Health-related quality of life (HRQoL) in a population at risk of type 2 diabetes: a cross-sectional study in two Latin American cities

Purpose: The purpose of this study was to describe the health-related quality of life (HRQoL) characteristics in a population at risk of developing type 2 diabetes in Barranquilla and Bogotá, Colombia. Methods: A cross-sectional study with 1135 participants older than 30 years-of-age recruited in Bogotá D.C., and Barranquilla by cluster sampling in 2018 to 2019. The Finnish Diabetes Risk Score (FINDRISC) was used to detect participants at risk of developing type 2 diabetes (T2D). HRQoL was assessed using the EQ-5D-3L questionnaire. Unadjusted and adjusted logistic regression models were used to calculate odds ratios (OR) and their corresponding 95% confidence intervals CI). Results: Moderate or extreme problems appeared more frequently in the dimensions of Pain/Discomfort (60.8%) and Anxiety/Depression (30.8%). The mean score of the EQ-VAS was 74.3 (± 17.3), significantly larger in the state of complete health (11111) compared with those with problems in more than one of the quality-of-life dimensions. Being female and living in Bogota D.C., were associated with greater odds of reporting problems in the Pain (OR 1.6; 95% CI 1.2–2.2) and Discomfort dimensions (OR 1.6; 95% CI 1.2–2.0) respectively and Anxiety/Depression (OR 1.9; 95% CI 1.3–2.7), (OR 9.1; 95% CI 6.6–12.4), respectively. Conclusions: As living place and sex were associated with dimensions of Pain/Discomfort and Anxiety/Depression in the HRQoL in people at risk of T2D, greater attention should be paid to these determinants of HRQoL to design and reorient strategies with a territorial and gender perspective to achieve better health outcomes. Plain English summary: Diabetes is one of the four non-communicable diseases with increasing prevalence in the world, which has made it a serious public health problem. In Colombia, in 2019 diabetes affected 8.4% of the Colombian adult population and more than one million Colombian adults of this age group have hidden or undetected diabetes. This disease is not only characterized by increased premature mortality, loss of productivity, and economic impact, but it also involves a deterioration in the quality of life of people with diabetes with their respective families. However, very Little is known about health-related quality of life (HRQoL) in a population at risk or with prediabetes. This study has evaluated the quality of life in patients at risk of diabetes and their behavior with some variables as sociodemographic, lifestyle, history, and established their diference in two territories of the Colombian Caribbean. The results of this study indicate that the HRQoL of people at risk of type 2 diabetes is afected by factors such as gender, city, dysglycemia, medication for hypertension and education level. Therefore, greater attention should be paid to these determinants of HRQL to design and implement strategies that reduce this risk of developing type 2 diabetes, prevent prediabetes and improve the quality of life in prediabetic or diabetic patients.This project was financed by the Colombian National Program for Science, Technology, and Health Innovation (COLCIENCIAS) in line with the theme focused on chronic non-transmutable diseases. This was in response to the 744 Call for projects in science, technology, and health innovation of 2016 in association with the Universidad del Norte of Barranquilla and the Colombian Diabetes Association.S

Estado del arte del proyecto

Los semilleros de investigación en la Universidad de la Costa se constituyen en un referente que dinamiza los procesos académicos institucionales en el marco de la función sustantiva de la investigación. Sus participantes, profesores y estudiantes generan conocimiento científico que posibilita, por una parte, avanzar en el saber disciplinar y por otra parte generar entre los estudiantes el gusto y la pasión por la investigación. La presente investigación tiene como propósito poder establecer los factores asociados a los semilleros de investigación que pudieran estar influyendo en el buen rendimiento académico de los estudiantes que participan en ellos. El semillero de investigación es un espacio para fomentar la cultura investigativa en la comunidad académica, permitiendo la apropiación de herramientas investigativas y el fortalecimiento de habilidades metodológicas, cognitivas y sociales que permitan el acercamiento y reconocimiento de la problemática en estudio

