Social Capital and Sexual Risk-Taking Behaviors Among Older Adults in the United States.

547048 research-article2014

JAGXXX10.1177/0733464814547048Journal of Applied GerontologyAmin

Article

Social Capital and Sexual Risk-Taking Behaviors Among Older Adults in the United States

Journal of Applied Gerontology 1–18 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464814547048 jag.sagepub.com

Iftekhar Amin1

Abstract Using the General Social Survey (GSS) 2012, a national household-based probability sample of non-institutionalized U.S. adults, this study examined the association of social capital and sexual risk behaviors among older adults aged 55 years and older. Of the 547 respondents, 87% reported not using condoms during their last intercourse, and nearly 15% reported engaging in sexual risk behaviors, such as casual sex, paid sex, male to male sex, and drug use. Binary logistic regression results showed that age, gender, marital status, education, race, sexual orientation, and sexual frequencies were significant predictors of older adults’ unprotected sex. Social capital was not a predictor of unprotected sex but was positively associated with other human immunodeficiency virus/sexually transmitted disease (HIV/STD) risk behaviors such as sex with strangers, having multiple sex partners, injecting drugs, and having male to male sex. Findings of this study highlight the importance of HIV/STD prevention programs for older adults.

Manuscript received: January 14, 2014; final revision received: July 16, 2014; accepted: July 20, 2014. 1University

of North Texas at Dallas, USA

Corresponding Author: Iftekhar Amin, Department of Counseling and Human Services, University of North Texas at Dallas, 7400 University Hills Blvd, Dallas, Texas 75241, USA. Email: [email protected]

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Journal of Applied Gerontology

Keywords sexuality in old age, sexual risk behavior, HIV/STI, older adults, sexually transmitted diseases

Introduction Human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs) are on the rise among older adults in the United States. From 2007 to 2011, reported cases of chlamydia among people aged 65 and older increased by 32%, and syphilis increased in the same age group by 52% (Centers for Disease Control and Prevention [CDC], 2011). It is estimated that by 2020 more than 50% of persons living with HIV infection would be aged 50 years or older (Brooks, Buchacz, Gebo, & Mermin, 2012). Many older adults, however, lack substantive knowledge about HIV/AIDS, perceive themselves to be at no risk of contracting HIV or sexually transmitted diseases (STDs; Ward, Disch, Levy, & Schensul, 2004), and engage in risk behaviors such as unprotected sex (Schick et al., 2010). Understanding the correlates of sexual risk behaviors of older adults is critical for developing HIV/STI prevention and intervention programs for this age group. Several factors are highlighted in literature as associated with older adults’ vulnerability to STIs, which include widowhood, the increased use of erectile dysfunction (ED) medications (Smith & Christakis, 2009), drug use with sex among HIV-positive persons (Grov, Golub, Parsons, Brennan, & Karpiak, 2010), racial differences in monogamous partnership (Harawa, Leng, Kim, & William, 2011), and the inaccurate perception of vulnerability (Ward, Disch, Levy, & Schensul, 2004). Research has shown that older adults’ vulnerability is based on their lack of avenues for seeking sexual health information or adequate sexual health services, such as absence of patient–provider conversation about sexual health and sexual screening (Lindau et al., 2007; Slinkard, 2011). More alarming is the unavailability of HIV and STD education/prevention materials specifically tailored for them even in majority of the state public health departments (Orel, Wright, & Wagner, 2004). Because discussion of sex or accessing information on sex could be difficult at old age, elders who have greater access to social capital, such as memberships in organizations and churches, social networks, and who live in environments with strong norms and trust may have greater access to safe sex information and option. Recent research has begun to document the positive influence of social capital on a wide range of health outcomes for older adults. However, the impact of social capital on older adults’ sexual health has been relatively unexplored. Social capital refers to “features of social life-networks, norms,


