John W. Ayers

Tracking the Rise in Popularity of Electronic Nicotine Delivery Systems ("Electronic Cigarettes") Using Search Query Surveillance

BACKGROUND Public interest in electronic nicotine delivery systems (ENDS) is undocumented. PURPOSE By monitoring search queries, ENDS popularity and correlates of their popularity were assessed in Australia, Canada, the United Kingdom (UK), and the U.S. METHODS English-language Google searches conducted from January 2008 through September 2010 were compared to snus, nicotine replacement therapy (NRT), and Chantix® or Champix®. Searches for each week were scaled to the highest weekly search proportion (100), with lower values indicating the relative search proportion compared to the highest-proportion week (e.g., 50=50% of the highest observed proportion). Analyses were performed in 2010. RESULTS From July 2008 through February 2010, ENDS searches increased in all nations studied except Australia, there an increase occurred more recently. By September 2010, ENDS searches were several-hundred-fold greater than searches for smoking alternatives in the UK and U.S., and were rivaling alternatives in Australia and Canada. Across nations, ENDS searches were highest in the U.S., followed by similar search intensity in Canada and the UK, with Australia having the fewest ENDS searches. Stronger tobacco control, created by clean indoor air laws, cigarette taxes, and anti-smoking populations, were associated with consistently higher levels of ENDS searches. CONCLUSIONS The online popularity of ENDS has surpassed that of snus and NRTs, which have been on the market for far longer, and is quickly outpacing Chantix or Champix. In part, the association between ENDSs popularity and stronger tobacco control suggests ENDS are used to bypass, or quit in response to, smoking restrictions. Search query surveillance is a valuable, real-time, free, and public method to evaluate the diffusion of new health products. This method may be generalized to other behavioral, biological, informational, or psychological outcomes manifested on search engines.

Seasonality in seeking mental health information on Google.

BACKGROUND Population mental health surveillance is an important challenge limited by resource constraints, long time lags in data collection, and stigma. One promising approach to bridge similar gaps elsewhere has been the use of passively generated digital data. PURPOSE This article assesses the viability of aggregate Internet search queries for real-time monitoring of several mental health problems, specifically in regard to seasonal patterns of seeking out mental health information. METHODS All Google mental health queries were monitored in the U.S. and Australia from 2006 to 2010. Additionally, queries were subdivided among those including the terms ADHD (attention deficit-hyperactivity disorder); anxiety; bipolar; depression; anorexia or bulimia (eating disorders); OCD (obsessive-compulsive disorder); schizophrenia; and suicide. A wavelet phase analysis was used to isolate seasonal components in the trends, and based on this model, the mean search volume in winter was compared with that in summer, as performed in 2012. RESULTS All mental health queries followed seasonal patterns with winter peaks and summer troughs amounting to a 14% (95% CI=11%, 16%) difference in volume for the U.S. and 11% (95% CI=7%, 15%) for Australia. These patterns also were evident for all specific subcategories of illness or problem. For instance, seasonal differences ranged from 7% (95% CI=5%, 10%) for anxiety (followed by OCD, bipolar, depression, suicide, ADHD, schizophrenia) to 37% (95% CI=31%, 44%) for eating disorder queries in the U.S. Several nonclinical motivators for query seasonality (such as media trends or academic interest) were explored and rejected. CONCLUSIONS Information seeking on Google across all major mental illnesses and/or problems followed seasonal patterns similar to those found for seasonal affective disorder. These are the first data published on patterns of seasonality in information seeking encompassing all the major mental illnesses, notable also because they likely would have gone undetected using traditional surveillance.

