Associations Between Profiles of Communication About Aging and Quality of Life for Middle-Aged and Older American Adults

Abstract

The association between how middle-aged and older adults talk about aging and their quality of life was examined using latent profile analysis and regression techniques. Two-hundred eight-six adults with an average age of 52.82 (range: 45–77) completed an online questionnaire, which assessed participants’ self-reported communication about aging, stress, health-related quality of life, and satisfaction with life. Controlling for social support and demographics, participants’ profile of communication about aging was found to predict satisfaction with life, stress, and mental health but not general or physical health.

Keywords

communication quality of life perceived stress satisfaction with life mental health

Introduction

Previous research has identified a number of social and relational factors that are linked to physical health, mental health, and quality of life as people age. For example, the protective effects of social support, defined as “the real or perceived availability of social resources” (Holt-Lunstad, Smith, & Layton, 2010, p. 2) on health is well established (Cohen, 2004; Cohen & Wills, 1985; Lakey & Orehek, 2011): Higher levels of perceived social support have been consistently linked to better health outcomes across age groups (Sherbourne, Meredith, Rogers, & Ware, 1992). Similar effects have been found for social integration, defined as “participation in a broad range of social relationships” (Cohen, 2004, p. 677). For instance, Yang et al. (2015) found that higher levels of social integration at previous points in time predicted lower physiological dysregulation (measured by blood pressure and inflammation levels) for older adults at subsequent points in time. The extent of people’s social relations (which includes both social integration and social support) has also been linked to mortality: In their meta-analysis of 148 studies (which primarily focused on older adults), Holt-Lunstad et al. (2010) found that stronger social relations were associated with a 50% increase in likelihood of survival.

Researchers have also found that the quality of people’s interpersonal relationships can influence both health and life satisfaction. For example, in his investigation of older adults’ life satisfaction, Nussbaum (1983) found associations between participants’ self-reported life satisfaction and closeness of interaction with family and friends (operationalized as a sum of participants’ closeness to a given person and the amount of enjoyment they derived from talking with that person). In addition, at least one study has found associations between the quality of intergenerational communication experienced by older adults and self-reported depression (Cai, Giles, & Noels, 1998).

A number of theoretical frameworks also address these issues. The communication predicament of aging model (Ryan, Giles, Bartolucci, & Henwood, 1986) posits that patronizing talk directed at older adults can have deleterious effects on mental and physical health, to the extent that older adults internalize negative stereotypes implicit in this kind of communication. Proponents of relational regulation theory have argued that social interactions in established relationships are a primary means by which people regulate thoughts, actions, and affect, and that this regulation has positive consequences for mental health (Lakey & Orehek, 2011). Studies by Floyd and colleagues, employing affection exchange theory, have demonstrated that expressing and receiving affection in close relationships are associated with better mental and physical health (e.g., Floyd, 2002, 2015; Floyd et al., 2005). Both relational regulation theory and affection exchange theory, however, do not address issues related to age or aging specifically.

Theory and research on social identity also highlight the potential importance of social evaluations to psychological and physical outcomes. Social identity theory posits that a positive sense of social identity is important to individuals’ self-concepts and self-esteem (Tajfel & Turner, 1986) and outlines strategies that individuals and groups pursue when they identify with a social group that is not held in positive regard. In Western society, attitudes toward older adults (and evaluations of older adults as a social group) are generally more negative than those toward younger adults (Kite, Stockdale, Whitley, & Johnson, 2005), and older adults can be the object of prejudice and discrimination based on their age (Nussbaum, Pitts, Huber, Kreiger, & Ohs, 2005; Palmore, 1999). Although there is limited research addressing the extent to which outcomes like life satisfaction and physical health follow directly from social group membership, social identification has been linked to symptoms of depression (Cruwys, Haslam, Dingle, Haslam, & Jetten, 2014), and belonging to socially stigmatized groups can be a source of chronic stress (e.g., Meyer, 1995). Chronic stress, in turn, has been associated with more negative mental and physical health outcomes (Williams, Jackson, & Anderson, 1997). Together, this suggests that knowing or believing that one is subject to negative social evaluations (by virtue of one’s group membership) may have consequences for health.

