Effects of Air Pollutants from Wildfires on Downwind Ecosystems: Observations, Knowledge Gaps, and Questions for Assessing Risk.

Wildfires have increased in frequency and area burned, trends expected to continue with climate change. Among other effects, fires release pollutants into the atmosphere, representing a risk to human health and downwind terrestrial and aquatic ecosystems. While human health risks are well studied, the ecological impacts to downwind ecosystems are not, and this gap may present a constraint on developing an adequate assessment of the ecological risks associated with downwind wildfire exposure. Here, we first screened the scientific literature to assess general knowledge about pathways and end points of a conceptual model linking wildfire generated pollutants and other materials to downwind ecosystems. We found a substantial body of literature on the composition of wildfire derived pollution and materials in the atmosphere and subsequent transport, yet little observational or experimental work on their effects on downwind ecological end points. This dearth of information raises many questions related to adequately assessing the ecological risk of downwind exposure, especially given increasing wildfire trends. To guide future research, we pose eight questions within the well-established US EPA ecological risk assessment paradigm that if answered would greatly improve ecological risk assessment and, ultimately, management strategies needed to reduce potential wildfire impacts.

Becoming-A Journey to Wellness: Engaging HBCU Students in Developing a Theory-Based YouTube Healthy Lifestyle Intervention.

Addressing disparities in overweight and obesity among African American college students is critical, as many of these students are heavier and gain more weight when compared with other groups. This report describes development of a theory-based YouTube series designed to improve Historically Black College and University (HBCU) student lifestyle behaviours.

Recommendations for Increasing Physician Provision of Pre-Exposure Prophylaxis: Implications for Medical Student Training.

There is growing evidence that pre-exposure prophylaxis (PrEP) prevents HIV acquisition. However, in the United States, approximately only 4% of people who could benefit from PrEP are currently receiving it, and it is estimated only 1 in 5 physicians has ever prescribed PrEP. We conducted a scoping review to gain an understanding of physician-identified barriers to PrEP provision. Four overarching barriers presented in the literature: Purview Paradox, Patient Financial Constraints, Risk Compensation, and Concern for ART Resistance. Considering the physician-identified barriers, we make recommendations for how physicians and students may work to increase PrEP knowledge and competence along each stage of the PrEP cascade. We recommend adopting HIV risk assessment as a standard of care, improving physician ability to identify PrEP candidates, improving physician interest and ability in encouraging PrEP uptake, and increasing utilization of continuous care management to ensure retention and adherence to PrEP.

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study.

BACKGROUND: Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. OBJECTIVE: This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. METHODS: This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users' tweets. RESULTS: Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants' tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). CONCLUSIONS: To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

Curricular Interventions in Medical Schools for Assessing Adverse Childhood Experience.

Scientific evidence is accumulating about the range of adverse health, mental health, and risky behavioral sequelae across the life continuum arising from exposure to Adverse Childhood Experiences (ACEs). Research findings show a clear relationship between the number of ACEs experienced by a person during childhood and the adverse health outcomes of adulthood. The purpose of this systematic review was to assess the extent to which medical schools are teaching medical students about ACEs. Published articles were identified through searches of several databases using a combination of major and minor MeSH terms. Out of a total of 715 publications screened, 13 studies were identified that focused on medical education efforts to address ACEs. Educational interventions were conducted in a variety of formats, including lectures, perspective-taking exercises, and small group discussions. Our systematic review found little evidence to suggest that medical schools are teaching students how to address ACEs among their patients.

Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus.

BACKGROUND: An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases. The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections. Internet data have shown promise in identifying human behaviors relevant for tracking and understanding other diseases. OBJECTIVE: Using Twitter posts regarding the 2015-16 Zika virus outbreak, we sought to identify and describe considerations and self-disclosures of a specific behavior change relevant to the spread of disease-travel cancellation. If this type of behavior is identifiable in Twitter, this approach may provide an additional source of data for disease modeling. METHODS: We combined keyword filtering and machine learning classification to identify first-person reactions to Zika in 29,386 English-language tweets in the context of travel, including considerations and reports of travel cancellation. We further explored demographic, network, and linguistic characteristics of users who change their behavior compared with control groups. RESULTS: We found differences in the demographics, social networks, and linguistic patterns of 1567 individuals identified as changing or considering changing travel behavior in response to Zika as compared with a control sample of Twitter users. We found significant differences between geographic areas in the United States, significantly more discussion by women than men, and some evidence of differences in levels of exposure to Zika-related information. CONCLUSIONS: Our findings have implications for informing the ways in which public health organizations communicate with the public on social media, and the findings contribute to our understanding of the ways in which the public perceives and acts on risks of emerging infectious diseases.

