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. Author manuscript; available in PMC: 2018 May 1.
Published in final edited form as: JAMA Pediatr. 2017 May 1;171(5):450–460. doi: 10.1001/jamapediatrics.2017.0141

Greater risk for viremia, immunosuppression, serious clinical events, and mortality with increasing age: the US perinatal HIV epidemic in its adolescence

Anne M Neilan 1, Brad Karalius 1, Kunjal Patel 1, Russell B Van Dyke 1, Mark J Abzug 1, Allison L Agwu 1, Paige L Williams 1, Murli Purswani 1, Deborah Kacanek 1, James M Oleske 1, Sandra K Burchett 1, Andrew Wiznia 1, Miriam Chernoff 1, George R Seage III 1, Andrea L Ciaranello 1, For the Pediatric HIV/AIDS Cohort Study and the International Maternal Adolescent and Pediatric AIDS Clinical Trials Network
PMCID: PMC5411314  NIHMSID: NIHMS847981  PMID: 28346597

Abstract

Importance

As perinatally HIV-infected youth (PHIVY) in the US grow older and more treatment-experienced, clinicians need updated information about the impact of age, CD4 count, viral load (VL), and antiretroviral drug (ARV) use on risks of opportunistic infections (OIs), key clinical events, and mortality in order to understand patient risks and improve care.

Objective

To determine the incidence or first occurrence during follow-up of key clinical events (including CDC-B and CDC-C events) and mortality among PHIVY stratified by age, CD4, and VL/ARV status.

Design

In the PHACS Adolescent Master Protocol (AMP) and IMPAACT P1074 multicenter cohort studies (2007–2015), we estimated event rates during person-time spent in key strata of age (7–12, 13–17, and 18–30 years), CD4 count (<200, 200–499, and ≥500 cells/μL), and VL/ARV status (< or ≥ 400 copies/mL; ARVs or no ARVs).

Setting

41 ambulatory sites in the US, including Puerto Rico.

Participants

1,562 participants in AMP and P1074 were eligible, 1446 PHIVY were included.

Exposure(s) for observational studies

Age, CD4 count, VL, ARV use.

Main outcomes

Clinical event rates stratified by person-time in age, CD4 count, and VL/ARV categories.

Results

During a mean follow-up of 4.9 years, higher incidences of CDC-B events, CDC-C events and mortality were observed as participants aged. Older PHIVY (13–17 and 18–30 year-olds) spent more time with VL ≥400 copies/mL and with CD4 <200/μL compared to 7–12 year-olds (30% and 44% vs. 22% of person-time with VL ≥400 copies/mL; 5% and 18% vs. 2% of person-time with CD4 <200/μL; p<0.01 for each comparison). We observed higher rates of CDC-B events, CDC-C events, bacterial infections, and mortality at lower CD4 counts, as expected. The mortality rate in older PHIVY was 6–12 times that of the general US population. Higher rates of sexually transmitted infections were also observed at lower CD4 counts, after adjusting for age.

Conclusions and relevance

Older PHIVY were at increased risk of viremia, immunosuppression, CDC-B events, CDC-C events, and mortality. Interventions to improve ART adherence and optimize models of care for PHIVY as they age are urgently needed to improve long-term outcomes among PHIVY.

Keywords: Perinatal HIV infection, antiretroviral therapy, HIV viral load, CD4 count, adolescence, youth

INTRODUCTION

Effective interventions to prevent mother-to-child HIV transmission and treat HIV-infected infants and children have shifted the US pediatric HIV epidemic; youth aged ≥13 years now represent the majority of perinatally HIV-infected youth (PHIVY) in the US.1–3 Rates of viremia and immunosuppression have decreased among PHIVY in the US since the implementation of effective combination antiretroviral therapy (cART), but may remain higher for older PHIVY.4,5 Compared to adults, PHIVY experience lower rates of HIV RNA viral load (VL) suppression and higher rates of loss to follow-up.6–8 As youth age and transition to adult care, their risks of opportunistic infections (OI), other serious clinical events, and mortality are not well described.9–14

Understanding the frequency of important clinical events for PHIVY, as well as the consequences of being prescribed cART without a suppressed VL, will provide critical information to design interventions for this group, who are at risk for severe illness, accumulation of resistance mutations, and secondary transmission.15,16 Our objectives were to determine the frequency of viremia and immunosuppression among PHIVY and young adults aged 7–30 years engaged in care in two large national cohort studies and to analyze associations between age, CD4 count, viremia, ARV use, and risks of significant clinical events and mortality.

