Hannah L F Cooper1, Sabriya Linton2, Mary E Kelley2, Zev Ross3, Mary E Wolfe2, Yen-Tyng Chen2, Maria Zlotorzynska2, Josalin Hunter-Jones2, Samuel R Friedman4, Don Des Jarlais5, Salaam Semaan6, Barbara Tempalski4, Elizabeth DiNenno6, Dita Broz6, Cyprian Wejnert6, Gabriela Paz-Bailey6. 1. Rollins School of Public Health at Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA. Electronic address: hcoope3@emory.edu. 2. Rollins School of Public Health at Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA. 3. ZevRoss SpatialAnalysis, 120 N Aurora St, Suite 3A, Ithaca, NY 14850, USA. 4. Institute for Infectious Disease Research, National Development and Research Institutes, 71 West 23rd Street, 4th Fl, New York, NY 10010, USA. 5. The Baron Edmond de Rothschild Chemical Dependency Institute, Beth Israel Medical Center, 160 Water Street, 24th floor, New York, NY 10038, USA. 6. Centers for Disease Control and Prevention, Corporate Square Building 8, Atlanta, GA 30333, USA.
Abstract
BACKGROUND: Substantial racial/ethnic disparities exist in HIV infection among people who inject drugs (PWID) in many countries. To strengthen efforts to understand the causes of disparities in HIV-related outcomes and eliminate them, we expand the "Risk Environment Model" to encompass the construct "racialized risk environments," and investigate whether PWID risk environments in the United States are racialized. Specifically, we investigate whether black and Latino PWID are more likely than white PWID to live in places that create vulnerability to adverse HIV-related outcomes. METHODS: As part of the Centers for Disease Control and Prevention's National HIV Behavioral Surveillance, 9170 PWID were sampled from 19 metropolitan statistical areas (MSAs) in 2009. Self-reported data were used to ascertain PWID race/ethnicity. Using Census data and other administrative sources, we characterized features of PWID risk environments at four geographic scales (i.e., ZIP codes, counties, MSAs, and states). Means for each feature of the risk environment were computed for each racial/ethnic group of PWID, and were compared across racial/ethnic groups. RESULTS: Almost universally across measures, black PWID were more likely than white PWID to live in environments associated with vulnerability to adverse HIV-related outcomes. Compared to white PWID, black PWID lived in ZIP codes with higher poverty rates and worse spatial access to substance abuse treatment and in counties with higher violent crime rates. Black PWID were less likely to live in states with laws facilitating sterile syringe access (e.g., laws permitting over-the-counter syringe sales). Latino/white differences in risk environments emerged at the MSA level (e.g., Latino PWID lived in MSAs with higher drug-related arrest rates). CONCLUSION: PWID risk environments in the US are racialized. Future research should explore the implications of this racialization for racial/ethnic disparities in HIV-related outcomes, using appropriate methods.
BACKGROUND: Substantial racial/ethnic disparities exist in HIV infection among people who inject drugs (PWID) in many countries. To strengthen efforts to understand the causes of disparities in HIV-related outcomes and eliminate them, we expand the "Risk Environment Model" to encompass the construct "racialized risk environments," and investigate whether PWID risk environments in the United States are racialized. Specifically, we investigate whether black and Latino PWID are more likely than white PWID to live in places that create vulnerability to adverse HIV-related outcomes. METHODS: As part of the Centers for Disease Control and Prevention's National HIV Behavioral Surveillance, 9170 PWID were sampled from 19 metropolitan statistical areas (MSAs) in 2009. Self-reported data were used to ascertain PWID race/ethnicity. Using Census data and other administrative sources, we characterized features of PWID risk environments at four geographic scales (i.e., ZIP codes, counties, MSAs, and states). Means for each feature of the risk environment were computed for each racial/ethnic group of PWID, and were compared across racial/ethnic groups. RESULTS: Almost universally across measures, black PWID were more likely than white PWID to live in environments associated with vulnerability to adverse HIV-related outcomes. Compared to white PWID, black PWID lived in ZIP codes with higher poverty rates and worse spatial access to substance abuse treatment and in counties with higher violent crime rates. Black PWID were less likely to live in states with laws facilitating sterile syringe access (e.g., laws permitting over-the-counter syringe sales). Latino/white differences in risk environments emerged at the MSA level (e.g., Latino PWID lived in MSAs with higher drug-related arrest rates). CONCLUSION: PWID risk environments in the US are racialized. Future research should explore the implications of this racialization for racial/ethnic disparities in HIV-related outcomes, using appropriate methods.
