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ORIGINAL ARTICLES |
From the Department of Psychiatry and Behavioral Medicine, Duke University Medical Center (B.H.B., I.C.S., J.C.B., R.S.S., M.N.F., R.B.W.); the Center for Urban and Regional Studies, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (W.M.R.); and the Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington (P.P.V.)
Address correspondence and reprint requests to Beverly H. Brummett, Box 2969 Duke University Medical Center, Durham, North Carolina, 27710. E-mail: brummett{at}duke.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONNOTESREFERENCES |
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Methods: The study sample consisted of 147 community recruited caregivers and 147 participants who did not have caregiving responsibilities. We hypothesized that negative neighborhood characteristics would magnify effects of caregiving on plasma glucose levels. Regression analyses were conducted to examine the interaction of three neighborhood characteristic measures with caregiving status in predicting fasting plasma glucose (FPG) and glycosylated hemoglobin concentration (HbA1c), with control for age, race, gender, relation to care recipient (spouse or relative), body mass index, income, and education.
Results: Of the three neighborhood measures, the one reflecting crime concerns significantly moderated the effect of caregiving on FPG (p < .002) and HbA1c (p < .001). For participants with better neighborhood characteristics, caregivers and noncaregivers were similar with respect to indicators of glucose metabolism; however, for participants with worse neighborhood characteristics, caregivers had higher levels of FPG and HbA1c, as compared with noncaregivers.
Conclusions: Poor health outcomes, such as impaired glucose control, may be found among caregivers who fear neighborhood crime.
Key Words: caregiving • glucose • neighborhood characteristics
Abbreviations: FPG = fasting plasma glucose; HbA1c = glycosylated hemoglobin concentration; SES = socioeconomic status; BMI = body mass index; STAI = Spielberger Trait Anxiety Scale; CES-D = Center for Epidemiologic Studies of Depression Scale; PSQI = Pittsburgh Sleep Quality Index; ISEL = Interpersonal Support Evaluation List.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONNOTESREFERENCES |
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Background
Residence in a neighborhood where residents fear crime and struggle with poor housing conditions negatively affect social and mental well-being (8) and physical health (9,10). Neighborhood physical characteristics indicative of higher crime rates have been associated with increased prevalence of coronary heart disease and increased levels of risky health behaviors (11). Moreover, individual perceptions of one’s neighborhood environment have been associated with poor physical and emotional health. Work by Wilson et al. (12) has shown that people who report that they are displeased with aspects of their neighborhood physical environment are 1.5 times more likely to report chronic health conditions. Similarly, longitudinal research has demonstrated that perceptions of neighborhood characteristics such as vandalism, burglary, litter, and robbery predict depressive symptoms, adjusted for baseline levels of depression (13). Finally, data from the National Longitudinal Survey of Youth suggests that neighborhood perceptions are more salient in shaping levels of maternal psychological distress than is objective neighborhood location, and these results were similar for racial groups comprised of African Americans, Mexican Americans, and Caucasians (14).
In addition to direct effects on health, neighborhood characteristics may also serve to magnify other naturally occurring social stressors experienced by residents. One such stressor that is becoming increasingly prevalent as the US population ages is caregiving. Consistent findings demonstrate that the burden associated with providing care for a loved one suffering from Alzheimer’s Disease is associated with negative health outcomes (15). For example, caregivers have been shown to have significant decreases in cellular immunity (16); compared with noncaregivers, male caregivers are more likely to have higher levels of triglycerides (17), and caregiving has been associated with elevated mortality (18). A recent meta-analysis, combining results of 23 studies, suggests that caregivers are at higher risk for health problems than noncaregivers (19). Related research has also shown that life stress not necessarily related to the role of caregiving (e.g., being the victim of a crime) may increase caregiver burden (20) and may negatively impact hemostatic function in caregivers (2).
