Current estimates indicate that some form of malnutrition  affects one in three people worldwide , and the 2016 Global Burden of Disease study has placed poor dietary habits as one of the leading risk factors for mortality globally . Over the past decade, there has been great momentum around addressing malnutrition in all its forms and commitment to actions that can accelerate progress to reduce its associated burden of morbidity and mortality. In 2012, the World Health Assembly (WHA) adopted the 2025 Global Targets for Maternal, Infant and Young Child Nutrition and in 2013, WHA adopted targets for non‐communicable diseases (NCDs), including several nutrition‐relevant targets . More recently, the United Nations elevated its efforts through a global declaration of 17 Sustainable Development Goals (SDGs), where at least 12 of the 17 goals feature indicators relevant to nutrition. In line with these targets, the decade of 2016‐2025 has been declared the Decade of Action on Nutrition . To this end, prioritizing critical actions to address school‐age children and adolescent nutrition, is necessary to achieve these milestones. At the forefront of malnutrition and poor dietary intake is the food system.According to the Food and Agriculture Organization (FAO) High Level Panel of Global Food and Nutrition Security, the food system is defined as ‘a system that embraces all the elements  and activities that relate to the production, processing, distribution and marketing, preparation and consumption of food and the outputs of these activities, including socio‐economic and environmental outcomes' . Importantly, this group identified three food system typologies , based on distinct inputs , outputs  and processes  Within traditional  food systems, there is a greater proportion of informal food markets , compared to formal food outlets, as food is produced locally by rural smallholder farmers, is minimally processed and dependent on seasons. Moreover, there is minimal food promotion through advertising, limited food quality control and increase food affordability as a significant portion of monthly household expenditure is allocated to food. Examples of countries with such food system type are: Bangladesh, Ethiopia, Indonesia, Nepal, Senegal and Zimbabwe.Emerging food systems are characterized by low urbanization and agricultural productivity, though there is still reliance on staple foods and household food budgets are considered moderate to high. Examples of countries with such food system type are: Cameroon, China, Honduras, Pakistan, Namibia, Philippines and Thailand.In transitioning food systems, there is a shift towards moderate agricultural productivity and lower dependence on staples as compared to emerging food systems. As well, household food budgets are still considered high compared to mixed food systems. Country examples from this food system type are: Brazil, Ecuador, Guyana, Malaysia, Mauritius, Russia, Suriname and Ukraine.In mixed food systems, there is improved infrastructure to support formal markets, though informal markets are still prominent. Furthermore, there is increased diversity of foods available, including staples, fresh produce, animal‐sourced foods and highly‐processed packaged foods. There is increased food promotion through advertisements and weakly implemented regulations on food safety and quality. Examples of countries with such food system type are: Barbados, Bulgaria, Estonia, Germany, Hungary, Italy and Switzerland.In modern  food systems, the highly urbanized environment promotes increased access and reliance on highly diverse and dense formal food markets . A high level of food information and promotion exists through various media platforms, including marketing and labelling on food packages, advertisements and public health campaigns. Economically, household food expenditure is reduced, though fresh produce and animal‐sourced foods are more expensive than staples and packaged products. Examples of countries with such food system type are: Australia, Canada, Denmark, Lebanon, Republic of Korea, Sweden and the US.In 2012, a global network of public‐interest organisations and researchers aiming to monitor, benchmark and support public and private sector actions to create healthy food environments and reduce obesity and NCDs was established. This network, named The International Network for Food and Obesity/Non‐Communicable Diseases Research, Monitoring and Action Support (INFORMAS), defines the food environment as ‘the collective physical, economic, policy and sociocultural surroundings, opportunities and conditions that influence people's food and beverage choices and nutritional status' . In the It is important to note that there are several proposed conceptual frameworks with a global lens available from Health Canada, Centre for Disease Control (CDC), USA, the FAO High Level Panel of Experts for Food Security and Nutrition, and the Global Panel on Agriculture and Food Systems for Nutrition , which borrow from the early work of Though these models have laid the foundation within the food environment space, none are tailored to the complexity of LMICs or are specific to school‐age children and adolescents. Rapid globalisation and urbanisation in LMICs has incited major changes to the landscape of food. This is observed upstream with trade liberalization and foreign direct investments by transnational food and beverage companies, including grocery and fast food retailers. As well, transformations in food processing and modernised supply chains have caused a systemic shift in the composition and packaging of foods, as well as the ease of acquiring these foods. In parallel, traditional domestic channels of food acquisition, such as through informal unregistered vendors (wet markets and street vendors) still account for a portion of the market share in LMICs. This dynamic and opportunistic environment is in a constant flux, posing a challenge in accurately estimating food availability, accessibility and affordability. Downstream, food preferences, dietary patterns, and habits have been negatively impacted as economical, convenience foods are inexpensive compared to healthy options. For school‐age children and adolescents, this corresponds to increased reliance on unhealthy, nutrient‐poor ultra‐processed foods and sugar‐sweetened beverages, as well as, increased snacking and eating away‐from‐home. Taken together, energy imbalances have led to population‐wide nutrition transition with continued burden of stunting and wasting. This double burden of malnutrition has been widely studied in LMICs, though there have been insufficient efforts to study the association to the food environment . Our review will focus on market price, as this is most relevant to environment level interventions. Promotion: Promotional advertising, marketing and branding directed at individuals.These dimensions and their associated measures are useful for understanding the food environment, especially in urbanised settings of LMICs. As well, the intersection of these dimensions directly or indirectly involves primary and secondary sectors such as agriculture, food manufacturing and food processing. We recognise policy is an important component of the food environment, however, we have not included policy as its own dimension. This is because we believe policies, regulations and guidelines  underlie all dimensions of the food environment. Likewise, we also acknowledge that conceptually food quality and food safety  are integral components of the food environment. However, food safety and food quality are in a constant flux, making it difficult to establish metrics of the food environment, particularly for school‐age children and adolescents. For this reason, we have chosen to place food quality and food safety under the food system and supply chain in our model .Additionally, it is hypothesised that the food environment is linked to consumer behaviour and purchasing, which includes knowledge, awareness, attitudes and preferences. As seen in We acknowledge the limitation in developing a single conceptual framework for both school‐age children and adolescents. School‐age children differ from adolescents in that, school‐age children have limited autonomy in the acquisition of food. Instead, parents and caretakers influence their consumption by making decisions on what is purchased for the home, what is eaten outside of the home and setting limits on the quantity of food consumed. On the other hand, adolescents have the ability to make their own decisions and purchase on their own. However, the focus of our review is to understand the impact of food environment interventions on diet‐related health outcomes in school‐age children and adolescents, where food purchasing is beyond the scope of what we have considered to be intrinsic properties of the food environment, whether this is done by parents, providers or adolescents.With global increases in obesity prevalence, especially in LMICs, there are various interventions that have been categorised and implemented with intent to improve the food environment . These iITS and quasi‐experimental studies evaluate a change or exposure that is outside the control of the researchers, proving more difficult to manipulate the exposure and randomise participants into intervention and control groups . These lIn addition, food environment interventions can be classified as preventive or management‐based .Interventions can also be defined as environmental or behavioural  identifies ten specific areas where policy and interventions can be used to improve food environments . Thus, organisational‐level interventions that change the physical environment such as improving the quality of foods available at lunchtime could have an impact on the dietary intake and consumption, and thus health outcomes, of school aged children and adolescents.We recognise also that not all children and adolescents attend school, especially in LMICs. However, we aim to capture these children and adolescents through community‐level interventions or organisational‐level interventions specific to the workplace (for those children and adolescents who are in the workforce). One limitation we acknowledge is that some children and adolescents who do not attend school might be better captured at the household level. However, the food environment at the household level is out of this review's scope as it can be affected by other factors, such as provider's preference and household income, which are not specific to the child or adolescent him‐ or herself.This review will include environmental interventions, both preventive and management‐based, targeted at school‐age children and adolescents in LMICs, which are implemented through market, community and organisational platforms . For theToday, many food environments promote access to unhealthy foods, influencing individuals' preferences and demands for foods with poor nutritional quality. This has contributed to the rise in obesity and non‐communicable diseases globally, especially in low‐ and middle‐income countries . In respEnvironmental interventions, such as those categorised in the NOURISHING framework, would primarily work at the policy and community levels of implementation within the CDC's logic model, or at the macro‐level as defined by In our model, policies, regulations or guidelines at every level  affect the food environment through