The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.

BACKGROUND: Adverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient's quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources. OBJECTIVE: This study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature). METHODS: In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions. RESULTS: Of the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner. CONCLUSIONS: There is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/47068.

Sensitivity of the familial high-risk approach for the prediction of future psychosis: a total population study.

Children who have a parent with a psychotic disorder present an increased risk of developing psychosis. It is unclear to date, however, what proportion of all psychosis cases in the population are captured by a familial high-risk for psychosis (FHR-P) approach. This is essential information for prevention research and health service planning, as it tells us the total proportion of psychosis cases that this high-risk approach would prevent if an effective intervention were developed. Through a prospective cohort study including all individuals born in Finland between January 1, 1987 and December 31, 1992, we examined the absolute risk and total proportion of psychosis cases captured by FHR-P and by a transdiagnostic familial risk approach (TDFR-P) based on parental inpatient hospitalization for any mental disorder. Outcomes of non-affective psychosis (ICD-10: F20-F29) and schizophrenia (ICD-10: F20) were identified in the index children up to December 31, 2016. Of the index children (N=368,937), 1.5% (N=5,544) met FHR-P criteria and 10.3% (N=38,040) met TDFR-P criteria. By the study endpoint, 1.9% (N=6,966) of the index children had been diagnosed with non-affective psychosis and 0.5% (N=1,846) with schizophrenia. In terms of sensitivity, of all non-affective psychosis cases in the index children, 5.2% (N=355) were captured by FHR-P and 20.6% (N=1,413) by TDFR-P approaches. The absolute risk of non-affective psychosis was 6.4% in those with FHR-P, and 3.7% in those with TDFR-P. There was notable variation in the sensitivity and total proportion of FHR-P and TDFR-P cases captured based on the age at which FHR-P/TDFR-P were determined. The absolute risk for psychosis, however, was relatively time invariant. These metrics are essential to inform intervention strategies for psychosis risk requiring pragmatic decision-making.

Mpox Discourse on Twitter by Sexual Minority Men and Gender-Diverse Individuals: Infodemiological Study Using BERTopic.

BACKGROUND: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals. OBJECTIVE: To fill this gap, this study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice. METHODS: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index. RESULTS: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81%), mpox vaccination (n=2242, 25.81%), and adverse events (n=85, 0.98%); sarcasm, jokes, and emotional expressions (n=1220, 14.04%); COVID-19 and mpox (n=636, 7.32%); government or public health response (n=532, 6.12%); mpox symptoms (n=238, 2.74%); case reports (n=192, 2.21%); puns on the naming of the virus (ie, mpox; n=75, 0.86%); media publicity (n=59, 0.68%); and mpox in children (n=58, 0.67%). Spearman rank correlation indicated significant negative correlation (ρ=-0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level. CONCLUSIONS: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data.

Pregnancy and birth complications and long-term maternal mental health outcomes: A systematic review and meta-analysis.

BACKGROUND: Few studies have examined the associations between pregnancy and birth complications and long-term (>12 months) maternal mental health outcomes. OBJECTIVES: To review the published literature on pregnancy and birth complications and long-term maternal mental health outcomes. SEARCH STRATEGY: Systematic search of Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PsycInfo®, PubMed® and Web of Science from inception until August 2022. SELECTION CRITERIA: Three reviewers independently reviewed titles, abstracts and full texts. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and appraised study quality. Random-effects meta-analyses were used to calculate pooled estimates. The Meta-analyses of Observational Studies in Epidemiology (MOOSE) guidelines were followed. The protocol was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022359017). MAIN RESULTS: Of the 16 310 articles identified, 33 studies were included (3 973 631 participants). Termination of pregnancy was associated with depression (pooled adjusted odds ratio, aOR 1.49, 95% CI 1.20-1.83) and anxiety disorder (pooled aOR 1.43, 95% CI 1.20-1.71). Miscarriage was associated with depression (pooled aOR 1.97, 95% CI 1.38-2.82) and anxiety disorder (pooled aOR 1.24, 95% CI 1.11-1.39). Sensitivity analyses excluding early pregnancy loss and termination reported similar results. Preterm birth was associated with depression (pooled aOR 1.37, 95% CI 1.32-1.42), anxiety disorder (pooled aOR 0.97, 95% CI 0.41-2.27) and post-traumatic stress disorder (PTSD) (pooled aOR 1.75, 95% CI 0.52-5.89). Caesarean section was not significantly associated with PTSD (pooled aOR 2.51, 95% CI 0.75-8.37). There were few studies on other mental disorders and therefore it was not possible to perform meta-analyses. CONCLUSIONS: Exposure to complications during pregnancy and birth increases the odds of long-term depression, anxiety disorder and PTSD.

