Next Article in Journal
Strong Associations Between the Pesticide Hexachlorocyclohexane and Type 2 Diabetes in Saudi Adults
Previous Article in Journal
Global School-Based Childhood Obesity Interventions: A Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 11
Issue 9
10.3390/ijerph110908962
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examination of Spatial Polygamy among Young Gay, Bisexual, and Other Men Who Have Sex with Men in New York City: The P18 Cohort Study

by
Dustin T. Duncan
1,2,3,4,*,
Farzana Kapadia
1,2,4,5 and
Perry N. Halkitis
1,2,3,4,5,6
1
Department of Population Health, School of Medicine, New York University, New York, NY 10016, USA
2
Global Institute of Public Health, New York University, New York, NY 10003, USA
3
Population Center, New York University, New York, NY 10012, USA
4
Center for Health, Identity, Behavior and Prevention Studies, New York University, New York, NY 10003, USA
5
Department of Nutrition, Food Studies and Public Health, Steinhardt School of Culture, Education and Human Development, New York University, New York, NY 10003, USA
6
Department of Applied Psychology, Steinhardt School of Culture, Education and Human Development, New York University, New York, NY 10003, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(9), 8962-8983; https://doi.org/10.3390/ijerph110908962
Submission received: 19 May 2014 / Revised: 21 August 2014 / Accepted: 22 August 2014 / Published: 28 August 2014
Browse Figures
Versions Notes

Abstract

:
The few previous studies examining the influence of the neighborhood context on health and health behavior among young gay, bisexual, and other men who have sex with men (YMSM) have predominantly focused on residential neighborhoods. No studies have examined multiple neighborhood contexts among YMSM or the relationships between sociodemographic characteristics, psychosocial factors, social support network characteristics, health behaviors, and neighborhood concordance. In this study, we assessed spatial polygamy by determining the amount of concordance between residential, social, and sex neighborhoods (defined as boroughs) in addition to examining individual-level characteristics that may be associated with neighborhood concordance. These data come from the baseline assessment of Project 18, a cohort of racially and ethnically diverse YMSM residing in the New York City metropolitan area. Participants (N = 598) provided information on their residential, social, and sex boroughs as well as information on their sociodemographic characteristics, psychosocial factors, social support network characteristics, and health behaviors (e.g., substance use and condomless sex). Descriptive analyses were conducted to examine the distribution of boroughs reported across all three contexts, i.e., residential, social, and sex boroughs. Next, concordance between: (1) residential and social boroughs; (2) residential and sex boroughs; (3) social and sex boroughs; and (4) residential, social, and sex boroughs was assessed. Finally, bivariable analyses were conducted to examine the relationships between sociodemographic characteristics, psychosocial factors, social support network characteristics, and health behaviors in relation to borough concordance. Approximately two-thirds of participants reported concordance between residential/socializing, residential/sex, and sex/socializing boroughs, whereas 25% reported concordance between all three residential/socializing/sex boroughs. Borough concordance varied by some individual-level characteristics. For example, White YMSM and YMSM reporting lower perceived socioeconomic status were significantly more likely to report residential/socializing/sex borough concordance (p < 0.001). With regard to psychosocial factors, YMSM who reported experiencing gay-related stigma in public forums were more likely to report discordant socializing/sex and residential/socializing/sex boroughs (p < 0.001). Greater frequency of communication with network members (≥weekly) was associated with less residential/social borough concordance (p < 0.05). YMSM who reported residential/socializing/sex borough concordance were more likely to report recent (last 30 days) alcohol use, recent marijuana use, and recently engaging in condomless oral sex (all p < 0.05). These findings suggest that spatial polygamy, or an individual moving across and experiencing multiple neighborhood contexts, is prevalent among urban YMSM and that spatial polygamy varies by multiple individual-level characteristics. Future research among YMSM populations should consider multiple neighborhood contexts in order to provide a more nuanced understanding of how and which neighborhood contexts influence the health and well-being of YMSM. This further examination of spatial polygamy (and individual-level characteristics associated with it) may increase understanding of the most appropriate locations for targeted disease prevention and health promotion interventions (e.g., HIV prevention interventions).

Graphical Abstract

1. Introduction

Disparities in health and health behaviors—including substance use, condomless sexual behaviors and HIV outcomes—persist among gay, bisexual, and other men who have sex with men (MSM) despite decades of behavioral research examining a broad range of individual-level factors. Moving beyond studies of individual-level factors, a focus on social and spatial contexts may help explain variation in health and health behaviors in MSM populations. Emerging research shows that neighborhoods can play an important role in influencing health and health behaviors (e.g., substance use and condomless sex) among MSM [1,2,3,4,5]. Similar to research in general (i.e., non-sexual minority) populations, existing research on neighborhoods and health among MSM populations suffer from a predominant focus on only one type of neighborhood environment or spatial context (i.e., the residential neighborhood). However, people move through multiple spatial contexts (e.g., travel between home, school, church, work, shopping centers, and socializing locations) [6]. As such, the exclusive focus on the residential environment (i.e., the “residential trap”) is problematic because it fails to capture the diversity of spatial contexts that one may experience [7]. For MSM, in particular, other spatial contexts—such as neighborhoods where MSM socialize and neighborhoods where MSM engage in sexual activity—may be salient and important to their health and health behaviors. To illustrate, in one recent study among young gay, bisexual, and other MSM in New York City, the majority of the participants socialized outside of their home neighborhoods and younger MSM often utilized their social circles to meet casual sex partners [8].
As originally conceptualized by Dr. John Odland (from Indiana University) and recently discussed by Dr. Stephen Matthews (from Penn State University), the concept of spatial polygamy not only asserts that individuals experience and interact in various contexts, but also that these varied spatial contexts influence and may be differentially associated with shaping health outcomes [6]. Thus, spatial polygamy may be more appropriate for considering whether and how different spatial contexts influence the health of MSM. For example, spatial polygamy may be linked to MSM health because those who travel to multiple neighborhoods could engage in sexual activity in these different contexts, increasing their HIV risk (especially if they engage in high-risk sexual behaviors across neighborhoods with a high HIV prevalence). While little is known about spatial polygamy and salient spatial contexts in general populations, and especially so for MSM populations, the extant research suggests that MSM move across spatial contexts [9,10,11]. For example, a qualitative study showed that MSM in New York City move through different neighborhoods—including to maintain connections with their various networks (e.g., their family and sexual networks) [11]. Interestingly and importantly, a recent study by Koblin and colleagues [10] of 706 MSM with an average age of approximately 32 years old who were from the New York City boroughs found that only 15% had concordant home, social, and sexual neighborhoods and that 31% of these men reported that none of their neighborhoods were the same [10]. In that study, concordance of neighborhoods differed significantly by race/ethnicity; Black and Latino men had less neighborhood concordance for all of their neighborhood types compared with their White counterparts [10].
While previous research on spatial polygamy among MSM populations is informative, there is a need for additional research to examine spatial polygamy in order to assess the extent that spatial polygamy may exist in other MSM populations (including young men who have sex with men (YMSM)), which may be different from MSM populations already studied. Compared to adult MSM, YMSM might be more spatially mobile because they may be more curious and more interested in exploration, including exploring different neighborhoods. In addition, YMSM might be more likely than adult MSM to leave their residential neighborhoods because they want to socialize in gay friendly neighborhoods (where they can be more open about their sexuality), increasing their spatial polygamy. Moreover, YMSM might be more spatially mobile, due to being more physically active, than older and elderly MSM populations, especially older and elderly MSM who may have a disability [12,13], and thus YMSM may be more likely to engage in more spatial polygamy. Furthermore, additional research is needed to examine whether sociodemographic characteristics, psychosocial factors, social support network characteristics, and health behaviors are associated with neighborhood concordance, which (to our best knowledge) has not been done to date. However, conceptual models linking neighborhood environments to health risk behaviors and related outcomes (including alcohol and drug use and sexual risk behavior) among MSM underscore that salient features could be sociodemographic characteristics, psychosocial factors, and social support network characteristics, among others [14].
Examination of spatial polygamy (and individual-level characteristics associated with it) may help researchers, practitioners, and policymakers determine the most appropriate location(s) for targeted disease prevention and health promotion interventions (e.g., HIV prevention interventions). For example, examining psychosocial factors such as experiences with gay-related stigma that might be related to spatial polygamy (because living in a homophobic neighborhood might encourage someone to socialize in a non-homophobic/another neighborhood) could help us determine what types of individuals are more (or less) likely to engage in spatial polygamy. Thus, among a sample of a new generation of YMSM in the New York City metropolitan area including Westchester County, New Jersey and Connecticut, the present study sought to assess spatial polygamy by determining the amount of concordance between residential, social, and sex neighborhoods. In this study, we also evaluated whether sociodemographic characteristics, psychosocial factors, social support network characteristics, and health behaviors (i.e., substance use and condomless sex) were associated with neighborhood concordance. New York City is an ideal location for this research because it is 304.8 square miles, with a population density of 27,440 persons per square mile, making it the most densely populated city in the United States. It contains 181 ZIP codes. Like other geographic locations, there are various ways to define a “neighborhood” in New York City and in this study we define neighborhoods as boroughs, due to the structure of the data. In New York City, there are five boroughs, each which is a county of New York State: The Bronx, Brooklyn, Manhattan, Queens, and Staten Island—which consolidated into a single city in 1898.

