Imperial College London

DrLorainneTudor Car

Faculty of MedicineSchool of Public Health

Honorary Senior Research Fellow
 
 
 
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Contact

 

l.tudor.car

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Publication Type
Year
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135 results found

Tudor Car L, 2021, Public perceptions of diabetes, healthy living and conversational agents in Singapore: A needs assessment, JMIR Formative Research, ISSN: 2561-326X

Journal article

Dhinagaran DA, Sathish T, Soong A, Theng Y-L, Best JD, Tudor Car Let al., 2021, Conversational agent for healthy lifestyle behaviour change: an online feasibility study, JMIR Formative Research, ISSN: 2561-326X

Journal article

Kingsland M, Barnes C, Doherty E, McCrabb S, Finch M, Cumpston M, Armstrong R, Tudor Car L, Doyle J, Wolfenden Let al., 2021, Identifying topics for future Cochrane Public Health reviews., J Public Health (Oxf)

Journal article

Teepe GW, Da Fonseca A, Kleim B, Jacobson NC, Salamanca Sanabria A, Tudor Car L, Fleisch E, Kowatsch Tet al., 2021, Just-in-time Adaptive Mechanisms of Popular Mobile Applications for Individuals with Depression: Systematic Review., J Med Internet Res

BACKGROUND: There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual's burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden of the person using the intervention by providing the right type of support at the right time. The right type and right time are determined by measuring the state of vulnerability and the state of receptivity. OBJECTIVE: With this work, we systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. METHODS: We systematically searched for apps addressing depression in the Apple App Store, the Google Play Store, and in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. Relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, two authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEExplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app's website, information on the app's website, and the app itself. All types of measurements (e.g., open questions, closed questions, device analytics) found in the apps were recorded. These measurements found were reviewed to investigate whether they were used to tailor content or timing along the JITAI mechanisms, to indicate progress, or as part of a component (e.g., describing a stressful situation). RESULTS: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Three apps did not use any measurements, 20 apps exclusivel

Journal article

Tudor Car L, 2021, The choice of behavioral change techniques in conversational agents in healthcare: a scoping review protocol, JMIR Research Protocols, Vol: 10, Pages: 1-5, ISSN: 1929-0748

Background:Conversational agents or chatbots are computer programs that simulate conversations with users. Conversational agents are increasingly used for delivery of behavior change interventions in health care. Behavior change is complex and comprises the use of one or several components collectively known as behavioral change techniques (BCTs).Objective:The objective of this scoping review is to identify the BCTs that are used in behavior change–focused interventions delivered via conversational agents in health care.Methods:This scoping review will be performed in line with the Joanna Briggs Institute methodology and will be reported according to the PRISMA extension for scoping reviews guidelines. We will perform a comprehensive search of electronic databases and grey literature sources, and will check the reference lists of included studies for additional relevant studies. The screening and data extraction will be performed independently and in parallel by two review authors. Discrepancies will be resolved through consensus or discussion with a third review author. We will use a data extraction form congruent with the key themes and aims of this scoping review. BCTs employed in the included studies will be coded in line with BCT Taxonomy v1. We will analyze the data qualitatively and present it in diagrammatic or tabular form, alongside a narrative summary.Results:To date, we have designed the search strategy and performed the search on April 26, 2021. The first round of screening of retrieved articles is planned to begin soon.Conclusions:Using appropriate BCTs in the design and delivery of health care interventions via conversational agents is essential to improve long-term outcomes. Our findings will serve to inform the development of future interventions in this area.International Registered Report Identifier (IRRID):PRR1-10.2196/30166JMIR Res Protoc 2021;10(7):e30166

Journal article

Tudor Car L, Myint Kyaw B, Nannan Panday RS, van der Kleij R, Chavannes N, Majeed A, Car Jet al., 2021, Digital health training programs for medical students: a scoping review, Journal of Medical Internet Research, Vol: 7, Pages: 1-11, ISSN: 1438-8871

Background: Medical schools worldwide are accelerating the introduction of digital health courses into their curricula. This review collated and analyzed the literature evaluating digital health education for medical students to inform development of future courses and identify areas where curricula may need to be strengthened.Methods: We carried out a scoping review following the Joanna Briggs Institute’s guidance and reported in line with PRISMA-ScR guidelines. We searched six major bibliographic databases and grey literature sources for the articles published from January 2000 to November 2019. Two authors independently screened the retrieved citations and extracted the data from the included studies. Discrepancies were resolved by consensus discussion between the authors. The findings were analyzed using thematic analysis and presented narratively.Results: A total of 34 studies focusing on different digital courses were included in this review. Most (n=22) were published from 2010 to 2019 and originated from the US (n=20). The reported digital health courses were mostly elective (n=20), integrated into the existing curriculum (n=24) and focused mainly on medical informatics (n=17). Most of the courses targeted medical students from first to third year (n=17) and the duration of the courses ranged from an hour to three academic years. Most (n=22) reported the use of blended education. Six of 34 delivered courses entirely digitally using online modules, offline learning, Massive Open Online Courses, and virtual patient simulations. The reported courses used various assessment approaches such as paper-based assessments, in person observations and/or online-based assessment. Thirty studies evaluated courses mostly using uncontrolled before and after design and generally reported improvements in students’ learning outcomes. ConclusionsDigital health courses reported in the literature were mostly elective, focused on a single area of digital health and lac

Journal article

See YKC, Smith HE, Car LT, Protheroe J, Wong WC, Bartlam Bet al., 2021, Health literacy and health outcomes in patients with low back pain: a scoping review, BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol: 21

Journal article

Tudor Car L, Kyaw BM, Nannan Panday RS, van der Kleij R, Chavannes N, Majeed A, Car Jet al., 2021, Digital Health Training Programs for Medical Students: Scoping Review (Preprint), DH

