Ronneberg - The Protective Effects of Religiosity on Depression - A 2-Year Prospective Study

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The Protective Effects of Religiosity on

Depression: A 2-Year Prospective Study

Corina R. Ronneberg, MS,* Edward Alan Miller, PhD, MPA,

Elizabeth Dugan, PhD, and Frank Porell, PhD

Department of Gerontology, John E. McCormack Graduate School of Policy & Global Studies, University

of Massachusetts Boston.

*Address correspondence to Corina R. Ronneberg, MS, Department of Gerontology, John E. McCormack Graduate School

of Policy & Global Studies, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA 02125. E-mail: Corina.

[email protected]

Received October 29 2013; Accepted June 3 2014.

Decision Editor: Rachel Pruchno, PhD

Purpose of the Study: Approximately 20% of older adults are diagnosed with depression

in the United States. Extant research suggests that engagement in religious activity, or

religiosity, may serve as a protective factor against depression. This prospective study

examines whether religiosity protects against depression and/or aids in recovery.

Design and Methods: Study data are drawn from the 2006 and 2008 waves of the Health

and Retirement Study. The sample consists of 1,992 depressed and 5,740 nondepressed

older adults (mean age = 68.12 years), at baseline (2006), for an overall sample size

of 7,732. Logistic regressions analyzed the relationship between organizational (service

attendance), nonorganizational (private prayer), and intrinsic measures of religiosity anddepression onset (in the baseline nondepressed group) and depression recovery (in the

baseline depressed group) at follow-up (2008), controlling for other baseline factors.

Results: Religiosity was found to both protect against and help individuals recover from

depression. Individuals not depressed at baseline remained nondepressed 2 years later

if they frequently attended religious services, whereas those depressed at baseline were

less likely to be depressed at follow-up if they more frequently engaged in private prayer.

Implications: Findings suggest that both organizational and nonorganizational forms of

religiosity affect depression outcomes in different circumstances (i.e., onset and recov-

ery, respectively). Important strategies to prevent and relieve depression among older

adults may include improving access and transportation to places of worship among

those interested in attending services and facilitating discussions about religious activi-

ties and beliefs with clinicians.

Key words: Organizational religiosity, Public religiosity, Nonorganizational religiosity, Private religiosity, Intrinsic

religiosity, Religion, Social support, Mental health

Depression is a major concern in the United States, as

more than 5% of the general population over 12 years old

reports being depressed at any given time (Pratt & Brody,

2008). The prevalence of depression becomes even more

alarming at older ages as 20% of those 65 years and older

report being depressed (Hurst, Williams, King, & Viken,

The Gerontologist , 2014, Vol. 00, No. 00, 1–12

doi:10.1093/geront/gnu073

Research Article

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2008; Paukert et al., 2008). Because depression is one of

the most common mental health issues facing older adults

(Administration on Aging, 2012), the study of depression

and its correlates has become a priority among scholars

looking to improve the quality of life among this popula-

tion (Administration on Aging, 2012).

Although conflicting results have been reported (e.g., posi-

tive, negative, or no relationship), recent investigations suggestthat involvement in religious activity may serve as a protective

factor against depression (Blay, Batista, Andreoli, & Gastal,

2008; Blazer, 2010; Hayward, Owen, Koenig, Steffens, &

Payne, 2012; King et al., 2007; Koenig, 2007, 2009; Law

& Sbarra, 2009; Smith, McCullough, & Poll, 2003). These

investigations also suggest that individuals who are more

religious may not only be more likely to recover from cer-

tain ailments such as acute myocardial infarction (Martin &

Levy, 2006) and severe mental illness (Webb, Charbonneau,

McCann, & Gayle, 2011), but do so more quickly, while

experiencing shorter hospitalization stays (Contrada et al., 2004). Together, these findings suggest a potentially impor-

tant avenue for preventing and/or promoting recovery from

depression, especially given the large role that religion plays

in the lives of most Americans, 90% of whom report believ-

ing in God or a universal spirit (Gallup, 2013) and 90% of

whom report engaging in prayer (Hill et al., 2000).

The role that religion plays in people’s lives becomes

more pronounced with age. One national survey, for

example, found that nearly 70% of adults 50 years or

older reported that religion is very important in their lives

compared with 44% of adults under 30 years old (Cohen

& Koenig, 2003). Older adults also exhibit higher levels

of religiosity or actual involvement in religious activities

(Boswell, Kahana, & Dilworth-Anderson, 2006). The fact

that religiosity appears to increase with age coupled with

the high prevalence of depression among older adults sug-

gests the need to further study the effects of religious beliefs

and activities on depression among the elderly.

