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. Author manuscript; available in PMC: 2016 Feb 15.
Published in final edited form as: Am J Health Behav. 2008 Nov-Dec;32(6):714–724. doi: 10.5555/ajhb.2008.32.6.714

Religious Affiliation and Health Behaviors and Outcomes: Data from the Nashville REACH 2010 Project

David G Schlundt 1, Monica D Franklin 1, Kushal Patel 2, Linda McClellan 3, Celia Larson 2, Sarah Niebler 1, Margaret Hargreaves 2
PMCID: PMC4753840  NIHMSID: NIHMS754689  PMID: 18442350

Abstract

Objectives

Using a large randomly selected community sample, we sought to clarify the relationships among religious involvement, membership in different types of religious groups, health behavior, and health outcomes.

Methods

A survey of Nashville residents (n = 3,014) was conducted for the Nashville REACH 2010 project. The survey included questions about religious affiliation, belief and practice as well as health behaviors and conditions.

Results

Bivariate analyses showed negative associations between religious involvement and health behaviors and outcomes along with many differences based on denominational affiliation. After controlling for demographic differences, there was a positive association between religious involvement and several health behaviors and outcomes. After controlling for demographic differences and individual differences in religious involvement, denominational affiliation was no longer associated with any of the health behaviors or health outcome measures.

Conclusions

One should not assume that involvement in any given faith community promotes better health. Rather, each religious group should be approached as a community of people who not only share religious beliefs, but who also have similarities in socioeconomic, ethnic, and cultural background. Health promotion in faith communities must take into account cultural context.

Keywords: REACH 2010, religion, health behaviors, religious affiliation

INTRODUCTION

Understanding the impact of religion on health has become an important question guiding research on health and healthcare practice. “Religion gives meaning to life … [and] spirituality and religious beliefs affect perceptions of pain, symptoms, and beliefs about the causes of illness and its course” (Walsh 1999). “Spirituality serves as a source of meaning and purpose, a framework within which people interpret their lives and experiences” (Johnson, Elbert-Avila et al. 2005). Multi-disciplinary research has indicated that the relationships between religion/spirituality and health outcomes are mostly positive (Aldridge 1991; Levin 1994; Hill 1995; Ellison and Levin 1998; Koenig 1999).

The finding of positive associations among religious belief, healthy behaviors, and better health outcomes is consistent across a number of populations. Recent studies have begun to use more stringent measurement, methodological, and data analytic techniques and scientists continue to find a positive association between religion and a variety of health variables (MacDonald 2000; Bussing, Ostermann et al. 2005; Carrico, Gifford et al. 2007). Using both prospective and cross sectional designs, measures of religious involvement have been associated with better physical and mental health and decreased mortality in older populations (George, Ellison et al. 2002). Religious attendance has been positively associated with decreased depression, improved physical health and lower blood pressure, boosted immune functioning, enhanced physical functioning and improved subjective health (Hill 1995).

Recent studies have also found a relationship between religious attendance and mortality. A meta-analysis published in 2000 (McCullough, Hoyt et al. 2000) indicated that religious involvement was significantly associated with lower mortality, and that this relationship was robust across a number of studies. More recently, Hill’s 2005 study found a relationship between these factors within a geriatric population, even after accounting for socio-demographic factors and health status (Hill 2005). Reviews of the literature provide a more thorough description of these associations (Koenig 2000; Levin 2001; Plante 2001; Oman and Thoresen 2002; Thoresen and Harris 2002; Jones 2004; Newberg and Lee 2005).

Scientists have explored three mechanisms to account for the positive association between religion and health: 1) positive physiological and emotional responses to religious activities 2) social support provided to individuals who belong to religious organizations, and 3) promotion of health-related behavior by religious institutions and belief systems (Levin 1994).

Beneficial physiological changes have been attributed to certain religious practices such as prayer and meditation. Herbert Benson, a cardiologist, termed this phenomenon the “relaxation response.” He theorized that the physiological changes associated with regular religious practices were beneficial by acting through the autonomic nervous system and endocrine pathways that make daily stress detrimental (Benson, 1975). For many, religion also provides a network of social support that helps in coping with physical, emotional and mental stressors (Davis 1994; Ferraro and Koch 1994; Droege 1995; Sutherland, Hale et al. 1995; Wilson 2000; Strawbridge, Shema et al. 2001). Religion can have a positive effect on health by directly influencing the health habits of individuals through the prohibition of health damaging behaviors (e.g., smoking) and/or the prescription of health enhancing behaviors (e.g., vegetarianism) (Levin 1996; Ellison and Levin 1998; George, Ellison et al. 2002; Eckersley 2007). Other potential mechanisms, such as psychosocial resources -- important for self-esteem, self-efficacy, and coping -- have also been discussed as potential mediating mechanisms through which religion impacts health (George, Ellison et al. 2002).