<sec> <title>BACKGROUND</title> <p>Medical schools worldwide are accelerating the introduction of digital health courses into their curricula. The COVID-19 pandemic has contributed to this swift and widespread transition to digital health and education. However, the need for digital health competencies goes beyond the COVID-19 pandemic because they are becoming essential for the delivery of effective, efficient, and safe care.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This review aims to collate and analyze studies evaluating digital health education for medical students to inform the development of future courses and identify areas where curricula may need to be strengthened.</p> </sec> <sec> <title>METHODS</title> <p>We carried out a scoping review by following the guidance of the Joanna Briggs Institute, and the results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched 6 major bibliographic databases and gray literature sources for articles published between January 2000 and November 2019. Two authors independently screened the retrieved citations and extracted the data from the included studies. Discrepancies were resolved by consensus discussions between the authors. The findings were analyzed using thematic analysis and presented narratively.</p> </sec> <sec> <title>RESULTS</title> <p>A total of 34 studies focusing on different digital courses were included in this review. Most of the studies (22/34, 65%) wer

Journal article

Tudor Car L, Tudor Car L, 2021, Virtual reality in medical students’ education: A scoping review protocol, BMJ Open, Vol: ``, Pages: 1-5, ISSN: 2044-6055

Background Virtual reality (VR) is a technology that produces a virtual manifestation of the real world. In recent years, VR has been increasingly used as a tool in medical education. The use of VR in medical education has large potential, as it allows for distance learning and training which may be challenging to deliver in real life. VR encompasses different tools and applications. There is a need to explore how VR has been employed in medical education to date.Objective The objective of this scoping review is to conceptualise the VR tools available and the applications of VR in undergraduate medical education as reported in the literature. This scoping review will identify any gaps in this field and provide suggestions for future research.Methods and analysis The relevant studies will be examined using the Joanna Briggs Institute methodological framework for scoping studies. A comprehensive search from a total of six electronic databases and grey literature sources will be performed. The reference list of included studies will be screened for additional studies. The screening and data extraction will be done in parallel and independently by two review authors. Any discrepancies will be resolved through consensus or discussion with a third review author. A data extraction form has been developed using key themes from the research questions. The extracted data will be qualitatively analysed and presented in a diagrammatic or tabular form, alongside a narrative summary, in line with Preferred Reporting Items for Systematic Reviews and Meta-Analysis: extension for Scoping Reviews reporting guidelines.Ethics and dissemination All data will be collected from published and grey literature. Ethics approval is therefore not a requirement. We will present our findings at relevant conferences and submit them for publications in peer-reviewed journals.

Journal article

Ng DY, Tudor Car L, Ng MJM, Lu J, Leung J, Goo TT, Chia CLKet al., 2021, Identifying barriers to early presentation in patients with locally advanced breast cancer (LABC) in Northern Singapore: qualitative study, PLoS One, Vol: 16, ISSN: 1932-6203

Introduction:Breast cancer is the leading cause of death in Singaporean women, with advanced stage rendering a poorer prognosis. This study aims to explore the barriers to early presentation, information needs and sources in patients with locally advanced breast cancer (LABC).Materials & methods:A convenience sample of patients who presented with locally advanced breast cancer to the Department of General Surgery in a teaching tertiary hospital were recruited for the study. We conducted semi-structured interviews face to face with the recruited patients. We recorded the interviews, transcribed them verbatim and analysed using thematic content analysis.Results:Twenty-three participants were recruited of which 12 were Chinese and 11 were Malay women. Mean age was 60 years (± 13 SD). The most common knowledge barrier resulting in delay was the misconception that a breast lump must be painful to be malignant. Other knowledge barriers include the lack of knowledge and misinformation from the internet or other social media platforms. Some perceived barriers include fear of diagnosis, fear of treatment and fear of imposing financial burden on family members. A significant proportion of participants were also not aware of a national breast screening programme.Conclusions:Our study has found that barriers to early presentation of women with locally advanced breast cancer remain similar and have persisted over the years despite targeted efforts. There is a need for a rethink of existing strategies and to develop new innovative ways to reach out to this group of patients.

Journal article

Moenninghoff A, Kramer JN, Hess AJ, Ismailova K, Teepe GW, Car LT, Mueller-Riemenschneider F, Kowatsch Tet al., 2021, Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 23, ISSN: 1438-8871

Journal article

Alattas A, Teepe GW, Leidenberger K, Fleisch E, Car LT, Salamanca-Sanabria A, Kowatsch Tet al., 2021, To What Scale Are Conversational Agents Used by Top-funded Companies Offering Digital Mental Health Services for Depression?, 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES), Publisher: SCITEPRESS, Pages: 801-808

Conference paper

Tudor KI, Pašiü MB, Škegro SN, Bakula M, Nemir J, Mustaþ F, Vidrih B, Pašiü H, Vujeviü L, Rajiþ F, Car LTet al., 2021, Lower urinary tract symptoms and depression in patients with multiple sclerosis, Psychiatria Danubina, Vol: 32, Pages: 113-121, ISSN: 0353-5053

Background: Both depression and lower urinary tract symptoms (LUTS) may be present in patients with multiple sclerosis (MS). The objective of this study was to give an insight on depression and LUTS in patients with MS in Croatia and to determine the possible association between LUTS and depression in patients with MS. Subjects and methods: This was a prospective cross-sectional study conducted in a tertiary healthcare center in Croatia. Hundred and one consecutive patients with MS (75 female, 26 male, mean age 42.09 (range 19-77) years, mean Expanded Disability Status Scale (EDSS) score 3.1 (range 0.0-7.0)) participated in this study. We evaluated LUTS and related quality of life (QoL) using three International Consultation on Incontinence Questionnaires (ICIQ) enquiring about overactive bladder (ICIQ-OAB), urinary incontinence short form (ICIQ-UI SF) and lower urinary tract symptoms related quality of life (ICIQLUTS-QoL). ICIQ-OAB and ICIQLUTS-QoL were for this purpose with permission successfully translated and validated into Croatian, while ICIQ-UI SF was already previously validated for the Croatian language. Information regarding treatment for depression was obtained during the medical interview. Data were analyzed and interpreted using IBM SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, N.Y., USA). Results: 89.10% (N=90) patients with MS reported urgency with urge urinary incontinence (UUI) present in 70.29% (N=71). 81.18% (N=82) patients reported nocturia, and 90.09% (N=91) reported feeling drowsy or sleepy during the day due to bladder symptoms. Neurological deficit measured by EDSS was found to positively correlate with LUTS on all three questionnaires: ICIQ-OAB (r=0.390, p<0.05), ICIQ-UI SF (r=0.477, p<0.01) and ICIQ-LUTSQoL (r=0.317, p<0.05). 25 patients were in treatment for depression. There were no significant differences between female and male patients regarding treatment for depression (Ȥ2=0.018, df=1, p>0.05). Results on

