C International Psychogeriatric Association 2015 International Psychogeriatrics (2015), 27:12, 1971–1977 doi:10.1017/S1041610215000708
Validation of an a priori, index model of successful aging in a population-based cohort study: the successful aging index ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Theodore D. Cosco,1 Blossom C. M. Stephan2 and Carol Brayne1 1
Cambridge Institute of Public Health, Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB20SR, UK 2 Institute of Health and Society, Newcastle University, The Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE24AX, UK
ABSTRACT
Background: Many definitions of successful aging (SA) exist in the absence of an established consensus definition. There are few examples of a priori application of SA models in real world contexts using external validation procedures. The current study aims to establish the predictive validity of an a priori, continuous model of SA with respect to service utilization. Methods: Individuals (n = 740; 64.2% female) aged 65 years and over (mean 75.9; SD 6.2), randomly selected from general practitioner registries in five sites across the United Kingdom included in the second and third combined screen and assessment waves of the Medical Research Council Cognitive Function and Aging Study (MRC CFAS; a longitudinal population-based cohort study) comprised the baseline and two-year follow-up in the current study. A Successful Aging Index (SAI) was created using items identified by systematic reviews of operational definitions and lay perspectives of SA, capturing physiological and psychosocial components. Demographic data and SAI components were collected at baseline. Outcome measures, i.e. health service use, informal care use, and functional service, were captured at two years follow-up. Results: Logistic regression revealed significant relationships between the SAI and six of eight service use outcomes in models adjusted for age, sex, education, and socio-economic status. Analysis of the area under the receiver operating characteristic (ROC) curve demonstrated sufficient predictive capabilities for all models, (range 0.65–0.86). Conclusions: The SAI demonstrated a strong association, and predictive accuracy, with respect to service use, providing preliminary support for the practical utility and usefulness of this measure. Key words: successful aging, healthy aging, model development, health service use, predictive validity
Introduction Many different models of successful aging (SA) exist in the absence of an overarching consensus definition (Cosco et al., 2014a). Since the initial coining of the phrase more than half a century ago (Havighurst, 1961), the operationalization and practical application of the phrase has been contentious (Depp and Jeste, 2006). Researchers from a variety of fields have developed an immense breadth of different definitions of SA, ranging from strictly biomedical conceptualizations capturing only physical and cognitive function, to single-item self-report measures of how an individual feels they have successfully aged (Cosco et al., 2014a). As a Correspondence should be addressed to: Theodore D. Cosco, Forvie Site, Robinson Way, Cambridge, CB20SR, UK. Phone: 07414983921. Email: [email protected]. Received 23 Jan 2015; revision requested 19 Mar 2015; revised version received 28 Mar 2015; accepted 31 Mar 2015. First published online 20 May 2015.
testament to the proliferation and heterogeneity of SA definitions, a 2006 review revealed 29 definitions whilst a review in 2014 revealed 105 definitions of SA (Depp and Jeste, 2006; Cosco et al., 2014a). Further complicating this issue is the discrepancy between researchers’ operationalizations of SA and laypersons’ perspectives on SA; recent systematic reviews have revealed that researchers generally posit unidimensional biomedically focused models of SA whilst laypersons have a multidimensional, psychosocially focused conceptualization of SA (Cosco et al., 2014b). The inclusion of lay perspectives into research has important implications for increasing the relevance of research outcomes and in complementing scientific theory (Jopp et al., 2015). Further, SA theorists have advocated for the inclusion of both subjective and objective components in models of SA (Bowling and Iliffe, 2006; Pruchno et al., 2010). If a construct researchers are examining does not have relevance
1972
T. D. Cosco et al.
to the demographic they are targeting, the potential impact of this research will be impeded. Although interest in positive states of aging and the heterogeneity of aging trajectories is on the rise, these theoretical developments have progressed at a much faster pace than practical applications of SA. Research into the theoretical underpinnings of SA has provided insight into the complexity of the construct (Cosco et al., 2014b), but examples where SA models have been applied in the framework of tangible health-related outcomes are limited (Young et al., 2009). The use of services in later life – both professional and informal – is on the rise (Kehusmaa et al., 2012), increasing the temporal and monetary value of their provision. Therefore, being able to identify individuals at greater prospective risk of the need for services is an important issue for policy makers, stakeholders, researchers, and clinicians to address. One such way may be to examine the degree to which individuals are aging “successfully.” SA models to date have been largely theoretical, e.g. Rowe and Kahn (1987), with few studies examining practical applications of the concept, e.g. Young, et al. (2009). The current study addresses the gap between theoretical and practical models of SA, and expands upon existing studies, by using a theoretically-driven, a priori model of SA to assess the predictive ability, with respect to service utilization, of a model of SA.
