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. Author manuscript; available in PMC: 2022 May 25.
Published in final edited form as: Subst Use Misuse. 2021 May 25;56(9):1387–1396. doi: 10.1080/10826084.2021.1928211

Perceived healthcare access among persons with and without HIV who use illicit stimulants: The role of cumulative risk

Shakiera T Causey 1,*, Sheri L Towe 1,*, Jeremiah Hartsock 1, Yunan Xu 1, Christina S Meade 1
PMCID: PMC8370044  NIHMSID: NIHMS1732235  PMID: 34034631

Abstract

Persons who use stimulant drugs have greater morbidity and mortality relative to non-users. HIV infection has the potential to contribute to even great disparity in health outcomes among persons who use stimulants. These health disparities likely result in part due to poorer access to healthcare. Our study used a cumulative risk model to examine the impact of multiple risk factors on healthcare access in a sample of persons with and without HIV who use stimulants. Our sample included 453 persons who reported recent use of illicit stimulants (102 HIV+, 351 HIV−). Participants completed clinical interviews, questionnaires, and a rapid oral HIV test. We constructed an 8-item cumulative risk index that included factors related to socioeconomic status, homelessness, legal history, and substance use. Participants with HIV (PHW) were older than participants without HIV and more likely to have health insurance. Participants with and without HIV reported similar prior treatment utilization, but PWH reported better healthcare access and lower cumulative risk scores. Regression analyses showed cumulative risk was a significant predictor of healthcare access (β = −0.20, p < 0.001) even after controlling for age, HIV status, and health insurance status. We did not observe an interaction of HIV status by cumulative risk. Access to care among persons who use stimulants, both with and without HIV, is negatively impacted by the accumulation of risk factors from a number of different domains. Understanding the cumulative effects of these factors is critical for developing interventions to facilitate access to care, thus reducing health disparities and improving health outcomes.

INTRODUCTION

Even amid an opioid epidemic in the United States, the abuse of stimulant drugs like cocaine and methamphetamine is an ongoing public health concern in the United States. According to prevalence estimates from the 2018 National Survey on Drug Use and Health, the rates of past year use among adults were 2.0% for cocaine, 0.3% for crack, 0.7% for methamphetamine, and 1.9% for prescription stimulant misuse (Substance Abuse and Mental Health Services Administration, 2019). People who use stimulants have greater morbidity and mortality due to higher risk of a range of health conditions (Borders et al., 2009), including cardiovascular and respiratory diseases, sexually transmitted infections, injuries, and mental health disorders (Borders, Booth, Stewart, Cheney, & Curran, 2015; S. T. Kim & Park, 2019; Laposata & Mayo, 1993). This health disparity may stem, in part, from reduced access to primary and preventive healthcare services. Even when persons who use drugs engage in substance abuse treatment, linkage from that treatment environment to general healthcare services often does not occur due to the separation of drug programs from primary care services (Laine et al., 2001).

Access to care is a key social determinant of health that is instrumental to reducing health disparities (Braveman & Gottlieb, 2014). Prior studies have found that persons who use drugs and alcohol over-utilize high-level services such as the emergency room, which are more costly (Cherpitel & Ye, 2008; Huynh, Ferland, Blanchette-Martin, Ménard, & Fleury, 2016; London, Utter, Battistella, & Wisner, 2009; McGeary & French, 2000), while underutilizing preventive and primary care services (McGeary & French, 2000). Persons who use drugs are about twice as likely to utilize emergency department services, and nearly seven times as likely to be hospitalized when compared to non-users (Laine et al., 2001; Stein, O’Sullivan, Ellis, Perrin, & Wartenberg, 1993). Multiple social determinants of health including socioeconomic and psychosocial factors (e.g., homelessness, mental illness) likely contribute to the underutilization of primary care and/or substance abuse treatment (Benjamin-Johnson, Moore, Gilmore, & Watkins, 2009; Cherpitel & Ye, 2008; Huynh et al., 2016). Identifying which factors contribute to diminished healthcare access among persons who use substance is critical for developing interventions aimed at reducing health disparities in this population. Increasing engagement with regular healthcare and preventative services will not only lead to better outcomes at the individual level, but also reduced cost to the healthcare system more broadly.

HIV infection has the potential to create even greater disparity in health outcomes among persons who use illicit stimulants. Stimulant use is a consistent predictor of HIV seroconversion (Carrico et al., 2019; Gouse et al., 2016), and the prevalence of stimulant use is disproportionately high among persons with HIV (PWH) compared to the general population (Gouse et al., 2016; Pence et al., 2008). In addition, stimulants are among the most prevalently used class of drugs among PWH, with higher rates of stimulant use [cocaine (5.9%), crack (3.1%), methamphetamines (6.1%), and amphetamines (1.6%)], compared to rates of prescription opioid use (2.9%) and prescription tranquilizers (2.4%) (Prevention, 2018). HIV infection requires routine care to monitor HIV viral load, immune function, and ARV response. For many PWH, their HIV care team may serve as primary care providers, delivering health management interventions and treatment of comorbid conditions (Cheng, Engelage, Grogan, Currier, & Hoffman, 2014; Selwyn, Budner, Wasserman, & Arno, 1993). Yet, despite federal funding to support HIV care for underserved populations (HRSA, 2018), only 62% of persons newly diagnosed with HIV in 2017 achieved viral suppression within six months of diagnosis (Harris et al., 2019), emphasizing the need for increased access to HIV care as a public health priority (CDC, 2017; Matsuzaki et al., 2018). Stimulant use is also associated with poorer HIV clinical outcomes, likely because persons who use stimulants are less likely to utilize HIV care services or adhere to their ARV medications (Crisp, Williams, Timpson, & Ross, 2004; C. O. Cunningham, Sohler, Berg, Shapiro, & Heller, 2006; Sohler et al., 2007; Tucker, Burnam, Sherbourne, Kung, & Gifford, 2003). Specifically, stimulant use is associated with unsuppressed HIV viral load, greater immunosuppression and opportunistic infections, and reduced life expectancy (Baum et al., 2009; Carrico et al., 2019; Carrico et al., 2014). Despite existence of a strong infrastructure to support HIV care, many PWH experience financial difficulties and social barriers that can impede uptake of and adherence to care (Kalichman et al., 2015; Palepu, Horton, Tibbetts, Meli, & Samet, 2004; Surratt, O’Grady, Levi-Minzi, & Kurtz, 2015). Given the extensive body of literature showing that persons with undetectable viral load cannot transmit the virus to others through sex, addressing barriers to healthcare access is imperative, both to optimize HIV clinical outcomes but also to reduce forward HIV transmission by achieving viral suppression (Matsuzaki et al., 2018).