Zika discourse in the Americas: A multilingual topic analysis of Twitter.

This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398).

Can online self-reports assist in real-time identification of influenza vaccination uptake? A cross-sectional study of influenza vaccine-related tweets in the USA, 2013-2017.

INTRODUCTION: The Centers for Disease Control and Prevention (CDC) spend significant time and resources to track influenza vaccination coverage each influenza season using national surveys. Emerging data from social media provide an alternative solution to surveillance at both national and local levels of influenza vaccination coverage in near real time. OBJECTIVES: This study aimed to characterise and analyse the vaccinated population from temporal, demographical and geographical perspectives using automatic classification of vaccination-related Twitter data. METHODS: In this cross-sectional study, we continuously collected tweets containing both influenza-related terms and vaccine-related terms covering four consecutive influenza seasons from 2013 to 2017. We created a machine learning classifier to identify relevant tweets, then evaluated the approach by comparing to data from the CDC's FluVaxView. We limited our analysis to tweets geolocated within the USA. RESULTS: We assessed 1 124 839 tweets. We found strong correlations of 0.799 between monthly Twitter estimates and CDC, with correlations as high as 0.950 in individual influenza seasons. We also found that our approach obtained geographical correlations of 0.387 at the US state level and 0.467 at the regional level. Finally, we found a higher level of influenza vaccine tweets among female users than male users, also consistent with the results of CDC surveys on vaccine uptake. CONCLUSION: Significant correlations between Twitter data and CDC data show the potential of using social media for vaccination surveillance. Temporal variability is captured better than geographical and demographical variability. We discuss potential paths forward for leveraging this approach.

A REVIEW OF WATER QUALITY RESPONSES TO AIR TEMPERATURE AND PRECIPITATION CHANGES 1: FLOW, WATER TEMPERATURE, SALTWATER INTRUSION.

Anticipated future increases in air temperature and regionally variable changes in precipitation will have direct and cascading effects on U.S. water quality. In this paper, and a companion paper by Coffey et al. (2019), we review technical literature addressing the responses of different water quality attributes to historical and potential future changes in air temperature and precipitation. The goal is to document how different attributes of water quality are sensitive to these drivers, to characterize future risk to inform management responses and to identify research needs to fill gaps in our understanding. Here we focus on potential changes in streamflow, water temperature, and salt water intrusion (SWI). Projected changes in the volume and timing of streamflow vary regionally, with general increases in northern and eastern regions of the U.S., and decreases in the southern Plains, interior Southwest and parts of the Southeast. Water temperatures have increased throughout the U.S. and are expected to continue to increase in the future, with the greatest changes in locations where high summer air temperatures occur together with low streamflow volumes. In coastal areas, especially the mid-Atlantic and Gulf coasts, SWI to rivers and aquifers could be exacerbated by sea level rise, storm surges, and altered freshwater runoff. Management responses for reducing risks to water quality should consider strategies and practices robust to a range of potential future conditions.

Integration of SWAT and HSPF for Simulation of Sediment Sources in Legacy Sediment-Impacted Agricultural Watersheds.

A total maximum daily load for the Chesapeake Bay requires reduction in pollutant load from sources within the Bay watersheds. The Conestoga River watershed has been identified as a major source of sediment load to the Bay. Upland loads of sediment from agriculture are a concern; however, a large proportion of the sediment load in the Conestoga River has been linked to scour of legacy sediment associated with historic millpond sites. Clarifying this distinction and identifying specific segments associated with upland vs. channel sources has important implications for future management. In order to address this important question, we combined the strengths of two widely accepted watershed management models - Soil and Water Assessment Tool (SWAT) for upland agricultural processes, and Hydrologic Simulation Program FORTRAN (HSPF) for instream fate and transport - to create a novel linked modeling system to predict sediment loading from critical sources in the watershed including upland and channel sources, and to aid in targeted implementation of management practices. The model indicates approximately 66% of the total sediment load is derived from instream sources, in agreement with other studies in the region and can be used to support identification of these channel source segments vs. upland source segments, further improving targeted management. The innovated linked SWAT-HSPF model implemented in this study is useful for other watersheds where both upland agriculture and instream processes are important sources of sediment load.

The Potential of Twitter as a Data Source for Patient Safety.

