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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
AIDS Behav. Author manuscript; available in PMC 2009 March 2.
Published in final edited form as:
PMCID: PMC2649661

Factors Associated with HIV Viral Load in a Respondent Driven Sample in Los Angeles


This study used a modified version of the Behavioral Model for Vulnerable Populations to examine the predisposing, enabling, and need factors associated with detectable viral load (VL). HIV status was measured using saliva and confirmed by blood. Of 835 persons enrolled, 193 were HIV positive and provided VL counts. A multistage logistic regression demonstrated that the predisposing factors of homelessness and recent substance abuse, particularly methamphetamine abuse, had a negative association with VL. The negative association of homelessness was lessened with the introduction of enabling and need utilization factors in the model. In contrast, the negative association with recent substance abuse on VL was sustained in the final model. Provision of HIV care and medications attenuated the negative association of homelessness within this sample. Guided policy to address substance abuse among those who are HIV positive is needed to improve biological outcomes.

Keywords: Homeless, HIV, substance abuse, viral load, respondent driven sampling


Although tremendous gains have been made in the treatment of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), disparities in access and subsequent receipt of care continue to be documented (Cunningham et al., 1995, 1998; King et al., 2004). The face of HIV has changed from the early 1980s and is now reflective of the intercalation of poverty and disease (CDC Factsheet, 2006; Cunningham et al., 2005; Diaz et al., 1994; Krueger et al., 1990). Individuals living in poverty are disproportionately affected both by unstable housing and substance use, which are contributors to lower access to and utilization of medical care (Katz et al., 2001; Kushel et al., 2001, 2006; Martens, 2001; Strathdee et al., 1998). These same factors correlate with transmission of HIV and represent possible contributors to HIV prevalence among the homeless that is three times that of the general population (Culhane et al. 2001).

Treating persons with multiple diagnoses (HIV, drug abuse, mental illness) is complicated—not only because of the independent and synergistic disease processes but also because of disparate access to care, limited or no insurance, and unmet subsistence needs (e.g., food, transportation, child care; Cunningham et al., 1999). When homelessness, i.e., persons without stable or permanent housing who may live on the streets, in a shelter or mission, single room occupancy facility, abandoned building or vehicle or ‘doubled up’ with friends or family members (DHHS/HRSA, 1999) is added to the equation, outcomes of HIV treatment can be even more compromised (Waldrop-Valverde and Valverde, 2005).

The Behavioral Model for Vulnerable Populations (Andersen et al., 2000; Gelberg, Andersen and Leake, 2000) provides a theoretical framework for analyzing health care utilization within vulnerable populations. The model can be used to demonstrate that utilization of health care and subsequent outcomes can be explained by: (1) variables that predispose persons to use care such as demographics and social structure; (2) variables that enable or create barriers to care such as income, insurance and (3) variables that describe the need for health care determined by individual perception or medical professional evaluation. The Andersen/Gelberg model has been well represented in assessing homeless persons’ health care utilization and outcomes (Barkin et al., 2003; Herndon et al., 2003; Kim et al, 2006; Lewis, Andersen, and Gelberg, 2003; Stein et al., 2000) and has been used to predict the treatment of HIV-positive individuals with highly active antiretroviral therapy (HAART) (Andersen et al., 2000).

HAART has reduced morbidity and mortality from HIV (Crum et al., 2006). Adherence to HAART is important as missed dosages can affect viral replication and can lead to drug resistance (Ho et al., 1995; Low-Beer et al., 2000). Several studies indicate that factors such as current substance use and unstable housing impede medication adherence and viral suppression (Arnsten et al., 2002; Duran et al., 2001; Knowlton et al., 2001; Lucas et al., 2001; O’ Connell, 2003; Pulvirenti et al., 2003; Turner et al., 2001), leading to more likely transmission of multidrug resistant strains of HIV (Little et al., 2002).

Due to the limited choice in remaining antiretrovirals for treatment, transmission of resistant HIV has both individual and community health implications. Prior studies on homelessness and HIV seropositive individuals have largely focused on the impact of homelessness and dual diagnoses on utilization of services (Gordon et al., 2006; Kim et al., 2006; Moss et al., 2004). Missing in this work is the extent to which predisposing, enabling and need factors predict successful outcomes from HIV medical care, such as achieving viral load levels that are undetectable.

