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Published online before print October 17, 2007, 10.1097/PSY.0b013e31815772a3
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Psychosomatic Medicine 69:748-755 (2007)
© 2007 American Psychosomatic Society

ORIGINAL ARTICLES

Multiple Sources of Psychosocial Disadvantage and Risk of Coronary Heart Disease

Rebecca C. Thurston, PhD and Laura D. Kubzansky, PhD, MPH

From the Department of Psychiatry (R.C.T.), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Society, Human Development and Health (L.D.K.), Harvard School of Public Health, Boston, Massachusetts.

Address correspondence and reprint requests to Rebecca C. Thurston, Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15217.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Objectives: To examine the co-occurrence of multiple sources of psychosocial disadvantage in association with risk of incident coronary heart disease (CHD). It was hypothesized that increased co-occurring psychosocial disadvantage would be associated with increased risk of CHD in a monotonic fashion. While both social and psychological disadvantage are associated with increased risk of CHD, indicators of psychosocial disadvantage are traditionally examined individually in relationship to CHD. However, multiple sources of psychosocial disadvantage tend to co-occur.

Methods: Hypotheses were examined using data from the First National Health and Nutrition Examination Survey and its follow-up studies (n = 6913). Indicators of psychosocial disadvantage (education, income, employment, single parenting, marital status, depressive and anxious symptoms) and covariates were derived from baseline interviews and incident CHD from hospital records/death certificates collected over 22 years of follow-up. Hypotheses were evaluated using Cox proportional hazards models.

Results: Results indicated that greater co-occurrence of psychosocial disadvantage conferred increased CHD risk. Relative to no disadvantage, one indicator of psychosocial disadvantage (relative risk (RR) = 1.28; 95% confidence interval (CI) = 1.10–1.48), two to three indicators of psychosocial disadvantage (RR = 1.56; 95% CI = 1.33–1.84), and four or more indicators of psychosocial disadvantage (RR = 2.63; 95% CI = 2.01–3.44) were associated with increased risk of incident CHD. Results persisted in covariate-adjusted models. A significant interaction by gender was observed such that the co-occurrence of psychosocial risk and its association with incident CHD were stronger among women than among men.

Conclusions: Results indicate the importance of considering patterns of co-occurring psychosocial risk factors in relationship to CHD.

Key Words: coronary heart disease • psychosocial risk • gender • socioeconomic status • depression • anxiety

Abbreviations: CHD = coronary heart disease; RR = relative risk; CI = confidence interval; NHANES I = First National Health and Nutrition Examination Survey; ICD-9 = International Classification of Diseases, Ninth Revision; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Social disadvantage and negative psychological states are associated with increased risk for cardiovascular morbidity and mortality. The association between low socioeconomic position—as indicated by low education, income, occupational status, or unemployment—and risk for coronary heart disease (CHD) is well documented (1). Psychological factors such as depression, anxiety, anger, or a hostile personality also are linked to risk for cardiovascular disease (2). Finally, social stressors such as job strain (1–3), low social support or networks (2), and marital/relationship stressors (2,4–8) have been associated with cardiovascular risk. Most studies examine each indicator's unique association with disease by adjusting for other available psychosocial, biological, and behavioral risk factors. However, adverse psychosocial and economic conditions often co-occur. Few studies have examined the co-occurrence of psychosocial risks and their joint relationship to disease risk (2).

Numerous theoretical perspectives have argued that social, economic, and psychological disadvantage are likely to co-occur (9–11). For example, the theory of fundamental causes (10) describes how social and economic conditions determine access to important resources and shape behavioral factors that, in turn, affect risk across multiple disease outcomes. Similarly, Ross and Wu posit that education shapes income and employment opportunities, sense of control, and well-being (9). Empirical findings have been consistent with these theories. For example, low educational attainment is associated with poorer employment opportunities, lower earning power, and fewer assets throughout life (9,12). Moreover, socioeconomic disadvantage is associated with increased distress (13) and mood and anxiety disorders (14), which may be due, in part, to increased stress exposure and fewer resources to buffer against these stressors (9,13,15). Finally, social factors such as divorce, widowhood, or single parent status are also associated with adverse psychological and economic conditions (16–18).

