Author: "Melek Somai" - Searchworks@Jio Institute Digital Library Search Results

31 results on '"Melek Somai"'

1. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Author: Marie-Laure Charpignon, Bella Vakulenko-Lagun, Bang Zheng, Colin Magdamo, Bowen Su, Kyle Evans, Steve Rodriguez, Artem Sokolov, Sarah Boswell, Yi-Han Sheu, Melek Somai, Lefkos Middleton, Bradley T. Hyman, Rebecca A. Betensky, Stan N. Finkelstein, Roy E. Welsch, Ioanna Tzoulaki, Deborah Blacker, Sudeshna Das, and Mark W. Albers
Subjects: Science
Abstract: Previous observational studies of the diabetes drugs metformin vs. sulfonylureas have yielded mixed results about whether metformin reduces the risk of dementia, relative to the sulfonylureas. Here, the authors apply a novel competing risks approach to emulate dementia-related target trials in electronic health records of diabetic patients and a complementary systems pharmacology evaluation on human neural cells.
Published: 2022
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2. Telemedicine and health disparities: Association between patient characteristics and telemedicine, in-person, telephone and message-based care during the COVID-19 pandemic

Author: Ling Tong, Ben George, Bradley H. Crotty, Melek Somai, Bradley W. Taylor, Kristen Osinski, and Jake Luo
Subjects: Telemedicine, Oncology, Remote Care, Health Disparity, Medicine (General), R5-920
Abstract: Telemedicine has been an essential form of care since the onset of the COVID-19 pandemic. However, telemedicine may exacerbate disparities for populations with limited digital literacy or access, such as older adults, racial minorities, patients of low income, rural residences, or limited English proficiency. From March 2020 to March 2022, this retrospective cohort study analyzed the use of in-person, phone/message, and telemedical care at a single tertiary care center in an oncology department. We investigated the association between economic, racial, ethnic, socioeconomic factors and forms of care, including in-person visits, telemedicine-based visits, and telephone/messages. The study results show that telemedicine utilization is lower among patients 65 and older, female patients, American Indian or Alaska Native patients, uninsured patients, and patients who require interpreters during clinical visits. As a result, it is unlikely that telemedicine will provide equal access to clinical care for all populations. On the other hand, in-person care utilization remains low in low-income and rural-living patients compared to the general population, while telephone and message use remains high in low-income and rural-living patients. We conclude that telemedicine is currently unable to close the utilization gap for populations of low socioeconomic status. Patients with low socioeconomic status use in-person care less frequently. For the disadvantaged, unusually high telephone or message utilization is unlikely to provide the same quality as in-person or telemedical care. Understanding the causes of disparity and promoting a solution to improve equal access to care for all patients is critical.
Published: 2022
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3. Bugs in the Virtual Clinic: Confronting Telemedicine’s Challenges Through Empathy and Support

Author: Bradley H Crotty and Melek Somai
Subjects: Medicine
Abstract: Although telemedicine has been an important conduit for clinical care during the COVID-19 pandemic, not all patients have been able to meaningfully participate in this mode of health care provision. Challenges with accessing telemedicine using consumer technology can interfere with the ability of patients and clinicians to meaningfully connect and lead to significant investments in time by clinicians and their staff. In this narrative case, we identify issues related to patients’ use of technology, make comparisons between telehealth adoption and the deployment of electronic health records, and propose that building intuitive and supported digital care experiences for patients is required to make virtual care sustainable.
Published: 2022
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4. Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis.

Author: Charitini Stavropoulou, Melek Somai, and John P A Ioannidis
Subjects: Medicine, Science
Abstract: The UK is one of the largest funders of health research in the world, but little is known about how health funding is spent. Our study explores whether major UK public and charitable health research funders support the research of UK-based scientists producing the most highly-cited research. To address this question, we searched for UK-based authors of peer-reviewed papers that were published between January 2006 and February 2018 and received over 1000 citations in Scopus. We explored whether these authors have held a grant from the National Institute for Health Research (NIHR), the Medical Research Council (MRC) and the Wellcome Trust and compared the results with UK-based researchers who serve currently on the boards of these bodies. From the 1,370 papers relevant to medical, biomedical, life and health sciences with more than 1000 citations in the period examined, we identified 223 individuals from a UK institution at the time of publication who were either first/last or single authors. Of those, 164 are still in UK academic institutions, while 59 are not currently in UK academia (have left the country, are retired, or work in other sectors). Of the 164 individuals, only 59 (36%; 95% CI: 29-43%) currently hold an active grant from one of the three funders. Only 79 (48%; 95% CI: 41-56%) have held an active grant from any of the three funders between 2006-2017. Conversely, 457 of the 664 board members of MRC, Wellcome Trust, and NIHR (69%; 95% CI: 65-72%) have held an active grant in the same period by any of these funders. Only 7 out of 655 board members (1.1%) were first, last or single authors of an extremely highly-cited paper. There are many reasons why the majority of the most influential UK authors do not hold a grant from the country's major public and charitable funding bodies. Nevertheless, the results are worrisome and subscribe to similar patterns shown in the US. We discuss possible implications and suggest ways forward.
Published: 2019
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5. Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities.

Author: Jake Luo, Ling Tong 0002, Bradley H. Crotty, Melek Somai, Bradley Taylor, Kristen Osinski, and Ben George
Published: 2021
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6. Mobile Maternal Health Applications in Developing Countries.

Author: Yuri Quintana, Jennifer McWhirter, Melek Somai, and Michelle Hacker
Published: 2016

7. Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities

Author: Ben George, Bradley K. Taylor, Bradley H. Crotty, Kristen Osinski, Ling Tong, Jake Luo, and Melek Somai
Subjects: Male, Telemedicine, Inequality, media_common.quotation_subject, MEDLINE, Ethnic group, Health Informatics, Health Information Management, Electronic Health Records, Humans, Medicine, Pandemics, Socioeconomic status, media_common, SARS-CoV-2, business.industry, Medical record, COVID-19, Odds ratio, Middle Aged, Metropolitan area, Computer Science Applications, Female, business, Demography
Abstract: Background The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients. Objectives This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area. Methods Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption. Results A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31–1.37), and have private insurance (OR 1.43; CI, 1.41–1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28–1.35), and have public insurance (OR 1.30; CI 1.27–1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, p = 0.01) had a strong correlation to video telemedicine adoption rate. Conclusion Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.
Published: 2021

8. From Pharmacovigilance to Clinical Care Optimization.

Author: Leo Anthony Celi, Edward T. Moseley, Christopher Moses, Padhraig Ryan, Melek Somai, David J. Stone, and Kai-ou Tang
Published: 2014
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9. Overcoming Eurocentric bias makes for better science

Author: Rana Dajani, Hamdi Mbarek, Said I. Ismail, Abdullah Awad, and Melek Somai
Subjects: General Medicine
Abstract: To understand disease, scientists are producing comprehensive omics datasets. However, the majority of these are Eurocentric. Recently, the inclusion of patients from Asia and the Middle East in genomic analyses uncovered unique loci linked to COVID-19 severity. This demonstrates that focusing on diversity and underrepresented populations can benefit all.
Published: 2022

10. Proposal: Medication Manager.

Author: Adarsha S. Bajracharya, Shira Fischer, and Melek Somai
Published: 2013

11. Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data

Author: Jeana M. Holt, Charles Spanbauer, Rachel Cusatis, Aaron N. Winn, AkkeNeel Talsma, Onur Asan, Melek Somai, Ryan Hanson, Jennifer Moore, Gregory Makoul, and Bradley H. Crotty
Subjects: Informatics, Research Design, Electronic Health Records, Humans, Female, Health Informatics, Medicare, United States, Aged
Abstract: Use the RE-AIM framework to examine the implementation of a patient contextual data (PCD) Tool designed to share patients' needs, values, and preferences with care teams ahead of clinical encounters.Observational study that follows initial PCD Tool scaling across primary care at a Midwestern academic health network. Program invitations, enrollment, patient submissions, and clinician views were tracked over a 1-year study period. Logistic regression modeled the likelihood of using the PCD Tool, accounting for patient covariates.Of 58,874 patients who could be contacted by email, 9,183 (15.6%) became PCD Tool users. Overall, 76% of primary care providers had patients who used the PCD Tool. Older age, female gender, non-minority race, patient portal activation, and Medicare coverage were significantly associated with increased likelihood of use. Number of office visits, medical issues, and behavioral health conditions also associated with use. Primary care staff viewed 18.7% of available PCD Tool summaries, 1.1% to 57.6% per clinic.The intervention mainly reached non-minority patients and patients who used more health services. Given the requirement for an email address on file, some patients may have been underrepresented. Overall, patient reach and adoption and clinician adoption, implementation, and maintenance of this Tool were modest but stable, consistent with a non-directive approach to fostering adoption by introducing the Tool in the absence of clear expectations for use.Healthcare organizations must implement effective methods to increase the reach, adoption, implementation, and maintenance of PCD tools across all patient populations. Assisting people, particularly racial minorities, with PCD Tool registration and actively supporting clinician use are critical steps in implementing technology that facilitates care.
Published: 2022

12. Clinical trials of neurointervention : 2007–2018

Author: Sunil A Sheth, David S Liebeskind, Hamidreza Saber, Melek Somai, Sandra Narayanan, Ashutosh P Jadhav, and Gary B Rajah
Subjects: Research Report, medicine.medical_specialty, Databases, Factual, Disease, Neurosurgical Procedures, 03 medical and health sciences, 0302 clinical medicine, Aneurysm, Interquartile range, Internal medicine, Humans, Medicine, Registries, 030212 general & internal medicine, Stroke, Clinical Trials as Topic, business.industry, Arteriovenous malformation, General Medicine, medicine.disease, Clinical trial, Stenosis, Venous thrombosis, Research Design, Surgery, Neurology (clinical), Nervous System Diseases, business, Vascular Surgical Procedures, 030217 neurology & neurosurgery
Abstract: BackgroundClinicalTrials.gov is one of the largest trials’ registries in the world.ObjectiveTo leverage the ClinicalTrials.gov database to define the portfolio of clinical trials of neurointervention.MethodsWe restricted our extraction to interventional clinical trials submitted between 2007 and 2018, and included MeSH terms that are part of the nervous system (n=19 344) or cardiovascular disease (n=19 234) categories and included a list of neurointerventional terms. The characteristics of trials, geographic distribution, dissemination, and funding sources were explored using descriptive and regression models.ResultsA total of 206 neurointerventional clinical trials across 1691 medical centers were identified. Acute stroke was the most studied conditions (68, 33%), followed by aneurysms (63, 31%), carotid stenosis (48, 24%), intracranial atherosclerotic disease (7, 3.5%), cerebral venous thrombosis (6, 3%), arteriovenous malformation (4, 2%), idiopathic intracranial hypertension (3, 1.5%), and others (6, 3%). Overall, 59 (29%) trials were completed, 79 (37%) were active trials (28% recruiting), and 22 (11%) were terminated or suspended. Academic centers and industry were the most common primary funding source (63% and 29%, respectively), with no funding source reported in 16 (7.7%) trials. Among 77 completed or terminated trials, only 9 (11.7%) trials reported findings within 12 months. Median time to publication for trials funded by academia was 1.66 years (interquartile range (IQR) 0.7–2.1) versus 2.1 years (IQR 1.2–3.25) for industry-funded studies.ConclusionsA low dissemination rate for results and a high rate of study non-completion, as well as lack of geographic dispersion of trials appear to be major challenges in the field.
Published: 2019