The need to further study the effects of religiosity on

depression is also suggested by current research. Most exist-

ing research in this area has been cross-sectional (Blay et al.,

2008; Branco, 2000; Lawler-Row & Elliott, 2009; Waddell

& Jacobs-Lawson, 2010; Yohannes, Koenig, Baldwin, & Connolly, 2008). That which is longitudinal has focused on

limited population subsets (e.g., African Americans elders

and adolescents with psychiatric conditions) (Dew et al.,

2010; Ellison & Flannelly, 2009), local or regional popula-

tions (Idler & Kasl, 1992; King et al., 2007; Koenig et al.,

1997; Koenig, George, & Peterson, 1998; Sun et al., 2012),

and non-U.S. samples (e.g., Australian, Lebanese, Israeli,

European elders, or Brazilian) (Braam et al., 2001; Chaaya,

Sibai, Fayad, & El-Roueiheb, 2007; Iecovich, 2001; Law &

Sbarra, 2009; Payman, George, & Ryburn, 2008). Sample

sizes also tend to be small and focus exclusively on, for

example, the effects of religiosity on depression recovery,

rather than both depression onset and recovery (Bosworth,

Park, McQuoid, Hays, & Steffens, 2003; Hayward et al.,

2012; Koenig et al., 1998). There is a lack of consistency

in measurement as well, with one or more religiosity meas-

ures tending to be employed operationalizing such concepts

as organizational religiosity, nonorganizational religiosity,intrinsic religiosity, religious salience, religious affiliation,

religious orthodoxy, and religious coping, though indica-

tors of the former three domains are most commonly used

(Blay et al., 2008; Bosworth et al., 2003; Braam, Beekman,

Deeg, Smit, & Tilburh, 1997; Braam et al., 2004; Branco,

2000; Hayward et al., 2012; Idler & Kasl, 1992; King et al.,

2007; Koenig et al., 1997, 1998; Levin, 2010; Schnittker,

2001; Sun et al., 2012).

The primary goals of this study are to assess depres-

sion levels both at baseline and 2 years later, in order to

determine (a) whether religiosity protects against depres-sion and (b) whether religiosity aids in the recovery from

depression. Shortcomings in extant research are addressed

in several ways. First, we use a larger, more representative

sample than the previous work, focusing expressly on older

adults (residing in the United States), and employ a longi-

tudinal perspective. Second, we employ a comprehensive

set of religiosity indicators, including organizational, non-

organizational, and intrinsic measures, as well as religious

salience, affiliation, and presence of both friends and rela-

tives at one’s place of worship.

Religiosity, Depression, and Older Adults

The biopsychosocial diathesis-stress model of depression

(BPDS) posits that there are certain interconnected bio-

logical, psychological, and social factors that can affect an

individual’s predisposition to depression (Schotte, Bossche,

Doncker, Claes, & Cosyns, 2006; Stein, 2005). These

include risk factors that can serve both as potential pre-

cursors of depression and potential protective factors that

can act as buffers against depression. Some individuals are

more vulnerable to depression due to biological factors

(e.g., age and gender), somatic factors (e.g., health status,chronic conditions, disability, or recent medical setbacks),

psychological factors (e.g., mental illness and serious alco-

holic use), and social influences (e.g., marital status, edu-

cation, income, social support, volunteerism, and adverse

life events). In this study, religiosity is conceptualized as a

protective factor, which may help buffer against one’s total

vulnerability for depression.

Religiosity typically refers not only to a belief in a higher

entity or something greater than oneself but also formal

involvement in organized religious activities and specific,

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measurable acts such as prayer, meditation, service attend-

ance, religious readings, and affiliation with a particular reli-

gion or place of worship (Hill et al., 2000; Iecovich, 2001;

Yohannes et al., 2008). A key characteristic of religion is that

it is organized in a hierarchical fashion with an identified

authority figure such as a priest, pastor, or rabbi presiding.

Although religion refers to someone’s belief system, religios-

ity is the actual application of such beliefs in daily life.The literature distinguishes between three general types

of religiosity: organizational, nonorganizational, and intrin-

sic. Organizational religiosity typically involves public or

group activities and is most commonly measured by one’s

religious service attendance (Koenig et al., 1998; Sun et al.,

2012). Nonorganizational religiosity, by contrast, is more

private and typically occurs on a person’s own time, alone,

encompassing activities such as reading religious texts,

praying, and/or meditating (Koenig et al., 1998; Sun et al.,

2012). Intrinsic religiosity is concerned with individuals’

subjective meaning of religiosity and how religious beliefsaffect everyday life (Sun et al., 2012). Studies have shown

that as individuals age, they are more likely to engage in

nonorganizational activities as opposed to organizational

modes of religious expression (Yohannes et al., 2008), pos-

sibly shifting to more private religious activities, perhaps

due to physical decline, rather than giving up on religious

involvement altogether.

Nearly 75% of older people who suffer from depres-

sion or anxiety partake in some kind of religious activity at

least monthly (Paukert et al., 2009). A meta-analysis of 147

studies found higher religiosity to be associated with fewer

depressive symptoms or indicators in more than three quar-

ters of the studies analyzed (Smith et al., 2003). Furthermore,

individuals who regularly attend religious services display

lower rates of depression when compared with individu-

als who either do not attend services or do attend services

but on a more sporadic basis (Blazer, 2010; Braam et al.,

2004; Koenig et al., 1997). In a prospective study focusing

on African Americans over the age of 55, it was found that

individuals who received guidance from their religion on a

regular basis were less likely to suffer from major depression

3–4 years later (Ellison & Flannelly, 2009).

