1
|
Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e49185. [PMID: 38241067 PMCID: PMC10837764 DOI: 10.2196/49185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/06/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.
Collapse
Affiliation(s)
- Emily C Clark
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Sophie Neumann
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Stephanie Hopkins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Alyssa Kostopoulos
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Leah Hagerman
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
| |
Collapse
|
2
|
Houghton S, Boy F, Bradley A, James R, Wardle H, Dymond S. Tracking online searches for gambling activities and operators in the United Kingdom during the COVID-19 pandemic: A Google Trends™ analysis. J Behav Addict 2023; 12:983-991. [PMID: 38141072 PMCID: PMC10786234 DOI: 10.1556/2006.2023.00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/01/2023] [Accepted: 09/10/2023] [Indexed: 12/24/2023] Open
Abstract
Background Whilst some research has explored the impact of COVID-19 on gambling behaviour, little is yet known about online search behaviours for gambling during this period. The current study explored gambling-related online searches before, during and after the outbreak of the COVID-19 pandemic in the UK. We also assessed whether search trends were related to Gambling Commission behavioural data over the same period. Methods Google Trends™ search data, covering thirty months from January 2020 to June 2022, for five gambling activities and five gambling operators were downloaded. Graphical displays of the weekly relative search values over this period were then produced to visualise trends in search terms, with key dates in COVID-19 policy and sporting events highlighted. Cross-correlations between seasonally adjusted monthly search data and behavioural indices were conducted. Results Sharp increases in internet searches for poker, slots, and bingo were evident during the first lockdown in the UK, with operator searches sharply decreasing over this period. No changes in gambling activity searches were highlighted during subsequent lockdowns, although small increases in operator-based searches were detected. Strong positive correlations were found between search data and industry data for sports betting and poker but not for slots. Conclusions Google Trends™ data may act as an indicator of population-level gambling behaviour. Substitution of preferred gambling activities for others may have occurred during the first lockdown when opportunities for sports betting were limited. Further research is needed to assess the effectiveness of internet search data in predicting gambling-related harm.
Collapse
Affiliation(s)
- Scott Houghton
- School of Psychology, Swansea University, Singleton Campus, Swansea, SA2 8PP, United Kingdom
- Department of Psychology, Northumbria University, Newcastle Upon Type, NE1 8ST, United Kingdom
| | - Frederic Boy
- iLab Innovation and Research Centre, School of Management, Swansea University, Bay Campus, Swansea, SA2 8PP, United Kingdom
| | - Alexander Bradley
- School of Education and Sociology, University of Portsmouth, Portsmouth, PO1 2HY, United Kingdom
| | - Richard James
- School of Psychology, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
| | - Heather Wardle
- School of Social and Political Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Simon Dymond
- School of Psychology, Swansea University, Singleton Campus, Swansea, SA2 8PP, United Kingdom
- Department of Psychology, Reykjavík University, Menntavegur 1, Nauthólsvík, 101Reykjavík, Iceland
| |
Collapse
|
3
|
Wang P, Chen C, Wang X, Zhang N, Lv D, Li W, Peng F, Wang X. Does seasonality affect snoring? A study based on international data from the past decade. Sleep Breath 2023; 27:1297-1307. [PMID: 36219385 PMCID: PMC9552723 DOI: 10.1007/s11325-022-02717-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/22/2022] [Accepted: 09/27/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Though snoring is often regarded as a harmless condition that coincides with sound sleep, it is a sleep disorder that can be a potential indicator of more severe conditions such as sleep apnea syndrome. In the present study, we investigated the association between seasonal variations and snoring. METHOD Search index for snoring (SIS) data were obtained from Google Trends and Baidu Index. SIS data were collected for the USA, India, Germany, Russia, Japan, Australia, China, and Brazil from 2011 to 2020, with the periodicity of the relationship between seasonal time series data and snoring evaluated using a time series decomposition model. RESULT The highest average SIS growth rates from 2011 to 2020 were observed for Brazil, Japan, and Germany, with average SIS values of 94%, 68%, and 49%, respectively. The SIS of the USA, Russia, Japan, Brazil, Australia, Germany, and India increased by 22.3%, 12.4%, 11.9%, 35.4%, 12.3%, 28.0%, and 55.8%, respectively, in comparison with their SIS values in 2019, whereas for China, it decreased by 13.7%. Relative to countries in the southern hemisphere, those in the northern hemisphere showed comparable SIS trends, increasing from September to February and decreasing from March to August. CONCLUSION The SIS data showed cyclical changes over the study period. The search index for snoring increased during the cold season or the heating season, suggesting that snoring is associated with seasonal changes.
