Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and...

Area of Science:

• Public Health
• Health Informatics
• Community Health

Background:

• The COVID-19 pandemic highlighted challenges in utilizing community-level data for public health crisis management.
• Local public health decision-making was hindered by difficulties in creating effective data-driven learning systems.
• Healthcare partners convened to share experiences and identify solutions for managing public health crises.

Purpose of the Study:

• To identify key tools and processes for leveraging data and learning systems to support equitable public health decision-making during the COVID-19 pandemic.
• To explore theoretical drivers for achieving herd immunity through qualitative interviews with public health leaders.
• To refine a theory of change and develop a Community Protection Dashboard tool based on collective experiences and insights.

Main Methods:

• Qualitative exploration of theoretical drivers through nine semi-structured telephonic interviews with 16 public health and community leaders across the U.S.
• Analysis of interview responses to identify key themes for effective public health responses.
• Reflection on COVID-19 experiences by partners in Dallas and Cincinnati.

Main Results:

• Four key themes emerged: real-time, accessible data balancing transparency and privacy; sustained public trust; adaptable infrastructures; and cohesive community coalitions.
• Revised theory of change emphasizing community collaboration and trust-building.
• Refinement of the Community Protection Dashboard tool.

Conclusions:

• Broad consensus exists among public health and community leaders on essential data and learning system components for managing public health responses.
• Findings offer guidance for data utilization in future public health crises and population health initiatives.