Methods Study characteristics The MRC CFAS is a population-based study of community dwelling individuals (n = 13,004) aged 65 years and over, conducted in five centers with identical methodology in England and Wales (Newcastle, Nottingham, Oxford, Cambridgeshire, and Gwynedd) (Brayne, 2006). Baseline interviewing began in 1991. A 20% (n = 2,640) stratified sample, selected based on cognitive ability, age, and center completed a more detailed assessment interview, with re-interviewing approximately every two years. Trained interviewers conducted face-toface interviews in participants’ place of residence. Questions concerning demographics, cognition (Mini-Mental State Exam: MMSE (Folstein et al., 1983)), activities of daily living (ADLs), instrumental activities of daily living (IADLs) (Townsend and Ryan, 1991), and psychosocial well-being were included in the interview. Data in the current study was taken from the MRC CFAS first combined screen and assessment (referred to here as the baseline interview) and the second combined screen and assessment conducted two
years later (referred to here as the two-year followup; data version v9.0). Further details of the sampling methods and interview are available at www.cfas.ac.uk (Brayne, 2006). All study centers obtained ethical approval from local research committees. Successful aging index Based on the results of recent systematic reviews of operational definitions (Cosco et al., 2014c) and lay perspectives of SA (Cosco et al., 2013) an a priori model of SA was constructed. Physical functioning (modified Katz ADLs (1970) and modified Lawton IADLs (1969)), and cognitive functioning (MMSE (Folstein et al., 1983)) were the two most commonly identified components of SA in the operational definitions of SA review and were, consequently, included in the model. In the lay perspectives review, the three most commonly identified components of SA were included in the model: personal resources (optimism: measured on a three-point Likert scale ranging from optimistic to pessimistic view of the future), engagement (interest: measured on a three-point Likert scale ranging from no loss of interest to persistent lower interest; loneliness: measured on a three-point Likert scale ranging from no feelings of loneliness to frequent/persistent feelings of loneliness) and self-awareness (self-rated health measured on a four-point Likert scale ranging from excellent to poor rating of one’s own health) (model previously described in Cosco et al. (2014c)). Through the inclusion of three components of the operational definitions review and the lay perspectives review, both perspectives were equally represented in the model. Due to shortcomings of the components expressed in a binary framework (as highlighted in Cosco et al. (2014c) a continuum-based index was developed to allow for higher resolution and greater granularity, i.e. the SAI. The SAI was created using a similar protocol used to create the frailty index (Searle et al., 2008), insofar that ordinal items were assigned values and the scores from each of the items used to create a continuous value (Table 1). Depending on the number of possible answers, fraction values were assigned accordingly, e.g. for a four-item Likert question, the answer corresponding to the highest level of functioning would receive 100, the next highest 67, 33, and finally 0 for the lowest possible level of functioning. For continuous variables, e.g. MMSE scores, previously used cut-off scores, i.e. ࣙ26, 25–22, 21–18, 17–0 (Yip et al., 2002; Xie et al., 2008; Cluett et al., 2010), were used to assigned fractional values, as per the protocol used in Searle et al. (2008). Diverging from the frailty index
Successful aging index
1973
Table 1. Successful aging index (SAI) items, values and calculation SURVEY ITEM
QUESTION
SCORE RESPONSES
VALUE
CALCULATION
.........................................................................................................................................................................................................................................................................................................................
Engagement
Change in level of interest
Engagement
Do you feel lonely?
Personal resources
How do you feel about the future?
Personal resources
View of own health
Cognitive functioning
Mini-Mental State Examination
Activities of daily living
Able to cut own toe nails Able to put on shoes and socks Able to go up and down stairs Able to wash all over or bathe Difficulty controlling bladder
Instrumental activities of daily living
Able to do the heavy housework Able to shop and carry heavy bags Able to prepare and cook a hot meal Able to reach an overhead shelf Able to tie knot in string Difficulty in household tasks Needs help to check money
No change Less interest - Infrequent Less interest - Persistent No Infrequently Frequently/persistently Optimistic Empty expectations Pessimistic Excellent Good Fair Poor 26–30 22–25 18–21 0–17 Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help No Occasionally wets Frequently wets Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help No Yes Impossible No Yes
100 50 0 100 50 0 100 50 0 100 67 33 0 100 67 33 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 0
