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Tselebis A, Zabuliene L, Milionis C, Ilias I. Pandemic and precocious puberty - a Google trends study. World J Methodol 2023; 13:1-9. [PMID: 36684480 PMCID: PMC9850652 DOI: 10.5662/wjm.v13.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/29/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Recent publications from several countries have reported that more young people (mainly girls) are experiencing precocious puberty (PP)/menarche during the coronavirus disease 2019 pandemic compared to the past. This variation is attributed to the stress of confinement, lack of exercise, obesity and disturbed sleep patterns. A common feature of the relevant papers, however, is the small number of reported cases of PP. Studies have shown that searches for diseases on the internet also reflect to some extent the epidemiology of these diseases.
AIM To estimate, through internet searches for PP, any changes in the epidemiology of PP.
METHODS We assessed in Google Trends searches for 21 PP-related terms in English internationally (which practically dwarf searches in other languages), in the years 2017-2021. Additionally, we assessed local searches for selected terms, in English and local languages, in countries where a rise in PP has been reported. Searches were collected in Relative Search Volumes format and analyzed using Kendall’s Tau test, with a statistical significance threshold of P < 0.05.
RESULTS Internationally, searches for three PP-related terms showed no noticeable change over the study period, while searches for eight terms showed a decrease. An increase was found over time in searches for nine PP-related terms. Of the 17 searches in English and local languages, in countries where a rise in PP has been reported, 5 showed a significant increase over time.
CONCLUSION Over the study period, more than half of the search terms showed little change or declined. The discrepancy between internet searches for PP and the reported increase in the literature is striking. It would be expected that a true increase in the incidence of PP would also be aptly reflected in Google trends. If our findings are valid, the literature may have been biased. The known secular trend of decreasing age of puberty may also have played a role.
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Affiliation(s)
- Athanasios Tselebis
- Department of Psychiatry, “Sotiria” General Chest Diseases Hospital, Athens GR-11527, Greece
| | - Lina Zabuliene
- Faculty of Medicine, Vilnius University, Vilnius LT-03101, Lithuania
| | - Charalampos Milionis
- Department of Endocrinology, Elena Venizelou General and Maternity Hospital, Athens GR-11521, Greece
| | - Ioannis Ilias
- Department of Endocrinology, Elena Venizelou General and Maternity Hospital, Athens GR-11521, Greece
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2
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Ilias I, Milionis C, Koukkou E. COVID-19 and thyroid disease: An infodemiological pilot study. World J Methodol 2022; 12:99-106. [PMID: 35721248 PMCID: PMC9157630 DOI: 10.5662/wjm.v12.i3.99] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/11/2022] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Google Trends searches for symptoms and/or diseases may reflect actual disease epidemiology. Recently, Google Trends searches for coronavirus disease 2019 (COVID-19)-associated terms have been linked to the epidemiology of COVID-19. Some studies have linked COVID-19 with thyroid disease.
AIM To assess COVID-19 cases per se vs COVID-19-associated Google Trends searches and thyroid-associated Google Trends searches.
METHODS We collected data on worldwide weekly Google Trends searches regarding “COVID-19”, “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)”, “coronavirus”, “smell”, “taste”, “cough”, “thyroid”, “thyroiditis”, and “subacute thyroiditis” for 92 wk and worldwide weekly COVID-19 cases' statistics in the same time period. The study period was split in half (approximately corresponding to the preponderance of different SARS-COV-2 virus variants) and in each time period we performed cross-correlation analysis and mediation analysis.
RESULTS Significant positive cross-correlation function values were noted in both time periods. More in detail, COVID-19 cases per se were found to be associated with no lag with Google Trends searches for COVID-19 symptoms in the first time period and in the second time period to lead searches for symptoms, COVID-19 terms, and thyroid terms. COVID-19 cases per se were associated with thyroid-related searches in both time periods. In the second time period, the effect of “COVID-19” searches on “thyroid’ searches was significantly mediated by COVID-19 cases (P = 0.048).
CONCLUSION Searches for a non-specific symptom or COVID-19 search terms mostly lead Google Trends thyroid-related searches, in the second time period. This time frame/sequence particularly in the second time period (noted by the preponderance of the SARS-COV-2 delta variant) lends some credence to associations of COVID-19 cases per se with (apparent) thyroid disease (via searches for them).
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Affiliation(s)
- Ioannis Ilias
- Department of Endocrinology, Diabetes & Metabolism, Elena Venizelou Hospital, Athens GR-11521, Greece
| | - Charalampos Milionis
- Department of Endocrinology, Diabetes & Metabolism, Elena Venizelou Hospital, Athens GR-11521, Greece
| | - Eftychia Koukkou
- Department of Endocrinology, Diabetes & Metabolism, Elena Venizelou Hospital, Athens GR-11521, Greece
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3
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Ravalli S, Roggio F, Lauretta G, Di Rosa M, D'Amico AG, D'agata V, Maugeri G, Musumeci G. Exploiting real-world data to monitor physical activity in patients with osteoarthritis: the opportunity of digital epidemiology. Heliyon 2022; 8:e08991. [PMID: 35252602 PMCID: PMC8889133 DOI: 10.1016/j.heliyon.2022.e08991] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 11/22/2021] [Accepted: 02/16/2022] [Indexed: 12/15/2022] Open
Abstract
Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. Current guidelines emphasize the importance of regular physical activity as a preventive measure against disease progression and as a valuable strategy for pain and functionality management. Despite this, most patients with osteoarthritis are inactive. Modern technological advances have led to the implementation of digital devices, such as wearables and smartphones, showing new opportunities for healthcare professionals and researchers to monitor physical activity and therefore engage patients in daily exercising. Additionally, digital devices have emerged as a promising tool for improving frequent health data collection, disease monitoring, and supporting public health surveillance. The leveraging of digital data has laid the foundation for developing a new concept of epidemiological study, known as "Digital Epidemiology". Analyzing real-world data can change the way we observe human behavior and suggest health interventions, as in the case of physical exercise and osteoarthritic patients. Furthermore, large-scale data could contribute to personalized and precision medicine in the future. Herein, an overview of recent clinical applications of wearables for monitoring physical activity in patients with osteoarthritis and the benefits of exploiting real-world data in the context of digital epidemiology are discussed.
