Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: AIDS Care. 2019 Feb 19;31(9):1131–1139. doi: 10.1080/09540121.2019.1576840

ANCILLARY SERVICE NEEDS AMONG PERSONS NEW TO HIV CARE AND THE RELATIONSHIP BETWEEN NEEDS AND LATE PRESENTATION TO CARE

Anne K Monroe 1, Catherine R Lesko 2, Geetanjali Chander 3, Bryan Lau 4, Jeanne Keruly 5, Heidi M Crane 6, K Rivet Amico 7, Sonia Napravnik 8, E Byrd Quinlivan 9, Michael J Mugavero 10
PMCID: PMC7328012  NIHMSID: NIHMS1534215  PMID: 30776911

Abstract

Ancillary service needs likely influence time to diagnosis and presentation for HIV care. The effect of both met and unmet needs on late presentation to HIV care is not well understood. We used baseline data from 348 people with HIV (PWH) with no prior HIV care who enrolled in iENGAGE (a randomized controlled trial (RCT) of an intervention to support retention in care) at one of four HIV clinics in the US. A standardized baseline questionnaire collected information on ancillary service needs, and whether each need was presently unmet. We examined covariates known to be associated with disease stage at presentation to care and their association with needs. We subsequently assessed the relationship of needs with CD4 accounting for those other covariates by estimating prevalence ratios (PR) using inverse probability weights. Most patients enrolling in the RCT were male (79%) and the majority were Black (62%); median age was 34 years. Prevalence of any reported individual need was 69%. One third of the sample had a baseline CD4 cell count <200, 42% between 200–499 and 27% ≥500. There was no statistically significant association between need or unmet need and baseline CD4. In general, psychiatric health and SU issues (depression, anxiety, and drug use) were consistently associated with higher prevalence of need (met and unmet). Additionally, Black race was associated with higher basic resource needs (housing: PR 1.67, 95%CI 1.08–2.59; transportation: PR 1.65, 95% CI 1.12–2.45). Ancillary service needs (met and unmet) were common among patients new to HIV care and impacted vulnerable subgroups. However, we found no evidence that reporting a specific individual need, whether met or unmet, was associated with a timely presentation to HIV care. The impact of needs on subsequent steps of the HIV care continuum will be examined with longitudinal data.

Keywords: retention, needs, treatment naive

Background

Even with decreases in undiagnosed HIV infection in the US, late presentation for care persists (Lesko, Cole, Zinski, Poole, & Mugavero, 2013). Late presentation causes a delay in receipt of antiretroviral therapy (ART), leading to both negative health consequences for people with HIV (PWH) (Danel C; Group, 2015) and increased HIV transmission risk (Cohen, Chen, McCauley, & Gamble, 2011). Increasing HIV testing and linkage to care to ensure early diagnosis and treatment are necessary steps for ending the AIDS epidemic and are both domestic and global priorities (UNAIDS 2018). Prior research has revealed differences in the associations between various demographic factors and time to diagnosis and presentation to care. For example, while two prior studies revealed an association between female sex and delay in HIV diagnosis (Hall HI, 2015; Hall, Tang, & Espinoza, 2016), another did not (Mayben et al., 2007). Furthermore, male sex was associated with delayed presentation to medical care in one nationally representative study (Robertson et al., 2016). Older age has been associated with late diagnosis (Hall HI, 2015; Leeper et al., 2014), however younger age has been associated with delayed presentation (Wolitski 2018). And Black race has been associated with delayed diagnosis (Hall HI, 2015) with different observed effects on delay in presentation – both association (Ulett et al., 2009) and inverse association (Robertson et al., 2016). These varying results stem from differences both in the definitions used and the populations studied.

The need for concrete services or resources may impact time to presentation to HIV care, and subsequently, HIV outcomes. For example, not having financial resources and not having a vehicle have been associated with later presentation to care (Leeper et al., 2014). Lack of housing (Aidala, Lee, Abramson, Messeri, & Siegler, 2007; Terzian et al., 2015), drug treatment (Altice, Kamarulzaman, Soriano, Schechter, & Friedland, 2010; Ashman, Conviser, & Pounds, 2002), transportation (Andersen et al., 2007), and mental health treatment (Ashman et al., 2002) have all been associated with worse ART adherence and HIV outcomes (Cornelius et al., 2017). Conversely, receipt of ancillary services such as case management, housing, food, transportation, mental health and substance abuse treatment has been associated with access to primary care (Chan, Absher, & Sabatier, 2002; Conviser & Pounds, 2002), and receipt of case management services has been associated with improved HIV outcomes. (Lo, MacGovern, & Bradford, 2002; Magnus et al., 2001)

Because of the known impact of ancillary services on HIV outcomes, the Ryan White CARE Act provides funding to address unmet needs for these vital services among PWH. Less is known, however, about how unmet needs for ancillary services impact time to presentation to HIV care. The iENGAGE study, a behavioral intervention to optimize HIV care continuum outcomes among individuals presenting for HIV care for the first time, offered an opportunity to examine this question and to describe the needs (both met and unmet) of people with HIV when they arrived at clinic for the first time.

One of the challenges for this analysis is that there are different definitions for what constitutes late presentation to care (Althoff et al., 2010; Antinori et al., 2011) and the date of infection is unknown for the overwhelming majority of people who test positive for HIV (Skar, Albert, & Leitner, 2013). Additionally, there are several ways that need or unmet need could impact CD4 at presentation to HIV care: need or unmet need could delay diagnosis of HIV or delay entry to care following diagnosis or delay both processes. Furthermore, a need or unmet need could create conditions in which time to HIV diagnosis and entry to care are unaffected, but physical health is compromised such that CD4 cell count decline is faster following infection. In this analysis, we conceptualized late presentation to care as first measured CD4 cell count that was clinically meaningfully suppressed.

The objective of our study was to describe the prevalence of needs in a sample of persons entering HIV care for the first time, and to evaluate the association between the presence of needs for ancillary services and late presentation to HIV care, indicated by low CD4 cell count at time to presentation to care. We hypothesized that a greater number of unmet needs would be associated with late presentation to HIV care, as indicated by an initial CD4 count <200 cells/mm3.