This manuscript analyzed survey and clinical data from a respondent-driven sample of individuals in Los Angeles County whose behaviors (i.e., being men who have sex with men, drug use) place them at the core of the HIV epidemic. The core refers to a group whose demographic, geographic, and behavioral characteristics contribute to their disproportionate role in the transmission of sexually transmitted infections (Yorke, Hethcote, and Nold, 1978). The objective of this paper is to apply the Behavioral Model for Vulnerable populations to a subset of HIV-seropositive participants enrolled in a larger study to identify factors associated with undetectable viral load. We hypothesized that introducing predisposing vulnerable factors identified in the literature such as homelessness, substance use, and mental illness, would have a negative association with the clinical outcome of viral load. We also hypothesized that introducing factors that enable health care utilization and represent subjective and objective need for health care will decrease and increase, respectively, the differences between HIV positive persons with detectable and undetectable viral loads.



Between August 2005 and August 2006, 835 participants were recruited into the Los Angeles site of the Sexual Acquisition and Transmission of HIV – Cooperative Agreement Program (NIDA’s SATH-CAP) using Respondent-Driven Sampling (RDS). RDS is a variant of chain-referral sampling used to reach hidden populations (Heckathorn, 1999, 2002); details of the recruitment method are described elsewhere (Heckathorn, 2002).

Twenty-five “seeds” who met core criteria were recruited via flyers posted at agencies, stores, social organizations, bars, sex clubs, and initiated the referral chains. Sixty- four percent of the 25 seeds were HIV seropositive. They were provided up to seven coupons to recruit individuals from their social network who met core criteria, and from their sexual network. Twenty-one waves were needed to populate the final sample which was comprised of individuals who met the following inclusion criteria: age 18 years and older; resided in Los Angeles County; provided informed consent; identified as either an MSM and/or a DU (powder or crack cocaine, heroin, methamphetamine, or reported injection of any drug in the past six months); a current a sex partner of an MSM or DU in the study; or a sex partner of a sex partner in the study. Forty-seven participants who were women, reported “other” race, or had missing viral load results were excluded from these analyses due to small cell sizes, resulting in a sample of 788. Of these, 193 men were HIV infected and provided labs documenting viral load.


All participants underwent a one-hour audio computer-assisted self-interview (ACASI) that included questions about demographics (age, gender, education, race, ethnicity, employment, housing status), medical care access and utilization, sexual practices, sexual partnerships, HIV-related risk behaviors, friendships and social network. Biological data were collected upon completion of the ACASI and included an oral fluid sample tested for HIV antibodies (Oraquik, Orasure Technologies, Bethlehem, PA) and blood specimens (10mL each) to confirm HIV infection (Western Blot) and to assay HIV viral load (Los Angeles County Public Health Department laboratory). Participants received $50 for completing study requirements, and were given coupons to recruit individuals in their social and sexual networks into the study for which they received $20 per successful recruit. The Human Subjects Protection Committee of the University of California, Los Angeles provided oversight of all activities in this project.


Health status regarding HIV disease was measured by viral load using a qualitative cutoff of 400 copies per mL to classify detectable and undetectable cases (Kushel et al., 2006; Moss et al., 2004).

The following variables were included within the predisposing category: age, number of years being HIV positive; gender, race/ethnicity; relationship status (single, married, separated or widowed); education (high school, some college and above); employment status; self-report of homelessness (yes/no); mental health and substance abuse. Substance use was evaluated as any drug use in the past 30 days; self-reported use of methamphetamine, or cocaine (powder or crack), or heroin, in the previous 30 days.

Variables within the enabling category included health insurance (yes/no) and the type of insurance (health maintenance, private, public: Medicaid, Medicare, Social Security Insurance and Social Security Disability Insurance; Veteran Administration, Champus and other); income during previous 30-day period ($0–500; $501–1000; > $1000); number of physician visits for previous 12 months; current care by a HIV physician specialist.

The final category, perceived and objective need, included current use of HIV medications.

Statistic Analysis

The sample weights created for viral load, the outcome variable, were estimated using RDS Analysis Tool version 5.6.0. (Heckathorn and Volz, 2006). A sensitivity test was conducted for the unweighted data and compared to the results from the weighted data. The point estimates, reported as odds ratios in this study, from two sets of data were not significantly different to each other, and the 95% confidence intervals (CI) although the unweighted estimates tended to be smaller than the weighted estimates. Thus, the unweighted results are presented as they are more conservative than the weighted results.