A
B
C
D
E
a
(a+b+c+d+e)/5 = F
b
c
d
e
f
(f+g+h+i+j+k+l)/7 = G
g
h
i
j
k
l (A+B+C+D+E+F+G)/7 = SAI
1974
T. D. Cosco et al.
procedures, each ADL (dressing oneself, putting on shoes, going up stairs, continence, cutting toenails) and IADL (doing heavy housework, shopping, preparing meals, reaching overhead shelf, tying a knot, housekeeping, getting on a bus, managing finances) item (measured on a three-point Likert scale ranging from able to perform task without difficulty to unable to perform task alone) was made into an index of its own, averaged, and entered into the full model as a single item. The physical functioning components were made into indexes of their own to allow for the equal representation of biomedical and psychosocial components. Inclusion of each constituent ADL or IADL into the integrated index would result in there being many more biomedical components than lay components. Items were averaged creating a value between 0 and 100, with higher values indicating higher functioning. Outcomes The predictive validity of the SAI was investigated by examining the relationship between the SAI score and study participants’ self-reported service use at two years follow-up. Three categories of self-reported day-to-day service provision were investigated: (1) health services, i.e. home help, care worker, community worker, community nurse; (2) functional services, i.e. meals on wheels, warden, paid help; and, (3) informal care, i.e. help provided by friends or relatives. Statistical procedures All analyses were conducted in STATA 12. Descriptive statistics were calculated to examine differences in the SAI by study demographics. Missingness at random was assessed via a χ2 test, examining the association between missingness and service use. Differences between service and non-service users were tested using χ2 tests for sex and socio-economic status and t-tests for age. Logistic regression was performed examining the association between the SAI and service use at two-years follow-up, in unadjusted models and models adjusted for sex, socio-economic status, education, and age. ROC curves were plotted for each logistic model and the area under the curve (AUC) was calculated to determine the predictive accuracy of each model. Logistic regression analyses were conducted unweighted and weighted for study design (i.e. backweighted to adjust for over sampling of individuals aged 75 years or older and sampling to the diagnostic interview). Score calibration was assessed for goodness of fit via Hosmer–Lemeshow test using deciles of SAI scores and service use. It is not possible to weight calculation of the AUC for
study and therefore only unweighted estimates are presented.
Results Sample characteristics In the full MRC CFAS sample, 1,651 individuals were captured at baseline, of whom 511 were excluded in the current study due to incomplete components of the SAI or the use of informant data rather than respondent data. At the twoyear follow-up, 740 individuals with SAI scores at baseline remained in the sample, 684 of whom had completed surveys on service use. The sample consisted of primarily women (64.2%), from manual occupations (68.1%) with an average age of 75.9 years (SD 6.2) and 9.9 (SD 2.1) years spent in full-time education. Informal help (23.0%), paid help (6.6%), and home help (5.8%) were the most commonly used services. As shown in Table 1 there were significant sex differences between service users and non-users in home help (X2 = 7.06, p = 0.008) and paid help (X2 = 9.51, p = 0.002) and there were significant age differences in use of home help (t(684) = −7.13, p < 0.001), care worker (t(684) = −3.95, p < 0.001), Meals-onWheels (t(684) = −2.89, p = 0.004), warden (t(684) = −2.74, p = 0.006), paid help warden (t(684) = −4.71, p < 0.001), other professional help (t(684) = −3.45, p < 0.001), and informal help (t(684) = −5.07, p < 0.001). Differences in paid help use were observed in manual employment (X2 = 23.64, p < 0.001) and education (t(681) = −5.15, p < 0.001). Individuals in the highest cognitive impairment category, i.e. (MMSE < 26), differed in their use of home help (X2 = 4.11, p = 0.04), a care worker (X2 = 60.73, p = 0.009), other professional services (X2 = 5.54, p = 0.02) and informal services (X2 = 4.37, p = 0.04). Missing data Save for care workers (X2 = 14.25, p < 0.001), and community nurse (X2 = 6.37 p = 0.01) and other paid help (X2 = 5.17, p = 0.02) there were no significant associations between service use and missingness, suggesting that missing values were missing at random. Predictive validity Values for the weighted data and unweighted data were nearly identical, saved for paid help which did not reach significance in the weighted regressions. Therefore only results from the unweighted analysis are presented here in Table 2 (see Appendix A for weighted results, available as supplementary
