Projected Life Expectancy for Adolescents With HIV in the US.

Importance: Life expectancy is a key measure of overall population health. Life expectancy estimates for youth with HIV in the US are needed in the current HIV care and treatment context to guide health policies and resource allocation. Objective: To compare life expectancy between 18-year-old youth with perinatally acquired HIV (PHIV), youth with nonperinatally acquired HIV (NPHIV), and youth without HIV. Design, Setting, and Participants: Using a US-focused adolescent-specific Monte Carlo state-transition HIV model, we simulated individuals from age 18 years until death. We estimated probabilities of HIV treatment and care engagement, HIV progression, clinical events, and mortality from observational cohorts and clinical trials for model input parameters. The simulated individuals were 18-year-old race and ethnicity-matched youth with PHIV, youth with NPHIV, and youth without HIV; 47%, 85%, and 50% were assigned male sex at birth, respectively. Individuals were categorized by US Centers for Disease Control and Prevention-defined HIV acquisition risk: men who have sex with men, people who ever injected drugs, heterosexually active individuals at increased risk for HIV infection, or average risk for HIV infection. Distributions were 3%, 2%, 12%, and 83% for youth with PHIV and youth without HIV, and 80%, 6%, 14%, and 0% for youth with NPHIV, respectively. Among the simulated youth in this analysis, individuals were 61% Black, 24% Hispanic, and 15% White, respectively. Exposures: HIV status by timing of acquisition. Main Outcomes: Life expectancy loss for youth with PHIV and youth with NPHIV: difference between mean projected life expectancy under current and ideal HIV care scenarios compared with youth without HIV. Uncertainty intervals reflect varying adolescent HIV-related mortality inputs (95% CIs). Results: Compared with youth without HIV (life expectancy: male, 76.3 years; female, 81.7 years), male youth with PHIV and youth with NPHIV had projected life expectancy losses of 10.4 years (95% CI, 5.5-18.1) and 15.0 years (95% CI, 9.3-26.8); female youth with PHIV and youth with NPHIV had projected life expectancy losses of 11.8 years (95% CI, 6.4-20.2) and 19.5 years (95% CI, 13.8-31.6), respectively. When receiving ideal HIV care, life expectancy losses were projected to improve for youth with PHIV (male: 0.5 years [95% CI, 0.3-1.8]: female: 0.6 years [95% CI, 0.4-2.1]) but were projected to persist for youth with NPHIV (male: 6.0 years [95% CI, 5.0-9.1]; female: 10.4 years [95% CI, 9.4-13.6]). Conclusions: This adolescent-focused microsimulation modeling analysis projected that youth with HIV would have shorter life expectancy than youth without HIV. Projected differences were larger for youth with NPHIV compared with youth with PHIV. Differences in mortality by sex at birth, sexual behavior, and injection drug use contributed to lower projected life expectancy among youth with NPHIV. Interventions focused on HIV care and social factors are needed to improve life expectancy for youth with HIV in the US.

Projecting the Clinical and Economic Impacts of Changes to HIV Care Among Adolescents and Young Adults in the United States: Lessons From the COVID-19 Pandemic.

BACKGROUND: During the COVID-19 pandemic, many US youth with HIV (YHIV) used telehealth services; others experienced disruptions in clinic and antiretroviral therapy (ART) access. METHODS: Using the Cost-effectiveness of Preventing AIDS Complications (CEPAC)-Adolescent HIV microsimulation model, we evaluated 3 scenarios: 1) Clinic: in-person care; 2) Telehealth: virtual visits, without CD4 or viral load monitoring for 12 months, followed by return to usual care; and 3) Interruption: complete care interruption with no ART access or laboratory monitoring for 6 months (maximum clinic closure time), followed by return to usual care for 80%. We assigned higher 1-year retention (87% vs 80%) and lower cost/visit ($49 vs $56) for Telehealth vs Clinic. We modeled 2 YHIV cohorts with non-perinatal (YNPHIV) and perinatal (YPHIV) HIV, which differed by mean age (22 vs 16 years), sex at birth (85% vs 47% male), starting CD4 count (527/µL vs 635/µL), ART, mortality, and HIV-related costs. We projected life months (LMs) and costs/100 YHIV over 10 years. RESULTS: Over 10 years, LMs in Clinic and Telehealth would be similar (YNPHIV: 11 350 vs 11 360 LMs; YPHIV: 11 680 LMs for both strategies); costs would be $0.3M (YNPHIV) and $0.4M (YPHIV) more for Telehealth than Clinic. Interruption would be less effective (YNPHIV: 11 230 LMs; YPHIV: 11 620 LMs) and less costly (YNPHIV: $1.3M less; YPHIV: $0.2M less) than Clinic. Higher retention in Telehealth led to increased ART use and thus higher costs. CONCLUSIONS: Telehealth could be as effective as in-person care for some YHIV, at slightly increased cost. Short interruptions to ART and laboratory monitoring may have negative long-term clinical implications.

