529333 research-article2014
JAHXXX10.1177/0898264314529333Journal of Aging and HealthHill et al.
Article
Religious Attendance and Biological Functioning: A Multiple Specification Approach
Journal of Aging and Health 2014, Vol. 26(5) 766–785 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264314529333 jah.sagepub.com
Terrence D. Hill, PhD1, Sunshine M. Rote, PhD2, Christopher G. Ellison, PhD3, and Amy M. Burdette, PhD4
Abstract Objective: This study explores the role of religious attendance across a wide range of biological markers. Method: The data are drawn from the National Social Life, Health, and Aging Project to assess continuous and categorical biomarker specifications. Results: Across specifications, higher levels of attendance are associated with lower levels of pulse rate and overall allostatic load. Depending on the specification, higher levels of attendance are also associated with lower levels of body mass, diastolic blood pressure, C-reactive protein, and Epstein–Barr virus. Attendance is unrelated to dehydroepiandrosterone, systolic blood pressure, and glycosylated hemoglobin across specifications. Discussion: The study confirms that religious attendance is associated with healthier biological functioning in later life. Additional research is needed to verify these patterns with other data sources and to test viable mediators of the association between religious attendance and biological risk.
1The
University of Utah, Salt Lake City, USA of Louisville, USA 3University of Texas at San Antonio, USA 4Florida State University, Tallahassee, USA 2University
Corresponding Author: Terrence D. Hill, PhD, Department of Sociology, The University of Utah, 380 S 1530 E, Room 301, Salt Lake City, UT 84112-0250, USA. Email: [email protected]
Hill et al.
767
Keywords religion, religious attendance, allostatic load, biological risk Over the past three decades, numerous studies have shown that religious involvement—indicated by observable feelings, beliefs, activities, and experiences in relation to spiritual, divine, or super-natural entities—tends to favor health and longevity in the elderly population. These patterns are remarkably consistent across a range of health indicators, including anger (Carr, 2003), depression (Idler, 1987; Idler & Kasl, 1997a; Strawbridge, Shema, Cohen, Roberts, & Kaplan, 1998), anxiety (Cicirelli, 2002; Krause, 2005), non-specific psychological well-being (Fry, 2001; Idler & Kasl, 1997a), life satisfaction (Levin, Markides, & Ray, 1996; Krause, 2003, 2005), cognitive functioning (Hill, Burdette, Angel, & Angel, 2006; Reyes-Ortiz et al., 2008; Van Ness & Kasl, 2003), self-rated health (Idler, McLaughlin, & Kasl, 2009; Krause, 1998, 2006), functional status (Benjamins, 2004; Idler, 1987; Idler & Kasl, 1997b; Park et al., 2008), and stroke (Wolinsky et al., 2009). Not surprisingly, religious involvement is also associated with lower risk of all-cause mortality (Ellison, Hummer, Cormier, & Rogers, 2000; Hill, Angel, Ellison, & Angel, 2005; Strawbridge, Cohen, Shema, & Kaplan, 1997) and mortality linked to circulatory diseases, respiratory diseases, and other specific causes (Hummer, Rogers, Nam, & Ellison, 1999; Oman, Kurata, Strawbridge, & Cohen, 2002; Rogers, Krueger, & Hummer, 2010). Although this body of work is extensive, researchers have only begun to explore the role of religious involvement across a wide range of biological markers. Biological markers or biomarkers are objective indicators (derived from independent assessments like blood and saliva, not self-reports) of physiological functioning (e.g., cardiovascular and immune functioning) that are known to predict health and mortality risks (Crimmins, Kim, & Vasunilashorn, 2010; Crimmins & Seeman, 2001; McDade, Williams, & Snodgrass, 2007). Like most health outcomes, biomarkers are not randomly distributed in society. They are shaped by repeated and patterned social, psychological, and behavioral processes (Crimmins et al., 2010; Crimmins & Seeman, 2001; McDade et al., 2007; Seeman, Dubin, & Seeman, 2003). In general, research shows that various indicators of religious involvement are associated with favorable biomarker profiles across sympathetic nervous, hypothalamic–pituitary–adrenal (HPA), cardiovascular, immune, and metabolic systems (Seeman et al., 2003; Seybold, 2007). When limited to studies of older adults, there is evidence that religious involvement is associated with lower levels of blood pressure (Koenig et al., 1998; Krause et al., 2002; Maselko, Kubzansky, Kawachi, Seeman, & Berkman, 2007), C-reactive
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protein (Gillum, King, Obisesan, & Koenig, 2008; King, Mainous, & Pearson, 2002; King, Mainous, Steyer, & Pearson, 2001), interleukin-6 (Koenig et al., 1997; Lutgendorf, Russell, Ullrich, Harris, & Wallace, 2004), white blood cells (King et al., 2001), epinephrine (Maselko et al., 2007), cortisol (Ironson et al., 2002), and overall allostatic load (Maselko et al., 2007). Body mass is one possible exception to these general tendencies. Several studies demonstrate that religious adults tend to weigh more, not less, than their less religious counterparts (Bruce, Sims, Miller, Elliott, & Ladipo, 2007; Idler & Kasl, 1997a; Kim, Sobal, & Wethington, 2003; Oman & Reed, 1998; Strawbridge et al., 1997). However, research also suggests that religious adults are less likely to be underweight (Musick et al., 2004), which is especially relevant to health and well-being in old age. Observed religious variations in biological functioning are clearly