Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Drug Alcohol Depend. 2021 May 29;225:108796. doi: 10.1016/j.drugalcdep.2021.108796

Religiosity and substance use in U.S. adults: Findings from a large-scale national survey

Ofir Livne a,*, Tovia Wengrower b, Daniel Feingold c, Dvora Shmulewitz d,e, Deborah S Hasin a,d,e, Shaul Lev-Ran b,f
PMCID: PMC8918021  NIHMSID: NIHMS1786823  PMID: 34119881

Abstract

Background:

In recent decades, the US religious landscape has undergone considerable change such as a decline in religious service attendance. These changes may indicate that religious social support structures have deteriorated, possibly leading to a decrease in strengths of associations with substance use. Considering this, and given limitations of past studies (e.g., limited control for potential confounders), large-scale general population studies are needed to reexamine associations between religiosity domains and substance use.

Methods:

This cross-sectional study used data from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (N = 36,309). In unadjusted and adjusted models, controlling for religiosity domains and other covariates, we examined associations between three religiosity domains (importance of religiosity/spirituality, service attendance, and religious affiliation) and DSM-5 SUD. Focusing on service attendance, we also examined associations with other substance use-related outcomes.

Results:

Among religiosity domains, only frequency of service attendance was associated with SUD across most substances. Frequent service attendees had lower odds of alcohol use disorder (adjusted OR [aOR] = 0.4, 95 % CI 0.33,0.51), tobacco use disorder (aOR = 0.3, 95 % CI 0.22,0.33) and cannabis use disorder (aOR = 0.4, 95 % CI 0.24,0.68), compared to non-service attendees. For alcohol and tobacco, the protective effect of frequent service attendance was more robust for SUD than for respective substance use.

Conclusions:

Despite decreasing rates of religious belief and practice in the US, service attendance independently lowered the odds of substance use and SUD across multiple substances. Results may inform religious leaders and clinicians about the value of utilizing religious social support structures in the prevention and treatment of substance use and SUD.

Keywords: Religiosity, Spirituality, Substance use, Addiction, NESARC-III, Service attendance, Religious affiliation

1. Introduction

Substance use and substance use disorders (SUD) are associated with a wide range of physical and mental health problems (Davis et al., 2013; Meier et al., 2012; Moore et al., 2007; Solowij et al., 2002) and cause substantial disease burden worldwide (Degenhardt et al., 2018). Thus, identifying factors that may be protective against these is crucial. A growing body of research indicates that religiosity lowers the risk of substance use and addiction (Koenig et al., 2012; Rosmarin and Koenig, 2020). As summarized by Koenig (2012), religiosity is a multidimensional construct, organized into beliefs, lifestyles, rituals, and symbols that is interpreted through institutional and internalized dimensions (Koenig et al., 2012; Schiller and Levin, 1988). Researchers examining the relationship between religiosity and psychiatric disorders, including addiction, commonly use two broad domains to measure religiosity. One of these is the subjective importance of religiosity, i.e., religious involvement experienced individually and interpreted subjectively. The term “spirituality” is occasionally used interchangeably with this domain and refers to a connection to the supernatural or mystical that encompasses organized religion (Koenig, 2012; Koenig et al., 2001; Schiller and Levin, 1988). The second domain is religious service attendance, which accounts for the institutionalized dimension of religious involvement, characterized by participation in social religious activities (Levin et al., 1995). While a growing number of studies suggest strong relationships between different religiosity domains and addiction, given the multidimensionality of religiosity, and the challenges in disentangling the effects of numerous known and unknown confounders and effect modifiers on these relationships, underlying mechanisms of these associations remain unclear (George et al., 2002; Moreira-Almeida et al., 2006).

Although a large body of literature suggests that religiosity is inversely associated with substance use (Blay et al., 2008; Chi et al., 2009; Francis and Mullen, 1993; Ghandour et al., 2009; Harden, 2010; Kendler et al., 2003, 1997; Miller et al., 2000; Mullen et al., 1996), several factors illustrate the need for further well-designed studies. First, analyses should be based on more recent US data. Opinion polls from the Religious Landscape Study,(Pew Research Center; Cox, 2019) and Gallup survey (Newport, 2017a) indicate that although religion remains an integral part in the lives of most Americans, the U.S. religiosity landscape has undergone substantial changes in recent decades; these changes include:(1) a rapid rise in the share of Americans with no religious affiliation (including self-described atheists, agnostics, and those who identify religiously, as “nothing in particular” [“nones”]), alongside substantial decline in Christianity (Pew Research Center, 2019); (2) a decline in belief in god and in the perceived importance of religious beliefs in one’s life (Pew Research Center, 2015a), and; (3) a decrease in frequency of service attendance (Newport, 2017a; Pew Research Center, 2018). Notably, the decline in religious service attendance, may indicate that important social support structures that are often found in religious institutions and congregations and that underly the protective effects on substance use (Bonelli and Koenig, 2013a; Koenig et al., 2012, 2015) may have diminished; however, this has not been studied with recent data. Second, only few previous studies have incorporated the most recent DSM-5 SUD diagnostic criteria as a measure of substance use, and none have yet directly compared the protective effect of religiosity on substance use vs. DSM-5 SUD. Such a comparison may provide a better understanding of the protective role of religiosity at different stages of substance use. Third, as indicated in a 2013 review (Bonelli and Koenig, 2013a), in which nine high-quality studies were reviewed, while numerous studies account for potential confounders in their examination of associations between religiosity and substance use, a surprisingly low number of studies controlled for other religiosity domains (Bonelli and Koenig, 2013b; Koenig et al., 2012; Nonnemaker et al., 2003). Considering the multidimensional nature of religiosity, whereby specific domains may be confounders of certain associations and mediators of others, studies that include several religiosity domains in their analyses may help disentangle complexity of associations with substance use. Additionally, while a majority of past studies focused on specific substances in their analyses (mainly alcohol), only a smaller number of studies included a wide array of substances in their analyses. As recently discussed in the literature (Vanderweele, 2017), conceptually, outcome-wide epidemiologic studies that examine associations between exposure and numerous outcomes simultaneously, rather than just one at a time across studies, could be more informative on the public health level. Considering this, more studies that include several substances as outcomes in their analytic model are warranted. These limitations indicate a need for a more current, large scale general population-based study that examines the association between several domains of religiosity and DSM-5 SUD, while accounting for these drawbacks.

Therefore, utilizing data from a large, nationally representative epidemiological study conducted in the US in 2012–2013, our aims were to: 1) report strengths of associations between three religiosity domains (importance of religiosity/spirituality [R/S], frequency of service attendance, and religious affiliation) and DSM-5 SUD, across several substances, while adjusting for numerous potential confounders; 2) explore differences in strengths of associations between service attendance, a strong indicator of religious social support structures, and different levels of substance use.