BACKGROUND: Error-reporting systems are widely regarded as critical components to improving patient safety, yet current systems do not effectively engage patients. We sought to assess Twitter as a source to gather patient perspective on errors in this feasibility study. METHODS: We included publicly accessible tweets in English from any geography. To collect patient safety tweets, we consulted a patient safety expert and constructed a set of highly relevant phrases, such as "doctor screwed up." We used Twitter's search application program interface from January to August 2012 to identify tweets that matched the set of phrases. Two researchers used criteria to independently review tweets and choose those relevant to patient safety; a third reviewer resolved discrepancies. Variables included source and sex of tweeter, source and type of error, emotional response, and mention of litigation. RESULTS: Of 1006 tweets analyzed, 839 (83%) identified the type of error: 26% of which were procedural errors, 23% were medication errors, 23% were diagnostic errors, and 14% were surgical errors. A total of 850 (84%) identified a tweet source, 90% of which were by the patient and 9% by a family member. A total of 519 (52%) identified an emotional response, 47% of which expressed anger or frustration, 21% expressed humor or sarcasm, and 14% expressed sadness or grief. Of the tweets, 6.3% mentioned an intent to pursue malpractice litigation. CONCLUSIONS: Twitter is a relevant data source to obtain the patient perspective on medical errors. Twitter may provide an opportunity for health systems and providers to identify and communicate with patients who have experienced a medical error. Further research is needed to assess the reliability of the data.

Perceived Attitudes About Substance Use in Anonymous Social Media Posts Near College Campuses: Observational Study.

BACKGROUND: Substance use is a major issue for adolescents and young adults, particularly college students. With the importance of peer influence and the ubiquitous use of social media among these age groups, it is important to assess what is discussed on various social media sites regarding substance use. One particular mobile app (Yik Yak) allowed users to post any message anonymously to nearby persons, often in areas with close proximity to major colleges and universities. OBJECTIVE: This study describes the content, including attitude toward substances, of social media discussions that occurred near college campuses and involved substances. METHODS: A total of 493 posts about drugs and alcohol on Yik Yak were reviewed and coded for their content, as well as the poster's attitude toward the substance(s) mentioned. RESULTS: Alcohol (226/493, 45.8%), marijuana (206/493, 41.8%), and tobacco (67/493, 13%) were the most frequently mentioned substances. Posts about use (442/493) were generally positive toward the substance mentioned (262/442, 59.3%), unless the post was about abstinence from the substance. Additionally, posts that commented on the substance use of others tended to be less positive (18/92, 19.6% positive) compared to posts about one's own use (132/202, 65.3% positive). CONCLUSIONS: This study provides a description of anonymous discussions on or near college campuses about drugs and alcohol, which serves as an example of data that can be examined from social media sites for further research and prevention campaigns.

#Healthy Selfies: Exploration of Health Topics on Instagram.

BACKGROUND: Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity. OBJECTIVE: This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research. METHODS: The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool. RESULTS: We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts). CONCLUSIONS: A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter.

Understanding emerging forms of cannabis use through an online cannabis community: An analysis of relative post volume and subjective highness ratings.

BACKGROUND: Legalization of medical and recreational cannabis has coincided with an increase in novel forms of cannabis use and a burgeoning cannabis product industry. This research seeks to understand the occurrence of discussions about these emerging and traditional forms of use in an online social media discussion forum. METHODS: We analyzed posts to a cannabis-specific forum on the Reddit social media platform posted from January 2010-December 2016. For each of various keywords describing smoking, vaping, edibles, dabbing, and butane hash oil (BHO) concentrate use, we analyzed (1) relative prevalence of posts mentioning these cannabis forms of use; (2) user-reported subjective ratings of "highness" on a scale of 1-10; (3) the ten most common words mentioned in posts; and (4) the frequency of adverse health effect terms. RESULTS: Form of use was mentioned in approximately 17.7% of 2.26 million posts; smoking was the most commonly mentioned form of cannabis use. From 2010-2016, relative post volume increased significantly for posts mentioning dabbing (3.63/1000 additional posts per year, p < .001), butane hash oil terms (3.16/1000, p < .001), and edible terms (2.84/1000, p = .002). Mean subjective highness was significantly greater for posts mentioning dabbing (mean = 7.8, p < .001), butane hash oil terms (mean = 7.5, p < .001), and edible terms (mean = 7.2, p < .001) but not significantly different for vaping (mean = 6.7, p = .19), when compared to smoking (mean = 6.8). CONCLUSIONS: Despite limitations in representativeness, findings indicate a significant increase in online discussion of emerging cannabis forms of use over time and greater subjective effects of dabbing, butane hash oil, and edible use.

