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Clinical Science

Higher Acuity Resource Utilization With Older Age and Poorer HIV Control in Adolescents and Young Adults in the HIV Research Network

Neilan, Anne M. MD, MPHa,b,c,d; Lu, Frances MPHe; Gebo, Kelly A. MD, MPHf; Diaz-Reyes, Rebeca MSPHf; Huang, Mingshu PhDe; Parker, Robert A. ScDd,e; Karalius, Brad MPHg; Patel, Kunjal DSc, MPHg,h; Voss, Cindy MAf; Ciaranello, Andrea L. MD, MPHb,c,d; Agwu, Allison L. MD, ScMf

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: April 1, 2020 - Volume 83 - Issue 4 - p 424-433
doi: 10.1097/QAI.0000000000002280

Abstract

INTRODUCTION

The US Centers for Disease Control and Prevention (CDC) reports that 22% of new HIV diagnoses occur in adolescents and young adults (AYA) aged 13–24 years in the United States.1 Nearly 61,000 AYA are now living with HIV in the United States.2 AYA with HIV experience poorer outcomes compared with adults at every step of the HIV care continuum, from diagnosis through starting antiretroviral therapy (ART) to virologic suppression.3–5 With combination ART (cART), hospitalizations for persons with HIV of all ages have declined.6,7 Despite improving trends, AYA with HIV are hospitalized more frequently than AYA without HIV.8 AYA who have lived with HIV their entire lives (youth with perinatally acquired HIV, PHIVY), and AYA who have newly acquired HIV (youth with non–perinatally acquired HIV, NPHIVY), may experience HIV differently despite traversing the same, uniquely challenging developmental time period of adolescence. Data on the impact of these poorer outcomes for US AYA with HIV on health care resource utilization, however, are limited, and none, to the best of our knowledge, examine the impact of mode of HIV acquisition, CD4, and HIV viral load (VL).8–11

Understanding health care resource utilization among AYA with HIV may inform policies and practices to improve outcomes among these vulnerable youth.12 Such data are also useful to inform health policy model-based economic projections,13,14 which rely on data derived from adults.15 For example, recent cost estimates to achieve presidential goals to end the HIV epidemic vary widely ($291 million to $25 billion/year).16–18 Our objectives were therefore to analyze resource utilization—including outpatient, emergency, and inpatient hospital care—among AYA with HIV in the HIV Research Network (HIVRN) by mode of HIV acquisition, age, CD4 count, viremia and antiretroviral (ARV) use, as well as to describe resource utilization associated with specific AIDS-defining conditions (ADCs).

METHODS

Study Population

We analyzed data from the HIVRN, a clinical cohort attending 5 pediatric and 13 adult US HIV clinics.19 Sites were hospital- (14) and community-based (4) and geographically diverse (Northeast: 8; South: 5; Midwest: 1; and West: 4). The study population included participants aged 13–30 years between January 2006 and December 2015 and with ≥1 CD4 count and VL measurement after enrollment during the study period. Demographic, laboratory, and medication data were extracted from electronic databases and by chart review. Data were combined across sites at the coordinating center, Johns Hopkins University, to produce a uniform database.19 Institutional review boards at participating sites approved the study. Reported/recorded route of transmission determined categorization as youth with perinatally acquired (PHIVY) or non–perinatally acquired HIV (NPHIVY). Race/ethnicity categories were self-reported as mutually exclusive. Sex/gender was recorded/reported as mutually exclusive male/female/transgender. Based on guidelines and practice patterns during the study period, as in previous work, we defined cART regimens as 1 of 2 mutually exclusive types expected to be suppressive: (1) ≥3 drugs from ≥2 classes or (2) a protease inhibitor (PI, excluding ritonavir alone) + 1 drug from another class.20–26 Although individual circumstances may justify alternative ART approaches, during the study period, they were not standard of care and were not expected to suppress VL.20–26 Given variable uptake of guideline-concordant ART approaches, we also examined the impact of including ≥3 nucleos(t)ide reverse transcriptase inhibitors in the cART definition in a sensitivity analysis. A change in ART regimen was defined as a change in ≥1 medications. Loss to follow-up was defined as no data recorded for >12 months for any reason other than documented care transfer.

Outcome Measures

Primary outcome measures included rates of outpatient visits, emergency department (ED) visits, and inpatient hospital days per person-year (PY). Outpatient visits included primary care (medical doctor, physician assistant, or nurse practitioner in the HIV clinic), nurse, and social worker visits. For specific ADCs, we assessed primary care visits, ED visits, and inpatient hospital days; social work and nurse visits were not routinely linked to ADCs. Because some ADCs occur exclusively (eg, malignant cervical dysplasia) or more frequently (eg, Kaposi sarcoma) by sex, in an additional analysis, we examined resource utilization associated with ADCs by recorded sex/gender.

We assessed utilization associated with specific ADCs, as defined by the CDC classification.27 A single ADC could be associated with multiple outpatient, ED, and inpatient events; in this case, we assigned each type of utilization (outpatient visits, ED visits, and inpatient days) to the same event. For co-occurring ADCs likely to differentially impact inpatient length of stay (LOS) (eg, pulmonary tuberculosis and cachexia), we assigned the LOS to the ADC of greater severity. For co-occurring ADCs likely to similarly impact LOS (eg, cryptococcal meningitis and toxoplasmosis), we assigned the LOS to both diagnoses. Co-occurring ADC designations are listed in eTables 1–3, Supplemental Digital Content, http://links.lww.com/QAI/B421.

Statistical Analyses

We estimated average utilization of outpatient visits (primary care, social work, and nurse visits), ED visits and inpatient hospital days per PY stratified by mode of acquisition (PHIVY vs. NPHIVY), age (13–17, 18–23, and 24–30 years), CD4 count (<200, 200–499, ≥500 cells/µL), and VL and ARV status (VL/ARV) at time of event. We defined VL/ARV status based on person-time (PT) spent in each of 3 categories: (1) suppressive ARVs: VL <400 copies/mL and prescribed any ARVs (ie, inclusive of suppressive non-cART regimens), (2) nonsuppressive cART: VL ≥400 copies/mL and prescribed cART, and (3) no ARVs: VL ≥400 copies/mL and not prescribed any ARVs.28 Virologic suppression was defined as VL <400 copies/mL based on historic assay lower limits of detection at participating HIVRN sites. PT spent in CD4 count and VL/ARV strata was estimated by calculating PT in between each change in CD4 count, VL/ARV status, and age strata. When available, the nearest measurement before baseline was also used. Last available CD4 count and VL were carried forward until the end of follow-up.

When estimating PT for outpatient, ED, and inpatient utilization rates, we excluded PT when patients had VL <400 copies/mL and were not prescribed ARVs, as well as when patients had VL ≥400 copies/mL while being prescribed an ARV regimen other than cART. To describe resource utilization associated with ADCs, all PT was included.

We report crude outpatient, ED, and inpatient utilization rates and 95% confidence intervals (CIs); rates simultaneously stratifying by all variables (mode of acquisition, age, CD4 count, and VL/ARV status) are reported in the supplemental appendix, http://links.lww.com/QAI/B421.

RESULTS

Study Population

Among 4540 participants, there were 12,641 PYs of active outpatient care (Table 1). We excluded 1568 PYs (12%) from the PT distribution (Tables 2 and 3) and outpatient, ED, and inpatient rate analyses (Figs. 1A–C and see eTables 4–9, Supplemental Digital Content, http://links.lww.com/QAI/B421). We excluded 126 PYs (1.0%) while participants had VL ≥400 copies/mL and were on a regimen other than cART; 122 PYs (1.0%) while participants had VL <400 copies/mL while off ARVs, 465 PYs (3.7%) while participants had VL <400 copies/mL and were missing ARV data, and 854 PYs (6.8%) while participants had VL ≥400 copies/mL and were missing ARV data, from a total of 1222 participants. To assess resource utilization associated with individual ADCs, all 12,641 PYs were analyzed (Fig. 2).

TABLE 1.
TABLE 1.:
Characteristics of HIVRN Participants
TABLE 2.
TABLE 2.:
Distribution of PT Stratified by Mode of Infection, Age, CD4 Count, and Viral Load/Antiretroviral Status
TABLE 3.
TABLE 3.:
Distribution of CD4 Cell Count and Viral Load/Antiretroviral Status by Age and Mode of Infection
FIGURE 1.
FIGURE 1.:
(A) Outpatient visits, (B) emergency medical care visits, and (C) inpatient days per PY. ARV, antiretroviral. Error bars indicate Poisson 95% CIs.
FIGURE 2.
FIGURE 2.:
Primary care outpatient visits, ED visits, and inpatient days per AIDS-defining condition. For the category Total for all ADCs, all AIDS-defining conditions (ADCs) are averaged. For the category Total infections, all individual infections are averaged. Total bacterial infections, total viral infections, total fungal infections, and total mycobacterial infections comprised individual bacterial, viral, fungal, and mycobacterial infections, respectively. Mycobacterial disease may comprise either tuberculosis or nontuberculous mycobacteria and thus is distinguished from, for example, disseminated Mycobacterium avium complex (MAC). CMV, cytomegalovirus; HSV, herpes simplex virus; PML, progressive multifocal leukoencephalopathy; TB, tuberculosis.

