Imperial College London

DrFelixGreaves

Faculty of MedicineSchool of Public Health

Clinical Senior Lecturer
 
 
 
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Contact

 

felix.greaves08

 
 
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Location

 

Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Publication Type
Year
to

138 results found

Naughton F, Hope A, Siegele-Brown C, Grant K, Barton G, Notley C, Mascolo C, Coleman T, Shepstone L, Sutton S, Prevost AT, Crane D, Greaves F, High Jet al., 2023, An Automated, Online Feasibility Randomized Controlled Trial of a Just-In-Time Adaptive Intervention for Smoking Cessation (Quit Sense)., Nicotine Tob Res

INTRODUCTION: Learned smoking cues from a smoker's environment are a major cause of lapse and relapse. Quit Sense, a theory-guided Just-In-Time Adaptive Intervention smartphone app, aims to help smokers learn about their situational smoking cues and provide in-the-moment support to help manage these when quitting. METHODS: A two-arm feasibility randomized controlled trial (N = 209) to estimate parameters to inform a definitive evaluation. Smoker's willing to make a quit attempt were recruited using online paid-for adverts and randomized to "usual care" (text message referral to NHS SmokeFree website) or "usual care" plus a text message invitation to install Quit Sense. Procedures, excluding manual follow-up for nonresponders, were automated. Follow-up at 6 weeks and 6 months included feasibility, intervention engagement, smoking-related, and economic outcomes. Abstinence was verified using cotinine assessment from posted saliva samples. RESULTS: Self-reported smoking outcome completion rates at 6 months were 77% (95% CI 71%, 82%), viable saliva sample return rate was 39% (95% CI 24%, 54%), and health economic data 70% (95% CI 64%, 77%). Among Quit Sense participants, 75% (95% CI 67%, 83%) installed the app and set a quit date and, of those, 51% engaged for more than one week. The 6-month biochemically verified sustained abstinence rate (anticipated primary outcome for definitive trial), was 11.5% (12/104) among Quit Sense participants and 2.9% (3/105) for usual care (adjusted odds ratio = 4.57, 95% CIs 1.23, 16.94). No evidence of between-group differences in hypothesized mechanisms of action was found. CONCLUSIONS: Evaluation feasibility was demonstrated alongside evidence supporting the effectiveness potential of Quit Sense. IMPLICATIONS: Running a primarily automated trial to initially evaluate Quit Sense was feasible, resulting in modest recruitment costs and researcher time, and high trial engagement. When invited, as part of trial participa

Journal article

Toolan M, Walpole S, Shah K, Kenny J, Jonsson P, Crabb N, Greaves Fet al., 2023, Environmental impact assessment in health technology assessment: principles, approaches, and challenges, INTERNATIONAL JOURNAL OF TECHNOLOGY ASSESSMENT IN HEALTH CARE, Vol: 39, ISSN: 0266-4623

Journal article

Thygesen JH, Tomlinson C, Hollings S, Mizani MA, Handy A, Akbari A, Banerjee A, Cooper J, Lai AG, Li K, Mateen BA, Sattar N, Sofat R, Torralbo A, Wu H, Wood A, Sterne JAC, Pagel C, Whiteley WN, Sudlow C, Hemingway H, Denaxas Set al., 2022, COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records, LANCET DIGITAL HEALTH, Vol: 4, Pages: E542-E557

Journal article

Panch T, Duralde E, Mattie H, Kotecha G, Celi LA, Wright M, Greaves Fet al., 2022, A distributed approach to the regulation of clinical AI., PLOS Digit Health, Vol: 1

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number of applications of clinical AI is increasing, which, amplified by the need for adaptations to account for the heterogeneity of local health systems and inevitable data drift, creates a fundamental challenge for regulators. Our opinion is that, at scale, the incumbent model of centralized regulation of clinical AI will not ensure the safety, efficacy, and equity of implemented systems. We propose a hybrid model of regulation, where centralized regulation would only be required for applications of clinical AI where the inference is entirely automated without clinician review, have a high potential to negatively impact the health of patients and for algorithms that are to be applied at national scale by design. This amalgam of centralized and decentralized regulation we refer to as a distributed approach to the regulation of clinical AI and highlight the benefits as well as the pre-requisites and challenges involved.

