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1. Bangladesh Endocrine Society (BES) Position Statement for Management of Diabetes and Other Endocrine Diseases in Patients with COVID-19

Author

Pathan F, Selim S, Fariduddin M, Rahman MH, Ashrafuzzaman SM, Afsana F, Qureshi NK, Hossain T, Saifuddin M, Kamrul-Hasan AB, and Mir AS

Subjects
covid-19, diabetes mellitus, sars cov-2, antidiabetic medications, endocrine diseases and covid-19, thyroid disease and covid-19, Specialties of internal medicine, RC581-951
Abstract

Faruque Pathan,1 Shahjada Selim,2 Md Fariduddin,2 Md Hafizur Rahman,3 SM Ashrafuzzaman,1 Faria Afsana,1 Nazmul Kabir Qureshi,4 Tanjina Hossain,5 M Saifuddin,6 AB Kamrul-Hasan,7 Ahmed Salam Mir8 On behalf of the BES Diabetes and COVID Task Force1Department of Endocrinology, BIRDEM General Hospital, Dhaka, Bangladesh; 2Department of Endocrinology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh; 3Bangladesh Endocrine Society, Dhaka, Bangladesh; 4National Healthcare Network (NHN) Uttara EC, Dhaka, Bangladesh; 5Department of Endocrinology, Green Life Medical College Hospital, Dhaka, Bangladesh; 6Department of Endocrinology, Dhaka Medical College Hospital, Dhaka, Bangladesh; 7Mymensingh Medical College Hospital, Dhaka, Bangladesh; 8Department of Endocrinology, BIHS General Hospital, Dhaka, BangladeshCorrespondence: Shahjada SelimDepartment of Endocrinology, Bangabandhu Sheikh Mujib Medical University, Shahbagh, Dhaka, 1000, BangladeshEmail selimshahjada@yahoo.comBackground: The year 2020 witnessed a largely unprecedented pandemic of coronavirus disease (COVID-19), caused by SARS COV-2. Many people with COVID-19 have comorbidities, including diabetes, hypertension and cardiovascular diseases, which are significantly associated with worse outcomes. Moreover, COVID-19 itself is allied with deteriorating hyperglycemia. Therefore, Bangladesh Endocrine Society has formulated some practical recommendations for management of diabetes and other endocrine diseases in patients with COVID-19 for use in both primary and specialist care settings.Objective: The objective of the article is to develop a guideline to protect the vulnerable group with utmost preference – the elderly and those with comorbid conditions. Therefore, to ensure the adequate protective measures and timely treatment for COVID-19 patients with diabetes, other endocrine diseases or any other comorbidities.Considering and Monitoring Issues: The risk of a fatal outcome from COVID-19 may be up to 50% higher in patients with diabetes than in non-diabetics.Patients with diabetes and COVID had CFR 7.3– 9.2%, compared with 0.9– 1.4% in patients without comorbidities.Diabetic ketoacidosis may be one of the causes of mortality in COVID-19.There is wide fluctuation of blood glucose in these patients, probably due to irregular diet, reduced exercise, increased glucocorticoids secretion, and use of glucocorticoids.HbA1c should be < 7.0% for the majority of the patients, this target may be relaxed in appropriate clinical settings.More emphasis should be given on day-to-day blood glucose levels. Hypoglycemia (< 3.9 mmol/l) must be avoided.Frequent monitoring of blood glucose is needed in critically ill patients.Conclusion: The fight against COVID-19 has been proven to be a challenging one. Therefore, all healthcare personnel should make the best use of updated knowledge and skills to ensure adequate protective measures and timely treatment for COVID-19 patients with diabetes, other endocrine diseases or any other comorbidities.Keywords: COVID-19, diabetes mellitus, SARS COV-2, antidiabetic medications, endocrine diseases and COVID-19, thyroid disease and COVID-19

Published
2021

2. Improved Pharmacodynamic Potential of Rosuvastatin by Self-Nanoemulsifying Drug Delivery System: An in vitro and in vivo Evaluation

Author

Verma R, Kaushik A, Almeer R, Rahman MH, Abdel-Daim MM, and Kaushik D

Subjects
snedds, rosuvastatin, in vitro lipolysis, nanotechnology, d-optimal mixture design, pharmacodynamic study, Medicine (General), R5-920
Abstract

Ravinder Verma,1 Ajeet Kaushik,2 Rafa Almeer,3 Md Habibur Rahman,4 Mohamed M Abdel-Daim,3,5 Deepak Kaushik1 1Department of Pharmaceutical Sciences, M.D. University, Rohtak, Haryana, 124001, India; 2NanoBioTech Laboratory, Health Systems Engineering, Department of Natural Sciences, Division of Sciences, Arts, & Mathematics, Florida Polytechnic University, Lakeland, FL, 33805-8531, USA; 3Department of Zoology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; 4Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh; 5Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, EgyptCorrespondence: Deepak KaushikDepartment of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, Haryana, 124001, IndiaTel +91-931580962Email deepkaushik1977@gmail.comMohamed M Abdel-DaimDepartment of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi ArabiaEmail abdeldaim.m@vet.suez.edu.egPurpose: The purpose of this proposed research was to investigate a nano-formulation developed using self-nanoemulsifying drug delivery system (SNEDDS) to improve the pharmacodynamic potential of rosuvastatin by assisting its transportation through lymphatic circulation.Methods: The utilized lipids, surfactants, and co-surfactants for SNEDDS were selected on the basis of solubility studies. The SNEDDS formulation was optimized by implementing a D-optimal mixture design, wherein the effect of concentration of Capmul MCM EP (X1), Tween 20 (X2) and Transcutol P (X3) as independent variables was studied on droplet size (Y1), % cumulative drug release (Y2) and self-emulsification time (Y3) as dependent variables. The optimized formulation was evaluated via in vitro parameters and in vivo pharmacodynamic potential in Wistar rats.Results: The D-optimal mixture design and subsequent ANOVA application resulted in the assortment of the optimized SNEDDS formulation that exhibited a droplet size of nano range (14.91nm), in vitro drug release of > 90% within 30 minutes, and self-emulsification time of 16 seconds. The in vivo pharmacodynamic study carried out using Wistar rats confirmed the better antihyperlipidemic potential of developed formulation in normalizing the lipidic level of serum in contrast to pure drug and marketed tablets.Conclusion: This research reports the application of D-optimal mixture design for successful and systematic development of rosuvastatin-loaded SNEDDS with distinctly enhanced in vitro and in vivo performance in comparison to marketed formulation. Eventually, improved anti-hyperlipidemic efficacy was envisaged which might be attributed to increased drug solubility and absorption. Overall, this study shows the utility of SNEDDS for improving the dissolution rate and bioavailability of poor aqueous-soluble drugs. The present SNEDDS formulation could be a promising approach and alternative to conventional dosage form.Keywords: SNEDDS, rosuvastatin, in vitro lipolysis, nanotechnology, D-optimal mixture design, pharmacodynamic study