13. Predictive analytics and machine learning in stroke and neurovascular medicine

Author: David S Liebeskind, Hamidreza Saber, Gary Rajah, Melek Somai, and Fabien Scalzo
Subjects: 0301 basic medicine, Computer science, Machine learning, computer.software_genre, Field (computer science), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Health care, Humans, Relevance (information retrieval), Diagnosis, Computer-Assisted, Pace, business.industry, Deep learning, General Medicine, Predictive analytics, Precision medicine, Stroke, Cerebrovascular Disorders, 030104 developmental biology, Science research, Neurology, Neural Networks, Computer, Neurology (clinical), Artificial intelligence, business, computer, Algorithms, 030217 neurology & neurosurgery
Abstract: Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost every industry. Accuracy and precision of predictive analytics have significantly increased over the past few years and are evolving at an exponential pace. There have been significant breakthroughs in using Predictive Analytics in healthcare where it is held as the foundation of precision medicine. Yet, although the research in the field is expanding with the profuse volume of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Regardless of the status of its current contribution, the field of predictive analytics is expected to fundamentally change the way we diagnose and treat diseases, as well as the conduct of biomedical science research. In this review, we describe the main tools and techniques in predictive analytics and will analyze the trends in application of these techniques over the recent years. We will also provide examples of its application in medicine and more specifically in stroke and neurovascular research and outline current limitations.
Published: 2019

14. Impact of Pre-visit Contextual Data Collection on Patient-Physician Communication and Patient Activation: a Randomized Trial

Author: Charles Spanbauer, AkkeNeel Talsma, Rachel Cusatis, Melek Somai, Joni S. Williams, Kathryn E. Flynn, Bradley H. Crotty, Onur Asan, Gregory Makoul, Jeana M Holt, Aaron N. Winn, and Purushottam W. Laud
Subjects: Patient Activation Measure, medicine.medical_specialty, Health information technology, business.industry, 010102 general mathematics, Percentage point, Affect (psychology), 01 natural sciences, law.invention, 03 medical and health sciences, 0302 clinical medicine, Contextual design, Randomized controlled trial, law, Health care, Internal Medicine, Physical therapy, Medicine, 030212 general & internal medicine, 0101 mathematics, Patient participation, business, Original Research
Abstract: BACKGROUND: Patient contextual data (PCD) are often missing from electronic health records, limiting the opportunity to incorporate preferences and life circumstances into care. Engaging patients through tools that collect and summarize such data may improve communication and patient activation. However, differential tool adoption by race might widen health care disparities. OBJECTIVE: Determine if a digital tool designed to collect and present PCD improves communication and patient activation; secondarily, evaluate if use impacts outcomes by race. DESIGN, SETTING, AND PARTICIPANTS: A pragmatic, two-armed, non-blinded, randomized controlled trial conducted during 2019 in a primary care setting. INTERVENTION: The PCD tool (PatientWisdom) invited patients to identify preferences, values, goals, and barriers to care. Patients were randomized to a standard pre-visit email or facilitated enrollment with dedicated outreach to encourage use of the tool. MAIN OUTCOMES AND MEASURES: Outcomes of interest were post-visit patient communication and patient activation measured by the Communication Assessment Tool (CAT) and Patient Activation Measure (PAM), respectively. Outcomes were evaluated using treatment-on-the-treated (TOT) and intention-to-treat (ITT) principles. KEY RESULTS: A total of 301 patients were enrolled. Facilitated enrollment resulted in a five-fold increase in uptake of the PCD tool. TOT analysis indicated that the PCD tool was associated with notable increases in specific CAT items rated as excellent: “treated me with respect” (+ 13 percentage points; p = 0.04), “showed interest in my ideas” (+ 14 percentage points; p = 0.03), “showed care and concern” (+ 16 percentage points; p = 0.02), and “spent about the right amount of time with me” (+ 11 percentage points; p = 0.05). There were no significant pre/post-visit differences in PAM scores between arms (− 4.41 percentage points; p = 0.58). ITT results were similar. We saw no evidence of the treatment effect varying by race in ITT or TOT analyses. CONCLUSIONS AND RELEVANCE: The inclusion of PCD enhanced essential aspects of patient-provider communication but did not affect patient activation. Outcomes did not differ by race. TRIAL REGISTRATION: Clincaltrials.gov identifier: NCT03766841 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-020-06583-7.
Published: 2021

15. Bugs in the Virtual Clinic: Confronting Telemedicine’s Challenges Through Empathy and Support (Preprint)

Author: Bradley H Crotty and Melek Somai
Abstract: UNSTRUCTURED Although telemedicine has been an important conduit for clinical care during the COVID-19 pandemic, not all patients have been able to meaningfully participate in this mode of health care provision. Challenges with accessing telemedicine using consumer technology can interfere with the ability of patients and clinicians to meaningfully connect and lead to significant investments in time by clinicians and their staff. In this narrative case, we identify issues related to patients’ use of technology, make comparisons between telehealth adoption and the deployment of electronic health records, and propose that building intuitive and supported digital care experiences for patients is required to make virtual care sustainable.
Published: 2020

16. Addressing depression and behavioral health needs through a digital program at scale

Author: Karen Fickel, Erin Green, Lawrence A. Miller, William Wong, Ryan Hanson, Bradley H. Crotty, Melek Somai, Christine Shen, Jaymes Burns, Zakariyah Sharif-Sidi, and Caitlin Dunn
Subjects: medicine.medical_specialty, Wilcoxon signed-rank test, medicine.medical_treatment, Specialty, Anxiety, 03 medical and health sciences, 0302 clinical medicine, Interquartile range, medicine, Humans, 030212 general & internal medicine, Depression (differential diagnoses), Cognitive Behavioral Therapy, Primary Health Care, business.industry, Depression, Health Policy, Mental health, Digital health, Anxiety Disorders, Cognitive behavioral therapy, Physical therapy, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract: Depression and anxiety disorders are prevalent mental health conditions; yet they are often unrecognized, under-addressed and/or under-treated, and specialty treatment for these conditions is oftentimes difficult to access. By acting either as a bridge to therapy or as a form of therapy, digital tools, such as those that provide internet-based cognitive behavioral therapy (iCBT), may help clinicians support their patients' mental health needs. At one academic health system, a digital mental health program was deployed in primary care and outpatient behavioral health programs to help patients meet needs identified through screening or clinical visits. Over the first two years of operation, 138 clinicians (40% of eligible clinicians) prescribed the program to 2,228 unique patients, from which 1,117 (48.9%) enrolled. Patients who enrolled tended to be younger and healthier than non-enrollees. On average, enrolled patients spent 114.6 minutes within the iCBT program. Clinical improvement was assessed using pre- and post PHQ-9 and GAD-7 scores for depression and anxiety, respectively. Pre/Post scores were compared using Wilcoxon Rank Sum test. Patients with at least moderate depression had an average 23% reduction in PHQ-9 scores (median change -3(interquartile range 7), p
Published: 2020

17. Augmenting patient safety through participation by design - An assessment of dual monitors for patients in the outpatient clinic

Author: Melek Somai, Avishek Choudhury, Onur Asan, and Bradley H. Crotty
Subjects: Information privacy, 020205 medical informatics, media_common.quotation_subject, Health Informatics, 02 engineering and technology, Health informatics, Ambulatory Care Facilities, 03 medical and health sciences, Patient safety, 0302 clinical medicine, Intervention (counseling), Physicians, 0202 electrical engineering, electronic engineering, information engineering, medicine, Second screen, Outpatient clinic, Electronic Health Records, Humans, Quality (business), 030212 general & internal medicine, media_common, Physician-Patient Relations, business.industry, medicine.disease, Curiosity, Medical emergency, Patient Safety, business
Abstract: Background Patients and physicians engaging together in the electronic health record (EHR) during clinical visits may provide opportunities to both improve patient understanding and reduce medical errors. Objective To assess the potential impact of a patient EHR display intervention on patient quality and safety. We hypothesized that if patients had a dedicated display with an explicit invitation to follow clinicians in the EHR that this would identify several opportunities to engage patients in their care quality and safety. Material and methods Physician-patient outpatient encounters (24 patients and 8 physicians) were videotaped. Encounters took place in a hospital-based general internal medicine outpatient clinic where physicians and patients had their respective EHR monitors. Following the visits, each patient and physician was interviewed for 30 min to understand their perception of the mirrored-screen setting. Results The following 7 themes were identified (a) curiosity, (b) opportunity to ask questions, (c) error identification, (d) control over medications, (e) awareness, (f) shared understanding & decision-making, (g) data privacy. These themes collectively comprised a conceptual model for how patient engagement in electronic health record use, through a dedicated second screen or an explicitly shared screen, relates to safety and quality opportunities. Therefore, the double EHR screen provides an explicit invitation for patients to join the process to influence safety. Conclusion Desired outcomes include real-time error identification and better-shared understanding and decision-making, leading to better downstream follow-through with care plans.
Published: 2020

18. The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial (Preprint)

Author: Jeana M Holt, Rachel Cusatis, Aaron Winn, Onur Asan, Charles Spanbauer, Joni S Williams, Kathryn E Flynn, Melek Somai, Purushottam Laud, and Bradley H Crotty
Abstract: BACKGROUND Patient-centered care is respectful of and responsive to individual patient preferences, needs, and values. To provide patient-centered care, clinicians need to know and incorporate patients’ context into their communication and care with patients. Patient contextual data (PCD) encompass social determinants of health and patients’ needs, values, goals, and preferences relevant to their care. PCD can be challenging to collect as a routine component of the time-limited primary care visit. OBJECTIVE This study aims to determine if patient-provider communication and patient activation are different for patient users and patient nonusers of an electronic health record (EHR)–integrated PCD tool and assess if the impact of using PCD on patient-provider communication and patient activation differs for Black and White patients. METHODS We describe a randomized controlled trial of a prospective cohort of non-Hispanic White and Black patients who receive primary care services at a midwestern academic health care system in the United States. We will evaluate whether providing PCD through a consumer informatics tool enhances patient-provider communication, as measured by the Communication Assessment Tool, and we will evaluate patient activation, as measured by the Patient Activation Measure for PCD tool users and nonusers. Furthermore, owing to racial disparities in care and communication, we seek to determine if the adoption and use of the tool might narrow the differences between patient groups. RESULTS The trial was funded in November 2017 and received local ethics review approval in February 2019. The study began recruitment in April 2019 and enrollment concluded in October 2019 with 301 participants. The analysis was completed in May 2020, and trial results are expected to be published in winter 2020. CONCLUSIONS Recently, there has been increased attention to the role of health information technology tools to enable patients to collaborate with providers through the sharing of PCD. The adoption of such tools may overcome the barriers of current EHRs by directly engaging patients to submit their contextual data. Effectively, these tools would support the EHR in providing a more holistic understanding of the patient. Research further supports that individuals who have robust digital engagement using consumer informatics tools have higher participation in treatment follow-up and self-care across populations. Therefore, it is critical to investigate interventions that elicit and share patients’ social risks and care preferences with the health care team as a mechanism to improve individualized care and reduce the gap in health outcomes. CLINICALTRIAL ClinicalTrials.gov NCT03766841; https://clinicaltrials.gov/ct2/show/NCT03766841 INTERNATIONAL REGISTERED REPORT RR1-10.2196/20309
Published: 2020

