Celiac disease (CD), an autoimmune disorder triggered by gluten, impacts about one percent of the population. Only one-third receive a diagnosis, leaving the majority unaware of their condition. Untreated CD can lead to gut lining damage, resulting in malnutrition, anemia, and osteoporosis. Our primary goal was to identify at-risk groups and assess the cost-effectiveness of active case finding in primary care.

Our methodology involved systematic reviews and meta-analyses focusing on the accuracy of CD risk factors (chronic conditions and symptoms) and diagnostic tests (serological and genetic). Prediction models, based on identified risk factors, were developed for identifying individuals who would benefit from CD testing in routine primary care. Additionally, an online survey gauged individuals’ preferences regarding diagnostic certainty before initiating a gluten-free diet. This information informed the development of economic models evaluating the cost-effectiveness of various active case finding strategies.

Individuals with dermatitis herpetiformis, a family history of CD, migraine, anemia, type 1 diabetes, osteoporosis, or chronic liver disease showed one and a half to two times higher risk of having CD. IgA tTG, and EMA demonstrated good diagnostic accuracy. Genetic tests showed high sensitivity but low specificity. Survey results indicated substantial variation in preference for certainty from a blood test before initiating a gluten-free diet. Cost-effectiveness analyses showed that, in adults, IgA tTG at a one percent pre-test probability (equivalent to population screening) was the most cost effective. For non-population screening strategies, IgA EMA plus HLA was most cost effective. There was substantial uncertainty in economic model results.

While population-based screening with IgA tTG appears the most cost effective in adults, decisions for implementation should not solely rely on economic analyses. Future research should explore whether population-based CD screening aligns with UK National Screening Committee criteria and requires a long-term randomized controlled trial of screening strategies.

Most patients with long-term conditions (LTC) receive regular blood tests to monitor disease progression and response to treatment and to detect complications. There is currently no robust evidence to inform recommendations on monitoring. Creating this evidence base is challenging because the benefits and harms of testing are dependent on what is done in response to the test results.

We identified a list of commonly used tests. We defined a series of filtering questions to determine whether there was evidence to support the rationale of monitoring, such as “Can the general practitioner do anything in response to an abnormal test result?” Through a series of rapid reviews we identified evidence to answer each question. The evidence was presented at a consensus meeting where clinicians and patients voted for inclusion, exclusion, or further analysis. A process evaluation was performed alongside this. Further analyses were performed using routinely collected healthcare data and by performing incidence analyses, emulating randomized controlled trials (RCTs), and modeling disease progression.

We tested this methodology on three common LTCs: chronic kidney disease (CKD), type 2 diabetes mellitus (T2DM), and hypertension. We found sufficient evidence to include hemoglobin A1C and estimated glomerular filtration rate (eGFR) for monitoring patients with T2DM; hemoglobin and eGFR for patients with CKD; and eGFR for patients with hypertension. The consensus panel excluded four tests, while 10 tests were selected for further analysis. The emulated RCTs will investigate the effect of regular monitoring with certain tests on health outcomes among routinely monitored patients. In addition, we will investigate the signal-to-noise ratio of each test over time using a modeling approach.

The cost effectiveness of the evidence-based testing panels needs to be tested in clinical practice. We are currently developing an intervention package and are planning to run a feasibility trial. This program of work has the potential to change how LTCs are monitored in primary care, ultimately improving patient outcomes and leading to more efficient use of healthcare resources.

Childhood bullying is a public health priority. We evaluated the effectiveness and costs of KiVa, a whole-school anti-bullying program that targets the peer context.

A two-arm pragmatic multicenter cluster randomized controlled trial with embedded economic evaluation. Schools were randomized to KiVa-intervention or usual practice (UP), stratified on school size and Free School Meals eligibility. KiVa was delivered by trained teachers across one school year. Follow-up was at 12 months post randomization. Primary outcome: student-reported bullying-victimization; secondary outcomes: self-reported bullying-perpetration, participant roles in bullying, empathy and teacher-reported Strengths and Difficulties Questionnaire. Outcomes were analyzed using multilevel linear and logistic regression models.

