Abstract
Background
People with HIV (PWH) have a disproportionately high burden of chronic comorbidities. However, how their multimorbidity patterns differ from those of the general population and how COVID-19 infection may affect this burden have not been well characterized.
Methods
We conducted a retrospective cohort study using the National COVID Cohort Collaborative (N3C), including adults aged ≥18 years with a confirmed COVID-19 diagnosis between January 1, 2018 and December 31, 2024. Fourteen chronic comorbidities were identified using N3C concept sets. Exploratory factor analysis was used to identify multimorbidity patterns before and after COVID-19 diagnosis.
Results
Among 7 500 765 patients with COVID-19, 52,015 (0.7%) were PWH. The median age was 48 years (IQR, 33–63) and 57.3% were female. PWH consistently had a higher multimorbidity burden than PWoH, and this disparity widened after COVID-19: the proportion with ≥ 3 comorbidities rose from 45.0% to 55.9% among PWH versus 22.2% to 29.7% among PWoH. Four multimorbidity factors were identified in both periods: transplant and multiorgan disease; cardiometabolic disorder; substance use, psychiatric disorder, and liver disease; and obesity. After COVID-19, the leading pattern among PWH shifted toward cardiometabolic disorder.
Conclusions
PWH experience a disproportionately high multimorbidity burden, with disparities further amplified after COVID-19 diagnosis and the leading pattern shifting toward cardiometabolic dominance. These findings highlight the need for integrated, multimorbidity-focused care for PWH.
Keywords: cardiometabolic disorder, COVID-19, HIV, multimorbidity
People with HIV (PWH) experience a disproportionately high burden of comorbidities compared with the general population, including cardiometabolic conditions, neuropsychiatric disorders, and chronic systemic organ dysfunctions [1–5]. Multimorbidity, commonly defined as the presence of 2 or more chronic conditions, is particularly prevalent among older PWH [6]. Compared with people without HIV (PWoH), PWH often experience earlier onset of chronic diseases and a higher risk of multimorbidity [7–11]. Recent studies have emphasized that although antiretroviral therapy (ART) has extended life expectancy for PWH, the years gained are not free of chronic disease burden [6, 12, 13].
The COVID-19 pandemic has profoundly affected population health worldwide, and its impact persists years after the initial outbreak [14–16]. Beyond acute infection, COVID-19 has been linked to long-term increases in comorbidities, excess mortality, and higher disability-adjusted life years in the general population [17, 18]. Evidence from the early stages of the pandemic suggested that PWH faced an increased risk of experiencing severe COVID-19 complications, hospitalization, and mortality, particularly among those with uncontrolled viral load or immunosuppression [19–21]. COVID-19 also disrupted HIV care engagement, medication adherence, and viral suppression [22, 23]. While substantial research has explored long-term outcomes of COVID-19 infection in the general population [24–26], post-COVID conditions among PWH remain poorly characterized. In addition, prior investigations have primarily focused on specific chronic conditions individually [27], rather than assessing the overall multimorbidity burden and how the burden shifts over time, and comparisons across age, sex, and race and ethnicity groups remain limited.
Leveraging the National Clinical Cohort Collaborative (N3C), one of the largest harmonized electronic health records (EHR) datasets of patients with and without COVID-19 infection in the United States [28], we sought to fill this gap by characterizing how the multimorbidity burden among PWH evolved across the COVID-19 pandemic at a national scale. The primary objectives of this study are: (1) to describe the multimorbidity burden among PWH and PWoH before and 3 years after COVID-19 diagnosis; (2) to identify and compare multimorbidity patterns in these populations using data-driven methods; and (3) to quantify and examine the associations of HIV status and sociodemographic factors with multimorbidity patterns.
METHODS
Data Source and Study Population
This study used data from N3C. The design, data collection processes, and data harmonization methods of N3C have been detailed previously (Supplementary Methods 1) [29]. Deidentified data were accessed through the N3C Data Enclave under a data-sharing agreement approved by the National Institutes of Health Institutional Review Board (IRB), with the Johns Hopkins University School of Medicine serving as the central IRB, and each investigator obtaining IRB approvals from their respective institutions. The study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [30].