consistent with the large body of research on religious variations in health and mortality risk. In fact, several researchers consider the broader health and mortality patterns to be at least partially mediated or explained by underlying biological differences (Gillum et al., 2008; Hill, 2010; Hill, Burdette, & Idler, 2011; Koenig, King, & Carson, 2012; Koenig, McCullough, & Larson, 2001; Lutgendorf et al., 2004; Seeman et al., 2003; Seybold, 2007). For example, Lutgendorf and colleagues (2004) demonstrate that the inverse association between religious attendance and all-cause mortality risk in older adults is fully mediated by lower levels of interleukin-6, a biomarker implicated in the development of heart disease, cancer, osteoporosis, frailty, and functional limitations. Although Gillum and colleagues (2008) report a similar pattern for C-reactive protein, their results are less certain because several potential mediators are entered simultaneously. Although this work suggests that biomarkers can help to explain religious variations in health and mortality risk, we still know very little about how religious involvement gets “under the skin” to contribute to favorable biomarker profiles. Nevertheless, previous research has proposed several social (e.g., social integration and social support), psychological (e.g., meaning and control beliefs), behavioral (e.g., drinking and smoking), and biological (e.g., stress) mechanisms (Hill, 2010; Hill, Burdette, & Idler, 2011; Koenig et al., 2001; Koenig et al., 2012; Seybold, 2007). For example, religious involvement (e.g., religious meaning systems) may help to buffer appraisals of stressful life conditions and, by extension, their physiological consequences. Instrumental support, the sense of control, and moderate drinking practices could help older adults to avoid stressful life conditions (events and appraisals) and chronic activation of the physiological stress response. In the event of stressful life conditions (and the activation of sympathetic systems), religious beliefs and practices, supportive relationships, strong self-concepts,
Hill et al.
769
and healthy lifestyles may also favor healthy coping strategies (and efficient activation of parasympathetic systems and various growth responses). Because stress, mental health, and unhealthy behaviors are reliably linked to religious involvement and the activation of nervous, HPA, cardiovascular, immune, and metabolic systems (McEwen, 1998, 2002), these factors (among others) may function as general mechanisms across markers of allostatic systems. We should note that these mechanisms cannot explain the anomalous positive association between religious attendance and body mass (an important marker of the metabolic system). Explanations for why religious adults tend to weigh more than less religious adults are not firmly established in the literature. However, there is some speculation that poor eating habits, lower rates of smoking, and the sedentary practice of religious media consumption may play a role (Cline & Ferraro, 2006; Kim et al., 2003). All of these processes are theoretically viable, but researchers must first establish basic associations between religious involvement and biological functioning before they can begin to legitimately explain them. In this article, we use data collected from a large national sample of older adults to examine associations between religious attendance and a range of biomarkers. We contribute to previous research in several ways. First, we attempt to replicate previous studies by observing biomarkers that have been emphasized in the literature (e.g., body mass, blood pressure, C-reactive protein, and overall allostatic load). Second, we examine associations with unexplored or understudied biomarkers (e.g., pulse rate, glycosylated hemoglobin, Epstein-Barr virus, and dehydroepiandrosterone). Finally, acknowledging different coding conventions in the literature, we assess (in the same analysis) how associations with religious attendance might vary depending on how biological functioning is specified.
Method Data We use data from the National Social Life, Health, and Aging Project (NSHAP) to formally test these associations. The NSHAP is a nationally representative sample of older adults aged 57 to 85 who live in the United States. The National Opinion Research Center, along with Principal Investigators at the University of Chicago, conducted 3,005 interviews during 2005 and 2006 yielding a sample of U.S. adults aged 57 to 85 years (Suzman, 2009). Data collection took place during face-to-face interviews in respondents’ homes. The overall response rate was 75.5% (see O’Muircheartaigh, Eckman, & Smith, 2009, for more information on the sampling design). Specific
770
Journal of Aging and Health 26(5)
biomarker collection procedures were selected to be carried out by NORC interviewers (Jaszczak, Lundeen, & Smith, 2009; Smith et al., 2009). All respondents were asked to participate in the height and weight assessment, saliva collection, and blood pressure and pulse rate screenings. Blood spot collection was randomized to 2,494 respondents (Nallanathan, Williams, McDade, & Lindau, 2008). Overall, 1,746 viable assays were obtained for glycosylated hemoglobin, 1,939 for C-reactive protein, and 1,977 for Epstein–Barr virus. In addition, 1,450 respondents had viable information across all biomarkers. All analyses are weighted to adjust for respondent selection and nonresponse based on age and urbanicity. Table 1 provides descriptive statistics for the study sample.
