Abstract
Substantial literature demonstrates the influence of the neighborhood environment on health behaviors and outcomes. But limited research examines on how gay and bisexual men experience and exist in various geographic and virtual spaces and how this relates to their sexual behavior. New York City Men 2 Men (NYCM2M) was a cross-sectional study designed to identify neighborhood-level characteristics within the urban environment that influence sexual risk behaviors, substance use, and depression among men who have sex with men (MSM) living in NYC. The sample was recruited using a modified venue-based time-space sampling methodology and through select websites and mobile applications. Whether key neighborhoods of human activity, where a participant resided (termed home), socialized (termed social), or had sex most often (termed sex), were the same or different was evaluated. “Congruence” (or the sameness) of home, social, and most often sex neighborhood was reported by 17 % of men, while 30 % reported that none of their neighborhoods were the same. The largest group of men (39 %) reported that their home and sex neighborhoods were the same but their social neighborhood was different while 10 % reported that their home neighborhood was different than their social and sex neighborhood; 5 % men reported same home and social neighborhoods with a different sex neighborhood. Complete neighborhood incongruence was highest among men who were Black and/or Latino, had lower education and personal income levels, and had greater financial insecurity. In adjusted analysis, serodiscordant condomless anal intercourse and condomless anal intercourse with partners from the Internet or mobile applications were significantly associated with having the same social and sex (but not home) neighborhoods. Understanding the complexity of how different spaces and places relate to the health and sexual behavior of MSM is essential for focusing interventions to best reach various populations of interest.
Keywords: HIV, Neighborhoods, Risk behaviors
Introduction
Men who have sex with men (MSM) comprise the largest proportion of all new HIV diagnoses in the USA [1]. Black and Latino MSM are disproportionately affected [1]. Recent literature has emphasized the importance of factors beyond those at the individual level in explaining race/ethnic disparities in HIV infection among MSM in the USA, including structural barriers such as poverty, stigma, and incarceration and how they create barriers to prevention and care services. [2–4] Substantial literature demonstrates the influence of the neighborhood environment, including built and social characteristics, on health behaviors and outcomes, such as mortality, coronary heart disease, self-rated health, depression, violence, drug use, sexual behavior, sexual partnering patterns, and sexually transmitted infections [5–10].
Among MSM, the neighborhood environment may operate in a number of ways; it may offer opportunities to connect with other gay people or it may manifest barriers to full expression and experience of a gay identity. Traditionally, a critical mass of gay people living in a neighborhood, along with services tailored to their needs, has been conceptualized as either “gay presence” or “gay space” [11, 12]. Gay neighborhood presence has been found to be positively associated with protective sexual behaviors such as consistent condom use [13]. Conversely, other studies have found living in a gay neighborhood was associated with methamphetamine use and condomless receptive anal intercourse, while protective against substance use dependency [14, 15]. A lack of significant neighborhood gay presence may result in gay and bisexual men migrating into urban areas to seek a more supportive, less homophobic environment [16, 17]. At the same time, gay and bisexual men born and raised in large urban areas such as New York City (NYC) also report challenging experiences of homophobia and heterosexism in neighborhoods with little gay space. Our qualitative research suggests that young men of color born in neighborhoods with little gay space often seek gay space and/or sex outside of their home neighborhoods; when these MSM are not out about their sexuality and/or their home neighborhoods are not supportive, sexual and partnering activity may take place far away from familiar home contexts or in risky environments [18].
In addition to neighborhood influences on health and well-being, feelings of connectedness to non-geographically bounded, identity-based communities may be important to sexual behavior among MSM. In a germinal study, O’Donnell and colleagues found that Latino MSM were less likely to engage in unprotected anal intercourse if they felt strong feelings of connectedness to the Latino community [19]. Similarly, Warren and colleagues found that stronger ethnic identity was protective of higher risk sex among Latino MSM [20]. More recently, Van Sluytman and colleagues [21] found that attachment to the Black community and/or gay Black community reduced the likelihood of psychological distress. Thus, connectedness to identity-based communities is also relevant to the sexual health and well-being of MSM. Likewise, connectedness to the gay community has been found to be important for MSM with a stronger attachment associated with decreased sexual risk [22]. This attachment may be shaped by social relations or ties and networks, including friends, families, and families of choice [12].
A limitation of many studies of neighborhood or spatial influence on health is the reliance on a single neighborhood space, typically residential neighborhood as the geographic unit of interest [23]. As a result, few studies have examined how gay and bisexual men exist in various geographic and virtual spaces and how this relates to sexual behavior. MSM, as is the general population, are likely influenced by multiple geographic and virtual environments [24, 25]. Here, we examined the congruence (or sameness) of three neighborhoods of potential influence: where the men live (home), where they socialize most often (social), and where they most often have sex (sexual). Then, we determined the association of sociodemographic characteristics, levels of outness and gay community attachment, and self-reported neighborhood factors (e.g., experiences of discrimination, neighborhood connectedness, social ties) with neighborhood congruence. Finally, we assessed the association of levels of neighborhood congruence with sexual risk behavior, including sexual partner seeking through the Internet or mobile applications. Connectedness to neighborhood and community is no longer bounded by physical space, with the advent of the Internet and the ability for individuals to congregate and create community online or in “virtual spaces.” The role of the Internet and portable, smart devices in partner seeking, connecting people, bridging physical divides, and other social activities for gay and bisexual men has been significant [26–28]. Multiple studies have found that many MSM meet their partners through the Internet, with an estimated 6.2 million gay and bisexual men in the USA using virtual tools for romantic and sexual encounters [26]. This increased connectivity and readily available outlets for casual sex may have increased sexual risk for some groups of MSM [26, 29].
Results of these analyses may help us better understand how the intersections among and characteristics of the various spaces where MSM live, socialize, and have sex influence HIV risk. This information may lead to more effective HIV prevention programs that target those most at risk while taking into account how existence across various life spaces influences risk.
