Getting HIV-positive people into care--and keeping them there.
Subject: HIV patients
HIV (Viruses)
Author: Mascolini, Mark
Pub Date: 03/22/2011
Publication: Name: Research Initiative/Treatment Action! Publisher: The Center for AIDS: Hope & Remembrance Project Audience: General; Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2011 The Center for AIDS: Hope & Remembrance Project ISSN: 1520-8745
Issue: Date: Spring, 2011 Source Volume: 16 Source Issue: 1
Topic: Event Code: 200 Management dynamics Computer Subject: Company business management
Organization: Government Agency: United States. Centers for Disease Control and Prevention
Accession Number: 263250027
Full Text: If HIV-positive people do get diagnosed, a dismaying fraction gets no care for months or even years. Among 48,413 US residents diagnosed with HIV in 2005 and 2006 in 37 states with name-based reporting, CDC statisticians estimated that only 55% had a CD4 count within 4 to 6 months of diagnosis, and only 64% had their CD4s tallied 19 to 24 months after testing positive (Figure 1). (1) Among 38,070 people diagnosed in those states in 2005 and 2006, 31% had neither a CD4 count nor a viral load test within 12 months of diagnosis.

Proportions of people late to care were evenly distributed among people infected during gay sex (30.9%), while injecting drugs (27.7%), either during gay sex or while injecting drugs (28.1%), and during heterosexual sex (32.8%). (1) In the same analysis, a bigger portion of African Americans got to care late (35.0%) than did Hispanics (29.2%) or whites (26.4%).

Linkage estimates both slippery and baleful

Although recent reports offer some data indicating sluggish entry into care after HIV diagnosis, researchers who study this issue say such data are, at best, approximations. Michael Mugavero and colleagues from the University of Alabama at Birmingham point to a simple reason for the sketchy quality of these findings: "the activities of testing and linkage [to care] are often uncoupled." (2) That uncoupling also helps explain why linkage remains so poor in the United States and elsewhere.

Research on entry to HIV care suggests the ponderous scope of the problem, while outlining a web of patient- and practice-related factors that contributes to dismal linkage numbers. A study that melded national prescription data with CDC HIV prevalence estimates reckoned that 314,000 of 1,135,000 people diagnosed with HIV in 2008 (28%) were not getting care for their infection. (3) The number of infected people not in care far outstripped the estimated 227,000 US residents with undiagnosed HIV infection.

Massing statistics from 28 entry-to-care studies that collected data from May 2005 through 2009, CDC investigators figured 72% of HIV-diagnosed people in the United States began care within 4 months of diagnosis, (4) a much higher proportion than the CDC's own calculation of the proportion of people with a CD4 count measured 4 to 6 months after diagnosis--or even 2 years after diagnosis (Figure 1). (1) The meta-analysis determined that a higher proportion of people who tested positive in an emergency department than in a community center entered care (76% versus 67%).

Longitudinal study of 1266 people diagnosed with HIV between July 1, 2005 and June 30, 2006 and followed through June 15, 2007 in Philadelphia charted a median 8-month time to entering care, with a range from 1 to 26 months. (5) Interviews of 1038 HIV-positive people in public hospitals in Miami or Atlanta found that 1 in 5 had not started care for their infection although they knew they had HIV for more than 5 years. (6) Two in 5 of these people had not seen an HIV clinician for more than 6 months. A study of 1928 people diagnosed with HIV in 2003 in New York City found that 369 (19%) did not have a viral load test or CD4 count within 3 months of diagnosis and 331 (17%) never had a recorded viral load or CD4 count through the end of 2006. (7)

Entry to care is a particular concern for HIV-positive prisoners who may or may not be taking antiretrovirals when released. A retrospective study of 1750 HIV-positive inmates released from Texas prisons from January 2004 through December 2007 found that only 28% had enrolled in an HIV clinic within 90 days. (8)

Even after HIV-positive people begin care, they report unmet medical and dental needs, according to an analysis of the HIV Cost and Services Utilization Study (HCSUS), the first nationally representative study of people in care for HIV. (9) Extrapolating from a 2864-person sample of people in care in January 1996, researchers figured that 58,000 of 230,900 HIV-positive people (25%) had unmet medical or dental needs, including 11,600 (5%) who had both unmet medical and dental needs. Low income, lack of insurance, and Medicaid insurance without dental benefits made unmet needs more likely.

Reasons for delayed entry bountiful and linked

Even a cursory review of published studies on starting care for HIV can mine a trove of reasons explaining why diagnosed people don't get to a doctor's office. The literature search for this article readily revealed dozens of predictors, often singled out in multivariate analyses. Three points emerge from this kind of inquest:

* Reasons for delayed entry to care can be divided into two groups--those inherent in a turbid US healthcare system and those reflecting the demographics and behaviors of HIV-diagnosed people.

* Demographic and behavioral reasons are interrelated, ramified, and nearly impossible to tease apart.

* Studies in diverse populations identify race or ethnicity, poverty, lack of insurance, mental illness, and substance abuse as common patient-related reasons for delayed care.

The article that begins this issue of RITA! examines one overriding reason HIV-positive people don't start care: they don't know they're infected. But ever-expanding testing will not solve the problem of delayed entry to care. Indeed, some research suggests HIV screening dollars would be better spent making sure people who test positive actually get their results and then making sure they get an HIV clinic appointment and show up for their first exam. (10) This simulation of HIV testing services determined that "interventions that improve the probability of success in later stages in the testing pathway are more cost-effective than investments devoted to earlier stages." (10)

Once people get a positive test result, all but the most motivated may quickly run afoul of a healthcare system aptly labeled "fragmented" and "fractured" by physicians who study why people diagnosed with a readily arrested infection do not get themselves quickly to an HIV clinic (Table 1). (2) As Michael Mugavero stresses in the interview starting on page 64 of this issue, pinpointing and correcting system-wide problems improve care for whole clusters of patients, while pinpointing and correcting individual patient problems help only that individual.

Mugavero and colleagues note that the CDC and local and state health departments run the country's HIV testing and prevention engines. (2) But treatment and support services rely on the Health Resources and Services Administration, the Center for Medicare and Medicaid Services, and private insurers. "Although varying levels of integration exist within and between service delivery organizations and funding agencies that provide testing and/or prevention and medical and/or supportive services," these experts write, "the vast majority of activities are uncoordinated." (2)

Even when newly diagnosed people do not suffer from denial or distrust of the whole healthcare system, a range of brass-tack realities may stand between them and a doctor: low income, little or no insurance, lack of transportation, child-care obstacles, depression, drug or alcohol abuse, physical or sexual abuse, getting time off from work, fear of stigma or discrimination, a cascade of competing priorities, and just being too sick. (A CD4 count under 50 cells/[mm.sup.3] made spotty clinic attendance about 50% more likely in one study of 1286 people with HIV. (11)) Complicating matters further, barriers to care often differ from one HIV population to another, observes Richard Moore of Johns Hopkins University. (12)

Holes in the health system that delay HIV care

Perhaps the biggest system-related obstacles to smooth entry to care are the walls--literal and figurative--between testing centers and treatment centers. A New York City Department of Health study found that 369 of 1928 people (19%) diagnosed with HIV in 2003 had not started care within 3 months and 331 (17%) never started care during the study. (13) Compared with people diagnosed at a center that offered both testing and treatment, those diagnosed at stand-alone testing facilities had a 50% to 90% higher risk of delayed care, depending on the type of facility.

At solitary HIV testing units, active case management of people who test positive gets more people into care than passive referral. Analysis of 270 HIV-positive people in the Antiretroviral Treatment Access Study (ARTAS) found that 31% had not seen an HIV clinician within 6 months of their diagnosis. (14) People who received short-term case management were almost 4 times more likely to enter care within 6 months than people merely referred to HIV care.