Measures Biomarkers. Our analyses include eight individual biomarkers. The HPA axis is indicated by dehydroepiandrosterone (DHEA), an androgen produced by the adrenal glands (Juster, McEwen, & Lupien, 2010). DHEA is measured through saliva specimens provided by the respondent (Mendoza, Curran, & Lindau, 2007). Specimens were placed on ice packs immediately following collection, and then transported to a freezer and shipped to a laboratory on dry ice for assessment (Mendoza et al., 2007). DHEA measurements are based on the average of two readings. Cardiovascular functioning is indicated by respondents’ systolic blood pressure, diastolic blood pressure, and pulse rate. These markers were obtained through the use of a blood pressure cuff on the respondent’s left arm. Blood pressure and pulse rate measurements are based on the average of two or three readings (when the first two readings varied greatly). Metabolic functioning is indicated by body mass index (BMI) and glycosylated hemoglobin (HbA1c). BMI is measured using direct measurements of respondents’ height and weight. We calculated BMI by dividing weight in pounds (lb) by height in inches (in.) squared, then multiplying by a conversion factor of 703 (formula = [weight (lb)]/[height (in.)]2 × 703; Williams, Pham-Kanter, & Leitsch, 2009). HbA1c, the ratio of glycosylated to nonglycosylated hemoglobin, is a marker of glucose metabolism (Gomero, McDade, Williams, & Lindau, 2008). HbA1c is measured through the use of dried blood spot technology (McDade et al., 2007). The dried blood spots were taken from the respondents’ finger with a lancet, applied to filter paper, and transported to laboratories for assessment (Gomero et al., 2008). Immune functioning is indicated by C-reactive protein (CRP) and Epstein– Barr virus (EBV). CRP, an acute phase protein, is a marker of inflammation (Nallanathan et al., 2008). EBV, a herpes virus, is an indirect marker of
771
Hill et al. Table 1. Descriptive Statistics for Study Variables. Mean/proportion (SD) Religious attendance Allostatic load (continuous) Count High (top quarter) Low (bottom quarter) BMI (continuous) High (top quarter) Low (bottom quarter) Diastolic BP (continuous) High diastolic BP Low diastolic BP Systolic BP (continuous) High systolic BP Low systolic BP Pulse rate (continuous) High pulse rate Low pulse rate CRP (continuous) High CRP Low CRP HbA1c (continuous) High HbA1c Low HbA1c EBV (continuous) High EBV Low EBV DHEA (continuous) High DHEA Low DHEA Age (65-74) Age (75+) Female Black Latino Other Less than high school High school Some college Income (LN)
1.93 (1.17) − .06 (3.30) 1.95 (1.42) .25 .25 29.10 (6.32) .25 .23 80.81 (12.07) .25 .26 137.14 (20.55) .25 .26 70.86 (12.17) .26 .25 3.20 (6.03) .24 .25 6.10 (1.03) .25 .25 156.62 (77.12) .25 .25 524.91 (55.22) .25 .25 .36 .30 .52 .17 .10 .02 .23 .26 .28 10.41 (.94)
Cut point
>1.84 ≤−2.28 >32.15 kg/m2 ≤24.82 kg/m2 >88.50 mmHg ≤72.50 mmHg >149.33 mmHg ≤122.50 mmHg >78.50 bpm ≤.00 bpm >3.55 mg/L ≤ .63 mg/L >6.3% ≤5.5% >221.33 VCAa ≤93.74 VCAa ≤18.70 pg/mLb >64.95 pg/mLb
Range
n
.00-3.00 −12.27-25.84 .00-8.00 .00-1.00 .00-1.00 14.06-75.60 .00-1.00 .00-1.00 46.50-130.00 .00-1.00 .00-1.00 46.50-130.00 .00-1.00
3005 1450 2790 2934 2934
.00-1.00
40.00-133.33 .00-1.00 .00-1.00 .00-100.00 .00-1.00 .00-1.00 4.50-14.20 .00-1.00 .00-1.00 12.72-371.35 .00-1.00 .00-1.00 .00-573.55 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-14.40
2931 1939 1746 1977 2398 3005 3005 3005 3005 3005
Source. National Social Life, Health, and Aging Project (2005/2006). Note. BMI = body mass index; CRP = C-reactive protein; EBV = Epstein–Barr virus; DHEA = dehydroepiandrosterone. aELISA units. bBased on DHEA before reverse coding.
