Do seasons have an influence on the incidence of depression? The use of an internet search engine query data as a proxy of human affect

"How" web searches change under stress

To adjust to stressful environments, people seek information. Here, we show that in response to stressful public and private events the high-level features of information people seek online alter, reflecting their motives for seeking knowledge. We first show that when people want information to guide action they selectively ask "How" questions. Next, we reveal that "How" searches submitted to Google increased dramatically during the pandemic (controlling for search volume). Strikingly, the proportion of these searches predicted weekly self-reported stress of ~ 17K individuals. To rule out third factors we manipulate stress and find that "How" searches increase in response to stressful, personal, events. The findings suggest that under stress people ask questions to guide action, and mental state is reflected in features that tap into why people seek information rather than the topics they search for. Tracking such features may provide clues regrading population stress levels.

Association between perceived neighborhood environment and depression among residents living in mega-communities in Guiyang, China: a cross-sectional study

BACKGROUND

Little was known about the relationship between perceived neighborhood environment and depression among residents living in mega-communities. Furthermore, the mediating effects of physical activity (PA) and anxiety in this relationship have not been investigated. Thus, this study aimed to comprehensively examine the association between perceived neighborhood environment and depression among residents living in mega-communities, and test whether PA and anxiety mediated the association.

METHODS

A cross-sectional study on perceived neighborhood environment and depression was conducted among individuals who lived in mega-communities (n = 665) in Guiyang, China from July to August 2022. Perceived neighborhood environment was assessed from the following six aspects: traffic, building quality, accessibility, neighborhood, indoor, and pollution. Depression was measured by the Patients Health Questionnaire-9. Structural equation model was used to evaluate the association between perceived neighborhood environment and depression, and test the mediating effect of PA and anxiety in this association.

RESULTS

We found that neighborhood (β = -0.144, p = 0.002) and PA (β = -0.074, p < 0.001) were both negatively associated with depression, while anxiety was positively associated with depression (β = 0.447, p < 0.001). Married residents were less likely to experience depression than residents of other marital status. PA played a mediator role in the relationship between accessibility and depression (β = 0.014, p = 0.033). PA mediated the relationship between neighborhood and depression (β = -0.032, p = 0.015). The mediating effect of anxiety in the relationship between perceived neighborhood environment and depression was not significant.

CONCLUSIONS

This study demonstrated that neighborhood, which was assessed by satisfaction with safety, hygiene, parking, greening, lighting, and building shape, was negatively associated with depression, and PA mediated the relationship.

Internet searches for "insomnia" and "suicide" mediated by stay-at-home behaviors in 45 countries during the first 12 months of the COVID-19 pandemic

BACKGROUND

This study aimed to investigate (1) the mental health impacts (i.e., insomnia and suicide ideas) of the COVID-19 pandemic and (2) the mediation effects of stay-at-home levels on those impacts.

METHODS

This study investigated monthly national COVID-19 deaths, stay-at-home levels, and internet searches for words for "insomnia" and "suicide" across 45 countries during the first year of the COVID-19 pandemic (March 1, 2020, to February 28, 2021). We used the changes of internet search volumes for "insomnia" and "suicide" (from the Google Trends database) to represent the mental health impacts, and the time of cell phone activity at the residence (from Google Location History) to estimate the stay-at-home effects. We computed the proportion mediated (PM) caused by stay-at-home levels in the COVID-19 impacts on insomnia and suicide ideas, respectively.

RESULTS

Throughout the first year of the COVID-19 pandemic, national COVID-19 deaths significantly correlated to increased internet searches for "insomnia" but decreased searches for "suicide". In addition, the mediation effect was significant in the first six-month of COVID-19-related increases in insomnia (PM = 42.6 %, p = 0.016), but this effect was not significant (PM = 13.1 %, p = 0.270) in the second six-month. By contrast, the mediation effect was not significant in the first six-month of COVID-19-related decrease in suicide ideation (PM = 8.1 %, p = 0.180), but this effect was significant (PM = 39.6 %, p = 0.014) in the second six-month.

CONCLUSIONS

Stay-at-home levels significantly mediated both increased insomnia and decreased suicide ideas, but within different time frames.

Psychological Effect of Discrete Outbreak Events of COVID-19 on Health Information Search in China

Community transmission events occasionally happened in the long-term pandemic, which led to repeated outbreaks of COVID-19. In addition to potential physical threats, the outbreaks could also lead to psychological stress and influence their health behaviors, especially for vulnerable people. It poses a great challenge to both physical and mental health management. However, little is known about the impacts of discrete outbreak events of COVID-19 on people's reactions to health concerns in the long-term pandemic period. In the current study, we discussed the impact of discrete outbreak events of COVID-19 on health information search for specific symptoms in China from a perceptive of susceptibility. The empirical study was conducted after the first wave of outbreak events ended in China from June to October 2020. Three typical outbreak events happened during this period, and a total of 1800 search index data for 60 cities in China crawled from Baidu search engine were included in the data set. Using the real-world searching data, we conducted a panel data analysis to examine the psychological effect of discrete outbreak events on the symptom search and the moderation effect of the geographical distance. It was found that discrete outbreak events significantly increased the symptom search, and its impacts were various in different regions. More health information search caused by discrete outbreak events was found in cities which were closer to the outbreak area. Implications for healthcare were discussed.

Infodemiology on diet and weight loss behavior before and during COVID-19 pandemic in Indonesia: Implication for public health promotion

Objective

This study set out to explore public interest through information search trends on diet and weight loss before and during the COVID-19 pandemic in Indonesia.

Methods

The Google Trends database was evaluated for the relative internet search popularity on diet-related search terms, including top and rising diet-related terms. The search range was before and during the COVID-19 pandemic (April 2018 to January 2022) in the Indonesia region. We analyzed the Relative Search Volume (RSV) data using line charts, correlation, and comparison tests.

Results

Search queries of “lose weight” was higher during the pandemic (58.34 ± 9.70 vs. 68.69 ± 7.72; p<0.05). No difference was found in diet-related searches before and after the pandemic. Public interest in the diet was higher after Eid al-Fitr (Muslims break fasting celebration day) and after the new year. Many fad diet (FD) terms were found on the top and rising terms.

Conclusion

After Eid al-Fitr and the new year were susceptible times for promoting a healthy diet in Indonesia. Potential need found before those times for education in inserting healthy food among fatty and sugary menus related to holidays and celebrations. Higher interest in “lose weight” was relevant to heightened obesity risk during the social restriction and heightened COVID-19 morbidity and mortality due to obesity. The high interest for rapid weight loss through FD needs to be resolved by promoting healthy diets with a more captivating message and messenger, like consistently using top terms in the keywords of the official healthy diet guidance. Future research could explore the relationship between diet and other behavior or with non-communicable diseases.

