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. Author manuscript; available in PMC: 2024 May 26.
Published in final edited form as: Subst Use Misuse. 2023 May 26;58(10):1295–1301. doi: 10.1080/10826084.2023.2215331

The Role of Perceived Neighborhood Cohesion in the Association between Depressive Symptoms and Cigarette Smoking

Alison C McLeish 1,2,3,4,*, Kandi L Walker 2,3,4,5, Rachel J Keith 2,3,4, Joy L Hart 2,3,4,5
PMCID: PMC10413331  NIHMSID: NIHMS1917922  PMID: 37232382

Abstract

Background:

Greater depression has been linked to increased smoking rates. However, the mechanisms underlying this association are not fully understood. It is possible that high perceived neighborhood cohesion may serve as one such mechanism given its associations with decreased depression and smoking. Having increased levels of depression likely impacts one’s perceptions of neighborhood cohesion, which could lead to further increases in depression and a need to manage these symptoms via cigarette smoking. As a first test of this theory, the current study examined the effect of neighborhood cohesion on the association between depressive symptoms and smoking frequency and quantity among past 30-day cigarette smokers.

Methods:

Participants were 201 combustible cigarette smokers (Mage = 48.33, SD = 11.64; 63.2% female; 68.2% White) who completed self-report measures as part of a larger study of environmental influences on cardiac health.

Results:

Greater depressive symptoms were associated with lower levels of perceived neighborhood cohesion, and there was a significant indirect effect of greater depressive symptoms on heavier smoking through decreased neighborhood cohesion (b = .07, SE = .04, 95% CI [.003, .15]). There was no significant indirect effect for daily smoking.

Conclusion:

These results suggest that neighborhood cohesion is an important contextual factor that serves as one explanatory mechanism for the well-established relationship between depression and smoking quantity. Thus, there may be utility in implementing interventions focused on increasing neighborhood cohesion as a way to decrease smoking behavior.

Keywords: cigarettes, depression, perceived neighborhood cohesion, smoking, social cohesion

Despite significant reductions in prevalence rates in recent years, cigarette smoking remains the leading preventable cause of death and disability in the United States, accounting for one in five deaths each year (U.S. Department of Health and Human Services, 2014). Thus, smoking remains a significant public health concern. The presence of comorbid psychopathology has been identified as a risk factor for the onset and maintenance of cigarette smoking as well as cessation difficulties. (Grant et al., 2004; Leventhal et al., 2012; Piper et al., 2010). In particular, there is a well-established association between depression (at both the symptom and disorder level) and cigarette smoking. Not only is smoking more common among individuals with depressive disorders (Cook et al., 2014; Ziedonis et al., 2008), but depressive symptoms and disorders are also associated with greater nicotine dependence, heavier smoking rate, and greater difficulties with smoking cessation (Cooper et al., 2016; Fluharty et al., 2017; Goodwin et al., 2011; Grant et al., 2004; Hitsman et al., 2003; Japuntich et al., 2007; McKenzie et al., 2010; Piper et al., 2010). Therefore, identifying mechanisms that underlie the depression-smoking association may provide novel targets for prevention and intervention efforts.

One potential mechanism to examine in this regard is perceived neighborhood cohesion, or feelings of connectedness to one’s neighborhood characterized by a sense of belongingness, mutual trust, and shared expectations, values, and goals (Kawachi & Subramanian, 2007; Sampson, 2003; Sampson et al., 1997). Perceived neighborhood cohesion is theorized to influence health and health-related behaviors through increased dissemination of health-related information, ability to collectively advocate for resources, social support, and reinforcement of healthy behaviors (Kawachi & Berman, 2000; Kawachi & Subramanian, 2007). Greater perceived neighborhood cohesion is linked to between a 16% and 25% reduction in the prevalence of smoking (Echeverría et al., 2008; Patterson et al., 2004; Shih et al., 2017), and these effects can be seen above and beyond objective measures of neighborhood cohesion (e.g., income, residential stability; Shih et al., 2017). Furthermore, an analysis of Jackson Heart Study data found that greater neighborhood cohesion was associated with lower odds of continued smoking over an 8-year period (Wang et al., 2018).