19. Digital Engagement: How Serious Are Hospitals?

Author: Melek Somai and Bradley H. Crotty
Subjects: business.industry, Communication, MEDLINE, medicine.disease, Medicare, Telemedicine, Hospitals, United States, Editorial, Patient Portals, Surveys and Questionnaires, Internal Medicine, Medicine, Electronic Health Records, Humans, Medical emergency, Patient Participation, business, Original Research, Aged
Abstract: BACKGROUND: Patient portals present the opportunity to expand patients’ access to their clinicians and health information. Yet patients and clinicians have expressed the need for more guidance on portal and secure messaging procedures to avoid misuse. Little information is currently available concerning whether and how expectations of portal and messaging usage are communicated to patients. OBJECTIVE: To identify the information made available to patients about patient portal use, and to assess ease in accessing such information. DESIGN: A national survey of publicly available portal information from hospital websites. The study team followed up with phone calls to each hospital to request any additional patient-directed materials (e.g., pamphlets) not located in the web search. PARTICIPANTS: A random sample of 200 acute-care hospitals, 50 from each of four US Census regions, selected from the US Centers for Medicare & Medicaid Hospital Compare dataset. MAIN MEASURES: Availability of patient portals, secure messaging, and related functionality; the content and ease of access to patient-directed information about portals. KEY RESULTS: Of the hospitals sampled, 177 (89%) had a patient portal; 116 (66%) of these included secure messaging functionality. Most portals with secure messaging (N = 65, 58%) did not describe appropriate patient messaging conduct. Although many included disclaimers that the service is not for emergencies, 23 hospitals only included this within the fine prints of their “Terms and Conditions” section. Content analysis of additional patient-directed materials revealed a focus on logistical content, features of the portals, and parameters of use. Of the three categories, logistical content (e.g., creating an account) was the most thorough. CONCLUSIONS: Although most of the sampled hospitals had patient portals, many fail to educate patients fully and set expectations for secure messaging. To improve patient engagement and minimize harm, hospitals and clinicians need to provide more information and set clearer guidelines for patients.
Published: 2020

20. Data Science in Global Health—Highlighting the Burdens of Human Papillomavirus and Cervical Cancer in the MENA Region Using Open Source Data and Spatial Analysis

Author: Sylvia Levy, Zied Mhirsi, and Melek Somai
Subjects: Cervical cancer, medicine.medical_specialty, business.industry, Reproductive tract, HPV infection, medicine.disease, Open source data, Environmental health, Epidemiology, medicine, Global health, National level, Human papillomavirus, business
Abstract: Cervical cancer is a top driver of death and disability across the MENA region with at least 7,601 deaths annually. Nearly all cases of cervical cancer are caused by Human papillomavirus (HPV), the most common viral infection of the reproductive tract. HPV infection can be prevented by widespread uptake of the HPV vaccine and progression to cervical cancer can be averted with regular HPV and cervical cancer screenings. Sadly, these effective interventions are not in broad use on a national and regional level in the MENA region. We developed a data-driven digital map that integrates multiple data sources about HPV vaccination and cervical cancer incidence and mortality for countries in the MENA region. The use of different data sources from international and national organisations offers integrative and comprehensive information about the epidemiological status of these preventable diseases and the current policy-effectiveness at the national level. Our platform is a one-stop analytical online application that can help policymakers in their decision-making and ease the process required to combine different data sources into a comprehensive platform.
Published: 2020

21. Analysis of Clinician and Patient Factors and Completion of Telemedicine Appointments Using Video

Author: Aaron N. Winn, Melek Somai, Yilu Dong, Michael Christopher Decker, Natalie Mortensen, Alexandra Polovneff, Noorie Hyun, Jeana M Holt, Bradley H. Crotty, and Purushottam W. Laud
Subjects: Adult, Male, Telemedicine, medicine.medical_specialty, Quality management, MEDLINE, Ethnic group, Health Informatics, Telehealth, Appointments and Schedules, Patient experience, Humans, Medicine, Socioeconomic status, Aged, Original Investigation, Aged, 80 and over, Primary Health Care, business.industry, Research, General Medicine, Odds ratio, Middle Aged, Telephone, Online Only, Cross-Sectional Studies, Family medicine, Ethnic and Racial Minorities, Videoconferencing, Female, Patient Participation, business
Abstract: Key Points Question Which patient and clinician factors are associated with a successful or failed video visit? Findings This quality improvement study of 137 846 video visits showed an overall 90% success rate. Patient rather than clinician factors were more systematically associated with successful completion of video visits, and clinician comfort with technology was associated with successful video visits or conversion to telephone visits. Meaning The findings suggest that, as policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should be kept in the forefront., Importance Telemedicine provides patients access to episodic and longitudinal care. Policy discussions surrounding future support for telemedicine require an understanding of factors associated with successful video visits. Objective To assess patient and clinician factors associated with successful and with failed video visits. Design, Setting, and Participants This was a quality improvement study of 137 846 scheduled video visits at a single academic health system in southeastern Wisconsin between March 1 and December 31, 2020, supplemented with patient experience survey data. Patient information was gathered using demographic information abstracted from the electronic health record and linked with block-level socioeconomic data from the US Census Bureau. Data on perceived clinician experience with technology was obtained using the survey. Main Outcomes and Measures The primary outcome of interest was the successful completion of a scheduled video visit or the conversion of the video visit to a telephone-based service. Visit types and administrative data were used to categorize visits. Mixed-effects modeling with pseudo R2 values was performed to compare the relative associations of patient and clinician factors with video visit failures. Results In total, 75 947 patients and 1155 clinicians participated in 137 846 scheduled video encounters, 17 190 patients (23%) were 65 years or older, and 61 223 (81%) patients were of White race and ethnicity. Of the scheduled video encounters, 123 473 (90%) were successful, and 14 373 (10%) were converted to telephone services. A total of 16 776 patients (22%) completed a patient experience survey. Lower clinician comfort with technology (odds ratio [OR], 0.15; 95% CI, 0.08-0.28), advanced patient age (66-80 years: OR, 0.28; 95% CI, 0.26-0.30), lower patient socioeconomic status (including low high-speed internet availability) (OR, 0.85; 95% CI, 0.77-0.92), and patient racial and ethnic minority group status (Black or African American: OR, 0.75; 95% CI, 0.69-0.81) were associated with conversion to telephone visits. Patient characteristics accounted for systematic components for success; marginal pseudo R2 values decreased from 23% (95% CI, 21.1%-26.1%) to 7.8% (95% CI, 6.3%-9.4%) with exclusion of patient factors. Conclusions and Relevance As policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should guide decisions. In particular, this quality improvement study suggests that underserved patients may become disproportionately vulnerable by cuts in coverage for telephone-based services., This quality improvement study evaluates patient and clinician factors to assess which factors are associated with the successful completion or failure of telemedicine video appointments.
Published: 2021

22. Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis

Author: Melek Somai, John P. A. Ioannidis, and Charitini Stavropoulou
Subjects: Science and Technology Workforce, 030204 cardiovascular system & hematology, Careers in Research, Database and Informatics Methods, 0302 clinical medicine, Citation analysis, Medicine and Health Sciences, 030212 general & internal medicine, Database Searching, Publication, Multidisciplinary, Publications, Health services research, Research Assessment, Medical research, 3. Good health, Professions, Charities, Citation Analysis, Medicine, Health Services Research, Philanthropic Funding of Science, Research Article, LB2300, Science Policy, Science, Scopus, MEDLINE, Research Grants, Library science, Research and Analysis Methods, Research Funding, 03 medical and health sciences, Research Support as Topic, Political science, Humans, Government Funding of Science, Z665, Descriptive statistics, business.industry, United Kingdom, Public Expenditures, Scholarly Communication, Health Care, People and Places, Scientists, Population Groupings, business, Health funding
Abstract: The UK is one of the largest funders of health research in the world, but little is known about how health funding is spent. Our study explores whether major UK public and charitable health research funders support the research of UK-based scientists producing the most highly-cited research. To address this question, we searched for UK-based authors of peer-reviewed papers that were published between January 2006 and February 2018 and received over 1000 citations in Scopus. We explored whether these authors have held a grant from the National Institute for Health Research (NIHR), the Medical Research Council (MRC) and the Wellcome Trust and compared the results with UK-based researchers who serve currently on the boards of these bodies. From the 1,370 papers relevant to medical, biomedical, life and health sciences with more than 1000 citations in the period examined, we identified 223 individuals from a UK institution at the time of publication who were either first/last or single authors. Of those, 164 are still in UK academic institutions, while 59 are not currently in UK academia (have left the country, are retired, or work in other sectors). Of the 164 individuals, only 59 (36%; 95% CI: 29-43%) currently hold an active grant from one of the three funders. Only 79 (48%; 95% CI: 41-56%) have held an active grant from any of the three funders between 2006-2017. Conversely, 457 of the 664 board members of MRC, Wellcome Trust, and NIHR (69%; 95% CI: 65-72%) have held an active grant in the same period by any of these funders. Only 7 out of 655 board members (1.1%) were first, last or single authors of an extremely highly-cited paper.\ud \ud There are many reasons why the majority of the most influential UK authors do not hold a grant from the country’s major public and charitable funding bodies. Nevertheless, the results are worrisome and subscribe to similar patterns shown in the US. We discuss possible implications and suggest ways forward.
Published: 2019

23. The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial

Author: Rachel Cusatis, Onur Asan, Aaron N. Winn, Jeana M Holt, Melek Somai, Joni S. Williams, Charles Spanbauer, Bradley H. Crotty, Kathryn E. Flynn, and Purushottam W. Laud
Subjects: medicine.medical_specialty, 020205 medical informatics, Health information technology, Computer applications to medicine. Medical informatics, R858-859.7, 02 engineering and technology, patient-centered care, 03 medical and health sciences, 0302 clinical medicine, Health care, Protocol, 0202 electrical engineering, electronic engineering, information engineering, Medicine, 030212 general & internal medicine, Social determinants of health, Patient participation, Patient Activation Measure, mobile phone, consumer health informatics, business.industry, General Medicine, physician-patient relations, Clinical trial, Informatics, Family medicine, randomized controlled trial, patient participation, business, Consumer health informatics, vulnerable populations
Abstract: Background Patient-centered care is respectful of and responsive to individual patient preferences, needs, and values. To provide patient-centered care, clinicians need to know and incorporate patients’ context into their communication and care with patients. Patient contextual data (PCD) encompass social determinants of health and patients’ needs, values, goals, and preferences relevant to their care. PCD can be challenging to collect as a routine component of the time-limited primary care visit. Objective This study aims to determine if patient-provider communication and patient activation are different for patient users and patient nonusers of an electronic health record (EHR)–integrated PCD tool and assess if the impact of using PCD on patient-provider communication and patient activation differs for Black and White patients. Methods We describe a randomized controlled trial of a prospective cohort of non-Hispanic White and Black patients who receive primary care services at a midwestern academic health care system in the United States. We will evaluate whether providing PCD through a consumer informatics tool enhances patient-provider communication, as measured by the Communication Assessment Tool, and we will evaluate patient activation, as measured by the Patient Activation Measure for PCD tool users and nonusers. Furthermore, owing to racial disparities in care and communication, we seek to determine if the adoption and use of the tool might narrow the differences between patient groups. Results The trial was funded in November 2017 and received local ethics review approval in February 2019. The study began recruitment in April 2019 and enrollment concluded in October 2019 with 301 participants. The analysis was completed in May 2020, and trial results are expected to be published in winter 2020. Conclusions Recently, there has been increased attention to the role of health information technology tools to enable patients to collaborate with providers through the sharing of PCD. The adoption of such tools may overcome the barriers of current EHRs by directly engaging patients to submit their contextual data. Effectively, these tools would support the EHR in providing a more holistic understanding of the patient. Research further supports that individuals who have robust digital engagement using consumer informatics tools have higher participation in treatment follow-up and self-care across populations. Therefore, it is critical to investigate interventions that elicit and share patients’ social risks and care preferences with the health care team as a mechanism to improve individualized care and reduce the gap in health outcomes. Trial Registration ClinicalTrials.gov NCT03766841; https://clinicaltrials.gov/ct2/show/NCT03766841 International Registered Report Identifier (IRRID) RR1-10.2196/20309
Published: 2020

