Traditional infectious disease epidemiology is built on the foundation of high quality and high accuracy data on disease and behavior. Digital infectious disease epidemiology, on the other hand, uses existing digital traces, re-purposing them to identify patterns in health-related processes. Medical claims are an emerging digital data source in surveillance; they capture patient-level data across an entire population of healthcare seekers, and have the benefits of medical accuracy through physician diagnoses, and fine spatial and temporal resolution in near real-time. Our work harnesses the large volume and high specificity of diagnosis codes in medical claims to improve our understanding of the mechanisms driving spatial variation in reported influenza activity each year. The mechanisms hypothesized to drive these patterns are as varied as: environmental factors affecting transmission or virus survival, travel flows between different populations, population age structure, and socioeconomic factors linked to healthcare access and quality of life. Beyond process mechanisms, the nature of surveillance data collection may affect our interpretation of spatial epidemiological patterns, particularly since influenza is a non-reportable disease with non-specific symptoms ranging from asymptomatic to severe. Considering the ways in which medical claims are generated, biases may arise from healthcare-seeking behavior, insurance coverage, and medical claims database coverage in study populations.
Objective
To assess the use of medical claims records for surveillance and epidemiological inference through a case study that examines how ecological and social determinants and measurement error contribute to spatial heterogeneity in reports of influenza-like illness across the United States.