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The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies.
This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada.
Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms “schizophrenia,” “autism,” “bipolar,” “depression,” “anxiety,” “OCD” (obsessive-compulsive disorder), and “suicide.” Control terms were overall search results for the terms “health” and “how.” Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term.
All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for “suicide.” The comparison term “health” also exhibited seasonal periodicity, while the term “how” did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does.
Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of “suicide.” Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners.
There is emerging evidence on the existence of seasonal patterns of public interest in psychiatric disorders and conditions [
Until recently, the sampling of populations for information on mental health was mostly conducted using epidemiological surveys, which have been widely used to study evidence on the level of disorders in the general population [
Limitations of existing epidemiological surveys are associated with a dearth of knowledge surrounding the possible existence of seasonal changes in public interest of psychiatric disorders and conditions. This knowledge gap is of importance, given the growing recognition of public interest in mental health and psychiatric issues [
Over the last two decades, progress has been made in our ability to assess public interest in mental health issues by tracking internet searches. The internet became the most relied upon health search resource as early as 2006 [
Internet searches are most frequently performed using the Google search engine. In 2017, Google search accounted for 67.5% of overall searches in Canada, over three times the number of Yahoo searches (21.5%) [
To date, however, no studies of internet queries have examined aspects of seasonality of interest in psychiatric conditions in smaller geographical areas that are relatively homogenous with regard to climate. This exploratory study investigated seasonal patterns of Google queries on mental health diagnoses and symptoms in Ontario, Canada’s most populous province and home to over 13 million individuals. Given the lack of existing research in this field and the inability to record specific population demographic information, the sample was limited to Ontario in order to reduce the possible influence of different climate zones.
Data were downloaded from Google Trends [
English search terms were captured from Google Trends data in Ontario for the 5-year period from August 2012 to August 2017. For the purpose of this study, primary search terms were similar to those chosen by Ayers and colleagues [
We measured seasonality of search interest in general aspects of health by downloading search results for the term “health” in the general health category. To measure the seasonality of even broader search interests that extend well beyond health-related issues, we downloaded search results for the content-agnostic term “how” that can be used as an adverb, a conjunction, or a noun. Due to the paucity of research in this area, these search terms were not based on previous study but were rather decided upon based on consensus opinion between the authors.
Using Google Trends’ related-term option, we extracted the top 10 related searches for each of our items in their respective categories. We then excluded searches that were ostensibly unrelated to the question of this study (eg, the term “Suicide Squad” denoting a blockbuster movie rather than a mental health query). Terms that included overlap between search terms (eg, “anxiety and depression”) were excluded from both search lists. Thereafter, the original search term (eg, “bipolar”) and the remaining related searches were used to calculate the mean value for the weekly data point of the 5-year time series.
Our primary aim was to assess whether a significant seasonal signature was detected for each of the search terms across a 5-year period. Using the R package WaveletComp [
Each series was decomposed in the time-frequency domain using a continuous wavelet transform. The resulting wavelet power spectrum was used to identify whether a significant 52-week periodic component was detected (
Phase angle differences were calculated to assess timing differences between the wave functions of our search terms. Phase angle (measured from peak to peak) is the angular position along a sinusoidal function from –180º to +180º, where these extreme values represent two waves that are completely out of phase, while 0º would represent waves that are completely in phase. In the case of this study, with a 52-week periodicity, a phase shift of ±180º would indicate that the peaks of two waves being compared (ie, peak search volume for two search terms) are occurring exactly 6 months apart, with a positive angle indicating that the second wave shifted later in the year and a negative value indicating that the second wave shifted earlier in the year, relative to the first wave. Phase angle difference was also calculated within each search term, across each of the 5 years in order to assess a shift in the peak search volume from year to year. In order to translate the phase angle difference value into approximate weeks of the year, the value in degrees was divided by 360º and multiplied by 52.
Finally, we measured differences in the magnitude of seasonal changes between our search terms. First, percent change in search volume from summer to winter was calculated for each of the 5 years of the study. Means and SD of percent change from summer to winter in search volume were calculated for each term. We then performed a one-way analysis of variance, followed by posthoc tests, to assess differences between search terms in their percent change in search volume between August 2012 and August 2017. A one-way analysis of variance was chosen over multiple
A significant 52-week seasonal component was found for all search terms, with the exception of “suicide” and our general control search term “how.” The waves for the terms showing a significant seasonal component are shown in
Change in relative search volume over time between search terms from August 2012 to August 2017. OCD: obsessive-compulsive disorder.
