Health & Place 33 (2015) 57–66

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Health & Place journal homepage: www.elsevier.com/locate/healthplace

How community physical, structural, and social stressors relate to mental health in the urban slums of Accra, Ghana Meredith J. Greif a,n, F. Nii-Amoo Dodoo b a b

Department of Sociology, Johns Hopkins University, 3400 N, Charles Street, Baltimore, MD 21218, USA The Pennsylvania State University, Regional Institute for Population Studies, USA

art ic l e i nf o

a b s t r a c t

Article history: Received 25 July 2014 Received in revised form 26 January 2015 Accepted 2 February 2015 Available online 11 March 2015

Urban health in developing counties is a major public health challenge. It has become increasingly evident that the dialog must expand to include mental health outcomes, and to shift focus to the facets of the urban environment that shape them. Population-based research is necessary, as empirical findings linking the urban environment and mental health have primarily derived from developed countries, and may not be generalizable to developing countries. Thus, the current study assesses the prevalence of mental health problems (i.e., depression, perceived powerlessness), as well as their community-based predictors (i.e., crime, disorder, poverty, poor sanitation, local social capital and cohesion), among a sample of 690 residents in three poor urban communities in Accra, Ghana. It uncovers that residents in poor urban communities in developing countries suffer from mental health problems as a result of local stressors, which include not only physical and structural factors but social ones. Social capital and social cohesion show complex, often unhealthy, relationships with mental health, suggesting considerable drawbacks in making social capital a key focus among policymakers. & 2015 Published by Elsevier Ltd.

Keywords: Mental health Social capital Social cohesion Urban poverty and disorder

1. Introduction Urban health in developing counties is a major public health challenge. Sub-Saharan Africa is the most rapidly urbanizing region in the world, and nearly all of this growth has been in urban slum settlements (Kjellstrom and Mercado, 2008). Numerous studies and reports have uncovered that these poor urban communities experience high rates of communicable (e.g., HIV) and, non-communicable diseases (e.g., respiratory disease, waterborne diseases, diabetes, stroke), injuries, and premature death (Ali, 2010; Butala et al., 2010; Kyobutungi et al., 2008; Sverdlik, 2011). The growing awareness that mental illness, particularly depression, is a tremendous contributor to the growing burden of disease worldwide, and especially in developing regions (Bird et al., 2010; Gupta et al., 2010; Izutsu et al., 2006; Lund et al., 2010; Prince et al. 2007), suggests that improving wellbeing among slum dwellers, a charge of the Millennium Development Goals (MDGs), requires significantly more attention to mental health outcomes than the few existing studies have given. Poor mental health (e.g., depression, anxiety, perceived powerlessness) may be prevalent in poor urban communities as a result of individual and household characteristics (e.g., poverty, illness),

n

Corresponding author. E-mail address: [email protected] (M.J. Greif).

http://dx.doi.org/10.1016/j.healthplace.2015.02.002 1353-8292/& 2015 Published by Elsevier Ltd.

as well as community characteristics. Slum communities are characterized by lacking infrastructure, insufficient sanitation, poor quality housing, overcrowding, joblessness, social mistrust, disorder, and crime (Izutsu et al., 2006; UN-HABITAT, 2006). A sizeable body of literature from the developed world evidences that routine exposure to these community-based stressors can contribute to mental health problems (Aneshensel and Sucoff, 1996; Haney, 2007; Kawachi and Berkman, 2001; Latkin and Curry, 2003; Ross, 2000; Ross and Mirowsky, 2009, 2001; Silver et al., 2002). Community-based social resources (e.g., social capital, social cohesion) may temper the relationship between local stressors and mental health, prompting scholars and policymakers to consider the utility of incorporating a concern for social capital and cohesion into plans for urban revitalization (Lang and Hornburg, 2010). However recent evidence of the downsides of social capital and cohesion in distressed communities illustrates the importance of exploring how, and for whom, these social resources may benefit mental health (Caughy et al., 2003; Curley, 2009; Mair et al., 2010; Mitchell and LaGory, 2002). Existing findings may not be generalizable to developing regions, due to cultural variations in the meaning and expression of poor mental health, as well as in residents' adaptation to community stressors (Abas and Broadhead, 1997; Barke et al., 2011; de Menil et al., 2012; Ruback et al., 2002). Population-based research is necessary to assess the prevalence and social determinants of mental

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M.J. Greif, F. Nii-Amoo Dodoo / Health & Place 33 (2015) 57–66

health problems in poor urban slum settlements. Further, identifying community-based social resources that contribute to, or diminish, mental health problems can assist urban planners, policymakers, and local community organizations in creating an environment that is conducive to physical and mental well-being (Smit et al., 2011). Accordingly, the current study will assess prevalence of mental health problems, as well as their community-based predictors, among a sample of residents in three poor urban communities in Accra, Ghana. Specifically, it will gauge the prevalence of two indicators of poor mental health: depression and perceived powerlessness. It will then assess residents' levels of concern regarding potentially problematic community characteristics (e.g., crime, disorder, poverty, unemployment), and then examine the contribution of such community stressors to mental distress. Finally, it will assess the significance of social resources (e.g., social capital, social cohesion) for mental health, both directly and indirectly as a potential moderator of the relationship between stressors and mental health.