Persons who use drugs experience a myriad of factors that may affect access to healthcare. The cumulative risk model purports that the accumulation of risk factors over the life course across multiple domains (e.g., socioeconomic, psychosocial, biological) adversely affects health outcomes (Sexton, 2012). Since risk factors typically do not operate in isolation, cumulative risk models may more accurately predict adverse outcomes than those focused on specific risk factors. While the cumulative risk model has been extensively applied to understand developmental outcomes (Evans, Li, & Whipple, 2013; Liu, Shelton, Eldred-Skemp, Goldsmith, & Suglia, 2019; Savolainen et al., 2018), it has also been utilized to predict adult health outcomes, including general health (Lantz, House, Mero, & Williams, 2005; Rhee, Marottoli, Cooney, & Fortinsky, 2020), cardiometabolic risk (Echouffo-Tcheugui, Caleyachetty, Muennig, Narayan, & Golden, 2016; Hatzenbuehler, Slopen, & McLaughlin, 2014), depression (Almeida et al., 2011; van der Waerden, Hoefnagels, Hosman, & Jansen, 2014), self-harm behavior (Bedi, Muller, & Classen, 2014), cancer mortality (Caleyachetty, Tehranifar, Genkinger, Echouffo-Tcheugui, & Muennig, 2015), and problematic substance use (Kopak, Proctor, & Hoffmann, 2017; Meier et al., 2016). A small number of papers have examined the impact of cumulative risk on healthcare access and utilization in adults, with the cumulative risk measurement focused on adverse childhood experiences such as sexual abuse and parental marital conflict (Chartier, Walker, & Naimark, 2007, 2010). However, this model has not been extended to understanding health outcomes in persons who use stimulant drugs, nor have risk factors focused on adult experiences been examined (e.g., employment, income, years of incarceration).

The primary aim of the present study is to examine associations between cumulative risk and perceived healthcare access among persons with and without HIV who use illicit stimulants. We hypothesized that the cumulative effect of multiple risk factors will result in decreased perceived access to care. Secondly, we hypothesized that PWH will have better perceived access due to linkage with HIV care services, but cumulative risk will nevertheless have an adverse impact on perceived access to care.

METHODS

Participants

Adults who reported recent use of illicit stimulant drugs were recruited via peer referrals, a repository contact database, and community outreach (e.g., flyers in infectious diseases clinics) from October 2016 to February 2019. Inclusion criteria were: 1) ≥18 years old; 2) self-reported stimulant use in the past 1 month; 3) English speaking; 4) willingness to have a rapid HIV test to verify self-reported HIV status; and 5) no acute intoxication. After providing written informed consent, participants completed a 2–3 hour visit that included a rapid HIV test, a urine drug screen to verify self-reports of recent drug use, clinical interviews, and questionnaires. The HIV test was an oral swab (OraQuick©) that tests for HIV-1/2 antibodies that provides results in 20 minutes. Study procedures were approved by the institutional review board at Duke University Health System. All data were collected at an off-campus office space in the community.

Measures

Clinical interviews.

Clinical interviews were administered by Bachelor’s-level interviewers who had received extensive training by PhD-level clinical psychologists (the second and fifth authors). The Addiction Severity Index-Lite (ASI-Lite), a semi-structured interview, assessed problem areas related to substance abuse, including medical status, employment and support, drug and alcohol use, legal status, family/social status, and psychiatric status (McLellan et al., 1992). Items include history of substance use treatment episodes, psychiatric treatment history, the number of lifetime medical hospitalizations, and the number of days in the past 30 days experiencing medical problems. Additionally, using a 5-point scale (0 = Not at all, 1 = Slightly, 2 = Moderately, 3 = Considerably, 4 = Extremely), participants rated (1) how troubled or bothered they are by current medical problems and (2) how important treatment for medical problems is to them now. Participants also completed a timeline follow-back exercise to assess frequency of substance use in the past 90 days, including days of stimulant use and polysubstance use (Robinson, Sobell, Sobell, & Leo, 2014; Sobell & Sobell, 1996). To assess past year substance use disorder criteria for the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), we administered Module E of the Structured Clinical Interview for DSM-5 (SCID-5) (First, 2015). Consistent with DSM-5 classification, participants who endorsed 6 or more criteria on the SCID-5 were diagnosed as meeting criteria for a severe stimulant use disorder in the past year. Finally, participants with HIV were asked their date of HIV diagnosis, whether they are currently receiving HIV treatment, and whether they are currently prescribed antiretroviral therapy. Of note, specific details about engagement in care (e.g., date of most recent visit, frequency of appointment attendance) was not assessed.

Questionnaires.

Additional demographic factors (e.g., race, gender, education, income) and questionnaires were collected using an audio computer-assisted self-interview (ACASI). To assess health insurance status, participants reported on whether they have any kind of health insurance on the ACASI. For the purposes of the present analyses, insurance status was coded as 0 to represent having some kind of health insurance and 1 to represent lacking health insurance. To assess prior medical conditions, participants completed the “Pre-existing Conditions” subset of items from the Veterans Aging Cohort Study (VACS) Patient Questionnaire (Smola et al., 2001). For these items, participants were shown a list of 24 medical conditions (e.g., hypertension, diabetes, heart attack) and for each condition they were asked whether a doctor has ever told them that they have that condition. Of note, the list of conditions on this questionnaire does not include HIV infection. We computed the total number of chronic medical conditions by summing the number of individual conditions endorsed.

Participants completed the 6-item scale developed by Cunningham et al. to assess perceived healthcare access (W. E. Cunningham et al., 1999; W. E. Cunningham et al., 1995). On this questionnaire, participants rated their agreement on statements related to affordability (“Sometimes I go without the medical care I need because it is too expensive”), availability (“It is hard for me to get medical care in an emergency”; “If I need hospital care I can get admitted without any trouble”; “I am able to get medical care whenever I need it”), convenience (“Places where I can get medical care are very conveniently located”), and access to specialists (“I have easy access to the medical specialists I need”). The 5-point agreement scale ranged from 1 (“strongly disagree”) to 5 (“strongly agree”). The item for affordability and the first item for availability (“It is hard for me to get medical care in an emergency”) were reverse-scored, so that a higher score indicated better access for every item. The scores for the 6 items were summed to create an overall access score.

Data Analytic Plan

Using standard questions from the ASI and the ACASI measures, a cumulative risk index including 8 factors was created as the primary independent variable. The selection of these 8 psychosocial risk factors was guided by the existing literature on characteristics that impact healthcare access, including socioeconomic status (Adler & Stewart, 2010), homelessness (Baggett, O’Connell, Singer, & Rigotti, 2010), legal history (Frank, Wang, Nunez-Smith, Lee, & Comfort, 2014; Kulkarni, Baldwin, Lightstone, Gelberg, & Diamant, 2010), and substance-related factors (Lorvick, Browne, Lambdin, & Comfort, 2018). Participants received one point for each of the following risk factors: less than high school education, annual household income < $10,000, unemployed for the majority of the past 3 years, homelessness in the past year, long-term incarceration (defined as >12 months in lifetime), current engagement in the criminal justice system (defined as being on probation or parole, or awaiting trial or sentencing), frequent polysubstance use (defined as >30 days in the past 90 days), and severe stimulant use disorder in the past year. Scores ranged from 0 to 8, with higher scores indicating greater risk.