2. Materials and methods

2.1. NESARC-III sample

The NESARC-III target population included civilians ≥18 years of age in households and selected group quarters (Grant et al., 2014, 2015a). Respondents were selected through multistage probability sampling, including primary sampling units (counties/groups of contiguous counties); secondary sampling units (SSU; groups of Census-defined blocks); and tertiary sampling units (households within SSUs) from which respondents were selected. Data were adjusted for nonresponse and weighted to represent the U.S. population based on the 2012 American Community Survey (U.S. Census Bureau, 2016). Face-to-face interviews in respondents’ homes were conducted with 36, 309 participants, after they gave informed consent. The household response rate was 72 %; person-level response rate, 84 %; and overall response rate, 60.1 %, comparable to other concurrent U.S. national surveys (Blackwell et al., 2014; Rockville, Md: Office of Applied Studies; 2002). NESARC-III methodology is described further elsewhere (Grant et al., 2015a). Institutional review boards at the National Institutes of Health and Westat approved the study protocol.

2.2. Measures

NESARC-III utilized the Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5), which is a structured, computer-assisted diagnostic interview designed for lay interviewers (Grant et al., 2015b).

2.2.1. Substance use variables (outcomes)

The following substances were included in our analyses: alcohol; tobacco (including use of cigarettes, pipes, cigars, and e-cigarettes); cannabis; and other drugs (including cocaine, hallucinogens, opioids, sedatives, inhalants/solvents, heroin, club drugs, stimulants, and ‘other drugs’). Twelve-month substance use disorder (SUD) diagnoses were classified using the DSM-5 SUD diagnostic criteria. Reliability and procedural validity of the SUD diagnoses and associated criteria scales is good to excellent (Grant et al., 2015b). For our primary analysis, dichotomous variables were constructed for each substance, indicating 12-month SUD (yes/no). For our secondary analyses, three level variables were constructed for each substance, indicating whether respondents: (1) did not use the substance within the past 12 months; (2) used the substance within the past 12 months, but did not meet the DSM-5 diagnostic criteria for SUD (labeled “use/no disorder”); and (3) met the diagnostic criteria for SUD within the past 12 months. To be consistent with prior studies, alcohol use was defined as 3 or more drinks per week (Chan et al., 2009; Kunzmann et al., 2018). Use of all other substances was defined as any amount of substance used.

2.2.2. Religiosity domains (predictors)

2.2.2.1. Importance of religiosity.

Subjective importance of religiosity/spirituality was assessed using the following question: “In general, how important are religious or spiritual beliefs in your daily life?” Responses included: 1) “not at all important”; 2) “not very important”; 3) “somewhat important”; 4) “very important”. Consistent with previous studies (Ransome et al., 2019; Ransome and Gilman, 2016), responses 1 and 2 were recoded into one category and all levels were reverse coded for consistency with public religiosity variables.

2.2.2.2. Religious service attendance.

Religious service attendance was ascertained using two questions. Respondents acknowledged whether they attended religious services. Those who responded positively were asked: “How often do you attend these services?”. Responses ranged from 1 (once a year) to 5 (twice a week or more). A 5-level attendance variable was created: 0 - none; 1 - once to few times a year (less than 12); 2 1–3 times a month; 3 - once a week; 4 - twice a week or more.

2.2.2.3. Religious affiliation.

Religious affiliation was determined by asking respondents the following question: “Which category best describes your religion?”. Respondents were presented with 56 different religions and denominations. These were combined into six major categories: none (including agnostic, atheist, and respondents answering “no affiliation”); Jewish; Muslim; Catholic; Protestant or other non-Catholic Christian (e.g, Mormon, Jehovah Witness, etc.); Other non-Christian religion (Buddhist, Sikh, etc.).

2.2.3. Covariates

2.2.3.1. Sociodemographic covariates.

The following sociodemographic characteristics were included: sex, race/ethnicity, age, marital status, education, 12-month personal income, urbanicity, and region.

2.2.3.2. Clinical covariates.

Psychiatric disorders included: any 12-month mood disorder (major depressive disorder, dysthymia, bipolar 1 disorder, and bipolar 2 disorder); any 12-month anxiety disorder (general anxiety disorder, social phobia, agoraphobia, specific phobias, and panic disorder); any personality disorder (borderline, schizotypal, and antisocial). Test-retest reliability was fair to moderate for depressive (κ = 0.39–0.40) and anxiety disorders (κ = 0.43–0.51) with generally good to excellent reliability for corresponding dimensional measures (Grant et al., 2015b). Test-retest reliability of the personality disorders was good (κ = 0.67–0.71), and higher for corresponding dimensional measures (Grant et al., 2003; Ruan et al., 2008).

2.2.3.3. Perceived social support.

The Interpersonal Support Evaluation List-12 (ISEL-12) consists of 12 statements concerning the perceived availability of potential social resources, used to measure perceived social support (Cohen et al., 1985). Items are arranged on a 4-point Likert scale coded 1 = definitely false, 2 = probably false, 3 = probably true, and 4 = definitely true with a total score range of 0–36. The ISEL-12 is a broadly employed measure of functional social support and has been widely used in health-related research (Aftyka et al., 2019; Crane and Constantino, 2003; Ghesquiere et al., 2017; Merz et al., 2014)

2.2.3.4. Self-reported physical health.

The 12-item Short-Form Health Survey (SF-12v2) assessed current physical health, using the respective summary scores. These are reliable and valid measures of current general health impairment used in population surveys (Gandek et al., 1998). SF-12v2 norm-based physical health score has a mean of 50, standard deviation of 10, and range of 0–100. Lower scores indicate greater disability.

2.3. Statistical analysis

Weighted prevalences were calculated for religiosity domains, 12-month substance use and SUD, and sociodemographic characteristics. In primary analyses, odds ratios (OR) and 95 % confidence intervals derived from logistic regression models indicated associations between religiosity domains and 12-month SUD in two models: uncontrolled (“unadjusted model”) and controlled for sociodemographic characteristics, other religiosity domains, 12-month psychiatric disorders, 12-month SUD (except for the substance modeled as outcome), self-reported physical health, and non-religious perceived social support (“adjusted model”). Similarly, in secondary analyses, OR and 95 % confidence interval indicated the association between service attendance and a three-level substance use outcome variable. Prior to these analyses, Spearman rank-order coefficients (Proc Corr; SAS 9.4) were assessed for all religiosity variables to ensure that religiosity covariates are not highly correlated, which could induce multicollinearity. Religiosity dimensions were correlated (p-values ≤.0001) at low-moderate degrees, with little concern for multicollinearity. Importance of R/S was positively correlated with service attendance (rs = 0.5) and religious affiliation (rs = 0.3). Service attendance was positively correlated with religious affiliation (rs = 0.2). Collinearity diagnostics (Alin, 2010) were performed using Proc Reg (SAS 9.4) and results showed little concern for multicollinearity for all outcome variables (Importance of R/S [tolerance = 0.7, VIF = 1.5]; service attendance [tolerance = 0.3, VIF = 3.7]; religious affiliation [tolerance = 0.9, VIF = 1.2]). All study analyses were conducted using SAS 9.4 (Copyright ©, 2013, SAS Institute Inc., Cary, NC, n.d.) and SUDAAN 11.0 (Research Triangle Institute. Release 11. Vol 1 and 2. Research Triangle Institute, 2021), to account for NESARC-III the complex sample design.