Table 1 reports baseline and follow-up characteristics. Overall, 15% were PHIVY and 28% were female. Among NPHIVY, the most frequently recorded HIV acquisition risk factor was male who has sex with males (63%). Race/ethnicity were recorded as 63% black, 16% white/other, 18% Hispanic, and 1% not reported. Baseline CD4 strata were <200 cells/µL in 13%; 200–499 cells/µL in 46%; and ≥500 cells/µL in 41%. At baseline, 81% were prescribed cART and 32% had VL <400 copies/mL. Among those with VL ≥400 copies/mL at baseline, 2% were prescribed ARVs but not cART, 15% were prescribed no ARVs, and 1% were missing ARV data. During the study period, on average, 2.5 CD4 count and 2.6 VL measurements were recorded per person per year, and patients were prescribed an average of 1.7 ART regimens. Mean follow-up was 2.8 PYs, and cumulative loss to follow-up was 18%. Of those lost to follow-up, 54% returned during the study period. There were 17 deaths.

PT Distribution

Among PHIVY, 43% of PT was spent from 13 to 17 years, 45% from 18 to 23 years, and 12% from 24 to 30 years (Table 2). Among NPHIVY, 1% of PT was spent from 13 to 17 years, 43% from 18 to 23 years, and 56% from 24 to 30 years. Among both PHIVY and NPHIVY, most PT was spent with CD4 count ≥500 cells/µL (61% and 54%, respectively). Both PHIVY and NPHIVY spent most PT on suppressive ARVs (69% and 66%), with 29% and 24% of PT on nonsuppressive cART, and 2% and 10% of PT off ART. Of PHIVY and NPHIVY, 87% and 71% of participants ever had VL <400 copies/mL during the study period. In a sensitivity analysis of PT distribution, given variable uptake of guideline-concordant ART approaches, we included ≥3 nucleos(t)ide reverse transcriptase inhibitors in the definition of cART expected to be suppressive; this comprised <1% of overall PT.

Among PHIVY, PT spent at CD4 ≥500 cells/µL was lower at older ages (13–17 years: 75%; 18–23 years: 52%; and 24–30 years: 40%, Table 3); among NPHIVY, there was no difference by age (13–17 years: 60%; 18–23 years: 53%; and 24–30 years: 55%). Among PHIVY, PT spent on suppressive ARVs was lower at older ages (13–17 years: 80%; 18–23 years: 62%; and 24–30 years: 58%); by contrast, among NPHIVY, PT spent on suppressive ARVs was higher at older ages (13–17 years: 53%; 18–23 years: 61%; and 24–30 years: 71%).