The study population consisted of adults aged 18 years or older who had a documented diagnosis of COVID-19 between January 1, 2018 and December 31, 2024. Individuals with missing age or sex information were excluded. The first COVID-19 diagnosis was determined using the N3C phenotype, which incorporates diagnosis codes, procedure codes, laboratory tests, or their combinations [31].
Measures
PWH in the N3C dataset were identified using a computational phenotype that incorporates HIV-specific diagnostic codes (SNOMED), laboratory measurements (LOINC), and medication records (RxNorm) [32, 33]. The detailed methodology has been described elsewhere [32, 34, 35].
Comorbidities were defined using N3C standardized concept sets [36]. We focused on 14 common chronic comorbidity conditions, selected to capture the broad systems affected in PWH, including immune-related conditions: cardiovascular disease, cerebrovascular and neurocognitive disorder, chronic digestive disease, diabetes mellitus, transplant-related immunosuppression, rheumatic diseases, kidney disease, liver disease, malignancies, obesity, psychiatric disorder, pulmonary disease, substance use disorders (SUD), and vascular disease. This selection was informed by the Charlson Comorbidity Index framework and prior N3C computational phenotype definitions [37], and we excluded acute conditions and genetic disorders to better reflect long-term changes. Detailed definitions and corresponding concept set for each comorbidity group are provided in Supplementary Table 1. For each patient, comorbidity counts were derived by comparing chronic condition diagnosis dates relative to the first COVID-19 diagnosis. The cumulative number of conditions was calculated separately for the pre- and post-COVID-19 and categorized into 4 groups: 0, 1–2, 3–5, and 6 or more. Counts were grouped rather than modeled continuously to allow for nonlinear, threshold-based associations and to yield stratum-specific odds ratios (ORs). The cut-points reflect both established constructs in the multimorbidity literature: multimorbidity is conventionally defined as ≥2 conditions and complex multimorbidity as ≥3 [38], and the observed distribution of counts in the N3C cohort, in which each category approximated a quartile of patients within each group.
Covariates included age, sex, race and ethnicity, and smoking status. Race and ethnicity were categorized as White non-Hispanic, Black or African American non-Hispanic, Hispanic or Latino, Asian non-Hispanic, American Indian/Alaska Native or Pacific Islander (AI/PI), other, and unknown. Smoking status was defined as a binary variable (current smoker vs not current smoker).
Statistical Analysis
We summarized the baseline sociodemographic characteristics and smoking status by HIV status. Comorbidities before COVID-19 were defined as any of 14 chronic conditions diagnosed before the first COVID-19 diagnosis, and comorbidities after COVID-19 were defined as those diagnosed within 3 years afterward. We then examined changes in comorbidity distribution by age, race and ethnicity, and sex. Adjusted logistic regression models were used to estimate the ORs for having higher multimorbidity categories (1–2, 3–5, and 6 or more) compared with none (base outcome) in PWH and PWoH.
We identified multimorbidity patterns through exploratory factor analysis with tetrachoric correlations of 14 chronic conditions. We determined the numbers and composition of multimorbidity factors empirically, balancing statistical indicators with clinical interpretability (details in Supplementary Methods 2). For each patient, we calculated a factor score for each multimorbidity factor as a weighted sum of their conditions, with weights given by the factor loadings (Supplementary Methods 3). We then used factor scores as continuous outcomes in separate multivariable linear regression models with a Gaussian link function to examine their associations with HIV status, age, sex, race and ethnicity, and smoking status.
To assess the robustness of our findings, we conducted a sensitivity analysis by excluding data from sites contributing fewer than 100 PWH records to the N3C dataset. Results from the sensitivity analysis were compared with those from the main analysis to evaluate the consistency of results. All analyses were conducted within the N3C Data Enclave using SparkR and SQL in the secure enclave environment.
RESULTS
Patient Characteristics
Table 1 summarized the baseline characteristics of the patients. We included 7 500 765 patients with COVID-19 diagnosis from the N3C, 52 015 (0.7%) were PWH. The flowchart of study population selection is shown in Supplementary Figure 1, and the distribution of patients contributed by each submitting institution is shown in Supplementary Figure 2. The median age of overall patients was 48 years (IQR: 33–63) at the first COVID-19 diagnosis. Compared with PWoH, PWH were more likely to be non-Hispanic Black (33.9% vs. 13.0%), Hispanic or Latino (16.8% vs. 12.5%), and male (65.2% vs. 42.7%). Smoking (22.7% vs. 9.3%) and alcohol use disorders (7.3% vs 2.1%) were also more prevalent among PWH.
