Redlining and Time to Viral Suppression Among Persons With HIV.

Importance: Structural racism in the US is evidenced in the discriminatory practice of historical racial redlining when neighborhoods were valued, in part, based on the community's racial and ethnic compositions. However, the influence of these systemic practices in the context of the HIV epidemic is not well understood. Objective: To assess the effect of redlining on time to viral suppression among people newly diagnosed with HIV. Design, Setting, and Participants: Observational study that included individuals diagnosed with HIV from January 1, 2011, to December 31, 2019, in New Orleans, Louisiana. At the time of their HIV diagnosis, these individuals lived in neighborhoods historically mapped by the Home Owners' Loan Corporation (HOLC). The HOLC lending risk maps classified neighborhoods into 1 of 4 color-coded grades: A (best), B (still desirable), C (definitely declining), and D (hazardous). Main Outcome and Measures: The primary outcome of interest was time to viral suppression (estimated as the time from the diagnosis date to the date of the first recorded viral load that was <200 copies/mL). Individual-level demographic factors were used to evaluate time to viral suppression along with a neighborhood measure of gentrification (based on US census tract-level characteristics for educational attainment, housing development and value, and household income) and a Cox gamma frailty model with census tract used as the frailty term. Results: Of 1132 individuals newly diagnosed with HIV, 871 (76.9%) were men and 620 (54.8%) were 25 to 44 years of age. Of the 697 individuals living in historically redlined neighborhoods (HOLC grade D), 100 (14.6%) were living in neighborhoods that were gentrifying. The median time to viral suppression was 193 days (95% CI, 167-223 days) for persons with HIV living in redlined neighborhoods compared with 164 days (95% CI, 143-185 days) for the 435 persons with HIV living in HOLC grade A, B, or C (nonredlined) neighborhoods. Among persons with HIV living in gentrifying neighborhoods, those living in redlined neighborhoods had a longer time to viral suppression compared with persons living in nonredlined neighborhoods (hazard ratio, 0.54 [95% CI, 0.36-0.82]). Conclusions and Relevance: These findings suggest the enduring effects of systemic racism on present-day health outcomes among persons with HIV. Regardless of their neighborhood's contemporary level of gentrification, individuals diagnosed with HIV while living in historically redlined neighborhoods may experience a significantly longer time to viral suppression.

Association of Neighborhood Social Determinants of Health with Acute Kidney Injury during Hospitalization.

BACKGROUND: Acute kidney injury (AKI) is common among hospitalized patients. However, the contribution of social determinants of health (SDOH) to AKI risk remains unclear. This study evaluated the association between neighborhood measures of SDOH and AKI development and recovery during hospitalization. METHODS: This is a retrospective cohort study of adults without end-stage kidney disease admitted to a large southern U.S. healthcare system from 10/2014 to 9/2017. Neighborhood SDOH measures included: 1) Socioeconomic status: Area Deprivation Index (ADI) scores, 2) Food access: Low Income Low Access (LILA) scores, 3) Rurality: Rural Urban Commuting Area (RUCA) scores, and (4) Residential segregation: dissimilarity and isolation scores. The primary study outcome was AKI based on serum creatinine (SCr)-KDIGO criteria. Our secondary outcome was lack of AKI recovery (requiring dialysis or elevated SCr at discharge). The association of SDOH measures with AKI was evaluated using generalized estimating equation models adjusted for demographics and clinical characteristics. RESULTS: Among 26,769 patients, 26% developed AKI during hospitalization. Compared with those who did not develop AKI, those who developed AKI were older (median 60 vs. 57 years), more commonly men (55% vs. 50%), and more commonly self-identified as Black (38% vs. 33%). Patients residing in most disadvantaged neighborhoods (highest ADI tertile) had 10% (95%CI: 1.02-1.19) greater adjusted odds of developing AKI during hospitalization than counterparts in least disadvantaged areas (lowest ADI tertile). Patients living in rural areas had 25% higher adjusted odds of lack of AKI recovery by hospital discharge (95% CI: 1.07, 1.46). Food access and residential segregation were not associated with AKI development or recovery. CONCLUSIONS: Hospitalized patients from the most socioeconomically disadvantaged neighborhoods and from rural areas had higher odds of developing AKI and not recovering from AKI by hospital discharge, respectively. A better understanding of the mechanisms underlying these associations is needed to inform interventions to reduce AKI risk during hospitalization among disadvantaged populations.

