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A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) “women” or “men” or “sex,” (2) “big data” or “artificial intelligence” or “NLP”, and (3) “disparities” or “differences.” From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process towards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

Background and Hypothesis Intestinal microbiota is intrinsically linked to human health. Evidence suggests that the composition and function of the microbiome differs in those with schizophrenia compared with controls. It is not clear how these alterations functionally impact people with schizophrenia. We performed a systematic review and meta-analysis to combine and evaluate data on compositional and functional alterations in microbiota in patients with psychosis or schizophrenia. Study design Original studies involving humans and animals were included. The electronic databases PsycINFO, EMBASE, Web of Science, PubMed/MEDLINE, and Cochrane were systematically searched and quantitative analysis performed. Study results Sixteen original studies met inclusion criteria (1376 participants: 748 cases and 628 controls). Ten were included in the meta-analysis. Although observed species and Chao 1 show a decrease in diversity in people with schizophrenia compared with controls (SMD = −0.14 and −0.66 respectively), that did not reach statistical significance. We did not find evidence for variations in richness or evenness of microbiota between patients and controls overall. Differences in beta diversity and consistent patterns in microbial taxa were noted across studies. We found increases in Bifidobacterium, Lactobacillus, and Megasphaera in schizophrenia groups. Variations in brain structure, metabolic pathways, and symptom severity may be associated with compositional alterations in the microbiome. The heterogeneous design of studies complicates a similar evaluation of functional readouts. Conclusions The microbiome may play a role in the etiology and symptomatology of schizophrenia. Understanding how the implications of alterations in microbial genes for symptomatic expression and clinical outcomes may contribute to the development of microbiome targeted interventions for psychosis.

Deficits in social and occupational function are widely reported in psychosis, yet no one measure of function is currently agreed upon as a gold standard in psychosis research. The aim of this study was to carry out a systematic review and meta-analysis of functioning measures to determine what measures were associated with largest effect sizes when measuring between-group differences, changes over time, or response to treatment. Literature searches were conducted based on PsycINFO and PubMed to identify studies for inclusion. Cross-sectional and longitudinal observational and intervention studies of early psychosis (≤5 years since diagnosis) that included social and occupational functioning as an outcome measure were considered. A series of meta-analyses were conducted to determine effect size differences for between-group differences, changes over time, or response to treatment. Subgroup analyses and meta-regression were carried out to account for variability in study and participant characteristics. One hundred and sixteen studies were included, 46 studies provided data (N = 13 261) relevant to our meta-analysis. Smallest effect sizes for changes in function over time and in response to treatment were observed for global measures, while more specific measures of social and occupational function showed the largest effect sizes. Differences in effect sizes between functioning measures remained significant after variability in study and participant characteristics were accounted for. Findings suggest that more specific measures of social function are better able to detect changes in function over time and in response to treatment.

ObjectivesXylazine is an alpha-2 agonist increasingly prevalent in the illicit drug supply. Our objectives were to curate information about xylazine through social media from People Who Use Drugs (PWUDs). Specifically, we sought to answer the following: 1) what are the demographics of Reddit subscribers reporting exposure to xylazine? 2) is xylazine a desired additive? and 3) what adverse effects of xylazine are PWUDs experiencing?MethodsNatural Language Processing (NLP) was used to identify mentions of “xylazine” from posts by Reddit subscribers who also posted on drug-related subreddits. Posts were qualitatively evaluated for xylazine-related themes. A survey was developed to gather additional information about the Reddit subscribers. This survey was posted on subreddits that were identified by NLP to contain xylazine-related discussions from March 2022 to October 2022.Results76 posts mentioning xylazine were extracted via NLP from 765,616 posts by 16,131 Reddit subscribers (January 2018 to August 2021). People on Reddit described xylazine as an unwanted adulterant in their opioid supply. 61 participants completed the survey. Of those that disclosed their location, 25/50 (50%) participants reported locations in the Northeastern United States. The most common eoute of xylazine use was intranasal use (57%). 31/59 (53%) reported experiencing xylazine withdrawal. Frequent adverse events reported were prolonged sedation (81%) and increased skin wounds (43%).ConclusionsAmong respondents on these Reddit forums, xylazine appears to be an unwanted adulterant. PWUDs may be experiencing adverse effects such as prolonged sedation and xylazine withdrawal. This appeared to be more common in the Northeast.

