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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread person-to-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings. We did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects meta-regressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047. Our search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0·18, 95% CI 0·09 to 0·38; risk difference [RD] −10·2%, 95% CI −11·5 to −7·5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2·02 per m; pinteraction=0·041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0·15, 95% CI 0·07 to 0·34, RD −14·3%, −15·9 to −10·7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12–16-layer cotton masks; pinteraction=0·090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0·22, 95% CI 0·12 to 0·39, RD −10·6%, 95% CI −12·5 to −7·7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings. The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance. World Health Organization.

Abstract Background Hypercalcemia of malignancy (HCM) is the most common metabolic complication of malignancies, but its incidence may be declining due to potent chemotherapeutic agents. The high mortality associated with HCM has declined markedly due to the introduction of increasingly effective chemotherapeutic drugs. Despite the widespread availability of efficacious medications to treat HCM, evidence-based recommendations to manage this debilitating condition are lacking. Objective To develop guidelines for the treatment of adults with HCM. Methods A multidisciplinary panel of clinical experts, together with experts in systematic literature review, identified and prioritized 8 clinical questions related to the treatment of HCM in adult patients. The systematic reviews (SRs) queried electronic databases for studies relevant to the selected questions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make recommendations. An independent SR was conducted in parallel to assess patients' and physicians' values and preferences, costs, resources needed, acceptability, feasibility, equity, and other domains relevant to the Evidence-to-Decision framework as well as to enable judgements and recommendations. Results The panel recommends (strong recommendation) in adults with HCM treatment with denosumab (Dmab) or an intravenous (IV) bisphosphonate (BP). The following recommendations were based on low certainty of the evidence. The panel suggests (conditional recommendation) (1) in adults with HCM, the use of Dmab rather than an IV BP; (2) in adults with severe HCM, a combination of calcitonin and an IV BP or Dmab therapy as initial treatment; and (3) in adults with refractory/recurrent HCM despite treatment with BP, the use of Dmab. The panel suggests (conditional recommendation) the addition of an IV BP or Dmab in adult patients with hypercalcemia due to tumors associated with high calcitriol levels who are already receiving glucocorticoid therapy but continue to have severe or symptomatic HCM. The panel suggests (conditional recommendation) in adult patients with hypercalcemia due to parathyroid carcinoma, treatment with either a calcimimetic or an antiresorptive (IV BP or Dmab). The panel judges the treatments as probably accessible and feasible for most recommendations but noted variability in costs, resources required, and their impact on equity. Conclusions The panel's recommendations are based on currently available evidence considering the most important outcomes in HCM to patients and key stakeholders. Treatment of the primary malignancy is instrumental for controlling hypercalcemia and preventing its recurrence. The recommendations provide a framework for the medical management of adults with HCM and incorporate important decisional and contextual factors. The guidelines underscore current knowledge gaps that can be used to establish future research agendas.

ObjectiveTo determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines.DesignA scoping review.Data sourcesMEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018.Eligibility criteriaWe included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context.Data extraction and synthesisWe summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items.ResultsOf 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16 543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%).ConclusionsMachine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health.Registration numberRegistered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