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cell-mediated immune function (Glaser et al., 1991; McDade et al., 2000). CRP and EBV are also measured through the use of dried blood spot technology (Nallanathan et al., 2008). In this article, we use a multiple specification approach to overcome the limitations of any particular coding scheme (e.g., categorical assessments can be arbitrary and insensitive, while continuous or dimensional assessments ignore regions of clinical significance). We examine four specifications of overall allostatic load (i.e., an index of the individual biomarkers) and three specifications of each individual biomarker. Our first specification treats each individual biomarker and overall allostatic load as continuous variables (Karlamangla, Singer, McEwen, Rowe, & Seeman, 2002; Maselko et al., 2007). Most of the individual biomarkers are transformed due to skewness in their original distributions. DHEA is top-coded at 575.55 pg/mL and reverse coded. Each individual biomarker was standardized and averaged to create the continuous measure of overall allostatic load. We use ordinary least squares (OLS) to estimate associations with all continuous specifications. Our second and third specifications use a high-risk cutoff criterion (Crimmins, Johnston, Hayward, & Seeman, 2003; Geronimus, Hicken, Keene, & Bound, 2006; Seeman, McEwen, Rowe, & Singer, 2001). Respondents who scored in the top 25th percentile of each biomarker (the bottom 25th percentile for DHEA) and the continuous overall allostatic load index are coded 1 and all others 0 (see Table 1 for specific cutoffs). We use binary logistic regression to estimate associations with all binary specifications. We created another overall allostatic load index by taking the sum of the number of biomarkers for which the respondent exceeded the high-risk criterion. We used Poisson regression to estimate associations with this count specification. Poisson regression models are best suited for count data and rely on the assumption that the dependent variable is equidispersed or that the conditional means equal conditional variances (Long & Freese, 2006). Because our count specification of allostatic load is not overdispersed, Poisson regression is preferable to negative binomial regression. Our fourth specification is based on McEwen’s (1998) definition of allostatic load: “ . . . wear and tear that results from chronic overactivity or underactivity of allostatic systems” (p. 171). To indicate “chronic overactivity or underactivity,” we coded three groups: (1) those in the high-risk (top 25th percentile, except for DHEA) portions of the distributions for each individual biomarker and the continuous allostatic load index, (2) those in the low-risk (bottom 25th percentile, except for DHEA) portions of the distributions for each individual biomarker and the continuous allostatic load index, and (3) those in the middle-risk (middle two quarters) portion of the distributions for each individual biomarker and the continuous allostatic load index. We use multinomial logistic regression to estimate associations with all multiple
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group specifications. In subsequent analyses, Group 3 (middle-risk) serves as the common comparison group. Religious attendance. Religious involvement is indicated by religious attendance, the most commonly used measure of religiousness. Religious attendance is measured with a single item. Respondents were asked, “Thinking about the past 12 months, about how often have you attended religious services?” Original response categories for this item included (0) never, (1) less than once a year, (2) about once or twice a year, (3) several times a year, (4) about once a month, (5) every week, and (6) several times a week. We recoded religious attendance by combining categories of attendance that are theoretically and empirically similar (Rote, Hill, & Ellison, 2013). For example, the categories of “less than once a year” and “about once or twice a year” capture sporadic yearly attendance and produce similar means on our focal outcomes. Our final religious attendance measure includes the following categories: (0) never attend, (1) attend less than once a year or about once or twice a year, (2) attend several times a year or about once a month, and (3) attend every week to several times a week. Subsequent analyses control for a number of factors that may influence the relationship between religious attendance and biological risk. Age is a categorical variable based on the sampling design of the NSHAP and includes those who are 57 to 64 years old (reference category), 65 to 74 years old, and 75 years or older. Gender is coded 1 for female and 0 for males. Race/ Ethnicity is based on respondents’ self-report of racial/ethnic identification and is coded as a multiple dummy variable indicating black, Hispanic, and other, with non-Hispanic White as the absent reference category. Education is a multiple dummy variable distinguishing those who have less than a high school degree, a high school degree or equivalent, some college, and a college degree or more (reference category). Income is based on household income per year. For the 29% of the sample missing on income, a series of follow-up questions were asked such as “is your household income at, over or under 50,000.” For respondents who reported an amount, for example, “at 50,000,” we imputed the exact amount. We then used a regression-based imputation procedure (impute) in Stata to retain the other 25% of our analytic sample missing on income (Ambler, Omar, & Royston, 2007). Missing values for all other variables are estimated using the same procedure.
Results Multivariate Analysis of Allostatic Load The first column of Table 2 presents the Poisson regression of the count specification of the full allostatic load index. In this analysis, the dependent
774
.94* (.89, .99) .89 (.78, 1.01) .86* (.76, .98) 1.07 (.98, 1.18) 1.25*** (1.10, 1.43) .94 (.81, 1.09) 1.26* (1.02, 1.55) 1.20 (.96, 1.50) 1.19 (.99, 1.43) 1.12 (.93, 1.34) .93*** (.90, .96) 78.11*** .02
Count of binary marker − .19* (.09) − .41 (.22) − .82*** (.22) .09 (.19) 1.26*** (.33) − .16 (.34) 1.02 (.75) .68 (.37) .69** (.27) .57* (.26) − .37** (.11) (7.22)*** (.05)
(Continuous) .85* (.74, .97) .69* (.49, .99) .56*** (.42, .76) 1.02 (.77, 1.35) 2.37*** (1.66, 3.37) .97 (.63, 1.47) 1.36 (.73, 2.56) 1.24 (.76, 2.04) 1.24 (.76, 2.04) 1.14 (.73, 1.78) .82* (.69, .98) 47.43*** .03
(High)
Binary logit
.86* (.75, .99) .68 (.45, 1.01) .55*** (.39, .76) .99 (.73, 1.35) 2.41*** (1.68, 3.46) 1.07 (.66, 1.74) 1.19 (.21, 2.74) 1.11 (.67, 2.14) 1.16 (.66, 1.74) 1.02 (.64, 1.64) .85 (.69, 1.04) 59.18*** .02
(High)
(Low) 1.05 (.92, 1.20) .92 (.66, 1.29) .91 (.64, 1.30) .91 (.67, 1.25) 1.06 (.60, 1.86) 1.35 (.74, 2.48) .61 (.22, 1.68) .68 (.39, 1.19) .84 (.54, 1.30) .75 (.52, 1.08) 1.11 (.90, 1.37)
Multinomial logit
Source. National Social Life, Health, and Aging Project (2005/2006). n = 1,450. Note. Presented are incidence rate ratios (Poisson count model) with 95% confidence intervals, unstandardized coefficients with standard errors (OLS model), and odds ratios (logit models) with 95% confidence intervals. *p < .05. **p < .01. ***p < .001.
Religious attendance Age (65-74) Age (75+) Female Black Latino Other
JAHXXX10.1177/0898264314529333Journal of Aging and HealthHill et al.
