These guidelines incorporate the recent advances in chronic cough pathophysiology, diagnosis and treatment. The concept of cough hypersensitivity has allowed an umbrella term that explains the exquisite sensitivity of patients to external stimuli such a cold air, perfumes, smoke and bleach. Thus, adults with chronic cough now have a firm physical explanation for their symptoms based on vagal afferent hypersensitivity. Different treatable traits exist with cough variant asthma (CVA)/eosinophilic bronchitis responding to anti-inflammatory treatment and non-acid reflux being treated with promotility agents rather the anti-acid drugs. An alternative antitussive strategy is to reduce hypersensitivity by neuromodulation. Low-dose morphine is highly effective in a subset of patients with cough resistant to other treatments. Gabapentin and pregabalin are also advocated, but in clinical experience they are limited by adverse events. Perhaps the most promising future developments in pharmacotherapy are drugs which tackle neuronal hypersensitivity by blocking excitability of afferent nerves by inhibiting targets such as the ATP receptor (P2X3). Finally, cough suppression therapy when performed by competent practitioners can be highly effective. Children are not small adults and a pursuit of an underlying cause for cough is advocated. Thus, in toddlers, inhalation of a foreign body is common. Persistent bacterial bronchitis is a common and previously unrecognised cause of wet cough in children. Antibiotics (drug, dose and duration need to be determined) can be curative. A paediatric-specific algorithm should be used.

Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot require updating in light of new Cochrane guidance on alternative synthesis methods. To update the ED plot, statistical significance was removed from the algorithm for within‐study synthesis and use of a sign test was considered to examine whether patterns of ED across studies could be due to chance alone. The revised methods were applied to an existing Cochrane review of the health impacts of housing improvements. The revised ED plot provides a method of data visualization in synthesis without meta‐analysis that incorporates information about study characteristics and study quality, using ED as a common metric, without relying on statistical significance to combine outcomes of single studies. The results of sign tests, when appropriate, suggest caution in over‐interpreting apparent patterns in effect direction, especially when the number of included studies is small. The revised ED plot meets the need for alternative methods of synthesis and data visualization when meta‐analysis is not possible, enabling a transparent link between the data and conclusions of a systematic review. ED plots may be particularly useful in reviews that incorporate nonrandomized studies, complex systems approaches, and diverse sources of evidence, due to the variety of study designs and outcomes in such reviews.

Background Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population. Methods We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised. Results We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context. Conclusion Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.

This article explores the need for conceptual advances and practical guidance in the application of the GRADE approach within public health contexts. We convened an expert workshop and conducted a scoping review to identify challenges experienced by GRADE users in public health contexts. We developed this concept article through thematic analysis and an iterative process of consultation and discussion conducted with members electronically and at three GRADE Working Group meetings. Five priority issues can pose challenges for public health guideline developers and systematic reviewers when applying GRADE: (1) incorporating the perspectives of diverse stakeholders; (2) selecting and prioritizing health and "nonhealth" outcomes; (3) interpreting outcomes and identifying a threshold for decision-making; (4) assessing certainty of evidence from diverse sources, including nonrandomized studies; and (5) addressing implications for decision makers, including concerns about conditional recommendations. We illustrate these challenges with examples from public health guidelines and systematic reviews, identifying gaps where conceptual advances may facilitate the consistent application or further development of the methodology and provide solutions. The GRADE Public Health Group will respond to these challenges with solutions that are coherent with existing guidance and can be consistently implemented across public health decision-making contexts.