Methods

Study sample

The iENGAGE study recruited individuals who were new to HIV care at four academic medical centers in cities with a diverse patient population: Baltimore, MD (Johns Hopkins HIV Clinic); Birmingham, AL (University of Alabama, Birmingham 1917 Outpatient Clinic); Chapel Hill, NC (University of North Carolina- Chapel Hill); and Seattle, WA (University of Washington). Patients enrolling in any of the four clinics were screened for prior HIV care and patients deemed new to care were invited to enroll in the iENGAGE study. Patients who consented to participate were randomized with equal probability to intervention or standard of care arms; the intervention provided intensive four-session behavioral intervention with the goals of helping patients adapt to their new HIV diagnosis and optimize retention in care and ART adherence through information, motivation, and behavioral skill building.

In this analysis, we evaluated baseline data from the iENGAGE study. At baseline, patients enrolled in iENGAGE completed detailed assessments (with the assistance of study staff if patients had any literacy limitations) of demographics, medical history, socioeconomic indicators, substance use and mental health symptoms, and need for ancillary services. The need for ancillary services questionnaire was delivered by computer-assisted self-interview (CASI).

There were 941 patients new to HIV care screened and 372 patients who agreed to participate in iENGAGE. We excluded 16 patients who did not complete baseline needs assessment, and 8 patients who did not have a CD4 cell count measured within 6 months (in either direction) of screening and enrollment into iENGAGE (6.5% of the sample). The analytic sample included 348 patients, all of whom entered HIV care within the previous 14 days.

Dependent variable

The first available CD4 cell count was abstracted from the medical record, and was categorized as ≤200 cells/mm3, 200–500 cells/mm3 or ≥500 cells/mm3. Patients without a CD4 count within 6 months of enrollment were excluded from the analytic sample as described above.

Primary independent variables

Need for ancillary services was assessed using the CDC Retention in Care (RiC) survey utilized for a multi-site retention in care trial (see Supplemental Materials 1). The survey items included perceptions of whether or not a given service was “needed” over the past 6 months (yes, no). A follow-up question for any service marked as needed asked if the participant was able to get the service (yes, sometimes, no). Participants were asked if they had a need for counseling, substance use (SU) treatment, housing, food, transportation, employment, benefits, or financial assistance.

Covariates

Depression was measured by PHQ-8 (Spitzer et al., 1994)and dichotomized as present (PHQ-8≥10) or absent. Anxiety was measured by PHQ-A and and dichotomized as present (panic symptoms or disorder) or absent. Social support was measured by MOS-4 (Sherbourne & Stewart, 1991) and reported as a summary score between 0 and 100. At-risk alcohol use was determined by an AUDIT-C (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) score of 3 or more for women or 4 or more for men. Drug use (amphetamines, cocaine, opiates or marijuana) was categorized into never, ever or current use.

Analysis

The burden of ancillary service needs, both met and unmet, among this cohort of patients new to HIV care was first described. To identify groups with highest prevalence of needs and unmet needs, associations between patient demographic and clinical characteristics were reported with estimated prevalence ratios using Poisson models with no offset, which approximate a log-binomial model but converge more reliably. Reported associations between patient demographic and clinical characteristics were conditional on all other covariates in the models.

The prevalence ratio for the association between needs and unmet needs with category of CD4 cell count at presentation to HIV care (200–499 vs. ≥500 and <200 vs. ≥500) was estimated. To do so, log-binomial models for CD4 cell count category as a function of presence of a particular need (or particular unmet need) were fit. Each individual need and unmet need was evaluated in a separate model. Covariates were balanced among patients who reported and who did not report each need or unmet need with inverse probability of exposure weights. Weights were estimated by fitting logistic models for the presence of each individual need or unmet need to estimate the probability of presence or absence of need or unmet need conditional on: age, male sex, black race, log10 viral load copies/mL, ever use of any illicit drug (crack/cocaine, amphetamine, opioid, marijuana or other drug), current use of any illicit drug, depression, anxiety, hazardous alcohol use, type of insurance (public, private, or none), and site of HIV care.

Results

The demographic and clinical characteristics of the study sample are shown in Table 1. The sample was predominantly male (79%) and black (62%), and the median (IQR) age was 34 (27, 45) years. Insurance type was mixed, with 44% of the sample reporting private insurance, 34% with public insurance, and 22% uninsured. A high proportion of the sample reported moderate or severe depressive symptoms (31%), anxiety symptoms (31%) or both (16%). A third of the sample had a baseline CD4 ≤ 200 cells/mm3, and the median (interquartile range [IQR]) baseline CD4 count was 344 (174, 554) cells/mm3.

Table 1.

Characteristics of 348 patients new to HIV care enrolled in the iENGAGE study

N(%) [unless otherwise indicated(*)]
Site
 1 145 (42)
 2 73 (21)
 3 74 (21)
 4 56 (16)
Male 275 (79)
Race/ethnicity
 Black 217 (62)
 White 98 (28)
 Hispanic 17 (5)
 Other 16 (5)
Age, median years (IQR)* 34 (27, 45)
Insurance type
 Private 152 (44)
 Public 117 (34)
 Uninsured 76 (22)
 Missing 3
Log10 viral load copies/mL, median (IQR)* 4.6 (3.8, 5.1)
Drug use, current
 Any illicit 60 (18)
 Crack/cocaine 32 (9)
 Amphetamines 21 (6)
 Heroin/opiates 17 (5)
Alcohol use, AUDIT-C score, median (IQR)* 2 (0, 4)
 Hazardous use 114 (33)
 Missing 7
PHQ-8 score, median (IQR)* 6 (3, 11)
 Moderate major depression (10–19) 89 (27)
 Severe major depression (≥20) 12 (4)
 Missing 21
PHQ-Anxiety
 Panic symptoms 72 (21)
 Panic disorder 35 (10)
 Missing 9
Anxiety AND depression (any) 51 (16)
Anxiety, no depression 48 (15)
Depression, no anxiety 45 (14)
No anxiety, no depression 176 (55)
Missing anxiety or depression 28
 
CD4 cells/μL, median (IQR)* 344 (174, 554)
 <200 cells/μL 108 (31)
 200–499 cells/μL 146 (42)
 ≥500 cells/μL 94 (27)
Open in a new tab

The overall prevalence of any ancillary service need was 69% (Table 2). The highest prevalence of any individual need was 36% for food assistance and the lowest prevalence was 3% for child care. Of the 10 people reporting a need for childcare, 1 was a woman (1% of women reported need for childcare) and 9 were men (3% of men reported need for childcare). All but one reported that the childcare need was unmet (one male reported that the need was met). Because of the low prevalence of need for child care we were unable to examine its association with covariates or CD4 cell count due to imprecision of estimates. The highest prevalence of any individual unmet need was 24% for financial assistance and the lowest prevalence was 2% for substance abuse treatment.

