ABSTRACT
Selection bias has long been central in methodological discussions across epidemiology and other fields. In epidemiology, the concept of selection bias has been continually evolving over time. In this issue of the Journal, Mathur and Shpitser (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) present simple graphical rules for using a Single World Intervention Graph (SWIG) to assess the presence of selection bias when estimating treatment effects in both the general population and a selected sample. Notably, the authors examine the setting in which the treatment affects selection, an issue not well-addressed in the existing literature on selection bias. To place the work by Mathur and Shpitser in context, we review the evolution of the concept of selection bias in epidemiology, with a primary focus on the developments in the last 20-30 years since the introduction of causal directed acyclic graphs (DAGs) to epidemiologic research.
ABSTRACT
Few prospective studies examine multilevel resilience resources and psychosocial factors in relation to cardiovascular health and disease. Recent research indicates that resilience resources are associated with a reduction in the incidence of cardiovascular disease-related events, but few studies have examined this relationship across different racial/ethnic populations or in large cohorts. Harmonization may address these limitations because it allows data from several cohorts to be analyzed together, potentially increasing sample size and in turn power overall and in minority populations. This paper describes the process involved in combining three cardiovascular health-focused cohorts: Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Mediators of Atherosclerosis in South Asians Living in America Study. Using a systematic process, we identified appropriate data harmonization techniques to use in harmonizing variables across cohorts. Variables included exposures (e.g., resilience resources), outcomes (e.g., American Heart Association's Life's Simple 7), and covariates (e.g., race and ethnicity). Post harmonization examinations included psychometric analyses of the harmonized variables. A total of 13,284 participants were included in the final harmonized dataset. This project provides opportunities for future research in resilience resources and informs future studies that need to harmonize data. Results based on the harmonized dataset could inform interventions and policies.
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Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.
Subject(s)
Hospitalization , Influenza, Human , Humans , Computer Simulation , Forecasting , Influenza, Human/epidemiology , Prospective Studies , Seasons , United States/epidemiology , Machine Learning , Public Health SurveillanceABSTRACT
We examined relationships between resilience resources (optimism, social support, and neighborhood social cohesion) and cardiovascular disease (CVD) incidence and assessed potential effect-measure modification by psychosocial risk factors (e.g., stress, depression) among adults without CVD in 3 cohort studies (2000-2018): the Jackson Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study. We fitted adjusted Cox models accounting for within-neighborhood clustering while censoring at dropout or non-CVD death. We assessed for effect-measure modification by psychosocial risks. In secondary analyses, we estimated standardized risk ratios using inverse-probability-weighted Aalen-Johansen estimators to account for confounding, dropout, and competing risks (non-CVD deaths) and obtained 95% confidence intervals (CIs) using cluster bootstrapping. For high and medium (versus low) optimism (n = 6,243), adjusted hazard ratios (HRs) for incident CVD were 0.94 (95% CI: 0.78, 1.13) and 0.90 (95% CI: 0.75, 1.07), respectively. Corresponding HRs were 0.88 (95% CI: 0.74, 1.04) and 0.92 (95% CI: 0.79, 1.06) for social support (n = 7,729) and 1.10 (95% CI: 0.94, 1.29) and 0.99 (95% CI: 0.85, 1.16) for social cohesion (n = 7,557), respectively. Some psychosocial risks modified CVD HRs. Secondary analyses yielded similar findings. For optimism and social support, an inverse relationship was frequently most compatible with the data, but a positive relationship was also compatible. For neighborhood social cohesion, positive and null relationships were most compatible. Thus, specific resilience resources may be potential intervention targets, especially among certain subgroups.
Subject(s)
Atherosclerosis , Cardiovascular Diseases , Adult , Humans , Cardiovascular Diseases/epidemiology , Incidence , Longitudinal Studies , Risk Factors , South Asian People , United StatesABSTRACT
Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g., mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies.
Subject(s)
Confounding Factors, Epidemiologic , Bias , Causality , Data Interpretation, Statistical , Epidemiologic Methods , HumansABSTRACT
There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.
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Population Health , Racism , Humans , United States , Health Status Disparities , Healthcare Disparities , CausalityABSTRACT
Social epidemiology is concerned with how social forces influence population health. Rather than focusing on a single disease (as in cancer or cardiovascular epidemiology) or a single type of exposure (e.g., nutritional epidemiology), social epidemiology encompasses all the social and economic determinants of health, both historical and contemporary. These include features of social and physical environments, the network of relationships in a society, and the institutions, politics, policies, norms and cultures that shape all of these forces. This commentary presents the perspective of several editors at the Journal with expertise in social epidemiology. We articulate our thinking to encourage submissions to the Journal that: 1) expand knowledge of emerging and underresearched social determinants of population health; 2) advance new empirical evidence on the determinants of health inequities and solutions to advance health equity; 3) generate evidence to inform the translation of research on social determinants of health into public health impact; 4) contribute to innovation in methods to improve the rigor and relevance of social epidemiology; and 5) encourage critical self-reflection on the direction, challenges, successes, and failures of the field.