Comparative Analysis of Clinical Practice Guidelines for the Pharmacological Treatment of Type 2 Diabetes Mellitus in Latin America

Purpose of review: Type 2 diabetes mellitus (T2DM) is one of the leading causes of death and disability in the world. The majority of diabetes deaths (> 80%) occur in low- and middle-income countries, which are predominant in Latin America. Therefore, the purpose of this article is to compare the clinical practice guideline (CPG) for the pharmacological management of T2DM in Latin America (LA) with international reference guidelines. Recent findings: Several LA countries have recently developed CPGs. However, the quality of these guidelines is unknown according to the AGREE II tool and taking as reference three CPGs of international impact: American Diabetes Association (ADA), European Diabetes Association (EASD), and Latin American Diabetes Association (ALAD). Ten CPGs were selected for analysis. The ADA scored > 80% on the AGREE II domains and was selected as the main comparator. Eighty percent of LA CPGs were developed before 2018. Only one was not recommended (all domains < 60%). The CPGs in LA have good quality but are outdated. They have significant gaps compared to the reference. There is a need for improvement, as proposing updates every three years to maintain the best available clinical evidence in all guidelines.This research was funded by SANOFI and was developed by Sapyens SAS BIC.S

Unraveling structural and compositional information in 3D FinFET electronic devices

Non-planar Fin Field Effect Transistors (FinFET) are already present in modern devices. The evolution from the well-established 2D planar technology to the design of 3D nanostructures rose new fabrication processes, but a technique capable of full characterization, particularly their dopant distribution, in a representative (high statistics) way is still lacking. Here we propose a methodology based on Medium Energy Ion Scattering (MEIS) to address this query, allowing structural and compositional quantification of advanced 3D FinFET devices with nanometer spatial resolution. When ions are backscattered, their energy losses unfold the chemistry of the different 3D compounds present in the structure. The FinFET periodicity generates oscillatory features as a function of backscattered ion energy and, in fact, these features allow a complete description of the device dimensions. Additionally, each measurement is performed over more than thousand structures, being highly representative in a statistical meaning. Finally, independent measurements using electron microscopy corroborate the proposed methodolog