Development and implementation of a distributed data network between an academic institution and state health departments to investigate variation in time to HIV viral suppression in the Deep South.

BACKGROUND: Achieving early and sustained viral suppression (VS) following diagnosis of HIV infection is critical to improving outcomes for persons with HIV (PWH). The Deep South of the United States (US) is a region that is disproportionately impacted by the domestic HIV epidemic. Time to VS, defined as time from diagnosis to initial VS, is substantially longer in the South than other regions of the US. We describe the development and implementation of a distributed data network between an academic institution and state health departments to investigate variation in time to VS in the Deep South. METHODS: Representatives of state health departments, the Centers for Disease Control and Prevention (CDC), and the academic partner met to establish core objectives and procedures at the beginning of the project. Importantly, this project used the CDC-developed Enhanced HIV/AIDS Reporting System (eHARS) through a distributed data network model that maintained the confidentiality and integrity of the data. Software programs to build datasets and calculate time to VS were written by the academic partner and shared with each public health partner. To develop spatial elements of the eHARS data, health departments geocoded residential addresses of each newly diagnosed individual in eHARS between 2012-2019, supported by the academic partner. Health departments conducted all analyses within their own systems. Aggregate results were combined across states using meta-analysis techniques. Additionally, we created a synthetic eHARS data set for code development and testing. RESULTS: The collaborative structure and distributed data network have allowed us to refine the study questions and analytic plans to conduct investigations into variation in time to VS for both research and public health practice. Additionally, a synthetic eHARS data set has been created and is publicly available for researchers and public health practitioners. CONCLUSIONS: These efforts have leveraged the practice expertise and surveillance data within state health departments and the analytic and methodologic expertise of the academic partner. This study could serve as an illustrative example of effective collaboration between academic institutions and public health agencies and provides resources to facilitate future use of the US HIV surveillance system for research and public health practice.

Lost potential and missed opportunities for DCD liver transplantation in the United States.

BACKGROUND: Donation after cardiac death(DCD) has been proposed as an avenue to expand the liver donor pool. METHODS: We examined factors associated with nonrecovery of DCD livers using UNOS data from 2015 to 2019. RESULTS: There 265 non-recovered potential(NRP) DCD livers. Blood type AB (7.8% vs. 1.1%) and B (16.9% vs. 9.8%) were more frequent in the NRP versus actual donors (p < 0.001). The median driving time between donor hospital and transplant center was similar for NRP and actual donors (30.1 min vs. 30.0 min; p = 0.689), as was the percentage located within a transplant hospital (20.8% vs. 20.9%; p = 0.984).The donation service area(DSA) of a donor hospital explained 27.9% (p = 0.001) of the variability in whether a DCD liver was recovered. CONCLUSION: A number of potentially high quality DCD donor livers go unrecovered each year, which may be partially explained by donor blood type and variation in regional and DSA level practice patterns.

Assessing Hispanic/Latino and Non-Hispanic White Social Determinants of Obesity Among a Community Sample of Residents in the Rural Southeast US.

Employing an ecological approach, we sought to identify social determinants of obesity among Hispanics/Latinos and non-Hispanic whites living in the Southeast US. Data on social determinants of obesity (individual, family, community and cultural/contextual) were collected from 217 participants [106 Hispanics/Latinos; 111 non-Hispanic whites]; height and weight  were objectively measured. We compared prevalence of overweight and obese between ethnic groups and BMI values within each group by social determinants. Hispanics had a 1.9-fold increase (OR 1.93, 95% CI: 1.05-3.55) in overweight prevalence compared to non-Hispanic whites after adjusting for age and gender. We found positive estimates between unfavorable family-level determinants and BMI among Hispanic/Latinos. In contrast, non-Hispanic whites who reported unfavorable neighborhood characteristics had higher BMI's. Findings highlight the need for targeted approaches for the prevention and control of obesity.

Area based stratified random sampling using geospatial technology in a community-based survey.