Univariate analysis comparisons of all predisposing, enabling, and need variables by viral load status (<400 copies per ml or > 400 copies per ml) were performed using analysis of variance (ANOVA) for continuous variables and Chi Square or Fisher’s exact test for categorical variables. In order to test our hypotheses in accord with the Behavioral Model, we established three multivariate stepwise models incorporating the predisposing, enabling and need variables sequentially to predict the probability of detectable versus undetectable viral loads in our study population [odds ratios (OR), 95% confidence intervals (CIs)]. These models have been explored for problems of multicollinearity and interactions among the variables in each stepwise model separately.


The Association of Participant Characteristics on Detectable Viral Load

Approximately 55% percent (n=107) of HIV-seropositive participants provided biological samples documenting undetectable viral loads. The predisposing variables showed that HIV-seropositive participants with detectable viral load were significantly more likely to consider themselves homeless (56% vs. 31%, Chi-Square, p <0.01) than those who did not identify as homeless. Participants who reported use of one or more drugs in the past 30 days (62% vs. 46%, Chi-Square, p < 0.05), and specifically report recent use of methamphetamine (60% vs. 39%, Chi-Square, p <0.05) were more likely to have detectable viral loads than those who did not report recent drug use. The enabling variables demonstrated that participants who reported seeing a physician for HIV care were significantly less likely to have detectable viral loads (83 % vs. 95%, Chi-Square, p<0.01). The need variables associated with viral load such that participants who reported taking their HIV medications were significantly less likely to have detectable viral loads (90% vs. 43%, Chi-Square, p <0.001) (See Table 1).

Table 1
Predisposing, Enabling and Need Variable by Viral Load (n=193).

Multivariate Analysis Results

Table 2 presents the odds ratios for all independent variables in the full model based on logic regression not adjusted for any other independent variables. The next three columns represent the multivariate logistic regression adjusting sequentially for the additional sets of variables. In column 2, the predisposing variables were entered into the first multivariate logistic regression model. Supporting our main hypothesis, participants who considered themselves homeless (Adjusted OR 2.11; 95% CI 0.93, 4.8) or reported using drugs in the past 30 days (Adjusted OR 2.61; 95% CI 1.24, 5.49), were significantly more likely to have detectable versus undetectable viral loads (see Table 2). The association of recent reported methamphetamine use with viral load is about 45% stronger than recent use of other drugs as measured in the crude model (OR 2.36; 95%CI 1.09, 5.08). Participants who considered themselves homeless were more likely to report recent use of methamphetamine (63.3%) than those who did not consider themselves homeless (38.3%), an association that was strongly correlated (R=0.248, p=0.009, Results not in Tables). This significant association precluded inclusion of the methamphetamine variable in the multivariate model as it correlated significantly with any drug use and with the homelessness variables. Column 3 enters enabling variables after controlling for the predisposing variables. No significant predictors were found among the enabling variables; however, their introduction affected the estimate of the covariate, homelessness, such that it is no longer statistically significant (Adjusted 2.06; 95% CI 0.88, 4.79). The introduction of enabling variables positively associated with reported recent drug use, as participants had higher odds of being detectable (Adjusted OR 2.59; 95% CI 1.22, 5.49). The final model seen in column 4 enters the need variables and controls for the predisposing and enabling variables. As in adjusted model 2, although statistically not significant, the covariate of homelessness has been further negatively affected with lower odds of being detectable compared to the first model. Participants who reported recent drug use, however, were still more likely to have detectable viral loads (Adjusted OR 2.46; 95% CI .99, 6.13) although the estimate becomes less precise.

Table 2
Selected Multivariate Logistic Regression Model of Factors Associated with Detectable Viral Load: Un-weighted Crude (OR) and Adjusted (aOR) Odds Ratios.