A 4-city ARTAS analysis randomized 316 people diagnosed from 2001 to 2003 to passive referral or to "intensive, short-term assistance" to help get them into care. (15) Almost two thirds of those receiving short-term help (64%) visited an HIV primary care clinician at least once in two consecutive 6-month periods, compared with 49% of those receiving passive referral (relative risk 1.41, 95% confidence interval [CI] 1.1 to 1.6, P = 0.006). The intervention significantly improved linkage to care in 12 of 26 subgroups assessed:

1. Males

2. Hispanics

3. People with less education

4. People within 6 months of their HIV diagnosis

5. Unstably housed people

6. People who reported no usual source of care or who usually used a hospital emergency room

7. People without depressive symptoms

8. People who did not use crack cocaine or inject drugs in the past 30 days

9. People who had not received help from others (such as family or friends in getting HIV care

10. People in the preparation or action stages of readiness to enter HIV care (versus precontemplation or contemplation stages)

11. People with positive beliefs and attitudes about HIV treatment and care

12. People who did not say feeling well was a reason they did not seek HIV care

The intervention (versus passive referral) appeared to improve linkage (but not significantly) in 10 other groups, while four groups seemed not to benefit at all:

1. People with higher education

2. People with symptoms of depression

3. People who used crack cocaine or injected drugs recently

4. People who had help from others in getting care

A 2006-2009 study of 355 crack users admitted to a hospital in Miami or Atlanta found that not being helped into HIV care at diagnosis almost tripled the risk of never starting HIV care (adjusted odds ratio [AOR] 2.83, 95% CI 1.56 to 5.15). (16) This study also found that lack of treatment for drug abuse quadrupled the risk of failure to start HIV care (AOR 4.13, 95% CI 2.24 to 7.62).

Even when testing centers or clinics have counselors to answer questions and help the newly diagnosed get into care, some people with a new HIV diagnosis say they never met a case manager. An ARTAS study of 316 HIV-positive people not getting medical care found that 55% claimed the counselor did not send them to a case manager, 27% complained that the case manager did not spend enough time with them, and 22% said the case manager did not answer all their questions. (17) Almost one quarter of these people were told--or thought they were told--that they did not need care for their HIV infection.

Longer time between the call to set up an appointment and the appointment itself raises the risk that a new HIV patient won't show up. Focusing on 522 people with a first HIV primary care appointment scheduled between August 2004 and August 2006, the University of Alabama team counted 162 potential patients (31%) who did not come to the clinic within 180 days of their clinic date. (18) A multivariate model to isolate predictors of failure to show up included age, gender, race, insurance status, local versus nonlocal residence, self-referral versus provider referral, and number of days from call to schedule an appointment to the new appointment date. Every 10 days between the call and the appointment date raised the no-show risk 32% (OR 1.32, 95% CI 1.14 to 1.53). (This finding inspired the Alabama group to fashion a program to get newly diagnosed people across the clinic threshold, as detailed below and in the interview with Michael Mugavero in this issue.)

Psychosocial, economic, and behavioral factors affecting entry to care

Broadly speaking, two groups contribute to sustained HIV incidence in the United States--gay and bisexual men and socially marginalized people who often belong to racial or ethnic minorities. Although these groups overlap, many gay and bisexual men have ready access to health care, harbor little suspicion of the healthcare system, and understand what behaviors put them at risk of HIV infection. Most socially marginalized minorities, in contrast, have poor access to healthcare, may mistrust the healthcare system, and may not know or may ignore factors that raise their risk of HIV. If socially marginalized people do become infected, the same variables that put them at risk of HIV can keep them from seeking care. Figure 2 outlines personal factors that affected entry to care in recent published reports.

* Race/ethnicity. A comparison of two US HIV populations published in 2006 delineates the differences between the HIV-care haves and the have-nots. (11) The study focused on two groups: (1) 1286 people from 16 sites across the country who were interviewed in 2001-2002 in a study of underserved HIV-positive people targeted for supportive outreach services, and (2) 2267 people in the HIV Cost and Services Utilization Study (HCSUS), a probability sample of people already getting HIV care.


Compared with the HCSUS group, the outreach sample had a higher proportion of blacks (59% versus 32%, P = 0.0001), Spanish speakers (9% versus 2%, P = 0.02), people with an annual income under $10,000 (75% versus 45%, P = 0.0001), heroin or cocaine users (58% versus 47%, P = 0.05), and people who were unemployed, homeless, or had no insurance. Higher proportions of people in the outreach group had fewer than 2 ambulatory visits for HIV care (26% versus 16%, P = 0.0001) and had not started antiretroviral therapy (82% versus 58%, P = 0.0001). In the outreach group, heavy alcohol use ballooned the risk of low ambulatory clinic attendance almost 75% (AOR 1.74, 95% CI 1.23 to 2.45), and a CD4 count under 50 cells/[mm.sup.3] raised that risk more than 50% (AOR 1.53, 95% CI 1.00 to 2.36).

The New York City study of 1928 people diagnosed with HIV in 2003 determined that nonwhite race or ethnicity upped the risk of delayed entry to care 80% (hazard ratio [HR] 1.8, 95% CI 1.5 to 2.0), birth outside the United States made delayed care 10% more likely (HR 1.1, 95% CI 1.0 to 1.2), and injection drug use raised the risk 30% (HR 1.3, 95% CI 1.1 to 1.5). (7)

Findings on whether Hispanic ethnicity, in itself, poses a hurdle to HIV care vary from study to study. CDC researchers interviewed 3942 HIV-positive people in 18 states from 2000 through 2004. (19) About one quarter of this group (28%) did not start care for more than 3 months after they tested positive. Multivariate analysis accounting for numerous variables determined that Hispanics had a 33% higher risk of delayed entry (OR 1.33, 95% CI 1.05 to 1.69). The equally large, just-noted outreach-versus-HCSUS study (11) found that Hispanics ran more than a doubled risk of poor clinic attendance in the HCSUS sample (AOR 2.34, 95% CI 1.56 to 3.52), but being Hispanic did not correlate with inconsistent HIV care in the group of underserved people targeted for supportive outreach services (AOR 0.81, 95% CI 0.39 to 1.69). The Philadelphia study of 1266 people handed a positive HIV result found that Hispanic ethnicity predicted earlier HIV care (HR 1.39, 95% CI 1.05 to 1.84). (5) In the 270-person ARTAS study, multivariate analysis linked Hispanic ethnicity (versus non-Hispanic black race) with a significantly higher likelihood of getting into care. (14)

The 2001-2003 randomized ARTAS study of 316 recently diagnosed people may help explain these between-study disparities in the impact of Hispanic ethnicity on entry to care. (15) ARTAS investigators randomized these people to passive referral or a "strengths-based linkage intervention" to ease entry into HIV primary care. The positive effect of the intervention was stronger in Hispanics than in other racial and ethnic groups combined (relative risk 1.53 versus 1.15), although that difference fell short of statistical significance (P = 0.157). This was a relatively small, 316-person study, and about 30% of participants were Hispanic; still, that result strongly hints that the Hispanics in this cohort (in Atlanta, Baltimore, Los Angeles, and Miami) responded better to linkage counseling than other groups did.

