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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: AIDS Care. 2020 Nov 10;33(5):594–606. doi: 10.1080/09540121.2020.1844864

Comorbidity patterns among people living with HIV: a hierarchical clustering approach through integrated electronic health records data in South Carolina

Xueying Yang 1,2,*, Jiajia Zhang 2,3, Shujie Chen 2,3, Sharon Weissman 4, Bankole Olatosi 1,5, Xiaoming Li 1,2
PMCID: PMC8068571  NIHMSID: NIHMS1648208  PMID: 33172284

Abstract

Comorbidity among people living with HIV (PLWH) is understudied although identifying its patterns and socio-demographic predictors would be beneficial for comorbidity management. Using electronic health records (EHR) data, 8,490 PLWH diagnosed between January 2005 and December 2016 in South Carolina were included in the current study. An initial list of 86 individual diagnoses of chronic conditions was extracted in the EHR data. After grouping individual diagnoses with a pathophysiological similarity, 24 diagnosis groups were generated. Hierarchical cluster analysis was applied to these 24 diagnosis groups and yielded four comorbidity clusters: “substance use and mental disorder” (e.g., alcohol use, depression, and illicit drug use); “metabolic disorder” (e.g., hypothyroidism, diabetes, hypertension, and chronic kidney disease); “liver disease and cancer” (e.g., hepatitis B, chronic liver disease, and non-AIDS defining cancers); and “cerebrovascular disease” (e.g., stroke and dementia). Multivariable logistic regression was conducted to investigate the association between socio-demographic factors and multimorbidity (defined as concurrence of ≥ 2 comorbidity clusters). The multivariable logistic regression showed that age, gender, transmission risk, race, initial CD4 counts, and viral load were significant factors associated with multimorbidity. The results suggested the importance of integrated clinical care that addresses the complexities of multiple, and potentially interacting comorbidities among PLWH.

Keywords: HIV/AIDS, Comorbidity patterns, Hierarchical cluster analysis, Electronic health records, South Carolina

Introduction

The introduction of antiretroviral therapy (ART) has changed the profile of the HIV epidemic. With the improved efficacy of ART, the mortality of people living with HIV (PLWH) has decreased (Wandeler et al., 2016). Due to this mortality reduction, HIV infection has transformed into a manageable chronic illness. At the same time, with a prolonged life expectancy, PLWH face challenges of co-occurring chronic conditions or comorbidities (Gebo, 2008). Results from several studies have shown that compared with age-matched uninfected individuals, PLWH might have a higher prevalence and an earlier onset of many chronic conditions, such as myocardial infarction (Althoff et al., 2015), diabetes mellitus (DM) (Guaraldi et al., 2011), non-AIDS defining cancers (Althoff et al., 2015; High et al., 2012; Valdez et al., 2016), chronic liver or kidney disease (Fischer et al., 2010; Lucas et al., 2007; Sulkowski & Thomas, 2003), and psychiatric disorders (Patel et al., 2018). With the increasing prevalence of comorbidities among PLWH, more and more attentions are paid to this important public health issue for at least two reasons. First, PLWH with comorbidity represent a higher mortality rate (Gijsen et al., 2001), higher healthcare costs, and poorer quality of life (Fortin et al., 2004). Second, the comorbid conditions complicate the HIV treatment and care (Gebo, 2008; Nachega et al., 2012) due to the polypharmacy, potential drug-drug interactions, and adverse effects.

The prevalence of PLWH who have co-occurred chronic conditions is alarming. In the U.S., the prevalence of PLWH with ≥2 chronic conditions reached as high as 65% (Kim et al., 2012). Often overlooked, the patients, providers, and healthcare systems face challenges in managing multiple diseases or conditions among PLWH. Although disease-specific strategies may be useful for treating or managing the individual illnesses, these strategies can also result in fragmentation of care and are often unable to address the complex needs of patients with concurrent chronic conditions (Starfield et al., 2003). The real challenges in the treatment and healthcare services are beyond just evaluating the individual comorbidity (Parekh & Barton, 2010). Chronic diseases can co-occur for several reasons. First, certain diseases share the same underlying genetic, environmental, or behavioral risk factors (e.g., obesity is a risk factor for a variety of chronic illnesses, such as osteoarthritis, diabetes, hypertension). Second, some disease clusters may arise as multiple sequelae of a primary condition (e.g., diabetes can lead to various complications, such as neuropathy, retinopathy). The patterns by which conditions tend to occur in conjunction with each other also play a key role in the complexity of treatment or healthcare management. Obtaining a general picture on the broad pattern of how diseases are clustering and where a particular disease of interest appears in the pattern as well as their temporal trends overtime will have far-reaching implications to HIV care management.

