Abstract
We assessed morbidity and mortality among Thai and Vietnamese adolescents and young adults with perinatally acquired human immunodeficiency virus (PHIV) compared with matched HIV-negative peers, 12–24 years of age. Data on serious adverse events (SAEs) were prospectively collected between 2013 and 2018 according to U.S. NIH Division of AIDS criteria. Of 288 youth, 142 had PHIV and 146 were HIV negative. At enrollment, the overall median age was 19 (interquartile range [IQR] 17–20) years, 67% were female, and 95% were Thai. Almost all PHIV youth (99%) were receiving antiretroviral therapy; 50% self-reported adherence ≥95%. Median CD4 was 579 (IQR 404–800) cells/mm3, and 24% had HIV-RNA ≥1,000 copies/mL. During follow-up, 31 (22%) PHIV youth and 9 (6%) HIV-negative youth had at least one SAE. The overall crude SAE rate was 4.66 (3.42–6.35) per 100 person-years (PY); 7.22 (5.08–10.26) per 100 PY among youth with PHIV and 2.10 (1.09–4.03) per 100 PY in HIV-negative youth (p < .001). All seven deaths that occurred were among those with PHIV and primarily due to opportunistic infections (e.g., pneumocystis pneumonia, tuberculous meningitis). In multivariate analyses, having PHIV, being <20 years of age, and having anogenital high-risk human papillomavirus (HPV) infection with types 16 and/or 18 increased risk of SAEs. Among PHIV youth, CD4 count <350 cells/mm3, HIV-RNA ≥1,000 copies/mL, advanced WHO stages, and having anogenital HPV 16 and/or 18 infection predicted higher incidence of SAEs; no prior use of alcohol was protective. These data emphasize the need for tailored interventions for adolescents with PHIV to prevent long-term morbidity and mortality.
Keywords: HIV, morbidity, mortality, adolescent, youth, Asia
Introduction
In 2020, there were an estimated 1.7 million adolescents (10–19 years old) living with human immunodeficiency virus (HIV) worldwide.1 While there has been substantial global scale-up of pediatric HIV testing and antiretroviral therapy (ART), AIDS-related deaths among adolescents have largely remained static over the past decade, while substantially decreasing among younger children and adults.2–4 Older adolescents and young adults with perinatally acquired HIV (PHIV) are at high risk (HR) of morbidity and mortality associated with life-long infection and systemic inflammation, and ART-related toxicities.5–7 Importantly, struggles with adherence during this age period increase treatment failure, care disengagement, and death.6,8–11
In Asia, access to pediatric ART in the mid-2000s led to growing numbers of older adolescents and young adults with PHIV in care.12 However, the challenges of managing health care during this developmental stage and as they transition to adults increase poor adherence, which is further compounded by emerging health issues, including mental health.13 As they become sexually active, there also are risks of sexually transmitted infections (STIs) and pregnancy and pregnancy-related complications.14–19 There are few direct comparisons of these outcomes to age-matched, HIV-negative peers, which would provide key insights into the relative burdens of disease and associations with poor outcomes. This analysis aimed to examine clinical outcomes as captured through serious adverse events (SAEs) and mortality between Thai and Vietnamese youth with and without PHIV in the context of a prospective cohort study of sexually active adolescents and young adults.
Methods
Study population
Longitudinal data were collected from sexually active adolescents and young adults 12–24 years of age, who were enrolled in a 3-year prospective cohort to study human papillomavirus (HPV) infection from 2013 to 2018. Participants were recruited through five clinical sites in Thailand and Vietnam. Primary study methods and results have previously been reported.20,21 Briefly, study groups were matched by HIV status (PHIV or HIV negative), sex, age range (12–15, ≥16–18, ≥19–21, and ≥22–24 years), and lifetime number of sexual partners (≤3 or >3). HIV-negative adolescents were recruited through a combination of strategies, including recruitment at local vocational schools and gynecology clinics at the same institution as the HIV clinics.
Participants were followed at 6-monthly study visits for females and annual visits for males, during which they were interviewed about their medical histories, mental well-being, and behaviors (e.g., sexual practices, substance use). Physical examinations, sample collection, and laboratory testing were conducted during each visit. No participant was treated as lost to follow-up, as they were able to return for their delayed study visits until study closure.