There are increasing calls to move beyond a single risk factor approach to understand how psychosocial risks together may affect disease (11,19). In isolating the unique variance in the outcome associated with an individual psychosocial risk factor, risk associated with psychosocial factors as a whole may be underestimated. Conversely, failure to control for correlated risk factors can lead erroneously to attributing variance in the outcome to the risk factor of interest rather than to correlated risk factors. Further, simultaneous inclusion of all psychosocial risk factors may result in problems of model over-fitting. Many of these estimation problems are particularly relevant when considering individuals with multiple psychosocial risk factors, a population often of great interest.

Understanding how biomedical and behavioral risk factors combine to increase disease risk has long been of interest, as illustrated by the well known Framingham risk score (20,21) or metabolic syndrome (22,23). Similarly, investigators have considered multiple aspects of either socioeconomic position or psychological functioning in relationship to disease risk (12,24,25). However, few have simultaneously considered both socioeconomic and psychosocial disadvantage. Preliminary work suggests the importance of this approach. Troxel and colleagues demonstrated an additive and graded association between stress, financial hardship, and discrimination with carotid intima media thickening among African American women (26). Others have shown interactive effects between psychosocial factors (19,27–29), although this research focuses largely on identifying vulnerable subgroups rather than risk associated with experience of multiple forms of psychosocial disadvantage. Moreover, most of this existing research has considered subclinical disease (26), mortality (19,29), or reinfarction (25) rather than incident disease. Many investigations have been restricted to one gender (19,26,27,29), and none of this research has been conducted with a nationally representative sample of the United States population.

Investigating both men and women is important, as gender may influence the patterning of psychosocial risk and its association with disease. Potentially reflecting, in part, women's lower social status (30,31), women earn less on the dollar (32), have lower financial return on their education (32), and consistently show increased risk of mood and anxiety disorders relative to men (33). Marital dissolution or single parenting may have a particularly adverse effect on women's emotional and economic condition (17,18). These social conditions may differentially affect men's and women's health. Certain indices of low socioeconomic position have particularly strong associations with CHD among women (1,34), which may be due to stronger socioeconomic gradients in body mass index (BMI) among women (34,35). Being married seems to be particularly beneficial to men's but not women's health (36). However, it is unknown whether multiple sources of disadvantage, in addition to socioeconomic factors, combine and relate to CHD differentially by gender.

The primary aims of this investigation are to examine both the overall co-occurrence of psychosocial disadvantage, as well as the association between co-occurring psychosocial disadvantage and incident CHD. We hypothesize an additive effect of psychosocial risk factors, whereby each additional psychosocial risk factor increases risk of CHD in a monotonic fashion. Secondary aims are to examine whether the co-occurrence of psychosocial disadvantage differentially relates to CHD across men and women, and to consider what biological (blood pressure, hypertension, diabetes, cholesterol, BMI) or behavioral factors (exercise, alcohol use, smoking) may account for any observed gender differences in the psychosocial gradient in CHD. We hypothesize that both the co-occurrence of psychosocial risk factors and its association with incident CHD will be stronger among women than among men. Based on earlier work (34) and existing literature documenting stronger associations between psychosocial factors and obesity among women (35,37), we also hypothesize that BMI will account for the largest portion of the observed gender differences in the association between psychosocial risk and incident CHD. We examine all associations in a 20-year nationally representative longitudinal study of the United States population.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Sample and Study Design
The First National Health and Nutrition Examination Survey (NHANES I) is a multistage, national probability survey conducted between 1971 and 1975 with the United States civilian noninstitutionalized population aged 1 to 74 years. It oversampled women of childbearing age, persons living in poverty areas, and elderly persons. The baseline assessment, conducted on the full cohort, included a medical examination, blood sample, and in-person structured interview. In addition, a representative subsample of adults aged 25 to 74 years (n = 6,913) (38) underwent more detailed medical and psychological assessments and comprised the sample in the present investigation. Nonresponse rates were 1.4% for the interview and 30.5% for the examination. Whereas interview nonresponders did not differ from participants on any demographic characteristics, examination nonresponders were older, had less education, and were more likely to reside in large urban centers compared with responders (p < .05). Details of the study design and sampling procedures are published elsewhere (39).

