The projected prevalence of comorbidities and multimorbidity in people with HIV in the United States through the year 2030
Notice bibliographique
Résumé
ABSTRACT Importance Estimating the medical complexity of people aging with HIV can inform clinical programs and policy to meet future healthcare needs. Objective To project the prevalence of comorbidities and multimorbidity among people with HIV (PWH) using antiretroviral therapy (ART) in the US through 2030. Design Agent-based simulation model Setting HIV clinics in the United States in the recent past (2020) and near future (2030) Participants In 2020, 674,531 PWH were using ART; 9% were men and 4% women with history of injection drug use; 60% were men who have sex with men (MSM); 8% were heterosexual men and 19% heterosexual women; 44% were non-Hispanic Black/African American (Black); 32% were non-Hispanic White (White); and 23% were Hispanic. Exposure(s) Demographic and HIV acquisition risk subgroups Main Outcomes and Measures Projected prevalence of anxiety, depression, stage ≥3 chronic kidney disease (CKD), dyslipidemia, diabetes, hypertension, cancer, end-stage liver disease (ESLD), myocardial infarction (MI), and multimorbidity (≥2 mental or physical comorbidities, other than HIV). Results We projected 914,738 PWH using ART in the US in 2030. Multimorbidity increased from 58% in 2020 to 63% in 2030. The prevalence of depression and/or anxiety was high and increased from 60% in 2020 to 64% in 2030. Hypertension and dyslipidemia decreased, diabetes and CKD increased, MI increased steeply, but there was little change in cancer and ESLD. Among Black women with history of injection drug use (oldest demographic subgroup in 2030), CKD, anxiety, hypertension, and depression were most prevalent and 93% were multimorbid. Among Black MSM (youngest demographic subgroup in 2030), depression was highly prevalent, followed by hypertension and 48% were multimorbid. Comparatively, 67% of White MSM were multimorbid in 2030 (median age in 2030=59 years) and anxiety, depression, dyslipidemia, CKD, and hypertension were highly prevalent. Conclusion and relevance The distribution of multimorbidity will continue to differ by race/ethnicity, gender, and HIV acquisition risk subgroups, and be influenced by age and risk factor distributions that reflect the impact of social disparities of the health on women, people of color, and people who use drugs. HIV clinical care models and funding are urgently required to meet the healthcare needs of people with HIV in the next decade. KEY POINTS Question How will the prevalence of multimorbidity change among people with HIV (PWH) using antiretroviral therapy in the US from 2020 to 2030? Findings In this agent-based simulation study using data from the NA-ACCORD and the CDC, multimorbidity (≥2 mental/physical comorbidities other than HIV) will increase from 58% in 2020 to 63% in 2030. The composition of comorbidities among multimorbid PWH vary by race/ethnicity, gender, and HIV acquisition risk group. Meaning HIV clinical programs and policy makers must act now to identify resources and care models to meet the increasingly complex medical needs of PWH over time, particularly mental healthcare needs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».