2. Methods

2.1. Study Design and Overview

Data for this study come from the baseline assessment of Project 18, a longitudinal study of 598 racially and ethnically diverse YMSM in the New York City metropolitan area with complete data at baseline. Full details on the Project 18 study have been described elsewhere [15,16,17,18,19,20,21]. Briefly, participants were recruited via active and passive strategies between June 2009 and May 2011—including community events and dance clubs, as well as social networking websites and dating websites. Participants were eligible for this study if they were 18 or 19 years old at the time of the baseline assessment, biologically male, lived in the New York City metropolitan area, reported having had sex (any physical contact that could lead to orgasm) with another male in the 6 months preceding the baseline assessment, and self-reported a HIV-negative or unknown serostatus. Men from a racial/ethnic minority group (i.e., Black, Hispanic, Asian-Pacific Islander, Multiracial/Other) were oversampled and thus were the majority of the sample (>70% non-White). The New York University Institutional Review Board approved the study protocol.

2.1.1. Characterization of Neighborhood Environments

In order to explore a range of neighborhood contexts that YMSM engage and interact in, this study sought to ascertain information about three different neighborhood contexts: the residential, social, and sex neighborhoods among study participants, which was based on our previous work highlighting these contexts as salient to MSM [8]. First, to characterize residential neighborhoods, participants were asked to report their current ZIP code of residence, which we geocoded in ArcGIS (Environmental Systems Research Institute (ESRI), Redlands, California, USA) based on procedures detailed in our prior work [22,23] using a ZIP code file from ESRI Data and Map current as of June 2011. Of the 598 participants who provided ZIP code data, 594 were geocoded while four were unable to be geocoded (due to invalid ZIP codes). In addition, all participants were asked to provide the names of their most recent (last 6 months) residential neighborhoods. Second, participants were asked to report on the neighborhoods where they had socialized most often during the last 6 months. Finally, participants were asked to provide names of the neighborhoods where they had sex most often during the past 6 months. All neighborhood information was obtained via survey. For each residential, social, and sex neighborhood, participants were asked to enter in the nearest cross streets or name of the neighborhood as opposed to geopolitical boundaries (e.g., ZIP codes). We did not obtain geopolitical boundaries for socializing and sex neighborhoods because we suspected this information might not be known for these neighborhoods in the same way that they are likely to be known for residential neighborhoods. Participants were asked to report the names of up to three neighborhoods for each neighborhood type (i.e., residential, social, and sex neighborhoods); however, there was often no data for the second and third neighborhood names for each neighborhood type. It was not clear if the data were missing or not entered as participants may have only reported one or two neighborhoods. As such, we restricted the neighborhood information to the first neighborhood name for each type. In addition, because these data were self-reported, there was significant variation in how this information was entered (e.g., from specific cross street intersections in Manhattan to New York City boroughs, to cities outside of New York State). Given the variability in responses obtained for residential, social, and sex neighborhoods, the data on neighborhoods were coded as follows: New York City (metropolitan area, out of metropolitan area), New Jersey, Connecticut and other areas. The New York City metropolitan area included each New York City borough and its respective county: The Bronx (Bronx County), Brooklyn (Kings County), Manhattan (New York County), Queens (Queens County), and Staten Island (Richmond County). Any residential, social, or sex neighborhoods outside of the New York City metropolitan area, but within New York State, were categorized as outside of the New York City metropolitan area (e.g., Westchester County). Participants reporting social or sex neighborhoods in locations/cities outside of New York State, but within the tri-state area (i.e., New Jersey and Connecticut), were categorized by that respective state. Finally, participants who reported social, or sex neighborhoods locations outside of the tri-state area (e.g., Los Angeles, California) were included in the “Other” category. Therefore, given the structure of the data, characterization of neighborhoods for the spatial polygamy analyses was based on New York City boroughs.

2.1.2. Individual-Level Characteristics

Individual-level characteristics were collected via audio computer-assisted self-interview (ACASI) software. A full description of these characteristics, including specific instruments used and their psychometric properties, has been described previously [15,16,17,18,19,20,21].
Sociodemographic Characteristics. The survey assessed race/ethnicity (Hispanic/Latino, Black, Asian-Pacific Islander/Multiracial/Other, and White), current school enrollment (yes, no), perceived familial socioeconomic status (lower, middle, upper), foreign-born status (yes, no), household composition (living with parents, friends/roommates, other (e.g., shelter)), sexual identity (exclusively homosexual versus not exclusively homosexual), currently in a relationship with another man (yes, no), and current housing status (stably housed versus unstably housed/homeless).
Psychosocial Factors. Ethnic identity was measured using the 12-item Multigroup Ethnic Identity Measure (e.g., “I have a lot of pride in my ethnic group”, etc.) [24] (coded as low versus high), experiences of gay-related stigma in both public (coded as low versus high) and personal (coded as low versus high) contexts was assessed via two subscales of the HIV Stigma Scale that we modified to assess stigma associated with sexual orientation including a two-item measure (e.g., “Most people think a person who is gay is disgusting”) and via a three-item measure (e.g., “I have been hurt by how people reacted to learning I’m gay”), respectively [25], having come out to their peers as measured by the proportion of friends who knew of the participant’s same-sex sexual behavior (“About how many of your friends know that you have had sex with a man?” [26] (coded as no/“some, few, none, don’t know” versus yes/“all, most”), gay community affinity was assessed from a one-item measure: “I feel a part of the gay community in New York City” [26] (coded as high versus medium/low), and internalized homophobia from a four-item measure (e.g., “Sometimes I dislike myself for being gay/bisexual”) [27] (coded as low versus high) was assessed.
Social Support Network Characteristics. Social support network characteristics were assessed via a social network inventory of up to 10 network members (e.g., family, friends, work associates, sexual partner) who study participants considered to be a “significant or important” person in their lives, consistent with our previous work [16,21]. Network size (dichotomized at median as ≤8 versus >8), average duration of network relationships (dichotomized as <2 years versus ≥2 years), frequency of communication with network members (dichotomized at ≤monthly versus ≥weekly), emotional support (low versus high), and material support (low versus high) were assessed.
Substance Use and Condomless Sex. Participants provided information on recent substance use and condomless sex using a 30-day calendar-based methodology [28]. Specifically, recent (last 30 days) alcohol use and recent marijuana use were assessed in the survey. In addition, recent (last 30 days) condomless oral sex and condomless insertive or receptive anal sex were also assessed in the survey. Recent substance use and condomless sex was coded was “yes” versus “no”.

