Lack of stable housing is a well-established determinant of health.1,2 Research studies have identified associations between housing instability and mental health outcomes including depression, anxiety, stress, and suicide in addition to physical health outcomes of poor self-reported health and quality of life, chronic diseases, overweight, and all-cause mortality.3–5 Negative health behaviors including alcohol and drug use, smoking, child maltreatment, and domestic violence are also associated with housing instability.3–5 Housing instability can also present barriers to chronic disease management and lead to higher levels of emergency department use and inpatient hospital stays.4,6–8
Housing instability is also associated with increased risk for sexually transmitted infections (STI) and human immunodeficiency virus (HIV). Studies have linked housing instability to multiple sex partners, unprotected sex, and exchange of sex for resources.9–11 Though far fewer studies have examined housing instability in relation to STI/HIV acquisition or prevalence, 1 study of low-income women in Baltimore found that homelessness and residential moves were both associated with self-reported recent STI diagnosis.10
There are numerous pathways by which housing instability may increase risk for STI. Housing instability and related residential moves may contribute to fragmenting of social networks in ways that increase sexual vulnerabilities. Housing instability may also increase economic vulnerabilities that place individuals at higher risk for STI. Additionally, housing instability is a risk factor for other determinants of STI risk such as substance use and poor mental health. Residential moves can also disrupt healthcare access, leading to reduced STI testing and treatment that results in ongoing transmission to sex partners and sustained or increased community prevalence.
Eviction, a legal process by which tenants are removed from rental properties, is a common but understudied form of housing instability. Approximately 2.3 million low-income renters are evicted in the United States every year, resulting in dramatic changes to daily life that can have devastating consequences.12,13 Most evictions occur as a result of nonpayment of rent,12 thus eviction is inextricably linked to poverty. In recent years, housing costs in the United States have risen dramatically relative to income levels such that currently there is no state in which a full-time minimum wage job provides sufficient income to affordably rent a 2-bedroom market-rate apartment, and a majority of low-income households spend more than 50% of their income on housing (in contrast to 30% that is deemed affordable).12,14 The high cost of private market rental housing is compounded by a growing shortage of federal rental assistance: currently only 1 in 5 eligible households receive subsidized rental assistance from the federal government.15 Collectively, these threats to stable and affordable housing have led to significant challenges for low-income renters striving to maintain residential stability.
Among housing problems, eviction may be a particularly salient event for negative health consequences. It represents an abrupt and forced move with immediate implications for economic difficulties, social networks, and access to health care, all of which are important determinants of health. However, few studies have considered the health consequences of eviction, in contrast to a much larger body of literature related to homelessness and a recently burgeoning literature on the health consequences of foreclosures that have been summarized in numerous reviews.1,3,4,16,17 A smaller body of literature has identified negative health outcomes associated with eviction including poor general health, depression, substance use, exposure to violence, poor treatment outcomes for people living with HIV, suicide, and all-cause mortality.18–24
We are not aware of any studies that have explicitly examined the association between eviction and STI risk. To address this dearth of knowledge, we conducted an ecological analysis of the county-level associations between eviction rates in 2014 and 2 common STI, chlamydia and gonorrhea, in the following year. This ecological design allowed us to measure the area-level associations between evictions and population-level measures of STI that may reflect not only increases in individual-level risk behaviors but also the broader impacts that evictions may have on social and community environments.
MATERIALS AND METHODS
The unit of analysis for this study was US counties, chosen because of the availability of both primary exposure (eviction rates) and outcome (STI rates) measures at this level. Chlamydia trachomatis (chlamydia) and Neisseria gonorrhoeae (gonorrhea) were selected for their relatively high burdens in the population that permitted robust statistical analysis. We obtained the annual chlamydia and gonorrhea rates per 100,000 population for 2015 (the most recent year for which data were available at the time of analysis) from the Centers for Disease Control and Prevention AtlasPlus website (https://www.cdc.gov/nchhstp/atlas/index.htm). The annual eviction rates, defined as the number of households that received an eviction judgment per 100 renter-occupied households in a year, are from the Eviction Lab National Database (www.evictionlab.org).13 Rates for 2014, available for 2500 counties (80% of 3142 total), were used to allow for a one-year period of time to influence population-level STI rates.