The influence of particular religious sects, or denominations, on health behavior and outcome has also been studied, but rarely as a primary focus (Schiller and Levin 1988; Levin 1994). In studies focused on the independent variable “religiosity”, there is typically a focus on a particular denomination of interest, not allowing for an investigation of differences in the impact of religion on health across religious groups or sects. Studies that have examined differences across religious affiliations or among denominations of a particular religion have found significant differences in body weight (Kim, Sobal et al. 2003), use of preventive healthcare (Benjamins 2004), life-satisfaction (Cohen 2002), alcohol use (Ford and Kadushin 2002), smoking (Gillum 2005) and breast cancer survival (Van Ness, Kasl et al. 2003).

Most past literature in the area of spirituality and health has focused on health outcomes (i.e. morbidity and mortality) rather than behavior. Studies of health behavior are beginning to emerge and typically examine healthcare utilization, especially utilization of preventive health services, as well as acceptance of treatment for illness/disability (Schiller and Levin 1988). Few studies have examined routine health behaviors such as eating habits and physical activity in non-clinical populations. While it is important to examine health outcomes, it is also important to examine the prevalence of unhealthy behaviors, which are linked to preventable illnesses. In light of the hypothesis that health behaviors and attitudes might mediate the relationship between religiosity and health, examining these associations across religious denominations is important..

There is also a small number of studies suggesting that not all of the impacts of religion on health are positive (Williams and Sternthal 2007). Rippentrop and colleagues in a study of chronic pain found that measures of spirituality were associated with worse health (Elizabeth Rippentrop, Altmaier et al. 2005). Pargament et al. distinguish between positive (e.g. spiritual support, benevolent religious reframing, collaborative religious coping, congregational support) and negative (e.g. spiritual discontent, punitive religious reframing, self-directing religious coping, congregational discontent) religious coping strategies (Pargament, Koenig et al. 2004). They argue that it is not the presence of religious beliefs per se that influences health, but rather how those beliefs translate into coping with the stressful circumstances of living. It is also important to consider whether some religious groups may encourage less than healthy behaviors because of the imbedding of religion in culture and socioeconomic status (Eckersley 2007)

A negative association between measures of religious involvement and physical health could arise when people intensify religious belief and practice in response to declining health (Chen and Koenig 2006). It is also possible that cultural differences between religious sects and denominations could have both positive and negative effects on health, especially if these are mediated through differential encouragement of healthy and unhealthy behaviors. For example, Kim and colleagues suggest that men in conservative Christian denominations may have a higher BMI because of lower rates of smoking (Kim, Sobal et al. 2003). Religious groups differ in the extent to which dietary practices are part of their religious belief system (Willett 2003).

Using a large randomly selected community sample, we sought to clarify the relationships among religious involvement, membership in different types of religious groups, health behavior, and health outcomes. We hypothesized that 1) there would be a positive association between religious involvement and both health behaviors and health outcomes, and 2) that there would be significant differences in health behaviors and outcomes across various religious sects and denominations.

METHODS

Data used for this study were collected through the Nashville REACH (Racial and Ethnic Approaches to Community Health) 2010 project, an initiative targeted towards reducing and eliminating health disparities in the African-American community of Nashville, Tennessee (McClellan and Schlundt 2006). As an assessment of the contexts and causes of health disparities, widespread changes in risk and protective behaviors, and reductions in health disparities in the target population, the coalition partners of Nashville REACH 2010 conducted a baseline phone survey in late 2000 and early 2001 (Miller, Schlundt et al. 2004).

The 25–30 minute survey was developed to assess access to health care, comorbid illness, health practices, socioeconomic status, and individual health status. Items were selected from previously used and validated questionnaires whenever possible including the Behavioral Risk Factor Surveillance System (Bolen, Rhodes et al. 2000), the medical outcomes study SF-12 (Ware, Kosinski et al. 1996), the Eating Behavior Patterns Questionnaire (Schlundt, Hargreaves et al. 2003), and the Eating Styles Questionnaire (Hargreaves, Schlundt et al. 1999). These surveys were pre-tested on a small sample and were then revised for clarity and length.