METHODS

Study Population

We evaluated participants in the Pediatric HIV/AIDS Cohort Study (PHACS) Adolescent Master Protocol (AMP) and the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) P1074 cohort studies. AMP enrolled 451 PHIVY aged 7–16 years between March 2007 and November 2009 at 15 US sites; follow-up is ongoing.2 P1074 enrolled 1,201 participants (87% PHIVY) with a mean age of 17.4 years (SD 5.4) between April 2009 and June 2013 at 40 US sites; follow-up was completed in June 2014.17 For this analysis, AMP participants were eligible if they had ≥1 visits between March 2007 and April 2015, and ≥1 CD4 and VL recorded after baseline; P1074 participants were eligible if they were PHIVY and had ≥1 chart abstraction with CD4 count and VL data recorded after baseline. AMP baseline was defined as date of study entry; P1074 baseline was defined as 1 year prior to study entry. Simultaneous co-enrollment in AMP and P1074 was not permitted. Based on guidelines, practice patterns, and trial data, we defined cART regimens as one of three 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, or (3) ≥3 nucleos(t)ide reverse transcriptase inhibitors.2,18–22 We excluded person-time when patients had VL <400 copies/mL and were not prescribed ARVs and when patients had VL ≥400 copies/mL while being prescribed an ARV regimen other than cART. Although individual patient circumstances may have warranted these approaches, they are not expected to suppress VL and were not standard of care during the study period.2,18–22 Lost to follow-up was defined as stopping data collection for any reason other than death, study completion, or site closure.

Clinical and Laboratory Data

Participant consent and data collection methods have been described previously for both studies.2,17 CD4 counts, VLs, ARV use, and clinical events were abstracted from medical records, and clinical events were coded using the Medical Dictionary for Regulatory Activities (MedDRA) version 18 by Frontier Science Technology and Research Foundation.23

Outcome Measures

Primary outcomes included mortality and first occurrence of CDC stage C/WHO stage 4 or CDC stage B/WHO stage 3 events during follow-up.24–27 Secondary outcomes included: bacterial pneumonia; serious bacterial infections; presumptive pelvic inflammatory disease (PID); other sexually transmitted infections (STI); pregnancy; mental health and neurodevelopmental conditions; asthma, atopy or allergy conditions; gastrointestinal conditions; cardiac conditions; anemia; pancreatitis or hepatitis; peripheral neuropathy; and metabolic or bone abnormalities, including renal conditions. Presumptive PID and pregnancy were limited to females aged ≥13; other STIs were limited to age ≥13 and stratified by sex due to sex-specific screening practices. Each MedDRA term was assigned a single diagnostic category, except for bacterial pneumonia and PID which were included in CDC-B/WHO-3 events, and also examined separately.

Statistical Analyses

We estimated incidence rates of key clinical events, stratified by the combination of time-varying age (7–12, 13–17, and 18–30 years), CD4 count (<200, 200–499, and ≥500/μL), and VL/ARV status. VL/ARV status was categorized as: 1) suppressive ARVs: VL <400 copies/mL and prescribed any ARVs, 2) non-suppressive cART: VL ≥400 copies/mL and prescribed cART, and 3) no ARVs: VL ≥400 copies/mL and not prescribed any ARVs.

We estimated incidence rates and assessed trends by age, CD4 count, and VL/ARV categories using Poisson regression models, accounting for within-subject correlation with robust standard variance estimators. For selected individual events (STIs, pregnancy and cardiac events), incidence rates by CD4 counts were adjusted for age using inverse probability weighting because person-time contributing to each age category was unevenly distributed by CD4 category.28–33 Only the first occurrence of each event after baseline for each participant was included.

To assign participants to baseline strata and calculate the total person-time contributed by all participants to each stratum, we used linear interpolation between adjacent CD4 and log10-transformed VL readings, and estimated dates when strata thresholds were crossed. The nearest CD4 and VL readings prior to baseline were used when available for interpolation, and last available CD4 count and VL were carried forward until the end of follow-up.

We used weighted repeated measures generalized estimating equation (GEE) models with an independence working correlation, identity link, normal distribution and robust variance estimators to determine the association between: age and person-time with VL ≥400 copies/mL, age and person-time with CD4 <200/μL, and VL/ARV status and person-time with CD4 <200/μL. For these GEE analyses, we additionally sub-divided the older age stratum into 18–21, 22–25, and 26–30 years.

To compare mortality rates between PHIVY and youth in the general US population, we calculated standardized mortality ratios (SMRs) standardized to CDC age, sex and race distributions.34 Confidence intervals (CI) for SMRs were calculated using the Boice-Monson method.

RESULTS

Characteristics of the study population

Of 1,467 PHIVY in AMP and/or P1074, we excluded 18 who did not have the combination of ARV, CD4 count and VL data at any point during follow-up, 1 who spent their entire person-time in the study with VL ≥400 while on a regimen other than cART, and 2 who spent their entire person-time with VL <400 and off ARVs. We excluded 92.3 person-years (PY) (1.4% of overall person-time) while participants were viremic and on a regimen other than cART and 59.4 PY (0.9% of overall person-time) while participants had VL <400 copies/mL while off ARVs, from a total of 247 participants. Table 1 reports additional baseline and follow-up characteristics. Mean age at baseline was 14.6 years, with 52% female participants; 66% identified as black, and 26% identified as Hispanic. Notably, patients who were lost to follow-up differed significantly from retained patients only in baseline age (15.8 vs. 14.4 years).

Table 1.