Previous studies have examined the patterns of chronic conditions using disease clusters among non-HIV populations (Cornell et al., 2008; Laux et al., 2008; Schafer et al., 2010). For example, using a set of 45 chronic conditions in the Veterans Health Administration data, six clusters were identified, i.e., metabolic, obesity, liver disease, neurovascular disease, stress, and dual diagnosis (Cornell et al., 2008). The patterns of chronic conditions among HIV patients might be different from other populations due to the chronic HIV infection and long-term use of ART (Green et al., 2010). Limited studies analyzed the comorbidity patterns among PLWH. One cross-sectional study identified three comorbidity clusters (i.e., metabolic, behavioral, substance use) among 1844 PLWH using exploratory factor analysis of 28 chronic conditions (Kim et al., 2012). Another study described temporal trends (8.2% in 2000 to 22.4% in 2009) of co-occurrence of two or more chronic conditions among 22,969 PLWH (Wong et al., 2018). However, most existing studies either used cross-sectional data (Schouten et al., 2014), self-report data (Wong et al., 2018), or had a limited number of clinical disease diagnoses (Kim et al., 2012). Much more remains to be learned about the concurrence of chronic diseases among PLWH and the factors associated with the patterns of such concurrence.

This study was designed to address these gaps by 1) identifying comorbidity clusters from a large set of chronic conditions among a population receiving HIV medical care from 2005 to 2016 in South Carolina (SC); 2) examining the patterns of comorbidity in terms of concurrence of comorbidity clusters as well as the temporal trend in the prevalence of different patterns of concurrence across a long follow up period (up to 12 years) for the entire cohort and certain demographic subgroups; and 3) investigating the associations between socio-demographic characteristics and multimorbidity (i.e., the concurrence of ≥2 comorbidity clusters).

Methods

Study design and data sources

This study is a population-based cohort study, with data being retrieved from an integrated system of electronic health record (EHR) data (i.e., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) in SC. The study population is PLWH with a confirmed HIV diagnosis from January 2005 and December 2016 and with a SC residence at diagnosis. The SC Department of Health and Environmental Control (SC DHEC) electronic HIV/AIDS reporting system (eHARS) data were used to identify PLWH. The eHARS data were linked to the SC Revenue and Fiscal Affairs Office (SC RFA) data to extract EHR data. The SC DHEC’s eHARS is a statewide confidential name-based reporting of HIV/AIDS began in 1986 (Centers for Disease Control and Prevention, 2006; Olatosi et al., 2009), to which reporting of CD4 and viral load tests has become mandatory by the government since January 1st, 2004 (Centers for Disease Control and Prevention, 2006; Marengoni et al., 2009), . The SC RFA is a state agency that houses all-payer data in SC and collects individual-level longitudinal health utilization data from various state agencies. Details of the research protocol are described elsewhere (Olatosi et al., 2019). After excluding participants whose age was less than 18 years at HIV diagnosis (n=148), 8,490 individuals were included in this analysis. The research protocol received approval from the relevant institutional review boards and regulatory committees.

Comorbid conditions and clinical diagnosis groups

The diagnoses of comorbid chronic conditions among PLWH were identified via the presence of International Classification of Diseases, 9th and 10th revision (ICD-9/10) Diagnostic Codes in the EHR dataset. A list of 86 individual diagnoses of common chronic conditions was compiled based on a review of the existing comorbidity literature in both the general and HIV-infected populations (Althoff et al., 2015; Kim et al., 2012; van den Bussche et al., 2011). Similar to existing studies (Kim et al., 2012), individual diagnoses reflected by the presence of ICD-9/10 codes were grouped together as a diagnosis group if diseases and syndromes had a close pathophysiological similarity or clinical relevance. Consequently, 24 diagnosis groups were defined using 86 individual diagnoses and were included in the analysis. The detailed individual diagnoses and diagnosis groups were displayed in Appendix 1.

Socio-demographic characteristics

The socio-demographic characteristics were extracted from the integrated database. These characteristics included age at HIV diagnosis (categorized as four groups: 18-24, 25-34, 35-44, 45+ years), sex (e.g., female, male), race (e.g., White, Black, Hispanics, others), and mode of HIV transmission (e.g., men who have sex with men [MSM], injection drug use [IDU], heterosexual, others). CD4 counts at HIV diagnosis were categorized as <200, 200-350, or >350 cells/mm3. Viral loads at HIV diagnosis were categorized as <200, 200-10,000, 10,000-100,000, or >100,000 copies/ml.

Statistical analysis

The analyses included hierarchical cluster analysis to identify comorbidity clusters, temporal trend analysis of the prevalence of concurrence of comorbidity clusters up to 12 years of follow up for the entire cohort as well as by socio-demographic groups, and multivariable logistic regression to identify the factors associated with multimorbidity which was defined as the concurrence of ≥2 comorbidity clusters in the current study. All analyses were performed with SAS (version 9.4; Cary, North Carolina, USA) and R software (version 3.6.2).