There is also an association between greater depressive symptoms and low levels of perceived neighborhood cohesion (Echeverría et al., 2008; Ruiz et al., 2018). Moreover, findings from a study conducted using a representative sample from the Netherlands suggest that perceptions of neighborhood cohesion play a larger role in depression severity than the perceived physical neighborhood environment (e.g., perceptions of distance to green and blue spaces, traffic density, pleasantness; Helbich et al., 2020). Longitudinal studies have found that baseline perceived neighborhood cohesion is associated with decreased risk for depressive symptoms by as much as a 22% (Kim et al., 2020; Ruiz et al., 2018). In one of the only studies to date to examine the associations between neighborhood cohesion, depression, and smoking, Pei et al. (2020) found that depressive symptoms mediated the association between perceptions of neighborhood cohesion and smoking among adolescents (Pei et al., 2020). However, while the majority of this work has examined the impact of perceived neighborhood cohesion on depressive symptoms, there are likely bidirectional associations between these two constructs, and it is likely that depressive symptoms and disorders also impact perceived neighborhood cohesion; thus, neighborhood cohesion could also help explain the association between depression and smoking.

Cognitive behavioral theories of depression posit that depression results from maladaptive thinking patterns combined with behavioral inactivity and avoidance, which reduces the chances of encountering sources of reward in one’s environment (Barlow, 2021). Thus, individuals with greater symptoms of depression may be more likely to have negative interpretations of interactions with neighbors or have a more negatively valenced recall of neighborhood-related events (Gotlib & Joormann, 2010). In addition, avoidance of neighborhood activities would reduce opportunities for experiencing positive affect and obtaining social support, an important protective factor in relation to depression (Gariépy & Honkaniemi, 2016; Robinette et al., 2013). Thus, the cognitive biases and decreased social interaction associated with depression likely negatively impacts one’s perceptions of neighborhood cohesion, which could result in smoking cigarettes as a coping method. Further, exploring the role of neighborhood cohesion in the depression-smoking association could identify important points of intervention beyond individual therapy. Such an approach is important given the severe shortage of mental health professionals in the U.S. (Health Resources and Services Administrations, 2021) as well as the stigma and cost associated with seeking mental health treatment. Moreover, because smoking cessation success rates are low (USDHHS, 2020), particularly for individuals with depression (Ranjit et al., 2020; Weinberger, Kashan, et al., 2017), it is critical to identify alternative strategies for intervention. As a first test of this model, the current study examined the indirect effect of perceived neighborhood cohesion in the association between depressive symptoms and smoking frequency and quantity (Figure 1) among past 30-day cigarette smokers.

Figure 1. Conceptual Model.

Figure 1

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Note. a path = effect of Xi on M; b paths = effect of M on Y; c paths = effect of Xi on Y without controlling for M; c’ paths = effect of Xi on Y controlling for M. Two separate models were conducted. Covariates included gender, race, and income.

Method

Participants

Participants were part of larger study of environmental influences on cardiac health, the Health, Environment and Action in Louisville study (HEAL; N =735; clinicaltrials.gov NCT03670524). Participants were recruited from a primarily residential 4-square-mile area of Louisville, KY with a total of approximately 25,000 residents. Inclusion criteria for the larger study were aged 25–70 years and ability to provide written, informed consent. Exclusion criteria included (1) diagnosis of HIV; (2) active treatment for cancer; (3) active bleeding, including wounds; (4) body weight less than 100 pounds; (5) body mass index > 40; and (6) pregnancy. Participants in the current study were 201 individuals who reported past 30-day combustible cigarette use. Participants were, on average, 48.33 years old (SD = 11.64), and the sample was predominantly female, White, non-Hispanic, and high school educated (Table 1), which generally mirrors the population in the study area. Participants reported smoking an average of 13.67 (SD = 10.40) cigarettes on 29–30 of the past 30 days.

Table 1.

Sample Demographic Information

% (n)
Gender (female) 63.2 (127)
Race
 White 68.2 (137)
 Black/African American 26.9 (54)
 American Indian or Alaskan Native 2.5 (5)
 Hawaiian or Pacific Islander .5 (1)
 Other 2.0 (4)
Ethnicity (Hispanic) 2.5 (5)
Annual Household Income
 < $20,000 39.8 (80)
 $20,000 – $44,999 33.3 (67)
 $45,000 – $64,999 16.9 (34)
 $65,000 – $89,999 8.0 (16)
 $90,000 – $124,999 1.5 (3)
 $125,000 or more .5 (1)
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Measures

Demographic Questionnaire

Participants were asked to provide general demographic information (i.e., age, gender identity, race, ethnicity, and income).

Smoking History

Participants self-reported smoking frequency (i.e., “On how many of the past 30 days did you smoke a cigarette?”) and quantity (i.e., “On average, when you smoked during the past 30 days, about how many cigarettes did you smoke a day?”). The smoking frequency variable was coded in groups of two days (e.g., 1 = 1–2 days, 2 = 3–4 days, 3 = 5–6 days). As 68.2% of the sample reported smoking 29–30 of the past 30 days, we dichotomized this variable into daily (29–30 of the past 30 days) and non-daily (fewer than 29 of the past 30 days) smoking.

