Pub Date : 2024-08-21DOI: 10.1016/S2589-7500(24)00124-9
Oscar Freyer , Isabella Catharina Wiest Dr med , Prof Jakob Nikolas Kather Dr med , Stephen Gilbert PhD
Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged as multifaceted tools that have potential for health-care delivery, diagnosis, and patient care. However, deployment of LLMs raises substantial regulatory and safety concerns. Due to their high output variability, poor inherent explainability, and the risk of so-called AI hallucinations, LLM-based health-care applications that serve a medical purpose face regulatory challenges for approval as medical devices under US and EU laws, including the recently passed EU Artificial Intelligence Act. Despite unaddressed risks for patients, including misdiagnosis and unverified medical advice, such applications are available on the market. The regulatory ambiguity surrounding these tools creates an urgent need for frameworks that accommodate their unique capabilities and limitations. Alongside the development of these frameworks, existing regulations should be enforced. If regulators fear enforcing the regulations in a market dominated by supply or development by large technology companies, the consequences of layperson harm will force belated action, damaging the potentiality of LLM-based applications for layperson medical advice.
{"title":"A future role for health applications of large language models depends on regulators enforcing safety standards","authors":"Oscar Freyer , Isabella Catharina Wiest Dr med , Prof Jakob Nikolas Kather Dr med , Stephen Gilbert PhD","doi":"10.1016/S2589-7500(24)00124-9","DOIUrl":"10.1016/S2589-7500(24)00124-9","url":null,"abstract":"<div><p>Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged as multifaceted tools that have potential for health-care delivery, diagnosis, and patient care. However, deployment of LLMs raises substantial regulatory and safety concerns. Due to their high output variability, poor inherent explainability, and the risk of so-called AI hallucinations, LLM-based health-care applications that serve a medical purpose face regulatory challenges for approval as medical devices under US and EU laws, including the recently passed EU Artificial Intelligence Act. Despite unaddressed risks for patients, including misdiagnosis and unverified medical advice, such applications are available on the market. The regulatory ambiguity surrounding these tools creates an urgent need for frameworks that accommodate their unique capabilities and limitations. Alongside the development of these frameworks, existing regulations should be enforced. If regulators fear enforcing the regulations in a market dominated by supply or development by large technology companies, the consequences of layperson harm will force belated action, damaging the potentiality of LLM-based applications for layperson medical advice.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e662-e672"},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001249/pdfft?md5=2df13b013a0e89af3fe332b6bcb83ed0&pid=1-s2.0-S2589750024001249-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1016/S2589-7500(24)00175-4
The Lancet Digital Health
{"title":"Balancing AI innovation with patient safety","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00175-4","DOIUrl":"10.1016/S2589-7500(24)00175-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Page e601"},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001754/pdfft?md5=0a8eaf69e78527e6cec35e921968cd32&pid=1-s2.0-S2589750024001754-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1016/S2589-7500(24)00176-6
{"title":"Correction to Lancet Digit Health 2024; 6: e605–13","authors":"","doi":"10.1016/S2589-7500(24)00176-6","DOIUrl":"10.1016/S2589-7500(24)00176-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Page e604"},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001766/pdfft?md5=11bd2424e9355f76c22900c4a010ada2&pid=1-s2.0-S2589750024001766-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1016/S2589-7500(24)00171-7
Jamie Elvidge , Dalia Dawoud
{"title":"Reporting standards to support cost-effectiveness evaluations of AI-driven health care","authors":"Jamie Elvidge , Dalia Dawoud","doi":"10.1016/S2589-7500(24)00171-7","DOIUrl":"10.1016/S2589-7500(24)00171-7","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e602-e603"},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001717/pdfft?md5=38bf3554f6e2ebc110a0128fd57b4837&pid=1-s2.0-S2589750024001717-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/S2589-7500(24)00140-7