Characteristics of PHACS AMP and IMPAACT P1074 participants at baseline

Study Participants, n (%)
AMP 421 (29%)
P1074 1,008 (70%)
Both 17 (1%)
Demographic characteristics Total participants (n=1,446)
Age at baseline, years, mean (SD) 14.6 (4.6)
Female sex, n (%) 759 (52%)
Year of birth, mean (SD) 1994 (4.6)
Race
 Black/African-American, n (%) 953 (66%)
 White/other, n (%) 470 (33%)
 Not reported, n (%) 23 (2%)
Hispanic ethnicity, n (%) 370 (26%)
 Not reported, n (%) 1 (0%)
Clinical characteristics
CD4 count at baseline, cells/μL, mean (SD) 712 (422)
Viral load <400 copies/mL at baseline, n (%) 934 (65%)
Prescribed cART at baseline, n (%) 1,330 (92%)
Prescribed ARVS but not cART at baseline, n (%) 68 (5%)
No ARVs at baseline, n (%) 48 (3%)
CD4 counts per person per year during follow-up, mean (SD) 3.6 (9.6)
Viral loads per person per year during follow-up, mean (SD) 3.6 (9.6)
Total ARV regimens per person, mean (SD) a 2.4 (1.6)
Years of follow-up under study protocols, mean (SD) 4.9 (1.3)
Cumulative loss to follow-up, n (%) 171 (12%)
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Data are presented as number (%), or mean (SD).

a

Regimen change was defined as a change in any single drug.

ARV, antiretroviral; cART, combination antiretroviral therapy; IMPAACT, International Maternal Pediatric Adolescent AIDS Clinical Trials Network; PHACS AMP, Pediatric HIV/AIDS Cohort Study Adolescent Master Protocol; VL, viral load.

Distribution of person-time

Participants contributed 19% of person-time between ages 7–12, 38% between 13–17, and 43% between 18–30; only 2% of total person-time was between ages 26–30 (Table 2). The majority of person-time was spent with CD4 counts ≥500/μL (65% of person-time, compared to 24% at 200–499/μL and 10% at <200/μL), and on suppressive ARVs (66% of person-time, compared to 28% on non-suppressive cART and 7% on no ARVs). Person-years spent in older age strata (ages 13–17 and 18–30), compared to ages 7–12, were more likely to be spent with CD4 counts <200/μL (5% and 18% vs. 2%, p<0.001 for each pair-wise comparison). For older PHIVY (age ≥18), more person-time was also spent with VL ≥400 copies/mL (either non-suppressive cART or no ARVs, 30% and 44% vs. 22% of person-time, p<0.001 for both). When the older age stratum was further divided into 18–21, 22–25, and 26–30 years, proportions of time spent being viremic and with low CD4 counts remained substantially higher for each sub-stratum compared to younger ages (Table 3). Use of non-suppressive cART was not associated with having CD4 <200/μL, compared to use of no ARVs (25% vs. 24%, p=0.72).

Table 2.

Distribution of person-time stratified by age, CD4 count and viral load and antiretroviral status

Distribution of person-time during follow-up Participants, n (%) a Person-time, years (%) b
Age (years)
 7–12 504 (35) 1,243 (19)
 13–17 921 (64) 2,459 (38)
 18–30 967 (67) 2,846 (43)
  18–21 871 (60) 1,887 (29)
  22–25 428 (30) 806 (12)
  26–30 87 (6) 153 (2)
CD4 count (cells/μL)
 ≥500 1,211 (84) 4,286 (65)
 200–499 850 (59) 1,597 (24)
 <200 318 (22) 664 (10)
VL/ARV status
 Suppressive ARVs 1,328 (92) 4,292 (66)
 Non-suppressive cART 941 (65) 1,810 (28)
 No ARVs 346 (24) 445 (7)
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a

Number of participants contributing person-time towards a given stratum.

b

Participants may contribute person-time to more than one stratum.

ARV, antiretroviral; cART, combination antiretroviral therapy; VL, viral load.

Table 3.

Person-time: Association between age, CD4 count and viral load and antiretroviral status

CD4 count, cells/μL a Viral load / ARV status b
<200 200–499 ≥500 Suppressive ARVs Non-suppressive cART No ARVs
Age, years
 7–12 19.2 (2%) 139.8 (11%) 1084.1 (87%) 958.6 (77%) 239.5 (19%) 45.1 (4%)
 13–17 129.3 (5%) 558.9 (23%) 1,770.4 (72%) 1,726.1 (70%) 587.3 (24%) 145.2 (6%)
 18–30 515.7 (18%) 898.5 (32%) 1,431.7 (50%) 1,607.3(56%) 983.6 (35%) 255.0 (9%)
  18–21 276.7 (15%) 592.4 (31%) 1,018.1 (54%) 1,087.0 (58%) 648.6 (34%) 151.6 (8%)
  22–25 202.3 (25%) 263.0 (33%) 340.8 (42%) 426.2 (53%) 291.0 (36%) 89.1 (11%)
  26–30 36.6 (24%) 43.1 (28%) 72.8 (48%) 94.2 (62%) 44.1 (29%) 14.3 (9%)
Viral load/ARV status c
 Suppressive ARVs 95.4 (2%) 699.2 (16%) 3,497.4 (81%)
 Non-suppressive cART 460.4 (25%) 704.7 (39%) 645.3 (36%)
 No ARVs 108.5 (24%) 193.3 (43%) 143.5 (32%)
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Row percents are presented.