Collapse
Affiliation(s)
- Ping Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China
- Shandong Academy of Chinese Medicine, Jinan, 250014, China
| | - Cai Chen
- Shandong Institute of Advanced Technology Chinese Academy of Sciences, Jinan, 250000, China
| | - Xingwei Wang
- Shandong Institute of Advanced Technology Chinese Academy of Sciences, Jinan, 250000, China
| | - Ningling Zhang
- Shandong Institute of Advanced Technology Chinese Academy of Sciences, Jinan, 250000, China
| | - Danyang Lv
- Shandong Institute of Advanced Technology Chinese Academy of Sciences, Jinan, 250000, China
| | - Wei Li
- Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
| | - Fulai Peng
- Shandong Institute of Advanced Technology Chinese Academy of Sciences, Jinan, 250000, China.
| | - Xiuli Wang
- Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, China.
| |
Collapse
|
4
|
Alibudbud R. Google Trends for health research: Its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology. Front Big Data 2023; 6:1132764. [PMID: 37050919 PMCID: PMC10083382 DOI: 10.3389/fdata.2023.1132764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
The high utilization of infodemiological tools for psychiatric and mental health topics signals the emergence of a new discipline. Drawing on the definition of infodemiology by Eysenbach, this emerging field can be termed “psychiatric and mental health infodemiology,” defined as the science of distribution and determinants of information in an electronic medium, including the internet, or in a population to inform mental health services and policies. Since Google Trends is one of its popular tools, this minireview describes its advantages, application, methodological considerations, and limitations in psychiatric and mental health research. The advantage of Google Trends is the nature of its data, which may represent the actual behavior rather than their users' stated preferences in real-time through automatic anonymization. As such, it can provide readily available data about sensitive health topics like mental disorders. Therefore, Google Trends has been used to explore public concerns, interests, and behaviors about psychiatric and mental health phenomena, service providers, and specific disciplines. In this regard, several methodological can be considered by studies using Google Trends, including documenting their exact keywords, query category, time range, location, and date of retrieval. Likewise, its limitations should be accounted for in its interpretation, including restricted representation of people who use the Google search engine, limited validity in areas with low internet penetration or freedom of speech, does not provide absolute search volumes, unknown sampled queries, and limited transparency in its algorithm, especially the terms and idioms it subsumes under its “topic” keywords.
Collapse
|
5
|
Levanti D, Monastero RN, Zamani M, Eichstaedt JC, Giorgi S, Schwartz HA, Meliker JR. Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities. AJPM FOCUS 2023; 2:100062. [PMID: 36573174 PMCID: PMC9773738 DOI: 10.1016/j.focus.2022.100062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.
