pmc92032131135Clin Infect DisClin Infect DisClinical infectious diseases : an official publication of the Infectious Diseases Society of America1058-48381537-6591404690051237596010.1093/cid/ciaf300HHSPA2103813ArticleIncidence of AIDS-Defining Conditions Among Adults With Perinatally Acquired HIV After Transition to Adult HIV Care in the United States and Canada, 2000–2022http://orcid.org/0000-0003-2602-4214HawNel Jason L.1LeskoCatherine R.1http://orcid.org/0000-0001-5878-5101NgDerek K.1http://orcid.org/0000-0001-5650-4310LamJennifer O.23GeboKelly4http://orcid.org/0000-0002-4822-6979SterlingTimothy R.5http://orcid.org/0000-0002-0053-3806RabkinCharles S.6LiJun7BuchaczKate7AgwuAllison48http://orcid.org/0000-0002-5068-6595AlthoffKeri1North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD)aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA;Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA;Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA;Department of Internal Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA;Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA;Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA;Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA;Department of Pediatrics, Johns Hopkins Medicine, Baltimore, Maryland, USA

NA-ACCORD collaborating cohorts and representatives are listed in the Acknowledgements.

Author contributions. N. L. J. H., C. R. L., D. K. N., A. A., and K. A. jointly conceptualized the research question. N. L. J. H. processed and analyzed the data and wrote the original draft of the manuscript. A. A. provided substantial edits from a clinical perspective, while C. R. L., D. K. N., and K. A. provided substantial edits from a methodological perspective. J. O. L., K. G., T. R. S., C. S. R., J. L., and K. B. also provided substantial edits in later versions of the draft.

Correspondence: N. J. L. Haw, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21224 (nhaw1@jh.edu); K. Althoff, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21224 (kalthoff@jh.edu).1882025241220252582025815962969For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.Background.

Little is known about the incidence of AIDS-defining conditions (ADCs) among people with perinatally acquired human immunodeficiency virus (PHIV) who transitioned to adult human immunodeficiency virus (HIV) care in the United States and Canada. We described the incidence among PHIV and compared it with that among those with non–perinatally acquired HIV (non-PHIV) and across calendar era.

Methods.

Using data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) from 2000–2022, we estimated weighted mean cumulative counts (MCCs) of ADCs, comparing people with PHIV and non-PHIV acquisition risk groups aged 18–40 years engaged in adult HIV care. The weights accounted for differences in characteristics among HIV acquisition risk groups as well as informative censoring. We calculated 95% confidence intervals using bootstrapping. We stratified results before and after 2012, when the immediate start of antiretroviral therapy was first recommended.

Results.

There were 5429 ADCs among 22 950 people with HIV. Among those with PHIV, the MCC of ADCs by 3 years in adult HIV care was 26 per 100 persons (95% confidence interval, 15–40) in 2000–2011 and 16 per 100 (5–21) in 2012–2022. Within each calendar era, weighted MCCs of ADCs among people with PHIV were similar or lower than in non-PHIV groups. Within each HIV acquisition risk group, weighted MCCs of ADCs were lower in 2000–2011 than in 2012–2022.

Conclusions.

People with PHIV who transitioned to adult HIV care did not experience a greater ADC incidence than those with non-PHIV. This emphasizes the importance of continued engagement in adult HIV care, as it provides critical opportunities for ADC prevention and management.

HIVperinatally acquiredAIDS-defining conditionslifetime survivors

The incidence of AIDS-defining conditions (ADCs) among people with human immunodeficiency virus (HIV) has decreased in the past 2 decades due to effective antiretroviral therapy (ART) [1, 2]. Continuous engagement in HIV care is important for adherence to ART, leading to sustained virological suppression [3], but ADCs persist even among young people with HIV retained in care [4]. Delayed ART initiation and suboptimal ART may be reasons for this persistence, as they complicate the immune restoration process afforded by ART [5].

