A Path Analysis of Dependence and Quality of Life in Alzheimer’s Disease

Abstract

Objective:

To determine the direct and indirect relationships of cognitive, functional, and behavioral factors and other medical comorbidities with the quality of life (QoL) of patients with Alzheimer’s disease (AD) according to the theoretical model of dependence.

Methods:

Observational and cross-sectional study. Cognitive and functional status, behavior, dependence, medical comorbidities, and QoL were assessed by using standardized instruments. A path analysis was used to model the direct and indirect relationships among clinical indicators according to the theoretically based model of dependence.

Results:

The sample consisted of 343 patients with AD (32.1% mild, 36.7% moderate, and 31.2% severe). Medical comorbidities, disease severity, and dependence level had a direct relationship with QoL. The functional disability and the behavior disturbances were indirectly related to QoL via dependence level, and the cognitive impairment was indirectly related to QoL via severity level.

Conclusion:

Direct and indirect effects exist between clinical indicators, dependence, and QoL.

Keywords

Alzheimer’s disease dementia dependence quality of life

Introduction

Alzheimer’s disease (AD) is the most common neurodegenerative dementia of old age and the leading chronic disease contributor to disability and dependence among older people worldwide.¹ Clinically, AD is characterized by a progressive cognitive decline that interferes with the ability to perform the activities of daily living,² and during the course of the disease, several behavioral and psychological symptoms appear and fluctuate, with remissions and recurrences.³ According to estimates, 27 million persons have AD worldwide, and as the life expectancy increases, it is expected that by 2050 there will be 115 million persons with AD.⁴ Alzheimer’s disease has a significant burden on the health and social care systems and on family and caregivers who provide the informal care to the patients.⁵ Current therapeutic approaches include pharmacological and nonpharmacological treatments; however, the effects are modest, symptomatic, and do not modify the course of the disease.⁶

As AD progresses, the cognitive performance decreases and the functional impairment increases, starting with mild difficulties to perform the instrumental activities of daily living and ending with the loss of any ability to perform the basic activities of daily living.⁷ Patients with AD need a progressive increase in assistance with the activities of daily living. At the final stages of the disease, the patients are absolutely dependent on their caregivers and require frequent monitoring by health professionals.⁸ Traditionally, dependence has been defined as the need for frequent human help or care beyond that habitually required by a healthy adult,⁹ and recently, several investigators have proposed the construct of dependence as a valid indicator of the disease progression.¹⁰ Defined as the level of assistance required by a patient with AD, the concept of dependence for AD appears as an interesting measure to assess the progression of the disease, because it has been shown to decline as the dementia advances,¹¹ and it is associated with cognitive and functional status, with behavioral and psychological symptoms,¹² and with caregiver’s burden.¹³

Due to the absence of preventive or curative therapies for AD, a primary goal of care for patients with AD is to maximize their quality of life (QoL). Over the last decades, the concept of QoL of patients with AD has emerged as an important key outcome for evaluating the effectiveness of therapeutic interventions, and several approaches to conceptualize and assess QoL in patients with AD have been developed.¹⁴ Regardless of the use of generic or AD-specific QoL questionnaires, a central problem for the assessment of QoL in patients with AD is the low reliability due to the cognitive impairment and the reduced self-awareness of the patients.¹⁵ In spite of the disagreement between the assessment performed by patient himself or by his caregiver,¹⁶ the use of proxy reports is widely accepted as an appropriate method of collecting information regarding QoL in patients with moderate or severe dementia.^17,18

According to the theoretical model of dependence, the specific level of dependence of a patient is due to the cognitive, functional, and behavioral impairments.¹⁰ In agreement with this approach, both dependence and comorbid conditions should be factors related to the QoL of the patients. The aims of this study were to identify the factors that contribute to the QoL in patients with AD according to the conceptual framework of dependence and to explore the direct and indirect relationships that exist between cognitive, functional, and behavioral factors and other medical comorbidities using a path analysis approach.