58.4∗ 9.8 1.8 77.4∗∗∗ 6.6 68.9∗ 51.0 10.1 10.0 2.3 2.3 79.8∗∗∗ 75 5.6 6.0 46.9 46.7 11.1∗∗∗ 9.9 2.7 2.1 78.6∗∗∗ 75.4 6.9 6.0 75.0 46.6 8.25 9.7 1.0 2.2 83.8∗∗ 75.2 7.7 5.8 53.3 10.1 2.4 80.3∗∗ 5.1
47.3 9.9 2.1 75.5 6.0
66.7 9.3 0.6 77.2 6.2
47.4 9.9 2.1 75.5 6.0
Discussion
67.5∗∗ 47.4 9.9 9.9 2.3 2.1 80.0∗∗∗ 75.5 6.2 6.0 < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.0001. ∗p
46.4 9.9 2.2 75 5.7 51.4 9.8 2.1 76.4 6.5 Mean SD Mean SD
1975
material attached to the electronic version of this paper at www.journals.cambridge.org/jid_IPG). Significant negative associations were observed between the provision of home help, care worker, community nurse, paid help, other professional help and informal care, and SAI score in the unadjusted and adjusted models. The Hosmer–Lemeshow test suggest good-fit for all service services reaching significance in the adjusted models, save for informal care (X2 = 26.55, p < 0.001). The AUC for each model is shown in Table 3 and ranged from strong for predicting community nurse use (AUC = 0.86, 95% CI: 0.79 to 0.92) to weak for informal care (AUC = 0.65, 95% CI: 0.61 to 0.69).
58.7∗ 46.5 10.0 9.9 1.7 2.1 80.4∗∗∗ 75.4 6.7 5.9
262 23.0 65.7 73.0∗ 478 41.9 63.4 65.5 23 2.0 65.2 82.6 75 663 6.6 58.1 80.0∗∗ 63.8 43.2∗∗∗ 67.6 611 53.5 61.9 71.2 4 0.4 50.0 50.0 682 59.8 63.9 68.2 11 1.0 72.7 81.8 675 59.2 63.7 67.9 13 1.1 53.9 84.6 673 59.0 64 67.8 27 2.4 70.4 73.1 659 57.8 63.6 67.9 66 5.8 78.8∗∗ 74.6 620 54.3 62.3 67.4 1,141 100.0 63.4 70.0
n % Female (%) Manual Employment (%) Dementia (%) Education (years) Age (years)
USER
..........................................................................................................................................................................................................................................................................................................................................................................................................................................................
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER USER
NONNON-
USER USER TOTAL
HELP HELP HELP NURSE WHEELS WORKER HELP
ON CARE HOME
Table 2. Sample characteristics, by service users and non-users
MEALS
COMMUNITY
WARDEN
OTHER
PROFESSIONAL PAID
INFORMAL
Successful aging index
The current study has taken a model of SA that is based on the literature and demonstrated a strong association between the SAI and a tangible, external outcome, i.e. prospective service use, providing preliminary evidence for the predictive validity of the SAI. The results suggest that the SAI, which incorporates both objective physiological measures and subjective psychosocial measures of SA, is an independent marker of aspects of all three categories of service use: health services, functional services, and informal help in older aged adults. Significant negative associations were demonstrated between SAI score and service use. There are however, limitations that must be taken into account in the interpretation of these results. The construction of the SAI was done using secondary data, matching variables informed by systematic reviews to proxy measures captured in CFAS. Although the most commonly identified components identified in the review were captured in CFAS, perhaps if there were more components within each of these categories, a greater level of granularity could be teased out. Data regarding the use of each service was collected via self-report. As with all longitudinal studies of older adults, attrition was a factor, resulting in missing values. The SAI is an average of all the constituent components; therefore if an item was missing their total score would not be comparable to other who had complete scores. Only respondent data was used to capture individuals’ subjective experiences, rather than an informant’s. Consequently, individuals who were unable to complete all of the SAI components themselves, for example, if they had severe dementia, were excluded, which may have resulted in a bias towards healthier individuals in the sample. Due to the level of missing data (30%) complete-case analysis was conducted rather than multiple imputations. As a result of attrition
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T. D. Cosco et al.
Table 3. SAI score and service use UNADJUSTED OR
95%
CI
P
AUC
ADJUSTED
95%
CI
OR
95%
CI
P
∗
AUC
95%
CI
.........................................................................................................................................................................................................................................................................................................................