Associations between soluble urokinase plasminogen activator receptor (suPAR) concentration and psychiatric disorders - A systematic review and meta-analysis.

BACKGROUND: There is some evidence of an association between inflammation in the pathogenesis of mental disorders. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker of chronic inflammation, which provides a more stable index of systemic inflammation than more widely used biomarkers. This review aims to synthesise studies that measured suPAR concentrations in individuals with a psychiatric disorder, to determine if these concentrations are altered in comparison to healthy participants. METHOD: Comprehensive literature searches from inception to October 2023 were conducted of five relevant databases (PubMed, Web of Science, Embase, Scopus, APA PsychInfo). Random-effects meta-analyses were performed to compare the standardised mean difference of blood suPAR levels (i.e. plasma or serum) for individuals with any psychiatric disorder relative to controls. Separate meta-analyses of suPAR levels were conducted for individuals with schizophrenia or other psychotic disorder and depressive disorder. Risk of bias was assessed using the Newcastle Ottawa Scale. Post-hoc sensitivity analyses included excluding studies at high risk of bias, and analyses of studies that measured suPAR concentrations either in serum or in plasma separately. RESULTS: The literature search identified 149 records. Ten full-text studies were screened for eligibility and 9 studies were included for review. Primary analyses revealed no significant difference in suPAR levels between individuals with any psychiatric disorder compared to controls (k = 7, SMD = 0.42, 95 % CI [-0.20, 1.04]). However, those with depressive disorder had elevated suPAR levels relative to controls (k = 3, SMD = 0.61, 95 % CI [0.34, 0.87]). Similarly, secondary analyses showed no evidence of a significant difference in suPAR levels in individuals with any psychiatric disorder when studies at high risk of bias were excluded (k = 6, SMD = 0.54, 95 % CI [-0.14, 1.22]), but elevated suPAR concentrations for those with schizophrenia or other psychotic disorder were found (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). Furthermore, studies that analysed plasma suPAR concentrations found elevated plasma suPAR levels in individuals with any psychiatric disorder relative to controls (k = 5, SMD = 0.84, 95 % CI [0.38, 1.29]), while studies measuring serum suPAR levels in any psychiatric disorder did not find a difference (k = 2, SMD = -0.61, 95 % CI [-1.27, 0.04]). For plasma, elevated suPAR concentrations were also identified for those with schizophrenia or other psychotic disorder (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). DISCUSSION: When studies measuring either only serum or only plasma suPAR were considered, no significant difference in suPAR levels were observed between psychiatric disorder groups, although significantly elevated suPAR levels were detected in those with moderate to severe depressive disorder. However, plasma suPAR levels were significantly elevated in those with any psychiatric disorder relative to controls, while no difference in serum samples was found. A similar finding was reported for schizophrenia or other psychotic disorder. The plasma findings suggest that chronic inflammatory dysregulation may contribute to the pathology of schizophrenia and depressive disorder. Future longitudinal studies are required to fully elucidate the role of this marker in the psychopathology of these disorders.

Health activism, vaccine, and mpox discourse: BERTopic based mixed-method analyses of tweets from sexual minority men and gender diverse (SMMGD) individuals in the U.S.