Methods
Ethics Statement
The New York Blood Center Institutional Review Board first approved this study and provided ongoing oversight. Institutional review boards at co-investigator institutions including New York University’s University Committee on Activities Involving Human Subjects, Hunter College Institutional Review Board, and the New York Academy of Medicine Institutional Review Board also reviewed the study. All participants provided written informed consent.
Study Sample and Recruitment
Methods for the NYCM2M Study have been previously published [30–32]. Briefly, individuals were eligible to participate if they report being a biological male at birth, were at least 18 years of age, resided in NYC, reported engaging in anal sex with a man in the past 3 months, and communicated in English or Spanish. Recruitment involved using a modified venue-based time-space sampling methodology and placement of ads on select social media and websites [33]. Recruitment occurred at the locations during designated sampling events. Men were systematically approached (e.g., every third man) at the sampling events, and eligible participants were asked to provide contact information. A similar process occurred for online recruitment as men were directed to the study website and those eligible were asked to provide contact information.
Study Visit
After providing informed consent, participants met with a staff member to complete the Neighborhood Locator Questionnaire which collected information on the location of four neighborhoods: home (where they currently live), social (where they socialize most often), and sexual (where they most recently had sex and most often have sex) using Google Earth to “drop a pin” at the closest intersection [30, 34], as well as place of birth and place where the majority of their childhood was spent. For each neighborhood, participants were also asked to identify the neighborhood name from a list of 347 neighborhoods within the 59 NYC community districts. Community districts range in population size from a little more than 50,000 residents to more than 200,000 [35].
Participants then completed an ACASI assessment and a social and sexual network questionnaire with an interviewer. Participants received HIV risk-reduction counseling and a rapid HIV antibody test was conducted. Those who tested HIV positive were referred for treatment and medical and social services, as needed. Upon completion of the visit, participants received $50 and a two-way public transportation card.
Outcome Measures
For these analyses, two dichotomous outcomes related to sexual risk in the last 3 months were defined: (1) serodiscordant/unknown status condomless anal intercourse (serodiscordant CAI) and (2) CAI with partners found using the Internet or mobile application.
Neighborhood Congruence
Using the home, social, and most often sex neighborhood names from the Neighborhood Locator Questionnaire, level of congruence was categorized as (1) all neighborhoods the same; (2) same home and social but different sex neighborhood; (3) same social and sex but different home neighborhood; (4) same home and sex but different social neighborhood; or (5) none of the neighborhoods the same.
Covariates
The questionnaire included questions regarding age, race/ethnicity, sexual identity, and measures of socioeconomic status (education, employment, annual personal income, and financial security). A measure of outness (“How many of the people you know or see day-to-day know you have sex with men?”) was included with responses ranging from not out to anyone to out to everyone. Gay community attachment was measured with seven items, such as “I feel a part of New York City’s gay community” and “Participating in New York City’s gay community is a positive thing for me.” These were measured by a 4-point response scale ranging from strongly disagree to strongly agree and a mean value was calculated for each participant (α = 0.81) [36].
The current analysis included data on place of birth (in NYC, outside of NYC in the USA, outside the USA), place where the participant spent most of their childhood (in their current home neighborhood, in another NYC neighborhood, outside NYC), and whether the participant would live in their current home neighborhood if they could live anywhere in NYC. Exposure to the neighborhood was measured with a question on duration of residence or going to the neighborhood to socialize. Social ties were assessed by asking participants how many relatives and friends were in their home and social neighborhoods. Neighborhood connectedness was measured with 12 items such as “I feel this place is a part of who I am” using a 4-point self-reported scale ranging from strongly disagree to strongly agree (α = 0.96) [37]. A mean value was calculated for each participant.
Experience of racial/ethnic and sexual orientation discrimination in the home and social neighborhoods in the prior 3 months was based on neighborhood lifetime and recent experience items as follows: “Have you ever experienced discrimination, been prevented from doing something, or been hassled or made to feel inferior in your home neighborhood because of your race, ethnicity, or color?,” “If yes, how many times did this happen?,” “How many times did this happen in the past 3 months?.” These questions were asked for the home and social neighborhoods separately. The same set of questions are asked in the context of sexual orientation discrimination: “Have you ever experienced discrimination, been prevented from doing something, or been hassled or made to feel inferior in your home neighborhood because of your sexual orientation?” also for both home and social neighborhoods.
Statistical Analysis
Correlates of Neighborhood Congruence
All analyses were conducted in SAS version 9.3 (SAS Institute Inc., SAS/Stat, NC, USA). Bivariate associations between sociodemographic characteristics, levels of outness and gay community attachment, and neighborhood variables with neighborhood congruence were conducted. Chi-square tests of association were used for categorical measures and linear models for overall tests of association for continuous measures.
Association with Behavior Outcomes
Sexual behavior outcomes (serodiscordant CAI, CAI with partners from the Internet or mobile application) were examined with logit models of association for each of these outcomes with neighborhood congruence. The relationships between the sexual behavior outcomes and neighborhood congruence, sociodemographics, and the correlates of neighborhood congruence (p value <0.1) as covariates were examined. Final models included neighborhood congruence, sociodemographics, and covariates with p value <0.05.
Results
Study Sample
A total of 398 venue-based recruitment events and four Internet-based advertisements were placed to recruit 1503 men from October 2010 through June 2013. Complete interview data were obtained for 1493 men, 778 from venue-based recruitment and 685 from Internet-based ads. The average age was 32.1 (SD = 10.3); 31.9 % of the sample was White (non-Hispanic); 30.4 % Latino; 25.2 % Black/African American; and 12.5 % reported another race/ethnicity (Table 1). Most men (87.3 %) self-identified as gay, homosexual, queer, or same-gender loving. About half (49.3 %) reported possessing at least a college degree, and 63.3 % were employed. Over a quarter of men (26.3 %) reported an average personal income of less than $10,000 per year, and 47.9 % reported that they did not have enough money for necessities in the prior 3 months.
