Publications
152 results found
Macdonald T, Dinnes J, Maniatopoulos G, et al., 2024, Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study., JMIR Res Protoc, Vol: 13, ISSN: 1929-0748
BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "de
Cezard GI, Denholm RE, Knight R, et al., 2024, Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study., Nat Commun, Vol: 15
Infection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a 'pre-vaccination' cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and 'vaccinated' and 'unvaccinated' cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.
Chidambaram S, Jain B, Jain U, et al., 2024, An introduction to digital determinants of health., PLOS Digit Health, Vol: 3
In recent years, technology has been increasingly incorporated within healthcare for the provision of safe and efficient delivery of services. Although this can be attributed to the benefits that can be harnessed, digital technology has the potential to exacerbate and reinforce preexisting health disparities. Previous work has highlighted how sociodemographic, economic, and political factors affect individuals' interactions with digital health systems and are termed social determinants of health [SDOH]. But, there is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health [DDOH]. This paper will, for the first time, propose a definition of DDOH and provide a conceptual model characterizing its influence on healthcare outcomes. Specifically, DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology. A better appreciation of DDOH by the various stakeholders at the individual and societal levels can be channeled towards policies that are more digitally inclusive. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity.
Smith AL, Greaves F, Panch T, 2023, Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models., PLOS Digit Health, Vol: 2
Sukriti KC, Tewolde S, Laverty AA, et al., 2023, Uptake and adoption of the NHSApp in England: an observational study, BRITISH JOURNAL OF GENERAL PRACTICE, ISSN: 0960-1643
Scott P, Heigl M, Mccay C, et al., 2023, Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project, LEARNING HEALTH SYSTEMS, ISSN: 2379-6146
Loebenberg G, Oldham M, Brown J, et al., 2023, Bot or Not? Detecting and Managing Participant Deception When Conducting Digital Research Remotely: Case Study of a Randomized Controlled Trial., J Med Internet Res, Vol: 25
BACKGROUND: Evaluating digital interventions using remote methods enables the recruitment of large numbers of participants relatively conveniently and cheaply compared with in-person methods. However, conducting research remotely based on participant self-report with little verification is open to automated "bots" and participant deception. OBJECTIVE: This paper uses a case study of a remotely conducted trial of an alcohol reduction app to highlight and discuss (1) the issues with participant deception affecting remote research trials with financial compensation; and (2) the importance of rigorous data management to detect and address these issues. METHODS: We recruited participants on the internet from July 2020 to March 2022 for a randomized controlled trial (n=5602) evaluating the effectiveness of an alcohol reduction app, Drink Less. Follow-up occurred at 3 time points, with financial compensation offered (up to £36 [US $39.23]). Address authentication and telephone verification were used to detect 2 kinds of deception: "bots," that is, automated responses generated in clusters; and manual participant deception, that is, participants providing false information. RESULTS: Of the 1142 participants who enrolled in the first 2 months of recruitment, 75.6% (n=863) of them were identified as bots during data screening. As a result, a CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) was added, and after this, no more bots were identified. Manual participant deception occurred throughout the study. Of the 5956 participants (excluding bots) who enrolled in the study, 298 (5%) were identified as false participants. The extent of this decreased from 110 in November 2020, to a negligible level by February 2022 including a number of months with 0. The decline occurred after we added further screening questions such as attention checks, removed the prominence of financial compensation from social media advertising
Shaw RJ, Rhead R, Silverwood RJ, et al., 2023, Associations between SARS-CoV-2 infection and subsequent economic inactivity and employment status: pooled analyses of five linked longitudinal surveys., medRxiv
INTRODUCTION: Following the acute phase of the COVID-19 pandemic, record numbers of people became economically inactive (i.e., neither working nor looking for work), or non-employed (including unemployed job seekers and economically inactive people). A possible explanation is people leaving the workforce after contracting COVID-19. We investigated whether testing positive for SARS-CoV-2 is related to subsequent economic inactivity and non-employment, among people employed pre-pandemic. METHODS: The data came from five UK longitudinal population studies held by both the UK Longitudinal Linkage Collaboration (UK LLC; primary analyses) and the UK Data Service (UKDS; secondary analyses). We pooled data from five long established studies (1970 British Cohort Study, English Longitudinal Study of Ageing, 1958 National Child Development Study, Next Steps, and Understanding Society). The study population were aged 25-65 years between March 2020 to March 2021 and employed pre-pandemic. Outcomes were economic inactivity and non-employment measured at the time of the last follow-up survey (November 2020 to March 2021, depending on study). For the UK LLC sample (n=8,174), COVID-19 infection was indicated by a positive SARS-CoV-2 test in NHS England records. For the UKDS sample we used self-reported measures of COVID-19 infection (n=13,881). Logistic regression models estimated odds ratios (ORs) with 95% confidence intervals (95%CIs) adjusting for potential confounders including sociodemographic variables, pre-pandemic health and occupational class. RESULTS: Testing positive for SARS-CoV-2 was very weakly associated with economic inactivity (OR 1.08 95%CI 0.68-1.73) and non-employment status (OR 1.09. 