The impacts of the “4+7” pilot policy on the volume, expenditures, and daily cost of Serotonin-Specific Reuptake Inhibitors (SSRIs) antidepressants: A quasi-experimental study

Objectives: The purpose of this study was to quantitatively evaluate the impacts of the”4 + 7” pilot policy on purchase volume, purchase expenditures, and daily cost and to find the changes in the use of SSRIs.

Methods: Data was collected covering 31 months, before, during, and after the “4 + 7” pilot policy was implemented in Shenzhen. Interrupted time-series (ITS) analysis was used to examine whether there had been a significant effect with the onset of the “4 + 7” pilot policy in March 2019.

Findings: The daily cost of policy-related drugs had a substantial drop of 2.93 yuan under the “4 + 7” pilot policy. The result has shown a 76.70% increase in volume and a 3.39% decrease in the expenditure on policy-related drugs. This study found that the “4 + 7” pilot policy increased the proportion of purchasing winning drugs, with an increment of 85.60 percent. After the implementation of the “4 + 7” pilot policy, policy-related drugs decreased by 443.55thousand Chinese yuan. The study indicated that volume of winning products significantly increased as shown in the regression with a level coefficient (β₂) of -224.17 (p < 0.001) and trend coefficient (β₃) of 15.74 (p < 0.001). The result revealed that both volume and expenditures on branded products showed a significant decrease in the regression in the post-intervention period (level coefficient of volume: β₂ = -57.65, p < 0.01, trend coefficient of volume: β₃ = -3.44, p < 0.01; level coefficient of expenditure: β₂ = -712.98, p < 0.01, trend coefficient of expenditure: β₃ = -40.10, p < 0.01).

Conclusion: The volume-based procurement has successfully led to price reductions and improved the affordability of medicines, especially for those with chronic diseases. The volume-based procurement has demonstrated initial success in reshaping the composition of the Chinese pharmaceutical market in favor of generics with high quality and low prices.

Contextual and individual factors associated with depressive symptoms in Latin American residents from eleven cities: Multilevel analysis

Depression is one of the most prevalent mental illnesses in the world. Its associated factors have been scarcely studied in the Latin American population. Our aim was to determine the contextual and individual factors associated with depressive symptoms (DS) in residents of 11 Latin American cities according to the 2019 CAF Survey (ECAF, acronym in Spanish). A multilevel analysis was performed to identify individual and contextual factors associated with DS. DS were measured by the short version of the Center for Epidemiological Studies Depression Scale (CES-D-10). Of the 10,206 participants, 42.0% had DS in the week prior to the survey. Regarding the individual factors studied, women (OR = 1.81, 95% CI: 1.65-1.99), smokers (OR = 1.30, 95% CI: 1.16-1.45), obese, junk food eaters more than twice a week, single people, and a greater number of people in the household were associated with greater probabilities of having DS. On the other hand, a secondary (OR = 0.77; 95% CI: 0.67-0.88) or superior educational level (OR = 0.58; 95% CI: 0.50-0.68) were associated as protective factors. Regarding contextual factors, higher contamination levels (OR = 1.07, 95% CI: 1.01-1.13) were associated with DS. Our study reports associations between individual and environmental factors and DS in residents of Latin America. These findings will allow the adoption of the necessary measures for decision-making and research to face this growing problem in developing countries.

Covid and mental health in America

Using 44 sweeps of the US Census Household Pulse Survey data for the period April 2020 to April 22 we track the evolution of the mental health of just over three million Americans during the COVID-19 pandemic. We find anxiety, depression and worry had two major peaks in 2020 but improved in 2021 and 2022. We show that a variable we construct based on daily inflows of COVID cases by county, aggregated up to state, is positively associated with worse mental health, having conditioned on state fixed effects and seasonality in mental health. However, the size of the effect declines in 2021 and 2022 as vaccination rates rise. For women and college educated men having a vaccine improved mental health. However, being vaccinated worsens mental health among less educated men.

Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season

Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment.

Utilizing Internet Search Volume to Monitor Stages of Change in Vaccine Hesitancy During the COVID-19 Outbreaks

Real-time vaccine hesitancy surveillance is needed to better understand changes in vaccination behaviors. We aim to understand the association between coronavirus disease 2019 (COVID-19) outbreaks and population vaccine hesitancy and to monitor the dynamic changes in vaccination behaviors. We used the autoregressive integrated moving average model to examine the association between daily internet search volume for vaccines and two waves of COVID-19 local outbreaks in Taiwan from 19 March to 25 May, 2021. During the small-scale outbreak, the search volume increased significantly for 7 out of 22 days with an average increase of 17.3% ± 10.7% from the expected search volume. During the large-scale outbreak, the search volume increased significantly for 14 out of 14 days, with an average increase of 58.4% ± 14.7%. There was a high correlation between the search volume and the number of domestic cases (r = 0.71, P < 0.001). Google Trends serves as a timely indicator to monitor the extent of population vaccine willingness.

A comprehensive evaluation of COVID-19 policies and outcomes in 50 countries and territories

The COVID-19 pandemic struck the world unguarded, some places outperformed others in COVID-19 containment. This longitudinal study considered a comparative evaluation of COVID-19 containment across 50 distinctly governed regions between March 2020 and November 2021. Our analysis distinguishes between a pre-vaccine phase (March–November 2020) and a vaccinating phase (December 2020–November 2021). In the first phase, we develop an indicator, termed lockdown efficiency (LE), to estimate the efficacy of measures against monthly case numbers. Nine other indicators were considered, including vaccine-related indicators in the second phase. Linear mixed models are used to explore the relationship between each government policy & hygiene education (GP&HE) indicator and each vital health & socioeconomic (VH&SE) measure. Our ranking shows that surveyed countries in Oceania and Asian outperformed countries in other regions for pandemic containment prior to vaccine development. Their success appears to be associated with non-pharmaceutical interventions, acting early, and adjusting policies as needed. After vaccines have been distributed, maintaining non-pharmacological intervention is the best way to achieve protection from variant viral strains, breakthrough infections, waning vaccine efficacy, and vaccine hesitancy limiting of herd immunity. The findings of the study provide insights into the effectiveness of emerging infectious disease containment policies worldwide.

Is Google Trends a useful tool for tracking mental and social distress during a public health emergency? A time-series analysis

BACKGROUND

Google Trends data are increasingly used by researchers as an indicator of population mental health, but few studies have investigated the validity of this approach during a public health emergency.