Table 1.
Sociodemographic characteristics of participants by neighborhood congruence, NYCM2M Study
| Characteristic | Total (n = 1493) | All the same (n = 248; 16.6 %) | Same home and social (n = 68; 4.6 %) | Same social and sex (n = 148; 9.9 %) | Same home and sex (n = 586; 39.2 %) | None the same (n = 443; 29.7 %) | p valuea | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | N | % | N | % | N | % | ||
| Age (mean, SD) | 32.1 (10.3) | 34.3 (11.1) | 33.2 (11.7) | 30.5 (9.1) | 32.8 (10.1) | 30.3 (9.9) | <0.001 | ||||||
| Race/ethnicity | |||||||||||||
| White | 474 | 31.9 | 101 | 21.3 | 26 | 5.5 | 41 | 8.6 | 190 | 40.1 | 116 | 24.5 | 0.004 |
| Black | 375 | 25.2 | 56 | 14.9 | 13 | 3.5 | 44 | 11.7 | 137 | 36.5 | 125 | 33.3 | |
| Hispanic | 452 | 30.4 | 58 | 12.8 | 20 | 4.4 | 42 | 9.3 | 176 | 38.9 | 156 | 34.5 | |
| All other | 186 | 12.5 | 30 | 16.1 | 9 | 4.8 | 21 | 11.3 | 82 | 44.1 | 44 | 23.7 | |
| Sexual identity | |||||||||||||
| Gay, homosexual, queer, same-gender loving, etc. | 1303 | 87.3 | 220 | 16.9 | 56 | 4.3 | 124 | 9.5 | 522 | 40.1 | 381 | 29.2 | 0.51 |
| Bisexual | 140 | 9.4 | 23 | 16.4 | 9 | 6.4 | 17 | 12.1 | 47 | 33.6 | 44 | 31.4 | |
| Straight, heterosexual/other | 50 | 3.4 | 5 | 10.0 | 3 | 6.0 | 7 | 14.0 | 17 | 34.0 | 18 | 36.0 | |
| Education | |||||||||||||
| ≤High school graduate | 253 | 16.9 | 27 | 10.7 | 15 | 5.9 | 36 | 14.2 | 85 | 33.6 | 90 | 35.6 | <0.001 |
| Some college | 504 | 33.8 | 69 | 13.7 | 26 | 5.2 | 47 | 9.3 | 193 | 38.3 | 169 | 33.5 | |
| College graduate+ | 736 | 49.3 | 152 | 20.7 | 27 | 3.7 | 65 | 8.8 | 308 | 41.8 | 184 | 25.0 | |
| Employment | |||||||||||||
| Working | 943 | 63.3 | 162 | 17.2 | 38 | 4.0 | 92 | 9.8 | 396 | 42.0 | 255 | 27.0 | 0.07 |
| Not working | 457 | 30.7 | 71 | 15.5 | 25 | 5.5 | 49 | 10.7 | 154 | 33.7 | 158 | 34.6 | |
| Working off the book/other | 90 | 6.0 | 14 | 15.6 | 5 | 5.6 | 7 | 7.8 | 35 | 38.9 | 29 | 32.2 | |
| Personal income | |||||||||||||
| <$10,000 | 385 | 26.3 | 57 | 14.8 | 22 | 5.7 | 35 | 9.1 | 126 | 32.7 | 145 | 37.7 | <0.001 |
| $10,000–39,999 | 608 | 41.5 | 88 | 14.5 | 22 | 3.6 | 66 | 10.9 | 256 | 42.1 | 176 | 28.9 | |
| $40,000–59,999 | 226 | 15.4 | 38 | 16.8 | 11 | 4.9 | 17 | 7.5 | 97 | 42.9 | 63 | 27.9 | |
| $60,000+ | 245 | 16.7 | 62 | 25.3 | 12 | 4.9 | 27 | 11.0 | 99 | 40.4 | 45 | 18.4 | |
| Financial insecurity in the last 3 months | |||||||||||||
| Not enough $ for rent, food, or utilities | |||||||||||||
| No | 773 | 52.1 | 135 | 17.5 | 34 | 4.4 | 66 | 8.5 | 328 | 42.4 | 210 | 27.2 | 0.039 |
| Yes | 710 | 47.9 | 113 | 15.9 | 34 | 4.8 | 82 | 11.5 | 257 | 36.2 | 224 | 31.5 | |
| Not enough $ for social activity | |||||||||||||
| No | 451 | 30.3 | 87 | 19.3 | 20 | 4.4 | 39 | 8.6 | 187 | 41.5 | 118 | 26.2 | 0.13 |
| Yes | 1039 | 69.7 | 161 | 15.5 | 48 | 4.6 | 109 | 10.5 | 398 | 38.3 | 323 | 31.1 | |
| Serodiscordant CAI in the last 3 months | |||||||||||||
| No | 1105 | 74.0 | 187 | 75.4 | 45 | 66.2 | 97 | 65.5 | 435 | 74.2 | 341 | 77.0 | 0.041 |
| Yes | 388 | 26.0 | 61 | 24.6 | 23 | 33.8 | 51 | 34.5 | 151 | 25.8 | 102 | 23.0 | |
| CAI with partners from Internet/mobile app in the last 3 months | |||||||||||||
| No | 425 | 33.5 | 81 | 39.3 | 15 | 26.8 | 27 | 22.1 | 187 | 36.5 | 115 | 31.0 | 0.006 |
| Yes | 842 | 66.5 | 125 | 60.7 | 41 | 73.2 | 95 | 77.9 | 325 | 63.5 | 31 | 69.0 | |
| Recruitment approach | |||||||||||||
| Venue based | 778 | 53.2 | 113 | 14.5 | 30 | 3.9 | 78 | 10.0 | 306 | 39.3 | 251 | 32.3 | 0.027 |
| Online | 685 | 46.8 | 132 | 19.3 | 38 | 5.5 | 69 | 10.1 | 263 | 38.4 | 183 | 26.7 | |
CAI condomless anal intercourse
aFor categorical characteristics, p value is for overall comparison between all levels
Neighborhood Congruence
Congruence of home, social, and most often sex neighborhood was reported by 248 (16.6 %) men, while 443 (29.7 %) reported that none of their neighborhoods were the same (Table 1). The largest group of men (39.2 %) reported that their home and sex neighborhoods were the same but their social neighborhood was different, while 148 (9.9 %) men reported that their home neighborhood was different than their social and sex neighborhood. Only 68 (4.6 %) men reported same home and social neighborhoods with a different sex neighborhood.