Methods

Participants

The sample consisted of patients included in the Study of the Cost of Dependence Associated to Alzheimer’s Disease (the CoDep-AD Study), a study of cost of illness in Spain. The CoDep-AD Study was an observational and cross-sectional multicenter study, performed in 21 memory clinics located in hospitals around different geographical sites of Spain, and was designed to assess the relationship between the economic cost of AD and the level of dependence. The CoDep-AD Study used a stratified sampling in order to achieve a minimum of 5 patients per center of each degree of severity according to Clinical Dementia Rating (CDR)¹⁹ score. Patients were included on the basis of a convenience sampling recruitment procedure in the outpatient consultation offices of the study investigators, and participants were eligible if they met the Diagnostic and Statistical Manual for Mental Disorder (Fourth Edition, Text Revision)²⁰ criteria for primary degenerative dementia of the Alzheimer type and the National Institute of Neurological Disorders and Stroke-Alzheimer’s Disease and Related Disorder Association criteria for probable AD.²¹ Inclusion criteria required patients to have a reliable caregiver. Exclusion criteria were the presence of disability due to causes other than AD (ie, osteoarthritis), a clinical status that may have a fatal outcome in the short term, and the participation in a clinical trial.

Study Procedure

Prior to the recruitment of the participants, all interviewers were trained on the objectives of the study, on the questionnaires and scales administration procedures, and on scoring criteria. Before the raters were allowed to administer the instruments included in the study protocol, they needed to complete an online form that included a video with a mock interview with a patient and his/her caregiver. In order to participate in the study, raters should adequately assess the clinical status of the patient using the study scales and questionnaires. Only certified raters participated in the data collection. The study procedure consisted of 2 visits with the memory clinic staff: 1 visit with the neurologist, psychiatrist, or geriatrist who collected the demographic and clinical information and 1 visit with the neuropsychologist who administered the questionnaires and scales to the patient and the caregiver in order to assess cognitive function, functional capacities, behavioral and psychological symptoms, dependence, and QoL. The informed consent was obtained from all the participants (patient and/or caregiver), and the study protocol was approved by all the institutional review boards of the participant centers. Data were collected from December 9, 2010, to July 6, 2012. The collected information was anonymized and met the confidentiality requirements for personal data protection in compliance with Spanish legislation. Only study researchers had access to the database.

Measures

The study included a set of standardized and validated instruments for the Spanish-speaking population. The dependence level was assessed with the Dependence Scale (DS),²² which is a brief instrument composed of 13 items with a hierarchical structure, with items increasing in the level of the assistance required by the patients. Reliability coefficients range from 0.66 to 0.93, and validity was established according to the relationship with several clinical indicators. A global dependence score is derived by summing the scores of all items, ranging from 0 to 15, with higher scores indicating a greater degree of dependence.

The cognitive function was assessed with the Mini-Mental State Examination (MMSE),²³ which provides a brief evaluation of orientation, registration, attention, recall, language, and constructional praxis. It is widely used as a screening tool and as a marker of cognitive change in patients with dementia. Scores range from 0 to 30 points.

The ability to perform the ADLs was assessed using the Disability Assessment for Dementia (DAD),²⁴ which is a scale that offers a broad assessment of ADLs: basic, instrumental, and leisure. It comprises 40 items, and the scores range between 0 and 80 points, which are later transformed into percentages. The lower scores indicate greater degree of functional disability. This scale presents an appropriate intraclass correlation coefficient (0.96) and internal consistency (Cronbach’s α = .59).

The behavioral and psychological symptoms were assessed with the Neuropsychiatric Inventory (NPI),²⁵ which is an instrument designed to measure 12 behavioral and psychological symptoms commonly found in persons with AD. The score for each disorder is calculated by multiplying the frequency (1-4) by the severity (1-3), and the sum of all of them provides an overall score (range: 0-144). This instrument has an overall internal consistency of 0.88.

The medical comorbidity was assessed using the Cumulative Illness Rating Scale (CIRS),²⁶ which consists of 14 items to quantify the burden of chronic diseases taking into account their severity. The CIRS has an appropriate interrater reliability (0.89) and satisfactory concurrent validity (0.73-0.084). Score ranges between 0 and 56 points, although a very high score is not biologically plausible because it would represent the concurrent failure of multiple systems that would not be compatible with life.