Data analyses were conducted using SPSS, version 26.0. Descriptive statistics were run to assess sample characteristics. Chi-squared tests and independent samples t-tests were used to compare participants with and without HIV on demographic characteristics and key variables, including treatment utilization, risk factors, and access scores. To examine the relationship between cumulative risk and healthcare access, we conducted a hierarchical linear regression. Age was entered in the first step. In the second step, we added HIV status and health insurance status (0 = insured, 1 = uninsured). To test the effect of cumulative risk over and above age, HIV status, and being uninsured, we added cumulative risk in the third step. Finally, to examine whether there is an interaction effect of HIV status and cumulative risk on healthcare access, we added an interaction term in the fourth step. Cumulative risk was mean-centered prior to the creation of the interaction term. Statistical significance for all analyses was set at p < 0.05 (two-tail).

RESULTS

Participant Characteristics

The sample included participants with (n=102) and without HIV (n=351). Table 1 summarizes the participant characteristics. The majority of participants identified as Black/African-American (87%) and male (57%) with no difference by HIV status. Participants without HIV largely identified as straight/heterosexual (87%), compared to 64% of PWH. PWH were significantly older and more likely to be insured than participants without HIV. Participants without HIV reported significantly more days of stimulant use and more days of polysubstance use in the past 90 days compared to PWH. While participants with and without HIV reported a similar number of lifetime medical hospitalizations (M = 3.77, SD = 8.69), PWH reported a significantly higher number of lifetime medical conditions on the VACS questionnaire. On average, participants reported 11.43 (SD = 12.38) days of medical problems in the past 30 days, that they felt moderately bothered by these problems (M = 2.17, SD = 1.63), and that it was moderately important to receive medical treatment now for these problems (M = 1.66, SD = 1.81). Days of medical problems and the associated ratings did not differ by HIV status. For prior substance use treatment, there was no difference by HIV status for number of alcohol treatment episodes (M = 0.70, SD = 2.22), but participants without HIV reported significantly more drug treatment episodes than PWH. Finally, PWH were more likely than participants without HIV to report a history of outpatient psychiatric treatment, but there was no difference between groups for history of any inpatient psychiatric treatment (overall 15%).

Table 1.

Participant Characteristics

HIV+ (n=102) HIV− (n=351) Statistic p-value
Age, M (SD) 48.72 (9.71) 45.68 (11.58) t(451) = −2.41 0.016
African-American race, n (%) 86 (84%) 306 (87%) X2(1) = 0.56 0.455
Female gender, n (%) 36 (35%) 161 (46%) X2(1) = 3.60 0.058
Sexual orientation, n (%) X2(1) = 37.96 < 0.001
 Straight/Heterosexual 65 (64%) 305 (87%)
 Gay/Lesbian 24 (23%) 17 (5%)
 Bisexual/Other 13 (13%) 29 (8%)
Days of stimulant use, past 90 days, M (SD) 26.39 (25.15) 35.74 (30.04) T(451) = 2.86 0.004
Days of polysubstance use, past 90 days, M (SD) 24.87 (26.23) 31.68 (30.40) T(451) = 2.05 0.041
Years since HIV diagnosis, M (SD) 16.22 (8.93) - - -
No health insurance, n (%) 28 (27%) 166 (47%) X2(1) = 12.71 < 0.001
VACS total number of conditions, M (SD) 4.49 (3.52) 2.90 (2.58) t(451) = −5.03 < 0.001
Lifetime medical hospitalizations, M (SD) 4.16 (9.61) 3.65 (8.42) t(451) = −0.52 0.606
Days of medical problems, past 30 days, M (SD) 13.53 (12.89) 10.83 (12.18) t(451) = −1.95 0.052
How troubled by medical problems, M (SD) 2.05 (1.56) 2.20 (1.65) t(451) = −0.82 0.411
How important is treatment for medical problems, M (SD) 1.86 (1.87) 1.60 (1.78) t(451) = −1.28 0.203
Number of alcohol abuse treatment episodes, M (SD) 0.65 (2.18) 0.72 (2.23) t(451) = 0.27 0.786
Number of drug abuse treatment episodes, M (SD) 1.36 (2.45) 2.05 (3.23) t(451) = 1.99 0.047
Any inpatient psychiatric hospitalizations, n (%) 13 (15%) 48 (16%) X2(1) = 0.14 0.707
Any outpatient psychiatric treatment, n (%) 28 (34%) 61 (22%) X2(1) = 4.71 0.030
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Note. M = Mean, SD = Standard Deviation

Among the 102 PWH, data on HIV characteristics were missing for 5 participants. Of the remaining 97 participants, 94% (n = 91) reported being currently in HIV care and 93% (n = 90) reported being prescribed antiretroviral therapy currently. Participants reported being diagnosed with HIV for 16.22 years on average (SD = 8.93; n = 94, as 3 participants reported they could not recall their date of diagnosis). The majority had been living with HIV for 10 or more years (72%; n = 68).

Cumulative Risk and Healthcare Access

There were significant differences between participants with and without HIV on scores for both the cumulative risk index and healthcare access (Table 2). On average, risk indices for participants without HIV were nearly one point higher than those of PWH. The groups were similar on the presence of 6 of the individual factors: less than high school diploma (47%), income (64%), unemployed (20%), long-term incarceration (32%), current engagement in the criminal justice system (19%), and severe stimulant use disorder (57%). Participants without HIV were significantly more likely to have the risk factors of past year homelessness and frequent polysubstance use. In contrast, PWH reported significantly higher perceived access to care than participants without HIV on both the overall score and 5 of the 6 individual items. The only item that the groups did not differ on was the item related to emergency care. Both groups disagreed that it is hard for them to get care in an emergency (M = 4.16, SD = 1.26).

Table 2.