Hierarchical cluster analysis

The hierarchical cluster algorithms were used to identify comorbidity clusters based on the 24 diagnosis groups. Cluster analysis is one of the most common used statistical methods to identify multimorbidity patterns among others and applied in several other studies (Ng et al., 2018; Prados-Torres et al., 2014). Given our expectation of multimorbidity among our study population, we applied a hierarchical cluster algorithm which was shown to be the most appropriate for classification problems where objects are related via some underlying systematic structure (Everitt, 2001). Specifically, we first computed the proximity between different comorbidities (“present” or “absent”) via the DGOWER method (Gower & Legendre, 1986) through the DISTANCE procedure. Second, we clustered the diagnosis groups based on their proximity using a hierarchical clustering algorithm, implemented by CLUSTER statement with Ward’s minimum-variance method. Because there is little consensus in the literature regarding how to determine the number and quality of clusters representing a meaningful grouping of objects, we used the following two clinically relevant criteria to identify disease clusters: 1) the groupings of the diseases (diagnosis groups) within the cluster match known epidemiological ties, and 2) most of the diseases within the cluster are known to respond to disease co-management approaches. Although these criteria are subjective, they were the commonly used criteria to determine the number of comorbidity clusters using hierarchical cluster analyses (Cornell et al., 2008). The number of concurrent comorbidity clusters was dichotomized (0-1 cluster vs. ≥ 2 clusters) to serve as a measure of multimorbidity in the current study.

Patterns and temporal trends of multimorbidity

To examine the patterns of multimorbidity, we plotted the percentage of concurrence of comorbidity clusters for the entire cohort using VennDiagram (Chen & Boutros, 2011). To examine the temporal trend of multimorbidity, we plotted the prevalence of different patterns of concurrences of comorbidity clusters up to 12 years from 2005 to 2016 by gender, race and residence type.

Factors associated with multimorbidity

To study the relationship between multimorbidity and socio-demographic factors, we conducted multivariable logistic regression analyses for the entire follow up duration (0-12 years) and at every 3years of follow up (0-3, 3-6, 6-9, 9-12 years). The dichotomized measure of multimorbidity serves as the dependent variable in the regression model. Socio-demographic characteristics, such as age, gender, and race, were included as independent variables. Odds ratio (OR) and respective 95% confidence interval (CI) between socio-demographic characteristics and the dependent variable were estimated to indicate the strengths of the associations.

Results

Characteristics of the study cohort

Among 8,490 HIV patients, 27.0% (n=2,290) were at least 45 years of age at HIV diagnosis, 75.0% (n=6,367) were male, 70.7% (n=6,004) were Black, and 82.6% (n=7,012) resided in the urban areas of SC. MSM constituted the largest HIV transmission risk group (49.8%, n=4,229). Each demographic variable was significantly associated with multimorbidity (p<.0001 or p=0.01) (Table 1). As shown in Figure 1, the prevalence of the 24 diagnosis groups ranged from 0.4% (cerebral infarction) to 38.5% (tobacco use).

Table 1.

Social demographics and multimorbidity among PLWH in South Carolina

Items Total
N (%)		 Multimorbidity p-value
Yes No
N (%) 8490 (100) 3017 (35.5) 5473 (64.5)
Age at HIV diagnosis (years) <.0001
 18-24 2069 (24.4) 476 (23.0) 1593 (77.0)
 25-34 2275 (26.8) 641 (28.2) 1634 (71.8)
 35-44 1856 (21.9) 782 (42.1) 1074 (57.9)
 45+ 2290 (27.0) 1118 (48.8) 1172 (51.2)
Sex <.0001
 Female 2123 (25.0) 1034 (48.7) 1089 (51.3)
 Male 6367 (75.0) 1983 (31.1) 4384 (68.9)
Race <.0001
 White 1872 (22.1) 709 (37.9) 1163 (62.1)
 Black 6004 (70.7) 2186 (36.4) 3818 (63.6)
 Hispanic/others 614 (7.2) 122 (19.9) 492 (80.1)
Residence 0.01
 Rural 1478 (17.4) 568 (38.4) 910 (61.6)
 Urban 7012 (82.6) 2449 (34.9) 4563 (65.1)
Risk <.0001
 Heterosexual 1824 (21.5) 832 (45.6) 992 (54.4)
 MSM 4229 (49.8) 1114 (26.3) 3115 (73.7)
 Injection drug use 491 (5.8) 265 (54.0) 226 (46.0)
 Others 1946 (22.9) 806 (41.4) 1140 (58.6)
Initial CD4 counts (cells/mm3) <.0001
 <200 2685 (31.6) 1165 (43.4) 1520 (56.6)
 200-350 1773 (20.9) 597 (33.7) 1176 (66.3)
 >350 4032 (47.5) 1255 (31.1) 2777 (68.9)
Initial viral load (copies/ml) <.0001
 <200 455 (5.4) 110 (24.2) 345 (75.8)
 200-10,000 2578 (30.4) 860 (33.4) 1718 (66.6)
 10,000-100,000 3170 (37.3) 1104 (34.8) 2066 (65.2)
 >100,000 2287 (26.9) 943 (41.2) 1344 (58.8)
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Figure 1. The prevalence of diagnosis groups of chronic diseases among PLWH in South Carolina from 2005 to 2016.

