Throughout the coronavirus disease 2019 (COVID-19) pandemic, health and social care workers have faced unprecedented professional demands, all of which are likely to have placed considerable strain on their psychological well-being.

To measure the national prevalence of mental health symptoms within healthcare staff, and identify individual and organisational predictors of well-being.

The COVID-19 Staff Wellbeing Survey is a longitudinal online survey of psychological well-being among health and social care staff in Northern Ireland. The survey included four time points separated by 3-month intervals; time 1 (November 2020; n = 3834) and time 2 (February 2021; n = 2898) results are presented here. At time 2, 84% of respondents had received at least one dose of a COVID-19 vaccine. The survey included four validated psychological well-being questionnaires (depression, anxiety, post-traumatic stress and insomnia), as well as demographic and organisational measures.

At time 1 and 2, a high proportion of staff reported moderate-to-severe symptoms of depression (30–36%), anxiety (26–27%), post-traumatic stress (30–32%) and insomnia (27–28%); overall, significance tests and effect size data suggested psychological well-being was generally stable between November 2020 and February 2021 for health and social care staff. Multiple linear regression models indicated that perceptions of less effective communication within their organisation predicted greater levels of anxiety, depression, post-traumatic stress and insomnia.

This study highlights the need to offer psychological support to all health and social care staff, and to communicate with staff regularly, frequently and clearly regarding COVID-19 to help protect staff psychological well-being.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Accounts of patient experiences are increasingly used in health technology assessment (HTA) processes. However, we know little about their impact on the decision-making process. This study aims to assess the level and the type of impact of patient input to highly specialised technologies (HSTs) and interventional procedures (IPs) guidance at the National Institute for Health and Care Excellence (NICE).

A questionnaire was developed to capture quantitative and qualitative data on the amount and type of impact of patient input into NICE HTAs. It was completed by committee members of the guidance-producing programs after a discussion of the considered topics. The data were analyzed by topic and overall, for each program, and compared across programs.

Patient input was assessed on ten pieces of HST guidance published between January 2015 and November 2019, and on twenty-six pieces of IP guidance scoped between February 2016 and October 2018. A total of 96 responses were collected for HST and 440 for IP. The level of impact of patient input was higher for HST than for IP. For HST, no respondents stated that it had no impact, whereas in IP, 35 percent of respondents did. The most common types of impact found for HST and IP were that it helped interpret the other evidence and that it provided new evidence.

The impact of patient input is not necessarily explicit in changing recommendations, but it provides context, reassurance, and new information to the committee for the decision-making process in HTAs.

Reimbursement agencies are increasingly using patient preference data to evaluate health technologies. Discrete choice experiments (DCE) are commonly used to elicit patient preferences, but they require large sample sizes to obtain meaningful results. For this reason, it is often not possible to use DCE to elicit patient preferences in rare diseases. This study assessed a swing weighting method for eliciting preferences from a small sample: patients with immunoglobulin A nephropathy (IgAN) in the United States (US) and China.

Attributes and levels were selected based on a review of clinical studies and qualitative research on patients. Computer-assisted, interview-based swing weighting exercises were piloted in a focus group with five participants each from the US and China. Preferences were then elicited in interviews with twenty-five patients in the US and fifteen patients in China. Consistency tests were used to assess internal validity. Qualitative data were collected on the reasons for patients’ preferences.

Preference consistency: The weights for one attribute were elicited twice. The difference between initial and consistency test weights was not statistically significant (p < 0.1), although this may partly reflect the small sample sizes. Trade-offs: Qualitative data were used to demonstrate the validity of interpreting participants’ ratings as trade-offs. Using the partial value function for end-stage renal disease as an example, qualitative data demonstrated that patients were able to provide face-valid reasons for different shaped, non-linear preference functions. Robustness of treatment evaluation: Three hypothetical treatment profiles (using the attribute swings) were constructed. Preferences for these treatment profiles were robust to variations in patients’ preferences; all patients preferred one specific profile. This finding was not sensitive to changes in weights.

This study supports the feasibility of collecting valid and robust preference data from small groups of patients using swing weighting. Further work could be done to test the performance of swing weighting in larger sample sizes.

The current study aims to describe the Mediterranean diet (MD) adherence across the US regions, and explore the predictive factors of MD adherence among US adults.

Cross-sectional secondary data analysis. MD adherence score (0–9) was calculated using the Block 98 FFQ. Hot spot analysis was conducted to describe the geospatial distribution of MD adherence across the US regions. Logistic regression explored predictors of MD adherence.

Nationwide community-dwelling residency in the USA.

Adults aged ≥45 years (n 20 897) who participated in the REasons for Geographic and Racial Differences in Stroke study and completed baseline assessment during January 2003 and October 2007.

The mean of MD adherence score was 4·36 (sd 1·70), and 46·5 % of the sample had high MD adherence (score 5–9). Higher MD adherence clusters were primarily located in the western and northeastern coastal areas of the USA, whereas lower MD adherence clusters were majorly observed in south and east-north-central regions. Being older, black, not a current smoker, having a college degree or above, an annual household income ≥ $US 75K, exercising ≥4 times/week and watching TV/video <4 h/d were each associated with higher odds of high MD adherence.

There were significant geospatial and population disparities in MD adherence across the US regions. Future studies are needed to explore the causes of MD adherence disparities and develop effective interventions for MD promotion in the USA.

Emergency Medical Services (EMS) systems have developed protocols for prehospital activation of the cardiac catheterization laboratory for patients with suspected ST-elevation myocardial infarction (STEMI) to decrease first-medical-contact-to-balloon time (FMC2B). The rate of “false positive” prehospital activations is high. In order to decrease this rate and expedite care for patients with true STEMI, the American Heart Association (AHA; Dallas, Texas USA) developed the Mission Lifeline PreAct STEMI algorithm, which was implemented in Los Angeles County (LAC; California USA) in 2015. The hypothesis of this study was that implementation of the PreAct algorithm would increase the positive predictive value (PPV) of prehospital activation.

This is an observational pre-/post-study of the effect of the implementation of the PreAct algorithm for patients with suspected STEMI transported to one of five STEMI Receiving Centers (SRCs) within the LAC Regional System. The primary outcome was the PPV of cardiac catheterization laboratory activation for percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG). The secondary outcome was FMC2B.

A total of 1,877 patients were analyzed for the primary outcome in the pre-intervention period and 405 patients in the post-intervention period. There was an overall decrease in cardiac catheterization laboratory activations, from 67% in the pre-intervention period to 49% in the post-intervention period (95% CI for the difference, -14% to -22%). The overall rate of cardiac catheterization declined in post-intervention period as compared the pre-intervention period, from 34% to 30% (95% CI, for the difference -7.6% to 0.4%), but actually increased for subjects who had activation (48% versus 58%; 95% CI, 4.6%-15.0%). Implementation of the PreAct algorithm was associated with an increase in the PPV of activation for PCI or CABG from 37.9% to 48.6%. The overall odds ratio (OR) associated with the intervention was 1.4 (95% CI, 1.1-1.8). The effect of the intervention was to decrease variability between medical centers. There was no associated change in average FMC2B.

The implementation of the PreAct algorithm in the LAC EMS system was associated with an overall increase in the PPV of cardiac catheterization laboratory activation.