The study protocol and regulatory documents were approved by institutional review boards at each participating study site and the coordinating center (TREAT Asia/amfAR, Thailand). Guardian consent and adolescent assent procedures were followed for participants <18 years of age who could not legally consent for themselves. These participants were formally consented for ongoing study participation after turning 18 or reaching the age of maturity according to local laws.
Clinical and behavioral evaluations
Participants were at least annually tested for HPV genotypes from liquid-based cytology samples from anogenital compartments, and for other STIs using cytology fluid for females and urine for males for Chlamydia trachomatis, Neisseria gonorrhoeae, and herpes simplex virus 2, and blood was sampled for syphilis testing. Nontreponemal antibody tests (rapid plasma reagin and venereal disease research laboratory) were used for initial syphilis screening, and treponemal antibody tests (treponema pallidum hemagglutination assay, fluorescent treponemal antibody absorption) were used for confirmatory testing.
HPV genotyping was done using the Linear Array test (LA HPV GT; Roche Molecular Systems, Inc.), including identification of 13 HR-HPV types (i.e., 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68). Testing for HPV and STIs was done at the Thai Red Cross AIDS Research Centre, Bangkok. Information on self-reported sexual behaviors and behavioral risks, including smoking, alcohol and drug use, and adherence, was collected using an audio computer-assisted self-interview tool (ACASI).22 Data on WHO stage, CD4 cell count, HIV RNA, and antiretroviral therapy were additionally obtained in youth with PHIV.
Clinical events after enrolment (baseline) that resulted in (a) inpatient hospitalization, (b) grade 4 laboratory values (according to the U.S. NIH Division of AIDS table),23 or (c) death were designated as SAEs. Data on individual SAEs were prospectively collected at the study sites and reviewed by the site clinicians, including start and stop dates, severity, relationship to the study procedures, and event patterns (e.g., single, continuous, intermittent). All SAEs were reported to the local Institutional Review Boards.
Statistical analysis
SAEs were reported into five main categories based on their frequency: infections, general symptoms, trauma, grade 4 laboratory values, and pregnancy complications. Formal comparisons of continuous characteristics were made by HIV status using a Wilcoxon test, and categorical characteristics were compared using Pearson's chi-square or Fisher's exact test, as appropriate. Follow-up time was calculated from the baseline study visit until the last study visit recorded in the database or death. The cumulative incidence of any SAE or of death alone was calculated by dividing the total number of events by the total person-years (PY) of follow-up (PYFU).
Generalized estimating equations (GEE) with a Poisson family, log link, and an exchangeable correlation matrix were used to assess factors associated with the incidence of any SAE or death alone (“events”). Covariates included sex at birth and HIV status (PHIV or HIV negative) and number of life-time partners at the baseline study visit. Time-updated covariates included age, body mass index (BMI), highest educational attainment, condom use, number of sexual partners in the previous 6 months, ever using alcohol and other substances, ever smoking cigarettes, laboratory-diagnosed STIs, HR-HPV infection, and HIV-specific covariates (i.e., CD4 cell count, HIV RNA load, WHO stage). HR-HPV infections were further divided into HPV 16 and/or 18 alone, which are the types most commonly associated with anogenital cancer.
We conducted a separate analysis among youth with PHIV to assess associations between incidence of SAE or death with potential risk factors and HIV-specific characteristics (noted above). The comparator for each HPV grouping analyses was of participants without the specified HPV genotypes (i.e., any HR-HPV vs. non HR-HPV, HPV 16/18 vs. non HPV 16/18). The linearity of continuous covariates was assessed against the log link function, and where these assumptions were not met, covariates were modeled as quartiles. Adjacent categories were collapsed together if the incident rate ratio (IRR) and size of the 95% confidence interval (CI) were similar. Covariates associated with the incidence of any SAE or death in univariate analyses were modeled by stepwise selection (p value <.15 to entry and p value <.10 for removal) in a multivariate model. Analyses were conducted using Stata version 14 (Statacorp LP, College Station, TX).
Results
Patient characteristics
Of 311 adolescents and young adults assessed for eligibility, 288 were included in the parent study; 142 were those with PHIV and 146 were matched, HIV-negative participants.20 Their median age was 19 (interquartile range [IQR] 17–20) years, 67% were female, 95% were Thai, 63% had reached grades 12 and below as their highest education attainment (Table 1), and median follow-up was 3.09 (IQR 2.97–3.29) years (Supplementary Table S1). A total of 18 (13%) PHIV participants were lost to follow-up over the course of the study (crude rate 3.84 [95% CI 2.42–6.09] per 100 PYFU). At baseline, among youth with PHIV, 99% were taking ART, with 50% self-reporting adherence at ≥95% (median adherence 93% [IQR 80–100]). Their median log10 HIV RNA was 1.60 (IQR 1.30–2.77) copies/mL, with 66% having viral suppression at <50 copies/mL. Median CD4 cell count was 579 (IQR 404–800) cells/mm3.