* Poverty and insurance. No one needs a psychosociologic treatise to understand why poverty can keep recently diagnosed people out of the doctor's office, especially in a country with a porous reimbursement scheme for poor people. But, as with ethnicity, the impact of poverty on getting into HIV care sometimes sorts oddly with intuition. The 18-state CDC study tied unemployment to a 23% higher risk of delayed entry to care (OR 1.23, 95% CI 1.04 to 1.45) in 3942 HIV-positive adults interviewed from 2000 through 2004. (19) Unemployment nearly doubled the risk of poor linkage to care in a 180-person Houston study described below, (20) but that association did not reach statistical significance (AOR 1.78, 95% CI 0.92 to 3.43, P = 0.09). In a study of 365 hospitalized HIV-positive crack users, annual income under $5000 hiked the odds of never being in care more than 8 times (AOR 8.17, 95% CI 3.35 to 19.94). (16)

The 3553-person outreach-versus-HCSUS study found that significantly more people in the underserved group targeted for HIV outreach than in the broadly based HCSUS sample had a yearly income under $10,000 (75% versus 45%, P = 0.0001). (11) In the relatively wealthier HCSUS contingent, low income raised the risk of poor HIV clinic attendance 35% (AOR 1.35, 95% CI 1.04 to 1.75). But in the relatively poorer outreach cohort, low income lowered odds of bad clinic attendance more than 25% (AOR 0.73, 95% CI 0.56 to 0.96). And the Philadelphia study of 1266 people with HIV determined that living in a census tract with a high poverty rate improved chances of earlier entry to care (HR 1.68, 95% CI 1.22 to 2.30). (5)

Divergent findings on how poverty affects entry to care may be partly explained by whether low-income HIV-positive people benefit from good case management, programs that usher them into care, and decent health insurance. The CDC researchers who interviewed 3942 HIV-positive people in 18 states figured that those without insurance had a 20% higher risk of delayed entry to care (OR 1.20, 95% CI 1.03 to 1.41). (19) And the 270-person ARTAS study linked public health insurance (versus no insurance) to a higher likelihood of seeing an HIV clinician. (14)

* Depression. Getting diagnosed with HIV probably triggers depression, at least short-term depression, more often than not. The Steps Study, a prospective observational cohort study of people in Houston with newly diagnosed HIV, found that two thirds of 180 participants screened positive for depression on the 20-item CES-D scale. (20) Women made up one third of the study group, 51% were black, 39% Hispanic, and 10% non-Hispanic white. About half of these people had not finished high school, half had no job, and two thirds earned less than $15,000 a year.

Multivariate analysis linked depression to female gender (AOR 5.71, 95% CI 1.76 to 18.5, P = 0.004), any substance abuse in the last 6 months (AOR 3.93, 95% CI 1.49 to 10.3, P = 0.009), low self-reported access to medical care on a 6-point scale (AOR 4.69, 95% CI 1.48 to 14.9, P = 0.009), and low self-efficacy (belief in one's ability to do things for oneself) (AOR 3.05, 95% CI 1.22 to 7.63, P = 0.03). (20) Income over $25,000 and a CD4 count of 200 to 350 cells/ [mm.sup.3] (versus under 200) independently lowered the odds of depression.

The Steps Study team defined successful linkage to HIV care as keeping an appointment in each of the first two 90-day periods after HIV diagnosis. Whereas 68% of people without depression entered care, 56% with depression got into care, a difference that fell short of statistical significance (P = 0.11). Multivariate analysis determined that depression doubled the risk of not starting HIV care, and that association nearly reached statistical significance (OR 2.00, 95% CI 0.96 to 4.14, P = 0.06).

These researchers proposed that "screening for depression should be undertaken at diagnosis of HIV seropositivity itself to identify persons at risk for poor follow-up and target them for unique interventions designed to bolster engagement in care." (20) But depressed people may be less likely to benefit from targeted interventions, results of the ARTAS trial suggest. (15) This study of 316 HIV-positive people randomized to passive referral to care or to a linkage intervention found that the linkage program worked better in people without depressive symptoms (relative risk 1.55 for people without depression versus 1.01 for people with depression, P = 0.052). (15) In fact, people with depression were one of only four groups that did not benefit from a linkage program in this 26-group analysis (see "Holes in the health system" above).

* Substance abuse. Interviewing 1038 HIV-positive people in two public hospitals in Miami and Atlanta, researchers found that 20% had never received HIV care, even though they knew they had HIV for more than 5 years. (6) Four in 10 of these people had not had HIV care in more than 6 months. Multivariate analysis determined that using crack cocaine and heavy drinking raised the risk of (1) never having an HIV clinician, (2) high-risk sexual behavior, and (3) not receiving antiretroviral therapy. These investigators proposed that "inpatient interventions that link and retain HIV-positive persons in primary care services could prevent HIV transmission and unnecessary hospitalizations."

The study that compared 1286 underserved HIV-positive people receiving an outreach intervention and 2267 HCSUS cohort members receiving HIV care found that heavy alcohol drinking independently raised the risk of inconsistent HIV clinic attendance in the outreach group (HR 1.74, 95% CI 1.23 to 2.45) but not in HCSUS (HR 1.00, 95% CI 0.73 to 1.37) (P = 0.02 for difference between outreach and HCSUS). (11)

Injection drug use emerged as an independent predictor of delayed HIV care in at least three studies. CDC interviews of 3942 HIV-positive people in 18 states found that people infected while injecting drugs were 40% more likely than heterosexually infected people to delay HIV care for 3 months or more after diagnosis (OR 1.40, 95% CI 1.08 to 1.82). (19) Using the same measure of delayed entry to care, the New York City Department of Health study of 1928 HIV-positive people figured that a history of injecting drugs (versus no history) raised the risk 30% (HR 1.3, 95% CI 1.1 to 1.5). (7) The ARTAS study of 270 people who had never seen an HIV clinician (74%) or had seen a clinician only once (26%) correlated never injecting drugs with a higher chance of seeing an HIV clinician within 6 months of enrolling in the study. (14)

* More factors. The studies referenced above and others uncovered an array of other patient-related variables that stand between an HIV diagnosis and speedy care. One unsurprising but nonetheless noteworthy finding is that people who feel sick seek care, and people who don't, don't. Multivariate analysis in the 270-person ARTAS study determined that having three or more HIV-related symptoms independently raised chances of seeing an HIV provider within 6 months. (14) And interviews with 130 HIV-positive people in Mississippi (81% black, 38% women) found that 47% who delayed HIV care more than 6 months listed "feeling good" as a reason, and 22% said "feeling good" was their main reason. (21) Three quarters of the people in this study reported feeling denial about their HIV diagnosis, an attitude that would lend itself to delaying care.

The 3942-person CDC study found that first-time testers were 33% more likely to delay care (OR 1.33, 95% CI 1.13 to 1.56) than were people who had an earlier negative test. (19) The CDC researchers cited a study that found gay men who got tested repeatedly for HIV had more positive health-related attitudes about testing, (22) and the CDC team speculated that those same positive attitudes may apply to health care in general and thus favor starting care. The same CDC study found that anonymous HIV testing (rather than confidential testing) made delayed care almost 25% more likely (OR 1.24, 95% CI 1.03 to 1.51). The researchers suggested that "those diagnosed with HIV anonymously may wish to preserve their anonymity and simply avoid medical care for this reason." (19)

Finally, the 270-person ARTAS study figured that HIV-positive people with higher education were significantly more likely to begin care for their infection. (14)

Clinical impact of late entry to HIV care

No one needs vast experience with HIV infection-or much imagination--to grasp why delaying the first visit to an HIV clinic portends ominous clinical outcomes for people who test positive. Infection with a virus that relentlessly unpins the immune system kills most people, in time, unless they start taking drugs to pin down that virus. Probably for these reasons, research on the clinical impact of late entry to HIV care is sparse. But when hard numbers go lacking, modelers gleefully fill the breach. A modeling study that evaluated starting antiretrovirals late--at a CD4 count under 200 cells/[mm.sup.3]--figured that tardy treatment takes 24 years off a normal life span. (23)

This novel analysis by modelers at Harvard and other centers tried to reckon life expectancy, compared with the general population, in four groups: (1) HIV-negative people with mortality risk profiles similar to people with HIV because of substance abuse and other high-risk behaviors, (2) HIV-positive people who begin antiretrovirals according to then-current guidelines, that is, when the CD4 count fell below 350 cells/ [mm.sup.3], and who go on to another regimen when one regimen fails, (3) HIV-positive people who do not start antiretrovirals till their CD4 count falls below 200 cells/[mm.sup.3], and (4) people who do not start the next available antiretroviral regimen after failure of one regimen.

The investigators derived demographic data from HIV-positive people in several cohorts. At HIV seroconversion, this group averaged 33 years of age, had an average viral load of about 65,000 copies/mL, and averaged 534 CD4 cells/ [mm.sup.3]. About half were black, 27% non-Hispanic white, and 21% Hispanic.