2. Significance of mental health in sub-Saharan Africa Mental health disorders, which account for approximately 13% of disease, are a powerful contributor to the burden of illness in subSaharan Africa (WHO, 2008). However, mental health problems have not been prioritized by government, policymakers, and stakeholders, due to competing health priorities, lack of sufficient funding and health care infrastructure, perceived insignificance, and stigma (Bird et al., 2010; Jacob et al., 2007). The symbiotic relationship between physical and mental health indicates that treating and preventing mental health problems can build upon existing efforts to alleviate physical health problems in the region. For example, experiencing depression, anxiety, and perceived powerlessness may lower inhibitions against risky behavior (e.g., inconsistent condom use, multiple sexual partners, drug and alcohol use), in turn heightening susceptibility to HIV and other communicable and non-communicable diseases (Hill et al., 2005; Lundberg et al., 2011). Mental health problems may also interfere in activities (e.g., employment, social involvement, exercise) that are beneficial to health (Canavan et al., 2013; Lund et al., 2010). Furthermore, poor mental health can inhibit adherence to medical treatment (e.g., antiretroviral therapy) (Nakimuli-Mpungu et al., 2012; Nel and Kagee, 2013), and directly weaken the immune system (Geronimus, 1992; Segerstrom and Miller, 2004). Preventing mental health problems, and not simply treating them, requires a broader understanding of their social determinants. Existing research on mental health in sub-Saharan Africa has primarily focused on individual and household risk factors, including poverty, unemployment, inadequate housing, food insecurity, gender, age, marital status, education, intimate partner violence, and HIV status (Bove and Veleggia, 2009; Cole and Tembo, 2011; de Menil et al., 2012; Dewing et al., 2013; Gruebner et al., 2012; Khumalo et al., 2012; Kuo et al., 2012; Patel and Kleinman, 2003; Myer et al., 2008; Olagunju et al., 2012; Patel et al., 2006; Pillay and Kriel, 2006; Pitpitan et al., 2012; Sipsma et al., 2013). To date there is scant population-based research on how the structural, institutional, and social dynamics in disadvantaged urban settings can contribute to mental health outcomes.

3. Urban context and mental health 3.1. Structural and institutional dynamics Numerous studies from developed countries have illustrated the importance of community context for mental health (Aneshensel and Sucoff, 1996; Haney, 2007; Kawachi and Berkman, 2001; Latkin and Curry, 2003; Ross and Mirowsky, 2009, 2001; Ross, 2000; Silver et al.,

2002). Disorder (i.e., visual, physical, and social conditions that may be viewed as threatening or noxious, including crime, poor sanitation, noise, drug and alcohol use) can heighten depression, anxiety, and sense of powerlessness (Geis and Ross, 1998; Haney, 2007; Latkin and Curry, 2003; Ross, 2000; Ross and Mirowsky, 2009; Ross et al., 2001, 2000; Simning et al., 2012; Wen et al., 2006). In fact, signs of local disorder may worsen mental health even more powerfully than unique life events (e.g., death of a loved one, unemployment), as they are chronic, uncontrollable, and difficult to escape (Pearlin, 1989). There is also evidence that local disorder may contribute to the relationship between urban poverty and mental health (Haney, 2007; Ross and Mirowsky, 2001; Ross et al., 2001), and may serve to explain the link between slum residence and poor health outcomes found in existing studies in sub-Saharan Africa. Living in disadvantaged communities can also directly worsen mental health. Communities are vital sites for the receipt of important services and resources (Ellen et al., 2001), particularly in less developed regions where access to extra-local resources is inhibited by limited or unaffordable transportation. Lack of local employment and educational opportunities significantly constrict prospects for economic success, heightening depression and powerlessness. Furthermore, paucity of health care facilities and professionals can inhibit screening and treatment of mental health disorders (Ellen et al., 2001). 3.2. Social dynamics An examination of local social dynamics is also vital to an understanding of the processes by which community context relates to mental health. A rapidly growing body of literature has suggested that local social dynamics, specifically social capital and social cohesion, are powerful contributors to mental health (Aneshensel and Sucoff, 1996; Carpiano, 2006; Caughy et al., 2003; Cornwell and Waite, 2009; Curley, 2009; Fitzpatrick et al., 2005; Fitzpatrick and LaGory, 2000; Gary et al., 2007; Gutman and Sameroff, 2004; Kawachi and Berkman, 2001; Kim, 2010; Omata, 2012; Pearlin, 1989; Phan et al., 2009; Ross et al., 2000; Schulz et al., 2006; Thoits, 1995; Usher, 2007; Xue et al., 2005). Conclusions regarding the benefits of social capital and social cohesion are mixed, however, which reflects the complexity of the social processes by which they shape mental health, as well as lack of scholarly consensus on how to define and operationalize them. Some scholars have defined social capital by the presence of social networks that produce norms of reciprocity and trust and, in turn, mutual benefit (Putnam, 1995). However, others have used this approach to conceptualize social cohesion, describing it as “trust, familiarity, values, and neighborhood network ties shared among residents….which serve as the basis from which social capital can be formed” (Carpiano, 2006). According to the latter perspective, social capital takes the form of actual or potential resources that derive from social networks (Bourdieu, 1986; Carpiano, 2006). Social capital can benefit mental health by providing an economic and psychological safety net in an environment that lacks sufficient opportunities to meet basic necessities (Aneshensel and Sucoff, 1996; Carpiano, 2006; Caughy et al., 2003; Cornwell and Waite, 2009; Curley, 2009; Fitzpatrick et al., 2005; Fitzpatrick and LaGory, 2000; Gary et al., 2007; Kawachi and Berkman, 2001; Kim, 2010; Omata, 2012; Pearlin, 1989; Phan et al., 2009; Ross et al., 2000; Sampson et al., 1997; Thoits, 1995; Usher, 2007; Xue et al., 2005). In disadvantaged contexts, social capital may be vital for assistance with day-to-day tasks and vital needs (e.g., economic assistance, providing food, lending goods, child care, household repairs, transportation). Further, it may provide emotional support and counteract the stressful cues in the neighborhood space (Aneshensel and Sucoff, 1996; Finfgeld-Connett, 2005). The mere presence of close-knit neighborhood social networks (i.e., social cohesion), however, does not inherently translate into social capital for all residents. Members of local social networks may be less inclined to aid newly arrived residents or those who are