Table 1.
Patients Characteristics at First COVID-19 Diagnosis by HIV Status in the N3C, January 1, 2018 to December 31, 2024
| Overall (N = 7 500 765) |
PWoH (N = 7 448 750) |
PWH (N = 52 015) |
|
|---|---|---|---|
| Age at COVID diagnosis | |||
| Median [Q1, Q3] | 48 (33,63) | 48 (33,63) | 49 (36,59) |
| Sex (%) | |||
| Female | 4 295 078 (57.3) | 4 276 970 (57.4) | 18 108 (34.8) |
| Male | 3 205 687 (42.7) | 3 171 780 (42.6) | 33 907 (65.2) |
| Race and ethnicity (%) | |||
| White non-Hispanic | 4 552 634 (60.7) | 4 532 040 (60.8) | 20 594 (39.6) |
| Black or African American non-Hispanic | 971 654 (13.0) | 954 018 (12.8) | 17 636 (33.9) |
| Hispanic or Latino | 939 325 (12.5) | 930 593 (12.5) | 8732 (16.8) |
| Asian non-Hispanic | 241 576 (3.2) | 240 397 (3.2) | 1179 (2.3) |
| AI/PIa | 55 241 (0.7) | 54 831 (0.7) | 410 (0.8) |
| Other | 161 891 (2.2) | 161 229 (2.2) | 662 (1.3) |
| Unknown | 578 444 (7.7) | 575 642 (7.7) | 2802 (5.4) |
| Current smoker (%) | |||
| No | 6 797 560 (90.6) | 6 757 372 (90.7) | 40 188 (77.3) |
| Yes | 703 205 (9.4) | 691 378 (9.3) | 11 827 (22.7) |
| Alcohol use disorder (%) | |||
| No | 7 343 311 (97.9) | 7 295 068 (97.9) | 48 243 (92.7) |
| Yes | 157 454 (2.1) | 153 682 (2.1) | 3772 (7.3) |
aAI/PI: American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander (non-Hispanic).
Changes in Prevalence of Comorbidities Among PWH and PWoH
Table 2 summarizes comorbidity counts before and after COVID-19 diagnosis. PWH had substantially higher odds of having multimorbidity than PWoH. Before COVID-19, the adjusted ORs comparing PWH with PWoH were 2.4 (95% CI, 2.3–2.4) for having 1–2 comorbidities, 4.4 (95% CI, 4.2–4.5) for 3–5 comorbidities, and 7.2 (95% CI, 6.9–7.5) for having 6 or more comorbidities. After COVID-19 diagnosis, these odds increased further to 2.8 (95% CI, 2.7–2.8), 5.2 (95% CI, 5.0–5.4), and 9.5 (95% CI, 9.1–9.9), respectively (Supplementary Table 2).
Table 2.
