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.

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials.

BACKGROUND: Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms. METHODS: We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI). RESULTS: We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes. CONCLUSIONS: Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

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.

Alcohol, cardiovascular disease and industry funding: A co-authorship network analysis of epidemiological studies.

INTRODUCTION: Alcohol's effects on cardiovascular disease (CVD) are controversial. Alcohol industry actors have shown particular interest in this subject, and been extensively involved through research funding, and in other ways, generating concerns about bias, particularly in reviews. MATERIAL & METHODS: We conducted a co-authorship network analysis of the primary studies included within a previous co-authorship study of 60 systematic reviews on the impact of alcohol on CVD. Additionally, we examined the relationships between declared alcohol industry funding and network structure. RESULTS: There were 713 unique primary studies with 2832 authors published between 1969 and 2019 located within 229 co-authorship subnetworks. There was industry funding across subnetworks and approximately 8% of all papers declared industry funding. The largest subnetwork dominated, comprising 43% of all authors, with sparse evidence of substantial industry funding. The second largest subnetwork contained approximately 4% of all authors, with largely different industry funders involved. Harvard affiliated authors who at the review level formed co-authorship subnetworks with industry funded authors were seen at the primary study level to belong to the largest epidemiological subnetwork. A small number of key authors make extensive alcohol industry funding declarations. CONCLUSIONS: There was no straightforward relationship between co-authorship network formation and alcohol industry funding of epidemiological studies on alcohol and CVD. More fine-grained attention to patterns of alcohol industry funding and to key nodes may shed further light on how far industry funding may be responsible for conflicting findings on alcohol and CVD.

Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: A Retrospective Cohort Study Using Longitudinal Social Media Data.

Background: Preterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and, together with low birthweight, the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pregnancy is associated with an increased risk of preterm birth; however, data remain limited by trimester of infection. The ability to study COVID-19 infection during the earlier stages of pregnancy has been limited by available sources of data. The objective of this study was to use self-reports in large-scale, longitudinal social media data to assess the association between trimester of COVID-19 infection and preterm birth. Methods: In this retrospective cohort study, we used natural language processing and machine learning, followed by manual validation, to identify pregnant Twitter users and to search their longitudinal collection of publicly available tweets for reports of COVID-19 infection during pregnancy and, subsequently, a preterm birth or term birth (i.e., a gestational age ≥37 weeks) outcome. Among the users who reported their pregnancy on Twitter, we also identified a 1:1 age-matched control group, consisting of users with a due date prior to January 1, 2020-that is, without COVID-19 infection during pregnancy. We calculated the odds ratios (ORs) with 95% confidence intervals (CIs) to compare the overall rates of preterm birth for pregnancies with and without COVID-19 infection and by timing of infection: first trimester (weeks 1-13), second trimester (weeks 1427), or third trimester (weeks 28-36). Results: Through August 2022, we identified 298 Twitter users who reported COVID-19 infection during pregnancy, a preterm birth or term birth outcome, and maternal age: 94 (31.5%) with first-trimester infection, 110 (36.9%) second-trimester infection, and 95 (31.9%) third-trimester infection. In total, 26 (8.8%) of these 298 users reported preterm birth: 8 (8.5%) were infected during the first trimester, 7 (6.4%) were infected during the second trimester, and 12 (12.6%) were infected during the third trimester. In the 1:1 age-matched control group, 13 (4.4%) of the 298 users reported preterm birth. Overall, the risk of preterm birth was significantly higher for pregnancies with COVID-19 infection compared to those without (OR 2.1, 95% CI 1.06-4.16). In particular, the risk of preterm birth was significantly higher for pregnancies with COVID-19 infection during the third trimester (OR 3.17, CI 1.39-7.21). Conclusion: The results of our study suggest that COVID-19 infection particularly during the third trimester is associated with an increased risk of preterm birth.

Priorities for research to support local authority action on health and climate change: a study in England.