Collapse
Affiliation(s)
| | | | - Mohammadzaman Zamani
- Department of Computer Science, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York
| | - Johannes C. Eichstaedt
- Department of Psychology, School of Humanities and Sciences, Stanford University, Palo Alto, California
| | - Salvatore Giorgi
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - H. Andrew Schwartz
- Department of Computer Science, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York
| | - Jaymie R. Meliker
- Program in Public Health, Department of Family, Population, & Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York
| |
Collapse
|
6
|
Lippke S, Warner LM. Understanding and overcoming challenges in times of personal or global crisis-Editorial on the Special Issue on Loneliness and Health. Appl Psychol Health Well Being 2023; 15:3-23. [PMID: 36478507 DOI: 10.1111/aphw.12420] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 11/13/2022] [Indexed: 12/12/2022]
Abstract
The ever-present interest in loneliness has increased during the last decade. Although loneliness is generally not as prevalent as other topics and not among the top 5 most read papers in the journal Applied Psychology: Health and Well-Being, it is closely connected to topics therein, such as well-being and health. Conceptualizing loneliness as indicator of risk, it may function as a cue for action. Accordingly, understanding loneliness, its development, prevalence, effects, and how to support individuals to prevent or overcome loneliness is key and the main aim of this special issue. Therefore, theories and models are reviewed in this paper and synthesized together with other aspects relating to the field of loneliness research and intervention. Accordingly, we propose an agenda including key determinants (e.g., risk groups), how to proceed (various research methods), intervention components (e.g., behavior change techniques, SDGs), and how to perform dissemination (open science practices, co-creative approaches, etc.). The original studies in this special issue provide stimulating examples. Moreover, the commentaries give new insights and inspiring ideas. Overall, this special issue aims to give readers a lens with which to re-examine their own research, enable innovation, and empower addressing loneliness and its interconnection synergistically.
Collapse
Affiliation(s)
| | - Lisa Marie Warner
- Institute of Psychosocial Research for Health Promotion and Intervention (IHPI), MSB Medical School Berlin, Berlin, Germany
| |
Collapse
|
7
|
Drachal K, González Cortés D. Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:421. [PMID: 36612742 PMCID: PMC9819235 DOI: 10.3390/ijerph20010421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/06/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.
Collapse
Affiliation(s)
- Krzysztof Drachal
- Faculty of Economic Sciences, University of Warsaw, 00-241 Warszawa, Poland
| | | |
Collapse
|
8
|
Niu Q, Liu J, Zhao Z, Onishi M, Kawaguchi A, Bandara A, Harada K, Aoyama T, Nagai-Tanima M. Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study. BMC Infect Dis 2022; 22:806. [PMID: 36309663 PMCID: PMC9617033 DOI: 10.1186/s12879-022-07790-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022] Open
Abstract
Background Coronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends. Methods HFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation. Results HFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic. Conclusions The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07790-9.
Collapse
|
9
|
Ciechanowski K, Banasik-Jemielniak N, Jemielniak D. What's hot and what's not in lay psychology: Wikipedia's most-viewed articles. CURRENT PSYCHOLOGY 2022:1-13. [PMID: 36248218 PMCID: PMC9553632 DOI: 10.1007/s12144-022-03826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 11/25/2022]
Abstract
We studied views of articles about psychology on 10 language editions of Wikipedia from July 1, 2015, to January 6, 2021. We were most interested in what psychology topics Wikipedia users wanted to read, and how the frequency of views changed during the COVID-19 pandemic and lockdowns. Our results show that the topics of interest to people seeking psychological knowledge changed during the pandemic. In addition, the interests differ noticeably among the languages. We made two important observations. The first was that during the pandemic, people in most countries looked for new ways to manage their stress without resorting to external help. This is understandable, given the increased stress of lockdown and the limited amount of professional help available. We also found that academic topics, typically covered in university classes, experienced a substantial drop in traffic, which could be indicative of issues with remote teaching. Supplementary Information The online version contains supplementary material available at 10.1007/s12144-022-03826-0.