Subject(s)
Epidemiology , Health Equity , Humans , Knowledge , Politics , Public Health , Social Determinants of Health , United States/epidemiologyABSTRACT
Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types.
Subject(s)
Selection Bias , Bias , Causality , HumansABSTRACT
BACKGROUND: Epidemiologic studies suggest cadmium exposure is associated with cardiovascular disease risk, including heart failure. However, prior findings may be influenced by tobacco smoking, a dominant source of cadmium exposure and risk factor for heart failure. The present study leverages up to 20 years of follow-up in the Danish Diet, Cancer and Health cohort to examine the relationship between urinary cadmium and incident heart failure among people who never smoked. METHODS: Between 1993 and 1997, 19,394 never-smoking participants (ages 50-64 years) enrolled and provided a urine sample. From this sample, we randomly selected a subcohort of 600 men and 600 women and identified 958 incident heart failure cases occurring between baseline and 2015. Using a case-cohort approach, we estimated adjusted hazard ratios (aHR) for heart failure in Cox proportional hazards models with age as the time scale. RESULTS: Participants had relatively low concentrations of urinary cadmium, as expected for never smokers (median = 0.20; 25th, 75th = 0.13, 0.32 µg cadmium/g creatinine). In adjusted models, we found that higher urinary cadmium was associated with a higher rate of incident heart failure overall (aHR = 1.1 per interquartile range difference [95% CI = 1.0, 1.2). In sex-stratified analyses, the association seemed restricted to men (aHR = 1.5 [95% CI = 1.2, 1.9]). CONCLUSIONS: In this cohort of people who never smoked tobacco, environmental cadmium was positively associated with incident heart failure, especially among men.
Subject(s)
Cadmium , Heart Failure , Cadmium/analysis , Cohort Studies , Denmark/epidemiology , Environmental Exposure/analysis , Female , Heart Failure/epidemiology , Humans , Male , Middle Aged , Risk Factors , SmokersABSTRACT
Understanding resilience in relation to HIV-related outcomes may help address racial/ethnic disparities, however, significant gaps in its measurement preclude in-depth study. Thus, this research aims to develop and evaluate the psychometric properties of long and short forms of the Multilevel Resilience Resource Measure for African American/Black Adults Living with HIV. To develop the items, we conducted a mixed methods study (N = 48) and reviewed published resilience measures. We completed content validity index analyses to ensure the items reflected the resilience construct. Next, we conducted 20 cognitive interviews and a field survey (N = 400). The long and short forms demonstrated acceptable to excellent psychometric properties based on factorial validity, internal consistency and convergent validity and on measurement invariance (conducted for the short form only). These measures provide a comprehensive framework to examine resilience and HIV-related outcomes and can inform resilience-building interventions to reduce racial and ethnic health disparities.
Subject(s)
Black or African American , HIV Infections , Adult , Black or African American/psychology , Black People , HIV Infections/psychology , Humans , Psychometrics , Racial Groups , Reproducibility of Results , Surveys and QuestionnairesABSTRACT
BACKGROUND: Psychosocial stressors increase the risks for cardiovascular disease across diverse populations. However, neighborhood level resilience resources may protect against poor cardiovascular health (CVH). This study used data from three CVH cohorts to examine longitudinally the associations of a resilience resource, perceived neighborhood social cohesion (hereafter referred to as neighborhood social cohesion), with the American Heart Association's Life's Simple 7 (LS7), and whether psychosocial stressors modify observed relationships. METHODS: We examined neighborhood social cohesion (measured in tertiles) and LS7 in the Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Mediators of Atherosclerosis in South Asians Living in America study. We used repeated-measures, modified Poisson regression models to estimate the relationship between neighborhood social cohesion and LS7 (primary analysis, n = 6,086) and four biological metrics (body mass index, blood pressure, cholesterol, blood glucose; secondary analysis, n = 7,291). We assessed effect measure modification by each psychosocial stressor (e.g., low educational attainment, discrimination). RESULTS: In primary analyses, adjusted prevalence ratios (aPR) and 95% confidence intervals (CIs) for ideal/intermediate versus poor CVH among high or medium (versus low) neighborhood social cohesion were 1.01 (0.97-1.05) and 1.02 (0.98-1.06), respectively. The psychosocial stressors, low education and discrimination, functioned as effect modifiers. Secondary analyses showed similar findings. Also, in the secondary analyses, there was evidence for effect modification by income. CONCLUSION: We did not find much support for an association between neighborhood social cohesion and LS7, but did find evidence of effect modification. Some of the effect modification results operated in unexpected directions. Future studies should examine neighborhood social cohesion more comprehensively and assess for effect modification by psychosocial stressors.