Journal article

Unsworth H, Wolfram V, Dillon B, Salmon M, Greaves F, Liu X, MacDonald T, Denniston AK, Sounderajah V, Ashrafian H, Darzi A, Ashurst C, Holmes C, Weller Aet al., 2022, Building an evidence standards framework for artificial intelligence-enabled digital health technologies, LANCET DIGITAL HEALTH, Vol: 4

Journal article

Tewolde S, Costelloe C, PowelI J, Papoutsi C, Reidy C, Gudgin B, Shenton C, Greaves Fet al., 2022, An observational study of uptake and adoption of the NHS App in England

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objectives</jats:title><jats:p>This study aimed to evaluate patterns of uptake and adoption of the NHS App. Data metrics from the NHS App were used to assess acceptability by looking at total app downloads, registrations, appointment bookings, GP health records viewed, and prescriptions ordered. The impact of the UK COVID-19 lockdown and introduction of the <jats:italic>COVID Pass</jats:italic> were also explored to assess App usage and uptake.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Descriptive statistics and an interrupted time series analysis were used to look at monthly NHS App metrics at a GP practice level from January 2019-May 2021 in the population of England. Interrupted time series models were used to identify changes in level and trend among App usage and the different functionalities before and after the first COVID-19 lockdown. The <jats:italic>Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)</jats:italic> guidelines were used for reporting and analysis.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Between January 2019 and May 2021, there were a total of 8,524,882 NHS App downloads and 4,449,869 registrations. There was a 4-fold increase in app downloads from April 2021 (650,558 downloads) to May 2021 (2,668,535 downloads) when the COVID Pass feature was introduced. Areas with the highest number of App registrations proportional to the GP patient population occurred in Hampshire, Southampton and Isle of Wight CCG, and the lowest in Blackburn with Darwen CCG. After the announcement of the first lockdown (March 2020), a positive and significant trend in the number of login sessions was observed at 602,124 (p=0.004)** logins a month. National NHS App appointment bookings ranged from 298 to 42

Journal article

Essen A, Stern AD, Haase CB, Car J, Greaves F, Paparova D, Vandeput S, Wehrens R, Bates DWet al., 2022, Health app policy: international comparison of nine countries' approaches, NPJ DIGITAL MEDICINE, Vol: 5, ISSN: 2398-6352

Journal article

Forde H, Penney TL, White M, Levy L, Greaves F, Adams Jet al., 2022, Understanding Marketing Responses to a Tax on Sugary Drinks: A Qualitative Interview Study in the United Kingdom, 2019, INTERNATIONAL JOURNAL OF HEALTH POLICY AND MANAGEMENT, Vol: 11, Pages: 2618-2629

Journal article

Garnett C, Perski O, Michie S, West R, Field M, Kaner E, Munafò MR, Greaves F, Hickman M, Burton R, Brown Jet al., 2021, Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness: a mixed methods approach, F1000Research, Vol: 10, Pages: 511-511

<ns3:p><ns3:bold>Background:</ns3:bold> Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. <ns3:italic>Drink Less</ns3:italic> is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining <ns3:italic>Drink Less</ns3:italic> to improve its usability and likely effectiveness.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of <ns3:italic>Drink Less</ns3:italic>, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app’s usability through user testing.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: ‘Behaviour Substitution’, ‘Information about Antecedents’ and ‘Insights’, were added to the app. One existing module – ‘Identity Change’ – was removed based on the initial evaluation of <ns3:italic>Drink Less</ns3:italic>. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals.</ns3:p><ns3:p> <ns3:bold>Conclusions:</ns3:bold> A mixed methods approach was used to refine the content and design of <ns3:italic>Drink Less</ns3:italic>, providing insights into how to improve its

Journal article

Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R, Kahn CE, Esteva A, Karthikesalingam A, Mateen B, Webster D, Milea D, Ting D, Treanor D, Cushnan D, King D, McPherson D, Glocker B, Greaves F, Harling L, Ordish J, Cohen JF, Deeks J, Leeflang M, Diamond M, McInnes MDF, McCradden M, Abramoff MD, Normahani P, Markar SR, Chang S, Liu X, Mallett S, Shetty S, Denniston A, Collins GS, Moher D, Whiting P, Bossuyt PM, Darzi Aet al., 2021, A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI, NATURE MEDICINE, Vol: 27, Pages: 1663-1665, ISSN: 1078-8956

