American Journal of Epidemiology Copyright © 1992 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved
Vol. 136, No 7 Printed in U. S.A.
Depressive Symptoms and Other Psychosocial Factors as Predictors of Stroke in the Elderly
A. Colantonio,1'2 S. V. Kasl,2 and A. M. Ostfeld2
aged; cerebrovascular disorders; depression; risk factors; social isolation
Epidemiologic studies have provided useful information on stroke risk factors. The
predominant risk factor for stroke is hypertension. Other risk factors that have been identified include cardiovascular comorbidity, diabetes mellitus, obesity, fibrinogen levels, hematocrit, family history of stroke, serum lipids, and cigarette and alcohol use (1-5). The reason for examining psychosocial variables with respect to stroke is that they have been shown to influence other important health outcomes. The effects of such psychosocial variables as depression, social support, social networks, and religiousness on stroke incidence have not been widely explored. Depression is one of the most important disorders in later life (6) and has been linked with greater overall mortality in older populations (7-9). It is also commonly experienced after a stroke with a prevalence of depression as high as 30 percent in the first year post stroke (10). Some evidence for higher rates of mortality from cardiovascular disease in clinically depressed patients has
Received for publication February 13, 1991, and in final form June 24, 1992 Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; ICD-9, International Classification of Diseases, Ninth Revision; RR, relative risk; YHAP, Yale Health and Aging Project. 1 Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA. 2 Department of Epidemiology and Public Health, Yale University, New Haven, CT. Reprint requests to Dr. A. Colantonio, Division of Critical Care Medicine, 648 Scaife Hall, Presbyterian University Hospital, Pittsburgh, PA 15213 This work has been supported in part by the Robert Wood Johnson Foundation (grant 9923, Psychosocial Predictors of Recovery in the Elderly); by the National Institute on Aging (grant N01AG02105, Establishment of Populations for the Epidemiologic Study of the Elderly, and grant AG00181, Epidemiology of Aging Program at the University of Pittsburgh); and by the Social Sciences and Humanities Research Council of Canada. The authors thank Dr. Lewis H Kuller for his helpful comments, Dr Rebecca Dersimonian for her assistance with statistical analysis, and Rita Wolk for her assistance with typing QO/l
Downloaded from http://aje.oxfordjournals.org/ at University of California, San Francisco on January 16, 2015
The aim of this paper is to assess the influence of selected psychosocial factors as predictors of stroke incidence in a probability sample of noninstitutionalized elderly. The main psychosocial factor of interest was depression. Marital status, social support, social networks, and religiousness were also assessed as potential antecedent or mediating factors. The data were obtained from a prospective longitudinal study based on 2,812 individuals aged 65 years and over living in New Haven, Connecticut. The incidence of stroke was monitored from the baseline interview in 1982 until December 1988. Depression, measured by the Center for Epidemiologic Studies Depression Scale (CES-D), was measured at baseline as were other predictor variables. Univariate Cox regression analyses revealed that higher CES-D scores were predictive of greater stroke incidence (p < 0.05). More frequent attendance at religious services was associated with lower incidence (p < 0.001). CES-D scores were also correlated with many measures of sociodemographic, health, and physical function factors in our multivariate analysis (p < 0.05). When combined with other significant predictor variables such as age, sex, hypertension, diabetes, physical function, and smoking, neither depression nor religious attendance retained its significance. Am J Epidemiol 1992;136:884-94.
Stroke in the Elderly: Psychosocial Factors
MATERIALS AND METHODS Subjects
The data for this study came from a large longitudinal research project, the Yale Health and Aging Project (YHAP). This survey is part of the Established Populations for Epidemiologic Studies of the Elderly Program, a collaborative program funded by the National Institute on Aging that consists of four epidemiologic cohort studies in four locations: New Haven, Connecticut; East Boston, Massachusetts; Iowa and Washington counties, Iowa; and Durham, Franklin, Granville, Vance, and Warren counties, North Carolina (18). The New Haven study is based on a probability sample of 2,812 noninstitutionalized men and women, 65 years of age and older, living in the city of New Haven, Connecticut, in 1982. The sampling frame is based on samples drawn from three housing strata reflecting the three most common types of housing for those aged 65 years and
older in New Haven: 1) public elderly housing that is age and income restricted, 2) private elderly housing that is age restricted, and 3) private community housing and apartments. For the community sample, housing units were randomly selected from a utilities listing. Men were oversampled in both the private and community strata since elderly women, in general, tend to outnumber elderly men. A computer-generated Kish selection procedure was used for the subsample of women. All individuals in the public housing stratum were included, however, because of a special interest in this group. Response rates were highest in the public housing stratum: 89 percent for men and 89 percent for women. In private housing, 82 percent of men and 84 percent of women agreed to participate. Response rates were lowest in the community: 79 percent of men and 76 percent of women agreed to participate. More details of this population are given elsewhere (19, 20). Only 24 subjects (1 percent) dropped out by the end of this study. Some 208 respondents who reported having had a stroke or whose medical records indicated a stroke prior to the beginning of the study were eliminated from the sample, leaving a sample size of 2,604. These strokefree participants were followed from baseline interview in 1982 until December 1988. Stroke incidence was ascertained by four overlapping methods: 1) monitoring hospital admissions of YHAP subjects in the two New Haven hospitals where nearly 90 percent of all hospitalizations of the cohort occurred, 2) death certificates, 3) Health Care Financing Administration data, and 4) self-reported strokes from annual YHAP contacts. In all these sources of information on stroke, hospital records were sought and evaluated to confirm stroke incidence. Stroke incidence from the YHAP cohort was ascertained predominately by monitoring hospitalizations of YHAP subjects. A trained nurse research assistant regularly reviewed admission and discharge records of these two hospitals for the hospitalizations of study subjects. If these records of participants indicated admission and/or discharge
Downloaded from http://aje.oxfordjournals.org/ at University of California, San Francisco on January 16, 2015
been presented (11). Yet the influence of depression specifically on stroke incidence has not been explored to date. Over the last 15 years, there has been growing evidence for the effect of social support and social networks on health status (12). Lack of social support has been associated with increased mortality risk (13) and delayed recovery from disease (14). Religiousness is hypothesized to influence health outcomes in a similar way as social support. Several studies, for instance, have found a significant relation between religious attendance, religious feelings, and health (15-17). The main aim of this paper is to investigate the influence of depression on stroke incidence in a population of noninstitutionalized elderly, controlling for relevant health and sociodemographic variables. We also examined the influence of social support, social networks, and religiousness as potential modifier or antecedent variables. We hypothesized that these variables would have a beneficial influence on stroke incidence, whereas the presence of depression would increase the risk.
