Internet searches for medical symptoms before seeking information on 12-step addiction treatment programs: A web-search log analysis

© 2019 George Nitzburg, Ingmar Weber, Elad Yom-Tov. 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

Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content

BACKGROUND: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. OBJECTIVE: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. METHODS: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. RESULTS: Results suggest a reduction in ILI rates of 14% (1-25%) and 17% (2-30%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. CONCLUSIONS: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool

Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions

Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content. Unfortunately, controversies occur across many topics and domains, with great changes over time. This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages. Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks. The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy. We train neural networks that can capture semantic information from texts using weak signal data. By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods. To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence. In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies

Inferring individual attributes from search engine queries and auxiliary information

Internet data has surfaced as a primary source for investigation of different aspects of human behavior. A crucial step in such studies is finding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. However, direct identification of users sharing this trait is often impossible, as the data available to researchers is usually anonymized to preserve user privacy. To facilitate research on specific topics of interest, especially in medicine, we introduce an algorithm for identifying a trait of interest in anonymous users. We illustrate how a small set of labeled examples, together with statistical information about the entire population, can be aggregated to obtain labels on unseen examples. We validate our approach using labeled data from the political domain. We provide two applications of the proposed algorithm to the medical domain. In the first, we demonstrate how to identify users whose search patterns indicate they might be suffering from certain types of cancer. In the second, we detail an algorithm to predict the distribution of diseases given their incidence in a subset of the population at study, making it possible to predict disease spread from partial epidemiological data

Structural Principles in Robo Activation and Auto-Inhibition

This is the author accepted manuscript. The final version is available from Elsevier (Cell Press) via the DOI in this record.Proper brain function requires high-precision neuronal expansion and wiring, processes controlled by the transmembrane Roundabout (Robo) receptor family and their Slit ligands. Despite their great importance, the molecular mechanism by which Robos’ switch from “off” to “on” states remains unclear. Here, we report a 3.6 Å crystal structure of the intact human Robo2 ectodomain (domains D1–8). We demonstrate that Robo cis dimerization via D4 is conserved through hRobo1, 2, and 3 and the C. elegans homolog SAX-3 and is essential for SAX-3 function in vivo. The structure reveals two levels of auto-inhibition that prevent premature activation: (1) cis blocking of the D4 dimerization interface and (2) trans interactions between opposing Robo receptors that fasten the D4-blocked conformation. Complementary experiments in mouse primary neurons and C. elegans support the auto-inhibition model. These results suggest that Slit stimulation primarily drives the release of Robo auto-inhibition required for dimerization and activation.ICRFIS

Global warming and Bergmann’s rule: do central European passerines adjust their body size to rising temperatures?

Recent climate change has caused diverse ecological responses in plants and animals. However, relatively little is known about homeothermic animals’ ability to adapt to changing temperature regimes through changes in body size, in accordance with Bergmann’s rule. We used fluctuations in mean annual temperatures in south-west Germany since 1972 in order to look for direct links between temperature and two aspects of body size: body mass and flight feather length. Data from regionally born juveniles of 12 passerine bird species were analysed. Body mass and feather length varied significantly among years in eight and nine species, respectively. Typically the inter-annual changes in morphology were complexly non-linear, as was inter-annual variation in temperature. For six (body mass) and seven species (feather length), these inter-annual fluctuations were significantly correlated with temperature fluctuations. However, negative correlations consistent with Bergmann’s rule were only found for five species, either for body mass or feather length. In several of the species for which body mass and feather length was significantly associated with temperature, morphological responses were better predicted by temperature data that were smoothed across multiple years than by the actual mean breeding season temperatures of the year of birth. This was found in five species for body mass and three species for feather length. These results suggest that changes in body size may not merely be the result of phenotypic plasticity but may hint at genetically based microevolutionary adaptations

Egg removal and intraspecific brood parasitism in the European starling ( Sturnus vulgaris )

From 1983 to 1986 we monitored 284 European starling ( Sturnus vulgaris ) nests in New Jersey for evidence of intraspecific brood parasitism and egg removal during the laying period. Egg removal occurred significantly more often at nests where intraspecific brood parasitism was detected (12 of 35 nests, 34%) than at unparasitized nests (23 of 249 nests, 9%). Brood parasitism (92% of parasitized nests) and egg removal (74% of nests with egg removal) were most common at nests where egg laying began in April of each year (i.e., early nests). Egg removal occurred at 26 (19%) and brood parasitism at 32 (23%) of 138 early nests. Both brood parasitism and egg removal were concentrated during the first four days in the laying period when brood parasitism is most likely to be successful and when host nests are most vulnerable to parasitism (Romagnano 1987). Both parasitism and removal usually involved a single egg at each nest. We detected brood parasitism and egg removal on the same day at five of 12 nests (42%) where both were observed. Because starlings do not remove foreign eggs from their nests once they begin laying (Stouffer et al. 1987) we hypothesize that parasite females sometimes removed host eggs while parasitizing nests.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46889/1/265_2004_Article_BF00295201.pd