATA/MP version 12 (StataCorp, 2011). Confirmatory factor analyses (CFAs) were conducted using Mplus version 7.1 (Muthén & Muthén, 1998-2012) with weighted least squares with mean- and variance-adjusted estimation (WLSMV) for dichotomous data (Schmitt, 2011) to examine the factor structure of the pooled items from the ADL and IADL. CFA is appropriate in this context as the dimensionalities of these items from the two scales have been explored in previous studies (Clark et al., 1997; Fortinsky et al., 2003; Kempen & Suurmeijer, 1990; Ng et al., 2006; Spector & Fleishman, 1998; Spector et al., 1987; Thomas et al., 1998). Hence, instead of using an exploratory factor analysis to derive the factor structure for a new set of variables, our research interest lies in investigating whether existing established dimensionalities apply to a different population, older adults with diabetes.
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Journal of Applied Gerontology
Based on prior research as stated above, three competing models were tested: (a) a one-factor model combining all ADL and IADL items into one factor, (b) a two-factor model with the ADL items on one factor and the IADL items on a second factor, and (c) a three-factor model with the ADL items, the physical IADLs, and the cognitive IADLs each forming separate factors. The three competing models were evaluated and compared with multiple fit indices, including the chi-square goodness-of-fit index, comparative fit index (CFI), Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). A model has good or acceptable fit when it has a nonsignificant chi-square test, CFI and TLI above 0.95 (Hu & Bentler, 1999; Kline, 2011), and RMSEA of .06 or less (Hu & Bentler, 1999). In addition, both unstandardized and standardized coefficients were examined; ideally, the standardized coefficients should be close to or greater than 0.70 (Kline, 2011).
Results After excluding 10 observations with missing responses on all 12 items, the final study sample comprised 2,158 elderly Medicare beneficiaries who resided in the community and had diabetes. Table 1 includes baseline sociodemographic characteristics, self-reported comorbidities, and self-rated health status of the study sample. More than half of the beneficiaries were between 65 and 80 years old, married, and had a high school education or less. The beneficiaries were also predominantly white (81.4%) and reported household income of US$50,000 or less (86.7%). The proportions of beneficiaries who indicated difficulties in any of the 12 functional activities are presented in Table 2. For the ADL items, the largest proportion of beneficiaries (34.5%) with diabetes faced difficulties in walking. Between 10% and 20% of the study population indicated difficulties with each of bathing/showering, dressing, and getting in and out of bed/chairs. Less than 10% reported difficulties with using the toilet or eating. With regard to IADL items, a substantial proportion of the diabetic elderly (40.4%) had difficulties with doing heavy housework. In contrast, approximately 1 in 10 beneficiaries reported problems using the telephone or managing money.
Internal Consistency and Concurrent Validity of the Combined ADL-IADL Scales The Cronbach’s alpha for the ADL (.798), IADL (.834), and combined ADLIADL (.890) scales displayed good internal consistency reliability. As the scores on the combined ADL-IADL scale increased, the proportion of beneficiaries with comorbidities increased correspondingly (Table 3). In particular,
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Ng et al. Table 1. Baseline Characteristics of Beneficiaries With Diabetes (N = 2,158). All beneficiaries (N = 2,158) Characteristics Age 65-69 70-74 75-79 80-84 85+ Gender Female Male Race (missing = 10) White African American Others Marital status (missing = 2) Single Married Education (missing = 8) Less than high school High school or vocational diploma Some college College degree or higher Income (missing = 1) US$50,000 Number of comorbiditiesa 0-1 2-3 4+ Self-rated health status (missing = 5) Excellent Very good Good Fair Poor
n
%
459 491 462 412 334
21.3 22.8 21.4 19.1 15.5
1,096 1,062
50.8 49.2
1,748 235 165
81.4 10.9 7.7
1,014 1,142
47.0 53.0
589 813 332 416
27.4 37.8 15.4 19.4
1,079 792 286
50.0 36.7 13.3
325 1,051 782
15.1 48.7 36.2
181 548 770 471 183
8.4 25.5 35.8 21.9 8.5
Data Source. Medicare Current Beneficiary Survey (2009). Note. Ten observations were dropped as they had missing values on all items of the Activities of Living scale and the Instrumental Activities of Living scale. aComorbidity categories are self-reported and include cardiac disease, hypertension, cerebrovascular disease (CVD), respiratory disease, cancer, arthritis, neurological conditions, psychiatric disorder, and osteoporosis or bone fracture.