Data on the following population-level covariates, considered potential confounders at the ecological level due to their known associations with STI rates, were also obtained for counties. Age and sex estimates are from the US Census Bureau for the year 2015 (www.census.gov/data/datasets/2016/demo/popest.counties_detail.html). Race and ethnicity estimates are from the Eviction Lab National Database from the 2015 5-year average American Community Survey estimates. Poverty, education, unemployment, and population density estimates are from US Census Bureau American FactFinder website for the year 2010 (https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml). Metropolitan status is defined according to the National Center for Health Statistics 2013 urban-rural classification scheme (https:/www.cdc.gov/nchs/data_access/urban_rural/html) and classified as metropolitan or nonmetropolitan. Geographical regions defined as US census regions (Northeast, Midwest, South, and West) are from the US Census Bureau (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf). For model efficiency and ease of interpretation of beta coefficients (eg, the change in STI rates per category rather than per unit change), continuous covariates were either dichotomized at an a priori meaningful cut point (age, ≥20% population between 15 and 29 years; female to male sex ratio, ≥1.0; race, ethnicity, and poverty; ≥10% population black, Hispanic, in poverty, respectively) or the median (population density: 45.2 population per square mile of land area; education, 13.1% no high school diploma or higher; unemployment, 7.5% among those age ≥ 16 years).
Eviction rates were trichotomized using tertile cut points (0.59 and 1.90 evictions per 100 renter-occupied households) to aid in interpretation of beta coefficients. We chose tertiles to allow assessment of patterns in STI rates across the 3 groups of low, medium and high such that trends (eg, dose–response) could be observed, and to allow comparison of noncontiguous (eg, high and low) groups. Median rates of chlamydia and gonorrhea were analyzed for descriptive results, and statistical significance was assessed with the Kruskal-Wallis test. To adjust for potential confounding at the county level using other ecological measures, we used linear regression modeling. Unadjusted models included only eviction rates, and adjusted models included all covariates described above. We also examined potential interactions by conducting stratified analyses for the associations between eviction rate categories and median chlamydia and gonorrhea rates. For parsimony, only certain variables were selected for this analysis. Poverty was selected for examination due to its importance for health. Geographic region and metropolitan status were selected to provide relevant information for state and local health officials.
A series of sensitivity analyses were conducted to assess the robustness of our findings and model assumptions. First, because chlamydia and gonorrhea rates were right skewed, we ran linear regression models using log-transformed rates. We also ran multinomial logistic regression models with chlamydia and gonorrhea rates that were trichotomized using tertile cut points (224 and 377 cases per 100,000 population for chlamydia and 23 and 72 cases per 100,000 population for gonorrhea). Second, because eviction rates and most covariates were continuous variables, we reran the linear regression models using the continuous rather than categorical measures. Finally, we reran the models using eviction rates from the same year as STI rates (2015) rather than allowing for the 1-year lag period.
The median county rates for the outcome variables of chlamydia and gonorrhea were 292.1 and 41.4 cases per 100,000 population, respectively. The median county-level eviction rate, the primary predictor variable of interest, was 1.15 evictions per 100 renter-occupied households. The median rates of these 3 variables by levels of the covariates are presented in Table 1. All covariates were significantly associated with eviction and both STI rates (P < 0.001) except education and eviction (P = 0.069).
The median rates of chlamydia in counties with low, medium, and high rates of eviction (using tertile splits) were 229, 270, and 358 cases per 100,000 population, respectively (P < 0.001) (Fig. 1A). The corresponding median rates of gonorrhea were 25, 37, and 75 cases per 100,000 population, respectively (P < 0.001) (Fig. 1B).