People with perinatally acquired HIV (PHIV) may be at higher lifetime ADC risk due to factors not experienced by people with non–perinatally acquired HIV (non-PHIV) at the same age. A 30-year old with PHIV today likely received suboptimal ART during childhood due to limited ART options [6], while a 30-year old with non-PHIV likely initiated ART when multiple effective ART regimens were available. People with PHIV also acquired HIV at an age of early immune system development, potentially leading to complications in immune restoration despite timely ART initiation [7]. They also may have challenges transitioning from pediatric to adult HIV care, including disruptions in care that delay ADC prevention and management [8]. Altogether, these challenges may increase the ADC risk despite early ART initiation in childhood and subsequent prolonged ART use. However, little is known about ADC incidence among people with PHIV after transition into adult HIV care, and such data may inform clinical guidelines to provide timely ADC screening, prophylaxis, and treatment.

In the current study, we described trends in ADC incidence after transition to adult HIV care among people with PHIV aged 18–40 years in the United States and Canada and compared them with findings in adults with non-PHIV before and after 2012, when the immediate start of ART was recommended [9–11].

METHODSStudy Population

The North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) is a large cohort collaboration of adults with HIV aged ≥18 years in the United States and Canada [12]. Enrolled participants had ≥2 visits to an adult HIV clinic within a 12-month window of enrollment. Each cohort provided standardized sociodemographic, clinical, and treatment data, which were harmonized by the Data Management Core (University of Washington, Seattle) and prepared for analysis at the Epidemiology/Biostatistics Core (Johns Hopkins University, Baltimore, Maryland) [12]. The study had institutional review board approval from the Johns Hopkins Bloomberg School of Public Health and locally at each cohort.

We included adults with HIV aged 18–40 years with a known HIV acquisition risk group and had ≥1 month of follow-up from January 2000 to December 2022 across 14 clinical cohorts (12 in the United States and 2 in Canada) that had ≥1 adult with PHIV. Since the geographic distribution, health system contexts, and exposure and outcome ascertainment protocols varied across cohorts, we included only cohorts that enrolled people with PHIV, for more homogenous comparisons between PHIV and non-PHIV. We followed adults from study entry, defined as the latest among enrollment date, cohort open date, ADC observation window [13] start date, or 1 January 2000. Three-fourths (74%) of participants with PHIV enrolled at age 18–25 years, the typical age of transition to adult HIV care for people transferring from pediatric HIV clinics [14], so we assumed that study entry was a proxy for the transition date to adult HIV care for people with PHIV. The participants’ study exit was defined as the earliest among cohort close date, ADC observation window close date, 31 December 2022, death date, 40th birth year, or loss to follow-up date, defined as the start of the 18-month period without a CD4 cell count or HIV viral load test [15].

HIV Acquisition Risk Group

The primary exposure of interest was perinatally acquired HIV (PHIV). HIV acquisition risk was ascertained through review of clinical records and patient interviews. Consistent with US Centers for Disease Control and Prevention definitions, we classified HIV acquisition risk as perinatal, male-to-male sexual contact (MMSC), heterosexual contact, or persons who inject drugs (PWID), which combined PWID only and those with both MMSC and PWID acquisition risk. We excluded participants who did not fall into one of these categories (eg, transfusion) or with unknown acquisition risk.

ADC Definitions

We defined ADCs as a clinical diagnosis of ADC reported by the provider in the health record [16] and ascertained by individual cohorts and harmonized by the Data Management Core. Supplementary File 1 contains the list of ADCs in this study. We also reported CD4 cell counts before the ADC, reporting the closest available test result up to a maximum window of 6 months. We also reported the most frequent ADC diagnoses among participants with PHIV and among non-PHIV.

Statistical Analysis

We used time from enrollment as the time scale for the analysis, representing years of follow-up in adult HIV care. To capture all ADCs, we modeled ADCs as recurrent events [17], so participants may have >1 ADC during study follow-up (Supplementary Figure 2.1 has a schematic explanation). At study entry, we reported HIV-related characteristics, such as CD4 cell counts, ART use (and specifically protease inhibitor and integrase inhibitor use), and undetectable viral load, determined based on the individual cohort’s lower limit of quantification (20–400 copies/mL).