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and trust—that enables participants to act together more effectively to pursue shared objectives” (Putnam, 1995, p. 664). According to Putnam (1995), social capital has the capacity for “bridging” (trust and reciprocity between different groups and communities) or for “bonding” (trust and reciprocity that reinforce bonds and connections within groups). Social capital has been shown to influence health outcomes in different ways. For example, in a study on smoking behaviors, social capital was shown to influence positive health outcomes by encouraging individuals to conform to healthy social norms that reduced risk behaviors (Giordano & Lindström, 2011). Social capital may also positively influence health behavior through the diffusion of health-related information in a trusted environment (Kawachi & Berkman, 2001). Participation in social activities such as voluntary/charity work, sport/ social club, religious organization, and political/community organization has been shown to have causal beneficial impact on health for individuals above age 50 (Sirven & Debrand, 2012). Social capital among neighbors may enhance older adults’ self-esteem and mutual respect (Cramm, Dijk, & Nieboer, 2013). A sense of belonging and trust in neighbors were shown to be associated with older adults’ perceived happiness and life satisfaction (Heenan, 2010; Nilsson, Rana, & Kabir, 2006; Theurer & Wister, 2010). Evidence shows that neighborhood social capital and social cohesion may not only influence the well-being of older adults but may also act as buffers against the adverse effects of being single and poor (Cramm et al., 2013). Social capital, particularly, can be beneficial in later life as older adults are at risks of losing social ties, and the loneliness may increase odds of unprotected sex (Grov et al., 2010). Social capital affects health behavior in several ways. Some studies suggest that the effect of social capital may be greater for older adults compared with younger populations. For example, Veenstra (2000) found that indicators of social capital, such as socialization with workmates, willingness to help a work-mate, attendance at religious services, participation in clubs and associations, the nature of the clubs, and relationships with neighbors, were all related to health for the elder subpopulation whereas unrelated to health of the middle-aged population. Although the reasons were not clear, the speculation was that social connections were particularly important at old age because aging older adults might find it increasingly challenging to participate in health enhancing activities

The Present Study For the present study, the objective was to examine whether higher social capital increases the likelihood of safer sex among older adults. Interpersonal


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trust, an indicator of social capital, may influence the norms of sexual behaviors and discourage individuals to engage in sexual risk behaviors to avoid negative sanctions, such as disapproval, criticism, and boycott. Social capital may also enhance the dissemination of health-related information among the individuals in the same social networks and encourage avoidance of sexual risk-taking behaviors. This study hypothesizes that older adults with greater social capital are more likely to engage in fewer sexual risk-taking behaviors compared with their counterparts with lower social capital. In this study, the term “older adults” refers to persons aged 55 and above. In the National Health Interview Surveys (NHIS), the age 55 has been reported to be “a useful benchmark” for assessing patterns in health characteristics in the older populations as people at this age “although approaching retirement, are usually still working; some are still engaged in raising families, and some are beginning to experience chronic health problems typical of older adults” (Schoenborn & Heyman, 2009, p. 2).

Data and Method Data Data for this study are from the General Social Survey (GSS) 2012, which is a national household-based probability sample of non-institutionalized U.S. adults aged 18 and older. Since 1972, GSS has collected data on a variety of topics of social importance to monitor societal change in the United States (Smith, Marsden, Hout, & Kim, 2011). From 57,061 respondents aged 18 and above who were interviewed in 2012 for this study, only those aged 55 or above were selected, which reduced the sample size to 718 respondents. Listwise deletion of cases with missing values on study variables further reduced the sample size to 548 respondents. The data presented here are for 548 respondents aged 55 and older interviewed in 2012.