Journal article

Karpathakis K, Libow G, Potts HWW, Dixon S, Greaves F, Murray Eet al., 2021, An Evaluation Service for Digital Public Health Interventions: User-Centered Design Approach, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 23, ISSN: 1438-8871

Journal article

Jombart T, Ghozzi S, Schumacher D, Taylor TJ, Leclerc QJ, Jit M, Flasche S, Greaves F, Ward T, Eggo RM, Nightingale E, Meakin S, Brady OJ, Medley GF, Hohle M, Edmunds WJet al., 2021, Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 376, ISSN: 0962-8436

Journal article

Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, Darzi A, Karthikesalingam A, Denniston AK, Mateen BA, Ting D, Treanor D, King D, Greaves F, Godwin J, Pearson-Stuttard J, Harling L, McInnes M, Rifai N, Tomasev N, Normahani P, Whiting P, Aggarwal R, Vollmer S, Markar SR, Panch T, Liu X, STARD-AI Steering Committeeet al., 2021, Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol, BMJ Open, Vol: 11, ISSN: 2044-6055

Introduction Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysis The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and dissemination Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate th

Journal article

Reitsma MB, Kendrick PJ, Ababneh E, Abbafati C, Abbasi-Kangevari M, Abdoli A, Abedi A, Abhilash ES, Abila DB, Aboyans V, Abu-Rmeileh NME, Adebayo OM, Advani SM, Aghaali M, Ahinkorah BO, Ahmad S, Ahmadi K, Ahmed H, Aji B, Akunna CJ, Al-Aly Z, Alanzi TM, Alhabib KF, Ali L, Alif SM, Alipour V, Aljunid SM, Alla F, Allebeck P, Alvis-Guzman N, Amin TT, Amini S, Amu H, Amul GGH, Ancuceanu R, Anderson JA, Ansari-Moghaddam A, Antonio CAT, Antony B, Anvari D, Arabloo J, Arian ND, Arora M, Asaad M, Ausloos M, Awan AT, Ayano G, Aynalem GL, Azari S, B DB, Badiye AD, Baig AA, Bakhshaei MH, Banach M, Banik PC, Barker-Collo SL, Bärnighausen TW, Barqawi HJ, Basu S, Bayati M, Bazargan-Hejazi S, Behzadifar M, Bekuma TT, Bennett DA, Bensenor IM, Berfield KSS, Bhagavathula AS, Bhardwaj N, Bhardwaj P, Bhattacharyya K, Bibi S, Bijani A, Bintoro BS, Biondi A, Birara S, Braithwaite D, Brenner H, Brunoni AR, Burkart K, Butt ZA, Caetano dos Santos FL, Cámera LA, Car J, Cárdenas R, Carreras G, Carrero JJ, Castaldelli-Maia JM, Cattaruzza MSS, Chang J-C, Chen S, Chu D-T, Chung S-C, Cirillo M, Costa VM, Couto RAS, Dadras O, Dai X, Damasceno AAM, Damiani G, Dandona L, Dandona R, Daneshpajouhnejad P, Darega Gela J, Davletov K, Derbew Molla M, Dessie GA, Desta AA, Dharmaratne SD, Dianatinasab M, Diaz D, Do HT, Douiri A, Duncan BB, Duraes AR, Eagan AW, Ebrahimi Kalan M, Edvardsson K, Elbarazi I, El Tantawi M, Esmaeilnejad S, Fadhil I, Faraon EJA, Farinha CSES, Farwati M, Farzadfar F, Fazlzadeh M, Feigin VL, Feldman R, Fernandez Prendes C, Ferrara P, Filip I, Filippidis F, Fischer F, Flor LS, Foigt NA, Folayan MO, Foroutan M, Gad MM, Gaidhane AM, Gallus S, Geberemariyam BS, Ghafourifard M, Ghajar A, Ghashghaee A, Giampaoli S, Gill PS, Glozah FN, Gnedovskaya EV, Golechha M, Gopalani SV, Gorini G, Goudarzi H, Goulart AC, Greaves F, Guha A, Guo Y, Gupta B, Gupta RD, Gupta R, Gupta T, Gupta V, Hafezi-Nejad N, Haider MR, Hamadeh RR, Hankey GJ, Hargono A, Hartono RK, Hassankhani H, Hay SI, Heidari G, Hertelet al., 2021, Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019, The Lancet, Vol: 397, Pages: 2337-2360, ISSN: 0140-6736