To contextualize the incidence of ADCs among people with PHIV, we reported unweighted and weighted incidence of ADCs of each HIV acquisition risk group. Propensity score–based weighting approaches may be conducted in descriptive studies to facilitate comparisons, and covariates in the propensity score model are not confounders but characteristics to make similar across exposure groups (see Supplementary File 2 for details) [18]. The weights were a product of inverse odds of exposure weights, which standardizes characteristics of each non-PHIV group to PHIV, and inverse probability of censoring weights, which standardizes characteristics across risk sets. The exposure weights were based on a multinomial logistic regression model of age, calendar year, and CD4 cell count at study entry, sex, and black race. The NA-ACCORD does not have HIV clinical and ART history for any participants before age 18 years, so age and calendar year at study entry controlled for potential birth cohort effects and duration of HIV infection, immunological history, and ART exposure. The censoring weights were based on a pooled logistic regression model of HIV acquisition risk group, calendar month, age of study entry, time-updated viral load and CD4 cell count, sex, and black race. See Supplementary File 2 for details on parametrization of covariates and propensity score diagnostics.

To describe the incidence of ADCs after transition in adult HIV care, we estimated the mean cumulative counts (MCCs) of ADCs in each HIV acquisition risk group. MCCs are interpretable as the average number of ADCs occurring in each person by a certain time in adult HIV care [19]. They were estimated nonparametrically, accounting for right censoring and competing risk for death [20]. The MCC estimator is also free of parametric (eg, Poisson) assumptions about event rates, which may not be appropriate for ADCs since history of ADCs is predictive of future ADCs [21]. We also reported MCC differences and ratios between people with PHIV and those from each non-PHIV group. The weighted MCC differences and ratios may be interpreted as the average controlled differences and ratios, respectively, of the ADC incidence between the PHIV group and each non-PHIV group, controlling for the characteristics in the propensity score model and accounting for informative censoring.

We stratified results by calendar era—2000–2011 or 2012–2022, a period encompassing contemporary guidelines such as immediate start of ART and increasing adoption of integrase inhibitors [9–11]. We obtained 95% confidence intervals (CIs) from the 2.5th and 97.5th percentiles of 1000 nonparametric bootstrap resamples. We conducted all analyses with SAS 9.4 software, using the WEIGHTEDMCC macro developed by Gaber and colleagues [20], and we created all figures using R 4.4.1 software (R Foundation). We conducted sensitivity analyses that accounted for cohort in the bootstrapping approach and excluded CD4 cells counts at study entry in the exposure weight propensity score model (See Supplementary File 2 for details).

RESULTS

There were 24 304 participants with HIV aged 18–40 years enrolled in 14 NA-ACCORD cohorts from 2000 to 2022. Of these, 22 950 belonged to MMSC, PWID, heterosexual, or perinatal HIV acquisition risk groups, contributing a total of 77 149 person-years of follow-up. Ninety-four participants (<1%) died during follow-up. Table 1 summarizes the sociodemographic and HIV clinical characteristics at baseline. There were 455 participants (2%) with PHIV; most of the study population acquired HIV through MMSC (15 858 [69%]). Across HIV acquisition risk groups, there were key differences in sex and race. Among people with PHIV, 55% were female and 58% were black. In contrast, only 18% in the PWID and 65% in the heterosexual contact group were female, and 41% in the MMSC, 16% in the PWID, and 63% in the heterosexual contact group were black. Overall, 6149 of 7881 nonblack male participants (78%) and 709 of 980 nonblack female participants (72%) were white.