Dependent Variable Sexual risk behavior was measured with two indicators: unprotected sex and other HIV/STD risk behaviors. The first variable, unprotected sex, was measured in GSS by a question “The last time you had sex, was a condom used?” In this study, the unprotected sex was a binary variable where condom “used” last time was coded as 0 and “not used” was coded as 1. “Other HIV/STD risk behaviors” was based on four risk behaviors: (a) whether the respondent had multiple sex partner in the past 5 years; (b) whether the respondent had sexual intercourse with a non-spousal partner that includes pickup sex, sex with


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acquaintances, paid sex, and sex with some other during the past year; (c) whether the respondent injected drugs within the past 3 years; and (d) whether the respondent had male to male sex past year. For each of the four questions, the positive response was coded as “1,” indicating the respondent was involved in the risk behavior, and the other responses were coded as “0,” indicating that the respondent did not take risk. The responses then were summed up and a dummy variable was created “other HIV/STD risks” where 0 indicated no risk behavior, and 1 indicated respondent was engaged in at least one risk behavior.

Predictor Variable The predictor variable was social capital. There are considerable differences in how social capital has been measured in the literature (Cramm et al., 2013; Veenstra, 2000). Three indicators have been widely used: trust, reciprocity, and co-operation (Heenan, 2010; Kawachi, Kennedy, Lochner, & ProthrowSmith, 1997; Theurer & Wister, 2010). In the present study, for measuring social capital, these three indicators were used. The three questions that have been used to assess perceived trust, helpfulness, and fairness of people were as follows: (a) “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” (b) “Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?” (c) “Would you say that people are fair or try to take advantage?” The responses included a positive, a negative, and a response “depends.” Each negative response was coded as 0 (no trust, none helpful, or none fair), the response “depends” was coded as 1, and positive response was coded as 2. Adding these three variables on perceived interpersonal trust, helpfulness, and fairness of people, a social capital index was created that ranged from 0 to 6, where higher score indicated greater social capital.

Covariates The individual demographic and socioeconomic variables included age, gender, race, marital status, education, sexual orientation, and sexual frequencies. Age was an interval-level variable. Race was measured from the GSS question: “What race do you consider yourself?” The options were “White,” “Black,” “Other,” and “not applicable.” From these responses, race was measured as a dummy variable that was coded “1” for White and “0” for nonWhite. Marital status was coded “1” for married and “0” for not married. Education was recorded as the highest year of school completed, which


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ranged from 0 years to 20 years. Three socioeconomic variables were added initially in the analysis: household income, religiosity, and employment status, but these were dropped from the final models due to their lack of significance (p > .10). Sexual frequency was assessed with the following question: “About how often did you have sex during the last 12 months?” The responses were as follows: not at all, once or twice, about once a month, 2 to 3 times a month, about once a week, 2 to 3 times a week, and more than 3 times a week. Because this is an ordinal-level measure, the options ranged from 0 to 6, where higher score represents greater frequencies of sex. Sexual orientation was measured by the following question: “Which of the following best describes you?” The options were as follows: (a) lesbian, gay, or homosexual; (b) bisexual; (c) heterosexual or straight. If the response was (a) or (b), the respondent was coded as lesbian, gay, or bisexual (LGB). If the respondent was male and had sex with “male” or “both male and female”the past year, then in the dummy variable 1 was coded as men having sex with men (MSM).

Data Analysis Descriptive statistics and bivariate analyses were conducted to examine the relationships among the dependent, predictor, and control variables. The two dependent variables were each analyzed with two binary logistic regression models. In Models 1 and 3, only individual demographic and socioeconomic characteristics were included to predict unprotected sex and “other HIV/STD risk behaviors.” In Model 2 and 4, social capital indicators were added to the demographic and socioeconomic variables. To detect any multicollinearity problems, collinearity statistics was examined. All of the tolerance levels were above the conventionally accepted “cutoff” of .2 (Hamilton, 1992; p. 134), which suggests that multicollinearity was not a problem. Adjusted R2 values were assessed for the goodness of fit of each of the models. All analyses were conducted with unweighted data using the SPSS for Windows Version 19.0 (SPSS, Inc., 2008).

