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.

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.

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.

965. Partnering with State Health Departments: A Road Map for Collaboration Using Public Health Enhanced HIV/AIDS Reporting System (eHARS)

Background

Academic and public health partnerships are a critical component of the Ending the HIV Epidemic: A Plan for America (EHE). The Enhanced HIV/AIDS Reporting System (eHARS) is a standardized document-based surveillance database used by state health departments to collect and manage case reports, lab reports, and other documentation on persons living with HIV. Innovative analysis of this data can inform targeted, evidence-based interventions to achieve EHE objectives. We describe the development of a distributed data network strategy at an academic institution in partnership with public health departments to identify geographic differences in time to HIV viral suppression after HIV diagnosis using eHARS data.

Figure 1. Distributed Data Network

Methods

This project was an outgrowth of work developed at the University of Alabama at Birmingham Center for AIDS Research (UAB CFAR) and existing relationships with the state health departments of Alabama, Louisiana, and Mississippi. At a project start-up meeting which included study investigators and state epidemiologists, core objectives and outcome measures were established, key eHARS variables were identified, and regulatory and confidentiality procedures were examined. The study methods were approved by the UAB Institutional Review Board (IRB) and all three state health department IRBs.

Results

A common data structure and data dictionary across the three states were developed. Detailed analysis protocols and statistical code were developed by investigators in collaboration with state health departments. Over the course of multiple in-person and virtual meetings, the program code was successfully piloted with one state health department. This generated initial summary statistics, including measures of central tendency, dispersion, and preliminary survival analysis.

Conclusion

We developed a successful academic and public health partnership creating a distributed data network that allows for innovative research using eHARS surveillance data while protecting sensitive health information. Next, state health departments will transmit summary statistics to UAB for combination using meta-analytic techniques. This approach can be adapted to inform delivery of targeted interventions at a regional and national level.

Disclosures

All Authors: No reported disclosures

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.

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.