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Schematic for calculating person-time with viral load (VL) exceeding 1500 copies/ml*. *Red circles represent time period spent living with increased transmission risk (detectable viral load)
Source publication
We examined the association between neighborhood disadvantages and percent person-time spent with increased transmission risk (VL > 1500 copies/ml) for people living with HIV (PLWH) in South Carolina (SC). The study population included PLWH diagnosed between 1/1/2014 and 12/31/2017, with two or more VL tests 6 months apart (n = 2076). Proportion of...
Citations
... In composite measure, making it difficult to assess the impact of each factor individually [68,74,75,85,87,101]. With reference to ART initiation, the two studies that used neighborhood-level percent of residents with education and median income as SES indicators presented contradicting results: Joy et al. found no association with late access to Highly Active Antiretroviral Therapy (HAART) [67], while Druyts et al. found an association among neighborhoods concentrated with Injection Drug Users (IDUs) who had a lower HAART initiation rate compared to neighborhoods concentrated with gay men [79]. ...
... This construct focuses specifically on the lack or insufficiency of resources. Of the 17 studies that assessed indicators of neighborhood deprivation, 3 examined outcomes on ART initiation [67,79,96], one study focused on ART adherence [81], six on HIV viral suppression [69,70,80,81,96,100], and 10 studies partly or fully combined their neighborhood deprivation indicators into a composite measure [70,72,74,75,78,80,85,87,94,97,101]. All three studies found an inverse relationship between percent unemployment and ART initiation [67,79,96]. ...
... As seen in Table 3, among the 25 studies using the percentage of households or residents living below the poverty line as a neighborhood characteristic, four studies assessed ART initiation [67,79,83,96], two studies assessed ART adherence [81,98], and 13 studies assessed HIV viral suppression [69, 73, 77, 81-84, 87, 89, 96, 99, 100]. Nine studies combined poverty indicators with other characteristics to create a composite measure, making it difficult to assess the impact of living below the poverty line alone [70,72,74,75,78,80,85,88,101]. Six studies found no association between the percentage of households or residents living below the poverty line and ART initiation [67,96] or HIV viral suppression [82,84,89,96,99]. ...
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.
... We investigate this outcome among Black people who have ever injected drugs (PWID) and are living with HIV in Baltimore city -a predominantly Black city where injection drug use is prevalent. This is a population that is disproportionately burdened by HIV [16,37,38], and may be particularly vulnerable to adverse neighborhood conditions due to the manner in which structural racism and stigmatization of drug use intersect [15,17,39,40]. ...
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.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-023-02133-x.
... In a longitudinal study of PWH receiving care in HIV specialty clinics in the U.S. (20 0 0-2014), pPT > 150 0 was 16.4% [14] . Another study examining pPT > 1500 among newly diagnosed PWH engaged in care using the South Carolina HIV surveillance data (2014-2017) reported 18.0% [15] . Patients in care at six HIV clinics in the U.S. (2009-2013) spent 26.3% of their observation time at increased risk of transmitting HIV during follow-up HIV care [10] . ...
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 2017-2019 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.
... 22 While prior studies have identified that neighborhood context may impact rates of HIV viral suppression, these works have focused on large metropolitan areas or, more recently, highincidence geographic regions (i.e. the Southern U.S.). [16][17][18][19] Neighborhood-level contributions to HIV outcomes have not been assessed in the U.S. Midwest. ...
Over the last four years, I have developed a research focus examining the intersections of race, place, and health. My M.D. Honors Thesis reflects a snapshot of these efforts. In this collection of brief research reports, I leverage area-based measures to investigate structural inequities in three contexts: the HIV epidemic in our hyperlocal community, the early stages of the COVID-19 pandemic, and clinical trials for novel COVID-19 therapeutics. I apply novel social epidemiologic tools to measure and explore disparate outcomes. And, in reflecting upon my findings, I discuss concrete implications for clinicians, researchers, and policymakers alike.
Chapter 1: Neighborhood-Level Deprivation and Racial Inequities in HIV Viral Suppression
Human immunodeficiency virus (HIV) is a treatable chronic disease. Yet, geographic and racial inequities across the HIV care continuum are persistent, even in the U.S. Midwest. Using the Area Deprivation Index, a novel measure of neighborhood-level disadvantage, I showed that Black-White disparities in HIV viral suppression among our clinic population are explained by neighborhood deprivation. Our findings highlighted how structural racism, through longstanding place-based disinvestment, directly contributes to disparate HIV outcomes.
Chapter 2: County-Level Social Vulnerability and COVID-19 Cases & Deaths
While it is now widely recognized that the COVID-19 pandemic has had an outsized impact on marginalized and minoritized communities, the pandemic’s inequitable trajectory was not as obvious during the early stages. Leveraging publicly available data as of mid-April 2020 and the validated CDC/ATSDR Social Vulnerability Index, I showed how greater prevalence of population-level characteristics like racial/ethnic minority status, limited English proficiency, poverty, unemployment, crowded housing, and poor transportation access are directly associated with disease incidence and death. Our findings informed risk prioritization efforts across the country and offer an evidence-based framework for allocation of scarce resources.