Collectively, this work provides strong evidence that the nature of people’s social interactions can influence their mental and physical well-being. However, a majority of these studies address social relations and social interaction at a general level; they do not address what people actually talk about, or say to each other. In addition, much of this research does not focus specifically on how people address issues of age and aging, even when older adults are the population of interest. As such, the extent to which other forms of communication related to age and aging are associated with physical and mental health is not as well known. This is the focus of the present study.

Communication and Aging

Researchers who study communication and aging contend that communication is a crucial component of how people approach the process of aging, and of positive experiences of aging (Giles, Davis, Gasiorek, & Giles, 2013; Nussbaum, 1985, 2007, 2016; Ryan et al., 1986). The communicative ecology model of successful aging (CEMSA) has proposed that how people communicate about topics related to age and aging can create and foster social environments (which the model refers to as ecologies) in which experiences of aging are more or less positive (Fowler, Gasiorek, & Giles, 2015). Drawing on a review of extant research on communication and aging, this model focuses on seven domains of communication behavior: self-categorizing as old (e.g., referring to one’s age in explanations or stories), expressing optimism about aging, teasing or joking with others about age, managing being the recipient of ageism, responding to messages about aging in mass media, talking with family about future care needs, and using current or emergent technology to stay connected to others (Fowler et al., 2015; Giles et al., 2013).

Subsequent research guided by the model has identified three distinct profiles of communication about age and aging, consisting of combinations of behaviors in these seven domains. The first, an engaged profile, is characterized by relatively low levels of self-categorizing as old and teasing others about age, but higher levels of expressing optimism about aging, skepticism toward anti-aging media messages, and communication with family about wishes for care. The second, a disengaged profile, is characterized by comparatively low levels of behavior in all domains of communication about age and aging. Third and finally, a bantering profile is characterized by relatively high levels of self-categorizing as old, teasing and joking about aging, expressing optimism, and skepticism about anti-aging media messages, coupled with moderate levels of communication with family about wishes for care. To date, these same three profiles have been identified and observed in four samples (two collected in the United States, two collected in New Zealand) of older, middle-aged, and younger adults (Gasiorek & Fowler, 2016; Gasiorek, Fowler, & Giles, 2015). Profiles offer some advantages over studying each domain individually: they provide insight into how particular communicative behaviors systematically co-occur and reflect the gestalt of individuals’ communication tendencies.

In these studies, researchers have also found differences between communication profiles in affect and anxiety toward aging, efficacy related to aging, and self-reported experiences of successful aging. Generally, people who exhibit an engaged profile of communication express the most positive affect, highest levels of efficacy, and highest levels of successful aging. People who exhibit a disengaged profile, in contrast, report the least positive experiences of successful aging. Those who exhibit a bantering profile appear to be ambivalent about aging, expressing moderate to high levels positive affect, and moderate levels of successful aging, but also higher levels of negative affect and anxiety than engaged communicators, as well as lower efficacy (Gasiorek & Fowler, 2016; Gasiorek et al., 2015).

Quality of Life and Communication About Aging

In this study, we extend this work to examine associations between profiles of communication about aging and health-related quality of life (HRQOL) in middle-aged and older adults. Quality of life is generally conceptualized as “the state of physical, mental, and social well-being” (Barile et al., 2013, p. 1202). HRQOL refers specifically to components of quality of life associated with mental and physical health, which can include perceived stress and global satisfaction with life (SWL; Barile et al., 2013; Hennessy, Moriarty, Zack, Scherr, & Brakbill, 1994; The WHOQOL Group, 1998).

How adults describe quality of life may also be an important factor in understanding healthy aging. For example, a mixed-methods investigation by Bryant, Corbett, and Kutner (2001) found that older adults whose self-assessed health was better than expected (given their age, number of chronic conditions, and physical ability) were more likely to define health as having something worthwhile and desirable to do, the ability to accomplish that activity, the resources to support that activity, and sufficient will or positive attitude. In these definitions, there was a notable lack of attention to physical conditions associated with aging, and instead a focus on meaningful activity and the involvement of friends and family. Benyamini, Leventhal, and Leventhal’s (2003) work corroborated these findings. They also found that older adults with poor quality of life were more likely consider physical symptoms as critical aspects of self-assessed health, and that social support from family and friends was a stronger consideration for those with poor, fair, and good health compared with those with very good or excellent health.