Between 8/11/2019–12/02/2021, 118 primary schools were recruited in four trial sites, 11 111 students in primary analysis (KiVa-intervention: n = 5944; 49.6% female; UP: n = 5167, 49.0% female). At baseline, 21.6% of students reported being bullied in the UP group and 20.3% in the KiVa-intervention group, reducing to 20.7% in the UP group and 17.7% in the KiVa-intervention group at follow-up (odds ratio 0.87; 95% confidence interval 0.78 to 0.97, p value = 0.009). Students in the KiVa group had significantly higher empathy and reduced peer problems. We found no differences in bullying perpetration, school wellbeing, emotional or behavioral problems. A priori subgroup analyses revealed no differences in effectiveness by socioeconomic gradient, or by gender. KiVa costs £20.78 more per pupil than usual practice in the first year, and £1.65 more per pupil in subsequent years.

The KiVa anti-bullying program is effective at reducing bullying victimization with small-moderate effects of public health importance.

The study was funded by the UK National Institute for Health and Care Research (NIHR) Public Health Research program (17-92-11). Intervention costs were funded by the Rayne Foundation, GwE North Wales Regional School Improvement Service, Children's Services, Devon County Council and HSBC Global Services (UK) Ltd.

Disparities in CHD outcomes exist across the lifespan. However, less is known about disparities for patients with CHD admitted to neonatal ICU. We sought to identify sociodemographic disparities in neonatal ICU admissions among neonates born with cyanotic CHD.

Annual natality files from the US National Center for Health Statistics for years 2009–2018 were obtained. For each neonate, we identified sex, birthweight, pre-term birth, presence of cyanotic CHD, and neonatal ICU admission at time of birth, as well as maternal age, race, ethnicity, comorbidities/risk factors, trimester at start of prenatal care, educational attainment, and two measures of socio-economic status (Special Supplemental Nutrition Program for Women, Infants, and Children [WIC] status and insurance type). Multivariable logistic regression models were fit to determine the association of maternal socio-economic status with neonatal ICU admission. A covariate for race/ethnicity was then added to each model to determine if race/ethnicity attenuate the relationship between socio-economic status and neonatal ICU admission.

Of 22,373 neonates born with cyanotic CHD, 77.2% had a neonatal ICU admission. Receipt of WIC benefits was associated with higher odds of neonatal ICU admission (adjusted odds ratio [aOR] 1.20, 95% CI 1.1–1.29, p < 0.01). Neonates born to non-Hispanic Black mothers had increased odds of neonatal ICU admission (aOR 1.20, 95% CI 1.07–1.35, p < 0.01), whereas neonates born to Hispanic mothers were at lower odds of neonatal ICU admission (aOR 0.84, 95% CI 0.76–0.93, p < 0.01).

Maternal Black race and low socio-economic status are associated with increased risk of neonatal ICU admission for neonates born with cyanotic CHD. Further work is needed to identify the underlying causes of these disparities.

Lewy Body Dementia (LBD) is predicted to be under-diagnosed in the general population. RBD is one of the four core clinical criteria for the diagnosis of LBD. Longitudinal studies of RBD show strong association with LBD, so there is potential for early identification of LBD and subsequent management. We aimed to screen 100% of patients referred to Trafford MATS for RBD.

We performed three Plan-Do-Study-Act (PDSA) cycles; in the first cycle we introduced a validated RBD screening question, from the DIAMOND-Lewy study, to the initial memory assessment proforma. This asked ‘Have you ever been told that you “act out your dreams” while sleeping (punched or flailed arms in the air, shouted or screamed)?’

In the second PDSA cycle, we delivered a RBD and LBD educational package to the specialist memory nurses who undertake the initial assessments. In the third PDSA cycle reminders were sent to the team to use the new assessment proforma.

We collated data from patients who had undergone an initial memory assessment between 06/04/21- 22/06/21 from the trusts electronic database.

Initial baseline data showed that 0% of initial assessments screened for RBD; at the end of PDSA one this was 100% and 75% at the end of PDSA two. This increased to 100% at the end of the last PDSA cycle. The main reason for non-completion of the screening question was use of the old proforma.

4/152 patients screened positive; patients were diagnosed with Alzheimer's disease, delirium, vascular dementia and mixed Alzheimer‘s disease and vascular dementia, respectively.

The introduction of a RBD screening question into the MATS initial assessment proforma improved screening for RBD. We think the variation in screening compliance rates was likely due to practitioners using old assessment proformas, hence sending reminders of the new proforma.