Cumulative risk and access to care

HIV+ (n=102) HIV− (n=351) Statistic p-value
Cumulative Risk total, M (SD) 2.53 (1.66) 3.27 (1.72) t(451) = 3.85 < 0.001
 Less than high school diploma, n (%) 46 (45%) 167 (48%) X2(1) = 0.20 0.659
 Income < $10,000, n (%) 57 (56%) 232 (66%) X2(1) = 3.57 0.059
 Unemployed, n (%) 14 (14%) 77 (22%) X2(1) = 3.32 0.068
 Homelessness in the past year, n (%) 22 (22%) 132 (38%) X2(1) = 9.06 0.003
 Long-term incarceration, n (%) 26 (25%) 119 (34%) X2(1) = 2.57 0.109
 Current engagement in criminal justice system, n (%) 14 (14%) 71 (20%) X2(1) = 2.19 0.139
 Frequent polysubstance use, n (%) 27 (26%) 142 (40%) X2(1) = 6.61 0.010
 Severe stimulant use disorder, n (%) 52 (51%) 207 (59%) X2(1) = 2.06 0.151
Perceived Access to Care, M (SD) 23.84 (5.42) 21.13 (5.80) t(451) = −4.22 < 0.001
 Can get admitted without any trouble, M (SD) 3.84 (1.52) 3.49 (1.53) t(451) = −2.04 0.042
 Hard to get care in an emergency, M (SD) 4.20 (1.29) 4.15 (1.25) t(451) = −0.36 0.720
 Sometimes go without because too expensive, M (SD) 4.06 (1.33) 3.35 (1.60) t(451) = −4.10 < 0.001
 Easy access to medical specialists, M (SD) 3.72 (1.54) 2.89 (1.57) t(451) = −4.67 < 0.001
 Places for care are conveniently located, M (SD) 3.99 (1.40) 3.65 (1.44) t(451) = −2.13 0.034
 Able to get care when needed, M (SD) 4.04 (1.36) 3.60 (1.47) t(451) = −2.69 0.007
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Note. M = Mean, SD = Standard Deviation

Association between Risk and Access

Results from hierarchical linear regression tested the relationship between cumulative risk and healthcare access after controlling for age, HIV status, and uninsured status (Table 3). In step 1, age was not a significant predictor, explaining less than 1% of the variance in healthcare access. In step 2, both HIV status and uninsured status were significant predictors, resulting in a significantly improved model that explained 18% of the variance. The model was further improved when cumulative risk was entered in step 3. With 21% of variance explained, cumulative risk was a significant predictor, and HIV status and uninsured status remained significant predictors. Finally, the HIV status X mean-centered cumulative risk interaction term entered in Step 4 was not significant and did not improve model fit.

Table 3.

Hierarchical regressions predicting access to care

Step 1 Step 2 Step 3 Step 4
Predictors B SE β p B SE β p B SE β p B SE β p
Age 0.02 0.02 0.04 0.444 −0.01 0.02 −0.03 0.530 −0.02 0.02 −0.05 0.272 −0.03 0.02 −0.05 0.239
HIV infection 1.87 0.61 0.13 0.002 1.54 0.60 0.11 0.011 1.69 0.62 0.12 0.007
No health insurance −4.45 0.51 −0.38 < 0.001 −4.06 0.51 −0.35 < 0.001 −4.06 0.51 −0.35 < 0.001
Cumulative risk −0.60 0.15 −0.18 < 0.001 −0.68 0.17 −0.20 < 0.001
HIV infection X Cumulative risk 0.37 0.35 0.05 0.301
R2 < 0.01 0.18 0.21 0.21
R2Δ < 0.01 0.17 0.03 < 0.01
FΔ 0.59 47.48 16.70 1.07
p of FΔ 0.444 < 0.001 < 0.001 0.301
Overall model F(1, 451) = 0.59 F(3, 449) = 31.89 F(4, 448) = 28.93 F(5, 447) = 23.36
Model p 0.444 < 0.001 < 0.001 < 0.001
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Note. B = unstandardized Beta coefficient; SE = standard error; β = standardized Beta coefficient

DISCUSSION

In this sample of persons who use illicit stimulants, our primary finding is that cumulative risk was associated with lower perceived access to care. Consistent with our hypothesis, PWH reported better access to care than participants without HIV overall and for nearly every individual question related to access – the only exception being that participants with and without HIV equally disagreed that accessing emergency care is hard. PWH were also significantly more likely to have health insurance. While we did not expect a difference in cumulative risk between these groups of persons who use stimulants, PWH had lower cumulative risk scores than participants without HIV. However, the link between cumulative risk and healthcare access was significant even after controlling for age, HIV status, and insurance status. Importantly, we did not observe an interaction effect between HIV status and cumulative risk, suggesting that the effects of cumulative risk are independent of HIV status. Our results underscore the additive impact that increasing risk factors have on healthcare access.

Our cumulative risk index included risk factors that have been shown to independently impact healthcare access. Our results support that the accumulation of these risk factors is an important predictor of access. Participants with and without HIV were similar on factors related to socioeconomic status (education, income, employment), legal history (long-term incarceration, current engagement in the criminal justice system), and severity of current stimulant use disorder. However, participants without HIV were significantly more like to report recent homelessness and frequent polysubstance use. Therefore, it is likely that those two factors are driving the difference in overall cumulative risk between participants with and without HIV. Given that PWH are more likely to have access to housing supports, it is not surprising that a higher proportion of participants without HIV experienced homelessness in the past year in our sample. The difference between groups in frequent polysubstance use is more notable. While participants without HIV had more days of polysubstance use relative to PWH, they also had more days of stimulant use specifically, though they did not differ from PWH on stimulant use disorder severity. It is possible that the difference between groups on the polysubstance risk factor is being driven by the differential frequency of stimulant use between groups.

In our sample, lacking health insurance was a significant predictor of healthcare access, which is in line with prior research on which factors most impact healthcare access and utilization across a number of different populations (Hawkins, O’Keefe, & James, 2010; Hsia et al., 2000; Washington, Bean-Mayberry, Riopelle, & Yano, 2011). Prior studies have also shown that insurance enrollment and coverage is associated with both healthcare access and improved health outcomes among PWH specifically (Furl, Watanabe-Galloway, Lyden, & Swindells, 2018; Ludema et al., 2016; Wohl et al., 2017). However, the type of health insurance (e.g. HMO, PPO, EPO, POS, etc.) could potentially impact if and when care is sought. Premiums and co-payments are often barriers to needed care among low-income patients, which is the impetus of the high prevalence of seeking emergency care over primary care (Kangovi et al., 2013; Vogel et al., 2019). Although emergency care is more expensive for healthcare systems, patients are able to receive palliative medical services regardless of ability to pay upfront (Kangovi et al., 2013). Additionally, it has been suggested that universal insurance coverage alone will not completely eliminate the significant health disparities that exist in the current healthcare system (Adler & Stewart, 2010). Our results, which show the impact of cumulative risk over and above the effect of lacking health insurance, support this suggestion.

Interestingly, there has been research to suggest that understanding intersecting identities (i.e. race, sexuality, class, gender, ability) and environment can improve or deteriorate health outcomes in people who use substances (A. B. Collins, Boyd, Cooper, & McNeil, 2019). Research suggests that individuals living in extreme poverty are more likely to experience other stressors which negatively impact their health, including housing insecurity, abuse, exposure to violence, criminal justice involvement, and substance dependence (Palumbo, Wiebe, Kassam-Adams, & Richmond, 2019). Persons who use stimulants commonly experience a multitude of these stressors and risk factors, which are likely to pose as barriers to healthcare access. The cumulative risk model can illuminate how interacting stressors compound and exacerbate existing medical conditions in adults who use stimulants. A point of innovation of our study is that we examine the relationship between adulthood experiences of socioeconomic disparity, addiction, involvement in the criminal justice system, and healthcare access among vulnerable populations. Understanding the mechanisms that connect these risk conditions can potentially inform how to mitigate risk factors that reduce healthcare access among persons who use stimulants.