For 33-year-olds in the general US population, estimated life expectancy was an additional 42.9 years at the time of this study. For the HIV-negative group with a risk profile similar to US residents with HIV, life expectancy at age 33 would be only 34.6 years (Figure 3). In other words, compared with the general population, this "HIV-like" group would lose 8.3 years in life expectancy. For the HIV-positive group that starts antiretrovirals at the 350-cell threshold, life expectancy at age 33 would be 22.7 years, so they would lose an estimated 20.2 years compared with the general population.

For 33-year-old HIV-positive people who start antiretroviral therapy at a CD4 count between 50 and 199 cells/[mm.sup.3], life expectancy would be only another 18.75 years, meaning they would lose 24.15 years compared with the general population, that is, 3.95 years more than the HIV-positive group that started antiretroviral therapy at the 350-cell cutoff. In this group that did not start antiretrovirals until their CD4 count lay between 50 and 199 cells/[mm.sup.3], life expectancy would be 18.2 years for those who took four antiretroviral regimens before quitting, 17.0 years for those who took three regimens, 15.6 years for those who took two, and 13.7 years for those who took only one. To put it another way, dropping out of care earlier and earlier robs more and more years from an HIV-positive person's life.

Of course much of the data summarized in the article on late HIV diagnosis in this issue of RITA! also suggest the clinical and monetary impact of delayed care after HIV diagnosis, because later diagnosis means later care.

Strategies to unstitch seams between HIV diagnosis and care

Indulgent readers will condone one more restatement of the obvious: HIV diagnosis earlier in the course of infection (the topic of the first article in this issue) will promote faster entry to care--or at least entry at a higher CD4 count and a less dire disease stage. But diagnosis with out linkage to care is almost pointless. And as Michael Mugavero and University of Alabama colleagues note, "although the importance of linkage to care is emphasized in the CDC guidelines, implementation has often focused on increasing the number of tests performed, with considerably less programmatic emphasis on linking patients to HIV care." (2)

In 2010 the US National HIV/AIDS Strategy outlined a plan to torque up access to care. (24) Though the goals are laudable, the recommended "action steps" are (in the nature of political manifestos) broad (Table 2). Still, the document's first action step accurately embodies a key finding of research discussed above and below: smoothing out "seams" that hem the newly diagnosed from speedy care should be a priority. Studies show that intense case management after diagnosis, and/or housing testing and care services inside the same walls, can ease the way into care. But even targeted programs can yield disappointing results in recalcitrant populations.

Apparently only one randomized trial, by the CDC's Antiretroviral Treatment and Access Study (ARTAS), has assessed entry-to-care tactics. (25) The ARTAS team randomized just-diagnosed people to case management or passive referral standard-of-care (which seems substandard when one considers the often lengthy lapse between a positive test and a handshake with an HIV clinician). This trial, published in 2005, involved 273 recently diagnosed people in Atlanta, Baltimore, Los Angeles, and Miami. The intervention included up to five contacts with a case manager over 90 days, while people in the passive referral group got only information about HIV and local care resources.

Significantly more people in the case-management group saw an HIV clinician at least once in 6 months (78% versus 60%, adjusted relative risk 1.36, P = 0.0005), and at least twice in 12 months (64% versus 49%, adjusted relative risk 1.41, P = 0.006). (25) People older than 40, those who had not used crack recently, Hispanics, and people who enrolled in the trial within 6 months of HIV diagnosis were significantly more likely to have at least two HIV clinic visits. The CDC team estimated that this type of case management costs $600 to $1200 yearly--a pittance when weighed against the cost of delayed care.

Although these results clearly demonstrate that one-on-one case management gets people into care more reliably than a stack of informational leaflets, the low 12-month visit rate in either study group suggests these people needed more help than they got.

Two other nonrandomized ARTAS studies evaluated case management and co-location, current jargon for having testing and treatment units under one roof. One of these later analyses focused on a larger group of 626 recently diagnosed people seen at 10 sites across the United States from 2004 through 2006. (26) All these people had up to five meetings with a linkage case manager over 90 days. In the first 6 months, 497 of the 626 study participants (79%) saw an HIV clinician, almost the same proportion as in the randomized trial. (25) People who had two or more case-management sessions and those seen at a site with testing and care under the same roof were significantly more likely to have an HIV office visit in 6 months. Other factors that favored linkage to care were age over 25, Hispanic ethnicity, stable housing, and no recent noninjection drug use. A separate analysis that used ARTAS data plus site visits and project director reports figured that sites with co-located testing and care had a substantially higher linkage rate than stand-alone sites, 87% versus 73%. (27)

Four studies assessed how well outreach programs get underserved HIV-positive groups into care--injection drug users, (28,29) nonwhites, (29,30) and people with unstable housing. (31) Street outreach for injection drug users (28) and peer-based outreach to people of color and injection drug users at 21 California sites (the California Bridge Project) (29) got only about half of study participants, at best, into care. Another California Bridge Project study focused on 325 out-of-treatment HIV-positive people who averaged 1.5 years since HIV diagnosis and their first meeting with project staff. (30) Almost three quarters of these people were nonwhite, and half were men who have sex with men. Case workers managed to link only 29% of this group to care--after an average 15.4 client contacts. Although these outreach programs linked half or fewer people to HIV care, the success rate surely reflects the hard-to-reach populations that the programs targeted.

An outreach program in New York City targeted 161 HIV-positive residents of single-occupancy hotels, 95% of whom were minorities and 59% of whom were active drug users. (31) Ninety-five study participants were assessed before receiving the intervention, while 66 were assessed after receiving the intervention. These people had better baseline access to care than the groups in the studies summarized above. (28-30) Three quarters of the pre-intervention group and 91% of the postintervention group already had a regular health care provider.

In the pre-intervention approach, an outreach worker went door-to-door in eight single-occupancy hotels and asked residents if they needed services and wanted to join a harm-reduction program. The intervention consisted of adding a physician to the door-to-door outreach team and asking residents if they wanted to see a physician right now. Comparing data from preintervention and postintervention interviews, multivariate analysis that accounted for drug use, HIV severity, and other factors determined that the intervention independently raised chances of having a regular provider (OR 5.3, P = 0.02), taking antiretrovirals (OR 5.7, P = 0.02), and having a better perception of quality of care (OR 4.9, P = 0.003).

Besides case management, outreach, and colocation of testing and care services, a few studies have pinpointed specific tactics that may cut the time between HIV diagnosis and care (Table 3). The already-discussed University of Alabama HIV clinic study found that every 10 days between the call to make a first clinic appointment and the appointment date magnified the no-show risk about 30%. (18) Other research shows the importance of having appointment times convenient for patients and having providers who speak the patient's language. (32,33) In a review article on improving US women's access to care, Mariam Aziz and Kimberly Smith of Chicago's Rush University Medical Center stressed the need to create a "woman-friendly environment" that offers child care and access to "case management, social workers, and gynecologic care, at a minimum." (34)

After Michael Mugavero and University of Alabama colleagues figured out why HIV-positive people failed to show up for their first HIV clinic appointment, (18) they devised a program, Project CONNECT, to help solve the problem. (35) Newly diagnosed people are scheduled for a clinic orientation visit within 5 days of their first call for an appointment (see Figure 1 in the Mugavero interview). That visit includes a semistructured interview, a psychosocial questionnaire, baseline lab tests, and (for the uninsured) a visit with a social worker. Patients needing substance abuse or mental health services get a prompt referral to appropriate services. Because the clinician has lab results and other data at the first patient encounter, care can begin immediately. During the first year that Project CONNECT was in place, the clinic's no-show rate dropped from 31% to 18% (P < 0.01). (2) CONNECT cut the risk of failure to establish HIV care almost 50% (OR 0.54, 95% CI 0.38 to 0.76).

San Francisco General Hospital (SFGH), which cares for large populations of gay men and poor, homeless, or uninsured people, created the Positive Health Access to Services and Treatment (PHAST) system to get newly diagnosed people into care. (36,37) All SFGH care settings use rapid HIV testing and a central diagnostic lab that pages positive results to a PHAST worker, who meets patients when they get a positive result.

The PHAST team member provides intensive on-the-spot support and education, schedules confirmatory testing, and performs clinic intake including CD4 count, viral load, and resistance testing. PHAST also helps newly diagnosed people with insurance applications and provides appointment reminders and primary care until the patient is transferred to a permanent HIV provider.

