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Innovative informatics methods for process mining in health care

Journal of Biomedical Informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

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The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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P10.25.A ATR INHIBITION IN EXPERIMENTAL GLIOMA: DISCOVERY AND VALIDATION OF MOLECULAR TARGETS FOR FUNCTIONALLY-INSTRUCTED COMBINATION THERAPIES

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii68–ii68页

作者： Walter, B Hirsch, S Kuhlburger, L Stahl, A Schnabel, L Haeusser, L A Tsiami, F Ploeger, S Aghaallaei, N Dick, A M Skokowa, J Schmees, C Templin, M Schenke-Layland, K Tatagiba, M Nahnsen, S Merk, D Tabatabai, G Hertie Institute for Clinical Brain Research Tübingen Germany Quantitative Biology Center Eberhard Karls University Tübingen Tübingen Germany Biomedical Data Science Department of Computer Science Eberhard Karls University Tübingen Tübingen Germany NMI Natural and Medical Sciences Institute at the University of Tübingen Reutlingen Germany German Consortium for Translational Cancer Research (DKTK) Partner site Tübingen Tübingen Germany Division of Translational Oncology Department of Internal Medicine II University Hospital Tübingen Tübingen Germany Institute of Biomedical Engineering Dept. for Medical Technologies and regenerative Medicine Eberhard Karls University Tübingen Tübingen Germany Center for Neuro-Oncology Comprehensive Cancer Center Tübingen-Stuttgart Eberhard Karls University Tübingen Tübingen Germany

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Reproducible brain-wide association studies require thousands of individuals (Mar, 10.1038/s41586-022-04492-9, 2022)

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NATURE 2022年第7911期605卷 E11-E11页

作者： Marek, Scott Tervo-Clemmens, Brenden Calabro, Finnegan J. Montez, David F. Kay, Benjamin P. Hatoum, Alexander S. Donohue, Meghan Rose Foran, William Miller, Ryland L. Hendrickson, Timothy J. Malone, Stephen M. Kandala, Sridhar Feczko, Eric Miranda-Dominguez, Oscar Graham, Alice M. Earl, Eric A. Perrone, Anders J. Cordova, Michaela Doyle, Olivia Moore, Lucille A. Conan, Gregory M. Uriarte, Johnny Snider, Kathy Lynch, Benjamin J. Wilgenbusch, James C. Pengo, Thomas Tam, Angela Chen, Jianzhong Newbold, Dillan J. Zheng, Annie Seider, Nicole A. Van, Andrew N. Metoki, Athanasia Chauvin, Roselyne J. Laumann, Timothy O. Greene, Deanna J. Petersen, Steven E. Garavan, Hugh Thompson, Wesley K. Nichols, Thomas E. Yeo, B. T. Thomas Barch, Deanna M. Luna, Beatriz Fair, Damien A. Dosenbach, Nico U. F. Department of Psychiatry Washington University School of Medicine St Louis MO USA Department of Neurology Washington University School of Medicine St Louis MO USA Department of Psychological and Brain Sciences Washington University in St Louis St Louis MO USA Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Psychology University of Pittsburgh Pittsburgh PA USA Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Department of Bioengineering University of Pittsburgh Pittsburgh PA USA Department of Biomedical Engineering Washington University in St Louis St Louis MO USA Department of Radiology Washington University School of Medicine St Louis MO USA Department of Neurological Surgery Washington University School of Medicine St Louis MO USA Program in Occupational Therapy Washington University School of Medicine St Louis MO USA Department of Pediatrics Washington University School of Medicine St Louis MO USA University of Minnesota Informatics Institute University of Minnesota Minneapolis MN USA Department of Psychology University of Minnesota Minneapolis MN USA Masonic Institute for the Developing Brain University of Minnesota Medical School Minneapolis MN USA Department of Pediatrics University of Minnesota Medical School Minneapolis MN USA Department of Psychiatry Oregon Health and Science University Portland OR USA Minnesota Supercomputing Institute University of Minnesota Minneapolis MN USA Institute of Child Development University of Minnesota Medical School Minneapolis MN USA Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Sleep and Cognition National University of Singapore Singapore Singapore Centre for Translational MR Research National University of Singapore Singapore Singapore N.1 Institute for Health Institute for Digital Medicine National University of Singapore Singapore Sing

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Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

Forecasting Thailand’s Precipitation with Cascading Model o...