Patient Health Questionnaire (PHQ-9)

The PHQ-9 is a nine-item self-report measure of depression severity (Kroenke et al., 2001). It is one of the most commonly used screening tools for depression and assesses symptoms of depression on a 0 (not at all) to 3 (nearly every day) Likert-type scale. The PHQ-9 has demonstrated excellent reliability and validity, is able to discriminate between those with and without diagnoses of depressive disorders and is responsive to treatment-related changes in symptoms (Kroenke et al., 2001; Kroenke et al., 2010; Löwe, Kroenke et al., 2004; Löwe, Spitzer, et al., 2004). Internal consistency for the PHQ-9 in the current sample was good (α = .89).

Perceived Neighborhood Cohesion

The neighborhood cohesion measure (Cagney et al., 2009) was developed to assess respondents’ degree of integration within their neighborhood (e.g., “How many neighbors do you have friendly talks with at least once a week”) and their perception of the neighborhood’s social cohesiveness (e.g., “How often in your neighborhood do you see neighbors and friends talking outside in the yard or on the street”). The three social cohesion items are rated on a 4-point Likert-type scale (1 = never to 4 = often). In the current study, the three integration items were rated on a 1 (0) to 7 (11+) scale. Items are summed with higher scores indicating greater cohesion (possible range: 3–33). The measure has shown good reliability and construct validity in terms of associations with neighborhood socioeconomic status and proportion of vacant dwellings (Cagney et al., 2009). Internal consistency in the current sample was good (α = .82).

Procedure

Individuals living in neighborhoods within HEAL study boundaries were recruited in summer 2018 and 2019 through mailed invitation letters, canvassing at community events, posting on social media, and distributing flyers to residences. Eligibility screening took place by telephone. Then, eligible participants attended an in-person study visit where they provided informed consent, participated in clinical measurements (e.g., blood pressure, weight) and provided blood and urine samples. Participants also completed the self-report measures used in the current study as part of a larger questionnaire battery completed while participants waited at health stations and/or prior to checkout. On average, study visits lasted 90 minutes, and participants were compensated for their time with a $50 prepaid Visa card. The Institutional Review Board approved all study materials and procedures prior to data collection. Study data were collected and managed using Research Electronic Data Capture (REDCap), a secure, web-based software platform designed to support data capture for research studies that provides (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for data integration and interoperability with external sources (Harris et al., 2019; Harris et al., 2009).

Analytic Strategy

Data from the full HEAL cohort were first examined for completeness. Based on our multipronged method of recruitment, it was not possible to calculate an exact response rate for the current sample. Using the estimated population of the study neighborhoods, a conservative estimate would be 2.94% (735/25,000), which likely is a significant underestimate. A total of 86 participants had missing data on one or more measures of interest for the current study (demographic data = 37 participants, smoking data = 35, PHQ-9 = 8, neighborhood cohesion = 6). However, given that none of the variables had more than 5% missing data (range = .7%−1.4%), we did not conduct a missing values analysis (Tabachnik & Fidell, 2019). An additional 448 participants were excluded because they had not smoked a cigarette in the past 30 days, resulting in a final sample size of 201. Analyses were conducted using SPSS version 28. First, sample descriptive statistics and zero-order correlations were examined. Then, mediation analyses were conducted using the PROCESS macro, and the 95-percentile confidence intervals (CI) for the indirect effects were estimated with bias-corrected bootstrap analyses (10,000 resamples; Hayes, 2022). A bootstrap confidence interval that does not include zero provides evidence of a significant indirect effect (Hayes, 2022). For all other analyses, a p-value less than .05 was considered significant. Separate models were created for both criterion variables: smoking frequency (coded as 0 = non-daily, 1 = daily) and smoking quantity. Depressive symptoms was entered as the predictor variable and perceived neighborhood cohesion as the mediator in each model (Figure 1). Gender (coded as 1 = female, 2 = male), race (coded as 1 = White, 2 = all other), and income (coded as 1 = < $45,000, 2= ≥ $45,000) were included as covariates in both models (Cornelius et al., 2022; Salk et al., 2017; Sireen et al., 2011). Due to the cross-sectional nature of the data, a series of analyses was conducted where the predictors and mediator were reversed (i.e., depressive symptoms as a mediator of the association between perceived neighborhood cohesion and smoking frequency and quantity).

Results

Bivariate Correlations

Descriptive statistics and bivariate correlations are presented in Table 2. Higher depression scores were significantly correlated with being female, having an annual household income less than $45,000, and lower perceived neighborhood cohesion. Greater perceived neighborhood cohesion was significantly associated with lower smoking quantity. Daily smoking was significantly associated with being White, having an annual household income less than $45,000, and higher smoking quantity. Higher smoking quantity was associated with being male and having an annual household income less than $45,000.