Figure 1

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Note: COPD=chronic obstructive pulmonary disease; NADC=non-AIDS defining cancers

Comorbidity clusters

The hierarchical cluster analysis identified four comorbidity clusters from the 24 diagnosis groups. As shown in Figure 2, the four comorbidity clusters were: 1) “substance use and mental disorders” (6 diagnosis groups: alcohol use, tobacco use, anxiety, depression, psychiatric disorders, illicit drug use); 2) “metabolic disorders” (10 diagnosis groups: hypothyroidism, anemia, diabetes, dyslipidemia, cardiac disorders, hypertension, ulcer disease, chronic obstructive pulmonary disease [COPD], osteoporosis/osteoarthritis, chronic kidney disease); 3) “liver disease and cancer” (4 diagnosis groups: hepatitis B, chronic liver disease, hepatitis C, non-AIDS defining cancers); and 4) “cerebrovascular disease” (4 diagnosis groups: stroke, cerebral infarction, peripheral vascular disease, dementia). The concurrence (in %) of comorbidity clusters among all the PLWH were shown in Figure 3, with 11.50% of the patients being diagnosed only with substance use and mental disorders (cluster 1), 12.94% only with metabolic disorders (cluster 2), 0.52% only with liver diseases and cancer (cluster 3) and 0.09% only with cerebrovascular disease (cluster 4). In the meantime, the 2 most frequent concurrent dyads were: clusters 1 and 2 (22.56%) and clusters 2 and 3 (1.30%). The 2 most frequent concurrent triads were clusters 1, 2, and 3 (6.22%) and clusters 1, 2, and 4 (2.41%). The proportion of patients who were diagnosed with all four clusters was low (1.32%).

Figure 2. Cluster dendrogram from hierarchical cluster analysis among the 24 diagnosis groups observed in the PLWH in South Carolina.

Figure 2

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Note: COPD=chronic obstructive pulmonary disease; NADC=non-AIDS defining cancers

Figure 3. Concurrence (in %) of comorbidity clusters among PLWH in South Carolina from 2005 to 2016.

Figure 3

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Note: cluster 1=substance use and mental disorders; cluster 2=metabolic disorders; cluster 3=liver disease and cancer; cluster 4=cerebrovascular disease

Temporal trends of multimorbidity

Among the study cohort, 39.41% (n=3,346) of the patients did not have any comorbid conditions, and 25.05% (n=2,127) were diagnosed with comorbid conditions in one comorbidity cluster. The overall prevalence of multimorbidity (concurrence of ≥2 comorbidity clusters) was 35.53% (n=3,017), with the rates of concurrence of two, three and four comorbidity clusters being 25.35% (n=2,153), 8.86% (n=752), and 1.32% (n=112), respectively.

Overall, the prevalence of multimorbidity has gradually increased over time for the entire cohort (6.6% in 2005 and 23.6% in 2016) (Figure 4). The temporal trends of the number of concurrent comorbidity clusters as well as trends by socio-demographic characteristics over 12 years of follow-up were plotted in Figure 4. In terms of residence type, there were more rural patients with one or two comorbidity clusters than urban patients at each year of follow-up, while the prevalence of three or four comorbidity clusters among rural patients was lower than urban patients. Female patients had a much higher prevalence of multimorbidity (i.e., the concurrence of 2 or more clusters) than male patients with an increase from 9.5% in 2005 to 27.4% in 2016 for the concurrence of two clusters. The prevalence of Black patients with one cluster was much higher than White patients, while the prevalence of multimorbidity among White patients was higher than Black patients overall all 12 years of follow-up.

Figure 4.