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Affiliation(s)
- Silvia Ravalli
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy
| | - Federico Roggio
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy.,Department of Psychology, Educational Science and Human Movement, University of Palermo, Via Giovanni Pascoli 6, 90144 Palermo, Italy
| | - Giovanni Lauretta
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy
| | - Michelino Di Rosa
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy
| | - Agata Grazia D'Amico
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
| | - Velia D'agata
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy
| | - Grazia Maugeri
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy
| | - Giuseppe Musumeci
- Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Via S. Sofia 87, 95123 Catania, Italy.,Research Center on Motor Activities (CRAM), University of Catania, 95123 Catania, Italy.,Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA
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4
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Shah SFH, Shah SA, Merchant SA. Investigating temporal patterns of public interest in skin whitening using Google Trends. Int J Dermatol 2020; 60:e160-e161. [PMID: 33372285 DOI: 10.1111/ijd.15380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 10/22/2020] [Accepted: 11/30/2020] [Indexed: 11/28/2022]
Affiliation(s)
- Syed F H Shah
- Faculty of Medicine, University of Cambridge, Cambridge, UK
| | - Syed A Shah
- School of Medicine, St. George's University of London, London, UK
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5
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Campo DS, Gussler JW, Sue A, Skums P, Khudyakov Y. Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches. PLoS One 2020; 15:e0243622. [PMID: 33284864 PMCID: PMC7721465 DOI: 10.1371/journal.pone.0243622] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005–2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.
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Affiliation(s)
- David S. Campo
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
- * E-mail:
| | - Joseph W. Gussler
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
- Georgia State University, Atlanta, Georgia, United States of America
| | - Amanda Sue
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Pavel Skums
- Georgia State University, Atlanta, Georgia, United States of America
| | - Yury Khudyakov
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
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He Z, Zhang CJP, Huang J, Zhai J, Zhou S, Chiu JWT, Sheng J, Tsang W, Akinwunmi BO, Ming WK. A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China. J Med Internet Res 2020; 22:e21685. [PMID: 32805703 PMCID: PMC7511225 DOI: 10.2196/21685] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/23/2020] [Accepted: 08/11/2020] [Indexed: 12/15/2022] Open
Abstract
A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.
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Affiliation(s)
- Zonglin He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.,Faculty of Medicine, International School, Jinan University, Guangzhou, China
| | - Casper J P Zhang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jian Huang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, St Mary's Campus, Imperial College London, London, United Kingdom
| | - Jingyan Zhai
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Shuang Zhou
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Joyce Wai-Ting Chiu
- Faculty of Medicine, International School, Jinan University, Guangzhou, China
| | - Jie Sheng
- College of Economics, Jinan University, Guangzhou, China
| | - Winghei Tsang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Babatunde O Akinwunmi
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard University, Boston, MA, United States.,Pulmonary & Critical Care Medicine Unit, Asthma Research Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Wai-Kit Ming
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
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Lippi G, Mattiuzzi C, Cervellin G. Is Digital Epidemiology the Future of Clinical Epidemiology? J Epidemiol Glob Health 2020; 9:146. [PMID: 31241874 PMCID: PMC7310749 DOI: 10.2991/jegh.k.190314.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Affiliation(s)
- Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
| | - Camilla Mattiuzzi
- Service of Clinical Governance, Provincial Agency for Social and Sanitary Services, Trento, Italy
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Sadilek A, Hswen Y, Bavadekar S, Shekel T, Brownstein JS, Gabrilovich E. Lymelight: forecasting Lyme disease risk using web search data. NPJ Digit Med 2020; 3:16. [PMID: 32047861 PMCID: PMC7000681 DOI: 10.1038/s41746-020-0222-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 12/19/2019] [Indexed: 02/02/2023] Open
Abstract
Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease.
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Affiliation(s)
| | - Yulin Hswen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA USA
| | | | | | - John S. Brownstein
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA USA
- Department of Pediatrics, Harvard Medical School, Massachusetts, USA
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Mattiuzzi C, Lippi G. Which lessons shall we learn from the 2019 novel coronavirus outbreak? ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:48. [PMID: 32154288 PMCID: PMC7036635 DOI: 10.21037/atm.2020.02.06] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/04/2020] [Indexed: 12/22/2022]
Affiliation(s)
- Camilla Mattiuzzi
- Service of Clinical Governance, Provincial Agency for Social and Sanitary Services, Trento, Italy
| | - Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
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