Testing interventions to reduce clinical inertia in the treatment of hypertension: rationale and design of a pragmatic randomized controlled trial.

BACKGROUND: Clinical inertia, or failure to intensify treatment when indicated, leads to suboptimal blood pressure control. Interventions to overcome inertia and increase antihypertensive prescribing have been modestly successful in part because their effectiveness varies based on characteristics of the provider, the patient, or the provider-patient interaction. Understanding for whom each intervention is most effective could help target interventions and thus increase their impact. METHODS: This three-arm, randomized trial tests the effectiveness of 2 interventions to reduce clinical inertia in hypertension prescribing compared to usual care. Forty five primary care providers (PCPs) caring for patients with hypertension in need of treatment intensification completed baseline surveys that assessed behavioral traits and were randomized to one of three arms: 1) Pharmacist e-consult, in which a clinical pharmacist provided patient-specific recommendations for hypertension medication management to PCPs in advance of upcoming visits, 2) Social norming dashboards that displayed PCP's hypertension control rates compared to those of their peers, or 3) Usual care (no intervention). The primary outcome was the rate of intensification of hypertension treatment. We will compare this outcome between study arms and then evaluate the association between characteristics of providers, patients, their clinical interactions, and intervention responsiveness. RESULTS: Forty-five primary care providers were enrolled and randomized: 16 providers and 173 patients in the social norming dashboards arm, 15 providers and 143 patients in the pharmacist e-consult arm, and 14 providers and 150 patients in the usual care arm. On average, the mean patient age was 64 years, 47% were female, and 73% were white. Baseline demographic and clinical characteristics of patients were similar across arms, with the exception of more Hispanic patients in the usual care arm and fewest in the pharmacist e-consult arm. CONCLUSIONS: This study can help identify interventions to reduce inertia in hypertension care and potentially identify the characteristics of patients, providers, or patient-provider interactions to understand for whom each intervention would be most beneficial. TRIAL REGISTRATION: Clinicaltrials.gov (NCT, Registered: NCT04603560).

Identifying amyotrophic lateral sclerosis through interactions with an internet search engine.

INTRODUCTION/AIMS: Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with an average time from onset of symptoms to diagnosis of about 1 year. Herein we examine the possibility that interactions with an internet search engine can identify people with ALS. METHODS: We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to: (1) 3276 control users; and (2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. RESULTS: The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (rank sum test, p < .05 with Bonferroni correction). Mild cognitive impairment could not be distinguished from ALS (p > .05). In the prospective analysis, the model reached an AUC of 0.74. DISCUSSION: Our results suggest that interactions with search engines should be further studied to understand the potential to act as a tool to assist in screening for ALS and to reduce diagnostic delay.

Using online search activity for earlier detection of gynaecological malignancy.

BACKGROUND: Ovarian cancer is the most lethal and endometrial cancer the most common gynaecological cancer in the UK, yet neither have a screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data can be used to differentiate between individuals with malignant and benign gynaecological diagnoses. METHODS: This is a prospective cohort study evaluating online search data in symptomatic individuals (Google user) referred from primary care (GP) with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. Informed written consent was obtained and online search data was extracted via Google takeout and anonymised. A health filter was applied to extract health-related terms for 24 months prior to GP referral. A predictive model (outcome: malignancy) was developed using (1) search queries (terms model) and (2) categorised search queries (categories model). Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate and 392 were recruited. Of those recruited, 108 did not complete enrollment, 12 withdrew and 37 were excluded as they did not track Google searches or had an empty search history, leaving a cohort of 235. RESULTS: The cohort had a median age of 53 years old (range 20-81) and a malignancy rate of 26.0%. There was a difference in online search data between those with a benign and malignant diagnosis, noted as early as 360 days in advance of GP referral, when search queries were used directly, but only 60 days in advance, when queries were divided into health categories. A model using online search data from patients (n = 153) who performed health-related search and corrected for sample size, achieved its highest sample-corrected AUC of 0.82, 60 days prior to GP referral. CONCLUSIONS: Online search data appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. Online search data needs to be evaluated in a larger dataset to determine its value as an early disease detection tool and whether its use leads to improved clinical outcomes.

Ethics of Medical Archival Internet Research Data.

Medical research based on internet archive data, which in some ways is quite different from other data-based studies, is becoming more and more common. Despite its uniqueness and the challenges that characterize it, clear ethical rules designed to guide practitioners in this field have not yet been written. This article points to the lacuna that exists in legal and ethical texts today and offers an ethically balancing alternative. Among other features, the balance is based on the famous three laws of robotics by Asimov and a series of values, including transparency, accountability, fairness, and privacy.

