An infodemiological study using search engine query data to explore the temporal variations of depression in Finland

Abstract

A majority of healthcare is undertaken by individuals without the involvement or knowledge of healthcare professionals. People often try to treat health problems themselves, often first consulting the Internet, with search engines as natural starting points. Health information seeking conducted in search engines generate big data, data that can provide valuable insights into patterns of symptoms and disease, especially for stigmatizing or sensitive health topics, like mental health problems. The aim of this article is to utilize search engine query data for trends and temporal variations of depression in Finland. The key findings of this study show that depression related Internet search query volumes are slightly increasing during the time periods studied. The results also show that search query volumes follow seasonal patterns with peaks during autumn and spring and troughs during the summer months and mid-winter. Of all weekdays, Sundays have the highest search volume for depression related queries. These results present new meaningful insights into the epidemiology of depression, as it can give insights into the part of the population that does not present for treatment or professional help. It can also help health professionals and other officials to understand broader patterns of mental illness when planning services and campaigns.

When in doubt, Google it: distress-related information seeking in Italy during the COVID-19 pandemic

“…Daily official data about new cases of COVID-19 and information-seeking about new cases of COVID-19 were entered as continuous independent variables, while the phase of managing the COVID-19 outbreak was entered as a categorical independent variable (coded as, 1 = Pre-COVID; 2 = COVID-19 outbreak; 3 = Phase 1; 4 = Phase 2; 5 = Phase 3; 6 = Second wave). Since available evidence showed weekly pattern of online information seeking about PD [ 13 ], the day of the week was entered in each model to control for any weekly variations in anxiety, depression, or insomnia. Specifically, this effect assessed whether people were more likely to search for PD people on specific days of the week.…”

“…Search queries on Google have been used to study diurnal variations in information seeking about depression: this help-seeking has specific patterns during the day, with a peak during the evening and night [ 7 ]. Moreover, search query volumes for depression-related symptoms followed weekly pattern with highest information seeking on Sundays [ 13 ]. Other studies were conducted in the COVID-19 pandemic context [ 14 – 16 ] and showing the feasibility of using Google Trends to monitor infectious diseases and COVID-19 [ 17 ].…”

Rhythmicity of health information behaviour

“…The infodemiology approach has been utilised to study many different health-related phenomena, like mining tweets for pandemics, different ailments and public health issues (Chew and Eysenbach, 2010; Paul and Dredze, 2011), analysing internet search trends for a multitude of illnesses and health issues, to complement epidemiological research (Abedi et al , 2015; Brigo and Trinka, 2015; Carneiro and Mylonakis, 2009; Seifter et al , 2010) as well as studying accessing and sharing information related to different topics (Wong et al , 2013; Matsuda et al , 2017). Temporal variations and patterns of health information behaviour have also been investigated using infodemiology metrics, mostly for specific health issues or diseases, from mental health problems (Arendt and Scherr, 2017; Ayers et al , 2013; Chen et al , 2018; Tana, 2018; Tana et al , 2018), to somatic diseases like Lyme disease (Pesälä et al , 2017), diabetes (Tkachenko et al , 2017) and disease and influenza outbreaks (Bragazzi et al , 2017; Kraut et al , 2017; Ortiz-Martínez and Jiménez-Arcia, 2017; Osuka et al , 2018; Seo and Shin, 2017). However, research on temporal variations and patterns of general health and wellness utilising infodemiology metrics is scarce, as most infodemiology research has focussed on specific diseases and their symptoms (Guy et al , 2011; Zeraatkar and Ahmadi, 2018).…”

The aspect of time in online health information behaviour

“…These studies include analysing social media data for pandemics (Chew and Eysenbach, 2010; Ortiz-Martínez and Jiménez-Arcia, 2017; Seo and Shin, 2017) as well as mood swings and depression (Chen et al, 2018). Search engine data has been utilised to investigate a multitude of illnesses and health issues, like mental health issues (Arendt and Scherr, 2017;Ayers et al, 2013;Tana et al, 2018;Tana, 2018), chronic diseases and symptoms (Basnet et al, 2016), diabetes (Tkachenko et al, 2017), different virus and influenza outbreaks (Bragazzi et al, 2017;Carneiro and Mylonakis, 2009;Kraut et al, 2017;Osuka et al, 2018), transient ischemic attack (Abedi et al, 2015), status epilepticus (Brigo and Trinka, 2015), exercise and weight loss (Madden, 2017), as well as Lyme disease (Pesälä et al, 2017;Seifter et al, 2010). Web traffic again has been utilized for studying accessing health information on the internet related to different topics, such as suicide-related information and pharmacovigilance (Wong et al, 2013;Matsuda et al, 2017).…”