Table 2.

Descriptive Statistics and Bivariate Correlations

1. 2. 3. 4. 5. 6. 7.
1. Gender 1 .054 .096 −.227** .049 −.054 .154*
2. Race 1 −.173* −.076 .055 −.243** −.114
3. Income 1 −.142* −.033 −.188** −.156*
4. Depression 1 −.251** .128 .123
5. Cohesion 1 −.039 −.162*
6. Daily smoking 1 .350**
7. Quantity 1
Mean / n 127 137 147 7.13 17.78 137 13.67
SD / % 63.2 68.2 73.1 5.95 4.78 68.2 10.40
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*

p < 0.05

**

p < 0.01

Note. Gender: coded 1 = female, 2 = male (n/% listed for females); Race: coded 1 = white, 2 = all other (n/% listed for white); Income: coded 1 = < $45,000, 2 = ≥ $45,000 (n/% listed for < $45,000); Depression = Perceived Health Questionnaire-9; Cohesion = Neighborhood Cohesion; Daily smoking: coded 0 = no, 1 = yes (n/% listed for yes); Quantity = average number of cigarettes on each smoking day.

Mediation Models

Results of the mediation analyses are presented in Table 3. For both models, greater depressive symptoms significantly predicted decreased perceived neighborhood cohesion (a path). For daily smoking, perceived neighborhood cohesion did not predict daily smoking (b path), the direct effect of depression on daily smoking (c’ path) was not significant (b = .03, Z = .0.99, p = .320, 95% CI [0.029, .089]), and there was no significant indirect effect of depression on daily smoking through perceived neighborhood cohesion (b = .001, SE = .008, 95% CI [−.015, .018]).

Table 3.

Indirect Effect of Depression on Smoking via Perceived Neighborhood Cohesion

Y Path R 2 b SE t/Z p CI (l) CI (u)
1 a: Depression → Neighborhood cohesion .068 −.208 .058 −3.62 .000 −.322 −.095
b: Neighborhood cohesion → Daily smoking −.005 .035 −.155 .877 −.073 .063
c’: Depression → Daily smoking .030 .030 .995 .320 −.029 .089
a*b: Depression → Cohesion → Daily smoking .001 .008 −.015 .018
2 a: Depression → Neighborhood cohesion .068 −.208 .058 −3.62 .000 −.322 −.095
b: Neighborhood cohesion → Smoking quantity .114 −.318 .152 −2.09 .038 −.618 −.018
c: Depression → Smoking quantity .094 .233 .123 1.89 .060 −.010 .477
c’: Depression → Smoking quantity .167 .126 1.32 .188 −.082 .416
a*b: Depression → Cohesion Smoking → quantity .067 .037 .003 .147
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Note: The standard error and 95% CI for the indirect effects (a*b) are obtained through bootstrapping with 10,000 re-samples. a paths = effect of X on M; b paths = effect of M on Y (not presented for dichotomous Y); c path = total effect of X on Y (not presented for dichotomous Y); c’ paths = direct effect of X on Y controlling for M; Depression = Patient Health Questionnaire-9; Daily smoking: coded 0 = no, 1 = yes; Smoking quantity = average number of cigarettes on each smoking day; covariates included gender, race, and income.

For smoking quantity, there was a significant b path (b = −.32, t = −2.09, p = .04, 95% CI [−.62, −.02]), but the c path was not significant (b = .23, t = 1.89, p = .06, 95% CI [−.01, .48]). There was, however, a significant indirect effect of depressive symptoms on smoking quantity through perceived neighborhood cohesion (b = .07, SE = .04, 95% CI [.003, .15]). After accounting for the effects of perceived neighborhood cohesion, the direct effect of depressive symptoms on smoking frequency (c’ path) remained non-significant (b = .17, t = 1.32, p = .188, 95% CI [−.08, .42]).

Specificity Analyses

Alternative models were tested by reversing the proposed mediator for each model. Specifically, perceived neighborhood cohesion was the predictor, depressive symptoms the indirect factor, and the outcomes remained the same. The indirect effects of the alternate models were not significant for daily smoking (b = −.009, SE = .010, 95%CI [−.033, .008]) or smoking quantity (b = −.050, SE = .041, 95%CI [−.139, .025]).