Chapter 3: Census Tract-Level Inequities in Access to COVID-19 Therapeutic Trials
Geography is a key determinant of access to health care yet is often unexplored as a determinant of clinical trial enrollment. Using publicly available data from ClinicalTrials.gov, I geocoded the locations of 2,095 COVID-19 biomedical trial sites and calculated the driving distance from each U.S. Census tract center of population to the nearest site. I identified that nearly one-third of the overall US population, over one-half of the Native American population, and over three-fourths of the rural population lived more than an hour away from the nearest trial site. Of further concern, Black and Hispanic populations lived closer to trial sites than other populations, yet several studies highlighted the underrepresentation of these populations in major COVID-19 trials. Our findings demonstrated that geographic accessibility alone may not improve representative trial enrollment in the absence of additional structural interventions.
... Since 2017, we have been utilizing a data science approach to examine treatment gaps among PLWH in SC [30,32]. The ongoing research extracted longitudinal electronic health records data of all PLWH in SC from multiple state agencies and health systems. ...
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.
... Instead, we must emphasize the role of structural injustice based on race in constraining the ability of people in disadvantaged and impoverished neighborhoods to protect themselves from HIV compared with people who inhabit resource and income-rich neighborhoods. Applied to our focus on neighborhoods and HIV vulnerability, the historical legacy of structural racism affects virtually every aspect of where Black people live in the U.S., and in turn their HIV ulnerability, access to HIV prevention services, and even HIV viral suppression [73,74]. Indeed, there is now ample empirical evidence that people in diverse Black communities (e.g., young heterosexual adults; gay, bisexual and other MSM) are at disproportionate risk for HIV despite engaging in fewer sexual and substance use risk behaviors than their White counterparts [75][76][77]. ...
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.
... The highest viral load, duration of viral suppression [percentage of days with viral suppression (<200 copies/ml)], nadir CD4 þ cell count, duration of immunodeficiency [i.e. percentage of days with low CD4 þ cell count (<500 cells/ml)], duration of HIV diagnosis and clinical AIDS diagnosis in this time period were also extracted as predictors. The detailed calculation of these percentages was described elsewhere [59]. Briefly, percentage of days with viral suppression was calculated as the mid-point in days between the last viral load less than 200 copies/ml to the first detectable date over the total follow up days. ...
Abstract
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 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 comorbidity burden based on the logistic least absolute shrinkage and selection operator (Lasso) regression analysis with 80% data for 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 0-21) with 2328 (28.2%) patients showing an increase of 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 count and duration of viral suppression.
Conclusions The application of machine learning methods to EHR data could identify important predictors of increased comorbidity burden among PLWH with a high accuracy. Results may enhance the understanding of comorbidity and provide the data-based evidence for integrated HIV and comorbidity care management of PLWH.
... Given that our study focuses on structural-level effects of exposures on each outcome, we believe that identification of structural-level factors associated with moderate adherence or below would be most amenable to community-level interventions [32]. This approach aligns with other studies that have explored the effects of neighborhood factors on viral transmission risk, for which the cutoff of 1500 copies/mL is used [33]. Viral suppression at the individual level was defined as an HIV RNA not detected or below the lower limit of detection (20 copies/mL), conducted using COBAS TaqMan HIV-1 v2.0 (E. ...
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.
... Higher ADI is associated with having a higher proportion of Black residents [14], and it has been associated with all-cause mortality [14], re-hospitalization [15], higher prevalence of chronic diseases, and worse chronic disease outcomes, such as diabetes [16], chronic obstructive pulmonary disease [17], alcohol abuse [18], and cancer [19][20][21][22]. An analysis of HIV surveillance data from New York City found that high-poverty neighborhoods had a lower likelihood of viral suppression maintenance [23], while a study in South Carolina found no difference in time with viral load above 1500 copies/ mL by the ADI [24]. Khazanchi and colleagues report that higher ADI was associated with a lower likelihood of HIV viral suppression. ...
... Neighborhood segregation is a form of structural racism in the United States (US) and a fundamental cause of racial health inequities due to downstream differences in socioeconomic mobility, educational attainment, built environments, and access to health services [3]. Living in a disadvantaged neighborhood has been linked to numerous adverse health outcomes including decreased rates of HIV viral suppression [4][5][6][7]; thus, policymakers and clinicians must consider socioecological context when proposing interventions. The Area Deprivation Index (ADI) is a validated, summative index of neighborhood-level inequalities composed of 17 education, employment, housing quality, and poverty measures drawn from 2011-2015 US Census American Community Survey data [8]. ...
... While prior studies have identified that neighborhood context may impact rates of HIV viral suppression, these works have focused on large metropolitan areas or, more recently, high-incidence geographic regions (ie, the Southern US) [4][5][6][7]. Neighborhood-level contributions to HIV outcomes have not been assessed in the US Midwest. ...
Combating disparities is a crucial goal of ongoing efforts to end the HIV epidemic. In multivariable analysis of a U.S. Midwest cohort, racial/ethnic disparities in HIV viral suppression are no longer robust after accounting for other sociodemographic factors. Neighborhood deprivation and low income are independently, inversely associated with viral suppression.