Sobering numbers on staying in care After HIV diagnosis and linkage to care, the third leg in the HIV-health tripod is staying in care, or retention. Using data from the CDC and other sources, researchers estimated that 79% of the 1.1 million HIV-positive people in the United States get diagnosed, 59% enter care, and only 40% stay in care. (38) (See Figure 1 in the article "Late HIV diagnosis.") Looking at specific HIV populations, other investigators usually make similarly depressing estimates of retention in care.

Meta-analysis of HIV linkage and retention studies with data from May 1995 through 2009 figured that, of 75,655 people who entered care, only 59% kept multiple HIV visits averaged across assessment intervals ranging from 6 months to 3 to 5 years. (39) A British study of 16,595 people in HIV care determined that 44% did not have a CD4 count for a year or more, and 40% of that group fell out of care for the duration of follow-up. (40) A 12,304-person EuroSIDA analysis published in 2008 defined loss to follow-up as no clinic visit, CD4 count, or viral load assay after January 1, 2006; the researchers counted 2712 people (22%) who met those criteria. (41) Recruitment of the analyzed cohorts began in May 1994 and ended in December 2005. In contrast to these findings, another prospective cohort, the French Hospital Database on HIV, found in 2006 that only 2950 of 34,835 people (8.5%) did not have a medical visit for at least 12 months after their last visit in 1999. (42) But loss to follow-up was 16.8% among people diagnosed with HIV in the past year.

Retrospective analysis of 2619 male US veterans who started antiretroviral therapy after January 1, 1998 determined that 36% went at least 3 months without seeing their HIV clinician in the first year of therapy. (43) Among 1636 people who entered the University of North Carolina Center for AIDS Research prospective clinical cohort from January 2001 through January 2008, 414 (25%) dropped out of care, defined as missing appointments for 18 months. (44) A New York City study of 842 people diagnosed with HIV from July 1 to September 30, 2005 found that 650 (77%) started care within 3 months. (45) Of those, only 45% maintained regular care, defined as at least one clinic visit every 6 months.

Why people with HIV drop out of care Many variables that shorten the odds of starting HIV care also explain why people later quit. Whether the study group lives in the United States or Western Europe, factors that make poor retention more likely often include younger age, minority or immigrant status, substance use, and a very low CD4 count (Figure 4). People with AIDS, in contrast, seem more likely to keep appointments than people without AIDS.

To analyze predictors of retention in HIV care, RITA! sifted results of 9 studies that used multivariate analysis to pinpoint retention predictors. The four biggest studies scrutinized large cohorts--34,835 people in the French Hospital Database on HIV, (42) 12,304 in EuroSIDA, (41) 2619 men cared for at US Veterans Affairs (VA) centers, (43) and 2411 or 1924 HIV-positive women (depending on the analysis) in the six-site US Women's Interagency HIV Study (WIHS). (46) (WIHS kept tabs on cohort visits, not on primary care HIV visits.) Smaller populations included 1636 people seen at the University of North Carolina HIV clinic, (44) 1007 patients in five French HIV clinics, (47) 650 people in New York City, (45) 567 people at the University of Alabama HIV clinic, (48) and 398 people in Los Angeles clinics. (49) The VA study differed from the others in the stringency of its definition of poor retention--missing a clinic visit in any one of four quarters in the first year of antiretroviral therapy. (43) The appendix following the references to this article details how each of these studies defined retention and outlines key results.


Younger age consistently boosted the risk of poor retention in the VA study, WIHS, and the clinic-based studies, (43-48) while older age favored good retention in EuroSIDA. (41) EuroSIDA linked female gender to better retention, (41) while the Los Angeles study found that Latina and African-American women were more likely to keep clinic appointments than Latino or African-American gay men. (49) The all-men VA study determined that black veterans ran a one-third higher risk of poor retention than white veterans. (43)

How HIV disease status affects retention seems a little trickier to reckon, but in the end none of these disease status results defy logic. First, people with an AIDS diagnosis were more likely to see their HIV physician regularly in the French Hospital Database, (42) EuroSIDA, (41) and the University of North Carolina clinic; (44) this consistent result reflects the likelihood that people who have an AIDS disease or had one earlier want their AIDS treated right away or want to avoid repeating the experience. Conversely, the New York City study identified early (non-AIDS) HIV infection as a predictor of poor retention. (45)

People with higher viral loads proved more likely to drop out of care at the University of North Carolina, (44) in WIHS, (46) and in the French clinic study. (47) Although those findings at first seem at odds with the AIDS results, it makes sense that people with higher viral loads are dropout risks because high loads often reflect poor antiretroviral adherence; and people with poor antiretroviral adherence are candidates for poor appointment adherence. High loads may also indicate lack of antiretroviral therapy, and taking antiretrovirals correlates with good retention, as discussed below.

WIHS (46) and the five French clinics (47) linked a lower CD4 count to poor clinic attendance, while EuroSIDA (41) and the Los Angles study (49) figured a higher CD4 count favored good clinic attendance. The VA and University of Alabama studies found that a higher initial CD4 count predicted poor retention, (43,48) while the French clinic study found that a lower initial CD4 count predicted good retention. (47) What do these mixed results mean? There's no way to know for sure because studies that demonstrate associations do not establish the direction of causality. But there's plenty of room for speculation:

A very low CD4 count may signal advanced HIV disease and imminent death, which would tend to keep people out of the clinic. Indeed, some people listed as "lost to follow-up" may have died. A higher CD4 count can reflect good adherence to antiretrovirals and to care in general and thus explain good clinic attendance. On the other hand, a high CD4 count when first entering care probably reflects asymptomatic disease, and people with no symptoms have less motivation to keep clinic appointments. The timing of the CD4 measurement is critical, as the five-clinic French study showed by evaluating CD4 count two ways. (47) In this 1007-person study, a low initial CD4 count favored good retention, (47) probably for the same reason that an AIDS diagnosis favors good retention: sick people who just learned they have HIV want to get care. The same French study found that a lower CD4 count during care made poor retention more likely, (47) perhaps because people whose CD4 count stays low despite being in care are not taking antiretrovirals and are missing appointments, or they are getting too sick to come to clinic.

The studies that isolated antiretroviral therapy as a retention predictor revealed the two sides of the treatment coin. EuroSIDA members who had begun antiretroviral therapy were more likely to stay in care, (41) while people not taking antiretrovirals in the five French clinics were more likely to drop Out. (47) One could theorize endlessly on what is cause and what is effect in these associations, but few would disagree with one interpretation: getting patients to the point where they can start antiretrovirals helps keep them from turning truant.

Minority status, figured different ways, heightens the risk of poor clinic attendance. In both French studies, (42,47) being born outside of France raised the risk of poor retention in care, and black race made poor retention more likely in the VA survey (43) and the New York City study. (45) But in the WIHS study of US women, white women were more likely than black women to miss WIHS study visits 7 through 10. (46) Why white race correlated with poor study attendance in WIHS is not clear; many women in this six-center cohort are socially marginalized regardless of race or ethnicity.

People without a primary care provider ran a higher risk of missing cohort appointments 7 through 10 in WIHS (46) and of dropping out of care in the French clinic study. (47) Women with no health insurance or with temporary housing were more likely to miss appointments in WIHS, (46) while having no phone number (a surrogate for poverty or transience) in the French study predicted dropping out of care. (47) Having insurance favored good retention at the University of North Carolina HIV clinic. (44) Substance abuse upped the odds of poor retention in WIHS (46) and the University of Alabama clinic, (48) while moderate (but not heavy) alcohol drinking made poor retention more likely in WIHS. (46) The Los Angeles analysis of Hispanic or black women or gay men was the only study that weighed the impact of HIV disclosure status. (49) People who told more "network members" they had HIV were more likely to stay in care. In the same study, Latino gays who felt more gay stigma made fewer clinic visits. In the VA study, men with a chronic nonviral disease such as diabetes, hypertension, or ischemic heart disease had about a 20% lower risk of poor retention (OR 0.81, 95% CI 0.66 to 0.99). (43)

Erratic appointment keeping and death HIV clinicians need no instruction on the baleful consequences of missing appointments or falling out of care completely. But it is instructive to see how consistently poor retention predicts death in diverse HIV populations studied in the past 5 years.

