Social Science & Medicine 120 (2014) 92e99
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Health benefits of religion among Black and White older adults? Race, religiosity, and C-reactive protein Kenneth F. Ferraro a, b, *, Seoyoun Kim a, b a b
Department of Sociology, Purdue University, Stone Hall, West Lafayette, IN 47907, USA Center on Aging and the Life Course, Purdue University, Hanley Hall, West Lafayette, IN 47907, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 June 2014 Received in revised form 5 August 2014 Accepted 20 August 2014 Available online 22 August 2014
The study investigates potential health benefits of religiosity to protect against chronic inflammation associated with the risk of cardiovascular diseases. The study uses longitudinal data from a representative survey of adults 57e85 years old at the beginning of the National Social Life, Health, and Aging Project. Linear regression models were used to analyze the association between religiosity, as measured by affiliation, attendance, and having a clergy confidant, and logged values of C-reactive protein (CRP) concentration (mg/L). Although religious attendance was not related to CRP among the White respondents, attendance was associated with lower CRPdand change in CRP over timedamong the Black respondents. There was no evidence that religious affiliation alone had any health benefit. The study provides evidence of the salutary effects of religious engagement on chronic inflammation among older adults, especially for Black Americans, which may be useful in reducing the prevalence of hypertension and cardiovascular disease. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Religiosity Social participation AfricaneAmerican Chronic inflammation
1. Introduction Is religion good, bad, or benign to health? This is a question that has long sparked scholarly interest. Freud and Marx generally viewed religion as a maladaptive response while Durkheim and James found that humanedivine relations can benefit both the social order and personal well-being. Research on religion and health has proliferated during the last two decades: more than 2200 quantitative publications examine the conditions under which there may be a link between religion and various facets of physical or mental health (George et al., 2013; Koenig et al., 2001). Most of these studies report that religion is beneficial to one or more health indicators, but some studies reveal that religion may also lead to poorer health outcomesdand still other studies find no association. Religion and health are each rich concepts, and the evidence-based conclusions from the extant research depend largely upon which elements of the two phenomena are studied. The present study seeks to contribute to this literature along two axes. First, given religion's role as a meaning system and its utility as a coping resource, it is logical that there are many studies of religion's
* Corresponding author. Department of Sociology, Purdue University, 700 West State Street, West Lafayette, IN 47907-2059, USA. E-mail address: [email protected] (K.F. Ferraro). http://dx.doi.org/10.1016/j.socscimed.2014.08.030 0277-9536/© 2014 Elsevier Ltd. All rights reserved.
effect on mental health and overall wellbeing. Even among the studies examining physical health, however, most rely on measures of self-reported health. Although self-reported measures are generally considered useful in a prognostic sense (Ferraro & Farmer, 1999), they are limited for explicating mechanisms for how social factors influence biological processes. Thus, a small but growing set of studies examine various biomarkers of health, which provide a window into preclinical risk of disease and the biological pathways by which religion may influence health. Second, race is a notable and relatively understudied source of variability in how religion may be related to health. Organized religious affiliation and expression are patterned along racial and ethnic lines in many nations. Relatively few empirical studies of religion and physical health, however, systematically examine racial and ethnic variability in these relationships; many studies either do not analyze racial variability or simply adjust for race when testing the relationship between indicators of religion and physical health outcomes (for an exemplar examining health, see Steffen et al., 2001). Black churches, however, are distinctive in the types of programs and services offered (Koch and Beckley, 2006), the sense of belonging among members (Martinez and Dougherty, 2013), and the religious experience cultivated to aid coping with adversity (Ellison et al., 2008). Perhaps greater attention to this variability will clarify who most benefits from (or is harmed by) religious
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participation. Evidence from previous research leads one to expect that Black people receive greater health benefits from their religious participation than is the case for White people (Krause, 2002). In addition, health disparities by race are substantial (LaVeist et al., 2011), calling for greater attention to the health needs of Black people (i.e., persons of African and AfroCaribbean descent) and the role of religion to reduce such disparities. If the relationship between religion and health varies by race in the United States, the implications may be far reaching to other nations and religions. Using a national sample of older Americans, we systematically examine Black/White differences in the relationship between several indicators of religion and a biomarker of chronic inflammation that is a precursor to several cardiovascular diseases. Across all age groups, Black men and women have higher rates of hypertension and heart disease mortality than their White counterparts (Centers for Disease Control and Prevention, 2014). As such, examining how religion may influence a biomarker that is highly predictive of heart disease is potentially significant for improving public health (Crimmins and Vasunilashorn, 2011). 2. Religion and physical health Although the manifest purpose of religion is not health promotion, a substantial body of research in recent decades has made important strides by explicating why religion may be related to physical health. Three hypotheses have received considerable attention during this time: constraining health-injurious behaviors, social integration, and consolation. Studies have shown that religious participation constrains some behaviors that are harmful to health such as smoking (Whooley et al., 2002) and substance abuse (Haber et al., 2012) but that its relationship with obesity is weaker and more complex (Cline & Ferraro, 2006). Scores of studies reveal that integration in religious networks and communities has a salutary effect on physical health (Strawbridge et al., 2001), but a smaller set of studies reveals that “religious support” can also have a dark side, whereby interpersonal conflict leads to psychological distress (Ellison et al., 2009). Whereas religion is widely viewed as a coping resource while facing stressors, several studies have shown that misfortunedincluding poor healthdmay also lead to religious participation (Ferraro & Kelley-Moore, 2000). As such, the relationships may be quite complex: religion may lead to or result from physical or emotional dysphoria. Underlying much of this research is a tenet that religion can shape physiological processes and outcomes. Cousins (1990) spoke of these processes as part of the “biology of hope”, and Seeman et al. (2003) sought evidence of biological pathways between religion/spirituality and health. Based on their critical review of the scientific literature, they concluded that “the evidence reported to date is generally consistent with the hypothesis that aspects of religiosity/spirituality may indeed be linked to physiological processesdincluding cardiovascular, neuroendocrine, and immune functiondthat are importantly related to health” (p. 61). In an effort to study biological pathways between religion and health, investigators have integrated biomarkers in their research. There are at least two main advantages of doing so. First, using biomarkers enables one to focus on specific pathways or mechanisms. Global measures of health remain important, but isolating biological pathways has the potential for breakthrough discoveries in determining if and how religion is beneficial to health. Second, biomarkers enable one to sidestep some of the fallibilities of selfreported health measures. Since religion frequently offers inspiration or consolation to people facing hardship, it is plausible that an optimistic outlook may lead to underreporting of health problems. Thus, it is understandable why Williams and Sternthal (2007, p.