Published
2021

5. Outcomes of polio eradication activities in Uttar Pradesh, India: the Social Mobilization Network (SM Net) and Core Group Polio Project (CGPP)

Author

Singh Vibha, Solomon Roma, Rahman MH, Weiss William M, and Ward Dora

Subjects
Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The primary strategy to interrupt transmission of wild poliovirus in India is to improve supplemental immunization activities and routine immunization coverage in priority districts with a focus on 107 high-risk blocks of western Uttar Pradesh and central Bihar. Villages or urban areas with a history of wild poliovirus transmission, or hard-to-reach or resistant populations are categorized as high-risk areas within blocks. The Social Mobilization Network (SM Net) was formed in Uttar Pradesh in 2003 to support polio eradication efforts through improved planning, implementation and monitoring of social mobilization activities in those high-risk areas. In this paper, we examine the vaccination outcomes in districts of SM Net where the CORE Group works. Methods We carried out a secondary data analysis of routine monitoring information collected by the SM Net and the Government of India. These data include information about vaccination outcomes in SM Net areas and non-SM Net areas within the districts where the CORE Group operates. Statistical analysis was used to compare, between SM Net and non-SM Net areas, vaccination outcomes considered sensitive to social mobilization efforts of the SM Net. We employed Generalized Estimating Equations (GEE) statistical method to account for Intra-cluster Correlation (ICC), and used 'Quasi-likelihood under the independence model criterion (QIC)' as the model selection method. Results Vaccination outcomes in SM Net areas were as high as or higher than in non-SM Net areas. There was considerable variation in vaccination outcomes between districts. Conclusions While not conclusive, the results suggest that the social mobilization efforts of the SM Net and the CORE Group are helping to increase vaccination levels in high-risk areas of Uttar Pradesh. Vaccination outcomes in CORE Group areas were equal or higher than in non-CORE, non-SM Net areas. This occurred even though SM Net areas are those with more community resistance to polio vaccination and/or are have harder-to-reach populations than non-SM Net areas. Other likely explanations for the relatively good vaccination performance in SM Net areas are not apparent.

Published
2011
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11. RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.

Author

Alam, SS, Chakma, A, Rahman, MH, Bin Mofidul, R, Alam, MM, Utama, IBKY, Jang, YM, Alam, SS, Chakma, A, Rahman, MH, Bin Mofidul, R, Alam, MM, Utama, IBKY, and Jang, YM

Abstract

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37

Published
2023

12. Complex interactions between diverse mobile genetic elements drive the evolution of metal-resistant bacterial genomes.

Author

Mahbub, KR, Chénard, C, Batinovic, S, Petrovski, S, Lauro, FM, Rahman, MH, Megharaj, M, Franks, AE, Labbate, M, Mahbub, KR, Chénard, C, Batinovic, S, Petrovski, S, Lauro, FM, Rahman, MH, Megharaj, M, Franks, AE, and Labbate, M

Abstract

In this study, we compared the genomes of three metal-resistant bacteria isolated from mercury-contaminated soil. We identified diverse and novel MGEs with evidence of multiple LGT events shaping their genomic structure and heavy metal resistance. Among the three metal-resistant strains, Sphingobium sp SA2 and Sphingopyxis sp SE2 were resistant to multiple metals including mercury, cadmium, copper, zinc and lead. Pseudoxanthomonas sp SE1 showed resistance to mercury only. Whole genome sequencing by Illumina and Oxford Nanopore technologies was undertaken to obtain comprehensive genomic data. The Sphingobium and Sphingopyxis strains contained multiple chromosomes and plasmids, whereas the Pseudoxanthomonas strain contained one circular chromosome. Consistent with their metal resistance profiles, the strains of Sphingobium and Sphingopyxis contained a higher quantity of diverse metal resistance genes across their chromosomes and plasmids compared to the single-metal resistant Pseudoxanthomonas SE1. In all three strains, metal resistance genes were principally associated with various novel MGEs including genomic islands (GIs), integrative conjugative elements (ICEs), transposons, insertion sequences (IS), recombinase in trio (RIT) elements and group II introns, indicating their importance in facilitating metal resistance adaptation in a contaminated environment. In the Pseudoxanthomonas strain, metal resistance regions were largely situated on a GI. The chromosomes of the strains of Sphingobium and Sphingopyxis contained multiple metal resistance regions, which were likely acquired by several GIs, ICEs, numerous IS elements, several Tn3 family transposons and RIT elements. Two of the plasmids of Sphingobium were impacted by Tn3 family transposons and ISs likely integrating metal resistance genes. The two plasmids of Sphingopyxis harboured transposons, IS elements, an RIT element and a group II intron. This study provides a comprehensive annotation of complex genomic re

Published
2023

21. Protozoal food vacuoles enhance transformation in Vibrio cholerae through SOS-regulated DNA integration.

Author

Rahman, MH, Mahbub, KR, Espinoza-Vergara, G, Ritchie, A, Hoque, MM, Noorian, P, Cole, L, McDougald, D, Labbate, M, Rahman, MH, Mahbub, KR, Espinoza-Vergara, G, Ritchie, A, Hoque, MM, Noorian, P, Cole, L, McDougald, D, and Labbate, M

Abstract

Vibrio cholerae, the bacterial pathogen responsible for the diarrheal disease cholera, resides in the aquatic environment between outbreaks. For bacteria, genetic variation by lateral gene transfer (LGT) is important for survival and adaptation. In the aquatic environment, V. cholerae is predominantly found in biofilms associated with chitinous organisms or with chitin "rain". Chitin induces competency in V. cholerae, which can lead to LGT. In the environment, V. cholerae is also subjected to predation pressure by protist. Here we investigated whether protozoal predation affected LGT using the integron as a model. Integrons facilitate the integration of mobile DNA (gene cassettes) into the bacterial chromosome. We report that protozoal predation enhances transformation of a gene cassette by as much as 405-fold. We show that oxidative radicals produced in the protozoal phagosome induces the universal SOS response, which in turn upregulates the integron-integrase, the recombinase that facilitates cassette integration. Additionally, we show that during predation, V. cholerae requires the type VI secretion system to acquire the gene cassette from Escherichia coli. These results show that protozoal predation enhances LGT thus producing genetic variants that may have increased capacity to survive grazing. Additionally, the conditions in the food vacuole may make it a "hot spot" for LGT by accumulating diverse bacteria and inducing the SOS response helping drive genetic diversification and evolution.