Discussion

The present study sought to examine whether perceived neighborhood cohesion helps to explain the association between depressive symptoms and cigarette smoking. Results indicated that greater depressive symptoms were associated with decreased perceived neighborhood cohesion, and there was a significant indirect effect of greater depressive symptoms on heavier smoking through decreased perceived neighborhood cohesion. These effects were not attributable to the variance accounted for by gender, race, or income, and tests of specificity also were not significant. There was no significant indirect effect for daily vs. non-daily smoking. Overall, these results suggest that perceived neighborhood cohesion is an important contextual factor that serves as one explanatory mechanism for the well-established relationship between depression and smoking quantity.

Although this study is the first to examine the effect of perceived neighborhood cohesion on the depression-smoking relationship, the current findings are largely in line with previous research demonstrating associations between depressive symptoms and disorders and heavier smoking (Fluharty et al., 2017) and lower perceived neighborhood cohesion (Echeverría et al., 2008; Helbich et al., 2020; Kim et al., 2020; Ruiz et al., 2018) as well as between perceived neighborhood cohesion and smoking (Echeverria et al., 2008; Patterson et al., 2004; Shih et al., 2017; Want et al., 2018). Interestingly, despite the significant findings for smoking quantity, there was not a significant indirect effect in terms of smoking frequency (i.e., whether participants were daily smokers). Recent work has found that the prevalence of past-year major depressive episodes among non-daily smokers has increased and is now equivalent to that of daily smokers (Weinberger et al., 2017). Thus, it is possible that smoking quantity, rather than frequency, is more important in terms of associations with depressive symptoms and neighborhood cohesion. However, as nearly 70% of the sample were daily smokers, there may not have been sufficient variability to detect significant effects. Thus, it will be important for future research to use samples with a larger proportion of non-daily smokers to better answer this question.

The present findings lend support to the theory that depression contributes to lower perceived neighborhood cohesion, which could contribute to further increases in depression symptomatology and the need to smoke as an affect regulatory strategy. Thus, these findings suggest that in addition to individual-focused interventions (e.g., psychotherapy to treat depression), interventions focused on increasing neighborhood cohesion could be an effective and efficient way to decrease smoking behavior. For example, there is some limited evidence that community intervention projects can improve neighborhood cohesion (Adams, Witten, & Conway, 2009; Brisson et al., 2019; Fong et al., 2021; Shen et al., 2017). However, more work is needed to determine how such programs need to be adapted to the specific neighborhood in which they are implemented. Given the efficacy of physical activity in reducing depression and smoking (Rebar et al., 2015; Roberts et al., 2012; Santos et al., 2021), community walking interventions also could be a method for not only improving neighborhood cohesion, but also depression and smoking outcomes. Such interventions could be particularly useful in situations where neighborhood cohesion is already fairly high, but the perception of cohesion is low due to the negative cognitive biases associated with depression.

There are, however, several limitations to the current study that warrant consideration. First, the present cross-sectional design does not permit causal-oriented hypothesis testing. Thus, despite non-significant tests of specificity, the directionality of the observed associations cannot be unambiguously determined. Longitudinal studies are needed to better understand how depressive symptoms and neighborhood cohesion impact each other and smoking behavior over time. Second, the current study relied solely on self-report measures, which increases the possibility for shared method variance and reporting errors. Future studies could build on the present work by utilizing a multi-method approach. For example, laboratory-based studies could experimentally manipulate mood and/or social cohesion and examine the effects on subsequent smoking behavior. Future studies would also benefit from a more detailed assessment of neighborhood (e.g., crime) and psychological (e.g., stress, coping) factors to determine their influence on these associations. Finally, the sample was relatively small and predominantly White and female, and it will be important for future studies to examine these associations in more diverse samples. Similarly, although we used a wide-reaching recruitment approach, our response rate may have been low, which could decrease the generalizability of the current findings. However, the fact that all participants came from the same neighborhood helped control for the effects of structural neighborhood influences. Moreover, while racial and ethnic make-up of the sample was not representative of the U.S. population as a whole, it was representative of adult smokers (Cornelius et al., 2022), which may mitigate these concerns somewhat.

Overall, the present findings indicate that neighborhood cohesion is a relevant factor to examine to better understand the association between depression and smoking. Moreover, neighborhood cohesion appears to serve as a mediational factor in the association between depression and smoking quantity. Future work is needed to extend the current findings to additional smoking outcomes (e.g., motivation to quit, smoking expectancies).

Funding Details:

This work was supported, in part, by grants from the National Institute of Environmental Health Sciences (Award Numbers R01 ES 029846 and P42 ES023716) as well as the National Heart, Lung, and Blood Institute (NHLBI) and the FDA Center for Tobacco Products (Award Number U54HL120163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Food and Drug Administration, or the University of Louisville. The funding sponsors had no role in study design; data collection, analyses, or interpretation; manuscript preparation; or the decision to publish the results.