To the extent that HRQOL represents a subjective assessment of one’s life at a given point in time, it is reasonable to expect that it could be subject to, and a product of, psychosocial factors such as one’s views about one’s life course, and current position in it. These views, it has been argued in the CEMSA, may be both shaped by and reflected in one’s communication about age and aging (Fowler et al., 2015). In this study, we tested whether individuals’ communication about aging was associated with their (a) SWL, (b) perceived stress, (c) self-reported general health, and the number of (d) mentally and (e) physically unhealthy days they experienced in the previous month. Given previous findings, we anticipated that an engaged profile would likely be associated with higher SWL, lower stress, and better self-reported health; a disengaged profile with the opposite pattern of outcomes; and a bantering profile associated with higher stress but lower SWL and worse self-reported health.

Method

Participants and Procedures

Participants were a sample of N = 286 middle-aged and older adults (defined as age 45 years or older) from the United States who completed an online questionnaire via a link posted on Amazon’s Mechanical Turk platform. Human subjects committee reviewed this study proposal and approved it as exempt. Participants’ average age was 52.82 years (SD = 6.24; range: 45–77), and the sample was 42% male. A majority of participants (88.5%) were Caucasian; 6.3% were African American; 4.2% were Asian or Asian American; 3.5% were Hispanic (non-White), and 1.0% self-identified as other (i.e., an ethnicity not listed).¹ The median level of educational attainment in the sample was a bachelor’s degree, and the median household income bracket for the sample was $50,001 to $70,000 per year. A majority of participants (60.1%) reported being married; 34.3% reported being single, and the remainder reported being in some form of nonmarriage partnership. Most participants lived in either a major city (42.3%) or a town (42.0%); the remaining 15.4% reported living in a rural area.

Materials

In the online questionnaire,² 18 items from Fowler et al. (2015) assessed participants’ communication about aging (see Supplementary materials for full list of items). SWL was measured with the Satisfaction with Life Scale (five items, Pavot & Diener, 1993; α = .90; sample items: “I am satisfied with life,” “In most ways my life is close to my ideal”). Perceived stress was measured with the short version of the Perceived Stress Scale (four items; Cohen, Kamarck, & Mermelstein, 1983; α = .78; sample item: “In the last month, how often have you felt that you were unable to control the important things in your life?”). General health and unhealthy days (mental and physical) were measured using the U.S. Centers for Disease Control and Prevention’s (2000) HRQOL-4 (four items). The MOS-SSS-6 (Holden, Lee, Hockey, Ware, & Dobson, 2014; α = .93), which asks participants to indicate how often different types of support are available to them (sample items, “Someone to take you to the doctor if you need it,” “Someone to do something enjoyable with,” 1 = none of the time, 5 = all of the time), assessed global social support. Composite indexes for each scale were created by taking the mean of individual item scores. As demographic controls, participants also reported their age, ethnicity, gender, relationship status, living situation (alone or with others), residential environment (city, town, rural), current employment status, total household income (in brackets, 0–6; 1 = below $30,000; 6 = above $130,000), and educational attainment (0–5; 1 = no formal degree, 2 = high school diploma or equivalent, 3 = Bachelor’s degree, 4 = Master’s degree, 5 = Doctoral degree).

Data Analysis

Data analysis proceeded in two steps. First, we conducted a latent profile analysis (LPA) with the 18 communication items as indicators. The purpose of this analysis was to determine whether the same three relative profiles of communication behavior found in previous work also were present in the current sample. In the second step, we used multiple regression to examine whether participants’ communication profile was uniquely associated with SWL, stress, general health, and unhealthy days experienced in the previous month. Participants’ communication profile was operationalized as their most likely profile membership (MLPM) from the LPA conducted in the first step of the analysis. MLPM was dummy coded such that for each profile participants received a 1 if this was their MLPM, and a 0 if it was not. Participants’ age, ethnicity (White or non-White), gender, relationship status (single or partnership), living situation (alone or with others), residential environment (rural or nonrural), employment status (currently employed or not), income bracket, and educational attainment were included as demographic controls. Because social support often has a strong communicative component but is conceptually distinct from the communication profiles of interest, it was also included as a control.