93
S49) urged greater attention “directed towards understanding the biological mechanisms through which religion ‘gets under the skin’.” 3. Race, religion, and chronic inflammation The current study responds to this call, and those of others (Seeman et al., 2003), while focused on a public health disparity of great significance. Cardiovascular diseases, including ischemic heart disease and stroke, are among the leading causes of death worldwide, and the rates are particularly high in developed nations. In the United States, heart disease accounts for about one quarter of all deaths among Black and White adults. Moreover, the rate of deaths attributable to cardiovascular disease is about 38% higher for Black than White men and nearly 41% higher for Black than White women (Go et al., 2013). The prevalence of hypertension is also nearly 38% higher among Black than White adults, and Black Americans are less likely to have their blood pressure under control (Centers for Disease Control and Prevention, 2014). Combined, there is a major disparity in circulatory system health between Black and White Americans. Long before cardiovascular diseases are diagnosed, however, there is an acute-phase protein in the blood that is highly predictive of cardiac events such as myocardial infarction and stroke for men and women (Ridker et al., 1998). C-reactive protein (CRP), synthesized in the liver, is a biomarker of chronic or systemic inflammation (Herd et al., 2012). As such, measuring it in population samples can be very useful as a harbinger of health risks, especially cardiovascular diseases (Sesso et al., 2003). It is anticipated that CRP will generally be higher in Black than White adults because of the levels of stress exposure attendant with minority status. The phenomenon has been described as weathering, whereby racial inequality and institutionalized racism accelerate health deterioration and premature agingdabove and beyond what one might anticipate due to differences in socioeconomic status (Geronimus et al., 2010). Indeed, prior research reveals that CRP is generally higher in Black adults (Wee et al., 2008), along with other biomarkers that are indicators of allostatic load (Geronimus et al., 2006). We are unaware, however, of any study that specifically asks whether religion can offset the generally higher levels of CRP in Black people. It is plausible that religious engagement can have such salubrious effects. Religion provides people with a meaning system and consolation when facing hardship (Ferraro & Koch, 1994). Many religions and forms of spirituality promote a lifestyle of reflection and meditation, which may have a calming effect on adherents. All religions have the potential for health benefits, but Black churches in the United States have long served a unique role in providing spiritual and social resources to parishioners (Ellison et al., 2001). Against an historical backdrop of institutionalized discrimination, the Black church became a vital social center for dealing with race-related issues, helping parishioners cope with adversity and take pride in their ancestry (Ammerman, 2005). Paris (1995, p. 48) characterized the emphasis on coping as survival theology enabling Black people to “endure pain and injustice while not affirming it.” Although there is evidence that Black people derive more mental health benefits from religion than is the case for White people (e.g., Bierman, 2006), it is unclear whether religion can similarly assuage chronic inflammation. Previous studies of religion and CRP generally reveal a modest inverse relationship (Holt-Lunstad et al., 2011) or that it is significant among selected groups only (e.g., persons with diabetes: King et al., 2002). Some studies examine categories of high CRP and report that people who do not regularly attend church have higher levels of the biomarker of chronic inflammation (and this pattern
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holds despite the use of alternative thresholds to define high CRP; cf. Gillum et al., 2008; King et al., 2001). Most of these studies rely on church attendance as the sole or main indicator of religion, but there are also studies that show that CRP is favorably influenced by yoga (Pullen et al., 2008) and spirituality (Holt-Lunstad et al., 2011). Findings from studies examining indicators of religious engagement and other biomarkers such as interleukin 6 are inconsistent: Koenig et al. (1997) reported a modest salutary effect, but Yeager et al. (2006) reported no significant association. Anyfantakis et al. (2013) reported that religious beliefs were associated with lower levels of cardiovascular risk (intima media thickness). Yet, none of these studies examines whether the effect of religion on the health outcome varies by race. Three basic research questions guide the analysis. 1. Is religion associated with chronic inflammation? Most prior research reveals a weak inverse relationship between attending religious services and CRP. As such, we anticipate the same but examine two additional indicators of religious life. 2. If religion is related to CRP at baseline, does it also influence change in CRP over the five-year period? Since most studies rely on crosssectional associations, evidence for a relationship between religion and change in CRP would strengthen claims of the salubrious effects of religion to protect against chronic inflammation. Aging is generally associated with rising CRP, but can religion retard this rise? We rely on longitudinal analyses to determine if religion can slow the weathering process. 3. Are the relationships between religion and CRP stronger for Black than White people? Given Black/White differences in religious affiliation and participation, we hypothesize that the protective effect of religion on CRP is stronger for Black adults than for their White counterparts (i.e., interaction hypotheses associated with the first two research questions).
4. Methods
related to their health needs and religiosity. The final W1 analytic sample is 1693. Post-stratification sample weights were not used in the analyses for two reasons. First, when most of the variables used to construct the post-stratification weights are included in the analytic models, unweighted estimates are preferred (Winship and Radbill, 1994). Second, given our focus on racial differences, using the unweighted sample, with its oversample of Black adults, maximizes the number of Black respondents for testing racial differences. By 2010e2011, when W2 data were collected, 220 respondents had died; 85 were in poor health; and an additional 97 respondents were not re-interviewed for various reasons including refusal. The final analytic sample size for W2 is 1124, and parameter estimates for the longitudinal analyses are adjusted to account for selective mortality. The cross-sectional analyses are useful for establishing baseline values and parameter estimates (Pepys and Hirschfield, 2003), but we emphasize findings that are consistent across the cross-sectional and longitudinal analyses. As described below in the Analysis section, the longitudinal analyses adjust for selective mortality.
4.2. Measurement 4.2.1. CRP concentration During the interview at both waves, a blood sample was collected via capillary finger stick and disposable lancet; up to five drops of blood were applied to filter paper for transport and storage. Blood-spot assays were completed at the Laboratory for Human Biology Research at Northwestern University (Williams and McDade, 2009). CRP measured via dried blood spots is highly correlated with matched plasma samples, but serum or plasma measurement of CRP is considered superior (McDade et al., 2004). Given the skewed distribution of CRP, parameter estimates were derived from its natural logarithm. All tabular results are based on ln CRP.