The Behavioral Model for Vulnerable Populations supported our main hypothesis that predisposing variables such as homelessness and recent drug use would affect the clinical outcome of detectable viral loads in a sample of HIV sero-positive men recruited into a larger study in Los Angeles. Persons who were homeless or reported recent drug use were more than two times more likely to have detectable viral loads compared to those persons not homeless or reporting recent drug use. However, when introducing enabling variables representing factors that facilitate HIV health care utilization (insurance, income and seeing an HIV specialist), the negative association of homelessness and VL was attenuated. Further, when introducing perceived and observed need for health care utilization variables such as taking medications for HIV; the association of homelessness with detectable viral load is further weakened. In our model, although homelessness itself can be predictive of having a detectable viral load, the provision of health care evidenced by having the ability to pay for care, to have an HIV specialist and to take HIV medications can diminish the association of homelessness with the HIV clinical outcome of viral load. This finding is similar to reports from San Francisco showing a high penetration of antiretroviral medical care among the homeless (Riley et al., 2005) and is also consistent with findings that persons who reported access to HIV specialists and adherence to antiretroviral medications were more likely to have better biological outcomes (Riley et al., 2005).

In contrast, participants who reported recent drug use were consistently less likely to have undetectable viral loads even when health care utilization enabling or need variables were introduced into the model. Paradoxically, the negative association of recent drug use on viral load appears to strengthen as enabling and need variables are brought in.

The co-occurrence of homelessness and substance abuse among HIV-seropositive individuals has been shown to worsen health outcomes and increase medical care costs (Aidala et al., 2005; Kim et al., 2006). The strength of the associations of viral load with recent methamphetamine use and viral load with overall recent substance use deserve consideration. Methamphetamine abuse has been identified as a negative predictor of antiretroviral adherence (Halkitis et al., 2005; Moss et al., 2004; Reback et al., 2003) and there are some suggestions that it may have direct immunological effects that could associate with poor biological outcomes (Gavrilin, Mathes, and Podell, 2002; Markowitz et al., 2005).

These analyses have several limitations. First, our sample was recruited using respondent-driven sampling (RDS). While RDS has been proposed as a bias-free sampling methodology to access “hidden” populations, such as MSM and drug users (Ramirez-Valles et al., 2005), its ability to produce representative samples is unclear (Martin et al., 2003). Second, potential self-report bias may have led to an under-reporting of behaviors that are stigmatized or unpopular such as admitting recent drug use or non-adherence to HIV medication regimens. Third, the single biological outcome variable of viral load, while correlated with control of viral replication, is only one aspect of biological control of HIV disease overall. Use of CD4 counts, which is reflective of overall immune status, would undoubtedly broaden the assessment of biological status for these participants. Fourth, although individuals in the sample who reported taking HIV medications were approximately 92% less likely to have detectable viral loads, we did not have a standardized measure of adherence. There are subpopulations of mutated viruses in which the virus is slow to replicate (Pastori et al., 2006), therefore, persons infected with the delta CCR5 mutation would have lower viral loads, with or without taking medications. However, it is highly improbable that all 107 individuals would have this mutation and is more likely an indirect measure of adherence to medications. Additional analyses of the entire sample from the cooperative agreement (SATH-CAP) need to occur to answer these questions.

Despite these limitations, the Behavioral Model for Vulnerable Populations provided a useful framework in analyzing the association of health care utilization factors on viral load among HIV positive men recruited by RDS sampling. Predisposing factors such as homelessness and reported recent drug use negatively influenced viral load. Enabling factors and need factors such as insurance, visits to an HIV specialist and taking medications had a positive association on viral load for persons who were homeless and HIV positive, but not on those persons who were HIV positive and reported recent substance abuse.

These findings are crucial to understanding factors that correlate with one of the primary biomarkers of HIV disease status in a population with significant healthcare disparities. The use of the Andersen/Gelberg health care utilization model clarified associations between key variables to isolate the relative contribution of homelessness, recent drug use and HIV medication taking on a key marker of HIV medical care. . Findings from this project illustrate the need for policy directed toward emphasizing drug use reduction strategies in groups of HIV-positive, individuals with healthcare disparities to improve HIV and general health outcomes. Further confirmatory studies to analyze the associations between drug use variables and HIV viral load are needed.