Table 1.
Baseline Characteristics of Study Participants by Incidence of Serious Adverse Event
| Baseline characteristics | PHIV |
HIV-negative |
Overall |
Total (N = 288) | p a | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| With SAE (N = 31) | Without SAE (N = 111) | N = 142 | With SAE (N = 9) | Without SAE (N = 137) | N = 146 | With SAE (N = 40) | Without SAE (N = 248) | |||
| Female | 22 (71) | 71 (64) | 93 (65) | 7 (78) | 92 (67) | 99 (68) | 29 (73) | 163 (66) | 192 (67) | .40 |
| Ethnicity | ||||||||||
| Thai | 30 (97) | 105 (95) | 135 (95) | 9 (100) | 129 (94) | 138 (95) | 39 (98) | 234 (94) | 273 (95) | .41 |
| Vietnamese | 1 (3) | 6 (5) | 7 (5) | 0 (0) | 8 (6) | 8 (5) | 1 (3) | 14 (6) | 15 (5) | |
| Age, years, median (IQR) | 18 (17–20) | 19 (17–20) | 18 (17–20) | 17 (16–18) | 19 (18–20) | 19 (18–20) | 18 (17–20) | 19 (18–20) | 19 (17–20) | .04 |
| BMI, kg/m2, median (IQR) | 19 (17–21) | 19 (17–22) | 19 (17–21) | 21 (19–23) | 20 (18–23) | 20 (18–23) | 19 (17–22) | 20 (18–22) | 20 (18–22) | .39 |
| PHIV | 31 (78) | 111 (45) | 142 (49) | NA | NA | NA | 31 (78) | 111 (45) | 142 (49) | <.001 |
| Sociodemographic data | ||||||||||
| Living situation | .10 | |||||||||
| With parent(s) | 9 (29) | 33 (30) | 42 (30) | 7 (78) | 90 (66) | 97 (66) | 16 (41) | 123 (49) | 139 (48) | |
| With nonparental relatives | 15 (48) | 39 (35) | 54 (38) | 1 (11) | 14 (10) | 15 (10) | 16 (40) | 53 (21) | 69 (24) | |
| With a foster family/shelter home | 1 (3) | 5 (5) | 6 (4) | 0 (0) | 0 (0) | 0 (0) | 1 (3) | 5 (2) | 6 (2) | |
| With a partner or spouse | 5 (16) | 20 (18) | 25 (18) | 0 (0) | 27 (20) | 27 (18) | 5 (13) | 47 (19) | 52 (18) | |
| On their own | 1 (3) | 14 (13) | 15 (11) | 1 (11) | 6 (4) | 7 (5) | 2 (5) | 20 (8) | 22 (8) | |
| Highest education | .02 | |||||||||
| Grades 1–12 | 29 (94) | 80 (72) | 109 (77) | 7 (78) | 64 (47) | 71 (49) | 36 (90) | 144 (58) | 180 (63) | |
| Preuniversity | 1 (3) | 15 (14) | 16 (11) | 1 (11) | 44 (32) | 45 (31) | 2 (5) | 59 (24) | 61 (21) | |
| University | 1 (3) | 12 (11) | 13 (9) | 1 (11) | 25 (18) | 26 (18) | 2 (5) | 37 (15) | 39 (14) | |
| Nonformal education | 0 (0) | 4 (4) | 4 (3) | 0 (0) | 4 (3) | 4 (3) | 0 (0) | 8 (3) | 8 (3) | |
| Currently employed | 14 (45) | 55 (50) | 69 (49) | 5 (56) | 54 (39) | 59 (40) | 19 (48) | 109 (44) | 128 (44) | .68 |
| Risk behavior and sexual history data | ||||||||||
| Ever tried alcohol | 25 (81) | 94 (85) | 119 (84) | 9 (100) | 126 (92) | 135 (92) | 34 (85) | 220 (89) | 254 (88) | .50 |
| Drank alcohol, past 3 months | 16 (64) | 51 (54) | 67 (56) | 7 (78) | 83 (66) | 90 (67) | 23 (68) | 133 (60) | 156 (61) | .42 |
| Ever smoked cigarettes | 15 (48) | 49 (44) | 64 (45) | 8 (89) | 75 (55) | 83 (57) | 23 (58) | 124 (50) | 147 (51) | .41 |
| Smoked cigarettes, past 3 months | 11 (73) | 27 (55) | 38 (59) | 4 (50) | 44 (59) | 48 (58) | 15 (65) | 71 (57) | 86 (58) | .45 |