The dementia severity was assessed using the CDR, which is a semistructured interview designed to assess the clinical severity of dementia according to the level of execution in 6 dimensions (memory, orientation, judgment and problem solving, social leisure, and personal care) by an algorithm. The score ranges from 0 (no dementia) to 3 (severe dementia).

The QoL was assessed with the proxy version of the generic preference instrument European Quality of Life-5 Dimensions (EQ-5D).²⁷ The EQ-5D describes health status according to 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has 3 possible responses: no problems, some problems, and severe problems. In the proxy version, the caregiver is asked to answer the questions from the patient’s point of view. Each of the 243 possible health states can be converted into a utility score by applying an algorithm based on valuations of health states from the Spanish general population.²⁸ The EQ-5D has appropriate reliability (between 69.8% and 99.7%) and has been shown to be applicable to patients with dementia and to their caregivers as proxies.²⁹

Statistical Analysis

We described the clinical and demographic characteristics of the patients by means of absolute and relative frequencies for qualitative variables and by means of central tendency and dispersion measures for quantitative variables. Bivariate analyses of the demographic and clinical characteristics of the patients according to the severity of the disease (using the CDR score as severity classification criteria) were performed using the Kruskall-Wallis test and the χ² test. Bivariate analyses of the relationship between QoL and clinical characteristics of patients were performed using the Spearman correlation coefficient.

We fit a path model according to the theoretical model of dependence (Figure 1). This model states that cognitive level and behavior are direct predictors of dependence and also that cognition and behavior each relates to functional capabilities which are also direct predictors of dependence. Likewise, dependence level in combination with other factors such as comorbid conditions is a predictor of health-care outcomes such as costs or QoL. We calculated several fit indices.³⁰ First, the χ² test was used, which indicates, when nonsignificant, that the model and the data are consistent. Second, χ² test is recommended as a measure of fit instead of test statistics, with the value less than 2 times its degree of freedom (df) as the rule of thumb for good fit. Third, the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR) were used. The acceptable model fitting values for these measures were defined as follows: CFI ≥ 0.95, TLI ≥ 0.95, RMSEA < 0.05, SRMR < 0.05.

Figure 1.

Theoretical model for dependence in Alzheimer’s disease.

All of the statistical contrasts were bilateral, and the confidence intervals were calculated using a 95% reliability level. Data processing and analysis were performed using the SPSS statistical program version 19.0 and the Mplus program version 5.0 for Windows.

Results

The sample consisted of 343 participants, the mean age was 78.9 years (standard deviation [SD] = 7.4), and 67.0% were women. The mean MMSE score (cognitive function) was 14.2 points (SD = 6.3), 43.3 points (SD = 27.6) for the DAD (functional capacity), 18.4 points (SD = 16.7) for the NPI (behavioral and psychological symptoms), 5.5 points (SD = 3.9) for the CIRS (medical comorbidity), 8.1 points (SD = 3.2) for the DS (dependence level), and 0.5 points (SD = 0.2) for the EQ-5D (QoL). Based on the CDR scores, the severity of dementia was mild in 32.1% of the patients, moderate in 36.7%, and severe in 31.2%. The results of the descriptive statistics according to the severity of dementia are shown in Table 1. As expected, all clinical indicators, except the medical comorbidity (CIRS score), were worse for patients with severe AD.

Table 1.

Demographic and Clinical Characteristics of the Patients According to Dementia Severity.