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rhage, and transient cerebral ischemias. Most of the strokes were attributed to thrombosis (n = 100). There were twelve hemorrhagic strokes. The remaining number of strokes (« = 45) were classified as ill defined/ other. Instruments and variables
Almost all independent variables were measured by means of a structured interview at baseline in 1982. Sociodemographic characteristics that were recorded included age, sex, years of education, level of income, race, and housing stratum. Health status measures were obtained mainly by self report. The presence of hypertension, however, was based on actual blood pressure readings taken by interviewers in 1982. Interviewers were trained according to the Hypertension Detection and Follow-up Program protocol (21). Blood pressure was measured with the use of a mercury column sphygmomanometer while the participant was seated. Mean diastolic and systolic blood pressure readings were calculated from the second and third readings. The presence of hypertension was coded as a binary variable. Subjects were coded as negative if systolic blood pressure was less than 140 mmHg and diastolic blood pressure was less than 90 mmHg and they were not taking antihypertensive medication. If the blood pressure of subjects had exceeded these cutoff points and/or they were taking antihypertensive medications, then respondents were coded as hypertensive. Use of an antihypertensive was determined by trained interviewers who examined all medication the respondent was taking within the 2 weeks before the interview and who then copied the medication name from the containers and coded. Cardiovascular comorbidity was measured by asking respondents whether a doctor ever told them they had had a heart attack, a coronary, a myocardial infarction, a coronary thrombosis, or a coronary occlusion. Responses to the London School of Hygiene Cardiovascular Questionnaire were used to ascertain angina and intermittent claudication (18). The frequency of alcohol
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for stroke between 1982 and November 1988 (according to the specified International Classification of Diseases, Ninth Revision (ICD-9), codes), then medical records were examined to ascertain diagnosis. Medical records were reviewed by a trained nurse researcher. Computerized tomography scans were available for 77 percent of cases to clarify diagnosis. A total of 136 incident strokes were ascertained by this method. Death certificates of all study subjects were also reviewed for ICD-9 codes that indicated stroke. Death certificates of the Connecticut State Department of Health Services were regularly checked for the deaths of YHAP subjects, and out-of-state death certificates were obtained by mail. They were coded by a single specially trained nosologist. All death certificates that indicated stroke or cerebrovascular disease as an underlying, immediate, and/or contributing cause of death were reviewed by a study physician expert in cerebrovascular disease to confirm diagnosis. Fifteen individuals were identified by this method. Hospital records were then sought to verify stroke diagnosis and date of stroke. In three cases, hospital records could not be retrieved. The type and date of stroke were then taken from the death certificate. Included in our number of strokes were self-reported strokes from annual interviews that were confirmed by medical records (n = 13) or by Health Care Financing Administration data (n = 1). Furthermore, Health Care Financing Administration data identified two cases that were hospitalized outside New Haven. All these methods of ascertaining stroke combined yielded a total of 167 strokes. The following ICD-9 codes were used to include strokes into the study: 431, 432.9, 433.0-434.9, and 436-437.1, which includes intracerebral hemorrhage, unspecified intracranial hemorrhage, occlusion and stenosis of precerebral and cerebral arteries, acute but ill-defined cerebrovascular disease, cerebral atherosclerosis, and other generalized ischemic cerebrovascular disease. The study excluded subarachnoid hemorrhage, nontraumatic extradural and subdural hemor-
Stroke in the Elderly Psychosocial Factors
The latter scale measured higher levels of physical function than the Katz Scale. Each of these items was adapted and used previously in the Framingham Disability Study (35). This scale will be referred to as the Rosow Scale throughout this paper. For the Rosow Scale, a score of 0 was given to each of the items assessed if the respondent performed the activity independently and 1 if the respondent needed assistance or was unable to perform the task. Thus, a score between 0 and 3 was possible. Depressive symptomatology was measured using the Center for Epidemiologic Studies Depression (CES-D) Scale. This 20item scale was designed to measure depressive symptomatology in the general population. The 20 CES-D items are scored on a standard four-point scale (0-3) with a potential range of 0-60, with a higher score indicating more symptomatology. A cutpoint of 16 or higher is used to distinguish between those with depressive symptomatology in the clinical range and those with lesser symptoms, based on tests with a clinical population (36). Scores for those with three or fewer missing items were created by obtaining a means score based on the items answered and multiplying by 20. This variable was treated as both continuous (with a potential range of 0-60) and dichotomous (not depressed vs. depressed) using a score of 16 as a cutoff point. Marital status was coded as married or not. Thus, those who were separated, divorced, never married, or widowed were grouped in the nonmarried category. The social support measure was based on a total of a cumulative score of availability of financial, instrumental, and emotional support. A higher score is indicative of more supports. Social networks were assessed with the social network index devised by Berkman and Syme (13). This index is composed of four types of social contacts: 1) marital status, 2) numbers of close friends and relatives, 3) church membership, and 4) membership in community organizations. This structural index of social ties weights marital status and intimate contacts with close friends and relatives more heavily than church affilia-
