A "Grounded in Health" Fair: Applying Environmental Health Approaches in a Cross-Specialty Community-Based Initiative

Area of Science:

• Public Health
• Preventive Medicine
• Environmental Health

Background:

• Individual and community wellness are influenced by clinical medicine, environmental factors, and social determinants of health.
• Preventive health services addressing these diverse factors often function independently, limiting their comprehensive impact.
• An integrated approach is needed to address the multifaceted drivers of health within communities.

Purpose of the Study:

• To describe a pilot community health fair in New Haven, Connecticut, that integrated diverse preventive services.
• To detail the structure, implementation, and practical considerations of a community-based health programming model.
• To share lessons learned from a pilot initiative for developing similar integrated community health programs.

Main Methods:

• The community health fair combined health insurance coverage assistance, stroke prevention outreach, environmental exposure assessment, and consumer product safety education.
• Services included blood pressure screening, stroke awareness materials, soil lead testing, chemical hazard audits, and health/dental coverage enrollment support.
• Implementation involved cross-specialty medical collaboration and partnerships with local community organizations.

Main Results:

• The event successfully integrated clinical, environmental, and social health services at a community level.
• Cross-sector and cross-specialty collaborations were crucial for the event's delivery.
• The pilot provided a framework and practical insights for future community-based preventive health initiatives.

Conclusions:

• Integrated community health fairs can address intersecting drivers of health by combining clinical and non-clinical preventive services.
• Cross-sector and interdisciplinary collaboration is essential for the successful implementation of such programs.
• Further research is needed to assess the acceptability, feasibility, and impact of this model for potential scalability.