Subject(s)
Atherosclerosis , Cardiovascular Diseases , Blood Glucose , Cardiovascular Diseases/epidemiology , Cholesterol , Humans , Risk Factors , Social Cohesion , United StatesABSTRACT
Resilience may help people living with HIV (PLWH) overcome adversities to disease management. This study identifies multilevel resilience resources among African American/Black (AA/B) PLWH and examines whether resilience resources differ by demographics and neighborhood risk environments. We recruited participants and conducted concept mapping at two clinics in the southeastern United States. Concept Mapping incorporates qualitative and quantitative methods to represent participant-generated concepts via two-dimensional maps. Eligible participants had to attend ≥ 75% of their scheduled clinic appointments and did not have ≥ 2 consecutive detectable HIV-1 viral load measurements in the past 2 years. Of the 85 AA/B PLWH who were invited, forty-eight participated. Twelve resilience resource clusters emerged-five individual, two interpersonal, two organizational/policy and three neighborhood level clusters. There were strong correlations in cluster ratings for demographic and neighborhood risk environment comparison groups (r ≥ 0.89). These findings could inform development of theories, measures and interventions for AA/B PLWH.
Subject(s)
Black or African American/psychology , HIV Infections/psychology , Health Equity , Resilience, Psychological , Adult , Anti-HIV Agents/therapeutic use , Black People/psychology , HIV Infections/drug therapy , HIV Infections/ethnology , Humans , Male , Middle Aged , Multilevel Analysis , Residence Characteristics , Southeastern United States/epidemiologyABSTRACT
BACKGROUND: and Purpose: Cadmium has been associated with risk of cardiovascular events, including stroke. Human cadmium exposure occurs primarily through diet and tobacco smoke. Recent cohort studies have found an association with stroke, but residual confounding from smoking, could not be ruled out. We therefore conducted a case-cohort study to evaluate whether cadmium is associated with stroke in never-smokers. METHODS: The Danish Diet Cancer and Health cohort consists of Danes 50-64 years old, recruited in 1993-1997. From never-smoking cohort members without previous cancer or stroke we sampled a sub-cohort of 1200 persons. We also identified all (n = 534) cases in the cohort with a validated stroke diagnosis between baseline and 2009. We quantified cadmium and creatinine concentrations from baseline urine samples and used cadmium per creatinine as our main exposure metric. We used Cox proportional hazards models to estimate hazard ratios (HRs) with age as time scale and adjusting for BMI, education and urinary cotinine with and without stratification by sex. RESULTS: The median urinary cadmium concentration was 0.21 µg cadmium/g creatinine in cases and 0.19 µg/g in the sub-cohort. The majority (83%) of stroke cases were diagnosed with ischemic stroke. The HR for stroke in the highest quartile of exposure (median 0.44 µg/g creatinine) was 1.11 (95% CI: 0.79-1.54) compared with the lowest quartile (median 0.10 µg/g creatinine). The HR per inter quartile range (IQR, 0.19 µg/g creatinine) was 1.02 (95% CI: 0.92-1.12). Among men, the HR per IQR higher levels of cadmium (0.16 µg/g creatinine) was 1.18 (95% CI: 0.92-1.52), and 1.00 (95% CI: 0.89-1.12) among women. Adjusting for creatinine or using osmolality instead of creatinine standardization generally attenuated observed relationships. CONCLUSIONS: Our results do not support that low levels of cadmium exposure among never-smokers are strongly associated with risk of stroke, although results varied somewhat by sex and method of accounting for urinary dilution.
Subject(s)
Cadmium , Stroke , Cohort Studies , Denmark/epidemiology , Environmental Exposure/analysis , Female , Humans , Male , Middle Aged , Smokers , Stroke/chemically induced , Stroke/epidemiologyABSTRACT
Receiving regular HIV care is crucial for maintaining good health among persons with HIV. However, racial and gender disparities in HIV care receipt exist. Discrimination and its impact may vary by race/ethnicity and gender, contributing to disparities. Data from 1578 women in the Women's Interagency HIV Study ascertained from 10/1/2012 to 9/30/2016 were used to: (1) estimate the relationship between discrimination and missing any scheduled HIV care appointments and (2) assess whether this relationship is effect measure modified by race/ethnicity. Self-reported measures captured discrimination and the primary outcome of missing any HIV care appointments in the last 6 months. Log-binomial models accounting for measured sources of confounding and selection bias were fit. For the primary outcome analyses, women experiencing discrimination typically had a higher prevalence of missing an HIV care appointment. Moreover, there was no statistically significant evidence for effect measure modification by race/ethnicity. Interventions to minimize discrimination or its impact may improve HIV care engagement among women.