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use was based on consumption during the past month. Amounts were ascertained separately for beer, wine, and liquor, the latter being defined as "things like whiskey, vodka, gin, brandy, or liquors." Smoking, measured as a dichotomous variable, was based on whether the individual reported being a smoker at the time of the interview or at any time in the past. Former smokers were placed in a category with smokers, because former smokers have been found to be at higher risk for stroke in comparison with nonsmokers (22-24). Body mass was assessed by Quetelet's Index (25). Cognitive impairment was measured using the Pfeiffer Scale, a 10-item screening instrument similar to the Kahn and Goldfarb Short Portable Mental Status Examination (26). In this scale, the one item "What is the name of this place?" was changed to "What is your address?" because the latter seemed more appropriate for communitydwelling respondents. The scores of the Pfeiffer Scale were utilized in analyses as a three-level ordinal variable with more than four errors representing the most impaired category. Other analyses of these data have used the same approach (27). Cognitive impairment has been associated with depressive symptomatology (28, 29), and the former was included in the analysis as a control variable. Physical functioning is another variable that has been strongly correlated with depressive symptomatology in the YHAP data (19). Physical functioning, measured by two instruments, served as a control variable for the CES-D scores. The Katz Activities of Daily Living Scale (30) was expanded to include walking. This modification conformed with a practice utilized in two national surveys of elderly populations (31, 32) and a recent study on elderly California Medicaid recipients (33). The Katz Scale was recorded as a dichotomous variable in which 0 was assigned if the individual performed all items independently, and 1 signified dependence in one or more activities of daily living. The other scale of gross-mobility function was based on three items developed from the work of Rosow and Breslau (34).
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Statistical analysis
A Cox proportional hazards model using the BMDP computer program package (37) was utilized for univariate and multivariate modeling. Log-(log(survival)) plots were generated for each of the independent variables to check the proportional hazards assumption. The "Proc Lifetest" procedure by SAS was utilized to conduct this nonparametric analysis (38). Kendall's tau-b correlation coefficients were computed using SAS to analyze correlations between the psychosocial variables and control variables. The strategy of the analysis was to establish whether psychosocial factors predicted stroke incidence over and above known risk factors for stroke and the net of selected control variables. We did not wish to include all potential risk factors available to us in the YHAP data in the model because many of the health measures were highly correlated and to include them all in the model
would lead to problems of multicollinearity. A stepwise regression was undertaken to arrive at a subset of health and sociodemographic variables that were used as control variables. Then, each psychosocial variable of interest was added to the model one by one, and its additional contribution to the base model reflecting the role of control variables was examined. RESULTS
Overall, respondents were on the average 74 years old at the initial interview, primarily female (59 percent), and white (79 percent). The greatest percentage lived in community housing (43 percent). Fifty-five percent of the population had a grade school education only. Of the individuals who reported income, 40 percent had annual reported incomes (for self and spouse if applicable) under $5,000. As mentioned previously, the total number of strokes that occurred over the 7-year follow-up period was 167. Subjects were followed for a total of 14,730.8 person-years, which yielded a total incidence rate of 0.01 per person-year. Tables 1-3 provide information on selected sociodemographic, psychosocial, and health characteristics of the study population with respective incidence rates. These tables provide an overview of cumulative incidence that does not account for differential mortality over time. Thus, they are not meant to reflect the proportional hazards analysis used in this study. Table 1 shows that the incidence increases sharply with age and is slightly higher for males, nonwhites, and less educated groups. In table 2, CES-D scores were divided into quartiles. The incidence was higher for the upper two quartiles of individuals with more symptomatology. Married individuals and those with more social networks had slightly lower incidence rates. Furthermore, the incidence was also distinctly lower for individuals who attended religious services once a week or more. The incidence appeared unrelated to other measures of religiousness and numbers of types of support. Table 3
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tions and group memberships. The index ranges from 1 to 12 with the lowest score given to the most isolated subjects. In our analysis, we dichotomized this index on the basis of the mean number of networks (x = 3.6). Therefore, a score of 3 or lower was indicative of fewer networks and a score greater than or equal to 4 included individuals in the higher category. Religiousness was assessed by three items. The first question that refers to public religiousness was measured by frequency of attendance at religious services (five levels). A higher score indicates more frequent attendance at religious services. Private religiousness was measured by two items assessing the subjective religious experience. The questions read as follows: "Aside from attendance at religious services, do you consider yourself to be 1) against religion, 2) not at all religious, 3) only slightly religious, 4) fairly religious, or 5) deeply religious," where deeply religious was given the highest score, and "How much is religion a source of strength and comfort to you? 1) none, 2) a little, or 3) a great deal," with 0 indicating none.