The median calendar year of study entry was 2014 (interquartile range [IQR], 2010–2017), with a median follow-up (IQR) of 2.3 (0.8–4.7) years. The distributions of calendar year of study entry and follow-up years were similar across groups. However, people with PHIV entered the study with a median age (IQR) of 21.6 (19.3–25.2) years, about 5 years younger than other HIV acquisition risk groups at entry to adult HIV care. There were also differences in HIV-related characteristics at study entry: among people with PHIV, 41% had an undetectable viral load at study entry, 52% had a CD4 cell count >500/μL, and 78% were on ART, of whom 71% were on protease inhibitor–containing regimens. All of these proportions were higher in the PHIV group than in other HIV acquisition risk groups, except for CD4 cell count.

A total of 5429 ADC events were observed during follow-up; 43% of these events occurred when the participant’s CD4 cell count was <200/μL. The distributions of CD4 cell counts when ADCs occurred differed across HIV acquisition risk groups. For more than half (55%) of ADCs among participants with PHIV, the latest CD4 cell count within a 6-month window before the ADC was <200/μL, higher than in non-PHIV acquisition risk groups (39% for the MMSC, 37% for the PWID, and 51% for the heterosexual contact group). The 5 most common ADCs reported among people with PHIV were HIV-associated wasting syndrome (n = 20), esophageal candidiasis (n = 18), Pneumocystis jirovecii pneumonia (n = 15), progressive multifocal leukoencephalopathy (n = 10), and recurrent bacterial pneumonia (n = 8). Across all 3 non-PHIV groups, the 5 most common ADCs reported were Kaposi sarcoma (n = 858), non-Hodgkin lymphoma (n = 487), herpes simplex infection (n = 486), P. jirovecii pneumonia (n = 376), and pulmonary tuberculosis (n = 340). Table 2 summarizes the frequency of ADCs and the unweighted and weighted MCCs for each group. Across all groups, <10% of participants experienced ≥1 ADC during follow-up, and <4% experienced ≥3 ADCs.

The unweighted results (Table 2) show that during the years 2000–2011, at 3 years in adult HIV care, there were 27 ADCs per 100 persons with PHIV (95% CI, 14–42), compared with 22 ADCs (18–26) per 100 persons in the MMSC, 33 (13–67) in the PWID, and 31 (23–39) in the heterosexual contact group. During 2012–2022, at 3 years in adult HIV care, there were 18 ADCs per 100 persons with PHIV (95% CI, 9–28), compared with 20 (16–23) in the MMSC, 13 (8–20) in the PWID, and 23 (18–29) in the heterosexual contact group.

Figure 1 shows the weighted MCC plots comparing PHIV with non-PHIV groups, stratified by calendar period. Supplementary File 3 contains the propensity score model’s diagnostic plots and tables (Supplementary Figure 3.1, 3.2, 3.3, and 3.4 and Supplementary Table 2.1), where good balance was achieved across covariates. The weighted results show similar ADC incidence between PHIV and non-PHIV acquisition risk groups. During 2000–2011, at 3 years in adult HIV care, there were 26 ADCs per 100 persons with PHIV (95% CI, 15–40), compared with 30 ADCs (23–43) per 100 persons in the MMSC, 36 (13–71) in the PWID, and 42 (33–61) in the heterosexual contact group. Within each HIV acquisition risk group, the ADC incidence was lower in 2012–2022 than in 2000–2011. At 3 years in adult HIV care, there were 16 ADCs per 100 persons with PHIV (95% CI, 5–21), compared with 18 ADCs (4–23) per 100 in the MMSC, 19 (5–33) in the PWID, and 30 (16–40) in the heterosexual contact group. Sensitivity analyses yielded similar results (Supplementary Table 2.1).