Prevalence of Chronic Comorbidities Before and 3 Years After COVID-19 Diagnosis PWoH and PWH
| Pre-COVID | Post-COVID | |||
|---|---|---|---|---|
| PWoH (N = 7 448 750) |
PWH (N = 52 015) |
PwoH (N = 7 448 750) |
PWH (N = 52 015) |
|
| Multimorbidity count | ||||
| 0 (%) | 3 253 884 (43.7) | 9617 (18.5) | 2 595 555 (34.8) | 5798 (11.1) |
| 1–2 (%) | 2 540 137 (34.1) | 18 970 (36.5) | 2 638 638 (35.4) | 17 162 (33.0) |
| 3–5 (%) | 1 384 958 (18.6) | 17 785 (34.2) | 1 758 686 (23.6) | 20 220 (38.9) |
| ≥6 (%) | 269 771 (3.6) | 5643 (10.8) | 455 871 (6.1) | 8835 (17.0) |
| Obesity (%) | 2 179 275 (29.3) | 17 129 (32.9) | 2 599 394 (34.9) | 19 565 (37.6) |
| Cerebrovascular and neurocognitive disorder (%) | 396 188 (5.3) | 3843 (7.4) | 556 696 (7.5) | 5404 (10.4) |
| Pulmonary disease (%) | 1 048 224 (14.1) | 14 127 (27.2) | 1 368 841 (18.4) | 17 577 (33.8) |
| Cardiovascular disease (%) | 2 202 690 (29.6) | 24 034 (46.2) | 2 671 937 (35.9) | 28 168 (54.2) |
| Transplant-related immunosuppressiona (%) | 32 050 (0.4) | 1896 (3.6) | 41 115 (0.6) | 2613 (5.0) |
| Rheumatic diseases (%) | 370 735 (5.0) | 4036 (7.8) | 487 285 (6.5) | 5299 (10.2) |
| Vascular disease (%) | 237 507 (3.2) | 2620 (5.0) | 343 447 (4.6) | 3772 (7.3) |
| Psychiatric disorder (%) | 1 040 701 (14.0) | 17 279 (33.2) | 1 399 337 (18.8) | 21 341 (41.0) |
| Digestive disease (%) | 174 144 (2.3) | 2319 (4.5) | 278 506 (3.7) | 3565 (6.9) |
| Diabetes mellitus (%) | 1 146 690 (15.4) | 12 949 (24.9) | 1 452 715 (19.5) | 16 240 (31.2) |
| Malignancies (%) | 529 073 (7.1) | 6506 (12.5) | 697 394 (9.4) | 8252 (15.9) |
| Kidney disease (%) | 545 373 (7.3) | 9517 (18.3) | 740 651 (9.9) | 11 999 (23.1) |
| Liver disease (%) | 369 085 (5.0) | 10 226 (19.7) | 544 193 (7.3) | 12 839 (24.7) |
| Substance use disorder (%) | 206 818 (2.8) | 8079 (15.5) | 283 867 (3.8) | 10 108 (19.4) |
aSolid organ transplantation including liver, lung, heart, kidney, and pancreas as well as bone marrow transplantation.
Figure 1 illustrates the distribution of multimorbidity categories across age, sex, and race and ethnicity, stratified by HIV status and COVID-19 diagnosis. Multimorbidity prevalence increased progressively with age. Before COVID-19, 53.0% of PWH aged 50–59 had 3 or more comorbidities, compared with 25.2% of PWoH. Among adults aged 70 years or older, 22.6% of PWH had 6 or more comorbidities, compared with 10.2% of PWoH in the same age. After COVID-19, the burden increased further. Among adults aged 70 years or older, 32.0% of PWH and 16.1% of PWoH had 6 or more comorbidities.
Figure 1.
Distribution of multimorbidity counts by (A) age group, (B) race and ethnicity, and (C) sex, stratified by HIV status and before versus after COVID-19 diagnosis.
Racial and ethnic disparities were also pronounced. AI/PI and non-Hispanic Black individuals were more likely to be classified in higher multimorbidity categories, particularly among PWH. Before COVID-19, 52.4% of AI/PI and 49.8% of Black PWH had more than 3 comorbidities, rising to 63.9% and 60.1%, respectively, after COVID-19. Sex differences were also evident. Although females constituted a smaller proportion of PWH, they had higher mean multimorbidity counts than males, a pattern opposite to that observed among PWoH (Supplementary Table 3).
Table 2 summarizes the prevalence of individual comorbidities before and after COVID-19 diagnosis by HIV status. Before COVID-19, PWH had a higher prevalence across all 14 chronic comorbidities compared with PWoH, with particularly large differences observed for cardiovascular disease (46.2% vs 29.6%), psychiatric disorders (33.2% vs 14.0%), pulmonary disease (27.2% vs 14.1%), diabetes mellitus (24.9% vs 15.4%), kidney disease (18.3% vs 7.3%), liver disease (19.7% vs 5.0%), and SUD (15.5% vs 2.8%).