Results Table 1 displays the descriptive statistics for all predictors and dependent variables. Of the 548 respondents, 46.4% were males. Their average age was 66 (range = 55-89; SD = 8.75) years. About half of the respondents were married, and 84.9% were Whites. Respondents on average completed 13.64 years of education. About one third of them were employed full-time, 9% were employed part-time, and 46% were retired. About half of the respondents had


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Amin Table 1. Descriptive Statistics for Study Variables and Respondents. Variable Sexual risk behaviors Condom usea Did not use condom Used condom Other HIV/STD risk behaviorsb Had sex with a non-spousal partner the past year Casual sex (pick up someone) Paid for or being paid for sex Sex with acquaintances Sex with some other persons MSM the past year Injected drugs within the past 3 years Multiple sex partners in the past 5 years Social capital indicators Social support index Demographic and socioeconomic characteristics Age (in years) Gender Female (reference) Male Marital status Not married (reference) Married Race Black (reference) White Education Highest year completed Work statusc Full-time employed Part-time employed Retired Other Household incomec $0-$39,999 $40,000-$74,999 $75,000-$149,999 $150,000 and above

M or % (SD)

Range

86.7 13.3

0-1 0-1

1.5 0.7 1.8 0.2 1.4 0.2 13.2

0-1

3.32 (2.31)

1-6

66.43 (8.76)

55-89

53.6 46.4

51.4 48.6

15.2 84.9

0-1

0-1

0-1

13.64 (3.03)

—

28.5 8.9 46.0 16.6

48.9 25.9 19.3 6.3

— — — — (continued)


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Table 1. (continued) Variable Religiosityc How often attended religious services? Never Less than once a year Once a year Several times a year Once a month 2-3 times a month Nearly every week Every week More than once a week Sexual variables Frequency of sex in the past 12 months Not at all Once or twice Once a month 2-3 times a month Weekly 2-3 times per week 4 and more times per week Sexual orientation Gay, lesbian, bisexual Heterosexual

M or % (SD)

Range

21.8 5.3 11.2 9.4 6.1 7.1 4.4 26.0 8.8

— — — — — — — — —

7.9 20.5 23.7 24.5 16.5 5.8 1.1

2.4 97.6

Note. MSM = men having sex with men; HIV = human immunodeficiency virus; STD = sexually transmitted disease. N = 547 persons above age 55. aCondom use = respondent did not use condom during last intercourse. bOther HIV/STD risk behaviors = respondent engaged in any of these behaviors: (a) had multiple sex partners in the past 5 years, (b) injected drugs within the past 3 years, (c) had sex with a stranger the past year, and/or (d) had MSM in the past 1 year. cVariable that was included in the initial analysis but was dropped due to lack of significance in any of the four models.

annual household income less than 40,000. Whereas 22% of them never attended religious services at church the past year, about one third of them attended once or more than once every week. About 43% believed that people can be trusted, 56% believed that people are helpful, and 60% believe that people are fair. About 2.4% of the respondents were LGBs. Most of them were sexually active, with one fourth having sex 2 to 3 times a month. About 17% had sex weekly and 6% had sex 2 to 3 times per week.


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Table 2. Correlationa of Demographic and Socioeconomic Characteristics, Social Capital Indicators, and Condom Use Behavior Among Adults Above Age 55. 1 1. Age 2. Male 3. Married 4. White 5. Education 6. Frequency of sex 7. Sexual orientation 8. Social capital 9. Unprotected sex 10. Other HIV/STD risks

2

3

4

5

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−.01 −.12** .13** .10* .07 .18** −.11** .03 .11** .08* −.22** .12* −.16** −.07 .09 .03 −.10* .13** .00 −.08* −.10* .07 .02 .07 .29** .28** .02 .18** −.09* .18** .17** −.02 .04 −.13** .31** −.25** −.13** .03 .09

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8

9

−.07 .19** .06 −.22** .05 −.19**

Note. HIV = human immunodeficiency virus; STD = sexually transmitted disease. aPearson correlation coefficient; male, married, White, sexual orientation, unprotected sex, and other HIV/ STD risk behaviors were all dummy variables. *p ≤ .05. **p ≤ .01. ***p ≤ .001 (two-tailed).