Objectives: To synthesize discussions among sexual minority men and gender diverse (SMMGD) individuals on mpox, given limited representation of SMMGD voices in existing mpox literature. Methods: BERTopic (a topic modeling technique) was employed with human validations to analyze mpox-related tweets (n = 8,688; October 2020-September 2022) from 2,326 self-identified SMMGD individuals in the U.S.; followed by content analysis and geographic analysis. Results: BERTopic identified 11 topics: health activism (29.81%); mpox vaccination (25.81%) and adverse events (0.98%); sarcasm, jokes, emotional expressions (14.04%); COVID-19 and mpox (7.32%); government/public health response (6.12%); mpox symptoms (2.74%); case reports (2.21%); puns on the virus' naming (i.e., monkeypox; 0.86%); media publicity (0.68%); mpox in children (0.67%). Mpox health activism negatively correlated with LGB social climate index at U.S. state level, ρ = -0.322, p = 0.031. Conclusions: SMMGD discussions on mpox encompassed utilitarian (e.g., vaccine access, case reports, mpox symptoms) and emotionally-charged themes-advocating against homophobia, misinformation, and stigma. Mpox health activism was more prevalent in states with lower LGB social acceptance. Public Health Implications: Findings illuminate SMMGD engagement with mpox discourse, underscoring the need for more inclusive health communication strategies in infectious disease outbreaks to control associated stigma.

Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

Risk of Psychosis Among Individuals Who Have Presented to Hospital With Self-harm: A Prospective Nationwide Register Study in Sweden.

BACKGROUND AND HYPOTHESIS: Recent research showed that young people who presented to hospital with self-harm in Finland had a significantly elevated risk of later psychosis. We investigated the prospective relationship between hospital presentation for self-harm and risk of psychosis in an unprecedentedly large national Swedish cohort. STUDY DESIGN: We used inpatient and outpatient healthcare registers to identify all individuals born between 1981 and 1993 who were alive and living in Sweden on their 12th birthday and who presented to hospital one or more times with self-harm. We compared them with a matched cohort, followed up for up to 20 years, and compared the cumulative incidence of psychotic disorders. Furthermore, we examined whether the strength of the relationship between hospital presentation for self-harm and later psychosis changed over time by examining for cohort effects. STUDY RESULTS: In total, 28 908 (2.0%) individuals presented to hospital with self-harm without prior psychosis diagnosis during the follow-up. For individuals who presented to hospital with self-harm, the cumulative incidence of diagnosed psychosis was 20.7% at 20 years follow-up (hazard radio = 13.9, 95% CI 13.3-14.6, P-value <5 × 10-308). There was no evidence of a dilution of the effect over time: while the incidence of hospital self-harm presentation increased, this did not result in an attenuation over time of the strength of the relationship between hospital self-harm presentation and subsequent psychosis. CONCLUSIONS: Individuals who present to hospital with self-harm in their teens and 20s represent an important risk group for psychosis prediction and prevention.

The Association between Threatened Miscarriage and Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder in Offspring by Age 14 Years.

OBJECTIVE: To examine the association between threatened miscarriage, and neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in offspring by age 14 years. METHODS: We used data from the Millennium Cohort Study, a nationally representative longitudinal study of children born in the UK. Data on threatened miscarriage and potential confounders were maternal-reported and collected at 9 months postpartum. Data on ASD and ADHD were based on maternal-reported doctor diagnoses and collected when children were aged 5, 7, 11 and 14 years. A diagnosis of ASD or ADHD was assumed if parents reported ASD or ADHD at age 5, 7, 11 or 14 years. Crude and adjusted logistic regression examined threatened miscarriage and ASD and ADHD relationship, adjusting for several sociodemographic, maternal and lifestyle factors. RESULTS: A total of 18,294 singleton babies were included at baseline, and 1,104 (6.0%) women experienced a threatened miscarriage during their pregnancy. Adjusted results suggested an association between threatened miscarriage and ASD (OR: 1.55, 95% CI 1.15, 2.08), and ADHD (OR: 1.51, 95% CI 1.09, 2.10) by age 14 years. E-values for threatened miscarriage and ASD were 2.47, while the lower limits of the 95% CI were 1.57. E-values for threatened miscarriage and ADHD were 2.39, while the corresponding lower limits of the 95% CI were 1.40. CONCLUSION: Threatened miscarriage was associated with an increased likelihood of ASD and ADHD by the age of 14 years, however, residual confounding cannot be ruled out. Placental pathology may be a potential mechanism for the observed associations.

Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis.

Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored. Methods: The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted. Discussion: This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

PhenoID, a language model normalizer of physical examinations from genetics clinical notes.

Background: Phenotypes identified during dysmorphology physical examinations are critical to genetic diagnosis and nearly universally documented as free-text in the electronic health record (EHR). Variation in how phenotypes are recorded in free-text makes large-scale computational analysis extremely challenging. Existing natural language processing (NLP) approaches to address phenotype extraction are trained largely on the biomedical literature or on case vignettes rather than actual EHR data. Methods: We implemented a tailored system at the Children's Hospital of Philadelpia that allows clinicians to document dysmorphology physical exam findings. From the underlying data, we manually annotated a corpus of 3136 organ system observations using the Human Phenotype Ontology (HPO). We provide this corpus publicly. We trained a transformer based NLP system to identify HPO terms from exam observations. The pipeline includes an extractor, which identifies tokens in the sentence expected to contain an HPO term, and a normalizer, which uses those tokens together with the original observation to determine the specific term mentioned. Findings: We find that our labeler and normalizer NLP pipeline, which we call PhenoID, achieves state-of-the-art performance for the dysmorphology physical exam phenotype extraction task. PhenoID's performance on the test set was 0.717, compared to the nearest baseline system (Pheno-Tagger) performance of 0.633. An analysis of our system's normalization errors shows possible imperfections in the HPO terminology itself but also reveals a lack of semantic understanding by our transformer models. Interpretation: Transformers-based NLP models are a promising approach to genetic phenotype extraction and, with recent development of larger pre-trained causal language models, may improve semantic understanding in the future. We believe our results also have direct applicability to more general extraction of medical signs and symptoms. Funding: US National Institutes of Health.

Patient-Reported Reasons for Antihypertensive Medication Change: A Quantitative Study Using Social Media.

INTRODUCTION: Hypertension is the leading cause of heart disease in the world, and discontinuation or nonadherence of antihypertensive medication constitutes a significant global health concern. Patients with hypertension have high rates of medication nonadherence. Studies of reasons for nonadherence using traditional surveys are limited, can be expensive, and suffer from response, white-coat, and recall biases. Mining relevant posts by patients on social media is inexpensive and less impacted by the pressures and biases of formal surveys, which may provide direct insights into factors that lead to non-compliance with antihypertensive medication. METHODS: This study examined medication ratings posted to WebMD, an online health forum that allows patients to post medication reviews. We used a previously developed natural language processing classifier to extract indications and reasons for changes in angiotensin receptor II blocker (ARB) and angiotensin-converting enzyme inhibitor (ACEI) treatments. After extraction, ratings were manually annotated and compared with data from the US Food and Drug administration (FDA) Adverse Events Reporting System (FAERS) public database. RESULTS: From a collection of 343,459 WebMD reviews, we automatically extracted 1867 posts mentioning changes in ACEIs or ARBs, and manually reviewed the 300 most recent posts regarding ACEI treatments and the 300 most recent posts regarding ARB treatments. After excluding posts that only mentioned a dose change or were a false-positive mention, 142 posts in the ARBs dataset and 187 posts in the ACEIs dataset remained. The majority of posts (97% ARBs, 91% ACEIs) indicated experiencing an adverse event as the reason for medication change. The most common adverse events reported mapped to the Medical Dictionary for Regulatory Activities were "musculoskeletal and connective tissue disorders" like muscle and joint pain for ARBs, and "respiratory, thoracic, and mediastinal disorders" like cough and shortness of breath for ACEIs. These categories also had the largest differences in percentage points, appearing more frequently on WebMD data than FDA data (p < 0.001). CONCLUSION: Musculoskeletal and respiratory symptoms were the most commonly reported adverse effects in social media postings associated with drug discontinuation. Managing such symptoms is a potential target of interventions seeking to improve medication persistence.