Figure 4

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Temporal trends of concurrences of comorbidity clusters across each follow-up year overall and by residence type, sex and race

Factors associated with multimorbidity

In the overall multivariable logistic regression model for the entire 12 years of follow up, HIV patients had an increased odds of developing multimorbidity if they were older (25-34: [OR: 1.26; 95%CI: 1.04, 1.52]; 35-44: [OR: 2.16; 95%CI: 1.78, 2.61]; 45+: [OR: 2.88; 95%CI: 2.39, 3.47]), had a history of injection drug use (OR: 1.85; 95%CI: 1.46, 2.33), and had higher (detectable) viral load levels at diagnosis (200-10,000: [OR:1.80; 95%CI:1.32, 2.45]; 10,000-100,000: [OR:2.06; 95%CI:1.51, 2.80]; >100,000 [OR: 2.28; 95%CI: 1.66, 3.14]); while male gender (OR: 0.47; 95%CI: 0.40, 0.55), Hispanic/other race (OR: 0.39; 95%CI: 0.29, 0.52), MSM (OR: 0.78; 95%CI: 0.65, 0.93), and higher initial CD4 counts (>350: [OR: 0.83; 95%CI: 0.72, 0.96]) were associated with lower odds of developing multimorbidity (Table 2). When the analyses were conducted for each 3-year follow-up period, similar associations were observed, except that the statistical significance related to MSM (during each 3-year follow-up period) and initial CD4 counts (during 9-12 years follow up) diminished (Table 2).

Table 2.

Associations between social demographics and multimorbidity by follow-up intervals

Items Total: 0-12YR
(OR, 95%CI)		 Years of follow up (OR, 95%CI)
0-3 YR 3-6 YR 6-9 YR 9-12 YR
N 8490 8490 7876 5629 3538
Age at HIV diagnosis (years)
 18-24 1 1 1 1 1
 25-34 1.26 (1.04,1.52) 1.30 (1.00,1.68) 1.22 (0.99,1.52) 1.31 (1.05,1.64) 1.23 (0.95,1.60)
 35-44 2.16 (1.78,2.61) 2.03 (1.58,2.62) 2.08 (1.68,2.57) 2.10 (1.68,2.62) 1.70 (1.32,2.21)
 45+ 2.88 (2.39,3.47) 3.49 (2.74,4.43) 2.77 (2.25,3.41) 2.85 (2.29,3.55) 2.41 (1.85,3.13)
Sex
 Female 1 1 1 1 1
 Male 0.47 (0.40,0.55) 0.43 (0.36,0.51) 0.39 (0.33,0.47) 0.40 (0.33,0.48) 0.43 (0.35,0.54)
Race
 White 1 1 1 1 1
 Black 0.74 (0.64,0.84) 0.75 (0.63,0.88) 0.73 (0.62,0.85) 0.70 (0.59,0.83) 0.62 (0.51,0.75)
 Hispanic or others 0.39 (0.29,0.52) 0.51 (0.36,0.72) 0.39 (0.28,0.55) 0.37 (0.26,0.54) 0.30 (0.19,0.46)
Residence
 Rural 1 1 1 1 1
 Urban 0.88 (0.76,1.02) 0.99 (0.82,1.18) 0.89 (0.76,1.05) 0.84 (0.70,1.00) 0.89 (0.72,1.11)
Transmission Risk
 Heterosexual 1 1 1 1 1
 MSM 1 0.78 (0.65,0.93) 0.99 (0.79,1.25) 0.97 (0.78,1.21) 0.97 (0.77,1.21) 0.90 (0.69,1.18)
 IDU 2 1.85 (1.46,2.33) 2.42 (1.85,3.17) 2.05 (1.57,2.67) 1.72 (1.30,2.29) 1.86 (1.32,2.63)
 Others 0.78 (0.65,0.93) 0.99 (0.79,1.25) 0.97 (0.78,1.21) 0.97 (0.77,1.21) 0.90 (0.69,1.18)
Initial CD4 counts (cells/mm3)
 <200 1 1 1 1 1
 200-350 0.88 (0.74,1.03) 0.79 (0.64,0.96) 0.87 (0.72,1.05) 0.95 (0.78,1.17) 1.03 (0.81,1.32)
 >350 0.83 (0.72,0.96) 0.76 (0.64,0.91) 0.76 (0.65,0.90) 0.94 (0.79,1.12) 1.07 (0.87,1.33)
Initial viral load (copies/ml)
 <200 1 1 1 1 1
 200-10,000 1.80 (1.32,2.45) 2.01 (1.31,3.08) 1.63 (1.16,2.3) 1.25 (0.88,1.79) 1.29 (0.85,1.96)
 10,000-100,000 2.06 (1.51,2.80) 2.45 (1.6,3.73) 1.83 (1.3,2.56) 1.53 (1.08,2.17) 1.54 (1.01,2.33)
 >100,000 2.28 (1.66,3.14) 3.14 (2.04,4.83) 2.09 (1.47,2.97) 1.67 (1.16,2.42) 1.95 (1.25,3.02)
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Note:

1

MSM=men who have sex with men

2

IDU=injection drug use

Discussion

Our study represents one of the first efforts to characterize comorbidity patterns (e.g., the clusters, concurrences, and temporal trends of concurrence of comorbid chronic conditions) from the integrated longitudinal EHR data among PLWH in SC. With hierarchical cluster analysis, we identified four comorbidity clusters from 24 diagnosis groups that were derived from 86 individual comorbid diagnoses. Each cluster contains chronic conditions with a common underlying pathological origin or process (e.g., hypertension, diabetes), or contains diagnoses that can be linked as some sequelae of one chronic condition (e.g., hepatitis B and chronic liver disease). This analytic approach helped us identify clinically meaningful groupings from a large number of chronic diseases. These findings could possibly be generalized to other settings with similar population characteristics (e.g., other Southern US states) and be used to inform the health care systems of the comorbidity clusters occurring within their patient populations. Understanding the disease clusters and the concurrence of different clusters would facilitate the design and implementation of chronic disease management programs and develop strategies to implement evidence-based guidelines for HIV patients with comorbid conditions, especially those with multimorbidity. Instead of focusing on individual diseases, clinical care could be transformed into “multimorbidity” perspectives. These strategies may improve patient-centered outcomes and increase the efficiency of clinical care and disease management. Also, patient education programs could offer tailored self-management guidelines and behavioral skills training to patients with a particular comorbidity pattern.

Most of the individual diagnoses identified in each cluster were clinically relevant and compatible with previous studies (Kim et al., 2012). The clustering of such chronic conditions could provide information for healthcare providers regarding integrated HIV care management and inform future intervention efforts. The first cluster identified in the current study was “substance use and mental disorder”. The emergence of this cluster was not surprising, since a similar cluster was identified in the previous literature (Cornell et al., 2008). This bio-behavioral link between mental disorder and substance use has also been supported by other clinical studies (Cook et al., 2013; Palfai et al., 2014). The second cluster was “metabolic disorders”. Most of the diseases in this cluster have well-established epidemiological ties and are similar to the cluster of the cardiovascular disease reported in another U.S. study (Kim et al., 2012). The concurrences of these conditions have long been known because they shared the same underlying risk factors (Choi et al., 2008). For example, hypertension and diabetes are well-known risk factors of cardiac disorders and share pathophysiological features and vascular mechanisms (Petrie et al., 2018). Osteoporosis is one of the most severe complications of COPD (Bitar et al., 2019) resulting from steroid use and inactivity. The association between thyroid dysfunction and anemia has been postulated for decades, although their clinical relevance was not always being found (Bitar et al., 2019; Floriani et al., 2018; Stevens et al., 2016). Chronic liver disease can be the sequelae consequences of hepatitis B or C infection. The high prevalence of hepatitis B or C infection among PLWH reinforced such causal relationships (dos Santos Marcon et al., 2018). However, the inclusion of non-AIDS defining cancers in this cluster merits further investigation. Similarly, the conditions in the cerebrovascular disease cluster, such as stroke or dementia, also have strong epidemiological links in terms of risk factors (Kalaria et al., 2016; Vijayan & Reddy, 2016).

Over one-third of patients were diagnosed with multimorbidity. There was an increasing temporal trend of the overall multimorbidity and individual comorbidity clusters over time. The trends seen in our study are consistent with existing literature and reflect the growing comorbidity burden among PLWH, although it may be difficult to directly compare the rates of multimorbidity with other studies due to the different definitions. Another U.S. study defined multimorbidity as having ≥2 individual diagnoses and observed an increase of multimorbidity from 8.2% in 2000 to 22.4% in 2009 (Wong et al., 2018), which is compatible with our findings (6.6% in 2005 and 23.6% in 2016). Our findings suggested the need to continue monitoring the concurrence of multiple conditions as PLWH live longer with effective ART. One of the implications of these findings is that providers will need to be prepared to manage multimorbidity. There is an absence of formal multimorbidity care guidelines for PLWH (Aberg et al., 2014). As providers face the challenge of caring for PLWH, a healthcare team with diverse medical expertise should be an integral part of optimized HIV management plan in the future (Rawat et al., 2018). Although guidelines often exist to address management of individual comorbid conditions, yet rarely address the complex multimorbidity and its associated impacts, such as drug-drug interactions and functional status for HIV positive patients burdened with multiple chronic diseases (Goulet et al., 2005). As the healthcare needs of PLWH continue to evolve, it is vital to address this challenge and ensure that they receive needed care.