Journal article

Nazeha N, Pavagadhi D, Kyaw BM, Car J, Larrain GJ, Tudor Car Let al., 2020, Digitally competent health workforce: a scoping review of educational frameworks, Journal of Medical Internet Research, Vol: 22, Pages: 1-20, ISSN: 1438-8871

Background: Digital health technologies can be key to improving health outcomes, provided health workers are adequately trained to utilize these technologies. There have been efforts to identify digital competencies for different health worker groups, however, an overview of these efforts has yet to be consolidated and analysed.Objective: The objective of this review is to identify and study the existing digital health competency frameworks for health workers and provide recommendations for future digital health training initiatives and framework development.Methods: A literature search was performed to collate digital health competency frameworks published from year 2000. Six databases, including grey literature sources such as OpenGrey, ResearchGate, Google Scholar, Google, and websites of relevant associations were searched in November 2019. Screening and data extraction were performed in parallel by reviewers. The included evidence is narratively described in terms of characteristics, evolution, and structural composition of frameworks. A thematic analysis was also performed to identify common themes across the included frameworks. Findings: Thirty frameworks were included in this review, a majority of which aimed at nurses, originated from high-income countries, published since 2016 and developed via literature reviews, followed by expert consultations. The thematic analysis uncovered 28 digital health competency domains across the included frameworks. The most prevalent domains were pertaining to basic IT literacy, health information management, digital communication, ethical/legal/regulatory requirements, and data privacy/security. The HITCOMP framework was found to be the most comprehensive framework, as it presented 21 out of the 28 identified domains, had the highest number of competencies, and targeted a wide variety of health workers.Conclusions: Digital health training initiatives should focus on competencies relevant to a particular health worker grou

Journal article

Tudor KI, Bošnjak Pašić M, Nađ Škegro S, Bakula M, Nemir J, Mustač F, Vidrih B, Pašić H, Vujević L, Rajič F, Tudor Car Let al., 2020, Lower Urinary Tract Symptoms and Depression in Patients with Multiple Sclerosis., Psychiatr Danub, Vol: 32, Pages: 511-519, ISSN: 0353-5053

BACKGROUND: Both depression and lower urinary tract symptoms (LUTS) may be present in patients with multiple sclerosis (MS). The objective of this study was to give an insight on depression and LUTS in patients with MS in Croatia and to determine the possible association between LUTS and depression in patients with MS. SUBJECTS AND METHODS: This was a prospective cross-sectional study conducted in a tertiary healthcare center in Croatia. Hundred and one consecutive patients with MS (75 female, 26 male, mean age 42.09 (range 19-77) years, mean Expanded Disability Status Scale (EDSS) score 3.1 (range 0.0-7.0)) participated in this study. We evaluated LUTS and related quality of life (QoL) using three International Consultation on Incontinence Questionnaires (ICIQ) enquiring about overactive bladder (ICIQ-OAB), urinary incontinence short form (ICIQ-UI SF) and lower urinary tract symptoms related quality of life (ICIQLUTS-QoL). ICIQ-OAB and ICIQLUTS-QoL were for this purpose with permission successfully translated and validated into Croatian, while ICIQ-UI SF was already previously validated for the Croatian language. Information regarding treatment for depression was obtained during the medical interview. Data were analyzed and interpreted using IBM SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, N.Y., USA). RESULTS: 89.10% (N=90) patients with MS reported urgency with urge urinary incontinence (UUI) present in 70.29% (N=71). 81.18% (N=82) patients reported nocturia, and 90.09% (N=91) reported feeling drowsy or sleepy during the day due to bladder symptoms. Neurological deficit measured by EDSS was found to positively correlate with LUTS on all three questionnaires: ICIQ-OAB (r=0.390, p<0.05), ICIQ-UI SF (r=0.477, p<0.01) and ICIQ-LUTSQoL (r=0.317, p<0.05). 25 patients were in treatment for depression. There were no significant differences between female and male patients regarding treatment for depression (χ2=0.018, df=1, p>0.05). Results

Journal article

Lozano R, Fullman N, Mumford JE, Knight M, Barthelemy CM, Abbafati C, Abbastabar H, Abd-Allah F, Abdollahi M, Abedi A, Abolhassani H, Abosetugn AE, Abreu LG, Abrigo MRM, Abu Haimed AK, Abushouk AI, Adabi M, Adebayo OM, Adekanmbi V, Adelson J, Adetokunboh OO, Adham D, Advani SM, Afshin A, Agarwal G, Agasthi P, Aghamir SMK, Agrawal A, Ahmad T, Akinyemi RO, Alahdab F, Al-Aly Z, Alam K, Albertson SB, Alemu YM, Alhassan RK, Ali M, Ali S, Alipour V, Aljunid SM, Alla F, Almadi MAH, Almasi A, Almasi-Hashiani A, Almasri NA, Al-Mekhlafi HM, Almulhim AM, Alonso J, Al-Raddadi RM, Altirkawi KA, Alvis-Guzman N, Alvis-Zakzuk NJ, Amini S, Amini-Rarani M, Amiri F, Amit AML, Amugsi DA, Ancuceanu R, Anderlini D, Andrei CL, Androudi S, Ansari F, Ansari-Moghaddam A, Antonio CAT, Antony CM, Antriyandarti E, Anvari D, Anwer R, Arabloo J, Arab-Zozani M, Aravkin AY, Aremu O, Ärnlöv J, Asaad M, Asadi-Aliabadi M, Asadi-Pooya AA, Ashbaugh C, Athari SS, Atout MMW, Ausloos M, Avila-Burgos L, Ayala Quintanilla BP, Ayano G, Ayanore MA, Aynalem YA, Aynalem GL, Ayza MA, Azari S, Azzopardi PS, B DB, Babaee E, Badiye AD, Bahrami MA, Baig AA, Bakhshaei MH, Bakhtiari A, Bakkannavar SM, Balachandran A, Balassyano S, Banach M, Banerjee SK, Banik PC, Bante AB, Bante SA, Barker-Collo SL, Bärnighausen TW, Barrero LH, Bassat Q, Basu S, Baune BT, Bayati M, Baye BA, Bedi N, Beghi E, Behzadifar M, Bekuma TTT, Bell ML, Bensenor IM, Berman AE, Bernabe E, Bernstein RS, Bhagavathula AS, Bhandari D, Bhardwaj P, Bhat AG, Bhattacharyya K, Bhattarai S, Bhutta ZA, Bijani A, Bikbov B, Bilano V, Biondi A, Birihane BM, Bockarie MJ, Bohlouli S, Bojia HA, Bolla SRR, Boloor A, Brady OJ, Braithwaite D, Briant PS, Briggs AM, Briko NI, Burugina Nagaraja S, Busse R, Butt ZA, Caetano dos Santos FL, Cahuana-Hurtado L, Cámera LA, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Castro F, Catalá-López F, Causey K, Cederroth CR, Cercy KM, Cerin E, Chandan JS, Chang AY, Charan Jet al., 2020, Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, The Lancet, Vol: 396, Pages: 1250-1284, ISSN: 0140-6736

BackgroundAchieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages.MethodsBased on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified

Journal article

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, S Roberts NL, Sylte DO, Henry NJ, LeGrand KE, Abdelalim A, Abdoli A, Abdollahpour I, Abdulkader RS, Abedi A, Abosetugn AE, Abushouk AI, Adebayo OM, Agudelo-Botero M, Ahmad T, Ahmed R, Ahmed MB, Eddine Aichour MT, Alahdab F, Alamene GM, Alanezi FM, Alebel A, Alema NM, Alghnam SA, Al-Hajj S, Ali BA, Ali S, Alikhani M, Alinia C, Alipour V, Aljunid SM, Almasi-Hashiani A, Almasri NA, Altirkawi K, Abdeldayem Amer YS, Amini S, Loreche Amit AM, Andrei CL, Ansari-Moghaddam A, T Antonio CA, Yaw Appiah SC, Arabloo J, Arab-Zozani M, Arefi Z, Aremu O, Ariani F, Arora A, Asaad M, Asghari B, Awoke N, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azari S, Azarian G, Badawi A, Badiye AD, Bagli E, Baig AA, Bairwa M, Bakhtiari A, Balachandran A, Banach M, Banerjee SK, Banik PC, Banstola A, Barker-Collo SL, Bärnighausen TW, Barrero LH, Barzegar A, Bayati M, Baye BA, Bedi N, Behzadifar M, Bekuma TT, Belete H, Benjet C, Bennett DA, Bensenor IM, Berhe K, Bhardwaj P, Bhat AG, Bhattacharyya K, Bibi S, Bijani A, Bin Sayeed MS, Borges G, Borzì AM, Boufous S, Brazinova A, Briko NI, Budhathoki SS, Car J, Cárdenas R, Carvalho F, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Catalá-López F, Cerin E, Chandan JS, Chanie WF, Chattu SK, Chattu VK, Chatziralli I, Chaudhary N, Cho DY, Kabir Chowdhury MA, Chu D-T, Colquhoun SM, Constantin M-M, Costa VM, Damiani G, Daryani A, Dávila-Cervantes CA, Demeke FM, Demis AB, Demoz GT, Demsie DG, Derakhshani A, Deribe K, Desai R, Nasab MD, da Silva DD, Dibaji Forooshani ZS, Doyle KE, Driscoll TR, Dubljanin E, Adema BD, Eagan AW, Eftekhari A, Ehsani-Chimeh E, Sayed Zaki ME, Elemineh DA, El-Jaafary SI, El-Khatib Z, Ellingsen CL, Emamian MH, Endalew DA, Eskandarieh S, Faris PS, Faro A, Farzadfar F, Fatahi Y, Fekadu W, Ferede TY, Fereshtehnejad S-M, Fernandes E, Ferrara P, Feyissa GT, Filip I, Fischer F, Folayan MO, Foroutan M, Francis JM, Franklin RC, Fukumoto T, Geberemariyam BS, Gebre AK, Gebreet al., 2020, Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017, Injury Prevention, Vol: 26, Pages: i96-i114, ISSN: 1353-8047

BACKGROUND: Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. METHODS: We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). FINDINGS: In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). INTERPRETATION: Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.

Journal article

Haagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, Lucchesi LR, Roberts NLS, Sylte DO, Adebayo OM, Ahmadi A, Ahmed MB, Aichour MTE, Alahdab F, Alghnam SA, Aljunid SM, Al-Raddadi RM, Alsharif U, Altirkawi K, Anjomshoa M, Antonio CAT, Appiah SCY, Aremu O, Arora A, Asayesh H, Assadi R, Awasthi A, Ayala Quintanilla BP, Balalla S, Banstola A, Barker-Collo SL, Bärnighausen TW, Bazargan-Hejazi S, Bedi N, Behzadifar M, Behzadifar M, Benjet C, Bennett DA, Bensenor IM, Bhaumik S, Bhutta ZA, Bijani A, Borges G, Borschmann R, Bose D, Boufous S, Brazinova A, Campuzano Rincon JC, Cárdenas R, Carrero JJ, Carvalho F, Castañeda-Orjuela CA, Catalá-López F, Choi J-YJ, Christopher DJ, Crowe CS, Dalal K, Daryani A, Davitoiu DV, Degenhardt L, De Leo D, De Neve J-W, Deribe K, Dessie GA, deVeber GA, Dharmaratne SD, Doan LP, Dolan KA, Driscoll TR, Dubey M, El-Khatib Z, Ellingsen CL, El Sayed Zaki M, Endries AY, Eskandarieh S, Faro A, Fereshtehnejad S-M, Fernandes E, Filip I, Fischer F, Franklin RC, Fukumoto T, Gezae KE, Gill TK, Goulart AC, Grada A, Guo Y, Gupta R, Haghparast Bidgoli H, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Haro JM, Hassankhani H, Hassen HY, Havmoeller R, Hendrie D, Henok A, Híjar M, Hole MK, Homaie Rad E, Hossain N, Hostiuc S, Hu G, Igumbor EU, Ilesanmi OS, Irvani SSN, Islam SMS, Ivers RQ, Jacobsen KH, Jahanmehr N, Jakovljevic M, Jayatilleke AU, Jha RP, Jonas JB, Jorjoran Shushtari Z, Jozwiak JJ, Jürisson M, Kabir A, Kalani R, Kasaeian A, Kelbore AG, Kengne AP, Khader YS, Khafaie MA, Khalid N, Khan EA, Khoja AT, Kiadaliri AA, Kim Y-E, Kim D, Kisa A, Koyanagi A, Kuate Defo B, Kucuk Bicer B, Kumar M, Lalloo R, Lam H, Lami FH, Lansingh VC, Leasher JL, Li S, Linn S, Lunevicius R, Machado FR, Magdy Abd El Razek H, Magdy Abd El Razek M, Mahotra NB, Majdan M, Majeed A, Malekzadeh R, Malik MA, Malta DC, Manda A-L, Mansournia MA, Massenburg BB, Maulik PK, Meheretu HAA, Mehndiratta MM, Melese A, Mendoza W, Mengesha MM, Meretoja TJ, Meretoja A Met al., 2020, Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017, Injury Prevention, Vol: 26, Pages: i12-i626, ISSN: 1353-8047