In addition to measures of health behaviors and outcomes, questions examining religious affiliation/denomination were also included. These questions included the following items: 1) What is your religious affiliation? 2) How often did you attend regular church services during the year? 3) How religious do you consider yourself to be? and 4) How much is religion/God a source of strength and comfort to you? For all but the religious affiliation question, which prompted individuals to identify their affiliation/denomination, the variables were scored on Likert scale ranging from 1–5 with higher scores indicating increased religiosity, frequency of church attendance and perception of religion as a source of strength and comfort.

Sampling Strategy

Sixteen thousand two hundred (n=16200) randomly selected residential telephone numbers were purchased from SDR Sampling Services, Inc. (Atlanta, GA). The sample was stratified by two geographic areas of interest; North Nashville (NN) and the rest of Nashville/Davidson County (NDC). NN was the geographic target area of the Nashville REACH 2010 project. Nine thousand (9000) residential numbers in NN and 7200 numbers in all other areas of NDC were randomly selected. Only household members who were at least 18 years of age were eligible to participate. The adult with the closest approaching birthday was selected to be interviewed, further ensuring randomization.

Survey Protocol

Telephone interviews, conducted by trained interviewers using a computer-assisted telephone interviewing system (CATI), occurred between 10:00 am and 8:30 pm Monday through Friday. Interviewers were trained and were monitored by a supervisor. Compared to other methods of gathering and entering interview data, a CATI system is more efficient and less likely to compromise the study’s internal validity. The system allowed for the creation of a “real time” electronic database, eliminating the need for a separate data entry process and automatically causing the interviewer to follow predetermined skip patterns. This process insured that all relevant questions were asked. The system was programmed to randomly select telephone numbers for dialing. The disposition of each number dialed was coded and automatically stored in a database that included the number of attempted calls, scheduled times for call-backs to achieve completed interviews, and the outcome of attempts to reach each number. This method of tracking data allowed an exact characterization of response rates. Overall, this process yielded a 32% response rate after adjusting for the following: disconnected numbers, reaching a fax/modem, non private residence, respondent physically unable to answer the survey, or ineligible respondent. Of those contacted, 51% in NN and 54% in NDC agreed to be interviewed. Of those starting the interview, 94% in NN and 95% in NDC completed the interview (Miller, Schlundt et al. 2004).

Health-related Variables

Both health behaviors and health outcomes were examined. Health behaviors included dietary behaviors and physical activity while health outcomes included physical and mental health status, chronic illness diagnoses, and obesity.

Health Behaviors

Health behavior scales examined physical activity and dietary behaviors. First, participants were asked if they participated in any regular physical activity during the past month. Those who responded affirmatively were asked to name their two most common activities and report on the usual frequency and duration of each activity. An activity index was created by multiplying the average METS (metabolic equivalent – a measure of the level of exertion) per activity by the frequency and duration of each activity for all participants (Ainsworth, Haskell et al. 1993). To normalize this distribution, the natural log was taken and all analyses were conducted with this log-transformed measure. Questions were also asked about frequency of participation in sedentary behaviors (reading, watching TV, using the internet, and driving). The amount of time spent per week in watching TV, using the internet, and driving was also asked. Two indices were created, one for ratings of the frequency of sedentary behaviors and the other by summing the reported time spent on the sedentary behaviors. Questions were also asked about frequency of active lifestyle behaviors (yard work, shopping, child care, and house work) and these ratings were summed to form an active lifestyle behavior index.

The dietary behavior items included in the survey addressed a number of eating behaviors including both healthy and unhealthy eating practices. A principal components factor analysis with varimax rotation of 36 eating questions was conducted and six sets of factor scores were created: 1) Barriers to Health Eating (e.g., high cost of low fat foods), 2) Healthy behaviors (e.g., eat a salad daily), 3) Eating Problems (e.g., overeating, poor food choices), 4) Frequent Snacking, 5) Weight Control Efforts (e.g., trying to lose weight), 6) High Fat Behaviors (e.g., add fat to vegetables, meat at every meal). Factor scores were used so that the eating behavior variables would be uncorrelated.