The largest such study involves 2619 HIV-positive men diagnosed in Veterans Affairs hospitals or clinics in 1997 and 1998. (50) All these men started antiretroviral therapy after January 1, 1997, saw an HIV clinician at least once after starting, and survived at least 1 year. Median pretreatment CD4 count stood at 228 cells/[mm.sup.3], median viral load measured about 38,000 copies/mL, and follow-up averaged more than 4 years. During years in which men made at least one clinic visit, 36% had visits in fewer than four quarters and 16% died during follow-up. Multivariate regression analysis determined that, compared with men who made a clinic visit in all four quarters, those who made visits in three, two, or one quarter all had a higher risk of dying during follow-up:

Hazard ratio for death compared with HIV clinic visits in 4 quarters per year:

* Visits in 3 quarters: HR 1.42 (95% CI 1.11 to 1.83), P < 0.01

* Visits in 2 quarters: HR 1.67 (95% CI 1.24 to 2.25), P < 0.001

* Visit in 1 quarter: HR 1.95 (95% CI 1.37 to 2.78),P < 0.001

A retrospective statewide study in South Carolina involved 2197 HIV-positive people at least 13 years olds who were diagnosed with HIV from 2004 through 2007 and entered care. (51) The investigators defined retention as optimal if a person visited a clinic once every 6 months in the first 2 years of care, suboptimal if they made visits in three of four 6-month intervals, sporadic if they made visits in two of four intervals, and dropout if they made no visits. Half of the study group failed to get to the clinic at least once every 6 months in the 2 years after diagnosis. Sporadic retention almost tripled the risk of death during follow-up (AHR 2.91, 95% CI 1.54 to 5.50) and dropping out quadrupled the risk (AHR 4.00, 95% CI 1.50 to 10.65).

University of Alabama researchers retrospectively analyzed mortality in 543 HIV-positive people who started outpatient care between January 2000 and December 2005, with follow-up through August 1, 2007. (52) Most people, 60%, missed at least one scheduled appointment during the first year of care. Death rates were 1.0 per 100 person-years among people who kept all clinic appointments in the first year of care and 2.3 deaths per 100 person-years among people who missed one or more visits (P = 0.02). Compared with people who always kept appointments, those who missed visits ran a tripled risk of death during follow-up (HR 2.90, 95% CI 1.28 to 6.56).

Even when people who drop out of care return, the higher death risk persists, according to results of a study in northern France. (53) From 1997 through 2006, researchers at 5 clinics around Lille classified 135 of 1007 HIV patients (13%) as lost to follow-up, defined as not coming to the clinic for 12 months, not known to be in care elsewhere, and not known to have died. Seventy-four of the 135 dropouts (55%) returned to care after a median of 19 months, and 33 of those 74 (45%) had a CD4 count below 200 cells/ [mm.sup.3] and/or AIDS when they came back. Compared with clinic patients who never dropped out, those returning to care were more likely to be younger (median 31 versus 35 years, P< 0.001) and injection drug users (12% versus 2.6%, P < 0.001), and less likely to have an AIDS illness upon entering care (11% versus 20%, P = 0.01). An analysis that adjusted for CD4 count and AIDS diagnosis at enrollment figured that people who returned to care after loss to follow-up had a 5 times higher risk of dying than people who never left care (OR 5.14, 95% CI 2.11 to 12.54).

Strategies to promote steady care: what works?

Although strategies to promote retention in care have not been studied as closely as antiretroviral adherence tactics, researchers have proposed and tested an armful of retention programs. Harvard's Elizabeth Horstmann and coworkers at other centers tabulated and reviewed eight such blueprints, (54) and others are easy to find. But perhaps the most practical overall advice comes from Thomas Giordano, who studies retention (and other things) at Houston's Baylor College of Medicine and Veterans Affairs Medical Center. Giordano concludes a list of 10 retention tips for HIV clinicians with these no-nonsense points: (55)

Patients know they should be in care:

--Reminders are likely not enough.

--Admonishments or encouragements will not work.

--Problem solve with your patients just as you would for adherence to medications.

The intervention studies reviewed by Horstmann (56-63) plus additional studies identified by RITA! (64-68) usually involve some type of case management, outreach, "system navigator," or ancillary services (Figure 5). Tactics range from the simple (answering medical questions (59,64)) to the complex (planning and launching a "social marketing campaign" (64)). Horstmann's recent review of retention strategy studies (54) found that facets of all such programs (56-63) improved appointment keeping by people with HIV. All of the additional retention strategy studies tracked down by RITA!, (64-68) which include programs for women, youth, and children, found that at least some components of these programs helped keep people in care.

The bottom line from all this work appears to be that doing something is better than doing nothing. But how much can any busy HIV practice do to keep patients coming back? Beyond broad programs such as case management, certain specific services worked across diverse populations:

* Clinic appointment reminders (59,61,68)

* Help with appointment scheduling and rescheduling (59,61,64,68)

* Service coordination via a "system navigator" or "buddy" (57,59,63,65,68)

* Mental health counseling and treatment (56-58,60,62)

* Substance abuse counseling and treatment (56-58)

* Housing assistance (57,58.65)

* Food and nutrition support (57-59,65,68)

* Transportation (56-58,65,66,68)


Some programs incorporate services that make sense but may be too costly for many clinics, such as child care, (57) translation services, (58) insurance help, (65) legal assistance, (58) and emergency financial aid. (57) Just as quickly making a first HIV clinic appointment improved entry to care in a study reviewed above, (18) making the first medical appointment within 72 hours of HIV diagnosis improved retention in a University of North Carolina study. (64)

Giving each HIV patient a "system navigator" or "buddy" has become a popular approach to improving retention, at least in studies of ways to bolster appointment keeping. (57,59,63,65) This personal ombudsman, often a peer, helps patients pick their way through an increasingly brambly sociomedicolegal thicket that can baffle even the most-motivated person. "Navigator" is not just a trendy rubric for "case manager" since the navigator typically joins the patient on clinic visits and works with the patient day to day. "Patient navigators," explains the University of Alabama's Michael Mugavero, "are often HIV-infected peers or near-peers who share similar cultural and socioeconomic backgrounds as the patient, often playing a distinct and complementary role to case managers and other supportive service providers." (2) However, the potential benefit of system navigators remains to be studied rigorously in a randomized trial.

In addition to the problem-solving advice listed at the beginning of this section, Thomas Giordano offers the following practical pointers on keeping HIV patients in care: (55)

* Track no-show and out-of-care rates.

* Examine your processes: Bringing patients back is much more difficult once they are out of care completely.

* Work with emergency room and inpatient services, community-based organizations, public health agencies, jails/prisons, and other Ryan White providers to identify patients poorly retained in care and to build or strengthen re-linkage processes.

* Build or strengthen outreach or peer navigator programs.

* Work with the resources you have: Spread the word about the importance of retention; have staff advocate with patients for retention.

* Improve the customer's experience.

* Minimize unmet needs: Strengthen substance use, mental health, case management, and social services.

* Minimize time between appointment making and appointment date.

* Pilot wider appointment availability, open access.

Summary points on linkage and retention


* Two years after getting a positive HIV test, one third of Americans still have not had a CD4 count, according to the CDC. (1)

* Health system problems that delay entry to care include separate HIV testing and care facilities, passive referral instead of active case management, and longer time between referral and the first scheduled visit. (2,18)

* Two HIV testing factors heighten the risk of delayed entry to care: testing positive the first time tested, and anonymous versus confidential HIV testing. (19)

* The only randomized trial of methods to improve linkage to care confirmed the superiority of case management over passive referral after HIV diagnosis. (25)

* Focused linkage programs at the University of Alabama (35) and San Francisco General Hospital (36,37) have had some success.