Home help Care worker Meals on wheels Community nurse Warden Paid help Other professional help Informal care
0.94 0.93 0.96 0.93 0.96 0.97 0.95
(0.93–0.96) (0.91–0.96) (0.93–0.99) (0.90–0.97) (0.90–1.00) (0.96–0.99) (0.92–0.97)
Validation of an a priori, index model of successful aging in a population-based cohort study: the successful aging index ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Theodore D. Cosco,1 Blossom C. M. Stephan2 and Carol Brayne1 1
Cambridge Institute of Public Health, Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB20SR, UK 2 Institute of Health and Society, Newcastle University, The Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE24AX, UK
ABSTRACT
Background: Many definitions of successful aging (SA) exist in the absence of an established consensus definition. There are few examples of a priori application of SA models in real world contexts using external validation procedures. The current study aims to establish the predictive validity of an a priori, continuous model of SA with respect to service utilization. Methods: Individuals (n = 740; 64.2% female) aged 65 years and over (mean 75.9; SD 6.2), randomly selected from general practitioner registries in five sites across the United Kingdom included in the second and third combined screen and assessment waves of the Medical Research Council Cognitive Function and Aging Study (MRC CFAS; a longitudinal population-based cohort study) comprised the baseline and two-year follow-up in the current study. A Successful Aging Index (SAI) was created using items identified by systematic reviews of operational definitions and lay perspectives of SA, capturing physiological and psychosocial components. Demographic data and SAI components were collected at baseline. Outcome measures, i.e. health service use, informal care use, and functional service, were captured at two years follow-up. Results: Logistic regression revealed significant relationships between the SAI and six of eight service use outcomes in models adjusted for age, sex, education, and socio-economic status. Analysis of the area under the receiver operating characteristic (ROC) curve demonstrated sufficient predictive capabilities for all models, (range 0.65–0.86). Conclusions: The SAI demonstrated a strong association, and predictive accuracy, with respect to service use, providing preliminary support for the practical utility and usefulness of this measure. Key words: successful aging, healthy aging, model development, health service use, predictive validity
Introduction Many different models of successful aging (SA) exist in the absence of an overarching consensus definition (Cosco et al., 2014a). Since the initial coining of the phrase more than half a century ago (Havighurst, 1961), the operationalization and practical application of the phrase has been contentious (Depp and Jeste, 2006). Researchers from a variety of fields have developed an immense breadth of different definitions of SA, ranging from strictly biomedical conceptualizations capturing only physical and cognitive function, to single-item self-report measures of how an individual feels they have successfully aged (Cosco et al., 2014a). As a Correspondence should be addressed to: Theodore D. Cosco, Forvie Site, Robinson Way, Cambridge, CB20SR, UK. Phone: 07414983921. Email: [email protected]. Received 23 Jan 2015; revision requested 19 Mar 2015; revised version received 28 Mar 2015; accepted 31 Mar 2015. First published online 20 May 2015.
testament to the proliferation and heterogeneity of SA definitions, a 2006 review revealed 29 definitions whilst a review in 2014 revealed 105 definitions of SA (Depp and Jeste, 2006; Cosco et al., 2014a). Further complicating this issue is the discrepancy between researchers’ operationalizations of SA and laypersons’ perspectives on SA; recent systematic reviews have revealed that researchers generally posit unidimensional biomedically focused models of SA whilst laypersons have a multidimensional, psychosocially focused conceptualization of SA (Cosco et al., 2014b). The inclusion of lay perspectives into research has important implications for increasing the relevance of research outcomes and in complementing scientific theory (Jopp et al., 2015). Further, SA theorists have advocated for the inclusion of both subjective and objective components in models of SA (Bowling and Iliffe, 2006; Pruchno et al., 2010). If a construct researchers are examining does not have relevance
1972
T. D. Cosco et al.
to the demographic they are targeting, the potential impact of this research will be impeded. Although interest in positive states of aging and the heterogeneity of aging trajectories is on the rise, these theoretical developments have progressed at a much faster pace than practical applications of SA. Research into the theoretical underpinnings of SA has provided insight into the complexity of the construct (Cosco et al., 2014b), but examples where SA models have been applied in the framework of tangible health-related outcomes are limited (Young et al., 2009). The use of services in later life – both professional and informal – is on the rise (Kehusmaa et al., 2012), increasing the temporal and monetary value of their provision. Therefore, being able to identify individuals at greater prospective risk of the need for services is an important issue for policy makers, stakeholders, researchers, and clinicians to address. One such way may be to examine the degree to which individuals are aging “successfully.” SA models to date have been largely theoretical, e.g. Rowe and Kahn (1987), with few studies examining practical applications of the concept, e.g. Young, et al. (2009). The current study addresses the gap between theoretical and practical models of SA, and expands upon existing studies, by using a theoretically-driven, a priori model of SA to assess the predictive ability, with respect to service utilization, of a model of SA.
