Comparison of Hand Eczema Search Terms in Iraq Before and During SARS-CoV-2 Pandemic Using Frequentist Statistics and Polynomial Models
DOI:
https://doi.org/10.20883/medical.e615Keywords:
Digital epidemiology, online systems, pharmacovigilance, spatio-temporal analysis, web searchAbstract
Introduction.SARS-CoV-2 pandemic spread around the world exponentially. People use disinfectants excessively as a form of protection from the novel coronavirus, which may result in contact eczema. This, in turn, may be monitored by the local health authorities. Our study explores the internet in order to detect significant changes in online information search behaviors associated with eczema in Iraq during the pandemic.
Material and Methods. We searched the internet, via Google Trends, using five search terms; "اكزيما", "الاكزيما", "اكزيما اليد", "كحول", and "مطهر"; these are the Arabic translation for "eczema", "the eczema", "hand eczema", "alcohol", and "disinfectant". We explored the temporal mapping covering two years, before and during the pandemic, using frequentist statistics, polynomial models, and neural networks to evaluate the time series which reflects web users' information-seeking behavior with regard to these terms.
Results. Spatial mapping conveyed data from six Iraq governorates, including Ninawa, Babil, Al-Najaf, Baghdad, Basrah, and Erbil. Basrah governorate had the highest score (interest) for the search term "the eczema" (الاكزيما), while Al-Najaf had the highest score regarding the search term "disinfectant" (مطهر). Temporal mapping exhibited high variability, the highest of which was for the "the eczema" (الاكزيما) and "alcohol" (كحول). Exploring the time series using polynomial models demonstrated a weak power over the two years. However, in the course of the pandemic year, all models possessed moderate power.
Conclusions. Changes in the human behavior during pandemic events are of prime importance for the pharmacovigilance experts. Pandemics may affect medical conditions, including hand eczema, as a manifestation of disinfectants overuse. Combining statistics and artificial intelligence facilitates screening, detecting, and collecting pharmacovigilance safety data.
Downloads
References
Safety monitoring of medicinal products. Guidelines for setting up and running a pharmacovigilance center. Uppsala, WHO Collaborating Centre for International Drug Monitoring, 2000.
Alomar M, Palaian S, Al‑Tabakha MM. Pharmacovigilance in perspective: drug withdrawals, data mining and policy implications. F1000Res. 2019; 8: 2109.
Foppiano M, Lombardo G. Worldwide pharmacovigilance systems and tolrestat withdrawal. The Lancet. 1997; 349(9049): 399–400.
Worldometer - real time world statistics [Internet]. Worldometer. 2020 [cited 22 January 2021]. Available from: https://www.worldometers.info/
Motyka MA, Al‑Imam A, Aljarshawi MHA. SARS‑CoV-2 pandemic as an anomie. Przestrzeń Społeczna (Social Space). 2;2020 (20): 111–144.
Wadman M. Public needs to prep for vaccine side effects. Science. 2020; 370(6520): 1022.
Logunov DY, Dolzhikova IV, Shcheblyakov DV, Tukhvatulin AI, Zubkova OV, Dzharullaeva AS, Kovyrshina AV, Lubenets NL, Grousova DM, Erokhova AS, Botikov AG. Safety and efficacy of an rAd26 and rAd5 vector‑based heterologous prime‑boost COVID-19 vaccine: an interim analysis of a randomised controlled phase 3 trial in Russia. The Lancet. 2021; 397(10275): 671–81.
COVID-19 vaccine tracker [Internet]. The Regulatory Affairs Professionals Society (RAPS). 2020 [cited 22 April 2021]. Available from: https://www.raps.org/news‑and‑articles/news‑articles/2020/3/covid-19-vaccine‑tracker
Papanikolaou V, Chrysovergis A, Ragos V, Tsiambas E, Katsinis S, Manoli A, Papouliakos S, Roukas D, Mastronikolis S, Peschos D, Batistatou A. From Delta to Omicron: S1-RBD/S2 mutation/deletion equilibrium in SARS‑CoV-2 defined variants. Gene. 2022; 814: 146134.
