JMIR Publications

ABSTRACT

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 (e.g., thermometers and antifever medications), cough and cold medications showed the strongest association with ILI-related purchasing behavior and higher estimated purchase probability under ILI (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.