Our finding that PWH reported better healthcare access than participants without HIV is consistent with other research in illicit drug using samples (W. E. Cunningham et al., 1999; Matsuzaki et al., 2018). One possibility is that PWH may have access to additional resources via their HIV care that are not as readily available to persons without HIV. The large majority of PWH in our sample were diagnosed with HIV 10 or more years ago and therefore likely have a similarly long history of engagement in HIV care. Engagement in HIV care may buffer PWH from compounded effects of cumulative risk because they are more likely to be linked to palliative care by HIV providers. Our study provides emergent policy implications that providing persons without HIV who use stimulants with similar resources afforded to PWH engaged in HIV care may mitigate the health effects of cumulative risk. Persons without HIV who use stimulants may benefit from having community support that provides universal insurance coverage and linkage to outpatient services and similar palliative care programs.

Despite differences in healthcare access between participants with and without HIV, there were few differences between groups on self-reported past treatment utilization in our sample. PWH were more likely to have had outpatient psychiatric treatment, and participants without HIV reported more episodes of prior drug treatment. Otherwise, participants reported a similar history of lifetime medical hospitalizations, medical problems in the past 30 days, and perceived need for treatment for medical problems. However, PWH reported significantly more lifetime chronic medical conditions than participants without HIV. In general, research supports that persons who use stimulants like cocaine have high comorbidity of chronic health conditions such as kidney and heart disease (Y. M. Kim, 2019). Research has also shown that PWH experience more non-HIV related comorbidities than persons without HIV (L. F. Collins et al., 2020; Kong, Pozen, Anastos, Kelvin, & Nash, 2019; Schouten et al., 2014), and PWH who use substances are at the greatest risk for multiple comorbidities (Altice, Kamarulzaman, Soriano, Schechter, & Friedland, 2010). Thus, this difference between participants with and without HIV on number of chronic health conditions in our sample is not surprising. Additionally, for PWH, ongoing engagement in HIV care may increase potential opportunities for providers to identify and treat comorbid medical conditions.

This study has several notable strengths, including a large community sample of 453 persons who use stimulants, verification of HIV status, and a cumulative risk index that was constructed based on the empirical literature. At the same time, there were some limitations. Our measures of healthcare access, treatment utilization, and medical conditions were based on self-report and we did not obtain any objective data on detailed healthcare utilization and access, such as frequency of healthcare visits, proximity to care, and types of treatment received. Relatedly, given the brevity of the visit, many constructs were assessed via single items (e.g., homelessness in the past year) without additional details (e.g., engagement in care within the past 6 months, information about premiums and co-pays associated with different types of insurance) that might have informed our conclusions. A comprehensive assessment of these factors, including a more evaluation of these factors over the life course, would be optimal for a future study assessing cumulative risk and access to care among persons who use stimulants. Additionally, a mixed methods approach in future work could provide a deeper understanding of the context of how multiple risk factors impact access to care among persons with and without HIV who use stimulants. Because our sample was comprised mostly of African American males with low socioeconomic status recruited in an urban community, our results might not generalize to other populations. Finally, another limitation of the study was that by assessing our predictors and outcome simultaneously using a cross-sectional design, there was an inability to establish a temporal relationship between cumulative risk and healthcare access.

In conclusion, our findings show that access to care among persons who use stimulants, both with and without HIV, is negatively impacted by the accumulation of risk factors from a number of different domains. Importantly, considering these factors in isolation is unlikely to fully mitigate the persistent health disparities that exist for this underserved population. Understanding the cumulative effects of these factors and considering them together is critical for developing interventions to facilitate access to care, and thus improve health outcomes. Future studies should consider how proximity to health care facilities in low-income neighborhoods, housing insecurity, and limited education compound to diminish access to care for both individuals with and without HIV. Among PWH who use stimulants, those who are uninsured may need additional support linking them to publicly funded programs that will help improve engagement in care and adherence to antiretroviral treatment. In contrast, persons without HIV who use stimulants who are more likely to have fewer economic and publicly funded resources need support linking them to low-cost or free clinical care in accessible locations. Of particular importance, engaging this population in more preventative healthcare and minimizing the use of emergency services for non-emergency medical issues would certainly improve health outcomes at the individual level, thus reducing health disparities, but it would also contribute to reduced cost to the larger overall healthcare system.

Funding:

This study was funded by grant DP2-DA040226 and T32-AI007392 from the United States National Institutes of Health.

Footnotes

Conflict of Interest: All authors declare no conflict of interest

Ethics approval: This study was approved by the Institutional Review Board of Duke University School of Medicine.

Availability of data and materials:

The datasets analyzed during the current study are not available to the general public due to ongoing analyses.