Stroke in the Elderly: Psychosocial Factors
TABLE 1. Distribution of stroke incidence by selected sociodemographic characteristics of the study population: New Haven, Connecticut, 19821988 %
Age (years) at baseline 65-74 75-84 >85
78/1,456 58/892 31/256
5.4 6.5 12.1
Sex Male Female
74/1,065 73/1,539
6.9 6.0
Housing stratum Public Private Community
43/670 53/805 71/1,129
6.4 6.6 6.3
128/2,058
6.2
38/535
7.1
Race White Nonwhite (88% black) Education (years) 9
89/1,350 69/1,177
6.6 5.9
TABLE 2. Distribution of stroke incidence by selected psychosocial characteristics of the study population: New Haven, Connecticut, 1982-1988
CES-D* scores 0-1.9 2-5.9 6-11.9 >12 Marital status Married Unmarried
No./total
%
25/530 37/698 51/631 48/648
4.7 5.3 8.1 7.4
5.7 55/971 110/1,633 6.7
Total size of network Few networks (1-3) Larger networks (>4)
95/1,372 6.9 67/1,178 5.7
No. of types of support (emotional, financial, instrumental) 0 1 2 3
13/287 45/637 65/922 42/717
4.5 7.1 7.0
Frequency at religious services Never/almost never
55/637
8.6
18/308 15/276 25/282 50/1,055
5.8 5.4 8.9 4.7
8/86 18/277 67/1,201 72/967
9.3 6.5 5.6
Once or twice a year Every few months Once or twice a month Once a week or more
5.9
indicates that the incidence of stroke was higher for subjects reporting hypertension, diabetes, prior myocardial infarction, angina, intermittent claudication, cognitive impairment, a history of smoking, and limitations in functioni. Correlations were examined between CES-D and potential sociodemographic and health control variables. Table 4 shows significant positive correlations between age, sex, history of myocardial infarction, diabetes, angina, cogni-
* CES-D, Center for Epidemiologic Studies Depression Scale.
tive impairment, and limitations in function. Each of the predictive factors of interest and sociodemographic, health, and psychosocial variables were examined in relation to the cumulative hazard for stroke incidence, using the Cox proportional hazards model. The results of this analysis are presented in table 5 for each variable separately. Age was the most significant sociodemographic variable of all in this analysis. Being female was protective against stroke incidence (relative risk (RR) = 0.78) but did not reach statistical significance at the 0.05 level. Other sociodemographic factors such as race and education did not appear as important.
Housing stratum (p = 0.86) was the least salient variable of this category of factors. Higher CES-D scores were predictive of greater stroke incidence in the univariate analysis. Because of the skewed distribution of this continuous variable, the logarithms of CES-D scores were taken, a procedure that improved the fit of this variable. Of the other variables, the frequency of attendance at religious services showed a significant negative association with incidence. Other variables such as marital status, the social network index, number of types of support, and the religiousness items did not achieve significance.
Religiosity Not at all/against Slightly Fairly Deeply Religion: source of strength None A little A great deal
7.5
10/154 6.5 37/581 6.4 110/1,776 6.2
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No./total
889
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Colantonio et al.
TABLE 3. Distribution of stroke incidence by selected health characteristics of the study population: New Haven, Connecticut, 1982-1988 %
Hypertension Yes No
111/1,550 46/969
7.2 4.7
Diabetes Yes No
46/377 132/2,222
12.2 5.9
Prior myocardial infarction Yes No
32/349 132/2,244
9.2 5.9
Anoina Yes No
18/158 147/2,439
11.4 6.0
Intermittent claudication Yes No
11/54 154/2,540
20.4 6.1
Cognitive impairment None Mild Severe
85/1,520 49/720 29/317
5.6 6.8 9.1
Smoking Current Past Never
34/519 52/749 79/1,321
6.6 6.9 6.0
123/2,200 42/385
5.6 10.9
63/1,447 60/787 35/255
4.4 7.6 13.7
Physical function (Katz Scale) 0 limitations >1 limitation Physical function (Rosow Scale) 0 limitations 1 -2 limitations 3 limitations
1982-1988 Variable*
TABLE 4. Kendall's tau-b correlation coefficients between selected health and sociodemographic control variables and CES-Dt scores: New Haven, Connecticut, elderly, 1982 Control variables
Correlation coefficient
Age Sex Education Hypertension Prior myocardial infarction Diabetes Intermittent claudication Smoker Cognitive impairment Physical function (Rosow Scale)
0 079*** 0.111*** -0.102*** 0.029 0.078*** 0.086*** 0.014 0.0 0.145*** -0.266***
* p < 0 0 5 ; " p < 0 . 0 1 ; *"p < 0.001. t CES-D, Center for Epidemiologic Studies Depression Scale
Sociodemographic variables Age (10-year intervals) Sex Race Education Psychosocial variables CES-Dt scores CES-D (logged) Marital status Social network index No of types of support Frequency at religious services Religiousness Religion: source of support Health variables Hypertension Diabetes Prior myocardial infarction Angina Intermittent claudication Body massj (Quetelet index) Smoking Alcohol intake§ Cognitive impairment (Pfeiffer Scale) Physical function variables Katz Scale Rosow Scale
Relative risk
95% Clf
1.69 0.78 0.91 0.85
1.37-2.09 0.57-1.06 0.63-1.30 0.62-1.17
1.02 1.23 0.83 0.77 1.03
1.00-1.03 1.05-1.44 0.60-1.15 0.56-1.05 0.88-1.21
0.86 0.99
0.79-0.94 0.82-1.20
1.04
0.82-1.33
1.64 2.56
1.17-2.31 1.82-3.60
1.82 2.04 4.41
1.23-2.67 1.25-3.33 2.39-8.14
0 97 1.21 1.03
0.93-1.00 0.89-1.64 0.79-1.35
1.38
1.12-1.69
2.45 1.59
1.73-3.48 1.40-1.82
* Reference categories: sex, male; race, nonwhite; education,
Vol. 136, No 7 Printed in U. S.A.