Callum Stewart PhD , Yatharth Ranjan MSc , Pauline Conde MSc , Shaoxiong Sun PhD , Yuezhou Zhang PhD , Zulqarnain Rashid PhD , Heet Sankesara BSc , Nicholas Cummins PhD , Petroula Laiou PhD , Xi Bai PhD , Prof Richard J B Dobson PhD , Amos A Folarin PhD
<div><h3>Background</h3><p>The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).</p></div><div><h3>Methods</h3><p>The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).</p></div><div><h3>Findings</h3><p>By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p<0·0001).</p></div><div><h3>Interpretation</h3><p>Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.</p></div><div><h3>Funding</h3><p>National
{"title":"Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study","authors":"Callum Stewart PhD , Yatharth Ranjan MSc , Pauline Conde MSc , Shaoxiong Sun PhD , Yuezhou Zhang PhD , Zulqarnain Rashid PhD , Heet Sankesara BSc , Nicholas Cummins PhD , Petroula Laiou PhD , Xi Bai PhD , Prof Richard J B Dobson PhD , Amos A Folarin PhD","doi":"10.1016/S2589-7500(24)00140-7","DOIUrl":"10.1016/S2589-7500(24)00140-7","url":null,"abstract":"<div><h3>Background</h3><p>The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).</p></div><div><h3>Methods</h3><p>The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).</p></div><div><h3>Findings</h3><p>By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p<0·0001).</p></div><div><h3>Interpretation</h3><p>Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.</p></div><div><h3>Funding</h3><p>National ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e640-e650"},"PeriodicalIF":23.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001407/pdfft?md5=41fde3576005e91fa40bb70d2e644041&pid=1-s2.0-S2589750024001407-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/S2589-7500(24)00141-9
L Nelson Sanchez-Pinto MD , María del Pilar Arias López MD , Halden Scott MD , Kristen Gibbons PhD , Michael Moor PhD , Prof R Scott Watson MD , Matthew O Wiens PhD , Prof Luregn J Schlapbach MD , Tellen D Bennett MD
The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and—with the right planning—have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.
{"title":"Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world","authors":"L Nelson Sanchez-Pinto MD , María del Pilar Arias López MD , Halden Scott MD , Kristen Gibbons PhD , Michael Moor PhD , Prof R Scott Watson MD , Matthew O Wiens PhD , Prof Luregn J Schlapbach MD , Tellen D Bennett MD","doi":"10.1016/S2589-7500(24)00141-9","DOIUrl":"10.1016/S2589-7500(24)00141-9","url":null,"abstract":"<div><p>The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and—with the right planning—have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e651-e661"},"PeriodicalIF":23.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001419/pdfft?md5=31f8722a8d750546a71029215e4dfdf0&pid=1-s2.0-S2589750024001419-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1016/S2589-7500(24)00139-0
Whitney W Au BBiomedSc , Francesco Recchia MSc , Daniel Y Fong PhD , Prof Stephen H S Wong PhD , Derwin K C Chan PhD , Catherine M Capio PhD , Clare C W Yu PhD , Sam W S Wong DPT , Prof Cindy H P Sit PhD , Prof Patrick Ip MD , Prof Ya-Jun Chen PhD , Prof Walter R Thompson PhD , Prof Parco M Siu PhD
<div><h3>Background</h3><p>Physical inactivity in children and adolescents has become a pressing public health concern. Wearable activity trackers can allow self-monitoring of physical activity behaviour and promote autonomous motivation for exercise. However, the effects of wearable trackers on physical activity in young populations remain uncertain.</p></div><div><h3>Methods</h3><p>In this systematic review and meta-analysis, we searched PubMed, Embase, SPORTDiscus, and Web of Science for publications from database inception up to Aug 30, 2023, without restrictions on language. Studies were eligible if they were randomised controlled trials or clustered randomised controlled trials that examined the use of wearable activity trackers to promote physical activity, reduce sedentary behaviours, or promote overall health in participants with a mean age of 19 years or younger, with no restrictions on health condition or study settings. Studies were excluded if children or adolescents were not the primary intervention cohort, or wearable activity trackers were not worn on users’ bodies to objectively track users’ physical activity levels. Two independent reviewers (WWA and FR) assessed eligibility