DISCUSSION

We estimated the incidence of ADCs among people living with HIV aged 18–40 years in the United States and Canada and compared the incidence in people with PHIV with those in non-PHIV groups. Among people with PHIV, 1 in 10 experienced ≥1 ADC following the transition to adult HIV care. The incidence of recurrent ADCs among people with PHIV by 3 years in adult HIV care was 26 per 100 people in 2000–2011, then lower at 16 per 100 in 2012–2022. People with PHIV did not experience a greater ADC incidence than those with non-PHIV in 2000–2022. As more people with PHIV age into adulthood, this study adds knowledge on the occurrence of ADCs during a time of universal ART eligibility and long-term availability of effective ARTs by showing that the incidence of ADCs in people with PHIV remain comparable to that in non-PHIV once they have successfully transitioned to adult HIV care. Most ADCs that occurred among people with PHIV after this transition occurred at low CD4 cell counts.

Our results were similar to those of other studies in North America that examined ADC incidence among adults living with HIV [1, 22–24]. Our study extended these results and found that the incidence decreased in 2012–2022 compared with 2000–2011 across all HIV acquisition risk groups. Our results among people with PHIV were also similar to other high-income contexts: a United Kingdom cohort found that 9% of people with PHIV had ≥1 incident ADC while in adult HIV care from 2006 to 2017 [25].

An important context in this study population is that the people with PHIV were a highly selected group: they successfully transitioned to adult HIV care after 2 decades of living with HIV and have been engaged in adult HIV care for ≥12 months. Compared with people with non-PHIV in our study, those with PHIV were more likely to be treatment experienced and virally suppressed at study entry. Most people with PHIV in our study were born at a time before highly active ART was available and were children when ADC rates were 14 times higher than they are currently with the availability of highly active ART [26]. The proportion with viral suppression at transition to adult HIV care in our study was similar to that in other HIV cohorts featuring people with PHIV transitioning to adulthood [27, 28]. Thus, the incidence of ADCs observed in our sample among people with PHIV may be less of a reflection of the cumulative exposure to HIV over 18–40 years and more a reflection of a selected subset of people with PHIV who had better natural immune response to HIV infection, better immunological response to early ART, and better social and clinical support in HIV care during childhood than people with PHIV who did not survive to transition to adult HIV care.

The proportion of adolescents with HIV who do not successfully transition to adult HIV care is substantial, with estimates ranging from 30% to 66% within the first year of transition [29–31]. Therefore, we emphasize the importance of continued engagement in HIV care and adherence to treatment throughout the life course for people with PHIV. Engagement in HIV care provides critical opportunities for ADC prevention, such as prophylaxis and more regular CD4 and viral load monitoring [32].

Our study has several strengths. The NA-ACCORD has many people with PHIV who have transitioned to adult HIV care, providing an opportunity to study the contemporary experience of people with PHIV. The distribution of people with PHIV in our study reflected the distribution of age, sex, and race of all people with PHIV in the United States and Canada [33, 34]. Second, we analyzed all ADCs per participant during follow-up, which more accurately captures the clinical experience of PWH, as compared to earlier studies that simply looked at first ADC.

Our study also had some limitations. While there are robust quality measures for outcome ascertainment, there may have been differences in clinical assessment across cohorts. For example, there may be differences in practices in International Classification of Diseases coding, as diagnosis codes are primarily used for nonresearch purposes. Some covariates like social determinants of health were not included in the propensity score model due to limitations in data availability. Our study population included only people engaged in HIV care, so the study results are not representative of the experience of all PWH. Future studies may consider collecting pediatric HIV history among people with PHIV to better capture the full history of ADCs, immunological and virological responses to ADCs, and multiple ART changes.

In conclusion, people with PHIV who have successfully transitioned to adult HIV care have an ADC incidence similar to that in their peers with non-PHIV and may not need any additional ADC prevention or management beyond what all people with HIV should receive. We encourage continued engagement in care for people with PHIV as it provides opportunities for ADC prevention and screening.

Supplementary Material

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Financial support.

