How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data
Abstract
:1. Introduction
2. Results
2.1. Public Interest in Antimicrobials and Antimicrobial Resistance
2.2. Public Interest in Preventive Measures
2.3. Correlations between Google Trends and COVID-19 Cases
3. Discussion
4. Materials and Methods
4.1. Data Sources
4.2. Statistical Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- ECDC. Antimicrobial Resistance Surveillance in Europe 2022–2020 Data. 2022. Available online: https://www.ecdc.europa.eu/en/publications-data/antimicrobial-resistance-surveillance-europe-2022-2020-data (accessed on 1 February 2022).
- Jansen, K.U.; Knirsch, C.; Anderson, A.S. The role of vaccines in preventing bacterial antimicrobial resistance. Nat. Med. 2018, 24, 10–19. [Google Scholar] [CrossRef] [PubMed]
- WHO. World Antimicrobial Awareness Week. Available online: https://www.who.int/campaigns/world-antimicrobial-awareness-week (accessed on 1 February 2022).
- Rawson, T.M.; Ming, D.; Ahmad, R.; Moore, L.S.P.; Holmes, A.H. Antimicrobial use, drug-resistant infections and COVID-19. Nat. Rev. Microbiol. 2020, 18, 409–410. [Google Scholar] [CrossRef] [PubMed]
- WHO. World Hand Hygiene Day. Available online: https://www.who.int/campaigns/world-hand-hygiene-day (accessed on 1 February 2022).
- Global Handwashing Partnership. Global Handwashing Day. Available online: https://globalhandwashing.org/global-handwashing-day (accessed on 1 February 2022).
- Keitoku, K.; Nishimura, Y.; Hagiya, H.; Koyama, T.; Otsuka, F. Impact of the World Antimicrobial Awareness Week on public interest between 2015 and 2020: A Google Trends analysis. Int. J. Infect. Dis. 2021, 111, 12–20. [Google Scholar] [CrossRef] [PubMed]
- Galido, A.; Ecleo, J.J.; Husnayain, A.; Chia-Yu Su, E. Exploring online search behavior for COVID-19 preventive measures: The Philippine case. PLoS ONE 2021, 16, e0249810. [Google Scholar] [CrossRef] [PubMed]
- Hartwell, M.; Greiner, B.; Kilburn, Z.; Ottwell, R. Association of Public Interest in Preventive Measures and Increased COVID-19 Cases After the Expiration of Stay-at-Home Orders: A Cross-Sectional Study. Disaster Med. Public Health Prep. 2020, 10, 1–5. [Google Scholar] [CrossRef] [PubMed]
- Greiner, B.; Ottwell, R.; Vassar, M.; Hartwell, M. Public Interest in Preventive Measures of Coronavirus Disease 2019 Associated with Timely Issuance of Statewide Stay-at-Home Orders. Disaster Med. Public Health Prep. 2020, 14, 765–768. [Google Scholar] [CrossRef] [PubMed]
- Eysenbach, G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am. J. Prev. Med. 2011, 40, S154–S158. [Google Scholar] [CrossRef] [PubMed]
- Anema, A.; Kluberg, S.; Wilson, K.; Hogg, R.S.; Khan, K.; Hay, S.I.; Tatem, A.J.; Brownstein, J.S. Digital surveillance for enhanced detection and response to outbreaks. Lancet Infect. Dis. 2014, 14, 1035–1037. [Google Scholar] [CrossRef] [Green Version]