| Ever used recreational drugs | 2 (6) | 14 (13) | 16 (11) | 2 (22) | 34 (25) | 36 (25) | 4 (10) | 48 (19) | 52 (18) | .15 |
| Amphetamine | 2 (100) | 8 (57) | 10 (63) | 1 (50) | 22 (67) | 23 (66) | 3 (75) | 30 (63) | 33 (63) | .65 |
| Used other drugs in past 3 months | 2 (100) | 6 (43) | 8 (50) | 1 (50) | 8 (24) | 9 (25) | 3 (75) | 14 (29) | 17 (33) | .06 |
| Lifetime partners, median (IQR) | 3 (2–10) | 2 (1–4) | 2 (1–5) | 1 (0–1) | 1 (1–1) | 1 (1–1) | 3 (2–8) | 2 (1–4) | 2 (1–5) | .13 |
| 1 | 7 (23) | 36 (32) | 43 (30) | 2 (22) | 38 (28) | 40 (27) | 9 (23) | 74 (30) | 83 (29) | .63 |
| 2 | 7 (23) | 23 (21) | 30 (21) | 2 (22) | 30 (22) | 32 (22) | 9 (23) | 53 (21) | 62 (22) | |
| ≥3 | 17 (55) | 52 (47) | 69 (49) | 5 (56) | 69 (50) | 74 (51) | 22 (55) | 121 (49) | 143 (50) | |
| Partners, past 6 months, median (IQR) | 1 (1–1) | 1 (1–1) | 1 (1–1) | 3 (2–5) | 3 (1–4) | 3 (1–4) | 1 (1–1) | 1 (1–1) | 1 (1–1) | .51 |
| None | 4 (13) | 11 (10) | 15 (11) | 3 (33) | 8 (6) | 11 (8) | 7 (18) | 19 (8) | 26 (9) | .08 |
| 1 | 20 (65) | 90 (81) | 110 (77) | 6 (67) | 106 (77) | 112 (77) | 26 (65) | 196 (79) | 222 (77) | |
| ≥2 | 7 (23) | 10 (9) | 17 (12) | 0 (0) | 23 (17) | 23 (16) | 7 (18) | 33 (13) | 40 (14) | |
| Condom use with vaginal sex receptive, past 6 months | ||||||||||
| Always | 6 (19) | 25 (23) | 31 (22) | 0 (0) | 11 (8) | 11 (8) | 6 (15) | 36 (15) | 42 (15) | .93 |
| Sometimes/never | 15 (48) | 45 (41) | 60 (42) | 6 (67) | 79 (58) | 85 (58) | 21 (53) | 124 (50) | 145 (50) | |
| Not applicable/not having sex this route | 10 (32) | 41 (37) | 51 (36) | 3 (33) | 47 (34) | 50 (34) | 13 (33) | 88 (35) | 101 (35) | |
| Laboratory-diagnosed STIs | 9 (29) | 25 (23) | 34 (24) | 3 (33) | 27 (20) | 30 (21) | 12 (30) | 52 (21) | 64 (22) | .20 |
| Syphilis | 1 (3) | 3 (3) | 4 (3) | 0 (0) | 3 (2) | 3 (2) | 1 (3) | 6 (2) | 7 (2) | .045 |
| Chlamydia | 7 (23) | 23 (21) | 30 (21) | 3 (33) | 21 (15) | 24 (16) | 10 (25) | 44 (18) | 54 (19) | .28 |
| Gonorrhea | 2 (6) | 3 (3) | 5 (4) | 0 (0) | 2 (1) | 2 (1) | 2 (5) | 5 (2) | 7 (2) | .26 |
| Herpes simplex virus type 2 | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 3 (2) | 3 (2) | 0 (0) | 3 (1) | 3 (1) | .51 |
| HPV genotype | ||||||||||
| Any HR-HPVb | 19 (61) | 53 (48) | 72 (51) | 4 (44) | 54 (39) | 58 (40) | 23 (58) | 107 (43) | 130 (45) | .09 |
| HPV 16 and/or 18 only | 11 (35) | 22 (20) | 33 (23) | 4 (44) | 30 (22) | 34 (23) | 15 (38) | 52 (21) | 67 (23) | .02 |
| HIV-related characteristics | ||||||||||
| CD4 cells count, cell/mm3, median (IQR) | 434 (67–715) | 606 (491–808) | 579 (404–800) | <.001 | ||||||
| Log10 HIV-RNA, copies/mL, median (IQR) | 4.03 (1.60–5.07) | 1.60 (1.30–1.86) | 1.60 (1.30–2.77) | <.001 | ||||||
| HIV-RNA ≥1,000 copies/mL | 17 (55) | 17 (15) | 34 (24) | <.001 | ||||||