BACKGROUND: The epidemiological transition of non-communicable diseases replacing infectious diseases as the main contributors to disease burden has been well documented in global health literature. Less focus, however, has been given to the relationship between sociodemographic changes and injury. The aim of this study was to examine the association between disability-adjusted life years (DALYs) from injury for 195 countries and territories at different levels along the development spectrum between 1990 and 2017 based on the Global Burden of Disease (GBD) 2017 estimates. METHODS: Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm-the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate. RESULTS: For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced. CONCLUSIONS: The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum.

Journal article

Wang H, Abbas KM, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A, Abolhassani H, Abreu LG, Abrigo MRM, Abushouk AI, Adabi M, Adair T, Adebayo OM, Adedeji IA, Adekanmbi V, Adeoye AM, Adetokunboh OO, Advani SM, Afshin A, Aghaali M, Agrawal A, Ahmadi K, Ahmadieh H, Ahmed MB, Al-Aly Z, Alam K, Alam T, Alanezi FM, Alanzi TM, Alcalde-Rabanal JE, Ali M, Alicandro G, Alijanzadeh M, Alinia C, Alipour V, Alizade H, Aljunid SM, Allebeck P, Almadi MAH, Almasi-Hashiani A, Al-Mekhlafi HM, Altirkawi KA, Alumran AK, Alvis-Guzman N, Amini-Rarani M, Aminorroaya A, Amit AML, Ancuceanu R, Andrei CL, Androudi S, Angus C, Anjomshoa M, Ansari F, Ansari I, Ansari-Moghaddam A, Antonio CAT, Antony CM, Anvari D, Appiah SCY, Arabloo J, Arab-Zozani M, Aravkin AY, Aremu O, Ärnlöv J, Aryal KK, Asadi-Pooya AA, Asgari S, Asghari Jafarabadi M, Atteraya MS, Ausloos M, Avila-Burgos L, Avokpaho EFGA, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azarian G, Babaee E, Badiye AD, Bagli E, Bahrami MA, Bakhtiari A, Balassyano S, Banach M, Banik PC, Barker-Collo SL, Bärnighausen TW, Barzegar A, Basu S, Baune BT, Bayati M, Bazmandegan G, Bedi N, Bell ML, Bennett DA, Bensenor IM, Berhe K, Berman AE, Bertolacci GJ, Bhageerathy R, Bhala N, Bhattacharyya K, Bhutta ZA, Bijani A, Biondi A, Bisanzio D, Bisignano C, Biswas RK, Bjørge T, Bohlouli S, Bohluli M, Bolla SRR, Borzì AM, Borzouei S, Brady OJ, Braithwaite D, Brauer M, Briko AN, Briko NI, Bumgarner BR, Burugina Nagaraja S, Butt ZA, Caetano dos Santos FL, Cai T, Callender CSKH, Cámera LLAA, Campos-Nonato IR, Cárdenas R, Carreras G, Carrero JJ, Carvalho F, Castaldelli-Maia JM, Castelpietra G, Castro F, Catalá-López F, Cederroth CR, Cerin E, Chattu VK, Chin KL, Chu D-T, Ciobanu LG, Cirillo M, Comfort H, Costa VM, Cowden RG, Cromwell EA, Croneberger AJ, Cunningham M, Dahlawi SMA, Damiani G, D'Amico E, Dandona L, Dandona R, Dargan PI, Darwesh AM, Daryani A, Das Gupta R, das Neves J, Davletov K, De Leo D, Denova-Gutiérrez E, Deribe K, Derveniset al., 2020, Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019, The Lancet, Vol: 396, Pages: 1160-1203, ISSN: 0140-6736

BackgroundAccurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019.Methods8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated usin

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

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

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, Roberts NLS, Sylte DO, Bertolacci GJ, Cunningham M, Henry NJ, LeGrand KE, Abdelalim A, Abdollahpour I, Abdulkader RS, Abedi A, Abegaz KH, Abosetugn AE, Abushouk AI, Adebayo OM, Adsuar JC, Advani SM, Agudelo-Botero M, Ahmad T, Ahmed MB, Ahmed R, Eddine Aichour MT, Alahdab F, Alanezi FM, Alema NM, Alemu BW, Alghnam SA, Ali BA, Ali S, Alinia C, Alipour V, Aljunid SM, Almasi-Hashiani A, Almasri NA, Altirkawi K, Abdeldayem Amer YS, Andrei CL, Ansari-Moghaddam A, T Antonio CA, Anvari D, Yaw Appiah SC, Arabloo J, Arab-Zozani M, Arefi Z, Aremu O, Ariani F, Arora A, Asaad M, Ayala Quintanilla BP, Ayano G, Ayanore MA, Azarian G, Badawi A, Badiye AD, Baig AA, Bairwa M, Bakhtiari A, Balachandran A, Banach M, Banerjee SK, Banik PC, Banstola A, Barker-Collo SL, Bärnighausen TW, Barzegar A, Bayati M, Bazargan-Hejazi S, Bedi N, Behzadifar M, Belete H, Bennett DA, Bensenor IM, Berhe K, Bhagavathula AS, Bhardwaj P, Bhat AG, Bhattacharyya K, Bhutta ZA, Bibi S, Bijani A, Boloor A, Borges G, Borschmann R, Borzì AM, Boufous S, Braithwaite D, Briko NI, Brugha T, Budhathoki SS, Car J, Cárdenas R, Carvalho F, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Catalá-López F, Cerin E, Chandan JS, Chapman JR, Chattu VK, Chattu SK, Chatziralli I, Chaudhary N, Cho DY, Choi J-YJ, Kabir Chowdhury MA, Christopher DJ, Chu D-T, Cicuttini FM, Coelho JM, Costa VM, Dahlawi SMA, Daryani A, Dávila-Cervantes CA, Leo DD, Demeke FM, Demoz GT, Demsie DG, Deribe K, Desai R, Nasab MD, Silva DDD, Dibaji Forooshani ZS, Do HT, Doyle KE, Driscoll TR, Dubljanin E, Adema BD, Eagan AW, Elemineh DA, El-Jaafary SI, El-Khatib Z, Ellingsen CL, Zaki MES, Eskandarieh S, Eyawo O, Faris PS, Faro A, Farzadfar F, Fereshtehnejad S-M, Fernandes E, Ferrara P, Fischer F, Folayan MO, Fomenkov AA, Foroutan M, Francis JM, Franklin RC, Fukumoto T, Geberemariyam BS, Gebremariam H, Gebremedhin KB, Gebremeskel LG, Gebremeskel GG, Gebremichael B, Gedefaw GA, Geta B, Geteet al., 2020, Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study, Injury Prevention, Vol: 26, Pages: i125-i153, ISSN: 1353-8047

