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Categorizing Comorbid Risk for People Living With HIV: A Latent Profile Analysis
Brianne Olivieri-Mui1,2, Sandra Shi2, Ellen P McCarthy2
1Department of Health Sciences, The Roux Institute, Northeastern University, Boston, MA.
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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