Characterizing the (Perceived) Newsworthiness of Health Science Articles: A Data-Driven Approach.

BACKGROUND: Health science findings are primarily disseminated through manuscript publications. Information subsidies are used to communicate newsworthy findings to journalists in an effort to earn mass media coverage and further disseminate health science research to mass audiences. Journal editors and news journalists then select which news stories receive coverage and thus public attention. OBJECTIVE: This study aims to identify attributes of published health science articles that correlate with (1) journal editor issuance of press releases and (2) mainstream media coverage. METHODS: We constructed four novel datasets to identify factors that correlate with press release issuance and media coverage. These corpora include thousands of published articles, subsets of which received press release or mainstream media coverage. We used statistical machine learning methods to identify correlations between words in the science abstracts and press release issuance and media coverage. Further, we used a topic modeling-based machine learning approach to uncover latent topics predictive of the perceived newsworthiness of science articles. RESULTS: Both press release issuance for, and media coverage of, health science articles are predictable from corresponding journal article content. For the former task, we achieved average areas under the curve (AUCs) of 0.666 (SD 0.019) and 0.882 (SD 0.018) on two separate datasets, comprising 3024 and 10,760 articles, respectively. For the latter task, models realized mean AUCs of 0.591 (SD 0.044) and 0.783 (SD 0.022) on two datasets-in this case containing 422 and 28,910 pairs, respectively. We reported most-predictive words and topics for press release or news coverage. CONCLUSIONS: We have presented a novel data-driven characterization of content that renders health science "newsworthy." The analysis provides new insights into the news coverage selection process. For example, it appears epidemiological papers concerning common behaviors (eg, alcohol consumption) tend to receive media attention.

Assessing the Validity of Online Drug Forums as a Source for Estimating Demographic and Temporal Trends in Drug Use.

OBJECTIVES: Addiction researchers have begun monitoring online forums to uncover self-reported details about use and effects of emerging drugs. The use of such online data sources has not been validated against data from large epidemiological surveys. This study aimed to characterize and compare the demographic and temporal trends associated with drug use as reported in online forums and in a large epidemiological survey. METHODS: Data were collected from the Web site, drugs-forum.com, from January 2007 through August 2012 (143,416 messages posted by 8087 members) and from the US National Survey on Drug Use and Health (NSDUH) from 2007 to 2012. Measures of forum participation levels were compared with and validated against 2 measures from the NSDUH survey data: percentage of people using the drug in past 30 days and percentage using the drug more than 100 times in the past year. RESULTS: For established drugs (eg, cannabis), significant correlations were found across demographic groups between drugs-forum.com and the NSDUH survey data, whereas weaker, nonsignificant correlations were found with temporal trends. Emerging drugs (eg, Salvia divinorum) were strongly associated with male users in the forum, in agreement with survey-derived data, and had temporal patterns that increased in synchrony with poison control reports. CONCLUSIONS: These results offer the first assessment of online drug forums as a valid source for estimating demographic and temporal trends in drug use. The analyses suggest that online forums are a reliable source for estimation of demographic associations and early identification of emerging drugs, but a less reliable source for measurement of long-term temporal trends.

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.

Terahertz-Triggered Phase Transition and Hysteresis Narrowing in a Nanoantenna Patterned Vanadium Dioxide Film.

We demonstrate that high-field terahertz (THz) pulses trigger transient insulator-to-metal transition in a nanoantenna patterned vanadium dioxide thin film. THz transmission of vanadium dioxide instantaneously decreases in the presence of strong THz fields. The transient THz absorption indicates that strong THz fields induce electronic insulator-to-metal transition without causing a structural transformation. The transient phase transition is activated on the subcycle time scale during which the THz pulse drives the electron distribution of vanadium dioxide far from equilibrium and disturb the electron correlation. The strong THz fields lower the activation energy in the insulating phase. The THz-triggered insulator-to-metal transition gives rise to hysteresis loop narrowing, while lowering the transition temperature both for heating and cooling sequences. THz nanoantennas enhance the field-induced phase transition by intensifying the field strength and improve the detection sensitivity via antenna resonance. The experimental results demonstrate a potential that plasmonic nanostructures incorporating vanadium dioxide can be the basis for ultrafast, energy-efficient electronic and photonic devices.