After COVID-19 diagnosis, the prevalence of most comorbidities increased in both PWH and PWoH. Among PWH, cardiovascular disease remained the most prevalent condition, exceeding 50% (54.2%), followed by psychiatric disorders (41.0%). Several conditions, including psychiatric disorders, liver disease, kidney disease, and SUD, showed marked absolute increases after COVID-19, with pre-existing disparities between PWH and PWoH persisting or widening.
Patterns of Multimorbidity Among PWH and PWoH
Figure 2 displays factor loadings from exploratory factor analysis, with detailed value provided in Supplementary Table 4. The scree plot supporting factor retention and variance explained are shown in Supplementary Figure 3. Four multimorbidity factors were identified across groups and time periods.
Figure 2.
Factor loadings from exploratory factor analysis of 14 chronic comorbidities, stratified by HIV status and COVID-19 period. (A and B) Show results for PWH (pre- and post-COVID-19); (C and D) show results for PWoH.
Among PWH (Figure 2A and 2B), the same 4 multimorbidity factors were present before and after COVID-19: Factor 1 reflected transplant and multiorgan disease, comprising transplant-related immunosuppression, kidney disease, and rheumatic diseases; Factor 2 captured cardiometabolic disorders, including cardiovascular disease, diabetes, kidney disease, and cerebrovascular and neurocognitive disorder; Factor 3 reflected SUD, psychiatric disorder, and liver disease; and Factor 4 was characterized by obesity. The structure was stable in composition, but the 2 leading factors reversed in prominence. Before COVID-19, the transplant and multiorgan disease factor was leading (explaining 16.1% of the total variance), followed by cardiometabolic disorder (15.6%); after COVID-19, cardiometabolic disorder became the leading factor (17.9%), with the transplant and multiorgan disease factor second (14.2%).
Among PWoH (panels C and D), the 4 multimorbidity factors remained the same over time. Factor 1 represented cardiometabolic disorders; Factor 2 captured SUD, psychiatric disorder, and liver disease; Factor 3 reflected transplant and multiorgan disease; and Factor 4 was characterized by obesity.
Correlates of Multimorbidity Patterns by HIV Status and Sociodemographic Factors
Factor scores were estimated by conducting an exploratory factor analysis in the overall study population. This analysis identified the same 4 multimorbidity factors as those observed among PWoH.
Figure 3 presents the results of multivariable linear regression analyses using factor scores as outcomes in the entire study population (Supplementary Table 5). Positive coefficients indicate higher multimorbidity burden relative to the reference group. HIV status was significantly associated with higher scores across Factors 1–3 but not Factor 4 (obesity). Smoking status was also a strong predictor of increased multimorbidity burden, particularly in the Factor 2 (SUD, psychiatric disorder, and liver disease). Compared with non-Hispanic White patients, non-Hispanic Black and AI/PI patients had the highest burden in the cardiometabolic and metabolic disease groups both before and after COVID-19 diagnosis. In contrast, non-Hispanic Asian individuals consistently exhibited lower burden across these factors. Female sex was associated with higher scores in Factors 1 and 2, but lower scores in Factors 3 and 4.
Figure 3.
Associations between sociodemographic factors and factor scores (A) before and (B) after COVID-19 diagnosis. Results of multivariable linear regression analyses using factor scores as outcomes in the entire study population. Positive coefficients indicate higher multimorbidity burden relative to the reference group.
Sensitivity Analysis
Results from sensitivity analyses excluding data from sites with fewer than 100 PWH patients (Supplementary Table 6–8) were highly consistent with those from the primary analysis.
DISCUSSION
Leveraging a nationwide EHR cohort, we found that PWH consistently experienced a substantially higher burden of multimorbidity than PWoH, with these disparities further amplified following COVID-19 diagnosis. The prevalence of most comorbidities increased in both PWH and PWoH after COVID-19, consistent with the broad health impact of the pandemic among PWH and PWoH, with the gap between PWH and PWoH further widened across the burden spectrum. Beyond increases in overall burden, the leading multimorbidity pattern among PWH shifted toward cardiometabolic dominance after COVID-19, while the overall 4-factor structure remained largely stable. Our findings underscore the necessity for improved screening, continuous monitoring of existing comorbidities, and comprehensive integrated care strategies that transcend single-disease management—particularly for aging PWH and populations experiencing a higher burden of multimorbidity after the COVID-19 pandemic.
