Construction of a Large, Public, HIV-Related Database in Support of Ending the HIV Epidemic Initiative: Learnings on Approach, Process, and Tools (Preprint)

Authors:
Preprint
Publisher website
Google Scholar
Abstract
BACKGROUND The HIV epidemic is a national focus within the United States (U.S.), Ending the HIV Epidemic (EHE) Initiative aiming to reduce new HIV infections by 90% by 2023 through scaling of prevention and treatment strategies. Data are crucial to understanding HIV-related needs, barriers to care, and the effectiveness of interventions. While several publicly available datasets exist, few integrate multiple topic domains such as HIV outcomes, social determinants of health (SDOH), and community-level factors. The lack of unified data and difficulty linking datasets across these domains hampers efforts to tailor HIV management and treatment strategies. Existing datasets are often siloed and difficult to analyze together, limiting potential for comprehensive research. Combining and integrating data across geographic and thematic strata offers significant potential to better understand the factors influencing HIV outcomes and to optimize intervention strategies. OBJECTIVE This study explores the feasibility of combining public HIV and community data to identify factors influencing HIV outcomes and interventions. We described the process of sourcing, extracting, and linking variables into a unified database to highlight key community factors and demonstrate the potential of this integrated approach for overcoming data access barriers in HIV research. METHODS A team of two researchers was integrated within a larger network to undergo this task. Approximately 350 hours were spent developing a single unified database. The build was conducted across three phases: 1) initial sourcing of datasets, 2) extracting and linking data, and 3) quality control/quality assurance. Experts were consulted to identify relevant data sources, and data was extracted and linked across multiple geographics levels. Quality control included manual spot checks and web scraping with Web Scraper, a third-party tool to verify accuracy of the extracted data. Data were uploaded to SAS for further analysis. RESULTS The resulting database comprises 242 variables sources from eight publicly available datasets. These variables were linked to 104 Ryan White-associated clinics across five U.S. states, covering geographic levels from state to individual clinic. All data were sourced and scraped between August 2022 and January 2023. Following data preparation, descriptive tables were generated, grouped into three domains: environmental variables, HIV-related variables, and implementation outcome variables. The final database and data dictionary are publicly accessible on the Yale Center for Implementation and Prevention Science (CMIPS) website for future research. CONCLUSIONS This integrated HIV and SDOH database overcomes key challenges in accessing fragmented data, offering valuable insights for addressing HIV outcomes. Despite barriers like technology learning curves and data availability, the project successfully created a resource that can inform EHE goals and guide future interventions. Expanding the database will enable deeper analysis and support ongoing effort to end the HIV epidemic.