* Of the 1.1 million HIV-positive people in the United States, perhaps only 40% get diagnosed, enter care, and stay in care. (38)

* Across nine retention studies, factors that most consistently predicted dropping out of care were younger age, minority or immigrant status, substance use, and a low CD4 count.

* Not taking antiretroviral therapy boosted the risk of dropping out of care in two large cohort studies. (41,47)

* Four studies concurred in finding that dropping out of HIV care, or dropping out and returning after a year, raised the risk of death. (50-53)

* Programs that promote good retention include case management, "system navigators," and ancillary care such as mental health and substance abuse services. (56-68)


Nine studies to pinpoint predictors of poor retention in care

RITA! found nine published studies that use multivariate analysis to identify independent predictors of retention in HIV care. Overall results are described and analyzed above under the subhead "Why people with HIV drop out of care." Definitions of retention and study details follow.

The French Hospital Database on HIV otters the biggest retention-in-care analysis, embracing 34,835 HIV-positive people enrolled since 1999. (42) The investigators defined loss to follow-up as not being seen for at least 12 months after a visit in 1999. Loss to follow-up was more frequent among people diagnosed in the past year than in those diagnosed more than 1 year ago (16.8% versus 7.1%). Among people diagnosed in the past year, loss to follow-up was 40% less likely among those infected during sex between men than in other transmission groups (OR 0.60, 95% CI 0.5 to 0.7) and 50% less likely in people with AIDS (OR 0.5, 95% C1 0.4 to 0.6). Immigrants were 30% more likely to drop out of care than natives (OR 1.3, 95% CI 1.0 to 1.5). Overall loss to follow-up proved 20% more likely among injection drug users than among men who have sex with men (OR 1.2, 95% CI 1.1 to 1.4).

A 12,304-person EuroSIDA analysis published in 2008 defined loss to follow-up as no follow-up visit, CD4 count, or viral load after January 1, 2006. (41) Women were almost 10% less likely to fall out of care than men (incidence rate ratio [IRR] 0.91, 95% CI 0.83 to 1.00, P = 0.039), while every 10 years of age made falling out of care almost 20% less likely (IRR 0.82, 95% CI 0.78 to 0.85, P < 0.0001). Every 2 times higher CD4 count lowered the chance of loss to follow-up 7% (IRR 0.93, 95% CI 0.91 to 0.96, P < 0.0001), and starting combination antiretroviral therapy lowered the risk more than 30% (IRR 0.69, 95% CI 0.63 to 0.76, P < 0.0001). Having AIDS made loss to follow-up about 15% less likely (IRR 0.84, 95% CI 0.77 to 0.92, P < 0.0001). Getting infected by injecting drugs rather than via other routes upped the risk of loss to follow-up about 35% (IRR 1.36, 95% CI 1.22 to 1.52, P < 0.0001).

The biggest US study to probe for retention variables involved 2619 male US veterans who started therapy after January 1, 1998. (43) This study differs from others reviewed here not only in its all-male all-veteran population (62% black or Hispanic, 22.5% with HCV, 28% with "socioeconomic instability"), but also in the stringent definition of poor retention in care--missing a primary care visit in any one of four quarters during the first year of antiretroviral therapy. Multivariate analysis isolated several predictors of poor retention: younger age (OR 1.41, 95% CI 1.13 to 1.75 for 40 to 49 versus 50 or older; higher odds ratios for 30 to 39 and 20 to 29 versus 50 or older), black versus white race (OR 1.34, 95% CI 1.11 to 1.62), CD4 count above 350 cells/[mm.sup.3] versus under 200 cells/[mm.sup.3] (OR 1.25, 95% CI 1.02 to 1.52), hepatitis C infection (OR 1.32, 95% CI 1.06 to 1.64), and illicit drug use (OR 1.42, 95% CI 1.08 to 1.87). Having a chronic medical condition other than HIV or HCV (such as diabetes, hypertension, or ischemic heart disease) lowered the risk of poor retention (OR 0.81, 95% CI 0.66 to 0.99).

Another large US analysis of retention in care involved more than 2000 HIV-positive women in the Women's Interagency HIV Study (WIHS), which regularly monitors HIV-positive and at-risk women at six centers. (46) This analysis identified factors that predict keeping WIHS visits, not visits to these women's primary care or HIV clinicians. Still, the study offers useful insights into why poor minority women, who make up the bulk of WIHS enrollees and reflect the US female HIV population, have trouble keeping medical appointments.

WIHS researchers determined reasons why women missed cohort visits 2 and 3 or cohort visits 7 through 10. Among 2411 HIV-positive women in the visit 2-3 analysis, factors that independently predicted nonattendance were temporary housing (OR 2.80, 95% CI 1.74 to 4.50, P < 0.001), moderate alcohol consumption (OR 1.46 for 1 to 13 drinks weekly versus none, 95% CI 1.04 to 2.07, P = 0.03), use of crack, cocaine, or heroin (OR 2.56, 95% CI 1.43 to 4.57, P < 0.01), lower CD4 count (OR 0.84 for each log2 higher CD4 count, 95% CI 0.74 to 0.95 P < 0.01), higher viral load (OR 1.37 for each log10 higher viral load, 95% CI 1.11 to 1.71, P < 0.01), and having a primary care provider (OR 2.14, 95% CI 1.30 to 3.52, P < 0.01). The last finding runs counter to results when the WIHS team analyzed visits 7 through 10.

Among 1924 women, missing WIHS visits 7 through 10 was more likely with younger age (OR 0.78 for each added 10 years of age, 95% CI 0.68 to 0.89, P < 0.001), white versus black race (OR 1.58, 95% CI 1.19 to 2.10, P < 0.001), not having a primary care provider (OR 0.77 for having a provider, 95% CI 0.60 to 0.99, P = 0.04), not having health insurance (OR 0.74 for having insurance, 95% CI 0.56 to 0.96, P = 0.03), higher viral load (OR 1.16 for each logl0 higher, 95% CI 1.04 to 1.30, P < 0.001), and nonattendance at a previous visit (OR range 3.31 to 30.7 depending on missed visit sequence, P < 0.001).

University of North Carolina (UNC) investigators assessed retention of 1636 people in the UNC Center for AIDS Research prospective clinical cohort alter enrollment between January 1, 2001 and January 1, 2008. (44) Most of these people (58%) were African American, and 32% were women. Defining dropout as failing to keep clinic visits for 18 months, the UNC researchers found that every 10 years of age raised chances of study retention 12% (AHR 1.12, 95% CI 1.00 to 1.25), having private insurance (versus none or "other") raised retention chances almost 50% (AHR 1.46, 95% CI 1.14 to 1.86), and having public insurance (versus none or "other") raised chances about 30% (AHR 1.31, 95% CI 1.03 to 1.66). Having AIDS made retention 30% more likely (AHR 1.30, 95% CI 1.03 to 1.64), but having a detectable viral load almost halved chances of staying in care (AHR 0.52, 95% CI 0.40 to 0.66, for more than 10,000 copies/mL versus under 400 copies/mL; AHR 0.58, 95% CI 0.45 to 0.75, for 400 to 10,000 copies/mL versus under 400 copies/mL). Living in an urban area made retention 30% less likely (AHR 0.70, 95% CI 0.55 to 0.88), and living farther from the clinic cut chances about 25% for every 50 miles (AHR 0.74, 95% CI 0.66 to 0.84).

A study of 1007 H IV-positive people in care from January 1997 to December 2006 in five French clinics identified risk factors for loss to follow-up, first, when people joined the study group and, second, during follow-up. (47) Baseline variables that raised the risk of dropping out of care were age under 30 versus over 40 (HR 1.66, 95% CI 1.04 to 2.64), trans mission by injection drug use rather than sex between men (HR 5.26, 95% CI 2.90 to 9.52), no phone number provided (HR 5.4, 95% CI 3.6 to 8.2), no primary care physician (HR 2.10, 95% CI 1.25 to 3.52), and sub-Saharan African origin (HR 2.09, 95% CI 1.36 to 3.22). Baseline variables that lowered the risk of loss to follow-up were a CD4 count below 200 cells/[mm.sup.3] versus over 349 cells/[mm.sup.3] (HR 0.49, 95% CI 0.32 to 0.76) and a CD4 count of 200 to 349 cells/[mm.sup.3] versus over 349 cells/[mm.sup.3] (HR 0.63, 95% CI 0.41 to 0.98). During follow-up, chances of falling out of care were higher with a most recent CD4 count under 200 cells/[mm.sup.3] (HR 2.06, 95% CI 1.16 to 3.66), not taking antiretroviral therapy (HR 4.20, 95% CI 2.66 to 6.61), and taking antiretrovirals but having a detectable viral load (HR 1.92, 95% CI 1.19 to 3.01).