Published
2022

24. Adaptation to an amoeba host drives selection of virulence-associated traits in Vibrio cholerae

Author

Hoque, MM, Noorian, P, Espinoza-Vergara, G, Manuneedhi Cholan, P, Kim, M, Rahman, MH, Labbate, M, Rice, SA, Pernice, M, Oehlers, SH, and McDougald, D

Subjects
parasitic diseases, 05 Environmental Sciences, 06 Biological Sciences, 10 Technology, Microbiology
Abstract

Predation by heterotrophic protists drives the emergence of adaptive traits in bacteria, and often these traits lead to altered interactions with hosts and persistence in the environment. Here we studied adaptation of the cholera pathogen, Vibrio cholerae during long-term co-incubation with the protist host, Acanthamoeba castellanii. We determined phenotypic and genotypic changes associated with long-term intra-amoebal host adaptation and how this impacts pathogen survival and fitness. We showed that adaptation to the amoeba host leads to temporal changes in multiple phenotypic traits in V. cholerae that facilitate increased survival and competitive fitness in amoeba. Genome sequencing and mutational analysis revealed that these altered lifestyles were linked to non-synonymous mutations in conserved regions of the flagellar transcriptional regulator, flrA. Additionally, the mutations resulted in enhanced colonisation in zebrafish, establishing a link between adaptation of V. cholerae to amoeba predation and enhanced environmental persistence. Our results show that pressure imposed by amoeba on V. cholerae selects for flrA mutations that serves as a key driver for adaptation. Importantly, this study provides evidence that adaptive traits that evolve in pathogens in response to environmental predatory pressure impact the colonisation of eukaryotic organisms by these pathogens.

Published
2021

25. State-of-the-Art of Establishing Test Procedures for Real Driving Gaseous Emissions from Light- and Heavy-Duty Vehicles

Author

Rahman, SMA, Fattah, IMR, Ong, HC, Ashik, FR, Hassan, MM, Murshed, MT, Imran, MA, Rahman, MH, Rahman, MA, Hasan, MAM, and Mahlia, TMI

Subjects
02 Physical Sciences, 09 Engineering
Abstract

Air pollution caused by vehicle emissions has raised serious public health concerns. Vehicle emissions generally depend on many factors, such as the nature of the vehicle, driving style, traffic conditions, emission control technologies, and operational conditions. Concerns about the certification cycles used by various regulatory authorities are growing due to the difference in emission during certification procedure and Real Driving Emissions (RDE). Under laboratory conditions, certification tests are performed in a ‘chassis dynamometer’ for light-duty vehicles (LDVs) and an ‘engine dynamometer’ for heavy-duty vehicles (HDVs). As a result, the test drive cycles used to measure the automotive emissions do not correctly reflect the vehicle’s real-world driving pattern. Consequently, the RDE regulation is being phased in to reduce the disparity between type approval and vehicle’s real-world emissions. According to this review, different variables such as traffic signals, driving dynamics, congestions, altitude, ambient temperature, and so on have a major influence on actual driving pollution. Aside from that, cold-start and hot-start have been shown to have an effect on on-road pollution. Contrary to common opinion, new technology such as start-stop systems boost automotive emissions rather than decreasing them owing to unfavourable conditions from the point of view of exhaust emissions and exhaust after-treatment systems. In addition, the driving dynamics are not represented in the current laboratory-based test procedures. As a result, it is critical to establish an on-road testing protocol to obtain a true representation of vehicular emissions and reduce emissions to a standard level. The incorporation of RDE clauses into certification procedures would have a positive impact on global air quality.

Published
2021

26. Hand hygiene practice and determinant factors among Community Health Care Providers in Community Clinic: an observational study in Bangladesh

Author

Hassan Mn, Khan Msi, Awal N, Hasanuzzaman M, and Rahman Mh

Subjects
medicine.medical_specialty, business.industry, Community health care, Hygiene, media_common.quotation_subject, Family medicine, Medicine, Observational study, business, media_common
Abstract

Background Healthcare-associated infections caused by poor hand hygiene (HH) practices can affect both the care seekers and the health care providers (HCPs) while providing primary health care. No study has been conducted on the compliance of HH practices among the primary level HCPs in Bangladesh. Objective The study aimed to assess the compliance rates of HH and its determinant factors among Community Health Care Providers (CHCPs) in Community Clinics (CC). Methods A cross-sectional study was conducted from September 2019 to February 2020 among 150 randomly selected CHCPs with functional HH facilities in Patuakhali district, Bangladesh. Structured interviews and observation tools were used to collect data. Results Good HH compliance among CHCPs was found to be 16.7% (95% CI: 11.3–21.3). Out of all 1218 possible HH opportunities, only 255 (20.9%) resulted in any HH action. Presence of 70% alcohol-based hand sanitizer at the point of care (aOR: 6.4, 95% CI: 1.1–38.3), HH training (aOR: 4.6, 95% CI: 1.1–18.9), displayed visual cues (aOR: 4.4, 95% CI: 1.1–17.7), knowledge about HH (aOR: 3.8, 95% CI: 1.1–13.6, p = 0.04), and number of HH opportunities (aOR: 0.6, 95% CI: 0.4–0.8) were factors associated with HH compliance. Discussion Overall HH compliance among CHCPs was quite low. It was recommended to implement multimodal HH improvement strategies including a continuous training program, supply of alcohol-based hand rub, reminder, provision of five moments of HH in the training modules, and feedback on HH performance.

Published
2021

27. Comparative Study on Antimicrobial Activity of Four Bangladeshi Medicinal Plants used as Antimicrobial Finishes on Cotton Fabric

Author

Ashraf M, Rahman Mh, and Rahman M

Subjects
Traditional medicine, parasitic diseases, General Medicine, Biology, Medicinal plants, Antimicrobial
Abstract

Considering the post COVID-19 pandemic situation, it can be assumed that using safer clothing will be a great attraction point for consumers across the world. In that scenario, effective antimicrobial finishes that are readily available and cheap would contribute to the consumer demand of safer clothing. This study scrutinized the antimicrobial activity of bleached cotton fabric treated with extracts of Ocimum tenuiflorum (Basil), Mentha spicata (Spearmint), Centella asiatica (Indian pennywort) and Azadirachta indica (Neem) as antimicrobial finishes. The study aims at assessing the comparative activity of four abovementioned natural medicinal plants, which can be used as ecofriendly antimicrobial finishes for textiles. Bleached cotton fabric samples were treated with plant extractions following exhaust method and then evaluated by using ASTM E2149-01 method against gram-positive bacteria Staphylococcus aureus and gram-negative bacteria Escherichia coli. The results depicted that all the four samples showed antimicrobial activity with more than 95% reduction of micro-organisms. Among the four, Ocimum tenuiflorum (Basil) showed the best result against both E. coli and S. aureus with reduction percentage of 98.81% and 99.16% respectively. Although Azadirachta indica (Neem) has comparatively the lowest performance against E. coli, its antimicrobial activity against S. aureus is very close to Ocimum tenuiflorum (Basil).