Depressive Symptoms and Other Psychosocial Factors as Predictors of Stroke in the Elderly
A. Colantonio,1'2 S. V. Kasl,2 and A. M. Ostfeld2
aged; cerebrovascular disorders; depression; risk factors; social isolation
Epidemiologic studies have provided useful information on stroke risk factors. The
predominant risk factor for stroke is hypertension. Other risk factors that have been identified include cardiovascular comorbidity, diabetes mellitus, obesity, fibrinogen levels, hematocrit, family history of stroke, serum lipids, and cigarette and alcohol use (1-5). The reason for examining psychosocial variables with respect to stroke is that they have been shown to influence other important health outcomes. The effects of such psychosocial variables as depression, social support, social networks, and religiousness on stroke incidence have not been widely explored. Depression is one of the most important disorders in later life (6) and has been linked with greater overall mortality in older populations (7-9). It is also commonly experienced after a stroke with a prevalence of depression as high as 30 percent in the first year post stroke (10). Some evidence for higher rates of mortality from cardiovascular disease in clinically depressed patients has
Received for publication February 13, 1991, and in final form June 24, 1992 Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; ICD-9, International Classification of Diseases, Ninth Revision; RR, relative risk; YHAP, Yale Health and Aging Project. 1 Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA. 2 Department of Epidemiology and Public Health, Yale University, New Haven, CT. Reprint requests to Dr. A. Colantonio, Division of Critical Care Medicine, 648 Scaife Hall, Presbyterian University Hospital, Pittsburgh, PA 15213 This work has been supported in part by the Robert Wood Johnson Foundation (grant 9923, Psychosocial Predictors of Recovery in the Elderly); by the National Institute on Aging (grant N01AG02105, Establishment of Populations for the Epidemiologic Study of the Elderly, and grant AG00181, Epidemiology of Aging Program at the University of Pittsburgh); and by the Social Sciences and Humanities Research Council of Canada. The authors thank Dr. Lewis H Kuller for his helpful comments, Dr Rebecca Dersimonian for her assistance with statistical analysis, and Rita Wolk for her assistance with typing QO/l
Downloaded from http://aje.oxfordjournals.org/ at University of California, San Francisco on January 16, 2015
The aim of this paper is to assess the influence of selected psychosocial factors as predictors of stroke incidence in a probability sample of noninstitutionalized elderly. The main psychosocial factor of interest was depression. Marital status, social support, social networks, and religiousness were also assessed as potential antecedent or mediating factors. The data were obtained from a prospective longitudinal study based on 2,812 individuals aged 65 years and over living in New Haven, Connecticut. The incidence of stroke was monitored from the baseline interview in 1982 until December 1988. Depression, measured by the Center for Epidemiologic Studies Depression Scale (CES-D), was measured at baseline as were other predictor variables. Univariate Cox regression analyses revealed that higher CES-D scores were predictive of greater stroke incidence (p < 0.05). More frequent attendance at religious services was associated with lower incidence (p < 0.001). CES-D scores were also correlated with many measures of sociodemographic, health, and physical function factors in our multivariate analysis (p < 0.05). When combined with other significant predictor variables such as age, sex, hypertension, diabetes, physical function, and smoking, neither depression nor religious attendance retained its significance. Am J Epidemiol 1992;136:884-94.
Stroke in the Elderly: Psychosocial Factors
MATERIALS AND METHODS Subjects
The data for this study came from a large longitudinal research project, the Yale Health and Aging Project (YHAP). This survey is part of the Established Populations for Epidemiologic Studies of the Elderly Program, a collaborative program funded by the National Institute on Aging that consists of four epidemiologic cohort studies in four locations: New Haven, Connecticut; East Boston, Massachusetts; Iowa and Washington counties, Iowa; and Durham, Franklin, Granville, Vance, and Warren counties, North Carolina (18). The New Haven study is based on a probability sample of 2,812 noninstitutionalized men and women, 65 years of age and older, living in the city of New Haven, Connecticut, in 1982. The sampling frame is based on samples drawn from three housing strata reflecting the three most common types of housing for those aged 65 years and
older in New Haven: 1) public elderly housing that is age and income restricted, 2) private elderly housing that is age restricted, and 3) private community housing and apartments. For the community sample, housing units were randomly selected from a utilities listing. Men were oversampled in both the private and community strata since elderly women, in general, tend to outnumber elderly men. A computer-generated Kish selection procedure was used for the subsample of women. All individuals in the public housing stratum were included, however, because of a special interest in this group. Response rates were highest in the public housing stratum: 89 percent for men and 89 percent for women. In private housing, 82 percent of men and 84 percent of women agreed to participate. Response rates were lowest in the community: 79 percent of men and 76 percent of women agreed to participate. More details of this population are given elsewhere (19, 20). Only 24 subjects (1 percent) dropped out by the end of this study. Some 208 respondents who reported having had a stroke or whose medical records indicated a stroke prior to the beginning of the study were eliminated from the sample, leaving a sample size of 2,604. These strokefree participants were followed from baseline interview in 1982 until December 1988. Stroke incidence was ascertained by four overlapping methods: 1) monitoring hospital admissions of YHAP subjects in the two New Haven hospitals where nearly 90 percent of all hospitalizations of the cohort occurred, 2) death certificates, 3) Health Care Financing Administration data, and 4) self-reported strokes from annual YHAP contacts. In all these sources of information on stroke, hospital records were sought and evaluated to confirm stroke incidence. Stroke incidence from the YHAP cohort was ascertained predominately by monitoring hospitalizations of YHAP subjects. A trained nurse research assistant regularly reviewed admission and discharge records of these two hospitals for the hospitalizations of study subjects. If these records of participants indicated admission and/or discharge
Downloaded from http://aje.oxfordjournals.org/ at University of California, San Francisco on January 16, 2015
been presented (11). Yet the influence of depression specifically on stroke incidence has not been explored to date. Over the last 15 years, there has been growing evidence for the effect of social support and social networks on health status (12). Lack of social support has been associated with increased mortality risk (13) and delayed recovery from disease (14). Religiousness is hypothesized to influence health outcomes in a similar way as social support. Several studies, for instance, have found a significant relation between religious attendance, religious feelings, and health (15-17). The main aim of this paper is to investigate the influence of depression on stroke incidence in a population of noninstitutionalized elderly, controlling for relevant health and sociodemographic variables. We also examined the influence of social support, social networks, and religiousness as potential modifier or antecedent variables. We hypothesized that these variables would have a beneficial influence on stroke incidence, whereas the presence of depression would increase the risk.