Figure 1
Behavioral Model for Vulnerable Populations


The research team would like to thank Dr. Ronald Andersen for his advice and expertise in the adaptation of the Andersen/Gelberg model and the following individuals for their assistance in the development of this manuscript: Mr. Gregory D. Victorianne; Ms Uyen Kao This project was funded by National Institute of Drug Abuse U01DA17394 (Shoptaw, PI); Minority supplement (Shoptaw PI./King)


  • Aidala A, Cross JE, Stall R, Harre D, Sumartojo E. Housing status and HIV risk behaviors: implications for prevention and policy. AIDS and Behavior. 2005;9(3):251–265. [PubMed]
  • Anderson R, Bozzette S, Shapiro M, St. Clair P, Morton S, Crystal S, Goldman D, Wenger N, Gifford A, Leibowitz A, Asch S, Berry S, Nakazono T, Heslin K, Cunningham W. the HCSUS Consortium. Access of vulnerable groups to antiretroviral therapy among persons in care for HIV disease in the United States. Health Services Research. 2000;35(2):389–416. [PMC free article] [PubMed]
  • Arnsten J, Demas P, Grant R, Gourevitch M, Farzadegan H, Howard A, Schoenbaum E. Impact of active drug use on antiretroviral therapy adherence and viral suppression in HIV-infected drug users. Journal of General Internal Medicine. 2002;17(5):377–381. [PMC free article] [PubMed]
  • Barkin SL, Balkrishan R, Manuel J, Andersen RM, Gelberg L. Health care utilization among homeless adolescents and young adults. Journal of Adolescent Health. 2003;32(4):253–256. [PubMed]
  • Centers for Disease Control and Prevention. Factsheet, HIV/AIDS Among African Americans. Atlanta: CDC; 2006.
  • Crum NF, Riffenburgh RH, Wegner S, Agan BK, Tasker SA, Spooner KM, Armstrong AW, Fraser s, Wallace MR. Triservice AIDS Clinical Consortium. Comparisons of causes of death and mortality rates among HIV-infected persons; analysis of the pre-, early, and late HAART (highly active antiretroviral therapy) eras. Journal of Acquired Immune Deficiency Syndrome. 2006;41(2):194–200. [PubMed]
  • Culhane DP, Gollub E, Kuhn R, Shpaner M. The co-occurrence of AIDS and homelessness: results from the integration of administrative databases for AIDS surveillance and public shelter utilisation in Philadelphia. Journal of Epidemiology and Community Health. 2001;55(7):515–520. [PMC free article] [PubMed]
  • Cunningham WE, Hays RD, Williams KW, Beck KC, Dixon WJ, Shapiro MF. Access to medical care and health-related quality of life for low-income persons with symptomatic human immunodeficiency virus. Medical Care. 1995;33(7):739–754. [PubMed]
  • Cunningham W, Hays R, Ettl M, Dixon W, Liu C-C, Beck K, Shapiro M. The prospective effect of access to medical care on health-related quality-of-life outcomes in patients with symptomatic HIV disease. Medical Care. 1998;36(3):295–306. [PubMed]
  • Cunningham WE, Andersen RM, Katz MH, Stein MD, Turner BJ, Crystal S, Zierler S, Kuromiya K, Morton SC, St Clair P, Bozzette SA, Shapiro MF. The impact of competing subsistence needs and barriers on access to medical care for persons with human immunodefiency virus receiving care in the United States. Medical Care. 1999;37(12):1270–1281. [PubMed]
  • Cunningham WE, Hays RD, Duan N, Andersen R, Nakazono TT, Bozzette SA, Shapiro MF. The effect of socioeconomic status on the survival of people receiving care for HIV infection in the United States. Journal of Health Care for the Poor and Underserved. 2005;16(4):655–676. [PubMed]
  • Derogatis LR. Pearson Assessments. Minnapolis, MN: 2000. Brief Symptom Inventory 18 (BSI-18)
  • Diaz T, Chu SY, Buehler JW, Boyd D, Checko PJ, Conti L, Davidson AJ, Hermann P, Herr M, Levy A, et al. Socioeconomic differences among people with AIDS: results from a multistate surveillance project. American Journal of Preventive Medicine. 1994;10(4):217–222. [PubMed]
  • Duran S, Spire B, Raffi F, Walter V, Bouhour D, Journot V, Cailleton V, Leport C, Moatti JP. the APROCO Cohort Study Group. Self-reported symptoms after initiation of protease inhibitor in HIV-infected patients and their impact on adherence to HAART. HIV Clinical Trials. 2001;2(1):38–45. [PubMed]
