Objectives To systematically reivew the observational evidence of the effect of school closures and school reopenings on SARS-CoV-2 community transmission. Setting Schools (including early years settings, primary schools and secondary schools). Intervention School closures and reopenings. Outcome measure Community transmission of SARS-CoV-2 (including any measure of community infections rate, hospital admissions or mortality attributed to COVID-19). Methods On 7 January 2021, we searched PubMed, Web of Science, Scopus, CINAHL, the WHO Global COVID-19 Research Database, ERIC, the British Education Index, the Australian Education Index and Google, searching title and abstracts for terms related to SARS-CoV-2 AND terms related to schools or non-pharmaceutical interventions (NPIs). We used the Cochrane Risk of Bias In Non-randomised Studies of Interventions tool to evaluate bias. Results We identified 7474 articles, of which 40 were included, with data from 150 countries. Of these, 32 studies assessed school closures and 11 examined reopenings. There was substantial heterogeneity between school closure studies, with half of the studies at lower risk of bias reporting reduced community transmission by up to 60% and half reporting null findings. The majority (n=3 out of 4) of school reopening studies at lower risk of bias reported no associated increases in transmission. Conclusions School closure studies were at risk of confounding and collinearity from other non-pharmacological interventions implemented around the same time as school closures, and the effectiveness of closures remains uncertain. School reopenings, in areas of low transmission and with appropriate mitigation measures, were generally not accompanied by increasing community transmission. With such varied evidence on effectiveness, and the harmful effects, policymakers should take a measured approach before implementing school closures; and should look to reopen schools in times of low transmission, with appropriate mitigation measures.

Summary: There is good evidence that behavioural weight management interventions improve physical health; however, the impact on mental health remains unclear. We evaluated the impact of behavioural weight management interventions on mental health‐related outcomes in adults with overweight or obesity at intervention‐end and 12 months from baseline. Eligible studies were randomized controlled trials (RCTs) or cluster RCTs of adult behavioural weight loss interventions reporting affect, anxiety, binge eating, body image, depression, emotional eating, quality of life, self‐esteem and stress. We searched seven databases from inception to 7 May 2019 and included 43 articles reporting 42 RCTs. Eighteen studies were deemed to be at high risk of bias. We conducted random‐effects meta‐analyses, stratified analyses and meta‐regression using Stata. Interventions generated greater improvements than comparators for depression, mental health‐related quality of life and self‐efficacy at intervention‐end and 12 months from baseline. There was no difference between groups for anxiety, overall quality of life, self‐esteem or stress at intervention‐end. There was insufficient evidence to assess the impact on anxiety, binge eating, body image, emotional eating, affect, life satisfaction, self‐esteem or stress at intervention‐end and/or 12 months from baseline. Although evidence suggests that interventions benefit some aspects of mental health, high‐quality, transparently reported RCTs measuring a range of mental health outcomes over longer durations are required to strengthen the evidence base.

Summary: The extent to which behavioral weight management interventions affect health inequalities is uncertain, as is whether trials of these interventions directly consider inequalities. We conducted a systematic review, synthesizing evidence on how different aspects of inequality impact uptake, adherence, and effectiveness in trials of behavioral weight management interventions. We included (cluster‐) randomized controlled trials of primary care‐applicable behavioral weight management interventions in adults with overweight or obesity published prior to March 2020. Data about trial uptake, intervention adherence, attrition, and weight change by PROGRESS‐Plus criteria (place of residence, race/ethnicity, occupation, gender, religion, education, socioeconomic status, social capital, plus other discriminating factors) were extracted. Data were synthesized narratively and summarized in harvest plots. We identified 91 behavioral weight loss interventions and 12 behavioral weight loss maintenance interventions. Fifty‐six of the 103 trials considered inequalities in relation to at least one of intervention or trial uptake (n = 15), intervention adherence (n = 15), trial attrition (n = 32), or weight outcome (n = 34). Most trials found no inequalities gradient. If a gradient was observed for trial uptake, intervention adherence, and trial attrition, those considered “more advantaged” did best. Alternative methods of data synthesis that enable data to be pooled and increase statistical power may enhance understanding of inequalities in behavioral weight management interventions.