Misinformation poses a serious challenge to clinical and policy decision-making in the health field. The COVID-19 pandemic amplified interest in misinformation and related terms and witnessed a proliferation of definitions. We aim to assess the definitions of misinformation and related terms used in health-related literature. We conducted a scoping review of systematic reviews by searching Ovid MEDLINE, Embase, Cochrane, and Epistemonikos databases for articles published within the last 5 years up till March 2023. Eligible studies were systematic reviews that stated misinformation or related terms as part of their objectives, conducted a systematic search of at least one database, and reported at least 1 definition for misinformation or related terms. We extracted definitions for the terms misinformation, disinformation, fake news, infodemic, and malinformation. Within each definition, we identified concepts and mapped them across misinformation-related terms. We included 41 eligible systematic reviews, out of which 32 (78%) reviews addressed the topic of public health emergencies (including the COVID-19 pandemic) and contained 75 definitions for misinformation and related terms. The definitions consisted of 20 for misinformation, 19 for disinformation, 10 for fake news, 24 for infodemic, and 2 for malinformation. \"False/inaccurate/incorrect\" was mentioned in 15 of 20 definitions of misinformation, 13 of 19 definitions of disinformation, 5 of 10 definitions of fake news, 6 of 24 definitions of infodemic, and 0 of 2 definitions of malinformation. Infodemic had 19 of 24 definitions addressing \"information overload\" and malinformation had 2 of 2 definitions with \"accurate\" and 1 definition \"used in the wrong context.\" Out of all the definitions, 56 (75%) were referenced from other sources. While the definitions of misinformation and related terms in the health field had inconstancies and variability, they were largely consistent. Inconstancies related to the intentionality in misinformation definitions (7 definitions mention \"unintentional,\" while 5 definitions have \"intentional\"). They also related to the content of infodemic (9 definitions mention \"valid and invalid info,\" while 6 definitions have \"false/inaccurate/incorrect\"). The inclusion of concepts such as \"intentional\" may be difficult to operationalize as it is difficult to ascertain one's intentions. This scoping review has the strength of using a systematic method for retrieving articles but does not cover all definitions in the extant literature outside the field of health. This scoping review of the health literature identified several definitions for misinformation and related terms, which showed variability and included concepts that are difficult to operationalize. Health practitioners need to exert caution before labeling a piece of information as misinformation or any other related term and only do so after ascertaining accurateness and sometimes intentionality. Additional efforts are needed to allow future consensus around clear and operational definitions.

Background This study presents the prevalence of burnout among the Canadian public health workforce after three years of the COVID-19 pandemic and its association with work-related factors. Methods Data were collected using an online survey distributed through Canadian public health associations and professional networks between November 2022 and January 2023. Burnout was measured using a modified version of the Oldenburg Burnout Inventory (OLBI). Logistic regressions were used to model the relationship between burnout and work-related factors including years of work experience, redeployment to pandemic response, workplace safety and supports, and harassment. Burnout and the intention to leave or retire as a result of the COVID-19 pandemic was explored using multinomial logistic regressions. Results In 2,079 participants who completed the OLBI, the prevalence of burnout was 78.7%. Additionally, 49.1% of participants reported being harassed because of their work during the pandemic. Burnout was positively associated with years of work experience, redeployment to the pandemic response, being harassed during the pandemic, feeling unsafe in the workplace and not being offered workplace supports. Furthermore, burnout was associated with greater odds of intending to leave public health or retire earlier than anticipated. Conclusion The high levels of burnout among our large sample of Canadian public health workers and its association with work-related factors suggest that public health organizations should consider interventions that mitigate burnout and promote recovery.

Background Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. Methods We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. Results We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI’s applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. Conclusions Experts are cautiously optimistic about AI’s impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits.

Background The COVID-19 pandemic has had an unprecedented impact in the global research production and has also increased research waste. Living evidence syntheses (LESs) seek to regularly update a body of evidence addressing a specific question. During the COVID-19 pandemic, the production and dissemination of LESs emerged as a cornerstone of the evidence infrastructure. This critical interpretive synthesis answers the questions: What constitutes an LES to support decision-making?; when should one be produced, updated, and discontinued?; and how should one be disseminated? Methods Searches included the Cochrane Library, EMBASE (Ovid), Health Systems Evidence, MEDLINE (Ovid), PubMed, and Web of Science up to 23 April 2024 and included articles that provide any insights on addressing the compass questions on LESs. Articles were selected and appraised, and their insights extracted. An interpretive and iterative coding process was used to identify relevant thematic categories and create a conceptual framework. Results Among the 16,630 non-duplicate records identified, 208 publications proved eligible. Most were non-empirical articles, followed by actual LESs. Approximately one in three articles were published in response to the COVID-19 pandemic. The conceptual framework addresses six thematic categories: (1) what is an LES; (2) what methodological approaches facilitate LESs production; (3) when to produce an LES; (4) when to update an LES; (5) how to make available the findings of an LES; and (6) when to discontinue LES updates. Conclusion LESs can play a critical role in reducing research waste and ensuring alignment with advisory and decision-making processes. This critical interpretive synthesis provides relevant insights on how to better organize the global evidence architecture to support their production. Trial registration PROSPERO registration: CRD42021241875.

