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Narrative reviews of paediatric NAFLD quote prevalences in the general population that range from 9% to 37%; however, no systematic review of the prevalence of NAFLD in children/adolescents has been conducted. We aimed to estimate prevalence of non-alcoholic fatty liver disease (NAFLD) in young people and to determine whether this varies by BMI category, gender, age, diagnostic method, geographical region and study sample size. We conducted a systematic review and meta-analysis of all studies reporting a prevalence of NAFLD based on any diagnostic method in participants 1-19 years old, regardless of whether assessing NAFLD prevalence was the main aim of the study. The pooled mean prevalence of NAFLD in children from general population studies was 7.6% (95%CI: 5.5% to 10.3%) and 34.2% (95% CI: 27.8% to 41.2%) in studies based on child obesity clinics. In both populations there was marked heterogeneity between studies (I2 = 98%). There was evidence that prevalence was generally higher in males compared with females and increased incrementally with greater BMI. There was evidence for differences between regions in clinical population studies, with estimated prevalence being highest in Asia. There was no evidence that prevalence changed over time. Prevalence estimates in studies of children/adolescents attending obesity clinics and in obese children/adolescents from the general population were substantially lower when elevated alanine aminotransferase (ALT) was used to assess NAFLD compared with biopsies, ultrasound scan (USS) or magnetic resonance imaging (MRI). Our review suggests the prevalence of NAFLD in young people is high, particularly in those who are obese and in males.

The prevalence of obesity has increased in the United Kingdom, and reliably measuring the impact on quality of life and the total healthcare cost from obesity is key to informing the cost-effectiveness of interventions that target obesity, and determining healthcare funding. Current methods for estimating cost-effectiveness of interventions for obesity may be subject to confounding and reverse causation. The aim of this study is to apply a new approach using mendelian randomisation for estimating the cost-effectiveness of interventions that target body mass index (BMI), which may be less affected by confounding and reverse causation than previous approaches. We estimated health-related quality-adjusted life years (QALYs) and both primary and secondary healthcare costs for 310,913 men and women of white British ancestry aged between 39 and 72 years in UK Biobank between recruitment (2006 to 2010) and 31 March 2017. We then estimated the causal effect of differences in BMI on QALYs and total healthcare costs using mendelian randomisation. For this, we used instrumental variable regression with a polygenic risk score (PRS) for BMI, derived using a genome-wide association study (GWAS) of BMI, with age, sex, recruitment centre, and 40 genetic principal components as covariables to estimate the effect of a unit increase in BMI on QALYs and total healthcare costs. Finally, we used simulations to estimate the likely effect on BMI of policy relevant interventions for BMI, then used the mendelian randomisation estimates to estimate the cost-effectiveness of these interventions. A unit increase in BMI decreased QALYs by 0.65% of a QALY (95% confidence interval [CI]: 0.49% to 0.81%) per year and increased annual total healthcare costs by £42.23 (95% CI: £32.95 to £51.51) per person. When considering only health conditions usually considered in previous cost-effectiveness modelling studies (cancer, cardiovascular disease, cerebrovascular disease, and type 2 diabetes), we estimated that a unit increase in BMI decreased QALYs by only 0.16% of a QALY (95% CI: 0.10% to 0.22%) per year. We estimated that both laparoscopic bariatric surgery among individuals with BMI greater than 35 kg/m2, and restricting volume promotions for high fat, salt, and sugar products, would increase QALYs and decrease total healthcare costs, with net monetary benefits (at £20,000 per QALY) of £13,936 (95% CI: £8,112 to £20,658) per person over 20 years, and £546 million (95% CI: £435 million to £671 million) in total per year, respectively. The main limitations of this approach are that mendelian randomisation relies on assumptions that cannot be proven, including the absence of directional pleiotropy, and that genotypes are independent of confounders. Mendelian randomisation can be used to estimate the impact of interventions on quality of life and healthcare costs. We observed that the effect of increasing BMI on health-related quality of life is much larger when accounting for 240 chronic health conditions, compared with only a limited selection. This means that previous cost-effectiveness studies have likely underestimated the effect of BMI on quality of life and, therefore, the potential cost-effectiveness of interventions to reduce BMI.

Background The statistical models developed for meta-analysis of diagnostic test accuracy studies require specialised knowledge to implement. This is especially true since recent guidelines, such as those in Version 2 of the Cochrane Handbook of Systematic Reviews of Diagnostic Test Accuracy, advocate more sophisticated methods than previously. This paper describes a web-based application - MetaBayesDTA - that makes many advanced analysis methods in this area more accessible. Results We created the app using R, the Shiny package and Stan. It allows for a broad array of analyses based on the bivariate model including extensions for subgroup analysis, meta-regression and comparative test accuracy evaluation. It also conducts analyses not assuming a perfect reference standard, including allowing for the use of different reference tests. Conclusions Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to researchers with varying levels of expertise. We anticipate that the application will encourage higher levels of uptake of more advanced methods, which ultimately should improve the quality of test accuracy reviews.

Background Latent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios. Methods We generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G 2 and χ 2 statistics indicated lack-of-fit globally or for each test pair. Results Across all settings, residual correlation plots, pairwise G 2 and χ 2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G 2 and χ 2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests. Conclusions Residual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

•We identified six randomised controlled trials.•These trials were from four countries and comprised 468 participants in total.•All but one trial investigated psychosocial interventions.•Overall there was weak evidence of a small positive effect of interventions. People with substance use disorder (SUD) are at significantly greater risk of suicide compared with the general population. In recent years the number of suicides resulting from drug poisoning in England and Wales has increased. We sought to identify and evaluate the effect of interventions to prevent suicide or reduce self-harm among people with SUD. We conducted a systematic review of randomised controlled trials (RCTs) of interventions for people with SUD that included suicide or self-harm-related primary outcomes. We searched Cochrane Central Register of Controlled Trials (CENTRAL), PsycINFO, PubMed, Embase and Web of Science from inception until 13th January 2019. Studies were assessed for bias using the Cochrane Risk of Bias 2 tool. A random effects meta-analysis of standardised mean differences (SMD) was conducted. We identified six RCTs from four countries (Australia, Iran, the United States of America and the United Kingdom) comprising 468 participants in total. All but one study investigated psychosocial interventions. On average across studies there was weak evidence of a small positive effect of interventions on suicide or self-harm outcomes (d=−0.20, 95% CI=−0.39–0.00). Studies were heterogeneous in terms of population, intervention, controls and outcome. There were some concerns regarding bias for all trials. All trials were liable to type II error. Evidence is currently lacking regarding the effectiveness of interventions to prevent suicide and reduce self-harm amongst people with SUD.

The prevalence of coeliac disease (CD) is around 1%, but diagnosis is challenged by varied presentation and non-specific symptoms and signs. This study aimed to identify diagnostic indicators that may help identify patients at a higher risk of CD in whom further testing is warranted. International guidance for systematic review methods were followed and the review was registered at PROSPERO (CRD42020170766). Six databases were searched until April 2021. Studies investigating diagnostic indicators, such as symptoms or risk conditions, in people with and without CD were eligible for inclusion. Risk of bias was assessed using the QUADAS-2 tool. Summary sensitivity, specificity, and positive predictive values were estimated for each diagnostic indicator by fitting bivariate random effects meta-analyses. 191 studies reporting on 26 diagnostic indicators were included in the meta-analyses. We found large variation in diagnostic accuracy estimates between studies and most studies were at high risk of bias. We found strong evidence that people with dermatitis herpetiformis, migraine, family history of CD, HLA DQ2/8 risk genotype, anaemia, type 1 diabetes, osteoporosis, or chronic liver disease are more likely than the general population to have CD. Symptoms, psoriasis, epilepsy, inflammatory bowel disease, systemic lupus erythematosus, fractures, type 2 diabetes, and multiple sclerosis showed poor diagnostic ability. A sensitivity analysis revealed a 3-fold higher risk of CD in first-degree relatives of CD patients. Targeted testing of individuals with dermatitis herpetiformis, migraine, family history of CD, HLA DQ2/8 risk genotype, anaemia, type 1 diabetes, osteoporosis, or chronic liver disease could improve case-finding for CD, therefore expediting appropriate treatment and reducing adverse consequences. Migraine and chronic liver disease are not yet included as a risk factor in all CD guidelines, but it may be appropriate for these to be added. Future research should establish the diagnostic value of combining indicators.

