Neighborhood Matters: Impact on Time Living with Detectable Viral Load for New Adult HIV Diagnoses in South Carolina

Neighborhood characteristics and HIV treatment outcomes: A scoping review

Recognizing challenges faced by people living with HIV is vital for improving their HIV treatment outcomes. While individual-level interventions play a crucial role, community factors can shape the impact of individual interventions on treatment outcomes. Understanding neighborhood characteristics' association with HIV treatment outcomes is crucial for optimizing effectiveness. This review aims to summarize the research scope on the association between neighborhood characteristics and HIV treatment outcomes. The databases PubMed, CINAHL (EBSCOhost), Embase (Elsevier), and PsychINFO (EBSCOhost) were searched from the start of each database to Nov 21, 2022. Screening was performed by three independent reviewers. Full-text publications of all study design meeting inclusion criteria were included in the review. There were no language or geographical limitations. Conference proceedings, abstract only, and opinion reports were excluded from the review. The search yielded 7,822 publications, 35 of which met the criteria for inclusion in the review. Studies assessed the relationship between neighborhood-level disadvantage (n = 24), composition and interaction (n = 17), social-economic status (n = 18), deprivation (n = 16), disorder (n = 8), and rural-urban status (n = 7) and HIV treatment outcomes. The relationship between all neighborhood characteristics and HIV treatment outcomes was not consistent across studies. Only 7 studies found deprivation had a negative association with HIV treatment outcomes; 6 found that areas with specific racial/ethnic densities were associated with poor HIV treatment outcomes, and 5 showed that disorder was associated with poor HIV treatment outcomes. Three studies showed that rural residence was associated with improved HIV treatment outcomes. There were inconsistent findings regarding the association between neighborhood characteristics and HIV treatment outcomes. While the impact of neighborhood characteristics on disease outcomes is highly recognized, there is a paucity of standardized definitions and metrics for community characteristics to support a robust assessment of this hypothesis. Comparative studies that define and assess how specific neighborhood indicators independently or jointly affect HIV treatment outcomes are highly needed.

Systematic Review of Neighborhood Factors Impacting HIV Care Continuum Participation in the United States

Social determinants have been increasingly implicated in accelerating HIV vulnerability, particularly for disenfranchised communities. Among these determinants, neighborhood factors play an important role in undermining HIV prevention. However, there has been little research comprehensively examining the impact of neighborhood factors on HIV care continuum participation in the US. To address this, we conducted a systematic review (PROSPERO registration number CRD42022359787) to determine neighborhood factors most frequently associated with diminished HIV care continuum participation. Peer-reviewed studies were included if published between 2013 - 2022, centralized in the US, and analyzed a neighborhood factor with at least one aspect of the HIV care continuum. The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Study quality was guided by LEGEND (Let Evidence Guide Every New Decision) evaluation guidelines. Systematic review analysis was conducted using Covidence software. There were 3,192 studies identified for initial screening. Forty-four were included for review after eliminating duplicates, title/abstract screening, and eligibility assessment. Social and economic disenfranchisement of neighborhoods negatively impacts HIV care continuum participation among persons living with HIV. In particular, five key neighborhood factors (socioeconomic status, segregation, social disorder, stigma, and care access) were associated with challenged HIV care continuum participation. Race moderated relationships between neighborhood quality and HIV care continuum participation. Structural interventions addressing neighborhood social and economic challenges may have favorable downstream effects for improving HIV care continuum participation.

Estimation of place-based vulnerability scores for HIV viral non-suppression: an application leveraging data from a cohort of people with histories of using drugs

The relationships between place (e.g., neighborhood) and HIV are commonly investigated. As measurements of place are multivariate, most studies apply some dimension reduction, resulting in one variable (or a small number of variables), which is then used to characterize place. Typical dimension reduction methods seek to capture the most variance of the raw items, resulting in a type of summary variable we call "disadvantage score". We propose to add a different type of summary variable, the "vulnerability score," to the toolbox of the researchers doing place and HIV research. The vulnerability score measures how place, as known through the raw measurements, is predictive of an outcome. It captures variation in place characteristics that matters most for the particular outcome. We demonstrate the estimation and utility of place-based vulnerability scores for HIV viral non-suppression, using data with complicated clustering from a cohort of people with histories of injecting drugs.