BACKGROUND: Evidence is needed to support local action to reduce the adverse health impacts of climate change and maximise the health co-benefits of climate action. Focused on England, the study identifies priority areas for research to inform local decision making. METHODS: Firstly, potential priority areas for research were identified from a brief review of UK policy documents, and feedback invited from public and policy stakeholders. This included a survey of Directors of Public Health (DsPH) in England, the local government officers responsible for public health. Secondly, rapid reviews of research evidence examined whether there was UK evidence relating to the priorities identified in the survey. RESULTS: The brief policy review pointed to the importance of evidence in two broad areas: (i) community engagement in local level action on the health impacts of climate change and (ii) the economic (cost) implications of such action. The DsPH survey (n = 57) confirmed these priorities. With respect to community engagement, public understanding of climate change's health impacts and the public acceptability of local climate actions were identified as key evidence gaps. With respect to economic implications, the gaps related to evidence on the health and non-health-related costs and benefits of climate action and the short, medium and longer-term budgetary implications of such action, particularly with respect to investments in the built environment. Across both areas, the need for evidence relating to impacts across income groups was highlighted, a point also emphasised by the public involvement panel. The rapid reviews confirmed these evidence gaps (relating to public understanding, public acceptability, economic evaluation and social inequalities). In addition, public and policy stakeholders pointed to other barriers to action, including financial pressures, noting that better evidence is insufficient to enable effective local action. CONCLUSIONS: There is limited evidence to inform health-centred local action on climate change. More evidence is required on public perspectives on, and the economic dimensions of, local climate action. Investment in locally focused research is urgently needed if local governments are to develop and implement evidence-based policies to protect public health from climate change and maximise the health co-benefits of local action.

Alcohol policy framing in South Africa during the early stages of COVID-19: using extraordinary times to make an argument for a new normal.

INTRODUCTION: Public health and alcohol industry actors compete to frame alcohol policy problems and solutions. Little is known about how sudden shifts in the political context provide moments for policy actors to re-frame alcohol-related issues. South Africa's temporary bans on alcohol sales during the COVID-19 pandemic offered an opportunity to study this phenomenon. METHODS: We identified Professor Charles Parry from the South African Medical Research Council as a key policy actor. Parry uses a Twitter account primarily to comment on alcohol-related issues in South Africa. We harvested his tweets posted from March 18 to August 31, 2020, coinciding with the first two alcohol sales bans. We conducted a thematic analysis of the tweets to understand how Parry framed alcohol policy evidence and issues during these 'extraordinary times.' RESULTS: Parry underlined the extent of alcohol-related harm during 'normal times' with scientific evidence and contested industry actors' efforts to re-frame relevant evidence in a coherent and well-constructed argument. Parry used the temporary sales restrictions to highlight the magnitude of the health and social harms resulting from alcohol consumption, particularly trauma, rather than the COVID-19 transmission risks. Parry portrayed the sales ban as a policy learning opportunity (or 'experiment') for South Africa and beyond. CONCLUSIONS: Crisis conditions can provide new openings for public health (and industry) actors to make salient particular features of alcohol and alcohol policy evidence.

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.

Reasons for Discontinuation or Change of Selective Serotonin Reuptake Inhibitors in Online Drug Reviews.

Importance: Selective serotonin reuptake inhibitors (SSRIs) are a commonly prescribed medication class to treat a variety of mental disorders. However, adherence to SSRIs is low, and uncovering the reasons for discontinuation among SSRI users is an important first step to improving medication persistence. Objective: To identify the reasons SSRIs are discontinued or changed, as reported by patients and caregivers in online drug reviews. Design, Setting, and Participants: This qualitative study used natural language processing and machine learning to extract mentions of changes in SSRI intake from 667 drug reviews posted on the online health forum WebMD from September 1, 2007, to August 31, 2021. The type of medication change, including discontinuation, switch to another medication, or dose change and the reason for the change were manually annotated. In each instance in which an adverse event was reported, the event was categorized using Medical Dictionary for Regulatory Activities primary system organ class (SOC) codes, and its relative frequency was compared with that in spontaneous reporting systems maintained by the US Food and Drug Administration and the UK Medicines and Healthcare Products Regulatory Agency. Main Outcomes and Measures: Reasons for SSRI medication change as assessed using SOC codes. Results: In total, 667 reviews posted by 659 patients or caregivers (516 [78%] of patients were female; 410 [62%] 25-54 years of age) were identified that indicated a medication change: 335 posts indicated SSRI discontinuation, 188 posts indicated dose change, and 179 posts indicated switched medications. Most authors 625 (95%) were patients. The most common reason for medication discontinuation or switching was adverse events experienced, and the most common reason for dose change was titration. Both uptitration and downtitration were initiated by either a health care professional or patient. The most common adverse events were classified by SOC codes as psychiatric disorders, including insomnia, loss of libido, and anxiety. Compared with those in regulatory data, psychiatric adverse events, adverse events recorded by investigations (mostly weight gain) and adverse events associated with the reproductive system (mostly erectile dysfunction) were reported disproportionately more often. Conclusions and Relevance: This qualitative study of online drug reviews found that useful information was provided directly by patients or their caregivers regarding their medication behavior, specifically, information regarding SSRI treatment changes that may inform interventions to improve adherence. These findings suggest that these reported adverse events may be associated with SSRI persistence and that people may feel more inclined to report such events on social media than to clinicians or regulatory agencies.

