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

BACKGROUND

Traditional disease surveillance systems often rely on clinical or laboratory-confirmed data, which may delay detection of emerging outbreaks. The National Retail Data Monitor (NRDM), established in 2002, collects anonymized over-the-counter (OTC) health product sales data from more than 28,000 retail outlets across the United States and provides a complementary data source for syndromic surveillance.

OBJECTIVE

This study evaluated the use of NRDM data for detecting and tracking influenza-like illness (ILI), including purchasing patterns suggestive of novel disease outbreaks.

METHODS

NRDM data from Allegheny County, Pennsylvania, collected between 2016 and 2021, were analyzed using a probabilistic modeling framework to estimate daily ILI activity from OTC purchasing patterns. An expectation-maximization algorithm was applied to data from the training period (June 2016-May 2019) to estimate conditional probabilities of OTC product purchases under ILI and non-ILI conditions. These estimates were then applied to daily sales data from the test period (June 2019-December 2021) to infer daily ILI counts. The framework was further extended into a monitoring system, termed NRDM Tracker, which uses daily log-likelihoods and false discovery rate-adjusted P values to detect statistically significant anomalies in purchasing behavior.

RESULTS

NRDM-derived ILI estimates were moderately correlated with emergency department (ED)-based ILI counts (r=0.66). Compared with other OTC product categories (eg, thermometers and antifever medications), cough and cold medications showed the strongest association with ILI-related purchasing behavior and higher estimated purchase probability under ILI conditions (0.792) than under non-ILI conditions (0.695). During the early phase of the COVID-19 pandemic in March 2020 in Allegheny County, when COVID-19 testing capacity was limited and public awareness and health-seeking behaviors were rapidly evolving, NRDM-based ILI signals increased before corresponding rises in confirmed ED-based COVID-19 counts. Moreover, NRDM Tracker identified significant anomalies in OTC purchasing behavior during major outbreak periods, including a pronounced decline in log-likelihood in March 2020 corresponding to the onset of the COVID-19 pandemic. Multiple product categories exhibited statistically significant deviations in sales during these periods.

CONCLUSIONS

OTC product sales data, analyzed using probabilistic modeling and anomaly detection methods, can provide timely insights into population-level health activity and may help identify anomalies associated with emerging disease outbreaks. NRDM-based surveillance systems may complement traditional clinical surveillance and strengthen local and national biosurveillance capabilities.