Correlates of Neighborhood Congruence
Several sociodemographic characteristics were associated with neighborhood congruence (Table 1). The mean age was youngest for men whose social and sex neighborhoods were the same and for men whose neighborhoods were all different. Men who were White, had higher education, and highest income were more likely to report all their neighborhoods were the same. Men who were Black, Latino, had lower education and income, and had higher financial insecurity were more likely to report that all their neighborhoods were different. Men who were recruited through online recruitment were more likely to report all their neighborhoods were the same, whereas those recruited through venue-based sampling were more likely to report that none of the neighborhoods were the same.
Men who reported lowest scores on the outness scale were more likely to report that none of their neighborhoods were the same, although this association was of borderline significance (Table 2). Gay community attachment was highest for men who reported same home and sex neighborhoods.
Table 2.
Outness and gay community attachment reported by participants by neighborhood congruence, NYCM2M Study
| Measure | Total (n = 1493) | All the same (n = 248) | Same home and social (n = 68) | Same social and sex (n = 148) | Same home and sex (n = 586) | None the same (n = 443) | p value |
|---|---|---|---|---|---|---|---|
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | ||
| Outness scale | 8.14 (2.25) | 8.38 (1.99) | 7.97 (2.69) | 8.20 (2.26) | 8.23 (2.16) | 7.90 (2.42) | 0.055 |
| Gay community attachment | 3.12 (0.54) | 3.06 (0.54) | 3.00 (0.48) | 3.10 (0.53) | 3.16 (0.50) | 3.11 (0.59) | 0.040 |
Men born in NYC were more likely to report that all their neighborhoods were different, whereas men born within the USA but outside of NYC and those born outside of the USA were more likely to report that all their neighborhoods were the same (Table 3). Likewise, men who grew up outside of NYC were more likely to report that all their neighborhoods were currently the same. With respect to men’s perspectives on their home neighborhood, those who reported that they would live in their current home neighborhood if they could live anywhere in NYC were more likely to report that their home, social, and sex neighborhoods were the same. Men with the shortest duration of residency in their home neighborhood (less than 1 year) were more likely to report same social and sex neighborhoods but different home neighborhood or that all of their neighborhoods were different. Men who had resided in their home neighborhood the longest (5 or more years) were more likely to report that all of their neighborhoods were different. Men with relatives in their home neighborhood were more likely to report that all of their neighborhoods were different whereas men with multiple friends in their home neighborhood were more likely to report that all of their neighborhoods were the same or that their home and social neighborhoods were the same. Home neighborhood connectedness was highest among men who reported that all of their neighborhoods were the same.
Table 3.
Neighborhood factors of study participants by neighborhood congruence, NYCM2M Study
| Characteristic | Total (n = 1493) | All the same (n = 248) | Same home and social (n = 68) | Same social and sex (n = 148) | Same home and sex (n = 586) | None the same (n = 443) | p valuea | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | N | % | N | % | N | % | ||
| Birthplace | |||||||||||||
| In NYC | 458 | 30.7 | 49 | 10.7 | 19 | 4.1 | 47 | 10.3 | 177 | 38.6 | 166 | 36.2 | 0.002 |
| US born, not NYC | 680 | 45.6 | 130 | 19.1 | 35 | 5.1 | 69 | 10.1 | 266 | 39.1 | 180 | 26.5 | |
| Born outside the USA | 355 | 23.8 | 69 | 19.4 | 14 | 3.9 | 32 | 9.0 | 143 | 40.3 | 97 | 27.3 | |
| Childhood | |||||||||||||
| Current home neighborhood | 107 | 7.3 | 11 | 10.3 | 7 | 6.5 | 12 | 11.2 | 39 | 36.4 | 38 | 35.5 | <0.001 |
| Other NYC neighborhood | 254 | 17.4 | 26 | 10.2 | 13 | 5.1 | 28 | 11.0 | 87 | 34.3 | 100 | 39.4 | |
| Outside NYC | 1103 | 75.3 | 210 | 19.0 | 48 | 4.4 | 103 | 9.3 | 447 | 40.5 | 295 | 26.7 | |
| Home neighborhood | |||||||||||||
| Would live in current neighborhood if had choice | |||||||||||||
| No | 960 | 64.4 | 103 | 10.7 | 37 | 3.9 | 115 | 12.0 | 393 | 40.9 | 312 | 32.5 | <0.001 |
| Yes | 531 | 35.6 | 145 | 27.3 | 31 | 5.8 | 33 | 6.2 | 192 | 36.2 | 130 | 24.5 | |