Health Outcome Variables

Health status was assessed with the SF-12 (Ware, Kosinski et al. 1996), a measure of general health status. Two component scores describing physical status (Physical Component Score) and mental health status (Mental Component Score) are derived from SF-12 items. Higher scores indicate better functioning. In addition, participant’s global ratings of overall health from poor to excellent were analyzed separately.

Several chronic illness variables assessed whether participants had been diagnosed with hypertension, high cholesterol, and diabetes. In addition, body mass index (BMI = kg/m2) was computed from self-reported height and weight. In order to account for individuals who had been diagnosed with more than one chronic illness, a chronic illness index was created which indicated how many of the chronic illness variables each individual had been diagnosed with, as well as whether or not the participant was obese (BMI ≥ 30). Higher scores on this index, which ranges from 0 to 4, indicate a greater number of chronic illnesses (obesity, diabetes, hypertension, high cholesterol).

Religious Variables

The survey question on religious affiliation/denomination had 29 categories the interviewers could use to code participant responses. The taxonomy of American religious groups presented by Steensland and colleagues was used to group participants into 5 categories: 1) No religious affiliation, 2) Catholic, 3) Evangelical Christian, 4) Mainstream Protestant, and 5) Other religious groups(Steensland 2000). The three other questions about religion were: 1) frequency of church attendance, 2) a rating of how religious one is, and 3) a rating of how much comfort one receives from religion/God. These three questions were scored in the same direction, and summed to create a Religious Involvement Index. The index ranged from 3 to 14.

Statistical Analysis

Interviews were sampled from two strata, North Nashville and the rest of Nashville/Davidson County. Interviews within each strata were weighted based on the selection probabilities for gender and race computed from the 2000 US census to allow the results to be more representative of the Nashville population. Case weights were used in SPSS version 15 (SPSS inc., Chicago, IL) to calculate descriptive statistics. To control for clustering within strata (North Nashville, Davidson County), inferential statistics were computed using case weights with SUDAAN version 9.0.1 (Research Triangle Institute, Research Triangle Park, NC).

RESULTS

The sample for this study was restricted to interviews that were either from White/Caucasian or Black/African American respondents. A total of 3209 interviews were completed across both the North Nashville and Nashville/Davidson County geographic areas. Race/ethnicity was known for 3140 interviews, and the final sample consisted of 3014 White or African American participants. Table 1 shows the weighted demographic characteristics of the sample by race/ethnicity and gender. Males reported higher incomes and more education than females, and Whites reported higher incomes and more education than Blacks. More males were employed than females, and more Whites were employed than Blacks. Women were older than men, and African Americans had more representation in the youngest and the oldest age groups than Whites. Weight differences by sex and gender were somewhat more complex. For White participants, the women were less often overweight and obese than the men. For African American participants, the distribution for women and men was more similar. More women than men refused to give their weight.

Table 1.

Characteristics of the REACH 2010 Survey Participants

White Black
Male Female Male Female
N = 527 582 842 1063
Variable Category % % % %
Income* ξ
<$15k 4.9 8.4 10.7 17.4
$15–25k 12.3 14.6 21.9 25.8
$25–50k 34.5 29.9 30.6 22.5
>$50k 32.2 21.1 14.4 8.6
Unknown 16.1 25.9 22.5 25.7
Education* ξ
<HS 10.2 17.2 18.0 24.2
HS or GED 24.6 31.2 30.4 31.0
Some college 22.3 22.4 25.0 22.8
College grad 41.7 29.1 26.1 21.1
Unknown 1.1 0.2 0.5 0.9
Employment* ξ
employed 69.9 52.0 61.4 48.9
unemployed 1.7 2.9 2.4 2.7
homemaker 0.6 9.6 0.2 5.5
student 2.3 1.9 3.2 1.6
retired 21.0 28.1 25.6 32.5
disabled 4.2 5.1 6.4 8.5
unknown 0.4 0.3 0.8 0.3
Age* ξ
18–24 5.1 5.7 7.1 6.9
24–39 19.2 13.4 12.0 10.4
35–44 20.2 16.3 15.7 15.8
54–54 17.1 18.0 23.1 15.6
55–64 16.2 14.6 15.6 15.4
65–74 21.3 30.4 24.5 33.4
75+ 1.0 1.5 1.9 2.4
Body Mass Index (BMI)* ξ
underweighta 0.9 4.3 2.0 2.4
normal weight 33.4 43.1 25.8 25.1
overweight 38.9 24.2 37.4 30.1
obese 20.1 15.6 27.0 26.6
extremely obese 1.3 2.4 2.6 5.4
no data 5.3 10.3 5.2 10.4
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Associated with Gender based on Pearson Chi-square test: * p < 0.0001

Associated with Race/Ethnicity based on Pearson Chi-square test: ξ p < 0.0001,

a

BMI is computed as kg/m2. To better display the distribution of BMI we used the following definitions: underweight = BMI < 18; normal weight = 18 ≤ BMI < 25; overweight = 25 ≤ BMI < 30; obese = 30 ≤ BMI < 40; and extremely obese = BMI ≥ 40.