Published
2021

30. Dynamics of the Sydney rock oyster microbiota before and during a QX disease event

Author

Nguyen, VK, King, WL, Siboni, N, Mahbub, KR, Rahman, MH, Jenkins, C, Dove, M, O'Connor, W, Seymour, JR, Labbate, M, Nguyen, VK, King, WL, Siboni, N, Mahbub, KR, Rahman, MH, Jenkins, C, Dove, M, O'Connor, W, Seymour, JR, and Labbate, M

Abstract

The Sydney rock oyster (SRO; Saccostrea glomerata) is the most intensively farmed oyster species in Australia however, Queensland unknown (QX) disease has resulted in substantial losses and impeded productivity. QX disease is caused by infection with the parasite Marteilia sydneyi, and like other diseases, outbreaks are driven by a series of complex environmental and host factors such as seasonality, seawater salinity and oyster genetics. A potential but understudied factor in QX disease is the SRO microbiota, which we sought to examine before and during a QX disease outbreak. Using 16S rRNA (V1 – V3 region) amplicon sequencing, we examined the microbiota of SROs deployed in an estuary where QX disease occurs, with sampling conducted fortnightly over 22 weeks. Marteilia sydneyi was detected in the SROs by PCR (QX-positive), 16 weeks after the first sampling event and sporonts were observed in the digestive gland two weeks later on. There were no apparent patterns observed between the microbiota of QX-positive SROs with and without digestive gland sporonts however, the microbiota of QX-positive SROs was significantly different from those sampled prior to detection of M. sydneyi and from those negative for M. sydneyi post detection. As a result, shifts in microbiota structure occurred before sporulation in the digestive gland and either before or shortly after pathogen colonisation. The microbiota shifts associated with QX-positive oysters were principally driven by a relative abundance increase of operational taxonomic units (OTUs) assigned to unclassified species of the Borrelia and Candidatus Hepatoplasma genera and a relative abundance decrease in an OTU assigned to an unclassified species of the Mycoplasma genus. Since Mycoplasma species are common microbiota features of SROs and other oysters, we propose that there may be an important ecological link between Mycoplasma species and the health state of SROs.