| Length of time in home neighborhood | |||||||||||||
| Less than 1 year | 499 | 33.5 | 67 | 13.4 | 24 | 4.8 | 62 | 12.4 | 179 | 35.9 | 167 | 33.5 | <0.001 |
| 1 to <2 years | 199 | 13.3 | 36 | 18.1 | 8 | 4.0 | 17 | 8.5 | 94 | 47.2 | 44 | 22.1 | |
| 2 to <5 years | 310 | 20.8 | 60 | 19.4 | 13 | 4.2 | 29 | 9.4 | 134 | 43.2 | 74 | 23.9 | |
| 5+ | 484 | 32.4 | 85 | 17.6 | 23 | 4.8 | 40 | 8.3 | 179 | 37.0 | 157 | 32.4 | |
| No. of relatives in home neighborhood | |||||||||||||
| 0 | 1106 | 74.2 | 205 | 18.5 | 49 | 4.4 | 109 | 9.9 | 456 | 41.2 | 287 | 25.9 | <0.001 |
| 1+ | 384 | 25.8 | 43 | 11.2 | 19 | 4.9 | 38 | 9.9 | 128 | 33.3 | 156 | 40.6 | |
| No. of friends in home neighborhood | |||||||||||||
| 0 | 620 | 41.5 | 54 | 8.7 | 14 | 2.3 | 62 | 10.0 | 255 | 41.1 | 235 | 37.9 | <0.001 |
| 1–2 | 406 | 27.2 | 69 | 17.0 | 18 | 4.4 | 46 | 11.3 | 171 | 42.1 | 102 | 25.1 | |
| 3–5 | 287 | 19.2 | 63 | 22.0 | 21 | 7.3 | 26 | 9.1 | 105 | 36.6 | 72 | 25.1 | |
| 6+ | 180 | 12.1 | 62 | 34.4 | 15 | 8.3 | 14 | 7.8 | 55 | 30.6 | 34 | 18.9 | |
| Experienced race/sex discrimination in neighborhood | |||||||||||||
| No | 1299 | 87.7 | 216 | 16.6 | 60 | 4.6 | 131 | 10.1 | 516 | 39.7 | 376 | 29.0 | 0.61 |
| Yes | 182 | 12.3 | 30 | 16.5 | 8 | 4.4 | 17 | 9.3 | 64 | 35.2 | 63 | 34.6 | |
| Connectedness mean (SD) | 2.39 (0.68) | 2.67 (0.61) | 2.60 (0.63) | 2.30 (0.68) | 2.37 (0.68) | 2.22 (0.68) | <0.001 | ||||||
| Social neighborhood | |||||||||||||
| Length of time going to social neighborhood | |||||||||||||
| Less than 1 year | 317 | 21.3 | 43 | 13.6 | 14 | 4.4 | 33 | 10.4 | 111 | 35.0 | 116 | 36.6 | 0.20 |
| 1 to <2 years | 196 | 13.2 | 29 | 14.8 | 13 | 6.6 | 23 | 11.7 | 78 | 39.8 | 53 | 27.0 | |
| 2 to <5 years | 395 | 26.5 | 74 | 18.7 | 19 | 4.8 | 37 | 9.4 | 156 | 39.5 | 109 | 27.6 | |
| 5+ | 582 | 39.1 | 102 | 17.5 | 22 | 3.8 | 54 | 9.3 | 239 | 41.1 | 165 | 28.4 | |
| No. of relatives in social neighborhood | |||||||||||||
| 0 | 1291 | 86.6 | 215 | 16.7 | 51 | 4.0 | 125 | 9.7 | 521 | 40.4 | 379 | 29.4 | 0.021 |
| 1+ | 199 | 13.4 | 33 | 16.6 | 17 | 8.5 | 22 | 11.1 | 64 | 32.2 | 63 | 31.7 | |
| No. of friends in social neighborhood | |||||||||||||
| 0 | 433 | 29.1 | 38 | 8.8 | 12 | 2.8 | 42 | 9.7 | 180 | 41.6 | 161 | 37.2 | <0.001 |
| 1–2 | 457 | 30.7 | 69 | 15.1 | 20 | 4.4 | 44 | 9.6 | 191 | 41.8 | 133 | 29.1 | |
| 3–5 | 346 | 23.3 | 70 | 20.2 | 18 | 5.2 | 33 | 9.5 | 140 | 40.5 | 85 | 24.6 | |
| 6+ | 251 | 16.9 | 71 | 28.3 | 18 | 7.2 | 29 | 11.6 | 73 | 29.1 | 60 | 23.9 | |
| Experienced race/sex discrimination in neighborhood | |||||||||||||
| No | 1375 | 92.7 | 225 | 16.4 | 58 | 4.2 | 137 | 10.0 | 547 | 39.8 | 408 | 29.7 | 0.10 |
| Yes | 109 | 7.4 | 22 | 20.2 | 10 | 9.2 | 11 | 10.1 | 35 | 32.1 | 31 | 28.4 | |
| Connectedness mean (SD) | 2.67 (0.68) | 2.67 (0.68) | 2.64 (0.69) | 2.61 (0.70) | 2.70 (0.67) | 2.66 (0.70) | 0.74 | ||||||
aFor categorical characteristics, p value is for overall comparison between all levels
With regard to men’s perspectives on their social neighborhood, men with relatives in their social neighborhood were more likely to report the same home and social (but not sex) neighborhood. Men with multiple friends in their social neighborhood were more likely to report that all of their neighborhoods were the same or that their home and social neighborhoods were the same.
Association of Neighborhood Congruence and Sexual Behavior Outcomes
In terms of sexual behavior outcomes in the prior 3 months, 26.0 % reported serodiscordant CAI and 66.5 % reported CAI with partners found by the Internet or mobile application. In bivariate analyses (Table 4), the odds of serodiscordant CAI was higher for men reporting same social and sex (but not home) neighborhoods (OR = 1.77; 95 % CI, 1.12, 2.80) compared with those reporting all the same neighborhoods. The odds of CAI with partners from the Internet or mobile application was higher for men reporting same social and sex (but not home) neighborhoods (OR = 2.01; 95 % CI, 1.20, 3.38) and those reporting none of the same neighborhoods (OR = 1.48; 95 % CI, 1.02, 2.14). In adjusted analysis (Table 4), serodiscordant CAI and CAI with partners from the Internet or mobile application were significantly associated with having the same social and sex (but not home) neighborhoods (serodiscordant CAI: adjusted odds ratio (aOR) = 1.98; 95 % CI, 1.23, 3.20; CAI with partners from the Internet or mobile application: aOR = 2.16; 95 % CI, 1.26, 3.72).