Extant research also demonstrates that religious involve-ment may benefit clinically depressed individuals (Koenig

et al., 1998; Murphy & Fitchett, 2009). Depressive symp-

toms have been shown to decrease across time in persons

engaged in organizational religiosity (Braam et al., 2004;

Koenig, 2007; Law & Sbarra, 2009; Levin, 2010; Smith

et al., 2003). However, mixed results abound regarding

nonorganizational modes of religious involvement. For

instance, one cross-sectional study found nonorganiza-

tional religiosity unrelated to depression (Koenig et al.,

1997), whereas another found an inverse relationship

between nonorganizational religiosity and depression

cross-sectionally, but a U-shaped association longitudinally

(King et al., 2007). Yet another study found nonorganiza-

tional religiosity to be associated with lower depression

severity after 3 months (Hayward et al., 2012). Findings

around intrinsic religiosity are also inconsistent. Parker and

coworkers (2003) found no relationship between intrinsic

religiosity and depression, King and coworkers (2007) apositive relationship, and Koenig and coworkers (1998)

quicker remission from depression.

Evidence suggests that the impact of religiosity on depres-

sion is stronger among women who also tend to be more

active participants in both organizational and nonorgani-

zational religious activities than men, including, for exam-

ple, religious affiliation and private prayer (Wink & Dillon,

2002; Yohannes et al., 2008). This tendency is reflected in

a 2002 Health and Retirement Study (HRS) of older adults

60 years or older, which found higher ratings of religious

importance to be a protective factor against depression inwomen—but not men (Waddell & Jacobs-Lawson, 2010).

Based on previous research and according to the BPDS

model of depression, this study hypothesizes that (a)

higher religiosity will be associated with a lower likelihood

of depression 2 years later among older adults without

depression at baseline (i.e., religiosity will protect against

depression onset) and (b) higher religiosity will be associ-

ated with a lower likelihood of depression 2 years later for

respondents depressed at baseline (i.e., religiosity will aid in

depression recovery).

Methods

Data Source

The sample in this study was drawn from the 2006 and

2008 waves of the HRS; the goal was to model depres-

sion in 2008 based on respondent characteristics in 2006.

The HRS is sponsored by the National Institute on Aging

(grant number NIA U01AG009740) and is conducted by

the University of Michigan (Health and Retirement Study,

2006 /2008). The HRS began collecting data in 1992 and

continues to do so every 2 years. The HRS is a nationally

representative study that contains rich information onmore than 22,000 community-dwelling older adults aged

50 or older, with respect to respondent health, functional

status, cognition, living arrangements, retirement, religious

affiliation, and involvement in assorted activities.

Sample

The sample utilized in this study includes the subset of

HRS respondents who completed the 2006 Leave-Behind

Questionnaire (n = 7,732). The rationale for utilizing the

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Leave-Behind Questionnaire is that it contains much more

comprehensive measures of religiosity than the basic HRS

survey. In all, 944, or 12% of respondents, were missing

information on at least one HRS item. Thus, for purposes

of our analyses, we employed multiple imputation of miss-

ing data to fill in the missing values. Twenty imputations

were conducted and pooled results were used in the analy-

ses reported.

Measurement

Dependent Variable (Depression)

Depression status (in 2008) is the outcome variable in

the present study. Depression is assessed with Center for

Epidemiological Studies Depression scale (CESD-8). The

presence of three or more symptoms, out of eight, indicates

a higher likelihood of being clinically depressed (Steffick,

2000). Therefore, respondents reporting three or more

depressive symptoms were coded 1 = depressed; those withzero, one, or two symptoms as 0 = not depressed.

Independent Variables (Religiosity)

Five religiosity questions are asked in the basic HRS.

Religious affiliation was self-reported as Protestant,

Catholic, Jewish, or none/other religion. Respondents were

asked about organizational religiosity, via the frequency

of attendance at religious services: high (more than once

a week or once a week), moderate (two to three times a

month), and low/none (one or more times a year or not at

all). Additionally, respondents were asked about the pres-

ence of both friends and relatives in one’s congregation (yes

or no). Lastly, respondents were asked to rate the impor-

tance of religiosity: very, somewhat, or not important.

Each of these items was coded as a series of dichotomous

variables.

The Leave-Behind Questionnaire includes two addi-

tional measures of religiosity. The first is an index of

religiosity, an intrinsic measure, composed of four items

(α = .92)—believe God watches over me, events unfold

according to a divine/greater plan, carry religious beliefs

into all dealings in life, find strength and comfort in reli-

gion. Possible scores range from 1 (strongly disagree) to6 (strongly agree) (averaged across the four items) where

higher scores indicate higher religiosity levels. The second

Leave-Behind Questionnaire item measured the frequency

of prayer in private contexts, a nonorganizational meas-

ure. The scores (1–8) on this item were reverse coded

so that higher scores denote higher frequency of private

prayer.

The potential for multicollinearity was examined

in several ways. Neither variance inflation factors

(all < 4.5) nor correlations (all < 0.62) among the seven

religiosity measures revealed problematic multicollin-

earity. Moreover, each of the seven religiosity variables

was entered one by one into the model and as a block,

both with and without covariates, both for the depressed

and nondepressed samples. Results on the religiosity

variables largely remained consistent across these vari-

ous specifications. The final model therefore includes all

seven religiosity variables described previously alongwith covariates.

Covariates

This study controls for biological, somatic, psychologi-

cal, and social factors that have been found to be associ-

ated with depression. Prior research suggests that older

adults, females, and non-Hispanic Blacks exhibit higher

rates of depression compared with their younger, male,

and white counterparts (Law & Sbarra, 2009; Pratt &

Brody, 2008). Age is measured as a continuous vari-

able (number of years); gender as a dichotomous vari-able, with female = 1 and male = 0; and race/ethnicity

as a series of dichotomous variables for white, black,

Hispanic, and other.

In addition, somatic or health conditions may be

related to depression status (Blazer, 2010; Centers for

Disease Control and Prevention, 2011a; Koenig, 1999; Lo

et al., 2010; Schotte et al., 2006). At baseline, respondents

were asked whether they had ever been diagnosed with

each of seven chronic ailments: high blood pressure, dia-

betes, cancer, lung disease, heart conditions, stroke, and

arthritis. The number of chronic conditions was summed

(0–7) with a higher count indicating greater comorbidity.

Respondents were also asked whether they had recently

experienced each of three somatic life events in the last

two years (since baseline): stroke, heart attack, and/or

cancer. It is important to account for the onset of illnesses

such as these, as recently experiencing a negative event has

been found to be associated with depression (Schnittker,

2001). A count (0–3) of somatic events was developed,

with a higher number indicating greater comorbidity.

Self-reported health status was measured using a series

of dichotomous indicator variables: excellent, very good,

good, fair, or poor. Functional limitations were measuredusing counts of both instrumental activities of daily living

(IADLs) (0–6) and basic activities of daily living (ADLs)

(0–5).

Alcohol abuse appears to be associated with depres-

sion (Blay et al., 2008; Braam et al., 2004; Centers

for Disease Control and Prevention, 2011a; Idler &

Kasl, 1992; Rodriguez, Schonfeld, King-Kallimanis, &

Amber, 2010). Consistent with previous research (Satre,

Gordon, & Weisner, 2007), respondents were identified

as a serious drinker, or abuser of alcohol, if they were



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(a) a woman that consumes more than two drinks per

occasion or (b) a man that consumes more than three

drinks per occasion. Also included is a dichotomous

variable indicating whether or not an individual had

ever been diagnosed with any emotional or psychiatric

condition(s) (Koenig et al., 1998). Both variables have

been shown to increase the risk for, or coexist with,

depression (Aina & Suman, 2006; Centers for Disease Control and Prevention, 2011b) and have been used as

covariates in similar studies.

Social factors have the potential to predispose individu-

als toward depression (Schotte et al., 2006). The influences

of social and economic considerations are reflected, in part,

in sociodemographic variables, including marital status

(married, divorced/separated, widowed, or never married),

education (in years), and household income (in quartile

earnings). Social considerations are also reflected, in part,

in living alone, volunteerism, and having family and friends

nearby. Certain recent adverse life events such as “seriouslosses, threatening occurrences, or difficulties in life” are

also predictive of depression (Schnittker, 2001; Schotte

et al., 2006, p. 314). These include experiencing divorce/

separation, death of a spouse/partner, a nursing home stay,

and residential move in the last 2 years. A count has been

created (0–4), where a higher number indicates experi-

encing more recent adverse social events. Lastly, whether

respondents were living in a nursing home as opposed to a

community setting at the time of the survey was recorded,

in addition to whether survey responses were provided by

a proxy or not.

Analytical Plan

Basic descriptive statistics are reported, followed by bivari-

ate analyses comparing the baseline characteristics of the

depressed and nondepressed samples (Table 1). Results

from multivariate analyses are presented next, utiliz-

ing logistic regression to model the relationship between

depression and religiosity, controlling for other baseline

factors. Two logistic regressions models were employed:

one with baseline depressed respondents and the second

with baseline nondepressed respondents (Table 2).

Results

Table 1 reports descriptive statistics for the entire sam-

ple, as well as differentiated by depression status in 2006.

Depressed and nondepressed samples had similar religious

affiliations (χ2 = 3.94 (3), p > .05). At 45% and 37%, respec-

tively, high frequency of religious service attendance wasmore likely to be reported by nondepressed than depressed

respondents, whereas depressed respondents were more

likely to report low or no service attendance than their non-

depressed counterparts (51% vs. 43%) (χ2 = 43.19 (2), p

< .001). A higher proportion of nondepressed respondents

(59%) reported having friends at their congregation than

depressed respondents (50%) (χ2 = 44.42 (1), p < .001);

approximately one quarter of each group reported shar-

ing their congregation with family (χ2 = 0.01 (1), p > .05).

There was a small but significant difference assigned to the

importance of religion, with depressed respondents being

Table 1. Descriptive and Bivariate Statistics for Baseline (2006) Depressed and Nondepressed Samples

Covariates Entire sample (n = 7,732) Depressed (n = 1,992) Nondepressed (n = 5,740) χ2(df )/ t

# (%)/mean (SD) # (%)/mean (SD) # (%)/mean (SD)

Religiosity factors

Religiosity (basic HRS questions)

Religious affiliation

Protestant 4,892 (63.3%) 1,276 (64.1%) 3,616 (70.0%) 3.94 (3)

Catholica 2,048 (26.5%) 501 (25.2%) 1,547 (27.0%)

Jewish 158 (2.0%) 48 (2.4%) 110 (1.9%)

None/other 614 (7.9%) 162 (8.1%) 452 (7.9)

Service attendance

High 3,324 (43.0%) 740 (37.2%) 2,584 (45.0%) 43.19 (2)***

Moderatea 917 (11.9%) 232 (11.7%) 685 (12.0%)

Low/none 3,493 (45.2%) 1,021 (51.3%) 2,472 (43.1%)

Friends in congregation 4,367 (56.5%) 998 (50.1%) 3,369 (58.7%) 44.42 (1)***

Relatives in congregation 1,891 (24.5%) 489 (26.6%) 1,402 (24.4%) 0.01 (1)

Importance of religion

Very important 5,273 (68.2%) 1,390 (69.8%) 3,883 (67.7%) 10.60 (2)**

Somewhat important 1,591 (20.6%) 422 (21.2%) 1,170 (20.4%)

Not importanta 871 (11.3%) 183 (9.2%) 688 (12.0%)



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Covariates Entire sample (n = 7,732) Depressed (n = 1,992) Nondepressed (n = 5,740) χ2(df )/ t

# (%)/mean (SD) # (%)/mean (SD) # (%)/mean (SD)

Religiosity (LBQ)

Index of religiosity 4.99 (1.39) 5.02 (1.33) 4.98 (1.40) −1.26

Frequency private prayer 6.11 (2.32) 6.25 (2.26) 6.06 (2.34) −3.17**

Biological variables

Demographic variables

Age 68.11 (1.39) 68.80 (11.44) 67.87 (10.40) −3.19***

Female 4,543 (58.8%) 1,317 (66.1%) 3,226 (56.2%) 59.96 (1)***

Race/ethnicity

Whitea 6,009 (77.7%) 1,462 (73.4%) 4,547 (79.2%) 33.84 (3)***

Black 1,004 (13.0%) 292 (14.8%) 712 (12.4%)

Hispanic 601 (7.7%) 204 (10.2%) 397 (6.9%)

Other 118 (1.5%) 34 (1.7%) 84 (1.5%)

Somatic variables

Health and functional limitation

Chronic conditions 1.92 (1.33) 2.39 (1.37) 1.75 (1.27) −18.46***

Self-reported health

Excellent 904 (11.7%) 70 (3.5%) 834 (14.5%) 1174.17 (4)***

Very good 2,350 (30.4%) 310 (15.6%) 2,040 (35.5%)

Gooda 2,395 (31.0%) 547 (27.5%) 1,848 (32.2%)

Fair 1,554 (20.1%) 702 (35.2%) 851 (14.8%)

Poor 543 (7.0%) 369 (18.5%) 174 (3.0%)

IADLs 0.34 (.82) 0.63 (1.07) 0.24 (0.70) −15.01***

ADLs 0.34 (.94) 0.80 (1.38) 0.18 (0.66) −19.48***

Somatic adverse life events 0.07 (0.27) 0.09 (0.30) 0.07 (0.26) −2.73**

Psychological variables

High alcohol use 351 (4.5%) 101 (5.2%) 250 (4.4%) 1.74 (1)

Psychological issues 1,244 (16.1%) 655 (32.9%) 589 (10.3%) 558.35 (1)***

Social variables

Sociodemographic variables

Marital status

Marrieda 5,039 (65.2%) 1,029 (51.7%) 4,010 (70.0%) 216.04 (3)***

Divorced/separated 966 (12.5%) 342 (17.2%) 624 (10.9%)

Widowed 1,504 (19.5%) 542 (27.2%) 962 (16.8%)

Never married 223 (2.9%) 79 (4.0%) 144 (2.5%)

Education level 12.58 (3.11) 11.78 (3.30) 12.86 (2.99) −13.53***

Household income

Quartile 1a 1,747 (22.6%) 722 (36.2%) 1,025 (17.9%) 347.93 (3)***

Quartile 2 1,922 (25.4%) 528 (26.5%) 1,394 (24.3%)

Quartile 3 1,985 (25.7%) 391 (19.6%) 1,594 (27.8%)

Quartile 4 2,078 (26.9%) 351 (17.6%) 1,727 (30.1%)