Footnotes

Disclosure Statement: The authors report there no conflict of interest.

Data Availability Statement:

The data that support the findings of this study are available from the corresponding author, A.C.M., upon reasonable request.

References

  1. Adams J, Witten K, & Conway K (2009). Community development as health promotion: Evaluating a complex locality-based project in New Zealand. Community Development Journal, 44(2), 140–57. 10.1093/cdj/bsm049 [DOI] [Google Scholar]
  2. Barlow DH. (Ed.). (2021). Clinical handbook of psychological disorders: A step-by-step treatment manual. Guilford. [Google Scholar]
  3. Brisson D, Peña SL, Mattocks N, Plassmeyer M, & McCune S (2019). Effects of theYour Family, Your Neighborhood intervention on neighborhood social processes. Social Work Research 43(4), 235–246. 10.1093/swr/svz020 [DOI] [Google Scholar]
  4. Cagney KA, Glass TA, Skarupski KA, Barnes LL, Schwartz BS, & Mendes de Leon CF. (2009). Neighborhood-level cohesion and disorder: measurement and validation in two older adult urban populations. Journal of Gerontology: Social Sciences 64(3), 415–424. 10.1093/geronb/gbn041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cook BL, Wayne GF, Kafali EN, Liu Z, Shu C, & Flores M (2014). Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. JAMA, 311(2),172–82. 10.1001/jama.2013.284985 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cooper J, Borland R, McKee SA, Yong HH, & Dugué PA (2016). Depression motivates quit attempts but predicts relapse: differential findings for gender from the International Tobacco Control study. Addiction, 111(8), 1438–1147. 10.1111/add.13290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cornelius M, E, Loretan CG, Wang TW, Jamal A, & Homa DM. (2022). Tobacco Product Use Among Adults- United States, 2020. Morbidity and Mortality Weekly Report, 71, 397–405. 10.15585/mmwr.mm7111a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Echeverría S, Diez-Roux AV, Shea S, Borrell LN, & Jackson S (2008). Associations of neighborhood problems and neighborhood social cohesion with mental health and health behaviors: The Multi-Ethnic Study of Atherosclerosis. Health & Place 14(4), 853–865. 10.1016/j.healthplace.2008.01.004 [DOI] [PubMed] [Google Scholar]
  9. Fong P, Cruwys T, Robinson SL, Haslam SL, Haslam C, Mance PL, & Fisher CL (2021). Evidence that loneliness can be reduced by a whole-of-community intervention to increase neighbourhood identification. Social Science & Medicine 277, 113909. 10.1016/j.socscimed.2021.113909 [DOI] [PubMed] [Google Scholar]
  10. Fluharty M, Taylor AE, Grabski M, & Munafo MR (2017). The association of cigarette smoking with depression and anxiety: A systematic review. Nicotine & Tobacco Research 19(1), 3–13. 10.1093/ntr/ntw140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gariépy G, Honkaniemi H, & Quesnel-Vallée A (2016). Social support and protection from depression: Systematic review of current findings in Western countries. The British Journal of Psychiatry, 209(4), 284–293. 10.1192/bjp.bp.115.169094 [DOI] [PubMed] [Google Scholar]
  12. Goodwin R, Pagura J, Spiwak R, Lemeshow AR, & Sareen J (2011). Predictors of persistent nicotine dependence among adults in the United States. Drug and Alcohol Dependence 118(2–3), 127–133. 10.1016/j.drugalcdep.2011.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gotlib I, & Joormann J (2010). Cognition and depression: Current status and future directions. Annual Review of Clinical Psychology, 6, 285–312. 10.1146/annurev.clinpsy.121208.131305 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Grant BF, Hasin DS, Chou SP, Stinson FS, & Dawson DA (2004). Nicotine dependence and psychiatric disorders in the United States: Results from the national epidemiologic survey on alcohol and related conditions. Archives of General Psychiatry 61(11), 1107–1115. 10.1001/archpsyc.61.11.1107 [DOI] [PubMed] [Google Scholar]
  15. Hayes AF (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd Ed). Guilford Press. [Google Scholar]
  16. Resources Health and Administration Services. (2021). Designated Health Professional Shortage Areas statistics: Designated HPSA quarterly summary, as of September 30, 2021. U.S. Department of Health & Human Services. https://data.hrsa.gov/topics/health-workforce/shortage-areas [Google Scholar]