M.J. Greif, F. Nii-Amoo Dodoo / Health & Place 33 (2015) 57–66

perceived as “outsiders” (Carpiano, 2006). However even less socially integrated residents can indirectly benefit from residence in a socially cohesive community, as it frequently exhibits higher levels of social control that ward off undesirable community conditions such as crime and disorder (Ross et al., 2001). Further, for many residents the perception of the availability of a cohesive social support structure may also prevent a cascade of negative emotions in the face of stressful events (Kawachi and Berkman, 2001). More recently scholars have uncovered that local social capital and social cohesion may not deliver the same benefits across all communities, and that they may in fact be detrimental to some residents' mental health. In disadvantaged settings, local social capital may prove ineffective in buffering the ill effects of local and household stressors, and may even amplify their effects (Belle, 1982; Caughy et al., 2003; Curley, 2009, De Silva et al., 2007; Durden et al., 2007; Offer, 2012; Phan et al., 2009; Usher, 2007; Warner and Rountree, 1997; Wen et al., 2006). Norms of balanced reciprocity are difficult to enforce in economically deprived settings (Offer, 2012), particularly in the setting of sub-Saharan Africa where norms of communalism and sharing are emphasized (Jensen and Gaie, 2010). Consequently, residents who rely heavily on their more financially secure neighbors for assistance may experience shame and fear of future social exclusion as a result of having few resources to share in return (Offer, 2012; Omata, 2012). Moreover, economically advantaged residents who provide assistance for their neighbors may experience distress about depleting their own resources (Belle, 1982; Curley, 2009; Durden et al., 2007; Kawachi and Berkman, 2001; Kleit, 2010; Mitchell and Lagory, 2002; Omata, 2012; Phan et al., 2009). Social interactions among neighbors may also invite discussion about personal problems and worries about community crime or other local stressors, which may be emotionally helpful for some residents but emotionally draining for others (Curley, 2009).

4. Significance of population-based research Caution is necessary in generalizing research findings linking mental health and community context from developed countries to developing ones. A handful of studies in developing countries have examined the relationship between slum residence and mental health. However, they rarely focus on sub-Saharan Africa or examine how specific facets of the social and structural environment (e.g., crime, disorder, joblessness, social capital) influence mental health (Ezpeleta et al., 2007; Gruebner et al., 2012; Izutsu et al., 2006; Parkar et al., 2012; Puertas et al., 2006; Ruback and Pandey, 2002; Ruback et al., 2002). There are significant regional variations in the cultural meanings of mental health, comfort about expressing symptoms indicative of mental health disorders, and stigma surrounding mental health disorders (Abas and Broadhead, 1997; Barke et al., 2011; Bass et al., 2007; de Menil et al., 2012; Ruback et al., 2002). Furthermore, there may be cultural disparities in expectations for the community environment, as well as in adaptation to community stressors (Ruback et al., 2002). Assessing the prevalence and social determinants of mental health problems in poor urban settlements in sub-Saharan Africa, as well as in other developing regions, must be prioritized. Only scant empirical research exists on the associations between local stressors, local social capital and cohesion, and mental health. To this end, the current study employs a sample of residents in urban poor communities in Accra, Ghana, to explore the prevalence of mental health problems (i.e., depression and perceived powerlessness) and residents' perceptions of community stressors (i.e., unemployment, poverty, crime, poor sanitation, drug selling, and insufficient health care) and local social capital and cohesion. Further, it will explore how community stressors, local social capital and cohesion, and a range of individual socioeconomic resources relate to mental health. Finally, a growing body of

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literature illustrates that social capital and cohesion may ameliorate, or conversely deepen, the toll of daily stressors on mental health in disadvantaged communities, and therefore raises questions about the utility of policy initiatives to promote local social capital as a means to improve residents' quality of life. Accordingly, this analysis will explore interactions among social capital, social cohesion, and a range of community and household stressors.