The bivariate associations between Denominational Affiliation and the Religious Involvement Index and demographic characteristics are presented in table 2. Males more often identified themselves as having no religious affiliation and females more often were associated with Evangelical Christian denominations. Whites were more likely to have no affiliation, or to be Catholic or Mainline Protestant while African Americans were more likely to be Evangelical Christians. Evangelical Christians had lower incomes and less educational attainment than Catholics and Mainline Protestants. The employment rate was highest among Catholics and those with no religious preference, and the disability rate highest among Evangelical Christians. Students more often indicated no religious preference, while more retired persons were Evangelical Christians or Mainline Protestants. Younger people more often had no religious preference. The Religious Involvement Index was higher for females than for males, and higher for Blacks than for Whites. Those who did not report income or education, also reported lower levels of religious involvement. Religious involvement was lower among students and those who did not report employment status. The Religious Involvement Index was also lower for the youngest and oldest age groups.

Table 2.

Association of Demographic Characteristics with Denominational Affiliation and Religious Involvement for Participants in the REACH 2010 Survey

None Catholic Evangelical Mainline
Protestant		 Other Religious
Involvement
N = 476 164 1718 532 124 3014
Percent Percent Percent Percent Percent Mean S.D
Gender* ξ
Male 60.5 48.8 40.7 45.3 49.2 10.6 2.6
Female 39.5 51.2 59.3 54.7 50.8 11.3 2.2
Race/Ethnicity* ξ
White 53.4 66.5 25.3 51.9 29.0 10.2 2.7
Black 46.6 33.5 74.7 48.1 71.0 11.5 2.1
Income*
<$15k 11.3 5.5 13.1 13.1 11.3 11.0 2.4
$15–25k 19.7 11.0 23.1 23.1 15.3 11.1 2.3
$25–50k 29.8 28.7 26.8 26.8 38.7 11.0 2.4
>$50k 15.5 38.4 13.0 13.0 15.3 11.0 2.5
Unknown 23.5 16.5 24.0 24.0 19.4 10.9 2.5
Education* ξ
<HS 18.5 3.7 23.0 11.1 11.2 10.9 2.3
HS oe GED 30.3 17.7 32.5 23.5 32.8 11.0 2.3
Some college 20.4 33.5 22.1 23.7 34.4 11.2 2.3
College grad 28.4 45.1 21.9 41.7 20.8 11.0 2.7
Unknown 2.5 0.0 0.4 0.0 0.8 7.6 3.9
Employment* ξ
Employed 66.5 67.7 53.7 52.3 63.7 10.8 2.5
Unemployed 2.5 0.6 2.7 2.1 1.6 10.5 2.7
Homemaker 3.6 4.3 4.3 3.0 4.8 11.1 2.5
Student 4.6 4.3 1.5 1.3 4.8 10.2 2.7
Retired 15.5 15.9 29.7 38.0 19.4 11.5 2.2
Disabled 5.9 6.7 7.8 3.2 5.6 11.0 2.4
Unknown 1.5 0.6 0.3 0.2 0.0 8.8 2.5
Age* ξ
18–24 11.8 7.3 5.8 3.8 4.9 10.5 2.4
24–39 20.4 18.3 11.8 7.9 15.4 10.5 2.7
35–44 19.6 15.2 16.6 13.7 19.5 10.7 2.4
54–54 16.2 26.8 19.0 14.5 24.4 11.0 2.5
55–64 13.5 11.6 16.0 17.9 9.8 11.2 2.3
65–74 15.4 19.5 29.3 40.5 22.8 11.4 2.1
75+ 3.2 1.2 1.6 1.7 3.3 10.3 3.5
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*

Association with denomination based on Pearson Chi-square test: p < 0.0001;

ξ

Association with Religious Involvement Pearson Chi-square test: p < 0.0001

Table 3 presents the bivariate associations of Denominational Affiliation and the Religious Involvement Index with the health outcomes and health behaviors. There was a significant bivariate association between denominational membership and all of the health indicators, all of the physical activity variables, and all but one of the eating measures. Religious Involvement was significantly correlated with most of the health outcome measures, none of the physical activity measures, and two of the eating measures. The significant correlations were, however, all less than r = 0.13, Evangelical Christians, Mainline Protestants, and Others were higher on BMI, diabetes prevalence, hypertension prevalence, high cholesterol, and chronic problems. Eating habits were healthier for Mainline Protestants, Catholics, and Others and less healthy for Evangelical Christians.