Nonseasonal search terms (raw data).
Investigation of individual disorders showed that mean percent difference from winter to summer months was greatest for OCD (45.2%, SD 4.9%; 95% CI 40.8%-49.6%) and schizophrenia (43.3%, SD 10.5%; 95% CI 34.1%-52.5%). This indicates that the average 5-year peak search volumes for these terms were 45% and 43% higher in the winter than the lowest point in the summer, respectively. Autism also showed a similarly marked change in search volume, with a 37.5% difference from winter to summer (SD 6.6%; 95% CI 31.6%-43.4%). The differences for the remaining search terms were as follows: 21.2% for anxiety (SD 7.6%; 95% CI 14.6%-27.8%), 26.6% for bipolar disorder (SD 3.9%; 95% CI 23.5%-29.7%), and 28.7% for depression (SD 2.5%; 95% CI 26.5%-30.9%). Our term for searches related to “health,” in general, showed a mean seasonal change of 31.6% (SD 6.4%; 95% CI 26.0%-37.2%).
The one-way analysis of variance comparing the mean percent change in search volume between all search terms was significant (
Comparison of percent change in peak search volume and mean phase angle difference between terms across 5 years (August 2012 to August 2017). Phase angle values represent the difference in the timing of peak search volume between search terms across the 5-year search period. A positive phase angle difference represents a comparison in which the peak search volume for the second term is occurring later in the year relative to the first term, while a negative value indicates that peak volume for the second term occurs earlier in the year relative to the first. The value in parentheses is the value of the phase angle difference represented in weeks of the year.
Comparison | Mean difference (%)a | Phase angle (degrees), weeks | |
Anxiety-autism | –16.2 | .009 | 10.1 (1.5) |
Anxiety-bipolar | –5.4 | .85 | 14.6 (2.1) |
Anxiety-depression | –7.5 | .56 | –22.3 (–3.2) |
Anxiety-OCDb | –22.1 | <.001 | –7.5 (–1.1) |
Anxiety-schizophrenia | –23.9 | <.001 | –10.8 (–1.6) |
Anxiety-health | –10.4 | .21 | –21.0 (–3.0) |
Autism-bipolar | 10.8 | .16 | –4.5 (–0.7) |
Autism-depression | 8.7 | .39 | –36.9 (–5.3) |
Autism-OCD | –5.9 | .80 | –22.1 (–3.2) |
Autism-schizophrenia | –7.7 | .53 | –25.4 (–3.7) |
Autism-health | 5.8 | .79 | –16.7 (–2.4) |
Bipolar-depression | –2.1 | >.99 | –32.4 (–4.7) |
Bipolar-OCD | –16.7 | .007 | –17.6 (–2.5) |
Bipolar-schizophrenia | –18.6 | .002 | –20.9 (–3.0) |
Bipolar-health | –4.9 | .89 | –12.2 (–1.8) |
Depression-OCD | –14.6 | .02 | 14.8 (2.1) |
Depression-schizophrenia | –16.4 | .008 | 11.5 (1.7) |
Depression-health | –2.8 | .99 | 20.2 (2.9) |
OCD-schizophrenia | –1.8 | >.99 | –3.3 (–0.5) |
OCD-health | 11.7 | .11 | 11.5 (1.7) |
Schizophrenia-health | 13.6 | .04 | –5.4 (–0.8) |
aMean percent change in search volume between each pair of search terms are the results of the Tukey Honestly Significant Difference posthoc comparisons following one-way analysis of variance.
bOCD: obsessive-compulsive disorder.
To assess differences in the timing for peak search volume between search terms across the 5-year period, the mean phase angle difference was estimated between all pairs of search terms (
Phase angle difference was also calculated within each search term between consecutive years. As seen in
Peak search volume of search terms in different years. OCD: obsessive-compulsive disorder.
Phase angle difference between years within each search term. The value in each cell is the phase angle difference for that year relative to the peak search volume for year 1 (column two). The value in parentheses is the phase angle difference converted to the corresponding number of weeks. A negative value indicates a shift earlier in the year, relative to the first, and a positive value indicates a shift later in the year, relative to the first.
Condition | Peak search, year 1 | Year 2, phase angle (weeks) | Year 3, phase angle (weeks) | Year 4, phase angle (weeks) | Year 5, phase angle (weeks) |
Anxiety | March, week 3 | –31.3º (–4.5) | –44.7º (–6.5) | –48.4º (–7.0) | –42.3º (–6.1) |
Autism | April, week 1 | –29.8º (–4.3) | –52.7º (–7.6) | –53.9º (–7.8) | –52.2º (–7.5) |
Bipolar | March, week 4 | –17.2º (–2.5) | –33.3º (–4.8) | –36.2º (–5.2) | –29.5º (–4.3) |
Depression | February, week 2 | –50.7º (–7.3) | –58.7º (–8.5) | –54.7º (–7.9) | –49.0º (–7.0) |
Obsessive-compulsive disorder | January, week 3 | 29.0º (4.2) | 19.5º (2.8) | 27.5 (4.0) | 38.3º (5.5) |
Schizophrenia | January, week 4 | 41.8º (6.0) | 34.0º (4.9) | 28.9º (4.2) | 27.0º (3.9) |
Health | February, week 3 | –31.3º (4.5) | –44.7º (6.5) | –48.4º (7.0) | –42.3º (6.1) |
The present study was the first to investigate seasonal patterns of Google searches on psychiatric conditions in Ontario, Canada. We found evidence of seasonal patterns for the following search terms and their related queries: “anxiety,” “autism,” “bipolar,” “depression,” “OCD,” “schizophrenia” and “health.” Specifically, we found that winter-to-summer search query differences were maintained for all items with the exception of “suicide” and our general search term (“how”).
In general, our results are in line with previous clinical [
Interestingly, the magnitude of the majority of winter-to-summer search interest peaks in this study were higher than those previously reported for Australia and the United States [
The finding of winter peaks and summer troughs in search volumes of general health and mental health terms in Ontario may have several possible explanations. First, it is possible that this finding reflects a general increase in overall internet search activity. Theoretically, a winter decrease in outdoor activity may leave more time for internet searches, in general. There is evidence that Canadians spend less time outdoors in winter than their US counterparts [
Our findings suggest a potential overlap between public interest in OCD and schizophrenia, as interest peaks for the two search terms were almost completely in phase, suggesting very similar peak times during the year. In addition, the mean 5-year seasonal search interest differences were the largest for OCD and schizophrenia terms (45% and 43%, respectively), which were even higher than those for seasonal affective disorder, the prototypical seasonal disorder. Differences in percent change in peak search volumes between OCD and schizophrenia terms were not significant, and the two terms differed significantly from bipolar disorder, depression, and anxiety. These search term similarities are intriguing, given evidence of marked endophenotype differences between OCD and schizophrenia [
We did not observe seasonal patterns of search interest for the term “suicide” and its related searches. Of our mental health-related search terms, “suicide” was the only one that does not pertain to a specific diagnostic category. Indeed, suicidal ideation and suicide rates are associated with multiple risk factors, some of which are not directly related to mental health disorders [
Strengths of the study include its focus on a well-defined geographical area that lacks extreme within-region climate variations; reliance on a 5-year period with weekly data points; and the choice of the continuous wavelet transform, which allowed the isolation and identification of a significant seasonal component for each time series. On the other hand, an important limitation of the study is that the study of internet queries presents unique validation challenges, as seeking information on mental health conditions may not necessarily correspond to actual mental illness in the individual performing the search. In addition, our ability to interpret the results is limited by the lack of demographic information on the sampled population. A third limitation of the present study is that, although there is evidence that the internet has become the most publicly available information search method, Google Trends data do not include absolute numbers. This limitation should be viewed in the context of the ability to quickly track very recent population behavior, which is a clear advantage. Thus, the use of internet query analysis for mental health planning and delivery should be viewed as complementary rather than a replacement for conventional population studies.
In conclusion, our study was the first to focus on mental health searches in Ontario. Seasonal components were detected for all mental health terms, with the exception of “suicide.” Overall, our study demonstrates the feasibility of performing longitudinal tracking of interest in mental health terms in Ontario, a complementary approach to traditional population health studies. Future studies should explore the association between internet search volumes and other online or offline markers of mental health.
obsessive-compulsive disorder
NS extracted the data and drafted the Introduction and Discussion sections. DC completed the analysis and drafted the Methods and Results sections. DS provided advice on all statistical analyses and contributed to all drafts of the manuscript. KR and RM provided advice on the methodology and contributed to all drafts of the manuscript.
None declared.