REFERENCES

  1. Adler NE, & Stewart J (2010). Health disparities across the lifespan: meaning, methods, and mechanisms. Annals of the New York Academy of Sciences, 1186, 5–23. doi: 10.1111/j.1749-6632.2009.05337.x [DOI] [PubMed] [Google Scholar]
  2. Almeida OP, Alfonso H, Pirkis J, Kerse N, Sim M, Flicker L, … Pfaff J (2011). A practical approach to assess depression risk and to guide risk reduction strategies in later life. International Psychogeriatrics, 23(2), 280–291. doi: 10.1017/s1041610210001870 [DOI] [PubMed] [Google Scholar]
  3. Altice FL, Kamarulzaman A, Soriano VV, Schechter M, & Friedland GH (2010). Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. Lancet, 376(9738), 367–387. doi: 10.1016/S0140-6736(10)60829-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baggett TP, O’Connell JJ, Singer DE, & Rigotti NA (2010). The unmet health care needs of homeless adults: a national study. American Journal of Public Health, 100(7), 1326–1333. doi: 10.2105/ajph.2009.180109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Baum MK, Rafie C, Lai S, Sales S, Page B, & Campa A (2009). Crack-cocaine use accelerates HIV disease progression in a cohort of HIV-positive drug users. Journal of Acquired Immune Deficiency Syndromes 50(1), 93–99. doi: 10.1097/QAI.0b013e3181900129 [DOI] [PubMed] [Google Scholar]
  6. Bedi R, Muller RT, & Classen CC (2014). Cumulative risk for deliberate self-harm among treatment-seeking women with histories of childhood abuse. Psychological Trauma: Theory, Research, Practice, and Policy, 6(6), 600–609. doi: 10.1037/a0033897 [DOI] [Google Scholar]
  7. Benjamin-Johnson R, Moore A, Gilmore J, & Watkins K (2009). Access to medical care, use of preventive services, and chronic conditions among adults in substance abuse treatment. Psychiatric services (Washington, D.C.), 60(12), 1676–1679. doi: 10.1176/ps.2009.60.12.1676 [DOI] [PubMed] [Google Scholar]
  8. Borders TF, Booth BM, Falck RS, Leukefeld C, Wang J, & Carlson RG (2009). Longitudinal changes in drug use severity and physical health-related quality of life among untreated stimulant users. Addictive Behaviors, 34(11), 959–964. doi: 10.1016/j.addbeh.2009.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Borders TF, Booth BM, Stewart KE, Cheney AM, & Curran GM (2015). Rural/urban residence, access, and perceived need for treatment among african american cocaine users. The Journal of Rural Health : Official Journal of the American Rural Health Association and the National Rural Health Care Association, 31(1), 98–107. doi: 10.1111/jrh.12092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Braveman P, & Gottlieb L (2014). The social determinants of health: it’s time to consider the causes of the causes. Public Health Reports, 129 Suppl 2, 19–31. doi: 10.1177/00333549141291S206 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Caleyachetty R, Tehranifar P, Genkinger JM, Echouffo-Tcheugui JB, & Muennig P (2015). Cumulative social risk exposure and risk of cancer mortality in adulthood. BMC Cancer, 15(1), 945. doi: 10.1186/s12885-015-1997-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carrico AW, Hunt PW, Neilands TB, Dilworth SE, Martin JN, Deeks SG, & Riley ED (2019). Stimulant Use and Viral Suppression in the Era of Universal Antiretroviral Therapy. Journal of Acquired Immune Deficiency Syndromes, 80(1), 89–93. doi: 10.1097/QAI.0000000000001867 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carrico AW, Shoptaw S, Cox C, Stall R, Li X, Ostrow DG, … Plankey MW (2014). Stimulant use and progression to AIDS or mortality after the initiation of highly active antiretroviral therapy. Journal of Acquired Immune Deficiency Syndromes, 67(5), 508–513. doi: 10.1097/qai.0000000000000364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. CDC. (2017). Selected National HIV Prevention and Care Outcomes in the United States.
  15. Centers for Disease Control and Prevention. (2018). HIV Surveillance Report: Behavioral and Clinical Characteristics of Persons with Diagnosed HIV Infection. Atlanta, Georgia: Division of HIV/AIDS Prevention and US Department of Health and Human Services. [Google Scholar]
  16. Chartier MJ, Walker JR, & Naimark B (2007). Childhood abuse, adult health, and health care utilization: results from a representative community sample. American Journal of Epidemiology, 165(9), 1031–1038. doi: 10.1093/aje/kwk113 [DOI] [PubMed] [Google Scholar]
  17. Chartier MJ, Walker JR, & Naimark B (2010). Separate and cumulative effects of adverse childhood experiences in predicting adult health and health care utilization. Child Abuse and Neglect, 34(6), 454–464. doi: 10.1016/j.chiabu.2009.09.020 [DOI] [PubMed] [Google Scholar]
  18. Cheng QJ, Engelage EM, Grogan TR, Currier JS, & Hoffman RM (2014). Who provides primary care? an assessment of HIV patient and provider practices and preferences. Journal of AIDS & Clinical Research, 5(11). doi: 10.4172/2155-6113.1000366 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Cherpitel CJ, & Ye Y (2008). Drug use and problem drinking associated with primary care and emergency room utilization in the US general population: data from the 2005 national alcohol survey. Drug and Alcohol Dependence, 97(3), 226–230. doi: 10.1016/j.drugalcdep.2008.03.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Collins AB, Boyd J, Cooper HLF, & McNeil R (2019). The intersectional risk environment of people who use drugs. Social Science Medicine, 234, 112384. doi: 10.1016/j.socscimed.2019.112384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Collins LF, Sheth AN, Mehta CC, Naggie S, Golub ET, Anastos K, … Ofotokun I (2020). The prevalence and burden of non-AIDS comorbidities among women living with or at risk for Human Immunodeficiency Virus infection in the United States. Clinical Infectious Diseases, [epub ahead of print]. doi: 10.1093/cid/ciaa204 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Crisp BR, Williams M, Timpson S, & Ross MW (2004). Medication compliance and satisfaction with treatment for HIV disease in a sample of african-american crack cocaine smokers. AIDS and Behavior, 8(2), 199–206. doi: 10.1023/B:AIBE.0000030250.33931.af [DOI] [PubMed] [Google Scholar]
  23. Cunningham CO, Sohler NL, Berg KM, Shapiro S, & Heller D (2006). Type of substance use and access to HIV-related health care. AIDS Patient Care and STDs, 20(6), 399–407. doi: 10.1089/apc.2006.20.399 [DOI] [PubMed] [Google Scholar]
  24. Cunningham WE, Andersen RM, Katz MH, Stein MD, Turner BJ, Crystal S, … Shapiro MF (1999). The impact of competing subsistence needs and barriers on access to medical care for persons with human immunodeficiency virus receiving care in the United States. Medical Care, 37(12), 1270–1281. doi: 10.1097/00005650-199912000-00010 [DOI] [PubMed] [Google Scholar]
  25. Cunningham WE, Hays RD, Williams KW, Beck KC, Dixon WJ, & Shapiro MF (1995). Access to medical care and health-related quality of life for low-income persons with symptomatic human immunodeficiency virus. Medical Care, 33(7), 739–754. doi: 10.1097/00005650-199507000-00009 [DOI] [PubMed] [Google Scholar]