5. Methods 5.1. Data This study employed data from the 2011 wave of the Regional Institute for Population Studies (RIPS) Urban Health and Poverty Project, collected in three poor communities in urban Accra: James Town, Ussher Town, and Agbogbloshie. All three localities are in the Ashiedu Keteke sub-metropolis of the Accra Metropolitan Assembly and are proximate to the central business district. James Town and Ussher Town are situated adjacent to each other, with populations of 16,221 and 27,624, respectively, while the population of Agbogbloshie is 8305 (Ghana Statistical Service, 2012). Conditions suggest somewhat more favorable living standards (e.g., higher levels of education and income) in James Town compared to Ussher Town and Agbogbloshie. Agbogbloshie evidences characteristics most indicative of a slum (e.g., fewer economic resources, lack of access to clean water and sanitation), and being situated near the banks of the Korle Lagoon, it is prone to flooding and significant morbidity and mortality (Joint UNEP/ OCHA Environment Unit 2011). A list of all enumeration areas (EA) demarcated by the Ghana Statistical Service was obtained for the three selected localities. EAs were randomly sampled from the list of EAs for each community, with the number proportionate to the total population of the community. Thus, the number of households in each locality was proportionate to the population size of that locality. This resulted in the stratified selection of five EA's from Agbogbloshie, eight from James Town, and sixteen from Ussher Town. In this way, the sample was representative. A manual listing exercise of all structures, and households within structures, was conducted in each EA. Each structure was identified and numbered, and households within the structures were also identified and numbered. The households identified in each structure were then listed cumulatively, and ultimately forty households were systematically selected from each EA to be sampled for the study. All men and women in their reproductive ages of 15 to 49 years and 15 to 59 years, respectively, were eligible to take part in the survey. The study sample consists of 690 women and men aged 15–49 and 15–59 years, respectively.1 5.2. Dependent and independent variables 5.2.1. Mental health The analysis examines two dependent variables: depression and perceived powerlessness. Depression is measured by a question that asks how often the respondent felt depressed in the last month (‘1’ ¼none of the time, ‘5’ ¼all of the time). Perceived powerlessness – the sense that life is shaped by forces outside one's control (Ross et al., 2001) – is measured by a question that asks whether respondents feel that what happens in their life is determined by factors outside their control (‘1’ ¼strongly disagree, ‘5’ ¼strongly agree). 1 In the case of multiple eligible individuals in a household, all were sampled. Typically this consisted of one male and one female (of reproductive age).

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Table 1 Descriptive Information on Mental Health, Community Stressors, Social Relations, Economic Resources, and Background Characteristics. Variable Mental health Depression Perceived powerlessness

Question

Mean Min. Max. SD

“How often in the past one month did you feel depressed?” (‘1’¼ none; ‘5’¼ all the time) 1.74 “I feel that what happens in my life is often determined by factors beyond my control” (‘1’¼ strongly disagree; 3.54 ‘5’¼ strongly agree)

1 1

5 5

1.08 1.24

“On a scale of 1 (low) to 5 (high), how much of a problem are the following in your community?” (alpha¼ 0.75) Crime Drug selling or use Deterioration and poor sanitation Groups of teenagers or adults causing trouble “On a scale of 1 (low) to 5 (high), how much of a problem are the following in your community?”

3.54 3.33 3.44 4.15 3.24 4.20

1 1 1 1 1 1

5 5 5 5 5 5

0.98 1.44 1.50 1.15 1.38 0.86

Lack of employment Poverty Local insufficient health care “On a scale of 1 (low) to 5 (high), how much of a problem is insufficient healthcare?“ Local social relations Social capital (alpha ¼.69) “Thinking about the neighbors with whom you interact most, how frequently do you do the following with them, from a scale of 1 (very infreqently) to 5 (very frequently)?“ Do favors for each other Lend money or goods Social cohesion (alpha ¼.60) “On a scale of 1 (strongly disagree) to 5 (strongly agree) how much do you agree that:” If there is a problem in the community, neighbors get together to deal with it There are adults in the community that youth can look up to as role models You can count on adults in this community to watch out that children are safe and don't get into trouble Organizational membership “Are you currently a member of an organization in this community?” (‘0’¼ no; ‘1¼yes) Socioeconomic resources Education “What is the highest level of education you have attained?” (‘0’¼ pre-school; ‘4’¼higher) Current employment “Are you currently working?” (‘0’¼ no; ‘1’¼yes) Assets scale (mean) “Does your household have a ___“? (‘0’¼ no; ‘1¼ yes) (fridge/freezer/tv/radio/phone/clock/stove/sofa/fan) Difficulty with bills “How difficult is it for you to meet monthly payments on your household bills?” (‘1’¼ not at all difficult; ‘5’¼ extremely difficult) External economic “How much can you rely on relatives outside of your household or friends for financial assistance?” (‘1’¼ not at assistance all; ‘4’¼ a lot) Relative economic standing “Consider the economic status of the residents in your community, where the people at the top (5) have the (in community) highest economic standing in the community and the people at the bottom (1) have the lowest standing. Which number (1 to 5) best represents where you stand at this time in your life, relative to other people in your current community?“ Demographic/background characteristics Overall health “How is your health in general?” (‘1’¼ poor; ‘5’¼ excellent) Religious involvement “In the past 1 month, how often did you attend religious services?” (‘1’¼ regularly;‘3’¼ never) Years in community “How long have you lived in the current community (years)?“ Married/cohabiting “Are you currently married or living with a partner?” (‘0’¼ no; ‘1’¼yes) Age (years) “How old were you on your last birthday?“ Male (‘1’¼male; ‘0’¼female)

4.19 4.21 2.96

1 1 1

5 5 5

1.08 .99 1.35

3.23

1

5

1.20

3.56 2.90 3.10 3.15 3.00 3.02 0.12

1 1 1 1 1 1 0

5 5 5 5 5 5 1

1.26 1.47 .89 1.30 1.20 1.18 0.32

2.08 0.65 0.49 2.89

0 0 0 1

4 1 1 5

0.82 0.48 0.28 0.99

1.72

1

4

0.94

2.37

1

5

0.96

3.85 0.60 5.08 0.48 29.77 0.48

1 1 0 0 14 0

5 3 42 1 59 1

0.99 0.49 7.98 0.50 10.45 0.50

Perceived community stressors Local disorder Local crime Local drug use/selling Local poor sanitation Teens Local Economic Disadvantage

5.2.2. Community stressors A host of variables assess respondents' perceptions of community problems. Respondents are asked rate on a scale of ‘1’ (low) to ‘5’ (high) how problematic they find the following: lack of employment, poverty, crime, drug selling/use, deterioration and poor sanitation, groups of teens/adults causing trouble, and insufficient health care. Empirical research shows that subjective reactions to local phenomena are equally, if not more, valuable than “objective” conditions when assessing how neighborhood conditions relate to health outcomes (Skogan, 1990). Factor analysis guided the creation of two scales using these variables: local economic disadvantage (alpha¼0.69) and local disorder (alpha¼0.75) (see Table 1). Factor analysis also showed that local insufficient health care did not load well with other community-based variables, and therefore it is included in models as a separate variable.

5.2.3. Local social dynamics A social capital scale consists of two variables that assess the extent to which respondents engage with local neighbors in terms of 1) lending goods and 2) doing favors (‘1’¼ very infrequently, ‘5’¼ very

frequently) (alpha¼ 0.69). A social cohesion scale captures respondents' perceptions of larger community social dynamics. It consists of three variables that assess the extent to which (1) local residents work together to deal with community problems, (2) watch out that children are safe, and (3) act as role models for youth (‘1’¼strongly disagree, ‘5’¼strongly agree) (alpha¼0.60). A community organizational membership variable is also included, as membership can benefit mental well-being through opportunities for solidarity and socializing (Carpiano, 2006). The variable is based on a single question that assesses whether respondents are members of any community groups (‘1’¼yes, ‘0’¼ no).

5.2.4. Socioeconomic characteristics Socioeconomic status has been consistently linked to mental health outcomes, and is captured here by a multidimensional set of variables. Objective measures of SES include education, current employment, and assets. Three additional variables identify more nuanced, subjective measures of SES that recent studies have shown to be highly meaningful to the understanding of health outcomes, perhaps more so than objective measures (Greif, 2012; Heflin and

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Iceland, 2009). These include difficulty paying bills, external financial assistance (i.e., the extent to which respondents can rely on friends and kin outside the household for financial assistance), and relative economic standing (in community). Relative economic standing (in community) assesses respondents' perception of their own economic standing compared to other residents in their community. “Subjective social status” variables such as this one have been linked to better emotional well-being above and beyond objective SES measures (Adler et al., 2000; Leu et al., 2008; Singh-Manoux et al., 2005).

Table 2 Multilevel analysis of depression a. Fixed Effects Community Stressors Local disorder Local economic disadvantage Local insufficient health care

5.2.5. Background/demographic characteristics Age, gender, marital status, years in the community, religious involvement and overall health are also included in the analysis in order to isolate the relationships among community stressors, social ties, and mental health.

Local Social Relations Social capital

5.3. Analytic approach

Socioeconomic Resources Education

Analyses will first explore the prevalence of mental health problems, perceived community stressors, and local social ties. Next, due to the data being hierarchically structured (individuals are nested in households, which are nested in EAs), multilevel regression models with random intercepts and fixed slopes were carried out. This approach statistically accounts for the violation regarding independence of observations (Bryke and Raudenbush, 1992). The association between community stressors and mental health will be examined in the first model, while the second model will include variables pertaining to local social ties. The third model will then incorporate socioeconomic characteristics to isolate the relationships between mental health and both community stressors and local social ties, while the fourth model goes further to include background/ demographic variables. Finally, it will assess whether local social capital and social cohesion also indirectly contribute to mental health by moderating its relationship with a range of stressors.