Our findings are consistent with prior studies on comorbidity distributions across age, sex, and race and ethnicity among PWH, while extending the evidence into the post-COVID-19 period, which few prior studies have examined at a national scale. We observed a clear stepwise increase in multimorbidity burden with advancing age among PWH. Prior to COVID-19, more than 60% of PWH aged 50 years or older had 3 or more chronic conditions, and this proportion increased by an additional 10.9% following a COVID-19 diagnosis. This age-related accumulation of comorbidities among PWH is consistent with findings from prior analyses conducted in the Multicenter AIDS Cohort Study and the Women's Interagency HIV Study between 2009 and 2019, which also reported higher multimorbidity burden among PWH, particularly in women [9]. In addition, even after accounting for sociodemographic covariates, PWH remained more concentrated in higher multimorbidity categories than PWoH, suggesting that the onset and progression of chronic comorbidities may occur earlier and advance more rapidly among PWH [39]. Together, these observations highlighted that chronic HIV infection is associated with an earlier onset of non-AIDS-related morbidity, a shorter comorbidity free life expectancy, and the presence of multimorbidity even among young adults with HIV [10, 13, 40]. This “aging” phenomenon has been explicitly shown in the setting of COVID-19 [34].
Cardiovascular disease, obesity, diabetes mellitus, pulmonary disease, and psychiatric disorders were the most prevalent comorbidities among PWH, echoing prior literature documenting a high burden of hypertension, dyslipidemia, endocrine disorders, and mental health conditions in this population [5, 41, 42]. However, the relative prominence of specific comorbidities among PWH appeared to evolve over time, with cardiometabolic conditions such as dyslipidemia, diabetes, and chronic kidney disease accounting for an increasing share of the overall burden [43, 44]. Beyond identifying which conditions were most common, our analysis quantified how multimorbidity patterns related to HIV status and sociodemographic characteristics. Together with the widening PWH versus PWoH disparity noted above, these results indicate that the post-COVID-19 increase in multimorbidity was not evenly distributed but concentrated among groups already at higher risk.
Our study provided a systematic, data-driven characterization of multimorbidity patterns among PWH and captured a transition toward cardiometabolic dominance after COVID-19. Cardiometabolic disorders emerged as the primary multimorbidity factor followed by the transplant and multiorgan disease factor. This pattern is likely driven by a triple threat unique to PWH that includes the synergy of HIV-specific immune activation, treatment-related metabolic dysfunction, and traditional risk factors such as obesity, tobacco use, and genetic predisposition [45–47]. Although ART has enabled effective viral suppression, it has been associated with metabolic disturbances including increased insulin resistance, alterations in adiposity, and dyslipidemia [48–52]. Evidence also suggests that long-term ART exposure contributed to organ-specific toxicities, particularly involving renal and hepatic function [53–55]. Crucially, even with viral suppression, PWH are likely to experience persistent chronic inflammation and immune dysregulation [56]. For example, the chronic immune activation might trigger the endothelial dysfunction and accelerates plaque formation, which is the primary drivers of atherosclerotic cardiovascular disease [57].
Psychiatric disorders, SUDs, and liver disease were identified as a prominent multimorbidity factor among both PWH and PWoH. The prevalence of these conditions was substantially higher among PWH. This pattern reflects a well-recognized syndemic of psychosocial and behavioral conditions in PWH, in which mental health disorders, substance use, and liver disease frequently co-occur and reinforce one another [58–61]. The COVID-19 pandemic may have further amplified this vulnerability through disruptions in mental health and addiction services and reduced continuity of care, contributing to the post-COVID-19 increase in these diseases [62, 63].
LIMITATIONS
This study has several limitations. First, the N3C dataset primarily collects data from large medical centers that are more likely to include patients with more severe clinical presentations and better access to health care, limiting generalizability to some extent. Second, our analysis defined the first COVID-19 diagnosis as the index date to distinguish pre- and post-COVID-19 multimorbidity burden. This approach did not account for disease severity at the time of infection, nor did it capture the temporal dynamics of different pandemic phases or multiple infections. Finally, given the nature of EHR data, some conditions may have been present prior to infection but only diagnosed after COVID-19 diagnosis, potentially inflating estimates of new disease onset. However, this misclassification is likely to be nondifferential between PWH and PWoH.