Most of the older adults (87%) did not use a condom during their last intercourse. About 15% reported being engaged in at least one of the following four sexual risk behaviors: (a) sex with a stranger the past year, (b) male to male sex the past year, (c) injecting drugs within the past 3 years, and (d) multiple sex partners the past 5 years. Among them, 13% reported having multiple sex partners in the past 5 years. About 0.2% injected drugs within the past 3 years. Other HIV/STD risk behaviors such as sex with strangers and male to male sex were not uncommon. Nearly 1.4% of the older adults reported having male to male sex or MSM the past year. Correlation coefficients among the variables are displayed in Table 2. The results of bivariate analyses showed that age (p < .01), being male (p < .05), being married (p < .01), being White (p < 0.01), and sexual orientation (p < .01) were significantly related to unprotected sex and other HIV/STD risk behaviors. Social capital was not associated with condom use or HIV/STD risk behaviors.

Unprotected Sex Table 3 shows the binary logistic regression results. Model 1 examines the effects of the demographic and socioeconomic variables on unprotected sex while controlling for the others. The overall model was significantly predictive of unprotected sex, (χ2 = 56.77, p < .001; Nagelkerke R2 = .39). In Model 1,


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Table 3. Binary Logistic Regression Models Predicting Older Adults’ Sexual RiskTaking Behavior. Unprotected sexa

Model 1

Variable

B (SE)c

Model 2

Odds ratiod

Social capital indicators Social capital Sociodemographic variables Age (in years) Gender Female (reference) Male Marital status Not married (reference) Married Race Non-White (reference) White Education (in years) Sexual variables Gay or bisexual (reference) Heterosexual Frequency of sex −2 log likelihood Nagelkerke R2

Other HIV/STD risk behaviorsb

B (SE)c

Model 3

Odds ratiod

−0.00 (0.13)

1.00

B (SE)c

Odds ratiod

Model 4 B (SE)c 0.20* (0.1)

1.22

0.96

0.13** (0.05)

1.14

0.15** (0.05)

1.14

−0.05 (0.03)

0.96

−0.04 (0.03)

−1.20* (0.53)

0.30

−1.44* (0.66)

0.24

1.14** (0.41)

3.14

1.16** (0.47)

1.95*** (0.56)

7.04

2.08** (0.68)

7.98

−2.80*** (0.42)

0.06

−2.48*** (0.48)

1.29* (0.56) −0.27** (0.96)

3.61

0.67 (0.71) −0.27* (0.12)

1.95

−0.09 (0.50) −0.01 (0.08)

0.91

−0.29 (0.57) −0.01 (0.08)

2.48** (0.90) 0.74*** (0.22) 125.79 .39

0.77

11.89 2.09

3.31** (1.21) 0.97*** (0.29) 87.43 .46

0.76

27.41 2.63

−1.71 (1.12) −0.05 (0.15) 178.12 .46

0.99

0.18 0.95

Odds ratiod

−1.26 (1.19) −0.06 (0.16) 140.37 .47

3.21

0.6

0.82 0.99

0.28 0.94

Note. N = 548 adults above age 55 years. HIV = human immunodeficiency virus; STD = sexually transmitted disease. aUnprotected sex = respondent did not use condom during last intercourse. bOther HIV/STD risk behaviors = respondent was engaged in any of these behaviors: (a) had multiple sex partners in the past 5 years, (b) injected drugs within the past 3 years, (c) had sex with a stranger the past year, and/or (d) had male to male sexual intercourse in the past 1 year. cB (SE) = unstandardized logistic regression coefficient estimate (B) and its standard error (SE). dOdds ratio = eB. †p ≤ .10. *p ≤ .05. **p ≤ .01. ***p ≤ .001 (one-tailed tests).

age, gender, marital status, race, education, sexual orientation, and sexual frequency were significant predictors of older adults’ unprotected sex. Age was positively associated with unprotected sex. With each additional year of age,