In 1982, 1987, and 1992, follow-up studies were conducted on the full surviving NHANES I cohort aged 25 to 74 years at the baseline examination (40–42), and in 1986 on participants aged 55 to 74 years at baseline (43). Assessments included an interview with the respondent or proxy (for decedents), measurements of weight and blood pressure, tracking of participants via National Death Index and acquisition of death certificates, and obtainment of all records for overnight hospital and nursing home stays in the study period.

The present investigation included members of the detailed subsample (n = 6913), all of whom were traced at one or more follow-ups. Those with evidence of cardiovascular disease by self-report or physical examination at baseline (n = 453) and missing values for one or more variables (n = 435) were excluded from the present investigation. Participants with missing data were more likely to be ethnic minority (p = .008), unemployed (p < .001), unmarried (p = .01), sedentary (p = .002), nondrinkers (p = .02), and to have high levels of depressive symptoms (p < .001) relative to those without missing data. The final sample available for analysis included 6025 participants (2750 men, 3275 women).

Measures
Psychosocial Disadvantage
Psychosocial disadvantage was defined following previous work in the area (2). However, as is often the case with secondary data analyses, indicators of the full range of psychosocial disadvantage were not available. Seven psychosocial indicators, obtained from the baseline NHANES I interview, were available for consideration: education, income, unemployment, single parenting, divorced or widowed marital status, depressive symptoms, and anxious symptoms. Education was categorized into levels of attainment (less than high school, high school graduate, some college, college graduate or higher). Household income was considered relative to 1973 (median year of NHANES I examination) poverty thresholds for reported family size (<100%, 100% to 200%, and >200%). Employment status was considered as follows: participants who reported working (part- or full-time) over the past 3 months were classified as employed; those who reported keeping house were classified as homemakers; those who reported being laid off, looking for work, or staying home were classified as unemployed; and those who specified being retired, unable to work, a student, ill, or other were classified as other. Single parents were those who reported being unmarried, the head of household, and living with one or more related individuals. Participants were classified as divorced or widowed if they described their current marital status as divorced, widowed, or separated. Depressive and anxious symptoms were obtained from the depressed mood and anxious/tense subscales of General Well Being Schedule, a validated measure with known psychometric properties (44), including strong internal consistency in the present investigation (depression subscale: {alpha} = 0.82; anxiety subscale: {alpha} = 0.85). Cut-points used here have been validated against the Center for Epidemiologic Studies of Depression clinical thresholds (45) and adopted by previous investigators (45–48): scale scores 0 to 12 indicated high symptoms; 13 to 18 indicated moderate symptoms; and 19 to 25 indicated low symptoms.

A psychosocial risk score was calculated for each participant using the seven indicators identified above. One point was assigned for each psychosocial characteristic in which the participant scored in the high-risk group (less than high school education, income <100% of poverty, unemployed, a single parent, divorced or widowed marital status, high depressive symptoms, high anxious symptoms). Points were then summed, yielding a possible psychosocial risk score range of 0 to 7. The index was considered as a continuous measure. Due to the small cell sizes in the higher end of the range, we also categorized the measure as none, one, two to three, and four or more psychosocial risk factors. The distribution of high risk for individual risk factors ranged from 1.2% (n = 73) of the sample unemployed to 39% (n = 2353) having less than a high school education. Bivariate associations between pairs of individual risk factors indicated that 17 of the 21 pairings were significant (data not shown). The median number of psychosocial risks was 1, the mode was 0, and the range was 0 to 6.

