Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland during the COVID-19 Pandemic : Infodemiological Approach Using Database Logs

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Mukka , M , Pesälä , S , Hammer , C , Mustonen , P , Jormanainen , V , Pelttari , H , Kaila , M & Helve , O 2021 , ' Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland during the COVID-19 Pandemic : Infodemiological Approach Using Database Logs ' , JMIR public health and surveillance , vol. 7 , no. 12 , e31961 . https://doi.org/10.2196/31961

Title: Analyzing Citizens' and Health Care Professionals' Searches for Smell/Taste Disorders and Coronavirus in Finland during the COVID-19 Pandemic : Infodemiological Approach Using Database Logs
Author: Mukka, Milla; Pesälä, Samuli; Hammer, Charlotte; Mustonen, Pekka; Jormanainen, Vesa; Pelttari, Hanna; Kaila, Minna; Helve, Otto
Contributor organization: University of Helsinki
HUS Head and Neck Center
Department of Public Health
Clinicum
HUS Children and Adolescents
Children's Hospital
Date: 2021-12-07
Language: eng
Number of pages: 10
Belongs to series: JMIR public health and surveillance
ISSN: 2369-2960
DOI: https://doi.org/10.2196/31961
URI: http://hdl.handle.net/10138/341141
Abstract: Background: The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. Objective: The aim of this study was (1) to assess whether citizens' and professionals' searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and (2) to test our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods: We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 2020 (49 weeks). Two major medical internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician's Database (PD), a database widely used among HCPs. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results: We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions: Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases.
Description: Publisher Copyright: © Milla Mukka, Samuli Pesälä, Charlotte Hammer, Pekka Mustonen, Vesa Jormanainen, Hanna Pelttari, Minna Kaila, Otto Helve. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.12.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
Subject: COVID-19
Health personnel
Information-seeking behavior
Medical informatics
SARS-CoV-2
Smell disorders
Statistical models
Taste disorders
3142 Public health care science, environmental and occupational health
3121 General medicine, internal medicine and other clinical medicine
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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