CONCLUSION
In this large national cohort study, multimorbidity burden increased after COVID-19 among both PWH and PWoH, with the increase most pronounced among PWH and the disparity widening across the burden spectrum. We further characterized multimorbidity patterns among PWH and observed a shift toward cardiometabolic dominance. These findings underscore the evolving complexity of chronic disease burden among PWH in the era of effective ART and post-COVID-19 recovery. As HIV care increasingly resembles chronic disease management, our results support proactive, multimorbidity-informed care: PWH warrant heightened vigilance for new comorbidities, and the presence of one condition should prompt monitoring for others that co-occur within the same multimorbidity pattern, particularly for aging PWH and populations with higher multimorbidity burden. Such integrated care strategies that extend beyond single-disease management are essential to improve long-term health outcomes and ensure quality care for an aging population living with HIV.
Supplementary Material
Notes
Disclaimer . The N3C Publication committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.
Financial support . This work was supported by the National Institutes of Health (NIMH R01MH131542; and NIAID K01AI162247 to J.S.). This research was conducted with data and tools accessed through the National COVID Cohort Collaborative (N3C) Data Enclave, supported by National Center for Advancing Translational Sciences (NCATS) Contract No. 75N95023D00001.
N3C attribution . The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave (https://ncats.nih.gov/research/research-activities/n3c/overview) and N3C Attribution & Publication Policy v1.2-2020-08-25b, supported by NCATS Contract No. 75N95023D00001. This research was under project N3C ImmunoSuppressed/Compromised Task Team: The Impact of COVID-19 on the ISC Population (DUR: RP-CA3365). This work was additionally supported by the U.S. National Institutes of Health (NIMH R01MH131542), and Dr. Sun is supported by NIAID K01AI162247. This research was made possible by patients whose information is included within the data, the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories), and the scientists who have contributed to the ongoing development of this community resource (https://doi.org/10.1093/jamia/ocaa196).
IRB . The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.
Author contributions . Conceptualization: J.S., R.C.P. Data curation: Y.W., J.A. Formal analysis: Y.W., A.S., J.A. Methodology: Y.W., A.S., J.A., D.L., J.S. Project administration: J.S., R.C.P. Supervision: J.S., R.C.P. Visualization: Y.W. Writing—original draft: Y.W., J.S. Writing—review & editing: All authors.
Individual acknowledgements for core contributors . Note to corresponding authors: Many people contributed to the core infrastructure of the N3C; while these individuals do not need to be indexed in medline we request that they be included in the in-text acknowledgement section of your manuscript. The text to paste is below; note there may be overlap between tiers 1, 2, and 3; this is to be expected. You are welcome to condense as you feel appropriate.
We gratefully acknowledge the following core contributors to N3C:
Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew M. Southerland, Andrew T. Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G. Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Donald E. Brown, Eilis Boudreau, Elaine L. Hill, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar B. Mehta, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J. Loomba, John B. Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Philip R.O. Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C. Patel, Richard A. Moffitt, Richard L. Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep K. Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steven G. Johnson, Tellen D. Bennett, Tiffany J. Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at COVID.cd2h.org/core-contributors.
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Patient consent statement . This study used deidentified data from the N3C Data Enclave under an approved data use agreement (Johns Hopkins IRB00249128). Individual patient consent was not required for use of deidentified data.