  • Gavrilin MA, Mathes LE, Podell M. Methamphetamine enhances cell-associated feline immunodeficiency virus replication in astrocytes. Journal of Neurovirology. 2002;8:240–249. [PubMed]
  • Gelberg L, Andersen RM, Leake BD. The behavioral model for vulnerable populations: application to medical care use and outcomes for homeless people. Health Services Research. 2000;34(6):1273–1302. [PMC free article] [PubMed]
  • Gordon AJ, McGinnis KA, Conigliaro J, Rodriguez-Barradas MC, Rabeneck L, Justice AC. VACS-3 Project Team. Associations between alcohol use and homelessness with healthcare utilization among human immunodeficiency virus-infected veterans. Medical Care. 2006;44 8 Suppl 2:S37–S43. [PubMed]
  • Halkitis P, Kutnick A, Slater S. The social realities of adherence to protease inhibitor regimens: substance use, health care and psychological states. Journal of Health Psychology. 2005;10(4):545–558. [PubMed]
  • Heckathorn D. Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Social Problems. 2002;49:11–34.
  • Heckathorn D, Broadhead R, Antony D, Weakliem D. AIDS and social networks: prevention through network mobilization. Social Focus. 1999;32:159–179.
  • Heckathorn DD, Volz E. Respondent-driven sampling analysis tool (RDSAT) 5.6.0. Ithaca, NY: Department of Sociology, Cornell University; 2006.
  • Herndon B, Asch SM, Kilbourne AM, Wang M, Lee M, Wenzel SL, Andersen R, Gelberg L. Prevalence and predictors of HIV testing among a probability sample of homeless women in Los Angeles County. Public Health Reports. 2003;118(3):261–269. [PMC free article] [PubMed]
  • Ho D, Neuman A, Perelson A, Chen W, Leonard J, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature. 1995;373:123–126. [PubMed]
  • Katz M, Cunningham W, Flieshman J, Anderson R, Kellogg T, Bozzette S, Shapiro M. Effect of case management on unmet needs and utilization of medical care and medications among HIV-infected persons. Annals of Internal Medicine. 2001;135(8):557–565. [PubMed]
  • Kim T, Kertesz S, Horton N, Tibbets N, Samet J. Episodic homelessness and healthcare utilization in a prospective cohort of HIV-infected persons with alcohol problems. BMC Health Services Research. 2006;6(19):1–10. [PMC free article] [PubMed]
  • King WD, Wong MD, Shapiro MF, Landon BE, Cunningham WE. Does racial concordance between HIV-positive patients and their physicians affect the time to receipt of protease inhibitors? Journal of General Internal Medicine. 2004;19(11):1146–1153. [PMC free article] [PubMed]
  • Knowlton A, Hoover D, Chung S, Celentano D, Vlahov D, Latkin C. Access to medical care and service utilization among injection drug users with HIV/AIDS. Drug and Alcohol Dependence. 2001;64(1):55–62. [PubMed]
  • Krueger LE, Wood RW, Diehr PH, Maxwell CL. Poverty and HIV seropositivity: the poor are more likely to be infected. AIDS Aug. 1990;4(8):811–814. [PubMed]
  • Kushel M, Colfax G, Ragland K, Heineman A, Palacio H, Bangsberg DR. Case management is associated with improved antiretroviral adherence and CD4 cell counts in homeless and marginally housed individuals with HIV infection. Clinical Infectious Diseases. 2006;43:234–242. [PubMed]
  • Kushel M, Vittinghoff E, Haas J. Factors associated with the health care utilization of homeless persons. Journal of the American Medical Association. 2001;285:200–206. [PubMed]
  • Lewis JH, Andersen RM, Gelberg L. Health care for homeless women. Journal of General Internal Medicine. 2003;18(11):921–928. [PMC free article] [PubMed]
  • Little S, Holte S, Routy J-P, Daar E, Markowitz M, Collier A, Koup R, Mellors J, Connick E, Conway B, Kilby M. Antiretroviral drug resistance among patients recently infected with HIV. New England Journal of Medicine. 2002;347:385–394. [PubMed]
  • Low-Beer S, Yip B, O’Shaughnessey M, Hogg R, Montaner J. Adherence to triple therapy and viral load response. Journal of Acquired Immune Deficiency Syndrome. 2000;23:360–361. [PubMed]