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1

2

3

4

0.11n (0.05) 0.02 (0.05)  0.02 (0.03)

0.09n (0.05) 0.01 (0.05)  0.01 (0.03)

0.13nn (0.05) 0.01 (0.05)  0.02 (0.03)

0.14nn (0.05) 0.03 (0.05)  0.02 (0.03)

0.12nnn (0.03) 0.03 (0.05) 0.05 (0.12)

0.12nnn (0.03) 0.05 (0.05) 0.11 (0.12)

0.16nnn (0.03) 0.05 0.04 0.12 (0.12)

 0.11n (0.05)  0.03 (0.08) 0.14 (0.16) 0.18nnn (0.04)  0.10n (0.05) 0.04 (0.04)

 0.08n (0.35)  0.11 (0.09) 0.21 (0.17) 0.15nnn (0.04)  0.14nn (0.05) 0.03 (0.04)

0.01 0.10 690

 0.19nnn (0.04)  0.02 (0.08)  0.02 (0.09) 0.01 (0.004)  0.36nnn (0.08) 0.01 0.10 690

Social cohesion Organizational membership

Current employment Assets Difficulty with bills External economic assistance Relative economic standing (in community) Demographic/Background Overall health Religious involvement Married/cohabiting Age Male

6. Results Descriptive characteristics of the sample are shown in Table 1. Respondents reported higher levels of powerlessness (mean¼3.54) than depression (mean¼1.74). Significant concern was expressed about all community characteristics measured here. Local poverty, unemployment, and poor sanitation (means of 4.21, 4.19, and 4.15, respectively) were considered most problematic by respondents. However, they also reported noteworthy levels of concern about crime (mean¼ 3.33), local drug selling (mean¼3.44), and teens/adults causing trouble (mean¼3.24). Meaningful levels of social capital were reported (scale mean¼ 3.23), with doing favors (mean¼3.56) occurring more regularly than lending money and goods (mean¼2.90). Respondents reported modest levels of social cohesion among community members (scale mean¼3.10), and low levels of individual involvement in local organizations (12%).2 Table 2 presents the results of the multilevel models pertaining to depression. Model 1 shows that local disorder is associated with higher levels of depression in this model (coefficient¼0.11, po0.05) as well as in all subsequent models. However community economic disadvantage (coefficient ¼.02, p40.05) and insufficient health care 2 We examined patterns across the three communities and found that residents in Agbogbloshie reported the highest levels of concern about local disorder and economic disadvantage (4.02, 4.30, respectively), followed by Ussher Town (3.55, 4.27) and then Jamestown (3.23, 4.0). Levels of social capital and social cohesion were highest in Ussher Town (3.31, 3.15, respectively), followed by James Town (3.23, 3.03), and then Agbogbloshie (2.96, 2.78). Organizational membership was most common in James Town (17%), compared to Ussher Town (9%) and Agbogbloshie (10%).

EA level variance Household level variance N

0.03 0.17 690

0.03 0.14 690

a

Standard errors in parentheses. p o 0.05. po 0.01. nnn po 0.001. n

nn

(coefficient¼  0.02, p40.05), do not appear to significantly relate to depression in this model. Model 2 illustrates that local social relations are relevant to depression, specifically through social capital among neighbors, which is associated with higher levels of depression (coefficient¼0.12, po0.001). Community social cohesion and organizational membership do not appear to be meaningfully associated with depression here, although their coefficients are in the same direction as the social capital variable. The inclusion of socioeconomic characteristics in Model 3 demonstrates the robustness of local disorder and social capital, and also shows that depression levels are higher among respondents who are less educated (coefficient¼  .11, po0.05), who lack external economic assistance (coefficient¼  0.10, po0.05), and who have difficulty paying their bills (0.18, po0.001). The inclusion of background/ demographic variables in Model 4 explains away the significant relationship between education and depression from the prior model, although the other previously observed associations remain. This model also shows that being male ( .36, po0.001) or in good health ( .19, po0.001) is protective of mental health. Findings in Table 3 likewise present results from multilevel models that examine the relationship between perceived powerlessness and

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Table 3 Multilevel analysis of perceived powerlessness a. Fixed effects Community Stressors Local disorder Local economic disadvantage Local insufficient health care

1

2

3

4

0.11n (0.05) 0.17nn (0.06) 0.04 (0.04)

0.10n (0.05) 0.16nn (0.06) 0.05 (0.03)

0.11n (0.06) 0.15nn (0.06) 0.05 (0.04)

0.12n (0.06) 0.16nn (0.06) 0.05 (0.04)

0.08n (0.04) 0.09 (0.05)  0.09 (0.05)

0.11nn (0.04) 0.09 (0.05)  0.05 (0.14)

0.11nn (0.04) 0.08 (0.04)  0.03 (0.14)

 0.08 (0.06) 0.06 (0.10) 0.11 (0.20) 0.11n (0.05)  0.01 (0.06) 0.01 (0.05)

 0.07 (0.06)  0.05 (0.10)  0.03 0.21 0.09n (0.05)  0.02 (0.06)  0.01 (0.05)