Daily Oral HIV Pre-exposure Prophylaxis Among Young Men Who Have Sex With Men in the United States: Cost-saving at Generic Drug Price.

BACKGROUND: Adherence and retention concerns raise questions about the effectiveness and cost-effectiveness of oral HIV pre-exposure prophylaxis (PrEP) in young men who have sex with men (YMSM). METHODS: Using an adolescent-focused simulation model, we compared annual HIV screening alone with tenofovir disoproxil fumarate/emtricitabine-based oral PrEP with every 3-month HIV screening in YMSM (aged 15-24) at increased risk of HIV. Data derived from published sources included: age-stratified HIV incidence/100 person-years (PY) on- or off-PrEP (0.6-10.1 or 0.4-6.4), PrEP retention at 6 years (28%), transmissions by HIV RNA level (0.0-78.4/100PY) and annual costs of antiretroviral therapy ($32 000-69 000), HIV care ($3100-34 600), and PrEP program/generic drug ($900/360). Outcomes included transmissions (percent of cohort infected), quality-adjusted life-years (QALYs), costs ($), and incremental cost-effectiveness ratios ($/QALY). We explored the sensitivity of findings to variation in HIV incidence and drug prices. RESULTS: Compared with annual screening alone, PrEP would increase QALYs (9.58 to 9.67), reduce new infections (37% to 30%), and decrease costs (by $5000) over 10 years. PrEP would remain cost-saving for HIV incidence off-PrEP ≥5.1/100PY or annual PrEP price ≤$1200. Over a lifetime horizon, PrEP would be cost-saving for HIV incidence off-PrEP ≥1.0/100PY, across all retention assumptions examined. PrEP would not be cost-effective at HIV incidence ≤0.1/100PY, regardless of drug price, due to programmatic costs. CONCLUSIONS: In US YMSM at increased risk of HIV, generic oral PrEP and every-3-month screening would be cost-saving compared with annual screening alone, even with high discontinuation and low adherence, over a range of HIV incidences.

Increasing the initiation of antiretroviral therapy through optimal placement of diagnostic technologies for pediatric HIV in Zimbabwe: A modeling analysis.

OBJECTIVES: Point-of-care (POC) devices for infant HIV testing provide timely result-return and increase antiretroviral (ART) initiation. We aimed to optimally locate POC devices to increase 30-day ART initiation in Matabeleland South, Zimbabwe. METHODS: We developed an optimization model to identify the locations for limited POC devices at health facilities, maximizing the number of infants who receive HIV test results and initiate ART within 30 days of testing. We compared location-optimization model results to non-model-based decision heuristics, which are more practical and less data-intensive. Heuristics assign POC devices based on demand, test positivity, laboratory result-return probability, and POC machine functionality. RESULTS: With the current placement of 11 existing POC machines, 37% of all tested infants with HIV were projected to receive results and 35% were projected to initiate ART within 30 days of testing. With optimal placement of existing machines, 46% were projected to receive results and 44% to initiate ART within 30 days, retaining three machines in current locations, moving eight to new facilities. Relocation based on the highest POC device functionality would be the best-performing heuristic decision (44% receiving results and 42% initiating ART withing 30 days); although, it still would not perform as well as the optimization-based approach. CONCLUSION: Optimal and ad hoc heuristic relocation of limited POC machines would increase timely result-return and ART initiation, without further, often costly, interventions. Location optimization can enhance decision-making regarding the placement of medical technologies for HIV care.

Prevalence of undiagnosed HIV among children in South Africa, Côte d'Ivoire and Zimbabwe: a model-based analysis to inform paediatric HIV screening programmes.