BackgroundEnding the global tobacco epidemic is a defining challenge in global health. Timely and comprehensive estimates of the prevalence of smoking tobacco use and attributable disease burden are needed to guide tobacco control efforts nationally and globally.MethodsWe estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We modelled multiple smoking-related indicators from 3625 nationally representative surveys. We completed systematic reviews and did Bayesian meta-regressions for 36 causally linked health outcomes to estimate non-linear dose-response risk curves for current and former smokers. We used a direct estimation approach to estimate attributable burden, providing more comprehensive estimates of the health effects of smoking than previously available.FindingsGlobally in 2019, 1·14 billion (95% uncertainty interval 1·13–1·16) individuals were current smokers, who consumed 7·41 trillion (7·11–7·74) cigarette-equivalents of tobacco in 2019. Although prevalence of smoking had decreased significantly since 1990 among both males (27·5% [26·5–28·5] reduction) and females (37·7% [35·4–39·9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0·99 billion (0·98–1·00) in 1990. Globally in 2019, smoking tobacco use accounted for 7·69 million (7·16–8·20) deaths and 200 million (185–214) disability-adjusted life-years, and was the leading risk factor for death among males (20·2% [19·3–21·1] of male deaths). 6·68 million [86·9%] of 7·69 million deaths attributable to smoking tobacco use were among current smokers.Int

Journal article

Unsworth H, Dillon B, Collinson L, Powell H, Salmon M, Oladapo T, Ayiku L, Shield G, Holden J, Patel N, Campbell M, Greaves F, Joshi I, Powell J, Tonnel Aet al., 2021, The NICE Evidence Standards Framework for digital health and care technologies - Developing and maintaining an innovative evidence framework with global impact, DIGITAL HEALTH, Vol: 7, ISSN: 2055-2076

Journal article

Naughton F, Brown C, High J, Notley C, Mascolo C, Coleman T, Barton G, Shepstone L, Sutton S, Prevost AT, Crane D, Greaves F, Hope Aet al., 2021, Randomised controlled trial of a just-in-time adaptive intervention (JITAI) smoking cessation smartphone app: the Quit Sense feasibility trial protocol, BMJ OPEN, Vol: 11, ISSN: 2044-6055

Journal article

Karpathakis K, Libow G, Potts HWW, Dixon S, Greaves F, Murray Eet al., 2021, An Evaluation Service for Digital Public Health Interventions: User-Centered Design Approach (Preprint)

<sec> <title>BACKGROUND</title> <p>Digital health interventions (DHIs) have the potential to improve public health by combining effective interventions and population reach. However, what biomedical researchers and digital developers consider an effective intervention differs, thereby creating an ongoing challenge to integrating their respective approaches when evaluating DHIs.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study aims to report on the Public Health England (PHE) initiative set out to operationalize an evaluation framework that combines biomedical and digital approaches and demonstrates the impact, cost-effectiveness, and benefit of DHIs on public health.</p> </sec> <sec> <title>METHODS</title> <p>We comprised a multidisciplinary project team including service designers, academics, and public health professionals and used user-centered design methods, such as qualitative research, engagement with end users and stakeholders, and iterative learning. The iterative approach enabled the team to sequentially define the problem, understand user needs, identify opportunity areas, develop concepts, test prototypes, and plan service implementation. Stakeholders, senior leaders from PHE, and a working group critiqued the outputs.</p> </sec> <sec> <title>RESULTS</title> <p>We identified 26 themes and 82 user needs from semistructured interviews (N=15), expressed as 46 Jobs To Be Done, which were then validated across the journey of evaluation design for a DHI. We identified seven essential concepts for evaluating DHIs: evaluation

Journal article

Gold N, Egan M, Londakova K, Mottershaw A, Harper H, Burton R, Henn C, Smolar M, Walmsley M, Arambepola R, Watson R, Bowen S, Greaves Fet al., 2021, Effect of alcohol label designs with different pictorial representations of alcohol content and health warnings on knowledge and understanding of low-risk drinking guidelines: a randomized controlled trial, ADDICTION, Vol: 116, Pages: 1443-1459, ISSN: 0965-2140

Journal article

Greaves F, Boysen M, 2021, NICE's approach to measuring value, BMJ-BRITISH MEDICAL JOURNAL, Vol: 372, ISSN: 0959-535X