One of the mechanisms that may link stress to cardiovascular mortality is impaired glucose metabolism. Impaired glucose metabolism, which has been shown to be exacerbated by psychological stress (21), is the underlying mechanism responsible for diabetes and has been associated with increased mortality from all causes and cardiovascular disease (22–24). It is also important to note that research has shown that glycosylated hemoglobin concentration is associated with increased risk for mortality both in individuals with diabetes as well as in individuals who are within the normal range of HbA1c concentration (22), suggesting it is important to examine correlates of glucose metabolism within nonclinical samples. It has been hypothesized that the stress of caregiving may act via the metabolic syndrome, of which impaired glucose metabolism is a primary component, to increase risk of cardiovascular disease (3).
We a priori hypothesized that the effects of caregiving on glucose levels would be magnified by negative neighborhood characteristics. To evaluate the extent to which any observed effects are attributable to the characteristics of the neighborhood environment and not merely an indirect reflection of socioeconomic status (SES), we adjusted for education and income. Age, gender, race, relation to care recipient (spouse or relative), and body mass index (BMI) were also controlled in the statistical analysis.
Methodology
Sampling Procedures
Participants were recruited to be part of a study designed to examine the underlying biological and behavioral mechanisms whereby stressful social and physical environments lead to health disparities among different socioeconomic groups. The study was approved by the Duke University Medical Center Institutional Review Board and enrollment began in May of 2001. Caregivers were recruited using flyers, ads in the local media, and community outreach efforts. Noncaregiver controls were recruited by asking caregivers to nominate two to five friends who live in their neighborhood and are similar with respect to demographic factors (i.e., gender, age, and race). All subjects gave informed consent before their participation in the study. Individuals who were experiencing any acute major medical or psychiatric disorders were excluded from the study. It is difficult to assess the exact point at which the caregiving role is assumed. However, following interviews with the participants that involved discussion of their caregiving role, it was evident that the majority of the participants in this study had been active as primary caregivers for a period of 3 months or longer.
Data were collected in two venues, a questionnaire battery was given to participants during a home visit by a nurse and returned on their visit to the General Clinical Research Center at Duke University Medical Center. The home assessment was scheduled during the same week as the physical examination. Home visits took place in the afternoon, and clinical visits were held in the morning between the hours of 8:00 and 10:00. During the clinical visit, participants received a general physical examination, and blood was drawn for assessment of glucose metabolism. In addition, before the conclusion of their clinical visit, participants took part in a cardiovascular reactivity protocol. Subjects enrolled in the study received $250 for their participation.
The full study sample consists of 344 participants. Because of missing data on one or more variables required for the present study, 24 participants were excluded. In addition, the neighborhood characteristics questionnaire was added to the study after data collection had begun, and therefore 26 participants did not complete this measure. This resulted in a study sample of 147 adults who reported significant caregiving responsibility for a relative (96% of which were parents) or spouse diagnosed with dementia and 147 noncaregivers who do not have such caregiving responsibilities (total n = 294). Participants excluded because of missing values on one or more of the variables of interest did not differ significantly with respect to age, race, gender, income, education, or measures of plasma glucose.
Measures
Group status (caregiver or noncaregiver), relation to care recipient (spouse or relative), race (African American or Caucasian), and gender were each coded as dichotomous variables. Age was measured in years. Education was measured in years of education completed. Income was measured in 20 categories, beginning with less than $10,000 and increasing by increments of $4,999 and ending at $100,000 or more. Height and weight measurements for calculation of BMI were collected at the General Clinical Research Center examination. BMI was expressed as weight in kilograms divided by height in meters squared (kg/m2) (25). The Spielberger Trait Anxiety Scale (STAI) (26) was used to assess chronic levels of anxiety. Higher scores on the STAI indicate higher levels of anxiety. The Center for Epidemiologic Studies of Depression Scale (CES-D) (27) was used to assess symptoms of depression. The CES-D is a 20-item self-report questionnaire. Higher scores represent depressive responses, and a score of 
16 is generally considered suggestive of a depressive disorder. The Pittsburgh Sleep Quality Index (PSQI) was used to measure the quality and patterns of sleep (28). The PSQI yields a global sleep rating, with higher scores indicating poorer sleep. The Interpersonal Support Evaluation List (ISEL) (29) was used to assess perceptions of social support. A shortened 16-item version of the ISEL was used in the present study (30,31). Items were rated on a 4-point scale, with a potential range of 0 to 48 for total ISEL scores. Higher scores reflect greater perceived support. Smoking was assessed with the following question, "Do you currently smoke cigarettes?" yes/no.