all three platforms . These include macro‐level legislation on taxation of unhealthy food, regulation of imports and exports, and policies on nutritional labelling and advertising, each of which would have an effect on the economy, various industries and society at‐large. The direct effects of these interventions would include food price regulation, taxation on unhealthy food and beverage, improved labelling and informative food packaging, and decreased airtime for TV advertisements of unhealthy food during children's programs. Examples of micro‐level legislation or policy would include school‐based policies that limit the selling of sugar‐sweetened beverages in a school cafeteria.Similar to other models and frameworks cited above, market‐level interventions in our logic model include those that affect change within the formal and informal retail space  Community‐level interventions affect widespread changes in physical infrastructure within the community and public health. These interventions include public health campaigns promoting healthy food and nutrition, transportation infrastructure improvements to provide better access and availability of healthy food options. Direct effects of these interventions include increasing the number of food outlets in the neighbourhood and providing subsidies for healthier options in stores. At the organisational‐level, interventions aim to influence institutions, such as work and school systems. These involve regulating the availability of food in work and school environments, such as increasing the number of healthy options at school canteens and cafeterias, and improving the physical environment, such as the implementation of water fountains in schools and removing advertisements .The combination of these direct effects from the market‐, community‐ and organisational‐level interventions work to improve the acquisition of healthier food and beverages for both households and individuals, in terms of both quantity and quality. Better availability and accessibility to healthy food and beverages allows children, adolescents and their families to make healthier food choices and improve their food intake. These intermediate effects lead to short‐term outcomes, such as improved diet and eating practices, reduced overweight and obesity, and long‐term outcomes, such as improved life expectancy and improved work and education productivity.As mentioned above, we acknowledge that school‐aged children and adolescents may experience the food environment differently as they transition from child to adolescent, or adolescent to adult. As children get older, they gain independence, autonomy and a greater range of choice in their acquisition of food and drink. This would affect their consumption and subsequently, their health outcomes. We also acknowledge that different environments might provide greater autonomy and choice. For example, a child eating lunch at school might have unhealthier options provided to them than at home where their parents are able to better control their diets. However, the focus of our review is to understand the impact of interventions outside of the home environment, apart from the strong influence that providers and caretakers might have on the diets of children and adolescents.Understanding if and how food environment interventions impact diet‐related health outcomes in school‐age children and adolescents in LMICs is critical for offering strategic recommendations for food environment improvement and encourages advocacy. Ultimately, this review will contribute to research within this field, with the overarching goal to improve health outcomes through recommended interventions. As mentioned, food environments in LMICs differ from those in high‐income countries in part due to the lack of appropriate infrastructure to access and distribute healthy foods. The lack of registered and formal outlets complicates measuring food environments in LMICs through geospatial and observational methodologies. As such, we acknowledge there are limitations associated with synthesizing information on food environments in LMICs, as food environments are not static. Despite this, we will attempt to capture information on the transitory nature of the food environment.To date, there is paucity of high‐quality summative evidence to link the food environment to diet‐related health outcomes in school‐age children and adolescents in LMICs. In fact, a lack of consistency in the definition of a food environment has presented challenges in synthesizing data. Currently, the only attempt to develop standard indicators of the food environment, relates to policy indicators. The Healthy Food Environment Policy Index (Food‐EPI), was developed by the INFORMAS group to assess the level of policy implementation against international best practices. Food‐EPI consists of seven domains in relation to policy  and six domains in relation to infrastructure support . Currently, Food‐EPI is being conducted in 30 countries worldwide, of which half are in progress in LMICs. To our knowledge, there are no systematic reviews on this topic that have been conducted or are in progress on LMIC populations.Related literature and systematic reviews have been focused in high‐income countries. These reviews have examined:► Food environment methodology ;► Food environment interventions ;► Food environment intervention cost‐effectiveness ;► Food environment exposures in association with dietary intake, dietary behaviour and or diet‐related health outcomes .Three on‐going reviews (registered with PROSPERO) are also focused in high‐income countries .It is also worth noting the Cochrane childhood obesity intervention reviews. A total of seven reviews have been published, one prevention‐focused  and six Moreover, our inclusion criteria contrasts to that of the Cochrane reviews in that we do not have a minimum intervention time, and we will not include dietary intake interventions alone or physical activity interventions alone . This is because the focus of our review aims to understand the impact of interventions on the physical environment, to which school‐age children and adolescents are exposed. Whereas, dietary intake or physical activity interventions serve to modify individual behaviour (i.e. changing their eating patterns or activity level). Finally, for all three reviews, the majority of included studies were conducted in high‐income settings, whereas our review is focused in LMIC settings.Our main objective for this review is to synthesise and assess literature on the impact of food environment interventions including interventions that attempt to influence food availability, accessibility, policy, pricing and promotion on diet‐related health outcomes in school‐age children and adolescents in LMICs, published since 2000.We will consider the following primary study types, including large‐scale programme evaluations, which assess the efficacy and/or effectiveness of interventions:1. Randomised controlled trials (RCTs): Experimental studies in which people are allocated to different interventions using methods that are random. This includes both individually‐randomised controlled trials, where the randomisation occurs on the level of individuals, and cluster‐randomised controlled trials (cRCTs), in which the randomisation occurs on the level of clusters of individuals (i.e. study groups or study sites). An example of this type of study would be the effect of promotional materials on overweight/obesity status. Children would be randomised by schools within a district, where the experiment group would have vending machines and promotional materials (posters on food/beverage products), while the control group would not have vending machines and promotional materials;2. Non‐randomised controlled trials (NRCTs) or quasi‐experimental: Experimental studies in which people  are allocated to the intervention or control group using methods that are not random, including exogenous geographical variation in the treatment allocation ; An example of this type of study would measure anthropometric outcomes of school‐age children and adolescents within schools that are < 400 meter radius to a food outlet/vendor, compared to those within schools that are > 400 meter radius to a food outlet/vendor. Children within each comparison group are not randomised, though are allocated to the exposure based on geographical variation;3.before measurements act as control to the after measurements once the intervention was implemented; Controlled before‐after (CBA) studies: Studies in which observations are made before and after the implementation of an intervention, both in a group that receives the intervention and in a control group that does not. Unlike in NRCTs, the allocation to the intervention and control groups is not determined by the investigators, but by nature or by other factors outside the control of the investigators. An example of this type of study would be anthropometric measurements of all school‐age children and adolescents are taken before and after the intervention of increased healthy food availability within a school cafeteria. The children's' 4. Interrupted‐time‐series studies: Studies that use observations at multiple time points before and after an intervention (the ‘interruption’). The design attempts to detect whether the intervention has had an effect greater than any underlying trend over time. An example of this type of study would be implementation of a region‐wide sugar tax on unhealthy food products. Anthropometric measurements of school‐age children and adolescents within this region would be taken three time points before and three time points after the implementation of this tax.5. Repeated measures studies (RMS): An ITS study where measurements are made in the same individuals at each time point.For the exact classification of study designs we will use the algorithm provided by EPOC, 2013.We will include only study designs that satisfy the following criteria specified by EPOC, 2013:► For cluster‐RCTs, NRCTs and CBA studies: studies with at least two intervention and two control sites► For ITS and RMS: a clearly defined point in time when the intervention occurred and at least three data points before and three data points after the intervention.In addition, we will include mixed methods studies . However, we will only include the quantitative aspects of these types of studies.We will exclude animal studies, observational , dissertations, conference abstracts and other systematic reviews (though reference lists of relevant systematic reviews will be reviewed for studies that meet our eligibility criteria).We will include participants aged 5‐9 years (school‐age children) and 10‐19.9 years (adolescents) from LMICs . ChildreThis review will include environmental interventions, both preventive and management‐based, targeted at school‐age children and adolescents in LMICs, which are implemented through market, community and organisational platforms , with thIn regards to market‐level food environment interventions, we will include interventions within consumer retail environments, both informal and formal,  aimed at impacting diet‐related health outcomes (including overweight and obesity). Some examples include: policy interventions on taxation, marketing or labelling of unhealthy food products; pricing interventions where point‐of‐sale vouchers/coupons are given to encourage healthy