Authors: Katie B Biello; Trace Kershaw; Robert Nelson; Matthew Hogben; Jeannette Ickovics; Linda Niccolai Journal: Am J Public Health Date: 2012-05-17 Impact factor: 9.308
Authors: Shira M Goldenberg; Steffanie A Strathdee; Manuel Gallardo; Tim Rhodes; Karla D Wagner; Thomas L Patterson Journal: Soc Sci Med Date: 2011-03-03 Impact factor: 4.634
Authors: William T Robinson; Jan M H Risser; Shanell McGoy; Adam B Becker; Hafeez Rehman; Mary Jefferson; Vivian Griffin; Marcia Wolverton; Stephanie Tortu Journal: J Urban Health Date: 2006-11 Impact factor: 3.671
Authors: Hannah Lf Cooper; Don C Des Jarlais; Barbara Tempalski; Brian H Bossak; Zev Ross; Samuel R Friedman Journal: Health Place Date: 2011-09-28 Impact factor: 4.078
Authors: Michael-John S Milloy; Brandon D L Marshall; Thomas Kerr; Jane Buxton; Tim Rhodes; Julio Montaner; Evan Wood Journal: AIDS Date: 2012-06-01 Impact factor: 4.177
Authors: Hannah L F Cooper; Don C Des Jarlais; Zev Ross; Barbara Tempalski; Brian Bossak; Samuel R Friedman Journal: Am J Public Health Date: 2010-11-18 Impact factor: 9.308
Authors: Don C D Jarlais; Hannah L F Cooper; Heidi Bramson; Sherry Deren; Angelos Hatzakis; Holly Hagan Journal: Curr Opin HIV AIDS Date: 2012-07 Impact factor: 4.283
Authors: Alex H Kral; Mohsen Malekinejad; Jason Vaudrey; Alexis N Martinez; Jennifer Lorvick; Willi McFarland; H Fisher Raymond Journal: J Urban Health Date: 2010-09 Impact factor: 3.671
Authors: Hannah L F Cooper; Kimberly Jacob Arriola; Regine Haardörfer; Colleen M McBride Journal: Am J Public Health Date: 2016-08-23 Impact factor: 9.308
Authors: Sabriya L Linton; Hannah L F Cooper; Yen-Tyng Chen; Mohammed A Khan; Mary E Wolfe; Zev Ross; Don C Des Jarlais; Samuel R Friedman; Barbara Tempalski; Dita Broz; Salaam Semaan; Cyprian Wejnert; Gabriela Paz-Bailey Journal: J Urban Health Date: 2020-02 Impact factor: 3.671
Authors: Don C Des Jarlais; Kamyar Arasteh; Courtney McKnight; Jonathan Feelemyer; Susan Tross; David Perlman; Samuel Friedman; Aimee Campbell Journal: Am J Public Health Date: 2017-05-18 Impact factor: 9.308
Authors: Sabriya L Linton; Hannah L F Cooper; Mary E Kelley; Conny C Karnes; Zev Ross; Mary E Wolfe; Yen-Tyng Chen; Samuel R Friedman; Don Des Jarlais; Salaam Semaan; Barbara Tempalski; Catlainn Sionean; Elizabeth DiNenno; Cyprian Wejnert; Gabriela Paz-Bailey Journal: Ann Epidemiol Date: 2016-08-08 Impact factor: 3.797
Authors: Hannah Lf Cooper; David H Cloud; Patricia R Freeman; Monica Fadanelli; Travis Green; Connor Van Meter; Stephanie Beane; Umedjon Ibragimov; April M Young Journal: Int J Drug Policy Date: 2020-03-26
Authors: Kristina T Phillips; Catherine Stewart; Bradley J Anderson; Jane M Liebschutz; Debra S Herman; Michael D Stein Journal: Drug Alcohol Depend Date: 2021-02-27 Impact factor: 4.492