Background: Foods high in fat, sugar and salt (HFSS) are known to contribute to overweight and obesity. In addition to overweight and obesity, smoking, alcohol consumption and physical inactivity are known risk factors for non-communicable diseases, including several cancers and cardiovascular disease. Methods: Secondary analysis of UK-representative cross-sectional survey data of 3293 adults aged 18+. Regression analyses were undertaken to understand the relationship between consumption of HFSS food and soft drinks, alcohol and tobacco and socio-demographics. Clustering analysis identified groupings of health risk factors. Results: Males, those aged 18–24 and those from the more deprived groups consumed ready meals and fast food most frequently. Most of the sample (77.3%) engaged in at least one health risk behaviour. Six clusters were identified in the clustering analysis. Older (65+) female respondents were more likely to be inactive. Smokers exhibiting additional risk behaviours were more likely to be of working age from more deprived groups, and men over 65 were more likely to consume harmful levels of alcohol with additional risk factors. Conclusion: Policies and services in the UK tend to focus on changing behaviour to address individual risk factors. This study shows that policies and interventions need to address multiple risk factors.

Introduction: The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) is a commonly used scale of mental wellbeing focusing entirely on the positive aspect of mental health. It has been widely used in a broad range of clinical and research settings, including to evaluate if interventions, programmes or pilots improve wellbeing. We aimed to systematically review all interventions that used WEMWBS and evaluate which interventions are the most effective at improving wellbeing. Methods: Eligible populations included children and adults, with no health or age restrictions. Any intervention study was eligible if the wellbeing outcome was measured using the 7 or 14-item WEMWBS scale assessed both pre- and post-intervention. We identified eligible intervention studies using three approaches: a database search (Medline, EMBASE, CINAHL, PyschInfo and Web of Science from January 2007 to present), grey literature search, and by issuing a call for evidence. Narrative synthesis and random-effects meta-analysis of standardised mean differences in the intervention group were used to summarise intervention effects on WEMWBS score. Results: We identified 223 interventions across 209 studies, with a total of 53,834 participants across all studies. Five main themes of interventions were synthesised: psychological (n = 80); social (n = 54); arts, culture and environment (n = 29); physical health promotion (n = 18); and other (n = 28). Psychological interventions based on resilience, wellbeing or self-management techniques had the strongest effect on wellbeing. A broad range of other interventions were effective at improving mental wellbeing, including other psychological interventions such as cognitive behavioural therapy, psychoeducation and mindfulness. Medium to strong effects were also seen for person-centred support/advice (social), arts-based, parenting (social) and social prescribing interventions. However, a major limitation of the evidence was that only 75 (36%) of studies included a control group. Conclusions: WEMWBS has been widely used to assess wellbeing across a diverse range of interventions, settings and samples. Despite substantial heterogeneity in individual intervention design, delivery and groups targeted, results indicate that a broad range of intervention types can successfully improve wellbeing. Methodological changes, such as greater use of control groups in intervention evaluation, can help future researchers and policy makers further understand what works for mental wellbeing.

Overweight and obesity is the second biggest preventable cause of cancer after smoking, causing ~3.4 million deaths worldwide. This study provides current UK data on awareness of the link between obesity and cancer by socio-demographic factors, including BMI, and explores to what degree healthcare professionals provide weight management advice to patients. Cross-sectional survey of 3293 adults completed an online survey in February/March 2016, weighted to be representative of the UK population aged 18+. Public awareness of the link between obesity and cancer is low (25.4% unprompted and 57.5% prompted). Higher levels of awareness existed for least deprived groups (P < 0.001), compared to more deprived groups. Most respondents had seen a healthcare practitioner in the past 12 months (91.6%) and 17.4% had received advice about their weight, although 48.4% of the sample were overweight/obese. Cancer is not at the forefront of people’s minds when considering health conditions associated with overweight or obesity. Socio-economic disparities exist in health knowledge across the UK population, with adults from more affluent groups being most aware. Healthcare professionals are uniquely positioned to provide advice about weight, but opportunities for intervention are currently under-utilized in healthcare settings.