Table 3.

Bivariate Analysis of the Relationship Between Denominational Affiliation and Religious Involvement with Health Behaviors and Health Outcomes

Nonea
n=438		 Catholic
n=154		 Evangelical
n-1577		 Mainline
Protestant
n=491		 Other
n=111		 Religious
Involvement
n=3014
Mean SD Mean SD Mean SD Mean SD Mean SD Pearson rc
Body Mass Index (BMI)b*** 27.07 5.74 26.97 6.13 28.21 6.38 27.00 5.88 28.50 5.78 0.06 ξξξ
Diabetesc *** 0.06 0.24 0.07 0.26 0.13 0.33 0.14 0.35 0.11 0.32 −3.74c ξξξ
Hypertensionc *** 0.32 0.47 0.34 0.48 0.45 0.50 0.45 0.50 0.34 0.48 −3.72c ξξξ
High Cholesterolc ** 0.19 0.40 0.24 0.43 0.25 0.43 0.27 0.45 0.28 0.45 −2.96c ξξ
Chronic Problems*** 0.57 0.74 0.65 0.80 0.82 0.87 0.86 0.86 0.74 0.80 0.09 ξξξ
Health Index*** 2.70 1.07 2.48 1.10 2.76 1.05 2.54 1.00 2.71 1.12 −0.02
SF-12 Physical*** 49.41 9.16 49.78 9.15 47.58 10.44 48.21 10.41 47.35 11.29 −0.04 ξ
Sf-12 Mental*** 53.50 8.87 53.91 8.71 53.38 9.09 55.24 7.89 53.39 9.29 0.07 ξξξ
Activity Index*** 2.81 3.59 3.92 3.49 2.49 3.49 2.98 3.48 2.57 3.44 −0.02
Sedentary Behaviors*** 9.60 2.30 9.87 2.04 9.26 2.27 9.47 2.24 9.54 2.19 −0.02
Sedentary Time* 15.25 12.36 18.53 30.91 13.20 14.65 13.52 12.84 14.72 12.32 −0.04
Active Lifestyle Behaviors* 9.49 2.25 9.64 2.37 9.51 2.27 9.83 2.40 9.15 2.08 0.04
Barriers to Healthy Eatingd −0.01 0.99 0.08 1.08 0.00 1.01 −0.06 0.92 0.15 1.07 0.00
Healthy Eating Behaviorsd *** −0.18 1.04 0.12 1.01 −0.03 0.97 0.18 1.00 0.22 1.04 0.13 ξξξ
Eating Problemsd ** 0.01 1.01 0.20 1.04 −0.04 1.00 0.08 0.98 −0.13 0.91 −0.02
Snackingd *** 0.09 1.00 −0.07 0.92 0.04 0.99 −0.21 1.01 0.06 1.07 −0.03
Emotional Eatingd ** −0.12 0.99 0.07 0.98 0.01 1.00 0.00 1.00 0.24 1.05 −0.01
Weight Loss Effortsd 0.04 0.99 −0.04 0.98 0.01 1.01 −0.08 0.97 0.12 1.03 −0.01
High Fat Behaviorsd *** −0.07 1.02 −0.23 0.92 0.08 1.02 −0.09 0.94 −0.15 0.90 0.10 ξξξ
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Denominations differ based on one-way analysis of variance: * p < 0.05, ** p < 0.01, *** p < 0.0001

Pearson correlation with Religious Involvement: ξ p < 0.05, ξ ξ p < 0.01, ξ ξ ξ p < 0.0001

a

Subject’s descriptions of their religious affiliation were grouped based on the taxonomy of American religious groups presented by Steensland et al. (2000)

b

Body Mass Index (BMI) is computed as m/kg2 and was analyzed as a continuous variable.

c

Because these variables are coded present (1) or absent (0), association with Denominational Affiliation was tested using logistic regression, and association with Religious involvement was tested using a t-test.

c

The eating variables are factor scores computed using the regression method. They all have a mean of zero, and a standard deviation of one.