Article
Religious Attendance and Biological Functioning: A Multiple Specification Approach
Journal of Aging and Health 2014, Vol. 26(5) 766–785 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264314529333 jah.sagepub.com
Terrence D. Hill, PhD1, Sunshine M. Rote, PhD2, Christopher G. Ellison, PhD3, and Amy M. Burdette, PhD4
Abstract Objective: This study explores the role of religious attendance across a wide range of biological markers. Method: The data are drawn from the National Social Life, Health, and Aging Project to assess continuous and categorical biomarker specifications. Results: Across specifications, higher levels of attendance are associated with lower levels of pulse rate and overall allostatic load. Depending on the specification, higher levels of attendance are also associated with lower levels of body mass, diastolic blood pressure, C-reactive protein, and Epstein–Barr virus. Attendance is unrelated to dehydroepiandrosterone, systolic blood pressure, and glycosylated hemoglobin across specifications. Discussion: The study confirms that religious attendance is associated with healthier biological functioning in later life. Additional research is needed to verify these patterns with other data sources and to test viable mediators of the association between religious attendance and biological risk.
1The
University of Utah, Salt Lake City, USA of Louisville, USA 3University of Texas at San Antonio, USA 4Florida State University, Tallahassee, USA 2University
Corresponding Author: Terrence D. Hill, PhD, Department of Sociology, The University of Utah, 380 S 1530 E, Room 301, Salt Lake City, UT 84112-0250, USA. Email: [email protected]
Hill et al.
767
Keywords religion, religious attendance, allostatic load, biological risk Over the past three decades, numerous studies have shown that religious involvement—indicated by observable feelings, beliefs, activities, and experiences in relation to spiritual, divine, or super-natural entities—tends to favor health and longevity in the elderly population. These patterns are remarkably consistent across a range of health indicators, including anger (Carr, 2003), depression (Idler, 1987; Idler & Kasl, 1997a; Strawbridge, Shema, Cohen, Roberts, & Kaplan, 1998), anxiety (Cicirelli, 2002; Krause, 2005), non-specific psychological well-being (Fry, 2001; Idler & Kasl, 1997a), life satisfaction (Levin, Markides, & Ray, 1996; Krause, 2003, 2005), cognitive functioning (Hill, Burdette, Angel, & Angel, 2006; Reyes-Ortiz et al., 2008; Van Ness & Kasl, 2003), self-rated health (Idler, McLaughlin, & Kasl, 2009; Krause, 1998, 2006), functional status (Benjamins, 2004; Idler, 1987; Idler & Kasl, 1997b; Park et al., 2008), and stroke (Wolinsky et al., 2009). Not surprisingly, religious involvement is also associated with lower risk of all-cause mortality (Ellison, Hummer, Cormier, & Rogers, 2000; Hill, Angel, Ellison, & Angel, 2005; Strawbridge, Cohen, Shema, & Kaplan, 1997) and mortality linked to circulatory diseases, respiratory diseases, and other specific causes (Hummer, Rogers, Nam, & Ellison, 1999; Oman, Kurata, Strawbridge, & Cohen, 2002; Rogers, Krueger, & Hummer, 2010). Although this body of work is extensive, researchers have only begun to explore the role of religious involvement across a wide range of biological markers. Biological markers or biomarkers are objective indicators (derived from independent assessments like blood and saliva, not self-reports) of physiological functioning (e.g., cardiovascular and immune functioning) that are known to predict health and mortality risks (Crimmins, Kim, & Vasunilashorn, 2010; Crimmins & Seeman, 2001; McDade, Williams, & Snodgrass, 2007). Like most health outcomes, biomarkers are not randomly distributed in society. They are shaped by repeated and patterned social, psychological, and behavioral processes (Crimmins et al., 2010; Crimmins & Seeman, 2001; McDade et al., 2007; Seeman, Dubin, & Seeman, 2003). In general, research shows that various indicators of religious involvement are associated with favorable biomarker profiles across sympathetic nervous, hypothalamic–pituitary–adrenal (HPA), cardiovascular, immune, and metabolic systems (Seeman et al., 2003; Seybold, 2007). When limited to studies of older adults, there is evidence that religious involvement is associated with lower levels of blood pressure (Koenig et al., 1998; Krause et al., 2002; Maselko, Kubzansky, Kawachi, Seeman, & Berkman, 2007), C-reactive
768
Journal of Aging and Health 26(5)
protein (Gillum, King, Obisesan, & Koenig, 2008; King, Mainous, & Pearson, 2002; King, Mainous, Steyer, & Pearson, 2001), interleukin-6 (Koenig et al., 1997; Lutgendorf, Russell, Ullrich, Harris, & Wallace, 2004), white blood cells (King et al., 2001), epinephrine (Maselko et al., 2007), cortisol (Ironson et al., 2002), and overall allostatic load (Maselko et al., 2007). Body mass is one possible exception to these general tendencies. Several studies demonstrate that religious adults tend to weigh more, not less, than their less religious counterparts (Bruce, Sims, Miller, Elliott, & Ladipo, 2007; Idler & Kasl, 1997a; Kim, Sobal, & Wethington, 2003; Oman & Reed, 1998; Strawbridge et al., 1997). However, research also suggests that religious adults are less likely to be underweight (Musick et al., 2004), which is especially relevant to health and well-being in old age. Observed religious variations in biological functioning are clearly consistent with the large body of research on religious