What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends

BACKGROUND Google Flu Trends (GFT) claimed to generate real-time, valid predictions of population influenza-like illness (ILI) using search queries, heralding acclaim and replication across public health. However, recent studies have questioned the validity of GFT. PURPOSE To propose an alternative methodology that better realizes the potential of GFT, with collateral value for digital disease detection broadly. METHODS Our alternative method automatically selects specific queries to monitor and autonomously updates the model each week as new information about CDC-reported ILI becomes available, as developed in 2013. Root mean squared errors (RMSEs) and Pearson correlations comparing predicted ILI (proportion of patient visits indicative of ILI) with subsequently observed ILI were used to judge model performance. RESULTS During the height of the H1N1 pandemic (August 2 to December 22, 2009) and the 2012-2013 season (September 30, 2012, to April 12, 2013), GFTs predictions had RMSEs of 0.023 and 0.022 (i.e., hypothetically, if GFT predicted 0.061 ILI one week, it is expected to err by 0.023) and correlations of r=0.916 and 0.927. Our alternative method had RMSEs of 0.006 and 0.009, and correlations of r=0.961 and 0.919 for the same periods. Critically, during these important periods, the alternative method yielded more accurate ILI predictions every week, and was typically more accurate during other influenza seasons. CONCLUSIONS GFT may be inaccurate, but improved methodologic underpinnings can yield accurate predictions. Applying similar methods elsewhere can improve digital disease detection, with broader transparency, improved accuracy, and real-world public health impacts.

Could Behavioral Medicine Lead the Web Data Revolution

Digital footprints left on search engines, social media, and social networking sites can be aggregated and analyzed as health proxies, yielding anonymous and instantaneous insights. On the one hand, nearly all the existing work has focused on acute diseases. This means the value-added from web surveillance is reduced, because the effectiveness of even high profile systems, such as Google Flu Trends, have been found inferior to already strong traditional surveillance.1 On the other hand, the future of web surveillance is promising in an area where traditional surveillance is largely incomplete: behavioral medicine, a multidisciplinary field incorporating medicine, social science, and public health and focusing on health behaviors and mental health. The proportion of illness (or death) attributable to health behaviors or psychological well-being has steadily increased over the last half century, while surveillance of these outcomes has remained largely unchanged. Investigators simply ask people about their health on surveys. However, surveys have well-known limitations, such as respondents’ reluctance to participate, social desirability biases, difficulty in accurately reporting behaviors, long lags between data collection and availability, and provisions (sometimes legal) curtailing the inclusion of politically sensitive topics like gun violence. Most importantly, the expense of surveys means many topics are either not covered or covered restrictively (e.g., clinical depression screeners are included in the Behavioral Risk Factor Surveillance System just every other year). Given the current budget climate, survey capacity will likely worsen before it improves. To overcome these limits, behavioral medicine should now embrace web data. First, behavioral medicine requires observing behavior or the manifestation of mental health problems. Doing so online is easier, more comprehensive, and more effective than with surveys, because many outcomes are passively exhibited there. For example, one study showed how precise health concerns changed during the United States recession of December 2008 through 2011, by systematically selecting Google search queries and using the content of each query to describe the concern and the change in volume to describe concern prevalence. “Stomach ulcer symptoms,” for example, were 228% (95%CI, 35–363) higher than expected during the recession, with queries thematically related to arrhythmia, congestion, pain (including many foci like head, tooth and back) also elevated.2 This approach highlights how web data can reveal largely assumption-free insights, via systematic data generation of hundreds of possible outcomes rather than arbitrary a priori selection of a few outcomes by investigators. Second, web data reflects more than the individual, because social context can also be captured online. Online networks can reveal how mechanistic drivers such as social norms spread and influence population health. For example, social patterns in obesity promotion and suppression have been described by pooling Facebook posts that encourage television watching