Incident CHD
Hospital/nursing home discharge reports and death certificates were used to identify CHD events. At each follow-up, participants reported all hospital or nursing home stays from the time of the last study contact. Hospitals/nursing homes were contacted with participant permission, and discharge reports were obtained for all overnight stays in the study period. All decedents were tracked via the National Death Index and death certificates were obtained. A CHD event was coded if International Classification of Diseases, Ninth Revision (ICD-9) codes 410 to 414 (ischemic heart disease) were listed on the discharge report or as the cause of death on the death certificate. For nonfatal events, the event date was the discharge date. If no discharge date was available, the admission date was used. For fatal events, the event date was the date of death on the death certificate. In the case of multiple events (e.g., myocardial infarction followed by CHD death), the first event was considered as the event and the participant was thereafter censored. Results were also considered excluding ICD-9 codes 412 (old myocardial infarction) and 413 (angina), restricting events to ICD-9 codes 410 (acute myocardial infarction), 411 (other acute/subacute ischemic heart disease), and 414 (other chronic ischemic heart disease). Because results were largely unchanged, results utilizing ICD-9 codes 410 to 414 are presented here.

Biological and Behavioral Variables
Gender, alcohol use (none, ≤2 servings/day, >2 servings/day), leisure time physical activity (sedentary/light, moderate, or regular exercise), smoking status (current versus never/former), and hypertension or diabetes status (past or present doctor-diagnosis and/or past or present medication use for the condition) were derived from responses to the baseline NHANES I interview. Age and race/ethnicity reported in the NHANES I interview were updated/corrected in 1982 to resolve discrepancies between interviews (40), with corrected values used in present analyses. Approximately 86.8% (n = 5227) of participants reported being white, 12.2% (n = 733) black, 0.5% (n = 32) Asian, 0.2% (n = 12) Native American, and 0.3% (n = 21) other race/ethnicity. Because there were small cell sizes across several minority racial/ethnic groups, participants were classified as white or nonwhite. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) values were obtained from one seated measurement taken during the NHANES I physical examination. BMI (ratio of weight in kilograms to standing height in meters squared (kg/m2)) was calculated from measures taken during the NHANES I examination.

Statistical Analyses
Gender differences in the psychosocial risk score were calculated via logistic regression. Relative risks (RRs) and 95% confidence intervals (CIs) of incident CHD associated with psychosocial risks were estimated in multivariate Cox proportional hazards regression. Time from baseline interview to the CHD event date, non-CHD death date, or the last date known alive was the follow-up time. Each model was estimated adjusting for age, race, and gender, and subsequently for biological and behavioral variables known to be related to CHD risk, including smoking, aerobic exercise, alcohol use, SBP, DBP, BMI, cholesterol, and hypertension and diabetes status. Interactions between psychosocial risk and CHD by gender were examined. Where a significant interaction was evident, results were presented by gender.

The impact of biological and behavioral variables on the interaction between psychosocial risk and gender on CHD was estimated by adjusting for blocks of these variables in age and race adjusted models: a) health behaviors (aerobic exercise, smoking, alcohol use); b) cardiovascular factors (hypertension, SBP, and DBP); c) metabolic factors (diabetes and cholesterol); and d) BMI. The impact of any one behavioral or biological risk factor alone on the interaction between psychosocial risk and gender was subsequently evaluated in age and race adjusted models. To evaluate the impact of any variable or blocks of variables on gender differences in the association between psychosocial risk and CHD, changes in the psychosocial risk-by-gender interaction term from the prior model were estimated via the formula 1 – log(hazard ratioadjusted)/log(hazard ratiounadjusted) (49). Differential associations between psychosocial risk and BMI by gender were estimated within multiple linear regression adjusting for age, including both main effects and the interaction term between psychosocial risk and gender.