Data availability statement. The following institutions whose data is released or pending: Available: Advocate Health Care Network—UL1TR002389: The Institute for Translational Medicine (ITM) • Aurora Health Care Inc—UL1TR002373: Wisconsin Network For Health Research • Boston University Medical Campus—UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University—U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University—UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children's Hospital Colorado—UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center—UL1TR001873: Irving Institute for Clinical and Translational Research • Dartmouth College—None (Voluntary) Duke University—UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children's Research Institute—UL1TR001876: Clinical and Translational Science Institute at Children's National (CTSA-CN) • George Washington University—UL1TR001876: Clinical and Translational Science Institute at Children's National (CTSA-CN) • Harvard Medical School—UL1TR002541: Harvard Catalyst • Indiana University School of Medicine—UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University—UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Louisiana Public Health Institute—None (Voluntary) • Loyola Medicine—Loyola University Medical Center • Loyola University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic—None (Voluntary) • Massachusetts General Brigham—UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester—UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina—UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation—None (Voluntary) • Montefiore Medical Center—UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours—U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem—UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago—UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN—INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University—UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center—UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey—UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University—U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus—U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University—UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo—UL1TR001412: Clinical and Translational Science Institute • The University of Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa—UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine—UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor—UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston—UL1TR001439: The Institute for Translational Sciences • The University of Utah—UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center—UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University—UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center—None (Voluntary) • University Medical Center New Orleans—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham—UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences—UL1TR003107: UAMS Translational Research Institute • University of Cincinnati—UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus—UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago—UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center—UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky—UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester—UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada—None (voluntary) • University of Minnesota—UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center—U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center—U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill—UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center—U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh—UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania—UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester—UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California—UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington—UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison—UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center—UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University—UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences—UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis—UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University—UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) Submitted: Icahn School of Medicine at Mount Sinai—UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis—UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine—UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles—UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego—UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco—UL1TR001872: UCSF Clinical and Translational Science Institute NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core—OTA-21-015A: Post-Acute Sequelae of SARS-CoV-2 Infection Initiative (RECOVER) Pending: Arkansas Children's Hospital—UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine—None (Voluntary) • Children's Hospital of Philadelphia—UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children's Hospital Medical Center—UL1TR001425: Center for Clinical and Translational Science and Training • Emory University—UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth—None (Voluntary) • Loyola University Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin—UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute—None (Voluntary) • Georgetown University—UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth—None (Voluntary) • Montana State University—U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center—UL1TR001445: Langone Health's Clinical and Translational Science Institute • Ochsner Medical Center—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute—UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research—None (Voluntary) • Stanford University—UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University—UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute—UL1TR002550: Scripps Research Translational Institute • University of Florida—UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center—UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio—UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital—UL1TR001863: Yale Center for Clinical Investigation.
Contributor Information
Yumeng Wang, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Ahmed Sabit, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Jerrod Anzalone, Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Dongmei Li, Clinical and Translational Science Institute, Department of Obstetrics and Gynecology, and Department of Public Health Sciences, University of Rochester, Rochester, New York, USA.
Eric Hurwitz, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Nada Fadul, Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Shixiong Wei, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Amy L Olex, C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, Virginia, USA.
Sandra E Safo, Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.
Jessica Y Islam, Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
Todd T Brown, Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA.
Christopher G Chute, Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA.
Melissa A Haendel, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Cara D Varley, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA.
Zachary Butzin-Dozier, Division of Biostatistics, University of California, Berkeley, Berkeley, California, USA.
Marlene Camacho-Rivera, Department of Community Health Sciences, SUNY Downstate School of Public Health, Brooklyn, New York, USA.
Emmanuel Nazaire Essam Nkodo, Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Rena C Patel, Division of Infectious Diseases, Department of Medicine and Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Jing Sun, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
National Clinical Cohort Collaborative (N3C):
Adam B Wilcox, Adam M Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E Williams, Andrew M Southerland, Andrew T Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G Caleb Alexander, Carolyn T Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A Eichmann, Diego Mazzotti, Donald E Brown, Eilis Boudreau, Elaine L Hill, Emily Carlson Marti, Emily R Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P Lehmann, Heidi Spratt, Hemalkumar B Mehta, J W Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J Loomba, John B Buse, Jomol Mathew, Joni L Rutter, Julie A McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M Walters, Ken Wilkins, Kenneth R Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M Portilla, Mariam Deacy, Mark M Bissell, Marshall Clark, Mary Emmett, Matvey B Palchuk, Melissa A Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A Francis, Penny Wung Burgoon, Philip R O Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C Patel, Richard A Moffitt, Richard L Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T Miller, Saiju Pyarajan, Sam G Michael, Samuel Bozzette, Sandeep K Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T O'Neil, Soko Setoguchi, Stephanie S Hong, Steven G Johnson, Tellen D Bennett, Tiffany J Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, and Xiaohan Tanner Zhang
Supplementary Data
Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
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