  17. Helbich M, & Hagenauer J, Roberts H. (2020). Relative importance of perceived physical and social neighborhood characteristics for depression: A machine learning approach. Social Psychiatry and Psychiatric Epidemiology 55(5), 599–610. 10.1007/s00127-019-01808-5 [DOI] [PubMed] [Google Scholar]
  18. Hitsman B, Borrelli B, McChargue DE, Spring B, & Niaura R (2003). History of depression and smoking cessation outcome: A metaanalysis. Journal of Consulting and Clinical Psychology 71(4), 657–663. 10.1037/0022-006x.71.4.657 [DOI] [PubMed] [Google Scholar]
  19. Japuntich SJ, Smith SS, Jorenby DE, Piper ME, Fiore MC, & Baker TB (2007). Depression predicts smoking early but not late in a quit attempt. Nicotine & Tobacco Research 9(6), 677–686. 10.1080/14622200701365301 [DOI] [PubMed] [Google Scholar]
  20. Kawachi I, & Berkman L (2000). Social cohesion, social capital, and health. In, Berkman LF, L.F. & Kawachi I (Eds.), Social Epidemiology (pp. 174–190). Oxford University Press. [Google Scholar]
  21. Kawachi I, & Subramanian SV (2007). Neighbourhood influences on health. Journal of Epidemiology and Community Health, 61(1), 3–4. 10.1136/jech.2005.045203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kim ES, Chen Y, Kawachi I, & VanderWeele TJ (2020). Perceived neighborhood social cohesion and subsequent health and well-being in older adults: An outcome-wide longitudinal approach. Health & Place, 66, 102420. 10.1016/j.healthplace.2020.102420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kroenke K, Spitzer RL, & Williams JBW (2001). The PHQ-9: Validity of a brief depression measure. Journal of General Internal Medicine, 16(9), 606–613. 10.1046/j.1525-1497.2001.016009606.x   [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kroenke K, Spitzer RL, Williams JBW, & Löwe B (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: A systematic review. General Hospital Psychiatry, 32(4), 345–359. 10.1016/j.genhosppsych.2010.03.006 [DOI] [PubMed] [Google Scholar]
  25. Leventhal AM, Japuntich SJ, Piper ME, Jorenby DE, Schlam TR, & Baker TB (2012). Isolating the role of psychological dysfunction in smoking cessation: Relations of personality and psychopathology to attaining cessation milestones. Psychology of Addictive Behaviors, 26(4), 838–849. 10.1037/a0028449 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Löwe B, Kroenke K, Herzog W, & Gräfe K (2004). Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). Journal of Affective Disorders, 81(1), 61–66. 10.1016/s0165-0327(03)00198-8 [DOI] [PubMed] [Google Scholar]
  27. Löwe B, Spitzer RL, Gräfe K, Kroenke K, Quenter A, Zipfel S, & Herzog W (2004). Comparative validity of three screening questionnaires for DSM-IV depressive disorders and physicians’ diagnoses. Journal of Affective Disorders, 78(2), 131–140. 10.1016/s0165-0327(02)00237-9 [DOI] [PubMed] [Google Scholar]
  28. McKenzie M, Olsson CA, Jorm AF, Romaniuk H, & Patton GC (2010). Association of adolescent symptoms of depression and anxiety with daily smoking and nicotine dependence in young adulthood: Findings from a 10-year longitudinal study. Addiction, 105(9), 1652–1659. 10.1111/j.1360-0443.2010.03002.x [DOI] [PubMed] [Google Scholar]
  29. Patterson JM, Eberly LE, Ding Y, & Hargreaves M (2004). Associations of smoking prevalence with individual and area level social cohesion. Journal of Epidemiology and Community Health, 58(8), 692–697. 10.1136/jech.2003.009167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Pei F, Want Y, Wu Q, Shockley McCarthy K, & Wu S (2020). The roles of neighborhood social cohesion, peer substance use, and adolescent depression in adolescent substance use. Children and Youth Services Review, 112, 104931. 10.1016/j.childyouth.2020.104931 [DOI] [Google Scholar]
  31. Piper ME, Smith SS, Schlam TR, Fleming MF, Bittrich AA, Brown JL, Leitzke CJ, Zehner ME, Fiore ME, & Baker TB (2010). Psychiatric disorders in smokers seeking treatment for tobacco dependence: Relations with tobacco dependence and cessation. Journal of Consulting and Clinical Psychology, 78(1), 13–23. 10.1037/a0018065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ranjit A, Latvala A, Kinnunen TH, Kaprio J, & Korhonen T (2020). Depressive symptoms predict smoking cessation in a 20-year longitudinal study of adult twins. Addictive Behaviors, 108, 106427. 10.1016/j.addbeh.2020.106427 [DOI] [PubMed] [Google Scholar]
  33. Rebar AL, Stanton R, Geard D, Short C, Duncan MJ, & Vandelanotte C (2015). A meta-meta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. Health Psychology Review, 9(3), 366–378. 10.1080/17437199.2015.1022901 [DOI] [PubMed] [Google Scholar]