CDR Score	1 (n = 110)	2 (n = 126)	3 (n = 106)	P
Age, years, mean (SD)	76.5 (7.33)	79.9 (6.54)	79.9 (8.03)	<.001
Gender, n (%)^a
Male	38 (34.5)	44 (34.9)	31 (29.2)	.605
Female	72 (65.5)	82 (65.1)	75 (70.8)
MMSE score, mean (SD)	19.3 (4.2)	14.8 (4.2)	8.2 (5.0)	<.001
DAD score, mean (SD)	70.3 (17.0)	42.1 (18.9)	16.6 (15.1)	<.001
NPI score, mean (SD)	13.2 (12.3)	18.2 ((16.8)	24.1 (18.7)	<.001
CIRS score, mean (SD)	5.3 (3.3)	5.4 (3.9)	5.9 (4.6)	.506
EQ-5D score, mean (SD)	0.7 (0.1)	0.5 (0.2)	0.3 (0.2)	<.001
DS score, mean (SD)	5.0 (2.3)	8.3 (2.0)	11.0 (3.2)	<.001

Abbreviations: CDR, Clinical Dementia Rating; CIRS, Cumulative Illness Rating Scale; DAD, Disability Assessment for Dementia; DS, Dependence Scale; EQ-5D, European Quality of Life-5 Dimensions; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; SD, standard deviation.

^aData not available in 1 case.

Table 2 shows the Spearman correlation coefficients between patient clinical characteristics and QoL (EQ-5D score) stratified by dementia severity (CDR score). Only functional capacity (DAD score) and dependence level (DS score) showed significant correlation coefficients with QoL (EQ-5D score), with increasing values according to disease severity. The behavioral and psychological symptoms (NPI score) showed a statistically significant correlation with QoL (EQ-5D score) only for mild patients, while the medical comorbidity (CIRS score) was associated with the QoL (EQ-5D score) only for severe patients. The cognitive function (MMSE score) was not correlated with the QoL (EQ-5D score) independent of the patient’s severity degree (CDR score).

Table 2.

Spearman Correlation Coefficients Between Patient Characteristics and EQ-5D Score Stratified by Dementia Severity.

CDR Score	1 (n = 110)	2 (n = 126)	3 (n = 107)
MMSE score	0.110	0.139	0.080
NPI score	−0.235^a	−0.142	−0.052
DAD score	0.264^a	0.400^a	0.450^a
DS score	−0.300^a	−0.509^a	−0.545^a
CIRS score	−0.019	−0.058	−0.247^a

^a P < .05.

The proposed model in Figure 1 was tested with our data, and the fit indices values indicated that it fit the data well. The χ²/df value was lower than 2 (1.70), the CFI and TLI values were higher than 0.95, and the model and the data were consistent (χ² test of absolute fit was not significant, and SRMR and RMSEA values were lower than 0.05; Table 3). The model explained 39% of the variance in the QoL (EQ-5D score), 49% of the variance in the functional capacity (DAD score), and 81% of the variance in the dependence level (DS score). Figure 2 shows the path diagram and results of the analysis in terms of standardized regression coefficients (β) for each path, wherein QoL results from several determinants. Table 4 shows the β coefficients of the indirect relationships of the predictors with the QoL (EQ-5D scores). According to this model, the 39% of the variation in QoL (EQ-5D scores) was explained by direct effects of dependence (−.619), by indirect effects of cognitive function (.335) and behavioral and psychological symptoms (−.107) mediated through functional capabilities, and by indirect effects of behavioral and psychological symptoms (−.043), functional capacity (.518), and medical comorbidity (−.045) mediated through dependence level.

Table 3.

Summary of Model Fit Statistics.

Model Fit Index	Model 1
χ² test	6.822
df	4
P	.1456
CFI	0.997
TLI	0.991
RMSEA	0.045
SRMR	0.019

Abbreviations: CFI, comparative fit index; df, degrees of freedom; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual; TLI, Tucker-Lewis Index.

Figure 2.

Path model for dependence and QoL. *P < .05; **P < .001. CIRS indicates Cumulative Illness Rating Scale; DAD, Disability Assessment for Dementia; DS, Dependence Scale; EQ-5D, European Quality of Life-5 Dimensions; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; QoL, quality of life; r ²: coefficient of determination.

Table 4.

Standardized β Coefficients and 95% Confidence Intervals (CIs) of the Indirect Relationships Between Predictors and EQ-5D Score.