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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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Network reinforcement driven drug repurposing for Covid-19 by exploiting disease-gene-drug associations

arXiv

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arXiv 2020年

作者： Nam, Yonghyun Yun, Jae-Seung Lee, Seung Mi Park, Ji Won Chen, Ziqi Lee, Brian Verma, Anurag Ning, Xia Shen, Li Kim, Dokyoon Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia United States Division of Endocrinology and Metabolism Department of Internal Medicine St. Vincent’s Hospital College of Medicine Catholic University of Korea Seoul Korea Republic of Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul Korea Republic of Department of Surgery Seoul National University College of Medicine Seoul Korea Republic of Computer Science and Engineering Department College of Engineering Ohio State University Columbus United States Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia United States Biomedical Informatics Department College of Medicine Ohio State University Columbus United States Translational Data Analytics Institute Ohio State University Columbus United States Institute for Biomedical Informatics University of Pennsylvania Philadelphia United States

Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN 10

Towards a Personalized Item Recommendation Approach in Socia...

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10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Guan, Chun Yuen, Kevin Kam Fung Yue, Yong Department of Computer Science University of Liverpool Liverpool United Kingdom Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China School of Business University of Social Sciences Singapore Singapore Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781538658369

In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be applied in recommendation systems. This paper proposes the Intuitionistic Fuzzy DBSCAN (IF-DBSCAN) for the personalized item recommendation in folksonomy. The IF-DBSCAN is used to cluster items with respect to the user-defined tags. The Intuitionistic Fuzzy Set (IFS) is used to represent tag values which are vague and uncertain. DBSCAN can cluster items with the tags represented by using IFSs into different groups. An example of movie recommendation is demonstrated for the applicability of the proposed method. © 2018 IEEE.

关键词： Recommender systems

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Resting-state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L Miller David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Jamaica Plain MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA

Background Age-related cognitive decline after 65 is a well-known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies explored the link between functional network connectivity (FNC) estimated from resting-state functional MRI (rs-fMRI) and cognitive scores in healthy adults. FNC is often assumed to be static over time. However, this assumption runs contrary to the dynamic nature of brain FNC, and dynamic FNC (dFNC) has been recently introduced to overcome this limitation. The current study investigated the relationship between dFNC estimated from 36,263 individuals from UK Biobank and cognition. Method We used the resting-state fMRI (duration: 5min) data of 37,784 (20,157 females) adults’ brains, demographic information (age:64.06± 7.51), and cognitive scores from the UK Biobank in which the cognitive scores include fluid intelligence (FI), reaction time (RT), and pairs matching (Pairs). We adapted group independent component analysis to extract 53 data-driven components for the whole brain using a fully automated approach (Fig.1: Step1). Next, we used the sliding window and Pearson correlation to estimate the dFNC among 53 components (Fig.1: Step2). We used k-means clustering to put dFNCs of all individuals into three separated states and calculated individual state vectors (Fig.1: Step3). Then, we estimated 32 dFNC features based on three estimated states and state vectors (Fig.1: Step4). Finally, we trained a two-fold cross-validation support vector regression to predict the cognitive scores (Fig.1: Step5). Result The estimated dFNC feature predicted FI score with high accuracy (the correlation between measure and predicted score is R = 0.043, p = 6.6e −17 , Fig.2A), mean time to correctly identify match in RT task (R = 0.065, p = 1.9 −34 , Fig.2B), and time to complete Pairs task (R = 0.049, p = 1.3e −20 , Fig.2C). Conclusion Here we explored the link between dFNC features and cogniti

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The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

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Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

关键词： Biogeography Epidemiology Infectious diseases Malaria

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