Table 4.
Multivariate associations with sexual behavior outcomes, NYCM2M Study
| Neighborhood congruence | Serodiscordant CAI (N = 1343) | CAI with partners from Internet/mobile app (N = 1195) | ||||
|---|---|---|---|---|---|---|
| N (%) | OR (95 %CI) | aOR (95 %CI) | N (%) | OR (95 %CI) | aOR (95 %CI) | |
| All the same | 61 (24.6) | Ref | Ref | 125 (60.7) | Ref | Ref |
| Same home and social | 23 (33.8) | 1.65 (0.91, 2.95) | 1.65 (0.89, 3.05) | 41 (73.2) | 1.77 (0.92, 3.41) | 1.58 (0.79, 3.16) |
| Same social and sex | 51 (34.5) | 1.77 (1.12, 2.80) | 1.98 (1.23, 3.20) | 95 (77.9) | 2.01 (1.20, 3.38) | 2.16 (1.26, 3.72) |
| Same home and sex | 151 (25.8) | 1.14 (0.80, 1.62) | 1.26 (0.87, 1.82) | 325 (63.5) | 1.11 (0.79, 1.56) | 1.08 (0.75, 1.55) |
| None the same | 102 (23.0) | 0.91 (0.62, 1.33) | 1.05 (0.71, 1.57) | 31 (69.0) | 1.48 (1.02, 2.14) | 1.33 (0.89, 1.98) |
All adjusted models control for age group, race/ethnicity, birthplace, education, employment, income, financial insecurity, recruitment source. Serodiscordant CAI: also controlled for gay community attachment. CAI with partners from the Internet: also controlled for friends in social neighborhood
CAI condomless anal intercourse
Discussion
Just one in six men in this study reported complete congruence among their home, social, and sex neighborhoods, with most reporting some incongruence among these neighborhoods. A previous study reported a higher proportion of men in NYC with congruence between any two types of neighborhoods (home, social, and sex) [38]. However, that analysis was based on the five boroughs within NYC, rather than specific neighborhoods within boroughs.
We conducted this analysis to assess whether this observed geographic incongruence translated into higher sexual risk behavior, perhaps reflecting disconnection within men’s sociosexual lives. As expected, and as observed in previous work in New York City [38], complete neighborhood incongruence was highest among MSM who reported being Black and/or Latino and had lower educational levels, lower personal income, and greater financial insecurity. These men were also more likely to be born and raised in New York City and thus may be less mobile for financial reasons and more anchored in the neighborhoods where they grew up, through kin networks. These men also had the lowest mean score on the outness scale. This ongoing connection to one’s natal neighborhood community (e.g., relatives in the home neighborhood) may be a source of social support or a source of stress [18, 25]. In contrast, men who were White, more highly educated, with higher personal income, and born outside of NYC were more likely to report congruence of all neighborhoods. Neighborhood congruence in this situation may be an extension of higher control over their living situation within specific “sought-after” neighborhoods, reflecting individual-level socioeconomic status and the historical socioeconomic forces that drive race- and income-based segregation in urban areas.
While we observed significant differences in gay community attachment by neighborhood congruence, the highest mean score for gay community attachment was among those men who had the same home and sex (but not social) neighborhoods, rather than those for whom all neighborhoods were the same. These findings suggest that physical proximity to neighborhoods with high levels of gay social establishments is not required for strong feeling of gay community attachment [12].
We found that neighborhood incongruence was associated with sexual risk. Participants for whom the social and sex (but not home) neighborhoods were the same had a greater odds of serodiscordant CAI and CAI with partners met through the Internet. This finding may reflect, to some degree, engagement in the “party and play” subculture where individuals travel or migrate into areas and engage in riskier sexual behavior [25]. It may also reflect that individuals have less control over the sexual situation when sex does not occur at home, for example, having access to condoms or feeling more in control or efficacious when at home or having less control and access to condoms at sex parties or other venues where sex occurs. Interestingly, the association of complete neighborhood incongruence and serodiscordant CAI and CAI with partners found by the Internet or mobile application did not emerge, suggesting that MSM with entirely incongruent neighborhoods may have developed compensatory risk-reduction skills needed to transverse and exist in multiple spaces.
Taken together, these findings suggest that it may not be congruence of the home and another neighborhood that is critical to risk reduction. While further qualitative or mixed-method research to isolate the effects of congruence on sexual behavior is needed, these findings support the need for targeting biomedical intervention outreach efforts, such as pre- and post-exposure prophylaxis, to neighborhoods where social and sex activities co-occur. Furthermore, based on the evidence that structural-level factors play a significant role in race disparities in HIV infection rates [3, 4, 39], these findings suggest a role for interventions to influence the environment of the multiple geographic spaces in which MSM exist.
This study has several limitations. The cross-sectional design of this study is a limitation as we are unable to assess causal associations, identifying what drives sexual decision-making and living choices. This design also limits our ability to look at how patterns of neighborhood choice (e.g., where one lives, where one prefers to have sex) change over time. This paper does, however, provide an initial snapshot of how MSM interact with different spaces and places in the urban environment. Longitudinal studies are needed to better understand how these relations change over time. Participants were recruited using a modified venue-based time-space sampling which allowed us to recruit a geographically and ethnically diverse sample. We used both the Internet and event/street spaces to reach as many men as possible; however, we likely missed some men who did not participate in the spaces chosen as recruitment venues. These findings are also limited by the New York City context. These results cannot be generalized beyond New York City; however, similar patterns may exist in other large cities with significant MSM populations which could lead to development of similar prevention interventions. More research is needed to understand how neighborhood/geography impacts the lives of men living in smaller cities and rural areas.
