Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases.

Area of Science:

• Public Health Surveillance
• Epidemiology
• Data Science in Health

Background:

• Traditional disease surveillance relies on delayed clinical data.
• Early detection of emerging outbreaks is crucial for public health.
• National Retail Data Monitor (NRDM) tracks over-the-counter (OTC) product sales.

Purpose of the Study:

• To evaluate NRDM data for public health surveillance.
• To develop a probabilistic model for estimating influenza-like illness (ILI) activity.
• To assess OTC sales as an early warning system for known and novel diseases.

Main Methods:

• Utilized NRDM data from Allegheny County, PA (2016-2021).
• Developed a probabilistic model using an expectation-maximization (EM) algorithm.
• Estimated daily ILI activity based on OTC purchasing patterns.

Main Results:

• The model estimated ILI counts correlated moderately with emergency department data (r = 0.66).
• Model performance can indicate unusual disease outbreaks.
• Changes in consumer purchasing behavior signal health trends.

Conclusions:

• OTC product sales surveillance is a viable tool for public health.
• This method provides early insights into potential outbreaks.
• It can detect both known and novel disease emergence.