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Harmonizing Implementation and Outcome Data Across HIV Prevention and Care Studies in Resource-Constrained Settings
Geri R Donenberg1,2, Katherine G Merrill1, Chisom Obiezu-Umeh3
1Center for Dissemination and Implementation Science, Department of Medicine, University of Illinois at Chicago, Chicago, IL USA.
Harmonizing measures across HIV studies in resource-limited settings strengthens research. This 2-year process created common data elements for implementation science, improving comparability and generalizability of findings.
Area of Science:
- Implementation Science
- Public Health Research
- Adolescent Health
Background:
- Establishing common data elements for research harmonization is crucial but rarely documented.
- The Prevention And Treatment through a Comprehensive Care Continuum for HIV-affected Adolescents in Resource Constrained Settings (PATC³H) consortium involved eight federally-funded studies.
- Harmonizing measures is essential for comparing results and strengthening scientific rigor, especially in resource-limited settings.
Purpose of the Study:
- To detail a rigorous, 2-year process for harmonizing measures across multiple HIV-focused studies.
- To develop a common set of implementation science constructs for use in low- and middle-income countries.
- To inform future data harmonization efforts and strengthen the generalizability of research findings.
Main Methods:
- Created a repository of measured constructs from each participating study.
- Classified and selected constructs for harmonization across key domains: implementation science, HIV prevention/care, demographics, sexual behavior, mental health, substance use, and economic assessment.
- Harmonized implementation science constructs using the RE-AIM framework and Implementation Outcomes Framework for staff and participants.
Main Results:
- Successfully harmonized measures across diverse domains, with a focus on implementation science constructs.
- Developed a codified set of implementation science measures for reach, adoption, implementation, maintenance, feasibility, acceptability, appropriateness, and fidelity.
- The process provides a replicable model for future data harmonization initiatives.
Conclusions:
- The described harmonization process and developed measures can enhance collaborative research and the comparability of findings.
- Recommendations for research consortia include ensuring representation, adopting a flexible and transparent approach, and addressing real-world events.
- Strengthening the literature on implementation science harmonization is vital for advancing research in resource-limited settings.
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