BACKGROUND Adverse drug events are a considerable public health burden resulting in disability, hospitalisation and death. Even those adverse drug events deemed non-serious can severely impact a patient’s quality of life and adherence to an intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on adverse drug events. Whilst many studies have been undertaken to detect adverse events on social media, a consensus has not yet been reached as to the value of social media in pharmacovigilance or its role in pharmacovigilance in relation to more traditional data sources. OBJECTIVE To evaluate and characterise the utility of social media in adverse drug event detection and pharmacovigilance as compared to other data sources. METHODS A scoping review will be undertaken. We will search 11 bibliographical databases as well as Google Scholar, handsearching and forward and backward citation searching. Records will be screened in Covidence by two independent reviewers at both title and abstract stage as well as full-text. Studies will be included if they used any type of social media (such as Twitter or patient forums) to detect any type of adverse event associated with any type of medication and then compared the results from social media to any other data source (such as spontaneous reporting systems or clinical literature). Data will be extracted using a data extraction sheet piloted by the authors. Important data on the types of methods used (such as machine learning), any limitations of the methods used, types of adverse events and drugs searched for and included, availability of data and code, details of the comparison data source, the results and conclusions will be extracted. RESULTS We will present descriptive summary statistics as well as identify any patterns in the types and timing of adverse drug events detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract adverse drug events from social media data. We will also summarize what how the data from social media compares to conventional data sources. The literature will be organised by the data source for comparison. Where possible we will analyse the impact of the types of adverse events, the social media platform used, and the methods employed. CONCLUSIONS This scoping review will provide a valuable summary of a large body of research and important information for pharmacovigilance, as well as, suggest future directions of further research in this area. Through the comparisons with other data sources, we will be able to conclude the added value of social media in monitoring adverse events of medications, in terms of type of adverse events and timing. CLINICALTRIAL

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user’s publicly available tweets (the user’s ‘timeline’). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user’s timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user’s timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

Statin discontinuation can have major negative health consequences. Studying the reasons for discontinuation can be challenging as traditional data collection methods have limitations. We propose an alternative approach using social media.We used natural language processing and machine learning to extract mentions of discontinuation of statin therapy from an online health forum, WebMD ( http://www.webmd.com ). We then extracted data according to themes and identified key attributes of the people posting for themselves.We identified 2121 statin reviews that contained information on discontinuing at least one named statin. Sixty percent of people posting declared themselves as female and the most common age category was 55-64 years. Over half the people taking statins did so for 6 months. By far the most common reason given (90%) was patient experience of adverse events, the most common of which were musculoskeletal and connective tissue disorders. The rank order of adverse events reported in WebMD was largely consistent with those reported to regulatory agencies in the US and UK. Data were available on age, sex, duration of statin use, and, in some instances, adverse event resolution and rechallenge. In some instances, details were presented on resolution of the adverse event and rechallenge.Social media may provide data on the reasons for switching or discontinuation of a medication, as well as unique patient perspectives that may influence continuation of a medication. This information source may provide unique data for novel interventions to reduce medication discontinuation.

Many individuals with early psychosis experience impairments in social and occupational function. Identification of modifiable predictors of function such as cognitive performance has the potential to inform effective treatments. Our aim was to estimate the strength of the relationship between psychosocial function in early psychosis and different domains of cognitive and social cognitive performance. We conducted a systematic review and meta-analysis of peer-reviewed, cross-sectional, and longitudinal studies examining cognitive predictors of psychosocial function. Literature searches were conducted in PsycINFO, PubMed, and reference lists of relevant articles to identify studies for inclusion. Of the 2565 identified, 46 studies comprising 3767 participants met inclusion criteria. Separate meta-analyses were conducted for 9 cognitive domains. Pearson correlation values between cognitive variables and function were extracted. All cognitive domains were related to psychosocial function both cross-sectionally and longitudinally. Importantly, these associations remained significant even after the effects of symptom severity, duration of untreated psychosis, and length of illness were accounted for. Overall, general cognitive ability and social cognition were most strongly associated with both concurrent and long-term function. Associations demonstrated medium effect sizes. These findings suggest that treatments targeting cognitive deficits, in particular those focusing on social cognition, are likely to be important for improving functional outcomes in early psychosis. [Abstract copyright: © The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.]