METHODS

Relative search volumes (RSV) for the topics depression, anxiety, self-harm, suicide, suicidal ideation, loneliness, and abuse were obtained from Google Trends. We used graphical and time-series approaches to compare daily trends in searches for these topics against population measures of these outcomes recorded using validated self-report scales (PHQ-9; GAD-7; UCLA-3) in a weekly survey (n = ~70,000) of the impact COVID-19 on psychological and social experiences in the UK population (21/03/2020 to 21/08/ 2020).

RESULTS

Self-reported levels of depression, anxiety, self-harm/suicidal ideation, self-harm, loneliness and abuse decreased during the period studied. There was no evidence of an association between self-reported anxiety, self-harm, abuse and RSV on Google Trends. Trends in Google topic RSV for depression and suicidal ideation were inversely associated with self-reports of these outcomes (p = 0.03 and p = 0.04, respectively). However, there was statistical and graphical evidence that self-report and Google searches for loneliness (p < 0.001) tracked one another.

LIMITATIONS

No age/sex breakdown of Google Trends data available. Survey respondents were not representative of the UK population and no pre-pandemic data were available.

CONCLUSION

Google Trends data do not appear to be a useful indicator of changing levels of population mental health during a public health emergency, but may have some value as an indicator of loneliness.

Predicting Norovirus in the United States Using Google Trends: Infodemiology Study

Background

Norovirus is a contagious disease. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of internet search trends, internet search has been a useful tool to manifest infectious disease outbreaks.

Objective

In this study, we tried to discover the correlation between internet search terms and norovirus infection.

Methods

The internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and internet search terms in New York, California, and the United States as a whole.

Results

Some Google search terms such as gastroenteritis, watery diarrhea, and stomach bug coincided with norovirus Google Trends. Some Google search terms such as contagious, travel, and party presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and coronavirus presented several months later than norovirus Google Trends. We found that fever, gastroenteritis, poison, cruise, wedding, and watery diarrhea were important factors correlated with norovirus Google Trends. In actual norovirus cases from New York, California, and the United States as a whole, some Google search terms presented with, earlier, or later than actual norovirus cases.

Conclusions

Our study provides novel strategy-based internet search evidence regarding the epidemiology of norovirus.

Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study

Background

With the prolonging of human life expectancy and subsequent population aging, osteoporosis (OP) has become an important public health issue.

Objective

This study aimed to understand the global public search interests and dynamic trends in “osteoporosis” using the data derived from Google Trends.

Methods

An online search was performed using the term “osteoporosis” in Google Trends from January 1, 2004, to December 31, 2019, under the category “Health.” Cosinor analysis was used to test the seasonality of relative search volume (RSV) for “osteoporosis.” An analysis was conducted to investigate the public search topic rising in RSV for “osteoporosis.”

Results

There was a descending trend of global RSV for “osteoporosis” from January 2004 to December 2014, and a slowly increasing trend from January 2015 to December 2019. Cosinor analysis showed significant seasonal variations in global RSV for “osteoporosis” (P=.01), with a peak in March and a trough in September. In addition, similar decreasing trends of RSV for “osteoporosis” were found in Australia, New Zealand, Ireland, and Canada from January 2004 to December 2019. Cosinor test revealed significant seasonal variations in RSV for “osteoporosis” in Australia, New Zealand, Canada, Ireland, UK, and USA (all P<.001). Furthermore, public search rising topics related to “osteoporosis” included denosumab, fracture risk assessment tool, bone density, osteopenia, osteoarthritis, and risk factor.

Conclusions

Our study provided evidence about the public search interest and dynamic trends in OP using web-based data, which would be helpful for public health and policy making.

Detecting epidemiological relevance of adenoid hypertrophy, rhinosinusitis, and allergic rhinitis through an Internet search

Purpose

This study aimed to detect the epidemiological relevance between adenoid hypertrophy (AH) and rhinosinusitis, and AH and allergic rhinitis (AR) through an Internet search.

Methods

Internet search query data from January 2011 to December 2019 in China were retrieved from the Baidu Index (BI). Spearman’s correlation coefficients were used to detect the correlation among the search volumes of AH, rhinosinusitis, and AR. We also collected search data from the first 5 months of 2020, when quarantine was implemented in China due to the coronavirus disease 2019 epidemic. Then, we compared the search data to those obtained during the same period in 2019 to assess the effects of isolation on AH and AR.

Results

Statistically significant relevance was found between the search variations of AH and rhinosinusitis during 2011–2019 (R = 0.643, P < 0.05). However, the relationship between AH and AR was weak (R =  − 0.239, P < 0.05) and that between rhinosinusitis and AR (R =  − 0.022, P > 0.05) was not relevant. The average monthly search volume of AH and rhinosinusitis had a strong correlation (R = 0.846, P < 0.01), but AH and AR and rhinosinusitis and AR were not correlated (R = – 0.350, P > 0.05; R = – 0.042, P > 0.05, respectively). AH and rhinosinusitis search volumes decreased consistently during the first 5 months of 2020 (isolation), whereas that for AR increased during January–February.

Conclusion

AH had an epidemiological relationship with rhinosinusitis, which was not consistent with AR. The decrease in public gathering effectively reduced the morbidities of AH and rhinosinusitis but not those of AR.

Global Dieting Trends and Seasonality: Social Big-Data Analysis May Be a Useful Tool

We explored online search interest in dieting and weight loss using big-data analysis with a view to its potential utility in global obesity prevention efforts. We applied big-data analysis to the global dieting trends collected from Google and Naver search engines from January 2004 to January 2018 using the search term “diet,” in selected six Northern and Southern Hemisphere countries; five Arab and Muslim countries grouped as conservative, semi-conservative, and liberal; and South Korea. Using cosinor analysis to evaluate the periodic flow of time series data, there was seasonality for global search interest in dieting and weight loss (amplitude = 6.94, CI = 5.33~8.56, p < 0.000) with highest in January and the lowest in December for both Northern and Southern Hemisphere countries. Seasonal dieting trend in the Arab and Muslim countries was present, but less remarkable (monthly seasonal seasonality, amplitude = 4.07, CI = 2.20~5.95, p < 0.000). For South Korea, seasonality was noted on Naver (amplitude = 11.84, CI = 7.62~16.05, p < 0.000). Our findings suggest that big-data analysis of social media can be an adjunct in tackling important public health issues like dieting, weight loss, obesity, and food fads, including the optimal timing of interventions.

Seasonality of Back Pain in Italy: An Infodemiology Study

BACKGROUND

E-health tools have been used to assess the temporal variations of different health problems. The aim of our infodemiology study was to investigate the seasonal pattern of search volumes for back pain in Italy.

METHODS

In Italian, back pain is indicated by the medical word "lombalgia". Using Google Trends, we selected the three search terms related to "lombalgia" with higher relative search volumes (RSV), (namely, "mal di schiena", "dolore alla schiena" and "dolore lombare"), representing the semantic preferences of users when performing web queries for back pain in Italy. Wikipedia page view statistics were used to identify the number of visits to the page "lombalgia". Strength and direction of secular trends were assessed using the Mann-Kendall test. Cosinor analysis was used to evaluate the potential seasonality of back pain-related RSV.

RESULTS

We found a significant upward secular trend from 2005 to 2020 for search terms "mal di schiena" (τ = 0.734, p < 0.0001), "dolore alla schiena" (τ = 0.713, p < 0.0001) and "dolore lombare" (τ = 0.628, p < 0.0001). Cosinor analysis on Google Trends RSV showed a significant seasonality for the terms "mal di schiena" (p_cos < 0.001), "dolore alla schiena" (p_cos < 0.0001), "dolore lombare" (p_cos < 0.0001) and "lombalgia" (p_cos = 0.017). Cosinor analysis performed on views for the page "lombalgia" in Wikipedia confirmed a significant seasonality (p_cos < 0.0001). Both analyses demonstrated a peak of interest in winter months and decrease in spring/summer.

CONCLUSIONS

Our infodemiology approach revealed significant seasonal fluctuations in search queries for back pain in Italy, with peaking volumes during the coldest months of the year.

Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study

BACKGROUND

Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic.

OBJECTIVE

This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: "insomnia," "depression," and "suicide."

METHODS

We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19-related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations.

RESULTS

The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19-related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries (ρ=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (ρ=-0.12, P=.63) or suicide (ρ=-0.07, P=.79).

CONCLUSIONS

Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic.

New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms

Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals.

An Extensive Search Trends-Based Analysis of Public Attention on Social Media in the Early Outbreak of COVID-19 in China

Background

A novel coronavirus (COVID-19) caused pneumonia broke out at the end of 2019 in Wuhan, China. Many cases were subsequently reported in other cities, which has aroused strong reverberations on the Internet and social media around the world.

Objective

The aim of this study was to investigate the reaction of global Internet users to the outbreak of COVID-19 by evaluating the possibility of using Internet monitoring as an instrument in handling communicable diseases and responding to public health emergencies.

Methods

The disease-related data were retrieved from China's National Health Commission (CNHC) and World Health Organization (WHO) from January 10 to February 29, 2020. Daily Google Trends (GT) and daily Baidu Attention Index (BAI) for the keyword "Coronavirus" were collected from their official websites. Rumors which occurred in the course of this outbreak were mined from Chinese National Platform to Refute Rumors (CNPRR) and Tencent Platform to Refute Rumors (TPRR). Kendall's Tau-B rank test was applied to check the bivariate correlation among the two indexes mentioned above, epidemic trends, and rumors.

Results

After the outbreak of COVID-19, both daily BAI and daily GT increased rapidly and remained at a high level, this process lasted about 10 days. When major events occurred, daily BAI, daily GT, and the number of rumors simultaneously reached new peaks. Our study indicates that these indexes and rumors are statistically related to disease-related indicators. Information symmetry was also found to help significantly eliminate the false news and to prevent rumors from spreading across social media through the epidemic outbreak.

Conclusion

Compared to traditional methods, Internet monitoring could be particularly efficient and economical in the prevention and control of epidemic and rumors by reflecting public attention and attitude, especially in the early period of an outbreak.

Suicidal Presentations to Emergency Departments in a Large Australian Public Health Service over 10 Years

This paper presents trends and characteristics for 32,094 suicidal presentations to two Emergency Departments (EDs) in a large health service in Australia across a 10-year period (2009–2018). Prevalence of annual suicidal presentations and for selected groups of consumers (by sex, age groups, and ethnicity) was determined from a machine learning diagnostic algorithm developed for this purpose and a Bayesian estimation approach. A linear increase in the number of suicidal presentations over 10 years was observed, which was 2.8-times higher than the increase noted in all ED presentations and 6.1-times higher than the increase in the population size. Females had higher presentation rates than males, particularly among younger age groups. The highest rates of presentations were by persons aged 15–24. Overseas-born persons had around half the rates of suicidal presentations than Australian-born persons, and Indigenous persons had 2.9-times higher rates than non-Indigenous persons. Of all presenters, 70.6% presented once, but 5.7% had five or more presentations. Seasonal distribution of presentations showed a peak at the end of spring and a decline in winter months. These findings can inform the allocation of health resources and guide the development of suicide prevention strategies for people presenting to hospitals in suicidal crisis.

The Association Between PM2.5 and Depression in China

While China has been experiencing unprecedented economic growth, depression is becoming one of the most striking social and mental health problems in recent years. Such a paradox to progress may partially be due to the notoriously poor air quality of the country. To verify this argument, we constructed an index of the prevalence of depression (IPD) using internet search query volumes in Baidu to proxy the potential depression and examined how IPD is associated with PM_2.5, the major air pollutant in China. Our results from 2-way fixed effects models reveal that a 100 μg·m⁻³ increase in previous week’s PM_2.5 in a city is significantly associated with 0.279 increase in its IPD, comparable to 7.34 hours decrease in weekly daylight, and such relationship is particularly pronounced in the spring and summer and in East and South areas. Our findings of large-scale pattern suggest that PM_2.5 at current levels in China poses serious mental health risks.

Google searches for the keywords of "wash hands" predict the speed of national spread of COVID-19 outbreak among 21 countries

This study hypothesized that national population health literacy might reflect on their keywords searching. We applied Google searches for "wash hands" and "face mask" during January 19 to February 18 as a surrogate of national population health literacy among 21 countries, and examine whether google searches for "wash hands" and "face masks" would protect from increased numbers of confirmed cases of among 21 countries We found the increased google searches for "wash hands" from January 19 to February 18, 2020, correlated with a lower spreading speed of COVID-19 from February 19 to March 10, 2020 among 21 countries (Pearson's correlation coefficient of -0.70, P < 0.001). The result highlights the importance of public awareness of hand washing in preventing COVID-19 disease spreading.

Winter peaks in web-based public inquiry into epistaxis

PURPOSE

Epistaxis represents the most frequent ear, nose, throat-related emergency symptom. Seasonal variation in epistaxis incidence, with peaks during winter months, is widely accepted, although the literature itself remains inconclusive. The objective of this study was to evaluate public inquiry into nose bleeding, by considering Google-based search query frequency on "Epistaxis"-related search terms and to assess possible seasonal variations globally.

METHODS

Epistaxis-related search terms were systematically collected and compared using Google Trends (GT). Relative search volumes for the most relevant epistaxis-related terms, covering a timeframe from 2004 to 2019 were analysed using cosinor time series analysis for the United States of America, Germany, the United Kingdom, Italy, Canada, Australia, and New Zealand.

RESULTS

Graphical representation revealed seasonal variations with peaks during winter months in the majority of countries included. Subsequent cosinor analysis revealed these variations to be significant (all p < 0.001).

CONCLUSION

Public interest in seeking epistaxis-related information through the Internet displayed seasonal patterns in countries from both hemispheres, with the highest interest during winter months. Further studies exploring causality with environmental factors are warranted.

Suppression of voluntary ethanol intake in mice under constant light and constant darkness

Seasonal variations in photoperiod are associated with alterations in human mood and behavior. Similarly, manipulation of the environmental lighting regimen can exert pronounced effects on affective behavior in experimental animals. These observations may be due, in part, to light-induced alterations in circadian rhythms, but it seems likely that other, non-circadian factors also contribute. Several studies have shown that voluntary alcohol (ethanol) consumption can be affected by lighting conditions in rodents, suggesting that photoperiodic variation may account for seasonal and geographic patterns of human alcohol consumption. Nevertheless, the existing animal data are somewhat inconsistent, and little work in this area has been performed in mice. In the present study, we monitored circadian activity rhythms and voluntary ethanol consumption under standard 12:12 light-dark (LD) cycles, and in constant light (LL) and constant darkness (DD). Experiment 1 employed male C3H/He inbred mice, while Experiment 2 employed males and females from a genetically heterogeneous line (WSC). Relative to LD conditions, ethanol intake and ethanol preference were reduced under both LL and DD in both experiments. Because similar effects were seen in both LL and DD, neither circadian disruption nor a classical photoperiodic mechanism are likely to account fully for these findings. Instead, we suggest that the absence of circadian entrainment may function as a mild stressor, resulting in reduced ethanol consumption.

Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study

Background

As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines.

Objective

The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States.

Methods

For each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year’s prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada.

Results

In the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24% improvement (0.72-0.55; P<.001) for diabetes; 18% improvement (1.20-0.99; P=.001) for obesity, and 34% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively.

Conclusions

Although our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models.

Syndromic surveillance using web data: a systematic review

During the recent years, a lot of debate is taken place about the evolution of Smart Healthcare systems. Particularly, how these systems can help people improve human conditions of health, by taking advantages of the new Information and Communication Technologies (ICT), regarding early prediction and efficient treatment. The purpose of this study is to provide a systematic review of the current literature available that focuses on information systems on syndromic surveillance using web data. All published items concern articles, books, reviews, reports, conference announcements, and dissertations. We used a variation of PRISMA Statements methodology to conduct a systematic review. The review identifies the relevant published papers from the year 2004 to 2018, systematically includes and explores them to extract similarities, gaps, and conclusions on the research that has been done so far. The results presented concern the year, the examined disease, the web data source, the geographic location/country, and the data analysis method used. The results show that influenza is the most examined infectious disease. The internet tools most used are Twitter and Google. Regarding the geographical areas explored in the published papers, the most examined country is the United States, since many scientists come from this country. There is a significant growth of articles since 2009. There are also various statistical methods used to correlate the data retrieved from the internet to the data from national authorities. The conclusion of all researches is that the Web can be a useful tool for the detection of serious epidemics and for a creation of a syndromic surveillance system using the Web, since we can predict epidemics from web data before they are officially detected in population. With the advance of ICT, Smart Healthcare can benefit from the monitoring of epidemics and the early prediction of such a system, improving national or international health strategies and policy decision. This can be achieved through the provision of new technology tools to enhance health monitoring systems toward the new innovations of Smart Health or eHealth, even with the emerging technologies of Internet of Things. The challenges and impacts of an electronic system based on internet data include the social, medical, and technological disciplines. These can be further extended to Smart Healthcare, as the data streaming can provide with real-time information, awareness on epidemics and alerts for both patients or medical scientists. Finally, these new systems can help improve the standards of human life.

Development of an addiction index and delineation 15-year trends of illicit drugs from the Taiwan national drug enhancement database

Illicit drug use contributes to substantial morbidity and mortality. Drug scheduling, a legal measure in drug enforcement, is often structured as a hierarchy based on addiction tendency, abuse trends, and harm, but may lack data-driven evidence when classifying substances. Our study aims to measure addiction tendency and use trends based on real-world data. We used the open access database of National Police Agency, Ministry of the Interior in Taiwan and analyzed all daily criminal cases of illicit drugs from 2013 to 2017 and monthly illicit drug enforcement data from the same database from 2002 to 2017. We hypothesized that repeat and frequent use despite legal consequence may be a reflection of addictive behavior, and empirical mode decomposition was applied in analysis to calculate addiction tendency indices and intrinsic 15-year use trends. Our analysis showed heroin has the highest addiction index, followed by methamphetamine. 3,4-Methyl enedioxy methamphetamine, marijuana, and ketamine had lower addictive propensities. This result is consistent with most drug scheduling hierarchies. 15-year use trends of substances were consistent with previous epidemiological studies.

The Association Between Internet Searches and Moisturizer Prescription in Japan: Retrospective Observational Study

BACKGROUND

Heparinoid is a medication prescribed in Japan for skin diseases, such as atopic dermatitis and dry skin. Heparinoid prescription has increased with instances of internet blogs recommending its use as a cosmetic.

OBJECTIVE

This study aimed to examine the prescription trends in moisturizer use and analyze their association with internet searches.

METHODS

We used a claims database to identify pharmacy claims of heparinoid-only prescriptions in Japan. Additionally, we used Google Trends to obtain internet search data for the period between October 1, 2007, and September 31, 2017. To analyze the association between heparinoid prescriptions and internet searches, we performed an autoregressive integrated moving average approach for each time series.

RESULTS

We identified 155,733 patients who had been prescribed heparinoid. The number of prescriptions increased from 2011 onward, and related internet searches increased from 2012 onward. Internet searches were significantly correlated with total heparinoid prescription (correlation coefficient=.25, P=.005). In addition, internet searches were significantly correlated with heparinoid prescription in those aged 20-59 years at -1-month lag in Google Trends (correlation coefficient=.30, P=.001).