95%CI 0.77-1.55) in the primary analyses. In secondary analyses, self-reported test-confirmed COVID-19 was not associated with either economic inactivity (OR 1.01 95%CI 0.70-1.44) or non-employment status (OR 1.03 95%CI 0.79-1.35). CONCLUSIONS: Among people employed pre-pandemic, te
Walpole SC, Weeks L, Shah K, et al., 2023, How can environmental impacts be incorporated in health technology assessment, and how impactful would this be?, EXPERT REVIEW OF PHARMACOECONOMICS & OUTCOMES RESEARCH, ISSN: 1473-7167
Edmunds CER, Gold N, Burton R, et al., 2023, The effectiveness of alcohol label information for increasing knowledge and awareness: a rapid evidence review, BMC PUBLIC HEALTH, Vol: 23
Forde H, Chavez-Ugalde Y, Jones RA, et al., 2023, The conceptualisation and operationalisation of 'marketing' in public health research: a review of reviews focused on food marketing using principles from critical interpretive synthesis, BMC PUBLIC HEALTH, Vol: 23
KC S, Reidy C, Laverty AA, et al., 2023, Adoption and Use of the NHS App in England: a mixed-methods evaluation, BJGP
Sukriti KC, Reidy C, Laverty AA, et al., 2023, Adoption and Use of the NHS App in England: a mixed-methods evaluation, BRITISH JOURNAL OF GENERAL PRACTICE, Vol: 73, ISSN: 0960-1643
Naughton F, Hope A, Siegele-Brown C, et al., 2023, An Automated, Online Feasibility Randomized Controlled Trial of a Just-In-Time Adaptive Intervention for Smoking Cessation (Quit Sense), NICOTINE & TOBACCO RESEARCH, Vol: 25, Pages: 1319-1329, ISSN: 1462-2203
Toolan M, Walpole S, Shah K, et 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
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- Citations: 2
Forde H, Penney TL, White M, et 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
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- Citations: 11
Bryazka D, Reitsma MB, Griswold MG, et al., 2022, Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020, The Lancet, Vol: 400, Pages: 185-235, ISSN: 0140-6736
BackgroundThe health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year.MethodsFor this analysis, we constructed burden-weighted dose–response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol.FindingsThe burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15–39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0–0) and 0·603 (0·400–1·00) standard drinks per day, and the NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0–0·403) to 1·87 (0·500–3·30) standard drinks per day and an NDE that ranged between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3–65·4) were aged 15–39 years and 76·9% (73·0–81·3) were male.InterpretationThere is stron
Thygesen JH, Tomlinson C, Hollings S, et 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
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- Citations: 17
Panch T, Duralde E, Mattie H, et 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.
Unsworth H, Wolfram V, Dillon B, et al., 2022, Building an evidence standards framework for artificial intelligence-enabled digital health technologies, LANCET DIGITAL HEALTH, Vol: 4
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- Citations: 5
Tewolde S, Costelloe C, PowelI J, et 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
Essen A, Stern AD, Haase CB, et al., 2022, Health app policy: international comparison of nine countries' approaches, NPJ DIGITAL MEDICINE, Vol: 5, ISSN: 2398-6352
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- Citations: 21
Garnett C, Perski O, Michie S, et 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
Sounderajah V, Ashrafian H, Rose S, et 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
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- Citations: 35
Karpathakis K, Libow G, Potts HWW, et al., 2021, An Evaluation Service for Digital Public Health Interventions: User-Centered Design Approach, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 23, ISSN: 1438-8871
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- Citations: 6
Jombart T, Ghozzi S, Schumacher D, et 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
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- Citations: 6
Sounderajah V, Ashrafian H, Golub RM, et 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
Reitsma MB, Kendrick PJ, Ababneh E, et 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
Gold N, Egan M, Londakova K, et 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
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- Citations: 8
Unsworth H, Dillon B, Collinson L, et 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
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- Citations: 28
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