Age-, CD4-, and VL- and ARV-Stratified Outpatient Visits

The proportion of individuals having any outpatient visit was 97% (PHIVY) and 98% (NPHIVY). Overall, for PHIVY and NPHIVY, there were 12.1 and 6.0 outpatient (including primary care, social work, and nurse) visits/PY, respectively. Among PHIVY, the overall outpatient visit rate was highest in the 18–23 years' group: 13.2/PY (95% CI: 13.0 to 13.5) vs. 13–17 years: 11.2 (95% CI: 11.0 to 11.4) and 24–30 years: 10.7 (95% CI: 10.4 to 11.1, Fig. 1A, left section). Of outpatient visits among PHIVY, primary care visits were higher at younger ages (see eFig. 1, Supplemental Digital Content, http://links.lww.com/QAI/B421); social work visits were greatest in the 18–23 years' age group (see eFig. 2, Supplemental Digital Content, http://links.lww.com/QAI/B421); and nurse visits were similar by age (see eFig. 3, Supplemental Digital Content, http://links.lww.com/QAI/B421). Among NPHIVY, overall outpatient visit rates were lower at older ages; 13–17 years: 8.7/PY (95% CI: 8.2 to 9.3); 18–23 years: 7.1/PY (95% CI: 7.0 to 7.2); and 24–30 years: 5.1/PY (95% CI: 5.0 to 5.1). For both PHIVY and NPHIVY, rates of overall outpatient visits (Fig. 1A, middle section) and primary care outpatient visits (see eFig. 1, Supplemental Digital Content, http://links.lww.com/QAI/B421) were higher during PT spent at lower CD4 counts. Considering VL/ARV status, for both NPHIVY and PHIVY, overall outpatient visit rates were highest during PT spent on nonsuppressive cART, whereas the lowest outpatient visit rates were during PT spent not prescribed ART (Fig. 1A, right section). Incorporating all age, CD4, and VL/ARV strata variables, compared with NPHIVY, PHIVY had similar rates of primary care outpatient visits (see eTable 5, Supplemental Digital Content, http://links.lww.com/QAI/B421) and generally higher rates of social work and nurse visits in the suppressive ARV and nonsuppressive cART strata (see eTables 6 and 7, Supplemental Digital Content, http://links.lww.com/QAI/B421).

Age-, CD4-, and VL- and ARV-Stratified ED Visits

The proportion of individuals having any ED visit was 37% (PHIVY) and 26% (NPHIVY). Overall, for PHIVY and NPHIVY, there were 0.4 and 0.3 visits/PY, respectively. Among PHIVY and NPHIVY, the lowest rates of ED visits were during PT spent at ages 13–17 years (0.2/PY and 0.1/PY, respectively), CD4 ≥500 cells/µL (0.2/PY and 0.3/PY), and on suppressive ART (0.3/PY and 0.3/PY, Fig. 1B). Among PHIVY and NPHIVY, rates of ED visits increased during PT spent at older ages or lower CD4 counts. Among PHIVY and NPHIVY, when considering VL/ARV status only, the highest rates of ED visits occurred during PT spent on nonsuppressive cART [PHIVY: 0.7/PY (95% CI: 0.7 to 0.8); NPHIVY: 0.6/PY (95% CI: 0.5 to 0.6)]. After stratifying by all age, CD4, and VL/ARV strata variables, among patients on nonsuppressive cART, the highest rates of ED visits occurred during PT with CD4 count <200 cells/µL (see eTable 8, Supplemental Digital Content, http://links.lww.com/QAI/B421).

Age-, CD4-, and VL- and ARV-Stratified Inpatient Hospital Days

The proportion of individuals having any hospitalizations was 26% (PHIVY) and 13% (NPHIVY). Overall, PHIVY and NPHIVY experienced 1.5 and 0.8 inpatient days/PY, respectively. Average LOS was 6.2 days (PHIVY) and 5.9 days (NPHIVY). Among PHIVY and NPHIVY, the oldest youth (25–30 years) spent the most days inpatient [2.9 (95% CI: 2.7 to 3.1) and 1.0 days (95% CI: 1.0 to 1.1), respectively], compared with other ages [13–17 years: 0.8 (95% CI: 0.8 to 0.9) and 0.3 days (95% CI: 0.2 to 0.4); 18–24 years: 1.9 (95% CI: 1.8 to 2.0) and 0.6 days (95% CI: 0.6 to 0.7)]. Among PHIVY and NPHIVY, the fewest inpatient days occurred during PT spent with CD4 ≥500 cells/µL [0.5 days (95% CI: 0.5 to 0.6) and 0.3 days (95% CI: 0.3 to 0.3), respectively] or suppressive cART [0.8 days (95% CI: 0.7 to 0.8) and 0.5 days (95% CI: 0.5 to 0.6)]. After stratifying by all age, CD4, and VL/ARV strata variables, PHIVY compared with NPHIVY generally had higher or similar rates of inpatient stays during PT spent at age 18–23 years and at most CD4 counts in the suppressive ARV therapy and nonsuppressive cART categories (see eTable 9, Supplemental Digital Content, http://links.lww.com/QAI/B421).