2.2. Statistical Analyses

First, we calculated the number of participants per residential ZIP code and used this information to create a map illustrating the distribution of residential neighborhoods for the Project 18 sample (See Figure 1). Second, descriptive analyses were conducted to present the distribution of neighborhoods (defined as boroughs) reported across all three contexts, i.e., residential, social, and sex neighborhoods. Next, using these data, we examined concordance between borough types to assess the extent of spatial polygamy or differences between borough contexts. Specifically, we examined concordance between: (1) residential and social boroughs; (2) residential and sex boroughs; (3) social and sex boroughs; and (4) residential, social, and sex boroughs. Bivariable analyses were conducted to examine the influence of sociodemographic characteristics, psychosocial factors, social network characteristics, and health behaviors in relation to borough concordance, using Chi-Square statistics. Thus, all individual-level variables were examined separately in relation to borough concordance. Statistical significance was determined by p < 0.05. Statistical Package for the Social Sciences (SPSS) version 21 (SPSS IBM, New York, USA) was used to perform the statistical analysis.
Ijerph 11 08962 g001 1024
Figure 1. Project 18 participants by residential ZIP code, New York City metropolitan area.
Figure 1. Project 18 participants by residential ZIP code, New York City metropolitan area.
Ijerph 11 08962 g001

3. Results

Figure 1 shows the spatial distribution of this sample of YMSM, by ZIP code, across the greater New York City metropolitan area. Participants resided in 122 of the 181 ZIP codes in New York City. The vast majority of the sample reported residing in the New York City metropolitan area with 69% of the sample reporting residence in one of the five New York City boroughs (Table 1). Of the five New York City boroughs, 32% and 15% of participants reported residence in Manhattan or Brooklyn, respectively, while approximately 10% reported either The Bronx or Queens as their borough of residence. Only 1% of the sample reported residing in Staten Island. With regard to socializing boroughs, 47% and 14% of this sample identified Manhattan or Brooklyn as their main boroughs for engaging in social activities. Overall, the distribution of sex boroughs more closely resembled the distribution of residential boroughs rather than socializing boroughs. In terms of borough concordance, approximately one third of YMSM in this sample reported discordant residential/social, residential/sex, and socializing/sex boroughs. In addition, 75% of participants in this study reported discordance across all three borough types (i.e., residential, socializing, and sex boroughs).
Table
Table 1. Distribution of self-reported residential, social and sex neighborhoods (boroughs) among YMSM in the Project 18 study, 2009–2011.
Table 1. Distribution of self-reported residential, social and sex neighborhoods (boroughs) among YMSM in the Project 18 study, 2009–2011.
Neighborhoods% (N)
Residential boroughs
 Manhattan32 (194)
 Brooklyn15 (91)
 The Bronx10 (59)
 Queens11 (66)
 Staten Island1 (4)
 New York (Non-metro NYC area)11 (67)
 New Jersey9 (53)
 Connecticut1 (7)
 Other9 (52)
Socializing boroughs
 Manhattan47 (278)
 Brooklyn14 (83)
 The Bronx6 (37)
 Queens8 (47)
 Staten Island1 (3)
 New York (Non-metro NYC area)10 (61)
 New Jersey6 (37)
 Connecticut1 (4)
 Other7 (43)
Sex boroughs
 Manhattan32 (191)
 Brooklyn14 (84)
 The Bronx10 (59)
 Queens9 (53)
 Staten Island1 (3)
 New York (Non-metro NYC area)10 (61)
 New Jersey8 (49)
 Connecticut1 (5)
 Other8 (49)
Residential and social borough concordance
 Yes66 (395)
 No34 (203)
Residential and sex borough concordance
 Yes68 (408)
 No32 (190)
Social and sex borough concordance
 Yes63 (378)
 No37 (220)
Residential, social, and sex borough concordance
 Yes25 (150)
 No75 (448)
Bivariable analyses indicated several notable differences in borough concordance by certain sociodemographic characteristics, psychosocial factors, social support network characteristics, as well as health behaviors (Table 2). First, Hispanic and Black YMSM were more likely to report borough discordance for all types of boroughs, whereas White YMSM were significantly more likely to report borough concordance, also across all borough concordance measures. For example, White YMSM were significantly more likely to report residential/socializing/sex borough concordance (46%) compared to Hispanic (29%) and Black (13%) YMSM (p < 0.001). Next, those reporting currently being enrolled in school reported more residential/social borough concordance (p < 0.05). Perceived familial socioeconomic status was also associated with borough concordance, with YMSM who reported a higher perceived socioeconomic status being more likely to report borough discordance while those reporting lower perceived socioeconomic status to be more likely to report borough concordance for socializing/sex and residential/socializing/sex boroughs. YMSM who reported residing with their parents were also more likely to report borough discordance across measures of residential/social, socializing/sex and residential/socializing/sex boroughs, while those who reported residing with their friends or a roommate reported more concordance for these borough types (all p < 0.05). Those who were exclusively homosexual reported more residential/social borough discordance (p < 0.01). With regard to psychosocial factors, YMSM who reported experiencing gay-related stigma in public forums were more likely to report discordant residential/social, socializing/sex and residential/socializing/sex boroughs (p < 0.05). YMSM who reported having come out to their peers were more likely to report concordant residential/sex and socializing/sex boroughs (both p < 0.05) while YMSM who reported greater gay community affinity were more likely to report discordant residential/social boroughs (p < 0.01). Regarding social support network characteristics, we found that greater frequency of communication with network members (≥weekly) was associated with less residential/social borough concordance (p < 0.05). In addition, YMSM who reported residential/socializing/sex borough concordance were more likely to report recent (last 30 days) alcohol use compared to those in discordant boroughs (88% versus 76%, p < 0.01). Self-reports of recent marijuana use were higher among those reporting both residential/socializing/sex as well as socializing/sex concordant boroughs (both p < 0.05). In terms of sexual behavior, YMSM in this study who reported concordance across all borough contexts were more likely to report engaging in condomless oral sex (all p < 0.05).

4. Discussion

Spatial polygamy may be particularly salient in urban environments such as New York City due to the proximity of neighborhoods whose characteristics, norms, and cultures vary dramatically. In order to evaluate spatial polygamy among a sample of YMSM in New York City, we inquired about their home, social, and sex boroughs. In this study, we found that approximately two-thirds of participants reported concordance between residential/socializing, residential/sex, and sex/socializing boroughs, whereas 25% reported concordance between all three residential/socializing/sex boroughs. In addition, borough concordance varied by some sociodemographic characteristics, psychosocial factors, social support network characteristics, as well as certain health behaviors.
Table
Table 2. Bivariable associations between sociodemographic characteristics, psychosocial factors, social support network characteristics substance use and sexual behaviors and borough concordance for the Project 18 participants.
Table 2. Bivariable associations between sociodemographic characteristics, psychosocial factors, social support network characteristics substance use and sexual behaviors and borough concordance for the Project 18 participants.
Residential/Social Borough ConcordanceResidential/Sex Borough ConcordanceSocial/Sex Borough ConcordanceResidential/Social/Sex Borough Concordance
NoYesNoYesNoYesNoYes
(N = 203)(N = 395)(N = 190)(N = 408)(N = 220)(N = 378)(N = 448)(N = 150)
%%%%%%%%
Sociodemographic characteristics
Race/ethnicity
Hispanic/Latino44364137 ***4535 ***4229 ***
Black2017231521151913
Other (including Asian Pacific Islander)1114171216121412
White2533203618382546
Currently enrolled in school(yes)8188 *848785868489
Perceived familial socioeconomic status
High353335333930 *3625 ***
Middle3936373737373933
Low2631283024332542
Foreign-born (yes)101112111012129
Household composition
Parents6150 *61516448 **6522 ***
Friends/roommates3137293828392855
Other (e.g., shelter)8131012813723
Sexual identity (exclusively homosexual)5037 **424144404241
Currently in male-male relationship (yes)2726312528262725
Current housing status (unstably housed)65945563
Psychosocial factors
Ethnic identity (high)3838354040374033
Experienced public gay-related stigma (high)5445 *52465842 ***5528 ***
Experienced personal gay-related stigma (high)4749445047484945
Has come out to peers (yes)71675974 ***6472 *6873
Gay community affinity (high)5138 **414345414243
Internalized Homophobia (high)7772727575747474
Social support network characteristics
Network size (>8 members)4749484947504656
Average duration of network relationships (≥2yrs)6467706565677057
Frequency of communication with network members (≥weekly)8779 *828284818478
Emotional support (high)8283737273727368
Material support (high)7272707373727274
Health behaviors
Alcohol use, last 30 days (yes)7980788077817688 **
Marijuana use. last 30 days (yes)454741494050 *4258 **
Condomless oral sex, last 30 days (yes)4858 *4858 *4361 ***5263 *
Condomless anal sex, last 30 days (yes)1921202120212117
Notes: * p < 0.05, ** p < 0.01, *** p < 0.001.
Our findings are similar to the existing research on spatial polygamy among adult MSM, including a recent study by Koblin and colleagues finding that few (15%) MSM in New York City had concordant home, social, and sexual neighborhoods; 31% men reported that none of their neighborhoods were the same. Similar to our study, previous research also showed that concordance of neighborhoods differed significantly by race/ethnicity. More specifically, a higher percentage of White men (22%) reported that all of the neighborhoods were the same compared to Black (10%) and Latino (11%) men, and a higher percentage of Black (35%) and Latino (37%) men reported that none of their neighborhoods were the same compared to their White counterparts (25%) [10]. However, the current study is the first study to examine spatial polygamy in YMSM and to evaluate the influence of multiple sociodemographic characteristics, psychosocial factors, social support network characteristics, and health behaviors in relation to neighborhood concordance. The Koblin study only examined and reported differences by race/ethnicity. Therefore, our study nicely builds on Koblin’s important work and provides a meaningful contribution to the literature. The present study examines adolescent and emerging adult MSM.
There are several plausible explanations for our findings. Spatial polygamy may be common among our sample of New York City YMSM for a number of reasons, including the porosity of neighborhoods in New York City, cost of living differences by borough may mean that young people cannot necessarily live where they work, play, and go to school. In addition, there is easily accessible and multiple modes of public transportation between boroughs in New York City. Participants in our study might travel between home and work/school—which is supported by a recent study conducted among a sample of MSM in Baltimore [9]. As persistent residential segregation and its concomitant influence on amenities in neighborhoods have been documented [29,30,31,32], White YMSM may be more likely to report borough concordance because they may be more likely to live in neighborhoods with amenities that promote social activities—including parks and movie theaters; thus, they would not need to go to another borough to be socially-engaged. In addition, racial/ethnic minority (e.g., Black and Hispanic) men have a higher likelihood of not disclosing their sexual identity to their friends/family [17] and having more internalized homophobia [16], which may be a factor in less borough concordance (e.g., they might travel outside of their residential boroughs to socialize in neighborhoods where they can be more open about their sexuality). Moreover, people living with their parents might be less likely to be “out” and therefore report more discordant boroughs. It is possible that YMSM reporting lower perceived socioeconomic status are more likely to report borough concordance because they might not have the financial means to travel to different neighborhoods. Previous research has shown that urban sexual minority youth are exposed to LGBT hate crimes in their residential neighborhoods, which may be stress-inducing [33,34]; as such, YMSM experiencing gay-related stigma in public forums may be more likely to report discordant socializing/sex and residential/socializing/sex boroughs because they may be seeking to socialize in neighborhoods where they can be open about their sexuality and relationships. Our findings of some significant differences in mobility across boroughs (spatial polygamy) and social support network characteristics are in line with previous research among MSM populations [11]. In particular, we found that greater frequency of communication with network members was associated with less residential/social borough concordance and this may be because participants are socializing with social network members in neighborhoods outside of where they live. Interestingly, YMSM who reported residential/socializing/sex borough concordance were more likely to report recent (last 30 days) alcohol use, recent marijuana use, and recently engaging in condomless oral sex. There is literature on the health effects of gay enclaves, some of which suggests that among MSM, gay enclaves are associated with increased substance use [2,3] and increased high-risk sexual behavior [3,4]—perhaps due to social norms that encourage drug use and condomless sex.

5. Study Limitations

Several limitations of this study should be noted. First, as previously described, similar to other geographic locations, there are various ways to define neighborhoods in New York City—including based on one’s perception [10] as well as using ego-centric buffers [35,36,37,38,39], census block groups [40,41,42,43,44], census tracts [45,46,47,48,49], United Hospital Fund (UHF) defined neighborhoods [50,51,52,53,54], community districts [55,56,57,58,59], ZIP codes [60,61,62,63,64], and boroughs/counties [8,65,66,67,68]. In this study, we analyzed boroughs due to the structure of the data, which is a coarse neighborhood definition. For example, we were not able to evaluate neighborhoods within the borough/county of Manhattan—which consists of many geographic units previously used to define neighborhoods (e.g., in Manhattan there are 42 ZIP codes (using 2011 ZIP code boundaries), 287 census tracts (using 2010 census tract boundaries), and 1,092 census block groups (using 2010 census block group boundaries)—many of which have defined neighborhood names, including West Village, Chelsea, Kips Bay, Hell’s Kitchen, Midtown, Upper East Side, and Harlem). Because borough/county was used as the neighborhood definition, our study might underestimate spatial polygamy and we recognize there is potential for spatial misclassification [69], which may explain some of the null associations found in this study. However, in addition to New York City-based research [8,65,66,67,68], it should be noted that county as a neighborhood definition has been used in significant previous health-related research on neighborhoods in other geographic locations in general populations [70,71,72,73,74,75,76], as well as similar research in sexual minority populations [77,78,79,80,81,82]. Further, it should be noted that some studies have examined neighborhood-level exposures in New York City as the neighborhood definition and unit of analysis [83,84], which is an even coarser neighborhood scale than used in the current study. Second, because we restricted the neighborhood information to the first neighborhood name for each type (as there was often no data for the second and third neighborhood names for each neighborhood type and as it was not clear to us if the data were missing or not entered as participants may have only reported one or two neighborhoods), we recognize that spatial polygamy may be underestimated. Third, in this study, we did not examine neighborhoods where these young men went to school, sought medical care, worked and had other family, which may be salient to their lives. Fourth, the “uncertain geographic context problem” is a concern. The uncertain geographic context problem notes that a problem in neighborhood health research is spatial uncertainty in the actual areas that exert contextual influences on the individuals being studied and the temporal uncertainty in the timing and duration in which individuals experience contextual influences [85,86]. No measures of exposure by neighborhood (e.g., time spent in residential versus socializing neighborhood), for instance, were included in the study because this information was not included in the survey. In addition, there may be some under-reporting of substance use and condomless sex due to socially desirable responding. However, our use of audio computer-assisted self-interviews (ACASI) is a method of survey administration used to reduce this potential misclassification. Also, results were not corrected for multiple comparisons. Furthermore, our study was conducted among a sample of predominantly racial/ethnic minority HIV-negative YMSM in New York City. Consequently, the findings from this study might only be generalizable to racial/ethnic minority HIV-negative YMSM in urban settings. We have no reason to believe that the results would vary by HIV status, but it is possible that if one has had their HIV-positive status compromised in one neighborhood, they may avoid it altogether. Lastly, spatial sampling [87,88,89] was not utilized, so the recruitment strategy did not take into account the geographic distribution of New York City. However, we note that recruitment took place in all of the New York City boroughs/ other areas in the NYC metro area and the sample comes from the vast majority of the New York City metropolitan area.

6. Future Research and Study Implications

Future research should continue to examine spatial polygamy, including among YMSM populations, to understand salient spatial contexts including residential, social and sexual neighborhood contexts but also other contexts (not examined in the current study) such as school and work neighborhood contexts—and including the actual characteristics of the various neighborhoods such median household income, percent racial/ethnic minority, crime statistics and/or HIV rates. Future research can examine spatial polygamy using smaller neighborhood definitions—which might be more socially meaningful than the neighborhoods analyzed in the current study. As shown in previous research, qualitative [11] and web-based [10] methods can be used to examine spatial polygamy among MSM. Survey methods, as done in this study, can also be employed to examine spatial polygamy among MSM. However, more nuanced and specific information can come from geocoded data on frequently traveled spatial contexts using Global Positioning Systems (GPS) technology, which also have a temporal dimension [90,91]. Any potential GPS research can help minimize the concerns related to the uncertain geographic context problem [85,86]. Furthermore, future research studies could examine correlates and effect of spatial polygamy. For instance, one could test whether spatial polygamy could be the result of an individual moving to a new neighborhood—leaving friends and sex partners in their old neighborhood. More broadly, future research can examine additional sociodemographic characteristics, psychosocial factors, social network characteristics, and health behaviors and examine the geographic location of an individual’s social network members. Additional and more specific information on associations between sociodemographic characteristics, psychosocial factors, social network characteristics, and health behaviors associated with spatial polygamy may increase understanding of the most appropriate locations for targeted disease prevention and health promotion interventions (e.g., HIV prevention interventions). This is especially warranted given the persistence of stark disparities in HIV between non-sexual minority and sexual minority (e.g., MSM) populations, as well as socioeconomic and racial/ethnic disparities in HIV/AIDS among YMSM [92,93,94]. Findings from this and other studies on spatial polygamy in conjunction with emerging research showing HIV “hot spots” (i.e., that HIV clusters spatially) [5,95,96,97,98] can be used to target HIV prevention strategies among MSM populations. Such targeted HIV prevention strategies should not only take into account potential HIV ‘hot spots’, but also consider other emerging work showing that sustained high viral load and durably suppressed viral load among HIV-infected individuals varies by neighborhood [68]. One study demonstrated that the percentage of HIV-infected New Yorkers varies by New York City borough and that certain boroughs (e.g., The Bronx) are more likely to have sustained high viral loads [68]. We highlight the importance of targeted HIV prevention and control programs because lower socioeconomic status MSM (who also in this study are more likely to be Black or Hispanic) have sex and live in neighborhoods, which are also likely to be low-income and as result probably have greater rates of untreated unsuppressed HIV. Thus, despite some evidence of Black YMSM not actually engaging in more condomless sexual acts [18,99], they have a higher likelihood of having sex with a man who is virally unsuppressed because he resides in a lower-income neighborhood.