or going outdoors, which ultimately explained variability in neighborhood obesity rates.3 Moreover, social support concepts are often expressed in web data, like observing specific instances of caregiving and confidence on Twitter. As a result, online behavioral medicine can move away from understanding aggregation based purely on location and towards understanding health in the context of our human interconnectedness. Third, web data are potentially the only source for real-time insights into behavioral medicine, where web data can be available almost immediately compared to a 365-day lag time between annual surveys. By harnessing these data around social events or interventions, programs can be evaluated as they are implemented, hypothetically generating real-time feedback to maximize their effectiveness. Web data in this vein also hold promise for guiding investigator resources. In 2011, when tobacco journals were debating snus (a smokeless tobacco product), and funders were soliciting proposals to understand the snus pandemic, electronic cigarettes already attracted more searches on Google than any other smoking alternative, snus included.4 In this same way, web data can guide traditional surveillance, like vetting the inclusion of questions on surveys using online proxies. Fourth, given all hypotheses are based on some data, web data can be an important source for identifying new hypotheses. Many hypotheses in behavioral medicine can be traced directly to data availability and can appear ad hoc to lay audiences. Many studies have explored birthdate seasonality in mental health problems. Why? Birthdates are routinely found in traditional surveillance, while some mental health problems are too rare to assess incidence or increased severity seasonality. As a result, obvious questions are never explored, until now. Is schizophrenia seasonal? Online interest in schizophrenia and its symptoms – as well as 8 other outcomes - peak in the winter.5 What is the healthiest day? Online interest in quitting smoking across the globe is highest on Monday.6 Behavioral medicine needs to escape the confines of limited data to more fully specify the next frontier of research questions, and going online is one such escape. Fifth, it is beyond present scientific limits for a hypothetical arm to reach out of the screen to inoculate against infection. In behavioral medicine, however, substantial resources have been used to develop online interventions that treat or prevent illness with effectiveness equivalent to their offline counterparts. For example, as early as the mid-1990s, investigators implemented online programs to promote behavioral health. A meta-analysis found these programs relatively increased quitting smoking 44%,7 yet a research agenda for harnessing the surveillance potential of the web has not been articulated. Improving the online surveillance capacity means online interventions can be better disseminated via online screening or linking subjects to existing online treatments (i.e., what advertisements for an online program are most effective?). Sixth, some of the most effective interventions in behavioral medicine involve changes in public policy. Web data can identify alerts for policy changes and pathways for health advocacy. For instance, by archiving online media, places considering policy changes can be identified, and this information can then be passed onto advocacy groups. Case in point, Brazilian President Lula’s laryngeal cancer prompted broad changes in media coverage of tobacco control, and soon after, Brazil became the largest smoke-free nation to date.8 By prospectively analyzing news media content, advocacy resources may be more cost-effectively spent during opportunistic times, including events like Lula’s diagnosis, will be possible. A major criticism is that web data have sampling biases. However, such biases are increasingly eroding at the population level as more people go online. In addition, several studies have demonstrated that valid trends reflecting the entire population, and even subsets of the population, can be extracted from online data. For example, computer science has already developed approaches for identifying the gender, ethnicity or education associated with a Twitter account using the content of a user’s Tweets. Going forward, the research community may mimic these studies and validate methods for obtaining high quality, actionable information in behavioral medicine, then further realizing the comparative value of web data to traditional data. Billions of digital footprints from nearly all parts of the United States and from countries around the world provide a powerful opportunity to expand the evidence-base across medicine. However, for the above reasons and more related reasons yet to be expressed, behavioral medicine potentially has the most to gain from web data and could be essential to the broader web data revolution.

Enhancing disease surveillance with novel data streams: challenges and opportunities

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Sorting out the competing effects of acculturation, immigrant stress, and social support on depression: a report on Korean women in California.

This research identifies stressors that correlate with depression, focusing on acculturation, among female Korean immigrants in California. Telephone interviews were conducted with female adults of Korean descent (N = 592) from a probability sample from 2006 to 2007. Sixty-five percent of attempted interviews were completed, of which over 90% were conducted in Korean. Analyses include descriptive reports, bivariate correlations, and structural equation modeling. Findings suggest that acculturation did not have a direct impact on depression and was not associated with social support. However, acculturation was associated with reduced immigrant stress which, in turn, was related to decreased levels of depression. Immigrant stress and social support were the principal direct influences on depression, mediating the effect for most other predictors. Stressful experiences associated with immigration may induce depressive feelings. Interventions should facilitate acculturation thereby reducing immigrant stress and expand peer networks to increase social support to assuage depression.

Adult Prescription Drug Use and Pediatric Medication Exposures and Poisonings

BACKGROUND AND OBJECTIVES: Nontherapeutic medication ingestions continue to be a major pediatric health problem, with recent increases in ingestions despite a number of public health interventions. It is unknown how changes in adult prescription drug use relate to pediatric medication poisonings. The objective of the study was to measure the association between changing adult prescription drug patterns and pediatric medication exposures and poisonings and identify high-risk classes of medications and pediatric age groups. METHODS: We measured monthly pediatric exposures and poisonings using the National Poison Data System and prescriptions written for adults using the National Ambulatory Medical Care Surveys for 2000 through 2009. Associations between adult prescriptions for oral hypoglycemics, antihyperlipidemics, β-blockers, and opioids and exposures and poisonings among children 0 to 5, 6 to 12, and 13 to 19 years were analyzed by using multiple time-series analysis. Emergency department visits, serious injuries, and hospitalizations stemming from these associations were described. RESULTS: Adult medication prescriptions were statistically significantly associated with exposures and poisonings in children of all ages, with the strongest association observed for opioids. Across medications, the greatest risk was among children 0 to 5 years old, followed by 13- to 19-year-olds. Rates of emergency department visits were highest for events related to hypoglycemics (60.1%) and β-blockers (59.7%), whereas serious injuries and hospitalizations occurred most frequently with opioids (26.8% and 35.2%, respectively) and hypoglycemics (19.5% and 49.4%, respectively). CONCLUSIONS: Increasing adult drug prescriptions are strongly associated with rising pediatric exposures and poisonings, particularly for opioids and among children 0 to 5 years old. These associations have sizable impacts, including high rates of serious injury and health care use.

A Novel Evaluation of World No Tobacco Day in Latin America

Background World No Tobacco Day (WNTD), commemorated annually on May 31, aims to inform the public about tobacco harms. Because tobacco control surveillance is usually annualized, the effectiveness of WNTD remains unexplored into its 25th year. Objective To explore the potential of digital surveillance (infoveillance) to evaluate the impacts of WNTD on population awareness of and interest in cessation. Methods Health-related news stories and Internet search queries were aggregated to form a continuous and real-time data stream. We monitored daily news coverage of and Internet search queries for cessation in seven Latin American nations from 2006 to 2011. Results Cessation news coverage peaked around WNTD, typically increasing 71% (95% confidence interval [CI] 61–81), ranging from 61% in Mexico to 83% in Venezuela. Queries indicative of cessation interest peaked on WNTD, increasing 40% (95% CI 32–48), ranging from 24% in Colombia to 84% in Venezuela. A doubling in cessation news coverage was associated with approximately a 50% increase in cessation queries. To gain a practical perspective, we compared WNTD-related activity with New Year’s Day and several cigarette excise tax increases in Mexico. Cessation queries around WNTD were typically greater than New Year’s Day and approximated a 2.8% (95% CI –0.8 to 6.3) increase in cigarette excise taxes. Conclusions This novel evaluation suggests WNTD had a significant impact on popular awareness (media trends) and individual interest (query trends) in smoking cessation. Because WNTD is constantly evolving, our work is also a model for real-time surveillance and potential improvement in WNTD and similar initiatives.

Assessing the Online Social Environment for Surveillance of Obesity Prevalence

Background Understanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data. Purpose This article explores the relationship between online social environment via web-based social networks and population obesity prevalence. Methods We performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook. Results Higher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set. Conclusions Activity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

Pokémon GO—A New Distraction for Drivers and Pedestrians

Pokemon GO, an augmented reality game, has swept the nation. As players move, their avatar moves within the game, and players are then rewarded for collecting Pokemon placed in real-world locations. By rewarding movement, the game incentivizes physical activity. However, if players use their cars to search for Pokemon they negate any health benefit and incur serious risk. Motor vehicle crashes are the leading cause of death among 16- to 24-year-olds, whom the game targets. Moreover, according to the American Automobile Association, 59% of all crashes among young drivers involve distractions within 6 seconds of the accident. We report on an assessment of drivers and pedestrians distracted by Pokemon GO and crashes potentially caused by Pokemon GO by mining social and news media reports. Language: en