A New York City study of 650 people diagnosed with HIV in 2005 and starting care within 3 months defined regular care as 1 or more clinic visit every 6 months and retention in care as the last visit within 6 months of the end of analysis on June 30, 2009. (43) Variables that raised the odds of not having regular care were age 13 to 24 versus 50 or over (AOR 3.0, 95% CI 1.5 to 6.0), black race (AOR 2.0, 95% CI 1.4 to 2.8), eligibility for antiretroviral therapy (AOR 1.5, 95% CI 1.1 to 2.2), and injection drug use (AOR 2.7, 95% CI 1.0 to 7.1). Factors that made quitting care more likely were age 13 to 24 versus 50 or over (AHR 1.9, 95% CI 1.1 to 3.4), nonhospital site of care (AHR 1.4, 95% CI 1.0 and 2.0), and early-stage (non-AIDS) disease (AHR 1.4, 95% CI 1.0 to 2.0).

The University of Alabama group studied 567 people starting outpatient care at their clinic between January 2000 and December 2005. (48) The investigators defined retention in care as the number of 6-month blocks during which a person attended at least one clinic visit over the 2 years after the first outpatient HIV primary care visit (range 1 to 4). Younger age independently predicted worse retention in the first 2 years of care (OR 0.7 per 10 years older, 95% CI 0.57 to 0.86), as did higher initial CD4 count (OR 2.65, 95% CI 1.6 to 4.38 for 200-349 cells/[mm.sup.3] versus under 200 cells/[mm.sup.3]; OR 2.48, 95% CI 1.6 to 3.86, for 350 cells/[mm.sup.3] or higher versus under 200 cells/[mm.sup.3]). Substance abuse also raised the risk of poor retention (OR 1.67, 95% CI 1.02 to 2.71), but having an affective mental health disorder cut the risk of poor retention (OR 0.45, 95% CI 0.31 to 0.67). To explain the last finding, the investigators speculated that people with diagnosed mental health problems may be getting treatment or special attention for that problem, whereas people with undiagnosed mental health problems would not be getting special care and thus would be more likely to miss clinic appointments.

A study of 398 HIV-positive people in Los Angeles included roughly equivalent proportions of Latino and African- American gay or bisexual men and Latina and African-American women. (49) They completed a 45-minute survey on demographics, HIV status disclosure, HIV-specific and general stress, gay- and HIV-related stigma, social support, and other health variables. The investigators rated 307 people as retained in care because they had 2 or more primary care visits in the 6 months before the interview and 91 people as not retained in care because they had 0 or 1 visit in the 6 months before the interview.

In the whole study group, HIV status disclosure to more personal network members was the primary predictor of good retention (OR 1.3, 95% CI 1.1 to 1.5). Among Latino gay men, gay stigma lowered chances of retention 10% (OR 0.9, 95% CI 0.8 to 0.9). Among people who disclosed their HIV status to one or more network members, predictors of retention in care were gender (OR 1.8 for women versus gay men, 95% CI 1.1 to 3.1) and disclosure of HIV status to more network members (OR 1.5, 95% CI 1.1 to 1.9). Among Latino gays who disclosed their HIV status, those with higher gay stigma scores were less likely to be retained in HIV care (OR 0.9, 95% CI 0.8 to 0.9). Among African-American men who had disclosed their HIV status to network members, general stress was associated with good retention (OR 1.2, 95% CI 1.1 to 1.3). Among Latina women who disclosed their HIV status, disclosing their status to more people quintupled chances of retention in care (OR 5.0, 95% CI 1.2 to 20.5). And among African-American women who disclosed their HIV status, those with a higher CD4 count in the last 6 months were 10 times more likely to stay in care (OR 10.4, 95% CI 2.1 to 51.2).


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Table 1. Health system problems that delay entry to care *

* Separate HIV testing and HIV care facilities

* Passive referral to care versus active case management

* Cursory case management after HIV diagnosis

* Lack of readily available support services, such as mental health
and substance abuse services

* Longer time between referral and first scheduled visit

* Clinic hours that do not accommodate work schedules and dependant
care needs

* Lack of culturally appropriate services in the clinic

* Underfunding of clinics that will see more patients with expanded
HIV testing (for example, through the Ryan White CARE Act)

* Health worker shortages and lack of clinicians specializing in
HIV care

* Most of these obstacles are reviewed by Mugavero and colleagues.

Table 2. US National HIV/AIDS Strategy to increase access
to HIV care e4

Recommended action steps        Targets by 2015

1. Establish a seamless         1. Increase the proportion
system to immediately link      of newly diagnosed patients
people to continuous,           linked to clinical care
coordinated quality care when   within 3 months of their HIV
they are diagnosed with HIV     diagnosis from 65% to 85%
                                (from 26,824 to 35,079
2. Take deliberate steps to     people).
increase the number and
diversity of available          2. Increase the proportion
providers of clinical care      of Ryan White HIV/AIDS
and related services for        Program clients who are in
people living with HIV.         care (at least 2 visits for
                                routine HIV medical care in
3. Support people living with   12 months at least 3 months
HIV with co-occurring health    apart) from 73% to 80% (from
conditions and those who have   237,924 to 260,739 people in
challenges meeting their        continuous care).
basic needs, such as housing.
                                3. Increase the percentage
                                of Ryan White HIV/AIDS
                                Program clients with
                                permanent housing from 82%
                                to 86% (from 434,000 to
                                455,800 people).

Table 3. Getting newly diagnosed people into HIV care: strategies
that work

* Active case management rather than passive referral upon HIV
diagnosis (25-27,35-37)

* Housing HIV testing services in the same building as an HIV
primary care clinic (26,27)

* Street and community outreach to find HIV-positive people not in
care (28-31)

* Shorter time between call for first appointment and first
appointment (18)

Figure 1. Among 48,413 people in
the United States diagnosed with HIV
infection in 2005 and 2006, fewer than
two thirds had their CD4 count measured
19 to 24 months after diagnosis.
This CDC calculation indicates that one
third of US residents diagnosed with
HIV in those years were getting no care
or grossly inadequate care 2 years after
testing positive. (1)

Time from HIV diagnosis to first CD4 count in US

1 month        45.2
2-3 months     51.6
4-6 months     55.4
7-12 months    59.5
13-18 months   62
19-24 months   64.2

Note: Table made from bar graph.

Figure 3. Compared with the general US population, HIV-negative
people with a mortality risk profile similar to HIV-positive people
(for example, because of substance abuse) would lose 8.3 years from
the expected life of a 33-year-old (first bar), according to a
modeling study. (23) HIV-positive people who start antiretroviral
therapy (ART) at the 350 CD4 threshold and continue treatment with
new regimens when one regimen fails would lose an estimated 20.2
years compared with 33-year-olds in the general population (second
bar). HIV-positive people who do not start antiretroviral therapy
until their CD4 count stands between 50 and 199 cells/[mm.sup.3]
would lose 24.15 years of life compared with 33-year-olds in the
general population (third bar). And HIV-positive people who start
treatment at 50 to 199 cells/ [mm.sup.3] but quit after two
combinations fail would lose 27.3 years of life compared with the
general population (fourth bar).

Estimated life years lost compared with
general US population

"HIV-like" risk         -8.3
Start ART at 350        -20.2
Start ART at 50-199     -24.15
Quit after 2 regimens   -27.3

Note: Table made for bar graph.
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