24. Association of Use of Online Symptom Checkers With Patients’ Plans for Seeking Care

Author: Nicole Fergestrom, Bradley H. Crotty, Aaron N. Winn, and Melek Somai
Subjects: Male, medicine.medical_specialty, 020205 medical informatics, Information Seeking Behavior, MEDLINE, Health Informatics, 02 engineering and technology, Medical care, 03 medical and health sciences, 0302 clinical medicine, Information seeking behavior, Research Letter, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, 030212 general & internal medicine, Association (psychology), Consumer Health Information, business.industry, Extramural, Research, General Medicine, Patient Acceptance of Health Care, Triage, Health Literacy, Online Only, Family medicine, Informatics, Compulsive Behavior, Female, Patient Participation, business
Abstract: This cross-sectional study examines the association of patient use of a free online symptom checker tool with patient plans for seeking medical care.
Published: 2019

25. CARING AND AGEING RE-IMAGINED IN EUROPE (CARE CAMPUS). AN INNOVATIVE APPROACH TO TACKLE THE HEALTHCARE CHALLENGES OF AGEING POPULATION IN EUROPE

Author: Melek Somai and Lefkos Middleton
Published: 2018

26. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

Author: Nancy, Fullman, Jamal, Yearwood, Solomon, M, Abay, Cristiana, Abbafati, Foad, Abd-Allah, Jemal, Abdela, Ahmed, Abdelalim, Zegeye, Abebe, Teshome, Abuka, Abebo, Victor, Aboyans, Haftom, Niguse, Abraha, Daisy, M, X, Abreu, Laith, Abu-Raddad, J, Akilew, Awoke, Adane, Rufus, Adesoji, Adedoyin, Olatunji, Adetokunboh, Tara, Ballav, Adhikari, Mohsen, Afarideh, Ashkan, Afshin, Gina, Agarwal, Dominic, Agius, Anurag, Agrawal, Sutapa, Agrawal, Aliasghar, Ahmad, Kiadaliri, Miloud, Taki, Eddine, Aichour, Mohammed, Akibu, Rufus, Olusola, Akinyemi, Tomi, Akinyemiju, F, Nadia, Akseer, Faris, Hasan, Lami, Al, Fares, Alahdab, Ziyad, Al-Aly, Khurshid, Alam, Tahiya, Alam, Deena, Alasfoor, Mohammed, I, Albittar, Kefyalew, Addis, Alene, Ayman, Al-Eyadhy, Syed, Danish, Ali, Mehran, Alijanzadeh, Syed, M, Aljunid, Ala’A, Alkerwi, François, Alla, Peter, Allebeck, Christine, Allen, Mahmoud, A, Alomari, Rajaa, Al-Raddadi, Ubai, Alsharif, Khalid, A, Altirkawi, Nelson, Alvis-Guzman, Azmeraw, T, Amare, Kebede, Amenu, Walid, Ammar, Yaw, Ampem, Amoako, Nahla, Anber, Catalina, Liliana, Andrei, Sofia, Androudi, Carl, Abelardo, Antonio, T, Valdelaine, E, Araújo, M, Olatunde, Aremu, Johan, Ärnlöv, Artaman, Al, Krishna, Kumar, Aryal, Hamid, Asayesh, Ephrem, Tsegay, Asfaw, Solomon, Weldegebreal, Asgedom, Rana, Jawad, Asghar, Mengistu, Mitiku, Ashebir, Netsanet, Abera, Asseffa, Tesfay, Mehari, Atey, Sachin, R, Atre, Madhu, Atteraya, S, Leticia, Avila-Burgos, Euripide, Frinel, Arthur, G, Avokpaho, Ashish, Awasthi, Beatriz, Paulina, Ayala, Quintanilla, Animut, Alebel, Ayalew, Henok, Tadesse, Ayele, Rakesh, Ayer, Tambe, Betrand, Ayuk, Peter, Azzopardi, Natasha, Azzopardi-Muscat, Tesleem, Kayode, Babalola, Hamid, Badali, Alaa, Badawi, Maciej, Banach, Amitava, Banerjee, Amrit, Banstola, Ryan, M, Barber, Miguel, Barboza, A, Suzanne, L, Barker-Collo, Till, Bärnighausen, Simon, Barquera, Lope, H, Barrero, Quique, Bassat, Sanjay, Basu, Bernhard, T, Baune, Shahrzad, Bazargan-Hejazi, Neeraj, Bedi, Ettore, Beghi, Masoud, Behzadifar, Meysam, Behzadifar, Bayu, Begashaw, Bekele, Abate, Bekele, Belachew, Saba, Abraham, Belay, Yihalem, Abebe, Belay, Michelle, L, Bell, Aminu, Bello, K, Derrick, A, Bennett, James, Bennett, R, Isabela, M, Bensenor, Derbew, Fikadu, Berhe, Eduardo, Bernabé, Robert, Steven, Bernstein, Mircea, Beuran, Ashish, Bhalla, Paurvi, Bhatt, Soumyadeep, Bhaumik, Zulfiqar, A, Bhutta, Belete, Biadgo, Ali, Bijani, Boris, Bikbov, Charles, Birungi, Stan, Biryukov, Hailemichael, Bizuneh, Ian, W, Bolliger, Kaylin, Bolt, Ibrahim, R, Bou-Orm, Kayvan, Bozorgmehr, Oliver, Jerome, Brady, Alexandra, Brazinova, Nicholas, J, Breitborde, K, Hermann, Brenner, Gabrielle, Britton, Traolach, S, Brugha, Zahid, Butt, A, Lucero, Cahuana-Hurtado, Ismael, Ricardo, Campos-Nonato, Julio, Cesar, Campuzano, Josip, Car, Mate, Car, Rosario, Cárdenas, Juan, Jesus, Carrero, Felix, Carvalho, Carlos, A, Castañeda-Orjuela, Jacqueline, Castillo, Rivas, Ferrán, Catalá-López, Kelly, Cercy, Julian, Chalek, Hsing-Yi, Chang, Jung-Chen, Chang, Aparajita, Chattopadhyay, Pankaj, Chaturvedi, Peggy, Pei-Chia, Chiang, Vesper, Hichilombwe, Chisumpa, Jee-Young, J, Choi, Hanne, Christensen, Devasahayam, Jesudas, Christopher, Sheng-Chia, Chung, Liliana, G, Ciobanu, Cirillo, Massimo, Danny, Colombara, Sara, Conti, Cyrus, Cooper, Leslie, Cornaby, Paolo, Angelo, Cortesi, Monica, Cortinovis, Alexandre, Costa, Pereira, Ewerton, Cousin, Michael, H, Criqui, Elizabeth, Cromwell, A, Christopher, Stephen, Crowe, John, Crump, A, Alemneh, Kabeta, Daba, Berihun, Assefa, Dachew, Abel, Fekadu, Dadi, Lalit, Dandona, Rakhi, Dandona, Paul, I, Dargan, Ahmad, Daryani, Maryam, Daryani, Jai, Das, Siddharth, Kumar, Das, José, Das, Neves, Nicole, Davis, Weaver, Kairat, Davletov, Barbora, De, Courten, Diego, Leo, De, Jan-Walter, De, Neve, Robert, Dellavalle, P, Gebre, Demoz, Kebede, Deribe, Don, C, Des, Jarlais, Subhojit, Dey, Samath, D, Dharmaratne, Meghnath, Dhimal, Shirin, Djalalinia, David, Teye, Doku, Kate, Dolan, Ray, E, Dorsey, Kadine, Priscila, Bender, Dos, Santos, Kerrie, E, Doyle, Tim, Driscoll, R, Manisha, Dubey, Eleonora, Dubljanin, Bruce, Bartholow, Duncan, Michelle, Echko, Dumessa, Edessa, David, Edvardsson, Joshua, R, Ehrlich, Erika, Eldrenkamp, Ziad, El-Khatib, Matthias, Endres, Aman, Yesuf, Endries, Babak, Eshrati, Sharareh, Eskandarieh, Alireza, Esteghamati, Mahdi, Fakhar, Tamer, Farag, Mahbobeh, Faramarzi, Emerito, Jose, Aquino, Faraon, André, Faro, Farshad, Farzadfar, Adesegun, Fatusi, Mir, Sohail, Fazeli, Valery, Feigin, L, Andrea, B, Feigl, Netsanet, Fentahun, Seyed-Mohammad, Fereshtehnejad, Eduarda, Fernandes, João, C, Fernandes, Daniel, Obadare, Fijabi, Irina, Filip, Florian, Fischer, Christina, Fitzmaurice, Abraham, D, Flaxman, Luisa, Sorio, Flor, Nataliya, Foigt, Kyle, J, Foreman, Joseph, Frostad, J, Thomas, Fürst, Neal, D, Futran, Emmanuela, Gakidou, Silvano, Gallus, Ketevan, Gambashidze, Amiran, Gamkrelidze, Morsaleh, Ganji, Abadi, Kahsu, Gebre, Tsegaye, Tewelde, Gebrehiwot, Amanuel, Tesfay, Gebremedhin, Yalemzewod, Assefa, Gelaw, Johanna, M, Geleijnse, Demeke, Geremew, Peter, W, Gething, Reza, Ghadimi, Khalil, Ghasemi, Falavarjani, Maryam, Ghasemi-Kasman, Paramjit, Singh, Gill, Ababi, Zergaw, Giref, Maurice, Giroud, Melkamu, Dedefo, Gishu, Giorgia, Giussani, William, W, Godwin, Srinivas, Goli, Hector, Gomez-Dantes, Philimon, N, Gona, Amador, Goodridge, Sameer, Vali, Gopalani, Yevgeniy, Goryakin, Alessandra, Carvalho, Goulart, Ayman, Grada, Max, Griswold, Giuseppe, Grosso, Harish, Chander, Gugnani, Yuming, Guo, Rahul, Gupta, Rajeev, Gupta, Tanush, Gupta, Tarun, Gupta, Vipin, Gupta, Juanita, A, Haagsma, Vladimir, Hachinski, Nima, Hafezi-Nejad, Gessessew, Bugssa, Hailu, Randah, Ribhi, Hamadeh, Samer, Hamidi, Graeme, J, Hankey, Hilda, Harb, L, Heather, C, Harewood, Sivadasanpillai, Harikrishnan, Josep, Maria, Haro, Hamid, Yimam, Hassen, Rasmus, Havmoeller, Caitlin, Hawley, Simon, I, Hay, Jiawei, He, Stephen, J, Hearps, C, Mohamed, I, Hegazy, Behzad, Heibati, Mohsen, Heidari, Delia, Hendrie, Nathaniel, J, Henry, Victor, Hugo, Herrera, Ballesteros, Claudiu, Herteliu, Desalegn, Tsegaw, Hibstu, Molla, Kahssay, Hiluf, Hans, W, Hoek, Enayatollah, Homaie, Rad, Nobuyuki, Horita, Dean, H, Hosgood, Mostafa, Hosseini, Seyed, Reza, Hosseini, Mihaela, Hostiuc, Sorin, Hostiuc, Damian, G, Hoy, Mohamed, Hsairi, Aung, Soe, Htet, Guoqing, Hu, John, J, Huang, Kim, Moesgaard, Iburg, Fachmi, Idris, Ehimario, Uche, Igumbor, Chad, Ikeda, Bogdan, Vasile, Ileanu, Olayinka, Ilesanmi, S, Kaire, Innos, Seyed, Sina, Naghibi, Irvani, Caleb, M, Irvine, S, Farhad, Islami, Troy, A, Jacobs, Kathryn, Jacobsen, H, Nader, Jahanmehr, Rajesh, Jain, Sudhir, Kumar, Jain, Mihajlo, Jakovljevic, M, Moti, Tolera, Jalu, Amr, Jamal, A, Mehdi, Javanbakht, Achala, Upendra, Jayatilleke, Panniyammakal, Jeemon, Ravi, Prakash, Jha, Vivekanand, Jha, Jacek, Józwiak, Oommen, John, Sarah, Charlotte, Johnson, Jost, Jonas, B, Vasna, Joshua, Mikk, Jürisson, Zubair, Kabir, Rajendra, Kadel, Amaha, Kahsay, Rizwan, Kalani, Chittaranjan, Kar, Marina, Karanikolos, André, Karch, Corine, Kakizi, Karema, Seyed, Karimi, M, Amir, Kasaeian, Dessalegn, Haile, Kassa, Getachew, Mullu, Kassa, Tesfaye, Dessale, Kassa, Nicholas, Kassebaum, J, Srinivasa, Vittal, Katikireddi, Anil, Kaul, Norito, Kawakami, Konstantin, Kazanjan, Seifu, Kebede, Peter, Njenga, Keiyoro, Grant, Rodgers, Kemp, Andre, Pascal, Kengne, Maia, Kereselidze, Ezra, Belay, Ketema, Yousef, Saleh, Khader, Morteza, Abdullatif, Khafaie, Alireza, Khajavi, Ibrahim, A, Khalil, Ejaz, Ahmad, Khan, Gulfaraz, Khan, Nuruzzaman, Md, Khan, Muhammad, Ali, Khan, Mukti, Nath, Khanal, Young-Ho, Khang, Mona, M, Khater, Abdullah, Tawfih, Abdullah, Khoja, Ardeshir, Khosravi, Jagdish, Khubchandani, Getiye, Dejenu, Kibret, Daniel, Ngari, Kiirithio, Daniel, Kim, Yun, Jin, Kim, Ruth, Kimokoti, W, Yohannes, Kinfu, Sanjay, Kinra, Adnan, Kisa, Niranjan, Kissoon, Sonali, Kochhar, Yoshihiro, Kokubo, Jacek, A, Kopec, Soewarta, Kosen, Parvaiz, A, Koul, Koyanagi, Ai, Michael, Kravchenko, Kewal, Krishan, Kristopher, J, Krohn, Barthelemy, Kuate, Defo, Anil, G, Kumar, Pushpendra, Kumar, Michael, Kutz, Igor, Kuzin, Hmwe, H, Kyu, Deepesh, Pravinkumar, Lad, Alessandra, Lafranconi, Dharmesh, Kumar, Lal, Ratilal, Lalloo, Hilton, Lam, Qing, Lan, Justin, J, Lang, Van, Lansingh, C, Sonia, Lansky, Anders, Larsson, Arman, Latifi, Jeffrey, Victor, Lazarus, Janet, Leasher, L, Paul, H, Lee, Yirga, Legesse, James, Leigh, Cheru, Tesema, Leshargie, Samson, Leta, Janni, Leung, Ricky, Leung, Miriam, Levi, Yongmei, Li, Juan, Liang, Misgan, Legesse, Liben, Lee-Ling, Lim, Stephen, S, Lim, Margaret, Lind, Shai, Linn, Stefan, Listl, Patrick, Y, Liu, Shiwei, Liu, Rakesh, Lodha, Alan, D, Lopez, Scott, Lorch, A, Stefan, Lorkowski, Paulo, A, Lotufo, Timothy, C, D, Lucas, Raimundas, Lunevicius, Grégoire, Lurton, Ronan, A, Lyons, Fadi, Maalouf, Erlyn, Rachelle, King, Macarayan, Mark, T, Mackay, Emilie, Maddison, R, Fabiana, Madotto, Hassan, Magdy, Abd, El, Razek, Mohammed, Magdy, Abd, Razek, El, Marek, Majdan, Reza, Majdzadeh, Azeem, Majeed, Reza, Malekzadeh, Rajesh, Malhotra, Deborah, Carvalho, Malta, Abdullah, Mamun, A, Helena, Manguerra, Treh, Manhertz, Mohammad, Ali, Mansournia, Lorenzo, Mantovani, G, Tsegahun, Manyazewal, Chabila, C, Mapoma, Christopher, Margono, Jose, Martinez-Raga, Sheila, Cristina, Ouriques, Martins, Francisco, Rogerlândio, Martins-Melo, Ira, Martopullo, Winfried, März, Benjamin, Ballard, Massenburg, Manu, Raj, Mathur, Pallab, K, Maulik, Mohsen, Mazidi, Colm, Mcalinden, Mcgrath, Martin, Mckee, Suresh, Mehata, Ravi, Mehrotra, Kala, M, Mehta, Varshil, Mehta, Toni, Meier, Fabiola, Mejia-Rodriguez, Kidanu, Gebremariam, Meles, Mulugeta, Melku, Peter, Memiah, Ziad, A, Memish, Walter, Mendoza, Degu, Abate, Mengiste, Desalegn, Tadese, Mengistu, Bereket, Gebremichael, Menota, George, Mensah, A, Atte, Meretoja, Tuomo, J, Meretoja, Haftay, Berhane, Mezgebe, Tomasz, Miazgowski, Renata, Micha, Robert, Milam, Anoushka, Millear, Ted, R, Miller, Mini, Gk, Shawn, Minnig, Andreea, Mirica, Erkin, M, Mirrakhimov, Awoke, Misganaw, Philip, B, Mitchell, Fitsum, Weldegebreal, Mlashu, Babak, Moazen, Karzan, Abdulmuhsin, Mohammad, Roghayeh, Mohammadibakhsh, Ebrahim, Mohammed, Mohammed, A, Mohammed, Shafiu, Mohammed, Ali, H, Mokdad, Glen, Liddell, D, Mola, Mariam, Molokhia, Fatemeh, Momeniha, Lorenzo, Monasta, Julio, Cesar, Montañez, Hernandez, Mahmood, Moosazadeh, Maziar, Moradi-Lakeh, Paula, Moraga, Lidia, Morawska, Ilais, Moreno, Velasquez, Rintaro, Mori, Shane, D, Morrison, Mark, Moses, Seyyed, Meysam, Mousavi, Ulrich, Mueller, O, Manoj, Murhekar, Gudlavalleti, Venkata, Satyanarayana, Murthy, Srinivas, Murthy, Jonah, Musa, Kamarul, Imran, Musa, Ghulam, Mustafa, Saravanan, Muthupandian, Chie, Nagata, Gabriele, Nagel, Mohsen, Naghavi, Aliya, Naheed, Gurudatta, A, Naik, Nitish, Naik, Farid, Najafi, Luigi, Naldi, Vinay, Nangia, Jobert, Richie, Njingang, Nansseu, K, M, Venkat, Narayan, Bruno, Ramos, Nascimento, Ionut, Negoi, 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Pedroso, Sandra, P, Pedroza, David, Pereira, M, Norberto, Perico, Hannah, Peterson, Max, Petzold, Niloofar, Peykari, Michael, Robert, Phillips, Frédéric, Piel, B, David, M, Pigott, Julian, David, Pillay, Michael, A, Piradov, Suzanne, Polinder, Constance, D, Pond, Maarten, Postma, J, Farshad, Pourmalek, Swayam, Prakash, Prakash, V, Narayan, Prasad, Noela, Marie, Prasad, Caroline, Purcell, Mostafa, Qorbani, Hedley, Knewjen, Quintana, Amir, Radfar, Anwar, Rafay, Alireza, Rafiei, Kazem, Rahimi, Afarin, Rahimi-Movaghar, Vafa, Rahimi-Movaghar, Mahfuzar, Rahman, Muhammad, Aziz, Rahman, Sajjad, Rahman, Ur, Rajesh, Kumar, Rai, Sree, Bhushan, Raju, Usha, Ram, Saleem, M, Rana, Zane, Rankin, Davide, Rasella, David, Laith, Rawaf, Salman, Rawaf, Sarah, E, Ray, Christian, Aspacia, Razo-García, Priscilla, Reddy, Robert, C, Reiner, Cesar, Reis, Marissa, B, Reitsma, Giuseppe, Remuzzi, Andre, M, Renzaho, N, Serge, Resnikoff, Satar, Rezaei, Mohammad, Sadegh, Rezai, Antonio, Ribeiro, L, Maria, Jesus, Rios, 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Arthur, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayalew, Animut Alebel, Ayele, Henok Tadesse, Ayer, Rakesh, Ayuk, Tambe Betrand, Azzopardi, Peter, Azzopardi-Muscat, Natasha, Babalola, Tesleem Kayode, Badali, Hamid, Badawi, Alaa, Banach, Maciej, Banerjee, Amitava, Banstola, Amrit, Barber, Ryan M., Barboza, Miguel A., Barker-Collo, Suzanne L., Bärnighausen, Till, Barquera, Simon, Barrero, Lope H., Bassat, Quique, Basu, Sanjay, Baune, Bernhard T., Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L., Bello, Aminu K., Bennett, Derrick A., Bennett, James R., Bensenor, Isabela M., Berhe, Derbew Fikadu, Bernabé, Eduardo, Bernstein, Robert Steven, Beuran, Mircea, Bhalla, Ashish, Bhatt, Paurvi, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A., Biadgo, Belete, Bijani, Ali, Bikbov, Bori, Birungi, Charle, Biryukov, Stan, Bizuneh, Hailemichael, Bolliger, Ian W., Bolt, Kaylin, Bou-Orm, Ibrahim R., Bozorgmehr, Kayvan, Brady, Oliver Jerome, Brazinova, Alexandra, Breitborde, Nicholas J.K., Brenner, Hermann, Britton, Gabrielle, Brugha, Traolach S., Butt, Zahid A., Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael Ricardo, Campuzano, Julio Cesar, Car, Josip, Car, Mate, Cárdenas, Rosario, Carrero, Juan Jesu, Carvalho, Felix, Castañeda-Orjuela, Carlos A., Castillo Rivas, Jacqueline, Catalá-López, Ferrán, Cercy, Kelly, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chisumpa, Vesper Hichilombwe, Choi, Jee-Young J., Christensen, Hanne, Christopher, Devasahayam Jesuda, Chung, Sheng-Chia, Ciobanu, Liliana G., Cirillo, Massimo, Colombara, Danny, Conti, Sara, Cooper, Cyru, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa Pereira, Alexandre, Cousin, Ewerton, Criqui, Michael H., Cromwell, Elizabeth A., Crowe, Christopher Stephen, Crump, John A., Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dadi, Abel Fekadu, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I., Daryani, Ahmad, Daryani, Maryam, Das, Jai, Das, Siddharth Kumar, Das Neves, José, Davis Weaver, Nicole, Davletov, Kairat, De Courten, Barbora, De Leo, Diego, De Neve, Jan-Walter, Dellavalle, Robert P., Demoz, Gebre, Deribe, Kebede, Des Jarlais, Don C., Dey, Subhojit, Dharmaratne, Samath D., Dhimal, Meghnath, Djalalinia, Shirin, Doku, David Teye, Dolan, Kate, Dorsey, E. Ray, Dos Santos, Kadine Priscila Bender, Doyle, Kerrie E., Driscoll, Tim R., Dubey, Manisha, Dubljanin, Eleonora, Duncan, Bruce Bartholow, Echko, Michelle, Edessa, Dumessa, Edvardsson, David, Ehrlich, Joshua R., Eldrenkamp, Erika, El-Khatib, Ziad, Endres, Matthia, Endries, Aman Yesuf, Eshrati, Babak, Eskandarieh, Sharareh, Esteghamati, Alireza, Fakhar, Mahdi, Farag, Tamer, Faramarzi, Mahbobeh, Faraon, Emerito Jose Aquino, Faro, André, Farzadfar, Farshad, Fatusi, Adesegun, Fazeli, Mir Sohail, Feigin, Valery L., Feigl, Andrea B., Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Fernandes, João C., Fijabi, Daniel Obadare, Filip, Irina, Fischer, Florian, Fitzmaurice, Christina, Flaxman, Abraham D., Flor, Luisa Sorio, Foigt, Nataliya, Foreman, Kyle J., Frostad, Joseph J., Fürst, Thoma, Futran, Neal D., Gakidou, Emmanuela, Gallus, Silvano, Gambashidze, Ketevan, Gamkrelidze, Amiran, Ganji, Morsaleh, Gebre, Abadi Kahsu, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M., Geremew, Demeke, Gething, Peter W., Ghadimi, Reza, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Gill, Paramjit Singh, Giref, Ababi Zergaw, Giroud, Maurice, Gishu, Melkamu Dedefo, Giussani, Giorgia, Godwin, William W., Goli, Sriniva, Gomez-Dantes, Hector, Gona, Philimon N., Goodridge, Amador, Gopalani, Sameer Vali, Goryakin, Yevgeniy, Goulart, Alessandra Carvalho, Grada, Ayman, Griswold, Max, Grosso, Giuseppe, Gugnani, Harish Chander, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gupta, Tarun, Gupta, Vipin, Haagsma, Juanita A., Hachinski, Vladimir, Hafezi-Nejad, Nima, Hailu, Gessessew Bugssa, Hamadeh, Randah Ribhi, Hamidi, Samer, Hankey, Graeme J., Harb, Hilda L., Harewood, Heather C., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassen, Hamid Yimam, Havmoeller, Rasmu, Hawley, Caitlin, Hay, Simon I., He, Jiawei, Hearps, Stephen J.C., Hegazy, Mohamed I., Heibati, Behzad, Heidari, Mohsen, Hendrie, Delia, Henry, Nathaniel J., Herrera Ballesteros, Victor Hugo, Herteliu, Claudiu, Hibstu, Desalegn Tsegaw, Hiluf, Molla Kahssay, Hoek, Hans W., Homaie Rad, Enayatollah, Horita, Nobuyuki, Hosgood, H. Dean, Hosseini, Mostafa, Hosseini, Seyed Reza, Hostiuc, Mihaela, Hostiuc, Sorin, Hoy, Damian G., Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J., Iburg, Kim Moesgaard, Idris, Fachmi, Igumbor, Ehimario Uche, Ikeda, Chad, Ileanu, Bogdan Vasile, Ilesanmi, Olayinka S., Innos, Kaire, Irvani, Seyed Sina Naghibi, Irvine, Caleb M.S., Islami, Farhad, Jacobs, Troy A., Jacobsen, Kathryn H., Jahanmehr, Nader, Jain, Rajesh, Jain, Sudhir Kumar, Jakovljevic, Mihajlo M., Jalu, Moti Tolera, Jamal, Amr A., Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Józwiak, Jacek, John, Oommen, Johnson, Sarah Charlotte, Jonas, Jost B., Joshua, Vasna, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kar, Chittaranjan, Karanikolos, Marina, Karch, André, Karema, Corine Kakizi, Karimi, Seyed M., Kasaeian, Amir, Kassa, Dessalegn Haile, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazanjan, Konstantin, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodger, Kengne, Andre Pascal, Kereselidze, Maia, Ketema, Ezra Belay, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalil, Ibrahim A., Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Md Nuruzzaman, Khan, Muhammad Ali, Khanal, Mukti Nath, Khang, Young-Ho, Khater, Mona M., Khoja, Abdullah Tawfih Abdullah, Khosravi, Ardeshir, Khubchandani, Jagdish, Kibret, Getiye Dejenu, Kiirithio, Daniel Ngari, Kim, Daniel, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohanne, Kinra, Sanjay, Kisa, Adnan, Kissoon, Niranjan, Kochhar, Sonali, Kokubo, Yoshihiro, Kopec, Jacek A., Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael, Krishan, Kewal, Krohn, Kristopher J., Kuate Defo, Barthelemy, Kumar, G. Anil, Kumar, Pushpendra, Kutz, Michael, Kuzin, Igor, Kyu, Hmwe H., Lad, Deepesh Pravinkumar, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lan, Qing, Lang, Justin J., Lansingh, Van C., Lansky, Sonia, Larsson, Ander, Latifi, Arman, Lazarus, Jeffrey Victor, Leasher, Janet L., Lee, Paul H., Legesse, Yirga, Leigh, Jame, Leshargie, Cheru Tesema, Leta, Samson, Leung, Janni, Leung, Ricky, Levi, Miriam, Li, Yongmei, Liang, Juan, Liben, Misgan Legesse, Lim, Lee-Ling, Lim, Stephen S., Lind, Margaret, Linn, Shai, Listl, Stefan, Liu, Patrick Y., Liu, Shiwei, Lodha, Rakesh, Lopez, Alan D., Lorch, Scott A., Lorkowski, Stefan, Lotufo, Paulo A., Lucas, Timothy C.D., Lunevicius, Raimunda, Lurton, Grégoire, Lyons, Ronan A., Maalouf, Fadi, Macarayan, Erlyn Rachelle King, Mackay, Mark T., Maddison, Emilie R., Madotto, Fabiana, Magdy Abd El Razek, Hassan, Magdy Abd El Razek, Mohammed, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malhotra, Rajesh, Malta, Deborah Carvalho, Mamun, Abdullah A., Manguerra, Helena, Manhertz, Treh, Mansournia, Mohammad Ali, Mantovani, Lorenzo G., Manyazewal, Tsegahun, Mapoma, Chabila C., Margono, Christopher, Martinez-Raga, Jose, Martins, Sheila Cristina Ourique, Martins-Melo, Francisco Rogerlândio, Martopullo, Ira, März, Winfried, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K., Mazidi, Mohsen, McAlinden, Colm, McGrath, John J., McKee, Martin, Mehata, Suresh, Mehrotra, Ravi, Mehta, Kala M., Mehta, Varshil, Meier, Toni, Mejia-Rodriguez, Fabiola, Meles, Kidanu Gebremariam, Melku, Mulugeta, Memiah, Peter, Memish, Ziad A., Mendoza, Walter, Mengiste, Degu Abate, Mengistu, Desalegn Tadese, Menota, Bereket Gebremichael, Mensah, George A., Meretoja, Atte, Meretoja, Tuomo J., Mezgebe, Haftay Berhane, Miazgowski, Tomasz, Micha, Renata, Milam, Robert, Millear, Anoushka, Miller, Ted R., Mini, G.K., Minnig, Shawn, Mirica, Andreea, Mirrakhimov, Erkin M., Misganaw, Awoke, Mitchell, Philip B., Mlashu, Fitsum Weldegebreal, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammadibakhsh, Roghayeh, Mohammed, Ebrahim, Mohammed, Mohammed A., Mohammed, Shafiu, Mokdad, Ali H., Mola, Glen Liddell D., Molokhia, Mariam, Momeniha, Fatemeh, Monasta, Lorenzo, Montañez Hernandez, Julio Cesar, Moosazadeh, Mahmood, Moradi-Lakeh, Maziar, Moraga, Paula, Morawska, Lidia, Moreno Velasquez, Ilai, Mori, Rintaro, Morrison, Shane D., Moses, Mark, Mousavi, Seyyed Meysam, Mueller, Ulrich O., Murhekar, Manoj, Murthy, Gudlavalleti Venkata Satyanarayana, Murthy, Sriniva, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nagata, Chie, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Naik, Gurudatta A., Naik, Nitish, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie Njingang, Narayan, K.M. Venkat, Nascimento, Bruno Ramo, Negoi, Ionut, Negoi, Ruxandra Irina, Newton, Charles R., Ngunjiri, Josephine Wanjiku, Nguyen, Grant, Nguyen, Long, Nguyen, Trang Huyen, Nichols, Emma, Ningrum, Dina Nur Anggraini, Nolte, Ellen, Nong, Vuong Minh, Norheim, Ole F., Norrving, Bo, Noubiap, Jean Jacques N., Nyandwi, Alypio, Obermeyer, Carla Makhlouf, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew Toyin, Olagunju, Tinuke Oluwasefunmi, Olivares, Pedro R., De Oliveira, Patricia Pereira Vasconcelo, Olsen, Helen E., Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Kanyin, Opio, John Nelson, Oren, Eyal, Ortega-Altamirano, Doris V., Ortiz, Alberto, Ozdemir, Raziye, Pa, Mahesh, Pain, Amanda W., Palone, Marcos Roberto Tovani, Pana, Adrian, Panda-Jonas, Songhomitra, Pandian, Jeyaraj D., Park, Eun-Kee, Parsian, Hadi, Patel, Teja, Pati, Sanghamitra, Patil, Snehal T., Patle, Ajay, Patton, George C., Paturi, Vishnupriya Rao, Paudel, Deepak, De Moares Pedroso, Marcel, Pedroza, Sandra P., Pereira, David M., Perico, Norberto, Peterson, Hannah, Petzold, Max, Peykari, Niloofar, Phillips, Michael Robert, Piel, Frédéric B., Pigott, David M., Pillay, Julian David, Piradov, Michael A., Polinder, Suzanne, Pond, Constance D., Postma, Maarten J., Pourmalek, Farshad, Prakash, Swayam, Prakash, V., Prasad, Narayan, Prasad, Noela Marie, Purcell, Caroline, Qorbani, Mostafa, Quintana, Hedley Knewjen, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahimi, Kazem, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Raju, Sree Bhushan, Ram, Usha, Rana, Saleem M., Rankin, Zane, Rasella, Davide, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E., Razo-García, Christian Aspacia, Reddy, Priscilla, Reiner, Robert C., Reis, Cesar, Reitsma, Marissa B., Remuzzi, Giuseppe, Renzaho, Andre M.N., Resnikoff, Serge, Rezaei, Satar, Rezai, Mohammad Sadegh, Ribeiro, Antonio L., Rios Blancas, 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Sd, Aljunid, Sm, Alomari, Ma, Altirkawi, Ka, Amare, At, Amoako, Ya, Andrei, Cl, Antonio, Cat, Araújo, Vem, Aryal, Kk, Asfaw, Et, Asgedom, Sw, Asghar, Rj, Ashebir, Mm, Asseffa, Na, Atey, Tm, Atre, Sr, Avokpaho, Efga, Ayala Quintanilla, Bp, Ayalew, Aa, Ayele, Ht, Ayuk, Tb, Babalola, Tk, Barber, Rm, Barboza, Ma, Barker-Collo, Sl, Barrero, Lh, Baune, Bt, Bekele, Bb, Belachew, Ab, Belay, Sa, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bennett, Jr, Bensenor, Im, Berhe, Df, Bhutta, Za, Bolliger, Iw, Bou-Orm, Ir, Brady, Oj, Breitborde, Njk, Butt, Za, Campos-Nonato, Ir, Campuzano, Jc, Carrero, Jj, Castañeda-Orjuela, Ca, Chang, Hy, Chang, Jc, Chiang, Pp, Chisumpa, Vh, Choi, Jj, Christopher, Dj, Chung, Sc, Ciobanu, Lg, Cortesi, Pa, Criqui, Mh, Cromwell, Ea, Crump, Ja, Daba, Ak, Dachew, Ba, Dadi, Af, Dargan, Pi, Das, Sk, De Neve, Jw, Dellavalle, Rp, Des Jarlais, Dc, Dharmaratne, Sd, Doku, Dt, Dorsey, Er, Dos Santos, Kpb, Doyle, Ke, Driscoll, Tr, Duncan, Bb, Ehrlich, Jr, El-Khatib, Zz, Endries, Ay, Faraon, Eja, Feigin, Vl, Feigl, Ab, Fereshtehnejad, Sm, Fernandes, Jc, Fijabi, Do, Flaxman, Ad, Foreman, Kj, Frostad, Jj, Futran, Nd, Gebre, Ak, Gebrehiwot, Tt, Gebremedhin, At, Gelaw, Ya, Geleijnse, Jm, Gething, Pw, Giref, Az, Gishu, Md, Godwin, Ww, Gona, Pn, Gopalani, Sv, Goulart, Ac, Gugnani, Hc, Haagsma, Ja, Hailu, Gb, Hamadeh, Rr, Hankey, Gj, Harb, Hl, Harewood, Hc, Haro, Jm, Hassen, Hy, Hay, Si, Hearps, Sjc, Hegazy, Mi, Henry, Nj, Herrera Ballesteros, Vh, Hibstu, Dt, Hiluf, Mk, Hoek, Hw, Hosgood, Hd, Hosseini, Sr, Hoy, Dg, Huang, Jj, Iburg, Km, Igumbor, Eu, Ileanu, Bv, Irvani, Ssn, Irvine, Cm, Jacobs, Ta, Jacobsen, Kh, Jain, Sk, Jakovljevic, Mb, Jalu, Mt, Jamal, Aa, Jayatilleke, Au, Jha, Rp, Johnson, Sc, Jonas, Jb, Karema, Ck, Karimi, Sm, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Ketema, Eb, Khafaie, Ma, Khalil, Ia, Khan, Ea, Khan, Mn, Khan, Ma, Khanal, Mn, Khang, Yh, Khater, Mm, Khoja, Ata, Kibret, Gd, Kiirithio, Dn, Kim, Yj, Kimokoti, Rw, Kopec, Ja, Koul, Pa, Krohn, Kj, Kumar, Ga, Kyu, Hh, Lad, Dp, Lal, Dk, Lang, Jj, Lansingh, Vc, Lazarus, Jv, Leasher, Jl, Lee, Ph, Leshargie, Ct, Liben, Ml, Lim, Ll, Lopez, Ad, Lorch, Sa, Lotufo, Pa, Lucas, Tcd, Lyons, Ra, Macarayan, Erk, Mackay, Mt, Maddison, Er, Malta, Dc, Mamun, Aa, Mansournia, Ma, Mantovani, Lg, Mapoma, Cc, Martins, Sco, Martins-Melo, Fr, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mehta, Km, Meles, Kg, Memish, Za, Mengiste, Da, Mengistu, Dt, Menota, Bg, Mensah, Ga, Meretoja, Tj, Mezgebe, Hb, Miller, Tr, Mini, Gk, Mirrakhimov, Em, Mitchell, Pb, Mlashu, Fw, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Mola, Gl, Montañez Hernandez, Jc, Morrison, Sd, Mousavi, Sm, Mueller, Uo, Murthy, Gv, Musa, Ki, Naik, Ga, Nansseu, Jrn, Narayan, Kv, Nascimento, Br, Negoi, Ri, Newton, Cr, Ngunjiri, Jw, Nguyen, Th, Ningrum, Dna, Nong, Vm, Norheim, Of, Noubiap, Jjn, Obermeyer, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olagunju, To, Olivares, Pr, Oliveira, Ppv, Olsen, He, Olusanya, Bo, Olusanya, Jo, Opio, Jn, Ortega-Altamirano, Dv, Pain, Aw, Palone, Mrt, Pandian, Jd, Park, Ek, Patil, St, Patton, Gc, Paturi, Vr, Pedroso, Mm, Pedroza, Sp, Pereira, Dm, Phillips, Mr, Piel, Fb, Pigott, Dm, Pillay, Jd, Piradov, Ma, Pond, Cd, Postma, Mj, Prasad, Nm, Quintana, Hk, Rahman, Ma, Rahman, Su, Rai, Rk, Raju, Sb, Rana, Sm, Rawaf, Dl, Ray, Se, Razo-García, Ca, Reiner, Rc, Reitsma, Mb, Renzaho, Amn, Ribeiro, Al, Rios