Journal article

Garnett C, Perski O, Michie S, West R, Field M, Kaner E, Munafò MR, Greaves F, Hickman M, Burton R, Brown Jet al., 2021, Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness: a mixed methods approach., F1000Res, Vol: 10

Background: Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. Drink Less is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining Drink Less to improve its usability and likely effectiveness. Methods: The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of Drink Less, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app's usability through user testing. Results: As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: 'Behaviour Substitution', 'Information about Antecedents' and 'Insights', were added to the app. One existing module - 'Identity Change' - was removed based on the initial evaluation of Drink Less. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals. Conclusions: A mixed methods approach was used to refine the content and design of Drink Less, providing insights into how to improve its usability and likely effectiveness. Drink Less is now ready for a confirmatory evaluation.

Journal article

Garnett C, Oldham M, Angus C, Beard E, Burton R, Field M, Greaves F, Hickman M, Kaner E, Loebenberg G, Michie S, Munafo M, Pizzo E, Brown Jet al., 2020, Evaluating the effectiveness of the smartphone app, Drink Less, compared with the NHS alcohol advice webpage, for the reduction of alcohol consumption among hazardous and harmful adult drinkers in the UK at 6-month follow-up: protocol for a randomised controlled trial, ADDICTION, Vol: 116, Pages: 412-425, ISSN: 0965-2140

Journal article

Murray CJL, Abbafati C, Abbas KM, Abbasi M, Abbasi-Kangevari M, Abd-Allah F, Abdollahi M, Abedi P, Abedi A, Abolhassani H, Aboyans V, Abreu LG, Abrigo MRM, Abu-Gharbieh E, Abu Haimed AK, Abushouk AI, Acebedo A, Ackerman IN, Adabi M, Adamu AA, Adebayo OM, Adelson JD, Adetokunboh OO, Afarideh M, Afshin A, Agarwal G, Agrawal A, Ahmad T, Ahmadi K, Ahmadi M, Ahmed MB, Aji B, Akinyemiju T, Akombi B, Alahdab F, Alam K, Alanezi FM, Alanzi TM, Albertson SB, Alemu BW, Alemu YM, Alhabib KF, Ali M, Ali S, Alicandro G, Alipour V, Alizade H, Aljunid SM, Alla F, Allebeck P, Almadi MAH, Almasi-Hashiani A, Al-Mekhlafi HM, Almulhim AM, Alonso J, Al-Raddadi RM, Altirkawi KA, Alvis-Guzman N, Amare B, Amare AT, Amini S, Amit AML, Amugsi DA, Anbesu EW, Ancuceanu R, Anderlini D, Anderson JA, Andrei T, Andrei CL, Anjomshoa M, Ansari F, Ansari-Moghaddam A, Antonio CAT, Antony CM, Anvari D, Appiah SCY, Arabloo J, Arab-Zozani M, Aravkin AY, Arba AAK, Aripov T, Ärnlöv J, Arowosegbe OO, Asaad M, Asadi-Aliabadi M, Asadi-Pooya AA, Ashbaugh C, Assmus M, Atout MMW, Ausloos M, Ausloos F, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azari S, Azene ZN, B DB, Babaee E, Badawi A, Badiye AD, Bagherzadeh M, Bairwa M, Bakhtiari A, Bakkannavar SM, Balachandran A, Banach M, Banerjee SK, Banik PC, Baraki AG, Barker-Collo SL, Basaleem H, Basu S, Baune BT, Bayati M, Baye BA, Bedi N, Beghi E, Bell ML, Bensenor IM, Berhe K, Berman AE, Bhagavathula AS, Bhala N, Bhardwaj P, Bhattacharyya K, Bhattarai S, Bhutta ZA, Bijani A, Bikbov B, Biondi A, Bisignano C, Biswas RK, Bjørge T, Bohlouli S, Bohluli M, Bolla SRR, Boloor A, Bose D, Boufous S, Brady OJ, Braithwaite D, Brauer M, Breitborde NJK, Brenner H, Breusov AV, Briant PS, Briggs AM, Britton GB, Brugha T, Burugina Nagaraja S, Busse R, Butt ZA, Caetano dos Santos FL, Cámera LLAA, Campos-Nonato IR, Campuzano Rincon JC, Car J, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castaldelli-Maia JM, Castelpietra G, Castro F, Catalá-López F, Causey K, Cederroth CR, Cercy KM Cet al., 2020, Five insights from the Global Burden of Disease Study 2019, The Lancet, Vol: 396, Pages: 1135-1159, ISSN: 0140-6736

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3·5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.