| HIV-RNA <50 copies/mL | 11 (35) | 83 (75) | 94 (66) | <.001 | ||||||
| Current WHO stage | ||||||||||
| Stage 1 or 2 | 26 (84) | 105 (95) | 131 (92) | .03 | ||||||
| Stage 3 or 4 | 5 (16) | 6 (5) | 11 (8) | |||||||
| ART regimen | ||||||||||
| NNRTI based | 13 (42) | 75 (68) | 88 (62) | .06 | ||||||
| PI based | 16 (52) | 30 (27) | 46 (32) | |||||||
| Other | 1 (3) | 5 (5) | 6 (4) | |||||||
| Unknown | 1 (3) | 1 (1) | 2 (1) | |||||||
| Self-report adherence, %, median (IQR) | 90 (50–100) | 95 (80–100) | 93 (80–100) | .65 | ||||||
| Self-report adherence ≥95% | 15 (48) | 56 (50) | 71 (50) | .84 | ||||||
Characteristics are described as medians (IQR) or N (%).
p Value shows the comparison between participants with and without SAE.
HR-HPV includes genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68.
ART, antiretroviral therapy; BMI, body mass index; HPV, human papillomavirus; HR, high risk; IQR, interquartile range; PHIV, perinatally acquired human immunodeficiency virus; SAE, serious adverse event; STIs, sexually transmitted infections, including syphilis, chlamydia, gonorrhea, and herpes simplex virus type 2.
All-cause SAEs
Seventy-eight SAEs, including 7 deaths, occurred among 40 participants: 67 (86%) were reported among 31 (22%) youth with PHIV and 11 (14%) were reported among 9 (6.2%) HIV-negative participants (Table 2). All deaths were among PHIV participants between the ages of 16–23 years (57% females). Almost all SAEs (n = 74; 95%) resulted in hospitalization with a median hospital stay of 7 (IQR 3–13) days (Supplementary Table S1). The crude rate of all SAEs was 7.22 (95% CI 5.08–10.26) per 100 PY for youth with PHIV and 2.10 (95% CI 1.09–4.03) per 100 PY for HIV-negative youth (p < .001), whereas the crude mortality was 1.49 (95% CI 0.71–3.13) per 100 PY in youth with PHIV and 0.76 (95% CI 0.36–1.60) per 100 PY in overall cohort.
Table 2.
Categories of Serious Adverse Events by Human Immunodeficiency Virus Status
| Primary causes | PHIV (N = 31) | HIV negative (N = 9) |
|---|---|---|
| Infections | 43 | 3 |
| Pneumoniaa | 16 | 0 |
| Sepsis | 6 | 0 |
| Tuberculosis | 5 | 0 |
| Cerebral | 4 | 0 |
| Disseminated infections, not otherwise specified | 2 | 0 |
| Otherb | 10 | 3 |
| General symptomsc | 10 | 0 |
| Trauma/accident | 10 | 4 |
| Grade 4 laboratory values23 | 4 | 0 |
| Pregnancy complication | 0 | 4 |
| Total | 67 | 11 |
The 67 SAEs among 31 youth with PHIV and 11 SAEs occurred among 9 HIV-negative youth.
Causes of pneumonia among PHIV: nocardia (n = 4), pneumocystis (n = 3), mycoplasma (n = 1), acinetobacter (n = 1), and unknown (n = 7).
Causes of other infections among PHIV: gastroenteritis (n = 3), pharyngitis, herpes zoster, syphilis, vaginal infection, urinary tract infection, influenza, and appendicitis (n = 1 each); among HIV negative: influenza (n = 1) and appendicitis (n = 2).
General symptoms among PHIV: seizure (n = 2), stroke, drug interaction, drug reaction, diarrhea, asthma, anemia, azotemia, and hypokalemic periodic paralysis (n = 1 each).
Infections (n = 43, 64%) were the main causes of SAEs, followed by general symptoms and trauma (n = 10, 15% each), and grade 4 abnormal laboratory values (n = 4, 6%) (Table 2). Of the SAEs in youth with PHIV, 24% were AIDS-related illnesses and 12% were tuberculosis related (not mutually exclusive). The seven deaths among PHIV were related to pneumocystis pneumonia (n = 2), meningitis (n = 2), disseminated multidrug-resistant tuberculosis, septic shock, and acute asthmatic attack (n = 1 each) (Supplementary Table S2). Trauma and pregnancy complications were the most common causes of SAEs in HIV-negative participants.
Factors associated with SAEs
On overall bivariate comparisons (Table 1), those with SAEs were more likely to have PHIV (78% vs. 45%, p < .001), a lower level of education (grade 12 and below vs. higher education level, p = .02), and infection with HPV types 16 and/or 18 (38% vs. 21%, p = .02). Among youth with PHIV, those with incident SAEs had significantly higher baseline log10 HIV RNA (4.03 copies/mL vs. 1.60 copies/mL, p < .001), less frequent virologic suppression (<50 copies/mL; 35% vs. 75%, p < .001), lower median CD4 cell counts (434 cells/mm3 vs. 606 cells/mm3, p < .001), and higher frequency of WHO stages 3 and 4 versus 1 and 2 (p = .03).
Factors associated with incident SAEs on a univariate GEE model included being PHIV (p < .001), lower educational attainment (grade 12 and lower, p = .001), age <20 years (p = .05), BMI <18.5 kg/m2 (p = .02), laboratory-diagnosed STIs (p = .01), any HR-HPV infection (p = .02), and HPV 16 and/or 18 infection (p = .004) (Table 3). No prior alcohol use was protective (p = .01). In an adjusted multivariate model, incident SAEs were associated with PHIV (adjusted IRR [aIRR] 5.65 [95% CI 2.89–11.05]; p < .001), age <20 years (aIRR 1.67 [95% CI 1.05–2.63]; p = .029), and having HR-HPV 16/18 infection (aIRR 1.91 [95% CI 1.19–1.06]; p = .007).
Table 3.
Factors Associated with Serious Adverse Events in the Cohort
| Covariates | Univariate, IRR (95% CI) | p | Multivariate, aIRR (95% CI) | p |
|---|---|---|---|---|
| PHIV | 5.91 (2.97–11.77) | <.001 | 5.65 (2.89–11.05) | <.001 |
| Sex, female | 1.71 (0.93–3.15) | .08 | 1.79 (0.99–3.23) | .053 |
| Age, <20 years | 1.60 (1.01–2.52) | .05 | 1.67 (1.05–2.63) | .029 |
| BMI, <18.5 kg/m2 | 1.76 (1.09–2.83) | .02 | ||
| Highest education level | .001 | |||
| Grades 1–12 | Ref | |||
| Preuniversity/university | 0.14 (0.04–0.47) | |||
| Nonformal education | 0.32 (0.14–0.74) | |||
| Alcohol use, ever | 0.44 (0.23–0.84) | .01 | ||
| Cigarette smoking, ever | 1.15 (0.7–1.89) | .58 | ||
| Recreational drug use, ever | 1.11 (0.64–1.94) | .71 | ||
| Laboratory-diagnosed STIs and HR-HPV | 1.56 (0.98–2.47) | .06 | ||
| Laboratory-diagnosed STIs | 1.91 (1.16–3.14) | .01 | ||
| Any HR-HPVa | 1.72 (1.09–2.73) | .02 | ||
| HPV 16 and/or 18 onlyb | 2.03 (1.26–3.26) | .004 | 1.91 (1.19–1.06) | .007 |
| PHIV cohort | ||||
| Sex, female | 2.06 (1.05–4.03) | .03 | ||
| Age, <20 years | 1.39 (0.85–2.28) | .19 | ||
| BMI, <18.5 kg/m2 | 1.31 (0.78–2.18) | .30 | ||
| CD4, cells/mm3 | <.001 | .001 | ||
| <200 | 8.38 (4.98–14.13) | 3.35 (1.62–6.95) | ||
| 200–350 | 0.84 (0.26–2.70) | 0.54 (0.16–1.85) | ||
| ≥350 | Ref | ref | ||
| HIV-RNA, ≥1,000 copies/mL | 5.80 (3.41–9.87) | <.001 | 3.25 (1.60–6.61) | .001 |
| Adherence to ART, <95% | 5.28 (3.04–9.17) | <.001 | ||
| WHO stages 3 or 4 vs. 1 or 2 | 3.90 (2.13–7.16) | <.001 | 2.38 (1.24–4.59) | .01 |
| Alcohol use, ever | 0.54 (0.28–1.04) | .06 | 0.37 (0.19–0.71) | .003 |
| Laboratory-diagnosed STIs and HR-HPV | 1.60 (0.96–2.67) | .07 | ||
| Laboratory-diagnosed STIs | 1.66 (0.99–2.79) | .06 | ||
| Any HR-HPVa | 1.81 (1.09–3.02) | .02 | ||
| HPV 16 and/or 18 only | 2.20 (1.34–3.63) | .002 | 1.97 (1.16–3.33) | .012 |
HR-HPV includes genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68.