0.01 0.37 690

0.01 (0.05) 0.07 (0.10) 0.19 (0.11) 0.01n (.004)  .05 (0.09) 0.01 0.37 690

Local Social Relations Social capital Social cohesion Organizational membership Socioeconomic Resources Education Current employment Assets Difficulty with bills External economic assistance Relative economic standing (in community) Demographic/background Overall health Religious involvement Married/cohabiting Age Male EA level variance Household level variance N

0.01 0.39 690

0.01 0.39 690

Multiplicative interaction terms between social capital, social cohesion, and all independent variables are used to explore whether community social ties moderate relationships among mental health and stressors, and similarly whether the relationship between community social ties and mental health depend on community (as well as individual) stressors. These interaction terms were entered one at a time in the full multilevel model that contained all independent variables (i.e., Model 4 in Tables 2 and 3). A handful of significant interaction effects emerge, and two are presented in Fig. 1. Overall, these findings underscore the nuanced relationship between community social resources and mental health, as increasingly evidenced by recent research. First regarding social capital, it exhibits significant interactions with relative economic standing (coefficient¼0.11, po0.01) and overall health (coefficient¼0.11, po0.01). As seen in Fig. 1, the direction of the relationship between mental health and social capital depends on levels of health and community status. Fig. 1 shows that local social capital contributes to feelings of powerlessness among residents who are in good overall health and are relatively economically advantaged in the community. However, local social capital can beneficial for health for residents who are in poor health and have low relative economic status. These patterns suggest that social capital may buffer the negative repercussions of poor health and relative economic deprivation for mental health. Furthermore, Table 4 shows a significant interaction between social capital and community economic disadvantage (coefficient ¼0.07, po0.05), such that close neighboring ties are more likely to contribute to depression in communities with higher levels of economic disadvantage. Table 4 also presents significant interactions pertaining to social cohesion, which underscore patterns discussed above that illustrate the advantages and drawbacks of local social ties for residents' mental health. Social cohesion is not likely to provide anti-depressive benefits when it occurs in economically disadvantaged communities (coefficient ¼ .12, po.05), and contributes to perceived powerlessness among households with more assets (coefficient¼0.51, po0.05). However, it also serves to weaken the detrimental relationship between local disorder and perceived powerlessness (coefficient¼  0.11, po.05), and buffers the psychological burden of struggling to pay bills (coefficient¼ 0.16, po0.01).

nnn

p o 0.001. a

Standard errors in parentheses. po 0.05. nn p o0.01. n

all independent variables. Local disorder emerges again as a powerful contributor to mental health problems (coefficient¼0.11, po0.01). Furthermore, exposure to local poverty and joblessness undermines residents' sense of personal control (coefficient¼ 0.17, po0.01). Both local disorder and local economic disadvantage maintain consistent relationships with perceived powerlessness across Models 2, 3, and 4, which also include community social dynamics, socioeconomic measures, and background/demographic measures, respectively. Models 2 through 4 illustrate a consistently positive relationship between social capital among neighbors and feelings of powerlessness. With respect to socioeconomic resources, those who struggle to pay their bills are more likely to perceive themselves as powerless, and the same holds true as people advance in age. However, as also seen in Table 2, a handful of independent variables (e.g., assets, marital status, religious involvement) never demonstrate significant relationships with mental health.3 3 We explored regression patterns across the three communities, and analyses did not uncover meaningful differences the relationship between mental health and measures of community-based stressors, local social dynamics, or socioeconomic resources. Results available upon request.

7. Discussion The current study underscores the importance of prioritizing mental health problems in poor urban settlements in developing countries. This study is among the first to employ a large data set to explore the presence of mental health problems – depression and feelings of powerlessness – in such communities in sub-Saharan Africa, and to further assess how they relate to the institutional, structural, and social characteristics of the urban environment. These findings have significant implications for strategies to reduce the burden of disease and improve overall health in slum communities, a charge of the Millennium Development Goals (MDGs). Numerous studies and reports have described the deplorable conditions found in urban slum communities, but few have empirically assessed the extent to which residents themselves view them as problematic and, consequently, experience poor mental health. Our findings showed that residents rated poverty, unemployment, and poor sanitation as highly problematic in their communities, and that they also expressed significant concern regarding the presence of local crime and drug selling. Exposure to these negative phenomena in the neighborhood space related to depression and feelings of powerlessness. Social capital among neighbors, as well as community social cohesion, proved to be simultaneously beneficial and detrimental to residents' mental health, depending on community characteristics and

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Fig. 1. Social Capital Interactions.

individuals' financial and physical capabilities. Neighboring ties, specifically those that entailed instrumental assistance with lending goods and doing favors, benefited mental health among residents facing extreme economic disadvantage and poor overall health. However, such interactions were emotionally burdensome among residents who were relatively advantaged economically, and in better health. These patterns speak to the mental health implications of collectivist norms in deprived settings, where balanced reciprocity is difficult to enforce. Residents most in need of economic and, perhaps, physical assistance benefited from such norms, but those with more resources and physical capabilities may feel emotionally encumbered by such expectations and experience (or fear) the depletion of their own resources, time, and energy. Social cohesion also proved more detrimental to mental health in more economically disadvantaged communities, as well as among residents who possessed more assets. These findings point to elevated distress as a result of having more tangible assets to share in a deprived setting (Belle, 1982; Curley, 2009; Durden et al., 2007; Kleit, 2010; Mitchell and LaGory, 2002; Phan et al., 2009). However, local social cohesion helped buffer the psychological perils of local disorder and difficulty paying bills. Thus, social cohesion may benefit residents' mental health regardless of whether they