a

Proportion of person-time with CD4 count <200 cells/μL according to age: Relative difference (95% confidence interval): 13–17 vs. 7–12 years: 4% (2 – 5%); 18–21 vs. 7–12 years: 13% (10 – 16%); 22–25 vs. 7–12 years: 24% (19 – 28%); 26–30 vs. 7–12 years: 22% (13 – 31%); p<0.001 for all comparisons.

b

Proportion of person-time with VL ≥400 copies/mL according to age: Relative difference (95% confidence interval): 13–17 vs. 7–12 years: 7% (3 – 11%); 18–21 vs. 7–12 years: 20% (15 – 24%); 22–25 vs. 7–12 years: 24% (18 – 30%); 26–30 vs. 7–12 years: 15% (5 – 25%); p<0.01 for all comparisons. The median frequency of HIV RNA measurements during follow-up for subjects while aged 7–12, 13–17, 18–21, 22–25, and 26–30 years was every 3.6, 3.5, 3.9, 4.3, and 4.3 months, respectively.

c

Proportion of person-time with CD4 count <200 cells/μL according to VL/ARV status: Relative difference (95% confidence interval): suppressive ARVs vs. no ARVs: −22% (−28 – −17%), p<0.001, non-suppressive cART vs. no ARVs: 1% (−5 – 7%), p=0.72. (Similar results were seen for the outcome of CD4 count <500/μL.)

ARV, antiretroviral; cART, combination antiretroviral therapy

Mortality

Overall, there were 29 deaths (0.4/100PY) (eTables 1–4). Seventy-nine percent of deaths occurred at CD4 <200/μL (3.5/100PY). Eighty-three percent of deaths occurred while VL was ≥400 copies/mL, with mortality rates of 0.9/100PY for non-suppressive cART and 1.6/100PY for no ARVs (p<0.001 for trend). All but one death (97%) occurred in 18–30 year olds (1.0/100PY, p<0.001 for trend). PHIVY between ages 15–19 and 20–29 had 5.6-fold (95%CI: 2.8–11.1) and 12.3-fold (95%CI: 8.0–18.9) higher mortality rates than youth of the same age in the US general population, respectively.34

First events during follow-up

CDC-C/WHO-4 events

There were 86 CDC-C/WHO-4 events (1.4/100PY; eTable 5). Higher rates of CDC-C/WHO-4 events were observed at lower CD4 counts (<200/μL: 9.6/100PY; 200–499/μL: 1.0/100PY; ≥500/μL: 0.4/100PY; p<0.001 for trend). Of 17 events that occurred at CD4 counts ≥500/μL (20% of total), only 2 were OIs (pulmonary tuberculosis and ocular toxoplasmosis); others were HIV-related kidney and cardiac disease. Higher rates of CDC-C/WHO-4 events occurred with higher VL (suppressive ARVs: 0.6/100PY; non-suppressive cART: 3.0/100PY; no ARVs: 2.4/100PY; p<0.001 for trend). Higher rates of CDC-C/WHO-4 events were also observed with older age (7–12: 0.7/100PY; 13–17: 0.9/100PY; 18–30: 2.1/100PY; p<0.001 for trend).

CDC B/WHO-3 events

There were 193 CDC-B/WHO-3 events (3.2/100PY; Figures 1 and 2; eTable 6). Higher rates of CDC-B/WHO-3 events were observed at lower CD4 counts (<200/μL: 12.5/100PY; 200–499/μL: 3.8/100PY; ≥500/μL: 1.8/100PY; p<0.001 for trend). CDC-B/WHO-3 events were also more common at higher VL (suppressive ARVs: 2.0/100PY; non-suppressive cART: 5.5/100PY; no ARVs: 7.1/100PY; p<0.001 for trend). These event rates also increased as participants aged (7–12: 2.4/100PY; 13–17: 2.9/100PY; 18–30: 3.9/100PY; p=0.01 for trend). For bacterial pneumonia (CDC-B/WHO-3 event) and serious bacterial infections, higher event rates were also observed at lower CD4 counts and higher viral loads, but no trends were observed by age (eTables 7 and 8).

Figure 1.

Figure 1

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Incidence of mortality and first occurrence of CDC-C/WHO-4 and CDC-B/WHO-3 events by CD4 count, viral load and antiretroviral status and age in AMP and P1074

VL: viral load; ARV: antiretroviral; cART: combination antiretroviral therapy

Exact Poisson 95% confidence intervals are presented in the error bars.

Figure 2.

Figure 2

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Incidence of mortality and first occurrence of all clinical outcomes in AMP and P1074

PID: pelvic inflammatory disease; STI: sexually transmitted infection; ND: neurodevelopmental

Exact Poisson 95% confidence intervals are presented in the error bars.