Published
2021

32. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019

Author

Roth, GA, Mensah, GA, Johnson, CO, Addolorato, G, Ammirati, E, Baddour, LM, Barengo, NC, Beaton, AZ, Benjamin, EJ, Benziger, CP, Bonny, A, Brauer, M, Brodmann, M, Cahill, TJ, Carapetis, J, Catapano, AL, Chugh, SS, Cooper, LT, Coresh, J, Criqui, M, DeCleene, N, Eagle, KA, Emmons-Bell, S, Feigin, VL, Fernández-Solà, J, Fowkes, G, Gakidou, E, Grundy, SM, He, FJ, Howard, G, Hu, F, Inker, L, Karthikeyan, G, Kassebaum, N, Koroshetz, W, Lavie, C, Lloyd-Jones, D, Lu, HS, Mirijello, A, Temesgen, AM, Mokdad, A, Moran, AE, Muntner, P, Narula, J, Neal, B, Ntsekhe, M, Moraes de Oliveira, G, Otto, C, Owolabi, M, Pratt, M, Rajagopalan, S, Reitsma, M, Ribeiro, ALP, Rigotti, N, Rodgers, A, Sable, C, Shakil, S, Sliwa-Hahnle, K, Stark, B, Sundström, J, Timpel, P, Tleyjeh, IM, Valgimigli, M, Vos, T, Whelton, PK, Yacoub, M, Zuhlke, L, Murray, C, Fuster, V, Beaton, A, Carapetis, JR, Chugh, S, Criqui, MH, DeCleene, NK, Fernández-Sola, J, Fowkes, FGR, Kassebaum, NJ, Koroshetz, WJ, Misganaw, AT, Mokdad, AH, Oliveira, GMM, Otto, CM, Owolabi, MO, Reitsma, MB, Rigotti, NA, Sable, CA, Shakil, SS, Sliwa, K, Stark, BA, Tleyjeh, II, Zuhlke, LJ, Abbasi-Kangevari, M, Abdi, A, Abedi, A, Aboyans, V, Abrha, WA, Abu-Gharbieh, E, Abushouk, AI, Acharya, D, Adair, T, Adebayo, OM, Ademi, Z, Advani, SM, Afshari, K, Afshin, A, Agarwal, G, Agasthi, P, Ahmad, S, Ahmadi, S, Ahmed, MB, Aji, B, Akalu, Y, Akande-Sholabi, W, Aklilu, A, Akunna, CJ, Alahdab, F, Al-Eyadhy, A, Alhabib, KF, Alif, SM, Alipour, V, Aljunid, SM, Alla, F, Almasi-Hashiani, A, Almustanyir, S, Al-Raddadi, RM, Amegah, AK, Amini, S, Aminorroaya, A, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Andrei, T, Andrei, CL, Ansari-Moghaddam, A, Anteneh, ZA, Antonazzo, IC, Antony, B, Anwer, R, Appiah, LT, Arabloo, J, Ärnlöv, J, Artanti, KD, Ataro, Z, Ausloos, M, Avila-Burgos, L, Awan, AT, Awoke, MA, Ayele, HT, Ayza, MA, Azari, S, B, DB, Baheiraei, N, Baig, AA, Bakhtiari, A, Banach, M, Banik, PC, Baptista, EA, Barboza, MA, Barua, L, Basu, S, Bedi, N, Béjot, Y, Bennett, DA, Bensenor, IM, Berman, AE, Bezabih, YM, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bijani, A, Bikbov, B, Birhanu, MM, Boloor, A, Brant, LC, Brenner, H, Briko, NI, Butt, ZA, Caetano dos Santos, FL, Cahill, LE, Cahuana-Hurtado, L, Cámera, LA, Campos-Nonato, IR, Cantu-Brito, C, Car, J, Carrero, JJ, Carvalho, F, Castañeda-Orjuela, CA, Catalá-López, F, Cerin, E, Charan, J, Chattu, VK, Chen, S, Chin, KL, Choi, J-YJ, Chu, D-T, Chung, S-C, Cirillo, M, Coffey, S, Conti, S, Costa, VM, Cundiff, DK, Dadras, O, Dagnew, B, Dai, X, Damasceno, AAM, Dandona, L, Dandona, R, Davletov, K, De la Cruz-Góngora, V, De la Hoz, FP, De Neve, J-W, Denova-Gutiérrez, E, Derbew Molla, M, Derseh, BT, Desai, R, Deuschl, G, Dharmaratne, SD, Dhimal, M, Dhungana, RR, Dianatinasab, M, Diaz, D, Djalalinia, S, Dokova, K, Douiri, A, Duncan, BB, Duraes, AR, Eagan, AW, Ebtehaj, S, Eftekhari, A, Eftekharzadeh, S, Ekholuenetale, M, El Nahas, N, Elgendy, IY, Elhadi, M, El-Jaafary, SI, Esteghamati, S, Etisso, AE, Eyawo, O, Fadhil, I, Faraon, EJA, Faris, PS, Farwati, M, Farzadfar, F, Fernandes, E, Fernandez Prendes, C, Ferrara, P, Filip, I, Fischer, F, Flood, D, Fukumoto, T, Gad, MM, Gaidhane, S, Ganji, M, Garg, J, Gebre, AK, Gebregiorgis, BG, Gebregzabiher, KZ, Gebremeskel, GG, Getacher, L, Obsa, AG, Ghajar, A, Ghashghaee, A, Ghith, N, Giampaoli, S, Gilani, SA, Gill, PS, Gillum, RF, Glushkova, EV, Gnedovskaya, EV, Golechha, M, Gonfa, KB, Goudarzian, AH, Goulart, AC, Guadamuz, JS, Guha, A, Guo, Y, Gupta, R, Hachinski, V, Hafezi-Nejad, N, Haile, TG, Hamadeh, RR, Hamidi, S, Hankey, GJ, Hargono, A, Hartono, RK, Hashemian, M, Hashi, A, Hassan, S, Hassen, HY, Havmoeller, RJ, Hay, SI, Hayat, K, Heidari, G, Herteliu, C, Holla, R, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Humayun, A, Iavicoli, I, Ibeneme, CU, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Iqbal, U, Irvani, SSN, Shariful Islam, Sheikh, Islam, RM, Iso, H, Iwagami, M, Jain, V, Javaheri, T, Jayapal, SK, Jayaram, S, Jayawardena, R, Jeemon, P, Jha, RP, Jonas, JB, Jonnagaddala, J, Joukar, F, Jozwiak, JJ, Jürisson, M, Kabir, A, Kahlon, T, Kalani, R, Kalhor, R, Kamath, A, Kamel, I, Kandel, H, Kandel, A, Karch, A, Kasa, AS, Katoto, PDMC, Kayode, GA, Khader, YS, Khammarnia, M, Khan, MS, Khan, MN, Khan, M, Khan, EA, Khatab, K, Kibria, GMA, Kim, YJ, Kim, GR, Kimokoti, RW, Kisa, S, Kisa, A, Kivimäki, M, Kolte, D, Koolivand, A, Korshunov, VA, Koulmane Laxminarayana, SL, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Kuate Defo, B, Kucuk Bicer, B, Kulkarni, V, Kumar, GA, Kumar, N, Kurmi, OP, Kusuma, D, Kwan, GF, La Vecchia, C, Lacey, B, Lallukka, T, Lan, Q, Lasrado, S, Lassi, ZS, Lauriola, P, Lawrence, WR, Laxmaiah, A, LeGrand, KE, Li, M-C, Li, B, Li, S, Lim, SS, Lim, L-L, Lin, H, Lin, Z, Lin, R-T, Liu, X, Lopez, AD, Lorkowski, S, Lotufo, PA, Lugo, A, M, NK, Madotto, F, Mahmoudi, M, Majeed, A, Malekzadeh, R, Malik, AA, Mamun, AA, Manafi, N, Mansournia, MA, Mantovani, LG, Martini, S, Mathur, MR, Mazzaglia, G, Mehata, S, Mehndiratta, MM, Meier, T, Menezes, RG, Meretoja, A, Mestrovic, T, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirrakhimov, EM, Mirzaei, H, Moazen, B, Moghadaszadeh, M, Mohammad, Y, Mohammad, DK, Mohammed, S, Mohammed, MA, Mokhayeri, Y, Molokhia, M, Montasir, AA, Moradi, G, Moradzadeh, R, Moraga, P, Morawska, L, Moreno