Measures of Social and Occupational Function in Early Psychosis: A Systematic Review and Meta-analysis.

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.

Pregnancy and birth complications associations with long-term adverse maternal mental health outcomes: a systematic review and meta-analysis protocol.

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.

Self-reported Xylazine Experiences: A Mixed-methods Study of Reddit Subscribers.

OBJECTIVES: Xylazine is an α 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? METHODS: Natural 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. RESULTS: Seventy-six posts 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. Sixty-one participants completed the survey. Of those who disclosed their location, 25 of 50 participants (50%) reported locations in the Northeastern United States. The most common route of xylazine use was intranasal use (57%). Thirty-one of 59 (53%) reported experiencing xylazine withdrawal. Frequent adverse events reported were prolonged sedation (81%) and increased skin wounds (43%). CONCLUSIONS: Among respondents on these Reddit forums, xylazine seems to be an unwanted adulterant. People who use drugs may be experiencing adverse effects such as prolonged sedation and xylazine withdrawal. This seemed to be more common in the Northeast.

Text mining biomedical literature to identify extremely unbalanced data for digital epidemiology and systematic reviews: dataset and methods for a SARS-CoV-2 genomic epidemiology study.

There are many studies that require researchers to extract specific information from the published literature, such as details about sequence records or about a randomized control trial. While manual extraction is cost efficient for small studies, larger studies such as systematic reviews are much more costly and time-consuming. To avoid exhaustive manual searches and extraction, and their related cost and effort, natural language processing (NLP) methods can be tailored for the more subtle extraction and decision tasks that typically only humans have performed. The need for such studies that use the published literature as a data source became even more evident as the COVID-19 pandemic raged through the world and millions of sequenced samples were deposited in public repositories such as GISAID and GenBank, promising large genomic epidemiology studies, but more often than not lacked many important details that prevented large-scale studies. Thus, granular geographic location or the most basic patient-relevant data such as demographic information, or clinical outcomes were not noted in the sequence record. However, some of these data was indeed published, but in the text, tables, or supplementary material of a corresponding published article. We present here methods to identify relevant journal articles that report having produced and made available in GenBank or GISAID, new SARS-CoV-2 sequences, as those that initially produced and made available the sequences are the most likely articles to include the high-level details about the patients from whom the sequences were obtained. Human annotators validated the approach, creating a gold standard set for training and validation of a machine learning classifier. Identifying these articles is a crucial step to enable future automated informatics pipelines that will apply Machine Learning and Natural Language Processing to identify patient characteristics such as co-morbidities, outcomes, age, gender, and race, enriching SARS-CoV-2 sequence databases with actionable information for defining large genomic epidemiology studies. Thus, enriched patient metadata can enable secondary data analysis, at scale, to uncover associations between the viral genome (including variants of concern and their sublineages), transmission risk, and health outcomes. However, for such enrichment to happen, the right papers need to be found and very detailed data needs to be extracted from them. Further, finding the very specific articles needed for inclusion is a task that also facilitates scoping and systematic reviews, greatly reducing the time needed for full-text analysis and extraction.

The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review.

BACKGROUND: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient's quality of life and adherence to intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on ADEs. While 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: The aim of the study is to evaluate and characterize the use of social media in ADE 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, hand-searching, and forward and backward citation searching. Records will be screened in Covidence by 2 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, and 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 ADEs detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract ADEs from social media data. We will also summarize how the data from social media compares to conventional data sources. The literature will be organized by the data source for comparison. Where possible, we will analyze the impact of the types of adverse events, the social media platform used, and the methods used. 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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47068.