variations in health and mortality risk. In fact, several researchers consider the broader health and mortality patterns to be at least partially mediated or explained by underlying biological differences (Gillum et al., 2008; Hill, 2010; Hill, Burdette, & Idler, 2011; Koenig, King, & Carson, 2012; Koenig, McCullough, & Larson, 2001; Lutgendorf et al., 2004; Seeman et al., 2003; Seybold, 2007). For example, Lutgendorf and colleagues (2004) demonstrate that the inverse association between religious attendance and all-cause mortality risk in older adults is fully mediated by lower levels of interleukin-6, a biomarker implicated in the development of heart disease, cancer, osteoporosis, frailty, and functional limitations. Although Gillum and colleagues (2008) report a similar pattern for C-reactive protein, their results are less certain because several potential mediators are entered simultaneously. Although this work suggests that biomarkers can help to explain religious variations in health and mortality risk, we still know very little about how religious involvement gets “under the skin” to contribute to favorable biomarker profiles. Nevertheless, previous research has proposed several social (e.g., social integration and social support), psychological (e.g., meaning and control beliefs), behavioral (e.g., drinking and smoking), and biological (e.g., stress) mechanisms (Hill, 2010; Hill, Burdette, & Idler, 2011; Koenig et al., 2001; Koenig et al., 2012; Seybold, 2007). For example, religious involvement (e.g., religious meaning systems) may help to buffer appraisals of stressful life conditions and, by extension, their physiological consequences. Instrumental support, the sense of control, and moderate drinking practices could help older adults to avoid stressful life conditions (events and appraisals) and chronic activation of the physiological stress response. In the event of stressful life conditions (and the activation of sympathetic systems), religious beliefs and practices, supportive relationships, strong self-concepts,
Hill et al.
769
and healthy lifestyles may also favor healthy coping strategies (and efficient activation of parasympathetic systems and various growth responses). Because stress, mental health, and unhealthy behaviors are reliably linked to religious involvement and the activation of nervous, HPA, cardiovascular, immune, and metabolic systems (McEwen, 1998, 2002), these factors (among others) may function as general mechanisms across markers of allostatic systems. We should note that these mechanisms cannot explain the anomalous positive association between religious attendance and body mass (an important marker of the metabolic system). Explanations for why religious adults tend to weigh more than less religious adults are not firmly established in the literature. However, there is some speculation that poor eating habits, lower rates of smoking, and the sedentary practice of religious media consumption may play a role (Cline & Ferraro, 2006; Kim et al., 2003). All of these processes are theoretically viable, but researchers must first establish basic associations between religious involvement and biological functioning before they can begin to legitimately explain them. In this article, we use data collected from a large national sample of older adults to examine associations between religious attendance and a range of biomarkers. We contribute to previous research in several ways. First, we attempt to replicate previous studies by observing biomarkers that have been emphasized in the literature (e.g., body mass, blood pressure, C-reactive protein, and overall allostatic load). Second, we examine associations with unexplored or understudied biomarkers (e.g., pulse rate, glycosylated hemoglobin, Epstein-Barr virus, and dehydroepiandrosterone). Finally, acknowledging different coding conventions in the literature, we assess (in the same analysis) how associations with religious attendance might vary depending on how biological functioning is specified.
Method Data We use data from the National Social Life, Health, and Aging Project (NSHAP) to formally test these associations. The NSHAP is a nationally representative sample of older adults aged 57 to 85 who live in the United States. The National Opinion Research Center, along with Principal Investigators at the University of Chicago, conducted 3,005 interviews during 2005 and 2006 yielding a sample of U.S. adults aged 57 to 85 years (Suzman, 2009). Data collection took place during face-to-face interviews in respondents’ homes. The overall response rate was 75.5% (see O’Muircheartaigh, Eckman, & Smith, 2009, for more information on the sampling design). Specific
770
Journal of Aging and Health 26(5)
biomarker collection procedures were selected to be carried out by NORC interviewers (Jaszczak, Lundeen, & Smith, 2009; Smith et al., 2009). All respondents were asked to participate in the height and weight assessment, saliva collection, and blood pressure and pulse rate screenings. Blood spot collection was randomized to 2,494 respondents (Nallanathan, Williams, McDade, & Lindau, 2008). Overall, 1,746 viable assays were obtained for glycosylated hemoglobin, 1,939 for C-reactive protein, and 1,977 for Epstein–Barr virus. In addition, 1,450 respondents had viable information across all biomarkers. All analyses are weighted to adjust for respondent selection and nonresponse based on age and urbanicity. Table 1 provides descriptive statistics for the study sample.