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older adults were 14% more likely to engage in unprotected sex (p < 0.01). Compared with older women, older men were 70% less likely to engage in unprotected sex (p < .05). Marital status was a strong predictor of condom use. Married adults above age 55 were 7 times more likely to have unprotected sex compared with those who were not married (p < .001; Model 1). Whites were 3.6 times more likely to have unprotected sex compared with non-Whites (p < .05). Among socioeconomic variables, education was negatively associated with unprotected sex. With each additional year of schooling, the likelihood of unprotected sex decreases by 23% (p < .01). Heterosexual older adults were 12 times more likely to have unprotected sex compared with their LGB counterpart (p < .01). Those who had sex at greater frequencies were 2 times more likely to report more unprotected sex (p < .001). In Model 2, the social capital variable was added to examine whether social capital had any association with unprotected sex. Model 2 was significantly predictive of unprotected sex but did not improve model fit compared with Model 1, (χ2 = 49.52, p < .001; Nagelkerke R2 = .46). The logistic regression results show that social capital did not have any statistically significant effects on condom use. With the inclusion of social capital in the Model, however, changes appeared in some of the demographic and socioeconomic predictors. In Model 2, race was no longer a predictor of unprotected sex. The significance of marital status and education was also reduced in Model 2. The most notable change occurred in the predictive power of sexual orientation. For LGBs, when social capital was controlled for, their likelihood of unprotected sex was 27 times greater than their heterosexual counterpart (p < .01).

Other HIV/STD Risks Model 3 examined the effects of sociodemographic and sexual frequency and sexual orientation variables on other HIV/STD risks (such as drug injecting, MSM, multiple sex partners, and sex with strangers). Model 3 was significantly predictive of other HIV/STD risk behaviors, (χ2 = 93.77, p < .001; Nagelkerke R2 = .47). Only gender and marital status appeared to be significant predictors of other HIV/STD risk behaviors. Men were 3 times more likely to engage in these HIV/STD risk behaviors compared with women (p < .01). Married older adults were 94% less likely to engage in other HIV/ STD risk behaviors compared with their not married counterparts (p < .001). None of the other variables in the model, age, race, education, sexual orientation, or sexual frequency, had any statistically significant effects on older adults’ “other HIV/STD risk behaviors.” In Model 4, social capital variable was added and it appeared to be a significant predictor of other HIV/STD risk behaviors (χ2 = 69.25, p < .001; Nagelkerke R2 = .46). Older adults with


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greater social capital were 22% more likely to engage in other HIV/STD risk behaviors (p < .05). With inclusion of social capital in Model 4, gender and marital status remained significant. In summary, for older adults, age, gender, marital status, race, education, sexual orientation, and sexual frequency were predictors for condom use behavior, but only gender and marital status were predictors for other HIV/ STD risk behaviors. Social capital did not predict unprotected sex, but it had a significant positive association with other HIV/STD risk behaviors.

Discussion The findings of this study provide important insights into the sexual behaviors of older adults in the United States by contributing evidence on demographic and socioeconomic risk factors for older adults’ sexual risk-taking behaviors. This study provides strong evidence that older adults are continuing to place themselves at risk of HIV/STIs by having unprotected sex and being engaged in other sexual risk behaviors. This study shows that sexual activity continues at advanced stages of life. Only 8% of the older adults reported not having sex at all, whereas the rest 92% were sexually active, which is consistent with findings from earlier studies (Lindau et al., 2007; Schick et al., 2010). Consistent with prior literature, findings of this study show that condom use declines with age (Schick et al., 2010). In addition, older women were less likely to use condoms than their male peers, which supports findings from earlier studies that focused on sexual behavior of older women (Patel, Gillespie, & Foxman, 2003). Older women’s diminished concern for being pregnant after menopause may partially explain their lack of condom use. The inaccurate perception of vulnerability may also contribute to older women’s lack of caution during sex (Ward et al., 2004). However, although condom use was higher among older men, this study found that they were more likely to take sexual risks by having sex with a stranger, with other men, with multiple sex partners, and with strangers. This corroborates previous finding that sexual desire, frequencies, and partners are typically greater for older men than women (Lindau et al., 2007; Patel et al., 2003). It is possible that older men and older women do not perceive the risks with the same level of severity. Previous research found that among the injection drug users (IDU), men who were engaged in high-risk sexual behaviors did not define casual sex as HIV risk behavior, whereas women who engaged in casual or commercial sex perceived themselves at heightened HIV risk (Mitchell & Latimer, 2009). Another explanation could be gender differences in sexual behavior, where women emphasize emotional aspects of relationships whereas men focus more on sensation seeking and engage in other HIV/STD risk behaviors (Bell, O’Neal, & Feng, 1999).