Journal article

Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, Abreu LG, Abrigo MRM, Abualhasan A, Abu-Raddad LJ, Abushouk AI, Adabi M, Adekanmbi V, Adeoye AM, Adetokunboh OO, Adham D, Advani SM, Agarwal G, Aghamir SMK, Agrawal A, Ahmad T, Ahmadi K, Ahmadi M, Ahmadieh H, Ahmed MB, Akalu TY, Akinyemi RO, Akinyemiju T, Akombi B, Akunna CJ, Alahdab F, Al-Aly Z, Alam K, Alam S, Alam T, Alanezi FM, Alanzi TM, Alemu BW, Alhabib KF, Ali M, Ali S, Alicandro G, Alinia C, Alipour V, Alizade H, Aljunid SM, Alla F, Allebeck P, Almasi-Hashiani A, Al-Mekhlafi HM, Alonso J, Altirkawi KA, Amini-Rarani M, Amiri F, Amugsi DA, Ancuceanu R, Anderlini D, Anderson JA, Andrei CL, Andrei T, Angus C, Anjomshoa M, Ansari F, Ansari-Moghaddam A, Antonazzo IC, Antonio CAT, Antony CM, Antriyandarti E, Anvari D, Anwer R, Appiah SCY, Arabloo J, Arab-Zozani M, Ariani F, Armoon B, Ärnlöv J, Arzani A, Asadi-Aliabadi M, Asadi-Pooya AA, Ashbaugh C, Assmus M, Atafar Z, Atnafu DD, Atout MMW, Ausloos F, Ausloos M, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azari S, Azarian G, Azene ZN, Badawi A, Badiye AD, Bahrami MA, Bakhshaei MH, Bakhtiari A, Bakkannavar SM, Baldasseroni A, Ball K, Ballew SH, Balzi D, Banach M, Banerjee SK, Bante AB, Baraki AG, Barker-Collo SL, Bärnighausen TW, Barrero LH, Barthelemy CM, Barua L, Basu S, Baune BT, Bayati M, Becker JS, Bedi N, Beghi E, Béjot Y, Bell ML, Bennitt FB, Bensenor IM, Berhe K, Berman AE, Bhagavathula AS, Bhageerathy R, Bhala N, Bhandari D, Bhattacharyya K, Bhutta ZA, Bijani A, Bikbov B, Bin Sayeed MS, Biondi A, Birihane BM, Bisignano C, Biswas RK, Bitew H, Bohlouli S, Bohluli M, Boon-Dooley AS, Borges G, Borzì AM, Borzouei S, Bosetti C, Boufous S, Braithwaite D, Breitborde NJK, Breitner S, Brenner H, Briant PS, Briko AN, Briko NI, Britton GB, Bryazka D, Bumgarner BR, Burkart K, Burnett RT, Burugina Nagaraja S, Butt ZA, Caetano dos Santos FL, Cahill LE, Cámeraet al., 2020, Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, The Lancet, Vol: 396, Pages: 1223-1249, ISSN: 0140-6736

BackgroundRigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.MethodsGBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quant