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Journal of Applied Gerontology
Table 2. Proportion of Beneficiaries With Difficulties in ADL or IADL (N = 2,158). Beneficiaries with difficulty (N = 2,158) ADL/IADL items
n
%
ADL scale Do you have any difficulty in bathing or showering? Yes 332 15.4 No 1,825 84.5 NA/missing 1 0.1 Do you have any difficulty in dressing? Yes 232 10.8 No 1,926 89.2 NA/missing — — Do you have any difficulty in eating? Yes 49 2.3 No 2,109 97.7 NA/missing — — Do you have any difficulty getting in or out of bed or chairs? Yes 369 17.1 No 1,783 82.6 NA/missing 6 0.3 Do you have any difficulty in walking? Yes 744 34.5 No 1,414 65.5 NA/missing — — Do you have any difficulty in using the toilet? Yes 164 7.6 No 1,994 92.4 NA/missing — — IADL scale Do you have any difficulty in doing light housework (such as washing dishes, straightening up, or light cleaning)? Yes 349 16.2 No 1,725 79.9 NA/missing 84 3.9 Do you have any difficulty in doing heavy housework (such as scrubbing floors or washing windows)? Yes 871 40.4 No 1,092 50.6 NA/missing 195 9.0 Do you have any difficulty in preparing (your/his/her) own meals? Yes 280 13.0 No 1,771 82.0 NA/missing 107 5.0 Do you have any difficulty in shopping for personal items (such as toilet items or medicines)? Yes 408 18.9 No 1,720 79.7 NA/missing 30 1.4 (continued)
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Ng et al. Table 2. (continued) Beneficiaries with difficulty (N = 2,158) ADL/IADL items
%
n
Do you have any difficulty in using the telephone? Yes 172 No 1,979 NA/missing 7 Do you have any difficulty in managing money (such as keeping track of expenses or paying bills)? Yes 203 No 1,886 NA/missing 69
8.0 91.7 0.3 9.4 87.4 3.2
Data Source. Medicare Current Beneficiary Survey (2009). Note. Available responses were “yes,” “no,” “doesn’t do,” “not ascertained,” “don’t know,” or “refused.” If the latter three are selected, responses were treated as missing. If “doesn’t do” is indicated, a followup question was asked to determine whether they did not do the particular activity because of health problems. Recoding: If they indicated yes to the follow-up question, they would be recoded as “yes.” If they indicated no to the follow-up question, they would be recoded as missing. Ten observations were dropped as they were missing on all items of the ADL and IADL. ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living; NA = not applicable.
Table 3. Proportion of Beneficiaries With Comorbidities, Stratified by Scores on the Combined ADL-IADL Scale. Scale score on the combined ADL-IADL
0 (%)
1-4 (%)
5-8 (%)
9-12 (%)
Heart Hypertension CVD Cancer Arthritis Neurological Psychiatric Bone Respiratory
43.7 80.3 9.6 17.6 51.8 3.8 8.3 15.0 14.2
61.1 86.4 17.0 26.0 72.7 9.5 15.9 21.7 24.0
63.4 88.6 26.4 24.0 78.1 22.0 27.2 31.2 36.2
73.1 87.5 46.2 27.9 84.6 52.9 57.7 55.8 73.1
Data Source. Medicare Current Beneficiary Survey (2009). Note. Comorbidity categories are self-reported and include cardiac disease, hypertension, CVD, respiratory disease, cancer, arthritis, neurological conditions, psychiatric disorder, and osteoporosis or bone fracture. ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living; CVD = cerebrovascular disease.
comparing a combined ADL-IADL score of 9–12 to 0, we observed a fivefold or more increase in cerebrovascular disease, neurological, psychiatric, and respiratory conditions.