4.1. Sample The study uses the first and second waves of data, hereafter W1 and W2, from the National Social Life, Health, and Aging project (NSHAP), a representative, population-based sampling of older adults in the United States. NSHAP W1 was collected in 2005e2006 and comprised of 3005 respondents with a response rate of 75.5%. During the interview, a random 83% sample (n ¼ 2494) was selected for blood spot collection, and 2120 individuals (85% response rate) provided samples, 1939 of which were usable (181 were excluded for logistical problems such as equipment malfunction). As is standard in the literature, 209 cases with very high levels of CRP (>8.6 mg/L), indicative of inflammatory response to acute conditions (e.g., flu, injury), were excluded (Herd et al., 2012). Given that only 37 cases had missing information on any of the covariates, they were eliminated from the analysis. The majority of W1 NSHAP respondents with CRP measured are non-Hispanic White (1475); Black respondents were oversampled (218); 110 persons identified themselves as other race (Hispanic Americans, Native Americans, Asian or Pacific Islander, or multiracial). The small number of respondents in the other race categories means that it is not feasible to statistically analyze each racial or ethnic group. In addition, the White and other non-Black categories are demographically similar to one another (e.g., marital status, age) but dissimilar from the Black subsample. Thus, the analysis focuses on the contrast between Black and non-Black (hereafter, White) respondents, which is consistent with our substantive interests in the distinctiveness of the Black population
4.2.2. Religion Three dimensions of religion are examined: religious affiliation, religious attendance, and clergy confidant. The final analyses included four categories for religious affiliation: Protestant, Catholic, other religion, or no religion. Preliminary analysis examined alternative classifications to test for additional differences by affiliation (Jew, evangelical Protestant and mainline Protestant), but no meaningful differences emerged. Thus, all Protestants were considered together, and Jews were treated as other religion. The measure for religious service attendance is based on an interview question asking how often respondents attended religious services during the past 12 months. Responses were rated on an ordinal-level scale ranging from 0 (never) to 6 (several times a week). A measure for clergy confidant was created using respondents' discussion network data. Respondents were asked “looking back over the last 12 months, who are the people with whom you most often discussed things that were important to you?” They were then probed to identify the relationship with each person in their network. Previous research reveals the importance of emotional support from a pastor as a coping resource (Krause et al., 2001), and this NSHAP measure relies on the respondent identifying people with whom they feel they can “discuss things that are important to them.” The variable is scored 1 if at least one of the people named was a religious figure such as a rabbi, priest, minister, or other clergy (zero, otherwise).
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Table 1 Range, coding, and descriptive statistics for study variables, National Social Life, Health, and Aging Project.a Variables
Range
Total sample (n ¼ 1693)
White (n ¼ 1475)
Black (n ¼ 218)
Blackb Log CRP, W1 (mg/L)c Log CRP, W2 (mg/L)c Religious affiliation Protestant Catholic Other religion No religion Religious service attendance [several per week] Clergy confidant Age Female Education [BA/BS or more] Low net worth Married Depressive symptoms [most of the time; 11 items] Contact with network members [daily; 6 alters] Closeness with network members [extremely] Tobacco use Physical activity [3þ times per week] Overweight (25 BMI < 30 kg/m2) Obese (BMI 30 kg/m2) Emphysema Asthma Diabetes Lipid medication
0,1 3.47e2.15 3.35e2.15
.13 .25 (1.06) .53 (.94)
.20 (1.05) .51 (.94)
.60 (1.04)*** .73 (.98)***
0,1 0,1 0,1 0,1 0e6 0,1 57e91 0,1 0e3 0,1 0,1 0e28 0e2190 0e4 0,1 0e4 0,1 0,1 0,1 0,1 0,1 0,1
.47 .29 .16 .07 3.35 (2.12) .05 69.48 (7.87) .51 2.53 (1.06) .26 .62 5.43 (5.10) 796.63 (365.84) 2.97 (.59) .15 3.21 (1.30) .38 .37 .11 .10 .21 .37
.45 .32 .14 .07 3.23 (2.14) .04 69.52 (7.96) .50 2.58 (1.05) .20 .65 5.31 (5.05) 780.57 (352.44) 2.97 (.58) .14 3.26 (1.26) .39 .36 .11 .10 .20 .38
.59*** .06*** .33*** .02* 4.11 (1.81)*** .05 69.18 (7.21) .60** 2.17 (1.06)*** .63*** .40*** 6.26 (5.35) 915.28 (436.24)*** 2.99 (.69) .20* 2.85 (1.49)*** .31* .50*** .09 .11 .33*** .33
*p < .05. **p < .01. ***p < .001. a Number of cases varies across items due to missing data. W2 N of cases is 1124. b Mean value for a binary variable represents percentage of cases that have a value of 1. Highest value of ordinal variables shown in brackets. c Mean values for raw CRP levels are 2.08 and 2.46 at W1 and W2, respectively.
4.2.3. Covariates In addition to the religion variables, several covariates were included because of their association with chronic inflammation (Herd et al., 2012). Age is coded in years, and sex is dichotomized with 1 indicating female. Race is coded as Black (1) and White (0). To measure socioeconomic resources, four categories of education (less than high school, high school graduate, some college, and bachelor's degree or more) and a binary variable for low net worth were included in the analyses. Participants were asked to estimate their net worth including all of their investments, properties, and other financial assets minus debt. A binary variable was created differentiating respondents in the lowest 20% of the household net worth from those in the top 80%. To account for the possibility that religiosity is an epiphenomenon of more general psychosocial resources, we controlled for marital status, depressive symptoms, total volume of contact with network members, and emotional closeness to network members. Marital status is a dummy variable with 1 indicating married or cohabiting with a partner (zero, otherwise). Measures for depressive symptoms are based on the 11-item Center for Epidemiologic Studies Depression Scale (CES-D) (a ¼ .93). In order to create the volume of contact with network members, we used a set of questions asking respondents to report the frequency of contact with up to six alters (members of their discussion network), ranging from “every day” to “less than once a year.” We then converted each category into an approximate number of times per year (e.g., once a month ¼ 12, every day ¼ 365) and summed the scores across network members to construct the total volume of contact (Cornwell et al., 2009). NSHAP respondents also reported how close they felt to each of their alters on a scale from 0 (not very close) to 4 (extremely close). This item was averaged across members to indicate overall closeness with network members. For lifestyle variables considered to be related to CRP, we use self-reported information. Tobacco use was defined by current
consumption of cigarettes, pipes, cigars, or chewing tobacco. Physical activity was measured with an item probing respondents' engagement in physical activities such as walking, dancing, or exercise (0 ¼ never, to 4 ¼ three or more times per week). Binary variables for overweight and obese were defined by body mass index (kg/m2, respectively, 25 BMI < 30 and BMI 30). Finally, the analysis adjusts for clinically relevant chronic health conditions that may be related to CRP. The measures include variables for whether the respondent was ever diagnosed by a physician for emphysema, asthma, and diabetes. Given the documented inverse relationship between lipid-lowering treatment and CRP (McDade et al., 2006), a binary variable for lipid medication use was included in the analyses. Supplementary analyses considered additional covariates (i.e., income, controlled hypertension, uncontrolled hypertension, Southern resident, former smokers, underweight, and self-reported physical and mental health) and alternative coding of variables (i.e., continuous measure of net worth). These were omitted then from the final analyses, however, because they were nonsignificant in multivariate specifications.