63

leverage it to form individual social capital. Social cohesion may temper the relationship between disorder and mental health by reducing objective levels of local disorder, or by creating a perception that local residents would be willing to intervene to reduce it. Moreover, a cohesive social environment in which residents look out for one another may help residents cope with financial insecurity in tangible ways, or provide the perception that assistance would be available if it became truly necessary. Caution is therefore necessary when contemplating the utility of social capital for mental health. In contexts that emphasize communalism and experience high rates of local and household stressors, there may be considerable drawbacks in making social capital a key focus among policymakers. Efforts to increase levels of social capital in such communities may be ineffective in diminishing mental health problems, or may even contribute to them (Stafford et al., 2008). Creating healthy environments in disadvantaged urban communities is crucial. Researchers, governments, international organizations, donors, and NGOs that have invested in the battle against HIV and other communicable and non-communicable disease in the region must be made aware of the utility of directing resources towards improving mental health, and similarly the urban environment, in diminishing the burden of disease. Improving sanitation – the community problem deemed most problematic by residents – should be prioritized. It will also be imperative to improve data collection on mental health in distressed communities, perhaps as part of routine surveillance at local clinics. Further, educating the populace about mental illness through mass media and community groups may reduce the stigma surrounding it and in turn encourage individuals to recognize and seek treatment for existing mental health problems (Livingston et al., 2013). This study is not without limitations. First, the analyses leaned on cross-sectional data, making it difficult to fully ascertain whether social connections in disadvantaged settings contribute to mental distress, or whether they are a response to it. Nevertheless, many researchers concur that social ties exert a direct and meaningful effect on mental health (Kawachi and Berkman, 2001). It would also be valuable to examine a wider range of local and household stressors, including noise, population density, household crowding, and objective measures on levels of crime (e.g., crime reports). Additionally, examining trust among neighbors, which tends to be weaker in disadvantaged communities (Ross et al., 2001), may further elucidate the relationship between local social capital and mental health found here (De Silva et al., 2007). While there is evidence that self-reported measures of community perceptions correlate strongly with objective measures (Skogan, 1990), it is important to consider whether respondents with mental health problems are more likely to systematically over report community problems. It is also vital to acknowledge that the stigma surrounding poor mental health in the region may lead respondents to withhold reporting information about their symptoms, leading to underestimates of the prevalence of mental health problems. The study also employed single-item measures to capture depression and perceived powerlessness, while other studies have more commonly used scale instruments to measure them. However, a number of studies have illustrated the validity of single item measures to assess feelings of depression and powerlessness (Ayalon et al., 2009; Hoeppner et al., 2011; Rosenzveig et al., 2014; Veldhuizen et al., 2014; Zimmerman et al., 2006). It has become increasingly evident that the dialog on urban health in developing regions must expand to include mental health outcomes, and to shift its focus to the specific facets of the urban environment that shape them. The current study uncovered that residents in poor urban communities in developing countries suffer from mental health problems that relate to the presence of local stressors, which include not only physical and structural factors but

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Table 4 Significant interactions between local social ties and stressors

a b

.

Dependent variable: perceived powerlessness Interaction between Social Capital and Relative Economic Standing (in community) Social capital Community social status Social capital  community social status Interaction between Social Capital and General Health Social capital General health Social capital  general health Interaction between Social Cohesion and Local Disorder Social cohesion Local disorder Social cohesion  local disorder Interaction between Social Cohesion and Assets Social cohesion Assets Social cohesion  assets Interaction between Social Cohesion and Difficulty with Bills Social cohesion Difficulty with bills Social cohesion  difficulty with bills Dependent variable: depression Interaction between Social Capital and Local Economic Disadvantage Social capital Local economic disadvantage Social capital  local economic disadvantage Interaction between social cohesion and local economic disadvantage Social cohesion Local economic disadvantage Social cohesion  local economic disadvantage

 .15n  .35n .11nn

(.10) (.14) (.04)

 .32n  .35n .11nn

(.16) (.14) (.04)

.46nn .42nn  .11n

(.19) (.15) (.05)

 .06  .20n .51n

(.08) (.09) (.25)

.56nnn .60nnn  .16nn

(.15) (.16) (.05)

 .13 .05 .07n

(.15) (.04) (.04)

 .47n .09 .12n

(.22) (.07) (.05)

a

Interaction terms entered separately in models that contain all independent variables. Standard errors in parentheses. n po 0.05. nn p o0.01. nnn p o0.001. b

social ones. Social capital and social cohesion showed complex, often unhealthy, relationships with mental health; these depart from existing knowledge on the consequences of social ties for mental in developing countries, yet align with a more recent body of literature on disadvantaged communities in developed countries. Large-scale population-based research is vital in order to account for meaningful cultural variations in norms that surround mental health, the community environment, and social relationships.

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