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There were several predictors of unprotected sex that were found to be significant. For instance, marital status was a strong contributing factor to unprotected sex. Married older adults were less likely to use a condom, which is not surprising given that marriage is often perceived as being a committed relationship and unprotected sex may be considered as less risky. However, married older adults were less likely to take other HIV/STD risks compared with their non-married peers. Thus, having a regular partner may reduce married adults risk taking after sex. To illustrate, prior research has shown widowhood as elevating risks for STD diagnoses for men (Smith & Christakis, 2009). More educated were more likely to practice safe sex, because awareness and knowledge about transmission of HIV/STIs are crucial for one’s motivation to use a condom. Heterosexual older adults were 12 times more likely to engage in unprotected sex compared with their LGB peers. It is possible that because heterosexual older adults are more likely to have married and steady partners, they are less likely to perceive unprotected sex as risky. It is worth noting that controlling for social capital substantially increased the observed differences in the risks of unprotected sex among these two groups. When social capital was added to the model, the odds of unprotected sex were 27 times more for the heterosexual older adults compared with their LGB counterpart. Thus, part of the reason that unprotected sex was higher among the heterosexual older adults than their LGB peers may be due to the social capital variation among these two groups. The finding that White older adults were more likely to engage in unprotected sex compared with their Black peers contradicts existing data that both HIV and STIs are significantly higher among Blacks compared with Whites (CDC, 2014; Harawa et al., 2011). When social capital was added to the model for other HIV/STD risk behaviors (Model 2), race was no longer significant, suggesting that differences in social capital between Whites and non-Whites influenced the significant effects of race on condom use. Further research is needed to examine how the differences in condom use could vary by race among older adults. Social capital was hypothesized to be predictive of lower sexual risk; however, it was not significantly related to condom use and had an unexpected effect on other HIV/STD risk behavior. It is noteworthy that there are some differences in these two types of sexual risk behaviors. Condom use may be a behavior of two married or regular and steady partners, and hence could be partially influenced by their relationship power and interpersonal trust versus a more collective social capital (Pulerwitz, Amaro, Jong, Gortmaker, & Rudd, 2002). However, the other HIV/STI risk behaviors exclusively refer to sex outside of marriage and thereby may be more prone to the influences of one’s trust and relationship with others in the social network. Although there is