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project

[EN] Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.The research leading to these results has received funding from the European Commission under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement no 600914.Fico, G.; Hernandez, L.; Cancela, J.; Dagliati, A.; Sacchi, L.; Martinez-Millana, A.; Posada, J.... (2019). What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project. BMC Medical Informatics and Decision Making. 19(1):1-16. https://doi.org/10.1186/s12911-019-0887-8116191World Health Statistics 2018, Monitoring health for the SDGs, World Health Organization. Available at: https://www.who.int/gho/publications/world_health_statistics/en/ , last Accessed 09 Aug 2019.Kane R, Priester R, Totten A. Meeting the challenge of chronic illness. Baltimore: The Johns Hopkins University Press; 2005.Colagiuri, S., Kent, J., Kainu, T., Sutherland, S., Vuik, S. Rising to the challenge: preventing and managing type 2 diabetes, report of the WISH diabetes forum. 2015. Available from: http://www.wish.org.qa/wp-content/uploads/.../WISH_Diabetes_Forum_08.01.15_WEB-1.pdf . Accessed 09 Aug 2019.IDFD Atlas. 2017. Available from: http://www.diabetesatlas.org/resources/2017-atlas.html . Accessed 11 Feb 2018.American Diabetes Association Consensus Panel. Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004;27(9):2262–5.Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163.Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900.Zarkogianni K, Litsa E, Mitsis K, Wu P, Kaddi CD, Cheng C, Wang MD, Nikita KS. A review of emerging technologies for the management of diabetes mellitus. IEEE Trans Biomed Eng. 2015;62(12):2735–49.Garg AX, Adhikari NK, McDonald H, Rosas-Arellano M, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.Roshanov PS, et al. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implement Sc. 2011;6(1):92.Roshanov PS, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657.Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. Int J Med Inform. 2015;84(12):1009–18.Patel VL, Kannampallil TG. Cognitive informatics in biomedicine and healthcare. J Biomed Inform. 2015;53:3–14.Zhang J. Human-centered computing in health information systems part 1: analysis and design. J Biomed Inform. 2005;38(1):1–3.Rinkus S, Walji M, Johnson-Throop KA, Malin M, Turley JP, Smith JW, Zhang J. Human-centered design of a distributed knowledge management system. J Biomed Inform. 2005;38:4–17.Nemeth CP, Nunnally M, O’Connor M, Klock PA, Cook R. Getting to the point: developing IT for the sharp end of healthcare. J Biomed Inform. 2005;38:18–25.Xiao Y. Artifacts and collaborative work in healthcare: methodological, theoretical and technological implications of the tangible. J Biomed Inform. 2005;38:26–33.Malhotra S, Laxmisan A, Keselman A, Zhang J, Patel VL. Designing the design phase of critical care devices: a cognitive approach. J Biomed Inform. 2005;38:34–50.Samaras GM, Horst RL. A systems engineering perspective on the human-centered design of health information systems. J Biomed Inform. 2005;38:61–74.Johnson CM, Johnson TR, Zhang J. A user-centered framework for redesigning health care interfaces. J Biomed Inform. 2005;38:75–87.Patterson ES, Boebbeling BN, Fung CH, Militello L, Anders S, Asch SM. Identifying barriers to the effective use of clinical reminders: bootstrapping multiple methods. J Biomed Inform. 2005;38:189–99.Laxmisan A, Malhotra S, Keselman A, Johnson TR, Patel VL. Decisions about critical events in device-related scenarios as a function of expertise. J Biomed Inform. 2005;38:200–12.Ginsburg GE. Human factors engineering: a tool for medical device evaluation in hospital procurement decision-making. J Biomed Inform. 2005;38:213–9.Reddy M, McDonald DW, Pratt W, Shabot MM. Technology, work, and information flows: lessons from the implementation of a wireless alert pager system. J Biomed Inform. 2005;38:229–38.Despont-Gros C, Mueller H, Lovis C. Evaluating user interactions with clinical information systems: a model based on human–computer interaction models. J Biomed Inform. 2005;38:244–55.World Health Organization, 2009. Practical guidance for scaling up health service innovations.European Commission, 2015. European scaling up strategy on active and healthy ageing.van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, Seydel ER. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13(4):e111.Sacchi L, Dagliati A, Segagni D, Leporati P, Chiovato L, Bellazzi R. Improving risk-stratification of diabetes complications using temporal data mining. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE; 2015. p. 2131–4.Sambo F, Di Camillo B, Franzin A, Facchinetti A, Hakaste L, Kravic J, Fico G, et al. A Bayesian Network analysis of the probabilistic relations between risk factors in the predisposition to type 2 diabetes. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE; 2015. p. 2119–22.Van Velsen L, van Gemert-Pijnen L, Nijland N, Beaujean D, Van Steenbergen J. Personas: the linking pin in holistic design for eHealth. proc. eTELEMED; 2012.Van Velsen L, Wentzel J, Van Gemert-Pijnen JE. Designing eHealth that matters via a multidisciplinary requirements development approach. JMIR Res Protoc. 2013;2(1):e21.Maurya A. Running lean: iterate from plan A to a plan that works. United States: O’Reilly Media, Inc.; 2012.Wentzel J, Van Limburg M, Karreman J, Hendrix R, Van Gemert-Pijnen L. Co-creation with stakeholders: a Web 2.0 Antibiotic Stewardship Program. Proceedings of The Fourth International Conference on eHealth, Telemedicine, and Social Medicine: January 30, 2012 to February 4, 2012; Valencia. 2012:196–202.Morgan DL. Focus groups as qualitative research. Thousand Oaks: Sage; 1997.Saaty T. How to structure and make choices in complex problems. Hum Syst Manag. 1982;3:255–61.Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977;15:234–81.Pecchia L, Bath PA, Pendleton N, Bracale M: Web-based system for assessing risk factors for falls in community-dwelling elderly people using the analytic hierarchy process. International Journal of the Analytic Hierarchy Process. 2010;2(2):135–57.Fico G, Gaeta E, Arredondo MT, Pecchia L. Analytic hierarchy process to define the most important factors and related technologies for empowering elderly people in taking an active role in their health. J Med Syst. 2015;39(9):1–7.Goepel KD. Implementation of an online software tool for the Analytic Hierarchy Process (AHP-OS). Int J Anal Hierarchy Process. 2018;10(3):469–87. https://doi.org/10.13033/ijahp.v10i3.590 .Nielsen J. Ten usability heuristics. United States: Nielsen Norman Group; 2005.Brooke J. SUS-A quick and dirty usability scale. Usability Eval Ind. 1996;189(194):4–7.Hassenzahl M, Burmester M, Koller F. AttrakDiff: Ein Fragebogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In: Mensch & computer. Germany: Vieweg+ Teubner Verlag; 2003, 2003. p. 187–96.International Organization for Standardization. Ergonomics of human-system interaction: part 210: human-centred design for interactive systems. United States: ISO; 2010.Sauro J, Lewis JR. Quantifying the user experience: practical statistics for user research. Burlington: Morgan Kaufmann; 2012.Gülcü C. The complete log4j manual. QOS. ch; 2003.Nantz B. Open source. NET development: programming with NAnt, NUnit, NDoc, and More. United States: Addison-Wesley Professional; 2004.Borsci S, Federici S, Lauriola M. On the dimensionality of the system usability scale: a test of alternative measurement models. Cogn Process. 2009;10(3):193–7.Borsci S, Federici S, Bacci S, Gnaldi M, Bartolucci F. Assessing user satisfaction in the era of user experience: comparison of the SUS, UMUX, and UMUX-LITE as a function of product experience. Int J Hum Comput Interact. 2015;31(8):484–95.Nielsen J, Landauer TK. A mathematical model of the finding of usability problems. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems. 1993. pp. 206–13. ACM.Dagliati A, Sacchi L, Tibollo V, Cogni G, Teliti M, Martinez-Millana A, et al. A dashboard-based system for supporting diabetes care. J Am Med Inform Assoc. 2018;25(5):538–47.Fico G, et al. User requirements for incorporating diabetes modeling techniques in disease management tools. In: 6th European conference of the international federation for medical and biological engineering. Switzerland: Springer International Publishing; 2015.Cancela J, Hernandez L, Fico G, Waldmeyer MTA. Heuristic evaluation of a toolset for type 2 diabetes mellitus management. In: XIV Mediterranean conference on medical and biological engineering and computing. Switzerland: Springer International Publishing; 2016, 2016. p. 982–7.Borsci S, Uchegbu I, Buckle P, Ni Z, Walne S, Hanna GB. Designing medical technology for resilience: integrating health economics and human factors approaches. Expert Rev Med Devices. 2018;15(1):15–26