Table 4 presents hierarchical linear regression analyses to test whether religious involvement and denominational membership add to the prediction of health outcomes and health behaviors after controlling for age, sex, race, education, income, and employment. Table 4 presents R2 after controlling for the demographic variables as a block. Next, Religious Involvement is added to the regression model and an F-test of R2 change is presented. After controlling for demographic differences and individual differences in Religious Involvement, denominational groups were added and an F-test of R2 change is presented.

Table 4.

Hierarchical Regression Analyses of Health Outcomes and Health Behaviors

Demographicsa Religious Index Denomination
n R2 df R2 df R2-
change		 F-change p-value R2 df R2-change F-change p-value
Body Mass Index (BMI)b 2762 0.0640 22 0.0649 1 0.0009 2.69 0.1009 0.0659 4 0.0010 0.70 0.5933
Chronic Problems 3012 0.2093 22 0.2094 1 0.0000 0.03 0.8620 0.2097 4 0.0004 0.34 0.8480
Health Index 3012 0.1614 22 0.1662 1 0.0048 17.11 0.0000 0.1680 4 0.0019 1.68 0.1521
SF-12 Physical 3012 0.2435 22 0.2436 1 0.0000 0.17 0.6803 0.2447 4 0.0012 1.17 0.3204
Sf-12 Mental 3012 0.0700 22 0.0729 1 0.0028 9.05 0.0027 0.0730 4 0.0001 0.11 0.9801
Activity Index 2599 0.1198 22 0.1276 1 0.0078 26.68 0.0000 0.1282 4 0.0006 0.50 0.7360
Sedentary Behaviors 3012 0.2667 22 0.2676 1 0.0009 3.61 0.0574 0.2678 4 0.0003 0.26 0.9063
Sedentary Time 3012 0.2226 22 0.2228 1 0.0003 0.97 0.3250 0.2228 4 0.0000 0.00 1.0000
Active Lifestyle Behaviors 3012 0.0363 22 0.0381 1 0.0018 5.49 0.0192 0.0381 4 0.0000 0.00 1.0000
Barriers to Health Eatingc 3012 0.0500 22 0.0501 1 0.0001 0.18 0.6693 0.0503 4 0.0003 0.21 0.9344
Healthy Eating Behaviorsc 3012 0.0534 22 0.0653 1 0.0119 37.99 0.0000 0.0666 4 0.0013 1.07 0.3683
Eating Problemsc 3012 0.0938 22 0.0940 1 0.0002 0.58 0.4475 0.0942 4 0.0002 0.17 0.9559
Snackingc 3012 0.0825 22 0.0826 1 0.0001 0.27 0.6053 0.0829 4 0.0003 0.22 0.9248
Emotional Eatingc 3012 0.0224 22 0.0227 1 0.0003 0.82 0.3645 0.0258 4 0.0031 2.34 0.0528
Weight Loss Effortsc 3012 0.0330 22 0.0332 1 0.0001 0.32 0.5745 0.0332 4 0.0000 0.00 1.0000
High Fat Behaviorsc 3012 0.0722 22 0.0757 1 0.0035 11.32 0.0008 0.0762 4 0.0005 0.41 0.8031
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P-values less than 0.05 are highlighted in boldface

a

Demographic variables were age, sex, race/ethnicity, education, income, and employment status. Age was entered as a continuous variable, and the other categorical variables were dummy coded using the categories in Table 1.

b

Body Mass Index (BMI) is computed as m/kg2 and was analyzed as a continuous variable.

c

The eating variables are factor scores computed using the regression method. They all have a mean of zero, and a standard deviation of one.

After controlling for demographic variables, the Religious Involvement Index resulted in a significant improvement in R2 for the overall rating of health, the SF-12 mental health scale, the physical activity index, active lifestyle behaviors, healthy eating behaviors, and high-fat eating behaviors. Denominational Affiliation did not improve R2 for any of the health outcomes or health behaviors after controlling for individual differences in demographics and Religious Involvement. Logistic regression analyses were run for diabetes, hypertension, and high cholesterol (not shown in Table 4). Religious Involvement Index and Denominational Affiliation did not improve the prediction of diabetes, hypertension, or high cholesterol prevalence after controlling for the demographic variables.