BACKGROUND: Most studies among Hispanics have focused on individual risk factors of obesity, with less attention on interpersonal, community and environmental determinants. Conducting community based surveys to study these determinants must ensure representativeness of disparate populations. We describe the use of a novel Geographic Information System (GIS)-based population based sampling to minimize selection bias in a rural community based study. METHODS: We conducted a community based survey to collect and examine social determinants of health and their association with obesity prevalence among a sample of Hispanics and non-Hispanic whites living in a rural community in the Southeastern United States. To ensure a balanced sample of both ethnic groups, we designed an area stratified random sampling procedure involving three stages: (1) division of the sampling area into non-overlapping strata based on Hispanic household proportion using GIS software; (2) random selection of the designated number of Census blocks from each stratum; and (3) random selection of the designated number of housing units (i.e., survey participants) from each Census block. RESULTS: The proposed sample included 109 Hispanic and 107 non-Hispanic participants to be recruited from 44 Census blocks. The final sample included 106 Hispanic and 111 non-Hispanic participants. The proportion of Hispanic surveys completed per strata matched our proposed distribution: 7% for strata 1, 30% for strata 2, 58% for strata 3 and 83% for strata 4. CONCLUSION: Utilizing a standardized area based randomized sampling approach allowed us to successfully recruit an ethnically balanced sample while conducting door to door surveys in a rural, community based study. The integration of area based randomized sampling using tools such as GIS in future community-based research should be considered, particularly when trying to reach disparate populations.

Reach of Effective, Nationally-Available, Low-Cost, Nonprofit Weight Loss Program in Medically Underserved Areas (MUAs).

Obesity rates are higher for ethnic minority, low-income, and rural communities. Programs are needed to support these communities with weight management. We determined the reach of a low-cost, nationally-available weight loss program in Health Resources and Services Administration medically underserved areas (MUAs) and described the demographics of the communities with program locations. This is a cross-sectional analysis of Take Off Pounds Sensibly (TOPS) chapter locations. Geographic information systems technology was used to combine information about TOPS chapter locations, the geographic boundaries of MUAs, and socioeconomic data from the Decennial 2010 Census. TOPS is available in 30 % of MUAs. The typical TOPS chapter is in a Census Tract that is predominantly white, urban, with a median annual income between $25,000 and $50,000. However, there are TOPS chapters in Census Tracts that can be classified as predominantly black or predominantly Hispanic; predominantly rural; and as low or high income. TOPS provides weight management services in MUAs and across many types of communities. TOPS can help treat obesity in the medically underserved. Future research should determine the differential effectiveness among chapters in different types of communities.

Multiple cluster analysis for the identification of high-risk census tracts for out-of-hospital cardiac arrest (OHCA) in Denver, Colorado.

BACKGROUND: Prior research has shown that high-risk census tracts for out-of-hospital cardiac arrest (OHCA) can be identified. High-risk neighborhoods are defined as having a high incidence of OHCA and a low prevalence of bystander cardiopulmonary resuscitation (CPR). However, there is no consensus regarding the process for identifying high-risk neighborhoods. OBJECTIVE: We propose a novel summary approach to identify high-risk neighborhoods through three separate spatial analysis methods: Empirical Bayes (EB), Local Moran's I (LISA), and Getis Ord Gi* (Gi*) in Denver, Colorado. METHODS: We conducted a secondary analysis of prospectively collected Emergency Medical Services data of OHCA from January 1, 2009 to December 31, 2011 from the City and County of Denver, Colorado. OHCA incidents were restricted to those of cardiac etiology in adults ≥18 years. The OHCA incident locations were geocoded using Centrus. EB smoothed incidence rates were calculated for OHCA using Geoda and LISA and Gi* calculated using ArcGIS 10. RESULTS: A total of 1102 arrests in 142 census tracts occurred during the study period, with 887 arrests included in the final sample. Maps of clusters of high OHCA incidence were overlaid with maps identifying census tracts in the below the Denver County mean for bystander CPR prevalence. Five census tracts identified were designated as Tier 1 high-risk tracts, while an additional 7 census tracts where designated as Tier 2 high-risk tracts. CONCLUSION: This is the first study to use these three spatial cluster analysis methods for the detection of high-risk census tracts. These census tracts are possible sites for targeted community-based interventions to improve both cardiovascular health education and CPR training.