Experience of crisis care may vary across different care models.

To explore the experience of care in standard care and ‘open dialogue’ (a peer-supported community service focused on open dialogue and involving social networks for adults with a recent mental health crisis) 3 months after a crisis.

We conducted semi-structured interviews with 11 participants (6 received open dialogue; 5 received treatment as usual (TAU)) in a feasibility study of open dialogue and analysed the data using a three-step inductive thematic analysis to identify themes that (a) were frequently endorsed and (b) represented the experiences of all participants.

Four themes emerged: (a) feeling able to rely on and access mental health services; (b) supportive and understanding family and friends; (c) having a choice and a voice; and (d) confusion and making sense of experiences. Generally, there was a divergence in experience across the two care models. Open dialogue participants often felt able to rely on and access services and involve their family and friends in their care. TAU participants described a need to rely on services and difficulty when it was not met, needing family and friends for support and wanting them to be more involved in their care. Some participants across both care models experienced confusion after a crisis and described benefits of sense-making.

Understanding crisis care experiences across different care models can inform service development in crisis and continuing mental healthcare services.

Adverse drug reactions (ADRs) are associated with increased morbidity, mortality, and resource utilization. Drug interactions (DDIs) are among the most common causes of ADRs, and estimates have cited that up to 22% of patients take interacting medications. DDIs are often due to the propensity for agents to induce or inhibit enzymes responsible for the metabolism of concomitantly administered drugs. However, this phenomenon is further complicated by genetic variants of such enzymes. The aim of this study is to quantify and describe potential drug-drug, drug-gene, and drug-drug-gene interactions in a community-based patient population.

A regional pharmacy with retail outlets in Arkansas provided deidentified prescription data from March 2020 for 4761 individuals. Drug-drug and drug-drug-gene interactions were assessed utilizing the logic incorporated into GenMedPro, a commercially available digital gene-drug interaction software program that incorporates variants of 9 pharmacokinetic (PK) and 2 pharmacodynamic (PD) genes to evaluate DDIs and drug-gene interactions. The data were first assessed for composite drug-drug interaction risk, and each individual was stratified to a risk category using the logic incorporated in GenMedPro. To calculate the frequency of potential drug-gene interactions, genotypes were imputed and allocated to the cohort according to each gene’s frequency in the general population. Potential genotypes were randomly allocated to the population 100 times in a Monte Carlo simulation. Potential drug-drug, gene-drug, or gene-drug-drug interaction risk was characterized as minor, moderate, or major.

Based on prescription data only, the probability of a DDI of any impact (mild, moderate, or major) was 26% [95% CI: 0.248-0.272] in the population. This probability increased to 49.6% [95% CI: 0.484-0.507] when simulated genetic polymorphisms were additionally assessed. When assessing only major impact interactions, there was a 7.8% [95% CI: 0.070-0.085] probability of drug-drug interactions and 10.1% [95% CI: 0.095-0.108] probability with the addition of genetic contributions. The probability of drug-drug-gene interactions of any impact was correlated with the number of prescribed medications, with an approximate probability of 77%, 85%, and 94% in patients prescribed 5, 6, or 7+ medications, respectively. When stratified by specific drug class, antidepressants (19.5%), antiemetics (21.4%), analgesics (16%), antipsychotics (15.6%), and antiparasitics (49.7%) had the highest probability of major drug-drug-gene interaction.

In a community-based population of outpatients, the probability of drug-drug interaction risk increases when genetic polymorphisms are attributed to the population. These data suggest that pharmacogenetic testing may be useful in predicting drug interactions, drug-gene interactions, and severity of interactions when proactively evaluating patient medication profiles.

Genomind, Inc.

Patients presenting to hospital with suspected coronavirus disease 2019 (COVID-19), based on clinical symptoms, are routinely placed in a cohort together until polymerase chain reaction (PCR) test results are available. This procedure leads to delays in transfers to definitive areas and high nosocomial transmission rates. FebriDx is a finger-prick point-of-care test (PoCT) that detects an antiviral host response and has a high negative predictive value for COVID-19. We sought to determine the clinical impact of using FebriDx for COVID-19 triage in the emergency department (ED).

We undertook a retrospective observational study evaluating the real-world clinical impact of FebriDx as part of an ED COVID-19 triage algorithm.

Emergency department of a university teaching hospital.

Patients presenting with symptoms suggestive of COVID-19, placed in a cohort in a ‘high-risk’ area, were tested using FebriDx. Patients without a detectable antiviral host response were then moved to a lower-risk area.

Between September 22, 2020, and January 7, 2021, 1,321 patients were tested using FebriDx, and 1,104 (84%) did not have a detectable antiviral host response. Among 1,104 patients, 865 (78%) were moved to a lower-risk area within the ED. The median times spent in a high-risk area were 52 minutes (interquartile range [IQR], 34–92) for FebriDx-negative patients and 203 minutes (IQR, 142–255) for FebriDx-positive patients (difference of −134 minutes; 95% CI, −144 to −122; P < .0001). The negative predictive value of FebriDx for the identification of COVID-19 was 96% (661 of 690; 95% CI, 94%–97%).

FebriDx improved the triage of patients with suspected COVID-19 and reduced the time that severe acute respiratory coronavirus virus 2 (SARS-CoV-2) PCR-negative patients spent in a high-risk area alongside SARS-CoV-2–positive patients.