- Brownstein, J.S.; Freifeld, C.C.; Madoff, L.C. Digital disease detection--harnessing the Web for public health surveillance. N. Engl. J. Med. 2009, 360, 2153–2155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mavragani, A.; Ochoa, G.; Tsagarakis, K.P. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. J. Med. Internet Res. 2018, 20, e270. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, P.; Zhang, J.; Wang, L.; Yang, S.; Luo, G.; Deng, C.; Wen, Y.; Zhang, Q. Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model. Sci. Rep. 2017, 7, 46469. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ginsberg, J.; Mohebbi, M.H.; Patel, R.S.; Brammer, L.; Smolinski, M.S.; Brilliant, L. Detecting influenza epidemics using search engine query data. Nature 2009, 457, 1012–1014. [Google Scholar] [CrossRef] [PubMed]
- Shin, S.Y.; Seo, D.W.; An, J.; Kwak, H.; Kim, S.H.; Gwack, J.; Jo, M.W. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Sci. Rep. 2016, 6, 32920. [Google Scholar] [CrossRef] [PubMed]
- Bragazzi, N.L.; Alicino, C.; Trucchi, C.; Paganino, C.; Barberis, I.; Martini, M.; Sticchi, L.; Trinka, E.; Brigo, F.; Ansaldi, F.; et al. Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis. PLoS ONE 2017, 12, e0185263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mavragani, A.; Gkillas, K. COVID-19 predictability in the United States using Google Trends time series. Sci. Rep. 2020, 10, 20693. [Google Scholar] [CrossRef] [PubMed]
- Kurian, S.J.; Bhatti, A.U.R.; Alvi, M.A.; Ting, H.H.; Storlie, C.; Wilson, P.M.; Shah, N.D.; Liu, H.; Bydon, M. Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis. Mayo Clin. Proc. 2020, 95, 2370–2381. [Google Scholar] [CrossRef] [PubMed]
- Sulyok, M.; Ferenci, T.; Walker, M. Google Trends Data and COVID-19 in Europe: Correlations and model enhancement are European wide. Transbound Emerg. Dis. 2021, 68, 2610–2615. [Google Scholar] [CrossRef] [PubMed]
- Pullan, S.; Dey, M. Vaccine hesitancy and anti-vaccination in the time of COVID-19: A Google Trends analysis. Vaccine 2021, 39, 1877–1881. [Google Scholar] [CrossRef] [PubMed]
- Maugeri, A.; Barchitta, M.; Agodi, A. Using Google Trends to Predict COVID-19 Vaccinations and Monitor Search Behaviours about Vaccines: A Retrospective Analysis of Italian Data. Vaccines 2022, 10, 119. [Google Scholar] [CrossRef] [PubMed]
- ECDC. ECDC Country Visit to Italy to Discuss Antimicrobial Resistance Issues. 2017. Available online: https://www.ecdc.europa.eu/en/publications-data/ecdc-country-visit-italy-discuss-antimicrobial-resistance-issues (accessed on 1 February 2022).
- Barchitta, M.; Maugeri, A.; La Rosa, M.C.; La Mastra, C.; Murolo, G.; Corrao, G.; Agodi, A. Burden of Healthcare-Associated Infections in Sicily, Italy: Estimates from the Regional Point Prevalence Surveys 2016–2018. Antibiotics 2021, 10, 1360. [Google Scholar] [CrossRef] [PubMed]
- Barchitta, M.; Maugeri, A.; La Rosa, M.C.; La Mastra, C.; Murolo, G.; Agodi, A. Three-Year Trends of Healthcare-Associated Infections and Antibiotic Use in Acute Care Hospitals: Findings from 2016–2018 Point Prevalence Surveys in Sicily, Italy. Antibiotics 2020, 10, 1. [Google Scholar] [CrossRef] [PubMed]
- Bordino, V.; Vicentini, C.; D’Ambrosio, A.; Quattrocolo, F.; Zotti, C.M. Burden of Healthcare-Associated Infections in Italy: Disability-Adjusted Life Years. Eur. J. Public Health 2020, 30, ckaa165.17. [Google Scholar] [CrossRef]
- Secondo Studio di Prevalenza Italiano Sulle Infezioni Correlate All’assistenza e Sull’uso di Antibiotici Negli Ospedali per Acuti–Protocollo ECDC; Dipartimento Scienze Della Salute Pubblica e Pediatriche: Università di Torino, Torino, Italy, 2018.