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

Journal article

Tudor Car L, Dhinagaran DA, Kyaw BM, Kowatsch T, Rayhan JS, Theng YL, Atun Ret al., 2020, Conversational agents in healthcare: a scoping review and conceptual analysis, Journal of Medical Internet Research, Vol: 22, Pages: 1-21, ISSN: 1438-8871

Background: Conversational agents also known as chatbots are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including healthcare. By enabling better accessibility, personalization and efficiency, conversational agents have the potential to improve patient care.Objectives: To review the current applications, gaps and challenges in the literature on conversational agents in healthcare and provide recommendations for their future research, design and application. Methods: We performed a scoping review. A broad literature search was done in Medline (Ovid), EMBASE (Ovid), PubMed, Scopus and Cochrane central with the search terms “conversational agents”, “conversational AI”, “chatbots” and associated synonyms. We also searched grey literature using sources such as OCLC World Cat database and Research Gate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by two review authors. The included evidence was analyzed narratively employing the principles of thematic analysis.Results: The literature search yielded 47 study reports (45 articles and two ongoing clinical trials) which matched the inclusion criteria. The identified conversational agents were largely smartphone applications-delivered (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case-studies describing chatbot development (n=18) were most prevalent and only 11 RCTs were identified. Three most commonly reported conversational agent applications in the literature were treatment and monitoring, healthcare service support, and patient education.Conclusions: The literature on conversational agents in healthcare is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, AI-driven and mobile application-delivered conversa

Journal article

Soriano JB, Kendrick PJ, Paulson KR, Gupta V, Abrams EM, Adedoyin RA, Adhikari TB, Advani SM, Agrawal A, Ahmadian E, Alahdab F, Aljunid SM, Altirkawi KA, Alvis-Guzman N, Anber NH, Andrei CL, Anjomshoa M, Ansari F, Antó JM, Arabloo J, Athari SM, Athari SS, Awoke N, Badawi A, Banoub JAM, Bennett DA, Bensenor IM, Berfield KSS, Bernstein RS, Bhattacharyya K, Bijani A, Brauer M, Bukhman G, Butt ZA, Cámera LA, Car J, Carrero JJ, Carvalho F, Castañeda-Orjuela CA, Choi J-YJ, Christopher DJ, Cohen AJ, Dandona L, Dandona R, Dang AK, Daryani A, de Courten B, Demeke FM, Demoz GT, De Neve J-W, Desai R, Dharmaratne SD, Diaz D, Douiri A, Driscoll TR, Duken EE, Eftekhari A, Elkout H, Endries AY, Fadhil I, Faro A, Farzadfar F, Fernandes E, Filip I, Fischer F, Foroutan M, Garcia-Gordillo MA, Gebre AK, Gebremedhin KB, Gebremeskel GG, Gezae KE, Ghoshal AG, Gill PS, Gillum RF, Goudarzi H, Guo Y, Gupta R, Hailu GB, Hasanzadeh A, Hassen HY, Hay SI, Hoang CL, Hole MK, Horita N, Hosgood HD, Hostiuc M, Househ M, Ilesanmi OS, Ilic MD, Irvani SSN, Islam SMS, Jakovljevic M, Jamal AA, Jha RP, Jonas JB, Kabir Z, Kasaeian A, Kasahun GG, Kassa GM, Kefale AT, Kengne AP, Khader YS, Khafaie MA, Khan EA, Khan J, Khubchandani J, Kim Y-E, Kim YJ, Kisa S, Kisa A, Knibbs LD, Komaki H, Koul PA, Koyanagi A, Kumar GA, Lan Q, Lasrado S, Lauriola P, La Vecchia C, Le TT, Leigh J, Levi M, Li S, Lopez AD, Lotufo PA, Madotto F, Mahotra NB, Majdan M, Majeed A, Malekzadeh R, Mamun AA, Manafi N, Manafi F, Mantovani LG, Meharie BG, Meles HG, Meles GG, Menezes RG, Mestrovic T, Miller TR, Mini GK, Mirrakhimov EM, Moazen B, Mohammad KA, Mohammed S, Mohebi F, Mokdad AH, Molokhia M, Monasta L, Moradi M, Moradi G, Morawska L, Mousavi SM, Musa KI, Mustafa G, Naderi M, Naghavi M, Naik G, Nair S, Nangia V, Nansseu JR, Nazari J, Ndwandwe DE, Negoi RI, Nguyen TH, Nguyen CT, Nguyen HLT, Nixon MR, Ofori-Asenso R, Ogbo FA, Olagunju AT, Olagunju TO, Oren E, Ortiz JR, Owolabi MO, P A M, Pakhale S, Pana A, Panda-Jonas S, Park E-K, Phamet al., 2020, Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017, The Lancet Respiratory Medicine, Vol: 8, Pages: 585-596, ISSN: 2213-2600