Reproductive system events

In female participants, higher rates of presumptive PID, other STIs, and pregnancies were observed with older age and with VL >400 copies/mL (eTables 9–11); higher rates of STIs (not including PID) and pregnancy were observed with lower CD4 counts. After adjusting for age, the association between increasing rates of pregnancies and lower CD4 counts no longer reached significance (<200/μL: 4.5/100PY; 200–499/μL: 4.8/100PY; ≥500/μL: 2.8/100PY; p=0.18); however, the trend of increased rates of first female STI at lower CD4 counts remained significant (<200/μL: 8.1/100PY; 200–499/μL: 5.9/100PY; ≥500/μL: 3.1/100PY; p<0.001). First STIs were infrequently reported in males (eTable 12).

Other clinical events

Mental health and neurodevelopmental conditions were among the most frequent conditions (4.0/100PY); no trends were observed by age, CD4 count, or VL/ARV strata (eTable 13). For asthma, atopy or allergy conditions, no trends were observed by age, CD4 count or VL/ARV strata (eTable 14). For gastrointestinal conditions, rates increased with lower CD4 counts (p<0.001); however, no trends were observed by age or VL/ARV strata (eTable 15). Higher rates of first non-AIDS-defining cardiac events were observed at lower CD4 counts (eTable 16), although this trend was no longer significant after adjusting for age (<200/μL: 2.1/100PY; 200–499/μL: 1.0/100PY; ≥500/μL: 0.7/100PY; p=0.08). Events potentially attributed to antiretroviral toxicity such as anemia (0.0/100PY), pancreatitis or hepatitis (0.4/100PY) and peripheral neuropathy (0.2/100PY) were among the least frequently reported events (eTables 17–19). Rates of first metabolic or bone abnormalities (1.5/100PY) showed no difference by age or CD4 count, but trended towards lower rates in patients with VL >400 copies/mL (suppressive ARVs: 1.7/100PY; non-suppressive cART: 1.1/100PY; no ARVs: 0.7/100PY; p=0.04) (eTable 20).

DISCUSSION

We analyzed rates of clinical events and mortality during 6,548 person-years of follow-up from 1,446 PHIVY aged 7–30 years in the AMP and P1074 cohort studies, stratified by time-updated age, CD4 count, and VL/ARV status. There were three key findings from this work. First, older youth were at highest risk for viremia, low CD4 counts, and serious clinical events, including mortality, CDC-C/WHO-4, and CDC-B/WHO-3 events. Viral load monitoring remained consistent across age ranges, suggesting ongoing engagement in care; high overall rates of viremia in participants aged 18–30 years are likely therefore due to suboptimal medication adherence or acceptance, or accumulated HIV viral resistance. Older PHIVY had greater early exposure to mono- or dual-regimens compared to younger PHIVY; the lack of viral suppression among those PHIVY prescribed cART is more likely related to poor medication adherence or acceptance, as resistance to newer ARVs, such as integrase inhibitors, is uncommon.35 Older youth also spent more time with CD4 counts <200/μL. Data from PHIVY aged 6–17 suggest that having CD4 counts <200/μL is associated with lower quality of life, psychiatric symptoms, and poor cognitive, academic, and social functioning, and data from adults suggest high risks of OIs and death.36,37 Our findings are consistent with a growing literature outlining the challenges to adhering to medications for PHIVY in adolescence, which intensify as PHIVY reach early adulthood38–43

Second, we observed relatively few clinical events and deaths during the follow-up period, during which cART was standard of care. These results add to prior reports of clinical events in AMP and P1074 by stratifying clinical event rates by time-updated age, CD4, and VL/ARV status.1,17 Our results are similar to other cohort studies of PHIVY in the US, UK and Ireland.9,13,44 For example, our observed incidence rates of mortality (0.4/100 PY), CDC-C/WHO-4 events (1.4/100 PY), CDC-B/WHO-3 events (3.2/100 PY), and bacterial pneumonia (1.4/100 PY) were comparable to those reported in the PACTG 219c cohort in 2006, 10 years into the cART-era (mortality: 0.5–0.8/100 PY; CDC-C: 1.5/100 PY; CDC-B: 5.0/100 PY; bacterial pneumonia 2.2/100 PY).5,12,44 However, the mortality rate in older adolescent (ages 15–19) and young adult (ages 20–29) PHIVY remained 6 and 12 times that of the US general population, respectively, after accounting for age, sex, and race. STIs among female participants and mental health and neurodevelopmental diagnoses were among the most commonly documented conditions. Higher STI rates (excluding PID) among female participants were associated with lower CD4 counts after adjusting for age. This finding has been previously reported, as well as the association of lower CD4 with herpes simplex virus reactivation and lack of human papilloma virus clearance.28–30,45–49 These data suggest a potential biological effect of immunosuppression; additionally, more frequent risk behavior among patients incompletely adherent to cART may contribute to higher STI rates.8,30 Pregnancy rates overall (3.5/100PY) and at older ages (13–17 years: 2.0/100PY and 18–30 years: 4.9/100PY) were similar to the general population (range for 15–29 years: 3.95–16.30/100PY).50 Pregnancy rates were higher among those with lower CD4 counts, although the strength of this relationship decreased after age adjustment. Complications potentially related to long-term antiretroviral therapy, including anemia, pancreatitis, hepatitis, peripheral neuropathy, and metabolic and bone abnormalities, were documented infrequently, likely reflecting use of less toxic ARVs over time.35,51