The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude. OBJECTIVE: This study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2. METHODS: We used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities. RESULTS: Our analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence. CONCLUSIONS: The 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions.

Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire.

BACKGROUND: Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists. OBJECTIVE: We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. METHODS: Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. RESULTS: A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. CONCLUSIONS: Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors.

BACKGROUND: Although autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls. OBJECTIVE: We aimed to examine whether this investigative tendency emerges in information searches conducted via the internet. METHODS: In total, 1746 search engine users stated that they had ASD in 2019. This group's naturally occurring responses following 1491 unique general queries and 78 image queries were compared to those of all other users of the search engine. The main dependent measure was scrolled distance, which denoted the extent to which additional results were scanned beyond the initial results presented on-screen. Additionally, we examined the number of clicks on search results as an indicator of the degree of search outcome exploitation and assessed whether there was a trade-off between increased search range and the time invested in viewing initial search results. RESULTS: After issuing general queries, individuals with self-stated ASD scanned more results than controls. The scrolled distance in the results page of general queries was 45% larger for the group of individuals with ASD (P<.001; d=0.45). The group of individuals with ASD also made the first scroll faster than the controls (P<.001; d=0.51). The differences in scrolled distance were larger for popular queries. No group differences in scrolled distance emerged for image queries, suggesting that visual load impeded the investigative behavior of individuals with ASD. No differences emerged in the number of clicks on search results. CONCLUSIONS: Individuals who self-stated that they had ASD scrutinized more general search results and fewer image search results than the controls. Thus, our results at least partially support the notion that individuals with ASD exhibit investigative behaviors and suggest that textual searches are an important context for expressing such tendencies.

Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data.

BACKGROUND: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension). OBJECTIVE: Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event. METHODS: We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions. RESULTS: The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session. CONCLUSIONS: The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study.

BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

Predicting eating disorders from Internet activity.

OBJECTIVE: Eating disorders (EDs) compromise the health and functioning of affected individuals, but it can often take them several years to acknowledge their illness and seek treatment. Early identification of individuals with EDs is a public health priority, and innovative approaches are needed for such identification and ultimate linkage with evidence-based interventions. This study examined whether Internet activity data can predict ED risk/diagnostic status, potentially informing timely interventions. METHOD: Participants were 936 women who completed a clinically validated online survey for EDs, and 231 of them (24.7%) contributed their Internet browsing history. A machine learning algorithm used key attributes from participants' Internet activity histories to predict their ED status: clinical/subclinical ED, high risk for an ED, or no ED. RESULTS: The algorithm reached an accuracy of 52.6% in predicting ED risk/diagnostic status, compared to random decision accuracy of 38.1%, a relative improvement of 38%. The most predictive Internet search history variables were the following: use of keywords related to ED symptoms and websites promoting ED content, participant age, median browsing events per day, and fraction of daily activity at noon. DISCUSSION: ED risk or clinical status can be predicted via machine learning with moderate accuracy using Internet activity variables. This model, if replicated in larger samples where it demonstrates stronger predictive value, could identify populations where further assessment is merited. Future iterations could also inform tailored digital interventions, timed to be provided when target online behaviors occur, thereby potentially improving the well-being of many individuals who may otherwise remain undetected.

Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines: Viewpoint.

Recent research has shown the efficacy of screening for serious medical conditions from data collected while people interact with online services. In particular, queries to search engines and the interactions with them were shown to be advantageous for screening a range of conditions including diabetes, several forms of cancer, eating disorders, and depression. These screening abilities offer unique advantages in that they can serve a broad strata of the society, including people in underserved populations and in countries with poor access to medical services. However, these advantages need to be balanced against the potential harm to privacy, autonomy, and nonmaleficence, which are recognized as the cornerstones of ethical medical care. Here, we discuss these opportunities and challenges, both when collecting data to develop online screening services and when deploying them. We offer several solutions that balance the advantages of these services with the ethical challenges they pose.

Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data.

BACKGROUND: Surveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown. OBJECTIVE: This study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional. METHODS: We collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions. RESULTS: On average, 15.49% (1792/12,367, SD 8.4%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=-0.47; P=.08). In addition, worrying symptoms were 14% more likely to be queried about. CONCLUSIONS: Our results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions.

Real-World Evidence on the Effect of Missing an Oral Contraceptive Dose: Analysis of Internet Search Engine Queries.