Social adverse life events 0.12 (0.12) 0.12 (0.33) 0.12 (0.33) −0.45

Social support variables

Living alone 1,673 (21.6%) 614 (30.8%) 1,059 (18.4%) 133.54 (1)***

Volunteer status 2,786 (36.0%) 475 (23.8%) 2,311 (40.2%) 172.90 (1)***

Relatives live near 2,157 (27.9%) 577 (29.0%) 1,580 (27.5%) 0.94 (1)

Friends live near 5,026 (65.0%) 1,241 (62.3%) 3,784 (70.0%) 11.80 (1)***

Have proxy respondent 283 (3.7%) 63 (3.2%) 220 (3.8%) 1.88 (1)

Live in nursing home 99 (1.3%) 48 (2.4%) 52 (0.9%) 18.96 (1)***

Notes: ADLs = activities of daily living; HRS = Health and Retirement Study; IADLs = instrumental activities of daily living; LBQ = Leave-Behind Questionnaire.aDenotes reference groups.

* p < .05. ** p < .01. *** p < .001.

Table 1. Continued



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Table 2. Logistic Regressions Modeling the Relationship Between Follow-Up (2008) Depression and Religiosity and Other

Covariates Among Baseline (2006) Depressed and Nondepressed Samples

Covariates Depressed at baseline

(n = 1,992)

Nondepressed at baseline

(n = 5,740)

Odds ratio p value Odds ratio p value

Religiosity factors

General HRS religiosity items

Religious affiliation

Protestant 0.91 0.465 1.03 0.768

Catholica — — — —

Jewish 2.05 0.040* 1.30 0.382

None/other 0.907 0.644 1.19 0.325

Frequency of attendance at religious services

High 1.36 0.062 0.65 0.001***

Moderatea — — — —

Low/none 1.18 0.330 0.75 0.035*

Friends in congregation 0.92 0.498 0.95 0.659

Relatives in congregation 1.13 0.336 0.92 0.432

Importance of religion

Very important 0.81 0.332 1.23 0.275

Somewhat important 1.00 0.995 1.01 0.977

Not importanta — — — —

Religiosity items (LBQ)

Index of religiosity 1.10 0.052 1.00 0.949

Frequency of private prayer 0.93 0.015* 0.98 0.476

Biological variables

Demographic variables

Age 0.99 0.099 1.00 0.319

Female 1.174 0.513 1.44 0.000***

Race/ethnicity

Whitea — — — —

Black 0.81 0.158 0.873 0.304

Hispanic 0.95 0.785 1.02 0.889

Other 1.04 0.971 0.67 0.304

Somatic variables

Health and functional limitation variables

Self-reported chronic conditions 1.06 0.127 1.09 0.020*

Self-reported health

Excellent 0.43 0.004** 0.54 0.000***

Very good 0.80 0.140 0.72 0.002**

Gooda — — — —

Fair 1.32 0.029* 1.77 0.000***

Poor 1.33 0.086 2.69 0.000***

IADLs 1.03 0.640 0.896 0.113

ADLs 1.04 0.412 1.13 0.058

Somatic adverse life events 1.57 0.008** 1.16 0.106

Psychological variables

Serious alcohol use 0.803 0.329 1.03 0.877

Psychological issues 1.607 0.000*** 1.93 0.000***

Social variables

Sociodemographic variables

Marital status

Marrieda — — — —

Divorced/separated 0.99 0.972 0.88 0.407

Widowed 0.72 0.048* 0.88 0.390

Never married 0.70 0.197 0.62 0.103



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more likely to report “very important” (70% vs. 68%)

and depressed respondents being less likely to report “not

important” (9% vs. 12%) (χ2 = 10.60 (2), p < .01). No

significant difference could be discerned between the two

groups on the religiosity index (t = −1.26, p > .05), but there

was a significant difference in regard to the frequency of pri-

vate prayer (t = −3.17, p < .01) with depressed respondentsreporting higher frequency (6.25 vs. 6.06 [out of 8]).

Significant differences could also be discerned with

respect to all covariates but high alcohol use (χ2 = 1.74 (1),

p > .05), social adverse life events (r = .45, p > .05), the pres-

ence of relatives living nearby (χ2 = 0.94 (1), p > .05), and

having a proxy respondent (χ2 = 1.88 (1), p > .05). Thus,

nondepressed respondents were more likely than depressed

respondents to be younger (r = −3.19, p < .001), male

(χ2 = 59.96, p < .001), non-Hispanic white (χ2 = 33.84, p <

.001), in excellent very good/good health (χ2 = 1174.17, (4),

p < .001), married (χ2

= 216.04, p < .001), have higher edu-cation (t = 13.53, p < .001), have higher income (χ2 = 347.93

(3), p < .001), volunteer (χ2 = 172.90, p < .001), and have

friends living nearby (χ2 = 11.80, p < .001). By contrast,

depressed respondents were more likely than nondepressed

respondents to be chronically ill (t = −18.46, p < .001),

IADL (t = −15.01, p < .001) and ADL (t = −19.48, p < .001)

impaired, suffer from adverse somatic life events (t = −2.73,

p < .01), have psychological issues (χ2 = 558.35, p < .001),

live alone (χ2 = 133.54 (1), p < .001), or live in a nursing

home (χ2 = 18.96 (1), p < .001).