Velásquez, I, Morze, J, Mubarik, S, Muruet, W, Musa, KI, Nagarajan, AJ, Nalini, M, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Nascimento, BR, Nayak, VC, Nazari, J, Nazarzadeh, M, Negoi, RI, Neupane Kandel, S, Nguyen, HLT, Nixon, MR, Norrving, B, Noubiap, JJ, Nouthe, BE, Nowak, C, Odukoya, OO, Ogbo, FA, Olagunju, AT, Orru, H, Ortiz, A, Ostroff, SM, Padubidri, JR, Palladino, R, Pana, A, Panda-Jonas, S, Parekh, U, Park, E-C, Parvizi, M, Pashazadeh Kan, F, Patel, UK, Pathak, M, Paudel, R, Pepito, VCF, Perianayagam, A, Perico, N, Pham, HQ, Pilgrim, T, Piradov, MA, Pishgar, F, Podder, V, Polibin, RV, Pourshams, A, Pribadi, DRA, Rabiee, N, Rabiee, M, Radfar, A, Rafiei, A, Rahim, F, Rahimi-Movaghar, V, Ur Rahman, MH, Rahman, Muhammad, Rahmani, AM, Rakovac, I, Ram, P, Ramalingam, S, Rana, J, Ranasinghe, P, Rao, SJ, Rathi, P, Rawal, L, Rawasia, WF, Rawassizadeh, R, Remuzzi, G, Renzaho, AMN, Rezapour, A, Riahi, SM, Roberts-Thomson, RL, Roever, L, Rohloff, P, Romoli, M, Roshandel, G, Rwegerera, GM, Saadatagah, S, Saber-Ayad, MM, Sabour, S, Sacco, S, Sadeghi, M, Saeedi Moghaddam, S, Safari, S, Sahebkar, A, Salehi, S, Salimzadeh, H, Samaei, M, Samy, AM, Santos, IS, Santric-Milicevic, MM, Sarrafzadegan, N, Sarveazad, A, Sathish, T, Sawhney, M, Saylan, M, Schmidt, MI, Schutte, AE, Senthilkumaran, S, Sepanlou, SG, Sha, F, Shahabi, S, Shahid, I, Shaikh, MA, Shamali, M, Shamsizadeh, M, Shawon, MSR, Sheikh, A, Shigematsu, M, Shin, M-J, Shin, JI, Shiri, R, Shiue, I, Shuval, K, Siabani, S, Siddiqi, TJ, Silva, DAS, Singh, JA, Mtech, AS, Skryabin, VY, Skryabina, AA, Soheili, A, Spurlock, EE, Stockfelt, L, Stortecky, S, Stranges, S, Suliankatchi Abdulkader, R, Tadbiri, H, Tadesse, EG, Tadesse, DB, Tajdini, M, Tariqujjaman, M, Teklehaimanot, BF, Temsah, M-H, Tesema, AK, Thakur, B, Thankappan, KR, Thapar, R, Thrift, AG, Timalsina, B, Tonelli, M, Touvier, M, Tovani-Palone, MR, Tripathi, A, Tripathy, JP, Truelsen, TC, Tsegay, GM, Tsegaye, GW, Tsilimparis, N, Tusa, BS, Tyrovolas, S, Umapathi, KK, Unim, B, Unnikrishnan, B, Usman, MS, Vaduganathan, M, Valdez, PR, Vasankari, TJ, Velazquez, DZ, Venketasubramanian, N, Vu, GT, Vujcic, IS, Waheed, Y, Wang, Y, Wang, F, Wei, J, Weintraub, RG, Weldemariam, AH, Westerman, R, Winkler, AS, Wiysonge, CS, Wolfe, CDA, Wubishet, BL, Xu, G, Yadollahpour, A, Yamagishi, K, Yan, LL, Yandrapalli, S, Yano, Y, Yatsuya, H, Yeheyis, TY, Yeshaw, Y, Yilgwan, CS, Yonemoto, N, Yu, C, Yusefzadeh, H, Zachariah, G, Zaman, SB, Zaman, MS, Zamanian, M, Zand, R, Zandifar, A, Zarghi, A, Zastrozhin, MS, Zastrozhina, A, Zhang, Z-J, Zhang, Y, Zhang, W, Zhong, C, Zou, Z, Zuniga, YMH, Murray, CJL, Roth, GA, Mensah, GA, Johnson, CO, Addolorato, G, Ammirati, E, Baddour, LM, Barengo, NC, Beaton, AZ, Benjamin, EJ, Benziger, CP, Bonny, A, Brauer, M, Brodmann, M, Cahill, TJ, Carapetis, J, Catapano, AL, Chugh, SS, Cooper, LT, Coresh, J, Criqui, M, DeCleene, N, Eagle, KA, Emmons-Bell, S, Feigin, VL, Fernández-Solà, J, Fowkes, G, Gakidou, E, Grundy, SM, He, FJ, Howard, G, Hu, F, Inker, L, Karthikeyan, G, Kassebaum, N, Koroshetz, W, Lavie, C, Lloyd-Jones, D, Lu, HS, Mirijello, A, Temesgen, AM, Mokdad, A, Moran, AE, Muntner, P, Narula, J, Neal, B, Ntsekhe, M, Moraes de Oliveira, G, Otto, C, Owolabi, M, Pratt, M, Rajagopalan, S, Reitsma, M, Ribeiro, ALP, Rigotti, N, Rodgers, A, Sable, C, Shakil, S, Sliwa-Hahnle, K, Stark, B, Sundström, J, Timpel, P, Tleyjeh, IM, Valgimigli, M, Vos, T, Whelton, PK, Yacoub, M, Zuhlke, L, Murray, C, Fuster, V, Beaton, A, Carapetis, JR, Chugh, S, Criqui, MH, DeCleene, NK, Fernández-Sola, J, Fowkes, FGR, Kassebaum, NJ, Koroshetz, WJ, Misganaw, AT, Mokdad, AH, Oliveira, GMM, Otto, CM, Owolabi, MO, Reitsma, MB, Rigotti, NA, Sable, CA, Shakil, SS, Sliwa, K, Stark, BA, Tleyjeh, II, Zuhlke, LJ, Abbasi-Kangevari, M, Abdi, A, Abedi, A, Aboyans, V, Abrha, WA, Abu-Gharbieh, E, Abushouk, AI, Acharya, D, Adair, T, Adebayo, OM, Ademi, Z, Advani, SM, Afshari, K, Afshin, A, Agarwal, G, Agasthi, P, Ahmad, S, Ahmadi, S, Ahmed, MB, Aji, B, Akalu, Y, Akande-Sholabi, W, Aklilu, A, Akunna, CJ, Alahdab, F, Al-Eyadhy, A, Alhabib, KF, Alif, SM, Alipour, V, Aljunid, SM, Alla, F, Almasi-Hashiani, A, Almustanyir, S, Al-Raddadi, RM, Amegah, AK, Amini, S, Aminorroaya, A, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Andrei, T, Andrei, CL, Ansari-Moghaddam, A, Anteneh, ZA, Antonazzo, IC, Antony, B, Anwer, R, Appiah, LT, Arabloo, J, Ärnlöv, J, Artanti, KD, Ataro, Z, Ausloos, M, Avila-Burgos, L, Awan, AT, Awoke, MA, Ayele, HT, Ayza, MA, Azari, S, B, DB, Baheiraei, N, Baig, AA, Bakhtiari, A, Banach, M, Banik, PC, Baptista, EA, Barboza, MA, Barua, L, Basu, S, Bedi, N, Béjot, Y, Bennett, DA, Bensenor, IM, Berman, AE, Bezabih, YM, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bijani, A, Bikbov, B, Birhanu, MM, Boloor, A, Brant, LC, Brenner, H, Briko, NI, Butt, ZA, Caetano dos Santos, FL, Cahill, LE, Cahuana-Hurtado, L, Cámera, LA, Campos-Nonato, IR, Cantu-Brito, C, Car, J, Carrero, JJ, Carvalho, F, Castañeda-Orjuela, CA, Catalá-López, F, Cerin, E, Charan, J, Chattu, VK, Chen, S, Chin, KL, Choi, J-YJ, Chu, D-T, Chung, S-C, Cirillo, M, Coffey, S, Conti, S, Costa, VM, Cundiff, DK, Dadras, O, Dagnew, B, Dai, X, Damasceno, AAM, Dandona, L, Dandona, R, Davletov, K, De la Cruz-Góngora, V, De