3. Results

3.1. Prevalence of substance use and sociodemographic characteristics

Prevalence rates of 12-month AUD, TUD, CUD and DUD were 14 %, 20 %, 2.5 %, and 1.7 %, respectively. The prevalence of 12-month substance use, without a respective DSM-5 SUD diagnosis for alcohol, tobacco, cannabis, and other drugs was 68 %, 25 %, 7%, and 6%, respectively. Table 1 shows 12-month prevalence of SUD by sociodemographic characteristics.

Table 1.

Past-year prevalence of substance use variables by sociodemographic characteristics, NESARC-III (N = 36,309).

AUD % (SE) TUD % (SE) CUD % (SE) DUD % (SE)
Sociodemographic characteristic
Gender
Female 10.4 (0.34) 17.0 (0.43) 1.7 (0.13) 1.6 (0.11)
Male 17.6 (0.42) 23.3 (0.54) 3.5 (0.19) 1.9 (0.11)
Race/ethnicity
White 14.00 (0.42) 22.32 (0.53) 2.23 (0.14) 1.9 (0.10)
Black 14.37 (0.72) 20.07 (0.64) 4.48 (0.39) 1.9 (0.21)
American
Indian / Alaska Native Asian / Native 19.20 (2.23) 29.59 (2.95) 5.31 (1.45) 2.0 (0.65)
Hawaiian / Pacific Islander 10.59 (0.87) 11.15 (0.95) 1.26 (0.28) 0.3 (0.13)
Hispanic 13.57 (0.44) 12.16 (0.54) 2.57 (0.21) 1.5 (0.18)
Age (years)
18–29 26.67 (0.74) 23.76 (0.69) 6.85 (0.40) 2.54 (0.19)
30–44 16.19 (0.55) 23.40 (0.68) 2.53 (0.21) 2.00 (0.18)
45–64 10.02 (0.32) 21.05 (0.63) 1.05 (0.10) 1.61 (0.13)
65 and older 2.35 (0.22) 8.45 (0.41) 0.22 (0.08) 0.55 (0.10)
Educational level
Some high school or less 11.45 (0.62) 27.95 (1.01) 3.15 (0.30) 2.47 (0.26)
High school graduate (or GED) 14.56 (0.47) 26.13 (0.66) 3.03 (0.20) 2.13 (0.16)
Some college or higher 14.09 (0.38) 15.77 (0.38) 2.21 (0.13) 1.40 (0.10)
Twelve-month personal income
$0–$19,999 16.21 (0.60) 28.51 (0.73) 4.88 (0.30) 3.34 (0.23)
$20,000–$34,999 13.99 (0.58) 23.30 (0.67) 2.53 (0.23) 1.87 (0.19)
$35,000–$69,999 13.20 (0.39) 19.69 (0.50) 2.09 (0.16) 1.40 (0.17)
$70,000 or greater 12.66 (0.43) 12.11 (0.56) 1.22 (0.14) 0.74 (0.12)
Marital status
Married / living with someone as if married 10.37 (0.30) 16.73 (0.52) 1.29 (0.10) 1.11 (0.09)
Widowed /divorced / Separated 11.38 (0.44) 25.18 (0.62) 1.86 (0.21) 2.23 (0.19)
Never married 25.02 (0.74) 24.00 (0.58) 6.35 (0.36) 2.86 (0.20)
Urbanicity
Urban 14.86 (0.34) 18.64 (0.42) 2.73 (0.12) 1.81 (0.09)
Rural 10.20 (0.55) 25.17 (1.05) 1.84 (0.21) 1.42 (0.17)
Region
Northeast 13.52 (0.55) 18.54 (0.71) 2.68 (0.26) 1.66 (0.17)
Midwest 14.99 (0.84) 22.70 (0.74) 2.31 (0.23) 1.73 (0.17)
South 12.27 (0.56) 21.54 (0.83) 2.27 (0.20) 1.63 (0.12)
West 15.65 (0.53) 16.31 (0.77) 3.08 (0.22) 1.92 (0.19)
Open in a new tab

Abbreviations: AUD, alcohol use disorder; TUD, tobacco use disorder; CUD, cannabis use disorder; DUD, drug use disorder.

3.2. Prevalence of religiosity domains

Over half (54 %; SE = 0.65) of the respondents reported religiosity or spirituality as very important to them. Of these, 58 % were female, 60 % white, 38 % aged 45–64, 59 % college educated, 60 % married, 46 % with a low 12-month personal income, and 65 % protestant/other Christian. Nearly half (49 %; SE = 0.55) of the respondents reported attending religious services at any frequency. Sociodemographic characteristics of these participants were generally similar to the characteristics of respondents reporting high levels of importance of religiosity/spirituality – 56 % were female, 62 % white, 37 % aged 45–64, 64 % college educated, 62 % married, 45 % low income, and 64 % Protestant/other non-Catholic Christian. Ninety percent (SE = 0.31) of the respondents identified with a religious affiliation. Of these, 56 % were Protestant/other non-Catholic Christian, 25 % were Catholic, 7.3 % were other non-Christian, 1.4 % were Jewish and 1% were Muslim.

3.3. Associations with substance use

3.3.1. Primary analysis

3.3.1.1. Importance of Religiosity/Spirituality.

With few exceptions, importance of religiosity/spirituality was inversely associated with 12-month SUD in an unadjusted model (Table 2). For all substances examined, the higher the degree of religiosity/spirituality, the lower the odds of 12-month SUD. In adjusted models, only associations between high degrees of importance of R/S and AUD remained significant (adjusted OR [aOR] = 0.79, 95 % CI 0.70,0.90; Table 2).

Table 2:

Associations between religiosity domains and 12-month substance use disorders among the NESARC-III sample (N = 36,309).