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Journal of Applied Gerontology
Confirmatory Factor Analysis Estimated coefficients and model fit indices for the one-factor, two-factor, and three-factor models are presented in Tables 4 and 5, respectively. The unidimensional model is illustrated in Figure 1. Standardized coefficients for nine items in the one-factor model are 0.70 or greater; three items have coefficients approaching 0.70: eating (0.696), getting in or out of bed or chairs (0.668), and using the telephone (0.637) (see Table 4). Similarly, most of the standardized coefficients in the two-factor and three-factor models were at least 0.70 (see Table 4). All three models had significant chi-square goodness-of-fit values (see Table 5). The three-factor model had a RMSEA value that was less than .06, and the RMSEA for the one-factor and two-factor models were above .06 but below .08. Incremental fit indices for all three models were excellent, with CFI and TLI values above 0.95. Because all three models exhibited reasonable fit based on the CFI, TLI, and RMSEA indices, no modifications were made to avoid increasing the complexity of the models. In the two-factor model, we observed a very strong correlation of .898 between the ADL and IADL factors. Likewise, the correlations among the latent constructs in the three-factor model were also strong to very strong: ADL with physical IADL (r = .910), physical IADL with cognitive IADL (r = .908), and cognitive IADL with ADL (r = .745). When we excluded beneficiaries reporting pre-diabetes, which represented only 3% of the study population, the findings did not change (see the online appendix).
Discussion Assessing and quantifying functional disability among older adults with diabetes is extremely important in the management of diabetes because many diabetes-related complications are associated with functional limitations (ADA, 2014; Durso, 2006; Sinclair et al., 2012). For example, nerve problems arising from diabetes may limit mobility, and uncontrolled diabetes can interfere with cognition and affects the ability to manage money (Chiu & Wray, 2011). The ADL and IADL scales are traditionally used as separate scales, and to the best of our knowledge, this is the first study examining the factor structure of the ADL and IADL items among a large representative sample of older adults with diabetes living in the community. Our incremental fit indices (CFI and TLI) and RMSEA values were satisfactory for the one-factor model, indicating that the ADL and IADL can be combined to form a single measure of functional disability for older adults
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1.000 (0.000); 0.905 (0.011) 0.989 (0.013); 0.895 (0.012) 0.770 (0.042); 0.696 (0.038) 0.738 (0.017); 0.668 (0.013) 0.957 (0.017); 0.866 (0.013) 0.959 (0.019); 0.867 (0.016) 0.704 (0.035); 0.637 (0.031) 0.901 (0.021); 0.815 (0.017) 1.051 (0.013); 0.951 (0.008) 0.987 (0.017); 0.893 (0.012) 1.039 (0.014); 0.939 (0.009) 1.034 (0.014); 0.935 (0.008)
One-factor 1.000 (0.000); 0.930 (0.010) 0.987 (0.015); 0.918 (0.012) 0.762 (0.041); 0.709 (0.038) 0.743 (0.017); 0.691 (0.014) 0.966 (0.017); 0.898 (0.013) 0.952 (0.019); 0.885 (0.016)
ADL
1.000 (0.000); 0.654 (0.032) 1.274 (0.061); 0.833 (0.017) 1.479 (0.072); 0.966 (0.007) 1.398 (0.071); 0.9914 (0.012) 1.457 (0.071); 0.952 (0.009) 1.454 (0.071); 0.950 (0.008)
IADL 1.000 (0.000); 0.929 (0.010) 0.988 (0.015); 0.918 (0.012) 0.763 (0.041); 0.709 (0.038) 0.744 (0.017); 0.692 (0.014) 0.966 (0.017); 0.897 (0.012) 0.953 (0.019); 0.885 (0.016)
ADL
1.000 (0.000); 0.726 (0.033) 1.297 (0.064); 0.942 (0.023)
IADL cognitive
Three-factor
1.000 (0.000); 0.963 (0.007) 0.944 (0.014); 0.910 (0.012) 0.985 (0.011); 0.948 (0.009) 0.982 (0.010); 0.946 (0.008)
IADL physical
Data Source. Medicare Current Beneficiary Survey (2009). Note. A total of 2,158 observations were included; 10 observations were excluded as they were missing on all items. The weighted least squares with mean and varianceadjusted estimator was used to derive unstandardized and standardized coefficients. The standardized coefficients reported are the STDYX coefficients from MPlus. All p values were highly significant at