CONCLUSIONS

Google searches related to heparinoid prescriptions showed a seasonal pattern and increased gradually over the preceding several years. Google searches were positively correlated with prescription trends. In addition, in a particular age group (20-59 years), prescriptions increased with the increase in internet searches. These results suggest that people obtained health-related information on the internet and that this affected their behavior and prescription requests.

The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland

Annual suicide figures are critical in identifying trends and guiding research, yet challenges arising from significant lags in reporting can delay and complicate real-time interventions. In this paper, we utilized Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland between 2004 and 2015. Official suicide rates are recorded by the Central Statistics Office in Ireland. While similar investigations using Google trends data have been carried out in other jurisdictions (e.g., United Kingdom, United Stated of America), such research had not yet been completed in Ireland. We compiled a collection of suicide- and depression-related search terms suggested by Google Trends and manually sourced from the literature. Monthly search rate terms at different lags were compared with suicide occurrences to determine the degree of correlation. Following two approaches based on vector autoregression and neural network autoregression, we achieved mean absolute error values between 4.14 and 9.61 when incorporating search query data, with the highest performance for the neural network approach. The application of this process to United Kingdom suicide and search query data showed similar results, supporting the benefit of Google Trends, neural network approach, and the applied search terms to forecast suicide risk increase. Overall, the combination of societal data and online behavior provide a good indication of societal risks; building on past research, our improvements led to robust models integrating search query and unemployment data for suicide risk forecasting in Ireland.

Chris Cornell, the Black Hole Sun, and the Seasonality of Suicide

PURPOSE

Seattle-inspired rock and roll superstar Chris Cornell died by suicide in May 2017. In the northern hemisphere, May represents the peak of the widely replicated but still unexplained seasonal spring rhythm in suicide. Years earlier, Cornell had suffered openly from recurrent bouts of severe depression, and his early musical lyrics do indeed suggest an enduring sensitivity to the vicissitudes of depressed and suicidal states. Cornell's most famous song, Black Hole Sun, suggests a mixed mood state, the incidence of which also peaks in the spring. The present work explores Cornell's May suicide from a chronobiologic perspective.

METHODS

Review of Cornell's lyrics and literature on suicide.

RESULTS

Cornell's lyrics contain clear indicators of mixed depressive and seasonal imagery, highlighting 3 fundamental axioms of suicidology: (1) the yearly suicide rhythm peaks in May in the northern hemisphere, (2) mixed depressive states are particularly lethal, and (3) the suicide risk increases dramatically when recovering from depression and mood turns mixed.

CONCLUSIONS

Cornell, in his life and music, left us with a novel and important hypothesis about the spring seasonality of suicide, namely, that the yearly suicide risk becomes maximal when winter turns to spring and there emerges a deadly mixed mood state under a May photoperiod, i.e., the suicide risk is maximal when a Black Hole Sun occurs in May. It is hoped that Cornell's legacy and sensitive hypothesis inspire research into the etiology and treatment of the spring seasonality of suicide risk and mixed mood states.

LIMITATIONS

The Cornell hypothesis was formulated based in part on several speculative inferences regarding the course of his functioning just prior to his suicide.

Birth seasonality and risk of autism spectrum disorder

Season of birth has been hypothesized to be a risk factor for autism spectrum disorder (ASD). However, the evidence has been mixed and limited due to methodological challenges. We examine ASD birth trends for 5,464,628 births across 5 countries. ASD birth prevalence data were obtained from the International Collaboration for Autism Registry Epidemiology database, including children born in Denmark, Finland, Norway, Sweden, and Western Australia. Empirical mode decomposition and cosinor modeling were used to assess seasonality. We show seasonal variation in ASD births for the countries of Finland and Sweden. There was a modest increase in risk for children born in the fall and a modest decrease in risk for children born in the spring. Solar radiation levels around conception and the postnatal period were inversely correlated with seasonal trends in ASD risk. In the first multinational study of birth seasonality of ASD, there was evidence supporting the presence of seasonal trends in Finland and Sweden. The observations that risk was highest for fall births (i.e., conceived in the winter) and lowest for spring births (i.e., conceived in the summer), and sunlight levels during critical neurodevelopmental periods explained much of the seasonal trends, are consistent with the hypothesis that a seasonally fluctuating risk factor may influence risk of ASD.

Positive and balancing selection on SLC18A1 gene associated with psychiatric disorders and human-unique personality traits

Maintenance of genetic variants susceptible to psychiatric disorders is one of the intriguing evolutionary enigmas. The present study detects three psychiatric disorder‐relevant genes (CLSTN2, FAT1, and SLC18A1) that have been under positive selection during the human evolution. In particular, SLC18A1 (vesicular monoamine transporter 1; VMAT1) gene has a human‐unique variant (rs1390938, Thr136Ile), which is associated with bipolar disorders and/or the anxiety‐related personality traits. 136Ile shows relatively high (20–61%) frequency in non‐African populations, and Tajima's D reports a significant peak around the Thr136Ile site, suggesting that this polymorphism has been positively maintained by balancing selection in non‐African populations. Moreover, Coalescent simulations predict that 136Ile originated around 100,000 years ago, the time being generally associated with the Out‐of‐Africa migration of modern humans. Our study sheds new light on a gene in monoamine pathway as a strong candidate contributing to human‐unique psychological traits.

Ambient air pollution exposure and emergency department visits for substance abuse

There is growing evidence supporting the notion that exposure to air pollution can contribute to cognitive and psychiatric disorders, including depression and suicide. Given the relationship between exposure to acute stressors and substance abuse, the present study assessed the association between exposure to ambient air pollution and emergency department (ED) visits for alcohol and drug abuse. ED visit data selected according to International Classification of Disease (ICD-9) coding 303 (alcohol dependence syndromes) and 305 (non-dependent abuse of drugs) were collected in five hospitals in Edmonton, Canada. A time-stratified case crossover design was used. Conditional logistic regression was applied to calculate odds ratios (OR) and 95% confidence intervals (95% CI). Season, temperature, and relative humidity were adjusted for using natural splines.

Results are reported for an increase in pollutant concentrations equivalent to one interquartile range (IQR). Statistically significant positive associations with substance abuse were observed for CO, NO₂ and particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) and 2.5 μm (PM_2.5). The strongest results were obtained in the cold period (October–March) for 1-day lagged CO (OR = 1.03, 95% CI: 1.01, 1.05, IQR = 0.4 ppm) and NO₂ (OR = 1.04, 95% CI: 1.01, 1.07, IQR = 12.8 ppb); ORs were also significant for CO and NO₂ with lags of 2 to 6 days and 2 to 7 days, respectively. The study suggests that, even at low levels, increases in ambient CO, NO₂, and PMs are associated with increased hospital admissions for substance abuse, possibly as a result of impacts of air quality on mental health or depression.

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data

BACKGROUND

Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals' overall mood state in relation to depression-like symptoms.

OBJECTIVE

The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking.

METHODS

Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day.

RESULTS

Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term "Depression," 33.5 pp for "Masennustesti," 30.6 pp for "Masennus," 12.8 pp for "Depression test," 12.0 pp for "Masennus testi," and 11.8 pp for "Masennus oireet." The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms "Masennus oireet" (P<.001), "Masennus" (P=.001), "Depression" (P=.005), and "Depression test" (P=.004). Higher search volumes for the terms "Masennus" (P=.14), "Masennustesti" (P=.07), and "Depression test" (P=.10) were present between the second and third time blocks.

CONCLUSIONS

Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes.

Acute Low Back Pain? Do Not Blame the Weather-A Case-Crossover Study

Objective

To investigate the influence of various weather parameters on the risk of developing a low back pain (LBP) episode.

Design

Case-crossover study.

Setting

Primary care clinics in Sydney, Australia.

Subjects

981 participants with a new episode of acute LBP.

Methods

Weather parameters were obtained from the Australian Bureau of Meteorology. Odds ratios (OR) and 95% confidence intervals (95% CI) were derived comparing two exposure variables in the case window-(1) the average of the weather variable for the day prior to pain onset and (2) the change in the weather variable from 2 days prior to 1 day prior to pain onset-with exposures in two control windows (1 week and 1 month before the case window).

Results

The weather parameters of precipitation, humidity, wind speed, wind gust, wind direction, and air pressure were not associated with the onset of acute LBP. For one of the four analyses, higher temperature slightly increased the odds of pain onset.

Conclusions

Common weather parameters that had been previously linked to musculoskeletal pain, such as precipitation, humidity, wind speed, wind gust, wind direction, and air pressure, do not increase the risk of onset for LBP.

Seasonal variation in major depressive episode prevalence in Canada

BACKGROUND

The purpose of this paper is to describe variation, over the months of the year, in major depressive episode (MDE) prevalence. This is an important aspect of the epidemiological description of MDE, and one that has received surprisingly little attention in the literature. Evidence of seasonal variation in MDE prevalence has been weak and contradictory. Most studies have sought to estimate the prevalence of seasonal affective disorder using cut-points applied to scales assessing mood seasonality rather than MDE. This approach does not align with modern classification in which seasonal depression is a diagnostic subtype of major depression rather than a distinct category. Also, some studies may have lacked power to detect seasonal differences. We addressed these limitations by examining the month-specific occurrence of conventionally defined MDE and by pooling data from large epidemiological surveys to enhance precision in the analysis.

METHOD

Data from two national survey programmes (the National Population Health Survey and the Canadian Community Health Survey) were used, providing ten datasets collected between 1996 and 2013, together including over 500,000. These studies assessed MDE using a short form version of the Composite International Diagnostic Interview (CIDI) for major depression, with one exception being a 2012 survey that used a non-abbreviated version of the CIDI. The proportion of episodes occurring in each month was evaluated using items from the diagnostic modules and statistical methods addressing complex design features of these trials. Overall month-specific pooled estimates and associated confidence intervals were estimated using random effects meta-analysis and a gradient was assessed using a meta-regression model that included a quadratic term.

RESULTS

There was considerable sampling variability when the month-specific proportions were estimated from individual survey datasets. However, across the various datasets, there was sufficient homogeneity to justify the pooling of these estimated proportions, producing large gains in precision. Seasonal variation was clearly evident in the pooled data. The highest proportion of episodes occurred in December, January and February and the lowest proportions occurred in June, July and August. The proportion of respondents reporting MDE in January was 70% higher than August, suggesting an association with implications for health policy. The pattern persisted with stratification for age group, sex and latitude.

CONCLUSIONS

Seasonal effects in MDE may have been obscured by small sample sizes in prior studies. In Canada, MDE has clear seasonal variation, yet this is not addressed in the planning of services. These results suggest that availability of depression treatment should be higher in the winter than the summer months.

Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context

Background

In many Western countries, after a motor vehicle collision, those involved seek health care for the assessment of injuries and for insurance documentation purposes. In contrast, in many less wealthy countries, there may be limited access to care and no insurance or compensation system.

Objective

The purpose of this infodemiology study was to investigate the global pattern of evolving Internet usage in countries with and without insurance and the corresponding compensation systems for whiplash injury.

Methods

We used the Internet search engine analytics via Google Trends to study the health information-seeking behavior concerning whiplash injury at national population levels in Europe.

Results

We found that the search for “whiplash” is strikingly and consistently often associated with the search for “compensation” in countries or cultures with a tort system. Frequent or traumatic painful injuries; diseases or disorders such as arthritis, headache, radius, and hip fracture; depressive disorders; and fibromyalgia were not associated similarly with searches on “compensation.”

Conclusions

In this study, we present evidence from the evolving viewpoint of naturalistic Internet search engine analytics that the expectations for receiving compensation may influence Internet search behavior in relation to whiplash injury.

Influence of legislations and news on Indian internet search query patterns of e-cigarettes

BACKGROUND

There is a paucity of data on the use of electronic nicotine delivery systems (ENDS) in India. In addition, the Indian internet search pattern for ENDS has not been studied. We aimed to address this lacuna. Moreover, the influence of the tobacco legislations and news pieces on such search volume is not known. Given the fact that ENDS could cause oral lesions, these data are pertinent to dentists.

METHODS

Using a time series analysis, we examined the effect of tobacco-related legislations and news pieces on total search volume (TSV) from September 1, 2012, to August 31, 2016. TSV data were seasonally adjusted and analyzed using time series modeling. The TSV clocked during the month of legislations and news pieces were analyzed for their influence on search pattern of ENDS.

RESULTS

The overall mean ± standard deviation (range) TSV was 22273.75 ± 6784.01 (12310-40510) during the study with seasonal variations. Individually, the best model for TSV-legislation and news pieces was autoregressive integrated moving average model, and when influence of legislations and news events were combined, it was the Winter's additive model. In the legislation alone model, the pre-event, event and post-event month TSV was not a better indicator of the effect, barring for post-event month of 2^nd legislation, which involved pictorial warnings on packages in the study period. Similarly, a news piece on Pan-India ban on ENDS influenced the model in the news piece model. When combined, no "events" emerged significant.