Variable	Estimate (SE)	95% CI
MMSE → DAD → DS → EQ-5D	0.335 (0.029)^a	0.278 to 0.391
MMSE → DS → EQ-5D	0.034 (0.020)	−0.358 to 0.426
NPI → DAD → DS → EQ-5D	−0.107 (0.021)^a	−0.065 to −0.0148
NPI → DS → EQ-5D	−0.043 (0.016)^b	−0.074 to −0.011
DAD → DS → EQ-5D	0.518 (0.034)^a	0.452 to 0.584
CIRS → DS → EQ-5D	−0.045 (0.015)^b	−0.074 to −0.016

^a P < .001.

^b P < .05.

Discussion

Our specific objectives were to explore the direct and indirect relationships of the QoL with cognitive function, functional impairment, behavioral symptoms, and medical comorbidities according to the theoretical model of dependence.

Regarding the validity of the theoretical model of dependence as a construct that reflects the consequences of deficits in cognition, function, and behavior, our results indicate that the 81% of the variance in the DS was explained by direct and indirect effects of cognitive function, functional capabilities, and behavioral symptoms. Our results are in agreement with those recently published regarding the direction of the associations between these measures^12,31

-35 and are consistent with those obtained by the largest study available exploring the psychometric properties of the DS.³⁵ This study used data from 3 separate studies and adjusted path models to evaluate the relationships among dependence, cognition, function, and behavior. For each data set, cognition and behavior measures had a significant effect on functional level, and the coefficients of function over dependence ranged between −0.569 and −0.646. These values are very similar to the value obtained in our study between functioning and dependence (−0.619). The path coefficients of each data set model were very similar, and the models explained about 50% of the variance in DS scores. However, our results show a large percentage of explained variance in the DS score, which may be explained by the inclusion of patients in all the stages of the disease in our study. We also provide new evidence regarding the effect of medical comorbidity on the level of dependence. The modest but significant association we detected suggests that medical comorbidity is a covariable to control when analyzing the dependence level in patients with AD.

With reference to the theoretically based model of dependence for AD and its association with the QoL, the path analysis further increased the understanding of the relationships between the clinical indicators and QoL when compared to the bivariate correlation analysis results. The dependence level was the variable with the highest degree of association with the QoL in the bivariate analysis and showed an increasing pattern of association with disease severity. The path model explained 39% of the variance in QoL, a value slightly higher than previously reported using the same set of measures in a multiple regression analysis approach (22%).³⁶ The use of a structural equation modeling, although similar in appearance with regression models, allows to capture complex direct and indirect relationships within a group of variables. Moreover, in this analytical approach, there is not a clear distinction between dependent and independent variables because such concepts only apply in relative terms, since a dependent variable in 1 model equation can become an independent variable in other components of the path.³⁷ In this sense, the use of a path analysis approach provides a deep understanding about the direct and indirect relationships existing within a set of related predictors and the outcome. For example, in our case, the classical multivariate regression analysis using the EQ-5D as dependent variable and the MMSE, NPI, DAD, DS, and CIRS scores as independent variables reported a coefficient of determination of 0.36, while the MMSE and the NPI scores did not reach statistical significance. Moreover, the use of DS and CIRS scores as unique independent variables reduced the coefficient of determination up to 0.30 (data not shown).

The bivariate analysis did not report a significant association between cognitive function and QoL. However, the path analysis detected a large effect of cognitive function on QoL mediated by the functional disability. Similarly, the bivariate analysis showed a modest association between behavioral disturbances and QoL, being significant only in cases with mild AD severity, and showed a lack of association between these variables in cases with more advanced disease. The association between behavioral disturbances, cognitive function, and QoL has been controversial, and there are discordant results depending on the samples characteristics (community-dwelling vs institutionalized individuals) and the measurement instruments used (self-reported vs proxy-reported measures of QoL).³⁸ However, according to our results, cognition and behavior disturbances reduce the QoL of the patients through its negative consequences on functional capacities. In turn, functional disability was the main contributor to QoL mediated by dependence level. According to the bivariate analysis results, it would be expected that in early stages of the disease, functional dependence had only a minimal contribution to the decreased QoL, but it would have a larger effect with the progression of the disease. These results are in agreement with previous research on the consequences of functional disability on the QoL in AD.³⁹