INTRODUCTION: To improve the diagnosis and survival of children living with HIV (CLWH), the World Health Organization recommends testing approaches beyond traditional infant HIV testing programmes. Information about undiagnosed HIV prevalence among children of varying ages in the general population is needed to guide innovative national/subnational case-finding and testing approaches. METHODS: We used the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)-Pediatric model to estimate the prevalence of undiagnosed HIV in 2-, 5- and 10-year-old children in South Africa, Côte d'Ivoire and Zimbabwe in 2018. We simulated cohorts of children born in 2008 (10-year-olds), 2013 (5-year-olds) and 2016 (2-year-olds). Country-/year-specific inputs for pregnant/breastfeeding women included: HIV prevalence (4.2-32.3%), HIV incidence (0.03-0.24%/month), knowledge of HIV status (27-89%) and antiretroviral drug coverage (36-95%). Paediatric inputs included early infant testing coverage (6-95%) and breastfeeding duration (0-20 months). We projected the proportion of surviving CLWH in whom HIV remained undiagnosed and the undiagnosed HIV prevalence among surviving children of each age in the general population. For children born in 2016, we projected survival and diagnosis of all CLWH through 2026. We conducted sensitivity analyses on model parameters. RESULTS: In 2018, the projected proportion of surviving CLWH whose HIV remained undiagnosed in South Africa/Côte d'Ivoire/Zimbabwe was 44.2%/55.8%/52.9% among 2-year-old CLWH; 29.0%/37.8%/33.2% among 5-year-old CLWH; and 18.3%/25.4%/23.1% among 10-year-old CLWH. Projected general population undiagnosed HIV prevalence in South Africa/Côte d'Ivoire/Zimbabwe was 0.44%/0.32%/0.68% among 2-year-olds; 0.25%/0.17%/0.41% among 5-year-olds; and 0.24%/0.14%/0.38% among 10-year-olds. Among all CLWH born in 2016, 50-54% were projected to die without HIV diagnosis (and subsequently without treatment) within 10 years after birth; 80-85% of these deaths occurred in the first 2 years. CONCLUSIONS: Projected population-level undiagnosed HIV prevalence is low and sharply decreases after age 2, with more CLWH dying than being diagnosed. Despite low undiagnosed prevalence in the general population of older children, we project that a large proportion of CLWH remain undiagnosed, suggesting that innovative strategies targeting untested children of all ages outside of health facility settings should be prioritized. Programmes could consider routine testing of the general population of children below 2 in all settings and children of all ages in high-prevalence settings.

Do not forget the children: a model-based analysis on the potential impact of COVID-19-associated interruptions in paediatric HIV prevention and care.

INTRODUCTION: The COVID-19 pandemic has affected women and children globally, disrupting antiretroviral therapy (ART) services and exacerbating pre-existing barriers to care for both pregnant women and paediatric populations. METHODS: We used the Spectrum modelling package and the CEPAC-Pediatric model to project the impact of COVID-19-associated care disruptions on three key populations in the 21 Global Plan priority countries in sub-Saharan Africa: (1) pregnant and breastfeeding women living with HIV and their children, (2) all children (aged 0-14 years) living with HIV (CLWH), regardless of their engagement in care and (3) CLWH who were engaged in care and on ART prior to the start of the pandemic. We projected clinical outcomes over the 12-month period of 1 March 2020 to 1 March 2021. RESULTS: Compared to a scenario with no care disruption, in a 3-month lockdown with complete service disruption, followed by 3 additional months of partial (50%) service disruption, a projected 755,400 women would have received PMTCT care (a 21% decrease), 187,800 new paediatric HIV infections would have occurred (a 77% increase) and 516,800 children would have received ART (a 35% decrease). For children on ART as of March 2020, we projected 507,200 would have experienced ART failure (an 80% increase). Additionally, a projected 88,400 AIDS-related deaths would have occurred (a 27% increase) between March 2020 and March 2021, with 51,700 of those deaths occurring among children engaged in care as of March 2020 (a 54% increase). CONCLUSIONS: While efforts will continue to curb morbidity and mortality stemming directly from COVID-19 itself, it is critical that providers also consider the immediate and indirect harms of this pandemic, particularly among vulnerable populations. Well-informed, timely action is critical to meet the health needs of pregnant women and children if the global community is to maintain momentum towards an AIDS-free generation.