{
  "abstract": "Background\n\nTraditional 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\n\nThis study evaluated the use of NRDM data for detecting and tracking influenza-like illness (ILI), including purchasing patterns suggestive of novel disease outbreaks.Methods\n\nNRDM 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\n\nNRDM-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\n\nOTC 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Informatics, University of Pittsburgh, 3420 Forbes Avenue, 8th Floor Murdoch Building, Pittsburgh, PA, 15213, United States, 1 4129252746.",
    "UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ye Ye",
      "orcid": "0000-0002-1138-9846"
    },
    {
      "affs": [
        0
      ],
      "name": "Jessi Espino",
      "orcid": "0000-0001-8104-2656"
    },
    {
      "affs": [
        0
      ],
      "name": "John M Aronis",
      "orcid": "0000-0001-5821-3672"
    },
    {
      "affs": [
        0
      ],
      "name": "Harry Hochheiser",
      "orcid": "0000-0001-8793-9982"
    },
    {
      "affs": [
        1
      ],
      "name": "Marian G Michaels",
      "orcid": "0000-0002-2556-0544"
    },
    {
      "affs": [
        0
      ],
      "name": "Gregory F Cooper",
      "orcid": "0000-0002-9276-773X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6v49pg9je44f1.bib",
  "cited_by": 0,
  "doi": "10.2196/91163",
  "fulltext_url": "https://europepmc.org/articles/PMC13686242",
  "id": "6v49pg9je44f1",
  "issns": [
    "2369-2960"
  ],
  "landing_url": "https://europepmc.org/article/MED/42905885",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2713|Data-Driven Disease Surveillance"
    ]
  ],
  "openalex": "W7203764430",
  "pdf_url": "https://europepmc.org/articles/PMC13686242?pdf=render",
  "pmcid": "PMC13686242",
  "pmid": "42905885",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6v49pg9je44f1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42905885",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42905885"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Data-Driven Disease Surveillance",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study.",
  "title_text": "Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/6v49pg9je44f1",
  "venue": "JMIR public health and surveillance",
  "versions": []
}