In unadjusted linear regression models, eviction rates were significantly associated with chlamydia and gonorrhea rates. For chlamydia, the beta coefficient for high compared to low eviction rates was 153.7 (95% confidence interval [CI], 134.1–173.3) and 38.4 (95% CI, 18.9–57.8) for moderate compared with low eviction rates. For gonorrhea, the beta coefficient for high compared with low eviction rates was 57.0 (95% CI, 49.7–64.2) and for moderate compared to low eviction rates 6.2 (95% CI, −1.0 to 13.4). In the linear regression models adjusted for all covariates, eviction rates remained statistically significantly associated with both chlamydia and gonorrhea rates at the county level though the magnitude of the effects were attenuated (Table 2). For chlamydia, the beta coefficient for high compared with low eviction rates was 63.8 (95% CI, 45.1–82.5), and 32.9 (95% CI, 17.0–48.8) for moderate compared to low eviction rates. For gonorrhea, the beta coefficient for high compared to low eviction rates was 20.4 (95% CI, 13.5–27.4). The difference in gonorrhea rates between medium and low eviction rate counties was not statistically significant (P = 0.26). Many of the other covariates were also associated in adjusted models: age, sex, race, ethnicity, poverty, and census region were associated with both chlamydia and gonorrhea, and employment was also associated with chlamydia.
In counties of both high and low poverty and metropolitan and nonmetropolitan status, significantly higher median chlamydia and gonorrhea rates were observed in counties with higher eviction rates (Table 3). In 3 of the 4 census regions, Midwest, South, and West, the same patterns seen nationally were also observed with higher rates of chlamydia and gonorrhea in the counties with the highest eviction rates. In the Northeast, however, the highest chlamydia and gonorrhea rates were observed in the counties with the highest and lowest eviction rates, and the lowest rates were observed in counties with medium rates of eviction (P = 0.019 for chlamydia and P = 0.210 for gonorrhea).
Results of sensitivity analyses indicated that these results were robust to model assumptions and different measurement approaches (see Appendix, SDC link: http://links.lww.com/OLQ/A304 for results). Briefly, models for log-transformed outcomes of chlamydia and gonorrhea rates to increase normality yielded similarly significant results for associations with eviction rates, goodness-of-fit statistics, adjusted R2 values, and residual diagnostics, thus the untransformed estimates are presented for ease of interpretation. Results from multinomial logistic regression models with trichotomized STI rates, linear regression models using continuous covariates, and using 2015 eviction rates yielded similar results for the association between eviction and STI rates as the primary analysis.
In this ecological study, we observed significant associations between county-level eviction rates and rates of both chlamydia and gonorrhea. These associations remained statistically significant after controlling for numerous potential confounders in adjusted models, and were observed across levels of poverty, in both metropolitan and nonmetropolitan counties, and in most regions of the United States. They were also robust to model assumptions and different approaches to measurement. To the best of our knowledge, this is the first report of an association between eviction and population-level prevalence of STI. This study extends a larger body of research on STI/HIV risk associated with homelessness.16,17 Although homelessness is important to consider due to high associated vulnerabilities, it alone fails to capture the wide range of forms that housing instability may take and the extent to which more common housing problems, in this case, eviction, may affect population health with respect to STI.
These findings suggest that eviction could increase sexual and/or social vulnerabilities in ways that may increase community levels of STI. For example, it could be that individuals who are evicted engage in risky sexual activity in exchange for housing or other needed resources (eg, food, transportation). In these situations, unprotected sex may result from mobility-associated difficulties accessing condoms, challenges negotiating condom use in relationships with power imbalances (perhaps of particular relevance for women), and/or a desire for more intimate sex as a way of coping with stress. Eviction may also disrupt long-term monogamous relationships that leads to the formation of new and/or casual partnerships that can increase risk for STI. This has been shown with other forms of forced mobility such as incarceration.25 The extent to which these mechanisms may operate for eviction is unknown and important for future study.