DISCUSSION

The Religious Involvement Index (church attendance, importance of religion, and religion as a source of comfort) had a small but positive correlation with higher BMI, and more chronic disease. There was also a small but significant correlation with better mental health. There was also a weak correlation with both healthy and unhealthy eating behaviors. After controlling for differences in age, sex, race, income, education, and employment status, there was a positive association between religious involvement and ratings of overall health (poor to excellent), frequency intensity and duration of physical activity, healthy lifestyle behaviors (e.g., yard work), healthy eating behaviors (e.g., more fruits and vegetables), and high fat behaviors (e.g., large Sunday meals, adding fat to vegetables). These results suggest that the relationship between religion and health is complex and multifaceted.

When the bivariate associations were examined, there were many differences in the Denominational Affiliation groups on health outcomes and health behaviors. However, once we controlled for demographic differences along with individual differences in Religious Involvement, Denominational Affiliation was no longer associated with any of the health behaviors or health outcomes. Denominational Affiliation is strongly confounded with age, gender, race, and socioeconomic status. People tend to join religious communities based on similarity of not only religious belief, but also similarity in cultural belief and practices. One we control for some of the more obvious indicators of cultural similarity (e.g., education, race, and income), there are no longer differences in these groups in health outcomes or health behaviors.

In these analyses, we grouped denominations into No Affiliation (16%), Catholic (5%), Evangelical Christian (57%), Mainline Protestant (18%), and Other (4%). Of these groups, the most heterogeneous was the Other group which included people who identified themselves as Jewish, Muslim, Buddhist, Unitarian, Seventh Day Adventist, Mormon, and Jehovah’s Witness. Within this group are sects (e.g, Mormon and 7thd Day Adventist) for which there is much evidence of better health than the general population(Willett 2003). However, in Nashville these groups are relatively uncommon compared to Evangelical Christians. It is possible, different results could be seen in other cities with a very different mix of religious affiliations.

Practically speaking, there are implications of this work for health promotion and disease prevention. The “church” or the “faith community” should not be treated as a monolithic entity. Those involved in health promotion end up working with specific churches or faith communities. One should not assume that involvement in any specific faith community promotes better health. Rather, each setting should be approached as a community of people who not only share religious beliefs, but who also have similarities in socioeconomic, ethnic, and cultural background. Each group has traditions surrounding food (e.g., serving fried chicken at church events) and physical activity (e.g., church sports teams or leagues) and these traditions may have both positive and negative impacts on health. The social norms, the local emphasis on health, and the resources of community members for making health promotion a priority will differ from one faith community to another.

The limitations of this study include the use of cross-sectional self-report methodology. Due to the cross-sectional nature, causation cannot be inferred. Additionally, each of the variables was based on a self-report telephone interview. Particularly with the health behavior and outcomes variables, individuals may not have accurate knowledge of their behaviors or may be unwilling to report certain information such as body weight and religious belief. Interviewers in the study took the respondent’s response to the question about religious affiliation and placed it in a category. Some respondents were more specific than others (e.g. Christian or Protestant vs. belonging to a specific denomination), and the categories used to code responses were far from exhaustive. Our Religious Involvement Index was based on only three questions. There are now many more detailed, multidimensional measures of religious belief and behavior. The sampling scheme used in this study relied on published telephone numbers. This strategy introduces biases by leaving out people without land-line telephone service and people who have unpublished land-line telephone numbers.

This study has significant implications for future research on the associations between religion and health. First, it suggests that more researchers should look for negative associations between religious behavior, religious affiliation and health. It caution researchers to consider that religion is imbedded in a socioeconomic/cultural context, and that it is crucial to separate religious belief and practice from the more general effects of education, income, and race/ethnicity. It confirms previous data that indicates that, generally, religious involvement is associated with improved health after controlling for differences in demographic characteristics. Additionally, it suggests that differences in health behavior and health outcome between religious groups should not necessarily be attributed to differences in religious belief and practice. Instead, these apparent differences may be due to socio-demographic similarities between individuals of certain religious denominations, or it may be that certain religious affiliations/denominations have a culture/environment (quite apart from their religious beliefs) that differentially encourages or discourages certain health practices. Future research should more closely examine not only broad religion/health associations, but also similarities and differences across various religious groups.

Acknowledgments

This project was supported by CDC REACH 2010 U50/CCU417280-0 and NIH Grant P01 DK 20593

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