food purchasing; increased healthy promotional material  on healthy food and beverage; increased availability of healthy food and beverages/decreased availability of unhealthy food and beverages; increased access to healthy mobile food vendors).In terms of community‐level food environment interventions, we will include studies where population policies or programs  have been implemented widely through households or the community.At the organisational‐level, we will include food environment interventions implemented through schools or workplaces  aimed at understanding the impact on diet‐related health outcomes (including overweight and obesity).In regards to multi‐component interventions, we will consider the following intervention study designs:► A 2‐arm study with an intervention group where the behavioural and environmental components cannot be separated, as compared to a control group with no intervention or business as usual;► A multi‐arm study with: i) an experimental group with both behavioural and environmental components, ii) an experimental group with only environmental components, iii) an experimental group with only behavioural components, as compared to a control group with no intervention or business as usual.A separate meta‐analysis will be conducted for each intervention type (single vs multi‐component), by dimension  and outcome, data permitting.As well, food assistance programs at the household (i.e. cash transfers) or school  and food fortification at the industrial (i.e. mass fortification), retail and household (i.e. targeted fortification or home fortification with multiple micronutrient powders), levels are important mediators in the relationship between the food environment and diet‐related health outcomes. However, these specific pathways are outside of the scope of our review.Comparators include participants with no intervention, those who receive the standard of care or business as usual. We will not combine studies with business as usual and active comparison groups in the same meta‐analysis. Comparisons may be between groups of consumers/students, schools, or geographical areas such as cities .Food environment interventions and their effect on primary and secondary outcomes will be prioritised throughout this review. Both direction of effects will be included. Our primary outcomes will be used as eligibility criteria for including studies .‐ Primary outcomesPrimary outcomes will encompass anthropometric and diet‐related health outcomes relevant to school‐age children and adolescents including :► Stunting► Wasting► BMI, age‐ and sex‐standardized body mass index (zBMI or BMI z‐scores), overweight, obesity► Lean mass %, fat mass %► Waist circumference, waist‐to‐hip ratio2. Diet‐related health outcomes :► Anaemia► Type 2 Diabetes► Metabolic Syndrome► Goitre► Xerophthalmia and Night blindness‐ Secondary outcomesSecondary outcomes will surround dietary intake and biomarkers of diet‐related health outcomes including ► Direct measures of consumption ► Direct measures of diet quality ► Place of consumption (i.e. Positive Change or Adverse Change in Place of Consumption)► Meal patterns ► Purchasing  of food purchased)‐ Adverse outcomes of the interventionFood environment interventions which modify the built and natural physical environments, legal and political environments, socio‐economic, and cultural environments, may cause adverse effects in:► Primary outcomes: increase in the prevalence or incidence of stunting or wasting; increase in the prevalence or incidence of overweight or increase in mean BMI;► Secondary outcomes: increases in mean lipid profile or mean systolic blood pressure, changes in place of consumption (i.e. change in place of dinner consumption such as at restaurant versus at the home) or negative changes in meal patterns  .Studies will not be excluded based on a pre‐specified minimum duration of follow‐up. However, a subgroup analysis based on follow‐up time will be conducted, depending on data availability. If studies assessed the intervention at multiple time points, we will use baseline and end of intervention time points. We will include the outcome measures most similar to those presented in the other studies included in any single meta‐analysis, and report any additional follow‐ups narratively.We will include studies that have conducted an intervention in any LMIC setting as defined by the World Bank (2018), as long as all other inclusion criteria are met. We anticipate the majority of studies will be conducted in urban cities, at the school or community level (including out‐of‐school childcare).The World Bank provides a comprehensive list of all the countries falling under this LMIC definition.A comprehensive search of the literature will be conducted from the year 2000 onwards, without language restrictions or search filters. According to previous systematic reviews on food environments in HIC, from 1990 to 2015, food environment research did not gain momentum until the year 2002 . We founWe will search the following academic health databases, as they cover most of the main nutrition, general health and medical journals that publish in this area:► BiblioMap ► Cochrane Central Register of Controlled Trials (CENTRAL)► Embase► PsycINFO► MEDLINE► TRoPHI We will use the search strategy described in We will modify this search appropriately for other databases and clinical trials registers. We will use the Ovid search interface for MEDLINE, Embase, PsycINFO and CENTRAL ► FAO► International Food Policy and Research Institute (IFPRI)► International Initiative for Impact Evaluation (3ie)► INFORMAS► National Cancer Institute, Epidemiology and Genomics Research Program► World Bank► World Food Programme (WFP)► World ObesityReference lists of relevant systematic reviews found within our database search will be reviewed for studies that meet our eligibility criteria.Many food environment interventions studies employ either a RCT or NRCT methodological design. Eligible study examples of each are described below:►Impact of a school‐based intervention program on obesity risk factors in Mexican children. Salud Pública de México. 2013 55 (3): S374‐S387. This study conducted a cluster randomised controlled trial, where schools were assigned to one of three conditions: basic intervention, plus intervention or control over two years. Baseline, midpoint (7 months and 11 months) and endpoint measurements of children's height, weight and BMI were taken. The basic program focused on improving norms related to nutrition and physical activity at the schools and was limited to using existing school infrastructure and resources. The nutrition component specifically related to increasing availability of healthy food  and beverages (particularly water), while reducing the availability of energy‐dense foods and sugar‐sweetened beverages, and reducing the number of eating opportunities during the school day. The plus intervention implemented all the components incorporated in the basic program and included additional financial investment and human resources. No changes were made to existing nutrition or physical activity practices in control schools. Safdie, M., Jennings‐Aburto, N., Lévesque, L., Janssen, I., Campirano‐Núñez, F., López‐Olmedo, N., Aburto, T. and Rivera, J. ►Outcome results for the Ma'alahi Youth Project, a Tongan community‐based obesity prevention programme for adolescents. Obesity Reviews. 2011 Oct 12: Suppl.2, 41‐50. This study is a 3‐year, quasi‐experimental cohort study that examined the effects of community‐based interventions on nutritional health status in Tongan adolescents. The community‐based interventions included food environment interventions such as food policies in schools, implementation of school gardens, fruit tree planting, regulation of school canteen and a water promotion campaign. Schools located in the districts of Houma, Nukunuku and Kolonga on the main island of Tongatapu were assigned to the intervention group. Secondary schools located on the island of Vava'u were assigned to the control comparison group. Baseline anthropometric data  were collected in 2005‐2006. Follow up data was collected almost 3 years later in 2008. Fotu KF, Millar L, Kremer P, Moodie M, Snowdon W, Utter J, Vivili J, Schultz, T, Malakellis M, McCabe MP, Roberts G and Swinburn BA. As included studies will be interventional in nature and the food environment is a complex domain in which to work, we anticipate there to be heterogeneity in the literature and do not anticipate non‐independence of findings to be problematic. We will ensure that any analysis will take possible sources of dependency into consideration. If there are two or more papers describing the same study, they will be combined and coded as a single study. We will cluster papers into exposures categories and ensure that no double counting of data occurs when synthesizing across exposures.1. General information► Study title (free text)► Authors (free text)► Year of publication (free text)► Location type ► Country► World Bank region► World Bank income‐level ► Study design► Length of study (free text)► Language► Type of data 2.► Setting ► City/Town (free text)► Type of population ► Context ► Sample size reported► Sample size analysed► Age unit ► Age value► Descriptive statistics value (free text)► Descriptive statistics unit ► Gender ► Gender value► Gender unit Study setting and populationAs we expect variability in reported age bands, we will make a post‐hoc decision in analysis of results at different age bands. If possible, we hope to analyse data by WHO age group classifications:► School‐age (5‐9.9 years)► Young Adolescents (10‐14.9 years)► Older Adolescents (15‐19.9 years)3. Exposures► Type of food environment ► Context ► Level collected ► Level reported ► Instruments/methodologies used ► Instruments/methodologies used validated? ► Instruments/methodologies used (free text and citation if available)► Exposures reported domain ► Exposures (free text)► Level of Exposures ► Other exposures (free text)► Study characterization of healthy/unhealthy sources of food or food products (free text)4. Intervention► Start of intervention (i.e. the date of implementation of the first intervention)► End of intervention (i.e. the date of when the last intervention component was discontinued)► Duration of the intervention (i.e. the time span from the start of the intervention to the end of the intervention)► Total duration of the follow‐up (i.e. the time span from the start)► Mode of delivery of intervention ► Type of intervention 1 ► Type of intervention 2 ► Who implemented the intervention? ► Context of the intervention (free text)► Unit of randomisation or control► Unit of analysis► Total number of persons in all intervention and control groups at baseline and post‐intervention► Number of individuals in each intervention and control group (at baseline and at post‐intervention)► If multiple time points, number of participants at each time point (attrition rate)► Reasons for attrition, if provided► Mode of analysis ► Which primary outcome(s) did the intervention address? ► Was a subgroup analysis done? ► Adverse Outcomes or Unintended Consequence ► General description of participants (context)► Was intervention targeted at specific age categories/bands?► Type of recruitment (how were participants recruited)► Date of baseline assessment► Date of last outcome assessment► Baseline assessment of intervention group► Baseline assessment of control group► Any baseline differences noted between study groups?