Methods Study characteristics The MRC CFAS is a population-based study of community dwelling individuals (n = 13,004) aged 65 years and over, conducted in five centers with identical methodology in England and Wales (Newcastle, Nottingham, Oxford, Cambridgeshire, and Gwynedd) (Brayne, 2006). Baseline interviewing began in 1991. A 20% (n = 2,640) stratified sample, selected based on cognitive ability, age, and center completed a more detailed assessment interview, with re-interviewing approximately every two years. Trained interviewers conducted face-toface interviews in participants’ place of residence. Questions concerning demographics, cognition (Mini-Mental State Exam: MMSE (Folstein et al., 1983)), activities of daily living (ADLs), instrumental activities of daily living (IADLs) (Townsend and Ryan, 1991), and psychosocial well-being were included in the interview. Data in the current study was taken from the MRC CFAS first combined screen and assessment (referred to here as the baseline interview) and the second combined screen and assessment conducted two
years later (referred to here as the two-year followup; data version v9.0). Further details of the sampling methods and interview are available at www.cfas.ac.uk (Brayne, 2006). All study centers obtained ethical approval from local research committees. Successful aging index Based on the results of recent systematic reviews of operational definitions (Cosco et al., 2014c) and lay perspectives of SA (Cosco et al., 2013) an a priori model of SA was constructed. Physical functioning (modified Katz ADLs (1970) and modified Lawton IADLs (1969)), and cognitive functioning (MMSE (Folstein et al., 1983)) were the two most commonly identified components of SA in the operational definitions of SA review and were, consequently, included in the model. In the lay perspectives review, the three most commonly identified components of SA were included in the model: personal resources (optimism: measured on a three-point Likert scale ranging from optimistic to pessimistic view of the future), engagement (interest: measured on a three-point Likert scale ranging from no loss of interest to persistent lower interest; loneliness: measured on a three-point Likert scale ranging from no feelings of loneliness to frequent/persistent feelings of loneliness) and self-awareness (self-rated health measured on a four-point Likert scale ranging from excellent to poor rating of one’s own health) (model previously described in Cosco et al. (2014c)). Through the inclusion of three components of the operational definitions review and the lay perspectives review, both perspectives were equally represented in the model. Due to shortcomings of the components expressed in a binary framework (as highlighted in Cosco et al. (2014c) a continuum-based index was developed to allow for higher resolution and greater granularity, i.e. the SAI. The SAI was created using a similar protocol used to create the frailty index (Searle et al., 2008), insofar that ordinal items were assigned values and the scores from each of the items used to create a continuous value (Table 1). Depending on the number of possible answers, fraction values were assigned accordingly, e.g. for a four-item Likert question, the answer corresponding to the highest level of functioning would receive 100, the next highest 67, 33, and finally 0 for the lowest possible level of functioning. For continuous variables, e.g. MMSE scores, previously used cut-off scores, i.e. ࣙ26, 25–22, 21–18, 17–0 (Yip et al., 2002; Xie et al., 2008; Cluett et al., 2010), were used to assigned fractional values, as per the protocol used in Searle et al. (2008). Diverging from the frailty index
Successful aging index
1973
Table 1. Successful aging index (SAI) items, values and calculation SURVEY ITEM
QUESTION
SCORE RESPONSES
VALUE
CALCULATION
.........................................................................................................................................................................................................................................................................................................................
Engagement
Change in level of interest
Engagement
Do you feel lonely?
Personal resources
How do you feel about the future?
Personal resources
View of own health
Cognitive functioning
Mini-Mental State Examination
Activities of daily living
Able to cut own toe nails Able to put on shoes and socks Able to go up and down stairs Able to wash all over or bathe Difficulty controlling bladder
Instrumental activities of daily living
Able to do the heavy housework Able to shop and carry heavy bags Able to prepare and cook a hot meal Able to reach an overhead shelf Able to tie knot in string Difficulty in household tasks Needs help to check money
No change Less interest - Infrequent Less interest - Persistent No Infrequently Frequently/persistently Optimistic Empty expectations Pessimistic Excellent Good Fair Poor 26–30 22–25 18–21 0–17 Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help No Occasionally wets Frequently wets Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help Yes - No difficulty Yes - Some difficulty No - Needs help No Yes Impossible No Yes
100 50 0 100 50 0 100 50 0 100 67 33 0 100 67 33 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 50 0 100 0
