JMIR Publications

ABSTRACT

Background:

The Ending the HIV Epidemic (EHE) initiative remains a national priority in the United States (U.S.), aiming to reduce new HIV infections by 90% by 2030. As we cross the initiative’s midpoint, there has been a renewed commitment to strengthening the HIV workforce’s capacity to plan, implement, and sustain effective HIV prevention, treatment, and care interventions. Despite substantial improvements in HIV outcomes, uneven implementation of evidence-based interventions reflects persistent gaps between available evidence and its translation into locally actionable practice. Achieving EHE goals requires tailoring implementation to the diverse epidemiological, social, and structural conditions shaping HIV outcomes across jurisdictions. Research increasingly highlights the value of integrated, contextual data to strengthen public health decision-making. Linking indicators spanning multiple conceptual domains across regional, local and individual levels can support a more robust understanding of the distinct drivers of HIV outcomes, yet existing data systems remain fragmented across domains and scales. A harmonized, multisource, multilevel database is therefore essential to support targeted, needs-based and data-driven implementation under the EHE initiative.

Objective:

This project has two objectives: (1) to build a high-quality contextual database integrating multiple sources of public data using transparent, replicable, and updateable methods, and (2) to develop and document systematic workflows for ongoing database updates, quality assurance, and to support future use aligned with open-science frameworks and standard data practices.

Methods:

This project will follow best practices in data architecture, acquisition, standardization, and quality assurance. For Objective 1, we will integrate data across multiple geographic levels (e.g., ZIP code, county) for the years 2020-2025, with measures categorized into conceptual domains (e.g., epidemiologic, sociodemographic) guided by established theoretical frameworks to facilitate future analyses. For Objective 2, we will develop a tiered data structure to enable transparent and reproducible data management, using a GitHub repository to store all documentation, processing scripts, and quality assurance logs to align with open science practices. Database construction and quality assurance methods were informed by targeted literature reviews in PubMed. Data sources will be identified from three inputs: existing data repositories, datasets identified through targeted literature reviews, and reports or grey-literature with consistent formatting and permissive terms of use suitable for web scraping. Stakeholder engagement will be integrated through all phases of database development, informing variable selection, usability, and validation to enable iterative refinement and revision.

Results:

Literature reviews were conducted from October to November of 2025, to inform database construction methods, source identification, and protocol development. Data acquisition will begin in May 2026.

Conclusions:

This contextual database will provide a reproducible and scalable data resource to support public health planning and advance implementation science by enabling more context-responsive decision-making under the EHE initiative.