► Follow‐up assessment of intervention group► Follow‐up assessment of control group► Post‐intervention assessment of intervention group► Post‐intervention assessment of control group► Measure of effect value (free text)► Measure of effect ► Measure of effect unit► Descriptive statistics value (free text)► Descriptive statistics unit 5. Primary outcomes► Type of primary outcome ► Anthropometric or diet‐related health outcome drop‐down , Normal, Overweight, Obese, Overweight/Obese, Underweight, Stunting, Wasting, HAZ, BAZ, Hyperglycaemia, Diabetes Status, Iron Status, Hypertension, Vitamin A Status, Hyperlipidaemia, Other)► How was outcome assessed? (tool or measurement)► Is the outcome tool or measurement validated? ► Outcome tool used validated how and when (free text)► Name of outcome tool or measurement (free text)► Reference for weight outcomes )► Anthropometric outcome or diet‐related health outcome unit ► Anthropometric outcome 1 descriptive statistics value (free text)► Anthropometric outcome 1 descriptive statistics value unit► Anthropometric outcome 1 (free text)► Context (how the author defined weight categories i.e. overweight vs obese)6. Secondary outcomes► Biomarker type ► Biomarker other (free text)► Biomarker unit ► Biomarker descriptive statistics value (free text)► Biomarker descriptive statistics value unit► Frequency of consumption value (free text)► Frequency of consumption unit ► Type of intake ► Instrument/tools of measurement )► Validated Tool ► Name of outcome tool or measurement (free text)► Food group categorization ► Energy intake/macronutrient/micronutrient reference ► Energy intake/macronutrient/micronutrient value (free text)► Energy intake/macronutrient/micronutrient units► Energy intake/macronutrient/micronutrient descriptive statistics value (free text)► Energy intake/macronutrient/micronutrient descriptive statistics units► Type of dietary score ► Dietary score value (free text)► Frequency of consumption value (free text)► Frequency of consumption unit ► Place of consumption , school (food brought from home), work (food bought at work), work (food brought from home), eating (purchased or home cooked food) outside of the home, eating out of the home )► Type of meal skipped ► Snacking ► Snacking context ► Snacking frequency ► Purchasing ► Place of purchase ► Type of food purchased ► Type of food purchased (free text)7. Other notes (Free text)After the candidate studies have been screened and the eligible studies have been selected, the two first authors (BC and CO) will code the studies following the guidelines described in the coding scheme below:Risk of bias of included studies will be independently assessed by two review authors using the EPOC‐adapted Cochrane Collaboration ‘Risk of bias’ tool . Inconsistencies will be resolved by discussion, and where necessary by consulting a third review author. The EPOC‐adapted Cochrane ‘Risk of bias’ tool has been validated, and is commonly used in Cochrane and non‐Cochrane reviews. For controlled study designs , the assessment is based on the following seven criteria:1.Random sequence generation (checking for possible selection bias)We will describe the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it produced comparable groups, for each included study. We will assess the method as:► low risk of bias ;► high risk of bias ;► unclear risk of bias.2.Allocation concealment (checking for possible selection bias)We will describe the method used to conceal the allocation sequence in sufficient detail and determine whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment, for each included study.We will assess the methods as:► low risk of bias ;► high risk of bias ;► unclear risk of bias.3.Blinding (checking for possible performance bias)We will describe the methods used, if any, to blind study participants and other individuals  from knowledge of which intervention a participant received, for each included study. We will consider if studies were at low risk of bias if they were blinded, or if we judged that the lack of blinding would be unlikely to affect results. We will assess blinding separately for different outcomes or classes of outcomes.► low risk of bias (e.g. if blinding was reported of participants and personnel)► high risk of bias (e.g. if blinding was not reported for participants and personnel)► unclear risk of bias.We will assess the methods as:4.Blinding (checking for detection bias)We will describe the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received, for each included study.► low risk of bias (e.g. if outcome assessors were blinded)► high risk of bias (e.g if outcome assessors were not blinded)► unclear risk of bias.We will assess the methods as:5.Incomplete outcome data ► low risk of bias ;► high risk of bias ;► unclear risk of bias.We will describe for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported, the numbers included in the analysis at each stage , reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information was reported, or could be supplied by the trial authors, we re‐included missing data in the analyses which we undertook. We will assess methods as:6.Selective reporting biasWe will describe each included study how we investigated the possibility of selective outcome reporting bias and what we found.► low risk of bias ;► high risk of bias ;► unclear risk of bias.We will assess the methods as:7.Other bias (checking for bias due to problems not covered by (1) to (6) above)We will describe each included study any important concerns we had about other possible sources of bias.► low risk of other bias;► high risk of other bias;► unclear whether there is risk of other bias.We will assess whether each study was free of other problems that could put it at risk of bias:We will make explicit judgements about whether studies were at high risk of bias, according to the criteria given in the Handbook . With reference to (1) to (7) above, we will assess the likely magnitude and direction of the bias and whether we considered it was likely to impact on the findings.For ITS study designs, we will use the following criteria to assess risk of bias:1. Intervention independent of other changes► Low risk if there are compelling arguments that the intervention occurred independently of other changes over time and the outcome was not influenced by other confounding variables/historic events during the study period;► High risk if reported that the intervention was not independent of other changes in time.2. Shape of the intervention effect pre‐specified► Low risk if point of analysis is the point of intervention or a rational explanation for the shape of the intervention effect was give by the author(s);► High risk if it is clear that the condition above is not met.3. Intervention unlikely to affect data collection► Low risk if reported that the intervention itself was unlikely to affect data collection;► High risk if the intervention itself was likely to affect data collection.4. Knowledge of the allocated interventions adequately prevented during the study► Low risk if the authors state explicitly that the primary outcome variables were assessed blindly, or the outcomes are objective;► High risk if the outcomes were not assessed blindly;► Unclear risk if not specified in the paper.5. Incomplete outcome data► Low risk if missing outcome measures were unlikely to bias the results;► High risk if the missing outcome data was likely to bias the results;► Unclear risk if not specified in the paper.6. Selective outcome reporting► low risk of bias ;► high risk of bias ;► unclear risk of bias, if not specified in the paper.7. Other bias► Low risk if there is no evidence of other risk of biases.After removal of duplicate studies, we will perform a multistage screening process to select studies that meet the eligibility criteria . Each tiIn the second step, two review authors (BC and CO) will assess all full texts in duplicate. Any disagreements will be resolved by discussion and, where necessary, by consulting a third review author. At this stage of the screening process, we will document the reasons for exclusion. We will exclude studies without primary outcomes. We will not exclude studies on the basis of reporting of an outcome . We will also contact the primary authors of the study for any insufficient or unclear information if required.EndNote will be used to collect and de‐duplicate studies and to initially screen title and abstracts. For full‐texts review and to document the exclusion decisions made, we will use the web‐based software application Covidence. Covidence has been developed to streamline the screening of literature and writing of systematic reviews. Any post‐hoc changes to the eligibility criteria that may occur at the data extraction stage will be reported explicitly in the final review.Data from the included studies will be independently coded and extracted by two review authors using the predefined data extraction forms. Data extraction forms will be matched. A third review author will be consulted in the event of any disagreements, and consensus will be sought. We will attempt to contact authors of the original reports to obtain further details if required.For dichotomous outcomes (e.g. rate of new‐onset obesity) results will be expressed as risk ratio (RR) with 95% confidence intervals (CI). Where continuous scales of measurement are used to assess the effects of a food environment exposure (e.g. consumption of fast food in times per week/per person or average BMI), the mean difference (MD) will be used, or the standardized mean difference (SMD) if different scales have been used in the primary studies. It is expected that most outcome measures will be continuous. We will include 95% CIs for MD, SMD and RR intervention effects.We will give special attention to the unit of randomisation, unit of analysis and underlying design issues for each included study, particularly identifying those studies with cross‐over and clustered designs. We will assess whether or not analysis and reporting were appropriately done. We will make decisions about whether or not to include studies with potential errors in analysis by consensus with the review authors, and consultation with a statistician where necessary.We will use the following questions to guide our decision:1. What is the unit of analysis issue?2. Can the issue be corrected by the review authors .3. Does the unit of analysis issue impact all aspects of the results and analysis or only parts of it?4. Does the unit of analysis issue represent an error that would warrant exclusion if all other aspects of the study indicated inclusion? Could some aspects of the study be used?Special attention will be given to cluster RCTs and cluster NRCTs, especially where analyses are not adjusted for clustering. We will attempt to adjust results for clustering according to the Cochrane Handbook . For registered but unpublished trials, we will contact the corresponding investigator to request relevant data.2, or Tau2 to estimate the absolute value of true heterogeneity between studies, and Chi² test to calculate the P value for the heterogeneity. We will consider it statistically significant if the P value for the Chi2 test is < 0.1.We will generate τIf we identify substantial heterogeneity, we will report this and explore possible