When interpreting the findings of this study, a number of limitations should be taken into account. First, although the path analysis allowed us to analyze direct and indirect effects simultaneously with multiple independent and dependent variables, the direction of causality between variables was based on the hypothetical relationships expressed within the theoretically based model. In this sense, it is important to emphasize that the path analysis cannot test causal directionality on relationships. Second, all disease measures, except cognition, were reported by the caregivers. This methodological approach is necessary when studying patients with moderate or severe dementia, and this limitation is unavoidable. Third, the study participants were recruited in specific memory clinics and according to specific inclusion criteria, which limit the generalization of the results to other patients with AD. The present article has also a number of strengths, such as using a large sample, which supports the confidence in the goodness-of-fit tests because it doubles the minimum requirement of 10 to 20 individuals for each variable in the model and ensures the robustness of the statistical analyses in terms of statistical power. Second, the investigators training procedures increased the internal validity of the study results due to the reduction in a potential information bias for use of the study scales and questionnaires by several raters (a bias arising from measurement error). Third, the models were fitted according to a theoretically based approach, not by using algorithms to adjust the model to the data, conferring to our results a strong external validity.

In summary, our findings suggest that almost 40% of the variation in the QoL level of patients with AD are explained by a direct effect of dependence and by indirect effects of cognition, functional capabilities, behavioral symptoms, and medical comorbidities through their effect on dependence level. These data have important clinical implications for health professionals working with patients and may help to develop specific interventions addressed to improve the QoL of the patients taking into account that the dependence level is an important mediator between clinical features of the disease and the QoL of the patients. Future research should be addressed to quantify the consequences for the patient’s QoL according to the changes in their dependence level.

Footnotes

Authors’ Note

The CoDep-AD Study Group refers to: Miquel Aguilar, Hospital Mútua de Terrassa (Terrassa); Jordi Alom, Hospital General Universitario de Elche (Elche); Guillermo Amer, Hospital Universitari Son Espases (Palma de Mallorca); Carmen Antúnez, Hospital Virgen de la Arrixaca (Murcia); Rafael Arroyo, Clínica Quirón (Barcelona); Miguel Barquero, Hospital La Fe (Valencia); Félix Bermejo, Hospital 12 de Octubre (Madrid); Fernando Castellanos, Hospital Virgen del Puerto (Plasencia); Manuel Franco, Hospital de Zamora (Zamora); Guillermo García, Hospital Ramón y Cajal (Madrid); José María García-Alberca, Instituto Andaluz de Neurociencia y Conducta (Málaga); Domènec Gil, Hospital Universitari Sagrat Cor de Jesús (Barcelona); Pedro Gil. Hospital Clínico San Carlos (Madrid); Albert Lleó, Hospital de Sant Pau (Barcelona); Manuel Martín, Clínica Psiquiátrica Padre Menni (Pamplona); Dolores Martínez, Hospital La Magdalena (Castelló de la Plana); Raimundo Mateos, Hospital de Santiago de Compostela (Santiago); Vicente Medrano, Hospital General Universitario de Elda (Elda); Jordi Peña, Hospital del Mar (Barcelona); Pilar Quílez, Hospital Mútua de Terrassa (Terrassa).

Acknowledgments

The authors are grateful to all the study participants who have generously given their time and collaborated in the study and S. Monserrat-Vila for the administrative support, acquisition of data, and data handling. The authors also want to thank the associated investigators of the Cost of Dependence Associated to Alzheimer’s Disease in Spain Study Group (CoDep-EA Study Group).

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Josep Garre-Olmo and Secundino López-Pousa are employees of Institut d’Assistència Sanitària and were paid contractors to Pfizer Inc and Janssen Alzheimer Immunotherapy Research & Development, LLC, in the development of this manuscript and the study design and data analysis.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was sponsored by Janssen Alzheimer Immunotherapy Research & Development, LLC, and Pfizer Inc. The funder participated in the design of the study. The funder had no part in the analysis and interpretation of the data, the writing of the report, and the decision to submit the article for publication.

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