We aimed to quantify attention-deficit hyperactivity disorder (ADHD) and autism assessment requests, and explore correlations with public interest and COVID-19 restrictions. We collected data on referrals to adult ADHD or autism services, Google searches for ‘autism’ or ‘ADHD’, birth gender ratios, ADHD prescriptions in England and COVID-19 restriction measures in four countries.

ADHD assessment demand tripled from July 2020 to January 2023, with Google searches for ADHD rising in parallel. Autism referrals and searches saw smaller, similarly timed rises. Female referrals outstripped males. ADHD prescriptions rose particularly in those aged 30–34 years. Google searches for ADHD unexpectedly rose from July 2020 in four countries, correlating with sustained intensity of national COVID-19 restrictions.

Public interest may have driven demand for ADHD assessments, with COVID-19 restrictions encouraging social media use facilitated by easy electronic information access. The public has decided that ADHD is important, independent of professional views. It is now critical that a consensus is reached to determine who benefits most from an ADHD diagnosis and medication.

Emergency department (ED) visits for epilepsy are common, costly, and often clinically unnecessary. Configuration of care pathways (CPs) that could divert people away from ED offer an alternative. The aim was to measure patient and carer preferences for alternative CPs and to explore the feasibility of implementing the preferred CPs in the National Health Service (NHS) England with a wider group of stakeholders.

Formative work (provider survey, service-user interviews, knowledge exchange, and think-aloud piloting) informed a discrete choice experiment (DCE) with six attributes: access to care plan, conveyance, time, epilepsy specialist today, general practitioner (GP) notification, and epilepsy specialist follow-up. This was hosted online with random assignment to two of three scenarios (home, public, or atypical). Logistic regression generated preference weights that were used to calculate the utility of CPs. The highest ranked CPs plus a status quo were discussed at three online knowledge exchange workshops. The nominal group technique was used to ascertain stakeholder views on preference evidence and to seek group consensus on optimal feasible alternatives.

A sample of 427 people with epilepsy and 167 friends or family completed the survey. People with epilepsy preferred paramedics to have access to care plan, non-conveyance, one to three hours, epilepsy specialists today, GP notification, and specialist follow-up within two to three weeks. Family and friends differed when considering atypical seizures, favoring conveyance to urgent treatment centers and shorter time. Optimal configuration of services from service users’ perspectives outranked current practice. Knowledge exchange (n=27 participants) identified the optimal CP as feasible but identified two scenarios for resource reallocation: care plan substitutes specialist advice today and times of strain on NHS resources.

Preferences differed to current practice but had minimal variation by seizure type or stakeholder. This study clearly identified optimal and feasible alternative CPs. The mixed-methods approach allowed for robust measurement of preferences, whilst knowledge exchange examined feasibility to enhance implementation of optimal alternative CPs in the future.

A collaborative evaluation of remote consultations in mental health services was undertaken by mental health service providers, experts by experience, academic institutions and a Health Innovation Network in south London, UK. ‘Learning healthcare systems’ thinking was applied. Workstream 1 reviewed international published evidence; workstream 2 synthesised findings from three health provider surveys of the perceptions and experiences of staff, patients and carers; and workstream 3 comprised an electronic survey on local projects.

Remote consultations can be acceptable to patients and staff. They improve access for some while restricting access for others, with digital exclusion being a key concern. Providing tailored choice is key.

The collaboration generated learning to inform choices by healthcare providers to embed or adapt remote delivery. A key output was freely downloadable survey questions for assessing the quantity and quality of appointments undertaken by phone or video or face to face.

Recruiting underrepresented people and communities in research is essential for generalizable findings. Ensuring representative participants can be particularly challenging for practice-level dissemination and implementation trials. Novel use of real-world data about practices and the communities they serve could promote more equitable and inclusive recruitment.

We used a comprehensive primary care clinician and practice database, the Virginia All-Payers Claims Database, and the HealthLandscape Virginia mapping tool with community-level socio-ecological information to prospectively inform practice recruitment for a study to help primary care better screen and counsel for unhealthy alcohol use. Throughout recruitment, we measured how similar study practices were to primary care on average, mapped where practices’ patients lived, and iteratively adapted our recruitment strategies.

In response to practice and community data, we adapted our recruitment strategy three times; first leveraging relationships with residency graduates, then a health system and professional organization approach, followed by a community-targeted approach, and a concluding approach using all three approaches. We enrolled 76 practices whose patients live in 97.3% (1844 of 1907) of Virginia’s census tracts. Our overall patient sample had similar demographics to the state for race (21.7% vs 20.0% Black), ethnicity (9.5% vs 10.2% Hispanic), insurance status (6.4% vs 8.0% uninsured), and education (26.0% vs 32.5% high school graduate or less). Each practice recruitment approach uniquely included different communities and patients.

Data about primary care practices and the communities they serve can prospectively inform research recruitment of practices to yield more representative and inclusive patient cohorts for participation.

Major depressive disorder (MDD) was previously associated with negative affective biases. Evidence from larger population-based studies, however, is lacking, including whether biases normalise with remission. We investigated associations between affective bias measures and depressive symptom severity across a large community-based sample, followed by examining differences between remitted individuals and controls.

