Housing Stability and Hepatitis C Infection for Young Adults Who Inject Drugs: Examining the Relationship of Consistent and Intermittent Housing Status on HCV Infection Risk

Abstract

Housing status affects drug using behaviors, but less is known about the relationship between housing patterns and hepatitis C virus (HCV) infection. HCV-negative young people who inject drugs (PWID) were enrolled into a prospective cohort (2003-2019) with quarterly study visits. We used Cox regression to estimate the independent association of recent housing status (housed vs. unhoused, housing stability, and housing trajectory) on HCV incidence. Among 712 participants, 245 incident HCV infections occurred over 963.8 person-years (py) (cumulative incidence 24.4/100 py). An inverse relationship between time housed and HCV incidence was observed (always unhoused 45.0/100 py, 95% confidence interval (CI) 37.1, 54.5; variably housed 18.0/100 py, 95% CI 15.0, 21.3; and always housed 7.0/100 py, 95% CI 3.0, 17.3). In Cox regression models controlling for confounders, those unhoused versus housed at baseline had a 1.9-fold increased infection risk (95% CI 1.4, 2.6). Those always unhoused versus always housed had a 1.5 times greater risk of HCV (95% CI 1.0, 2.3), and those spending a portion of time in stable housing a lower risk (adjusted relative hazard 0.05, 95% CI 0.3, 0.9) with a similar trend for those being housed for less time. Young adult PWID experiencing both recent and chronic states of being unhoused are at elevated risk for HCV infection. Importantly for this group of PWID, our findings indicate that some frequency of residential housing significantly reduces HCV infection risk.

Keywords: Hepatitis C virus; Housing status; People who inject drugs.

Fig. 1

Longitudinal housing status in cohort participants, by quarter. a Housing status. b Main place lived in the past 3 months. Vertical axis represents individual participants, ordered by housing trajectory

Fig. 2

Frequency of main place lived in the past 3 months, by cohort wave

Fig. 3

Four-panel figure of Kaplan-Meier curves with incidence rates listed below. a Baseline housing status. b Baseline housing type. c Housing trajectory. d Percent-time housed

Fig. 4

Unadjusted and adjusted Cox models with different exposure variables (n = 721). Variables included in the multivariable Cox models included age at enrollment, gender, recent injecting frequency*, recent needle/ancillary equipment sharing*, and number of recent injecting partners*. a Baseline housing status and recently housed (per 3 months). b Recent housing stability (per 3 months). c Housing status over time (cumulative). d Percent-time housed (cumulative). *Time-varying variable