Objectives: Considerable literature has examined the COVID-19 pandemic’s negative mental health sequelae. It is recognised that most people experiencing mental health problems present to primary care and the development of interventions to support GPs in the care of patients with mental health problems is a priority. This review examines interventions to enhance GP care of mental health disorders, with a view to reviewing how mental health needs might be addressed in the post-COVID-19 era. Methods: Five electronic databases (PubMed, PsycINFO, Cochrane Library, Google Scholar and WHO ‘Global Research on COVID-19’) were searched from May – July 2021 for papers published in English following Arksey and O’Malley’s six-stage scoping review process. Results: The initial search identified 148 articles and a total of 29 were included in the review. These studies adopted a range of methodologies, most commonly randomised control trials, qualitative interviews and surveys. Results from included studies were divided into themes: Interventions to improve identification of mental health disorders, Interventions to support GPs, Therapeutic interventions, Telemedicine Interventions and Barriers and Facilitators to Intervention Implementation. Outcome measures reported included the Seven-item Generalised Anxiety Disorder Scale (GAD-7), the Nine-item Patient Health Questionnaire (PHQ-9) and the ‘The Patient Global Impression of Change Scale’. Conclusion: With increasing recognition of the mental health sequelae of COVID-19, there is a lack of large scale trials researching the acceptability or effectiveness of general practice interventions. Furthermore there is a lack of research regarding possible biological interventions (psychiatric medications) for mental health problems arising from the pandemic.

Introduction There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources. Aim The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources. Methods We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and Comparisons® and Clinical Pharmacology®. We compared the proportion, relative frequencies and rank of each category of adverse event from each source using MedDRA® primary System Organ Class codes. Results Compared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators. Conclusions Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients. Electronic supplementary material The online version of this article (10.1007/s40264-020-00998-1) contains supplementary material, which is available to authorized users.

In the last three decades, early intervention for psychosis (EIP) services have been established worldwide and have resulted in superior symptomatic and functional outcomes for people affected by psychotic disorders. These improved outcomes are a result of reducing delays to treatment and the provision of specialised, holistic interventions. The COVID-19 pandemic poses significant challenges to the delivery of these services, such as undetected cases or long delays to treatment. Furthermore, the COVID-19 pandemic will likely increase the mental health needs of communities, including the incidence of psychotic disorders. In this perspective piece, we provide suggestions as to how EIP services can adapt within this environment, such as utilising novel technologies. Finally, we argue that despite the economic consequences of the pandemic, the funding for mental health services, including EI services, should be increased in line with the need for these services during and beyond the pandemic.

BackgroundDespite that medication is taken during more than 90% of pregnancies, the fetal risk for most medications is unknown, and the majority of medications have no data regarding safety in pregnancy.ObjectiveUsing beta-blockers as a proof-of-concept, the primary objective of this study was to assess the utility of Twitter data for a cohort study design—in particular, whether we could identify (1) Twitter users who have posted tweets reporting that they took a beta-blocker during pregnancy and (2) their associated pregnancy outcomes.MethodsWe searched for mentions of beta-blockers in 2.75 billion tweets posted by 415,690 users who announced their pregnancy on Twitter. We manually reviewed the matching tweets to first determine if the user actually took the beta-blocker mentioned in the tweet. Then, to help determine if the beta-blocker was taken during pregnancy, we used the timestamp of the tweet reporting intake and drew upon an automated natural language processing (NLP) tool that estimates the date of the user’s prenatal time period. For users who posted tweets indicating that they took or may have taken the beta-blocker during pregnancy, we drew upon additional NLP tools to help identify tweets that report their adverse pregnancy outcomes, including miscarriage, stillbirth, preterm birth, low birth weight, birth defects, and neonatal intensive care unit admission.ResultsWe retrieved 5114 tweets, posted by 2339 users, that mention a beta-blocker, and manually identified 2332 (45.6%) tweets, posted by 1195 (51.1%) of the users, that self-report taking the beta-blocker. We were able to estimate the date of the prenatal time period for 356 pregnancies among 334 (27.9%) of these 1195 users. Among these 356 pregnancies, we identified 257 (72.2%) during which the beta-blocker was or may have been taken. We manually verified an adverse pregnancy outcome—preterm birth, neonatal intensive care unit admission, low birth weight, birth defects, or miscarriage—for 38 (14.8%) of these 257 pregnancies.ConclusionsOur ability to detect pregnancy outcomes for Twitter users who posted tweets reporting that they took or may have taken a beta-blocker during pregnancy suggests that Twitter can be a complementary resource for cohort studies of drug safety in pregnancy.