7. Conclusions

These findings suggest that spatial polygamy, or an individual moving across and experiencing multiple neighborhood contexts, is prevalent among urban YMSM and that spatial polygamy varies by multiple individual-level characteristics. Future research among YMSM populations should consider multiple neighborhood contexts in order to provide a more nuanced understanding of how and which neighborhood contexts influence the health and well-being of YMSM. This further examination of spatial polygamy (and individual-level characteristics associated with it) may increase understanding of the most appropriate locations for targeted disease prevention and health promotion interventions (e.g., HIV prevention interventions).

Acknowledgments

We wish to thank the three anonymous reviewers who offered very helpful comments and suggestions, which allowed us to greatly improve this manuscript. We thank Priya Kanayson for creating the Project 18 neighborhood spatial polygamy dataset; Jeff Blossom for geocoding the ZIP code data, for creating the figure used in this study and for commenting on an early version of this manuscript; James Williams, Timothy Allomong, Kenneth Pass and William Goedel for commenting on an early version of this manuscript; and Shilpa Dutta, Maliyhah Al-Bayan and James Williams for their assistance with the preparation of this manuscript. We also thank the participants of the study and the research staff who contributed to the project. Dustin Duncan was supported by his New York University School of Medicine Start-up Research Fund to work on this project. The project was funded by the National Institute on Drug Abuse (NIDA) (Grant # R01DA025537, Perry Halkitis, Principal Investigator). The content does not necessarily reflect the opinions or views of NIDA or the National Institutes of Health (NIH).

Author Contributions

Dustin T. Duncan conceived the study, supervised the geocoding and mapping of residential ZIP codes, interpreted the results, and drafted the article. Farzana Kapadia assisted with the study design, supervised creating the neighborhood spatial polygamy dataset, performed the statistical analysis, and critically revised the manuscript for intellectual content. Perry N. Halkitis interpreted the results and critically revised the manuscript for intellectual content. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Frye, V.; Koblin, B.; Chin, J.; Beard, J.; Blaney, S.; Halkitis, P.; Vlahov, D.; Galea, S. Neighborhood-level correlates of consistent condom use among men who have sex with men: A multi-level analysis. AIDS Behav. 2010, 14, 974–985. [Google Scholar] [CrossRef] [PubMed]
  2. Carpiano, R.M.; Kelly, B.C.; Easterbrook, A.; Parsons, J.T. Community and drug use among gay men: The role of neighborhoods and networks. J. Health Soc. Behav. 2011, 52, 74–90. [Google Scholar] [CrossRef] [PubMed]
  3. Buttram, M.E.; Kurtz, S.P. Risk and protective factors associated with gay neighborhood residence. Amer. J. Mens Health 2013, 7, 110–118. [Google Scholar] [CrossRef]
  4. Kelly, B.C.; Carpiano, R.M.; Easterbrook, A.; Parsons, J.T. Sex and the community: The implications of neighbourhoods and social networks for sexual risk behaviours among urban gay men. Sociol. Health Ill. 2012, 34, 1085–1102. [Google Scholar] [CrossRef]
  5. Raymond, H.F.; Chen, Y.H.; Syme, S.L.; Catalano, R.; Hutson, M.A.; McFarland, W. The role of individual and neighborhood factors: HIV acquisition risk among high-risk populations in San Francisco. AIDS Behav. 2014, 18, 346–356. [Google Scholar] [CrossRef] [PubMed]
  6. Matthews, S.A. Spatial polygamy and the heterogeneity of place: Studying people and place via egocentric methods. In Communities, Neighborhoods, and Health; Springer: New York, NY, USA, 2011; pp. 35–55. [Google Scholar]
  7. Chaix, B.; Merlo, J.; Evans, D.; Leal, C.; Havard, S. Neighbourhoods in eco-epidemiologic research: Delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Soc. Sci. Med. 2009, 69, 1306–1310. [Google Scholar]
  8. Dragowski, E.A.; Halkitis, P.N.; Moeller, R.W.; Siconolfi, D.E. Social and sexual contexts explain sexual risk taking in young gay, bisexual, and other young men who have sex with men, ages 13–29 years. J. HIV/AIDS Soc. Serv. 2013, 12, 236–255. [Google Scholar]
  9. Tobin, K.E.; Cutchin, M.; Latkin, C.A.; Takahashi, L.M. Social geographies of African American men who have sex with men (MSM): A qualitative exploration of the social, spatial and temporal context of HIV risk in Baltimore, Maryland. Health Place 2013, 22, 1–6. [Google Scholar] [CrossRef] [PubMed]
  10. Koblin, B.A.; Egan, J.E.; Rundle, A.; Quinn, J.; Tieu, H.V.; Cerda, M.; Ompad, D.C.; Greene, E.; Hoover, D.R.; Frye, V. 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. [Google Scholar] [CrossRef]
  11. Egan, J.E.; Frye, V.; Kurtz, S.P.; Latkin, C.; Chen, M.; Tobin, K.; Yang, C.; Koblin, B.A. 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, S35–S50. [Google Scholar] [CrossRef] [PubMed]
  12. Sawatzky, R.; Liu-Ambrose, T.; Miller, W.C.; Marra, C.A. Physical activity as a mediator of the impact of chronic conditions on quality of life in older adults. Health Qual. Life Outcomes 2007, 5. [Google Scholar] [CrossRef]
  13. Motl, R.W.; McAuley, E. Physical activity, disability, and quality of life in older adults. Phys. Med. Rehabil. Clin. N. Am. 2010, 21, 299–308. [Google Scholar] [CrossRef] [PubMed]
  14. Frye, V.; Latka, M.H.; Koblin, B.; Halkitis, P.N.; Putnam, S.; Galea, S.; Vlahov, D. The urban environment and sexual risk behavior among men who have sex with men. J. Urban. Health 2006, 83, 308–324. [Google Scholar] [CrossRef] [PubMed]
  15. Storholm, E.D.; Siconolfi, D.E.; Halkitis, P.N.; Moeller, R.W.; Eddy, J.A.; Bare, M.G. Sociodemographic factors contribute to mental health disparities and access to services among young men who have sex with men in New York City. J. Gay Lesbian Mental Health 2013, 17. [Google Scholar] [CrossRef]
  16. Kapadia, F.; Siconolfi, D.E.; Barton, S.; Olivieri, B.; Lombardo, L.; Halkitis, P.N. Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men. AIDS Behav. 2013, 17, 1819–1828. [Google Scholar] [CrossRef] [PubMed]
  17. Siconolfi, D.E.; Kapadia, F.; Halkitis, P.N.; Moeller, R.W.; Storholm, E.D.; Barton, S.C.; Solomon, T.M.; Jones, D. Sexual health screening among racially/ethnically diverse young gay, bisexual, and other men who have sex with men. J. Adolescent Health 2013, 52, 620–626. [Google Scholar] [CrossRef]
  18. Halkitis, P.N.; Kapadia, F.; Siconolfi, D.E.; Moeller, R.W.; Figueroa, R.P.; Barton, S.C.; Blachman-Forshay, J. Individual, psychosocial, and social correlates of unprotected anal intercourse in a new generation of young men who have sex with men in New York City. Amer. J. Public Health 2013, 103, 889–895. [Google Scholar] [CrossRef]
  19. Halkitis, P.N.; Moeller, R.W.; Siconolfi, D.E.; Storholm, E.D.; Solomon, T.M.; Bub, K.L. Measurement model exploring a syndemic in emerging adult gay and bisexual men. AIDS Behav. 2013, 17, 662–673. [Google Scholar] [CrossRef] [PubMed]
  20. Halkitis, P.N.; Figueroa, R.P. Sociodemographic characteristics explain differences in unprotected sexual behavior among young HIV-negative gay, bisexual, and other YMSM in New York City. AIDS Patient Care Stds. 2013, 27, 181–190. [Google Scholar] [CrossRef] [PubMed]
  21. Kapadia, F.; Halkitis, P.N.; Barton, S.; Siconolfi, D.E.; Figueroa, R.E.P. Associations between social support network-characteristics and receipt of emotional and material support among a sample of sexual minority youth. J. Gay Lesbian Soc. Serv. 2014, 26, 279–302. [Google Scholar] [CrossRef]
  22. Duncan, D.T.; Castro, M.C.; Blossom, J.C.; Bennett, G.G.; Gortmaker, S.L. Evaluation of the positional difference between two common geocoding methods. Geospat. Health 2011, 5, 265–273. [Google Scholar] [PubMed]
  23. Duncan, D.T.; Aldstadt, J.; Whalen, J.; Melly, S.J.; Gortmaker, S.L. Validation of walk score for estimating neighborhood walkability: an analysis of four U.S. metropolitan areas. Int. J. Environ. Res. Public Health 2011, 8, 4160–4179. [Google Scholar] [CrossRef]
  24. Phinney, J.S. The multigroup ethnic identity measure: A new scale for use with diverse groups. J. Adolescent Res. 1992, 7, 156–176. [Google Scholar] [CrossRef]
  25. Frost, D.M.; Parsons, J.T.; Nanín, J.E. Stigma, concealment and symptoms of depression as explanations for sexually transmitted infections among gay men. J. Health Psychol. 2007, 12, 636–640. [Google Scholar] [CrossRef] [PubMed]
  26. O’Donnell, L.; Agronick, G.; San Doval, A.; Duran, R.; Myint-U, A.; 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, 457–471. [Google Scholar]
  27. Thiede, H.; Valleroy, L.A.; MacKellar, D.A.; Celentano, D.D.; Ford, W.L.; Hagan, H.; Koblin, B.A.; LaLota, M.; McFarland, W.; Shehan, D.A. Regional patterns and correlates of substance use among young men who have sex with men in 7 U.S. urban areas. Amer. J. Public Health 2003, 93, 1915–1921. [Google Scholar] [CrossRef]
  28. Sobell, L.C.; Sobell, M.B. Alcohol Timeline Followback Users’ Manual; Addiction Research Foundation: Toronto, Canada, 1995. [Google Scholar]
  29. Massey, D.S.; Denton, N. American Apartheid: Segregation and the Making of the Underclass; Harvard University Press: Cambridge, MA, USA, 1993. [Google Scholar]
  30. Marsh, B.; Parnell, A.M.; Joyner, A.M. Institutionalization of racial inequality in local political geographies. Urban Geogr. 2010, 31, 691–709. [Google Scholar] [CrossRef]
  31. Logan, J.R.; Stults, B. The Persistence of Segregation in the Metropolis: New Findings from the 2010 Census. Census Brief. Prepared for Project US2010, 2011. Available online: http://www.s4.brown.edu/us2010/ (accessed on 15 May 2014).
  32. Duncan, D.T.; Kawachi, I.; White, K.; Williams, D.R. The geography of recreational open space: Influence of neighborhood racial composition and neighborhood poverty. J. Urban Health 2013, 90, 618–631. [Google Scholar] [CrossRef] [PubMed]
  33. Duncan, D.T.; Hatzenbuehler, M.L.; Johnson, R.M. Neighborhood-level LGBT hate crimes and current illicit drug use among sexual minority youth. Drug Alcohol Depend. 2014, 135, 65–70. [Google Scholar] [CrossRef] [PubMed]
  34. Duncan, D.T.; Hatzenbuehler, M.L. Lesbian, gay, bisexual, and transgender hate crimes and suicidality among a population-based sample of sexual-minority adolescents in Boston. Amer. J. Public Health 2014, 104, 272–278. [Google Scholar] [CrossRef]