BackgroundPrevious attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017.MethodsUsing data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs.FindingsIn 2017, 544·9 million people (95% uncertainty interval [UI] 506·9–584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8–7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578–

Journal article

Han TC, Kyaw BM, Smith H, Tan C, Tudor Car Let al., 2020, The use of smartphones to detect diabetic retinopathy: a scoping review and meta-analysis of diagnostic test accuracy studies, Journal of Medical Internet Research, Vol: 22, ISSN: 1438-8871

Background: Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphones ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible and easier to use than conventional methods. Objective: To determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. Methods: We performed an electronic search on MEDLINE, Embase and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR, to an accurate or commonly-employed reference standard, e.g. indirect ophthalmoscopy, slit-lamp biomicroscopy and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data and assessed the quality of included studies using the QUADAS-2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random-effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the PRISMA-DTA guidelines. Results: Nine studies involving 1430 participants were included. Most were of high quality, except one study with limited applicability due to its reference standard. The pooled sensitivity and specificity for detecting: any DR was 87% (95% CI 74%–94%) and 94% (81%–98%); mild non-proliferative DR (NPDR) was 39% (10%–79%) and 95% (91%–98%); moderate NPDR was 71% (57%–81%) and 95% (88%–98%); severe NPDR was 80% (49%–94%) and 97% (88%–99%); PDR was 92% (79%–97%) and 99% (96%–99%); diabetic macular edema was 79% (63%–89%) and 93% (82%–97%); and referral-warranted DR was 91% (86%–94%) and 8

Journal article

Carrillo Larco R, Tudor Car L, Pearson-Stuttard J, Panch T, Miranda JJ, Atun Ret al., 2020, Machine learning health-related applications in low- and middle-income countries: A scoping review protocol, BMJ Open, Vol: 10, ISSN: 2044-6055

Introduction Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs.Methods and analysis This scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations.Ethics and dissemination The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.

Journal article

Foong HF, Kyaw BM, Upton Z, Tudor Car Let al., 2020, Facilitators and barriers of using digital technology for the management of diabetic foot ulcers: A qualitative systematic review, INTERNATIONAL WOUND JOURNAL, Vol: 17, Pages: 1266-1281, ISSN: 1742-4801

Journal article

Martinengo L, Yeo NJY, Markandran KDO, Olsson M, Kyaw BM, Car LTet al., 2020, Digital health professions education on chronic wound management: A systematic review, INTERNATIONAL JOURNAL OF NURSING STUDIES, Vol: 104, ISSN: 0020-7489

Journal article

Driscoll T, Steenland K, Pearce N, Rushton L, Hutchings SJ, Straif K, Abate D, Acharya D, Agrawal A, Alahdab F, Alene KA, Androudi S, Anjomshoa M, Antonio CAT, Aremu O, Ataro Z, Badaw A, Banoub JAM, Barker-Collo SL, Bedi N, Bennett DA, Bernstein R, Beuran M, Bhattacharyya K, Bijani A, Butt ZA, Carrero JJ, Castaneda-Orjuela CA, Chimed-Ochir O, Dandona L, Dandona R, Dang AK, Daryani A, Desalegn BB, Dharmaratne SD, Djalalinia S, Dubljanin E, Ebrahimpour S, El-Khatib Z, Fareed M, Fareed M, Faro A, Fernandes E, Fischer F, Fukumoto T, Gallus S, Gebremichae TG, Gezae KE, Grada A, Guo Y, Gupta R, Haj-Mirzaian A, Haj-Mirzaian A, Hamidi S, Hasan M, Hasankhani M, Hay SI, Hoang CL, Hole MK, Hosgood HD, Hostiuc M, Hostiuc S, Irvani SSN, Islam SMS, Jakovljevic M, Jha RP, Jonas JB, Kahsay A, Kasaeian A, Kawakami N, Khader YS, Khafaie MA, Khan EA, Khosravi MH, Khubchandani J, Kim YJ, Kimokoti RW, Kisa A, Kogevinas M, Kosen S, Koul PA, Koyanagi A, Defo BK, Kumar GA, Lal DK, Latif A, Leigh J, Levi M, Li S, Linn S, Mahotra NB, Majdan M, Malekzadeh R, Mansournia MA, Martins-Melo FR, Massenburg BB, Mehta V, Melese A, Melku M, Memish ZA, Mendoza W, Meretoja TJ, Mestrovic T, Mini GK, Mirrakhimov EM, Moazen B, Mezerji NMG, Mohammed S, Mokdad AH, Monasta L, Moodley Y, Moosazadeh M, Moradi G, Morawska L, Morrison SD, Mousav SM, Mustafa G, Nangia V, Nego I, Negoi RI, Nguyen CT, Nguyen TH, Nixon MR, Ofori-Asenso R, Ogbo FA, Olagunju AT, Olusanya BO, Mahesh PA, Panda-Jonas S, Park E-K, Pati S, Qorbani M, Rafay A, Rafiei A, Rahim F, Rahimi-Movaghar V, Rajati F, Reiner RC, Rezaei S, Roever L, Ronfani L, Roshandel G, Saddik B, Safir S, Sahraian MA, Samy AM, Schwebel DC, Sepanlou SG, Serdar B, Shaikh MA, Sheikh A, Shigematsu M, Shiri R, Shirkoohi R, Si S, Silva JP, Sinha DN, Soofi M, Soriano JB, Sreeramareddy CT, Stanaway JD, Stokes MA, Sufiyan MB, Sutradhar I, Tabares-Seisdedos R, Takahashi K, Tefera YM, Temsah M-H, Tovani-Palone MR, Tran BX, Tran KB, Car LT, Ullah I, Valdez PR, van Boven JFM, Vaset al., 2020, Global and regional burden of chronic respiratory disease in 2016 arising from non-infectious airborne occupational exposures: a systematic analysis for the Global Burden of Disease Study 2016, Occupational and Environmental Medicine, Vol: 77, Pages: 142-150, ISSN: 1351-0711