Table 2.

Prevalence of ancillary service needs and whether or not needs were met

Service N(%) Reporting Need N(%) Reporting Unmet Need % of those with Need in whom Need went Unmet
Food assistance 127 (36) 81 (23) 64
Financial assistance 121 (35) 84 (24) 69
Benefits assistance 113 (32) 77 (22) 68
Counseling 112 (32) 58 (17) 52
Transportation 112 (32) 72 (21) 64
Housing 85 (24) 59 (17) 69
Employment assistance 83 (24) 71 (21) 86
Substance abuse treatment 28 (8) 8 (2) 29
Child care 10 (3) 9 (3) 90
 
Any need 241 (69) 174 (50) 72
 Median (IQR) number of needs 2 (0, 4) 0.5 (0, 3)
Open in a new tab

Table 3 shows prevalence ratios for associations between patient demographic and clinical characteristics and ancillary service needs. While patterns varied across needs, in general, drug use and depression were consistently associated with higher prevalence of need. Black race was associated with higher need, in particular higher basic resource needs (housing: PR 2.38, 95%CI 1.23–4.59; financial assistance: PR 1.80, 95%CI 1.15–2.82; and employment assistance: PR 1.73, 95%CI 1.02, 2.91). Log10 viral load at baseline and public insurance (versus no insurance) were not generally associated with presence of needs. However, having private insurance was strongly and consistently associated with reduced needs.

Table 3.

Association between baseline characteristics and reported ancillary service needs

Food assistance Financial assistance Benefits assistance Counseling
10-year increase in age 0.87 (0.73, 1.03) 0.84 (0.70, 0.99)* 0.93 (0.79, 1.10) 1.02 (0.90, 1.15)
Male sex 1.11 (0.65, 1.88) 0.94 (0.58, 1.52) 1.11 (0.67, 1.81) 0.71 (0.51, 1.00)
Black race 1.16 (0.75, 1.80) 1.80 (1.15, 2.82)* 1.42 (0.88, 2.29) 0.91 (0.67, 1.24)
1-log increase in viral load 0.98 (0.84, 1.14) 1.07 (0.92, 1.23) 0.94 (0.79, 1.12) 0.98 (0.87, 1.10)
Ever used drugs 0.81 (0.48, 1.37) 0.75 (0.43, 1.32) 0.57 (0.30, 1.09) 1.12 (0.75, 1.66)
Current drug use 1.83 (1.03, 3.24)* 2.41 (1.36, 4.28)* 2.09 (1.09, 3.99)* 1.64 (1.08, 2.47)*
Depression 1.64 (1.10, 2.44)* 2.07 (1.41, 3.03)* 1.55 (1.04, 2.30)* 1.72 (1.26, 2.35)*
Anxiety 1.39 (0.94, 2.05) 1.44 (0.97, 2.12) 2.11 (1.41, 3.16)* 1.45 (1.05, 2.00)*
Hazardous alcohol use 1.21 (0.77, 1.91) 0.86 (0.54, 1.35) 1.11 (0.69, 1.80) 0.93 (0.67, 1.28)
Public insurance 0.93 (0.55, 1.60) 0.73 (0.45, 1.17) 0.77 (0.47, 1.25) 1.04 (0.69, 1.56)
Private insurance 0.59 (0.36, 0.96)* 0.63 (0.39, 1.03) 0.38 (0.22, 0.65)* 1.33 (0.90, 1.96)
Transportation Housing Employment assistance Substance abuse treatment
10-year increase in age 0.87 (0.73, 1.04) 0.94 (0.78, 1.13) 1.01 (0.86, 1.19) 1.24 (0.66, 2.34)
Male sex 0.84 (0.50, 1.41) 0.83 (0.47, 1.46) 1.39 (0.78, 2.45) 1.08 (0.17, 6.89)
Black race 1.35 (0.82, 2.23) 2.38 (1.23, 4.59)* 1.73 (1.02, 2.91)* 1.94 (0.32, 11.69)
1-log increase in viral load 1.03 (0.86, 1.22) 1.07 (0.89, 1.29) 0.93 (0.78, 1.10) 0.78 (0.57, 1.05)
Ever used drugs 0.82 (0.44, 1.54) 1.12 (0.58, 2.15) 0.51 (0.27, 0.98)* 0.55 (0.10, 2.90)
Current drug use 2.02 (1.02, 3.99)* 1.74 (0.91, 3.30) 2.33 (1.14, 4.76)* 3.56 (0.53, 23.85)
Depression 1.17 (0.76, 1.81) 1.22 (0.76, 1.98) 1.44 (0.94, 2.20) 1.98 (0.47, 8.32)
Anxiety 0.93 (0.59, 1.46) 1.49 (0.91, 2.43) 1.32 (0.84, 2.08) 1.68 (0.51, 5.53)
Hazardous alcohol use 1.07 (0.67, 1.73) 1.31 (0.78, 2.21) 0.87 (0.51, 1.46) 5.75 (1.35, 24.46)*
Public insurance 0.93 (0.57, 1.54) 0.58 (0.31, 1.07) 0.87 (0.51, 1.46) 0.56 (0.08, 4.09)
Private insurance 0.35 (0.18, 0.66)* 0.42 (0.22, 0.79)* 0.39 (0.21, 0.72)* 0.26 (0.03, 1.98)
Open in a new tab
*

p<0.05

Table 4 shows prevalence ratios for associations between patient demographic and clinical characteristics and reported unmet needs. The patterns of associations observed with reported needs (Table 3) were generally consistent with the patterns of associations for reported unmet needs although there were some differences. Older age was associated with reduced need for financial assistance (PR=0.84, 95%CI 0.70–0.99) and with counseling (PR=0.78, 95%CI 0.64–0.96). Additionally, there was a trend toward reduced need for food assistance, benefits assistance, and transportation among older individuals as well. Again, psychiatric health issues (current drug use, depression, anxiety) were associated with prevalence of unmet needs. Individuals with hazardous alcohol use were much more likely to report an unmet substance abuse treatment need than individuals not reporting hazardous alcohol use (PR=5.75, 95%CI 1.35–24.46). Again, private insurance was inversely associated with unmet needs.