4.3. Analysis The analyses used ordinary least squares (OLS) regression to model the initial level of CRP (W1) and change in CRP (by regressing W2 CRP on W1 CRP). All independent variables are measured at W1, specified as lagged predictors in longitudinal analyses. To examine group differences, we report race-stratified analyses at both waves and tested for differences in slopes by race. In supplementary analyses, we estimated six multiplicative models on the total sample with a product term for each religion variable and race (tested separately in 3 equations each for W1 and W2). The conclusions were consistent with the race-stratified
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Table 2 Regression of log CRP on independent variables in the NSHAP (unstandardized coefficients and standard errors). Variablesa
Wave 1 log CRP
Wave 2 log CRP
White (n ¼ 1475)
Catholic Other religion No religion Religious service attendance Clergy confidant Age Female Education Low net worth Married Depressive symptom Contact with network members Closeness with network members Tobacco use Physical activity Overweight Obese Emphysema Asthma Diabetes Lipid medication Wave 1 log CRP Selection l Constant Adjusted R2
Black (n ¼ 218)
Coefficient
SE
Coefficient
.11 .10 .17 .03 .31* .01 .23** .05 .04 .12 .00 .00 .02 .19 .05 .30*** .76*** .10 .08 .03 .27***
.08 .10 .14 .02 .15 .00 .07 .04 .09 .08 .01 .00 .06 .10 .03 .09 .09 .11 .12 .09 .07
.43 .39 eb .12*c .16 .02 .27 .03 .10 .05 .01 .00 .05 .02 .08 .37 .71** .92**c .52 .23 .24
.27 .16
.45
1.67 .28
White (n ¼ 990) SE .45 .22 .06 .39 .01 .26 .11 .23 .22 .02 .00 .14 .24 .08 .28 .27 .33 .30 .20 .21
1.28
Black (n ¼ 134)
Coefficient
SE
Coefficient
.07 .05 .07 .02 .16 .00 .03 .00 .12 .14* .00 .00 .03 .03 .02 .07 .24** .22* .15 .04 .06 .25*** .11 .23 .27
.06 .09 .11 .01 .12 .01 .06 .03 .08 .07 .01 .00 .05 .09 .03 .07 .08 .09 .10 .07 .06 .02 .25 .43
.42 .27 eb .13*c .51 .06 .06 .09 .14 .01 .02 .00 .30* .11 .03 .66** 1.04*** .01 .17 .15 .19 .19*** 1.15* 4.25 .56
SE .38 .18 .05 .33 .04 .23 .09 .19 .19 .02 .00 .12 .21 .07 .23 .23 .28 .27 .17 .18 .04 .48 1.18
*p < .05. **p < .01. ***p < .001. a The range of each independent variable is provided in Table 1. b Zero-restriction due to insufficient cases (n < 5). c Significant racial difference in slopes.
analyses presented in Table 2 (and one of the multiplicative models was used to generate a figure displaying a statistical interaction). Since the analysis examined racial differences in relationships between religion variables and CRP, some religious affiliations appear infrequently in the Black sample. Thus, we excluded an independent variable from parameter estimation if there were not at least five cases in the cross-classification of religion and race. Although re-interview rates were high in the follow-up study (i.e., 87.8% of the W1 survivors were re-interviewed), sample attrition may nonetheless produce biased parameter estimates in longitudinal analyses. Since the majority of attrition was due to death, we employed Heckman's (1979) selection bias models to adjust for the differential selectivity due to death. We first estimated a probit model to distinguish respondents who participated at the follow-up interview from those who died. Predictors of mortality in the probit model included age, female, and tobacco use along with several variables that were not included in the substantive equation predicting CRP (i.e., difficulty in activities of daily living (ADL), employment, underweight [BMI 18.5], and selfrated health). The selection instrument (l) based on the inverse Mills ratio was subsequently estimated and included in the substantive regression models.
examining the sample by race, however, CRP is notably higher at both waves for Black than White respondents. Black adults attended religious services more frequently than did White adults (mean of 4.11 v. 3.23). Although not shown in Table 1, 59.82% of Black adults attended religious services at least once a week, but only 44.31% of White respondents did the same. About 5% of the total sample reported having a clergy confidant in their network, and this did not vary significantly by race. Most of the other differences by race in Table 1 are as expected. Table 2 displays the results of ordinary least squares regression analyses for log CRP at W1 and W2, stratified by race. The first
5. Results Table 1 displays the descriptive statistics for each variable, both for the total sample and by race. Mean log CRP levels for the total sample were .25 and .53 at W1 and W2, respectively. (Corresponding raw values of CRP were 2.08 and 2.46, reflecting average risk for inflammation [3 mg/L typically indicates high risk]). Across waves, respondents generally experienced an increase in CRP, consistent with the existing literature (Ferrucci et al., 2005). By
Fig. 1. Predicted values of wave 2 raw C-reactive protein by religious service attendance for Black and White respondents (estimates adjusted for all independent variables, including W1 raw CRP).
K.F. Ferraro, S. Kim / Social Science & Medicine 120 (2014) 92e99
equation (column) is a regression of W1 log CRP on the predictors for White respondents; the second equation is for Black respondents. These cross-sectional analyses reveal that for White respondents, having a clergy confidant is associated with a lower CRP level (b ¼ .31, p < .05). This means that the log CRP of White older adults who reported having at least one clergy member as a confidant were .31 lower than those who did not have a clergy confidant. The equation for the White respondents also revealed that CRP was higher for women, overweight and obese persons, and those not taking lipid medication. Continuing with the cross-sectional analyses, the second equation shows that frequent religious service attendance is associated with lower CRP among Black respondents (b ¼ .12, b ¼ .21, p < .05), and the test of difference in slopes reveals that this is different from what was observed in the White subsample. CRP was also higher among Black adults who were obese or had emphysemadand the slope for emphysema is significantly different than what was observed in the White sample. Whereas fewer than five Black respondents identified as having no religion, this variable was omitted from the equation (zero restriction). Note, however, that the adjusted R2 in the Black subsample is notably higher than in the White subsample (.28 compared to .16). The longitudinal analyses are displayed in the right-half of Table 2 (Wave 2 log CRP), and these equations include two additional variables: W1 log CRP and the selection variable (l to account for differential mortality). With W1 log CRP in the analyses, these equations reflect change in log CRP over the 5-year interval. In the White sample, none of the religion variables is associated with change in CRP. The analysis reveals, however, that CRP rose significantly for persons who were unmarried or obese, or had emphysema. In the Black subsample, religious service attendance is negatively associated with log CRP (b ¼ .13, b ¼ .24, p < .05). Compared to those who never or infrequently attended religious services, people who attended frequently experienced smaller increases in CRP. Note also that this slope is significantly different across the racial groups. Among Black respondents, CRP also was less likely to rise among persons reporting closeness with network members. Both overweight and obesity were associated with a rise in CRP. For the Black subsample, the adjustment for mortality selection is important because those who died would likely have had higher W2 CRP. To graphically illustrate the distinctive effect of religious service attendance on CRP by race, Fig. 1 plots raw CRP levels at W2 for the Black and White subsamples after adjusting for all of the variables shown in the longitudinal analyses from Table 2 (including W1 raw CRP). As reflected in Table 2, the slope between religious service attendance and CRP among White respondents is essentially flat. By contrast, CRP is highest among Black people with low levels of religious attendance and lowest among those who attend religious services at least weekly. There is a stark difference between Black and White respondents at low levels of religious participation, but CRP among Black people who attend religious services frequently is fairly similar to the average levels of CRP among White people. Fig. 1 clarifies that frequent service attendance has a protective effect against elevated CRP for Black adults but does not pay similar dividends for White adults. To assess the robustness of the results, supplementary analyses were completed. First, we trichotomized raw CRP based on clinically significant thresholds (