Background Global prevalence of overweight and obesity are alarming. For tackling this public health problem, preventive public health and policy actions are urgently needed. Some countries implemented food taxes in the past and some were subsequently abolished. Some countries, such as Norway, Hungary, Denmark, Bermuda, Dominica, St. Vincent and the Grenadines, and the Navajo Nation (USA), specifically implemented taxes on unprocessed sugar and sugar‐added foods. These taxes on unprocessed sugar and sugar‐added foods are fiscal policy interventions, implemented to decrease their consumption and in turn reduce adverse health‐related, economic and social effects associated with these food products. Objectives To assess the effects of taxation of unprocessed sugar or sugar‐added foods in the general population on the consumption of unprocessed sugar or sugar‐added foods, the prevalence and incidence of overweight and obesity, and the prevalence and incidence of other diet‐related health outcomes. Search methods We searched CENTRAL, Cochrane Database of Systematic Reviews, MEDLINE, Embase and 15 other databases and trials registers on 12 September 2019. We handsearched the reference list of all records of included studies, searched websites of international organisations and institutions, and contacted review advisory group members to identify planned, ongoing or unpublished studies. Selection criteria We included studies with the following populations: children (0 to 17 years) and adults (18 years or older) from any country and setting. Exclusion applied to studies with specific subgroups, such as people with any disease who were overweight or obese as a side‐effect of the disease. The review included studies with taxes on or artificial increases of selling prices for unprocessed sugar or food products that contain added sugar (e.g. sweets, ice cream, confectionery, and bakery products), or both, as intervention, regardless of the taxation level or price increase. In line with Cochrane Effective Practice and Organisation of Care (EPOC) criteria, we included randomised controlled trials (RCTs), cluster‐randomised controlled trials (cRCTs), non‐randomised controlled trials (nRCTs), controlled before‐after (CBA) studies, and interrupted time series (ITS) studies. We included controlled studies with more than one intervention or control site and ITS studies with a clearly defined intervention time and at least three data points before and three after the intervention. Our primary outcomes were consumption of unprocessed sugar or sugar‐added foods, energy intake, overweight, and obesity. Our secondary outcomes were substitution and diet, expenditure, demand, and other health outcomes. Data collection and analysis Two review authors independently screened all eligible records for inclusion, assessed the risk of bias, and performed data extraction.Two review authors independently assessed the certainty of the evidence using the GRADE approach. Main results We retrieved a total of 24,454 records. After deduplicating records, 18,767 records remained for title and abstract screening. Of 11 potentially relevant studies, we included one ITS study with 40,210 household‐level observations from the Hungarian Household Budget and Living Conditions Survey. The baseline ranged from January 2008 to August 2011, the intervention was implemented on September 2011, and follow‐up was until December 2012 (16 months). The intervention was a tax ‐ the so‐called 'Hungarian public health product tax' ‐ on sugar‐added foods, including selected foods exceeding a specific sugar threshold value. The intervention includes co‐interventions: the taxation of sugar‐sweetened beverages (SSBs) and of foods high in salt or caffeine. The study provides evidence on the effect of taxing foods exceeding a specific sugar threshold value on the consumption of sugar‐added foods. After implementation of the Hungarian public health product tax, the mean consumption of taxed sugar‐added foods (measured in units of kg) decreased by 4.0% (standardised mean difference (SMD) −0.040, 95% confidence interval (CI) −0.07 to −0.01; very low‐certainty evidence). The study was at low risk of bias in terms of performance bias, detection bias and reporting bias, with the shape of effect pre‐specified and the intervention unlikely to have any effect on data collection. The study was at unclear risk of attrition bias and at high risk in terms of other bias and the independence of the intervention. We rated the certainty of the evidence as very low for the primary and secondary outcomes. The Hungarian public health product tax included a tax on sugar‐added foods but did not include a tax on unprocessed sugar. We did not find eligible studies reporting on the taxation of unprocessed sugar. No studies reported on the primary outcomes of consumption of unprocessed sugar, energy intake, overweight, and obesity. No studies reported on the secondary outcomes of substitution and diet, demand, and other health outcomes. No studies reported on differential effects across population subgroups. We could not perform meta‐analyses or pool study results. Authors' conclusions There was very limited evidence and the certainty of the evidence was very low. Despite the reported reduction in consumption of taxed sugar‐added foods, we are uncertain whether taxing unprocessed sugar or sugar‐added foods has an effect on reducing their consumption and preventing obesity or other adverse health outcomes. Further robustly conducted studies are required to draw concrete conclusions on the effectiveness of taxing unprocessed sugar or sugar‐added foods for reducing their consumption and preventing obesity or other adverse health outcomes.

A new tool to assess Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I) was published in Autumn 2016. ROBINS-I uses the Cochrane-approved risk of bias (RoB) approach and focusses on internal validity. As such, ROBINS-I represents an important development for those conducting systematic reviews which include non-randomised studies (NRS), including public health researchers. We aimed to establish the applicability of ROBINS-I using a group of NRS which have evaluated non-clinical public health natural experiments. Five researchers, all experienced in critical appraisal of non-randomised studies, used ROBINS-I to independently assess risk of bias in five studies which had assessed the health impacts of a domestic energy efficiency intervention. ROBINS-I assessments for each study were entered into a database and checked for consensus across the group. Group discussions were used to identify reasons underpinning lack of consensus for specific questions and bias domains. ROBINS-I helped to systematically articulate sources of bias in NRS. However, the lack of consensus in assessments for all seven bias domains raised questions about ROBINS-I’s reliability and applicability for natural experiment studies. The two RoB domains with least consensus were selection (Domain 2) and performance (Domain 4). Underlying the lack of consensus were difficulties in applying an intention to treat or per protocol effect of interest to the studies. This was linked to difficulties in determining whether the intervention status was classified retrospectively at follow-up, i.e. post hoc. The overall risk of bias ranged from moderate to critical; this was most closely linked to the assessment of confounders. The ROBINS-I tool is a conceptually rigorous tool which focusses on risk of bias due to the counterfactual. Difficulties in applying ROBINS-I may be due to poor design and reporting of evaluations of natural experiments. While the quality of reporting may improve in the future, improved guidance on applying ROBINS-I is needed to enable existing evidence from natural experiments to be assessed appropriately and consistently. We hope future refinements to ROBINS-I will address some of the issues raised here to allow wider use of the tool.