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compelling evidence in the existing literature that social capital has beneficial effects on health behaviors, this study found an unexpected relationship. It appeared that greater social capital (trust, norms, and reciprocity) was associated with increased likelihood of HIV/STD risk behaviors, such as having sex with a stranger, injecting drugs, MSM, and sex with multiple sex partners. Particularly, this contradicts the widely suggested inverse association of social capital and sexual risk behaviors that document social capital’s positive association with protective behaviors (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003). One possible explanation for this negative association could be the influence of peers on risk behaviors. Because the peer networks and the information channeled via peers have strong effects on individuals’ risky behaviors, strong peer support may not necessarily result in safe health behaviors if the peers themselves are involved in risky behaviors (Jaccard, Blanton, & Dodge, 2005). Although a supportive network may help individuals to acquire factual knowledge and adopt safer lifestyles (Campbell & MacPhail, 2002), risky social environment and networks, on the contrary, can increase the risks (Thornton, 2009). Thus, older adults may be “picking up” sexually risky behaviors from their social networks. When considering the overall results on social capital, it would be premature to conclude that it has no direct effect on older adults’ sexual risk behaviors. Future studies should more closely investigate explanations for positive association of social capital and sexual risk behaviors for this subpopulation. Several limitations of the study need to be acknowledged. First, there may have been a tendency for older adults to underreport their sexual risk behaviors due to the sensitivity of the topic and demand for socially desirable responses. Second, this is a retrospective study, and historical data may be inaccurate due to decay in memory over time. Third, there were some limitations in measurement of variables provided in the GSS 2012 version. For instance, no data in GSS 2012 were available for relationship type and therefore, sexual behaviors could not be assessed separately by relationship to last sexual partner. Research has shown that condom use decreases for those who have regular and ongoing partners as opposed to not having steady partners (Anderson, 2003). Also, social capital measurement was limited by unavailability of data for adults above age 55 on membership in voluntary organizations (Sirven & Debrand, 2012). Furthermore, in this study, only 1.4 of the older adults reported having MSM in the past year, which appears to be lower compared with the rates suggested by other studies. For example, Schick et al. (2010) found that 9% of men reported having a same-sex partner during the most recent sex. CDC (2013) estimates that only 4% of men in the United States are MSM. However, these rates are based on adult population, not specifically on older adults. Also, it is possible that the rate was lower because


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the current study calculated MSM based on only the past year. There was a question in the GSS about male to male sex since age 18; if MSM since age 18 was considered, then 2.9% of the older adults would have reported male to male sex. However, despite these limitations, this study allowed us to highlight the associations of demographic and socioeconomic characteristics that can lead to further our knowledge of sexuality at old age. This study has important public health policy implications. First, older adults’ limited use of condoms underscores the importance of sexual health education. For older adults, there are not many avenues for seeking information about safe sex, and as a result, older adults may lack knowledge about the transmission of HIV and perceive themselves at lower risks compared with younger people (Orel, Spence, & Steele, 2005). Studies suggest that for older patients, patient–provider conversation about sexual health and sexual screening is not very common (Lindau et al., 2007). In addition, HIV and STI education/prevention materials specifically tailored for older adults may not be available even in majority of the state public health departments (Orel et al., 2004). It is crucial that health-care providers be informed about the risk factors of HIV/ STIs among older adults so that they are able to discuss sexual health with their older patients and advise about safer sex. Second, the differential condom use behaviors among older males and females emphasize the need for health-care providers to discuss sexual behaviors with older female patients and help them redefine the condom not merely as a contraceptive but as essential for HIV/STI prevention. Third, racial variation of condom use and other HIV/STD risk behaviors suggests that prevention messages need to be tailored to the specific racial/ethnic groups of older adults, which have not received much attention. Fourth, the association of marital status with unprotected sex emphasizes the importance of focusing interventions for married older couples. This study was one of the first to examine the influence of social capital on sexual risk behaviors among older adults. A strength of this study was the use of a recent, large nationally representative sample of older adults, and future studies may benefit from additionally taking a longitudinal view of the impact of social capital. Several sociodemographic factors were found to be predictive of sexual risk, with social capital having an unexpected role in HIV/STD risk behaviors. This will add to our ability to focus sexual health interventions on specific risk factors among older adults. With older people living longer and remaining sexually active, sexual health education may be an essential and effective health promotion strategy for this age group. Author’s Note This research used the General Social Survey (GSS), which is a “public-use” data set, and does not require obtaining Institutional Review Board approval.


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

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

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Author Biography Iftekhar Amin, PhD is an Assistant Professor of gerontology in the Department of Counseling and Human Services at University of North Texas at Dallas. His research interests include examining social factors that influence sexuality in later life, transnational caregiving, and retirement.


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