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Health benefits of religion among Black and White older adults? Race, religiosity, and C-reactive protein Kenneth F. Ferraro a, b, *, Seoyoun Kim a, b a b
Department of Sociology, Purdue University, Stone Hall, West Lafayette, IN 47907, USA Center on Aging and the Life Course, Purdue University, Hanley Hall, West Lafayette, IN 47907, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 June 2014 Received in revised form 5 August 2014 Accepted 20 August 2014 Available online 22 August 2014
The study investigates potential health benefits of religiosity to protect against chronic inflammation associated with the risk of cardiovascular diseases. The study uses longitudinal data from a representative survey of adults 57e85 years old at the beginning of the National Social Life, Health, and Aging Project. Linear regression models were used to analyze the association between religiosity, as measured by affiliation, attendance, and having a clergy confidant, and logged values of C-reactive protein (CRP) concentration (mg/L). Although religious attendance was not related to CRP among the White respondents, attendance was associated with lower CRPdand change in CRP over timedamong the Black respondents. There was no evidence that religious affiliation alone had any health benefit. The study provides evidence of the salutary effects of religious engagement on chronic inflammation among older adults, especially for Black Americans, which may be useful in reducing the prevalence of hypertension and cardiovascular disease. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Religiosity Social participation AfricaneAmerican Chronic inflammation
1. Introduction Is religion good, bad, or benign to health? This is a question that has long sparked scholarly interest. Freud and Marx generally viewed religion as a maladaptive response while Durkheim and James found that humanedivine relations can benefit both the social order and personal well-being. Research on religion and health has proliferated during the last two decades: more than 2200 quantitative publications examine the conditions under which there may be a link between religion and various facets of physical or mental health (George et al., 2013; Koenig et al., 2001). Most of these studies report that religion is beneficial to one or more health indicators, but some studies reveal that religion may also lead to poorer health outcomesdand still other studies find no association. Religion and health are each rich concepts, and the evidence-based conclusions from the extant research depend largely upon which elements of the two phenomena are studied. The present study seeks to contribute to this literature along two axes. First, given religion's role as a meaning system and its utility as a coping resource, it is logical that there are many studies of religion's
* Corresponding author. Department of Sociology, Purdue University, 700 West State Street, West Lafayette, IN 47907-2059, USA. E-mail address: [email protected] (K.F. Ferraro). http://dx.doi.org/10.1016/j.socscimed.2014.08.030 0277-9536/© 2014 Elsevier Ltd. All rights reserved.
effect on mental health and overall wellbeing. Even among the studies examining physical health, however, most rely on measures of self-reported health. Although self-reported measures are generally considered useful in a prognostic sense (Ferraro & Farmer, 1999), they are limited for explicating mechanisms for how social factors influence biological processes. Thus, a small but growing set of studies examine various biomarkers of health, which provide a window into preclinical risk of disease and the biological pathways by which religion may influence health. Second, race is a notable and relatively understudied source of variability in how religion may be related to health. Organized religious affiliation and expression are patterned along racial and ethnic lines in many nations. Relatively few empirical studies of religion and physical health, however, systematically examine racial and ethnic variability in these relationships; many studies either do not analyze racial variability or simply adjust for race when testing the relationship between indicators of religion and physical health outcomes (for an exemplar examining health, see Steffen et al., 2001). Black churches, however, are distinctive in the types of programs and services offered (Koch and Beckley, 2006), the sense of belonging among members (Martinez and Dougherty, 2013), and the religious experience cultivated to aid coping with adversity (Ellison et al., 2008). Perhaps greater attention to this variability will clarify who most benefits from (or is harmed by) religious
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participation. Evidence from previous research leads one to expect that Black people receive greater health benefits from their religious participation than is the case for White people (Krause, 2002). In addition, health disparities by race are substantial (LaVeist et al., 2011), calling for greater attention to the health needs of Black people (i.e., persons of African and AfroCaribbean descent) and the role of religion to reduce such disparities. If the relationship between religion and health varies by race in the United States, the implications may be far reaching to other nations and religions. Using a national sample of older Americans, we systematically examine Black/White differences in the relationship between several indicators of religion and a biomarker of chronic inflammation that is a precursor to several cardiovascular diseases. Across all age groups, Black men and women have higher rates of hypertension and heart disease mortality than their White counterparts (Centers for Disease Control and Prevention, 2014). As such, examining how religion may influence a biomarker that is highly predictive of heart disease is potentially significant for improving public health (Crimmins and Vasunilashorn, 2011). 2. Religion and physical health Although the manifest purpose of religion is not health promotion, a substantial body of research in recent decades has made important strides by explicating why religion may be related to physical health. Three hypotheses have received considerable attention during this time: constraining health-injurious behaviors, social integration, and consolation. Studies have shown that religious participation constrains some behaviors that are harmful to health such as smoking (Whooley et al., 2002) and substance abuse (Haber et al., 2012) but that its relationship with obesity is weaker and more complex (Cline & Ferraro, 2006). Scores of studies reveal that integration in religious networks and communities has a salutary effect on physical health (Strawbridge et al., 2001), but a smaller set of studies reveals that “religious support” can also have a dark side, whereby interpersonal conflict leads to psychological distress (Ellison et al., 2009). Whereas religion is widely viewed as a coping resource while facing stressors, several studies have shown that misfortunedincluding poor healthdmay also lead to religious participation (Ferraro & Kelley-Moore, 2000). As such, the relationships may be quite complex: religion may lead to or result from physical or emotional dysphoria. Underlying much of this research is a tenet that religion can shape physiological processes and outcomes. Cousins (1990) spoke of these processes as part of the “biology of hope”, and Seeman et al. (2003) sought evidence of biological pathways between religion/spirituality and health. Based on their critical review of the scientific literature, they concluded that “the evidence reported to date is generally consistent with the hypothesis that aspects of religiosity/spirituality may indeed be linked to physiological processesdincluding cardiovascular, neuroendocrine, and immune functiondthat are importantly related to health” (p. 61). In an effort to study biological pathways between religion and health, investigators have integrated biomarkers in their research. There are at least two main advantages of doing so. First, using biomarkers enables one to focus on specific pathways or mechanisms. Global measures of health remain important, but isolating biological pathways has the potential for breakthrough discoveries in determining if and how religion is beneficial to health. Second, biomarkers enable one to sidestep some of the fallibilities of selfreported health measures. Since religion frequently offers inspiration or consolation to people facing hardship, it is plausible that an optimistic outlook may lead to underreporting of health problems. Thus, it is understandable why Williams and Sternthal (2007, p.
