{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T18:42:16Z","timestamp":1792608136491,"version":"4.5.0"},"reference-count":39,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study evaluated the use of NRDM data for detecting and tracking influenza-like illness (ILI), including purchasing patterns suggestive of novel disease outbreaks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\u2013adjusted\n                      <jats:italic>P<\/jats:italic>\n                      values to detect statistically significant anomalies in purchasing behavior.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      NRDM-derived ILI estimates were moderately correlated with emergency department (ED)\u2013based ILI counts (\n                      <jats:italic>r<\/jats:italic>\n                      =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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/91163","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T08:20:05Z","timestamp":1787127605000},"page":"e91163-e91163","source":"Crossref","is-referenced-by-count":0,"title":["Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1138-9846","authenticated-orcid":false,"given":"Ye","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8104-2656","authenticated-orcid":false,"given":"Jessi","family":"Espino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5821-3672","authenticated-orcid":false,"given":"John M","family":"Aronis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8793-9982","authenticated-orcid":false,"given":"Harry","family":"Hochheiser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2556-0544","authenticated-orcid":false,"given":"Marian G","family":"Michaels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9276-773X","authenticated-orcid":false,"given":"Gregory F","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"1 Pt 1","key":"R1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.7326\/0003-4819-156-1-201201030-00003","article-title":"Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes","volume":"156","author":"Elkin","journal-title":"Ann Intern Med"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174970","article-title":"A study of the transferability of influenza case detection systems between two large healthcare systems","volume":"12","author":"Ye","journal-title":"PLoS ONE"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.5888\/pcd21.230417","article-title":"Public health surveillance in electronic health records: lessons from PCORnet","volume":"21","author":"Ghildayal","journal-title":"Prev Chronic Dis"},{"issue":"8","key":"R4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008117","article-title":"Real-time estimation of disease activity in emerging outbreaks using internet search information","volume":"16","author":"Aiken","journal-title":"PLoS Comput Biol"},{"issue":"7232","key":"R5","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1038\/nature07634","article-title":"Detecting influenza epidemics using search engine query data","volume":"457","author":"Ginsberg","journal-title":"Nature"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/s40537-023-00847-9","article-title":"Search-engine-based surveillance using artificial intelligence for early detection of coronavirus disease outbreak","volume":"10","author":"Wang","journal-title":"J Big Data"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/data4010006","article-title":"Using Twitter for public health surveillance from monitoring and prediction to public response","volume":"4","author":"Jordan","journal-title":"Data (Basel)"},{"issue":"116882","key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116882","article-title":"An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US","volume":"198","author":"Zhang","journal-title":"Expert Syst Appl"},{"key":"R9","unstructured":"Best practices for the design, implementation, analysis and evaluation of participatory surveillance systems for influenza and other respiratory pathogens. World Health Organization. URL: https:\/\/www.who.int\/publications\/i\/item\/9789240095038 [Accessed 28-09-2026]"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.5210\/ojphi.v5i1.4456","article-title":"Flu near you: an online self-reported influenza surveillance system in the USA","volume":"5","author":"Chunara","journal-title":"Online J Public Health Inform"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s44482-025-00010-6","article-title":"Discerning the seasonal variations in respiratory virus exposure across networked populations through participatory surveillance","volume":"1","author":"Chan","journal-title":"npj Digit Public Health"},{"issue":"8","key":"R12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0303230","article-title":"Flexible and scalable participatory syndromic and virological surveillance for respiratory infections: our experiences in The Netherlands","volume":"20","author":"Smit","journal-title":"PLoS ONE"},{"issue":"9","key":"R13","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1016\/j.cmi.2026.03.005","article-title":"Self-testing for five respiratory viruses in adults \u226550 years of age in Germany for longitudinal monitoring of community-acquired acute respiratory infections","volume":"32","author":"Nacov","journal-title":"Clin Microbiol Infect"},{"issue":"6","key":"R14","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.16190","article-title":"Smart thermometer-based participatory surveillance to discern the role of children in household viral transmission during the COVID-19 pandemic","volume":"6","author":"Tseng","journal-title":"JAMA Netw Open"},{"issue":"3","key":"R15","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.jbi.2013.02.003","article-title":"A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza","volume":"46","author":"Villamar\u00edn","journal-title":"J Biomed Inform"},{"issue":"5","key":"R16","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1197\/jamia.M1357","article-title":"Design