Telemedicine Use Among People With HIV in 2021: The Hybrid-Care Environment

BACKGROUND

Telemedicine use for the care of people with HIV (PWH) significantly expanded during the COVID-19 pandemic. During 2021, vaccine uptake increased and patients were encouraged to resume in-person care, resulting in a mixture of in-person and telemedicine visits. We studied how different patient populations used telemedicine in this hybrid-care environment.

METHODS

Using observational data from patients enrolled in the Johns Hopkins HIV Clinical Cohort, we analyzed all in-person and telemedicine HIV primary care visits completed in an HIV clinic from January 1st, 2021, to December 31st, 2021. We used log-binomial regression to investigate the association between patient characteristics and the probability of completing a telemedicine versus in-person visit and the probability of completing a video versus telephone visit.

RESULTS

A total of 5518 visits were completed by 1884 patients; 4282 (77.6%) visits were in-person, 800 (14.5%) by phone, and 436 (7.9%) by video. The relative risk (RR) of completing telemedicine vs. in-person visits was 0.65 (95% Confidence Interval (CI): 0.47, 0.91) for patients age 65 years or older vs. age 20-39 years; 0.84 (95% CI: 0.72, 0.98) for male patients vs. female patients; 0.81 (95% CI: 0.66, 0.99) for Black vs. White patients; 0.62 (95% CI: 0.49, 0.79) for patients in the highest vs. lowest quartile of Area Deprivation Index; and 1.52 (95% CI: 1.26, 1.84) for patients >15 miles vs. <5 miles from clinic.

CONCLUSIONS

In the second year of the pandemic, overall in-person care was used more than telemedicine and significant differences persist across subgroups in telemedicine uptake.

Person-time spent with HIV viral load above 1500 copies/mL among Miami-Dade County Ryan White Program clients, 2017-2019: a retrospective analysis

HIV transmission risk significantly increases at HIV viral load (VL) >1500 copies/mL. We sought to determine the percentage of person-time spent with VL >1500 copies/mL (pPT >1500) and the associations of demographic, clinical, and psychosocial factors and this outcome among persons with HIV receiving care. A retrospective analysis of data from clients enrolled in the Ryan White Program from 2017 to 2019 was performed. We assessed pPT >1500 in HIV care by utilizing consecutive VL pairs and calculating the length of time between each pair and the corresponding time spent for the observation period. The association between pPT >1500 and selected client characteristics were analyzed using a random-effects zero-inflated negative binomial model. Among the 6390 clients, 42% were aged 50 or older, 52% MSM, and 59% Hispanic. Overall, 7.5% of clients spent, on average, 27.4 days per year at substantial risk of transmitting HIV. Younger age, AIDS diagnosis, and reported drug use in the preceding 12 months were associated with higher pPT >1500. Tailored interventions should be implemented to meet the unique HIV needs of groups with consistent viremia to significantly minimize transmission risk.