ObjectivesTo explore patient, carer and clinician experiences of the QbTest and its impact on patient outcomes for attention deficit hyperactivity disorder (ADHD) diagnosis and medication management.DesignMixed-methods systematic review.Data sourcesMEDLINE, EMBASE, PsycINFO, CINAHL, ClinicalTrials.gov and WHO ICTRP (from inception to September 2024).Study selectionPrimary studies, of any design, that evaluated any version of the QbTest (QbMini <5 years, QbTest 6–12 or 12–60 years, QbCheck for remote assessment via webcam or QbMT smartphone version), for ADHD diagnosis and/or medication management and provided data on any of the following outcomes, were eligible: time to assessment/diagnostic decision, use of services, impact on clinical decision-making, healthcare professionals’ confidence in assessment, intervention use, morbidity, mortality, health-related quality of life, cost, ease of use, experience and acceptability of the test to patients, carers and clinicians.Data extraction and synthesisTwo reviewers independently screened titles and abstracts and assessed potentially relevant reports for inclusion. One reviewer conducted data extraction and risk of bias (RoB) assessment, checked by a second reviewer. Mixed-methods synthesis followed the convergent-integrated approach.ResultsWe identified 10 eligible studies (9 QbTest; 1 QbCheck), including 1 randomised controlled trial (RCT), 2 feasibility RCTs, 5 before-and-after studies, 1 mixed-methods study and 1 diagnostic study. Most studies enrolled children in the UK and included surveys or interviews with patients, carers or clinicians. The RCT and before-and-after studies were judged at high/serious RoB. Six survey components and two qualitative interview components were judged at some concerns of RoB. We identified one ongoing study of the QbMT and no studies for QbMini. We organised themes emerging from the qualitative synthesis into two broad conceptual categories: views around the helpfulness of the QbTest (contribution to ADHD diagnosis, treatment decision-making, communication with caregivers) and barriers to QbTest implementation (practical barriers and acceptability of the test to patients and caregivers). Findings suggested that the addition of the QbTest may reduce time to diagnosis, improve clinician confidence in the diagnostic decision, increase the proportion of patients with a diagnostic decision and reduce cost and number of clinic appointments. The QbTest appeared to be generally well received by clinicians, patients and carers. However, barriers to test implementation were reported. Clinicians cited staffing, room requirements and issues with technology, and patients highlighted the test length and repetitive nature. Little data exist on the use of the QbTest for medication management.ConclusionsThe available evidence suggests the QbTest may be a useful addition to ADHD assessment in children and young people. Further well-designed RCTs with qualitative substudies are required to assess the impact of the QbTest on patient outcomes, user experience and cost, particularly for medication management and in adults, where evidence is scarce. Such RCTs should include economic analyses, direct comparisons to other continuous performance tests with motion trackers and subgroup analyses including age, sex, ethnicity and comorbidities.PROSPERO registration numberCRD42023482963.

Background Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial. Methods Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison. Results Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data. Conclusions Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice.

Introduction With the scaling up of vertical HIV transmission prevention programmes, the HIV‐related population profile of children in South Africa has shifted. We described temporal changes in HIV‐related characteristics of children, aged ≤3 years (up to the third birthday), with infectious disease hospitalisations across the Western Cape province. Methods We used routinely collected electronic data to identify children born in the Western Cape with infectious disease hospital records for lower respiratory tract infections, diarrhoea, meningitis and tuberculous meningitis, from 2008 to 2021. Linked maternal and child unique identifiers were used to extract pregnancy, HIV‐related, laboratory, pharmacy and hospitalisation data. We described temporal changes in child HIV exposure and acquisition status, timing of maternal HIV diagnosis and antiretroviral therapy (ART) start, infant exposure to maternal ART and timing thereof, and maternal CD4 and HIV viral load closest to delivery. We used logistic and multinomial regression to assess changes in characteristics between the Pre‐Option B+ (2008–2013), Option B+ (2013–2016) and Universal ART periods (2016–2021). Results Among 52,811 children aged ≤3 years with hospitalisations, the proportion living with HIV dreased from 7.0% (2008) to 1.1% (2021), while those exposed to HIV and uninfected increased from 14.0% (2008) to 16.1% (2021) with a peak of 18.3% in 2017. Among mothers with HIV (n = 9873), the proportion diagnosed with HIV and starting ART before pregnancy increased from 20.2% to 69.2% and 5.8% to 59.0%, respectively, between 2008 and 2021. Children hospitalised during the Universal ART period had eight times higher odds (Odds Ratio: 8.41; 95% CI: 7.36–9.61) of exposure to maternal ART versus children admitted Pre‐Option B+. Among mothers of children exposed to HIV and uninfected with CD4 records (n = 7523), the proportion with CD4 <350 cells/μl decreased from 90.6% (2008) to 27.8% (2021). Conclusions In recent years, among children hospitalised with infectious diseases, there were fewer children with perinatally acquired HIV, while an increased proportion of those without HIV acquisition are exposed to maternal HIV and ART. There is a need to look beyond paediatric HIV prevalence and consider child exposure to HIV and ART among children without HIV, when assessing the HIV epidemic's impact on child health services.