Table 4.

Association with baseline characteristics and reported unmet ancillary service needs

Food assistance Financial assistance Benefits assistance Counseling
10-year increase in age 0.87 (0.73, 1.03) 0.84 (0.70, 0.99)* 0.93 (0.79, 1.10) 0.78 (0.64, 0.96)*
Male sex 1.11 (0.65, 1.88) 0.94 (0.58, 1.52) 1.11 (0.68, 1.81) 0.43 (0.25, 0.74)*
Black race 1.16 (0.75, 1.80) 1.80 (1.15, 2.82)* 1.42 (0.88, 2.29) 1.14 (0.66, 1.97)
1-log increase in viral load 0.98 (0.84, 1.14) 1.07 (0.92, 1.23) 0.94 (0.79, 1.12) 1.19 (1.00, 1.42)*
Ever used drugs 0.81 (0.48, 1.37) 0.75 (0.43, 1.32) 0.57 (0.30, 1.09) 1.04 (0.57, 1.93)
Current drug use 1.83 (1.03, 3.24)* 2.41 (1.36, 4.28)* 2.09 (1.09, 4.00)* 2.27 (1.20, 4.28)*
Depression 1.64 (1.10, 2.44)* 2.07 (1.41, 3.03)* 1.55 (1.04, 2.30)* 1.93 (1.21, 3.07)*
Anxiety 1.39 (0.94, 2.05) 1.44 (0.97, 2.12) 2.11 (1.41, 3.16)* 1.13 (0.69, 1.87)
Hazardous alcohol use 1.21 (0.77, 1.91) 0.86 (0.54, 1.35) 1.11 (0.69, 1.80) 0.67 (0.39, 1.17)
Public insurance 0.93 (0.55, 1.58) 0.73 (0.45, 1.17) 0.77 (0.47, 1.25) 0.63 (0.34, 1.18)
Private insurance 0.59 (0.36, 0.96)* 0.63 (0.39, 1.03) 0.38 (0.22, 0.65)* 0.89 (0.50, 1.61)
Transportation Housing Employment assistance Substance abuse treatment
10-year increase in age 0.87 (0.73, 1.04) 0.94 (0.78, 1.13) 1.01 (0.86, 1.19) 1.24 (0.66, 2.34)
Male sex 0.84 (0.50, 1.41) 0.83 (0.47, 1.46) 1.39 (0.78, 2.45) 1.08 (0.17, 6.89)
Black race 1.35 (0.82, 2.23) 2.38 (1.23, 4.59)* 1.73 (1.02, 2.91)* 1.94 (0.32, 11.69)
1-log increase in viral load 1.03 (0.86, 1.22) 1.07 (0.89, 1.29) 0.93 (0.78, 1.10) 0.78 (0.57, 1.05)
Ever used drugs 0.82 (0.44, 1.54) 1.12 (0.58, 2.15) 0.51 (0.27, 0.98)* 0.55 (0.10, 2.90)
Current drug use 2.02 (1.02, 3.99)* 1.74 (0.91, 3.30) 2.33 (1.14, 4.76)* 3.56 (0.53, 23.85)
Depression 1.17 (0.76, 1.81) 1.22 (0.76, 1.98) 1.44 (0.94, 2.20) 1.98 (0.47, 8.32)
Anxiety 0.93 (0.59, 1.46) 1.49 (0.91, 2.43) 1.32 (0.84, 2.08) 1.68 (0.51, 5.53)
Hazardous alcohol use 1.07 (0.67, 1.73) 1.31 (0.78, 2.21) 0.79 (0.47, 1.35) 5.75 (1.35, 24.46)
Public insurance 0.93 (0.57, 1.54) 0.58 (0.31, 1.07) 0.87 (0.51, 1.46) 0.56 (0.08, 4.09)
Private insurance 0.35 (0.18, 0.66)* 0.42 (0.22, 0.79)* 0.39 (0.21, 0.72)* 0.26 (0.03, 1.98)
Open in a new tab

Prevalence ratios were examined between unmet needs and initial CD4 count. There were no statistically significant associations (p<0.05) between needs (Table 5) or unmet needs (Table 6) and initial CD4 category. Indeed, most estimated prevalence ratios were quite close to the null value of 1. Standardization typically weakened (moved closer to the null) the associations between need and low initial CD4, while it typically strengthened (moved further from the null) the associations between unmet need and low initial CD4. Although not statistically significant, having an unmet ancillary need was typically associated with a lower prevalence of CD4 cell count <500 at initial presentation to care (Table 6).

Table 5.

Crude and standardized prevalence ratios for the association between ancillary needs and first CD4 cell count upon entry to HIV care for the first time among 348 patients enrolled in the iENGAGE study

Crude Standardized
<200 (ref) vs ≥500 200–499 (ref) vs ≥500 <200 (ref) vs ≥500 200–499 (ref) vs ≥500
Food assistance 0.81 (0.59, 1.12) 0.92 (0.74, 1.15) 0.89 (0.62, 1.26) 0.96 (0.74, 1.24)
Financial assistance 0.87 (0.64, 1.20) 0.85 (0.67, 1.08) 0.91 (0.64, 1.25) 0.82 (0.60, 1.08)
Benefits assistance 1.01 (0.73, 1.39) 1.09 (0.87, 1.35) 0.90 (0.46, 1.40) 0.93 (0.58, 1.28)
Counseling 0.74 (0.52, 1.06) 0.92 (0.73, 1.16) 0.87 (0.56, 1.20) 1.03 (0.79, 1.31)
Transportation 1.14 (0.85, 1.54) 0.96 (0.76, 1.21) 0.94 (0.62, 1.33) 0.89 (0.65, 1.16)
Housing 0.90 (0.62, 1.29) 0.97 (0.76, 1.24) 0.81 (0.37, 1.41) 0.77 (0.41, 1.16)
Employment assistance 0.78 (0.52, 1.19) 1.09 (0.87, 1.37) 0.93 (0.30, 1.71) 0.99 (0.53, 1.46)
Substance abuse treatment 0.75 (0.37, 1.55) 1.07 (0.75, 1.51) 0.40 (0.03, 1.65) 0.62 (0.11, 1.54)
Open in a new tab

Table 6.

Crude and standardized prevalence ratios for the association between unmet ancillary needs and first CD4 cell count upon entry to HIV care for the first time among 348 patients enrolled in the iENGAGE study

Crude Standardized
<200 (ref) vs ≥500 200–499 (ref) vs ≥500 <200 (ref) vs ≥500 200–499 (ref) vs ≥500
Food assistance 0.95 (0.66, 1.36) 1.00 (0.78, 1.28) 1.20 (0.74, 1.74) 1.09 (0.81, 1.40)
Financial assistance 0.78 (0.53, 1.14) 0.88 (0.68, 1.14) 0.71 (0.39, 1.10) 0.76 (0.51, 1.05)
Benefits assistance 0.72 (0.47, 1.12) 1.04 (0.82, 1.31) 0.58 (0.14, 1.32) 0.89 (0.39, 1.43)
Counseling 0.89 (0.59, 1.33) 0.91 (0.68, 1.22) 0.59 (0.15, 1.41) 0.62 (0.23, 1.14)
Transportation 0.90 (0.62, 1.29) 0.78 (0.58, 1.06) 0.66 (0.31, 1.11) 0.73 (0.38, 1.06)
Housing 0.85 (0.56, 1.29) 0.87 (0.64, 1.18) 0.66 (0.25, 1.28) 0.64 (0.28, 1.03)
Employment assistance 0.69 (0.43, 1.11) 1.04 (0.81, 1.32) 0.51 (0.18, 1.01) 0.91 (0.41, 1.39)
Substance abuse treatment Inestimable 0.87 (0.46, 1.62) Inestimable 0.56 (0.07, 1.58)
Open in a new tab

Discussion

Prevalence of ancillary service needs (met and unmet) was high among patients new to HIV care and particularly high among disproportionately impacted vulnerable subgroups. Factors associated with met and unmet needs included being black, having depression, anxiety, or substance abuse. Having private insurance was associated with a lower prevalence of resource needs. In this sample of patients new to HIV care, both reported and unmet needs were not statistically significantly associated with CD4 cell count at first presentation to HIV care.

Our finding that the overall prevalence of any ancillary service need was 69% is comparable to findings by Katz et al, who showed that in a national probability sample of PWH, 67% had at least one need and that 27% of the overall sample had an unmet need for at least one service in the previous 6 months (Katz et al., 2000). This report highlights that close to two decades after Katz’s work, substantial ancillary service needs persist, as do unmet needs. Our finding that the presence of needs or unmet needs was not statistically significantly associated with late presentation to HIV care was somewhat unexpected. We did observe an overall trend towards lower CD4 cell count at presentation to HIV care among persons with an ancillary services need, and in particular with unmet ancillary services need. We may have been underpowered to detect a statistically significant association. We may be missing data on individuals who have ancillary service needs that continue to prevent them linking successfully to HIV care. We consider these data important to publish although they represent individuals who successfully linked to care. Our results cannot speak to the impact of need and unmet need among people with newly diagnosed HIV who did not link to care in this study.

Consistent associations of psychiatric health problems on both met and unmet ancillary service needs were seen. SU disorder is very common among PWH (Hartzler et al., 2017), and disparities in engagement at all steps of the continuum persist among individuals with substance use (Giordano et al., 2005; King et al., 2009; Monroe et al., 2016; Rebeiro et al., 2013; Torian & Wiewel, 2011). For individuals with SU, dual diagnosis with a mental health disorder is common, with up to 38% of PLWH having both a mental health and substance use disorder (Tegger et al., 2008). Individuals with SU have increased mortality from HIV and substance use -related deaths (DeLorenze, Weisner, Tsai, Satre, & Quesenberry Jr, 2011; Lloyd-Smith et al., 2006; Samji, Chen, Salters, Montaner, & Hogg, 2014). Mental health disorders also negatively impact ART adherence, virologic suppression, and mortality (J. Ickovics & Meade, 2002; J. R. Ickovics et al., 2006; Pence, Miller, Gaynes, & Eron Jr, 2007). People with mental health or substance use disorders may be disenfranchised from the health care system. People with mental health or SU disorders may avoid engaging in HIV care due to stigma and/or difficulty accessing resources and may not be able to navigate social services and/or hospital structures; care is fragmented therefore needs are not met (Jain, Maulsby, Kinsky, Charles, & Holtgrave, 2016; Mizuno et al., 2015). For clinic patients who cannot engage with typical structures, extra resources may be needed to interact with behavioral health providers, housing agencies, social welfare and benefits offices, and other social service providers outside of clinic (Sarango, de Groot, Hirschi, Umeh, & Rajabiun, 2017)

As demonstrated with our data, Black individuals have more need for housing, food, and employment resources compared with members of other races. This is likely a reflection of structural inequalities: Blacks are more likely both to live in poverty (Bureau.) and to live in areas of concentrated poverty compared with whites (“Architecture of Segregation,” 2015). Residential segregation contributes to increased HIV risk and worse HIV outcomes (Robinson & Moodie-Mills 2012). To continue progress on ending HIV, there must be progress on ending racial inequity (Robinson & Moodie-Mills 2012).