of studies and contacted authors of studies if more information was needed to assess eligibility. We also searched reference lists from relevant systematic reviews and meta-analyses. Systematic review software Covidence was used for study screening and data extraction. Study characteristics including study setting, participant characteristics, intervention characteristics, comparator, and outcome measurements were extracted from eligible studies. The two primary outcomes were objectively measured daily steps and moderate-to-vigorous physical activity. We used a random-effects model with Hartung–Knapp adjustments to calculate standardised mean differences. Between-study heterogeneity was examined using Higgins <em>I</em><sup>2</sup> and Cochran Q statistic. Publication bias was assessed using Egger's regression test. This systematic review was registered with PROSPERO, CRD42023397248.</p></div><div><h3>Findings</h3><p>We identified 9619 studies from our database research and 174 studies from searching relevant systematic reviews and meta-analyses, of which 105 were subjected to full text screening. We included 21 eligible studies, involving 3676 children and adolescents (1618 [44%] were female and 2058 [56%] were male, mean age was 13·7 years [SD 2·7]) in our systematic review and meta-analysis. Ten studies were included in the estimation of the effect of wearable activity trackers on objectively measured daily steps and 11 were included for objectively measured moderate-to-vigorous physical activity. Compared with controls, we found a significant increase in objectively measured daily steps (standardised mean difference 0·37 [95% CI 0·09 to 0·65; p=0·013]; Q 47·60 [p<0·0001]; <em>I</em><sup>2</sup> 72·7% [95% CI 53·4 to 84·0]), but not for moderate-to-vig
{"title":"Effect of wearable activity trackers on physical activity in children and adolescents: a systematic review and meta-analysis","authors":"Whitney W Au BBiomedSc , Francesco Recchia MSc , Daniel Y Fong PhD , Prof Stephen H S Wong PhD , Derwin K C Chan PhD , Catherine M Capio PhD , Clare C W Yu PhD , Sam W S Wong DPT , Prof Cindy H P Sit PhD , Prof Patrick Ip MD , Prof Ya-Jun Chen PhD , Prof Walter R Thompson PhD , Prof Parco M Siu PhD","doi":"10.1016/S2589-7500(24)00139-0","DOIUrl":"10.1016/S2589-7500(24)00139-0","url":null,"abstract":"<div><h3>Background</h3><p>Physical inactivity in children and adolescents has become a pressing public health concern. Wearable activity trackers can allow self-monitoring of physical activity behaviour and promote autonomous motivation for exercise. However, the effects of wearable trackers on physical activity in young populations remain uncertain.</p></div><div><h3>Methods</h3><p>In this systematic review and meta-analysis, we searched PubMed, Embase, SPORTDiscus, and Web of Science for publications from database inception up to Aug 30, 2023, without restrictions on language. Studies were eligible if they were randomised controlled trials or clustered randomised controlled trials that examined the use of wearable activity trackers to promote physical activity, reduce sedentary behaviours, or promote overall health in participants with a mean age of 19 years or younger, with no restrictions on health condition or study settings. Studies were excluded if children or adolescents were not the primary intervention cohort, or wearable activity trackers were not worn on users’ bodies to objectively track users’ physical activity levels. Two independent reviewers (WWA and FR) assessed eligibility of studies and contacted authors of studies if more information was needed to assess eligibility. We also searched reference lists from relevant systematic reviews and meta-analyses. Systematic review software Covidence was used for study screening and data extraction. Study characteristics including study setting, participant characteristics, intervention characteristics, comparator, and outcome measurements were extracted from eligible studies. The two primary outcomes were objectively measured daily steps and moderate-to-vigorous physical activity. We used a random-effects model with Hartung–Knapp adjustments to calculate standardised mean differences. Between-study heterogeneity was examined using Higgins <em>I</em><sup>2</sup> and Cochran Q statistic. Publication bias was assessed using Egger's regression test. This systematic review was registered with PROSPERO, CRD42023397248.