For each outcome variable, two regressions were conducted, one with the first profile (disengaged) as the reference profile, and one with the second (engaged) profile as the reference profile. Linear regressions were conducted for SWL, perceived stress, and general health as outcomes (as these were continuous variables with relatively normal distributions). Negative binomial regressions were conducted for mentally and physically unhealthy days as outcomes (as these were count variables with strongly skewed distributions; see Zhou et al., 2014). Significant dispersion coefficients for all count dependent variables confirmed the appropriateness of this distribution choice (Atkins & Gallop, 2007). All analyses were conducted in Mplus 7.2 (Muthén & Muthén, 1998–2016).

Results

In the first step of analyses, we tested a two, three, four, and five profile model and determined that the three-profile model best fit the data. This replicated the same three relative profiles of communication behavior identified in previous studies, each using different samples (see Table 1 for LPA fit statistics and supplemental materials for communication item means by profile, and for LPA plot). The sample’s MLPM breakdown was similar to previous studies, with 34.5% (n = 99) in the disengaged profile (P1), 19.2% (n = 55) in the engaged profile (P2), and 46.3% (n = 133) in the bantering profile (P3).

Table 1.

Latent Profile Analysis Fit Statistics.

Profiles	LogL	BIC	Entropy	LMR	Δ BIC
1			1.00
2	−9834.23	19979.74	0.90	0.00
3	−9660.42	19739.65	0.92	0.01	240.09
4	−9546.06	19618.46	0.92	0.11	121.19
5	−9478.70	19591.25	0.91	0.35	27.20

BIC = Bayesian information criterion; LMR = Lo-Mendell-Rubin likelihood ratio test.

Bold indicates final latent profile solution.

Table 2 presents the results of regressions predicting SWL, stress, general health; Table 3 presents regression results predicting mentally and physically unhealthy days. Communication profile membership was a significant predictor of SWL and perceived stress but not general health. Those in the engaged (β = .17, p = .004) and bantering profiles (β = .21, p < .001) reported higher SWL than those in the disengaged profile. There were, however, no significant differences between engaged and bantering profiles. In addition, those in the bantering profile (β = .20, p = .007) reported higher levels of stress than those in the engaged profile; there were, however, no other differences between profiles on perceived stress. Communication profile was not a significant predictor of general health (all comparison p’s > .18).

Table 2.

Predictors of Self-Reported Satisfaction With Life (SWL), Perceived Stress, and General Health (Linear Regression).

	SWL		Perceived stress		General health
	β	p	β	p	β	p
Age	−0.007	.897	−0.188	<.001	−0.036	.523
Education level	0.136	.017	−0.031	.584	−0.157	.008
Employed	0.012	.830	−0.070	.219	−0.200	.001
Income	0.076	.211	−0.074	.213	−0.099	.115
Live alone	0.099	.155	−0.095	.162	−0.053	.466
Male	−0.067	.218	−0.028	.596	0.087	.121
Rural environment	−0.017	.748	−0.017	.743	0.020	.723
Single	−0.109	.113	−0.023	.737	0.073	.309
White	−0.071	.181	−0.009	.861	−0.017	.754
Social support	0.363	<.001	−0.458	<.001	−0.268	<.001
Reference disengaged [P1]
Engaged [P2]	0.174	.004	−0.096	.11	0.065	.306
Bantering [P3]	0.210	<.001	0.077	.20	0.083	.185
Reference engaged [P2]
Disengaged [P1]	−0.211	.004	0.116	.11	0.078	.306
Bantering [P3]	−0.011	.881	0.198	.007	−0.002	.984

SWL = satisfaction with life. Bold indicates statistically significant coefficients. P1, P2, and P3 refer to communication profiles. General health is coded such that lower scores indicated better health. SWL R²= 0.25; stress R²= 0.28; general health R²= 0.20.

Table 3.

Predictors of Self-Reported Mentally and Physically Unhealthy Days (Negative Binomial Regression).