Reduction in the Risk of Peripheral Neuropathy and Lower Decrease in Kidney Function with Metformin, Linagliptin or Their Fixed-Dose Combination Compared to Placebo in Prediabetes: A Randomized Controlled Trial

Objective: To compare the effect of glucose-lowering drugs on peripheral nerve and kidney function in prediabetes. Methods: Multicenter, randomized, placebo-controlled trial in 658 adults with prediabetes treated for 1 year with metformin, linagliptin, their combination or placebo. Endpoints are small fiber peripheral neuropathy (SFPN) risk estimated by foot electrochemical skin conductance (FESC < 70 μSiemens) and estimated glomerular filtration rate (eGFR). Results: Compared to the placebo, the proportion of SFPN was reduced by 25.1% (95% CI:16.3-33.9) with metformin alone, by 17.3% (95% CI 7.4-27.2) with linagliptin alone, and by 19.5% (95% CI 10.1-29.0) with the combination linagliptin/metformin (p < 0.0001 for all comparisons). eGFR remained +3.3 mL/min (95% CI: 0.38-6.22) higher with the combination linagliptin/metformin than with the placebo (p = 0.03). Fasting plasma glucose (FPG) decreased more with metformin monotherapy -0.3 mmol/L (95%CI: -0.48; 0.12, p = 0.0009) and with the combination metformin/linagliptin -0.2 mmol/L (95% CI: -0.37; -0.03) than with the placebo (p = 0.0219). Body weight (BW) decreased by -2.0 kg (95% CI: -5.65; -1.65, p = 0.0006) with metformin monotherapy, and by -1.9 kg (95% CI: -3.02; -0.97) with the combination metformin/linagliptin as compared to the placebo (p = 0.0002). Conclusions: in people with prediabetes, a 1 year treatment with metformin and linagliptin, combined or in monotherapy, was associated with a lower risk of SFPN, and with a lower decrease in eGFR, than treatment with placebo.This research was funded by European Commission, FP7 EC-GA No. 279074; Boehringher Ingelheim, Ingelheim am Rhein, Germany (IIS Program. Grant number 1218.166); Merck Healthcare KGaA, Darmstadt, Germany (IIS number: EMR200084_621), Instituto de Salud Carlos III, Spain PI11/01653. The study funders were not involved in the design of the study, the collection, analysis and interpretation of data or writing the report and did not impose any restrictions regarding the publication of the report.S

Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetes

Objective: There are currently five widely used definition of prediabetes. We compared the ability of these to predict 5-year conversion to diabetes and investigated whether there were other cut-points identifying risk of progression to diabetes that may be more useful. Research design and methods: We conducted an individual participant meta-analysis using longitudinal data included in the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox regression models were used to obtain study-specific HRs for incident diabetes associated with each prediabetes definition. Harrell's C-statistics were used to estimate how well each prediabetes definition discriminated 5-year risk of diabetes. Spline and receiver operating characteristic curve (ROC) analyses were used to identify alternative cut-points. Results: Sixteen studies, with 76 513 participants and 8208 incident diabetes cases, were available. Compared with normoglycemia, current prediabetes definitions were associated with four to eight times higher diabetes risk (HRs (95% CIs): 3.78 (3.11 to 4.60) to 8.36 (4.88 to 14.33)) and all definitions discriminated 5-year diabetes risk with good accuracy (C-statistics 0.79-0.81). Cut-points identified through spline analysis were fasting plasma glucose (FPG) 5.1 mmol/L and glycated hemoglobin (HbA1c) 5.0% (31 mmol/mol) and cut-points identified through ROC analysis were FPG 5.6 mmol/L, 2-hour postload glucose 7.0 mmol/L and HbA1c 5.6% (38 mmol/mol). Conclusions: In terms of identifying individuals at greatest risk of developing diabetes within 5 years, using prediabetes definitions that have lower values produced non-significant gain. Therefore, deciding which definition to use will ultimately depend on the goal for identifying individuals at risk of diabetes.This work was supported by the National Health and Medical Research Council of Australia (grant number 1103242). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contract nos. HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I. ES was supported by NIH/NIDDK grant K24DK106414. The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN2682018000031, HHSN2682018000041, HHSN2682018000051, HHSN2682018000061 and HHSN2682018000071 from the National Heart, Lung, and Blood Institute (NHLBI). The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The Melbourne Collaborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The Multi-Ethnic Study of Atherosclerosis was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040 and UL1-TR-001079 from NCRR. The Population Study of Women in Gothenburg (PSWG) was financed in part by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement ALFGBG-720201. VIVA Study received grants 95/0029 and 06/90270 from the Instituto de Salud Carlos III, Spain.S

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO