Jounuil of Gerontology: PSYCHOLOGICAL SCIENCES 1990. Vol. 45. No. 2. P58-63
Copyright 1990 by The Genmiohgical Society of America
Cognitive Deterioration in Alzheimer's Disease Behavioral and Health Factors Linda Teri,1 James P. Hughes,2 and Eric B. Larson3 "Department of Psychiatry and Behavioral Sciences, department of Biostatistics, and department of Medicine, University of Washington.
P
ROGRESSIVE cognitive deterioration is the hallmark of Dementia of the Alzheimer's Type (DAT). Despite this, very little is known about the nature of this decline, or about the factors influencing its progression. The original DSM-III diagnostic criteria for Primary Degenerative Dementia (DAT) indicated that the nature of cognitive deterioration was "uniformly progressive" (APA, 1980, p. 126), consistent with the clinical experience at that time. More recently, however, DSM-III-R (revised, APA, 1987) has defined the deterioration as "generally progressive," omitting the term "uniformly" to acknowledge recent clinical and empirical evidence that the rate of decline may vary, influenced by factors inherently or tangentially related to the disease itself. For example, two recent studies followed DAT patients over time and found heterogeneous rates of decline across patients, with some patients progressing at a faster rate than others (Botwinick, Storandt, & Berg, 1986; Berg et al., 1984). In addition, other studies have indicated that the pattern of cognitive deficits that decline also varies between DAT patients (Kaszniak, Wilson, Fox, & Stebbins, 1986). Unfortunately, little is known about what factors may be associated with different rates of decline. Of three variables that have been hypothesized to be associated with progression — age of onset, family history, and duration of deficits — none have yielded consistent results. For example, Huff, Growdon, Corkin, and Rosen (1987) found duration of cognitive deficits unrelated to rate of decline, but Thai and Grundman (1986) found duration associated with more rapid decline. In addition, it is unclear how these variables influence survival. Kaszniak et al. (1986) found that earlier age of onset was not associated with decreased survival time (from disease onset), but more recent studies have reported that earlier age of onset was associated with decreased
P58
survival time (Seltzer & Sherwin, 1983; Barclay, Zemcov, Blass, & McDowell, 1985). Thus, although DAT may progress at different rates, age of onset, duration, and family history fail to yield consistent findings to explain these different rates. Knowledge about causes or associations of differential rates of progression in DAT could have theoretical as well as practical use. From a theoretical viewpoint, understanding the mechanism of decline may shed light on the pathogenesis and phenomenology of DAT. From a practical viewpoint, knowledge about risk factors for more rapid progression may assist health professionals and families to plan for patient care and prevent coexistent problems from contributing to deterioration. The types of factors that might influence decline can be gleaned from clinical writings. It has long been acknowledged that coexistent emotional, social, and physical distress can exacerbate medical conditions (e.g., Sarason, Sarason, Potter, & Antoni, 1985). One would therefore hypothesize that DAT patients with coexistent behavioral or health problems might deteriorate more rapidly than patients without such difficulties. To date, however, no study has investigated the role of emotional, physical, or behavioral factors on the progression of cognitive decline in DAT. One study (Barclay et al., 1985) investigated how these factors related to mortality. They found that male gender, presenile onset, and increased severity of behavioral impairment [as measured by the Haycox (1984) scale] were positively associated with decreased survival; the presence of vascular disease [as measured by the Hachinski et al. (1975) ischemic score] was not associated with survival. Whether these factors or mortality relate to cognitive decline is unknown.
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
Alzheimer's disease is characterized by progressive cognitive decline. However, little is known about the "typical" rate of decline, the degree of individual heterogeneity evident in decline, or the types of factors that influence such decline. This study investigated these questions in a sample of 106 patients with Alzheimer's disease, assessed at 1-5 points in time, spanning up to three years. At each time point, the Mini-Mental State Exam, a measure of global cognitive function, was administered to all patients. Measures of behavioral disturbance (including the presence/ absence of hallucinations, depression, incontinence, wandering, and agitation), health status (including presence/ absence of neurological, cardiovascular, and other diseases), and descriptive information (such as gender, age at time of onset, and duration of deficits) were obtained at entry into the study. A two-stage random effects regression model was fit to the data and then used to assess the effect of these behavioral, health, and descriptive measures on the rate of decline. Results indicate that the rate of cognitive decline in Alzheimer's disease is quite variable. Patients with various health and behavioral problems declined at a rate between 1.4 and 5 times faster than patients without such problems. Alcohol abuse, additional neurological disease, and agitation were significantly related to rate of decline. Overall number of problems was not. The association of these problems with accelerated decline may have prognostic and treatment implications.
COGNITIVE DETERIORATION
This study investigated the relationship of cognitive decline in DAT to behavioral, health, or descriptive variables that might influence the rate of deterioration. It was hypothesized that patients with severe behavioral, cognitive, and health problems at intake would have a greater rate of deterioration than patients without such problems. It was further hypothesized that patients with multiple problems would have an accelerated rate of decline, as compared to patients with a single problem. Patients' gender, age, education, and prior occupation were hypothesized to be unrelated to rate of cognitive decline, while younger age at onset and longer duration of cognitive deficits were hypothesized to be related to an increased rate of decline. METHODS
Measures. — All subjects were given the following tests as part of the overall diagnostic evaluation described above. The Mini-Mental
State Exam (MMSE) (Folstein et al.,
1975) is a brief measure used for screening organic impairment. It provides a score of global cognitive deficits and taps a range of cognitive tasks including orientation, verbal
reasoning, visual perceptual skills, language, and memory. Scores range from 0-30 with scores of 27-30 considered mild to normal, and scores below 24 indicative of definite organic involvement (Anthony, LeResche, Niaz, Von Korff, & Folstein, 1982; Klein etal., 1985). The MMSE has been shown in previous studies to have good test-retest reliability and to discriminate demented from nondemented individuals (Folstein et al., 1975; Klein et al., 1985; Thai and Grundman, 1986). The MMSE was administered to patients by a trained research nurse, physician, and psychiatrist or psychologist. As Table 1 shows, the mean score for this sample at intake was 18.2 (SD = 6.7); the majority of subjects were in the moderately to severely impaired range (85% scored between 24 and 0). Sixteen additional items evaluated the presence or absence of 10 behaviors and 6 health problems reported in the clinical literature as problematic and prevalent for DAT patients. These 16 items were rated as present or absent based upon data obtained during the evaluation. The behaviors were restlessness, agitation, wandering, hallucinations, suspiciousness or paranoia, incontinence, problems with personal hygiene, depression, awareness of memory loss, and falling. Two behaviors may benefit from definition: restlessness meant that the patient was mildly motorically active (fidgety, uneasy, impatient); agitation meant that the patient was extremely active (frenzied, excitable, unable to sit still). The six health items were: thyroid disease, other neurological disease, head trauma, gait disturbance, alcohol and drug abuse. None of the health problems was thought to be the primary cause of the dementia. Thyroid disease included symptoms (such as weight loss, dry skin, cold insensitivity and constipation) thought to indicate underlying thyroid disease; other neurological disease included any other known neurological disease (such as Parkinson's disease, cerebrovascular accident, transient ischemic attack, periph-
Table 1. Characteristics of the Sample (N = 106) Patients Age (M ± SD) Gender Men Women Initial Mini-Mental State Exam (M ± SD) Duration of cognitive deficits (months) (M ± SD) Educational level Less than high school High school graduate More than high school Unknown Marital status Married Widowed Other Caregiver respondent Spouse Child Other
77 ± 6.7 30 76 18.2 ± 6.7 46.6 ± 26.0 29 19 39 19 41 49 16 25 51 30
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
Subjects. — Subjects were selected from a cohort of 200 patients entered into a prospective study of the evaluation of dementia (Larson, Reifler, Sumi, Canfield, & Chinn, 1985). They were patients at the outpatient Geriatrics and Family Services Clinic at the University of Washington Hospital (Reifler, Larson, & Teri, 1987). Patients had most often been referred by a concerned family member or a general physician. A comprehensive geriatric evaluation was conducted on each patient, including (a) a 1-2 hour intake interview with the patient and concerned family members, conducted by an experienced geriatric psychiatrist or psychologist, (b) a complete physical and neurological exam conducted by an internist (E.B.L.) that included appropriate diagnostic tests such as thyroid function tests, complete blood count, chemistry battery, B12 and folate levels, EEG, VDRL. and CT scan, (c) a neuropsychological screening battery, including the WAIS-R (Wechsler, 1981), the Wechsler Memory Scale (Wechsler, 1945), and the Folstein MiniMental State Exam (Folstein, Folstein, & McHugh, 1975), and (d) a home visit conducted by a geriatric social worker, to assess environmental safety issues, patients' functional status, and daily living skills. Subjects for this study were those who met DSM-III criteria for Primary Degenerative Dementia (PDD) based on the evaluation described above and on a meeting between the psychiatrist, psychologist, internist, and neurologist/neuropathologist during which time diagnoses for individual subjects were discussed. Only those subjects for whom each rater agreed that DSM-III criteria for PDD were met and for whom PDD was the primary diagnosis were considered for inclusion in this study. Additionally, since the primary interest was in the change in cognitive status, only patients with two or more cognitive evaluations (MMSE) were included (N = 106). The mean age of subjects was 77 years (range 60-94); 70% were female. Table 1 summarizes the demographic characteristics and initial information on all subjects.
P59
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TER1 ETAL.
Procedures. — Subjects were followed during the course of their clinical care. The median MMSE follow-up time was 11 months. However, 21 patients were followed fewer than four months due to logistical problems (usually geographic distances). Subsequent analyses include these patients; the methods described herein effectively give relatively less weight to subjects with short follow-up. Replicate analyses without these subjects gave essentially the same results as those reported below. Statistical methods. — The statistical methods used to quantify change in the MMSE are based on a model described by Laird and Ware (1982). The model they propose can be written as y, = X(a + Z ^ + e, (1) where Ys = vector of observations (over time) for the ith individual; Xi = matrix of independent variables (design matrix) (see, for example De Groot, 1986) for the fixed effects; a = fixed effects parameters (to be estimated); Z, = design matrix for the random effects; b, = random effects parameters (to be estimated); e, = random errors. In this model the fixed effects are typically population parameters (such as sex, race, average rate of decline over time, etc.) and the random effects are the deviations of individuals from the population parameters. For example, if MMSEs are observed over time, a slope parameter in the fixed effects portion of the model would estimate the average rate of decline in MMSE over time for the entire sample. A slope parameter in the random effects portion of the model (which is estimated for each individual) would quantify each individual's deviation from the average slope. One of the strengths of this model is that subjects can have different numbers of MMSE scores at differing time intervals. Other
models for repeated measures (e.g., Greenhouse & Geisser, 1959; Koch, 1967) require "balanced" designs in which subjects are observed at the same time intervals. Also, the random effects portion of equation (1) provides the structure necessary to account for the nonindependence of observations taken on the same individual. An excellent review article on this and similar models is Feldman (1988). In this study the relationship between various behavioral and health characteristics (evaluated once at patient enrollment) and the rate of decline of the MMSE (obtained from scores at varying time intervals) was examined. Using the model shown in equation (1), the fixed effects consist of a y-intercept (corresponding to the mean MMSE at intake), various "main effects" (such as presence or absence of a given behavior/health effect), and various effect-by-time interaction terms. The main effects parameterize the differences in the intake MMSE attributable to the behavior/health variables. However, since intake occurs at some arbitrary point in the course of the disease, the main effects can be difficult to interpret. Of greater interest are the effect x time interaction terms. These model the relationship between the behavioral/health variables and rate of decline in the MMSE over time. A positive interaction between a behavioral/ health variable and time means that the variable decreases (makes less negative) the rate of decline, and a negative interaction means that the rate of decline is increased by the behavioral/health variable. The random effects portion of the model includes a subject-specific slope and intercept. This essentially fits a simple linear regression (MMSE vs time) for each subject. Conceptually, the fixed effect slope and intercept are the averages of these subject-specific slopes and intercepts. The variation of the subject-specific parameters (particularly the slopes) around the fixed-effects parameters is of interest because it shows how much variation in rate of decline individuals exhibit around the overall regression line. Only linear effects of time were investigated; there were too few repeated observations on each individual to fit higher order effects. RESULTS
Figure 1 shows a plot of the total percent change in the MMSE [(last MMSE-first MMSE)/first MMSE] on a per month basis. The mean deviation of follow-up was 1.1 years (SD = .66). As already stated, all subjects had two or more follow-up intervals (41% had 2, 39% had 3, 11% had 4, and 3% [n = 3] had 5). For most subjects, decline in MMSE over time is evident but the degree of decrement is variable. There is, in fact, considerable variability in the percent change for those cases where the total duration of follow-up was less than four months. Stated differently, two MMSEs measured less than four months apart are not a reliable measure of change. The regression model described above reduces the influence of these cases. Table 2 shows the frequency of occurrence of the behavioral and health measures that were included in this study. One measure, known drug abuse, was not present in any subject; consequently, it was dropped from further analysis. Multivariate analyses of these data proceeded in the following manner: first, a basic model incorporating the demo-
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
eral neuropathy) that was substantiated by history and clinical findings; head trauma included trauma sufficiently severe to result in concussion, loss of consciousness, or requiring medical attention; gait disturbance included any problem with a presumed neurological cause (i.e., nonarthritic or musculoskeletal); alcohol and drug abuse included current or past documentation of abuse. In the case of alcohol abuse, the amount of alcohol consumption and the temporal relationship of alcohol to onset of problems were determined to be unrelated to the cognitive problems experienced. This is admittedly somewhat problematic given the difficulty in differentiating ETOH-dementia from Alzheimer's disease. However, rather than exclude patients with ETOH (alcohol) use from the study, it was decided to include them in order to evaluate whether ETOH use was an important "additional health factor" in determining subsequent cognitive decline. (This idea was supported by ensuing analyses as will become evident later.) Each item was rated as present or absent by a trained geriatric internist (E.B.L.) based upon information obtained from observing the patient or provided by the family.
COGNITIVE DETERIORATION
P61
Table 2. Frequencies of Problems (N = 106)
if)
10
20 30 Duration (months)
40
Figure I: Percent change in MMSE (per month) vs duration of followup.
graphic factors, age and sex, and estimated duration of the disease was developed. From this model, subject-specific rates of decline could be estimated. The aveiage rate of decline between individuals with and without the various health and behavioral factors shown in Table 2 was then compared. Factors that showed a potentially significant effect on rate of decline in this univariate testing were added to the multivariate model. The multivariate model was then refined to eliminate factors which were no longer significant in the multivariate context. Results are reported on this final model. Age, sex, and duration of disease, as well as their interactions with time, were included in a basic regression model. Results from this model indicated that only age has any effect on rate of decline; even in this case, the effect was only marginally significant (/? = .07/ 1-tailed). Both age and duration were significantly negatively related with initial MMSE (j7 < .05). Also, as expected, the MMSE declined with time (slope = —3.25;/? < .01). Sex had no significant effect on initial MMSE or rate of decline. Table 3 shows the results of a series of Mests using the predicted slopes (one for each subject) from the basic model described in the previous paragraph (the predicted slopes in this table are actually the deviations from the population average slope of - 3.25 reported in the previous paragraph). This screening step was used to determine which factors should be added to the multivariate model. From Table 3, the behavioral factors (awareness, agitation, wandering, hygiene, and number of [behavioral] problems), and the health factors (thyroid disease, alcohol abuse, and other neurological disease) showed a potentially significant effect on slope and were retained for further analysis. As some of these factors may be measuring a similar behavior (e.g., agitation and wandering), it was anticipated that not all of the factors would remain significant when entered into the model simultaneously. The eight factors noted above were added to the basic model, and a stepwise backward elimination procedure was
'N = W = W =
Copyright 1990 by The Genmiohgical Society of America
Cognitive Deterioration in Alzheimer's Disease Behavioral and Health Factors Linda Teri,1 James P. Hughes,2 and Eric B. Larson3 "Department of Psychiatry and Behavioral Sciences, department of Biostatistics, and department of Medicine, University of Washington.
P
ROGRESSIVE cognitive deterioration is the hallmark of Dementia of the Alzheimer's Type (DAT). Despite this, very little is known about the nature of this decline, or about the factors influencing its progression. The original DSM-III diagnostic criteria for Primary Degenerative Dementia (DAT) indicated that the nature of cognitive deterioration was "uniformly progressive" (APA, 1980, p. 126), consistent with the clinical experience at that time. More recently, however, DSM-III-R (revised, APA, 1987) has defined the deterioration as "generally progressive," omitting the term "uniformly" to acknowledge recent clinical and empirical evidence that the rate of decline may vary, influenced by factors inherently or tangentially related to the disease itself. For example, two recent studies followed DAT patients over time and found heterogeneous rates of decline across patients, with some patients progressing at a faster rate than others (Botwinick, Storandt, & Berg, 1986; Berg et al., 1984). In addition, other studies have indicated that the pattern of cognitive deficits that decline also varies between DAT patients (Kaszniak, Wilson, Fox, & Stebbins, 1986). Unfortunately, little is known about what factors may be associated with different rates of decline. Of three variables that have been hypothesized to be associated with progression — age of onset, family history, and duration of deficits — none have yielded consistent results. For example, Huff, Growdon, Corkin, and Rosen (1987) found duration of cognitive deficits unrelated to rate of decline, but Thai and Grundman (1986) found duration associated with more rapid decline. In addition, it is unclear how these variables influence survival. Kaszniak et al. (1986) found that earlier age of onset was not associated with decreased survival time (from disease onset), but more recent studies have reported that earlier age of onset was associated with decreased
P58
survival time (Seltzer & Sherwin, 1983; Barclay, Zemcov, Blass, & McDowell, 1985). Thus, although DAT may progress at different rates, age of onset, duration, and family history fail to yield consistent findings to explain these different rates. Knowledge about causes or associations of differential rates of progression in DAT could have theoretical as well as practical use. From a theoretical viewpoint, understanding the mechanism of decline may shed light on the pathogenesis and phenomenology of DAT. From a practical viewpoint, knowledge about risk factors for more rapid progression may assist health professionals and families to plan for patient care and prevent coexistent problems from contributing to deterioration. The types of factors that might influence decline can be gleaned from clinical writings. It has long been acknowledged that coexistent emotional, social, and physical distress can exacerbate medical conditions (e.g., Sarason, Sarason, Potter, & Antoni, 1985). One would therefore hypothesize that DAT patients with coexistent behavioral or health problems might deteriorate more rapidly than patients without such difficulties. To date, however, no study has investigated the role of emotional, physical, or behavioral factors on the progression of cognitive decline in DAT. One study (Barclay et al., 1985) investigated how these factors related to mortality. They found that male gender, presenile onset, and increased severity of behavioral impairment [as measured by the Haycox (1984) scale] were positively associated with decreased survival; the presence of vascular disease [as measured by the Hachinski et al. (1975) ischemic score] was not associated with survival. Whether these factors or mortality relate to cognitive decline is unknown.
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
Alzheimer's disease is characterized by progressive cognitive decline. However, little is known about the "typical" rate of decline, the degree of individual heterogeneity evident in decline, or the types of factors that influence such decline. This study investigated these questions in a sample of 106 patients with Alzheimer's disease, assessed at 1-5 points in time, spanning up to three years. At each time point, the Mini-Mental State Exam, a measure of global cognitive function, was administered to all patients. Measures of behavioral disturbance (including the presence/ absence of hallucinations, depression, incontinence, wandering, and agitation), health status (including presence/ absence of neurological, cardiovascular, and other diseases), and descriptive information (such as gender, age at time of onset, and duration of deficits) were obtained at entry into the study. A two-stage random effects regression model was fit to the data and then used to assess the effect of these behavioral, health, and descriptive measures on the rate of decline. Results indicate that the rate of cognitive decline in Alzheimer's disease is quite variable. Patients with various health and behavioral problems declined at a rate between 1.4 and 5 times faster than patients without such problems. Alcohol abuse, additional neurological disease, and agitation were significantly related to rate of decline. Overall number of problems was not. The association of these problems with accelerated decline may have prognostic and treatment implications.
COGNITIVE DETERIORATION
This study investigated the relationship of cognitive decline in DAT to behavioral, health, or descriptive variables that might influence the rate of deterioration. It was hypothesized that patients with severe behavioral, cognitive, and health problems at intake would have a greater rate of deterioration than patients without such problems. It was further hypothesized that patients with multiple problems would have an accelerated rate of decline, as compared to patients with a single problem. Patients' gender, age, education, and prior occupation were hypothesized to be unrelated to rate of cognitive decline, while younger age at onset and longer duration of cognitive deficits were hypothesized to be related to an increased rate of decline. METHODS
Measures. — All subjects were given the following tests as part of the overall diagnostic evaluation described above. The Mini-Mental
State Exam (MMSE) (Folstein et al.,
1975) is a brief measure used for screening organic impairment. It provides a score of global cognitive deficits and taps a range of cognitive tasks including orientation, verbal
reasoning, visual perceptual skills, language, and memory. Scores range from 0-30 with scores of 27-30 considered mild to normal, and scores below 24 indicative of definite organic involvement (Anthony, LeResche, Niaz, Von Korff, & Folstein, 1982; Klein etal., 1985). The MMSE has been shown in previous studies to have good test-retest reliability and to discriminate demented from nondemented individuals (Folstein et al., 1975; Klein et al., 1985; Thai and Grundman, 1986). The MMSE was administered to patients by a trained research nurse, physician, and psychiatrist or psychologist. As Table 1 shows, the mean score for this sample at intake was 18.2 (SD = 6.7); the majority of subjects were in the moderately to severely impaired range (85% scored between 24 and 0). Sixteen additional items evaluated the presence or absence of 10 behaviors and 6 health problems reported in the clinical literature as problematic and prevalent for DAT patients. These 16 items were rated as present or absent based upon data obtained during the evaluation. The behaviors were restlessness, agitation, wandering, hallucinations, suspiciousness or paranoia, incontinence, problems with personal hygiene, depression, awareness of memory loss, and falling. Two behaviors may benefit from definition: restlessness meant that the patient was mildly motorically active (fidgety, uneasy, impatient); agitation meant that the patient was extremely active (frenzied, excitable, unable to sit still). The six health items were: thyroid disease, other neurological disease, head trauma, gait disturbance, alcohol and drug abuse. None of the health problems was thought to be the primary cause of the dementia. Thyroid disease included symptoms (such as weight loss, dry skin, cold insensitivity and constipation) thought to indicate underlying thyroid disease; other neurological disease included any other known neurological disease (such as Parkinson's disease, cerebrovascular accident, transient ischemic attack, periph-
Table 1. Characteristics of the Sample (N = 106) Patients Age (M ± SD) Gender Men Women Initial Mini-Mental State Exam (M ± SD) Duration of cognitive deficits (months) (M ± SD) Educational level Less than high school High school graduate More than high school Unknown Marital status Married Widowed Other Caregiver respondent Spouse Child Other
77 ± 6.7 30 76 18.2 ± 6.7 46.6 ± 26.0 29 19 39 19 41 49 16 25 51 30
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
Subjects. — Subjects were selected from a cohort of 200 patients entered into a prospective study of the evaluation of dementia (Larson, Reifler, Sumi, Canfield, & Chinn, 1985). They were patients at the outpatient Geriatrics and Family Services Clinic at the University of Washington Hospital (Reifler, Larson, & Teri, 1987). Patients had most often been referred by a concerned family member or a general physician. A comprehensive geriatric evaluation was conducted on each patient, including (a) a 1-2 hour intake interview with the patient and concerned family members, conducted by an experienced geriatric psychiatrist or psychologist, (b) a complete physical and neurological exam conducted by an internist (E.B.L.) that included appropriate diagnostic tests such as thyroid function tests, complete blood count, chemistry battery, B12 and folate levels, EEG, VDRL. and CT scan, (c) a neuropsychological screening battery, including the WAIS-R (Wechsler, 1981), the Wechsler Memory Scale (Wechsler, 1945), and the Folstein MiniMental State Exam (Folstein, Folstein, & McHugh, 1975), and (d) a home visit conducted by a geriatric social worker, to assess environmental safety issues, patients' functional status, and daily living skills. Subjects for this study were those who met DSM-III criteria for Primary Degenerative Dementia (PDD) based on the evaluation described above and on a meeting between the psychiatrist, psychologist, internist, and neurologist/neuropathologist during which time diagnoses for individual subjects were discussed. Only those subjects for whom each rater agreed that DSM-III criteria for PDD were met and for whom PDD was the primary diagnosis were considered for inclusion in this study. Additionally, since the primary interest was in the change in cognitive status, only patients with two or more cognitive evaluations (MMSE) were included (N = 106). The mean age of subjects was 77 years (range 60-94); 70% were female. Table 1 summarizes the demographic characteristics and initial information on all subjects.
P59
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TER1 ETAL.
Procedures. — Subjects were followed during the course of their clinical care. The median MMSE follow-up time was 11 months. However, 21 patients were followed fewer than four months due to logistical problems (usually geographic distances). Subsequent analyses include these patients; the methods described herein effectively give relatively less weight to subjects with short follow-up. Replicate analyses without these subjects gave essentially the same results as those reported below. Statistical methods. — The statistical methods used to quantify change in the MMSE are based on a model described by Laird and Ware (1982). The model they propose can be written as y, = X(a + Z ^ + e, (1) where Ys = vector of observations (over time) for the ith individual; Xi = matrix of independent variables (design matrix) (see, for example De Groot, 1986) for the fixed effects; a = fixed effects parameters (to be estimated); Z, = design matrix for the random effects; b, = random effects parameters (to be estimated); e, = random errors. In this model the fixed effects are typically population parameters (such as sex, race, average rate of decline over time, etc.) and the random effects are the deviations of individuals from the population parameters. For example, if MMSEs are observed over time, a slope parameter in the fixed effects portion of the model would estimate the average rate of decline in MMSE over time for the entire sample. A slope parameter in the random effects portion of the model (which is estimated for each individual) would quantify each individual's deviation from the average slope. One of the strengths of this model is that subjects can have different numbers of MMSE scores at differing time intervals. Other
models for repeated measures (e.g., Greenhouse & Geisser, 1959; Koch, 1967) require "balanced" designs in which subjects are observed at the same time intervals. Also, the random effects portion of equation (1) provides the structure necessary to account for the nonindependence of observations taken on the same individual. An excellent review article on this and similar models is Feldman (1988). In this study the relationship between various behavioral and health characteristics (evaluated once at patient enrollment) and the rate of decline of the MMSE (obtained from scores at varying time intervals) was examined. Using the model shown in equation (1), the fixed effects consist of a y-intercept (corresponding to the mean MMSE at intake), various "main effects" (such as presence or absence of a given behavior/health effect), and various effect-by-time interaction terms. The main effects parameterize the differences in the intake MMSE attributable to the behavior/health variables. However, since intake occurs at some arbitrary point in the course of the disease, the main effects can be difficult to interpret. Of greater interest are the effect x time interaction terms. These model the relationship between the behavioral/health variables and rate of decline in the MMSE over time. A positive interaction between a behavioral/ health variable and time means that the variable decreases (makes less negative) the rate of decline, and a negative interaction means that the rate of decline is increased by the behavioral/health variable. The random effects portion of the model includes a subject-specific slope and intercept. This essentially fits a simple linear regression (MMSE vs time) for each subject. Conceptually, the fixed effect slope and intercept are the averages of these subject-specific slopes and intercepts. The variation of the subject-specific parameters (particularly the slopes) around the fixed-effects parameters is of interest because it shows how much variation in rate of decline individuals exhibit around the overall regression line. Only linear effects of time were investigated; there were too few repeated observations on each individual to fit higher order effects. RESULTS
Figure 1 shows a plot of the total percent change in the MMSE [(last MMSE-first MMSE)/first MMSE] on a per month basis. The mean deviation of follow-up was 1.1 years (SD = .66). As already stated, all subjects had two or more follow-up intervals (41% had 2, 39% had 3, 11% had 4, and 3% [n = 3] had 5). For most subjects, decline in MMSE over time is evident but the degree of decrement is variable. There is, in fact, considerable variability in the percent change for those cases where the total duration of follow-up was less than four months. Stated differently, two MMSEs measured less than four months apart are not a reliable measure of change. The regression model described above reduces the influence of these cases. Table 2 shows the frequency of occurrence of the behavioral and health measures that were included in this study. One measure, known drug abuse, was not present in any subject; consequently, it was dropped from further analysis. Multivariate analyses of these data proceeded in the following manner: first, a basic model incorporating the demo-
Downloaded from http://geronj.oxfordjournals.org/ at New York University on July 1, 2015
eral neuropathy) that was substantiated by history and clinical findings; head trauma included trauma sufficiently severe to result in concussion, loss of consciousness, or requiring medical attention; gait disturbance included any problem with a presumed neurological cause (i.e., nonarthritic or musculoskeletal); alcohol and drug abuse included current or past documentation of abuse. In the case of alcohol abuse, the amount of alcohol consumption and the temporal relationship of alcohol to onset of problems were determined to be unrelated to the cognitive problems experienced. This is admittedly somewhat problematic given the difficulty in differentiating ETOH-dementia from Alzheimer's disease. However, rather than exclude patients with ETOH (alcohol) use from the study, it was decided to include them in order to evaluate whether ETOH use was an important "additional health factor" in determining subsequent cognitive decline. (This idea was supported by ensuing analyses as will become evident later.) Each item was rated as present or absent by a trained geriatric internist (E.B.L.) based upon information obtained from observing the patient or provided by the family.
COGNITIVE DETERIORATION
P61
Table 2. Frequencies of Problems (N = 106)
if)
10
20 30 Duration (months)
40
Figure I: Percent change in MMSE (per month) vs duration of followup.
graphic factors, age and sex, and estimated duration of the disease was developed. From this model, subject-specific rates of decline could be estimated. The aveiage rate of decline between individuals with and without the various health and behavioral factors shown in Table 2 was then compared. Factors that showed a potentially significant effect on rate of decline in this univariate testing were added to the multivariate model. The multivariate model was then refined to eliminate factors which were no longer significant in the multivariate context. Results are reported on this final model. Age, sex, and duration of disease, as well as their interactions with time, were included in a basic regression model. Results from this model indicated that only age has any effect on rate of decline; even in this case, the effect was only marginally significant (/? = .07/ 1-tailed). Both age and duration were significantly negatively related with initial MMSE (j7 < .05). Also, as expected, the MMSE declined with time (slope = —3.25;/? < .01). Sex had no significant effect on initial MMSE or rate of decline. Table 3 shows the results of a series of Mests using the predicted slopes (one for each subject) from the basic model described in the previous paragraph (the predicted slopes in this table are actually the deviations from the population average slope of - 3.25 reported in the previous paragraph). This screening step was used to determine which factors should be added to the multivariate model. From Table 3, the behavioral factors (awareness, agitation, wandering, hygiene, and number of [behavioral] problems), and the health factors (thyroid disease, alcohol abuse, and other neurological disease) showed a potentially significant effect on slope and were retained for further analysis. As some of these factors may be measuring a similar behavior (e.g., agitation and wandering), it was anticipated that not all of the factors would remain significant when entered into the model simultaneously. The eight factors noted above were added to the basic model, and a stepwise backward elimination procedure was
'N = W = W =