causes in pre‐specified subgroup analyses.To assess the potential for publication bias, we will compare mean effect sizes of published studies with those from unpublished sources. If the number of eligible studies is sufficient (recommended minimum of ten studies), funnel plots will be inspected to assess whether estimates from individual studies are symmetrically distributed around the mean effect size. As well, we will critically assess study bias and outcomes based on funding source (i.e. corporate funding versus studies with independent funding). Depending on data availability, we will conduct a sensitivity analysis comparing mean effect sizes from both types of funding.We will present the synthesis of quantitative evidence from the included studies through narrative and statistical analysis of comparable interventions/outcomes using meta‐analysis. Meta‐analysis is useful in synthesizing quantitative evidence from multiple studies as it considers the statistical power of the effect estimated in these various studies. We will first categorise the studies based on type of study design , and then by intervention type, since we will consider both single and multi‐component interventions and outcomes in our systematic review. For multi‐component interventions, we will determine which arms are most relevant to our review objectives and meet our inclusion criteria for meta‐analyses purposes, though descriptions of all intervention arms per study will be presented in a ‘Characteristics of Included Studies’ table. For example, in a three‐armed RCT , we will only include arms 1 and 3 in our analysis. However, in a two‐armed multi‐component RCT  we will analyse these studies separately from two armed single component RCT .2, or Tau2 to estimate the absolute value of true heterogeneity between studies. Moreover, to ensure no unit‐of‐analysis errors are made, we will combine groups to create a single pair‐wise comparisons for each meta‐analysis. Additionally, studies that include both school‐age children and adolescents, but do not disaggregate outcome data will be analysed as a separate group (i.e. 5 to 12 years). We do not plan on conducting any moderator analyses, as we are not comparing effects for different types of food environment domains (i.e. pricing vs promotion). Where meta‐analysis is deemed inappropriate due to substantial heterogeneity, we will summarise the findings of the included studies narratively.Depending on the outcome, we will calculate the standardised mean difference (for continuous outcomes) or risks ratio (for dichotomous outcomes), which are appropriate for comparison between groups across similar types of treatment effects. Where multiple measures are reported for an outcome in a single study, we will utilize the most commonly reported measure across all included studies. We will attempt to pool all studies within a given study design and or intervention category, assessing the same outcome, in the same population group by conducting a random‐effects meta‐analysis using Review Manager 5.3. We will generate τA random‐effects meta‐analysis model will be used given the high level of variation in effect sizes anticipated across studies due to the wide diversity in the design, scale, and implementation of their interventions. Therefore, we will apply the random‐effects model in conducting the subgroup analyses including all between and within subgroup weighting for the specified characteristics below. With this approach, we will estimate and report the variance in effect size across the studies within each subgroup and pool these subgroup estimates based on the assumption that the between study variance is likely to be the same for all subgroups. Combined mean effect estimates of the subgroups will be computed based on the random‐effects weights and pooled tau2. Comparisons for statistically significant differences will be tested using an analysis of variance Q statistic that follows a chi‐square distribution.From our scoping review, we have identified subgroup analyses that would be most appropriate to investigate heterogeneity (if data permits):► Age group (school‐age vs adolescents)► Geographical location ► Duration of the intervention and follow‐up .► RandomizationWe will conduct a comparative analysis to test for sensitivity of the results of the review by:► comparing results if we include studies into our predetermined age bands that may have been pooled separately due to age range of participants ;► determining whether results differ when studies at high risk of bias are excluded (from any dimension)We do not plan to include qualitative research.We will use the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system for grading the body of evidence for outcomes. In the context of GRADE, the quality of a body of evidence is understood as the extent to which one can be confident that an estimate of an effect is close to the true effect  or as very serious (downgrading by two levels):► Risk of bias of individual studies by dimension (limitations in the design and implementation of available studies suggesting high likelihood of bias)► Indirectness of evidence ► Unexplained heterogeneity or inconsistency of results ► Imprecision of results ► High probability of publication biasMoreover, there are three factors that can increase the quality of evidence, particularly in relation to quasi‐experimental studies including:► Large effects ► Dose response relationships► If all plausible residual confounding & biases (would have either reduced the demonstrated effect or increased the effect if no effect was observed)Lead review author:Content: BC, CO, EK, DR and ZAB have content expertise in the area of global child health and nutrition.Systematic review methods: EK, MFG, DR and ZAB have expertise and experience in conducting systematic reviews and meta‐analyses.Statistical analysis: EK, MFG, DR and ZAB have expertise in statistical methods, particularly those methods used in clinical research and epidemiology.Information retrieval: BC, CO, and EK have expertise and experience in retrieving and abstracting information from the literature using systematic methods.Funding was received from The Joannah and Brian Lawson Centre for Child Nutrition, The University of Toronto.None to declare.Review submission: March 2019We intend on updating the review in three years.By completing this form, you accept responsibility for preparing, maintaining and updating the review in accordance with Campbell Collaboration policy. Campbell will provide as much support as possible to assist with the preparation of the review.A draft review must be submitted to the relevant Coordinating Group within two years of protocol publication. If drafts are not submitted before the agreed deadlines, or if we are unable to contact you for an extended period, the relevant Coordinating Group has the right to de‐register the title or transfer the title to alternative authors. The Coordinating Group also has the right to de‐register or transfer the title if it does not meet the standards of the Coordinating Group and/or Campbell.You accept responsibility for maintaining the review in light of new evidence, comments and criticisms, and other developments, and updating the review at least once every five years, or, if requested, transferring responsibility for maintaining the review to others as agreed with the Coordinating Group.The support of the Coordinating Group in preparing your review is conditional upon your agreement to publish the protocol, finished review, and subsequent updates in the Campbell Library. Campbell places no restrictions on publication of the findings of a Campbell systematic review in a more abbreviated form as a journal article either before or after the publication of the monograph version in Campbell Systematic Reviews. Some journals, however, have restrictions that preclude publication of findings that have been, or will be, reported elsewhere and authors considering publication in such a journal should be aware of possible conflict with publication of the monograph version in Campbell Systematic Reviews. Publication in a journal after publication or in press status in Campbell Systematic Reviews should acknowledge the Campbell version and include a citation to it. Note that systematic reviews published in Campbell Systematic Reviews and co‐registered with Cochrane may have additional requirements or restrictions for co‐publication. Review authors accept responsibility for meeting any co‐publication requirements.I understand the commitment required to undertake a Campbell review, and agree to publish in the Campbell Library. Signed on behalf of the authors:


Outcome selection, measurement and reporting for the evaluation of new surgical procedures and devices is inconsistent and lacks standardization. A core outcome set may promote the safe and transparent evaluation of surgical innovations. This systematic review examined outcome selection, measurement and reporting in studies conducted within the IDEAL  framework to examine current practice and inform the development of a core outcome set for early‐phase studies of surgical procedures/devices.Web of Science and Scopus citation searches were performed to identify author‐reported IDEAL/IDEAL‐D studies for any surgical procedure/device. Outcomes were extracted verbatim, including contextual information regarding outcome selection and measurement. Outcomes were categorized to inform a conceptual framework of outcome domains relevant to evaluating innovation.Some 48 studies were identified. Outcome selection, measurement and reporting varied widely across studies in different IDEAL stages. From 1737 outcomes extracted, 22 domains specific to evaluating innovation were conceptualized under seven broad categories: procedure completion success/failure; modifications; unanticipated events; surgeons' experiences; patients' experiences; resource use specific to the innovative procedure/device; and other innovation‐specific outcomes. Most innovation‐specific outcomes were measured and reported in only a small number of studies.This review highlighted the need for guidance and standardization in outcome selection and reporting in the evaluation of new surgical procedures/devices. Novel outcome domains specific to innovation have been identified to establish a core outcome set for future evaluations of surgical innovations. This review of IDEAL/IDEAL‐D studies examined outcome selection, measurement and reporting to examine current practice and identify outcome domains unique to evaluating innovation. The findings have informed the development of a core set of outcome domains to measure and report in all early studies of innovative surgical procedures/devices to promote standardized, safe and transparent evaluation.Potential domains identified Unlike pharmaceuticals, where the introduction and evaluation of new medicines into clinical practice is highly regulated, the process for introducing new procedures and devices in surgery is inconsistent and lacks standardization. This has resulted in several high‐profile cases of potentially harmful interventions becoming established in clinical practice without robust evaluation, including vaginal mesh and metal‐on‐metal hip implantsA key problem in the effective evaluation of new surgical procedures and devices is the lack of standardization in outcome selection, measurement and reporting. Studies