Blancas, Mj, Rivera, Ja, Roth, Ga, Ruhago, Gm, Sabde, Yd, Sahraian, Ma, Saldanha, Rf, Salomon, Ja, Samy, Am, Sanabria, Jr, Sancheti, Pk, Sanchez-Niño, Md, Santric Milicevic, Mm, Sarker, Ar, Saylan, Mi, Schmidt, Mi, Schneider, Ijc, Schumacher, Ae, Schutte, Ae, Schwebel, Dc, Sepanlou, Sg, Servan-Mori, Ee, Shaikh, Ma, Shariful Islam, Sm, Shfare, Mt, Shrime, Mg, Shukla, Sr, Sigfusdottir, Id, Silberberg, Dh, Silva, Da, Silva, Jp, Silveira, Dga, Singh, Ja, Singh, Np, Sinha, Dn, Sinke, Ah, Soares Filho, Am, Sobaih, Bha, Sorensen, Rjd, Soriano, Jb, Soyiri, In, Sposato, La, Sreeramareddy, Ct, Stanaway, Jd, Stein, Dj, Stokes, Ma, Sufiyan, Mb, Suliankatchi, Ra, Sunguya, Bf, Sur, Pj, Sykes, Bl, Sylaja, Pn, Tadakamadla, Sk, Tadesse, Ah, Taffere, Gr, Tariku, At, Temsah, Mh, Tesema, Ag, Tesfaye, Dj, Thompson, Mj, To, Qg, Tran, Bx, Tran, Kb, Tripathy, Sp, Tuem, Kb, Ukwaja, Kn, Uthman, Oa, Uzochukwu, Bsc, Valdez, Pr, van Boven, Jfm, Vladimirov, Sk, Vlassov, Vv, Vollset, Se, Wallin, Mt, Walson, Jl, Wang, Yp, Wassie, Mm, Weaver, Mr, Weintraub, Rg, Weldegwergs, Kg, West, Te, White, Rg, Whiteford, Ha, Wolfe, Cd, Wondimkun, Ya, Wyper, Gma, Yan, Ll, Yimer, Nb, Yirsaw, Bd, Yoon, Sj, Younis, Mz, Zaki, Me, Zaman, Sb, Zenebe, Zm, Zimsen, Srm, Zuhlke, Lj, Murray, Cjl, and Lozano, R.
Subjects: Peformance, Coverage, Dánartíðni, Lífslíkur, Life expectancy, GBD, Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best), universal health coverage, Article, access quality health care, health care access and quality index, Nations, Healthcare Acce, Cause-specific mortality, Psychology, Healthcare Access and Quality Index, Mælitæki, States, Medicine (all), Health care, Þjóðir, Public Health, Global Health, Social Medicine and Epidemiology, Quality, Heilbrigðisþjónusta, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Sálfræði, Indicator, Amenable mortality, Transition, we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0–100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8–98·1) in Iceland, followed by 96·6 (94·9–97·9) in Norway and 96·1 (94·5–97·3) in the Netherlands, to values as low as 18·6 (13·1–24·4) in the Central African Republic, 19·0 (14·3–23·7) in Somalia, and 23·4 (20·2–26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1–93·6) in Beijing to 48·0 (43·4–53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6–68·8) in Goa to 34·0 (30·3–38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view—and subsequent provision—of quality health care for all populations, Trends, Inequalities
Abstract: Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97.1 (95% UI 95.8-98.1) in Iceland, followed by 96.6 (94.9-97.9) in Norway and 96.1 (94.5-97.3) in the Netherlands, to values as low as 18.6 (13.1-24.4) in the Central African Republic, 19.0 (14.3-23.7) in Somalia, and 23.4 (20.2-26.8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91.5 (89.1-936) in Beijing to 48.0 (43.4-53.2) in Tibet (a 43.5-point difference), while India saw a 30.8-point disparity, from 64.8 (59.6-68.8) in Goa to 34.0 (30.3-38.1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4.8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20.9-point to 17.0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17.2-point to 20.4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view and subsequent provision of quality health care for all populations., Bill & Melinda Gates Foundation. Barbora de Courten is supported by a National Heart Foundation Future Leader Fellowship (100864). Ai Koyanagi’s work is supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII —General Branch Evaluation and Promotion of Health Research—and the European Regional Development Fund (ERDF-FEDER). Alberto Ortiz was supported by Spanish Government (Instituto de Salud Carlos III RETIC REDINREN RD16/0019 FEDER funds). Ashish Awasthi acknowledges funding support from Department of Science and Technology, Government of India through INSPIRE Faculty scheme Boris Bikbov has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 703226. Boris Bikbov acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Panniyammakal Jeemon acknowledges support from the clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance (2015–20). Job F M van Boven was supported by the Department of Clinical Pharmacy & Pharmacology of the University Medical Center Groningen, University of Groningen, Netherlands. Olanrewaju Oladimeji is an African Research Fellow hosted by Human Sciences Research Council (HSRC), South Africa and he also has honorary affiliations with Walter Sisulu University (WSU), Eastern Cape, South Africa and School of Public Health, University of Namibia (UNAM), Namibia. He is indeed grateful for support from HSRC, WSU and UNAM. EUI is supported in part by the South African National Research Foundation (NRF UID: 86003). Ulrich Mueller acknowledges funding by the German National Cohort Study grant No 01ER1511/D, Gabrielle B Britton is supported by Secretaría Nacional de Ciencia, Tecnología e Innovación and Sistema Nacional de Investigación de Panamá. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Behzad Heibati would like to acknowledge Air pollution Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran. Syed Aljunid acknowledges the National University of Malaysia for providing the approval to participate in this GBD Project. Azeem Majeed and Imperial College London are grateful for support from the Northwest London National Insititute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care. Tambe Ayuk acknowledges the Institute of Medical Research and Medicinal Plant Studies for office space provided. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). João Fernandes gratefully acknowledges funding from FCT–Fundação para a Ciência e a Tecnologia (grant number UID/Multi/50016/2013). Jan-Walter De Neve was supported by the Alexander von Humboldt Foundation. Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (201900). Kazem Rahimi was supported by grants from the Oxford Martin School, the NIHR Oxford BRC and the RCUK Global Challenges Research Fund. Laith J Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008) who provided the main funding for generating the data provided to the GBD-IHME effort. Liesl Zuhlke is funded by the national research foundation of South Africa and the Medical Research Council of South Africa. Monica Cortinovis acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Chuanhua Yu acknowleges support from the National Natural Science Foundation of China (grant number 81773552 and grant number 81273179) Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Charles Shey Wiysonge’s work is supported by the South African Medical Research Council and the National Research Foundation of South Africa (grant numbers 106035 and 108571). John J McGrath is supported by grant APP1056929 from the John Cade Fellowship from the National Health and Medical Research Council and the Danish National Research Foundation (Niels Bohr Professorship). Quique Bassat is an ICREA (Catalan Institution for Research and Advanced Studies) research professor at ISGlobal. Richard G White is funded by the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/P002404/1), the Bill & Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276/OPP1135288, CORTIS: OPP1137034/OPP1151915, Vaccines: OPP1160830), and UNITAID (4214-LSHTM-Sept15; PO 8477-0-600). Rafael Tabarés-Seisdedos was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. Mihajlo Jakovljevic acknowleges contribution from the Serbian Ministry of Education Science and Technological Development of the Republic of Serbia (grant OI 175 014). Shariful Islam is funded by a Senior Fellowship from Institute for Physical Activity and Nutrition, Deakin University and received career transition grants from High Blood Pressure Research Council of Australia. Sonia Saxena is funded by various grants from the NIHR. Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral program (reference number CD15/00019 from the Instituto de Salud Carlos III (ISCIII–Spain) and the Fondos Europeo de Desarrollo Regional. Stefanos was awarded with a 6 months visiting fellowship funding at IHME from M-AES (reference no. MV16/00035 from the Instituto de Salud Carlos III). S Vittal Katikreddi was funded by a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the MRC (MC_UU_12017/13 & MC_ UU_12017/15) and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). Traolach S Brugha has received funding from NHS Digital UK to collect data used in this study. The work of Hamid Badali was financially supported by Mazandaran University of Medical Sciences, Sari, Iran. The work of Stefan Lorkowski is funded by the German Federal Ministry of Education and Research (nutriCARD, Grant agreement number 01EA1411A). Mariam Molokhia’s research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We also thank the countless individuals who have contributed to GBD 2016 in various capacities.
Published: 2018