Objectives This paper presents detailed analysis of the global and regional burden of chronic respiratory disease arising from occupational airborne exposures, as estimated in the Global Burden of Disease 2016 study.Methods The burden of chronic obstructive pulmonary disease (COPD) due to occupational exposure to particulate matter, gases and fumes, and secondhand smoke, and the burden of asthma resulting from occupational exposure to asthmagens, was estimated using the population attributable fraction (PAF), calculated using exposure prevalence and relative risks from the literature. PAFs were applied to the number of deaths and disability-adjusted life years (DALYs) for COPD and asthma. Pneumoconioses were estimated directly from cause of death data. Age-standardised rates were based only on persons aged 15 years and above.Results The estimated PAFs (based on DALYs) were 17% (95% uncertainty interval (UI) 14%–20%) for COPD and 10% (95% UI 9%–11%) for asthma. There were estimated to be 519 000 (95% UI 441,000–609,000) deaths from chronic respiratory disease in 2016 due to occupational airborne risk factors (COPD: 460,100 [95% UI 382,000–551,000]; asthma: 37,600 [95% UI 28,400–47,900]; pneumoconioses: 21,500 [95% UI 17,900–25,400]. The equivalent overall burden estimate was 13.6 million (95% UI 11.9–15.5 million); DALYs (COPD: 10.7 [95% UI 9.0–12.5] million; asthma: 2.3 [95% UI 1.9–2.9] million; pneumoconioses: 0.58 [95% UI 0.46–0.67] million). Rates were highest in males; older persons and mainly in Oceania, Asia and sub-Saharan Africa; and decreased from 1990 to 2016.Conclusions Workplace exposures resulting in COPD, asthma and pneumoconiosis continue to be important contributors to the burden of disease in all regions of the world. This should be reducible through improved prevention and control of relevant exposures.

Journal article

Driscoll T, Rushton L, Hutchings SJ, Straif K, Steenland K, Abate D, Abbafat C, Acharya D, Adebayo OM, Afshari M, Akinyemiju T, Alahdab F, Anjomshoa M, Antonio CAT, Aremu O, Ataro Z, Quintanilla BPA, Banoub JAM, Barker-Collo SL, Barnighausen TW, Barrero LH, Bedi N, Behzadifar M, Behzadifar M, Benavides FG, Beuran M, Bhattacharyya K, Bijani A, Cardenas R, Carrero JJ, Carvalho F, Castaneda-Orjuela CA, Cerin E, Cooper C, Dandona L, Dandona R, Dang AK, Daryani A, Desalegn BB, Dharmaratne SD, Dubljanin E, El-Khatib Z, Eskandarieh S, Fareed M, Faro A, Fereshtehnejad S-M, Fernandes E, Filip I, Fischer F, Fukumoto T, Gallus S, Gebremichael TG, Gezae KE, Gill TK, Goulart BNG, Grada A, Guo Y, Gupta R, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Hamzeh B, Hassankhani H, Hawkins DM, Hay SI, Hegazy MI, Henok A, Hoang CL, Hole MK, Rad EH, Hossain N, Hosseini M, Hostiuc S, Hu G, Ilesanmi OS, Irvani SSN, Islam SMS, Jakovljevic M, Jha RP, Jonas JB, Shushtar ZJ, Jozwiak JJ, Jurisson M, Kahsay A, Karami M, Karimi N, Kasaeian A, Kawakami N, Khader YS, Khan EA, Khubchandani J, Kim YJ, Kisa A, Defo BK, Kumar GA, Kumar M, Lami FH, Latif A, Leigh J, Levi M, Li S, Linn S, Lopez JCF, Lunevicius R, Mahotra NB, Majdan M, Malekzadeh R, Mansournia MA, Massenburg BB, Mehta V, Melese A, Memish ZA, Mendoza W, Mengistu G, Mengistu G, Meretoja TJ, Mestrovic T, Mestrovic T, Miazgowski T, Miller TR, Mini GK, Mirrakhimov EM, Moazen B, Mezerji NMG, Mohammed S, Mohebi F, Mokdad AH, Molokhia M, Monasta L, Moodley Y, Moosazadeh M, Morad G, Moradi-Lakeh M, Morawska L, Morrison SD, Mousav SM, Mustafa G, Najaf F, Nangia V, Negoi I, Negoi RI, Neupane S, Nguyen CT, Nguyen TH, Nixon MR, Ofori-Asenso R, Ogbo FA, Olagunju AT, Olusanya BO, Otstavnov SS, Mahesh PA, Panda-Jonas S, Park E-K, Prakash S, Qorbani M, Radfar A, Rafay A, Rahim F, Reiner RC, Renzaho AMN, Roever L, Ronfani L, Saddik B, Safari-Faramani R, Safi S, Safiri S, Salamati P, Salimi Y, Samy AM, Schwebel DC, Sepanlou SG, Serdar B, Shaikh MA Set al., 2020, Global and regional burden of disease and injury in 2016 arising from occupational exposures: a systematic analysis for the Global Burden of Disease Study 2016, Occupational and Environmental Medicine, Vol: 77, Pages: 133-141, ISSN: 1351-0711

Objectives This study provides an overview of the influence of occupational risk factors on the global burden of disease as estimated by the occupational component of the Global Burden of Disease (GBD) 2016 study.Methods The GBD 2016 study estimated the burden in terms of deaths and disability-adjusted life years (DALYs) arising from the effects of occupational risk factors (carcinogens; asthmagens; particulate matter, gases and fumes (PMGF); secondhand smoke (SHS); noise; ergonomic risk factors for low back pain; risk factors for injury). A population attributable fraction (PAF) approach was used for most risk factors.Results In 2016, globally, an estimated 1.53 (95% uncertainty interval 1.39–1.68) million deaths and 76.1 (66.3–86.3) million DALYs were attributable to the included occupational risk factors, accounting for 2.8% of deaths and 3.2% of DALYs from all causes. Most deaths were attributable to PMGF, carcinogens (particularly asbestos), injury risk factors and SHS. Most DALYs were attributable to injury risk factors and ergonomic exposures. Men and persons 55 years or older were most affected. PAFs ranged from 26.8% for low back pain from ergonomic risk factors and 19.6% for hearing loss from noise to 3.4% for carcinogens. DALYs per capita were highest in Oceania, Southeast Asia and Central sub-Saharan Africa. On a per capita basis, between 1990 and 2016 there was an overall decrease of about 31% in deaths and 25% in DALYs.Conclusions Occupational exposures continue to cause an important health burden worldwide, justifying the need for ongoing prevention and control initiatives.

Journal article

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