	Mentally unhealthy days			Physically unhealthy days
	B	p	IRR	B	p	IRR
Age	0.022	.266	1.022	0.018	.333	1.018
Education	−0.254	.061	0.776	−0.01	.952	0.990
Employed	−0.433	.075	0.649	−0.639	.007	0.528
Income	−0.137	.125	0.872	−0.157	.096	0.855
Live alone	−0.806	.003	0.447	−0.301	.415	0.740
Male	−0.176	.474	0.839	−0.254	.338	0.776
Rural environment	−0.072	.817	0.931	−0.225	.395	0.799
Single	−0.002	.994	0.998	0.027	.942	1.027
White	0.561	.102	1.752	0.155	.696	1.168
Social support	−0.493	<.001	0.611	−0.226	.087	0.798
Reference disengaged [P1]
Engaged [P2]	−0.492	.182	0.611	0.038	.923	1.039
Bantering [P3]	−0.692	.005	0.501	−0.207	.440	0.813
Reference engaged [P2]
Disengaged [P1]	0.492	.182	1.636	−0.038	.923	0.963
Bantering [P3]	−0.200	.573	0.819	−0.244	.508	0.783

IRR = incidence rate ratio. Coefficients are unstandardized. Bold indicates statistically significant coefficients.

Communication profile membership was also a significant predictor of mentally unhealthy days but not physically unhealthy days. People who communicated with a disengaged profile reported 50% more mentally unhealthy days than those with a bantering profile (p = .005); however, no other significant effects for profile membership were found. No significant differences in physically unhealthy days were found between communication profiles (all comparison p’s > .44).

Table 4 presents the adjusted and unadjusted means for the quality of life outcomes, which provide additional insights into the associations between communication, model covariates, and self-reported quality of life. It was notable that after adjustment for covariates in the model, the mean number of mentally unhealthy days rose from 2.59 days (unadjusted) to 3.86 days (adjusted) for those with an engaged communication profile. In a similar but reverse effect, after the adjusted for the covariates, the mean number of mentally unhealthy days for those in the bantering communication profile group were reduced (from 4.08 to 3.67 days).

Table 4.

Adjusted and Unadjusted Means for Self-Reported Quality of Life Outcomes by Communication Profile.

	SWL	Perceived stress	General health	Mentally unhealthy days	Physically unhealthy days
Disengaged [P1]	3.18/3.14	2.35/2.38	2.47/2.51	5.39/5.42	4.64/4.89
Engaged [P2]	3.59/3.80	2.13/1.91	2.63/2.41	3.86/2.59	4.13/3.65
Bantering [P3]	3.57/3.51	2.48/2.55	2.63/2.70	3.67/4.08	3.98/3.99

SWL = satisfaction with life. Means are presented as adjusted/unadjusted.

We also conducted post hoc analyses to determine whether, in addition to direct effects, social support moderated the associations between profile membership and the quality of life outcomes. These analyses yielded nonsignificant effects for all outcomes of interest except SWL. Here, the association between social support and SWL was stronger for those exhibiting a disengaged profile compared with an engaged profile (interaction β = .74, p = .029); and marginally stronger for those exhibiting a bantering profile compared with an engaged profile (interaction β = .67, p = .065).

Discussion

Controlling for self-reported social support and demographics, individuals’ profiles of communication about aging were found to predict life satisfaction, perceived stress, and mentally unhealthy days, but not general health or physically unhealthy days. People who exhibited a disengaged communication profile—characterized by comparatively low levels of behavior in all domains of communication about age and aging—generally had the least positive self-reported quality of life (i.e., the most unhealthy days, lowest SWL), though it was those in the bantering profile that reported the highest levels of stress. These results are broadly consistent with previous findings regarding these profiles, in which middle-aged and older adults with an engaged profile reported higher levels of positive affect about aging than those with a disengaged profile, and those with a bantering profile reporting higher levels of negative affect than those with an engaged profile (Gasiorek et al., 2015).

More specifically, this study found that those with engaged and bantering communication profiles reported better SWL than those with a disengaged profile, and those with an engaged profile reported less stress than those with a bantering profile. Across almost all quality of life outcomes, those in the disengaged group reported the least healthy scores. Compared with those with engaged and bantering profiles, they reported lower SWL and more mentally unhealthy days, even after adjustment for a host of covariates. This suggests that engaging in conversations about aging to any extent may be associated with better self-reported SWL and subjective health than no engagement. However, engaging in a manner that is consistently constructive and positive (which is what defines an engaged profile) may be associated with lower levels of perceived stress.