27. CARE CAMPUS. A EUROPEAN CONSORTIUM MODEL TO SUPPORT FORMAL AND INFORMAL CAREGIVING TRAINING

Author: George W. Leeson, Arlinda Cerga, Amie N Heap, Eric Asaba, Alexandra Manson, Jan-Olov Hoog, Lena Alksten, Caroline Manus, Mario Ottiglio, Fabien Lanterri, Gideon Shimshon, Anneliese Lilienthal, Melek Somai, Barbara Gomez, Mike Hodin, Maria Hagströmer, Mark Belan, Vincente Traver, Christine Boutet-Rixe, Sarah Harper, Miia Kivipelto, Susanne Guidetti, Suzanne Pathkiller, Trevor Brocklebank, Karen Abbott, Kristal Morales Pérez, Sylvia Nissim, Lefkos T. Middleton, Charles Consel, Carl Johan Sundberg, João Malva, Theng Yin Leng, Helene Villars, Stéphanie Giraud, Elizabeth Muir, and Laurie Owen
Subjects: business.industry, Family caregivers, Process (engineering), media_common.quotation_subject, Public sector, Control (management), Public relations, Private sector, Health care, Quality (business), Sociology, business, Curriculum, media_common
Abstract: Today’s health and social care systems are facing a challenge in how to effectively address caregiving for ageing populations facing cognitive disorders and frailty. Scholars and policy makers are now identifying a rise of “hidden form of care”, e.g. informal caregiving, as a phenomenon in support for ageing populations. Across Europe for instance, the rise in the older old adult population has led to a rapid expansion of the number of carers, both professional (formal) and informal. The latter, representing mostly family members caring for their loved ones, truly represents a “hidden form of care”. This can be a problem if formal and informal caregivers are not fully integrated into the healthcare continuum or are not given a systematic support to carry out caregiving in a relevant and safe way. There is currently no comprehensive European-wide legal framework and support mechanisms, in terms of training and education for this group. CARE Campus, an EIT Health programme within the Educational Campus Pillar, is a new model of collaboration between academic institutions, the private sector, and the public sector whose main aim is to support the development of a comprehensive training for formal and informal caregivers in Europe. The initial phase of the development encompasses nine (09) online training modules with a quality control process to ensure that the curriculum is evidence-based, compliant with the national and local regulations, and addresses the needs of caregivers across Europe. The objective is to support formal, informal, and family caregivers and reduce the burden on health care systems, whilst improving the quality of care for older adults.
Published: 2018

28. A bibliometric overview of e-cigarette publications from 2007 to 2016

Author: Filippos T. Filippidis and Melek Somai
Subjects: business.industry, Library science, Medicine, business
Published: 2017

29. From Pharmacovigilance to Clinical Care Optimization

Author: Kai-ou Tang, David J. Stone, Christopher Moses, Melek Somai, Edward T. Moseley, Leo Anthony Celi, and Padhraig Ryan
Subjects: Information Systems and Management, Standardization, business.industry, Process (engineering), Data science, Computer Science Applications, Variety (cybernetics), Risk analysis (engineering), Intensive care, Pharmacovigilance, Health care, Medicine, Observational study, business, Digitization, Perspectives, Information Systems
Abstract: In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies. Even well-conducted, randomized controlled trials are likely to have excluded a large proportion of individuals because of any number of issues. The digitization of medical care, which yields rich and accessible drug data amenable to analytic techniques, provides an opportunity to capture the required information via observational studies. We propose the development of an open, accessible database containing properly de-identified data, to provide the substrate for the required improvement in pharmacovigilance. A range of stakeholders could use this to identify delayed and low-frequency adverse events. Moreover, its power as a research tool could extend to the detection of complex interactions, potential novel uses, and subtle subpopulation effects. This far-reaching potential is demonstrated by our experience with the open Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) intensive care unit database. The new database could also inform the development of objective, robust clinical practice guidelines. Careful systematization and deliberate standardization of a fully digitized pharmacovigilance process is likely to save both time and resources for healthcare in general.
Published: 2014

30. Mobile teledermatology for a prompter and more efficient dermatological care in rural Mongolia

Author: S. Nergyi, María Teresa García-Romero, Melek Somai, A. Nyamdorj, Kenneth E. Paik, Leo Anthony Celi, Usman Iqbal, Shabbir Syed-Abdul, Wen-Shan Jian, V. Nikore, Chih-Wei Huang, Yu-Chuan Li, K. Byamba, and Phung Anh Nguyen
Subjects: Male, Teledermatology, MEDLINE, Dermatology, Rural Health, Skin Diseases, Article, law.invention, Nursing, Ambulatory care, Randomized controlled trial, law, Ambulatory Care, Medicine, Cluster Analysis, Humans, Referral and Consultation, business.industry, Rural health, Mongolia, medicine.disease, Telemedicine, Multicenter study, Female, Medical emergency, business
Published: 2014

31. A cannonball through the chest: disseminated tuberculosis, threatening the aortic arch

Author: Henry J, Feldman, Melek, Somai, and Ezra, Dweck
Subjects: Diagnosis, Differential, Male, Sternum, Tuberculosis, Miliary, Aortic Diseases, Humans, Aorta, Thoracic, Middle Aged, Thoracic Wall, Tomography, X-Ray Computed, Abscess
Abstract: In 2012 the World Health Organization reported 8.7 million new cases of Tuberculosis worldwide, causing 1.4 million deaths (1). Despite modern drug therapy, this disease continues to present in novel ways and mimic other diseases causing misdiagnosis.We report this case to educate on the reason to suspect atypical Tuberculosis presentation, even if a common disease is diagnosed, when Tuberculosis remains in the differential. We also demonstrate that with globalization and patient moving between countries, that these presentations can occur in locations, where such atypical manifestations are very uncommon.We report on a 48 year old man with one month of malaise, fever, productive cough, night sweats, chills, pleuritic chest pain, weight loss and progressive non-painful swelling on his thorax. Initial diagnoses of interstitial pneumonia and a thoracic subcutaneous abscess were made. Needle drainage was attempted, with thick purulent material returned. When the sternum was not struck with the needle, a thoracic computed tomography scan was performed. A milliary pattern was noted in the lungs, with a large abscess present anteriorly, completely obliterating the manubrium, approaching the aorta with distant lesions. Subsequent analysis showed the material to be pan-sensitive M. Tuberculosis.The issue that this case raises is that when tuberculosi is in the differential, even common diseases may in fact be atypical manifestations of tuberculosis. In addition, when a shallow surgical procedure is going to be performed on the thoracic soft tissues, particularly when tuberculosis is suspected, imaging of the thorax should be obtained.
Published: 2014

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31 results on '"Melek Somai"'

1. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

2. Telemedicine and health disparities: Association between patient characteristics and telemedicine, in-person, telephone and message-based care during the COVID-19 pandemic

3. Bugs in the Virtual Clinic: Confronting Telemedicine’s Challenges Through Empathy and Support

4. Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis.

5. Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities.

6. Mobile Maternal Health Applications in Developing Countries.

7. Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities

8. From Pharmacovigilance to Clinical Care Optimization.

9. Overcoming Eurocentric bias makes for better science

10. Proposal: Medication Manager.

11. Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data

12. Clinical trials of neurointervention : 2007–2018

13. Predictive analytics and machine learning in stroke and neurovascular medicine

14. Impact of Pre-visit Contextual Data Collection on Patient-Physician Communication and Patient Activation: a Randomized Trial

15. Bugs in the Virtual Clinic: Confronting Telemedicine’s Challenges Through Empathy and Support (Preprint)

16. Addressing depression and behavioral health needs through a digital program at scale

17. Augmenting patient safety through participation by design - An assessment of dual monitors for patients in the outpatient clinic

18. The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial (Preprint)

19. Digital Engagement: How Serious Are Hospitals?

20. Data Science in Global Health—Highlighting the Burdens of Human Papillomavirus and Cervical Cancer in the MENA Region Using Open Source Data and Spatial Analysis

21. Analysis of Clinician and Patient Factors and Completion of Telemedicine Appointments Using Video

22. Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis

23. The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial

24. Association of Use of Online Symptom Checkers With Patients’ Plans for Seeking Care

25. CARING AND AGEING RE-IMAGINED IN EUROPE (CARE CAMPUS). AN INNOVATIVE APPROACH TO TACKLE THE HEALTHCARE CHALLENGES OF AGEING POPULATION IN EUROPE

26. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

27. CARE CAMPUS. A EUROPEAN CONSORTIUM MODEL TO SUPPORT FORMAL AND INFORMAL CAREGIVING TRAINING

28. A bibliometric overview of e-cigarette publications from 2007 to 2016

29. From Pharmacovigilance to Clinical Care Optimization

30. Mobile teledermatology for a prompter and more efficient dermatological care in rural Mongolia

31. A cannonball through the chest: disseminated tuberculosis, threatening the aortic arch

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