This work was supported by the National Institutes of Health (NIH; grants U01AI069918, F31AI124794, F31DA037788, G12MD 007583, K01AI093197, K01AI131895, K01AI157849, K23EY013707, K24AI065298, K24AI118591, K24DA000432, KL2TR000421, N01CP01 004, N02CP055504, N02CP91027, P30AI027757, P30AI027763, P30A I027767, P30AI036219, P30AI050409, P30AI050410, P30AI094189, P30A I110527, P30MH62246, R01AA016893, R01AG053100, R01AI177010, R01DA011602, R01DA012568, R24AI067039, R34DA045592, U01AA 013566, U01AA020790, U01AI038855, U01AI038858, U01AI068634, U01AI068636, U01AI069432, U01AI069434, U01DA036297, U01DA 036935, U10EY008057, U10EY008052, U10EY008067, U01HL146192, U01HL146193, U01HL146194, U01HL146201, U01HL146202, U01HL 146203, U01HL146204, U01HL146205, U01HL146208, U01HL146240, U01HL146241, U01HL146242, U01HL146245, U01HL146333, U24AA 020794, U54GM133807, UL1RR024131, UL1TR000004, UL1TR000083, UL1TR002378, Z01CP010214, and Z01CP010176); the Centers for Disease Control and Prevention (contracts CDC-200-2006-18797 and CDC-200-2015-63931); the Agency for Healthcare Research and Quality (contract 90047713); the Health Resources and Services Administration (contract 90051652); the Grady Health System; the Canadian Institutes of Health Research (grants CBR-86906, CBR-94036, HCP-97105, and TGF-96118); the Ontario Ministry of Health and Long Term Care; the Government of Alberta, Canada; and the following institutes of the NIH: National Institute of Allergy and Infectious Diseases, National Cancer Institute, National Heart, Lung, and Blood Institute, Eunice Kennedy Shriver National Institute of Child Health & Human Development, National Human Genome Research Institute, National Institute for Mental Health, National Institute on Drug Abuse, National Institute on Aging, National Institute of Dental & Craniofacial Research, National Institute of Neurological Disorders and Stroke, National Institute of Nursing Research, National Institute on Alcohol Abuse and Alcoholism, National Institute on Deafness and Other Communication Disorders, and National Institute of Diabetes and Digestive and Kidney Diseases.

Potential conflicts of interest.

C. R. L., D. K. N., T. R. S., J. L., A. A., and K. A. have received research grants from the NIH to their respective institutions. K. G. has received royalties from UpToDate; has served on a scientific advisory board for Shionogi and Pfizer (noncompensated); has received personal consulting fees from Spark HealthCare, Premier HealthCare, Harrison Consulting, and MedEd Learning; and has served on scientific advisory boards of Shionogi and Pfizer. A. A. has previously served on the scientific advisory boards of Gilead and Merck and as a site investigator under a clinical research contract with Gilead managed through Johns Hopkins University. K. A. has received royalties from Coursera. All other authors report no potential conflicts.