AIDS-Defining Conditions and Mortality

The proportion of individuals experiencing any ADC was 11% (PHIVY) and 7% (NPHIVY). The proportion of females, males, and transgender individuals experiencing any ADC was 8%, 6%, and 5% respectively. Overall, the rate of any ADC (first diagnosis or recurrent) was 4.5/100PY (including and excluding malignant cervical neoplasm). On average, there were 1.3 primary care outpatient visits, 0.3 ED visits, and 9.8 inpatient days per ADC (Fig. 2). Candida esophagitis (17%), pneumocystis (11%), and cytomegaloviral disease (9%) were the most frequently occurring ADCs. The ADCs with the greatest utilization per event for primary care outpatient visits were malignant cervical neoplasm (6.0 visits/diagnosis), mycobacterial disease (4.0 visits/diagnosis), and Kaposi sarcoma (3.4 visits/diagnosis); for ED visits: disseminated Mycobacterium avium complex (MAC; 0.6 visits/diagnosis) and progressive multifocal leukoencephalopathy (0.6 visits/diagnosis); and, for inpatient days: Burkitt lymphoma (28.0 days/diagnosis), disseminated MAC (24.7 days/diagnosis), and progressive multifocal leukoencephalopathy (19.6 days/diagnosis, Fig. 2). ADC-associated resource utilization by sex/gender is reported in eFigs. 4–6, Supplemental Digital Content, http://links.lww.com/QAI/B421. The mortality rate was 0.2/100PY for PHIVY and 0.1/100PY for NPHIVY.

DISCUSSION

We described outpatient visits, ED visits, and inpatient days among AYA with HIV aged 13–30 years in the HIVRN according to mode of HIV acquisition, and time-updated age, CD4 count, and VL/ARV status. We also assessed outpatient visits, ED visits, and inpatient hospital days associated with specific ADCs. This analysis had 3 key findings.

First, among both PHIVY and NPHIVY, we found that inpatient and ED care resource utilization increased and primary care outpatient utilization decreased with PT spent at older ages, at lower CD4 counts, and with unsuppressed VL. We account for the potential confounding of the younger cohort being predominantly PHIVY by assessing PT and stratifying simultaneously by mode of transmission, age, CD4, and VL/ARV status. These findings expand on those of a previous study which found higher rates of hospitalizations among 17- to 24-year-old PHIVY compared with 5- to 16-year-old PHIVY in the HIVRN.8 Our findings are also consistent with Medicaid data associating poor adherence with higher total hospital days.29 The finding of declining outpatient utilization as age increases is consistent with national trends for adolescents without HIV. Age, rather than a change in virologic suppression, appears to be driving declining outpatient utilization in NPHIVY, who spent a larger fraction of PT virologically suppressed at older ages; this likely reflects a shift from regular outpatient engagement to symptom/event-driven care in acute care settings.30 Few studies examine trends in national utilization rates across the adolescent and young adult age spectrum31; 1 type I diabetes study found that outpatient visits declined and emergency care visits increased with age.32 These data underscore the importance of improving access to lower-acuity care and preventive services for AYA with HIV as for other chronic conditions.33

Second, we observed that overall PHIVY had higher rates of overall utilization compared with NPHIVY. After accounting for the greater time spent at lower CD4 count, at VL ≥400 copies/mL, and younger age among PHIVY (see eTables 4–9, Supplemental Digital Content, http://links.lww.com/QAI/B421), the observation of greater utilization among PHIVY vs. NPHIVY generally persisted for social work and nurse visits as well as for hospitalizations in the 18- to 23-year-old age group. Although distinguishing between outcomes among PHIVY and NPHIVY is critical to improving HIV-related health outcomes, data with this degree of granularity are not often reported, likely in part due to small numbers of PHIVY in the United States and, particularly in international settings, difficulty identifying the route of infection for patients diagnosed in adolescence.34,35 Higher rates of viremia and advanced immunosuppression have been previously reported in older PHIVY compared with younger PHIVY.28,36 We found that both PHIVY and NPHIVY in HIVRN experienced increased ED visits and inpatient hospital days with older age, lower CD4 count, and unsuppressed VL. However, we also observed differences between PHIVY and NPHIVY. Social work visits increased during ages 18–23 years for PHIVY, which may reflect the substantial challenges PHIVY face related to chronic illness as they transition from long-term pediatric to adult providers and navigate emergence into early adulthood.37 For NPHIVY, conversely, social work visits declined by age strata, along with primary care outpatient visits. Higher resource utilization rates among PHIVY compared with NPHIVY in certain categories may reflect the longer duration of HIV illness, the persistence of childhood care engagement patterns, or sex differences (female PHIVY vs. NHIPVY: 52% vs. 23%), in particular related to reproductive health care.38,39