Background: Existing studies have established an association between pregnancy, birth complications, and mental health in the first few weeks postpartum. However, there is no clear understanding of whether pregnancy and birth complications increase the risk of adverse maternal mental outcomes in the longer term. Research on maternal adverse mental health outcomes following pregnancy and birth complications beyond 12 months postpartum is scarce, and findings are inconsistent. Objective: This systematic review and meta-analysis will examine the available evidence on the association between pregnancy and birth complications and long-term adverse maternal mental health outcomes. Methods and analysis: We will include cohort, cross-sectional, and case-control studies in which a diagnosis of pregnancy and/or birth complication (preeclampsia, pregnancy loss, caesarean section, preterm birth, perineal laceration, neonatal intensive care unit admission, major obstetric haemorrhage, and birth injury/trauma) was reported and maternal mental disorders (depression, anxiety disorders, bipolar disorders, psychosis, and schizophrenia) after 12 months postpartum were the outcomes. A systematic search of PubMed, Embase, CINAHL, PsycINFO, and Web of Science will be conducted following a detailed search strategy until August 2022. Three authors will independently review titles and abstracts of all eligible studies, extract data using pre-defined standardised data extraction and assess the quality of each study using the Newcastle-Ottawa Scale. We will use random-effects meta-analysis for each exposure and outcome variable to calculate overall pooled estimates using the generic inverse variance method. This systematic review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Ethical consideration: The proposed systematic review and meta-analysis is based on published data; ethics approval is not required. The results will be presented at scientific meetings and publish in a peer-reviewed journal. PROSPERO registration: CRD42022359017

Opioid use disorder (OUD) is a major public health crisis for which buprenorphine-naloxone is an effective evidence-based treatment. Analysis of Reddit data yields detailed information about firsthand experiences with buprenorphine-naloxone that has the potential to inform treatment of OUD.We conducted a thematic analysis of posts about buprenorphine-naloxone from a Reddit forum in which Reddit users anonymously discuss topics related to opioid use. We used an application programming interface to retrieve posts about buprenorphine-naloxone, then applied natural language processing to generate meta-information and curate samples of salient posts. We manually categorized posts according to their content and conducted natural language processing-aided analysis of posts about buprenorphine tapering strategies, withdrawal symptoms, and adjunctive substances/behaviors useful in the tapering process.A total of 16,146 posts from 1933 redditors were retrieved from the /r/suboxone subreddit. Thematic analysis of sample posts (N = 200) revealed descriptions of personal experiences (74%), nonpersonal accounts (24%), and other content (2%). Among redditors who reported tapering to termination (N = 40), 0.063 mg and 0.125 mg were the most common termination doses. Fatigue, gastrointestinal disturbance, and mood disturbance were the most frequent adverse effects, and loperamide and vitamins/dietary supplements the most frequently discussed adverse effects adjunctive substances/behaviors respectively.Discussions on Reddit are rich in information about buprenorphine-naloxone. Information derived from analysis of Reddit posts about buprenorphine-naloxone may not be available elsewhere and may help providers improve treatment of people with OUD through better understanding of the experiences of people who have used buprenorphine-naloxone.

Toll-like receptors (TLRs) are crucial transmembrane receptors that form part of the innate immune response. They play a role in the recognition of various microorganisms and their elimination from the host. TLRs have been proposed as vital immunomodulators in the regulation of multiple neonatal stressors that extend beyond infection such as oxidative stress and pain. The immune system is immature at birth and takes some time to become fully established. As such, babies are especially vulnerable to sepsis at this early stage of life. Findings suggest a gestational age-dependent increase in TLR expression. TLRs engage with accessory and adaptor proteins to facilitate recognition of pathogens and their activation of the receptor. TLRs are generally upregulated during infection and promote the transcription and release of proinflammatory cytokines. Several studies report that TLRs are epigenetically modulated by chromatin changes and promoter methylation upon bacterial infection that have long-term influences on immune responses. TLR activation is reported to modulate cardiorespiratory responses during infection and may play a key role in driving homeostatic instability observed during sepsis. Although complex, TLR signaling and downstream pathways are potential therapeutic targets in the treatment of neonatal diseases. By reviewing the expression and function of key Toll-like receptors, we aim to provide an important framework to understand the functional role of these receptors in response to stress and infection in premature infants.