Disclaimer. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) collaborating cohorts and representatives. AIDS Clinical Trials Group Longitudinal Linked Randomized Trials: Constance A. Benson and Ronald J. Bosch; AIDS Link to the IntraVenous Experience: Gregory D. Kirk; Emory-Grady HIV Clinical Cohort: Vincent Marconi and Jonathan Colasanti; Fenway Health HIV Cohort: Kenneth H. Mayer and Chris Grasso; HIV Outpatient Study: K. B. and J. L.; HIV Research Network: K. G., and Richard D. Moore; Johns Hopkins HIV Clinical Cohort: Richard D. Moore; John T. Carey Special Immunology Unit Patient Care and Research Database, Case Western Reserve University: George Yendewa; Kaiser Permanente Mid-Atlantic States: Michael A. Horberg; Kaiser Permanente Northern California: Michael J. Silverberg; Longitudinal Study of Ocular Complications of AIDS: Jennifer E. Thorne; MACS/WIHS Combined Cohort Study: Todd Brown, Phyllis Tien, and Gypsyamber D’Souza; Maple Leaf Medical Clinic: Graham Smith, Mona Loutfy, and Meenakshi Gupta; The McGill University Health Centre, Chronic Viral Illness Service Cohort: Marina B. Klein; Multicenter Hemophilia Cohort Study–II: C. S. R.; Ontario HIV Treatment Network Cohort Study: Abigail Kroch, Ann Burchell, Adrian Betts, and Joanne Lindsay; Parkland/UT Southwestern Cohort: Ank Nijhawan; Retrovirus Research Center, Universidad Central del Caribe, Bayamon Puerto Rico: Angel M. Mayor; Southern Alberta Clinic Cohort: M. John Gill; Study of the Consequences of the Protease Inhibitor Era: Jeffrey N. Martin and Steven G. Deeks; Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy: J. L. and John T. Brooks; University of Alabama at Birmingham 1917 Clinic Cohort: Michael S. Saag, Michael J. Mugavero, and Greer Burkholder; University of California at San Diego: Laura Bamford and Maile Karris; University of North Carolina at Chapel Hill HIV Clinic Cohort: Joseph J. Eron and Sonia Napravnik; University of Washington HIV Cohort: Mari M. Kitahata and Heidi M. Crane; Vanderbilt Comprehensive Care Clinic HIV Cohort: T. R. S., David Haas, Peter Rebeiro, and Megan Turner; Veterans Aging Cohort Study: Lesley Park, Kathleen McGinnis, and Amy Justice; Executive Committee: Richard D. Moore, K. A., C. R. L., Mari M. Kitahata, Jennifer S. Lee, Michael A. Horberg, Marina B. Klein, Rosemary G. McKaig, and Aimee M. Freeman; Administrative Core: Richard D. Moore, K. A., and Aimee M. Freeman; Data Management Core: Mari M. Kitahata, Stephen E. Van Rompaey, Heidi M. Crane, Liz Morton, Justin McReynolds, and William B. Lober; and Epidemiology and Biostatistics Core: C. R. L., N. J. L. H., Brenna Hogan, Elizabeth Humes, Chunyan Zheng, Sally Coburn, and Lucas Gerace.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Data availability.

Data are not publicly available.

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Weighted mean cumulative counts (MCCs) of AIDS-defining conditions, comparing perinatal with other human immunodeficiency virus (HIV) acquisition routes among people with HIV aged 18–40 years in the United States and Canada, 2000–2022. MCC differences and ratios were calculated after 3 years in adult HIV care, with perinatally acquired HIV as the comparison group. Parenthetical ranges for MCC difference and ratios represent 95% confidence intervals, calculated from 1000 nonparametric bootstrap resamples of the data.

Demographic and Clinical Characteristics of Adults With HIV Aged 18–40 Years in the NA-ACCORD, by HIV Acquisition Risk Group, 2000–2022