Subject(s)
Discrimination, Psychological , HIV Infections/psychology , Patient Compliance/psychology , Social Stigma , Women's Health/ethnology , Adult , Anti-HIV Agents/therapeutic use , Appointments and Schedules , Attitude of Health Personnel , Cohort Studies , Ethnicity/statistics & numerical data , Female , HIV Infections/drug therapy , Health Status Disparities , Humans , Middle Aged , Patient Participation , Prevalence , Prospective Studies , Quality of Life , United States/epidemiologyABSTRACT
BACKGROUND: Increasing evidence suggests that psychological well-being (PWB) is associated with lower disease and mortality risk, and may be enhanced with relatively low-cost interventions. Yet, dissemination of these interventions remains limited, in part because insufficient attention has been paid to distinct PWB dimensions, which may impact physical health outcomes differently. METHODS: This essay first reviews the empirical evidence regarding differential relationships between all-cause mortality and multiple dimensions of PWB (e.g., life purpose, mastery, positive affect, life satisfaction, optimism). Then, individual-level positive psychology interventions aimed at increasing PWB and tested in randomized-controlled trials are reviewed as these allow for easy implementation and potentially broad outreach to improve population well-being, in concert with efforts targeting other established social determinants of health. RESULTS: Several PWB dimensions relate to mortality, with varying strength of evidence. Many of positive psychology trials indicate small-to-moderate improvements in PWB; rigorous institution-level interventions are comparatively few, but preliminary results suggest benefits as well. Examples of existing health policies geared towards the improvement of population well-being are also presented. Future avenues of well-being epidemiological and intervention research, as well as policy implications, are discussed. CONCLUSIONS: Although research in the fields of behavioral and psychosomatic medicine, as well as health psychology have substantially contributed to the science of PWB, this body of work has been somewhat overlooked by the public health community. Yet, the growing interest in documenting well-being, in addition to examining its determinants and consequences at a population level may provoke a shift in perspective. To cultivate optimal well-being-mental, physical, social, and spiritual-consideration of a broader set of well-being measures, rigorous studies, and interventions that can be disseminated is critically needed.
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Mental Health , Public Health , Health Promotion , Humans , Policy , Randomized Controlled Trials as TopicABSTRACT
OBJECTIVE: We investigated the technical model of motivational interviewing (MI) in a dual-outcome intervention (i.e., alcohol, sexual risk; N = 164; 57% female). METHOD: We identified latent classes of client change statements, based on the proportion of change talk (CT) over the session. We then examined whether outcomes were related to CT class, and whether the relations between MI skill and outcomes varied by CT class. RESULTS: We found three classes of alcohol-CT and two classes of sexual risk-CT. While CT class membership did not predict outcomes directly, greater therapist MI-consistent skill was associated with fewer heavy drinking days in the increasing alcohol-CT class. For sexual risk outcomes, therapist MI-consistent skill was associated with reduced odds of condomless sex for the low sexual risk-CT class. CONCLUSIONS: The relation of therapist MI consistency to outcomes appears to be a function of client CT during the session.
Subject(s)
Alcoholism/therapy , Counselors , Motivational Interviewing , Outcome Assessment, Health Care , Professional Competence , Risk Reduction Behavior , Risk-Taking , Unsafe Sex/prevention & control , Adult , Female , Humans , MaleABSTRACT
Reducing racial/ethnic disparities in human immunodeficiency virus (HIV) disease is a high priority. Reductions in HIV racial/ethnic disparities can potentially be achieved by intervening on important intermediate factors. The potential population impact of intervening on intermediates can be evaluated using observational data when certain conditions are met. However, using standard stratification-based approaches commonly employed in the observational HIV literature to estimate the potential population impact in this setting may yield results that do not accurately estimate quantities of interest. Here we describe a useful conceptual and methodological framework for using observational data to appropriately evaluate the impact on HIV racial/ethnic disparities of interventions. This framework reframes relevant scientific questions in terms of a controlled direct effect and estimates a corresponding proportion eliminated. We review methods and conditions sufficient for accurate estimation within the proposed framework. We use the framework to analyze data on 2,329 participants in the CFAR [Centers for AIDS Research] Network of Integrated Clinical Systems (2008-2014) to evaluate the potential impact of universal prescription of and ≥95% adherence to antiretroviral therapy on racial disparities in HIV virological suppression. We encourage the use of the described framework to appropriately evaluate the potential impact of targeted interventions in addressing HIV racial/ethnic disparities using observational data.