Model 1a Model 2b
AUD Odds Ratio (95 % CI) TUD Odds Ratio (95 % CI) CUD Odds Ratio (95 % CI) DUD Odds Ratio (95 % CI) AUD Odds Ratio (95 % CI) TUD Odds Ratio (95 % CI) CUD Odds Ratio (95 % CI) DUD Odds Ratio (95 % CI)
Religiosity Domains Importance of Religion/Spirituality
Not important Ref Ref Ref Ref Ref Ref Ref Ref
Somewhat important 0.8 (0.73,0.89) 0.9 (0.86,1.04) 0.6 (0.50,0.76) 0.8 (0.59,1.02) 0.9 (0.86,1.09) 1.1 (0.93,1.19) 0.9 (0.69,1.19) 0.9 (0.67,1.39)
Very important 0.4 (0.36,0.44) 0.6 (0.55,0.66) 0.3 (0.30,0.44) 0.6 (0.48,0.78) 0.8 (0.70,0.90) 1.2 (1.01,1.30) 1.1 (0.79,1.40) 1.0 (0.70,1.54)
Service Attendance
No attendance Ref Ref Ref Ref Ref Ref Ref Ref
Once per year / a few times per year 0.7 (0.57,0.76) 0.7 (0.59,0.75) 0.6 (0.49,0.84) 0.5 (0.35,0.74) 0.9 (0.74,1.01) 0.8 (0.70,0.89) 1.0 (0.75,1.35) 0.7 (0.47,1.066)
1–3 times per month 0.7 (0.59,0.73) 0.5 (0.45,0.56) 0.5 (0.35,0.61) 0.6 (0.42,0.77) 0.9 (0.83,1.05) 0.6 (0.51,0.66) 0.8 (0.57,1.02) 0.9 (0.65,1.28)
Once per week 0.4 (0.32,0.41) 0.3 (0.25,0.30) 0.2 (0.13,0.25) 0.4 (0.30,0.55) 0.6 (0.55,0.74) 0.4 (0.34,0.43) 0.4 (0.27,0.66) 0.9 (0.62,1.24)
Twice per week or more 0.2 (0.17,0.24) 0.2 (0.17,0.23) 0.1 (0.09,0.24) 0.3 (0.22,0.47) 0.4 (0.33,0.51) 0.3 (0.22,0.33) 0.4 (0.24,0.68) 0.8 (0.54,1.26)
Religious Affiliation
None Ref Ref Ref Ref Ref Ref Ref Ref
Jewish 0.4 (0.29,0.61) 0.5 (0.33,0.66) 0.3 (0.12,0.85) 1.0 (0.47,2.26) 0.8 (0.51,1.20) 0.9 (0.65,1.40) 0.9 (0.35,2.37) 2.4 (1.03,5.66)
Catholic 0.5 (0.45,0.60) 0.6 (0.52,0.67) 0.3 (0.19,0.34) 0.5 (0.36,0.70) 1.1 (0.91,1.33) 1.1 (0.93,1.24) 0.6 (0.44,0.83) 0.9 (0.62,1.44)
Protestant /Other Christian 0.5 (0.43,0.53) 0.9 (0.78,0.98) 0.4 (0.32,0.48) 0.6 (0.45,0.79) 0.9 (0.81,1.11) 1.2 (1.09,1.44) 0.7 (0.55,0.98) 0.9 (0.60,1.24)
Muslim 0.2 (0.12,0.42) 0.6 (0.43,0.85) 0.3 (0.08,1.06) 0.4 (0.19,0.99) 0.4 (0.22,0.71) 1.2 (0.83,1.68) 0.6 (0.17, 2.42) 0.9 (0.37,2.21)
Other non-Christian religion 0.5 (0.40,0.58) 0.7 (0.60,0.85) 0.6 (0.45,0.85) 0.8 (0.53,1.15) 0.8 (0.63,0.93) 1.0 (0.87,1.21) 1.0 (0.73,1.46) 1.1 (0.71,1.69)
Open in a new tab

Note: Significant ORs appear in boldface.

Abbreviations: AUD, alcohol use disorder; TUD, tobacco use disorder; CUD, cannabis use disorder; DUD, drug use disorder.

a

Unadjusted.

b

Adjusted for sociodemographic characteristics (sex, race/ethnicity, age, educational level, 12-month personal income, marital status, urbanicity, region); other religiosity variables (among importance of R/S, service attendance, religious affiliation); mood disorders (major depressive disorder, dysthymia, bipolar 1 disorder, and bipolar 2 disorder); 12-month anxiety disorders (general anxiety disorder, social phobia, agoraphobia, specific phobias, and panic disorder); personality disorders (borderline, schizotypal, and antisocial); any 12-month SUD (excluding substance used as outcome); self-reported physical health; non-religious social support.

3.3.1.2. Service attendance.

Frequency of service attendance was inversely associated with SUD for all substances in an unadjusted model (Table 2). Compared to non-attendance, the more frequently participants attended religious services, the lower the odds of 12-month SUD. In an adjusted model, associations remained significant only between more frequent service attendance levels (once a week and twice a week or more) and all outcomes, except for DUD. Respondents attending religious service twice or more per week had substantially lower odds of AUD, TUD, and DUD compared to respondents not attending religious services (aOR = 0.41, 95 % CI 0.33,0.52; aOR = 0.27, 95 % CI 0.22,0.33; aOR = 0.40, 95 % CI 0.24,0.68, respectively; Table 2).

3.3.1.3. Religious affiliation.

In an unadjusted model, respondents affiliated with a religion had lower odds of 12-month SUD for all outcomes, compared to those with no religious affiliation, with few exceptions (Table 2). In an adjusted model, only few associations remained significant; for instance, Muslim and other non-Christian affiliations were associated with lower odds of 12-month AUD compared to those with no religious affiliation (aOR = 0.39, 95 % CI 0.22,0.71; aOR = 0.76, 95 % CI 0.63,0.93, respectively; Table 2).

3.3.2. Secondary analyses

Frequency of service attendance was inversely associated with substance use (without a respective SUD) and SUD for all substances in an unadjusted model, with few exceptions (Table 3). Compared to non-attendance, the more frequently participants attended religious services, the lower the odds of both 12-month “use/no disorder” and SUD, for all substances. In an adjusted model, some results differed; specifically, frequency of service attendance was no longer inversely associated with 12-month other drug use and DUD. Only more frequent service attendance levels (once a week; twice a week or more) remained associated with substance use outcome levels, with few exceptions and strengths of all associations were attenuated (Table 3). For alcohol and tobacco, odds of SUD were lower than odds of substance use/no disorder (aOR = 0.42, 95 % CI 0.22,0.46 vs. aOR = 0.38, 95 % CI 0.31,0.48; aOR = 0.33, 95 % CI 0.25,0.44 vs. aOR = 0.24, 95 % CI 0.20,0.29, respectively; Table 3).

Table 3.

Associations between frequency of service attendance and substance use outcomes among the NESARC-III sample (N = 36,309).

