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  1. Book: Evidence based decisions and economics

    Shemilt, Ian

    health care, social welfare, education and criminal justice

    (Evidence-based medicine)

    2010  

    Title variant Evidence-based decisions and economics
    Author's details ed. by Ian Shemilt
    Series title Evidence-based medicine
    Keywords Economics, Medical ; Evidence-Based Medicine
    Language English
    Size X, 206 S. : Ill., graph. Darst.
    Edition 2. ed.
    Publisher Wiley-Blackwell
    Publishing place Oxford u.a.
    Publishing country Great Britain
    Document type Book
    Note Includes bibliographical references and index
    HBZ-ID HT016344485
    ISBN 978-1-4051-9153-1 ; 1-4051-9153-8
    Database Catalogue ZB MED Medicine, Health

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  2. Article ; Online: Methods used to conceptualize dimensions of health equity impacts of public health interventions in systematic reviews.

    Hollands, Gareth J / South, Emily / Shemilt, Ian / Oliver, Sandy / Thomas, James / Sowden, Amanda J

    Journal of clinical epidemiology

    2024  Volume 169, Page(s) 111312

    Abstract: Objectives: Our aims were to, first, identify and summarize the use of methods, frameworks, and tools as a conceptual basis for investigating dimensions of equity impacts of public health interventions in systematic reviews including an equity focus. ... ...

    Abstract Objectives: Our aims were to, first, identify and summarize the use of methods, frameworks, and tools as a conceptual basis for investigating dimensions of equity impacts of public health interventions in systematic reviews including an equity focus. These include PROGRESS-Plus, which identifies key sociodemographic characteristics that determine health outcomes. Second, we aimed to document challenges and opportunities encountered in the application of such methods, as reported in systematic reviews.
    Study design and setting: We conducted a methodological study, comprising an overview of systematic reviews with a focus on, or that aimed to assess, the equity impacts of public health interventions. We used electronic searches of the Cochrane Database of Systematic Reviews, the Database of Promoting Health Effectiveness Reviews (DoPHER), and the Finding Accessible Inequalities Research in Public Health Database, supplemented with automated searches of the OpenAlex dataset. An active learning algorithm was used to prioritize title-abstract records for manual screening against eligibility criteria. We extracted and analyzed a core dataset from a purposively selected sample of reviews, to summarize key characteristics and approaches to conceptualizing investigations of equity.
    Results: We assessed 322 full-text reports for eligibility, from which we included 120 reports of systematic reviews. PROGRESS-Plus was the only formalized framework used to conceptualize dimensions of equity impacts. Most reviews were able to apply their intended methods to at least some degree. Where intended methods were unable to be applied fully, this was usually because primary research studies did not report the necessary information. A general rationale for focusing on equity impacts was often included, but few reviews explicitly justified their focus on (or exclusion of) specific dimensions. In addition to practical challenges such as data not being available, authors highlighted significant measurement and conceptual issues with applying these methods which may impair the ability to investigate and interpret differential impacts within and between studies. These issues included investigating constructs that lack standardized operationalization and measurement, and the complex nature of differential impacts, with dimensions that may interact with one another, as well as with particular temporal, personal, social or geographic contexts.
    Conclusion: PROGRESS-Plus is the predominant framework used in systematic reviews to conceptualize differential impacts of public health interventions by dimensions of equity. It appears sufficiently broad to encompass dimensions of equity examined in most investigations of this kind. However, PROGRESS-Plus does not necessarily ensure or guide critical thinking about more complex pathways, including interactions between dimensions of equity, and with wider contextual factors, and important practical, measurement and conceptual challenges remain. The findings from investigations of equity impacts in systematic reviews could be made more useful through more explicitly rationalized and considered approaches to the design, conduct and reporting of both primary research and the reviews themselves.
    Language English
    Publishing date 2024-03-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 639306-8
    ISSN 1878-5921 ; 0895-4356
    ISSN (online) 1878-5921
    ISSN 0895-4356
    DOI 10.1016/j.jclinepi.2024.111312
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Conference proceedings: Evaluation of a new approach to automation in the evidence surveillance workflow

    Thomas, James / Shemilt, Ian

    2022  , Page(s) 22irm25

    Event/congress Information Retrieval Meeting (IRM 2022); Cologne; 2022
    Keywords Medizin, Gesundheit ; systematic review ; evidence surveillance ; machine learning ; automation
    Publishing date 2022-06-08
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/22irm25
    Database German Medical Science

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  4. Article ; Online: The crime, mental health, and economic impacts of prearrest diversion of people with mental health problems: A systematic review.