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S49) urged greater attention “directed towards understanding the biological mechanisms through which religion ‘gets under the skin’.” 3. Race, religion, and chronic inflammation The current study responds to this call, and those of others (Seeman et al., 2003), while focused on a public health disparity of great significance. Cardiovascular diseases, including ischemic heart disease and stroke, are among the leading causes of death worldwide, and the rates are particularly high in developed nations. In the United States, heart disease accounts for about one quarter of all deaths among Black and White adults. Moreover, the rate of deaths attributable to cardiovascular disease is about 38% higher for Black than White men and nearly 41% higher for Black than White women (Go et al., 2013). The prevalence of hypertension is also nearly 38% higher among Black than White adults, and Black Americans are less likely to have their blood pressure under control (Centers for Disease Control and Prevention, 2014). Combined, there is a major disparity in circulatory system health between Black and White Americans. Long before cardiovascular diseases are diagnosed, however, there is an acute-phase protein in the blood that is highly predictive of cardiac events such as myocardial infarction and stroke for men and women (Ridker et al., 1998). C-reactive protein (CRP), synthesized in the liver, is a biomarker of chronic or systemic inflammation (Herd et al., 2012). As such, measuring it in population samples can be very useful as a harbinger of health risks, especially cardiovascular diseases (Sesso et al., 2003). It is anticipated that CRP will generally be higher in Black than White adults because of the levels of stress exposure attendant with minority status. The phenomenon has been described as weathering, whereby racial inequality and institutionalized racism accelerate health deterioration and premature agingdabove and beyond what one might anticipate due to differences in socioeconomic status (Geronimus et al., 2010). Indeed, prior research reveals that CRP is generally higher in Black adults (Wee et al., 2008), along with other biomarkers that are indicators of allostatic load (Geronimus et al., 2006). We are unaware, however, of any study that specifically asks whether religion can offset the generally higher levels of CRP in Black people. It is plausible that religious engagement can have such salubrious effects. Religion provides people with a meaning system and consolation when facing hardship (Ferraro & Koch, 1994). Many religions and forms of spirituality promote a lifestyle of reflection and meditation, which may have a calming effect on adherents. All religions have the potential for health benefits, but Black churches in the United States have long served a unique role in providing spiritual and social resources to parishioners (Ellison et al., 2001). Against an historical backdrop of institutionalized discrimination, the Black church became a vital social center for dealing with race-related issues, helping parishioners cope with adversity and take pride in their ancestry (Ammerman, 2005). Paris (1995, p. 48) characterized the emphasis on coping as survival theology enabling Black people to “endure pain and injustice while not affirming it.” Although there is evidence that Black people derive more mental health benefits from religion than is the case for White people (e.g., Bierman, 2006), it is unclear whether religion can similarly assuage chronic inflammation. Previous studies of religion and CRP generally reveal a modest inverse relationship (Holt-Lunstad et al., 2011) or that it is significant among selected groups only (e.g., persons with diabetes: King et al., 2002). Some studies examine categories of high CRP and report that people who do not regularly attend church have higher levels of the biomarker of chronic inflammation (and this pattern
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holds despite the use of alternative thresholds to define high CRP; cf. Gillum et al., 2008; King et al., 2001). Most of these studies rely on church attendance as the sole or main indicator of religion, but there are also studies that show that CRP is favorably influenced by yoga (Pullen et al., 2008) and spirituality (Holt-Lunstad et al., 2011). Findings from studies examining indicators of religious engagement and other biomarkers such as interleukin 6 are inconsistent: Koenig et al. (1997) reported a modest salutary effect, but Yeager et al. (2006) reported no significant association. Anyfantakis et al. (2013) reported that religious beliefs were associated with lower levels of cardiovascular risk (intima media thickness). Yet, none of these studies examines whether the effect of religion on the health outcome varies by race. Three basic research questions guide the analysis. 1. Is religion associated with chronic inflammation? Most prior research reveals a weak inverse relationship between attending religious services and CRP. As such, we anticipate the same but examine two additional indicators of religious life. 2. If religion is related to CRP at baseline, does it also influence change in CRP over the five-year period? Since most studies rely on crosssectional associations, evidence for a relationship between religion and change in CRP would strengthen claims of the salubrious effects of religion to protect against chronic inflammation. Aging is generally associated with rising CRP, but can religion retard this rise? We rely on longitudinal analyses to determine if religion can slow the weathering process. 3. Are the relationships between religion and CRP stronger for Black than White people? Given Black/White differences in religious affiliation and participation, we hypothesize that the protective effect of religion on CRP is stronger for Black adults than for their White counterparts (i.e., interaction hypotheses associated with the first two research questions).
4. Methods
related to their health needs and religiosity. The final W1 analytic sample is 1693. Post-stratification sample weights were not used in the analyses for two reasons. First, when most of the variables used to construct the post-stratification weights are included in the analytic models, unweighted estimates are preferred (Winship and Radbill, 1994). Second, given our focus on racial differences, using the unweighted sample, with its oversample of Black adults, maximizes the number of Black respondents for testing racial differences. By 2010e2011, when W2 data were collected, 220 respondents had died; 85 were in poor health; and an additional 97 respondents were not re-interviewed for various reasons including refusal. The final analytic sample size for W2 is 1124, and parameter estimates for the longitudinal analyses are adjusted to account for selective mortality. The cross-sectional analyses are useful for establishing baseline values and parameter estimates (Pepys and Hirschfield, 2003), but we emphasize findings that are consistent across the cross-sectional and longitudinal analyses. As described below in the Analysis section, the longitudinal analyses adjust for selective mortality.
4.2. Measurement 4.2.1. CRP concentration During the interview at both waves, a blood sample was collected via capillary finger stick and disposable lancet; up to five drops of blood were applied to filter paper for transport and storage. Blood-spot assays were completed at the Laboratory for Human Biology Research at Northwestern University (Williams and McDade, 2009). CRP measured via dried blood spots is highly correlated with matched plasma samples, but serum or plasma measurement of CRP is considered superior (McDade et al., 2004). Given the skewed distribution of CRP, parameter estimates were derived from its natural logarithm. All tabular results are based on ln CRP.