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3–4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3–4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

Social anxiety disorder (SAD) is common. It usually starts in adolescence, and without treatment can disrupt key developmental milestones. Existing generic treatments are less effective for young people with SAD than with other anxiety disorders, but an adaptation of an effective adult therapy (CT-SAD-A) has shown promising results for adolescents.

The aim of this study was to conduct a qualitative exploration to contribute towards the evaluation of CT-SAD-A for adoption into Child and Adolescent Mental Health Services (CAMHS).

We used interpretative phenomenological analysis (IPA) to analyse the transcripts of interviews with a sample of six young people, six parents and seven clinicians who were learning the treatment.

Three cross-cutting themes were identified: (i) endorsing the treatment; (ii) finding therapy to be collaborative and active; challenging but helpful; and (iii) navigating change in a complex setting. Young people and parents found the treatment to be useful and acceptable, although simultaneously challenging. This was echoed by the clinicians, with particular reference to integrating CT-SAD-A within community CAMHS settings.

The acceptability of the treatment with young people, their parents and clinicians suggests further work is warranted in order to support its development and implementation within CAMHS settings.

Traditional dietary assessment methods in research can be challenging, with participant burden to complete an interview, diary, 24 h recall or questionnaire and researcher burden to code the food record to obtain a nutrient breakdown. Self-reported assessment methods are subject to recall and social desirability biases, in addition to selection bias from the nature of volunteering to take part in a research study. Supermarket loyalty card transaction records, linked to back of pack nutrient information, present a novel opportunity to use objective records of food purchases to assess diet at a household level. With a large sample size and multiple transactions, it is possible to review variation in food purchases over time and across different geographical areas.

This study uses supermarket loyalty card transactions for one retailer's customers in Leeds, for 12 months during 2016. Fruit and vegetable purchases for customers who appear to shop regularly for a ‘complete’ shop, buying from at least 7 of 11 Living Cost and Food Survey categories, were calculated. Using total weight of fruits and vegetables purchased over one year, average portions (80g) per day, per household were generated. Descriptive statistics of fruit and vegetable purchases by age, gender and Index of Multiple Deprivation of the loyalty card holder were generated. Using Geographical Information Systems, maps of neighbourhood purchases per month of the year were created to visualise variations.

The loyalty card holder transaction records represent 6.4% of the total Leeds population. On average, households in Leeds purchase 3.5 portions of fruit and vegetables per day, per household. Affluent and rural areas purchase more fruit and vegetables than average with 22% purchasing more than 5 portions/day. Conversely poor urban areas purchase less, with 18% purchasing less than 1 portion/day. Highest purchases are in the winter months, with lowest in the summer holidays. Loyalty cards registered to females purchased 0.4 portions per day more than male counterparts. The over 65 years purchased 1.5 portions per day more than the 17–24 year olds. A clear deprivation gradient is observed, with the most deprived purchasing 1.5 portions less per day than the least deprived.

Loyalty card transaction data offer an exciting opportunity for measuring variation in fruit and vegetable purchases. Variation is observed by age, gender, deprivation, geographically across a city and throughout the seasons. These insights can inform both policymakers and retailers regarding areas for fruit and vegetable promotion.

Supermarket transaction data, generated from loyalty cards, offers a novel source of food purchase information. Data are available for large sample sizes, over sustained periods of time, allowing for habitual purchasing patterns to be generated. In the UK, recommended dietary patterns to achieve a healthy diet are pictorially illustrated using the Eatwell Guide. Foods include: Fruit and vegetables; starchy products including potatoes, bread, pasta, rice; dairy or dairy alternatives; proteins such as beans, pulses, fish, eggs and meat; oils and spreads; and advice to limit foods high in salt, fat and sugar. Through mapping of foods purchased to the categories of the Eatwell Guide it is possible to review population performance against these national recommendations.

All loyalty card transaction records for purchases made in a UK supermarket chain, by residents of Yorkshire and the Humber during 2016 were included in this research. Customers who purchased foods from 7 or 11 Living Cost and Food Survey (LCFS) categories on ten or more occasions throughout the year were included in the sample, as these customers were considered to be purchasing the majority of their foods from the supermarket. All foods purchased were mapped to the Eatwell Guide food groups via the LCFS categories.

Households purchased: 25% of their total spend on fruits and vegetables, compared with 39% recommended; 13% on starchy products compared to 37% recommended; 23% of protein rich foods compared with 12% recommended; 12% dairy and alternatives compared to 8%; oils and spreads 2% compared to 1% recommended; and 25% foods that should be limited compared to 3% (recommended, but not pictorially illustrated on the plate).

Supermarket transaction data is a novel source of food purchase information which can be used to illustrate dietary behaviours in the UK population. However, it represents foods purchased, not consumed and is at a household level, not individual. Food purchases outside the home are not included. That said, it is arguably an objective measure for dietary assessment. From this study, it is clear to see that food purchases do not match the recommendations. Purchases of high sugar, high fat and high salt snacks constitute a significant proportion of spending, when they should in fact be limited. Protein rich products are also over-represented. Fruit and vegetables and starchy products are under-represented. This insight can benefit both retailers and policy makers for understanding the food purchase behaviours of our society.