Two main logistic regression models were estimated. The

first model is composed of individuals who were depressed

at baseline (n = 1,992) (Table 2). Two religiosity variables

were significant. The odds of being depressed at follow-up

were two times higher among depressed respondents with

a Jewish affiliation (odds ratio [OR] = 2.05, p < .05) but

lower for those with more frequent engagement in privateprayer (OR = 0.93, p < .05).

The first model also indicates that depressed individuals

who were in excellent health (OR = 0.43, p < .01), wid-

owed (OR = 0.72, p < .05), had higher household income

(OR = 0.73, p < .05; OR = 0.66, p < .05), or who lived

in a nursing home (OR = 0.41, p < .05) had a decreased

likelihood of being depressed at follow-up. In contrast,

depressed persons who reported more somatic life events

(OR = 1.57, p < .01), had psychological issues (OR = 1.61,

p < .001), reported more social adverse life events

(OR = 1.46, p < .05), and lived closer to their relatives(OR = 1.31, p < .05) were more likely to remain depressed

2 years later.

The second model is composed of individuals who were

not depressed at baseline (n = 5,740) (Table 2). Service

attendance was the only religiosity variable to prove sig-

nificant. In particular, nondepressed respondents with high

service attendance were 35% less likely to be depressed

at follow-up (OR = 0.65, p < .01), and respondents with

low/no service attendance were 25% less likely to become

depressed (OR = 0.75, p < .05), in comparison to those with

Covariates Depressed at baseline

(n = 1,992)

Nondepressed at baseline

(n = 5,740)

Odds ratio p value Odds ratio p value

Education level 0.98 0.155 0.99 0.333

Household income

Quartile 1a — — — —

Quartile 2 0.73 0.017* 0.93 0.528

Quartile 3 0.81 0.189 0.82 0.143

Quartile 4 0.66 0.023* 0.69 0.017*

Social adverse life events 1.461 0.011* 1.42 0.002**

Social support variables

Living alone 1.12 0.421 1.35 0.035*

Volunteer status 1.16 0.227 0.90 0.275

Relatives live near 1.31 0.018* 1.23 0.025*

Friends live near 0.954 0.659 0.91 0.291

Have proxy respondent 0.000 1.000 0.236 0.000***

Live in a nursing home 0.411 0.049* 0.741 0.538

−2 log likelihood: 1941.1*** −2 log likelihood: 3648.2***

Notes: ADLs = activities of daily living; HRS = Health and Retirement Study; IADLs = instrumental activities of daily living; LBQ = Leave-Behind Questionnaire.aDenotes reference groups.

* p < .05. ** p < .01. *** p < .001.

Table 2. Continued



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moderate service attendance. Other factors associated with

increased likelihood of depression onset included female

gender (OR = 1.44, p < .001), chronic illness (OR = 1.09,

p < .05), fair or poor health (OR = 1.77, OR = 2.69, both

p < .001), psychological issues (OR = 1.93, p < .001),

adverse social events (OR = 1.42, p < .01), living alone

(OR = 1.35, p < .05), and having relatives live nearby

(OR = 1.23, p < .05). By contrast, factors that appeared toguard against depression onset included reporting excellent

or very good health (OR = 0.54, p < .001; OR = 0.72, p

< .01), higher income (OR = 0.69, p < .05), and having a

proxy respondent (OR = 0.24, p < .001).

Discussion

This study sought to understand whether religiosity (a)

protects against future depression and (b) plays a role in

depression recovery, among older adults. Consistent with

prior research (Blazer, 2010; Koenig, 2007; Smith et al., 2003), results provide evidence supporting both of these

expectations, though the specific aspect of religiosity found

to protect against depression (frequent service attendance)

was different from the component found to aid in depres-

sion recovery (private prayer frequency). Relative to those

with moderate service attendance, individuals who were

not depressed at baseline (in 2006) were less likely to be

depressed 2 years later if they frequently attended reli-

gious services. It was expected that high service attend-

ance would protect against depression, perhaps due to the

availability, promotion, or benefits of social support found

in one’s place of worship. In particular, the protective path-

way stemming from service attendance may derive from

the comparatively higher levels of social capital resulting

from engagement in public modes of behavior, specifically

the interpersonal relationships formed and sustained by

active participation in a religious congregation. The pres-

ence of more social connections, in turn, may reduce the

likelihood of isolation and loneliness, two factors associ-

ated with depression.