Model 1a Model 2b
Alcohol Tobacco Cannabis Other Drugs Alcohol Tobacco Cannabis Other Drugs
Use, no AUD AUD Use, no TUD TUD Use, no CUD CUD Use, no DUD DUD Use, no AUD AUD Use, no TUD TUD Use, no CUD CUD Use, no DUD DUD
Frequency of service attendance
No attendance Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref
Once per year / a few times per year 0.86 (0.72, 1.02) 0.65 (0.56, 0.75) 0.88 (0.73, 1.07) 0.66 (0.58, 0.75) 0.53 (0.44, 0.64) 0.64 (0.49, 0.83) 0.65 (0.52, 0.82) 0.70 (0.35, 0.73) 0.91 (0.75, 1.09) 0.78 (0.67, 0.90) 0.92 (0.75, 1.14) 0.75 (0.66, 0.85) 0.85 (0.68, 1.06) 0.97 (0.72, 1.31) 0.94 (0.75 1.19) 1.03 (0.85, 1.24)
1–3 times per month 0.73 (0.63, 0.84) 0.64 (0.57, 0.72) 0.69 (0.60, 0.81) 0.50 (0.45, 0.56) 0.57 (0.48, 0.67) 0.46 (0.35, 0.60) 0.68 (0.56, 0.82) 0.57 (0.42, 0.77) 0.77 (0.67, 0.90) 0.91 (0.80, 1.02) 0.71 (0.59, 0.86) 0.55 (0.49, 0.62) 0.98 (0.82, 1.17) 0.76 (0.57, 1.03) 1.026 (0.85, 1.24) 0.91 (0.65, 1.27)
Once per week 0.69 (0.61, 0.79) 0.34 (0.30, 0.39) 0.41 (0.34, 0.49) 0.27 (0.25, 0.30) 0.23 (0.19, 0.28) 0.18 (0.12, 0.26) 0.41 (0.34, 0.51) 0.40 (0.29, 0.54) 0.70 (0.61, 0.81) 0.62 (0.53, 0.72) 0.46 (0.37, 0.58) 0.35 (0.31, 0.39) 0.56 (0.44, 0.67) 0.39 (0.25, 0.60) 0.78 (0.63, 0.95) 0.83 (0.59, 1.18)
Twice per week or more 0.37 (0.30, 0.47) 0.18 (0.15, 0.22) 0.32 (0.23, 0.41) 0.20 (0.16, 0.23) 0.13 (0.09, 0.16) 0.15 (0.09, 0.24) 0.41 (0.33, 0.52) 0.32 (0.22, 0.46) 0.42 (0.33, 0.54) 0.38 (0.31, 0.48) 0.33 (0.25, 0.44) 0.24 (0.20, 0.29) 0.32 (0.24, 0.43) 0.35 (0.21, 0.59) 0.83 (0.66, 1.05) 0.79 (0.52, 1.21)
Open in a new tab

Note: Significant ORs appear in boldface.

Abbreviations: AUD, alcohol use disorder; TUD, tobacco use disorder; CUD, cannabis use disorder; DUD, drug use disorder.

a

Unadjusted.

b

Adjusted for sociodemographic characteristics (sex, race/ethnicity, age, educational level, 12-month personal income, marital status, urbanicity, region); other religiosity variables (among importance of R/S, service attendance, religious affiliation); mood disorders (major depressive disorder, dysthymia, bipolar 1 disorder, and bipolar 2 disorder); 12-month anxiety disorders (general anxiety disorder, social phobia, agoraphobia, specific phobias, and panic disorder); personality disorders (borderline, schizotypal, and antisocial); any 12-month SUD (excluding substance used as outcome); self-reported physical health; non-religious social support.

4. Discussion

In this study, we used a large, nationally representative sample of the U.S. population to evaluate associations between several religiosity domains and SUD. Further, we focused on religious service attendance as it has been shown to carry a strong protective effect against substance use, compared to other religiosity domains. Specifically, we explored the protective effect of service attendance on different level of substance use, across several substances. While our crude models indicated that, with few exceptions, all religiosity domains were inversely associated with 12-month DSM-5 SUD for all substances, in adjusted models in which we controlled for potential confounders, only frequent levels of service attendance remained significantly associated with SUD. Findings also indicated that service attendance is inversely associated with different levels of substance use.

One strength of the current study is our inclusion of a wide array of potential confounders, including other religiosity domains seldomly accounted for in previous studies. Another strength is our use of an outcome-wide analytic approach, whereby we included several substances in a single study, utilizing DSM-5 SUD diagnoses. Altogether, these provide a more current and comprehensive portrayal of the relationships between religiosity and addiction in the U.S. Among all religiosity domains, frequent religious service attendance carried a strong and independent protective effect against SUD among US adults. These findings indicate that although religious beliefs and practices have declined in the US over the past decade, frequent attendance of religious services remains a strong protective factor from alcohol, tobacco, and cannabis use disorders, but not from other drug use disorders. The cross-sectional design of the current study could not account for changes in these associations over time. Future studies that report trends in these associations may shed further light on a possible decrease in the protective effect of social support structures, a primary underlying mechanism of the protective effect of service attendance on substance use. This is especially true in light of a growing number of studies indicating that attrition from religious membership is driven by US congregants’ increasing negative perceptions of congregational leaders (Waggoner, 2006). Additionally, recent Pew Research Center opinion polls point to considerable rates of respondents that report their dislike for sermons and feelings of being unwelcomed in religious services (Pew Research Center, 2018). Our findings that both the importance of R/S and religious affiliation are largely independently associated with SUD contrast with findings from previous studies, as reviewed in Koenig’s Handbook of Religion and Health (Koenig et al., 2012). This contrast may stem from inconsistent outcome measurements in the different studies performed over the years, as well as from lack of control for certain confounders in those studies. Nevertheless, our findings should be taken into account in the context of an increase in the portion of religious “nones” in the US and the decrease in perceived importance of faith and religion among Americans in recent decades. Future longitudinal studies that account for changes in the strength of relationships between religiosity and substance use can inform clinicians about the validity of current faith-based therapeutic interventions.

Findings from our secondary analysis revealed that the protective effects of frequent service attendance on AUD and TUD were more robust than the protective effects on respective substance use (without SUD). These results are generally aligned with findings from previous studies indicating that frequent service attendance plays a role in lowering the probability of transition from substance use to dependence (Walton-Moss et al., 2013). our findings suggest that the emotional support provided within organized religious gatherings (e.g., participation in prayer groups, scripture-study groups, or other religion-related recreational activities), all of which create a sense of bonding, cohesiveness, and solidarity (Krause et al., 2001), is not only effective in preventing continued substance use, but also, and to a greater degree, in the transition from existing substance use to SUD; this is especially true in the case of alcohol and tobacco use. Future studies that explore the relationship between religiosity domains and addiction and that differentiate between substance use and SUD may increase researchers’ ability to disentangle the mechanisms underlying the protective effects of service attendance on the cycle of addiction; this in turn can provide more insight into the nuances of potential preventive and therapeutic attributes of religiosity. Religious leaders and congregation representatives should be aware that substance users are less inclined to attend religious gatherings; targeting them via outreach programs and other religious service-related programs, may enhance their likelihood to attend service and hence, greatly reduce their risk of developing SUD. Further, clinicians should become more familiar with Spirituality/Religiosity enhanced Screening, Brief Intervention, and Referral to Treatment (SBIRT) models– evidence-based diagnostic and therapeutic models that systematically identify and target interventions to reduce or prevent problematic alcohol or illicit drug use. The revised R/S version of SBIRT can identify both R/S sources of potential strength or support (e.g., active practice of religious faith, sense of belonging and meaning in life) and S/R beliefs that may be neutral or negative (Babor et al., 2017; Rosmarin and Koenig, 2020).

Notably, in most of the adjusted models, religiosity domains were not significantly associated with other drug use and DUD. As these variables included a wide array of drugs, including more addictive substances such as opioids and cocaine, it is possible that users of these substances may benefit from the protective effects of religiosity domains less than other substance users. In light of the ongoing opioid epidemic, studies that examine more specific associations between religiosity domains and different levels of opioid use may be of considerable value.

Social interaction with members of one’s religious group outside of religious services is a component of the public religiosity domain that has been studied only seldomly in addiction research. In NESARC-III, 75 % of religious service attendants had some level of social interaction with members of their religious community outside of the setting of religious services. Considering the relatively understudied relationships between religious social group size and service attendance, and the unknown validity and reliability of this NESARC-III measure, we did not include it in the current study. Further studies that provide information on social support structures outside of religious services may improve our understanding of the protective role of religious social structures. Findings concerning the potential mediator effect of non-religious social support on associations between religiosity and mental health outcomes are mixed. While some studies indicate that non-religious social networks mediate associations between religiosity and mental health outcomes, others do not (Corrêa et al., 2011; Edlund et al., 2010). In alignment with previous studies (Benda, 2002), in the current study non-religious social support, measured using ISEL-12, was considered to be a potential confounder of associations between religiosity domains and substance use outcomes. Further reports that utilize valid and reliable measures to elucidate the role of non-religious support in the relationship between religiosity and substance use, are warranted.

Study limitations are noted. The NESARC-III is a cross-sectional study, which limits investigators’ ability to establish causality. There is a possibility, for instance, that substance users are more likely to leave religious congregations because they feel rejected. There is some possibility of recall bias, because NESARC-III was based on self-reports, although our use of 12-month substance use outcomes may somewhat mitigate this concern. Further, substance use may be underreported in NESARC-III, due to response bias, which may stem from social desirability (Paulhus and Reid, 1991). Additionally, among other religiosity domains that have been studied over the years in the context of substance use, are negative and positive religious coping (Gallucci et al., 2018; Medlock et al., 2017; Parenteau, 2017; Pargament et al., 2005) and private religious practices. These domains were not accounted for in adjusted models, as they were not queried in NESARC-III. Additional studies that can incorporate other religiosity domains in their analyses should be carried out; such studies can increase our understanding of the intricacies of the protective effects of each religiosity domain on substance use. Spirituality and religiosity could not be investigated separately in this study; considering that the more modern definition of spirituality expands beyond religion (Koenig et al., 2012) and that individuals identifying themselves as “spiritual but not religious” make up at least a quarter of the U.S. population (Lipka and Gecewicz, 2017), further studies that use these domains separately are warranted. Finally, NESARC-III data were collected almost a decade ago; studies that rely on more recent large-scale nationally representative data are necessary to provide a more current portrayal of the associations examined in this study. Nevertheless, considering that opinion polls indicate that the changes in the US religious landscape have been ongoing for over two decades (Pew Research Center, 2015b), our use of such a large-scale epidemiologic survey that utilized reliable and valid measures of substance use, for which data were collected in the midst of the above-mentioned religious shifts, findings from the current study are of considerable value.

4.1. Conclusions

In summary, drawing on a representative sample of the US population, this study presents an overview of the relationship between several religiosity domains and SUD, and a comprehensive examination of the relationship between service attendance, a strong substance use protective factor, and substance use outcomes. In adjusted models, controlling for a wide array of potential confounders, frequency of service attendance remained a strong and independent protective factor for both substance use and SUD. Attaining a broader understanding of the social support mechanisms underlying this specific religiosity domain may have considerable individual and public health implications.

Acknowledgements

NIDAT32DA031099(PI: Hasin).

Role of funding source

The NESARC–III study was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) with supplemental support by the National Institute on Drug Abuse, and by the Intramural Research Program of the NIAAA.

Footnotes

Declaration of Competing Interest

The authors report no declarations of interest.

References

  1. Aftyka A, Rozalska I, Pawlak A, Mazur A, Bednarek A, Zarzycka D, 2019. Internal consistency and accuracy of Interpersonal Support Evaluation List (ISEL-40) in mothers of healthy children and those with a medical history. Ann. Agric. Environ. Med 26, 85–91. 10.26444/aaem/91783. [DOI] [PubMed] [Google Scholar]
  2. Alin A, 2010. Multicollinearity. Wiley interdiscip. Rev. Comput. Stat 2, 370–374. 10.1002/wics.84. [DOI] [Google Scholar]
  3. Babor TF, Del Boca F, Bray JW, 2017. Screening, Brief Intervention and Referral to Treatment: implications of SAMHSA’s SBIRT initiative for substance abuse policy and practice. Addiction 112, 110–117. 10.1111/add.13675. [DOI] [PubMed] [Google Scholar]
  4. Benda BB, 2002. A survival analysis of dimensions of religion among homeless substance abusers: going into the remotest regions. Marriage Fam. A Christ. J 5, 99–114. [Google Scholar]
  5. Blackwell DL, Lucas JW, Clarke TC, 2014. Summary health statistics for U.S. adults: national health interview survey, 2012. Vital Health Stat 10 (10), 1–171. [PubMed] [Google Scholar]
  6. Blay SL, Batista AD, Andreoli SB, Gastal FL, 2008. The relationship between religiosity and tobacco, alcohol use, and depression in an elderly community population. Am. J. Geriatr. Psychiatry 16, 934–943. 10.1097/JGP.0b013e3181871392. [DOI] [PubMed] [Google Scholar]
  7. Bonelli RM, Koenig HG, 2013a. Mental disorders, religion and spirituality 1990 to 2010: a systematic evidence-based review. J. Relig. Health 52, 657–673. 10.1007/s10943-013-9691-4. [DOI] [PubMed] [Google Scholar]
  8. Chan AM, von Mühlen D, Kritz-Silverstein D, Barrett-Connor E, 2009. Regular alcohol consumption is associated with increasing quality of life and mood in older men and women: the Rancho Bernardo Study. Maturitas 62, 294–300. 10.1016/j.maturitas.2009.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chi FW, Kaskutas LA, Sterling S, Campbell CI, Weisner C, 2009. Twelve-Step affiliation and 3-year substance use outcomes among adolescents: social support and religious service attendance as potential mediators. Addiction 10.1111/j.1360-0443.2009.02524.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cohen S, Mermelstein R, Kamarck T, Hoberman HM, 1985. Measuring the functional components of social support. Social Support: Theory, Research and Applications Springer; Netherlands, Dordrecht, pp. 73–94. 10.1007/978-94-009-5115-0_5. [DOI] [Google Scholar]
  11. Copyright ©, 2013. Base SAS ® 9.4 Procedures Guide Statistical Procedures n.d., second edition. SAS Institute Inc., Cary, NC, U. [Google Scholar]
  12. Corrêa AAM, Moreira-Almeida A, Menezes PR, Vallada H, Scazufca M, 2011. Investigating the role played by social support in the association between religiosity and mental health in low income older adults: results from the São Paulo Ageing & Health Study (SPAH). Braz J Psychiatry 33, 157–164. 10.1590/S1516-44462011000200011. [DOI] [PubMed] [Google Scholar]
  13. Cox DA, 2019. The decline of religion in American family life. Am. Enterp. Inst Accessed 5.12.20) https://www.aei.org/research-products/report/the-decline-of-religion-in-american-family-life/. [Google Scholar]
  14. Crane PA, Constantino RE, 2003. Use of the Interpersonal Support Evaluation List (ISEL) to guide intervention development with women experiencing abuse. Issues Ment. Health Nurs 24, 523–541. 10.1080/01612840305286. [DOI] [PubMed] [Google Scholar]
  15. Davis GP, Compton MT, Wang S, Levin FR, Blanco C, 2013. Association between cannabis use, psychosis, and schizotypal personality disorder: findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Schizophr. Res 151, 197–202. 10.1016/j.schres.2013.10.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Degenhardt L, et al. , 2018. C. The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry 5, 987–1012. 10.1016/S2215-0366(18)30337-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Edlund MJ, Harris KM, Koenig HG, Han X, Sullivan G, Mattox R, Tang L, 2010. Religiosity and decreased risk of substance use disorders: is the effect mediated by social support or mental health status? Soc. Psychiatry Psychiatr. Epidemiol 45, 827–836. 10.1007/s00127-009-0124-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Francis LJ, Mullen K, 1993. Religiosity and attitudes towards drug use among 13–15 year olds in England. Addiction 88, 665–672. 10.1111/j.1360-0443.1993.tb02079.x. [DOI] [PubMed] [Google Scholar]
  19. Gallucci AR, Hackman C, Wilkerson A, 2018. Examining the relationship between religious coping and the misuse of prescription stimulants among a sample of undergraduate students. Subst. Use Misuse 10.1080/10826084.2017.1416405. [DOI] [PubMed] [Google Scholar]
  20. Gandek B, Ware JE, Aaronson NK, Alonso J, Apolone G, Bjorner J, Brazier J, Bullinger M, Fukuhara S, Kaasa S, Leplège A, Sullivan M, 1998. Tests of data quality, scaling assumptions, and reliability of the SF-36 in eleven countries: results from the IQOLA Project. International Quality of Life Assessment. J. Clin. Epidemiol 51, 1149–1158. [DOI] [PubMed] [Google Scholar]
  21. George LK, Ellison CG, Larson DB, 2002. Explaining the relationships between religious involvement and health. Psychol. Inq 10.1207/S15327965PLI1303_04. [DOI] [Google Scholar]
  22. Ghandour LA, Karam EG, Maalouf WE, 2009. Lifetime alcohol use, abuse and dependence among university students in Lebanon: exploring the role of religiosity in different religious faiths. Addiction 104, 940–948. 10.1111/j.1360-0443.2009.02575. [DOI] [PubMed] [Google Scholar]
  23. Ghesquiere A, Schwartz, Theresa, Wang Y, Mauro C, Skritskaya N, Shear MK, 2017. Performance and psychometric properties of the Interpersonal Support Evaluation List (ISEL) in older adults with Complicated Grief. J. Affect. Disord 218, 388–393. 10.1016/j.jad.2017.05.004. [DOI] [PubMed] [Google Scholar]
  24. Grant BF, Dawson DA, Stinson FS, Chou PS, Kay W, Pickering R, 2003. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample. Drug Alcohol Depend 71, 7–16. [DOI] [PubMed] [Google Scholar]
  25. Grant BF, Amsbary M, Chu A, et al. , 2014. Source and Accuracy Statement: National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) National Institute on Alcohol Abuse and Alcoholism, Rockville, MD. [Google Scholar]
  26. Grant BF, Goldstein RB, Saha TD, Chou SP, Jung J, Zhang H, Pickering RP, Ruan WJ, Smith SM, Huang B, Hasin DS, Patricia Chou S, Jung J, Zhang H, Pickering RP, June Ruan W, Smith SM, Huang B, Hasin DS, Chou SP, Jung J, Zhang H, Pickering RP, Ruan WJ, Smith SM, Huang B, Hasin DS, 2015a. Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions III. JAMA Psychiatry 72, 757–766. 10.1001/jamapsychiatry.2015.0584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Grant BF, Goldstein RB, Smith SM, Jung J, Zhang H, Chou SP, Pickering RP, Ruan WJ, Huang B, Saha TD, Aivadyan C, Greenstein E, Hasin DS, 2015b. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5): reliability of substance use and psychiatric disorder modules in a general population sample. Drug Alcohol Depend 148, 27–33. 10.1016/j.drugalcdep.2014.11.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Harden KP, 2010. Does religious involvement protect against early drinking? A behavior genetic approach. J. Child Psychol. Psychiatry Allied Discip 51, 763–771. 10.1111/j.1469-7610.2010.02247.x. [DOI] [PubMed] [Google Scholar]
  29. Kendler KS, Gardner CO, Prescott CA, 1997. Religion, psychopathology, and substance use and abuse: a multimeasure, genetic-epidemiologic study. Am. J. Psychiatry 154, 322–329. 10.1176/ajp.154.3.322. [DOI] [PubMed] [Google Scholar]
  30. Kendler KS, Liu XQ, Gardner CO, McCullough ME, Larson D, Prescott CA, 2003. Dimensions of religiosity and their relationship to lifetime psychiatric and substance use disorders. Am. J. Psychiatry 160, 496–503. 10.1176/appi.ajp.160.3.496. [DOI] [PubMed] [Google Scholar]