Analyses were conducted using SAS V8.2 (SAS Institute, Cary, North Carolina). Tests were two-sided at {alpha} = 0.05. Models were subsequently estimated to account for the complex survey design, incorporating sample weights, clustering, and stratification within SAS callable SUDAAN V9.0 (Research Triangle Institute, Research Triangle Park, NC, USA). Although there was some attenuation of findings, conclusions were unchanged; therefore, given these findings and known limitations of NHANES I sampling weights (50), results unadjusted for the complex survey design are presented here.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 NOTES
 REFERENCES
 
Over the follow-up period (mean = 15.1, standard deviation = 5.9, range = 0–21.9 years), 1082 incident CHD events (663 nonfatal, 419 fatal) were recorded via hospital/nursing home records and death certificates. Baseline participant characteristics by number of psychosocial risks are presented in Table 1. Women were overrepresented in high-risk categories, at almost two-fold odds of having ≥2 risks (odds ratio (OR) = 1.97; 95% CI = 1.75–2.21), and over four-fold odds of having ≥4 psychosocial risks (OR = 4.07; 95% CI = 2.95–5.62) relative to men (Table 2).


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  		TABLE 1. Participant Characteristics by Number of Psychosocial Risks

 

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  		TABLE 2. Co-occurrence of Psychosocial Risk by Gender

 

Co-occurrence of psychosocial risk factors was associated with increased risk for CHD. When considered as a continuous variable, each additional psychosocial risk was associated with increased risk of CHD in models adjusted for age, race, and gender (RR = 1.22; 95% CI = 1.16–1.29) and in fully adjusted models (RR = 1.14; 95% CI = 1.08–1.20). Relative to no psychosocial risks, one psychosocial risk (RR = 1.28; 95% CI = 1.10–1.48), two to three psychosocial risks (RR = 1.56; 95%CI = 1.33–1.84), and four or more psychosocial risks (RR = 2.63; 95% CI = 2.01–3.44) were associated with increased risk of incident CHD in a monotonic fashion (Figure 1). This pattern was also evident in fully adjusted models (one: RR = 1.16; 95% CI = 1.00–1.35; two to three: RR = 1.31; 95% CI = 1.10–1.55; four or more: RR = 1.94; 95% CI = 1.47–2.56). Brief examination of fully adjusted models indicated that, of the biological and behavioral factors, health behaviors seemed to have the most appreciable effect on the association between psychosocial risk and CHD (data not shown). However, although somewhat attenuated, associations between psychosocial risk and CHD remained significant even in fully adjusted models.


Figure 17
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  		Figure 1. Number of psychosocial risk factors and risk of incident coronary heart disease.

 

A significant interaction between the psychosocial risk score and gender was observed (p = .003), with the association between psychosocial risk and incident CHD stronger among women versus men (men: RR = 1.16; 95% CI = 1.07–1.26; women: RR = 1.26; 95% CI = 1.18–1.35) (Table 3). We evaluated biological and behavioral factors that may account for this gender difference by adjusting for blocks of variables and calculating the reduction in the hazard ratio corresponding to the interaction between gender and psychosocial risk. The factor most sizably attenuating the interaction between gender and psychosocial risk was BMI (Table 4), alone reducing it by 37% and rendering the interaction term nonsignificant. When examining the relationship between the psychosocial risk score and BMI for men and women separately, a sizable gradient in BMI according to psychosocial risk was observed among women but not among men (Figure 2). When psychosocial risk factors were considered individually, BMI displayed significant graded associations with six of the seven individual psychosocial risk factors among women but not men (data not shown).


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  		TABLE 3. Number of Psychosocial Risks and Incident CHD by Gender, n = 6025

 

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  		TABLE 4. Biological and Behavioral Variables Accounting for the Interaction Between Psychosocial Risk and Gender, n = 6025

 

Figure 27
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  		Figure 2. Body mass index by number of psychosocial risk factors.