Collapse
Affiliation(s)
- Kaśmir Ciechanowski
- MINDS (Management in Networked and Digital Societies) Department, Kozminski University, Warsaw, Poland
| | | | - Dariusz Jemielniak
- MINDS (Management in Networked and Digital Societies) Department, Kozminski University, Warsaw, Poland
| |
Collapse
|
10
|
Kim J, Han J, Chun BC. Trends of Internet Search Volumes for Major Depressive Disorder Symptoms During the COVID-19 Pandemic in Korea: An Interrupted Time-Series Analysis. J Korean Med Sci 2022; 37:e108. [PMID: 35411728 PMCID: PMC9001188 DOI: 10.3346/jkms.2022.37.e108] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 03/14/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The effect of coronavirus disease 2019 (COVID-19) pandemic on public mental health has been increasing. Additionally, the underlying psychological stressors remain unexplored, and few studies have been conducted nationally on the social distancing measures. Therefore, the present study aimed to identify the psychological impacts of the implementation of social distancing measures by analyzing the Internet search trends of major depressive disorder (MDD) symptoms. METHODS Using Naver® Trends' relative search volumes (RSVs), we analyzed the average search volumes and trend changes of 16 terms, adopted from the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for diagnosing MDD. An interrupted time-series analysis was performed using the data from January 1, 2016 to December 31, 2020. Furthermore, changes in RSVs, according to the intensity of the social distancing measures implemented from January 1 to December 31, 2020, were determined using Wilcoxon rank sum tests. RESULTS Of the 16 terms, the search trends of 'feeling guilty' (P < 0.001) and 'wanting to die' (P = 0.002) showed a significant increase as of February 29, 2020, when the social distancing measures were officially implemented. Additionally, the average search volumes for 'hopelessness' (P = 0.003), 'sexual desire' (P < 0.001), 'insomnia' (P = 0.002), 'hypersomnia' (P < 0.001), 'restlessness' (P < 0.001), and 'feeling lethargic' (P < 0.001) increased significantly. Moreover, gender analysis showed that the average search volume of 'depressed mood' (P = 0.033) and the trend of 'impaired concentration' (P < 0.001) increased in males only. However, the average search volume of 'feeling lethargic' (P = 0.001) and the trend of 'feeling guilty' (P = 0.001) increased in females only. The average search volumes for 'depressed mood' (P < 0.001), 'impaired concentration' (P = 0.038), and 'indecisiveness' (P < 0.001) were significantly higher during the enforcement of level 2 or higher social distancing measures. CONCLUSION Our results reported the negative effect of COVID-19 preventive measures on public mental health in South Korea, especially for MDD symptoms. Moreover, the findings suggested the utility of Naver Trends as a feasible big data source to assess future mental health crises.
Collapse
Affiliation(s)
- Jieun Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
| | - Juhui Han
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
| | - Byung Chul Chun
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
- Graduate School of Public Health, Korea University, Seoul, Korea.
| |
Collapse
|
11
|
Gimbrone C, Rutherford C, Kandula S, Martínez-Alés G, Shaman J, Olfson M, Gould MS, Pei S, Galanti M, Keyes KM. Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data. PLoS One 2021; 16:e0260931. [PMID: 34936666 PMCID: PMC8694413 DOI: 10.1371/journal.pone.0260931] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/19/2021] [Indexed: 12/23/2022] Open
Abstract
During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.
Collapse
Affiliation(s)
- Catherine Gimbrone
- Department of Epidemiology, Columbia University, New York, NY, United States of America
| | - Caroline Rutherford
- Department of Epidemiology, Columbia University, New York, NY, United States of America
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America
| | - Gonzalo Martínez-Alés
- Department of Epidemiology, Columbia University, New York, NY, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America
| | - Mark Olfson
- Department of Epidemiology, Columbia University, New York, NY, United States of America
- Department of Psychiatry, Columbia University, New York, NY, United States of America
| | - Madelyn S. Gould
- Department of Epidemiology, Columbia University, New York, NY, United States of America
- Department of Psychiatry, Columbia University, New York, NY, United States of America
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America
| | - Marta Galanti
- Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America
| | - Katherine M. Keyes
- Department of Epidemiology, Columbia University, New York, NY, United States of America
| |
Collapse
|