CONCLUSIONS

These findings suggest that search for information on ENDS is increasing and that these tobacco control policies and news items, targeting tobacco usage reduction, have only a short-term effect on the rate of searching for information on ENDS.

Effect of Tobacco Control Policies on Information Seeking for Smoking Cessation in the Netherlands: A Google Trends Study

Background

The impact of tobacco control policies on measures of smoking cessation behaviour has often been studied, yet there is little information on their precise magnitude and duration. This study aims to measure the magnitude and timing of the impact of Dutch tobacco control policies on the rate of searching for information on smoking cessation, using Google Trends search query data.

Methods

An interrupted time series analysis was used to examine the effect of two types of policies (smoke-free legislation and reimbursement of smoking cessation support (SCS)) on Google searches for ‘quit smoking’. Google Trends data were seasonally adjusted and analysed using autoregressive integrated moving average (ARIMA) modelling. Multiple effect periods were modelled as dummy variables and analysed simultaneously to examine the magnitude and duration of the effect of each intervention. The same analysis was repeated with Belgian search query data as a control group, since Belgium is the country most comparable to the Netherlands in terms of geography, language, history and culture.

Results

A significant increase in relative search volume (RSV) was found from one to four weeks (21–41%) after the introduction of the smoking ban in restaurants and bars in the Netherlands in 2008. The introduction of SCS reimbursement in 2011 was associated with a significant increase of RSV (16–22%) in the Netherlands after 3 to 52 weeks. The reintroduction of SCS in 2013 was associated with a significant increase of RSV (9–21%) in the Netherlands from 3 to 32 weeks after the intervention. No effects were found in the Belgian control group for the smoking ban and the reintroduction of SCS in 2013, but there was a significant increase in RSV shortly before and after the introduction of SCS in 2011.

Conclusions

These findings suggest that these tobacco control policies have short-term or medium-term effects on the rate of searching for information on smoking cessation, and therefore potentially on smoking cessation rates.

Relationship between Acute Phase of Chronic Periodontitis and Meteorological Factors in the Maintenance Phase of Periodontal Treatment: A Pilot Study

The acute phase of chronic periodontitis may occur even in patients during supportive periodontal therapy. However, the details are not fully understood. Since the natural environment, including meteorology affects human health, we hypothesized that weather conditions may affect occurrence of acute phase of chronic periodontitis. The aim of this study was to investigate the relationship between weather conditions and acute phase of chronic periodontitis in patients under supportive periodontal therapy. Patients who were diagnosed with acute phase of chronic periodontitis under supportive periodontal therapy during 2011–2013 were selected for this study. We performed oral examinations and collected questionnaires and meteorological data. Of 369 patients who experienced acute phase of chronic periodontitis, 153 had acute phase of chronic periodontitis without direct-triggered episodes. When using the autoregressive integrated moving average model of time-series analysis, the independent covariant of maximum hourly range of barometric pressure, maximum hourly range of temperature, and maximum daily wind speed were significantly associated with occurrence of acute phase of chronic periodontitis (p < 0.05), and 3.1% of the variations in these occurrence over the study period were explained by these factors. Meteorological variables may predict occurrence of acute phase of chronic periodontitis.

[On the seasonality of dermatoses: a retrospective analysis of search engine query data depending on the season]

BACKGROUND

The volume of search engine queries about disease-relevant items reflects public interest and correlates with disease prevalence as proven by the example of flu (influenza). Other influences include media attention or holidays.

STUDY GOAL

The present work investigates if the seasonality of prevalence or symptom severity of dermatoses correlates with search engine query data.

METHODS

The relative weekly volume of dermatological relevant search terms was assessed by the online tool Google Trends for the years 2009-2013. For each item, the degree of seasonality was calculated via frequency analysis and a geometric approach.

RESULTS

Many dermatoses show a marked seasonality, reflected by search engine query volumes. Unexpected seasonal variations of these queries suggest a previously unknown variability of the respective disease prevalence. Furthermore, using the example of allergic rhinitis, a close correlation of search engine query data with actual pollen count can be demonstrated.

DISCUSSION

In many cases, search engine query data are appropriate to estimate seasonal variability in prevalence of common dermatoses. This finding may be useful for real-time analysis and formation of hypotheses concerning pathogenetic or symptom aggravating mechanisms and may thus contribute to improvement of diagnostics and prevention of skin diseases.

Patients with migraine are right about their perception of temperature as a trigger: time series analysis of headache diary data

BACKGROUND

Researches to date on the association between headache and weather have yielded inconsistent results. Only a limited number of studies have examined the clinical significance of self-reported weather sensitivity. This study aimed to identify the difference in the association of headache with temperature between migraine patients with and without temperature sensitivity.

METHODS

66 migraine patients (75.8 % female; mean age 43.3 ± 12.9 years) provided their 1-year headache diaries from 2007 to a headache clinic in Taipei, Taiwan. 34 patients (51.5 %) reported sensitivity to temperature change but 32 (48.5 %) did not. Time series of daily headache incidence was modeled and stratified by temperature sensitivity. Empirical mode decomposition was used to identify temporal weather patterns that were correlated to headache incidence, and regression analysis was used to examine the amount of variance in headache incidence that could be explained by temperature in different seasons.

RESULTS

Among all migraine patients, temperature change accounted for 16.5 % of variance in headache incidence in winter and 9.6 % in summer. In winter, the explained variance increased to 29.2 % among patients with temperature sensitivity, but was not significant among those without temperature sensitivity. Overall, temperature change explained 27.0 % of the variance of the mild headache incidence but only 4.8 % of the incidence of moderate to severe headache during winter.

CONCLUSIONS

This diary-based study provides evidence to link the perception of temperature sensitivity and headache incidence in migraine patients. Those who reported temperature sensitivity are more likely to have headache increase during the winter, particular for mild headaches.

Natural light exposure, sleep and depression among day workers and shiftworkers at arctic and equatorial latitudes

OBJECTIVES

This study aimed to investigate the relationship between individual natural light exposure, sleep need, and depression at two latitudes, one extreme with a few hours of light per day during winter, and the other with equal hours of light and darkness throughout the year.

METHODS

This cross-sectional study included a sample of Brazilian workers (Equatorial, n = 488 workers) and a Swedish sample (Arctic, n = 1,273).

RESULTS

The reported mean total natural light exposure per 4-week cycle differed significantly between the Equatorial and Arctic regions. However, shiftworkers from both sites reported similar hours of natural light exposure. Short light exposure was a predictor for insufficient sleep.

CONCLUSION

Reduced exposure to natural light appears to increase the perception of obtaining insufficient sleep. Arctic workers were more prone to develop depression than Equatorial workers.