of a national retail data monitor for public health surveillance","volume":"10","author":"Wagner","journal-title":"J Am Med Inform Assoc"},{"issue":"32\u201339","key":"R17","first-page":"32","volume":"53","author":"Espino","journal-title":"MMWR Suppl"},{"key":"R18","unstructured":"Sabhnani MR Neill DB Moore AW . Detecting anomalous patterns in pharmacy retail data. Presented at: KDD-2005 Workshop on Data Mining Methods for Anomaly Detection; Aug 21, 2005; Chicago, Illinois. URL: https:\/\/www.cs.cmu.edu\/~neill\/papers\/ADKDD-Sabhnani.pdf [Accessed 28-09-2026]"},{"issue":"3","key":"R19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0059273","article-title":"Association of over-the-counter pharmaceutical sales with influenza-like-illnesses to patient volume in an urgent care setting","volume":"8","author":"Liu","journal-title":"PLoS ONE"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-10-39","article-title":"Prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the San Francisco Bay Area","volume":"10","author":"Kirian","journal-title":"BMC Med Inform Decis Mak"},{"issue":"3","key":"R21","doi-asserted-by":"publisher","first-page":"547","DOI":"10.2105\/AJPH.2004.061358","article-title":"Diarrheal illness detected through syndromic surveillance after a massive power outage: New York City, August 2003","volume":"96","author":"Marx","journal-title":"Am J Public Health"},{"issue":"1","key":"R22","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/BF03160942","article-title":"Disease surveillance and nonprescription medication sales can predict increases in poison exposure","volume":"4","author":"Krenzelok","journal-title":"J Med Toxicol"},{"issue":"Suppl","key":"R23","first-page":"190","volume":"54","author":"Johnson","journal-title":"Morb Mortal Wkly Rep"},{"key":"R24","volume":"53","author":"Heffernan","journal-title":"MMWR Suppl"},{"key":"R25","doi-asserted-by":"publisher","DOI":"10.2196\/57349","article-title":"A Bayesian system to detect and track outbreaks of influenza-like illnesses including novel diseases: algorithm development and validation","volume":"10","author":"Aronis","journal-title":"JMIR Public Health Surveill"},{"issue":"4","key":"R26","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1093\/jamia\/ocab204","article-title":"An atomic approach to the design and implementation of a research data warehouse","volume":"29","author":"Visweswaran","journal-title":"J Am Med Inform Assoc"},{"key":"R27","doi-asserted-by":"crossref","unstructured":"McLachlan GJ Krishnan T . The EM Algorithm and Extensions. John Wiley & Sons; 2008. ISBN: 9780470191613","DOI":"10.1002\/9780470191613"},{"issue":"3-4","key":"R28","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality (complete samples)","volume":"52","author":"Shapiro","journal-title":"Biometrika"},{"issue":"1","key":"R29","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","journal-title":"Journal of the Royal Statistical Society Series B"},{"issue":"1","key":"R30","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1214\/ss\/1056397487","article-title":"Multiple hypothesis testing in microarray experiments","volume":"18","author":"Dudoit","journal-title":"Statist Sci"},{"issue":"106\u2013111","key":"R31","first-page":"106","volume":"53","author":"Metzger","journal-title":"MMWR Suppl"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-20502-2","article-title":"Case growth analysis to inform local response to COVID-19 epidemic in a diverse U.S community","volume":"12","author":"de Oliveira Otto","journal-title":"Sci Rep"},{"key":"R33","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1661753","article-title":"Monitoring OTC drug sales for early detection of respiratory infectious disease outbreaks","volume":"13","author":"Li","journal-title":"Front Public Health"},{"issue":"2","key":"R34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0282101","article-title":"Surveillance of communicable diseases using social media: a systematic review","volume":"18","author":"Pilipiec","journal-title":"PLoS ONE"},{"issue":"1","key":"R35","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.33737","article-title":"Social media role and its impact on public health: a narrative review","volume":"15","author":"Kanchan","journal-title":"Cureus"},{"issue":"3","key":"R36","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/s44197-024-00272-y","article-title":"Internet-based surveillance systems and infectious diseases prediction: an updated review of the last 10 years and lessons from the COVID-19 pandemic","volume":"14","author":"McClymont","journal-title":"J Epidemiol Glob Health"},{"issue":"1","key":"R37","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-026-12905-7","article-title":"Survey on mathematical modeling of infectious disease dynamics: insights and applications","volume":"26","author":"Eshtewy","journal-title":"BMC Infect Dis"},{"issue":"10","key":"R38","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.93929","article-title":"Infectious disease surveillance in the era of big data and AI: opportunities and pitfalls","volume":"17","author":"Idahor","journal-title":"Cureus"},{"issue":"8051","key":"R39","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1038\/s41586-024-08564-w","article-title":"Artificial intelligence for modelling infectious disease epidemics","volume":"638","author":"Kraemer","journal-title":"Nature"}],"container-title":["JMIR Public Health and Surveillance"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T18:11:54Z","timestamp":1792606314000},"score":1,"resource":{"primary":{"URL":"https:\/\/publichealth.jmir.org\/2026\/1\/e91163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,21]]},"references-count":39,"URL":"https:\/\/doi.org\/10.2196\/91163","relation":{},"ISSN":["2369-2960"],"issn-type":[{"value":"2369-2960","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,21]]},"article-number":"v12i1 Pt 1e91163"}}