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

COVID-19 Testing Among People with HIV: A Population Level Analysis Based on Statewide Data in South Carolina

People with HIV (PWH) are at an elevated risk of developing severe COVID-19 outcomes because of compromised immunity and more comorbidities. However, existing literature suggests a lower rate of COVID-testing among PWH. This study aimed to explore the temporal trend of county-level COVID-19 testing rate and multi-level predictors of COVID-19 ever-testing among PWH in South Carolina (SC). Leveraging linked statewide HIV and COVID-19 datasets, we defined the study population as all adult (18 + years) PWH who were alive on March 2020 and living in SC. PWH with a COVID-19 testing record between March 2020 and October 2021 were defined as COVID-19 ever-testers. Logistic regression and generalized mixed models were used to investigate the association of PWH's demographic profile, HIV clinical characteristics (e.g., CD4 count, viral load), comorbidities, and social factors with COVID-19 testing among PWH. Among 15,660 adult PWH, 8,005 (51.12%) had ever tested for COVID-19 during the study period (March 2020-October 2021). PWH with older age, being male, and Hispanics were less likely to take COVID-19 testing, while men who have sex with men or injection drug users were more likely to take COVID-19 testing. PWH with higher recent viral load (10,000-100,000 copies/ml vs. <200 copies/ml: adjusted odds ratio [AOR]: 0.64, 95%CI: 0.55-0.75) and lower CD4 counts (> 350 cells/mm3 vs. <200 cells/mm3: AOR: 1.25, 95%CI: 1.09-1.45) had lower odds for COVID-19 testing. Additionally, PWH with lower comorbidity burden and those living in rural areas were less likely to be tested for COVID-19. Differences in COVID-19 test-seeking behaviors were observed among PWH in the current study, which could help provide empirical evidence to inform the prioritization of further disease monitoring and targeted intervention. More efforts on building effective surveillance and screening systems are needed to allow early case detection and curbing disease transmission among older, male, Hispanic, and immune-suppressed PWH, especially in rural areas.

Characterizing multimorbidity in ALIVE: comparing single and ensemble clustering methods

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses.

Comorbidity Burden and Health Care Utilization by Substance use Disorder Patterns among People with HIV in Florida

Substance use disorder (SUD), a common comorbidity among people with HIV (PWH), adversely affects HIV clinical outcomes and HIV-related comorbidities. However, less is known about the incidence of different chronic conditions, changes in overall comorbidity burden, and health care utilization by SUD status and patterns among PWH in Florida, an area disproportionately affected by the HIV epidemic. We used electronic health records (EHR) from a large southeastern US consortium, the OneFlorida + clinical research data network. We identified a cohort of PWH with 3 + years of EHRs after the first visit with HIV diagnosis. International Classification of Diseases (ICD) codes were used to identify SUD and comorbidity conditions listed in the Charlson comorbidity index (CCI). A total of 42,271 PWH were included (mean age 44.5, 52% Black, 45% female). The prevalence SUD among PWH was 45.1%. Having a SUD diagnosis among PWH was associated with a higher incidence for most of the conditions listed on the CCI and faster increase in CCI score overtime (rate ratio = 1.45, 95%CI 1.42, 1.49). SUD in PWH was associated with a higher mean number of any care visits (21.7 vs. 14.8) and more frequent emergency department (ED, 3.5 vs. 2.0) and inpatient (8.5 vs. 24.5) visits compared to those without SUD. SUD among PWH was associated with a higher comorbidity burden and more frequent ED and inpatient visits than PWH without a diagnosis of SUD. The high SUD prevalence and comorbidity burden call for improved SUD screening, treatment, and integrated care among PWH.

Simian immunodeficiency virus-infected rhesus macaques with AIDS co-develop cardiovascular pathology and encephalitis

Despite effective antiretroviral therapy, HIV co-morbidities remain where central nervous system (CNS) neurocognitive disorders and cardiovascular disease (CVD)-pathology that are linked with myeloid activation are most prevalent. Comorbidities such as neurocogntive dysfunction and cardiovascular disease (CVD) remain prevalent among people living with HIV. We sought to investigate if cardiac pathology (inflammation, fibrosis, cardiomyocyte damage) and CNS pathology (encephalitis) develop together during simian immunodeficiency virus (SIV) infection and if their co-development is linked with monocyte/macrophage activation. We used a cohort of SIV-infected rhesus macaques with rapid AIDS and demonstrated that SIV encephalitis (SIVE) and CVD pathology occur together more frequently than SIVE or CVD pathology alone. Their co-development correlated more strongly with activated myeloid cells, increased numbers of CD14+CD16+ monocytes, plasma CD163 and interleukin-18 (IL-18) than did SIVE or CVD pathology alone, or no pathology. Animals with both SIVE and CVD pathology had greater numbers of cardiac macrophages and increased collagen and monocyte/macrophage accumulation, which were better correlates of CVD-pathology than SIV-RNA. Animals with SIVE alone had higher levels of activated macrophage biomarkers and cardiac macrophage accumulation than SIVnoE animals. These observations were confirmed in HIV infected individuals with HIV encephalitis (HIVE) that had greater numbers of cardiac macrophages and fibrosis than HIV-infected controls without HIVE. These results underscore the notion that CNS and CVD pathologies frequently occur together in HIV and SIV infection, and demonstrate an unmet need for adjunctive therapies targeting macrophages.

Prevalence and Correlates of Cardiovascular, Pulmonary, Cancer, and Mental Health Comorbidities Among Adults With HIV Who Smoke

ABSTRACT

Using data from the D.C. Cohort Longitudinal HIV Study, we examined (a) diagnosed mental health and (b) cardiovascular, pulmonary, or cancer (CPC) comorbidity among adults with HIV who smoked. Among 8,581 adults, 4,273 (50%) smoked; 49% of smokers had mental health, and 13% of smokers had a CPC comorbidity. Among smokers, non-Hispanic Black participants had a lower risk for mental health (prevalence ratio [PR]: 0.69; 95% confidence interval [CI] [0.62-0.76]) but a higher risk for CPC (PR: 1.17; 95% CI [0.84-1.62]) comorbidity. Male participants had a lower risk for mental health (PR: 0.88; 95% CI [0.81-0.94]) and CPC (PR: 0.68; 95% CI [0.57-0.81]) comorbidity. All metrics of socioeconomic status were associated with a mental health comorbidity, but only housing status was associated with a CPC comorbidity. We did not find any association with substance use. Gender, socioeconomic factors, and race/ethnicity should inform clinical care and the development of smoking cessation strategies for this population.

Defining multimorbidity in people with HIV - what matters most?

PURPOSE OF REVIEW

Although multimorbidity (defined as the coexistence of multiple conditions) presents significant health challenges to people with HIV, there is currently no consensus on how it should be defined among this population. This review aimed to examine the definition of multimorbidity in existing studies among people with HIV ( n  = 22).

RECENT FINDINGS

Variation in the definition of multimorbidity (in terms of the number and nature of conditions included) across studies among people with HIV was observed, with less than half (45%) reporting a selection criteria for conditions. The number of conditions considered ranged from 4 to 65. Certain conditions (e.g. stroke, myocardial infarction and chronic kidney disease) and risk factors (e.g. hypertension) were more frequently included, while other symptoms (e.g. joint pain, peripheral neuropathy and sleeping problems) and mental health conditions (e.g. anxiety and panic attacks) were rarely included in the definition of multimorbidity.

SUMMARY

The definition of multimorbidity among people with HIV is highly variable, with certain conditions overlooked. We propose recommendations that researchers should consider when defining multimorbidity among this population to not only enable comparisons between studies/settings but also to ensure studies consider a person-centred approach that can accurately capture multimorbidity among people with HIV.

Multimorbidity patterns and association with mortality in 0.5 million Chinese adults

BACKGROUND

Few studies have assessed the relationship between multimorbidity patterns and mortality risk in the Chinese population. We aimed to identify multimorbidity patterns and examined the associations of multimorbidity patterns and the number of chronic diseases with the risk of mortality among Chinese middle-aged and older adults.

METHODS

We used data from the China Kadoorie Biobank and included 512,723 participants aged 30 to 79 years. Multimorbidity was defined as the presence of two or more of the 15 chronic diseases collected by self-report or physical examination at baseline. Multimorbidity patterns were identified using hierarchical cluster analysis. Cox regression was used to estimate the associations of multimorbidity patterns and the number of chronic diseases with all-cause and cause-specific mortality.

RESULTS

Overall, 15.8% of participants had multimorbidity. The prevalence of multimorbidity increased with age and was higher in urban than rural participants. Four multimorbidity patterns were identified, including cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), gastrointestinal and hepatorenal multimorbidity (gallstone disease, chronic kidney disease, cirrhosis, peptic ulcer, and cancer), and mental and arthritis multimorbidity (neurasthenia, psychiatric disorder, and rheumatoid arthritis). During a median of 10.8 years of follow-up, 49,371 deaths occurred. Compared with participants without multimorbidity, cardiometabolic multimorbidity (hazard ratios [HR] = 2.20, 95% confidence intervals [CI]: 2.14 - 2.26) and respiratory multimorbidity (HR = 2.13, 95% CI:1.97 - 2.31) demonstrated relatively higher risks of mortality, followed by gastrointestinal and hepatorenal multimorbidity (HR = 1.33, 95% CI:1.22 - 1.46). The mortality risk increased by 36% (HR = 1.36, 95% CI: 1.35 - 1.37) with every additional disease.

CONCLUSION

Cardiometabolic multimorbidity and respiratory multimorbidity posed the highest threat on mortality risk and deserved particular attention in Chinese adults.

Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019

BACKGROUND

The rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies.

METHODS

We identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.

RESULTS

We harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.

CONCLUSIONS

We identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP.