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Mathematical modeling studies are frequently conducted to guide policy in global health. However, the contribution of mathematical modeling studies to World Health Organization (WHO) guideline recommendations, and the quality of evidence contributed by these studies remains unknown. We conducted a systematic review of the WHO Guidelines Review Committee database to identify guideline recommendations that included evidence from mathematical modeling studies since inception of the Guidelines Review Committee on 1 December, 2007. We included WHO guideline recommendations citing a mathematical modeling study in the primary evidence base. We defined a mathematical model as a framework that predicted epidemiologic, health or economic impact of an intervention or decision in the clinical or public health context. The primary outcome was inclusion of evidence from mathematical modeling studies in a guideline recommendation. We evaluated each unique modeling study across multiple domains of quality. Between 1 December 2007 and 1 April 2019, the WHO Guidelines Review Committee approved 154 guidelines providing 1619 guideline recommendations. Mathematical modeling studies informed 46 WHO guidelines (29.9%) and 101 unique guideline recommendations (6.2%). Modeling evidence addressed topics related to infectious diseases in 38 guidelines (82.6%) and 81 recommendations (80.2%), most commonly for HIV and tuberculosis. Evidence from modeling studies was assessed in the GRADE evidence profile for 12 recommendations (12.9%) and GRADE evidence-to-decision framework for 45 recommendations (44.6%). Modeling-informed recommendations were more likely than other recommendations within the same guidelines to be issued with a "conditional" rather than "strong" strength of recommendation (53.5% versus 37.8%), and the evidence underlying modeling-informed recommendations was more likely to be assessed as very low quality (41.6% versus 24.1%). Upon review of individual modeling studies, we estimated that 33.8% of models performed a calibration, 29.4% of models performed a validation of results, and 20.6% of models reported a change in the study conclusion in the sensitivity analysis. While policy recommendations in WHO guidelines are informed by evidence from modeling studies, the validity of modeling studies included in guidelines development is heterogeneous. Quality assessment is needed to support the evaluation and incorporation of evidence from mathematical modeling studies in guidelines development.

BackgroundIn February 2021, over one hundred scientists and policy experts participated in a web-based Workshop to discuss the ways that divergent evaluations of evidence and scientific uncertainties are used to delay timely protection of human health and the environment from exposures to hazardous agents. The Workshop arose from a previous workshop organized by the European Environment Agency (EEA) in 2008 and which also drew on case studies from the EEA reports on 'Late Lessons from Early Warnings' (2001, 2013). These reports documented dozens of hazardous agents including many chemicals, for which risk reduction measures were delayed for decades after scientists and others had issued early and later warnings about the harm likely to be caused by those agents.ResultsWorkshop participants used recent case studies including Perfluorooctanoic acid (PFOA), Extremely Low Frequency - Electrical Magnetic Fields (ELF-EMF fields), glyphosate, and Bisphenol A (BPA) to explore myriad reasons for divergent outcomes of evaluations, which has led to delayed and inadequate protection of the public's health. Strategies to overcome these barriers must, therefore, at a minimum include approaches that 1) Make better use of existing data and information, 2) Ensure timeliness, 3) Increase transparency, consistency and minimize bias in evidence evaluations, and 4) Minimize the influence of financial conflicts of interest.ConclusionThe recommendations should enhance the production of "actionable evidence," that is, reliable evaluations of the scientific evidence to support timely actions to protect health and environments from exposures to hazardous agents. The recommendations are applicable to policy and regulatory settings at the local, state, federal and international levels.

BackgroundEvidence-to-decision (EtD) frameworks provide a structured and transparent approach for groups of experts to use when formulating recommendations or making decisions. While extensively used for clinical and public health recommendations, EtD frameworks are not in widespread use in environmental health. This review sought to identify, compare and contrast key EtD frameworks for decisions on interventions used in clinical medicine, public health or environmental health. This information can be used to develop an EtD framework suitable for formulating recommendations for interventions in environmental health.MethodsWe identified a convenience sample of EtD frameworks used by a range of organizations. We searched Medline for systematic reviews of frameworks. We summarized the decision criteria in the selected frameworks and reviews in a qualitative manner.FindingsFourteen organizations provided 18 EtD frameworks; most frameworks focused on clinical medicine or public health interventions; four focused on environmental health and three on economic considerations. Harms of interventions were examined in all frameworks and benefits in all but one. Other criteria included certainty of the body of evidence (15 frameworks), resource considerations (15), feasibility (13), equity (12), values (11), acceptability (11), and human rights (2). There was variation in how specific criteria were defined. The five identified systematic reviews reported a similar spectrum of EtD criteria.InterpretationThe EtD frameworks examined encompassed similar criteria, with tailoring to specific audience needs. Existing frameworks are a useful starting point for development of one tailored to decision-making in environmental health.FunderJPB Foundation.

Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision-making, particularly when data from systematic reviews of primary studies do not adequately answer a research question. However, systematic reviewers and guideline developers may struggle with using the results of modeling studies, because, at least in part, of the lack of a common understanding of concepts and terminology between evidence synthesis experts and mathematical modellers. The use of a common terminology for modeling studies across different clinical and epidemiological research fields that span infectious and non-communicable diseases will help systematic reviewers and guideline developers with the understanding, characterisation, comparison, and use of mathematical modeling studies. This glossary explains key terms used in mathematical modeling studies that are particularly salient to evidence synthesis and knowledge translation in clinical medicine and public health.

Guideline writers have a responsibility to generate recommendations that are both evidence-based and free of bias. How best to quantify and report potential conflicts has been unclear. This extension of the Reporting Items for practice Guidelines in HealThcare (RIGHT) checklist provides a checklist-based approach to reviewing, assessing, and reporting conflicts of interest for guideline-writing bodies. Background: Conflicts of interest (COIs) of contributors to a guideline project and the funding of that project can influence the development of the guideline. Comprehensive reporting of information on COIs and funding is essential for the transparency and credibility of guidelines. Objective: To develop an extension of the Reporting Items for practice Guidelines in HealThcare (RIGHT) statement for the reporting of COIs and funding in policy documents of guideline organizations and in guidelines: the RIGHT-COI&F checklist. Design: The recommendations of the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network were followed. The process consisted of registration of the project and setting up working groups, generation of the initial list of items, achieving consensus on the items, and formulating and testing the final checklist. Setting: International collaboration. Participants: 44 experts. Measurements: Consensus on checklist items. Results: The checklist contains 27 items: 18 about the COIs of contributors and 9 about the funding of the guideline project. Of the 27 items, 16 are labeled as policy related because they address the reporting of COI and funding policies that apply across an organization's guideline projects. These items should be described ideally in the organization's policy documents, otherwise in the specific guideline. The remaining 11 items are labeled as implementation related and they address the reporting of COIs and funding of the specific guideline. Limitation: The RIGHT-COI&F checklist requires testing in real-life use. Conclusion: The RIGHT-COI&F checklist can be used to guide the reporting of COIs and funding in guideline development and to assess the completeness of reporting in published guidelines and policy documents. Primary Funding Source: The Fundamental Research Funds for the Central Universities of China. [ABSTRACT FROM AUTHOR]

Introduction: Several antiretroviral drugs are being considered for the treatment of COVID-19, the disease caused by a newly identified coronavirus, (SARS-CoV-2). We systematically reviewed the clinical outcomes of using antiretroviral drugs for the prevention and treatment of coronaviruses and planned clinical trials. Methods: Three databases were screened from inception to 30 March 2020 for studies reporting clinical outcomes of patients with SARS, MERS or COVID-19 treated with antiretrovirals. Results: From an initial screen of 433 titles, two randomized trials and 24 observational studies provided clinical outcome data on the use of antiretroviral drugs; most studies reported outcomes using LPV/r as treatment. Of the 21 observational studies reporting treatment outcomes, there were three studies among patients with SARS, six studies among patients with MERS and 12 studies among patients with COVID-19. In one randomized trial 99 patients with severe COVID-19 illness were randomized to receive LPV/r (400/100 mg twice a day) and 100 patients to standard of care for 14 days: LPV/r was not associated with a statistically significant difference in time to clinical improvement, although LPV/r given within 12 days of symptoms was associated with shorter time to clinical improvement; 28 day mortality was numerically lower in the LPV/r group (14/99) compared to the control group (25/100), but this difference was not statistically significant. The second trial found no benefit. The certainty of the evidence for the randomized trials was low. In the observational studies 3 out of 361 patients who received LPV/r died; the certainty of evidence was very low. Three studies reported a possible protective effect of LPV/r as post-exposure prophylaxis. Again, the certainty of the evidence was very low due to uncertainty due to limited sample size. Conclusions: On the basis of the available evidence it is uncertain whether LPV/r and other antiretrovirals improve clinical outcomes or prevent infection among patients at high risk of acquiring COVID-19. Keywords: antiretroviral therapy; HIV; MERS; SARS; coronavirus; COVID-19, 1 | INTRODUCTION Several antiretroviral drugs are being considered for use in the treatment of COVID-19, the disease caused by a newly identified coronavirus, (SARS-CoV-2). Protease inhibitors have been considered [...]

