Brianne Olivieri-Mui1,2, Sandra Shi2, Ellen P McCarthy2, Monty Montano3, Ira Wilson4, Gahee Oh2, Justin Manjourides1, Dae Hyun Kim2. 1. Department of Health Sciences, The Roux Institute, Northeastern University, Boston, MA. 2. Hebrew SeniorLife, Harvard Medical School, The Marcus Institute for Aging Research, Boston, MA. 3. Harvard Medical School, Brigham and Women's Hospital, Boston, MA; and. 4. Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI.
Abstract
BACKGROUND: Categorizing clinical risk amidst heterogeneous multimorbidity in older people living with HIV/AIDS (PLWH) may help prioritize and optimize health care engagements. METHODS: PLWH and their prevalent conditions in 8 health domains diagnosed before January 1, 2015 were identified using 2014-2016 Medicare claims and the Chronic Conditions Data Warehouse. Latent profile analysis identified 4 distinct clinical subgroups based on the likelihood of conditions occurring together [G1: healthy, G2: substance use (SU), G3: pulmonary (PULM), G4: cardiovascular conditions (CV)]. Restricted mean survival time regression estimated the association of each subgroup with the 365 day mean event-free days until death, first hospitalization, and nursing home admission. Zero-inflated Poisson regression estimated hospitalization frequency in 2-year follow-up. RESULTS: Of 11,196 older PLWH, 71% were male, and the average age was 61 (SD 9.2) years. Compared with healthy group, SU group had a mean of 30 [95% confidence interval: (19.0 to 40.5)], PULM group had a mean of 28 (22.1 to 34.5), and CV group had a mean of 22 (15.0 to 22.0) fewer hospitalization-free days over 1 year. Compared with healthy group (2.8 deaths/100 person-years), CV group (8.4) had a mean of 4 (3.8 to 6.8) and PULM group (7.9) had a mean of 3 (0.7 to 5.5) fewer days alive; SU group (6.0) was not different. There was no difference in restricted mean survival time for nursing home admission. Compared with healthy group, SU group had 1.42-fold [95% confidence interval: (1.32 to 1.54)], PULM group had 1.71-fold (1.61 to 1.81), and CV group had 1.28-fold (1.20 to 1.37) higher rates of hospitalization. CONCLUSION: Identifying clinically distinct subgroups with latent profile analysis may be useful to identify targets for interventions and health care optimization in older PLWH.
BACKGROUND: Categorizing clinical risk amidst heterogeneous multimorbidity in older people living with HIV/AIDS (PLWH) may help prioritize and optimize health care engagements. METHODS: PLWH and their prevalent conditions in 8 health domains diagnosed before January 1, 2015 were identified using 2014-2016 Medicare claims and the Chronic Conditions Data Warehouse. Latent profile analysis identified 4 distinct clinical subgroups based on the likelihood of conditions occurring together [G1: healthy, G2: substance use (SU), G3: pulmonary (PULM), G4: cardiovascular conditions (CV)]. Restricted mean survival time regression estimated the association of each subgroup with the 365 day mean event-free days until death, first hospitalization, and nursing home admission. Zero-inflated Poisson regression estimated hospitalization frequency in 2-year follow-up. RESULTS: Of 11,196 older PLWH, 71% were male, and the average age was 61 (SD 9.2) years. Compared with healthy group, SU group had a mean of 30 [95% confidence interval: (19.0 to 40.5)], PULM group had a mean of 28 (22.1 to 34.5), and CV group had a mean of 22 (15.0 to 22.0) fewer hospitalization-free days over 1 year. Compared with healthy group (2.8 deaths/100 person-years), CV group (8.4) had a mean of 4 (3.8 to 6.8) and PULM group (7.9) had a mean of 3 (0.7 to 5.5) fewer days alive; SU group (6.0) was not different. There was no difference in restricted mean survival time for nursing home admission. Compared with healthy group, SU group had 1.42-fold [95% confidence interval: (1.32 to 1.54)], PULM group had 1.71-fold (1.61 to 1.81), and CV group had 1.28-fold (1.20 to 1.37) higher rates of hospitalization. CONCLUSION: Identifying clinically distinct subgroups with latent profile analysis may be useful to identify targets for interventions and health care optimization in older PLWH.
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