Projected Aging Among People with HIV in the United States: A Modeling Analysis in 24 States
Zalesak, A.; Kasaie, P.; Dansky, Z.; Althoff, K. N.; Dowdy, D.; Shah, M.; Fojo, A. T.; Schnure, M.
Show abstract
ImportanceAs the population living with HIV in the US ages, state-level projections of the aging dynamics among people with diagnosed HIV (PWDH) will be needed to inform local planning and intervention efforts. ObjectiveWe sought to explore how aging dynamics of the population with HIV in the US are expected to differ at the state level between 2025 and 2040. Design, Setting, and ParticipantsWe projected epidemic trajectories from 2025 to 2040 in 24 US states comprising 86% of PWDH in the US using a calibrated model of HIV transmission. Main Outcomes and MeasuresWe estimated change in median age of PWDH over age 13, from 2025 to 2040, for each state. ResultsWe project that by 2040, the median age of adult PWDH in the 24 states will rise from 51 to 62 years, and over half of adult PWDH will be over the age of 65. Our projections suggest substantial heterogeneities in age distributions by state. More populous and urban states with higher median ages of PWDH in 2025 are projected to experience even further aging of the population with diagnosed HIV in the coming 15 years. By contrast, more rural and less populous states tend to have younger-aged HIV epidemics that were not projected to age substantially over time. Conclusions and RelevanceAlthough the overall population of persons with diagnosed HIV in the US is projected to age substantially, these effects will unfold differently across states. In the coming years, healthcare systems will need to plan to adapt to changing state-level demographic patterns among PWDH. Key PointsO_ST_ABSQuestionC_ST_ABSHow will the age distribution of people living with diagnosed HIV change between 2025 and 2040? FindingsUsing a calibrated model of HIV transmission in 24 US states, we project that by 2040, the median age of adult PWDH in the 24 states will rise from 51 to 62 years, and over half of adult PWDH will be over the age of 65. Our projections suggest substantial differences in age distribution by state. MeaningIn the coming years, federal and local healthcare planning will need to adapt to changing state-level demographic patterns among PWDH.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Population Impact and Efficiency of Improvements to HIV PrEP Under Conditions of High ART Coverage among San Francisco Men Who Have Sex with Men 92%
- Predicting HIV Incidence in the SEARCH Trial: A Mathematical Modelling Study 91%
- Cost-Effectiveness of Interventions to Improve HIV Pre-Exposure Prophylaxis Initiation, Adherence, and Persistence among Men Who Have Sex with Men 91%
Similar papers in this journal
- Projected resurgence of COVID-19 in the United States in July—December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination 91%
- Changes in transmission of Enterovirus D68 (EV-D68) in England inferred from seroprevalence data 89%
- Disentangling the relationship between cancer mortality and COVID-19 89%
Similar papers in this journal
- A Decision Analytics Model to Optimize Investment in Interventions Targeting the PrEP Cascade of Care 92%
- National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the \"first 90\" from program and survey data 92%
- A Moving Target: Impacts of Lowering Viral Load Suppression Cutpoints on Progress Towards HIV Epidemic Control Goals 91%
Similar papers in this journal
- The impact of vaccination on COVID-19 outbreaks in the United States 92%
- Population immunity to SARS-CoV-2 in US states and counties due to infection and vaccination, January 2020-November 2021 91%
- Mathematical modeling to inform vaccination strategies and testing approaches for COVID-19 in nursing homes 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.