Counterintuitively, individuals with low service attend-

ance who were not depressed at baseline were also less

likely to be depressed 2 years later relative to those withmoderate service attendance. It is possible that persons

with low or no service attendance may be less likely to

be depressed at follow-up because they are more likely to

engage in other, less public forms of religiosity that, in turn,

provide protective benefits from depression and other ail-

ments. That this may be the case is suggested by the moder-

ate, significant inverse correlation between private prayer

frequency and level of service attendance (r = −.496, p <

.05). Thus, whereas the high service attendance group may

be disproportionately devoted to organizational forms of

religiosity, the low service attendance group may be dis-

proportionately devoted to nonorganizational forms. In

contrast, the moderate service attendance group may not

be disproportionately devoted to either form of religious

behavior and, as such, may be less likely to experience the

benefits that derive from each.

Consistent with expectations, persons who started

out depressed at baseline were less likely to be depressed2 years later if they more frequently engaged in private

prayer. This finding suggests that persons who become

depressed may turn to their faith for support and as a

means of coping from adverse life events—financial,

health, social, or otherwise. Subsequent engagement in

private prayer may serve, in part, to cultivate hope for

the future, potentially activating cognitive resources that

eventually counter depression.

Interestingly, Jewish respondents were much more likely

to remain depressed at follow-up than other respondents.

One possible explanation for this finding could be thelong-term, negative implications that belonging to a reli-

gious minority has on mental health (Berger, 1977). This

may be particularly important for the population surveyed

because anti-Semitism was much more visible and prev-

alent during our respondents’ formative years than it is

today. Another possible explanation could be that Jewish

elders may not benefit in the same way from religious

involvement as members of other religious affiliations.

Take Christian doctrine, for example, which emphasizes

the afterlife or Heaven at which point the body may be

restored and a reunion takes place with long deceased

loved ones (Gillman, 2007). This belief can be great source

of solace, hope, and comfort for those going through hard

times (e.g., depression), which may, in turn, support cop-

ing and recovery. This is in contrast to Jewish doctrine,

which in downplaying the hereafter in favor of the “here

and now,” may not provide the same level of solace, hope,

and comfort (Gillman, 2007).

One interesting yet surprising finding emerging from

this study is that having relatives living nearby was associ-

ated with depression at follow-up among both the baseline

depressed and nondepressed samples analyzed. It is plausi-

ble that there are certain unwanted expectations inherentwhen family members live closer—whether, for example,

caring for a frail and disabled parent or other relative in

need of long-term care or watching a young grandchild

in need of after school care, that results in burdens and

stresses that might not otherwise exist if family lived fur-

ther away. Simply measuring proximity, moreover, does

not account for the frequency or quality of the interactions

that take place. For example, some relationships, even with

relatives, may not be pursued no matter how convenient, if

those relationships are not fulfilling.



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There are several limitations worth noting. First, depres-

sion is measured using the CESD-8, a self-report tool,

rather than using the clinical diagnosis of depression by a

health or mental health professional. Although the latter

may be the gold standard, the CESD-8 is a commonly used

and accepted measure of depression in studies such as this

one where clinical diagnosis was not possible (e.g., Steffick,

2000). Second, data from the CESD-8 were not utilized todevelop a measure of depression based on a continuous

count of depressive symptoms but instead used to place

individuals into depressed or nondepressed categories based

on the presence of three or more of the eight symptoms

assessed. One implication is that potentially useful varia-

tion may have been missing. This “cutoff approach,” how-

ever, is typically employed in studies utilizing the CESD-8

(Steffick, 2000). A third limitation is related to the length of

the study. Given the episodic nature of depression, a 2-year

longitudinal study may miss signs of depression occur-

ring after the time period analyzed. Future research shouldextend the follow-up period studied over a longer period

of time as additional waves of the HRS become available.

Last, the Health and Retirement Study only includes meas-

ures of religiosity but not spirituality. Thus, this study is

focused exclusively on the former but not the latter. This

limitation is important to point out because extant research

suggests that spirituality may be associated with lower

rates of depression and mental illness as well (Skarupski,

Fitchett, Evans, & Mendes de Leon, 2010). Moreover, a

growing body of research suggests that spirituality to be a

unique construct, though related to religiosity (Underwood

& Teresi, 2002). Beyond this understanding experts hold

differing views regarding the distinction between religiosity

and spirituality, some maintaining that religiosity may be a

part of spirituality, whereas others viewing spirituality as a

part of religiosity (Hill et al., 2000; MacKinlay, 2006).

Conclusion

Several implications for policy and practice follow from

the results of this study. One is related to transportation

availability and the provision of better access to places of

worship so that older adults who are interested in religiousservices are able to attend and subsequently benefit from

organizational, or public, forms of religiosity. Moreover,

given the high prevalence of depression among older

adults, clinicians should be cognizant of the benefits asso-

ciated with both religious service attendance and involve-

ment in private prayer, assess individuals’ religious needs

and involvement, and determine whether their clients face

any barriers to attending services or pursuing their faith if

they so desire. Through these assessments, clinicians could

help connect interested clients to such services within their

communities or help them overcome any barriers they may

be experiencing, hindering involvement private prayer. Care

plans or therapy goals can be developed, which address

these issues as well.

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Ronneberg - The Protective Effects of Religiosity on Depression - A 2-Year Prospective Study

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