- Maugeri, A.; Barchitta, M.; Basile, G.; Agodi, A. Applying a hierarchical clustering on principal components approach to identify different patterns of the SARS-CoV-2 epidemic across Italian regions. Sci. Rep. 2021, 11, 7082. [Google Scholar] [CrossRef] [PubMed]
- Maugeri, A.; Barchitta, M.; Agodi, A. A Clustering Approach to Classify Italian Regions and Provinces Based on Prevalence and Trend of SARS-CoV-2 Cases. Int. J. Environ. Res. Public Health 2020, 17, 5286. [Google Scholar] [CrossRef]
- Rivera-Izquierdo, M.; Benavente-Fernández, A.; López-Gómez, J.; Láinez-Ramos-Bossini, A.J.; Rodríguez-Camacho, M.; Valero-Ubierna, M.D.C.; Martín-delosReyes, L.M.; Jiménez-Mejías, E.; Moreno-Roldán, E.; Lardelli-Claret, P.; et al. Prevalence of Multi-Resistant Microorganisms and Antibiotic Stewardship among Hospitalized Patients Living in Residential Care Homes in Spain: A Cross-Sectional Study. Antibiotics 2020, 9, 324. [Google Scholar] [CrossRef] [PubMed]
- Rivera-Izquierdo, M.; Láinez-Ramos-Bossini, A.J.; Rivera-Izquierdo, C.; López-Gómez, J.; Fernández-Martínez, N.F.; Redruello-Guerrero, P.; Martín-delosReyes, L.M.; Martínez-Ruiz, V.; Moreno-Roldán, E.; Jiménez-Mejías, E. OXA-48 Carbapenemase-Producing Enterobacterales in Spanish Hospitals: An Updated Comprehensive Review on a Rising Antimicrobial Resistance. Antibiotics 2021, 10, 89. [Google Scholar] [CrossRef] [PubMed]
- Barchitta, M.; Maugeri, A.; La Rosa, M.C.; La Mastra, C.; Murolo, G.; Basile, G.; Agodi, A. Carbapenem Consumption and Rate of carbapenemresistant gram-negative bacteria: Results from the Sicilian Surveillance System. Ann. Ig. 2021, 33, 289–296. [Google Scholar] [CrossRef]
- Barchitta, M.; Quattrocchi, A.; Maugeri, A.; La Rosa, M.C.; La Mastra, C.; Sessa, L.; Cananzi, P.; Murolo, G.; Oteri, A.; Basile, G.; et al. Antibiotic Consumption and Resistance during a 3-Year Period in Sicily, Southern Italy. Int. J. Environ. Res. Public Health 2019, 16, 2253. [Google Scholar] [CrossRef] [Green Version]
- European Commission. A European One Health Action Plan Against Antimicrobial Resistance (AMR). 2017. Available online: https://ec.europa.eu/health/amr/ (accessed on 1 February 2022).
- Paget, J.; Lescure, D.; Versporten, A.; Goossens, H.; Schellevis, F.; van Dijk, L. Antimicrobial Resistance and Causes of Non-Prudent Use of Antibiotics in Human Medicine in the UE; European Union: Brussels, Belgium, 2017. [Google Scholar]
- Charani, E.; Edwards, R.; Sevdalis, N.; Alexandrou, B.; Sibley, E.; Mullett, D.; Franklin, B.D.; Holmes, A. Behavior change strategies to influence antimicrobial prescribing in acute care: A systematic review. Clin. Infect. Dis 2011, 53, 651–662. [Google Scholar] [CrossRef] [Green Version]
- Pinder, R.; Sallis, A.; Berry, D.; Chadborn, T. Behaviour Change and Antibiotic Prescribing in Healthcare Settings: Literature Review and Behavioural Analysis; Department of Health & Public Health England: London, UK, 2015. [Google Scholar]
- Kosiyaporn, H.; Chanvatik, S.; Issaramalai, T.; Kaewkhankhaeng, W.; Kulthanmanusorn, A.; Saengruang, N.; Witthayapipopsakul, W.; Viriyathorn, S.; Kirivan, S.; Kunpeuk, W.; et al. Surveys of knowledge and awareness of antibiotic use and antimicrobial resistance in general population: A systematic review. PLoS ONE 2020, 15, e0227973. [Google Scholar] [CrossRef]