Among the 2 HPV-related variables, only HPV 16 and/or 18 was included in the adjusted models.
aIRR, adjusted incident rate ratio; CI, confidence interval.
In an adjusted stepwise model among PHIV youth alone, incident SAEs were associated with lower current CD4 count (<200 cells/mm3; aIRR 3.35 [95% CI 1.62–6.95]; p = .001), virologic failure (HIV RNA ≥1,000 copies/mL; aIRR 3.25 [95% CI 1.60–6.61]; p = .001), higher WHO stages (stages 3 or 4; aIRR 2.38 [95% CI 1.24–4.59]; p = .01), and HPV 16 and/or 18 infection (aIRR 1.97 [95% CI 1.16–3.33]; p = .012). No prior alcohol use was protective (aIRR 0.37 [95% CI 0.19–0.71]; p = .003).
Discussion
In this longitudinal cohort study of older adolescents and young adults, those with PHIV had a six times higher incidence of SAEs than their matched HIV-negative controls over a median of 3 years of follow-up. The overall SAE incidence among youth with PHIV (7.22 per 100 PY) was on the high end of the range reported in South Africa and Uganda (2.9–8.5 per 100 PY),24,25 and higher than 0.3–0.9 per 100 PY reported in the United Kingdom.11 Infections were the major causes, particularly among PHIV youth and those younger than 20 years.
This is consistent with earlier studies of complications of life-long treatment among youth with PHIV, who often struggle with adherence to their ART from childhood onward.26,27 Poor adherence increases during adolescence, and the sustained periods of unsuppressed viral load they experience lead to CD4 cell count decline, immune system compromise, and risk of opportunistic infections.28,29 This was also reflected in the associations with lower CD4 and more frequent viral failure in our cohort.
The correlation of lower educational attainment with SAEs is likely driven by 62% of the 67 PHIV youth with SAEs, who were below age-appropriate educational attainment, which compares with 45% of the 11 HIV-negative youth with SAEs. Notably, lower alcohol use was protective in both the overall and subanalysis of PHIV youth, which is consistent with other studies showing associations with poor adherence and treatment failure.30,31
The association with HPV 16 and/or 18 is hypothesized to be related to prior history of sexual activity, even if current sexual behavior was not considered HR (e.g., multiple partners), or reporting was subject to social desirability bias. Other research in this cohort has shown that anogenital HPV infection is more persistent among HPV youth than those without HIV.15 Tailored interventions that proactively address culturally sensitive topics like substance use and sexual behavior32,33 are increasingly needed to detect and address inadequate ART adherence and challenges to retention in care, especially among those with treatment failure and weakened immune systems.
The number of deaths occurring only among PHIV youth, and mostly due to severe and opportunistic infections, was unexpected and concerning. For example, among the 16 cases of pneumonia reported, 3 were due to Pneuomocystis jirovecii (Pneumocystis pneumonia [PCP]), which led to 2 deaths. While we did not collect data on use of trimethorprim-sulfamethoxazole, these youth were all in routine HIV care and would have presumably had access to CD4-guided PCP prophylaxis as well as effective ART. These infectious complications are likely a result of long-term poor adherence leading to treatment failure and eventual resistance to ART, which has been seen among adolescents across all country-income settings.6,34,35
These outcomes reflect the perennial demands of managing lifelong adherence among PHIV children who then become adolescents. While the majority of Asian PHIV youth are able to make the transition through adolescence without serious morbidity, the challenges inherent in this developmental stage make it harder for others to cope with the daily requirements of ART, which they often are left to manage without sufficient social and financial support interventions.17,36,37 There have been efforts to expand the scope of adherence interventions to include direct financial support (e.g., cash transfers), differentiated service delivery, and peer navigator programs.38–40 However, it is likely that combinations of these interventions will be needed to ensure better adherence.41,42 In addition, these programs should start at the transition between childhood to adolescence to build resilience through adulthood.