Neighborhood Characteristics
Neighborhood characteristics were measured using 21 items from a questionnaire designed to assess the participant’s ratings of specific problems. The design and implementation of the neighborhood assessment procedure was supervised by Dr. William Rohe, Professor of City and Regional Planning and Director of the Center for Urban and Regional Studies at the University of North Carolina Center, Chapel Hill, North Carolina. Questionnaire items were obtained from those used in the Chicago Neighborhood Study conducted by the National Opinion Research Center at the University of Chicago and the Safe and Secure Neighborhood Study funded by the National Institute of Justice.
To provide measures of different categories of neighborhood characteristics a Principal Components factor analysis was conducted using SAS (SAS Institute: Cary, NC) PROC FACTOR with an orthogonal transformation matrix. Depending on the question, the item response scale was 1 to 4 or 1 to 3. Examination of the scree plot suggested that 3 factors be retained. We then generated factor scores for each participant based on this 3 factor solution. Table 1 provides the loadings for each item with respect to the 3 factors. A few of the items had loadings that were indistinguishable across the 3 factors, and other items did not share face validity with their respective factor. Thus, we refined our factors by omitting 8 items. These items are noted in Table 1. As can be seen, items with strong loadings on the first factor refer primarily to concerns regarding crime (F1-Crime). The second factor relates mainly to dissatisfaction with perceptions of the neighborhood (F2-Perceptions). The final factor concerns neighborhood decline, both present and future. A higher score on each of the factors indicates worse neighborhood conditions.
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For 9 participants who failed to complete only 1 of the 13 items on the neighborhood questionnaire, a value was imputed using the mean score for that item. We conducted a sensitivity analysis using only participants who had complete data for the neighborhood questionnaire, and the removal of the 9 participants did not substantively alter the findings.
Indicators of Glucose Metabolism
FPG (mg/dl) and glycosylated hemoglobin concentration (HbA1c%) were assayed in all participants. HbA1c is the standard surrogate marker for measurement of average plasma glucose (32). Glucose attaches irreversibly to hemoglobin, and this glycosylated hemoglobin can be separated from other hemoglobin in a widely available assay. The amount of this hemoglobin fraction, HbA1c, formed is proportional to the amount of circulating glucose. Taking into consideration its continuous formation and the life span of the red blood cell, a single measurement of HbA1c gives an estimate of the average plasma glucose over the previous 3 months. The upper end of normal in the nationally standardized assay is 6%, corresponding to a plasma glucose of approximately 120 mg/dl. It is still necessary to obtain some primary glucose data, particularly the FPG, which provides data reflecting predominantly the basal hepatic glucose output, whereas the average glucose will reflect both hepatic glucose output and glucose tolerance (the rise in glucose following a nutrient load).
HbA1c has been related to stress in patients with diabetes (33–35), to cardiovascular mortality and morbidity (22), and to the outcome of stroke (36). HbA1c was determined at the Franklin Site Laboratory (Duke University Medical Center) by ion based high performance liquid chromatography, using a Tosoh (version G7; Tosoh Bioscience LLC, Montgomeryville, PA) analyzer. Because chromatographical methods of measuring HbA1c may be altered by changes in temperature, precautions were taken to assure that temperature was maintained at an appropriate and consistent level for all blood samples. Of the 294 participants, 16 were potentially diabetic (i.e., self-report of existing diagnosis of diabetes and/or FPG > 126).