885
886
Colantonio et al.
rhage, and transient cerebral ischemias. Most of the strokes were attributed to thrombosis (n = 100). There were twelve hemorrhagic strokes. The remaining number of strokes (« = 45) were classified as ill defined/ other. Instruments and variables
Almost all independent variables were measured by means of a structured interview at baseline in 1982. Sociodemographic characteristics that were recorded included age, sex, years of education, level of income, race, and housing stratum. Health status measures were obtained mainly by self report. The presence of hypertension, however, was based on actual blood pressure readings taken by interviewers in 1982. Interviewers were trained according to the Hypertension Detection and Follow-up Program protocol (21). Blood pressure was measured with the use of a mercury column sphygmomanometer while the participant was seated. Mean diastolic and systolic blood pressure readings were calculated from the second and third readings. The presence of hypertension was coded as a binary variable. Subjects were coded as negative if systolic blood pressure was less than 140 mmHg and diastolic blood pressure was less than 90 mmHg and they were not taking antihypertensive medication. If the blood pressure of subjects had exceeded these cutoff points and/or they were taking antihypertensive medications, then respondents were coded as hypertensive. Use of an antihypertensive was determined by trained interviewers who examined all medication the respondent was taking within the 2 weeks before the interview and who then copied the medication name from the containers and coded. Cardiovascular comorbidity was measured by asking respondents whether a doctor ever told them they had had a heart attack, a coronary, a myocardial infarction, a coronary thrombosis, or a coronary occlusion. Responses to the London School of Hygiene Cardiovascular Questionnaire were used to ascertain angina and intermittent claudication (18). The frequency of alcohol
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for stroke between 1982 and November 1988 (according to the specified International Classification of Diseases, Ninth Revision (ICD-9), codes), then medical records were examined to ascertain diagnosis. Medical records were reviewed by a trained nurse researcher. Computerized tomography scans were available for 77 percent of cases to clarify diagnosis. A total of 136 incident strokes were ascertained by this method. Death certificates of all study subjects were also reviewed for ICD-9 codes that indicated stroke. Death certificates of the Connecticut State Department of Health Services were regularly checked for the deaths of YHAP subjects, and out-of-state death certificates were obtained by mail. They were coded by a single specially trained nosologist. All death certificates that indicated stroke or cerebrovascular disease as an underlying, immediate, and/or contributing cause of death were reviewed by a study physician expert in cerebrovascular disease to confirm diagnosis. Fifteen individuals were identified by this method. Hospital records were then sought to verify stroke diagnosis and date of stroke. In three cases, hospital records could not be retrieved. The type and date of stroke were then taken from the death certificate. Included in our number of strokes were self-reported strokes from annual interviews that were confirmed by medical records (n = 13) or by Health Care Financing Administration data (n = 1). Furthermore, Health Care Financing Administration data identified two cases that were hospitalized outside New Haven. All these methods of ascertaining stroke combined yielded a total of 167 strokes. The following ICD-9 codes were used to include strokes into the study: 431, 432.9, 433.0-434.9, and 436-437.1, which includes intracerebral hemorrhage, unspecified intracranial hemorrhage, occlusion and stenosis of precerebral and cerebral arteries, acute but ill-defined cerebrovascular disease, cerebral atherosclerosis, and other generalized ischemic cerebrovascular disease. The study excluded subarachnoid hemorrhage, nontraumatic extradural and subdural hemor-
Stroke in the Elderly Psychosocial Factors
The latter scale measured higher levels of physical function than the Katz Scale. Each of these items was adapted and used previously in the Framingham Disability Study (35). This scale will be referred to as the Rosow Scale throughout this paper. For the Rosow Scale, a score of 0 was given to each of the items assessed if the respondent performed the activity independently and 1 if the respondent needed assistance or was unable to perform the task. Thus, a score between 0 and 3 was possible. Depressive symptomatology was measured using the Center for Epidemiologic Studies Depression (CES-D) Scale. This 20item scale was designed to measure depressive symptomatology in the general population. The 20 CES-D items are scored on a standard four-point scale (0-3) with a potential range of 0-60, with a higher score indicating more symptomatology. A cutpoint of 16 or higher is used to distinguish between those with depressive symptomatology in the clinical range and those with lesser symptoms, based on tests with a clinical population (36). Scores for those with three or fewer missing items were created by obtaining a means score based on the items answered and multiplying by 20. This variable was treated as both continuous (with a potential range of 0-60) and dichotomous (not depressed vs. depressed) using a score of 16 as a cutoff point. Marital status was coded as married or not. Thus, those who were separated, divorced, never married, or widowed were grouped in the nonmarried category. The social support measure was based on a total of a cumulative score of availability of financial, instrumental, and emotional support. A higher score is indicative of more supports. Social networks were assessed with the social network index devised by Berkman and Syme (13). This index is composed of four types of social contacts: 1) marital status, 2) numbers of close friends and relatives, 3) church membership, and 4) membership in community organizations. This structural index of social ties weights marital status and intimate contacts with close friends and relatives more heavily than church affilia-
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use was based on consumption during the past month. Amounts were ascertained separately for beer, wine, and liquor, the latter being defined as "things like whiskey, vodka, gin, brandy, or liquors." Smoking, measured as a dichotomous variable, was based on whether the individual reported being a smoker at the time of the interview or at any time in the past. Former smokers were placed in a category with smokers, because former smokers have been found to be at higher risk for stroke in comparison with nonsmokers (22-24). Body mass was assessed by Quetelet's Index (25). Cognitive impairment was measured using the Pfeiffer Scale, a 10-item screening instrument similar to the Kahn and Goldfarb Short Portable Mental Status Examination (26). In this scale, the one item "What is the name of this place?" was changed to "What is your address?" because the latter seemed more appropriate for communitydwelling respondents. The scores of the Pfeiffer Scale were utilized in analyses as a three-level ordinal variable with more than four errors representing the most impaired category. Other analyses of these data have used the same approach (27). Cognitive impairment has been associated with depressive symptomatology (28, 29), and the former was included in the analysis as a control variable. Physical functioning is another variable that has been strongly correlated with depressive symptomatology in the YHAP data (19). Physical functioning, measured by two instruments, served as a control variable for the CES-D scores. The Katz Activities of Daily Living Scale (30) was expanded to include walking. This modification conformed with a practice utilized in two national surveys of elderly populations (31, 32) and a recent study on elderly California Medicaid recipients (33). The Katz Scale was recorded as a dichotomous variable in which 0 was assigned if the individual performed all items independently, and 1 signified dependence in one or more activities of daily living. The other scale of gross-mobility function was based on three items developed from the work of Rosow and Breslau (34).
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Statistical analysis
A Cox proportional hazards model using the BMDP computer program package (37) was utilized for univariate and multivariate modeling. Log-(log(survival)) plots were generated for each of the independent variables to check the proportional hazards assumption. The "Proc Lifetest" procedure by SAS was utilized to conduct this nonparametric analysis (38). Kendall's tau-b correlation coefficients were computed using SAS to analyze correlations between the psychosocial variables and control variables. The strategy of the analysis was to establish whether psychosocial factors predicted stroke incidence over and above known risk factors for stroke and the net of selected control variables. We did not wish to include all potential risk factors available to us in the YHAP data in the model because many of the health measures were highly correlated and to include them all in the model
would lead to problems of multicollinearity. A stepwise regression was undertaken to arrive at a subset of health and sociodemographic variables that were used as control variables. Then, each psychosocial variable of interest was added to the model one by one, and its additional contribution to the base model reflecting the role of control variables was examined. RESULTS
Overall, respondents were on the average 74 years old at the initial interview, primarily female (59 percent), and white (79 percent). The greatest percentage lived in community housing (43 percent). Fifty-five percent of the population had a grade school education only. Of the individuals who reported income, 40 percent had annual reported incomes (for self and spouse if applicable) under $5,000. As mentioned previously, the total number of strokes that occurred over the 7-year follow-up period was 167. Subjects were followed for a total of 14,730.8 person-years, which yielded a total incidence rate of 0.01 per person-year. Tables 1-3 provide information on selected sociodemographic, psychosocial, and health characteristics of the study population with respective incidence rates. These tables provide an overview of cumulative incidence that does not account for differential mortality over time. Thus, they are not meant to reflect the proportional hazards analysis used in this study. Table 1 shows that the incidence increases sharply with age and is slightly higher for males, nonwhites, and less educated groups. In table 2, CES-D scores were divided into quartiles. The incidence was higher for the upper two quartiles of individuals with more symptomatology. Married individuals and those with more social networks had slightly lower incidence rates. Furthermore, the incidence was also distinctly lower for individuals who attended religious services once a week or more. The incidence appeared unrelated to other measures of religiousness and numbers of types of support. Table 3
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tions and group memberships. The index ranges from 1 to 12 with the lowest score given to the most isolated subjects. In our analysis, we dichotomized this index on the basis of the mean number of networks (x = 3.6). Therefore, a score of 3 or lower was indicative of fewer networks and a score greater than or equal to 4 included individuals in the higher category. Religiousness was assessed by three items. The first question that refers to public religiousness was measured by frequency of attendance at religious services (five levels). A higher score indicates more frequent attendance at religious services. Private religiousness was measured by two items assessing the subjective religious experience. The questions read as follows: "Aside from attendance at religious services, do you consider yourself to be 1) against religion, 2) not at all religious, 3) only slightly religious, 4) fairly religious, or 5) deeply religious," where deeply religious was given the highest score, and "How much is religion a source of strength and comfort to you? 1) none, 2) a little, or 3) a great deal," with 0 indicating none.