A
B
C
D
E
a
(a+b+c+d+e)/5 = F
b
c
d
e
f
(f+g+h+i+j+k+l)/7 = G
g
h
i
j
k
l (A+B+C+D+E+F+G)/7 = SAI
1974
T. D. Cosco et al.
procedures, each ADL (dressing oneself, putting on shoes, going up stairs, continence, cutting toenails) and IADL (doing heavy housework, shopping, preparing meals, reaching overhead shelf, tying a knot, housekeeping, getting on a bus, managing finances) item (measured on a three-point Likert scale ranging from able to perform task without difficulty to unable to perform task alone) was made into an index of its own, averaged, and entered into the full model as a single item. The physical functioning components were made into indexes of their own to allow for the equal representation of biomedical and psychosocial components. Inclusion of each constituent ADL or IADL into the integrated index would result in there being many more biomedical components than lay components. Items were averaged creating a value between 0 and 100, with higher values indicating higher functioning. Outcomes The predictive validity of the SAI was investigated by examining the relationship between the SAI score and study participants’ self-reported service use at two years follow-up. Three categories of self-reported day-to-day service provision were investigated: (1) health services, i.e. home help, care worker, community worker, community nurse; (2) functional services, i.e. meals on wheels, warden, paid help; and, (3) informal care, i.e. help provided by friends or relatives. Statistical procedures All analyses were conducted in STATA 12. Descriptive statistics were calculated to examine differences in the SAI by study demographics. Missingness at random was assessed via a χ2 test, examining the association between missingness and service use. Differences between service and non-service users were tested using χ2 tests for sex and socio-economic status and t-tests for age. Logistic regression was performed examining the association between the SAI and service use at two-years follow-up, in unadjusted models and models adjusted for sex, socio-economic status, education, and age. ROC curves were plotted for each logistic model and the area under the curve (AUC) was calculated to determine the predictive accuracy of each model. Logistic regression analyses were conducted unweighted and weighted for study design (i.e. backweighted to adjust for over sampling of individuals aged 75 years or older and sampling to the diagnostic interview). Score calibration was assessed for goodness of fit via Hosmer–Lemeshow test using deciles of SAI scores and service use. It is not possible to weight calculation of the AUC for
study and therefore only unweighted estimates are presented.
Results Sample characteristics In the full MRC CFAS sample, 1,651 individuals were captured at baseline, of whom 511 were excluded in the current study due to incomplete components of the SAI or the use of informant data rather than respondent data. At the twoyear follow-up, 740 individuals with SAI scores at baseline remained in the sample, 684 of whom had completed surveys on service use. The sample consisted of primarily women (64.2%), from manual occupations (68.1%) with an average age of 75.9 years (SD 6.2) and 9.9 (SD 2.1) years spent in full-time education. Informal help (23.0%), paid help (6.6%), and home help (5.8%) were the most commonly used services. As shown in Table 1 there were significant sex differences between service users and non-users in home help (X2 = 7.06, p = 0.008) and paid help (X2 = 9.51, p = 0.002) and there were significant age differences in use of home help (t(684) = −7.13, p < 0.001), care worker (t(684) = −3.95, p < 0.001), Meals-onWheels (t(684) = −2.89, p = 0.004), warden (t(684) = −2.74, p = 0.006), paid help warden (t(684) = −4.71, p < 0.001), other professional help (t(684) = −3.45, p < 0.001), and informal help (t(684) = −5.07, p < 0.001). Differences in paid help use were observed in manual employment (X2 = 23.64, p < 0.001) and education (t(681) = −5.15, p < 0.001). Individuals in the highest cognitive impairment category, i.e. (MMSE < 26), differed in their use of home help (X2 = 4.11, p = 0.04), a care worker (X2 = 60.73, p = 0.009), other professional services (X2 = 5.54, p = 0.02) and informal services (X2 = 4.37, p = 0.04). Missing data Save for care workers (X2 = 14.25, p < 0.001), and community nurse (X2 = 6.37 p = 0.01) and other paid help (X2 = 5.17, p = 0.02) there were no significant associations between service use and missingness, suggesting that missing values were missing at random. Predictive validity Values for the weighted data and unweighted data were nearly identical, saved for paid help which did not reach significance in the weighted regressions. Therefore only results from the unweighted analysis are presented here in Table 2 (see Appendix A for weighted results, available as supplementary
58.4∗ 9.8 1.8 77.4∗∗∗ 6.6 68.9∗ 51.0 10.1 10.0 2.3 2.3 79.8∗∗∗ 75 5.6 6.0 46.9 46.7 11.1∗∗∗ 9.9 2.7 2.1 78.6∗∗∗ 75.4 6.9 6.0 75.0 46.6 8.25 9.7 1.0 2.2 83.8∗∗ 75.2 7.7 5.8 53.3 10.1 2.4 80.3∗∗ 5.1
47.3 9.9 2.1 75.5 6.0
66.7 9.3 0.6 77.2 6.2
47.4 9.9 2.1 75.5 6.0
Discussion
67.5∗∗ 47.4 9.9 9.9 2.3 2.1 80.0∗∗∗ 75.5 6.2 6.0 < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.0001. ∗p
46.4 9.9 2.2 75 5.7 51.4 9.8 2.1 76.4 6.5 Mean SD Mean SD
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material attached to the electronic version of this paper at www.journals.cambridge.org/jid_IPG). Significant negative associations were observed between the provision of home help, care worker, community nurse, paid help, other professional help and informal care, and SAI score in the unadjusted and adjusted models. The Hosmer–Lemeshow test suggest good-fit for all service services reaching significance in the adjusted models, save for informal care (X2 = 26.55, p < 0.001). The AUC for each model is shown in Table 3 and ranged from strong for predicting community nurse use (AUC = 0.86, 95% CI: 0.79 to 0.92) to weak for informal care (AUC = 0.65, 95% CI: 0.61 to 0.69).