Subject(s)
Anti-HIV Agents/therapeutic use , Ethnicity/statistics & numerical data , HIV Infections/epidemiology , Healthcare Disparities/ethnology , Racial Groups/statistics & numerical data , Adult , Female , HIV , HIV Infections/drug therapy , HIV Infections/ethnology , Health Status Disparities , Humans , Male , Observational Studies as Topic , United States/epidemiologyABSTRACT
The impact of survival-related selection bias has not always been discussed in relevant studies of racial health disparities. Moreover, the analytic approaches most frequently employed in the epidemiologic literature to minimize selection bias are difficult to implement appropriately in racial disparities research. This difficulty stems from the fact that frequently employed analytic techniques require that common causes of survival and the outcome are accurately measured. Unfortunately, such common causes are often unmeasured or poorly measured in racial health disparities studies. In the absence of accurate measures of the aforementioned common causes, redefining the target population or changing the study design represents a useful approach for reducing the extent of survival-related selection bias. To help researchers recognize and minimize survival-related selection bias in racial health disparities studies, we illustrate the aforementioned selection bias and how redefining the target population or changing the study design can be useful.
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Black or African American , Health Status Disparities , Selection Bias , Survival , White People , Humans , Research DesignABSTRACT
We use a socioecological model of health to define resilience, review the definition and study of resilience among persons living with human immunodeficiency virus (PLWH) in the existing peer-reviewed literature, and discuss the strengths and limitations of how resilience is defined and studied in HIV research. We conducted a review of resilience research for HIV-related behaviors/outcomes of antiretroviral therapy (ART) adherence, clinic attendance, CD4 cell count, viral load, viral suppression, and/or immune functioning among PLWH. We performed searches using PubMed, PsycINFO and Google Scholar databases. The initial search generated 14,296 articles across the three databases, but based on our screening of these articles and inclusion criteria, n = 54 articles were included for review. The majority of HIV resilience research defines resilience only at the individual (i.e., psychological) level or studies individual and limited interpersonal resilience (e.g., social support). Furthermore, the preponderance of HIV resilience research uses general measures of resilience; these measures have not been developed with or tailored to the needs of PLWH. Our review suggests that a socioecological model of health approach can more fully represent the construct of resilience. Furthermore, measures specific to PLWH that capture individual, interpersonal, and neighborhood resilience are needed.
Subject(s)
HIV Infections/psychology , Resilience, Psychological , Anti-HIV Agents/therapeutic use , CD4 Lymphocyte Count , HIV Infections/drug therapy , Humans , Patient Compliance , Retrospective Studies , Social Support , Viral LoadABSTRACT
Retention in care and viral suppression are critical to delaying HIV progression and reducing transmission. Neighborhood socioeconomic context (NSEC) may affect HIV care receipt. We therefore assessed NSEC's impact on retention and viral suppression in a diverse HIV clinical cohort. HIV-positive adults with ≥1 visit at the Vanderbilt Comprehensive Care Clinic and 5-digit ZIP code tabulation area (ZCTA) information between 2008 and 2012 contributed. NSEC z-score indices used neighborhood-level socioeconomic indicators for poverty, education, labor-force participation, proportion of males, median age, and proportion of residents of black race by ZCTA. Retention was defined as ≥2 HIV care visits per calendar year, >90 days apart. Viral suppression was defined as an HIV-1 RNA <200 copies/mL at last measurement per calendar year. Modified Poisson regression was used to estimate risk ratios (RR) and 95% confidence intervals (CI). Among 2272 and 2541 adults included for retention and viral suppression analyses, respectively, median age and CD4 count at enrollment were approximately 38 (1st and 3rd quartile: 30, 44) years and 351 (176, 540) cells/µL, respectively, while 24% were female, and 39% were black. Across 243 ZCTAs, median NSEC z-score was 0.09 (-0.66, 0.48). Overall, 79% of person-time contributed was retained and 74% was virally suppressed. In adjusted models, NSEC was not associated with retention, though being in the 4th vs. 1st NSEC quartile was associated with lack of viral suppression (RR = 0.88; 95% CI: 0.80-0.97). Residing in the most adverse NSEC was associated with lack of viral suppression. Future studies are needed to confirm this finding.