  31. Koenig HG, 2012. Religion, spirituality, and health: the research and clinical implications. ISRN Psychiatry 2012, 1–33. 10.5402/2012/278730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Koenig HG, Larson DB, Larson SS, 2001. Religion and coping with serious medical illness. Ann. Pharmacother 10.1345/aph.10215. [DOI] [PubMed] [Google Scholar]
  33. Koenig H, King D, Carson VB, 2012. Handbook of Religion and Health Oxford University Press. 10.1093/acprof:oso/9780195118667.001.0001. Chapter 11. [DOI] [Google Scholar]
  34. Koenig HG, Al Zaben FN, Al Shohaib S, 2015. Religion and health: clinical considerations and applications. International Encyclopedia of the Social & Behavioral Sciences, second edition. Elsevier Inc, pp. 263–268. 10.1016/B978-0-08-097086-8.14026-7 [DOI] [Google Scholar]
  35. Krause N, Ellison CG, Shaw BA, Marcum JP, Boardman JD, 2001. Church-Based Social Support and Religious Coping. J. Sci. Study Relig 40, 637–656. 10.1111/0021-8294.00082. [DOI] [Google Scholar]
  36. Kunzmann AT, Coleman HG, Huang W-Y, Berndt SI, 2018. The association of lifetime alcohol use with mortality and cancer risk in older adults: a cohort study. PLoS Med 15 (6) 10.1371/journal.pmed.1002585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Levin JS, Taylor RJ, Chatters LM, 1995. A multidimensional measure of religious involvement for african americans. Sociol. Q 36, 157–173. 10.1111/j.1533-8525.1995.tb02325.x. [DOI] [Google Scholar]
  38. Lipka M, Gecewicz C, 2017. More Americans Now Say They’re Spiritual but Not Religious Pew Research Center; (Accessed 6.28.20). https://www.pewresearch.org/fact-tank/2017/09/06/more-americans-now-say-theyre-spiritual-but-not-religious/. [Google Scholar]
  39. Medlock MM, Rosmarin DH, Connery HS, Griffin ML, Weiss RD, Karakula SL, McHugh RK, 2017. Religious coping in patients with severe substance use disorders receiving acute inpatient detoxification. Am. J. Addict 26, 744–750. 10.1111/ajad.12606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Meier MH, Caspi A, Ambler A, Harrington HL, Houts R, Keefe RSE, McDonald K, Ward A, Poulton R, Moffitt TE, 2012. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc. Natl. Acad. Sci 10.1073/pnas.1206820109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Merz EL, Roesch SC, Malcarne VL, Penedo FJ, Llabre MM, Weitzman OB, Navas-Nacher EL, Perreira KM, Gonzalez F, Ponguta LA, Johnson TP, Gallo LC, 2014. Validation of interpersonal support evaluation List-12 (ISEL-12) scores among english- and spanish-speaking Hispanics/Latinos from the HCHS/SOL sociocultural ancillary study. Psychol. Assess 26, 384–394. 10.1037/a0035248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Miller L, Davies M, Greenwald S, 2000. Religiosity and substance use and abuse among adolescents in the national comorbidity survey. J. Am. Acad. Child Adolesc. Psychiatry 10.1097/00004583-200009000-00020. [DOI] [PubMed] [Google Scholar]
  43. Moore THM, Zammit S, Lingford-Hughes A, Barnes TRE, Jones PB, Burke M, Lewis G, 2007. Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet 370, 319–328. 10.1016/S0140-6736(07)61162-3. [DOI] [PubMed] [Google Scholar]
  44. Moreira-Almeida A, Neto FL, Koenig HG, 2006. Religiousness and mental health: a review. Rev. Bras. Psiquiatr 10.1590/s1516-44462006005000006. [DOI] [PubMed] [Google Scholar]
  45. Mullen K, Williams R, Hunt K, 1996. Irish descent, religion, and alcohol and tobacco use. Addiction 91, 243–254. 10.1111/j.1360-0443.1996.tb03185.x. [DOI] [PubMed] [Google Scholar]
  46. Newport F, 2017. Church Leaders and Declining Religious Service Attendance
  47. Nonnemaker JM, McNeely CA, Blum RW, 2003. Public and private domains of religiosity and adolescent health risk behaviors: evidence from the National Longitudinal Study of Adolescent Health. Soc. Sci. Med 57, 2049–2054. 10.1016/S0277-9536(03)00096-0. [DOI] [PubMed] [Google Scholar]
  48. Parenteau SC, 2017. Religious coping and substance use: the moderating role of sex. J. Relig. Health 56, 380–387. 10.1007/s10943-015-0166-7. [DOI] [PubMed] [Google Scholar]
  49. Pargament KI, Magyar-Russell GM, Murray-Swank NA, 2005. The sacred and the search for significance: religion as a unique process. J. Soc. Issues 10.1111/j.1540-4560.2005.00426.x. [DOI] [Google Scholar]
  50. Paulhus DL, Reid DB, 1991. Enhancement and denial in socially desirable responding. J. Pers. Soc. Psychol 60, 307–317. 10.1037/0022-3514.60.2.307. [DOI] [Google Scholar]
  51. Pew Research Center, 2015a. U.S. Public Becoming Less Religious Accessed 3.10.21). https://www.pewforum.org/2015/11/03/u-s-public-becoming-less-religious/.
  52. Pew Research Center, 2015b. America’s Changing Religious Landscape Accessed 3.10.21. https://www.pewforum.org/2015/05/12/americas-changing-religious-landscape/.
  53. Pew Research Center, 2018. Why Americans Go to Religious and Church Services (Accessed 3.10.21). https://www.pewforum.org/2018/08/01/why-americans-go-to-religious-services/.
  54. Pew Research Center, 2019. U.S., Decline of Christianity Continues at Rapid Pace (Accessed 5.23.20). https://www.pewforum.org/2019/10/17/in-u-s-decline-of-christianity-continues-at-rapid-pace/.
  55. Ransome Y, Gilman SE, 2016. The role of religious involvement in black–White differences in alcohol use disorders. J. Stud. Alcohol Drugs 10.15288/jsad.2016.77.792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ransome Y, Haeny AM, McDowell YE, Jordan A, 2019. Religious involvement and racial disparities in opioid use disorder between 2004–2005 and 2012–2013: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug Alcohol Depend 205. 10.1016/j.drugalcdep.2019.107615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Research Triangle Institute. Release 11. Vol 1 and 2. Research Triangle Park, N.R.T.I 2012. S.L.M., n.d. SUDAAN® Statistical Software for Analyzing Correlated Data. [Google Scholar]
  58. Rockville, Md: Office of Applied Studies, 2002. NHSDA series H-17, D.P.S. 02–3758.. n.d. Substance Abuse and Mental Health Services Administration. Results From the 2001 National Household Survey on Drug Abuse, Volume I: Summary of National Findings. [Google Scholar]
  59. Rosmarin DH, Koenig H, 2020. Handbook of Spirituality, Religion, and Mental Health, Handbook of Spirituality, Religion, and Mental Health Chapter 7.. 10.1016/c2017-0-04790-0. [DOI] [Google Scholar]
  60. Ruan WJ, Goldstein RB, Chou SP, Smith SM, Saha TD, Pickering RP, Dawson DA, Huang B, Stinson FS, Grant BF, 2008. The alcohol use disorder and associated disabilities interview schedule-IV (AUDADIS-IV): reliability of new psychiatric diagnostic modules and risk factors in a general population sample. Drug Alcohol Depend 92, 27–36. 10.1016/j.drugalcdep.2007.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Schiller PL, Levin JS, 1988. Is there a religious factor in health care utilization?: a review. Soc. Sci. Med 27, 1369–1379. 10.1016/0277-9536(88)90202-X. [DOI] [PubMed] [Google Scholar]
  62. Solowij N, Stephens RS, Roffman RA, Babor T, Kadden R, Miller M, Christiansen K, McRee B, Vendetti J, 2002. Cognitive functioning of long-term heavy Cannabis users seeking treatment. JAMA 287, 1123. 10.1001/jama.287.9.1123. [DOI] [PubMed] [Google Scholar]
  63. U.S. Census Bureau, 2016. United States Census Bureau. Poverty [WWW Document]. URL U.S. Department of Commerce. https://www.census.gov/topics/income-poverty/poverty.html. [Google Scholar]
  64. Vanderweele TJ, 2017. Outcome-wide epidemiology. Epidemiology 28, 399–402. 10.1097/EDE.0000000000000641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Waggoner B, 2006. Some Losses Inevitable, but Churches Can Guard the Back Door - Lifeway Research (Accessed 3.15.21. https://lifewayresearch.com/2006/10/26/some-losses-inevitable-but-churches-can-guard-the-back-door/.
  66. Walton-Moss B, Ray EM, Woodruff K, 2013. Relationship of spirituality or religion to recovery from substance abuse: a systematic review. J. Addict. Nurs 24, 217–226. 10.1097/JAN.0000000000000001. [DOI] [PubMed] [Google Scholar]

RESOURCES