Statistical Analysis
Multiple linear regression analyses were used to examine the hypothesis that neighborhood characteristics moderate the effects of caregiving on indicators of glucose metabolism. On the basis of our hypothesis that negative neighborhood characteristics would enhance effects of caregiving on glucose metabolism, we examined each neighborhood characteristic factor separately as a modifier of group status (caregiver versus noncaregiver) with respect to the outcomes of FPG and HbA1c. In primary analyses, age, gender, race, relation to care recipient, education, income level, and BMI were controlled, and alpha was set at p< .01.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONNOTESREFERENCES |
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In unadjusted-main effect analyses (n=294), the only neighborhood characteristic that was significantly associated with glucose function was F1-Crime (HbA1c, r = .12, p < .039). The relationships between our neighborhood characteristic factors and each measure of SES were F1-Crime, income r = –.27 (p < .001) and education r = –.10 (p < .089); F2-Perceptions, income r = –.20 (p < .001) and education r = –0.18 (p < .003); and F3-Decline, income r = –.10 (p < .095) and education r = .03 (p < .662).
In a model containing only covariates (age, gender, race, relation to care recipient, education, income, and BMI) age and BMI were positively associated with FPG and HbA1c. Race was unrelated to FPG, but African Americans were more likely to have elevated HbA1c. Males had higher levels of FPG, but gender was unrelated to HbA1c. Income, education, and relation to care recipient (spouse or relative) were not significantly related to either measure of glucose regulation.
Primary Analyses
F1-Crime was a significant modifier of the association of group status (caregiver versus noncaregiver) for both FPG (p < .002) and HbA1c (p < .001). The form of the interaction was similar for both measures. Specifically, within individuals who had lower levels of F1-Crime, the difference in glucose metabolism between caregivers and controls was minimal; however, within individuals with higher levels of F1-Crime, caregivers had higher levels of glucose and HbA1c, as compared with controls. Figure 1 depicts the form of the interactions for FPG and HbA1c. Table 3 presents the results of these regression models. F2-Neighbors and F3-Decline did not significantly moderate the relationship between caregiving and glucose metabolism (p values .20–.35). For each of the neighborhood characteristics, the above analyses were recalculated excluding the 16 potentially diabetic participants, and the results were not significantly altered.
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Secondary Analyses
Secondary analyses (adjusted for covariates) were conducted to examine anxiety ratings, symptoms of depression, sleep quality, social support, and BMI as potential mediators of the association between glucose measures in caregivers with higher F1-Crime scores. Because the observed F1-Crime x Caregiving interaction suggests that the relation between caregiving status and glucose metabolism is only within individuals who have higher F1-Crime ratings, we conducted mediation analyses within this group. Caregiving status remained significantly associated with anxiety ratings, symptoms of depression, sleep quality, and social support. However, these potential mediators were not significantly associated with glucose metabolism (p values > .05) within this subgroup, and therefore none of the above constructs were viable sources of mediation in the present sample. Finally, BMI did not meet the test for mediation, as it was not associated with caregiving status.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONNOTESREFERENCES |
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The modifying effect of neighborhood characteristics was only present for one of the three factors assessed in the present study, crime concerns. Related research examining moderators of the impact of caregiving has shown that being a victim of crime increases the impact of caregiver burden (20). Additional research has shown that measures indicative of deprived neighborhoods, such as high crime rates, are associated with loss of physical function in older adults (10) and with increased prevalence of cardiovascular disease (11). Dissatisfaction with the other two neighborhood environment measures, although displeasing, may not engender the same sort of negative emotion and/or behavior that fear of crime may lead to. For example, feeling that the neighborhood is generally declining or that neighbors are sometimes unpleasant may not stop an individual from getting out and walking about: a behavior that fear of crime may inhibit.
Stress is one of the mechanisms that may underlie the present association between poor neighborhood characteristics and glucose functioning in caregivers. Increased cardiovascular and neuroendocrine function in response to stress is a biologically plausible contributor to the pathogenesis and course of major health outcomes (38). A good deal of research has demonstrated that caregivers have heightened levels of subjective stress (15), as well as stress hormones (e.g., cortisol) that can increase glucose levels (19). Likewise, neighborhood characteristics, such as high crime rates, have been associated with a multivariable index of well-being (39). Finally, stress has been related to glucose regulation in diabetic patients (33,35). Thus, these relationships make stress a likely candidate for linking caregiving burden in adverse physical surroundings with the outcome of poorer glucose regulation. In the present study, we examined two measures of distress (i.e., STAI, and CES-D scores), and neither qualified as a potential mediator. It is possible that these indicators of stress did not adequately capture the form of stress that is associated with the impaired glucose found in the present study.
Other potential mechanisms have been proposed that may underlie associations between stressful circumstances and health outcomes. For example, poor health habits have been found among people living in poorer neighborhoods (40) and among caregivers (16). Moreover, poor health habits have been shown to mediate the relationship between caregiving and glucose dysregulation (3). In the present sample, the number of current smokers was small and assessment of other health habits, such as exercise and alcohol consumption, was limited. Thus, we were unable to adequately test mediational models concerning these health habits. We did however examine the potential effect of BMI as mediator, and it was not a viable candidate.
Although speculative, there are other plausible mechanisms that may account for the present findings. For example, it is possible that fear of crime and lack of free time may restrict social activity and increase the potential for social isolation. However, although caregivers in the present sample reported lower levels of perceived social support, our measure of perceived support did not qualify as a potential mediator of association between crime concerns and glucose metabolism. It is also possible that concerns regarding safety may indirectly affect access to medical care, as individuals may be less likely to make trips to the pharmacy and/or the physician. Finally, as in any observational study, it is possible that other unmeasured constructs may have influenced this relationship.
There are some limitations that should be noted when interpreting the present results. The participants in this study were recruited by various means and therefore may not be a representative sample. In addition, it should be noted that findings from research examining perceptions of neighborhood characteristics may differ from those of studies using more objective measures. Indeed, the relation between perceptions of crime rates versus actual crime rates is complex and depends on factors such as age, gender, race, where one lives, and who one associates with (41). Although the association between fear of crime and actual victimization experiences is inconsistent across studies (42), certain findings seem to suggest that the gap between perceptions and actual risk may be sizable (43).
Measures of SES, in unadjusted models, were moderately related to the present assessments of neighborhood characteristics. In addition, income but not education was modestly related to glucose values. However, income and education were controlled in our primary analyses, making it unlikely that the moderating effect of the neighborhood characteristic rating can be accounted for by SES. Related work by Ross and Mirowsky (9) has shown that living in a disadvantaged neighborhood affects health over and above the impact of personal socioeconomic characteristics. We examined interaction models without control for income and education, and the results were not substantially different. Thus, in the present sample SES seemed to have little effect on the observed associations. This may be attributed, in part, to the fact that our measure of crime concerns was based on perceptions rather than actual crime rates that are likely to be higher in lower SES neighborhoods.
Research has shown that mortality goes up in a linear fashion with increasing HbA1c (22), and the National Alliance for Caregiving and the AARP (44) indicates that by the year 2007, the number of households in the US providing care for persons aged 50 and over is likely to reach 39 million. Thus, the present results suggest that the effects of caregiving and crime concerns could contribute to higher mortality at a level of considerable public health significance. Although a remedy for poor neighborhood conditions may be hard to achieve, there are treatment strategies that have been shown effective in reducing fear of crime among the elderly (45). Interventions aimed at reducing such distress in caregiver populations may wish to target individuals who report concerns regarding crime in their area, and it should be noted that these may not necessarily be individuals of lower SES.
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NOTES |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONNOTESREFERENCES |
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Supported by the National Institute on Aging Grant R01AG19605, with co-funding by National Institute of Environmental Health Sciences; by the Clinical Research Unit Grant M01RR30; and by the National Institutes of Mental Health Grant R01MH57663.
DOI:10.1097/01.psy.0000174171.24930.11
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