BACKGROUND: Oral contraceptives (OCs) are a unique chronic medication with which a memory slip may result in a threat that could change a person's life course. Subjective concerns of missed OC doses among women have been addressed infrequently. Anonymized queries to internet search engines provide unique access to concerns and information gaps faced by a large number of internet users. OBJECTIVE: We aimed to quantitate the frequency of queries by women seeking information in an internet search engine, after missing one or more doses of an OC; their further queries on emergency contraception, abortion, and miscarriage; and their rate of reporting a pregnancy timed to the cycle of missing an OC. METHODS: We extracted all English-language queries submitted to Bing in the United States during 2018, which mentioned a missed OC and subsequent queries of the same users on miscarriage, abortion, emergency contraceptives, and week of pregnancy. RESULTS: We identified 26,395 Bing users in the United States who queried about missing OC pills and the fraction that further queried about miscarriage, abortion, emergency contraceptive, and week of pregnancy. Users under the age of 30 years who asked about forgetting an OC dose were more likely to ask about abortion (1.5 times) and emergency contraception (1.7 times) (P<.001 for both), while users at ages of 30-34 years were more likely to query about pregnancy (2.1 times) and miscarriage (5.4 times) (P<.001 for both). CONCLUSIONS: Our data indicate that many women missing a dose of OC might not have received sufficient information from their health care providers or chose to obtain it online. Queries about abortion and miscarriage peaking in the subsequent days indicate a common worry of possible pregnancy. These results reinforce the importance of providing comprehensive written information on missed pills when prescribing an OC.

HPV vaccine, Twitter, and gay, bisexual and other men who have sex with men.

This study aimed to quantify human papillomavirus (HPV) vaccine Twitter messaging addressing gay, bisexual and other men who have sex with men (GB+MSM) and describes messaging by vaccine sentiment (attitudes towards vaccine) and characteristics (topic of messaging). Between August 2014 and July 2015, we collected 193 379 HPV-related tweets and classified them by vaccine sentiment and characteristics. We analysed a subsample of tweets containing the terms 'gay', 'bisexual' and 'MSM' (N = 2306), and analysed distributions of sentiment and characteristics using chi-square. HPV-related tweets containing GB+MSM terms occupied 1% of our sample. The subsample had a largely positive vaccine sentiment. However, a proportion of 'gay' and 'bisexual' tweets did not mention the vaccine, and a proportion of 'gay' and 'MSM' tweets had a negative sentiment. Topics varied by GB+MSM term-HPV risk messaging was prevalent in 'bisexual' (25%) tweets, and HPV transmission through sex/promiscuity messaging was prevalent in 'gay' (18%) tweets. Prevention/protection messaging was prevalent only in 'MSM' tweets (49%). Although HPV vaccine sentiment was positive in GB+MSM messaging, we identified deficits in the volume of GB+MSM messaging, a lack of focus on vaccination, and a proportion of negative tweets. While HPV vaccine promotion has historically focused on heterosexual HPV transmission, there are opportunities to shape vaccine uptake in GB+MSM through public health agenda setting using social media messaging that increases knowledge and minimizes HPV vaccine stigma. Social media-based HPV vaccine promotion should also address the identities of those at risk to bolster vaccine uptake and reduce the risk of HPV-attributable cancers.

Mental illness and bipolar disorder on Twitter: implications for stigma and social support.

Background: Mental illness (MI), and particularly, bipolar disorder (BD), are highly stigmatized. However, it is unknown if this stigma is also represented on social media.Aims: Characterize Twitter-based stigma and social support messaging ("tweets") about mental health/illness (MH)/MI and BD and determine which tweets garnered retweets.Methods: We collected tweets about MH/MI and BD during a three-month period and analyzed tweets from dates with the most tweets ("spikes"), an indicator of topic interest. A sample was manually content analyzed, and the remainder were classified using machine learning (logistic regression) by topic, stigma, and social support messaging. We compared stigma and support toward MH/MI versus BD and used logistic regression to quantify tweet features associated with retweets, to assess tweet reach.Results: Of the 1,270,902 tweets analyzed, 94.7% discussed MH/MI and 5.3% discussed BD. Spikes coincided with a celebrity's death and a MH awareness campaign. Although the sample contained more support than stigma messaging, BD tweets contained more stigma and less support than MH/MI tweets. However, stigma messaging was infrequently retweeted, and users often retweeted personal MH experiences.Conclusions: These findings demonstrate opportunities for social media advocacy to reduce stigma and increase displays of social support towards people living with BD.

Adverse events associated with colonoscopy; an examination of online concerns.