Stroke in the Elderly: Psychosocial Factors
TABLE 1. Distribution of stroke incidence by selected sociodemographic characteristics of the study population: New Haven, Connecticut, 19821988 %
Age (years) at baseline 65-74 75-84 >85
78/1,456 58/892 31/256
5.4 6.5 12.1
Sex Male Female
74/1,065 73/1,539
6.9 6.0
Housing stratum Public Private Community
43/670 53/805 71/1,129
6.4 6.6 6.3
128/2,058
6.2
38/535
7.1
Race White Nonwhite (88% black) Education (years) 9
89/1,350 69/1,177
6.6 5.9
TABLE 2. Distribution of stroke incidence by selected psychosocial characteristics of the study population: New Haven, Connecticut, 1982-1988
CES-D* scores 0-1.9 2-5.9 6-11.9 >12 Marital status Married Unmarried
No./total
%
25/530 37/698 51/631 48/648
4.7 5.3 8.1 7.4
5.7 55/971 110/1,633 6.7
Total size of network Few networks (1-3) Larger networks (>4)
95/1,372 6.9 67/1,178 5.7
No. of types of support (emotional, financial, instrumental) 0 1 2 3
13/287 45/637 65/922 42/717
4.5 7.1 7.0
Frequency at religious services Never/almost never
55/637
8.6
18/308 15/276 25/282 50/1,055
5.8 5.4 8.9 4.7
8/86 18/277 67/1,201 72/967
9.3 6.5 5.6
Once or twice a year Every few months Once or twice a month Once a week or more
5.9
indicates that the incidence of stroke was higher for subjects reporting hypertension, diabetes, prior myocardial infarction, angina, intermittent claudication, cognitive impairment, a history of smoking, and limitations in functioni. Correlations were examined between CES-D and potential sociodemographic and health control variables. Table 4 shows significant positive correlations between age, sex, history of myocardial infarction, diabetes, angina, cogni-
* CES-D, Center for Epidemiologic Studies Depression Scale.
tive impairment, and limitations in function. Each of the predictive factors of interest and sociodemographic, health, and psychosocial variables were examined in relation to the cumulative hazard for stroke incidence, using the Cox proportional hazards model. The results of this analysis are presented in table 5 for each variable separately. Age was the most significant sociodemographic variable of all in this analysis. Being female was protective against stroke incidence (relative risk (RR) = 0.78) but did not reach statistical significance at the 0.05 level. Other sociodemographic factors such as race and education did not appear as important.
Housing stratum (p = 0.86) was the least salient variable of this category of factors. Higher CES-D scores were predictive of greater stroke incidence in the univariate analysis. Because of the skewed distribution of this continuous variable, the logarithms of CES-D scores were taken, a procedure that improved the fit of this variable. Of the other variables, the frequency of attendance at religious services showed a significant negative association with incidence. Other variables such as marital status, the social network index, number of types of support, and the religiousness items did not achieve significance.
Religiosity Not at all/against Slightly Fairly Deeply Religion: source of strength None A little A great deal
7.5
10/154 6.5 37/581 6.4 110/1,776 6.2
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TABLE 3. Distribution of stroke incidence by selected health characteristics of the study population: New Haven, Connecticut, 1982-1988 %
Hypertension Yes No
111/1,550 46/969
7.2 4.7
Diabetes Yes No
46/377 132/2,222
12.2 5.9
Prior myocardial infarction Yes No
32/349 132/2,244
9.2 5.9
Anoina Yes No
18/158 147/2,439
11.4 6.0
Intermittent claudication Yes No
11/54 154/2,540
20.4 6.1
Cognitive impairment None Mild Severe
85/1,520 49/720 29/317
5.6 6.8 9.1
Smoking Current Past Never
34/519 52/749 79/1,321
6.6 6.9 6.0
123/2,200 42/385
5.6 10.9
63/1,447 60/787 35/255
4.4 7.6 13.7
Physical function (Katz Scale) 0 limitations >1 limitation Physical function (Rosow Scale) 0 limitations 1 -2 limitations 3 limitations
1982-1988 Variable*
TABLE 4. Kendall's tau-b correlation coefficients between selected health and sociodemographic control variables and CES-Dt scores: New Haven, Connecticut, elderly, 1982 Control variables
Correlation coefficient
Age Sex Education Hypertension Prior myocardial infarction Diabetes Intermittent claudication Smoker Cognitive impairment Physical function (Rosow Scale)
0 079*** 0.111*** -0.102*** 0.029 0.078*** 0.086*** 0.014 0.0 0.145*** -0.266***
* p < 0 0 5 ; " p < 0 . 0 1 ; *"p < 0.001. t CES-D, Center for Epidemiologic Studies Depression Scale
Sociodemographic variables Age (10-year intervals) Sex Race Education Psychosocial variables CES-Dt scores CES-D (logged) Marital status Social network index No of types of support Frequency at religious services Religiousness Religion: source of support Health variables Hypertension Diabetes Prior myocardial infarction Angina Intermittent claudication Body massj (Quetelet index) Smoking Alcohol intake§ Cognitive impairment (Pfeiffer Scale) Physical function variables Katz Scale Rosow Scale
Relative risk
95% Clf
1.69 0.78 0.91 0.85
1.37-2.09 0.57-1.06 0.63-1.30 0.62-1.17
1.02 1.23 0.83 0.77 1.03
1.00-1.03 1.05-1.44 0.60-1.15 0.56-1.05 0.88-1.21
0.86 0.99
0.79-0.94 0.82-1.20
1.04
0.82-1.33
1.64 2.56
1.17-2.31 1.82-3.60
1.82 2.04 4.41
1.23-2.67 1.25-3.33 2.39-8.14
0 97 1.21 1.03
0.93-1.00 0.89-1.64 0.79-1.35
1.38
1.12-1.69
2.45 1.59
1.73-3.48 1.40-1.82
* Reference categories: sex, male; race, nonwhite; education,