 

Several additional analyses were conducted. First, to explore a possible interactive effect between psychosocial risk factors, all two- and three-way interactions were evaluated. Of 56 possible interactions, only two were significant (p < .05), with depressive symptoms more strongly associated with incident CHD among single parents (two-way interaction), particularly divorced/widowed single parents (three-way interaction). Given that several significant interactions are likely just by chance, we view these results cautiously, and posit that psychosocial risk factors work together primarily in an additive, rather than an interactive, fashion. Second, to address the possibility that those with greater psychosocial risk may have poorer baseline health not captured by measured variables, which might account for increased CHD risk, all analyses of incident CHD events were repeated excluding the first 3 years of follow-up. Conclusions were unchanged. Finally, we separately examined associations between psychosocial risk and fatal versus nonfatal incident CHD. Associations between the psychosocial risk score and fatal and nonfatal events were similar.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
NOTES
 REFERENCES
 
These results indicate in a nationally representative, 20-year, longitudinal study of the United States population, that the co-occurrence of psychosocial risk factors is associated with increased risk of incident CHD in a monotonic fashion. These results underscore the importance of considering psychosocial risk factors not only individually, but in combination in relationship to disease. Because multiple forms of psychosocial disadvantage frequently co-occur among individuals, there have been calls for greater attention to how psychological, economic, and social risk factors may jointly be associated with health outcomes (11,19). This investigation is a first step in doing precisely that.

Considering multiple sources of psychosocial disadvantage simultaneously makes possible a stronger and more accurate representation of the lived experience of the individual and its relationship to disease. In reality, psychosocial risks rarely occur in isolation. Thus, greater insight into how the social and psychological environment influences health may be gained by considering how sources of risk co-occur and jointly relate to disease. Findings with the psychosocial risk score suggest that the co-occurrence of any two psychosocial risk factors confers greater risk than that of one, and that as the constellation of risk factors increases, so too does risk of disease, regardless of which risk factors comprise the constellation. We note that this psychosocial risk score does not serve as (nor should be compared with) any one indicator of any particular risk, but rather indicates the CHD risk associated with different levels of psychosocial disadvantage, broadly categorized.

In the present investigation, the co-occurrence of psychosocial risk was particularly apparent among women. Although strong conclusions are limited by the psychosocial risk factors available for study, results suggest that any one psychosocial indicator may confer increased disadvantage among women relative to men. It has been shown previously that certain psychosocial risk factors co-occur more often among women. For example, low education and single parenting confer greater risk of low income among women (32,51), and particularly pronounced educational gradients in distress are noted among women (31). However, this study extends these findings to multiple social, economic, and psychological indicators.

Risk of incident CHD associated with multiple psychosocial risks was also stronger among women. There is some prior evidence that individual factors such as low education or low income may confer stronger risk of CHD among women (1,34). However, this study is the first to examine how co-occurring psychosocial risk may be differentially associated with CHD by gender. Thus, not only may psychosocial disadvantage be more concentrated among women, but it may also confer greater cardiovascular risk for women.

The reasons why women may be disproportionately vulnerable to the clustering of psychosocial risk are not fully clear. However, differential associations between psychosocial risk and metabolic factors, particularly BMI, played an important role in gender differences in relationships between psychosocial risk and CHD. Among women only, positive and graded associations were observed between BMI and almost every psychosocial risk factor when considered alone or in combination. Previous research has indicated stronger associations between education and CHD among women (34) that were accounted for, in a large part, by educational gradients in BMI among women (34,35). The present study suggests that these findings apply well beyond the effects of educational attainment to a wide range of risk factors, including their co-occurrence. Reasons for the stronger association between psychosocial risk and BMI among women are not known, although in the case of socioeconomic and affective factors, the relationships are likely birectional. Women in socially disadvantaged positions may be more likely to develop obesity due to greater restriction of physical activity in adverse environments (52), more sedentary work environments (53), stronger impact of food insecurity on weight (54), greater stress-related eating behavior (55), social class gradients in parity (56), and more pronounced social class-related dietary and exercise behavioral norms (57) among women. Moreover, due to the stigmatizing effects of obesity that are particularly pronounced among women, obesity may confer greater downward social mobility and increased negative affect for women relative to men (58). It is also important to consider that weaker psychosocial gradients in BMI among men may be, in part, due to strong social gradients in smoking among men (59). Finally, as suggested by the theory of fundamental causes (10), no one pathway can fully explain the link between social disadvantage and adverse health outcomes, and pathways that operate at one time and place may change over time, although the association between adversity and health is maintained. Similarly, no single factor is likely to fully account for gender differences in associations between psychosocial risk and CHD, and it is possible that pathways may vary over time.