Studies in Thailand have demonstrated successful HIV care transition among PHIV youth who received combinations of intensive peer support, formal transition preparations, and active involvement of both pediatric and adult health care providers who communicated around care coordination before and after clinic transfers.43–45 Given their HRs of SAEs, including death, national and regional HIV surveillance programs should be strengthened to track survival of PHIV youth through adulthood and monitor outcomes of HIV care transitions.46–49
The limitations in our study included the use of medical record review to capture co-morbidities or HIV-related conditions before enrollment of PHIV youth, which could have led to incomplete assessments of clinical stage at baseline. Although we allowed for inclusion of younger adolescents, the median age of our cohort was 19 years at enrollment, and our results would not have captured the risks of morbidity and mortality experienced by those with PHIV, who died before reaching this age, leading to survival bias.
Some SAEs may have been the result of a common underlying cause (e.g., an opportunistic infection leading to death), but could have been concurrently reported and included separately in the analysis. The parent study itself was focused on HPV infections and sexual behavior risks, and laboratory evaluations of non-STI-related infectious complications were limited. However, being able to match PHIV youth to HIV-negative youth on factors not specific to HPV and closely monitoring them over time allowed us to capture events that may have been missed in routine care.
In conclusion, sexually active PHIV youth in this cohort had substantially higher rates of SAEs, including death, than their matched HIV-negative peers. The frequency of infectious complications, despite continuous access to HIV treatment and care support, points to the need for more effective interventions to sustain adherence in PHIV youth and guide transition through young adult life.
Supplementary Material
Acknowledgments
The authors gratefully acknowledge the participation of adolescents and young adults and their families, and the contributions of all study staff, as well as the coordinating team, Waropart Pongchaisit, Kamonrat Kosaphan, and Theeradej Boonmangum. The TREAT Asia ACASI used in this study was based on the PHACS ACASI (Perinatal HIV/AIDS Cohort Study), with permission (phacsstudy.org).
Steering Committee:
S. Gatechompol, S. Kerr, and C. Ruengpanyathip, HIV-NAT, Bangkok, Thailand; K. Chokephaibulkit, M. Thamkhantho, A. Chalermchockcharoenkit, and S. Sricharoenchai, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; R. Hansudewechakul, J. Achalapong, and V. Wanchaitanawong, Chiangrai Prachanukroh Hospital, Chiangrai, Thailand; D.L.D. Hanh, D.N.Y. Dung, and T.D. Thang, Hung Vuong Hospital, Ho Chi Minh City, Vietnam; D.N.H. Tran and K.H. Truong, Children's Hospital 1, Ho Chi Minh City, Vietnam; S. Chaithongwongwatthana, W. Termrungruanglert, S. Triratanachat, and S. Sirivichayakul, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; J.M. Palefsky, University of California, San Francisco, USA; N. Phanuphak, N. Teeratakulpisarn, and T. Pankam, Thai Red Cross AIDS Research Centre, Bangkok, Thailand; and AH Sohn, J. Ross, and T. Singtoroj, TREAT Asia/amfAR–The Foundation for AIDS Research, Bangkok, Thailand.
Contributor Information
Collaborators: on Behalf of the HPV in Adolescents Study
Authors' Contributions
T.S., S.T., N.P., S.K., and A.H.S. contributed to the conceptualization and design of the study. K.C., S.G., R.H., H.L.D.D., and D.N.H.T. contributed to data collection and coordination of the study. S.T. and S.K. conducted the formal analyses. T.S. and A.H.S. wrote the original draft of the article. All authors critically revised, edited, and approved the final version for publication.
Author Disclosure Statement
A.H.S. reports grant funding to her institution from ViiV Healthcare. Other authors declare no conflict of interests.
Funding Information
The study was an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, and SEARCH/Thai Red Cross AIDS Research Centre with funds provided by the U.S. National Institutes of Health (NIH), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD; R01HD073972), and additional support from AIDS+ Austria. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.
Supplementary Material
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