la Hoz, FP, De Neve, J-W, Denova-Gutiérrez, E, Derbew Molla, M, Derseh, BT, Desai, R, Deuschl, G, Dharmaratne, SD, Dhimal, M, Dhungana, RR, Dianatinasab, M, Diaz, D, Djalalinia, S, Dokova, K, Douiri, A, Duncan, BB, Duraes, AR, Eagan, AW, Ebtehaj, S, Eftekhari, A, Eftekharzadeh, S, Ekholuenetale, M, El Nahas, N, Elgendy, IY, Elhadi, M, El-Jaafary, SI, Esteghamati, S, Etisso, AE, Eyawo, O, Fadhil, I, Faraon, EJA, Faris, PS, Farwati, M, Farzadfar, F, Fernandes, E, Fernandez Prendes, C, Ferrara, P, Filip, I, Fischer, F, Flood, D, Fukumoto, T, Gad, MM, Gaidhane, S, Ganji, M, Garg, J, Gebre, AK, Gebregiorgis, BG, Gebregzabiher, KZ, Gebremeskel, GG, Getacher, L, Obsa, AG, Ghajar, A, Ghashghaee, A, Ghith, N, Giampaoli, S, Gilani, SA, Gill, PS, Gillum, RF, Glushkova, EV, Gnedovskaya, EV, Golechha, M, Gonfa, KB, Goudarzian, AH, Goulart, AC, Guadamuz, JS, Guha, A, Guo, Y, Gupta, R, Hachinski, V, Hafezi-Nejad, N, Haile, TG, Hamadeh, RR, Hamidi, S, Hankey, GJ, Hargono, A, Hartono, RK, Hashemian, M, Hashi, A, Hassan, S, Hassen, HY, Havmoeller, RJ, Hay, SI, Hayat, K, Heidari, G, Herteliu, C, Holla, R, Hosseini, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Humayun, A, Iavicoli, I, Ibeneme, CU, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Iqbal, U, Irvani, SSN, Shariful Islam, Sheikh, Islam, RM, Iso, H, Iwagami, M, Jain, V, Javaheri, T, Jayapal, SK, Jayaram, S, Jayawardena, R, Jeemon, P, Jha, RP, Jonas, JB, Jonnagaddala, J, Joukar, F, Jozwiak, JJ, Jürisson, M, Kabir, A, Kahlon, T, Kalani, R, Kalhor, R, Kamath, A, Kamel, I, Kandel, H, Kandel, A, Karch, A, Kasa, AS, Katoto, PDMC, Kayode, GA, Khader, YS, Khammarnia, M, Khan, MS, Khan, MN, Khan, M, Khan, EA, Khatab, K, Kibria, GMA, Kim, YJ, Kim, GR, Kimokoti, RW, Kisa, S, Kisa, A, Kivimäki, M, Kolte, D, Koolivand, A, Korshunov, VA, Koulmane Laxminarayana, SL, Koyanagi, A, Krishan, K, Krishnamoorthy, V, Kuate Defo, B, Kucuk Bicer, B, Kulkarni, V, Kumar, GA, Kumar, N, Kurmi, OP, Kusuma, D, Kwan, GF, La Vecchia, C, Lacey, B, Lallukka, T, Lan, Q, Lasrado, S, Lassi, ZS, Lauriola, P, Lawrence, WR, Laxmaiah, A, LeGrand, KE, Li, M-C, Li, B, Li, S, Lim, SS, Lim, L-L, Lin, H, Lin, Z, Lin, R-T, Liu, X, Lopez, AD, Lorkowski, S, Lotufo, PA, Lugo, A, M, NK, Madotto, F, Mahmoudi, M, Majeed, A, Malekzadeh, R, Malik, AA, Mamun, AA, Manafi, N, Mansournia, MA, Mantovani, LG, Martini, S, Mathur, MR, Mazzaglia, G, Mehata, S, Mehndiratta, MM, Meier, T, Menezes, RG, Meretoja, A, Mestrovic, T, Miazgowski, B, Miazgowski, T, Michalek, IM, Miller, TR, Mirrakhimov, EM, Mirzaei, H, Moazen, B, Moghadaszadeh, M, Mohammad, Y, Mohammad, DK, Mohammed, S, Mohammed, MA, Mokhayeri, Y, Molokhia, M, Montasir, AA, Moradi, G, Moradzadeh, R, Moraga, P, Morawska, L, Moreno Velásquez, I, Morze, J, Mubarik, S, Muruet, W, Musa, KI, Nagarajan, AJ, Nalini, M, Nangia, V, Naqvi, AA, Narasimha Swamy, S, Nascimento, BR, Nayak, VC, Nazari, J, Nazarzadeh, M, Negoi, RI, Neupane Kandel, S, Nguyen, HLT, Nixon, MR, Norrving, B, Noubiap, JJ, Nouthe, BE, Nowak, C, Odukoya, OO, Ogbo, FA, Olagunju, AT, Orru, H, Ortiz, A, Ostroff, SM, Padubidri, JR, Palladino, R, Pana, A, Panda-Jonas, S, Parekh, U, Park, E-C, Parvizi, M, Pashazadeh Kan, F, Patel, UK, Pathak, M, Paudel, R, Pepito, VCF, Perianayagam, A, Perico, N, Pham, HQ, Pilgrim, T, Piradov, MA, Pishgar, F, Podder, V, Polibin, RV, Pourshams, A, Pribadi, DRA, Rabiee, N, Rabiee, M, Radfar, A, Rafiei, A, Rahim, F, Rahimi-Movaghar, V, Ur Rahman, MH, Rahman, Muhammad, Rahmani, AM, Rakovac, I, Ram, P, Ramalingam, S, Rana, J, Ranasinghe, P, Rao, SJ, Rathi, P, Rawal, L, Rawasia, WF, Rawassizadeh, R, Remuzzi, G, Renzaho, AMN, Rezapour, A, Riahi, SM, Roberts-Thomson, RL, Roever, L, Rohloff, P, Romoli, M, Roshandel, G, Rwegerera, GM, Saadatagah, S, Saber-Ayad, MM, Sabour, S, Sacco, S, Sadeghi, M, Saeedi Moghaddam, S, Safari, S, Sahebkar, A, Salehi, S, Salimzadeh, H, Samaei, M, Samy, AM, Santos, IS, Santric-Milicevic, MM, Sarrafzadegan, N, Sarveazad, A, Sathish, T, Sawhney, M, Saylan, M, Schmidt, MI, Schutte, AE, Senthilkumaran, S, Sepanlou, SG, Sha, F, Shahabi, S, Shahid, I, Shaikh, MA, Shamali, M, Shamsizadeh, M, Shawon, MSR, Sheikh, A, Shigematsu, M, Shin, M-J, Shin, JI, Shiri, R, Shiue, I, Shuval, K, Siabani, S, Siddiqi, TJ, Silva, DAS, Singh, JA, Mtech, AS, Skryabin, VY, Skryabina, AA, Soheili, A, Spurlock, EE, Stockfelt, L, Stortecky, S, Stranges, S, Suliankatchi Abdulkader, R, Tadbiri, H, Tadesse, EG, Tadesse, DB, Tajdini, M, Tariqujjaman, M, Teklehaimanot, BF, Temsah, M-H, Tesema, AK, Thakur, B, Thankappan, KR, Thapar, R, Thrift, AG, Timalsina, B, Tonelli, M, Touvier, M, Tovani-Palone, MR, Tripathi, A, Tripathy, JP, Truelsen, TC, Tsegay, GM, Tsegaye, GW, Tsilimparis, N, Tusa, BS, Tyrovolas, S, Umapathi, KK, Unim, B, Unnikrishnan, B, Usman, MS, Vaduganathan, M, Valdez, PR, Vasankari, TJ, Velazquez, DZ, Venketasubramanian, N, Vu, GT, Vujcic, IS, Waheed, Y, Wang, Y, Wang, F, Wei, J, Weintraub, RG, Weldemariam, AH, Westerman, R, Winkler, AS, Wiysonge, CS, Wolfe, CDA, Wubishet, BL, Xu, G, Yadollahpour, A, Yamagishi, K, Yan, LL, Yandrapalli, S, Yano, Y, Yatsuya, H, Yeheyis, TY, Yeshaw, Y, Yilgwan, CS, Yonemoto, N, Yu, C, Yusefzadeh, H, Zachariah, G, Zaman, SB, Zaman, MS, Zamanian, M, Zand, R, Zandifar, A, Zarghi, A, Zastrozhin, MS, Zastrozhina, A, Zhang, Z-J, Zhang, Y, Zhang, W, Zhong, C, Zou, Z, Zuniga, YMH, and Murray, CJL