Journal article

Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abolhassani H, Aboyans V, Abrams EM, Abreu LG, Abrigo MRM, Abu-Raddad LJ, Abushouk AI, Acebedo A, Ackerman IN, Adabi M, Adamu AA, Adebayo OM, Adekanmbi V, Adelson JD, Adetokunboh OO, Adham D, Afshari M, Afshin A, Agardh EE, Agarwal G, Agesa KM, Aghaali M, Aghamir SMK, Agrawal A, Ahmad T, Ahmadi A, Ahmadi M, Ahmadieh H, Ahmadpour E, Akalu TY, Akinyemi RO, Akinyemiju T, Akombi B, Al-Aly Z, Alam K, Alam N, Alam S, Alam T, Alanzi TM, Albertson SB, Alcalde-Rabanal JE, Alema NM, Ali M, Ali S, Alicandro G, Alijanzadeh M, Alinia C, Alipour V, Aljunid SM, Alla F, Allebeck P, Almasi-Hashiani A, Alonso J, Al-Raddadi RM, Altirkawi KA, Alvis-Guzman N, Alvis-Zakzuk NJ, Amini S, Amini-Rarani M, Aminorroaya A, Amiri F, Amit AML, Amugsi DA, Amul GGH, Anderlini D, Andrei CL, Andrei T, Anjomshoa M, Ansari F, Ansari I, Ansari-Moghaddam A, Antonio CAT, Antony CM, Antriyandarti E, Anvari D, Anwer R, Arabloo J, Arab-Zozani M, Aravkin AY, Ariani F, Ärnlöv J, Aryal KK, Arzani A, Asadi-Aliabadi M, Asadi-Pooya AA, Asghari B, Ashbaugh C, Atnafu DD, Atre SR, Ausloos F, Ausloos M, Ayala Quintanilla BP, Ayano G, Ayanore MA, Aynalem YA, Azari S, Azarian G, Azene ZN, Babaee E, Badawi A, Bagherzadeh M, Bakhshaei MH, Bakhtiari A, Balakrishnan S, Balalla S, Balassyano S, Banach M, Banik PC, Bannick MS, Bante AB, Baraki AG, Barboza MA, Barker-Collo SL, Barthelemy CM, Barua L, Barzegar A, Basu S, Baune BT, Bayati M, Bazmandegan G, Bedi N, Beghi E, Béjot Y, Bello AK, Bender RG, Bennett DA, Bennitt FB, Bensenor IM, Benziger CP, Berhe K, Bernabe E, Bertolacci GJ, Bhageerathy R, Bhala N, Bhandari D, Bhardwaj P, Bhattacharyya K, Bhutta ZA, Bibi S, Biehl MH, Bikbov B, Bin Sayeed MS, Biondi A, Birihane BM, Bisanzio D, Bisignano C, Biswas RK, Bohlouli S, Bohluli M, Bolla SRR, Boloor A, Boon-Dooley AS, Borges G, Borzì AM, Bourne R, Brady OJ, Brauer M, Brayne C, Breet al., 2020, Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, The Lancet, Vol: 396, Pages: 1204-1222, ISSN: 0140-6736

BackgroundIn an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries.MethodsGBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of

Journal article

Jombart T, Ghozzi S, Schumacher D, Leclerc QJ, Jit M, Flasche S, Greaves F, Ward T, Eggo RM, Nightingale E, Meakin S, Brady OJ, Medley GF, Höhle M, Edmunds WJet al., 2020, Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

<jats:title>Abstract</jats:title><jats:p>As several countries gradually release social distancing measures, rapid detection of new localised COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (Automatic Selection of Models and Outlier Detection for Epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterise the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggest ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. We illustrate our method using publicly available data of NHS Pathways reporting potential COVID-19 cases in England at a fine spatial scale, for which we provide a template automated analysis pipeline. ASMODEE is implemented in the free R package <jats:italic>trendbreaker</jats:italic>.</jats:p>

Journal article

Nguyen V, Aldridge R, Blackburn R, Hayward A, Greaves F, Flowers Jet al., 2020, Does the NHS Diabetes Prevention Programme prevent diabetes? A population-based matched cohort study, Publisher: OXFORD UNIV PRESS, Pages: V437-V437, ISSN: 1101-1262

Conference paper

Ram B, Venkatraman T, Foley K, Honeyford K, Ells L, van Sluijs E, Hargreaves D, Greaves F, Viner R, Saxena Set al., 2020, Impact of school-based physical activity interventions in primary schools: measuring what matters, European Public Health Conference, Publisher: Oxford University Press, Pages: 1-2, ISSN: 1101-1262