A comparison of the adjusted and unadjusted means for the quality of life provides additional insights into the associations between communication, subjective health, and SWL. The differences observed between adjusted and unadjusted outcome scores suggest that demographic and social factors (i.e., covariates in the model) were also associated with participants’ communication profiles, as well as their perceived quality of life. Given that previous research on these profiles has not identified consistent predictors of profile membership (Gasiorek & Fowler, 2016; Gasiorek et al., 2015), these differences in adjusted and unadjusted means may be a function of differential social support, which past studies with these profiles have not measured.

A number of the covariates included in the regression analyses were found to be statistically significant predictors of quality of life. Of note, older age was associated with lower perceived stress, higher education was associated with higher SWL, and being employed was associated with better general health and fewer physically unhealthy days. In addition, living alone was associated with fewer mentally unhealthy days (after adjusting for social support, a primary benefit of shared living spaces), consistent with some past research on living arrangements and mental health (e.g., Michael, Berkman, Colditz, & Kawachi, 2001). Interestingly, social support was a strong predictor of almost all outcomes (with the exception of physically unhealthy days), while income was never statistically significant. This suggests that after taking into account communication profiles of individuals, global social support still provides an added value to individuals’ quality of life, while income may not necessarily do so. Furthermore, social support has been found to help facilitate communication between individuals and improve their quality of life (Helgeson, 2003; Strine, Chapman, Balluz, & Mokdad, 2008), though the strength of these associations may depend on the type of support (e.g., emotional and instrumental), the age of the individual, and be specific to particular indicators of quality of life.

This study had a number of limitations but also a number of strengths. First, its data are cross sectional; therefore, the causal direction of the association between communication profiles and quality of life cannot be determined. It is possible that communicating about age and aging in particular ways (e.g., positively and constructively) fosters and promotes environments conducive to good health and SWL, as the CEMSA suggests (Fowler et al., 2015). However, it is also possible that experiencing good health and SWL leads people to communicate about age and aging in particular ways (e.g., positively and constructively). Indeed, we believe it is quite likely these relationships are likely bi-directional. It is also possible that people who talk about aging in different ways may also interpret questions about quality of life differently, particularly to the extent that people’s way of communicating about age reflects how they approach or view their lives more generally. Previous research suggests that individuals differentially consider factors like their ties with family and friends when responding to quality of life inventories (Benyamini et al., 2003; Bryant et al., 2001). Whether and to what extent different profiles of communication about aging correspond to different conceptualizations of quality of life is an interesting question for future research.

A second limitation of this study is that participants were relatively young (for a studying on issues aging), with middle-aged adults constituting a majority of the sample. It is possible that the relationships investigated here could differ across the lifespan (e.g., Yang et al., 2015); systematically investigating these issues with different age-group samples is another direction for future work. Finally, as an initial investigation into a topic that most major national surveys do not address, this study also relied on a relatively small (and not necessarily representative) sample. This limits the statistical power of our analyses and the potential generalizability of our results. Given the associations this study identified despite these limitations, a longitudinal investigation into the nature of these associations with a larger sample size is warranted.

These limitations notwithstanding, this study makes a meaningful contribution to our understanding of how communication is associated with HRQOL. To the best of our knowledge, this study represents the first investigation of communication about aging, subjective health, and quality of life in middle-aged and older adults. We demonstrate that how people communicate about age and aging is consequential and can make a unique contribution to individuals’ mental health, perceived stress, and self-reported life satisfaction. Given that quality of life is an established predictor of mortality (Brown, Thompson, Zack, Arnold, & Barile, 2015), these findings have potentially important implications for adults’ life trajectories. It also confirms and contributes to a growing body of research indicating that communication, and the way that people talk about age and aging are important components of experiences of successful and resilient aging.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Supplementary Material

Supplementary Material for this article is available online.

Author Biographies

Jessica Gasiorek is an assistant professor in the Department of Communicology at the University of Hawai'i at Mānoa. Her research addresses message processing and issues related to communication and aging. Her published work includes both empirical articles and book chapters on communication accommodation theory, communication and aging, and communication in multilingual medical contexts.

John P. Barile is an associate professor in the Department of Psychology at the University of Hawai'i at Mānoa. Dr. Barile’s research concerns ecological determinants of health-related quality of life. This line of research includes the study of individual level factors, such as socioeconomic status, aging and ethnicity, as well as contextual factors, such as housing conditions and community violence.

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