58.7∗ 46.5 10.0 9.9 1.7 2.1 80.4∗∗∗ 75.4 6.7 5.9
262 23.0 65.7 73.0∗ 478 41.9 63.4 65.5 23 2.0 65.2 82.6 75 663 6.6 58.1 80.0∗∗ 63.8 43.2∗∗∗ 67.6 611 53.5 61.9 71.2 4 0.4 50.0 50.0 682 59.8 63.9 68.2 11 1.0 72.7 81.8 675 59.2 63.7 67.9 13 1.1 53.9 84.6 673 59.0 64 67.8 27 2.4 70.4 73.1 659 57.8 63.6 67.9 66 5.8 78.8∗∗ 74.6 620 54.3 62.3 67.4 1,141 100.0 63.4 70.0
n % Female (%) Manual Employment (%) Dementia (%) Education (years) Age (years)
USER
..........................................................................................................................................................................................................................................................................................................................................................................................................................................................
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER
NON-
USER USER USER
NONNON-
USER USER TOTAL
HELP HELP HELP NURSE WHEELS WORKER HELP
ON CARE HOME
Table 2. Sample characteristics, by service users and non-users
MEALS
COMMUNITY
WARDEN
OTHER
PROFESSIONAL PAID
INFORMAL
Successful aging index
The current study has taken a model of SA that is based on the literature and demonstrated a strong association between the SAI and a tangible, external outcome, i.e. prospective service use, providing preliminary evidence for the predictive validity of the SAI. The results suggest that the SAI, which incorporates both objective physiological measures and subjective psychosocial measures of SA, is an independent marker of aspects of all three categories of service use: health services, functional services, and informal help in older aged adults. Significant negative associations were demonstrated between SAI score and service use. There are however, limitations that must be taken into account in the interpretation of these results. The construction of the SAI was done using secondary data, matching variables informed by systematic reviews to proxy measures captured in CFAS. Although the most commonly identified components identified in the review were captured in CFAS, perhaps if there were more components within each of these categories, a greater level of granularity could be teased out. Data regarding the use of each service was collected via self-report. As with all longitudinal studies of older adults, attrition was a factor, resulting in missing values. The SAI is an average of all the constituent components; therefore if an item was missing their total score would not be comparable to other who had complete scores. Only respondent data was used to capture individuals’ subjective experiences, rather than an informant’s. Consequently, individuals who were unable to complete all of the SAI components themselves, for example, if they had severe dementia, were excluded, which may have resulted in a bias towards healthier individuals in the sample. Due to the level of missing data (30%) complete-case analysis was conducted rather than multiple imputations. As a result of attrition
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Table 3. SAI score and service use UNADJUSTED OR
95%
CI
P
AUC
ADJUSTED
95%
CI
OR
95%
CI
P
∗
AUC
95%
CI
.........................................................................................................................................................................................................................................................................................................................
Home help Care worker Meals on wheels Community nurse Warden Paid help Other professional help Informal care
0.94 0.93 0.96 0.93 0.96 0.97 0.95
(0.93–0.96) (0.91–0.96) (0.93–0.99) (0.90–0.97) (0.90–1.00) (0.96–0.99) (0.92–0.97)