    Schucan Bird, Karen / Shemilt, Ian

    Criminal behaviour and mental health : CBMH

    2019  Volume 29, Issue 3, Page(s) 142–156

    Abstract: Background: Prearrest diversion strategies are being adopted across the Western world, enabling the police to identify and divert people suspected of having mental disorder towards health and community services rather than the criminal justice system.!## ...

    Abstract Background: Prearrest diversion strategies are being adopted across the Western world, enabling the police to identify and divert people suspected of having mental disorder towards health and community services rather than the criminal justice system.
    Aims: To quantify longer-term criminal justice and mental health outcomes after prearrest diversion of people with suspected mental disorder and consider economic correlates.
    Methods: A systematic review of published literature on longer term outcomes after prearrest diversion.
    Results: Only two quasi-experimental studies, with four independent samples, could be included. Findings for criminal and mental health outcomes were inconclusive, but potential for adverse outcomes was identified. Ten studies with cost data suggested that prearrest diversion can lead to overall cost savings.
    Conclusions: There is still inadequate evidence on which to base prearrest diversion programmes. Although some benefits have been identified by the review, so have possible harms. Future research and funding strategies must build in high-quality, systematic evaluation of outcomes before implementing a theoretically attractive strategy more widely.
    MeSH term(s) Community Mental Health Services/organization & administration ; Crime ; Criminal Law ; Criminals/psychology ; Humans ; Mental Disorders/psychology ; Mental Health ; Police ; Psychotic Disorders
    Language English
    Publishing date 2019-04-10
    Publishing country England
    Document type Journal Article ; Systematic Review
    ZDB-ID 2042697-5
    ISSN 1471-2857 ; 0957-9664
    ISSN (online) 1471-2857
    ISSN 0957-9664
    DOI 10.1002/cbm.2112
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Machine learning reduced workload for the Cochrane COVID-19 Study Register: development and evaluation of the Cochrane COVID-19 Study Classifier.

    Shemilt, Ian / Noel-Storr, Anna / Thomas, James / Featherstone, Robin / Mavergames, Chris

    Systematic reviews

    2022  Volume 11, Issue 1, Page(s) 15

    Abstract: Background: This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 ... ...

    Abstract Background: This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies.
    Methods: A ML classifier for retrieving COVID-19 research studies (the 'Cochrane COVID-19 Study Classifier') was developed using a data set of title-abstract records 'included' in, or 'excluded' from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records 'included' in, or 'excluded' from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records 'included' in, or 'excluded' from, the CCSR between the 4th and 19th of January 2021.
    Results: The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were 'included' in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were 'included' in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded).
    Conclusions: The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.
    MeSH term(s) COVID-19 ; Data Collection ; Humans ; Machine Learning ; SARS-CoV-2 ; Workload
    Language English
    Publishing date 2022-01-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2662257-9
    ISSN 2046-4053 ; 2046-4053
    ISSN (online) 2046-4053
    ISSN 2046-4053
    DOI 10.1186/s13643-021-01880-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research.

    Shemilt, Ian / Arno, Anneliese / Thomas, James / Lorenc, Theo / Khouja, Claire / Raine, Gary / Sutcliffe, Katy / Preethy, D'Souza / Kwan, Irene / Wright, Kath / Sowden, Amanda

    Wellcome open research

    2024  Volume 6, Page(s) 210

    Abstract: Background: Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, ...