Exploring gender differences in uptake of GP partnership roles: a qualitative mixed-methods study.

BACKGROUND: The unadjusted gender pay gap in general practice is reported to be 33.5%. This reflects partly the differential rate at which women become partners, but evidence exploring gender differences in GPs' career progression is sparse. AIM: To explore factors affecting uptake of partnership roles, focusing particularly on gender differences. DESIGN AND SETTING: Convergent mixed-methods research design using data from UK GPs. METHOD: Secondary analysis of qualitative interviews and social media analysis of UK GPs' Twitter commentaries, which informed the conduct of asynchronous online focus groups. Findings were combined using methodological triangulation. RESULTS: The sample comprised 40 GP interviews, 232 GPs tweeting about GP partnership roles, and seven focus groups with 50 GPs. Factors at individual, organisational, and national levels influence partnership uptake and career decisions of both men and women GPs. Desire for work-family balance (particularly childcare responsibilities) presented the greatest barrier, for both men and women, as well as workload, responsibility, financial investment, and risk. Greater challenges were, however, reported by women, particularly regarding balancing work-family lives, as well as prohibitive working conditions (including maternity and sickness pay) and discriminatory practices perceived to favour men and full-time GPs. CONCLUSION: There are some long-standing gendered barriers that continue to affect the career decisions of women GPs. The relative attractiveness of salaried, locum, or private roles in general practice appears to discourage both men and women from partnerships presently. Promoting positive workplace cultures through strong role models, improved flexibility in roles, and skills training could potentially encourage greater uptake.

Psychological well-being of hospice staff: systematic review.

BACKGROUND: Poor psychological well-being among healthcare staff has implications for staff sickness and absence rates, and impacts on the quality, cost and safety of patient care. Although numerous studies have explored the well-being of hospice staff, study findings vary and the evidence has not yet been reviewed and synthesised. Using job demands-resources (JD-R) theory, this review aimed to investigate what factors are associated with the well-being of hospice staff. METHODS: We searched MEDLINE, CINAHL and PsycINFO for peer-reviewed quantitative, qualitative or mixed-methods studies focused on understanding what contributes to the well-being of hospice staff who provide care to patients (adults and children). The date of the last search was 11 March 2022. Studies were published from 2000 onwards in the English language and conducted in Organisation for Economic Co-operation and Development countries. Study quality was assessed using the Mixed Methods Appraisal Tool. Data synthesis was conducted using a result-based convergent design, which involved an iterative, thematic approach of collating data into distinct factors and mapping these to the JD-R theory. RESULTS: A total of 4016 unique records were screened by title and abstract, 115 full-text articles were retrieved and reviewed and 27 articles describing 23 studies were included in the review. The majority of the evidence came from studies of staff working with adult patients. Twenty-seven individual factors were identified in the included studies. There is a strong and moderate evidence that 21 of the 27 identified factors can influence hospice staff well-being. These 21 factors can be grouped into three categories: (1) those that are specific to the hospice environment and role, such as the complexity and diversity of the hospice role; (2) those that have been found to be associated with well-being in other similar settings, such as relationships with patients and their families; and (3) those that affect workers regardless of their role and work environment, that is, that are not unique to working in a healthcare role, such as workload and working relationships. There was strong evidence that neither staff demographic characteristics nor education level can influence well-being. DISCUSSION: The factors identified in this review highlight the importance of assessing both positive and negative domains of experience to determine coping interventions. Hospice organisations should aim to offer a wide range of interventions to ensure their staff have access to something that works for them. These should involve continuing or commencing initiatives to protect the factors that make hospices good environments in which to work, as well as recognising that hospice staff are also subject to many of the same factors that affect psychological well-being in all work environments. Only two studies included in the review were set in children's hospices, suggesting that more research is needed in these settings. PROSPERO REGISTRATION NUMBER: CRD42019136721 (Deviations from the protocol are noted in Table 8, Supplementary material).