Consistent with existing literature (Vance et al., 2011), older age was significantly associated with multimorbidity among PLWH in SC. The scale up of ART has dramatically improved survival outcomes, and as PLWH age, the incidence of comorbidity is expected to increase (Shiels et al., 2011). Increased survival of HIV patients will likely contribute to higher multimorbidity rates in the future and will play a vital role in the evolving care needs of PLWH. The clinical outlook of multimorbidity may be shaped by factors apart from aging. Several other contributing factors that alter the trajectory of multimorbidity were suggested in our logistic regression model. First, considerable differences in multimorbidity were associated with gender. Female patients had a higher multimorbidity prevalence than male patients. Although males accounted for a much larger proportion of PLWH than females (75% vs. 25%), the concurrence of comorbidity clusters among females was always higher than males over time, as suggested in Figure 4. This finding corroborates previous studies, which suggested a larger growth of neuropsychiatric disorders (Schafer et al., 2010) or chronic kidney disease (Wong et al., 2018) in females than in males. Second, injection drug use was associated with multimorbidity in the entire cohort and each 3-year follow-up interval. This finding is supported by extant literature, which reported the higher incidence of certain comorbid conditions (e.g., end-stage renal disease, liver disease) among IDU patients than non-IDU individuals (Lesko et al., 2016).

Our findings also suggested some racial and ethnic disparities in multimorbidity with Hispanic/others or Black having lower odds of developing multimorbidity than Whites. The prevalence of multimorbidity among Hispanic/others was also lower than the Whites at each follow-up year (e.g., 5.5% vs. 9.2% at 1st year of follow-up and 12.0% vs. 29.8% at 12th year of follow-up) (data were not shown). Results from another U.S. study suggested that Hispanics were more likely than Whites to have hypertension and chronic kidney disease; conversely, Whites were more likely to have hypercholesterolemia, liver disease, or cancer (Wong et al., 2018). As shown in Figure 4, Whites have a higher prevalence of multimorbidity than Black in each follow-up year. This finding is not consistent with previous studies (Palella et al., 2019). Such a discrepancy might be explained by the different definitions of multimorbidity. The specific causes for such racial and ethnic disparities merit further investigation. Individuals with advanced immunosuppression (low CD4 counts and high viral load) at initial HIV diagnosis were more likely to develop multimorbidity, especially during the early stage of HIV infection. These findings were consistent with a number of studies (Bower et al., 2009; Dubrow et al., 2017; Bacik, 2016; Kim et al., 2012) and underscored the growing importance of early HIV testing to prevent non-HIV related comorbidities (Mocroft et al., 2010). These demographic disparities in multimorbidity underscore the importance of tailored intervention efforts for different subgroups.

The current study has several limitations. First, the comorbidity clustering based on the diagnosis groups may be an artifact of the algorithm. Hierarchical algorithms produce exclusive clusters that one diagnosis group can only exist in one cluster. In reality, some diagnosis groups might have clinically meaningful associations with more than one cluster. Some other clustering methods (e.g., fuzzy clustering algorithm or latent-class cluster analysis) may produce clusters that show different concurrence structure of comorbid conditions (Everitt, 2001; Vermunt, 2002). However, such an alternative approach may not necessarily produce a finding that would be meaningful for health care intervention planning. Second, our definition and analytic approach may yield an underestimate of the multimorbidity among PLWH. There is no universally accepted definition or calculation of multimorbidity in the literature. We followed some existing approaches for both PLWH and other populations (Marengoni et al., 2009) and defined multimorbidity based on the concurrences of comorbidity clusters generated from 86 common chronic conditions. Although our analysis based on these clusters has produced clinically interpretable and relevant results, each cluster contains multiple comorbid conditions or diagnosis groups. Third, data were unavailable from the EHR on some factors that might alter the trajectory of multimorbidity, such as the toxicity profiles of the ART regimen (Hughes et al., 2009).

Despite these limitations, we identified the comorbidity patterns and found their rising temporal trends longitudinally in a population-based cohort of PLWH receiving clinical care in the Southern US. Our attempt to characterize comorbidity patterns from EHR data will provide a critical first step in defining the scope of the problem and inform interventions to address the management of concurrent chronic conditions in the context of HIV. Several demographic and HIV-related characteristics (e.g., older age, injection drug use, and low initial CD4 counts) were associated with multimorbidity. Based on these observations, there is an expanding need for clinical care that addresses the complexities of multiple, and potentially interacting diseases among PLWH. The complexity of simultaneously caring for multiple diseases among PLWH engenders a need for coordinated, interdisciplinary teams of care providers. Continued monitoring of comorbidity epidemiology through a broader lens will be needed to minimize disparities, and inform the healthcare systems about the growing healthcare needs of PLWH.

Acknowledgments

The research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI127203. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Appendix

Appendix 1.