Third, although ADCs were infrequent, they contributed substantially to resource utilization. Similar to adults, candidiasis and pneumocystis were the most common ADCs.40 The average LOS for any ADC was 11.7 days. The overall average LOS for any hospitalization was 6.2 (PHIVY) and 5.9 (NPHIVY) days. Although comparisons are limited because of differences in calendar year and age groupings, our reported LOS is similar to national data. Nationally, mean LOS declined (6.8 vs. 6.1 days) for HIV-related hospital stays from 2006 to 2013; however, cost per stay increased over the same period ($12,589–$13,300, inflation-adjusted).40 Previous studies among AYA have reported higher rates of ADCs with poorer HIV disease control.28,41 In addition to advancing interventions to improve HIV management for AYA and thus avert ADCs, vaccine-preventable or -mitigatable conditions such as pneumococcal pneumonia and malignant cervical neoplasm present opportunities for optimizing care.

This analysis has several limitations. First, our results are limited to patients engaged in care, and out-of-care PT utilization patterns are unknown. Second, data capture is limited to HIVRN sites, and some visits (eg, mental health and substance use) may occur at outside sites; however, a statewide insurance claims study at an HIVRN site demonstrated that 91% of hospital admissions occurred at the same hospital, suggesting that any underestimation of these utilization rates due to care received elsewhere may be modest.42 Third, by design, we did not analyze whether hospital admissions may have been preceded by outpatient and ED visits, which is how charges are often captured. Fourth, given small numbers of events and the limitations of the database, we did not assess for differences by calendar year, insurance type, or by care site (eg, pediatric vs. adult centers)40,43; a previous analysis comparing care at pediatric and adult HIVRN sites found no differences in ART initiation rates, but did find higher rates of ART discontinuation at adult sites.44

In conclusion, AYA with HIV had higher resource utilization with more ED visits and inpatient days during time spent at older ages, lower CD4 counts, or unsuppressed VL. Although ADCs were rare, associated resource utilization was substantial. Interventions to improve outpatient care engagement and durable virologic suppression as AYA with HIV age may improve outcomes for this growing population as they transition to adulthood.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the participants and study staff in the HIVRN study as well as the sponsoring agencies. The authors also thank Ms. Julia Foote for assistance in preparing the manuscript for publication.

Participating Sites: Alameda County Medical Center, Oakland, California (Howard Edelstein, M.D.); Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Richard Rutstein, M.D.); Drexel University, Philadelphia, Pennsylvania (Amy Baranoski, M.D., Sara Allen, C.R.N.P.); Fenway Health, Boston, Massachusetts (Stephen Boswell, M.D. and Kenneth Mayer, M.D.); Johns Hopkins University, Baltimore, Maryland (Kelly Gebo, M.D., Richard Moore, M.D., and Allison Agwu M.D.); Montefiore Medical Group, Bronx, New York (Robert Beil, M.D.); Montefiore Medical Center, Bronx, New York (Uriel Felsen, M.D.); Mount Sinai St. Luke's and Mount Sinai West, New York, New York (Judith Aberg, M.D. and Antonio Urbina, M.D.); Oregon Health and Science University, Portland, Oregon (P. Todd Korthuis, M.D.); Parkland Health and Hospital System, Dallas, Texas (Ank Nijhawan, M.D. and Muhammad Akbar, M.D.); St. Jude's Children's Research Hospital and University of Tennessee, Memphis, Tennessee (Aditya Gaur, M.D.); Tampa General Health Care, Tampa, Florida (Charurut Somboonwit, M.D.); Trillium Health, Rochester, New York (William Valenti, M.D.); University of California, San Diego, California (W. Christopher Mathews, M.D.); University of Wisconsin-Madison, Madison, Wisconsin (Ryan Westergaard, M.D.); Sponsoring Agencies; Agency for Healthcare Research and Quality, Rockville, Maryland (Fred Hellinger, Ph.D. and John Fleishman, Ph.D.); Health Resources and Services Administration, Rockville, Maryland (Robert Mills, Ph.D., Faye Malitz, M.S.).

Data Coordinating Center: Johns Hopkins University (Richard Moore, M.D., Jeanne Keruly, C.R.N.P., Kelly Gebo, M.D., Cindy Voss, M.A., Charles Collins, M.P.H., and Rebeca Diaz-Reyes, M.S.P.H.).

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Keywords:

resource utilization; antiretroviral therapy; HIV viral load; CD4 count; adolescence; youth

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