Participants from Generation Scotland (N = 1109) completed the: (i) Bristol Emotion Recognition Task (BERT), (ii) Face Affective Go/No-go (FAGN), and (iii) Cambridge Gambling Task (CGT). Individuals were classified as MDD-current (n = 43), MDD-remitted (n = 282), or controls (n = 784). Analyses included using affective bias summary measures (primary analyses), followed by detailed emotion/condition analyses of BERT and FAGN (secondary analyses).

For summary measures, the only significant finding was an association between greater symptoms and lower risk adjustment for CGT across the sample (individuals with greater symptoms were less likely to bet more, despite increasingly favourable conditions). This was no longer significant when controlling for non-affective cognition. No differences were found for remitted-MDD v. controls. Detailed analysis of BERT and FAGN indicated subtle negative biases across multiple measures of affective cognition with increasing symptom severity, that were independent of non-effective cognition [e.g. greater tendency to rate faces as angry (BERT), and lower accuracy for happy/neutral conditions (FAGN)]. Results for remitted-MDD were inconsistent.

This suggests the presence of subtle negative affective biases at the level of emotion/condition in association with depressive symptoms across the sample, over and above those accounted for by non-affective cognition, with no evidence for affective biases in remitted individuals.

Without a robust evidence base to support recommendations for medical services at mass gatherings (MGs), levels of care will continue to vary and preventable morbidity and mortality will exist. Accordingly, researchers and clinicians publish case reports and case series to capture and explain some of the health interventions, health outcomes, and host community impacts of MGs. Streamlining and standardizing post-event reporting for MG medical services and associated health outcomes could improve inter-event comparability, thereby supporting and promoting growth of the evidence base for this discipline. The present paper is focused on theory building, proposing a set of domains for data that may support increasingly comprehensive, yet lean, reporting on the health outcomes of MGs. This paper is paired with another presenting a proposal for a post-event reporting template.

The conceptual categories of data presented are based on a textual analysis of 54 published post-event medical case reports and a comparison of the features of published data models for MG health outcomes.

A comparison of existing data models illustrates that none of the models are explicitly informed by a conceptual lens. Based on an analysis of the literature reviewed, four data domains emerged. These included: (i) the Event Domain, (ii) the Hazard and Risk Domain, (iii) the Capacity Domain, and (iv) the Clinical Domain. These domains mapped to 16 sub-domains.

Data modelling for the health outcomes related to MGs is currently in its infancy. The proposed illustration is a set of operationally relevant data domains that apply equally to small, medium, and large-sized events. Further development of these domains could move the MG community forward and shift post-event health outcomes reporting in the direction of increasing consistency and comprehensiveness.

Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

Without a robust evidence base to support recommendations for first aid, health, and medical services at mass gatherings (MGs), levels of care will continue to vary. Streamlining and standardizing post-event reporting for MG medical services could improve inter-event comparability, and prospectively influence event safety and planning through the application of a research template, thereby supporting and promoting growth of the evidence base and the operational safety of this discipline. Understanding the relationships between categories of variables is key. The present paper is focused on theory building, providing an evolving conceptual model, laying the groundwork for exploring the relationships between categories of variables pertaining the health outcomes of MGs.

A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.

A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into “inputs,” “modifiers,” “actuals,” and “outputs” and organized temporally into pre-, during, post-event, and reporting phases.

Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.

Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting.

Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources.

Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain.

The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as “overlays,” depending on the event category and type.

This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.

This review discusses the need for consistency in mass-gathering research and evaluation from a psychosocial perspective.

Mass gatherings occur frequently throughout the world. Having an understanding of the complexities of mass gatherings is important to determine required health resources. Factors within the environmental, psychosocial, and biomedical domains influence the usage of health services at mass gatherings. A standardized approach to data collection is important to identify a consistent reporting standard for the psychosocial domain.

This research used an integrative literature review design. Manuscripts were collected using keyword searches from databases and journal content pages from 2003 through 2018. Data were analyzed and categorized using the existing minimum data set as a framework.

In total, 31 manuscripts met the inclusion criteria. The main variables identified were use of alcohol or drugs, crowd behavior, crowd mood, rationale, and length of stay.

Upon interrogating the literature, the authors have determined that the variables fall under the categories of alcohol or drugs; maladaptive and adaptive behaviors; crowd behavior, crowd culture, and crowd mood; reason for attending event (motivation); duration; and crowd demographics. In collecting psychosocial data from mass gatherings, an agreed-upon set of variables that can be used to collect de-identified psychosocial variables for the purpose of making comparisons across societies for mass-gathering events (MGEs) would be invaluable to researchers and event clinicians.

This paper discusses the need for consistency in mass-gathering research and evaluation from an environmental reporting perspective.

Mass gatherings occur frequently throughout the world. Having an understanding of the complexities of mass gatherings is important to inform health services about the possible required health resources. Factors within the environmental, psychosocial, and biomedical domains influence the usage of health services at mass gatherings. A minimum data set (MDS) has been proposed to standardize collection of biomedical data across various mass gatherings, and there is a need for an environmental component. The environmental domain includes factors such as the nature of the event, availability of drugs or alcohol, venue characteristics, and meteorological factors.

This research used an integrative literature review design. Manuscripts were collected using keyword searches from databases and journal content pages from 2003 through 2018. Data were analyzed and categorized using the existing MDS as a framework.

In total, 39 manuscripts were identified that met the inclusion criteria.

In collecting environmental data from mass gatherings, there must be an agreed-upon MDS. A set of variables can be used to collect de-identified environmental variables for the purpose of making comparisons across societies for mass-gathering events (MGEs).