may measure benefits and harms in different ways, so that results cannot be directly compared or combined. Furthermore, investigators can choose which outcomes to measure and report, introducing bias. There is a need for standardized and transparent measurement and reporting of outcomes to improve safe and efficient evaluation of surgical innovation for introduction into clinical practice.The development and use of a core outcome set (COS) – a minimum set of outcome domains to be measured and reported in all early‐phase evaluations of innovative surgical procedures and devices – may be a solution to addressing inconsistent and heterogeneous outcome selection and reportingThe IDEAL  framework describes a pathway for evaluating surgical innovations from first‐in‐man through to long‐term evaluationThis study aimed to examine outcome selection, measurement and reporting in author‐reported IDEAL/IDEAL‐D studies to identify current practice, and serve as one of multiple data sources to help conceptualize novel domains relevant to evaluating innovation to inform the development of a COSA systematic review and content analysis of author‐reported IDEAL/IDEAL‐D studies was undertaken following PRISMA guidelinesElectronic searches were performed to identify studies reported as following the IDEAL/IDEAL‐D framework. Searches were undertaken in databases with citation tools (Web of Science and Scopus) to identify all publications citing any of ten key IDEAL/IDEAL‐D papersTitles and abstracts were screened independently for eligibility by two reviewers. Reports or protocols for studies described as IDEAL or IDEAL‐D in the title or abstract were included. Eligible studies were: studies of innovative invasive procedures; studies of innovative devices; or studies where the innovation was something other than an invasive procedure/device but the study involved an invasive procedure, for example radiological imaging for guided biopsy. The latter category of studies was included owing to the potential value of such studies for identifying outcomes relevant to evaluating innovation. Invasive procedures were defined using an existing published definition and included ‘purposeful or deliberate access to the body gained via an incision, percutaneous puncture, where instrumentation is used in addition to the puncture needle, or instrumentation via a natural orifice’A standardized electronic data extraction form ; detailed, but with no hypothesized effect for any outcomes; general rationale (for example ‘to examine safety’); or no rationale providedData analysis was undertaken in two stages. Extracted verbatim outcomes were categorized individually into domains to develop a preliminary framework of outcome domains relevant to innovation. Categorization was performed by one reviewer. During this process, the conceptual framework was modified iteratively until data saturation was reached and no new outcome domains were identified . A provisional conceptual framework of domains was subsequently attained. A subsample of outcomes  were independently categorized by a second reviewer with clinical expertise to ensure methodological rigour. Discrepancies were resolved by discussion with the wider study team.In the second stage of analysis, all outcomes were recategorized using the provisional conceptual framework by a second independent reviewer. Source documents (IDEAL/IDEAL‐D study publications) were referred to for context, for example, to determine whether clinical outcomes were anticipated or unanticipated. Outcomes that were not specific enough to categorize  were coded as ‘too broad for categorization’. Outcomes that did not specifically address the evaluation of the innovation but were routinely measured data variables of interest to the clinical specialty were coded as common data elementsOutcome domains within the provisional conceptual framework were further classified by the authors as being innovation‐specific or shared with effectiveness studies. Innovation‐specific domains included non‐traditional outcome domains  considered to be conceptually specific to evaluating the ongoing process of innovation when the procedure/device was new or evolving. Examples included modifications to the surgical procedure/device and whether the procedure was completed successfully. Domains shared with effectiveness studies encompassed outcomes that would commonly be measured in later‐phase studiesStudy characteristics, measured/reported outcomes  and contextual information relevant to selecting or measuring outcomes or evaluating innovation were compared across IDEAL stages specifically reported by the authors. Descriptive statistics were summarized using Microsoft Excel®  and STATA® statistical software .Assessment of factors relevant to systematic reviews of effectiveness  and data syntheses  were not appropriate given the exploratory aims of this review. Findings were synthesized and tabulated using descriptive statistics and a narrative summary directed at the study aims.Fig. After removal of duplicates, the search yielded 1207 records citing any of the ten key IDEAL/IDEAL‐D publications. Of these, 136 (11·3 per cent) included IDEAL as a text word in the title or abstract. Initial screening identified 62 of 136 records (45·6 per cent) for full‐text eligibility assessment. Finally, 48 relevant publications  were included in the analysisTable Included publications are detailed in Table Characteristics of studies across the different IDEAL stages (stated in 46) are summarized in Table Most studies (37 of 48) provided a rationale for outcome selection. Often (20 studies) this was a general rationale (for example ‘to examine safety’). Fewer studies (12) provided detail on the expected or anticipated effect on outcomes. Instead, outcomes were reported to be measured without explanation or elaboration regarding their selection. There were no observed differences in the level of detail provided in the rationale between studies of different IDEAL stages  mentioned the learning curve. The frequency of mentioning the learning curve did not differ by IDEAL stage.Table A total of 1737 outcomes were extracted from the 48 studies and categorized into outcome domains. The derived conceptual framework comprised 32 domains, 22 of which were considered to be conceptually specific to evaluating innovation and ten to be shared with effectiveness studies (Table Some 107 outcomes (6·2 per cent) were categorized as relating to the success or failure of performing the innovative procedure or using the innovative device (Some 92 outcomes (5·3 per cent) were categorized as relating to modifications. This included modifications to the individual steps of the planned innovative procedure . Modification outcomes also included changes to any accompanying intervention related to the innovative procedure. For example, one studyModifications were reported in 24 studies. The number of studies reporting modifications was broadly in line with the IDEAL framework, the majority being IDEAL stage 2a (innovations in early development). Most modifications were reported using narrative text alone (11 studies) and less frequently using flow diagrams, figures or graphs.Outcomes that related to an unexpected advantage or disadvantage were categorized as unanticipated events. These were further defined as occurring during or after the procedure. At least one unanticipated event was reported in eight of 42 studies (excluding protocols). These were mostly disadvantages, such as clinical complications  were categorized as relating to surgeons' experiences of performing the innovative procedure or experiences of operators using the new device (if not a surgeon). Examples included ergonomics, such as pain or discomfort when carrying out the procedure, or surgeons' reports of how difficult or easy it was to perform the procedure. Further outcomes categorized under this domain included descriptions of problematic points in undertaking the procedure and impressions of the learning experience. Although 23 studies included outcomes relating to surgeon or operator experiences, only threeTable Overall, 215 outcomes (12·4 per cent) were categorized as assessing patients' experiences. The majority  were measured after operation and were similar to those measured in effectiveness studies, such as patient‐reported quality of life and pain. A minority of outcomes , however, were specifically relevant to the experiences of undergoing the innovative procedure or receiving treatment with the innovative device. One studyTable A small number of outcomes  measured resource use specific to the innovative procedure/device. Examples included the cost of the procedure/device or associated costs (such as equipment sterilization costs) and the number of further treatments required owing to complications that had developed as a result of the innovative procedure. Some 31 studies included any kind of outcome relating to resource use , they were considered relevant outcomes important for the evaluation of innovation.Although the majority of outcomes extracted reflected those traditionally measured and reported in effectiveness studiesThe heterogeneity observed in the number of studies measuring and reporting outcomes of specific relevance to evaluating innovation highlights the need for more detailed guidance and improved standardization of outcomes that are important to measure when evaluating surgical innovation. The IDEAL framework provides clear guidance regarding the characteristics of studies at each stage of evaluation of surgical innovation, but uses only broad terms to describe the types of outcome that should be measured at different stages of evaluation. The lack of specific guidance around the selection and measurement of recommended outcomes may be one reason why the widespread uptake of IDEAL has been relatively limitedThe review was conducted using robust and established methodology, but has limitations. Methods to identify IDEAL/IDEAL‐D studies selected publications that had included IDEAL as a text word in the title or abstract. This pragmatic filtering step to identify relevant studies was based on the rationale that studies firmly engaging with the IDEAL framework, and therefore most likely to have more thoroughly considered outcomes in the context of evaluating innovation, may be more likely to describe their study as an IDEAL study in the title or abstract. It is accepted that some studies undertaken within the IDEAL framework may have been missed as a result of this search strategy. The number of studies identified (48) may be considered small given that the IDEAL framework was published 10 years ago. This number is, however, consistent with a recent reviewThe identification of innovation‐specific outcome domains is an important first step in developing a generic COS to standardize outcome selection, measurement and reporting in studies of novel surgical procedures. Such a COS would be appropriate for use before definitive evaluation within an RCT and is intended to support standardized evaluation of surgical procedures and medical devices throughout the stages of the innovation lifecycle, to facilitate safe and transparent introduction. The present review is one of several data sources to inform a conceptual framework for outcome domains for the COS to ensure its validity and comprehensivenessAppendix S1: supporting informationClick here for additional data file.


Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right‐TLE cohorts . Extrapolating our multivariate results to duration equals zero  we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non‐overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality.Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure , which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects , was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z‐scores) and a complementary multivariate approach , incorporating multiple white matter tracts into a single unified analysis. For patients with right‐TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left‐TLE, the FA of two limbic tracts  were significantly negatively associated with epilepsy duration  have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure to capture the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. ATRanterior thalamic radiationCGcingulum gyrusCHcingulum hippocampusFfornixFAfractional anisotropyMRImagnetic resonance imagingROIregion of interestTLEtemporal lobe epilepsyUFuncinate fasciculus1Epilepsy affects over 50 million people worldwide, with around 60% of patients presenting with focal seizures, most commonly being of temporal lobe origin . However, this correction has the effect of inflating the false negative rate (Type 2 error) leading to the increased probability of overlooking genuine relationships. Third, univariate analyses do not account for the natural covariance between tracts in individuals . The individuals with TLE were recruited from the National Hospital for Neurology and Neurosurgery epilepsy surgery programme, with diagnoses made by consultant neurologists specialising in epilepsy on the basis of clinical history, seizure semiology, and prolonged video‐EEG telemetry with ictal and interictal EEG, high resolution MRI, neuropsychological and neuropsychiatric assessments. Where applicable, 2.22 s − 1 ) along with 6 non‐diffusion weighted scans. The gradient directions were calculated and ordered as described elsewhere  using a Q‐space diffeomorphic reconstruction (QSDR) .Diffusion images were initially corrected for signal drift  are used throughout the paper to calculate the univariate and multivariate distances.The effects of sex and healthy aging in each tract were removed using a robust linear model . If the selected subject was a healthy control, we removed them from the population prior to random subset selection, to mitigate the bias of that control subject when calculating their distance. A subset of size 24 was selected as this is the maximum size in which at least 1,000 unique sub‐samples can be inferred from the population of (n‐1) controls. A z‐score distance is calculated for the subject by subtracting the mean of the sample control distribution from the subject FA residual and scaling by the sample distribution standard deviation. For each subject, the process is repeated 1,000 times to create a distribution of z‐score distances. Finally, median z‐scores for each subject are reported, providing a robust, single valued measure of univariate distance from the control distribution.We quantified the univariate distance of each subject from the control distribution. Robust z‐score values were calculated to mitigate the effects of outliers in the control data. First, we selected a subject and a random subset of 24 controls from the total control population , the Mahalanobis distance is a measure of distance from a reference distribution in multiple dimensions whilst accounting for the covariance structure. Penalising data points that fail to adhere to the natural structure, two subjects with similar z‐scores in each individual dimension can have vastly different Mahalanobis distances Figure .x represents a vector of subject FA residual values, µ the average tract FA residual values calculated from the healthy controls, and C a matrix representing the natural covariance structure exhibited in the healthy control population. We derived the Mahalanobis distance from the population of healthy controls. Distances are calculated using Equation The Mahalanobis distance assumes normality in the reference distribution. Therefore, univariate assessments of normality in the control subject distribution were conducted using the Lilliefors test  from the control data (28), calculating a Mahalanobis distance for each sample and reporting the median value. Non‐linear shrinkage estimators of the covariance matrix were used in place of the sample covariance matrix to minimise the estimation error of the inverted matrix  to quantify the association observed between the univariate and multivariate distances and epilepsy duration. A non‐parametric alternative to the Pearson correlation, the Spearman correlation is a measure of the monotonic relationship between two variables which is more robust to outliers in the dataset. Hypothesising a more negative z‐score and more positive Mahalanobis distance with a greater epilepsy duration, significant correlations were assessed using a one‐tailed test. Per patient group, ten univariate correlations were computed (one per tract) and two multivariate correlations . Reporting significance at the α=0.05 threshold a Bonferroni correction was applied to account for h multiple comparisons . Significant correlation thresholds for samples of size n were approximated using a Student's t distribution with (n‐2) degrees of freedom and test statistic (t) shown in Equation We investigated the association of the computed uni‐ and multi‐variate measures with epilepsy duration. Analysing patients with left and right TLE separately, we used Spearman correlations Assessments into the effects of laterality on the correlational analysis were conducted by combining the Mahalanobis distances of all patients into a single ipsilateral and contralateral measure of distance and correlating these with epilepsy duration.β0 from Equation We also investigated if the ipsilateral and contralateral Mahalanobis distances calculated in patients exhibit white matter deviations from the healthy population at onset . Using robust linear regression models of all patients' ipsilateral and contralateral Mahalanobis distances, estimates of the Mahalanobis distance at duration zero were calculated by regressing out the effects of duration and considering the intercept  calculated and surgical outcomes. Hypothesising that larger distances would relate to poorer outcomes, we used one‐tailed, two sample 2.9κ) provides a measure of the robustness the data from each tract has to outliers in the dataset. An ideal measure would either always show a significant result (κ=100%) or never show a significant result (κ=0%). Where κ deviates far from the extremities we interpret this as being inconsistent and therefore has the potential to lead to different results depending on the sample chosen or specific methodology. This thus leads to varied reporting in the literature of (non)significant results. In order to assess the stability of our robustness analysis to cohorts of various sizes we also repeated the analysis for subsample sizes ranging from 20 to 30 patients per group.To assess the robustness of the univariate and multivariate correlational analyses to outliers in the data and potentially explain some of the variability seen in the literature we used Jackknife resampling. A random subsample of 30 left‐TLE and 30 right‐TLE patients was taken and the association of each of the ten tract FA values and the ipsilateral/contralateral Mahalanobis distances with duration were calculated. Samples of size 30 were chosen as it is the maximum size in which at least 1,000 unique sub‐samples can be inferred from the population of 33 patients. Repeating 1,000 times and reporting the proportion of samples yielding significant correlations . In right‐TLE, there was no significant association between FA and duration in any tract .Evaluation of associations between univariate z‐score distances and surgery outcome revealed significant results for right‐TLE patients only. After correction for multiple comparisons the contralateral uncinate fasciculus remained significant, showing that FA values which deviate the least from healthy controls relate to a better seizure free outcome , with increased distance as duration progresses   and a weaker non‐significant correlation in the contralateral hemisphere . Intercepts of the ipsilateral and contralateral progression.Combining all patients into a single unified analysis  appeared to originate from the control distribution mean (DM=2.137)  and contralateral  Mahalanobis distances of patients at epilepsy onset  were not significantly different from healthy controls. Analysis of patients with (TLE‐HS) and without (TLE‐NHS) hippocampal sclerosis independently revealed significant correlations between epilepsy duration and the ipsilateral Mahalanobis distance in both groups , with larger distances in those who did not become seizure free. No significant associations with surgery outcome were found using the ipsilateral Mahalanobis distance  we found no significant difference to controls. Third, our robustness analysis revealed that a number of univariate associations were susceptible to outliers in the dataset, whereas results obtained using multivariate Mahalanobis distances were more stable.p < .05, h = 10), with an additional seven regions showing associations prior to correction. Significant associations were present in (1) ipsilateral fornix and (2) contralateral cingulum gyrus of left‐TLE patients. Similar associations were shown previously, with a whole brain TBSS analysis by  it should also be noted that different consistency values would be observed if the number of tracts studied varied. Based on this and the differences in inter‐study sample sizes, it is likely that the Bonferroni correction accounts for some of the inconsistencies which exist in the literature. Consistency values associated with the correlations between epilepsy duration and Mahalanobis distances show a strong robustness to outliers. Bonferroni correction of the Mahalanobis distance analysis based on number of tracts is also not required and is a distinct advantage of the multivariate approach.All patients studied here later underwent anterior temporal lobe surgery. This thus offered the opportunity to investigate the relationship to post‐surgical seizure‐freedom. We found significant differences between outcome groups for right TLE patients in a univariate approach  and the multivariate approach —Tables Our univariate analysis of associations between epilepsy duration and z‐score distances in ten white matter structures revealed notable differences based on patient laterality. We found stronger and more widespread correlations in left‐TLE, with two significant correlations surviving multiple comparisons correction. No significant correlations were seen in the right‐TLE patient group Table . ConsistAssociations between epilepsy duration and the multivariate Mahalanobis distances revealed similarities regardless of laterality Figure . StrongeOur cross‐sectional analysis of epilepsy progression has limitations. First, In order to compare patients, we removed the effects of healthy ageing using regression. This procedure assumes FA alterations in all subjects follow a similar natural linear trajectory. It is likely that FA alterations in some patients are underestimated whereas others are overestimated. These residuals may have an effect of altering the magnitude of associations between FA and duration. Second, the use of cross‐sectional data only provides associations with epilepsy onset and progression, rather than giving direct causal evidence. Third, although FA is the most widely used diffusion MRI metric in the literature, it nonspecific in its measurement which can be influenced by various different factors including axonal density and myelination (Concha et al., Collectively, our results show that the Mahalanobis distance can be used alongside the traditional univariate analyses for a more complete understanding of the progressive nature of epilepsy and its association with white matter abnormalities. More robust to outliers than the traditional univariate z‐score approach, the Mahalanobis distance is a complementary method which can be used to compare the overall epilepsy burden in a given hemisphere with clinical variables. Future studies with large cohorts and multi‐site data (Hatton et al., TO, YW and PT designed the study. JdT, SV, GW and JD oversaw data collection. TO and PT analysed the data. TO drafted the paper. TO, JdT, SV, GW, JD, YW and PT revised the paper.https://publons.com/publon/10.1111/ejn.15055.The peer review history for this article is available at Supplementary MaterialClick here for additional data file.