HIV Acquisition Risk Group
CharacteristicOverall (N = 22 950)Perinatal (n = 455)MMSC (n = 15 858)PWID (n = 1237)Heterosexual Contact (n = 5400)
Sex and race, no. (%)
 Nonblack male7881 (34)51 (11)6626 (42)728 (59)476 (9)
 Black male7833 (34)112 (25)6496 (41)150 (12)1075 (20)
 Nonblack female980 (4)63 (14)…133 (11)784 (15)
 Black female2507 (11)151 (33)…45 (4)2311 (43)
Median (IQR) value among incident and right-censored study participants
 Age at study entry, ya27.1 (23.8–31.1)21.6 (19.3–25.2)27.1 (23.9–30.9)28.4 (24.9–32.1)27.4 (23.6–31.6)
 Year at study entry2014 (2010–2017)2013 (2011–2017)2014 (2011–2017)2014 (2010–2018)2013 (2009–2017)
 Follow-up, y2.3 (0.8–4.7)2.3 (0.9–4.7)2.2 (0.8–4.6)2.1 (0.8–4.6)2.4 (0.8–5.1)
HIV-related characteristics at study entrya
 Undetectable VL, no. (%)6195 (27)185 (41)4410 (28)327 (26)1273 (24)
CD4 cell count, no. (%)
 <200/μL2545 (11)86 (19)1531 (10)108 (9)820 (15)
 200/μL to <350/μL3254 (14)51 (11)2145 (14)160 (13)898 (17)
 350–500/μL4237 (18)76 (17)2975 (19)192 (16)994 (18)
 >500/μL12 174 (53)222 (49)8729 (55)717 (58)2506 (46)
 On ART, no. (%)9822 (43)355 (78)6628 (42)565 (46)2334 (43)
 PI use among those on ART, no. (%)2895 (29)252 (71)1415 (21)191 (34)1037 (44)
 II use among those on ART, no. (%)4685 (47)136 (38)3440 (52)286 (51)823 (35)
ADCs
 Total no. of ADC events542910534822481594
CD4 cell count closest to ADC, no. (%)a
 < 200/μL2273 (42)54 (51)1326 (38)88 (35)803 (50)
 200/μL to <350/μL884 (16)15 (14)618 (18)53 (21)269 (17)
 350–500/μL589 (11)13 (12)420 (12)29 (12)163 (10)
 >500/μL1560 (29)17 (16)1044 (30)68 (27)326 (20)

Abbreviations: ADC, AIDS-defining condition; ART, antiretroviral therapy; HIV, human immunodeficiency virus; II, integrase inhibitor; IQR, interquartile range; MMSC, male-to-male sexual contact; NA-ACCORD, North American AIDS Cohort Collaboration on Research and Design; PI, protease inhibitor; PWID, persons who inject drugs; VL, viral load.

The latest available measurement within a 6-month window; missing values round to 100%.

Distribution of Adults With HIV Aged 18–40 Years in the NA-ACCORD, by Number of AIDS-Defining Conditions (ADCs) During Follow-up, Mean Cumulative Counts of ADCs, and HIV Acquisition Risk, 2000–2022

HIV Acquisition RiskParticipants Under Follow-up, No.Participants by No. of ADCs During Follow-up, No (%)aTotal No. of ADCs ObservedMCC of ADCs per 100 Persons During Follow-up (95% CI)b
UnweightedWeighted
0 ADCs1 ADC2 ADCs≥3 ADCs1 y3 y1 y3 y
2000–2011
 Perinatal154138 (90)9 (6)<5<52715 (8–23)27 (14–42)14 (7–22)26 (15–40)
 MMSC50604809 (95)143 (3)43 (1)65 (1)67413 (10–15)22 (18–26)16 (12–27)30 (23–43)
 PWID456433 (95)12 (3)<5 (1)7 (2)10525 (6–57)33 (13–67)20 (5–50)36 (13–71)
 Heterosexual contact22872130 (93)82 (4)27 (1)48 (2)51214 (10–18)31 (23–39)20 (14–29)42 (33–61)
2012–2022
 Perinatal402374 (93)10 (3)6 (2)12 (3)787 (3–12)18 (9–28)6 (2–11)16 (5–21)
 MMSC14 03813 351 (95)377 (3)97 (1)213 (2)280811 (8–14)20 (16–23)9 (3–12)18 (4–23)
 PWID1032977 (95)35 (3)7 (1)13 (1)1438 (4–12)13 (8–20)12 (2–25)19 (5–33)
 Heterosexual contact44674192 (94)116 (3)48 (1)111 (3)108213 (10–16)23 (18–29)17 (8–26)30 (16–40)

Abbreviations: ADCs, AIDS-defining conditions; CI, confidence interval; HIV, human immunodeficiency virus; MCC, mean cumulative count; MMSC, male-to-male sexual contact; NA-ACCORD, North American AIDS Cohort Collaboration on Research and Design; PWID, persons who inject drugs.

Counts of <5 participants are given as <5.

During follow-up in adult HIV care. The 95% CIs were calculated using 1000 nonparametric bootstrap resamples of the data.