  35. Patel, M.M.; Quinn, J.W.; Jung, K.H.; Hoepner, L.; Diaz, D.; Perzanowski, M.; Rundle, A.; Kinney, P.L.; Perera, F.P.; Miller, R.L. Traffic density and stationary sources of air pollution associated with wheeze, asthma, and immunoglobulin E from birth to age 5 years among New York City children. Environ. Res. 2011, 111, 1222–1229. [Google Scholar] [CrossRef] [PubMed]
  36. Cornell, A.G.; Chillrud, S.N.; Mellins, R.B.; Acosta, L.M.; Miller, R.L.; Quinn, J.W.; Yan, B.; Divjan, A.; Olmedo, O.E.; Lopez-Pintado, S.; Kinney, P.L.; Perera, F.P.; Jacobson, J.S.; Goldstein, I.F.; Rundle, A.G.; Perzanowski, M.S. Domestic airborne black carbon and exhaled nitric oxide in children in NYC. J. Expo. Sci. Environ. Epidemiol. 2012, 22, 258–266. [Google Scholar] [CrossRef] [PubMed]
  37. Rundle, A.; Neckerman, K.M.; Freeman, L.; Lovasi, G.S.; Purciel, M.; Quinn, J.; Richards, C.; Sircar, N.; Weiss, C. Neighborhood food environment and walkability predict obesity in New York City. Environ. Health Perspect. 2009, 117, 442–447. [Google Scholar] [CrossRef] [PubMed]
  38. Lovasi, G.S.; Jacobson, J.S.; Quinn, J.W.; Neckerman, K.M.; Ashby-Thompson, M.N.; Rundle, A. Is the environment near home and school associated with physical activity and adiposity of urban preschool children? J. Urban Health 2011, 88, 1143–1157. [Google Scholar] [CrossRef]
  39. Park, Y.; Neckerman, K.; Quinn, J.; Weiss, C.; Jacobson, J.; Rundle, A. Neighbourhood immigrant acculturation and diet among Hispanic female residents of New York City. Public Health Nutr. 2011, 14, 1593–1600. [Google Scholar] [CrossRef] [PubMed]
  40. Kwate, N.O.; Loh, J.M.; White, K.; Saldana, N. Retail redlining in New York City: Racialized access to day-to-day retail resources. J. Urban Health 2013, 90, 632–652. [Google Scholar] [CrossRef] [PubMed]
  41. Kwate, N.O.; Yau, C.Y.; Loh, J.M.; Williams, D. Inequality in obesigenic environments: Fast food density in New York City. Health Place 2009, 15, 364–373. [Google Scholar] [CrossRef] [PubMed]
  42. Kwate, N.O.; Lee, T.H. Ghettoizing outdoor advertising: Disadvantage and ad panel density in black neighborhoods. J. Urban Health 2007, 84, 21–31. [Google Scholar] [CrossRef] [PubMed]
  43. Gordon, C.; Purciel-Hill, M.; Ghai, N.R.; Kaufman, L.; Graham, R.; van Wye, G. Measuring food deserts in New York City’s low-income neighborhoods. Health Place 2011, 17, 696–700. [Google Scholar] [CrossRef] [PubMed]
  44. Barr, R.G.; Diez-Roux, A.V.; Knirsch, C.A.; Pablos-Mendez, A. Neighborhood poverty and the resurgence of tuberculosis in New York City, 1984–1992. Amer. J. Public Health 2001, 91, 1487–1493. [Google Scholar] [CrossRef]
  45. Janevic, T.; Borrell, L.N.; Savitz, D.A.; Herring, A.H.; Rundle, A. Neighbourhood food environment and gestational diabetes in New York City. Paediatr. Perinat. Epidemiol. 2010, 24, 249–254. [Google Scholar] [CrossRef] [PubMed]
  46. Neckerman, K.M.; Lovasi, G.S.; Davies, S.; Purciel, M.; Quinn, J.; Feder, E.; Raghunath, N.; Wasserman, B.; Rundle, A. Disparities in urban neighborhood conditions: evidence from GIS measures and field observation in New York City. J. Public Health Policy 2009, 30, S264–S285. [Google Scholar] [CrossRef] [PubMed]
  47. Rundle, A.; Roux, A.V.D.; Free, L.M.; Miller, D.; Neckerman, K.M.; Weiss, C.C. The urban built environment and obesity in New York City: A multilevel analysis. Amer. J. Health Promot. 2007, 21, 326–334. [Google Scholar] [CrossRef]
  48. Beard, J.R.; Cerda, M.; Blaney, S.; Ahern, J.; Vlahov, D.; Galea, S. Neighborhood characteristics and change in depressive symptoms among older residents of New York City. Amer. J. Public Health 2009, 99, 1308–1314. [Google Scholar] [CrossRef]
  49. Weiss, C.C.; Purciel, M.; Bader, M.; Quinn, J.W.; Lovasi, G.; Neckerman, K.M.; Rundle, A.G. Reconsidering access: Park facilities and neighborhood disamenities in New York City. J. Urban Health 2011, 88, 297–310. [Google Scholar] [CrossRef] [PubMed]
  50. Frank, J.W.; Hong, C.S.; Subramanian, S.V.; Wang, E.A. Neighborhood incarceration rate and asthma prevalence in New York City: A multilevel approach. Amer. J. Public Health 2013, 103, 38–44. [Google Scholar] [CrossRef]
  51. Black, J.L.; Macinko, J. The changing distribution and determinants of obesity in the neighborhoods of New York City, 2003–2007. Amer. J. Epidemiol. 2010, 171, 765–775. [Google Scholar] [CrossRef]
  52. White, K.; Borrell, L.N. Racial/ethnic residential segregation: Framing the context of health risk and health disparities. Health Place 2011, 17, 438–448. [Google Scholar] [CrossRef] [PubMed]
  53. Czarnecki, K.D.; Goranson, C.; Ellis, J.A.; Vichinsky, L.E.; Coady, M.H.; Perl, S.B. Using geographic information system analyses to monitor large-scale distribution of nicotine replacement therapy in New York City. Prev. Med. 2010, 50, 288–296. [Google Scholar] [CrossRef] [PubMed]
  54. Cooper, H.; des Jarlais, D.; Ross, Z.; Tempalski, B.; Bossak, B.H.; Friedman, S.R. Spatial access to sterile syringes and the odds of injecting with an unsterile syringe among injectors: A longitudinal multilevel study. J. Urban Health 2012, 89, 678–696. [Google Scholar] [CrossRef] [PubMed]
  55. Galea, S.; Ahern, J.; Rudenstine, S.; Wallace, Z.; Vlahov, D. Urban built environment and depression: A multilevel analysis. J. Epidemiol. Community Health 2005, 59, 822–827. [Google Scholar] [CrossRef] [PubMed]
  56. Karpati, A.M.; Bassett, M.T.; McCord, C. Neighbourhood mortality inequalities in New York City, 1989–1991 and 1999–2001. J. Epidemiol. Community Health 2006, 60, 1060–1064. [Google Scholar] [CrossRef] [PubMed]
  57. Ahern, J.; Cerda, M.; Lippman, S.A.; Tardiff, K.J.; Vlahov, D.; Galea, S. Navigating non-positivity in neighbourhood studies: an analysis of collective efficacy and violence. J. Epidemiol. Community Health 2013, 67, 159–165. [Google Scholar] [CrossRef] [PubMed]
  58. Cerda, M.; Ransome, Y.; Keyes, K.M.; Koenen, K.C.; Tardiff, K.; Vlahov, D.; Galea, S. Revisiting the role of the urban environment in substance use: The case of analgesic overdose fatalities. Amer. J. Public Health 2013, 103, 2252–2260. [Google Scholar] [CrossRef]
  59. Huynh, M.; Maroko, A.R. Gentrification and preterm birth in New York City, 2008–2010. J. Urban Health 2014, 91, 211–220. [Google Scholar] [CrossRef] [PubMed]
  60. Villanueva, C.; Aggarwal, B. The association between neighborhood socioeconomic status and clinical outcomes among patients 1 year after hospitalization for cardiovascular disease. J. Community Health 2013, 38, 690–697. [Google Scholar] [CrossRef] [PubMed]
  61. Brouse, C.H.; Hillyer, G.C.; Basch, C.E.; Neugut, A.I. Geography, facilities, and promotional strategies used to encourage indoor tanning in New York City. J. Community Health 2011, 36, 635–639. [Google Scholar] [CrossRef] [PubMed]
  62. Jack, D.; Neckerman, K.; Schwartz-Soicher, O.; Lovasi, G.S.; Quinn, J.; Richards, C.; Bader, M.; Weiss, C.; Konty, K.; Arno, P.; Viola, D.; Kerker, B.; Rundle, A. Socio-economic status, neighbourhood food environments and consumption of fruits and vegetables in New York City. Public Health Nutr. 2013, 16, 1197–1205. [Google Scholar] [CrossRef] [PubMed]
  63. Lovasi, G.S.; Schwartz-Soicher, O.; Neckerman, K.M.; Konty, K.; Kerker, B.; Quinn, J.; Rundle, A. Aesthetic amenities and safety hazards associated with walking and bicycling for transportation in New York City. Ann. Behav. Med. 2013, 45, S76–S85. [Google Scholar] [CrossRef] [PubMed]
  64. Lim, S.; Harris, T.G. Neighborhood contributions to racial and ethnic disparities in obesity among New York City adults. Amer. J. Public Health 2014. [Google Scholar] [CrossRef]
  65. Nair, H.P.; Torian, L.V.; Forgione, L.; Begier, E.M. Evaluation of HIV incidence surveillance in New York City, 2006. Public Health Rep. 2011, 126, 28–38. [Google Scholar] [PubMed]
  66. Caravanos, J.; Weiss, A.L.; Blaise, M.J.; Jaeger, R.J. A survey of spatially distributed exterior dust lead loadings in New York City. Environ. Res. 2006, 100, 165–172. [Google Scholar] [CrossRef] [PubMed]
  67. Frick, M.; Castro, M.C. Tobacco retail clustering around schools in New York City: Examining “place” and “space”. Health Place 2013, 19, 15–24. [Google Scholar] [CrossRef] [PubMed]
  68. Terzian, A.S.; Bodach, S.D.; Wiewel, E.W.; Sepkowitz, K.; Bernard, M.A.; Braunstein, S.L.; Shepard, C.W. Novel use of surveillance data to detect HIV-infected persons with sustained high viral load and durable virologic suppression in New York City. PLoS One 2012, 7. [Google Scholar] [CrossRef]
  69. Duncan, D.T.; Kawachi, I.; Subramanian, S.V.; Aldstadt, J.; Melly, S.J.; Williams, D.R. Examination of how neighborhood definition influences measurements of youths’ access to tobacco retailers: A methodological note on spatial misclassification. Amer. J. Epidemiol. 2014, 179, 373–381. [Google Scholar] [CrossRef]
  70. Osypuk, T.L.; Galea, S. What level macro? Choosing appropriate levels to assess how place influences population health. In Macrosocial Determinants of Population Health; Springer: New York, NY, USA, 2007; pp. 399–435. [Google Scholar]
  71. D’Agostino-McGowan, L.; Gennarelli, R.L.; Lyons, S.A.; Goodman, M.S. Using small-area analysis to estimate county-level racial disparities in obesity demonstrating the necessity of targeted interventions. Int. J. Environ. Res. Public Health 2013. [Google Scholar] [CrossRef]
  72. Dwyer-Lindgren, L.; Freedman, G.; Engell, R.E.; Fleming, T.D.; Lim, S.S.; Murray, C.J.; Mokdad, A.H. Prevalence of physical activity and obesity in U.S. counties, 2001–2011: A road map for action. Popul. Health Metrics 2013, 11. [Google Scholar] [CrossRef]
  73. Dwyer-Lindgren, L.; Mokdad, A.H.; Srebotnjak, T.; Flaxman, A.D.; Hansen, G.M.; Murray, C.J. Cigarette smoking prevalence in U.S. counties: 1996–2012. Popul. Health Metrics 2014, 12. [Google Scholar] [CrossRef]
  74. Zhang, Z.; Zhang, L.; Penman, A.; May, W. Using small-area estimation method to calculate county-level prevalence of obesity in Mississippi, 2007–2009. In Prev. Chronic Dis.; 2011; 8, p. A85. Available online: http://www.cdc.gov/pcd/issues/2011/jul/10_0159.htm (accessed on 15 May 2014). [Google Scholar]
  75. Peterson, N.A.; Lowe, J.B.; Reid, R.J. Tobacco outlet density, cigarette smoking prevalence, and demographics at the county level of analysis. Subst. Use Misuse 2005, 40, 1627–1635. [Google Scholar] [CrossRef] [PubMed]
  76. Reid, R.J.; Peterson, N.A.; Lowe, J.B.; Hughey, J. Tobacco outlet density and smoking prevalence: Does racial concentration matter? Drugs-Educ. Prev. Policy 2005, 12, 233–238. [Google Scholar] [CrossRef]
  77. Boehmer, U.; Ozonoff, A.; Timm, A. County-level association of sexual minority density with breast cancer incidence: Results from an ecological study. Sex. Res. Soc. Policy 2011, 8, 139–145. [Google Scholar] [CrossRef]
  78. Boehmer, U.; Ozonoff, A.; Miao, X. An ecological approach to examine lung cancer disparities due to sexual orientation. Public Health 2012, 126, 605–612. [Google Scholar] [CrossRef] [PubMed]
  79. Hatzenbuehler, M.L.; Keyes, K.M. Inclusive anti-bullying policies and reduced risk of suicide attempts in lesbian and gay youth. J. Adolesc. Health 2013, 53, S21–S26. [Google Scholar] [CrossRef] [PubMed]
  80. Hatzenbuehler, M.L.; Pachankis, J.E.; Wolff, J. Religious climate and health risk behaviors in sexual minority youths: A population-based study. Amer. J. Public Health 2012, 102, 657–663. [Google Scholar] [CrossRef]
  81. Hatzenbuehler, M.L.; Wieringa, N.F.; Keyes, K.M. Community-level determinants of tobacco use disparities in lesbian, gay, and bisexual youth: Results from a population-based study. Arch. Pediatr. Adolesc. Med. 2011, 165, 527–532. [Google Scholar] [PubMed]
  82. Hatzenbuehler, M.L. The social environment and suicide attempts in lesbian, gay, and bisexual youth. Pediatrics 2011, 127, 896–903. [Google Scholar] [CrossRef] [PubMed]
  83. Loomis, B.R.; Kim, A.E.; Busey, A.H.; Farrelly, M.C.; Willett, J.G.; Juster, H.R. The density of tobacco retailers and its association with attitudes toward smoking, exposure to point-of-sale tobacco advertising, cigarette purchasing, and smoking among New York youth. Prev. Med. 2012, 55, 468–474. [Google Scholar] [CrossRef] [PubMed]
  84. Kim, A.E.; Loomis, B.R.; Busey, A.H.; Farrelly, M.C.; Willett, J.G.; Juster, H.R. Influence of retail cigarette advertising, price promotions, and retailer compliance on youth smoking-related attitudes and behaviors. J. Public Health Manag. Pract. 2013, 19, 1–9. [Google Scholar] [CrossRef] [PubMed]
  85. Kwan, M.-P. The uncertain geographic context problem. Ann. Assn. Amer. Geogr. 2012, 102, 958–968. [Google Scholar] [CrossRef]
  86. Kwan, M.-P. How GIS can help address the uncertain geographic context problem in social science research. Ann. GIS 2012, 18, 245–255. [Google Scholar] [CrossRef]
  87. Delmelle, E. Spatial sampling. In The SAGE Handbook of Spatial Analysis; Fotheringham, A.S., Rogerson, P.A., Eds.; SAGE Publications: London, UK, 2009; pp. 183–206. [Google Scholar]
  88. Downs, T.J.; Ogneva-Himmelberger, Y.; Aupont, O.; Wang, Y.; Raj, A.; Zimmerman, P.; Goble, R.; Taylor, O.; Churchill, L.C.; Lemay, C.A. Vulnerability-based spatial sampling stratification for the National Children’s Study, Worcester County, Massachusetts: Capturing health-relevant environmental and sociodemographic variability. Environ. Health Perspect. 2010, 118, 1318–1325. [Google Scholar] [CrossRef] [PubMed]
  89. Lee, C.; Moudon, A.V.; Courbois, J.-Y.P. Built environment and behavior: Spatial sampling using parcel data. Ann. Epidemiol. 2006, 16, 387–394. [Google Scholar] [CrossRef] [PubMed]
  90. Boruff, B.J.; Nathan, A.; Nijenstein, S. Using GPS technology to (re)-examine operational definitions of “neighbourhood” in place-based health research. Int. J. Health Geographics 2012, 11. [Google Scholar] [CrossRef] [Green Version]
  91. Chaix, B.; Meline, J.; Duncan, S.; Merrien, C.; Karusisi, N.; Perchoux, C.; Lewin, A.; Labadi, K.; Kestens, Y. GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? Health Place 2013, 21, 46–51. [Google Scholar] [CrossRef] [PubMed]
  92. Centers for Disease Control and Prevention. HIV among Gay and Bisexual Men. Available online: http://www.cdc.gov/hiv/pdf/msm_fact_sheet_final_2014.pdf (accessed on 15 May 2014).
  93. New York City Department of Health and Mental Hygiene. HIV Epidemiology & Field Services Semiannual Report. 2012. Available online: http://www.nyc.gov/html/doh/downloads/pdf/dires/2012-1st-semi-rpt.pdf (accessed on 15 May 2014). [Google Scholar]
  94. New York City Department of Health and Mental Hygiene. New HIV Diagnoses Rising in New York City among Men Who Have Sex with Men. 2007. Available online: http://www.nyc.gov/html/doh/html/pr2007/pr079-07.shtml (accessed on 15 May 2014). [Google Scholar]
  95. El-Sadr, W.M.; Mayer, K.H.; Hodder, S.L. AIDS in America—Forgotten but not gone. N. Engl. J. Med. 2010, 362, 967–970. [Google Scholar]
  96. Wiewel, E.W.; Hanna, D.B.; Begier, E.M.; Torian, L.V. High HIV prevalence and diagnosis rates in New York City black men. J. Community Health 2011, 36, 141–149. [Google Scholar] [CrossRef] [PubMed]
  97. Shepard, C.W.; Gortakowski, H.W.; Nasrallah, H.; Cutler, B.H.; Begier, E.M. Using GIS-based density maps of HIV surveillance data to identify previously unrecognized geographic Foci of HIV burden in an urban epidemic. Public Health Rep. 2011, 126, 741–749. [Google Scholar] [PubMed]
  98. Nunn, A.; Yolken, A.; Cutler, B.; Trooskin, S.; Wilson, P.; Little, S.; Mayer, K. Geography should not be destiny: Focusing HIV/AIDS implementation research and programs on microepidemics in U.S. neighborhoods. Amer. J. Public Health 2014, 104, 775–780. [Google Scholar] [CrossRef]
  99. Forney, J.C.; Miller, R.L.; City Project Study Team. Risk and protective factors related to HIV-risk behavior: A comparison between HIV-positive and HIV-negative young men who have sex with men. AIDS Care 2012, 24, 544–552. [Google Scholar] [CrossRef] [PubMed]

Share and Cite

MDPI and ACS Style

Duncan, D.T.; Kapadia, F.; Halkitis, P.N. 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, 8962-8983. https://doi.org/10.3390/ijerph110908962

AMA Style

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. International Journal of Environmental Research and Public Health. 2014; 11(9):8962-8983. https://doi.org/10.3390/ijerph110908962

Chicago/Turabian Style

Duncan, Dustin T., Farzana Kapadia, and Perry N. Halkitis. 2014. "Examination of Spatial Polygamy among Young Gay, Bisexual, and Other Men Who Have Sex with Men in New York City: The P18 Cohort Study" International Journal of Environmental Research and Public Health 11, no. 9: 8962-8983. https://doi.org/10.3390/ijerph110908962

APA Style

Duncan, D. T., Kapadia, F., & Halkitis, P. N. (2014). Examination of Spatial Polygamy among Young Gay, Bisexual, and Other Men Who Have Sex with Men in New York City: The P18 Cohort Study. International Journal of Environmental Research and Public Health, 11(9), 8962-8983. https://doi.org/10.3390/ijerph110908962

Article Metrics

Back to TopTop