Finally, our person-time results add a valuable dimension to the literature base of cross-sectional and longitudinal studies on viremia in PHIVY. In the AMP and P1074 cohorts described here, 66% of person-time was spent with VL <400 copies/mL, similar to that observed in the HIV Research Network (63% of PHIVY aged 12–21 with VL <400 copies/mL from 2009–2012). However, viral suppression is higher than reported in the Adolescent Trials Network (ATN, 37% of PHIVY with VL below the lower level of detection) and in a recent meta-analysis (pooled North America estimate of youth with suppressed VL: 53% (range: 28–75%) of youth aged 12–24 years from 1990–2013).4,8,52 While lower suppression rates in the meta-analysis cohorts may reflect the inclusion of non-perinatally HIV-infected youth, lower suppression rates for PHIVY in the ATN cohort may reflect older age in that cohort (17.9 years versus 14.6 years). In contrast to data from adults, we found that having VL ≥400 copies/mL while being prescribed cART did not improve immunosuppression compared to having VL ≥400 copies/mL while taking no ARVs at all.53–61 The 35% of person-time spent with VL ≥400 copies/mL in our study raises critical concerns not only for individual patient outcomes, but also for the HIV epidemic among US youth.30,35,62 Youth who are viremic despite being engaged in care are more likely to have resistant virus and to report lower condom use than non-viremic youth, and thus are at risk of secondary horizontal or vertical transmission.63–65

This analysis of PHIVY in two large cohort studies had several limitations. First, the smallest proportion of person-time (19%) was spent at ages 7–12; nevertheless, we had adequate power for all statistical comparisons. Next, data collection protocols in the two cohorts may have impacted incidence rate estimates for specific events: 1) because we analyzed first events, we could not identify “recurrent bacterial pneumonia,” potentially underestimating CDC-C events; 2) STI screening was not performed at study-specific intervals but per local care practice; 3) care sought by participants at non-study healthcare facilities was recorded only if reported to study staff. Additionally, P1074 recorded only events deemed “clinically significant” by healthcare providers, whereas AMP recorded all events; we addressed this limitation by including only events that met the reporting threshold for both studies (e.g. laboratory-value diagnoses were excluded). Furthermore, PHIVY least engaged in care and at highest risk for adverse outcomes may not have been included. Conversely, to permit consistent coding of events between the AMP and P1074 studies, we excluded events prior to baseline, which may have led to overestimation of some first event rates. Finally, adherence data were not included in the analysis, because adherence measures were collected only in one study (AMP). Despite these counterbalancing possibilities, this analysis provides the most recent, detailed data available about clinical risks in US PHIVY over time. This information will be critical information for US policy makers as research and programmatic funding shifts for pediatric HIV in the US.66

In summary, we find that serious clinical events, including OIs and death, are rare in PHIVY receiving suppressive ART, but viremia, lower CD4 counts, and rates of serious clinical events and mortality increase throughout adolescence and young adulthood. Interventions to improve ART adherence and optimize models of care as perinatally HIV-infected youth age are urgently needed to improve long-term outcomes among this growing and vulnerable population.

Supplementary Material

Supplement

Acknowledgments

PHACS

We thank the children and families for their participation in PHACS, and the individuals and institutions involved in the conduct of PHACS. The study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development with co-funding from the National Institute on Drug Abuse, the National Institute of Allergy and Infectious Diseases, the Office of AIDS Research, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, the National Institute on Deafness and Other Communication Disorders, the National Institute of Dental and Craniofacial Research, and the National Institute on Alcohol Abuse and Alcoholism, through cooperative agreements with the Harvard T.H. Chan School of Public Health (HD052102) (Principal Investigator: George Seage; Project Director: Julie Alperen) and the Tulane University School of Medicine (HD052104) (Principal Investigator: Russell Van Dyke; Co-Principal Investigators: Kenneth Rich, Ellen Chadwick; Project Director: Patrick Davis). Data management services were provided by Frontier Science and Technology Research Foundation (PI: Suzanne Siminski), and regulatory services and logistical support were provided by Westat, Inc (PI: Julie Davidson). The following institutions, clinical site investigators and staff participated in conducting PHACS AMP and AMP Up in 2015, in alphabetical order: Ann & Robert H. Lurie Children’s Hospital of Chicago: Ram Yogev, Margaret Ann Sanders, Kathleen Malee, Scott Hunter; Baylor College of Medicine: William Shearer, Mary Paul, Norma Cooper, Lynnette Harris; Bronx-Lebanon Hospital Center: Murli Purswani, Mahboobullah Baig, Anna Cintron; Children’s Diagnostic & Treatment Center: Ana Puga, Sandra Navarro, Patricia A. Garvie, James Blood; Boston Children’s Hospital: Sandra K. Burchett, Nancy Karthas, Betsy Kammerer; Jacobi Medical Center: Andrew Wiznia, Marlene Burey, Molly Nozyce; Rutgers - New Jersey Medical School: Arry Dieudonne, Linda Bettica, James Oleske; St. Christopher’s Hospital for Children: Janet S. Chen, Maria Garcia Bulkley, Latreaca Ivey, Mitzie Grant; St. Jude Children’s Research Hospital: Katherine Knapp, Kim Allison, Megan Wilkins; San Juan Hospital/Department of Pediatrics: Midnela Acevedo-Flores, Heida Rios, Vivian Olivera; Tulane University School of Medicine: Margarita Silio, Medea Gabriel, Patricia Sirois; University of California, San Diego: Stephen A. Spector, Kim Norris, Sharon Nichols; University of Colorado Denver Health Sciences Center: Elizabeth McFarland, Juliana Darrow, Emily Barr, Paul Harding; University of Miami: Gwendolyn Scott, Grace Alvarez, Anai Cuadra.