BackgroundA growing number of small studies suggest that school-based physical activity initiatives can help children achieve the recommended 60 minutes of physical activity per day. However, the heterogeneity of outcomes and measures used in small studies prevents pooling of results to demonstrate whether short-term health benefits are sustained. Qualitative studies suggest many benefits that are not represented by outcomes in trials to date. The aim of this study was to generate a list of outcomes that have been studied to develop a core outcome set (COS) acceptable to key stakeholders for future studies evaluating school-based physical activity initiatives.MethodsWe searched six databases (MEDLINE, EMBASE, PsycINFO, CINAHL, CENTRAL and Cochrane Database of Systematic Reviews) systematically for reviews of school-based physical activity interventions, and extracted relevant studies to identify the outcomes and measures used in each paper. A long list was generated from the literature and a previous workshop with stakeholders. This study is registered with COMET (#1322), and with PROSPERO (CRD42019146621).Results75/121 cited studies drawn from 53/2409 reviews met our inclusion criteria. We grouped 65 outcomes into 3 domains: (i) physical activity and health (ii) social and emotional health, and (iii) educational attainment. We will conduct two Delphi survey rounds with four stakeholder groups (health professionals, researchers, educators and parents) to rate the importance of each outcome. A core outcome set will be generated from a consensus process.ConclusionsThere is currently a large variation of outcomes and measures studied that precludes evidence synthesis of the impact of school-based physical activity interventions. Consensus methods are needed to focus research on the outcomes that matter the most to key stakeholders and to provide tools for future studies to assess long-term impact.Key messagesVariations in outcomes studied precludes evidence synt

Conference paper

Alturkistani A, Qavi A, Anyanwu PE, Greenfield G, Greaves F, Costelloe Cet al., 2020, Patient portal functionalities and patient outcomes among patients with diabetes: systematic review (Preprint), Publisher: JMIR Publications

Background:Patient portal use could help improve diabetes patients’ care and health outcomes due to the features such as appointment booking, e-messaging, repeat prescription ordering that enable patient-centred care and improved patient self-management of the disease.Objective:To assess health and healthcare quality outcomes associated with the use of tethered (portals that are connected to the electronic healthcare record) patient portals by adult patients (18 years or older) with diabetes.Methods:We searched the databases including Medline, Embase and Scopus and reported the review methodology using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three independent reviewers screened titles and abstracts, and two reviewers assessed full-texts of relevant studies and performed data extraction and quality assessments of the included studies. We used the Cochrane Collaboration Risk of Bias Tool and the National Heart, Lung and Blood Institute (NIH) Study Quality Assessment Tools to assess the risk of bias of the included studies. Data were summarised through narrative synthesis.Results:Twelve studies were included in this review. Nine studies reported outcomes related to glycaemic control and most of them found statistically significant associations between using a patient portal and glycaemic control. Some studies also found an inverse association or no association between patient portal use and blood pressure, LDL cholesterol or BMI. Studies reported mixed outcomes regarding the use of patient portals and healthcare utilisation measures such as office visits, emergency department visits and hospitalisations. Few studies reported overall improved quality of care for diabetes patients who used patient portals.Conclusions:Studies mostly reported improved health outcomes for diabetes patients who used patient portals. However, the limitations of studying the effects of patient portals exist that do not guarantee whether

Working paper

Alturkistani A, Qavi A, Anyanwu PE, Greenfield G, Greaves F, Costelloe Cet al., 2020, Patient portal functionalities and patient outcomes among diabetes patients: a systematic, Journal of Medical Internet Research, Vol: 22, Pages: 1-9, ISSN: 1438-8871

Background:Patient portal use could help improve diabetes patients’ care and health outcomes due to the functionalities such as appointment booking, e-messaging, repeat prescription ordering that enable patient-centred care and improve the patient’s self-management of the disease.Objective:To summarise the evidence regarding the use of patient portal (portals that are connected to the electronic healthcare record) or patient portal functionality (e.g. appointment booking or e-messages) and their reported associations with health and healthcare quality outcomes among adult diabetes patients.Methods:We searched the databases including Medline, Embase and Scopus and reported the review methodology using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three independent reviewers screened titles and abstracts, and two reviewers assessed full-texts of relevant studies and performed data extraction and quality assessments of the included studies. We used the Cochrane Collaboration Risk of Bias Tool and the National Heart, Lung and Blood Institute (NHLBI) Study Quality Assessment Tools to assess the risk of bias of the included studies. Data was summarised through narrative synthesis.Results:Twelve studies were included in this review. Five studies reported overall patient portal use and its association with diabetes health and healthcare quality outcomes. Six studies reported E-messaging or email use associated outcomes and two studies reported prescription refill associated outcomes. Reported associations included the association between patient portal use and blood pressure, LDL cholesterol or BMI. Few studies reported outcomes regarding the use of patient portals and healthcare utilisation measures such as office visits, emergency department visits and hospitalisations. Limited number of studies reported overall quality of care for diabetes patients who used patient portals.Conclusions:The included studies mostly r

Journal article

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