A limitation of the project was that some patients did not have a bed partner, which makes identification of the disorder more difficult.

Since the completion of the project, we have circulated a news bulletin through the Dementia United charity to raise awareness of our QI project nationally and also discussed the project with the Lewy Body society. Whilst our project has not yet identified a patient with LBD, we feel that introducing this screening question is a very easy and reproducible change to implement and RBD should be screened for in all memory patients.

Evidence suggests that cognitive subtypes exist in schizophrenia that may reflect different neurobiological trajectories. We aimed to identify whether IQ-derived cognitive subtypes are present in early-phase schizophrenia-spectrum disorder and examine their relationship with brain structure and markers of neuroinflammation.

161 patients with recent-onset schizophrenia spectrum disorder (<5 years) were recruited. Estimated premorbid and current IQ were calculated using the Wechsler Test of Adult Reading and a 4-subtest WAIS-III. Cognitive subtypes were identified with k-means clustering. Freesurfer was used to analyse 3.0 T MRI. Blood samples were analysed for hs-CRP, IL-1RA, IL-6 and TNF-α.

Three subtypes were identified indicating preserved (PIQ), deteriorated (DIQ) and compromised (CIQ) IQ. Absolute total brain volume was significantly smaller in CIQ compared to PIQ and DIQ, and intracranial volume was smaller in CIQ than PIQ (F(2, 124) = 6.407, p = 0.002) indicative of premorbid smaller brain size in the CIQ group. CIQ had higher levels of hs-CRP than PIQ (F(2, 131) = 5.01, p = 0.008). PIQ showed differentially impaired processing speed and verbal learning compared to IQ-matched healthy controls.

The findings add validity of a neurodevelopmental subtype of schizophrenia identified by comparing estimated premorbid and current IQ and characterised by smaller premorbid brain volume and higher measures of low-grade inflammation (CRP).

The MAVORIC trial compared mogamulizumab to vorinostat in patients with mycosis fungoides (MF) or Sézary syndrome (SS), subtypes of cutaneous T-cell lymphoma. However, the treatment comparison within MAVORIC may not represent an HTA relevant population from a UK perspective: (i) 72.6 percent of patients randomized to vorinostat switched to mogamulizumab and (ii) vorinostat is not used in current clinical practice in the UK. This study explores methods to adjust treatment effect estimates using different crossover adjustment methods and Real-World Evidence.

This medicine is subject to additional monitoring. This will allow quick identification of new safety information. See www.mhra.gov.uk/yellowcard for how to report side effects.

An advanced stage (stage ≥IIB MF and all SS) population was included. Three methods were considered for treatment crossover adjustment. A synthetic control arm was created using the Hospital Episodes Statistics (HES) dataset. Predicted survival for the MAVORIC control arm, post-crossover adjustment, was compared to the HES to inform the selection of the appropriate methods for adjustment. A direct comparison between mogamulizumab (reweighted to represent the distribution of MF/SS patients in the HES) and the synthetic control was also conducted.

Following crossover adjustment of the vorinostat arm, using the inverse probability of censoring weighting method, the overall survival (OS) hazard ratio (HR) estimate for mogamulizumab vs. vorinostat was 0.45 (95% confidence interval (CI): 0.19, 1.07). This adjustment method was considered the most appropriate based on an assessment of assumptions and a comparison of OS between the adjusted vorinostat data and the HES data. The OS HR estimate for reweighted mogamulizumab vs. synthetic control from HES was 0.33 (CI: 0.21, 0.50).

Real World Evidence from the HES database can be used to validate crossover adjustment methods and to better reflect current clinical practice in the UK. Results using both methods support each other.

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom non-remission in first-episode psychosis.

Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 to 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 to 2009 from a further 11 English early intervention services. The one-year non-remission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for non-remission, which was externally validated.

The prediction model showed good discrimination (C-statistic of 0.74 (0.72, 0.76) and adequate calibration with intercept alpha of 0.13 (0.03, 0.23) and slope beta of 0.99 (0.87, 1.12). Our model improved the net-benefit by 16% at a risk threshold of 50%, equivalent to 16 more detected non-remitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases.

Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of non-remission at initial clinical contact.