IMPAACT

We thank the children and families for their participation in P1074 and the individuals and institutions involved in the conduct of P1074, as follows: New Jersey Medical School: Arry Dieudonne, MD, Linda Bettica, LPN, Anthony Scolpino, BA, James Oleske MD, MPH; UCLA–Los Angeles/Brazil AIDS Consortium: Yvonne Bryson, MD, Michele Carter, RN, Jaime Deville, MD, Karin Nielsen, MD; Texas Children’s Hospital: Michelle Del Rey, RN, Chivon McMullen-Jackson, BSN, RN, ADN, William Shearer, MD, PhD, Mary Paul, MD; Lurie Children’s Hospital of Chicago: Ram Yogev, MD, Margaret Ann Sanders, MPH, Ruth Williams, RN, Lynn Heald, PNP; Columbia University Medical Center (staff not specified); University of Miami Pediatric Perinatal HIV/AIDS: Gwendolyn Scott, MD, Charles Mitchell, MD, Claudia Florez, MD, Grace Alvarez, MD; University of California, San Diego, Mother-Child-Adolescent Program: Stephen Spector, MD, Rolando Viani, MD, MTP, Kimberly Norris, RN, BSN, Lisa Stangl, RN, NP; Duke University Medical Center: John Swetnam, Margaret Donnelly, PA-C, Joan Wilson, RN, MSN, Sunita Patil, PhD; Metropolitan Hospital: Mahrukh Bamji, MD, Indu Pathak, MD, Savita Manwanim, MD, Ekta Patel, MD; Boston Children’s Hospital: Sandra Burchett, MD, MS, Nancy Karthas, RN, MS, CPNP, Catherine Kneut, RN, MS, CPNP, Charlotte Mao, MD, MPH; Boston Medical Center Pediatric HIV Program: Ellen Cooper, MD, Diana Clarke, PharmD, Debra McLaud, RN, Pablo Leitz, MPH; New York University, New York: Aditya Kaul, MD, Nagamah Deygoo, MS, William Borkowsky, MD, Siham Akleh, RN; Jacobi Medical Center, Bronx: Michael Rosenberg, MD, Joanna Dobroszycki, MD, Karen Kassen, RN, BSN, Marlene Burey, NP; Children’s National Medical Center, Washington, DC: Steven Zeichner, MD, PhD, Connie Trexler, RN; Seattle Children’s Hospital: Ann Melvin, MD, MPH, Gloria Bowen, MA, Amanda Robson Nuss, BS, Carrie Pettler, MPH; University of South Florida– Tampa: Carina Rodriguez, MD, Patricia Emmanuel, MD, Denise Casey, RN, Alicia Marion, ARNP; San Juan City Hospital: Nicolas Rosario-Matos, MD, Wanda Marrero-Figueroa, BSN-RN, Carlos Ortega, BA, Lizbeth Fabregas, BS, MS; SUNY Stony Brook: Sharon Nachman, MD, Denise Ferraro, FNP, Erin Infanzon, Michele Kelly, NP; Children’s Hospital of Michigan: Chokechai Rongkavilit, MD, Ayanna Walters, RN, Eric McGrath, MD; Howard University, Washington DC: Sohail Rana, MD, Chandni Parikh, PNP, Caroline Reed, FNP, Patricia Houston, MS; Harbor UCLA Medical Center: Margaret Keller, MD, Michael Bolaris, MD, Judy Hayes, RN, Yolanda Gonzalez, RN; University of Southern California School of Medicine–Los Angeles County: Eva Operskalski, PhD, MBA, James Homans, MD, MPH, LaShonda Spencer, MD, Andrea Kovacs, MD; University of Florida Health Science Center: Mobeen Rathore, MD, Nizar Maraqa, MD, Saniyyah Mahmoudi, MSN, ARNP, Tabetha Gayton, PhD, ARNP; University of Colorado Denver: Hannah Bernath, MPH, Kerry Hahn, CCRP, Jennifer Dunn, MS, RN, FNP, Jennifer Englund, BS; South Florida Children’s Diagnostic and Treatment Center Fort Lauderdale: Ana Puga, MD, Amy Inman, Zulma Eysallenne, RN, James Blood, MSW; Strong Memorial Hospital, University of Rochester Medical Center: Geoffrey Weinberg, MD, Barbra Murante, MS, RN, PNP; Rush University Cook County Hospital, Chicago: Kenneth Boyer, MD, Jamie Martinez, MD, James McAuley, MD, Maureen Haak; Children’s Hospital of Los Angeles: Nancy Flores, Diane Tucker, MSN, Julie McAvoy, MPH, Marvin Belzer, MD; University of California San Francisco: Diane Wara, MD, Theodore Ruel, MD, Mica Muskat, NP, Nicole Tilton, NP; Johns Hopkins University Baltimore: Allison Agwu, MD, ScM, Thuy Anderson, RN, BSN, Aleisha Collinson-Streng, RN, BSN, ACRN, Kaye Park, MPH; Miller Children’s Hospital: Audra Deveikis, MD, Jagmohan Batra, MD, Tempe Chen, MD, David Michalik, DO; University of Maryland Baltimore: Douglas Watson, MD, Maria Johnson, DDS, Susan Lovelace, MSN, Corinda Hilyard; Tulane University New Orleans: Russell Van Dyke, MD, Margarita Silio, MD, Thomas Alchediak, MD, Sheila Bradford, RN; University of Alabama, Birmingham: Dorothy Shaw, BA, Sharan Robbins, BA, MAE, Newana Beatty, CCRC, Marilyn Crain, MD, MPH; The Children’s Hospital of Philadelphia: Carol Vincent, PhD, CRNP, Richard Rutstein, MD, Steven Douglas, MD, Sheri McDougall, MSHed, CCRC; Bronx-Lebanon Hospital: Anna Marie Emeh, MD, Mary Elizabeth Vachon, MPH, Levi Cherian, Murli Purswani, MD, FAAP; St Jude’s Children’s Hospital: Katherine Knapp, MD, Patricia Flynn, MD, Judy Glenn, LPN, Thomas Wride, MS; University of Puerto Rico Pediatric HIV/AIDS Research Program: Irma Febo, MD, Ruth Santos-Otero, RN, MPH, Maritza Cruz-Rodriguez, BA; Western New England Maternal Pediatric Adolescent AIDS: Katherine Luzuriaga, MD, Christina Hermos, MD, Jesica Pagano-Therrien, CPNP, Donna Picard BSN, RN, BC.