    Abstract Background: Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, comprehensive, continuously updated source, with adjunctive machine learning, could enable more efficient searching, selection and prioritisation workflows for updating (living) reviews and maps, though research is needed to establish this. Microsoft Academic Graph (MAG) is a potentially comprehensive single source which also contains metadata that can be used in machine learning to help efficiently identify eligible studies. This study sought to establish whether: (a) MAG was a sufficiently sensitive single source to maintain our living map of COVID-19 research; and (b) eligible records could be identified with an acceptably high level of specificity.
    Methods: We conducted an eight-arm cost-effectiveness analysis to assess the costs, recall and precision of semi-automated workflows, incorporating MAG with adjunctive machine learning, for continually updating our living map. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Our systematic review software, EPPI-Reviewer, was adapted to incorporate MAG and associated machine learning workflows, and also used to collect data on recall, precision, and manual screening workload.
    Results: The semi-automated MAG-enabled workflow dominated conventional workflows in both the base case and sensitivity analyses. At one month our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified 469 additional, eligible articles for inclusion in our living map, and cost £3,179 GBP per week less, compared with conventional methods relying on Boolean searches of Medline and Embase.
    Conclusions: We were able to increase recall and coverage of a large living map, whilst reducing its production costs. This finding is likely to be transferrable to OpenAlex, MAG's successor database platform.
    Language English
    Publishing date 2024-03-26
    Publishing country England
    Document type Journal Article
    ISSN 2398-502X
    ISSN 2398-502X
    DOI 10.12688/wellcomeopenres.17141.2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Machine learning reduced workload for the Cochrane COVID-19 Study Register

    Ian Shemilt / Anna Noel-Storr / James Thomas / Robin Featherstone / Chris Mavergames

    Systematic Reviews, Vol 11, Iss 1, Pp 1-

    development and evaluation of the Cochrane COVID-19 Study Classifier

    2022  Volume 8

    Abstract: Abstract Background This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 ...

    Abstract Abstract Background This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. Methods A ML classifier for retrieving COVID-19 research studies (the ‘Cochrane COVID-19 Study Classifier’) was developed using a data set of title-abstract records ‘included’ in, or ‘excluded’ from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between the 4th and 19th of January 2021. Results The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were ‘included’ in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were ‘included’ in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). Conclusions The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.
    Keywords Machine learning ; Study classifiers ; Searching ; Information retrieval ; Methods/methodology ; Systematic reviews ; Medicine ; R
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: GRADE guidance 23: considering cost-effectiveness evidence in moving from evidence to health-related recommendations.

    Xie, Feng / Shemilt, Ian / Vale, Luke / Ruiz, Francis / Drummond, Michael F / Lord, Joanne / Herrmann, Kirsten H / Rojas, María Ximena / Zhang, Yuan / Canelo-Aybar, Carlos / Alonso-Coello, Pablo / Shamliyan, Tatyana / Schünemann, Holger J

    Journal of clinical epidemiology

    2023  Volume 162, Page(s) 135–144

    Abstract: Background: This is the 23rd in a series of articles describing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the certainty of evidence and strength of recommendations for systematic reviews, health ... ...

    Abstract Background: This is the 23rd in a series of articles describing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the certainty of evidence and strength of recommendations for systematic reviews, health technology assessments, and clinical guideline development.
    Objectives: We outline how resource utilization and cost-effectiveness analyses are integrated into health-related recommendations, using the GRADE Evidence to Decision (EtD) frameworks.
    Study design and setting: Through iterative discussions and refinement, in-person, and online meetings, and through e-mail communication, we developed draft guidance to incorporate economic evidence in the formulation of health-related recommendations. We developed scenarios to operationalize the guidance. We presented a summary of the results to members of the GRADE Economic Evaluation Project Group.
    Results: We describe how to estimate the cost of preventing (or achieving) an event to inform assessments of cost-effectiveness of alternative treatments, when there are no published economic evaluations. Evidence profiles and Summary of Findings tables based on systematic reviews of cost-effectiveness analyses can be created to provide top-level summaries of results and quality of multiple published economic evaluations. We also describe how this information could be integrated in GRADE's EtD frameworks to inform health-related recommendations. Three scenarios representing various levels of available cost-effectiveness evidence were used to illustrate the integration process.
    Conclusion: This GRADE guidance provides practical information for presenting cost-effectiveness data and its integration in the development of health-related recommendations, using the EtD frameworks.
    MeSH term(s) Humans ; Evidence-Based Medicine ; Cost-Benefit Analysis ; Systematic Reviews as Topic ; GRADE Approach ; Technology Assessment, Biomedical
    Language English
    Publishing date 2023-08-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 639306-8
    ISSN 1878-5921 ; 0895-4356
    ISSN (online) 1878-5921
    ISSN 0895-4356
    DOI 10.1016/j.jclinepi.2023.08.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book: Evidence-based decisions and economics