  26. Echouffo-Tcheugui JB, Caleyachetty R, Muennig PA, Narayan KM, & Golden SH (2016). Cumulative social risk and type 2 diabetes in US adults: The National Health and Nutrition Examination Survey (NHANES) 1999–2006. European Journal of Preventive Cardiology, 23(12), 1282–1288. doi: 10.1177/2047487315627036 [DOI] [PubMed] [Google Scholar]
  27. Evans GW, Li D, & Whipple SS (2013). Cumulative risk and child development. Psychological Bulletin, 139(6), 1342–1396. doi: 10.1037/a0031808 [DOI] [PubMed] [Google Scholar]
  28. First M, Williams JBW, Karg RS, Spitzer RL. (2015). Structural Clinical Interview for DSM-5-Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV (R. Spitzer, Trans.). In Williams J (Ed.). Arlington, VA: American Psychiatric Association [Google Scholar]
  29. Frank JW, Wang EA, Nunez-Smith M, Lee H, & Comfort M (2014). Discrimination based on criminal record and healthcare utilization among men recently released from prison: a descriptive study. Health & Justice, 2, 6. doi: 10.1186/2194-7899-2-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Furl R, Watanabe-Galloway S, Lyden E, & Swindells S (2018). Determinants of facilitated health insurance enrollment for patients with HIV disease, and impact of insurance enrollment on targeted health outcomes. BMC Infect Dis, 18(1), 132. doi: 10.1186/s12879-018-3035-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gouse H, Joska JA, Lion RR, Watt MH, Burnhams W, Carrico AW, & Meade CS (2016). HIV testing and sero-prevalence among methamphetamine users seeking substance abuse treatment in Cape Town. Drug and Alcohol Review, 35(5), 580–583. doi: 10.1111/dar.12371 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Harris NS, Johnson AS, Huang Y-LA, Kern D, Fulton P, Smith DK, … Hall IH (2019). Vital signs: status of human immunodeficiency virus testing,viral suppression, and HIV preexposure prophylaxis —United States, 2013–2018. MMWR Morbidity And Mortality Weekly Report(68), 1117–1123. doi: 10.15585/mmwr.mm6848e1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hatzenbuehler ML, Slopen N, & McLaughlin KA (2014). Stressful life events, sexual orientation, and cardiometabolic risk among young adults in the United States. Health Psychology, 33(10), 1185–1194. doi: 10.1037/hea0000126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hawkins AS, O’Keefe AM, & James X (2010). Health care access and utilization among ex-offenders in Baltimore: Implications for policy. Journal of Health Care for the Poor and Underserved, 21(2), 649–665. doi: 10.1353/hpu.0.0287 [DOI] [PubMed] [Google Scholar]
  35. Hsia J, Kemper E, Sofaer S, Bowen D, Kiefe CI, Zapka J, … Limacher M (2000). Is insurance a more important determinant of healthcare access than perceived health? Evidence from the Women’s Health Initiative. Journal of Women’s Health & Gender-Based Medicine, 9(8), 881–889. doi: 10.1089/152460900750020919 [DOI] [PubMed] [Google Scholar]
  36. Huynh C, Ferland F, Blanchette-Martin N, Menard JM, & Fleury MJ (2016). Factors influencing the frequency of emergency department utilization by individuals with substance use disorders. The Psychiatric Quarterly, 87(4), 713–728. doi: 10.1007/s11126-016-9422-6 [DOI] [PubMed] [Google Scholar]
  37. Kalichman SC, Hernandez D, Kegler C, Cherry C, Kalichman MO, & Grebler T (2015). Dimensions of Poverty and Health Outcomes Among People Living with HIV Infection: Limited Resources and Competing Needs. Journal of Community Health, 40(4), 702–708. doi: 10.1007/s10900-014-9988-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kangovi S, Barg FK, Carter T, Long JA, Shannon R, & Grande D (2013). Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Afflictions (Millwood), 32(7), 1196–1203. doi: 10.1377/hlthaff.2012.0825 [DOI] [PubMed] [Google Scholar]
  39. Kim ST, & Park T (2019). Acute and Chronic Effects of Cocaine on Cardiovascular Health. International Journal of Molecular Science, 20(3). doi: 10.3390/ijms20030584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kim YM (2019). Comparing Medical Comorbidities Between Opioid and Cocaine Users: A Data Mining Approach. Addiction Health, 11(4), 223–233. doi: 10.22122/ahj.v11i4.242 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kong AM, Pozen A, Anastos K, Kelvin EA, & Nash D (2019). Non-HIV comorbid conditions and polypharmacy among people living with HIV age 65 or older compared with HIV-negative individuals age 65 or older in the United States: A retrospective claims-based analysis. AIDS Patient Care and STDs, 33(3), 93–103. doi: 10.1089/apc.2018.0190 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Kopak AM, Proctor SL, & Hoffmann NG (2017). The cumulative risk associated with demographic background characteristics among substance use treatment patients. Addiction Research and Theory, 25(3), 216–224. doi: 10.1080/16066359.2016.1265109 [DOI] [Google Scholar]
  43. Kulkarni SP, Baldwin S, Lightstone AS, Gelberg L, & Diamant AL (2010). Is incarceration a contributor to health disparities? Access to care of formerly incarcerated adults. Journal of Community Health, 35(3), 268–274. doi: 10.1007/s10900-010-9234-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Laine C, Hauck WW, Gourevitch MN, Rothman J, Cohen A, & Turner BJ (2001). Regular outpatient medical and drug abuse care and subsequent hospitalization of persons who use illicit drugs. Journal of American Medical Association, 285(18), 2355–2362. doi: 10.1001/jama.285.18.2355 [DOI] [PubMed] [Google Scholar]
  45. Lantz PM, House JS, Mero RP, & Williams DR (2005). Stress, life events, and socioeconomic disparities in health: results from the Americans’ Changing Lives Study. Journal of Health and Social Behavior, 46(3), 274–288. doi: 10.1177/002214650504600305 [DOI] [PubMed] [Google Scholar]
  46. Laposata EA, & Mayo GL (1993). A review of pulmonary pathology and mechanisms associated with inhalation of freebase cocaine (“crack”). The American Journal of Forensic Medicine and Pathology, 14(1), 1–9. doi: 10.1097/00000433-199303000-00001 [DOI] [PubMed] [Google Scholar]
  47. Liu R, Shelton RC, Eldred-Skemp N, Goldsmith J, & Suglia SF (2019). Early Exposure to Cumulative Social Risk and Trajectories of Body Mass Index in Childhood. Childhood Obesity, 15(1), 48–55. doi: 10.1089/chi.2018.0116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. London JA, Utter GH, Battistella F, & Wisner D (2009). Methamphetamine use is associated with increased hospital resource consumption among minimally injured trauma patients. The Journal of Trauma, 66(2), 485–490. doi: 10.1097/TA.0b013e318160e1db [DOI] [PubMed] [Google Scholar]
  49. Lorvick J, Browne EN, Lambdin BH, & Comfort M (2018). Polydrug use patterns, risk behavior and unmet healthcare need in a community-based sample of women who use cocaine, heroin or methamphetamine. Addictive Behaviors, 85, 94–99. doi: 10.1016/j.addbeh.2018.05.013 [DOI] [PubMed] [Google Scholar]