CEPAC

The authors gratefully acknowledge Simone Frank and Alex Bulteel for assistance with manuscript preparation, as well as Dr. Kenneth Freedberg and the Cost-effectiveness of Preventing AIDS Complications (CEPAC)-Pediatric research team in the Medical Practice Evaluation Center at Massachusetts General Hospital for providing feedback on study design and interpretation.

Dr. Patel and Mr. Karalius had full access to all data reported in this manuscript

FUNDING SOURCES

The Pediatric HIV/AIDS Cohort Study (PHACS) was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development with co-funding from the National Institute on Drug Abuse, the National Institute of Allergy and Infectious Diseases, the Office of AIDS Research, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, the National Institute on Deafness and Other Communication Disorders, the National Heart Lung and Blood Institute, the National Institute of Dental and Craniofacial Research, and the National Institute on Alcohol Abuse and Alcoholism, through cooperative agreements with the Harvard T.H. Chan School of Public Health (HD052102) and the Tulane University School of Medicine (HD052104).

Overall support for the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) group was provided by the National Institute of Allergy and Infectious Diseases of the NIH under (grants UM1AI068632 [IMPAACT Leadership and Operations Center], UM1AI068616 [IMPAACT Statistical and Data Management Center], and UM1AI106716 [IMPAACT Laboratory Center]), with cofunding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Institute of Mental Health.

This research was also funded by the National Institute of Allergy and Infectious Diseases (T32 AI007433 (AMN), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD079214 (ALC, GRS, KP)) and the Charles Hood Foundation Child Health Research Award (ALC, AMN, KP).

Footnotes

Cohort Registration:

Pediatric HIV/AIDS Cohort Study (PHACS) Adolescent Master Protocol (AMP) (http://www.phacsstudy.org); NCT01418014

International Maternal Pediatric Adolescent AIDS Clinical Trials Group (IMPAACT) P1074 (http://impaactnetwork.org); NCT01061164

AUTHOR ROLES

All authors contributed substantively to this manuscript in the following ways: Study design (AMN, KP, RVD, ALA, GRS III, ALC), data analysis (KP, BK), interpretation of results (all authors), drafting the manuscript (AMN, KP, BK, ALC), critical revision of the manuscript (all authors) and final approval of submitted version (all authors).

Conflicts of Interest and Financial Disclosures

The authors have no conflicts of interest or financial disclosures.

Role of the funding sources

The conclusions and opinions expressed in this article are those of the authors and do not necessarily reflect those of the National Institutes of Health or U.S. Department of Health and Human Services or other funders. The funding sources had no role in study design; collection, analysis, or interpretation of data; manuscript preparation; or decision to submit the paper for publication.

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