A Systematic Review of Neighborhood-Level Influences on HIV Vulnerability

A better understanding of the social-structural factors that influence HIV vulnerability is crucial to achieve the goal of ending the HIV epidemic by 2030. Given the role of neighborhoods in HIV outcomes, synthesis of findings from such research is key to inform efforts toward HIV eradication. We conducted a systematic review to examine the relationship between neighborhood-level factors (e.g., poverty) and HIV vulnerability (via sexual behaviors and substance use). We searched six electronic databases for studies published from January 1, 2007 through November 30, 2017 (PROSPERO CRD42018084384). We also mapped the studies' geographic distribution to determine whether they aligned with high HIV prevalence areas and/or the "Ending the HIV Epidemic: A Plan for the United States". Fifty-five articles met inclusion criteria. Neighborhood disadvantage, whether measured objectively or subjectively, is one of the most robust correlates of HIV vulnerability. Tests of associations more consistently documented a relationship between neighborhood-level factors and drug use than sexual risk behaviors. There was limited geographic distribution of the studies, with a paucity of research in several counties and states where HIV incidence/prevalence is a concern. Neighborhood influences on HIV vulnerability are the consequence of centuries-old laws, policies and practices that maintain racialized inequities (e.g., racial residential segregation, inequitable urban housing policies). We will not eradicate HIV without multi-level, neighborhood-based approaches to undo these injustices. Our findings inform future research, interventions and policies.

Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol

Background

Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support.

Methods

The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits.

Discussion

With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.

Neighborhood Deprivation and Racial/Ethnic Disparities in Human Immunodeficiency Virus Viral Suppression: A Single-center, Cross-sectional Study in the United States Midwest

Combating disparities is a crucial goal of ongoing efforts to end the human immunodeficiency virus (HIV) epidemic. In a multivariable analysis of a cohort in the Midwestern United States, racial/ethnic disparities in HIV viral suppression were no longer robust after accounting for other sociodemographic factors. Neighborhood deprivation and low income were independently inversely associated with viral suppression.

Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach

OBJECTIVES

An understanding of the predictors of comorbidity among people living with HIV (PLWH) is critical for effective HIV care management. In this study, we identified predictors of comorbidity burden among PLWH based on machine learning models with electronic health record (EHR) data.

METHODS

The study population are individuals with a HIV diagnosis between January 2005 and December 2016 in South Carolina (SC). The change of comorbidity burden, represented by the Charlson Comorbidity Index (CCI) score, was measured by the score difference between pre- and post-HIV diagnosis, and dichotomized into a binary outcome variable. Thirty-five risk predictors from multiple domains were used to predict the increase in comorbidity burden based on the logistic least absolute shrinkage and selection operator (Lasso) regression analysis using 80% data for model development and 20% data for validation.

RESULTS

Of 8253 PLWH, the mean value of the CCI score difference was 0.8 ± 1.9 (range from 0 to 21) with 2328 (28.2%) patients showing an increase in CCI score after HIV diagnosis. Top predictors for an increase in CCI score using the LASSO model included older age at HIV diagnosis, positive family history of chronic conditions, tobacco use, longer duration with retention in care, having PEBA insurance, having low recent CD4+ cell count and duration of viral suppression.

CONCLUSION

The application of machine learning methods to EHR data could identify important predictors of increased comorbidity burden among PLWH with high accuracy. Results may enhance the understanding of comorbidities and provide the evidence based data for integrated HIV and comorbidity care management of PLWH.

Longitudinal Associations Between Neighborhood Factors and HIV Care Outcomes in the WIHS

Identifying structural determinants affecting HIV outcomes is important for informing interventions across heterogeneous geographies. Longitudinal hierarchical generalized mixed-effects models were used to quantify the associations between changes in certain structural-level factors on HIV care engagement, medication adherence, and viral suppression. Among women living with HIV in the WIHS, ten-unit increases in census-tract level proportions of unemployment, poverty, and lack of car ownership were inversely associated with viral suppression and medication adherence, while educational attainment and owner-occupied housing were positively associated with both outcomes. Notably, increased residential stability (aOR 5.68, 95% CI 2.93, 9.04) was positively associated with HIV care engagement, as were unemployment (aOR: 1.59, 95% CI 1.57, 1.60), lack of car ownership (aOR 1.14, 95% CI 1.13, 1.15), and female-headed households (aOR 1.23, 95% CI 1.22, 1.23). This underscores the importance of understanding neighborhood context, including factors that may not always be considered influential, in achieving optimal HIV-related outcomes.