Background COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. Objective The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. Methods We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. Results We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies. Conclusions Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies.

BACKGROUND COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. OBJECTIVE The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. METHODS We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. RESULTS We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies. CONCLUSIONS Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies.

BackgroundCoronavirus Disease 2019 (Covid-19) during pregnancy is associated with an increased risk of maternal death, intensive care unit (ICU) admission, and preterm birth; however, many people who are pregnant refuse to receive Covid-19 vaccination because of a lack of safety data.ObjectiveThe objective of this preliminary study was to assess whether we could identify (1) users who have reported on Twitter that they received Covid-19 vaccination during pregnancy or the periconception period, and (2) reports of their pregnancy outcomes.MethodsWe searched for reports of Covid-19 vaccination in a large collection of tweets posted by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that report their pregnancy outcomes.ResultsUpon manually verifying the content of tweets detected automatically, we identified 150 users who reported on Twitter that they received at least one dose of Covid-19 vaccination during pregnancy or the periconception period. Among the 60 completed pregnancies, we manually verified at least one reported outcome for 45 (75%) of them.ConclusionsGiven the limited availability of data on Covid-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of Covid-19 vaccination in pregnant populations. Directions for future work include developing machine learning algorithms to detect a larger number of users for observational studies.

Background Pre-exposure prophylaxis (PrEP) is highly effective at preventing the acquisition of HIV. There is a substantial gap, however, between the number of people in the United States who have indications for PrEP and the number of them who are prescribed PrEP. Although Twitter content has been analyzed as a source of PrEP-related data (eg, barriers), methods have not been developed to enable the use of Twitter as a platform for implementing PrEP-related interventions. Objective Men who have sex with men (MSM) are the population most affected by HIV in the United States. Therefore, the objectives of this study were to (1) develop an automated natural language processing (NLP) pipeline for identifying men in the United States who have reported on Twitter that they are gay, bisexual, or MSM and (2) assess the extent to which they demographically represent MSM in the United States with new HIV diagnoses. Methods Between September 2020 and January 2021, we used the Twitter Streaming Application Programming Interface (API) to collect more than 3 million tweets containing keywords that men may include in posts reporting that they are gay, bisexual, or MSM. We deployed handwritten, high-precision regular expressions—designed to filter out noise and identify actual self-reports—on the tweets and their user profile metadata. We identified 10,043 unique users geolocated in the United States and drew upon a validated NLP tool to automatically identify their ages. Results By manually distinguishing true- and false-positive self-reports in the tweets or profiles of 1000 (10%) of the 10,043 users identified by our automated pipeline, we established that our pipeline has a precision of 0.85. Among the 8756 users for which a US state–level geolocation was detected, 5096 (58.2%) were in the 10 states with the highest numbers of new HIV diagnoses. Among the 6240 users for which a county-level geolocation was detected, 4252 (68.1%) were in counties or states considered priority jurisdictions by the Ending the HIV Epidemic initiative. Furthermore, the age distribution of the users reflected that of MSM in the United States with new HIV diagnoses. Conclusions Our automated NLP pipeline can be used to identify MSM in the United States who may be at risk of acquiring HIV, laying the groundwork for using Twitter on a large scale to directly target PrEP-related interventions at this population.

Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population. Identifying the demographics of social media users enables us to measure the representativeness. Extracting race or ethnicity from social media data can be difficult and researchers may choose from a multitude of different approaches. Methods: We present a scoping review to identify the methods used to extract race or ethnicity from Twitter datasets. We searched 16 electronic databases and carried out reference checking in order to identify relevant articles. Sifting of each record was undertaken independently by at least two researchers with any disagreement discussed. The research could be grouped by the methods applied to extract race or ethnicity.Results: From 1093 records we identified 56 that met our inclusion criteria. The majority focus on Twitter users based in the US. A range of types of data were used including Twitter profile -pictures, bios, and/or location, and the content in the tweets themselves. The methods used were wide ranging and included using manual inference, linkage to census data, commercial software, language/dialect recognition and machine learning. Not all studies evaluated their methods. Those that did found accuracy to vary from 45% to 93% with significantly lower accuracy identifying non-white race categories. There may be some ethical questions over some of the methods used, particularly using photos or dialect, as well as questions surrounding accuracy.Conclusion: There is no standard approach or guidelines for extracting race or ethnicity from Twitter or other social media. Social media researchers must use careful interpretation of race or ethnicity and not over-promise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers, and be guided by concerns of equity and social justice.

The increase of social media usage across the globe has fueled efforts in public health research for mining valuable information such as medication use, adverse drug effects and reports of viral infections that directly and indirectly affect human health. Despite its significance, such information can be incredibly rare on social media. Mining such non-traditional sources for disease monitoring requires natural language processing techniques for extracting symptom mentions and normalizing them to standard terminologies for interpretability. In this work, we present the first version of a social media mining tool called SEED that detects symptom and disease mentions from social media posts such as Twitter and DailyStrength and further normalizes them into the UMLS terminology. Using multi-corpus training and deep learning models, the tool achieves an overall F1 score of 0.85 for extracting mentions of symptoms on a health forum dataset and an F1 score of 0.72 on a balanced Twitter dataset significantly improving over previously systems on the datasets. We apply the tool on recently collected Twitter posts that self-report COVID19 symptoms to observe if the SEED system can extract novel diseases and symptoms that were absent in the training data. By doing so, we describe the advantages and shortcomings of the tool and suggest techniques to overcome the limitations. The study results also draw attention to the potential of multi-corpus training for performance improvements and the need for continual training on newly obtained data for consistent performance amidst the ever-changing nature of the social media vocabulary.

BackgroundThere has been an increasing recognition of the influence of social, behavioral, economic, and environmental factors on overall patient health. The purpose of this project was to leverage the ICD-10 codes to identify and link social determinants of health (SDoH) to patients with a high probability of utilizing acute care services and to determine if social service intervention reduced care utilization.MethodsWe analyzed retrospective data for active patients at a Department of Veterans Affairs Medical Center (VAMC) from 2015-2017. Eleven categories of SDoH were developed based on existing literature of the social determinants; the relevant ICD-10 codes were divided among these categories. Emergency Room (ER) visits, hospital admissions, and social work visits were determined for each patient in the cohort.ResultsIn a cohort of 44,401 patients, the presence of ICD-10 codes within the EHR in the 11 SDoH categories was positively correlated with increased acute care utilization. Veterans with at least one SDoH risk factor were 71% (95%CI: 68% - 75%) more likely to use the ED and 71% (95%CI: 65%-77%) more likely to be admitted to the hospital. Utilization decreased with social service interventions.ConclusionThis project demonstrates a potentially meaningful method to capture patient social risk profiles through existing EHR data in the form of ICD-10 codes, which can be used to identify the highest risk patients for intervention with the understanding that not all SDoH codes are uniformly used and some SDoHs may not be captured.

There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources.The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources.We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and ComparisonsCompared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators.Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients.

BackgroundA growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness.ObjectiveThis study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods.MethodsWe present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both.ResultsOf the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown.ConclusionsThere is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

Objective We address a first step toward using social media data to supplement current efforts in monitoring population-level medication nonadherence: detecting changes to medication treatment. Medication treatment changes, like changes to dosage or to frequency of intake, that are not overseen by physicians are, by that, nonadherence to medication. Despite the consequences, including worsening health conditions or death, 50% of patients are estimated to not take medications as indicated. Current methods to identify nonadherence have major limitations. Direct observation may be intrusive or expensive, and indirect observation through patient surveys relies heavily on patients’ memory and candor. Using social media data in these studies may address these limitations. Methods We annotated 9830 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended by a physician. We used active and transfer learning from 12 972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus. To validate our CNN and explore future directions, we annotated 1956 positive tweets as to whether they reflect nonadherence and categorized the reasons given. Results Our CNN achieved 0.50 F1-score on this new corpus. The manual analysis of positive tweets revealed that nonadherence is evident in a subset with 9 categories of reasons for nonadherence. Conclusion We showed that social media users publicly discuss medication treatment changes and may explain their reasons including when it constitutes nonadherence. This approach may be useful to supplement current efforts in adherence monitoring.