Measures Biomarkers. Our analyses include eight individual biomarkers. The HPA axis is indicated by dehydroepiandrosterone (DHEA), an androgen produced by the adrenal glands (Juster, McEwen, & Lupien, 2010). DHEA is measured through saliva specimens provided by the respondent (Mendoza, Curran, & Lindau, 2007). Specimens were placed on ice packs immediately following collection, and then transported to a freezer and shipped to a laboratory on dry ice for assessment (Mendoza et al., 2007). DHEA measurements are based on the average of two readings. Cardiovascular functioning is indicated by respondents’ systolic blood pressure, diastolic blood pressure, and pulse rate. These markers were obtained through the use of a blood pressure cuff on the respondent’s left arm. Blood pressure and pulse rate measurements are based on the average of two or three readings (when the first two readings varied greatly). Metabolic functioning is indicated by body mass index (BMI) and glycosylated hemoglobin (HbA1c). BMI is measured using direct measurements of respondents’ height and weight. We calculated BMI by dividing weight in pounds (lb) by height in inches (in.) squared, then multiplying by a conversion factor of 703 (formula = [weight (lb)]/[height (in.)]2 × 703; Williams, Pham-Kanter, & Leitsch, 2009). HbA1c, the ratio of glycosylated to nonglycosylated hemoglobin, is a marker of glucose metabolism (Gomero, McDade, Williams, & Lindau, 2008). HbA1c is measured through the use of dried blood spot technology (McDade et al., 2007). The dried blood spots were taken from the respondents’ finger with a lancet, applied to filter paper, and transported to laboratories for assessment (Gomero et al., 2008). Immune functioning is indicated by C-reactive protein (CRP) and Epstein– Barr virus (EBV). CRP, an acute phase protein, is a marker of inflammation (Nallanathan et al., 2008). EBV, a herpes virus, is an indirect marker of
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Hill et al. Table 1. Descriptive Statistics for Study Variables. Mean/proportion (SD) Religious attendance Allostatic load (continuous) Count High (top quarter) Low (bottom quarter) BMI (continuous) High (top quarter) Low (bottom quarter) Diastolic BP (continuous) High diastolic BP Low diastolic BP Systolic BP (continuous) High systolic BP Low systolic BP Pulse rate (continuous) High pulse rate Low pulse rate CRP (continuous) High CRP Low CRP HbA1c (continuous) High HbA1c Low HbA1c EBV (continuous) High EBV Low EBV DHEA (continuous) High DHEA Low DHEA Age (65-74) Age (75+) Female Black Latino Other Less than high school High school Some college Income (LN)
1.93 (1.17) − .06 (3.30) 1.95 (1.42) .25 .25 29.10 (6.32) .25 .23 80.81 (12.07) .25 .26 137.14 (20.55) .25 .26 70.86 (12.17) .26 .25 3.20 (6.03) .24 .25 6.10 (1.03) .25 .25 156.62 (77.12) .25 .25 524.91 (55.22) .25 .25 .36 .30 .52 .17 .10 .02 .23 .26 .28 10.41 (.94)
Cut point
>1.84 ≤−2.28 >32.15 kg/m2 ≤24.82 kg/m2 >88.50 mmHg ≤72.50 mmHg >149.33 mmHg ≤122.50 mmHg >78.50 bpm ≤.00 bpm >3.55 mg/L ≤ .63 mg/L >6.3% ≤5.5% >221.33 VCAa ≤93.74 VCAa ≤18.70 pg/mLb >64.95 pg/mLb
Range
n
.00-3.00 −12.27-25.84 .00-8.00 .00-1.00 .00-1.00 14.06-75.60 .00-1.00 .00-1.00 46.50-130.00 .00-1.00 .00-1.00 46.50-130.00 .00-1.00
3005 1450 2790 2934 2934
.00-1.00
40.00-133.33 .00-1.00 .00-1.00 .00-100.00 .00-1.00 .00-1.00 4.50-14.20 .00-1.00 .00-1.00 12.72-371.35 .00-1.00 .00-1.00 .00-573.55 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-1.00 .00-14.40
2931 1939 1746 1977 2398 3005 3005 3005 3005 3005
Source. National Social Life, Health, and Aging Project (2005/2006). Note. BMI = body mass index; CRP = C-reactive protein; EBV = Epstein–Barr virus; DHEA = dehydroepiandrosterone. aELISA units. bBased on DHEA before reverse coding.
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cell-mediated immune function (Glaser et al., 1991; McDade et al., 2000). CRP and EBV are also measured through the use of dried blood spot technology (Nallanathan et al., 2008). In this article, we use a multiple specification approach to overcome the limitations of any particular coding scheme (e.g., categorical assessments can be arbitrary and insensitive, while continuous or dimensional assessments ignore regions of clinical significance). We examine four specifications of overall allostatic load (i.e., an index of the individual biomarkers) and three specifications of each individual biomarker. Our first specification treats each individual biomarker and overall allostatic load as continuous variables (Karlamangla, Singer, McEwen, Rowe, & Seeman, 2002; Maselko et al., 2007). Most of the individual biomarkers are transformed due to skewness in their original distributions. DHEA is top-coded at 575.55 pg/mL and reverse coded. Each individual biomarker was standardized and averaged to create the continuous measure of overall allostatic load. We use ordinary least squares (OLS) to estimate associations with all continuous specifications. Our second and third specifications use a high-risk cutoff criterion (Crimmins, Johnston, Hayward, & Seeman, 2003; Geronimus, Hicken, Keene, & Bound, 2006; Seeman, McEwen, Rowe, & Singer, 2001). Respondents who scored in the top 25th percentile of each biomarker (the bottom 25th percentile for DHEA) and the continuous overall allostatic load index are coded 1 and all others 0 (see Table 1 for specific cutoffs). We use binary logistic regression to estimate associations with all binary specifications. We created another overall allostatic load index by taking the sum of the number of biomarkers for which the respondent exceeded the high-risk criterion. We used Poisson regression to estimate associations with this count specification. Poisson regression models are best suited for count data and rely on the assumption that the dependent variable is equidispersed or that the conditional means equal conditional variances (Long & Freese, 2006). Because our count specification of allostatic load is not overdispersed, Poisson regression is preferable to negative binomial regression. Our fourth specification is based on McEwen’s (1998) definition of allostatic load: “ . . . wear and tear that results from chronic overactivity or underactivity of allostatic systems” (p. 171). To indicate “chronic overactivity or underactivity,” we coded three groups: (1) those in the high-risk (top 25th percentile, except for DHEA) portions of the distributions for each individual biomarker and the continuous allostatic load index, (2) those in the low-risk (bottom 25th percentile, except for DHEA) portions of the distributions for each individual biomarker and the continuous allostatic load index, and (3) those in the middle-risk (middle two quarters) portion of the distributions for each individual biomarker and the continuous allostatic load index. We use multinomial logistic regression to estimate associations with all multiple
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group specifications. In subsequent analyses, Group 3 (middle-risk) serves as the common comparison group. Religious attendance. Religious involvement is indicated by religious attendance, the most commonly used measure of religiousness. Religious attendance is measured with a single item. Respondents were asked, “Thinking about the past 12 months, about how often have you attended religious services?” Original response categories for this item included (0) never, (1) less than once a year, (2) about once or twice a year, (3) several times a year, (4) about once a month, (5) every week, and (6) several times a week. We recoded religious attendance by combining categories of attendance that are theoretically and empirically similar (Rote, Hill, & Ellison, 2013). For example, the categories of “less than once a year” and “about once or twice a year” capture sporadic yearly attendance and produce similar means on our focal outcomes. Our final religious attendance measure includes the following categories: (0) never attend, (1) attend less than once a year or about once or twice a year, (2) attend several times a year or about once a month, and (3) attend every week to several times a week. Subsequent analyses control for a number of factors that may influence the relationship between religious attendance and biological risk. Age is a categorical variable based on the sampling design of the NSHAP and includes those who are 57 to 64 years old (reference category), 65 to 74 years old, and 75 years or older. Gender is coded 1 for female and 0 for males. Race/ Ethnicity is based on respondents’ self-report of racial/ethnic identification and is coded as a multiple dummy variable indicating black, Hispanic, and other, with non-Hispanic White as the absent reference category. Education is a multiple dummy variable distinguishing those who have less than a high school degree, a high school degree or equivalent, some college, and a college degree or more (reference category). Income is based on household income per year. For the 29% of the sample missing on income, a series of follow-up questions were asked such as “is your household income at, over or under 50,000.” For respondents who reported an amount, for example, “at 50,000,” we imputed the exact amount. We then used a regression-based imputation procedure (impute) in Stata to retain the other 25% of our analytic sample missing on income (Ambler, Omar, & Royston, 2007). Missing values for all other variables are estimated using the same procedure.
Results Multivariate Analysis of Allostatic Load The first column of Table 2 presents the Poisson regression of the count specification of the full allostatic load index. In this analysis, the dependent
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.94* (.89, .99) .89 (.78, 1.01) .86* (.76, .98) 1.07 (.98, 1.18) 1.25*** (1.10, 1.43) .94 (.81, 1.09) 1.26* (1.02, 1.55) 1.20 (.96, 1.50) 1.19 (.99, 1.43) 1.12 (.93, 1.34) .93*** (.90, .96) 78.11*** .02
Count of binary marker − .19* (.09) − .41 (.22) − .82*** (.22) .09 (.19) 1.26*** (.33) − .16 (.34) 1.02 (.75) .68 (.37) .69** (.27) .57* (.26) − .37** (.11) (7.22)*** (.05)
(Continuous) .85* (.74, .97) .69* (.49, .99) .56*** (.42, .76) 1.02 (.77, 1.35) 2.37*** (1.66, 3.37) .97 (.63, 1.47) 1.36 (.73, 2.56) 1.24 (.76, 2.04) 1.24 (.76, 2.04) 1.14 (.73, 1.78) .82* (.69, .98) 47.43*** .03
(High)
Binary logit
.86* (.75, .99) .68 (.45, 1.01) .55*** (.39, .76) .99 (.73, 1.35) 2.41*** (1.68, 3.46) 1.07 (.66, 1.74) 1.19 (.21, 2.74) 1.11 (.67, 2.14) 1.16 (.66, 1.74) 1.02 (.64, 1.64) .85 (.69, 1.04) 59.18*** .02
(High)
(Low) 1.05 (.92, 1.20) .92 (.66, 1.29) .91 (.64, 1.30) .91 (.67, 1.25) 1.06 (.60, 1.86) 1.35 (.74, 2.48) .61 (.22, 1.68) .68 (.39, 1.19) .84 (.54, 1.30) .75 (.52, 1.08) 1.11 (.90, 1.37)
Multinomial logit
Source. National Social Life, Health, and Aging Project (2005/2006). n = 1,450. Note. Presented are incidence rate ratios (Poisson count model) with 95% confidence intervals, unstandardized coefficients with standard errors (OLS model), and odds ratios (logit models) with 95% confidence intervals. *p < .05. **p < .01. ***p < .001.
Religious attendance Age (65-74) Age (75+) Female Black Latino Other