This paper raises important questions to consider as new biomedical and behavioral combination HIV prevention programs and structural-level interventions are implemented. These new HIV prevention methodologies require a more nuanced understanding of where and how to reach both individuals and communities. Understanding the complexity of how different spaces and places impact the health and behavior of MSM is essential to identifying where to focus various interventions to best reach the different populations of interest.
Acknowledgments
Thank you to the men who agreed to participate in this research. The long-established generosity of the LBGT community in the giving of themselves for research continues to allow us to understand and intervene with health disparities of our communities. Thank you to the outstanding study staff of Project Achieve who make this work possible. This study is supported by a grant to the New York Blood Center from the National Institute of Child Health and Human Development (NICHD) R01 HD059729. DCO was also supported by the Center for Drug Use and HIV Research (P30DA011041).
References
- 1.Centers for Disease Control and Prevention. HIV Surveillance Report. 2014.
- 2.Maulsby C, Millett G, Lindsey K, Kelley R, Johnson K, Montoya D, et al. HIV among Black men who have sex with men (MSM) in the United States: a review of the literature. AIDS Behav. 2014;18(1):10–25. doi: 10.1007/s10461-013-0476-2. [DOI] [PubMed] [Google Scholar]
- 3.Levy ME, Wilton L, Phillips G, 2nd, Glick SN, Kuo I, Brewer RA, et al. Understanding structural barriers to accessing HIV testing and prevention services among Black men who have sex with men (BMSM) in the United States. AIDS Behav. 2014;18:972–97. doi: 10.1007/s10461-014-0719-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Millett GA, Peterson JL, Flores SA, Hart TA, Jeffries WL, Wilson PA, et al. Comparisons of disparities and risks of HIV infection in Black and other men who have sex with men in Canada, UK, and USA: a meta-analysis. Lancet. 2012;380(9839):341–8. doi: 10.1016/S0140-6736(12)60899-X. [DOI] [PubMed] [Google Scholar]
- 5.Burns PA, Snow RC. The built environment & the impact of neighborhood characteristics on youth sexual risk behavior in Cape Town. South Africa Health Place. 2012;18(5):1088–100. doi: 10.1016/j.healthplace.2012.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Odoi A, Busingye D. Neighborhood geographic disparities in heart attack and stroke mortality: comparison of global and local modeling approaches. Spat Spatiotemporal Epidemiol. 2014;11:109–23. doi: 10.1016/j.sste.2014.10.001. [DOI] [PubMed] [Google Scholar]
- 7.Hatzenbuehler ML, Keyes KM, Hasin DS. State-level policies and psychiatric morbidity in lesbian, gay, and bisexual populations. Am J Public Health. 2009;99(12):2275–81. doi: 10.2105/AJPH.2008.153510. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Schule SA, Bolte G. Interactive and independent associations between the socioeconomic and objective built environment on the neighbourhood level and individual health: a systematic review of multilevel studies. PLoS One. 2015;10(4):e0123456. doi: 10.1371/journal.pone.0123456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Messer LC, Maxson P, Miranda ML. The urban built environment and associations with women’s psychosocial health. J Urban Health. 2013;90(5):857–71. doi: 10.1007/s11524-012-9743-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Jitnarin N, Heinrich KM, Haddock CK, Hughey J, Berkel L, Poston WS. Neighborhood environment perceptions and the likelihood of smoking and alcohol use. Int J Environ Res Public Health. 2015;12(1):784–99. doi: 10.3390/ijerph120100784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Frye V, Latka M, Koblin BA, Halkitis PN, Putnam S, Galea S, et al. The urban environment and sexual risk behavior among men who have sex with men. J Urban Health. 2006;83:308–24. doi: 10.1007/s11524-006-9033-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kelly BC, Carpiano RM, Easterbrook A, Parsons JT. Exploring the gay community question: neighborhood and network influences on the experience of community among urban gay men. Sociol Quarter. 2014;55:23–48. [Google Scholar]
- 13.Frye V, Koblin B, Chin J, Beard J, Blaney S, Halkitis P, et al. Neighborhood-level correlates of consistent condom use among men who have sex with men: a multi-level analysis. AIDS Behav. 2010;14(4):974–85. doi: 10.1007/s10461-008-9438-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Buttram ME, Kurtz SP. Risk and protective factors associated with gay neighborhood residence. Am J Mens Health. 2013;7(2):110–8. doi: 10.1177/1557988312458793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Carpiano RM, Kelly BC, Easterbrook A, Parsons JT. Community and drug use among gay men: the role of neighborhoods and networks. J Health Soc Behav. 2011;52(1):74–90. doi: 10.1177/0022146510395026. [DOI] [PubMed] [Google Scholar]
- 16.Lewis NM. Rupture, resilience, and risk: relationships between mental health and migration among gay-identified men in North America. Health Place. 2014;27:212–9. doi: 10.1016/j.healthplace.2014.03.002. [DOI] [PubMed] [Google Scholar]
- 17.Ueno K, Vaghela P, Ritter LJ. Sexual orientation, internal migration, and mental health during the transition to adulthood. J Health Soc Behav. 2014;55(4):461–81. doi: 10.1177/0022146514556509. [DOI] [PubMed] [Google Scholar]
- 18.Frye V, Egan JE, Van Tieu H, Cerda M, Ompad D, Koblin BA. “I didn’t think I could get out of the fucking park.” Gay men’s retrospective accounts of neighborhood space, emerging sexuality and migrations. Soc Sci Med. 2014;104:6–14. doi: 10.1016/j.socscimed.2013.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.O’Donnell L, Agronick G, San DA, Duran R, Myint U, Stueve A. Ethnic and gay community attachments and sexual risk behaviors among urban Latino young men who have sex with men. AIDS Educ Prev. 2002;14(6):457–71. doi: 10.1521/aeap.14.8.457.24109. [DOI] [PubMed] [Google Scholar]
- 20.Warren JC, Fernandez MI, Harper GW, Hidalgo MA, Jamil OB, Torres RS. Predictors of unprotected sex among young sexually active African American, Hispanic, and White MSM: the importance of ethnicity and culture. AIDS Behav. 2008;12(3):459–68. doi: 10.1007/s10461-007-9291-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Van Sluytman L, Spikes P, Nandi V, Van Tieu H, Frye V, Patterson J, et al. Ties that bind: community attachment and the experience of discrimination among Black men who have sex with men. Cult Health Sex. 2015:1–14. [DOI] [PMC free article] [PubMed]
- 22.Chng CL, Geliga-Vargas J. Ethnic identity, gay identity, sexual sensation seeking and HIV risk taking among multiethnic men who have sex with men. AIDS Educ Prev. 2000;12(4):326–39. [PubMed] [Google Scholar]
- 23.Matthews SA. Spatial polygamy and the heterogeneity of place: studying people and place via egocentric methods. In: Burton LM, Matthews SA, Leung M, Kemp SP, Takeuchi DT, eds. Communities, neighborhoods and health: expanding the boundaries of place. New York, NY: Springer; 2011.