BACKGROUND In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. OBJECTIVE The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. METHODS Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out “reported speech” (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. RESULTS Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state–level geolocations. CONCLUSIONS We have made the 13,714 tweets identified in this study, along with each tweet’s time stamp and US state–level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

Background Evidence is accruing to suggest that microbiota-gut-brain signalling plays a regulatory role in cardiorespiratory physiology. Chronic intermittent hypoxia (CIH), modelling human sleep apnoea, affects gut microbiota composition and elicits cardiorespiratory morbidity. We investigated if treatment with prebiotics ameliorates cardiorespiratory dysfunction in CIH-exposed rats. Methods Adult male rats were exposed to CIH (96 cycles/day, 6.0% O2 at nadir) for 14 consecutive days with and without prebiotic supplementation (fructo- and galacto-oligosaccharides) beginning two weeks prior to gas exposures. Findings CIH increased apnoea index and caused hypertension. CIH exposure had modest effects on the gut microbiota, decreasing the relative abundance of Lactobacilli species, but had no effect on microbial functional characteristics. Faecal short-chain fatty acid (SCFA) concentrations, plasma and brainstem pro-inflammatory cytokine concentrations and brainstem neurochemistry were unaffected by exposure to CIH. Prebiotic administration modulated gut microbiota composition and diversity, altering gut-metabolic (GMMs) and gut-brain (GBMs) modules and increased faecal acetic and propionic acid concentrations, but did not prevent adverse CIH-induced cardiorespiratory phenotypes. Interpretation CIH-induced cardiorespiratory dysfunction is not dependant upon changes in microbial functional characteristics and decreased faecal SCFA concentrations. Prebiotic-related modulation of microbial function and resultant increases in faecal SCFAs were not sufficient to prevent CIH-induced apnoea and hypertension in our model. Our results do not exclude the potential for microbiota-gut-brain axis involvement in OSA-related cardiorespiratory morbidity, but they demonstrate that in a relatively mild model of CIH, sufficient to evoke classic cardiorespiratory dysfunction, such changes are not obligatory for the development of morbidity, but may become relevant in the elaboration and maintenance of cardiorespiratory morbidity with progressive disease. Funding Department of Physiology and APC Microbiome Ireland, University College Cork, Ireland. APC Microbiome Ireland is funded by Science Foundation Ireland, through the Government's National Development Plan.

Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

There is clear evidence of physiological effects of the gut microbiota on whole-body function in health and disease. Microbiota-gut-brain axis signalling is recognised as a key player in behavioural disorders such as depression and anxiety. Recent evidence suggests that the gut microbiota affects neurocontrol networks responsible for homeostatic functions that are essential for life. We consider the evidence suggesting the potential for the gut microbiota to shape cardiorespiratory homeostasis. In various animal models of disease, there is an association between cardiorespiratory morbidity and perturbed gut microbiota, with strong evidence in support of a role of the gut microbiota in the control of blood pressure. Interventions that target the gut microbiota or manipulate the gut-brain axis, such as short-chain fatty acid supplementation, prevent hypertension in models of obstructive sleep apnoea. Emerging evidence points to a role for the microbiota-gut-brain axis in the control of breathing and ventilatory responsiveness, relevant to cardiorespiratory disease. There is also evidence for an association between the gut microbiota and disease severity in people with asthma and cystic fibrosis. There are many gaps in the knowledge base and an urgent need to better understand the mechanisms by which gut health and dysbiosis contribute to cardiorespiratory control. Nevertheless, there is a growing consensus that manipulation of the gut microbiota could prove an efficacious adjunctive strategy in the treatment of common cardiorespiratory diseases, which are the leading causes of morbidity and mortality.

The COVID-19 pandemic is a global health emergency, the scale, speed and nature of which is beyond anything most of us have experienced in our lifetimes. The mental health burden associated with this pandemic is also likely to surpass anything we have previously experienced. In this editorial, we seek to anticipate the nature of this additional mental health burden and make recommendations on how to mitigate against and prepare for this significant increase in mental health service demand.