In addition to direct impacts on sexual behaviors and partnerships, eviction may also impact STI risk through other health-related consequences. Stress associated with unaffordable housing costs and the threat of displacement can lead to increased alcohol consumption and drug use.3–5 Other research demonstrates that challenges associated with maintaining stable housing can consume emotional and physical energy in ways that may marginalize health concerns.8 Furthermore, research finds that residential mobility can disrupt engagement in healthcare, resulting in missed appointments and lack of medication adherence,7 and therefore, potentially reduced rates of STI testing that increases duration of infection and ongoing transmission.
Beyond these possible direct and indirect individual-level risks, high eviction rates may result in altered community dynamics that facilitate STI transmission. The mobility that results from evictions could expand sexual networks in a way that increases STI transmission at the community level. This finding is consistent with literature on other forms of mobility and STI/HIV transmission dynamics including migration associated with labor (eg, mining, farming, oil exploration) and community displacement associated with urban renewal.26–28
The value of this ecological study is an examination of eviction as a social force at the community level. Housing instability is likely to affect not just individuals but also communities.1,2 Other ecological studies have found associations between STI rates and community-level measures of social phenomena such as incarceration and residential segregation.29,30 Collectively, these studies highlight the importance of multiple contextual factors of social environments that are important for STI transmission. Indeed, we observed several other county-level covariates to be associated with STI rates, such as population-level measures of race, ethnicity, sex, and poverty. Though the focus of this analysis was on eviction, these findings provide additional empirical evidence for numerous social determinants of STI risk. These factors may interact in complex and synergistic ways, and/or they may reflect underlying social conditions that increase vulnerability. A better understanding of these mechanisms, perhaps using a complex systems approach and/or individual-level studies, should be an important research priority.
The association between eviction and STI rates may be complicated by utilization of STI screening services. Though our primary hypothesis was that eviction rates are associated with higher STI rates, it is also possible that eviction is associated with reduced screening and therefore lower rates of diagnosed STI. This could occur if individuals are not aware of locations for screening in new neighborhoods, or if the life challenges associated with eviction make accessing these services more difficult. This could explain the unexpected finding of higher chlamydia rates in counties with low eviction rates in the Northeast region of the United States. In the Northeast, STI rates are relatively low and population density is relatively high. It is possible that in this setting, lower evictions and higher residential stability is associated with greater access to STI screening services and thus detection, but this deserves further study. If screening differs at the county level in a way that is associated with eviction, our findings could be biased. However, it should also be noted that if eviction results in decreased screening and detection, then our findings of general patterns of higher rates in high eviction counties are underestimating the magnitude of the true association. This study is limited in its ability to answer these important questions, and could be addressed in future research.
Other limitations of this study include the following. The ecological design is limited for causal inference, and studies at the individual level and longitudinal studies can add to this body of research. The measure of eviction relies on court reports and does not capture other types of forced moves (ie, informal evictions) from rental properties, for example, moving under pressure prior to receiving an impending eviction order to avoid having a record.12 This could also bias our results if patterns of informal evictions vary systematically by STI rates. STI rates at the county level are not publicly available by sex, precluding analyses that could reveal different effects for women and men. Though cross-sectional in nature, we did consider STI rates in the year after eviction rates to allow for a population-level effect to be realized. However, future studies that are longitudinal in nature will be needed to better ascertain the temporal relationship between eviction and STI.
Though more empirical research is needed to better understand the relationship between eviction and STI risk, these results raise some considerations for prevention. Structural interventions that alter the environment or context in which risk occurs have been shown effective for HIV/STI prevention31,32; therefore, policy interventions to promote affordable and stable housing could both improve housing options and reduce STI prevalence. The STI management services could also be provided in ways thought to maximize efficiency and impact33 in a targeted way in areas of high housing instability. Ultimately, working to reduce the frequency of evictions may not only facilitate housing stability but also promote health and STI/HIV prevention.
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