Global search

Find data and evidence

Type at least 2 characters. Use arrow keys to review and Enter to open.

Peer reviewedOpen accessAIDS

The forecasted prevalence of comorbidities and multimorbidity in people with HIV in the United States through the year 2030: A modeling study

PLOS Medicine·

Keri N. Althoff, Cameron Stewart, Elizabeth Humes, Lucas Gerace, Cynthia Boyd, Kelly Gebo, Amy C. Justice, Emily P. Hyle, Sally B. Coburn, Raynell Lang, Michael J. Silverberg, Michael A. Horberg, Viviane D. Lima, M. John Gill, Maile Karris, Peter F. Rebeiro, Jennifer Thorne, Ashleigh J. Rich, Heidi Crane, Mari Kitahata, Anna Rubtsova, Cherise Wong, Sean Leng, Vincent C. Marconi, Gypsyamber D’Souza, Hyang Nina Kim, Sonia Napravnik, Kathleen McGinnis, Gregory D. Kirk, Timothy R. Sterling, Richard D. Moore, Parastu Kasaie

DOI
10.1371/journal.pmed.1004325
PMID
38215160
PMCID
PMC10833859
OpenAlex
W4390828082
Study type
Guideline
Publisher
Public Library of Science (PLoS)
Article type
journal-article
Integrity
current

Why this research matters now

The forecasted rise in comorbidity and multimorbidity among people aging with HIV has implications for clinical workforce preparedness, subspecialty care linkages, and resource allocation. Clinicians will need to remain current on evolving comorbidity-specific guidelines, and policymakers must support extended clinical capacity to meet complex healthcare needs.

01

Structured evidence summary

Research question

The study aimed to forecast the prevalence of specific comorbidities and multimorbidity among people with HIV receiving antiretroviral therapy in the United States through 2030.

Study design

Agent-based simulation modeling using the PEARL model, informed by CDC HIV surveillance data on new diagnoses and longitudinal NA-ACCORD data on comorbidity risk from 2009 to 2017. Simulations were replicated 200 times and forecasted outcomes through 2030.

Population and setting

People with HIV who initiated ART in the United States, stratified into 15 demographic subgroups by race/ethnicity (Hispanic, non-Hispanic White, non-Hispanic Black/African American), gender, and HIV acquisition risk (men who have sex with men, people with history of injection drug use, heterosexual men and women). The forecasted 2020 population was 670,000 individuals.

Main findings

The population of people with HIV receiving ART is forecasted to grow from 670,000 in 2020 to 908,000 by 2030. Multimorbidity prevalence is projected to increase from 63% in 2020 to 70% in 2030. Depression and anxiety prevalence is expected to rise from 60% to 64%. Dyslipidemia, diabetes, chronic kidney disease, and myocardial infarction are forecasted to increase, while hypertension prevalence is expected to decrease. Substantial heterogeneity exists across demographic subgroups, with Black women with history of injection drug use projected to have the highest multimorbidity at 92% in 2030.

Public-health relevance

The forecasted rise in comorbidity and multimorbidity among people aging with HIV has implications for clinical workforce preparedness, subspecialty care linkages, and resource allocation. Clinicians will need to remain current on evolving comorbidity-specific guidelines, and policymakers must support extended clinical capacity to meet complex healthcare needs.

Important limitations

The forecasts assume that trends in new HIV diagnoses, mortality, and comorbidity risk observed from 2009 to 2017 will continue through 2030, and do not account for influences occurring outside this historical period. This summary is limited to the supplied single-article abstract and metadata; the original paper is required for decision-grade interpretation.

GIDS interpretation

This modeling study was classified under AIDS, United States, and multiple health topics including surveillance, treatment, and health policy. The GIDS links reflect the study's focus on forecasting HIV comorbidity burden to inform clinical and policy planning, rather than reporting a live surveillance signal.

02

Related GIDS surveillance

Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.

03

Evidence relationships

This article has 12 auditable classifier relationships to diseases, places, topics, and study design.

about diseaseaddresses topicaddresses topicaddresses topicaddresses topicevaluates interventionhas pathogen typeinforms policy domainstudied instudied population settingstudies pathogenuses study design