Characterizing asset-based studies in public health: development of a framework.

Asset-based approaches are becoming more common within public health interventions; however, due to variations in terminology, it can be difficult to identify asset-based approaches. The study aimed to develop and test a framework that could distinguish between asset-based and deficit-based community studies, whilst acknowledging there is a continuum of approaches. Literature about asset-based and deficit-based approaches were reviewed and a framework was developed based on the Theory of Change model. A scoring system was developed for each of the five elements in the framework based on this model. Measurement of community engagement was built in, and a way of capturing how much the study involved an asset approach. The framework was tested on 13 studies examining community-based interventions to investigate whether it could characterize asset-based versus deficit-based studies. The framework demonstrated how much the principles underpinning asset-based approaches were present and distinguished between studies where the approach was deficit-based to those that had some elements of an asset-based approach. This framework is useful for researchers and policymakers when determining how much of an intervention is asset-based and identifying which elements of asset-based approaches lead to an intervention working.

Deficit-based approaches are a common approach to addressing public health issues within a community and involve identifying a health problem or need and finding a way to solve these. However, asset-based approaches, those that involve the community using its assets, or strengths, to enable community members to have more control over their health and wellbeing, are increasingly common. The terminology used to describe these methods varies greatly so it can be difficult to identify whether an approach is more deficit-based or asset-based. To address this a framework was developed to identify and score elements of asset-based studies. We did this by reviewing academic information describing asset-based approaches and built into this a scoring system. This framework was used to assess and measure the degree to which 13 community-based studies took an asset-based approach. The framework was able to identify studies which were more asset-based in their approach compared to those which were more deficit-focused, acknowledging that some studies may have elements of each approach. This framework will be useful for people working in health policy and research who want a resource to help identify asset-based approaches in practice and which aspects of the approach were important for its success in the community.

Cardiovascular health promotion: A systematic review involving effectiveness of faith-based institutions in facilitating maintenance of normal blood pressure.

Globally, faith institutions have a range of beneficial social utility, but a lack of understanding remains regarding their role in cardiovascular health promotion, particularly for hypertension. Our objective was assessment of modalities, mechanisms and effectiveness of hypertension health promotion and education delivered through faith institutions. A result-based convergent mixed methods review was conducted with 24 databases including MEDLINE, Embase and grey literature sources searched on 30 March 2021, results independently screened by three researchers, and data extracted based on behaviour change theories. Quality assessment tools were selected by study design, from Cochrane risk of bias, ROBINS I and E, and The Joanna Briggs Institute's Qualitative Assessment and Review Instrument tools. Twenty-four publications contributed data. Faith institution roles include cardiovascular health/disease teaching with direct lifestyle linking, and teaching/ encouragement of personal psychological control. Also included were facilitation of: exercise/physical activity as part of normal lifestyle, nutrition change for cardiovascular health, cardiovascular health measurements, and opportunistic blood pressure checks. These demand relationships of trust with local leadership, contextualisation to local sociocultural realities, volitional participation but prior consent by faith / community leaders. Limited evidence for effectiveness: significant mean SBP reduction of 2.98 mmHg (95%CI -4.39 to -1.57), non-significant mean DBP increase of 0.14 mmHg (95%CI -2.74 to +3.01) three months after interventions; and significant mean SBP reduction of 0.65 mmHg (95%CI -0.91 to -0.39), non-significant mean DBP reduction of 0.53 mmHg (95%CI -1.86 to 0.80) twelve months after interventions. Body weight, waist circumference and multiple outcomes beneficially reduced for cardiovascular health: significant mean weight reduction 0.83kg (95% CI -1.19 to -0.46), and non-significant mean waist circumference reduction 1.48cm (95% CI -3.96 to +1.00). In addressing the global hypertension epidemic the cardiovascular health promotion roles of faith institutions probably hold unrealised potential. Deliberate cultural awareness, intervention contextualisation, immersive involvement of faith leaders and alignment with religious practice characterise their deployment as healthcare assets.