- Srinivasan, A.; Song, X.; Richards, A.; Sinkowitz-Cochran, R.; Cardo, D.; Rand, C. A survey of knowledge, attitudes, and beliefs of house staff physicians from various specialties concerning antimicrobial use and resistance. Arch. Intern. Med. 2004, 164, 1451–1456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- García, C.; Llamocca, L.P.; García, K.; Jiménez, A.; Samalvides, F.; Gotuzzo, E.; Jacobs, J. Knowledge, attitudes and practice survey about antimicrobial resistance and prescribing among physicians in a hospital setting in Lima, Peru. BMC Clin. Pharmacol. 2011, 11, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thriemer, K.; Katuala, Y.; Batoko, B.; Alworonga, J.P.; Devlieger, H.; Van Geet, C.; Ngbonda, D.; Jacobs, J. Antibiotic prescribing in DR Congo: A knowledge, attitude and practice survey among medical doctors and students. PLoS ONE 2013, 8, e55495. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Navarro-San Francisco, C.; Del Toro, M.D.; Cobo, J.; De Gea-García, J.H.; Vañó-Galván, S.; Moreno-Ramos, F.; Rodríguez-Baño, J.; Paño-Pardo, J.R. Knowledge and perceptions of junior and senior Spanish resident doctors about antibiotic use and resistance: Results of a multicenter survey. Enferm. Infecc. Microbiol. Clin. 2013, 31, 199–204. [Google Scholar] [CrossRef] [PubMed]
- Bai, Y.; Wang, S.; Yin, X.; Bai, J.; Gong, Y.; Lu, Z. Factors associated with doctors’ knowledge on antibiotic use in China. Sci. Rep. 2016, 6, 23429. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Barchitta, M.; Sabbatucci, M.; Furiozzi, F.; Iannazzo, S.; Maugeri, A.; Maraglino, F.; Prato, R.; Agodi, A.; Pantosti, A. Knowledge, attitudes and behaviors on antibiotic use and resistance among healthcare workers in Italy, 2019: Investigation by a clustering method. Antimicrob. Resist. Infect. Control. 2021, 10, 134. [Google Scholar] [CrossRef] [PubMed]
- Elgibaly, O.; Daef, E.; Elghazally, S.A.; Hassan, H.M.; ElsaidTash, R.M.; Bahgat, S.M.; ELantouny, N.G.; Zarzour, A.A.; Othman, M.M.A.; El-Sokkary, R.H. Knowledge, perception, and confidence of healthcare workers about COVID-19 preventive measures during the first wave of the pandemic: A cross-sectional study from Egypt. Germs 2021, 11, 179–188. [Google Scholar] [CrossRef] [PubMed]
- Rivera-Izquierdo, M.; Valero-Ubierna, M.D.C.; Martínez-Diz, S.; Fernández-García, M.; Martín-Romero, D.T.; Maldonado-Rodríguez, F.; Sánchez-Pérez, M.R.; Martín-delosReyes, L.M.; Martínez-Ruiz, V.; Lardelli-Claret, P.; et al. Clinical Factors, Preventive Behaviours and Temporal Outcomes Associated with COVID-19 Infection in Health Professionals at a Spanish Hospital. Int. J. Environ. Res. Public Health 2020, 17, 4305. [Google Scholar] [CrossRef] [PubMed]
- Barchitta, M.; Quattrocchi, A.; Maugeri, A.; Rosa, M.C.; Mastra, C.; Basile, G.; Giuffrida, G.; Rinaldi, F.M.; Murolo, G.; Agodi, A. The “Obiettivo Antibiotico” Campaign on Prudent Use of Antibiotics in Sicily, Italy: The Pilot Phase. Int. J. Environ. Res. Public Health 2020, 17, 77. [Google Scholar] [CrossRef] [PubMed]
- Nishimura, Y.; Hagiya, H.; Keitoku, K.; Koyama, T.; Otsuka, F. Impact of the world hand hygiene and global handwashing days on public awareness between 2016 and 2020: Google trends analysis. Am. J. Infect. Control. 2022, 50, 141–147. [Google Scholar] [CrossRef] [PubMed]
- Google. Google Trends. Available online: https://trends.google.it/trends/?geo=IT (accessed on 1 February 2022).
- GitHub. Italian Data. Available online: https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioni (accessed on 1 February 2022).
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Maugeri, A.; Barchitta, M.; Basile, G.; Agodi, A. How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data. Antibiotics 2022, 11, 379. https://doi.org/10.3390/antibiotics11030379
Maugeri A, Barchitta M, Basile G, Agodi A. How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data. Antibiotics. 2022; 11(3):379. https://doi.org/10.3390/antibiotics11030379
Chicago/Turabian StyleMaugeri, Andrea, Martina Barchitta, Guido Basile, and Antonella Agodi. 2022. "How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data" Antibiotics 11, no. 3: 379. https://doi.org/10.3390/antibiotics11030379