Pottie et al discuss the clinical guideline for homeless and vulnerably housed people, and people with lived homelessness experience. Homelessness encompasses all individuals without stable, permanent and acceptable housing, or lacking the immediate prospect, means and ability of acquiring it. Clinical assessment and care of homeless and vulnerably housed populations should include tailoring approaches to a person's gender, age, Indigenous heritage, ethnicity and history of trauma; and advocacy for comprehensive primary health care. As initial steps in the care of homeless and vulnerably housed populations, permanent supportive housing is strongly recommended, and income assistance is also recommended. Case-management interventions, with access to psychiatric support, are recommended as an initial step to support primary care and to address existing mental health, substance use and other morbidities.

IntroductionThe Grading of Recommendations Assessment, Development and Evaluation (GRADE) and similar Evidence to Decision (EtD) frameworks require its users to judge how substantial the effects of interventions are on desirable and undesirable people-important health outcomes. However, decision thresholds (DTs) that could help understand the magnitude of intervention effects and serve as reference for interpretation of findings are not yet available.The objective of this study is an approach to derive and use DTs for EtD judgments about the magnitude of health benefits and harms. We hypothesise that approximate DTs could have the ability to discriminate between the existing four categories of EtD judgments (Trivial, Small, Moderate, Large), support panels of decision-makers in their work, and promote consistency and transparency in judgments.Methods and analysisWe will conduct a methodological randomised controlled trial to collect the data that allow deriving the DTs. We will invite clinicians, epidemiologists, decision scientists, health research methodologists, experts in Health Technology Assessment (HTA), members of guideline development groups and the public to participate in the trial. Then, we will investigate the validity of our DTs by measuring the agreement between judgments that were made in the past by guideline panels and the judgments that our DTs approach would suggest if applied on the same guideline data.Ethics and disseminationThe Hamilton Integrated Research Ethics Board reviewed this study as a quality improvement study and determined that it requires no further consent. Survey participants will be required to read a consent statement in order to participate in this study at the beginning of the trial. This statement reads: You are being invited to participate in a research project which aims to identify indicative DTs that could assist users of the GRADE EtD frameworks in making judgments. Your input will be used in determining these indicative thresholds. By completing this survey, you provide consent that the anonymised data collected will be used for the research study and to be summarised in aggregate in publication and electronic tools.PROTOCOL registration numberNCT05237635.

Background Guidelines and quality indicators (for example as part of a quality assurance scheme) aim to improve health care delivery and health outcomes. Ideally, the development of quality indicators should be grounded in evidence-based, trustworthy guideline recommendations. However, anecdotally, guidelines and quality assurance schemes are developed independently, by different groups of experts who employ different methodologies. We conducted an extension and update of a previous systematic review to identify, describe and evaluate approaches to the integrated development of guidelines and related quality indicators. Methods On May 24th, 2019 we searched in Medline, Embase and CINAHL and included studies if they reported a methodological approach to guideline-based quality indicator development and were published in English, French, or German. Results: Out of 16,034 identified records, we included 17 articles that described a method to integrate guideline recommendations development and quality indicator development. Added to the 13 method articles from original systematic review we included a total 30 method articles. We did not find any evaluation studies. In most approaches, guidelines were a source of evidence to inform the quality indicator development. The criteria to select recommendations (e.g. level of evidence or strength of the recommendation) and to generate, select and assess quality indicators varied widely. We found methodological approaches that linked guidelines and quality indicator development explicitly, however none of the articles reported a conceptual framework that fully integrated quality indicator development into the guideline process or where quality indicator development was part of the question formulation for developing the guideline recommendations. Conclusions In our systematic review we found approaches which explicitly linked guidelines with quality indicator development, nevertheless none of the articles reported a comprehensive and well-defined conceptual framework which integrated quality indicator development fully into the guideline development process.