    Shemilt, Ian

    health care, social welfare, education, and criminal justice

    (Evidence-based medicine)

    2010  

    Series title Evidence-based medicine
    MeSH term(s) Economics, Medical ; Evidence-Based Medicine
    Language English
    Size x, 206 p. :, ill.
    Edition 2nd ed. /
    Publisher Wiley-Blackwell
    Publishing place Chichester, West Sussex ; Hoboken, NJ
    Document type Book
    Note Rev. ed. of: Evidence-based health economics / edited by Cam Donaldson, Miranda Mugford, Luke Vale. c2002.
    ISBN 9781405191531 ; 1405191538
    Database Catalogue of the US National Library of Medicine (NLM)

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  10. Article ; Online: What do we know about the effects of exposure to 'Low alcohol' and equivalent product labelling on the amounts of alcohol, food and tobacco people select and consume? A systematic review.

    Shemilt, Ian / Hendry, Vivien / Marteau, Theresa M

    BMC public health

    2017  Volume 17, Issue 1, Page(s) 29

    Abstract: Background: Explicit labelling of lower strength alcohol products could reduce alcohol consumption by attracting more people to buy and drink such products instead of higher strength ones. Alternatively, it may lead to more consumption due to a 'self- ... ...

    Abstract Background: Explicit labelling of lower strength alcohol products could reduce alcohol consumption by attracting more people to buy and drink such products instead of higher strength ones. Alternatively, it may lead to more consumption due to a 'self-licensing' mechanism. Equivalent labelling of food or tobacco (for example "Low fat" or "Low tar") could influence consumption of those products by similar mechanisms. This systematic review examined the effects of 'Low alcohol' and equivalent labelling of alcohol, food and tobacco products on selection, consumption, and perceptions of products among adults.
    Methods: A systematic review was conducted based on Cochrane methods. Electronic and snowball searches identified 26 eligible studies. Evidence from 12 randomised controlled trials (all on food) was assessed for risk of bias, synthesised using random effects meta-analysis, and interpreted in conjunction with evidence from 14 non-randomised studies (one on alcohol, seven on food and six on tobacco). Outcomes assessed were: quantities of the product (i) selected or (ii) consumed (primary outcomes - behaviours), (iii) intentions to select or consume the product, (iv) beliefs associated with it consumption, (v) product appeal, and (vi) understanding of the label (secondary outcomes - cognitions).
    Results: Evidence for impacts on the primary outcomes (i.e. amounts selected or consumed) was overall of very low quality, showing mixed effects, likely to vary by specific label descriptors, products and population characteristics. Overall very low quality evidence suggested that exposure to 'Low alcohol' and equivalent labelling on alcohol, food and tobacco products can shift consumer perceptions of products, with the potential to 'self-licence' excess consumption.
    Conclusions: Considerable uncertainty remains about the effects of labels denoting low alcohol, and equivalent labels, on alcohol, food and tobacco selection and consumption. Independent, high-quality studies are urgently needed to inform policies on labelling regulations.
    MeSH term(s) Adult ; Alcohol Drinking/epidemiology ; Food/statistics & numerical data ; Food Preferences ; Humans ; Product Labeling/methods ; Product Labeling/statistics & numerical data ; Tobacco Products/statistics & numerical data
    Language English
    Publishing date 2017-01-12
    Publishing country England
    Document type Journal Article ; Review
    ISSN 1471-2458
    ISSN (online) 1471-2458
    DOI 10.1186/s12889-016-3956-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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