  50. Ludema C, Cole SR, Eron JJ Jr., Edmonds A, Holmes GM, Anastos K, … Adimora AA (2016). Impact of health insurance, ADAP, and income on HIV viral suppression among US women in the Women’s Interagency HIV Study, 2006–2009. Journal of Acquired Immune Deficiency Syndromes, 73(3), 307–312. doi: 10.1097/QAI.0000000000001078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Matsuzaki M, Vu QM, Gwadz M, Delaney JAC, Kuo I, Trejo MEP, … Christopoulos K (2018). Perceived access and barriers to care among illicit drug users and hazardous drinkers: findings from the Seek, Test, Treat, and Retain data harmonization initiative (STTR). BMC Public Health, 18(1), 366. doi: 10.1186/s12889-018-5291-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. McGeary KA, & French MT (2000). Illicit drug use and emergency room utilization. Health Services Research, 35(1 Pt 1), 153–169. [PMC free article] [PubMed] [Google Scholar]
  53. McLellan AT, Kushner H, Metzger D, Peters R, Smith I, Grissom G, … Argeriou M (1992). The Fifth Edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9(3), 199–213. doi: 10.1016/0740-5472(92)90062-s [DOI] [PubMed] [Google Scholar]
  54. Meier MH, Hall W, Caspi A, Belsky DW, Cerdá M, Harrington HL, … Moffitt TE (2016). Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment. Psychological Medicine, 46(4), 877–889. doi: 10.1017/s0033291715002482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Palepu A, Horton NJ, Tibbetts N, Meli S, & Samet JH (2004). Uptake and adherence to highly active antiretroviral therapy among HIV-infected people with alcohol and other substance use problems: the impact of substance abuse treatment. Addiction (Abingdon, England), 99(3), 361–368. doi: 10.1111/j.1360-0443.2003.00670.x [DOI] [PubMed] [Google Scholar]
  56. Palumbo AJ, Wiebe DJ, Kassam-Adams N, & Richmond TS (2019). Neighborhood Environment and Health of Injured Urban Black Men. Journal of Racial and Ethnic Health Disparities, 6(6), 1068–1077. doi: 10.1007/s40615-019-00609-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pence BW, Thielman NM, Whetten K, Ostermann J, Kumar V, & Mugavero MJ (2008). Coping strategies and patterns of alcohol and drug use among HIV-infected patients in the United States Southeast. AIDS Patient Care STDS, 22(11), 869–877. doi: 10.1089/apc.2008.0022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rhee TG, Marottoli RA, Cooney LM Jr., & Fortinsky RH (2020). Associations of social and behavioral determinants of health index with self-rated health, functional limitations, and health services use in older adults. Journal of American Geriatratric Society. doi: 10.1111/jgs.16429 [DOI] [PubMed] [Google Scholar]
  59. Robinson SM, Sobell LC, Sobell MB, & Leo GI (2014). Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use. Psychology of AddictiveBbehaviors, 28(1), 154–162. doi: 10.1037/a0030992 [DOI] [PubMed] [Google Scholar]
  60. Savolainen J, Eisman A, Mason WA, Schwartz JA, Miettunen J, & Jarvelin MR (2018). Socioeconomic disadvantage and psychological deficits: Pathways from early cumulative risk to late-adolescent criminal conviction. Journal of Adolescence, 65, 16–24. doi: 10.1016/j.adolescence.2018.02.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Schouten J, Wit FW, Stolte IG, Kootstra NA, van der Valk M, Geerlings SE, … Reiss P (2014). Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV cohort study. Clinical Infectious Diseases, 59(12), 1787–1797. doi: 10.1093/cid/ciu701 [DOI] [PubMed] [Google Scholar]
  62. Selwyn PA, Budner NS, Wasserman WC, & Arno PS (1993). Utilization of on-site primary care services by HIV-seropositive and seronegative drug users in a methadone maintenance program. Public Health Reports (Washington, D.C. : 1974), 108(4), 492–500. [PMC free article] [PubMed] [Google Scholar]
  63. Sexton K (2012). Cumulative risk assessment: an overview of methodological approaches for evaluating combined health effects from exposure to multiple environmental stressors. International Journal of Environmental Research on Public Health, 9(2), 370–390. doi: 10.3390/ijerph9020370 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Smola S, Justice AC, Wagner J, Rabeneck L, Weissman S, Rodriguez-Barradas M, & Team, V. P. (2001). Veterans aging cohort three-site study (VACS 3): overview and description. Journal of Clinical Epidemiology, 54 Suppl 1, S61–76. doi: 10.1016/s0895-4356(01)00448-6 [DOI] [PubMed] [Google Scholar]
  65. Sobell LC, & Sobell MB (1996). Timeline Follow-back User’s Guide: A Calendar Method for Assessing Alcohol and Drug Use. Toronto: Addiction Research Foundation. [Google Scholar]
  66. Sohler NL, Wong MD, Cunningham WE, Cabral H, Drainoni ML, & Cunningham CO (2007). Type and pattern of illicit drug use and access to health care services for HIV-infected people. AIDS Patient Care STDS, 21 Suppl 1, S68–76. doi: 10.1089/apc.2007.9985 [DOI] [PubMed] [Google Scholar]
  67. Stein MD, O’Sullivan PS, Ellis P, Perrin H, & Wartenberg A (1993). Utilization of medical services by drug abusers in detoxification. Journal of Substance Abuse, 5(2), 187–193. doi: 10.1016/0899-3289(93)90062-g [DOI] [PubMed] [Google Scholar]
  68. Substance Abuse and Mental Health Services Administration. (2019). Key substance use and mental health indicators in the United States: Results from the 2018 National Survey on Drug Use and Health HHS Publication No. PEP19–5068, NSDUH Series H-54. Retrieved from https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHNationalFindingsReport2018/NSDUHNationalFindingsReport2018.pdf
  69. Surratt HL, O’Grady CL, Levi-Minzi MA, & Kurtz SP (2015). Medication adherence challenges among HIV positive substance abusers: the role of food and housing insecurity. AIDS Care, 27(3), 307–314. doi: 10.1080/09540121.2014.967656 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Tucker JS, Burnam MA, Sherbourne CD, Kung FY, & Gifford AL (2003). Substance use and mental health correlates of nonadherence to antiretroviral medications in a sample of patients with human immunodeficiency virus infection. American Journal of Medicine, 114(7), 573–580. doi: 10.1016/s0002-9343(03)00093-7 [DOI] [PubMed] [Google Scholar]
  71. van der Waerden JE, Hoefnagels C, Hosman CM, & Jansen MW (2014). Defining subgroups of low socioeconomic status women at risk for depressive symptoms: the importance of perceived stress and cumulative risks. The International Journal of Social Psychiatry, 60(8), 772–782. doi: 10.1177/0020764014522751 [DOI] [PubMed] [Google Scholar]
  72. Vogel JA, Rising KL, Jones J, Bowden ML, Ginde AA, & Havranek EP (2019). Reasons Patients Choose the Emergency Department over Primary Care: a Qualitative Metasynthesis. Journal of General Internal Medicine, 34(11), 2610–2619. doi: 10.1007/s11606-019-05128-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Washington DL, Bean-Mayberry B, Riopelle D, & Yano EM (2011). Access to care for women veterans: delayed healthcare and unmet need. Journal of General Internal Medicine, 26Suppl2(Suppl 2), 655–661. doi: 10.1007/s11606-011-1772-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Wohl DA, Kuwahara RK, Javadi K, Kirby C, Rosen DL, Napravnik S, & Farel C (2017). Financial Barriers and Lapses in Treatment and Care of HIV-Infected Adults in a Southern State in the United States. AIDS Patient Care STDS, 31(11), 463–469. doi: 10.1089/apc.2017.0125 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets analyzed during the current study are not available to the general public due to ongoing analyses.

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