- 24.Egan JE, Frye V, Greene E, Rundle A, Quinn J, Nandi V, et al. Where do gay, bisexual and other MSM in NYC live, socialize and have sex? A spatial analysis of neighborhoods by race/ethnicity. In: 142nd Annual Meeting and Exposition of the American Public Health Association. New Orleans, LA. 2014.
- 25.Egan JE, Frye V, Kurtz SP, Latkin C, Chen M, Tobin K, et al. Migration, neighborhoods, and networks: approaches to understanding how urban environmental conditions affect syndemic adverse health outcomes among gay, bisexual and other men who have sex with men. AIDS Behav. 2011;15(Suppl 1):S35–50. doi: 10.1007/s10461-011-9902-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Grov C, Breslow AS, Newcomb ME, Rosenberger JG, Bauermeister JA. Gay and bisexual men’s use of the Internet: research from the 1990s through 2013. J Sex Res. 2014;51(4):390–409. doi: 10.1080/00224499.2013.871626. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Patel VV, Masyukova M, Sutton D, Horvath KJ. Social media use and HIV-related risk behaviors in young Black and Latino gay and Bi men and transgender individuals in New York City: implications for online interventions. J Urban Health. 2016;93(2):388–99. doi: 10.1007/s11524-016-0025-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Hirshfield S, Grov C, Parsons JT, Anderson I, Chiasson MA. Social media use and HIV transmission risk behavior among ethnically diverse HIV-positive gay men: results of an online study in three U.S. states. Arch Sex Behav. 2015;44(7):1969–78. doi: 10.1007/s10508-015-0513-5. [DOI] [PubMed] [Google Scholar]
- 29.Lewnard JA, Berrang-Ford L. Internet-based partner selection and risk for unprotected anal intercourse in sexual encounters among men who have sex with men: a meta-analysis of observational studies. Sex Transm Infect. 2014;90(4):290–6. doi: 10.1136/sextrans-2013-051332. [DOI] [PubMed] [Google Scholar]
- 30.Koblin BA, Egan JE, Rundle A, Quinn J, Tieu HV, Cerda M, et al. Methods to measure the impact of home, social, and sexual neighborhoods of urban gay, bisexual, and other men who have sex with men. PLoS One. 2013;8(10):e75878. doi: 10.1371/journal.pone.0075878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Usher D, Frye V, Shinnick J, Greene E, Baez E, Benitez J, et al. Recruitment by a geospatial networking application for research and practice: the New York City experience. J Acquir Immune Defic Syndr. 2014;(in press). [DOI] [PMC free article] [PubMed]
- 32.Tieu HV, Nandi V, Frye V, Stewart K, Oquendo H, Bush B, et al. Concurrent partnerships and HIV risk among men who have sex with men in New York City. Sex Transm Dis. 2014;41(3):200–8. doi: 10.1097/OLQ.0000000000000090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.MacKellar DA, Gallagher KM, Finlayson T, Sanchez T, Lansky A, Sullivan PS. Surveillance of HIV risk and prevention behaviors of men who have sex with men—a national application of venue-based, time-space sampling. Public Health Rep. 2007;122(Suppl 1):39–47. doi: 10.1177/00333549071220S107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Google I. Google Earth (version 5.1.3533.1731) [Software]. Available from: earth.google.com. 2009.
- 35.New York City Department of City Planning. NYC community districts. 2012.
- 36.Frost DM, Meyer IH. Measuring community connectedness among diverse sexual minority populations. J Sex Res. 2012;49(1):36–49. doi: 10.1080/00224499.2011.565427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Cameron JE. A three factor model of social identity. Self Identity. 2004;3:239–62. doi: 10.1080/13576500444000047. [DOI] [Google Scholar]
- 38.Duncan DT, Kapadia F, Halkitis PN. Examination of spatial polygamy among young gay, bisexual, and other men who have sex with men in New York City: the P18 Cohort Study. Int J Environ Res Public Health. 2014;11(9):8962–83. doi: 10.3390/ijerph110908962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Millett GA, Ding H, Marks G, Jeffries WL, Bingham T, Lauby J, et al. Mistaken assumptions and missed opportunities: correlates of undiagnosed HIV infection among Black and Latino men who have sex with men. J Acquir Immune Defic Syndr. 2011;58(1):64–71. doi: 10.1097/QAI.0b013e31822542ad. [DOI] [PubMed] [Google Scholar]