Abstract

Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019.Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019.Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside hi

Published
2020

44. Barriers to green supply chain management: An emerging economy context

Author

Tumpa, TJ, Ali, SM, Rahman, MH, Paul, SK, Chowdhury, P, and Rehman Khan, SA

Subjects
Environmental Sciences
Abstract

© 2019 Elsevier Ltd Green supply chain management is attracting increasing attention as a way to decrease the adverse environmental effects of industries worldwide. However, considering the context of an emerging economy like Bangladesh, green supply chain management is still in its inception and has not been widely embraced in the textile industry, and therefore barriers hindering its adoption in emerging economy context demand a comprehensive investigation. This research reviews the viewpoints and hurdles in adopting green supply chain management practices in the context of the Bangladeshi textile industry. A questionnaire survey of Bangladeshi textile practitioners of operations and supply chain management division, having a sample size of thirty, was undertaken to identify the barriers, and a hierarchical cluster analysis technique was used in the detailed analysis of this data. Opinions were sought from experts on the significance of the resulting clusters, considering the relative importance of the barriers. Fifteen barriers to the adoption of green supply chain management were identified in the review of the literature, with these barriers then analyzed by using the data collected from Bangladeshi textile industry practitioners. The research indicates that the most important barrier is that there is low demand from customers and financial constraint resulting from short term little financial benefit to businesses, with lack of government regulations also a commonly faced barrier in adopting green supply chain initiatives. This study will provide valuables insights to practitioners and relevant policy makers about the barriers prevailing in the emerging economies towards the adoption of green supply chain management practices, which, in turn, can guide to undertake appropriate steps for alleviating those barriers.

Published
2019

45. A grey approach to predicting healthcare performance

Author

Rahman, MH, Tumpa, TJ, Ali, SM, and Paul, SK

Subjects
Electrical & Electronic Engineering
Abstract

© 2018 Elsevier Ltd The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value.

Published
2019

48. Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh

Author

Moktadir, MA, Rahman, T, Rahman, MH, Ali, SM, and Paul, SK

Subjects
Environmental Sciences
Abstract

© 2017 Elsevier Ltd Sustainable manufacturing practices and the circular economy have recently received significant attention in academia and within industries to improve supply chain practices. Manufacturing industries have started adopting sustainable manufacturing practices and a circular economy in their supply chain to mitigate environmental concerns, as sustainable manufacturing practices and a circular economy result in the reduction of waste generation and energy and material usage. The leather industry, in spite of it contributing remarkably to a country's economic growth and stability, does not bear a good image because of its role in polluting the environment. Therefore, the leather industries of Bangladesh are trying to implement sustainable manufacturing practices as a part of undertaking green supply chain initiatives to remedy their image with the buyer and to comply with government rules and regulations. The main contribution of this study is to assess, prioritize and rank the drivers of sustainable manufacturing practices in the leather industries of Bangladesh. We have used graph theory and a matrix approach to examine the drivers. The results show that knowledge of the circular economy is paramount to implementing sustainable manufacturing practices in the leather industry of Bangladesh. This study will assist managers of leather companies to formulate strategies for the optimum utilization of available resources, as well as for the reduction of waste in the context of the circular economy.

Published
2018

49. Examining price and service competition among retailers in a supply chain under potential demand disruption

Author

Ali, SM, Rahman, MH, Tumpa, TJ, Moghul Rifat, AA, and Paul, SK

Subjects
ComputingMilieux_GENERAL, Marketing
Abstract

© 2017 Elsevier Ltd Supply chain disruptions management has attracted significant attention among researchers and practitioners. The paper aims to examine the effect of potential market demand disruptions on price and service level for competing retailers. To investigate the effect of potential demand disruptions, we consider both a centralized and a decentralized supply chain structure. To analyze the decentralized supply chain, the Manufacturing Stackelberg (MS) game theoretical approach was undertaken. The analytical results were tested using several numerical analyses. It was shown that price and service level investment decisions are significantly influenced by demand disruptions to retail markets. For example, decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions. This research may aid managers to analyze disruptions prone market and to make appropriate decision for price and service level. The manufacturer or the retailers will also be able to better determine when to close a market based on the proposed analysis by considering anticipated disruptions. The benefits and usefulness of the proposed approach are explained through a real-life case adopted from a toy supply chain in Bangladesh.

Published
2018

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