  34. Robert V, Maddison R, Simpson C, Bullen C, & Prapavessis H (2012). The acute effects of exercise on cigarette cravings, withdrawal symptoms, affect, and smoking behaviour: Systematic review update and meta-analysis. Psychopharmacology, 222(1), 1–15. 10.1007/s00213-012-2731-z [DOI] [PubMed] [Google Scholar]
  35. Robinette JW, Charles ST, Mogle JA, & Almeida DM (2013). Neighborhood cohesion and daily well-being: results from a diary study. Social Science & Medicine, 96, 174–182. 10.1016/j.socscimed.2013.07.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Ruiz M, Scholes S, & Bobak M (2018). Perceived neighbourhood social cohesion and depressive symptom trajectories in older adults: A 12-year prospective cohort study. Social Psychiatry and Psychiatric Epidemiology, 53(10), 1081–1090. 10.1007/s00127-018-1548-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Salk RH, Hyde JS, & Abramason LY (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783–822. 10.1037/bul0000102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sampson RJ (2003). The neighborhood context of well-being. Perspectives in Biology and Medicine, 46(Suppl. 3), S53–S64. [PubMed] [Google Scholar]
  39. Sampson RJ, Raudenbush SW, & Earls F (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924. 10.1126/science.277.5328.918 [DOI] [PubMed] [Google Scholar]
  40. Santos CP, Proença M, dos Santos Gouveia T, de Oliveria CBS, Tacao GY, Trevisan IB, Ramos EMC, & Ramos D (2021). Effectiveness of aerobic exercise on smoking cessation in adults: A systematic review and meta-analysis. Journal of Physical Activity & Health, 18(2), 230–242. 10.1123/jpah.2019-0339 [DOI] [PubMed] [Google Scholar]
  41. Shen C, Wan A, Kwok LT, Pang S, Wang X, Stewart SM, Lam TH, & Chan SS (2017). A community based intervention program to enhance neighborhood cohesion: The Learning Families Project in Hong Kong. PLoS ONE, 12, e0182722. 10.1371/journal.pone.0182722 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shih RA, Parast L, Pedersen ER, Troxel WM, Tucker JS, Miles JNV, Kraus L, & D’Amico EJ (2017). Individual, peer, and family factor modification of neighborhood-level effects on adolescent alcohol, cigarette, e-cigarette, and marijuana use. Drug and Alcohol Dependence, 180, 76–85. 10.1016/j.drugalcdep.2017.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sireen J, Afifi TO, McMillan KA, & Asmundson GJG (2011). Relationship between household income and mental disorders: Findings from a population-based longitudinal study. Archives of General Psychiatry, 68(4), 419–427. 10.1001/archgenpsychiatry.2011.15 [DOI] [PubMed] [Google Scholar]
  44. Tabachnick BG, & Fidell LS (2019). Using multivariate statistics (7th ed.). Pearson. [Google Scholar]
  45. U.S. Department of Health and Human Services. (2014). The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. [Google Scholar]
  46. U.S. Department of Health and Human Services. (2020). Smoking cessation: A report of thesurgeon general. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. [Google Scholar]
  47. Wang X, Auchincloss AH, Barber S, Mayne SL, Griswold ME, Sims M, & Diez Roux AV (2017). Neighborhood social environment as risk factors to health behavior among African Americans: The Jackson Heart Study. Health & Place, 45, 199–207. 10.1016/j.healthplace.2017.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Weinberger AH, Gbedemah M, Wall MM, Hasin DS, Zvolensky MJ, Chaiton M, & Goodwin RD (2017). Depression among non-daily smokers compared to daily smokers and never-smokers in the United States: An emerging problem. Nicotine & Tobacco Research, 19(9), 1062–1072. 10.1093/ntr/ntx009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Weinberger AH, Kashan RS, Shpigel DM, Esan H, Taha F, Lee CJ, Funk AP, & Goodwin RD (2017). Depression and cigarette smoking behavior: A critical review of population-based studies. American Journal of Drug and Alcohol Abuse, 43(4), 416–431. 10.3109/00952990.2016.1171327 [DOI] [PubMed] [Google Scholar]
  50. Ziedonis D, Hitsman B, Beckham JC, Zvolensky MJ, Adler LE, Audrain-McGovern J, Breslau N, Brown RA, George TP, Williams J, Calhoun PS, & Riley WT (2008). Tobacco use and cessation in psychiatric disorders: National Institute of Mental Health report. Nicotine & Tobacco Research, 10(12), 1691–715. 10.1080/14622200802443569 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, A.C.M., upon reasonable request.

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