In our sample, individuals with private insurance were less likely to have other unmet needs, likely reflecting higher economic status. Having insurance is associated with better outcomes along the HIV care continuum (Hughes, Mattson, Scheer, Beer, & Skarbinski, 2014; Muthulingam, Chin, Hsu, Scheer, & Schwarcz, 2013; Yehia, Fleishman, Metlay, Moore, & Gebo, 2012). Although the number of people with HIV who have insurance has increased since the Affordable Care Act (ACA) (Kaiser Family Foundation 2016), insurance alone does not address social determinants of health. The services provided in the Ryan White HIV/AIDS Program can help fill in the needs for additional services to improve their health (Weiser et al., 2015). This study began enrollment in 2014 after the implementation of the Affordable Care Act, and participants were not directly queried about the role of the Affordable Care Act in their own personal insurance coverage.

The limitations of our study include the potential for response bias due to sensitive and potentially stigmatizing circumstances queried. Additionally, the date of HIV diagnosis was not captured; CD4 count at entry to care may be an imperfect proxy of time elapsed between diagnosis and entry to care. Low CD4 at entry to care could be the result of delayed diagnosis or delayed entry to care following diagnosis Additionally, the study population excludes patients not linked to care and patients who linked but did not enroll in study. The strengths of our study include that we have a geographically diverse sample population and a unique sample comprised of individuals who have never received HIV care before. Previous studies that have tried to make inference about initial presentation to HIV care have generally had to rely on non-specific criteria to identify patients, such as ‘no prior ART’ or ‘no prior AIDS-defining conditions’ and we did not have that limitation.

In conclusion, patients presenting to HIV care for the first time should prompt providers to screen for both met and unmet needs pertaining to substance abuse treatment, financial needs, housing, food and transportation access. Longitudinal data from this trial will enable us to investigate whether having multiple needs enhances intervention delivery and outcomes because of the patient’s drive to work with the care team to meet his or her needs or whether having multiple needs hinders intervention delivery and outcomes because of the patient’s underlying social instability. Our findings highlight the importance of the Ryan White HIV/AIDS Program to provide clinics and community-based organizations resources to address the needs of PWH.

Acknowledgments

Funding

R01 AI 103661 05 (Mugavero)

K23 MH 105284 (Monroe)

Conflicts of Interest

KRA had a Gilead Sciences educational grant from 2016–2017 through the University of Michigan

Contributor Information

Anne K. Monroe, Johns Hopkins University School of Medicine, 1830 E. Monument Street, 8th Floor, Baltimore, MD 21287.

Catherine R. Lesko, 615 N. Wolfe Street, Room E7634, Baltimore, Maryland 21205.

Geetanjali Chander, Johns Hopkins University School of Medicine, 1830 E. Monument Street, 8th Floor, Baltimore, MD 21287.

Bryan Lau, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Room E7150, Baltimore, Maryland 21205.

Jeanne Keruly, Adult Ryan White Services, Johns Hopkins University, 1830 E. Monument Street, Room 8014, Baltimore, MD 21287.

Heidi M. Crane, University of Washington, Harborview Medical Center, 325 9th Ave, Box 359931, Seattle, WA 998104.

K. Rivet Amico, Department of Health Behavior and Health Education, School of Public Health, University of Michigan, 3830 School of Public Health, 1415 Washington Heights, Ann Arbor, Michigan, 48109-2029.

Sonia Napravnik, School of Medicine, CB# 7215, 130 Mason Farm Rd, 2101 Bioinformatics Building, Chapel Hill, NC 27599-7215.

E. Byrd Quinlivan, Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, 2nd Fl, CB #7020, Chapel Hill, NC 27599-7030.

Michael J. Mugavero, Division of Infectious Diseases, 908 20th Street South, Birmingham, AL 35205, University of Alabama at Birmingham (UAB).

References

  1. Aidala A, Lee G, Abramson DM, Messeri P, & Siegler A (2007). Housing need, housing assistance, and connection to HIV medical care. AIDS and Behavior, 11(6 Suppl), 101–115. doi: 10.1007/s10461-007-9276-x [DOI] [PubMed] [Google Scholar]
  2. Althoff KN, Gange SJ, Klein MB, Brooks JT, Hogg RS, Bosch RJ, . . . Moore RD (2010). Late presentation for human immunodeficiency virus care in the United States and Canada. Clin Infect Dis, 50(11), 1512–1520. doi: 10.1086/652650 [DOI] [PMC free article] [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. Andersen M, Hockman E, Smereck G, Tinsley J, Milfort D, Wilcox R, . . . Thomas R (2007). Retaining women in HIV medical care. Journal of the Association of Nurses in AIDS Care, 18(3), 33–41. doi: 10.1016/j.jana.2007.03.007 [DOI] [PubMed] [Google Scholar]
  5. Antinori A, Coenen T, Costagiola D, Dedes N, Ellefson M, Gatell J, . . . De Wolf F (2011). Late presentation of HIV infection: a consensus definition. HIV Med, 12(1), 61–64. doi: 10.1111/j.1468-1293.2010.00857.x [DOI] [PubMed] [Google Scholar]
  6. Ashman JJ, Conviser R, & Pounds MB (2002). Associations between HIV-positive individuals’ receipt of ancillary services and medical care receipt and retention. AIDS Care, 14(Suppl 1), S109–118. doi: 10.1080/09540120220149993a [DOI] [PubMed] [Google Scholar]
  7. Bishaw A US Census Bureau. Areas with concentrated poverty: 1999. Census 2000 Special Reports 2005. [Google Scholar]
  8. Chan D, Absher D, & Sabatier S (2002). Recipients in need of ancillary services and their receipt of HIV medical care in California. AIDS Care, 14(Suppl 1), S73–83. doi: 10.1080/0954012021000009665 [DOI] [PubMed] [Google Scholar]
  9. Cohen MS, Chen YQ, McCauley M, & Gamble T (2011). Prevention of HIV-1 infection with early antiretroviral therapy. New England Journal of Medicine, 365(6), 493–505 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Conviser R, & Pounds MB (2002). Background for the studies on ancillary services and primary care use. AIDS care, 14 Suppl 1(March), S7–14. doi: 10.1080/09540120220149993 [DOI] [PubMed] [Google Scholar]
  11. Cornelius T, Jones M, Merly C, Welles B, Kalichman MO, & Kalichman SC (2017). Impact of food, housing, and transportation insecurity on ART adherence: a hierarchical resources approach. AIDS Care, 29(4), 449–457. doi: 10.1080/09540121.2016.1258451 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Danel C MR, Gabillard D, et al. ;. TEMPRANO ANRS 12136 Study Group. A trial of early antiretroviral and isoniazid preventive therapy in Africa. . N Engl J Med. , 373(9), 808–822 [DOI] [PubMed] [Google Scholar]
  13. DeLorenze GN, Weisner C, Tsai AL, Satre DD, & Quesenberry CP Jr (2011). Excess Mortality Among HIV‐Infected Patients Diagnosed With Substance Use Dependence or Abuse Receiving Care in a Fully Integrated Medical Care Program. Alcoholism: Clinical and Experimental Research, 35(2), 203–210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kaiser Family Foundation. Medicaid and HIV. October 14, 2016. https://www.kff.org/hivaids/fact-sheet/medicaid-and-hiv Accessed February 26, 2018
  15. Giordano TP, Visnegarwala F, White AC Jr., Troisi CL, Frankowski RF, Hartman CM, & Grimes RM (2005). Patients referred to an urban HIV clinic frequently fail to establish care: factors predicting failure. AIDS Care, 17(6), 773–783. doi: 10.1080/09540120412331336652 [DOI] [PubMed] [Google Scholar]
  16. Group, T. I. S. S. (2015). Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med, 373, 795–807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hall HI SR, Szwarcwald CL,Green T. (2015). Time From Infection With the Human Immunodeficiency Virus to Diagnosis, United States [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hall HI, Tang T, & Espinoza L (2016). Late Diagnosis of HIV Infection in Metropolitan Areas of the United States and Puerto Rico. AIDS Behav, 20(5), 967–972. doi: 10.1007/s10461-015-1241-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hartzler B, Dombrowski JC, Crane HM, Eron JJ, Geng EH, Christopher Mathews W, . . . Donovan DM (2017). Prevalence and Predictors of Substance Use Disorders Among HIV Care Enrollees in the United States. AIDS Behav, 21(4), 1138–1148. doi: 10.1007/s10461-016-1584-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hughes AJ, Mattson CL, Scheer S, Beer L, & Skarbinski J (2014). Discontinuation of antiretroviral therapy among adults receiving HIV care in the United States. J Acquir Immune Defic Syndr, 66(1), 80–89. doi: 10.1097/qai.0000000000000084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ickovics J, & Meade C (2002). Adherence to HAART among patients with HIV: breakthroughs and barriers. AIDS care, 14(3), 309–318 [DOI] [PubMed] [Google Scholar]
  22. Ickovics JR, Milan S, Boland R, Schoenbaum E, Schuman P, Vlahov D, & Group, H. E. R. S. (2006). Psychological resources protect health: 5-year survival and immune function among HIV-infected women from four US cities. Aids, 20(14), 1851–1860 [DOI] [PubMed] [Google Scholar]
  23. Jain KM, Maulsby C, Kinsky S, Charles V, & Holtgrave DR (2016). 2015–2020 national HIV/AIDS strategy goals for HIV linkage and retention in care: Recommendations from program implementers. American Journal of Public Health, 106(3), 399–401. doi: 10.2105/AJPH.2015.302995 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jargowsky P Architecture of Segregation. The Century Foundation; August 9, 2015. http://apps.tcf.org/architecture-of-segregation Accessed February 26, 2018 [Google Scholar]
  25. Katz MH, Cunningham WE, Mor V, Andersen RM, Kellogg T, Zierler S, . . . Shapiro MF (2000). Prevalence and predictors of unmet need for supportive services among HIV-infected persons: impact of case management. Med Care, 38(1), 58–69 [DOI] [PubMed] [Google Scholar]
  26. King WD, Larkins S, Hucks-Ortiz C, Wang P-C, Gorbach PM, Veniegas R, & Shoptaw S (2009). Factors associated with HIV viral load in a respondent driven sample in Los Angeles. AIDS and Behavior, 13(1), 145–153 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Leeper C, Mugavero M, Willig J, Chamot E, Kerbawy S, & Zinski A (2014). Social determinants of late presentation to HIV care. Journal of Health Disparities Research and Practice, 7(1), 425419491 [Google Scholar]
  28. Lesko CR, Cole SR, Zinski A, Poole C, & Mugavero MJ (2013). A systematic review and meta-regression of temporal trends in adult CD4(+) cell count at presentation to HIV care, 1992–2011. Clin Infect Dis, 57(7), 1027–1037. doi: 10.1093/cid/cit421 [DOI] [PubMed] [Google Scholar]