Updated generic search filters for finding studies of adverse drug effects in Ovid medline and Embase may retrieve up to 90% of relevant studies.

BACKGROUND: The most current objectively derived search filters for adverse drug effects are 15 years old and other strategies have not been developed and tested empirically. OBJECTIVE: To develop and validate search filters to retrieve evidence on adverse drug effects from Ovid medline and Ovid Embase. METHODS: We identified systematic reviews of adverse drug effects in Epistemonikos. From these reviews, we collated their included studies which we then randomly divided into three tests and one validation set of records. We constructed a search strategy to maximise relative recall using word frequency analysis with test set one. This search strategy was then refined using test sets two and three and validated on the final set of records. RESULTS: Of 107 systematic reviews which met our inclusion criteria, 1948 unique included studies were available from medline and 1980 from Embase. Generic adverse drug effects searches in medline and Embase achieved 90% and 89% relative recall, respectively. When specific adverse effects terms were added recall was improved. CONCLUSION: We have derived and validated search filters that retrieve around 90% of records with adverse drug effects data in medline and Embase. The addition of specific adverse effects terms is required to achieve higher recall.

The emperor has no clothes: a synthesis of findings from the Transformative Research on the Alcohol industry, Policy and Science research programme.

BACKGROUND AND AIMS: The Transformative Research on the Alcohol industry, Policy and Science (TRAPS) programme investigates the alcohol industry, with an innovative focus on public health sciences. TRAPS adds to an under-developed literature on the study of alcohol industry influence on alcohol science and policymaking. This paper provides a synthesis of TRAPS findings to inform future research. METHODS: We conducted an interpretive review of TRAPS research findings across its component studies, identifying and integrating the key contributions made by individual studies to the literature on alcohol policymaking and science, and identifying areas where TRAPS progress was limited. This produced themes for consideration in future research agenda setting. RESULTS: TRAPS explored the interventions of the alcohol industry in science and policymaking using various methods, including systematic reviews and qualitative interviews. These studies identified the industry's activities in several key areas, such as the debate over minimum unit pricing (MUP), cardiovascular health and alcohol research and a long-running public relations programme developed in close connection with the tobacco industry. Collectively, the research shows that alcohol policymaking has involved a contest between the research community and alcohol industry actors about whether and how science should be used to inform policy. CONCLUSIONS: The TRAPS programme demonstrates the need for a transdisciplinary approach to understand the nature of corporate political activity; the crucial role industry involvement in science plays in the development of corporate political power; and how public health actors have successfully overcome industry opposition to evidence-based policies. Advances in alcohol policy should be underpinned by strong, reflexive public health sciences, alert to the role of industry in the alcohol harms under study and thorough in their investigation of the alcohol industry as an object of study in itself.

General practitioners' wellbeing during the COVID-19 pandemic: Novel methods with social media data.

BACKGROUND: It is difficult to engage busy healthcare professionals in research. Yet during the COVID-19 pandemic, gaining their perspectives has never been more important. OBJECTIVE: To explore social media data for insights into the wellbeing of UK General Practitioners (GPs) during the Covid-19 pandemic. METHODS: We used a combination of search approaches to identify 381 practising UK NHS GPs on Twitter. Using a two stage social media analysis, we firstly searched for key themes from 91,034 retrieved tweets (before and during the pandemic). Following this we used qualitative content analysis to provide in-depth insights from 7145 tweets related to wellbeing. RESULTS: Social media proved a useful tool to identify a cohort of UK GPs; following their tweets longitudinally to explore key themes and trends in issues related to GP wellbeing during the pandemic. These predominately related to support, resources and public perceptions and fluctuations were identified at key timepoints during the pandemic, all achieved without burdening busy GPs. CONCLUSION: Social media data can be searched to identify a cohort of GPs to explore their wellbeing and changes over time.

Patient-Reported Reasons for Switching or Discontinuing Statin Therapy: A Mixed Methods Study Using Social Media.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.