  • Lucas G, Cheever L, Chaisson R. Detrimental effects of continued illicit drug use on the treatment of HIV-1 infection. Journal of Acquired Immune Deficiency Syndrome. 2001;27:251–259. [PubMed]
  • Markowitz D, Mohri H, Mehandru S, Shet A, Kalyanaraman LBR, Kim A, Chung C, Jean-Pierre P, Horowitz A, La Mar M, Wrin T, Parkin N, Poles M, Petropoulos C, Mullen M, Boden D, Ho DD. Infection with multidrug resistant, dual-tropic HIV-1 and rapid progression to AIDS, a case report. The Lancet. 2005;365:1031–1038. [PubMed]
  • Martens W. A review of physical and mental health in homeless persons. Public Health Review. 2001;29:13–33. [PubMed]
  • Martin JL, Wiley J, Osmond D. Social networks and unobserved heterogeneity in risk for AIDS. Population Research and Policy Review. 2003;22:65–90.
  • Moss A, Hahn J, Perry S, Charlebois E, Guzman D, Clark R, Bangsberg D. Adherence to highly active antiretroviral therapy in the homeless population in San Francisco: A prospective study. Clinical Infectious Diseases. 2004;39:1190–1198. [PubMed]
  • O’Connell J, Braitstein P, Hogg R. Age, adherence and injection drug use predict virological suppression among men and women enrolled in a population-based antiretroviral drug treatment programme. Antiretroviral Therapy. 2003;8:569–576. [PubMed]
  • Pastori C, Weiser B, Barassi C, Uberti-Foppa C, Ghezzi S, Longhi R, Calori G, Burger H, Kemal K, Poli G, Lazzarin A, Lopalco L. Long-lasting CCR5 internalization by antibodies in a subset of long-term nonprogressors: a possible protective effect against disease progression. Blood. 2006;107(12):4825–4833. [PubMed]
  • Pulvirenti J, Glowacki R, Muppiddi U. Hospitalized HIV-infected patients in the HAART era: A view from the inner city. AIDS Patient Care STDS. 2003;17:565–573. [PubMed]
  • Rameriz-Valles J, Heckethorn D, Vazquez R, Diaz R, Campbell From networks to populations: The development and application of respondent-driven sampling among IDUs and Latino gay men. AIDS and Behavior. 2005;9(4):387–402. [PubMed]
  • Reback CJ, Larkins S, Shoptaw S. Methamphetamine abuse as a barrier to HIV medication adherence among gay and bisexual men. AIDS Care. 2003;15(6):775–785. [PubMed]
  • Riley ED, Bangsberg DR, Guzman D, Perry S, Moss AR. Antiretroviral therapy, hepatitis C virus, and AIDS mortality among San Francisco's homeless and marginally housed. Journal of Acquired Immune Deficiency Syndrome. 2005;38(2):191–195. [PubMed]
  • Strathdee S, Palepu A, Cornelisse P, Yip B, O'Shaughnessy M, Montaner J, Schechter M, Hogg R. Barriers to use of free antiretroviral therapy in injection drug users. Journal of the American Medical Association. 1998;280:547–549. [PubMed]
  • Stein JA, Andersen RM, Koegel P, Gelberg L. Predicting health services utilization among homeless adults: a prospective analysis. Journal of Health Care for the Poor and Underserved. 2000;11(2):212–230. [PubMed]
  • Strathdee S, Palepu A, Cornelisse P, Yip B, O'Shaughnessy M, Montaner J, Schechter M, Hogg R. Barriers to use of free antiretroviral therapy in injection drug users. Journal of the American Medical Association. 1998;280:547–549. [PubMed]
  • Turner BJ, Fleishman JA, Wenger N, London AS, Burnam MA, Shapiro MF. Effects of drug abuse and mental disorders on use and type of antiretroviral therapy in HIV-infected persons. Journal of General Internal Medicine. 2001;16:625–633. [PMC free article] [PubMed]
  • U.S. Department of Health and Human Services, Health Resources and Services Adminsitration (DHHS/HRSA) Principles of Practice: A Clinical Resource Guide for Health Care for the Homeless Programs. Bureau of Primary Health Care; 1999. March 1, PAL 99-12).
  • Waldrop-Valverde D, Valverde E. Homelessness and psychological distress as contributors to antiretroviral nonadherence in HIV-positive injecting drug users. AIDS, Patient Care and STDs. 2005;19(5):326–334. [PubMed]
  • Yorke JA, Hethcote HW, Nold A. Dynamics and control of the transmission of gonorrhea. Sexually Transmitted Diseases. 1978;5(2):51–56. [PubMed]