4.1. Sample The study uses the first and second waves of data, hereafter W1 and W2, from the National Social Life, Health, and Aging project (NSHAP), a representative, population-based sampling of older adults in the United States. NSHAP W1 was collected in 2005e2006 and comprised of 3005 respondents with a response rate of 75.5%. During the interview, a random 83% sample (n ¼ 2494) was selected for blood spot collection, and 2120 individuals (85% response rate) provided samples, 1939 of which were usable (181 were excluded for logistical problems such as equipment malfunction). As is standard in the literature, 209 cases with very high levels of CRP (>8.6 mg/L), indicative of inflammatory response to acute conditions (e.g., flu, injury), were excluded (Herd et al., 2012). Given that only 37 cases had missing information on any of the covariates, they were eliminated from the analysis. The majority of W1 NSHAP respondents with CRP measured are non-Hispanic White (1475); Black respondents were oversampled (218); 110 persons identified themselves as other race (Hispanic Americans, Native Americans, Asian or Pacific Islander, or multiracial). The small number of respondents in the other race categories means that it is not feasible to statistically analyze each racial or ethnic group. In addition, the White and other non-Black categories are demographically similar to one another (e.g., marital status, age) but dissimilar from the Black subsample. Thus, the analysis focuses on the contrast between Black and non-Black (hereafter, White) respondents, which is consistent with our substantive interests in the distinctiveness of the Black population
4.2.2. Religion Three dimensions of religion are examined: religious affiliation, religious attendance, and clergy confidant. The final analyses included four categories for religious affiliation: Protestant, Catholic, other religion, or no religion. Preliminary analysis examined alternative classifications to test for additional differences by affiliation (Jew, evangelical Protestant and mainline Protestant), but no meaningful differences emerged. Thus, all Protestants were considered together, and Jews were treated as other religion. The measure for religious service attendance is based on an interview question asking how often respondents attended religious services during the past 12 months. Responses were rated on an ordinal-level scale ranging from 0 (never) to 6 (several times a week). A measure for clergy confidant was created using respondents' discussion network data. Respondents were asked “looking back over the last 12 months, who are the people with whom you most often discussed things that were important to you?” They were then probed to identify the relationship with each person in their network. Previous research reveals the importance of emotional support from a pastor as a coping resource (Krause et al., 2001), and this NSHAP measure relies on the respondent identifying people with whom they feel they can “discuss things that are important to them.” The variable is scored 1 if at least one of the people named was a religious figure such as a rabbi, priest, minister, or other clergy (zero, otherwise).
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Table 1 Range, coding, and descriptive statistics for study variables, National Social Life, Health, and Aging Project.a Variables
Range
Total sample (n ¼ 1693)
White (n ¼ 1475)
Black (n ¼ 218)
Blackb Log CRP, W1 (mg/L)c Log CRP, W2 (mg/L)c Religious affiliation Protestant Catholic Other religion No religion Religious service attendance [several per week] Clergy confidant Age Female Education [BA/BS or more] Low net worth Married Depressive symptoms [most of the time; 11 items] Contact with network members [daily; 6 alters] Closeness with network members [extremely] Tobacco use Physical activity [3þ times per week] Overweight (25 BMI < 30 kg/m2) Obese (BMI 30 kg/m2) Emphysema Asthma Diabetes Lipid medication
0,1 3.47e2.15 3.35e2.15
.13 .25 (1.06) .53 (.94)
.20 (1.05) .51 (.94)
.60 (1.04)*** .73 (.98)***
0,1 0,1 0,1 0,1 0e6 0,1 57e91 0,1 0e3 0,1 0,1 0e28 0e2190 0e4 0,1 0e4 0,1 0,1 0,1 0,1 0,1 0,1
.47 .29 .16 .07 3.35 (2.12) .05 69.48 (7.87) .51 2.53 (1.06) .26 .62 5.43 (5.10) 796.63 (365.84) 2.97 (.59) .15 3.21 (1.30) .38 .37 .11 .10 .21 .37
.45 .32 .14 .07 3.23 (2.14) .04 69.52 (7.96) .50 2.58 (1.05) .20 .65 5.31 (5.05) 780.57 (352.44) 2.97 (.58) .14 3.26 (1.26) .39 .36 .11 .10 .20 .38
.59*** .06*** .33*** .02* 4.11 (1.81)*** .05 69.18 (7.21) .60** 2.17 (1.06)*** .63*** .40*** 6.26 (5.35) 915.28 (436.24)*** 2.99 (.69) .20* 2.85 (1.49)*** .31* .50*** .09 .11 .33*** .33
*p < .05. **p < .01. ***p < .001. a Number of cases varies across items due to missing data. W2 N of cases is 1124. b Mean value for a binary variable represents percentage of cases that have a value of 1. Highest value of ordinal variables shown in brackets. c Mean values for raw CRP levels are 2.08 and 2.46 at W1 and W2, respectively.
4.2.3. Covariates In addition to the religion variables, several covariates were included because of their association with chronic inflammation (Herd et al., 2012). Age is coded in years, and sex is dichotomized with 1 indicating female. Race is coded as Black (1) and White (0). To measure socioeconomic resources, four categories of education (less than high school, high school graduate, some college, and bachelor's degree or more) and a binary variable for low net worth were included in the analyses. Participants were asked to estimate their net worth including all of their investments, properties, and other financial assets minus debt. A binary variable was created differentiating respondents in the lowest 20% of the household net worth from those in the top 80%. To account for the possibility that religiosity is an epiphenomenon of more general psychosocial resources, we controlled for marital status, depressive symptoms, total volume of contact with network members, and emotional closeness to network members. Marital status is a dummy variable with 1 indicating married or cohabiting with a partner (zero, otherwise). Measures for depressive symptoms are based on the 11-item Center for Epidemiologic Studies Depression Scale (CES-D) (a ¼ .93). In order to create the volume of contact with network members, we used a set of questions asking respondents to report the frequency of contact with up to six alters (members of their discussion network), ranging from “every day” to “less than once a year.” We then converted each category into an approximate number of times per year (e.g., once a month ¼ 12, every day ¼ 365) and summed the scores across network members to construct the total volume of contact (Cornwell et al., 2009). NSHAP respondents also reported how close they felt to each of their alters on a scale from 0 (not very close) to 4 (extremely close). This item was averaged across members to indicate overall closeness with network members. For lifestyle variables considered to be related to CRP, we use self-reported information. Tobacco use was defined by current
consumption of cigarettes, pipes, cigars, or chewing tobacco. Physical activity was measured with an item probing respondents' engagement in physical activities such as walking, dancing, or exercise (0 ¼ never, to 4 ¼ three or more times per week). Binary variables for overweight and obese were defined by body mass index (kg/m2, respectively, 25 BMI < 30 and BMI 30). Finally, the analysis adjusts for clinically relevant chronic health conditions that may be related to CRP. The measures include variables for whether the respondent was ever diagnosed by a physician for emphysema, asthma, and diabetes. Given the documented inverse relationship between lipid-lowering treatment and CRP (McDade et al., 2006), a binary variable for lipid medication use was included in the analyses. Supplementary analyses considered additional covariates (i.e., income, controlled hypertension, uncontrolled hypertension, Southern resident, former smokers, underweight, and self-reported physical and mental health) and alternative coding of variables (i.e., continuous measure of net worth). These were omitted then from the final analyses, however, because they were nonsignificant in multivariate specifications.