Specific chronic conditions and diagnoses groups that were extracted from the EHR data among PLWH in South Carolina

Diagnosis groups Specific disease name ICD-9 code ICD-10 code
Cardiac Disorders Ischemic heart disease 410-414 I20-I25
Cardiac insufficiency 428 I50
Cardiac Arrhythmias 427 I44-I49
Hypertension Hypertension 401 I10
Dyslipedemia Dyslipedemia 272.4 E78.5
Hypercholesterolaemia 272.0 E78.0
Hyperglyceridemia 272.1 E78.1
Peripheral vascular disease Atherosclerosis 440 I70
Other peripheral arterial occlusive disease 443 I73
Stroke Transient ischemic Stroke 435 G45
Ischemic stroke 433 I60, I61
Hemorrhagic stroke (aneurysm, arteriovenous malformation) 431 I62
Cerebral infarction Cerebral infarction 434 I63
Dementia Dementia 290.0, 290.10, 290.11, 290.13, 290.20, 290.21, 290.3, 290.40-290.43, 290.8, 290.9, 294.10, 294.11, 294.21, 331.19, 331.82 F01.0-F01.3, F01.50, F01.51, F01.8, F01.9, F02.0, F02.80, F02.81, F03.90, F03.91, G31.09, G31.83
Chronic kidney disease Chronic kidney disease 585 N18
COPD COPD 490-496 J40-J47
Hepatitis B Hepatitis B 070.20, 070.21, 070.22, 070.23, 070.30, 070.31, 070.32, 070.33 B16.0, B16.1, B16.2, B16.9, B18.1, B18.0, B18.1, B19.10, B19.11
Hepatitis C Hepatitis C 070.51, 070.41, 070.54, 070.70, 070.71, 070.44 B17.10, B17.11, B18.2, B19.20, B19.21
Chronic liver disease Fibrosis and cirrhosis of liver 571 K74
Nonalcoholic fatty liver disease K76.0
Toxic liver disease 573.8 K71
Osteoporosis/osteoarthritis Osteoporosis 733 M80-M82
Osteoarthritis 711-715 M15-M19
Arthritis 716
Ulcer disease Gastric ulcer 531 K25
Gastroesophageal reflux disease 530 K21
Duodenal ulcer 532 K26
Peptic ulcer 533 K27
Non-AIDS defining cancers Penis_and_othermale_genitalorgans 187 C60, C63
Bladder 188 C67
Kidney_and_renal_pelvis 189 C64, C65
Leukemias 204-208 C91-C95
Melanoma 172, 173 C43
Myeloma 203 C90
Ovary 183 C56
Testis 186.9 C62
Thyroid 193 C73
Uterus 179, 182 C54, C55
Anus 154.2, 154.3, 154.8 C21
Head and neck 140-149 C00-C14, C30-C32
Liver_and_intrahepatic_bile_ducts 155 C22
Stomach 151 C16
Hodgkin_lymphoma 201 C81
Trachea 162.0 C33
Bronchus_and_lung 162.2-162.5, 162.8, 162.9 C34
Prostate 185 C61
Colorectum 153, 154 C18-C20
Breast 174, 175, 198.81 C50, C79.81
Skin 172, 173, 198.2 C43, C44, C79.1
Larynx 161 C32
Brain_andother_nervoussystem 191, 192 C70-C72
Bone_and_joint 152.0-152.3, 170, 171 C40, C41
Small_intestine 152.8, 152.9 C17
Pancreas 157 C25
Gall_bladder 156.0-156.2, 156.8, 156.9 C23
Esophagus 150 C15
Vulva C51
Vagina C52
Otherfemale_genital_organs 181, 184 C57, C58
Diabetes Diabetes 250 E10-E14
Depression Depression 296.2, 296.3 F32
Anxiety Anxiety 300, 309.21 F40, F41, F93.0
Psychiatric disorder Bipolar disorder 296.0, 296.4-296.8 F31
Schizoaffective_disorders 295 F25
Persist_mood_disord 300.4, 301.13 F34
Obsessive-compulsive disorder 300.3 F42
Schizophrenia 295 F20
Personality_disorder 301 F60
Alcohol use Alcohol abuse 305 F10
Alcohol dependence 303 K70
Alcohol disease 357.5, 425.5, 535.30, 535.31, 571 G31.2, G62.1, G72.1, I42.6, K86.0, K29.21
Alcohol psychosis 291 K29.20
Tobacco use Tobacco use 305.1 Z72.0
Nicotine dependence F17
Illicit drug use Cannabis 305.20-305.23 F12
Hallucinogen 305.30-305.33 F16
Opioid 305.50-305.53 F11
Cocaine 305.60-305.63 F14
Amphetamine 305.70-305.73
Other stimulant F15
Antidepressant 305.80-305.83
Other psychoactive substance 305.90-305.93 F19
Anemia Nutritional anemia 280-282 D50-D53
Hemolytic anemia 283-285 D55-D59
Aplastic and other anemias and other bone marrow failure syndromes D60-D64
Hypothyroidism Hypothyroidism 243, 244 E03.9
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Footnotes

Conflicts of Interest and Source of Funding

The authors declare that they have no conflict of interest.

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