Since the discovery that a retrovirus, human immunodeficiency virus type one HIV‐1), causes the acquired immune deficiency syndrome (AIDS), more than 77 million people have become infected with HIV and 37 million have died over the last 40 years [, causes To date only one HIV vaccine trial has demonstrated modest efficacy. The RV144 study conducted in Thailand investigated a heterologous prime/boost vaccine regimen consisting of ALVAC HIV vcp1572) plus AIDSVAX B/E gp120  plus AID. In addithe first to determine whether broadly protective antibodies could reduce HIV acquisition, that is could a vaccine regimen that elicited high levels of non‐neutralizing antibodies be developed that would enhance the protective efficacy beyond that seen in RV144; and second, to discern whether neutralizing antibodies could actually prevent acquisition. As no vaccine as yet has been able to elicit anything but strain‐specific neutralization, the neutralizing antibody approach was to use the passive infusion of broadly neutralizing antibodies brought on by the revolution of B‐cell engineering and cloning to evaluate whether high levels of monoclonal antibodies could reduce HIV acquisition.In 2014, the HIV Vaccine Trials Network (HVTN) outlined a strategy to execute a series of large‐scale efficacy trials that would define the approach needed for a successful HIV vaccine. This strategy was broken into two frameworks: Between 2015 and 2019, the HVTN initiated five large‐scale efficacy trials: three in sub‐Saharan Africa and two in North and South America. Three were directed at stimulating non‐neutralizing antibodies and one set of integrated trials evaluated the passive infusion of the monoclonal antibody VRC01 in the antibody‐mediated prevention (AMP) trials. The past 12 months have started to bring in the results of these trials. The earliest returns from non‐neutralizing antibodies have been disappointing. HVTN 702, which was built upon the regimen of RV144, was stopped in January 2020 for lack of efficacy. HVTN 702 was based upon the same regimen used in RV144 except adapted to the subtype C region. Despite evidence of high levels of binding antibodies, ADCP and ADCC activity, no efficacy was noted . The oneWhile the desire to develop neutralizing antibody vaccines to HIV has been there since inception, to date, only strain‐specific immune responses have been elicited by any candidate vaccine. Essentially the inability to elicit broadly neutralizing antibodies to HIV to cover its strain diversity has been a major flaw in the HIV vaccine field for the first 30 years of vaccine development. The quest to overcome this was provided by B‐cell cloning technology, which demonstrated that broadly neutralizing antibodies or antibodies that could neutralize a wide diversity of strains could be isolated from about 15% of HIV‐infected people who had longstanding and often uncontrolled infection . This reTo evaluate this concept, the AMP trials, one in southern Africa (HVTN 703/HPTN 081) and the other in the Americas (HVTN 704/HPTN 085), were conducted to determine whether the infusion of broadly neutralizing antibodies targeting the CD4‐binding site called VRC01 could be effective in reducing HIV acquisition. The results of these studies recently published propose the answer was yes, with marked efficacy . HoweverMost relevant to HIV vaccine development have been recent breakthroughs in developing approaches to initiate the early germline that are the precursors of broadly neutralizing antibodies to the CD4 binding site, MPER, and V3 regions of the virus , 15, 16.The year 2021 brings optimism that the technologies to develop broadly neutralizing vaccines have been developed and major progress in this arena will be achieved. If we can do this, we can apply the momentum, enthusiasm and resources that COVID‐19 vaccines have brought to the world to make the possibility of achieving an HIV vaccine a reality.HIV Vaccine Awareness Day remains a critical reminder that without this critical biomedical tool, an AIDS‐free generation is unlikely to be achieved. On this day, we also acknowledge and appreciate the many people and their communities who have walked these miles with scientists in the quest to find an effective HIV vaccine.The authors have declared no conflict of interest.All authors have contributed to the preparation of the manuscript, read and approved the final draft.


The images of this article are clinical pictures of peritonsillar abscess subsequently complicated by Ludwig's angina of a 68‐year‐old man. A 68‐year‐old man with ulcerative colitis visited the otolaryngology clinic with a 2‐day history of sore throat and trismus and was diagnosed with a peritonsillar abscess that necessitated needle aspiration. The patient refused the intervention and was administered oral and intravenous antibiotics; however, he subsequently developed swelling and pain in the right side of the neck and treated with frequent doses of oral nonsteroidal anti‐inflammatory drug (NSAID). Five days later, the patient was referred to the emergency department with severe acute abdominal pain, without respiratory distress or dysphonia. Physical examination revealed submandibular swelling and redness Figure , but shoPeritonsillar abscess is one of the commonest deep‐neck infections and is associated with life‐threatening complications such as descending mediastinitis or parapharyngeal and retropharyngeal abscesses.The authors have stated explicitly that there are no conflicts of interest in connection with this article.


The COVID‐19 pandemic caused by the novel coronavirus SARS‐CoV‐2 continues to have a major impact on healthcare and social systems throughout the world. As the clinical and epidemiological features of COVID‐19 have many parallels with influenza, it is important to ensure optimal management of both respiratory diseases as we anticipate their continued co‐circulation. In particular, there is a need to ensure that effective surveillance and diagnostic capacities are in place to monitor these and other respiratory viruses, as this will underpin decisions on the appropriate clinical management of the respective diseases. As such, we propose a series of key recommendations for stakeholders, public health authorities, primary care physicians and surveillance bodies that will help mitigate the combined risks of concurrent influenza epidemics and the COVID‐19 pandemic. We advocate the judicious use of influenza vaccines and antivirals, particularly among groups at high risk of complications, with healthcare workers also considered a priority for vaccination. It is likely that the increased use of emerging technologies such as telemedicine and contact tracing will permanently change our approach to managing infectious disease. The use of these technologies, alongside existing pharmaceutical strategies, will ensure that we achieve a holistic approach to the global public health measures needed to deal with the combined threat of influenza and COVID‐19. Ensuring that this approach is optimal will be key as we move from a reactive pandemic response towards preparing for the long‐term management of the remarkable clinical burden associated with these respiratory pathogens. As the epidemiology, clinical manifestations and management of COVID‐19 and influenza share many features, there is a need to address and deliver targeted surveillance, diagnosis and clinical management of both respiratory diseases. SARS‐CoV‐2 and influenza viruses can realistically be expected to co‐circulate for the foreseeable future, leading to the requirement for holistic long‐term public health measures to simultaneously manage both respiratory infections and their complications effectively. The World Health Organization (WHO) has highlighted the importance of ensuring that best practices for influenza care are maintained during times of increased diversion of resources and attention to COVID‐19. It remains essential to ensure that we are well prepared to deal with future influenza seasons, with the expectation that additional COVID‐19 waves will coincide, at least in part, with circulation of seasonal influenza in the years to come. The availability of influenza vaccines and antivirals, alongside our understanding of influenza transmission dynamics, is at a state of maturity; this is in stark contrast to the situation with SARS‐CoV‐2, where currently licensed vaccines are lacking and very limited therapeutic options are available, with many features of virus transmission still unknown. As both infections result in their most severe manifestations in older adult and immunocompromised populations, the potential for co‐infection with increased severity is evident. This highlights the importance of fully optimizing available strategies for influenza management.Here, we provide key recommendations for maintaining influenza management during the ongoing COVID‐19 pandemic. In addition, we suggest that vigilance and anticipation are required as we move from a reactive pandemic response phase towards one of preparing and planning for the likely co‐circulation of these two major infectious causes of morbidity and mortality with global impact.20) of 1.28 based on data collected over many influenza seasons0 of 2.79,To effectively prepare for ongoing co‐circulation of SARS‐CoV‐2 and influenza viruses, it is important to first appreciate the differences in transmission dynamics between the viruses. Seasonal influenza has a median basic reproduction number  often including fever, dry cough and fatigue, it is virtually impossible to distinguish early‐stage COVID‐19 from influenza based on symptoms alone.Surveillance data on the circulation of influenza viruses and/or SARS‐CoV‐2 and their associated diseases will help in the diagnostic and treatment decision‐making process, and public engagement in symptom reporting via mobile devices may help anticipate local or regional outbreaks. The efficacy of therapeutics for early‐stage COVID‐19 still requires further assessment, but it is likely that the majority of new antiviral therapeutics will be evaluated first in patients with more severe illness in secondary care. However, with increasing awareness of respiratory pathogens among healthcare professionals, in the future we may see a paradigm shift regarding increasing antiviral prescriptions in the primary care setting. Diagnostic testing and surveillance activities are necessary to ensure containment strategies such as isolation, contact tracing and quarantining of individuals, or containment measures in care homes or institutional/hospital settings can be implemented. Although diagnostic testing and surveillance will inevitably vary by national and regional policies, effective disease control for both viruses requires significant capacity for testing and information management, linked to implementation of control measures for COVID‐19The WHO's Global Influenza Surveillance and Response System (GISRS) has already played an important role in the response and monitoring of the spread of COVID‐19 through the establishment of sentinel surveillance networks for ILIs and severe acute respiratory infections.4Another important consideration in the clinical management of ILIs is the potential for co‐infection of SARS‐CoV‐2 with influenza virus (or other respiratory pathogens such as pneumococci), which has already been documented in up to ~30% of cases.5The disease burden of seasonal influenza epidemics is considerable; healthcare systems tended to be stretched beyond their capacity during intensive influenza seasons.While it is unclear if and when effective vaccines will be available against COVID‐19, we should continue to focus on maximizing the impact of the available control measures for influenza. Influenza vaccination of risk groups and healthcare workers should be the cornerstone of seasonal influenza management. Although this is recommended by WHO and several other national and international health authorities, vaccination uptake among high‐risk groups and healthcare workers is low, and often falls far below the recommended 70% among older adults even in most high‐income countries.It is recognized that influenza vaccine effectiveness can vary greatly ,6The continued co‐circulation of SARS‐CoV‐2 and influenza viruses is expected to present global challenges to healthcare systems. It is important that we anticipate and consider a holistic approach to this double threat, which may present a severe challenge due to a simultaneous burden of both diseases. We propose a series of key recommendations for stakeholders, public health authorities, primary care physicians and surveillance systems that will help to mitigate the combined risks of concurrent influenza and COVID‐19 epidemics ; Writing‐review & editing . Thea Kølsen Fischer: Writing‐original draft ; Writing‐review & editing . Jean‐Michel Heraud: Writing‐original draft ; Writing‐review & editing . Aeron C Hurt: Conceptualization (lead); Writing‐original draft ; Writing‐review & editing . Arnold Monto: Writing‐original draft ; Writing‐review & editing . albert DM Osterhaus: Writing‐original draft ; Writing‐review & editing . Yuelong Shu: Writing‐original draft ; Writing‐review & editing . John Tam: Writing‐original draft ; Writing‐review & editing .https://publons.com/publon/10.1111/irv.12824.The peer review history for this article is available at