ObjectiveTo assess the effectiveness of interventions aimed to increase retinal screening among people with diabetes.MethodsA systematic literature search was conducted of multiple electronic bibliographic databases up to May 2005. Studies were included if interventions were used to promote screening for diabetic retinopathy in any language and with any study design.ResultsForty-eight studies (12 randomized controlled trials [RCTs], four nonrandomized studies, and 32 pre-post studies) with a total of 162,157 participants, examined a wide range of interventions, which focused on one or more of the following: (1) patients or populations, (2) providers or practices, and (3) healthcare system infrastructure and processes. Four of five RCTs focusing on patients demonstrated that interventions increased screening significantly, with relative risk ranging from 1.05 (95% confidence interval [CI]=1.01-1.08) to 2.01 (95% CI=1.48-2.73). Five RCTs with a focus on the system all demonstrated significant increases in screening with relative risk ranging from 1.12 (95% CI=1.03-1.22) to 5.56 (95% CI=2.19-14.10). Thirty-six non-RCTs, which included interventions with single or multiple foci, also generally demonstrated positive effects.ConclusionsIncreasing patient awareness of diabetic retinopathy, improving provider and practice performance, and improving healthcare system infrastructure and processes, can significantly increase screening for diabetic retinopathy. Further research should explore strategies for increasing the rate of retinal screening among diverse or disadvantaged populations and the economic efficiency of effective interventions in large community populations.

Objective: This study aimed to examine selective outcome reporting (SOR) and selective analysis reporting (SAR) in randomized controlled trials (RCTs) and to explore the usefulness of trial registries for identifying SOR and SAR. Study Design and Setting: We selected one "index outcome" for each of three comparative effectiveness reviews (CERs) of pharmacotherapy and extracted data on this outcome from trial registries and from study publications. Results: Among 50 RCTs published since 2005 and reporting the index outcome, only 50% were listed in registries; 90% of RCTs were assessed as having SOR or SAR. The index outcome in the registry was different from that in the publication in 75% of trials in two CERs, and not specified at all in the third. Reported outcomes and analyses were not consistent between the publication's methods section and the results section in 33% and 46% of the two CERs where the index outcome was a benefit. There were no statistically significant predictors of SOR and SAR in our small sample where some predictors lacked variability. Conclusion: The SOR and SAR were frequent in this pilot study, and the most common type of SOR was the publication of outcomes that were not pre-specified. Trial registries were of little use in identifying SOR and of no use in identifying SAR.

Background: Selective outcome and analysis reporting (SOR and SAR) occur when only a subset of outcomes measured and analyzed in a study is fully reported, and are an important source of potential bias. Key methodological issues: We describe what is known about the prevalence and effects of SOR and SAR in both randomized controlled trials (RCTs) and non-randomized studies (NRS), and the effects of SOR and SAR on summary effect estimates and conclusions in systematic reviews of the effectiveness of healthcare interventions. Guidance: Review authors should always suspect SOR and SAR in reviews that include NRS, assess primary studies for the risk of bias, and make reasonable attempts to retrieve study protocols or other documentation developed before study recruitment began. There are clues that may suggest SOR or SAR in NRS, including differences between the methods and results sections of the publication, study funder, and differences between study protocol or registration information and the study report. Conclusion: Existing evidence about reporting biases in primary studies comes almost exclusively from methodological reviews of RCTs. The prevalence and impact of SOR and SAR in NRS are likely even greater than in RCTs but it is difficult to identify and confirm selective reporting in NRS.

Keywords: clinical practice guideline; cross-sectional analysis; guideline protocol EN clinical practice guideline cross-sectional analysis guideline protocol 3 9 7 04/05/23 20230301 NES 230301 A clinical practice guideline (CPG; hereafter referred to as "guideline") protocol is a document that outlines and maps out the plan or steps for developing a guideline, as well as the methods to be used.[1] The preparation and publication of a guideline protocol helps clarify the purpose and scope of the guideline, facilitates the guideline development process, helps to ensure the integrity and quality of the final version of the guideline, and supports efforts to avoid duplication among guideline development groups.[1] Reporting standards for protocols have been shown to improve the quality of the corresponding study protocol. Methods used to research consensus on recommendations Forty-four guideline protocols (91.7%) described the consensus process for formulating recommendations based on evidence. Updating the guideline development methodology of the Healthcare Infection Control Practices Advisory Committee (HICPAC). The most common method planned for reaching consensus was the Delphi survey, which was featured in 28 guideline protocols (63.6%), of which six guidelines (21.4%) reported that the modified Delphi survey would be used. [Extracted from the article]