BackgroundStrategies to reduce alcohol consumption would contribute to substantial health benefits in the population, including reducing cancer risk. The increasing accessibility and applicability of digital technologies make these powerful tools suitable to facilitate changes in behaviour in young people which could then translate into both immediate and long-term improvements to public health.ObjectiveWe conducted a review of systematic reviews to assess the available evidence on digital interventions aimed at reducing alcohol consumption in sub-populations of young people [school-aged children, college/university students, young adults only (over 18 years) and both adolescent and young adults (<25 years)].MethodsSearches were conducted across relevant databases including KSR Evidence, Cochrane Database of Systematic Reviews (CDSR) and Database of Abstracts of Reviews of Effects (DARE). Records were independently screened by title and abstract and those that met inclusion criteria were obtained for full text screening by two reviewers. Risk of bias (RoB) was assessed with the ROBIS checklist. We employed a narrative analysis.ResultsTwenty-seven systematic reviews were included that addressed relevant interventions in one or more of the sub-populations, but those reviews were mostly assessed as low quality. Definitions of “digital intervention” greatly varied across systematic reviews. Available evidence was limited both by sub-population and type of intervention. No reviews reported cancer incidence or influence on cancer related outcomes. In school-aged children eHealth multiple health behaviour change interventions delivered through a variety of digital methods were not effective in preventing or reducing alcohol consumption with no effect on the prevalence of alcohol use [Odds Ratio (OR) = 1.13, 95% CI: 0.95–1.36, review rated low RoB, minimal heterogeneity]. While in adolescents and/or young adults who were identified as risky drinkers, the use of computer or mobile device-based interventions resulted in reduced alcohol consumption when comparing the digital intervention with no/minimal intervention (−13.4 g/week, 95% CI: −19.3 to −7.6, review rated low RoB, moderate to substantial heterogeneity).In University/College students, a range of E-interventions reduced the number of drinks consumed per week compared to assessment only controls although the overall effect was small [standardised mean difference (SMD): −0.15, 95% CI: −0.21 to −0.09]. Web-based personalised feedback interventions demonstrated a small to medium effect on alcohol consumption (SMD: −0.19, 95% CI: −0.27 to −0.11) (review rated high RoB, minimal heterogeneity). In risky drinkers, stand-alone Computerized interventions reduced short (SMD: −0.17, 95% CI: −0.27 to −0.08) and long term (SMD: −0.17, 95% CI: −0.30 to −0.04) alcohol consumption compared to no intervention, while a small effect (SMD: −0.15, 95% CI: −0.25 to −0.06) in favour of computerised assessment and feedback vs. assessment only was observed. No short-term (SMD: −0.10, 95% CI: −0.30 to 0.11) or long-term effect (SMD: −0.11, 95% CI: −0.53 to 0.32) was demonstrated for computerised brief interventions when compared to counsellor based interventions (review rated low RoB, minimal to considerable heterogeneity). In young adults and adolescents, SMS-based interventions did not significantly reduce the quantity of drinks per occasion from baseline (SMD: 0.28, 95% CI: −0.02 to 0.58) or the average number of standard glasses per week (SMD: −0.05, 95% CI: −0.15 to 0.05) but increased the risk of binge drinking episodes (OR = 2.45, 95% CI: 1.32–4.53, review rated high RoB; minimal to substantial heterogeneity). For all results, interpretation has limitations in terms of risk of bias and heterogeneity.ConclusionsLimited evidence suggests some potential for digital interventions, particularly those with feedback, in reducing alcohol consumption in certain sub-populations of younger people. However, this effect is often small, inconsistent or diminishes when only methodologically robust evidence is considered. There is no systematic review evidence that digital interventions reduce cancer incidence through alcohol moderation in young people. To reduce alcohol consumption, a major cancer risk factor, further methodologically robust research is warranted to explore the full potential of digital interventions and to form the basis of evidence based public health initiatives.

Fenfluramine, tradename Fintepla®, was appraised within the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process as Technology Appraisal 808. Within the STA process, the company (Zogenix International) provided NICE with a written submission and a mathematical health economic model, summarising the company's estimates of the clinical effectiveness and cost-effectiveness of fenfluramine for patients with Dravet syndrome (DS). This company submission (CS) was reviewed by an evidence review group (ERG) independent of NICE. The ERG, Kleijnen Systematic Reviews in collaboration with Maastricht University Medical Centre, produced an ERG report. This paper presents a summary of the ERG report and the development of the NICE guidance. The CS included a systematic review of the evidence for fenfluramine. From this review the company identified and presented evidence from two randomised trials (Study 1 and Study 1504), an open-label extension study (Study 1503) and 'real world evidence' from a prospective and retrospective study. Both randomised trials were conducted in patients up to 18 years of age with DS, whose seizures were incompletely controlled with previous anti-epileptic drugs. A Bayesian network meta-analysis was performed to compare fenfluramine with cannabidiol plus clobazam. There was no evidence of a difference between any doses of fenfluramine and cannabidiol in the mean convulsive seizure frequency (CSF) rate during treatment. However, fenfluramine increased the number of patients achieving ≥ 50% reduction in CSF frequency from baseline compared to cannabidiol. The company used an individual-patient state-transition model (R version 3.5.2) to model cost-effectiveness of fenfluramine. The CSF and convulsive seizure-free days were estimated using patient-level data from the placebo arm of the fenfluramine registration studies. Subsequently, a treatment effect of either fenfluramine or cannabidiol was applied. Utility values for the economic model were obtained by mapping Pediatric Quality of Life Inventory data from the registration studies to EuroQol-5D-3L Youth (EQ-5D-Y-3L). The company included caregiver utilities in their base-case, as the severe needs of patients with DS have a major impact on parents and caregivers. There were several key issues. First, the company included caregiver utilities in the model in a way that when patients in the economic model died, the corresponding caregiver utility was also set to zero. Second, the model was built in R statistical software, resulting in transparency issues. Third, the company assumed the same percentage reduction for convulsive seizure days as was estimated for CSF. Fourth, during the final appraisal committee meeting, influential changes were made to the model that were not in line with the ERG's preferences (but were accepted by the appraisal committee). The company's revised and final incremental cost effectiveness ratio (ICER) in line with committee preferences resulted in fenfluramine dominating cannabidiol. Fenfluramine was recommended as an add-on to other antiepileptic medicines for treating seizures associated with DS in people aged 2 years and older in the National Health Service (NHS).