</p></div><div><h3>Findings</h3><p>We identified 9619 studies from our database research and 174 studies from searching relevant systematic reviews and meta-analyses, of which 105 were subjected to full text screening. We included 21 eligible studies, involving 3676 children and adolescents (1618 [44%] were female and 2058 [56%] were male, mean age was 13·7 years [SD 2·7]) in our systematic review and meta-analysis. Ten studies were included in the estimation of the effect of wearable activity trackers on objectively measured daily steps and 11 were included for objectively measured moderate-to-vigorous physical activity. Compared with controls, we found a significant increase in objectively measured daily steps (standardised mean difference 0·37 [95% CI 0·09 to 0·65; p=0·013]; Q 47·60 [p<0·0001]; <em>I</em><sup>2</sup> 72·7% [95% CI 53·4 to 84·0]), but not for moderate-to-vig","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e625-e639"},"PeriodicalIF":23.8,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001390/pdfft?md5=36380a4a62a32c50449fd9f8bf44ceca&pid=1-s2.0-S2589750024001390-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00099-2
Juliane F Oliveira PhD , Prof Andrêza L Alencar PhD , Maria Célia L S Cunha PhD , Adriano O Vasconcelos PhD , Gerson G Cunha PhD , Ray B Miranda BSc , Fábio M H S Filho BSc , Corbiniano Silva PhD , Emanuele Gustani-Buss PhD , Ricardo Khouri PhD , Thiago Cerqueira-Silva PhD , Prof Luiz Landau PhD , Prof Manoel Barral-Netto MD , Pablo Ivan P Ramos PhD
<div><h3>Background</h3><p>Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.</p></div><div><h3>Methods</h3><p>In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017–23). We constructed a graph-based representation of Brazil's mobility network. The Ford–Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25–April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6–March 1, 2021), for the purposes of model validation.</p></div><div><h3>Findings</h3><p>We found that flights alone transported 79·9 million (95% CI 58·3–101·4 million) passengers annually within Brazil during 2017–22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25–April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6–16, 2021.</p></div><div><h3>Interpretation</h3><p>By providing essential clues for effective pathogen
{"title":"Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study","authors":"Juliane F Oliveira PhD , Prof Andrêza L Alencar PhD , Maria Célia L S Cunha PhD , Adriano O Vasconcelos PhD , Gerson G Cunha PhD , Ray B Miranda BSc , Fábio M H S Filho BSc , Corbiniano Silva PhD , Emanuele Gustani-Buss PhD , Ricardo Khouri PhD , Thiago Cerqueira-Silva PhD , Prof Luiz Landau PhD , Prof Manoel Barral-Netto MD , Pablo Ivan P Ramos PhD","doi":"10.1016/S2589-7500(24)00099-2","DOIUrl":"10.1016/S2589-7500(24)00099-2","url":null,"abstract":"<div><h3>Background</h3><p>Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.</p></div><div><h3>Methods</h3><p>In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017–23). We constructed a graph-based representation of Brazil's mobility network. The Ford–Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25–April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6–March 1, 2021), for the purposes of model validation.</p></div><div><h3>Findings</h3><p>We found that flights alone transported 79·9 million (95% CI 58·3–101·4 million) passengers annually within Brazil during 2017–22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25–April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6–16, 2021.</p></div><div><h3>Interpretation</h3><p>By providing essential clues for effective pathogen ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 8","pages":"Pages e570-e579"},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000992/pdfft?md5=b74d298dd27f122d3107c7fb202b0a16&pid=1-s2.0-S2589750024000992-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00145-6
Benjamin L Smarr
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Pub Date : 2024-08-01DOI: 10.1016/S2589-7500(24)00115-8
{"title":"The next generation of evidence synthesis for diagnostic accuracy studies in artificial intelligence","authors":"","doi":"10.1016/S2589-7500(24)00115-8","DOIUrl":"10.1016/S2589-7500(24)00115-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 8","pages":"Pages e541-e542"},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001158/pdfft?md5=63d2ae9f56a06a00c10c7d09f23c13cb&pid=1-s2.0-S2589750024001158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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