Tracking OTC Sales Can Significantly Improve Disease Monitoring

Data from the National Retail Data Monitor (NRDM) was found significantly effective as a potential supplement to traditional disease monitoring systems, according to a study published in bioRxiv.¹ Throughout the researchers’ exhibition of the NRDM system, they found the database to provide actionable insights on public health efforts regarding influenza-like illnesses (ILIs) through identifying over-the-counter (OTC) product sales.

“Detection of disease outbreaks is critical to public health surveillance, particularly in the early identification of emerging outbreaks,” wrote the authors of the study. “Traditional surveillance approaches, such as sentinel systems operated by the CDC, rely on confirmed case reports from selected health care systems.”

Although the CDC is seemingly the US authority on disease outbreak monitoring, it relies on accurate case reports of clinical data, which often translates to delays in reporting and a lack of timeliness in addressing disease outbreaks.

READ MORE: Older Adults Lack Understanding of OTC Drugs Despite Common Use

A Variety of Approaches in Disease Monitoring

To supplement the surveillance of public health initiatives, a variety of approaches have since been introduced and have become common in disease monitoring. These approaches include surveillance through electronic health record (EHR) data, online search engine queries, and social media.¹

“The conventional approach is slow and lacks real-time capabilities, prompting the adoption of digital technologies to track disease spread, model epidemiological patterns, and aid in public health decision-making,” wrote authors of a study published in PeerJ Computer Science.² “Real-time infectious diseases monitoring is crucial for developing both immediate and prolonged public health strategies and preventive actions.”

Through EHR data, as well as online sources providing more insights into what patients are searching for, technology has revolutionized the way disease surveillance is conducted by the country’s leading health experts.

On top of the technology that has emerged throughout the end of the 20th and beginning of the 21st centuries, artificial intelligence (AI) has become the premier technology, overtaking industries and markets across the world. In infectious disease monitoring specifically, the CDC has vowed to use AI for innovation and operational efficiency in its work to fight infectious disease.³

OTC Sales Approach to Public Health Surveillance

“An additional, underutilized approach involves analyzing OTC product sales, which can reflect self-treatment behaviors,” continued the authors of the current study.¹ “Since these purchases often precede formal medical care, anomalies in OTC sales can signal emerging population-level health events. To support early outbreak detection nationwide, the NRDM was launched in December 2002 as a novel public health surveillance system.”

The NRDM was originally developed for public health purposes and can be used for the “rapid detection” of unusual or deviating purchasing behavior from retail customers. Through the use of the NRDM system as well as other data sources, the researchers explored how this novel platform could provide further insights into the nation’s collective approach to disease surveillance.

Researchers’ main goal in their exploration of the NRDM was to determine whether or not OTC product sales could inform the detection of a disease outbreak.

The NRDM’s Impact on Disease Monitoring

Through NRDM data, researchers collected OTC product sales in Allegheny County, Pennsylvania, from June 1, 2016, to December 30, 2021. Each sale came from a collection of 270 stores across the county.¹

Based on individuals’ purchasing patterns, as well as data from emergency department (ED) visits relating to ILIs, they developed a model that estimates daily disease activity in a specific area based on OTC sales.

After data extraction, they found the cough and cold category as the most frequently purchased OTC products. Indeed, average daily sales were 3168 for cough and cold products.

“By leveraging OTC product sales and applying a probabilistic modeling approach, we were able to estimate daily ILI activity and identify abnormal population-level purchasing patterns,” wrote the authors.¹ “The resulting ILI estimates from NRDM data exhibited moderately strong correlation with ILI-related ED visits, which provides support for the model’s alignment with measurable clinical rates of ILI disease in the region.”

Because the NRDM’s findings and model matched closely with ED data from ILI-related visits, researchers were able to confidently confirm the efficacy of its disease monitoring system. They even uncovered insights from the COVID-19 pandemic’s volatile peaks and further confirmed the success of the NRDM.

With these findings presented alongside a rapidly evolving technological landscape, public health officials are gradually uncovering new ways to predict and prevent infectious disease outbreaks. While they confirmed the success of the NRDM system, researchers still believe further refinement and development is necessary.

“By identifying significant deviations in symptom-related OTC product sales, the system could offer timely and actionable public health insights,” they concluded.¹ “Continued refinement and integration with other data sources and modeling frameworks may further strengthen its role in the national biosurveillance ecosystem.”

READ MORE: OTC Resource Center

Ready to impress your pharmacy colleagues with the latest drug information, industry trends, and patient care tips? Sign up today for our free Drug Topics newsletter.

REFERENCES

1. Ye Y, Espino J, Aronis JM, et al. Using over-the-counter retail medication sales to detect and track influenza-like illnesses including novel diseases. bioRxiv (Cold Spring Harbor Laboratory). November 9, 2025. https://doi.org/10.1101/2025.11.07.25339792

2. Aryffin HAK, Arif M, Pitchay SA, et al. Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review. PeerJ Comput Sci. 2025;11:e2874-e2874. https://doi.org/10.7717/peerj-cs.2874

3. CDC’s vision for using artificial intelligence in public health. CDC. August 22, 2025. Accessed December 2, 2025. https://www.cdc.gov/data-modernization/php/ai/cdcs-vision-for-use-of-artificial-intelligence-in-public-health.html