4.3. Analysis The analyses used ordinary least squares (OLS) regression to model the initial level of CRP (W1) and change in CRP (by regressing W2 CRP on W1 CRP). All independent variables are measured at W1, specified as lagged predictors in longitudinal analyses. To examine group differences, we report race-stratified analyses at both waves and tested for differences in slopes by race. In supplementary analyses, we estimated six multiplicative models on the total sample with a product term for each religion variable and race (tested separately in 3 equations each for W1 and W2). The conclusions were consistent with the race-stratified
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Table 2 Regression of log CRP on independent variables in the NSHAP (unstandardized coefficients and standard errors). Variablesa
Wave 1 log CRP
Wave 2 log CRP
White (n ¼ 1475)
Catholic Other religion No religion Religious service attendance Clergy confidant Age Female Education Low net worth Married Depressive symptom Contact with network members Closeness with network members Tobacco use Physical activity Overweight Obese Emphysema Asthma Diabetes Lipid medication Wave 1 log CRP Selection l Constant Adjusted R2
Black (n ¼ 218)
Coefficient
SE
Coefficient
.11 .10 .17 .03 .31* .01 .23** .05 .04 .12 .00 .00 .02 .19 .05 .30*** .76*** .10 .08 .03 .27***
.08 .10 .14 .02 .15 .00 .07 .04 .09 .08 .01 .00 .06 .10 .03 .09 .09 .11 .12 .09 .07
.43 .39 eb .12*c .16 .02 .27 .03 .10 .05 .01 .00 .05 .02 .08 .37 .71** .92**c .52 .23 .24
.27 .16
.45
1.67 .28
White (n ¼ 990) SE .45 .22 .06 .39 .01 .26 .11 .23 .22 .02 .00 .14 .24 .08 .28 .27 .33 .30 .20 .21
1.28
Black (n ¼ 134)
Coefficient
SE
Coefficient
.07 .05 .07 .02 .16 .00 .03 .00 .12 .14* .00 .00 .03 .03 .02 .07 .24** .22* .15 .04 .06 .25*** .11 .23 .27
.06 .09 .11 .01 .12 .01 .06 .03 .08 .07 .01 .00 .05 .09 .03 .07 .08 .09 .10 .07 .06 .02 .25 .43
.42 .27 eb .13*c .51 .06 .06 .09 .14 .01 .02 .00 .30* .11 .03 .66** 1.04*** .01 .17 .15 .19 .19*** 1.15* 4.25 .56
SE .38 .18 .05 .33 .04 .23 .09 .19 .19 .02 .00 .12 .21 .07 .23 .23 .28 .27 .17 .18 .04 .48 1.18
*p < .05. **p < .01. ***p < .001. a The range of each independent variable is provided in Table 1. b Zero-restriction due to insufficient cases (n < 5). c Significant racial difference in slopes.
analyses presented in Table 2 (and one of the multiplicative models was used to generate a figure displaying a statistical interaction). Since the analysis examined racial differences in relationships between religion variables and CRP, some religious affiliations appear infrequently in the Black sample. Thus, we excluded an independent variable from parameter estimation if there were not at least five cases in the cross-classification of religion and race. Although re-interview rates were high in the follow-up study (i.e., 87.8% of the W1 survivors were re-interviewed), sample attrition may nonetheless produce biased parameter estimates in longitudinal analyses. Since the majority of attrition was due to death, we employed Heckman's (1979) selection bias models to adjust for the differential selectivity due to death. We first estimated a probit model to distinguish respondents who participated at the follow-up interview from those who died. Predictors of mortality in the probit model included age, female, and tobacco use along with several variables that were not included in the substantive equation predicting CRP (i.e., difficulty in activities of daily living (ADL), employment, underweight [BMI 18.5], and selfrated health). The selection instrument (l) based on the inverse Mills ratio was subsequently estimated and included in the substantive regression models.
examining the sample by race, however, CRP is notably higher at both waves for Black than White respondents. Black adults attended religious services more frequently than did White adults (mean of 4.11 v. 3.23). Although not shown in Table 1, 59.82% of Black adults attended religious services at least once a week, but only 44.31% of White respondents did the same. About 5% of the total sample reported having a clergy confidant in their network, and this did not vary significantly by race. Most of the other differences by race in Table 1 are as expected. Table 2 displays the results of ordinary least squares regression analyses for log CRP at W1 and W2, stratified by race. The first
5. Results Table 1 displays the descriptive statistics for each variable, both for the total sample and by race. Mean log CRP levels for the total sample were .25 and .53 at W1 and W2, respectively. (Corresponding raw values of CRP were 2.08 and 2.46, reflecting average risk for inflammation [3 mg/L typically indicates high risk]). Across waves, respondents generally experienced an increase in CRP, consistent with the existing literature (Ferrucci et al., 2005). By
Fig. 1. Predicted values of wave 2 raw C-reactive protein by religious service attendance for Black and White respondents (estimates adjusted for all independent variables, including W1 raw CRP).
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equation (column) is a regression of W1 log CRP on the predictors for White respondents; the second equation is for Black respondents. These cross-sectional analyses reveal that for White respondents, having a clergy confidant is associated with a lower CRP level (b ¼ .31, p < .05). This means that the log CRP of White older adults who reported having at least one clergy member as a confidant were .31 lower than those who did not have a clergy confidant. The equation for the White respondents also revealed that CRP was higher for women, overweight and obese persons, and those not taking lipid medication. Continuing with the cross-sectional analyses, the second equation shows that frequent religious service attendance is associated with lower CRP among Black respondents (b ¼ .12, b ¼ .21, p < .05), and the test of difference in slopes reveals that this is different from what was observed in the White subsample. CRP was also higher among Black adults who were obese or had emphysemadand the slope for emphysema is significantly different than what was observed in the White sample. Whereas fewer than five Black respondents identified as having no religion, this variable was omitted from the equation (zero restriction). Note, however, that the adjusted R2 in the Black subsample is notably higher than in the White subsample (.28 compared to .16). The longitudinal analyses are displayed in the right-half of Table 2 (Wave 2 log CRP), and these equations include two additional variables: W1 log CRP and the selection variable (l to account for differential mortality). With W1 log CRP in the analyses, these equations reflect change in log CRP over the 5-year interval. In the White sample, none of the religion variables is associated with change in CRP. The analysis reveals, however, that CRP rose significantly for persons who were unmarried or obese, or had emphysema. In the Black subsample, religious service attendance is negatively associated with log CRP (b ¼ .13, b ¼ .24, p < .05). Compared to those who never or infrequently attended religious services, people who attended frequently experienced smaller increases in CRP. Note also that this slope is significantly different across the racial groups. Among Black respondents, CRP also was less likely to rise among persons reporting closeness with network members. Both overweight and obesity were associated with a rise in CRP. For the Black subsample, the adjustment for mortality selection is important because those who died would likely have had higher W2 CRP. To graphically illustrate the distinctive effect of religious service attendance on CRP by race, Fig. 1 plots raw CRP levels at W2 for the Black and White subsamples after adjusting for all of the variables shown in the longitudinal analyses from Table 2 (including W1 raw CRP). As reflected in Table 2, the slope between religious service attendance and CRP among White respondents is essentially flat. By contrast, CRP is highest among Black people with low levels of religious attendance and lowest among those who attend religious services at least weekly. There is a stark difference between Black and White respondents at low levels of religious participation, but CRP among Black people who attend religious services frequently is fairly similar to the average levels of CRP among White people. Fig. 1 clarifies that frequent service attendance has a protective effect against elevated CRP for Black adults but does not pay similar dividends for White adults. To assess the robustness of the results, supplementary analyses were completed. First, we trichotomized raw CRP based on clinically significant thresholds (