  29. Lloyd-Smith E, Brodkin E, Wood E, Kerr T, Tyndall MW, Montaner JS, & Hogg RS (2006). Impact of HAART and injection drug use on life expectancy of two HIV-positive cohorts in British Columbia. Aids, 20(3), 445–450 [DOI] [PubMed] [Google Scholar]
  30. Lo W, MacGovern T, & Bradford J (2002). Association of ancillary services with primary care utilization and retention for patients with HIV/AIDS. AIDS care, 14(Suppl 1), S45–S57. doi: 10.1080/0954012022014992049984 [DOI] [PubMed] [Google Scholar]
  31. Sarango M, de Groot A, Hirschi M, Umeh C, Rajabiun S. The Role of Patient Navigators in Building a Medical Home for Multiply Diagnosed HIV-Positive Homeless Populations. J Public Health Manag Pract. , 23(3), 276–282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Magnus M, Schmidt N, Kirkhart K, Schieffelin C, Fuchs N, Brown B, & Kissinger PJ (2001). Association between ancillary services and clinical and behavioral outcomes among HIV-infected women. AIDS patient care and STDs, 15(3), 137–145. doi: 10.1089/108729101750123607 [DOI] [PubMed] [Google Scholar]
  33. Mayben JK, Kramer JR, Kallen MA, Franzini L, Lairson DR, & Giordano TP (2007). Predictors of delayed HIV diagnosis in a recently diagnosed cohort. AIDS patient care and STDs, 21(3), 195–204 [DOI] [PubMed] [Google Scholar]
  34. Mizuno Y, Purcell DW, Knowlton AR, Wilkinson JD, Gourevitch MN, & Knight KR (2015). Syndemic vulnerability, sexual and injection risk behaviors, and HIV continuum of care outcomes in HIV-positive injection drug users. AIDS Behav, 19(4), 684–693. doi: 10.1007/s10461-014-0890-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Monroe AK, Lau B, Mugavero MJ, Mathews WC, Mayer KC, Napravnik S, . . . Chandler G (2016). Heavy alcohol use is associated with worse retention in HIV care. Journal of Acquired Immune Deficiency Syndromes, 1–5. doi: 10.1097/QAI.0000000000001083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Muthulingam D, Chin J, Hsu L, Scheer S, & Schwarcz S (2013). Disparities in engagement in care and viral suppression among persons with HIV. J Acquir Immune Defic Syndr, 63(1), 112–119. doi: 10.1097/QAI.0b013e3182894555 [DOI] [PubMed] [Google Scholar]
  37. Pence BW, Miller WC, Gaynes BN, & Eron JJ Jr (2007). Psychiatric illness and virologic response in patients initiating highly active antiretroviral therapy. JAIDS Journal of Acquired Immune Deficiency Syndromes, 44(2), 159–166 [DOI] [PubMed] [Google Scholar]
  38. Rebeiro P, Althoff KN, Buchacz K, Gill J, Horberg M, Krentz H, . . . Gange SJ (2013). Retention among North American HIV-infected persons in clinical care, 2000–2008. J Acquir Immune Defic Syndr, 62(3), 356–362. doi: 10.1097/QAI.0b013e31827f578a [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Robertson M, Wei SC, Beer L, Adedinsewo D, Stockwell S, Dombrowski JC, . . . Skarbinski J (2016). Delayed entry into HIV medical care in a nationally representative sample of HIV-infected adults receiving medical care in the USA. AIDS Care - Psychological and Socio-Medical Aspects of AIDS/HIV, 28(3), 325–333. doi: 10.1080/09540121.2015.1096891 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Robinson R, Moodie-Mills AC, HIV/AIDS Inequality: Structural Barriers to Prevention, Treatment, and Care in Communities of Color. Center for American Progress; July 27, 2012. https://www.americanprogress.org/issues/lgbt/reports/2012/07/27/11834/hivaids-inequality-structural-barriers-to-prevention-treatment-and-care-in-communities-of-color/ Accessed Feb 26, 2018 [Google Scholar]
  41. Samji H, Chen Y, Salters K, Montaner JSG, & Hogg RS (2014). Correlates of unstructured antiretroviral treatment interruption in a cohort of HIV-positive individuals in British Columbia. AIDS and Behavior, 18(11), 2240–2248. doi: 10.1007/s10461-014-0776-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Saunders JB, Aasland OG, Babor TF, de la Fuente JR, & Grant M (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption--II. Addiction, 88(6), 791–804 [DOI] [PubMed] [Google Scholar]
  43. Sherbourne CD, & Stewart AL (1991). The MOS social support survey. Soc Sci Med, 32(6), 705–714 [DOI] [PubMed] [Google Scholar]
  44. Skar H, Albert J, & Leitner T (2013). Towards estimation of HIV-1 date of infection: a time-continuous IgG-model shows that seroconversion does not occur at the midpoint between negative and positive tests. PLoS One, 8(4), e60906. doi: 10.1371/journal.pone.0060906 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Spitzer RL, Williams JB, Kroenke K, Linzer M, deGruy FV 3rd, Hahn SR, . . . Johnson JG (1994). Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. Jama, 272(22), 1749–1756 [PubMed] [Google Scholar]
  46. Tegger MK, Crane HM, Tapia KA, Uldall KK, Holte SE, & Kitahata MM (2008). The effect of mental illness, substance use, and treatment for depression on the initiation of highly active antiretroviral therapy among HIV-infected individuals. AIDS patient care and STDs, 22(3), 233–243 [DOI] [PubMed] [Google Scholar]
  47. Terzian AS, Irvine MK, Hollod LM, Lim S, Rojas J, & Shepard CW (2015). Effect of HIV housing services on engagement in care and treatment, New York City, 2011. AIDS and Behavior, 19(11), 2087–2096. doi: 10.1007/s10461-015-1003-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Torian LV, & Wiewel EW (2011). Continuity of HIV-related medical care, New York City, 2005–2009: Do patients who initiate care stay in care? AIDS Patient Care and STDs, 25(2), 79–88. doi: 10.1089/apc.2010.0151 [DOI] [PubMed] [Google Scholar]
  49. Ulett KB, Willig JH, Lin HY, Routman JS, Abroms S, Allison J, . . . Mugavero MJ (2009). The therapeutic implications of timely linkage and early retention in HIV care. AIDS Patient Care STDS, 23(1), 41–49. doi: 10.1089/apc.2008.0132 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. UNAIDS. 90–90-90 An Ambitious Target to Help end the HIV epidemic http://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf Accessed February 26, 2018.
  51. Weiser J, Beer L, Frazier EL, Patel R, Dempsey A, Hauck H, & Skarbinski J (2015). Service Delivery and Patient Outcomes in Ryan White HIV/AIDS Program-Funded and -Nonfunded Health Care Facilities in the United States. JAMA Intern Med, 175(10), 1650–1659. doi: 10.1001/jamainternmed.2015.4095 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Yehia B, Fleishman J, Metlay J, Moore R, & Gebo K (2012). Sustained Viral Suppression in HIV-Infected Patients Receiving Antiretroviral Therapy. JAMA: the journal of the American Medical Association, 308(4), 339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wolitski R, Improvement Needed STAT: Linkage to HIV Care. September 27, 2017. https://www.hiv.gov/blog/improvement-needed-stat-linkage-hiv-care. Accessed February 26, 2018

RESOURCES