BACKGROUND: Colonoscopy as a screening and diagnostic tool is generally safe and well-tolerated, and significant complications are rare. The rate of more mild adverse effects is difficult to estimate, particularly when such effects do not result in hospital admission. We aimed to identify the rate and timing of adverse effects as reported by users querying symptoms on an internet search engine. METHODS: We identified queries made to Bing originating from users in the United States containing the word "colonoscopy" during a 12-month period and identified those queries in which the timing of colonoscopy could be estimated. We then identified queries from those same users for medical symptoms during the time span from 5 days before through 30 days after the colonoscopy date. RESULTS: Of 641,223 users mentioning colonoscopy, 7013 (1.1%) had a query that enabled identification of their colonoscopy date. The majority of queries about colonoscopy preceded the procedure, and concerned diet. 28% of colonoscopy-related queries were made afterwards, and included queries about diarrhea and cramps, with 2.6% of users querying respiratory symptoms after the procedure, including cough (1.2%) and pneumonia (0.6%). Respiratory symptoms rose significantly at days 7-10 after the colonoscopy. CONCLUSIONS: Internet search queries for respiratory symptoms rose approximately one week after queries relating to colonoscopy, raising the possibility that such symptoms are an under-reported late adverse effect of the procedure. Given the widespread use of colonoscopy as a screening modality and the rise of anesthesia-assisted colonoscopy in the United States in recent years, this signal is of potential public health concern.

Symptoms Prompting Interest in Celiac Disease and the Gluten-Free Diet: Analysis of Internet Search Term Data.

BACKGROUND: Celiac disease, a common immune-based disease triggered by gluten, has diverse clinical manifestations, and the relative distribution of symptoms leading to diagnosis has not been well characterized in the population. OBJECTIVE: This study aimed to use search engine data to identify a set of symptoms and conditions that would identify individuals at elevated likelihood of a subsequent celiac disease diagnosis. We also measured the relative prominence of these search terms before versus after a search related to celiac disease. METHODS: We extracted English-language queries submitted to the Bing search engine in the United States and identified those who submitted a new celiac-related query during a 1-month period, without any celiac-related queries in the preceding 9 months. We compared the ratio between the number of times that each symptom or condition was asked in the 14 days preceding the first celiac-related query of each person and the number of searches for that same symptom or condition in the 14 days after the celiac-related query. RESULTS: We identified 90,142 users who made a celiac-related query, of whom 6528 (7%) exhibited sustained interest, defined as making a query on more than 1 day. Though a variety of symptoms and associated conditions were also queried before a celiac-related query, the maximum area under the receiver operating characteristic curve was 0.53. The symptom most likely to be queried more before than after a celiac-related query was diarrhea (query ratio [QR] 1.28). Extraintestinal symptoms queried before a celiac disease query included headache (QR 1.26), anxiety (QR 1.10), depression (QR 1.03), and attention-deficit hyperactivity disorder (QR 1.64). CONCLUSIONS: We found an increase in antecedent searches for symptoms known to be associated with celiac disease, a rise in searches for depression and anxiety, and an increase in symptoms that are associated with celiac disease but may not be reported to health care providers. The protean clinical manifestations of celiac disease are reflected in the diffuse nature of antecedent internet queries of those interested in celiac disease, underscoring the challenge of effective case-finding strategies.

Do Search Engine Helpline Notices Aid in Preventing Suicide? Analysis of Archival Data.

BACKGROUND: Search engines display helpline notices when people query for suicide-related information. OBJECTIVE: In this study, we aimed to examine if these notices and other information displayed in response to suicide-related queries are correlated with subsequent searches for suicide prevention rather than harmful information. METHODS: Anonymous suicide-related searches made on Bing and Google in the United States, the United Kingdom, Hong Kong, and Taiwan in a span of 10 months were extracted. Descriptive analyses and regression models were fit to the data to assess the correlation with observed behaviors. RESULTS: Display of helpline notices was not associated with an observed change in the likelihood of or future suicide searches (P=.42). No statistically significant differences were observed in the likelihood of people making future suicide queries (both generally and specific types of suicide queries) when comparing search engines in locations that display helpline notices versus ones that do not. Pages with higher rank, being neutral to suicide, and those shown among more antisuicide pages were more likely to be clicked on. Having more antisuicide Web pages displayed was the only factor associated with further searches for suicide prevention information (hazard=1.18, P=.002). CONCLUSIONS: Helpline notices are not associated with harm. If they cause positive change in search behavior, it is small. This is possibly because of the variability in intent of users seeking suicide-related information. Nonetheless, helpline notice should be displayed, but more efforts should be made to improve the visibility and ranking of suicide prevention Web pages.

Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis.

BACKGROUND: Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information. OBJECTIVE: Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information. METHODS: We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried. RESULTS: In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified. CONCLUSIONS: Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness.