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

@article{ye2026using,
  title = {{Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study.}},
  author = {Ye Ye and Jessi Espino and John M Aronis and Harry Hochheiser and Marian G Michaels and Gregory F Cooper},
  journal = {JMIR public health and surveillance},
  year = {2026},
  month = oct,
  doi = {10.2196/91163},
  url = {https://europepmc.org/article/MED/42905885},
  note = {CC BY},
}

TY  - JOUR
TI  - Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study.
AU  - Ye Ye
AU  - Jessi Espino
AU  - John M Aronis
AU  - Harry Hochheiser
AU  - Marian G Michaels
AU  - Gregory F Cooper
PY  - 2026
DA  - 2026/10/21
JO  - JMIR public health and surveillance
DO  - 10.2196/91163
UR  - https://europepmc.org/article/MED/42905885
AB  - 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.
SN  - 2369-2960
ER  -