Several study limitations deserve mention. First, the psychosocial risk score, derived by weighting and summing individual risk factors, is a simple approach to examining co-occurrence of psychosocial risk, ignoring the complex causal associations between these psychosocial variables. However, the goal of this investigation was not to model the precise form of the interrelationships between psychosocial factors. Rather, this investigation was an attempt to capture broad patterns of risk associated with multiple indices of psychosocial disadvantage in a parsimonious fashion. Given the simplicity of this index, we find the graded association between the number of psychosocial risks and CHD striking. Second, this score is not conceptualized as tapping a single underlying latent construct of which low education, low income, unemployment, single parenting, divorced/widowed marital status, and negative affect are measures. These risks are manifest conditions and are conceptualized as etiologically distinct circumstances that may combine to contribute to disease risk. Third, a fairly narrow range of psychosocial factors were available for consideration. Future research should consider other risk factors that may include job strain, social isolation/low social support, and hostility/anger. The limited availability of risk factors may have affected the ability to adequately evaluate gender differences in psychosocial risk. Fourth, due to the need to consider multiple psychosocial characteristics simultaneously, there was limited power to evaluate differences in these associations by other ascribed characteristics such as race/ethnicity that may influence the patterning of psychosocial risk and the relationship of psychosocial risk to disease. To address these questions, further work on the co-occurrence of psychosocial risk and its relationship to CHD should be conducted with studies designed for this purpose. Fifth, because there was a higher likelihood of missing data among those with psychosocial risk factors (e.g., unemployed, unmarried, high depressive symptoms), those with the highest psychosocial risk may have been excluded, limiting the range and potentially biasing estimates toward the null. Given the pattern of missing data, those with high psychosocial risk factors or minority race/ethnicity in the study may not be fully representative of the population. Finally, although this was a prospective investigation, with psychosocial factors assessed at baseline and disease assessed over a 20-year period among initially disease-free individuals, the causal direction of associations cannot be assumed.

This investigation had notable strengths. First, it was conducted with a nationally representative sample of the United States population. This study characteristic increases the generalizability of results, which is particularly important in describing broad population patterns in psychosocial risk and disease. Second, it is one of the few investigations of co-occurring psychosocial risk that includes both men and women, enabling the investigation of gender differences and factors that may account for these differences. Third, this investigation is a longitudinal study over a 20-year period, allowing the examination of incident CHD in a prospective fashion among initially disease-free individuals established by a detailed physical examination. Fourth, CHD events are measured via hospital records and death certificates, allowing an objective quantification of events.

The present investigation points to the value of moving beyond a sole individual risk factor approach to considering how multiple forms of psychosocial disadvantage may jointly be associated with risk of CHD. These multiple forms of disadvantage may together comprise a type of "psychosocial risk syndrome," akin to a metabolic syndrome, in which high scores may place an individual at high risk for disease. These findings also indicate the importance of considering how gender may modify these associations, with the co-occurrence of psychosocial disadvantage as well as its associated disease risk more pronounced among women than among men. Finally, interventions to address the immediate health impact of social deprivation and psychological distress may consider targeting those populations experiencing multiple forms of psychosocial disadvantage. Long-term preventive efforts should consider the underlying structural conditions that may give rise to concentrated economic, social, and psychological disadvantage (10).


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
REFERENCES
 
Received for publication July 19, 2006; revision received March 20, 2007.

This work was supported by Health and Society Scholars Implementation Grant 045821 from the Robert Wood Johnson Foundation.

DOI:10.1097/PSY.0b013e31815772a3


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 

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