Demographic and Behavioral Correlates of Unstable Housing among Men who Have Sex with Men in the United States Open Access

Burns-Lynch, Claire (Spring 2020)

Permanent URL: https://etd.library.emory.edu/concern/etds/ww72bc736?locale=pt-BR%2A
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Abstract

Introduction Men who have sex with men (MSM) and persons who experience homelessness are disproportionately affected by HIV. Risk factors for HIV, such as substance use, mental illness, and history of incarceration, are higher among those experiencing unstable housing. This analysis evaluated the association between HIV status and having experienced unstable housing in the past 12 months among MSM in the United States and assessed other factors associated with unstable housing.

Methods Data from the 2019 American Men’s Internet Survey were used. Chi-square analysis was used to compare the prevalence of various demographic and behavioral characteristics between stably and unstably housed groups. Bivariate relationships between all predictors and housing status were done among the full sample and then separately stratified by HIV status. Logistic regression evaluating the association between HIV status and experiences of unstable housing was performed.

Results Housing instability was experienced by 7.6% of respondents. Unstably housed individuals were more likely to be a race other than white and younger, make under $40,000, used illicit drugs, either paid for or been paid for sexual acts, or have a mental illness. HIV status was not significantly associated with unstable housing (PR=0.92). Race, age (15-24 years), income, education, exchange sex, and mental illness were associated with unstable housing. Black race was associated with a substantial increase in risk of unstable housing (PR=3.26) among MSM living with HIV (PLHIV) compared to Black MSM not living with HIV (PR=1.48). Mental illness prevalence was higher in MSM not living with HIV (PR=2.74) compared to PLHIV (PR=1.33).

Discussion Consistent with other literature, factors significantly associated with unstable housing were: age, race, income, education, exchange sex, and mental illness. MSM, MSM of color, MSM with lower education levels, those using drugs, and those with mental illness are at increased risk of unstable housing and must be targeted by prevention measures and messaging. Limitations of the study include that homelessness, strictly defined, was uncommon in this population, potentially limiting the association between HIV status and unstable housing. Additionally, the online enrollment of participants could have resulted in an unrepresentative sample of MSM experiencing unstable housing.

Table of Contents

Table of Contents

1.    Introduction …………………………………………………………………………1

2.    Methods ……………………………………………………………………………..3

2.1 Sample ………………………………………………………………………3

2.2 Outcome Variable Creation …………………………………………………4

2.3 Statistical Analysis …………………………………………………………...4

3.    Results ………………………………………………………………………………5

3.1 Demographics ………………………………………………………………5

3.2 Bivariate Analysis ……………………………………………………………6

3.3 Multivariate Logistic Regression ……………………………………………..7

3.4 Stratified Analysis ……………………………………………………………7

4.    Discussion …………………………………………………………………………...8

5.    References …………………………………………………………………………...12

6.    Tables ……………………………………………………………………………….15

Table 1: Distribution of characteristics of MSM who Completed the 2018

American Men’s Internet Survey and had Known HIV Status by Housing Status

in the Past 12 …………………………………………………………………….15

Table 2: Unadjusted associations between unstable housing and other risk factors

 for MSM who completed the 2018 American Men’s Internet Survey by housing

status in the past 12 months ……………………………………………………..17

Table 3: Results of multivariate logistic regression to detect variables predictive

of unstable housing among MSM who completed the 2018 American Men’s

Internet Survey …………………………………………………………………..19

Table 4: Unadjusted associations of variables predictive of unstable housing,

stratified by HIV status among MSM who completed the 2018 American Men’s

Internet Survey …………………………………………………………………..20

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