Minamoto K, Watanabe T, Diepgen TL. Self‑reported hand eczema among dental workers in Japan - a cross‑sectional study. Contact Derm. 2016; 75(4): 230–239.
Agner T, Andersen KE, Brandao FM, Bruynzeel DP, Bruze M, Frosch P, et al. Hand eczema severity and quality of life: a cross‐sectional, multicentre study of hand eczema patients. Contact Derm. 2008; 59(1): 43–7.
Al‑Imam A, Abdul‑Wahaab IT, Konuri VK, Sahai A. Reconciling artificial intelligence and non‑Bayesian models for pterygomaxillary morphometrics. Folia Morphol. 2021; 80(3): 625–641.
Al‑Imam A, Motyka MA, Jędrzejko MZ. Conflicting Opinions in Connection with Digital Superintelligence. IAES Int J Artif Intell. 2020; 9(2): 336–348.
Al‑Imam A, Al‑Lami F. Machine Learning for Potent Dermatology Research and Practice. J Dermatol Dermatol Surg. 2020; 24(1): 1–4.
Al‑Imam, A. Inferential Analysis of Big Data in Real‑Time: One Giant Leap for Spatiotemporal Digital Epidemiology in Dentistry. Oral Implantol. 2019; 12(1): 1–14.
Google Trends. 2020 [cited 22 January 2021]. Available from: https://trends.google.com/
Singh M, Pawar M, Bothra A, Choudhary N. Overzealous hand hygiene during the COVID 19 pandemic causing an increased incidence of hand eczema among general population. J Am Acad Dermatol. 2020; 83(1): e37-e41.
Blicharz L, Czuwara J, Samochocki Z, et al. Hand eczema‑A growing dermatological concern during the COVID-19 pandemic and possible treatments. Dermatol Ther. 2020; 33(5): e13545.
Abatemarco D, Perera S, Bao SH, Desai S, Assuncao B, Tetarenko N, Danysz K, Mockute R, Widdowson M, Fornarotto N, Beauchamp S. Training augmented intelligent capabilities for pharmacovigilance: applying deep‑learning approaches to individual case safety report processing. Pharm Med. 2018; 32(6):391–401.
Al‑Imam A, Gorial FI, Al‑shalchy A. A Novel Unusual Manifestation of CH‑Alpha as Acute Metabolic Disturbances: Case Report and Big Data Analytics. J Fac Med Baghdad. 2020; 62(1,2): 41–47.
Al‑Imam A, Khalid U, Al‑Doori HJ. Clustering Analysis of Coronavirus Disease 2019 Pandemic. Asian J Med Sci. 2020; 12(2): 108–113.
Al‑Imam A, Khalid U, Al‑Doori HJ. Predictive Epidemiology for SARS‑CoV-2 Pandemic in Iraq. Asian J Med Sci. 2021; 12(3): 121–124
Schmider J, Kumar K, LaForest C, Swankoski B, Naim K, Caubel PM. Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing. Clin Pharmacol Ther. 2019; 105(4): 954–61.
Al‑Imam A, Motyka MA, Al‑Doori HJ. Surface Web Merits for SARS‑CoV-2 Pandemic in Iraq. J Fac Med Baghdad. 2020; 62(4): 117–127.
Arora VS, McKee M, Stuckler D. Google Trends: Opportunities and limitations in health and health policy research. Health Policy. 2019; 123(3): 338–341.
Lund B, Beckstrom M. The Integration of Tor into Library Services: An Appeal to the Core Mission and Values of Libraries. Public Libr Q. 2021; 40(1): 60–76.
Vaughan L, Chen Y. Data mining from web search queries: A comparison of google trends and baidu index. J Assoc Inf Sci Technol. 2015; 66(1): 13–22.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 The copyright to the submitted manuscript is held by the Author, who grants the Journal of Medical Science (JMS) a nonexclusive licence to use, reproduce, and distribute the work, including for commercial purposes.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Accepted 2022-03-18
Published 2022-03-31