Detecting spatio-temporal mortality clusters of European countries by sex and ag

[EN] Background: Mortality decreased in European Union (EU) countries during the last century. Despite these similar trends, there are still considerable differences in the levels of mortality between Eastern and Western European countries. Sub-group analysis of mortality in Europe for different age and sex groups is common, however to our knowledge a spatio-temporal methodology as in this study has not been applied to detect significant spatial dependence and interaction with time. Thus, the objective of this paper is to quantify the dynamics of mortality in Europe and detect significant clusters of mortality between European countries, applying spatio-temporal methodology. In addition, the joint evolution between the mortality of European countries and their neighbours over time was studied. Methods: The spatio-temporal methodology used in this study takes into account two factors: time and the geographical location of countries and, consequently, the neighbourhood relationships between them. This methodology was applied to 26 European countries for the period 1990-2012. Results: Principally, for people older than 64 years two significant clusters were obtained: one of high mortality formed by Eastern European countries and the other of low mortality composed of Western countries. In contrast, for ages below or equal to 64 years only the significant cluster of high mortality formed by Eastern European countries was observed. In addition, the joint evolution between the 26 European countries and their neighbours during the period 1990-2012 was confirmed. For this reason, it can be said that mortality in EU not only depends on differences in the health systems, which are a subject to national discretion, but also on supra-national developments. Conclusions: This paper proposes statistical tools which provide a clear framework for the successful implementation of development public policies to help the UE meet the challenge of rethinking its social model (Social Security and health care) and make it sustainable in the medium term.The authors are grateful for the financial support provided by the Ministry of Economy and Competitiveness, project MTM2013-45381-P. Adina Iftimi gratefully acknowledges financial support from the MECyD (Ministerio de Educacion, Cultura y Deporte, Spain) Grant FPU12/04531. Francisco Montes is grateful for the financial support provided by the Spanish Ministry of Economy and Competitiveness, project MTM2016-78917-R. The research by Patricia Carracedo and Ana Debon has been supported by a grant from the Mapfre Foundation.Carracedo-Garnateo, P.; Debón Aucejo, AM.; Iftimi, A.; Montes-Suay, F. (2018). Detecting spatio-temporal mortality clusters of European countries by sex and ag. International Journal for Equity in Health. 17:1-19. https://doi.org/10.1186/s12939-018-0750-zS11917Anderson TW, Goodman LA. Statistical Inference about Markov Chains. Ann Math Stat. 1957; 28(1):89–110.Anselin L. Local Indicators of Spatial Association–LISA. Geographical Anal. 1995; 27(2):93–115.Bilbao-Ubillos J. Is there still such a thing as the ‘European social model’?. Int J Soc Welf. 2016; 25:110–25.Bivand R. spdep: Spatial Dependence:Weighting Schemes, Statistics and Models. 2012. R package version 0.5-53. http://CRAN.R-project.org/package=spdep .Bivand R, Hauke J, Kossowski T. Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods. Geographical Anal. 2013; 45(2):150–79.Bivand R, Keitt T, Rowlingson B. rgdal: Bindings for the Geospatial Data Abstraction Library. 2016. R package version 1.1-10. https://CRAN.R-project.org/package=rgdal .Bivand R, Lewin-Koh N. maptools: Tools for Reading and Handling Spatial Objects. 2016. R package version 0.8-39 https://CRAN.R-project.org/package=maptools .Bonneux L, Huisman C. de Beer J. Mortality in 272 European regions, 2002-2004: an update. Eur J Epidemiol. 2010; 25(1):77–85. Reporting year: 2010.Charpentier A. Computational Actuarial Science with R. Chapman y Hall/CRC. 2014.Cliff AD, Ord JK. Spatial autocorrelation. London: Pion; 1973.Cutler D, Deaton A, Lleras-Muney A. The Determinants of Mortality. J Econ Perspect. 2006; 20(3):97–120.Debón A, Chaves L, Haberman S, Villa F. Characterization of between-group inequality of longevity in European Union countries. Insur Math Econ. 2017; 75:151–65.Fleiss J, Levin B, Paik M. Statistical Methods for Rates and Proportions: Wiley; 2013.Gordon M. Gmisc: Descriptive Statistics, Transition Plots, and More. 2016. R package version 1.3.1. https://CRAN.R-project.org/package=Gmisc .Hinde A. Demographic methods. Routledge: Routledge; 1998.Hyndman RJ, Booth H, Tickle L, Maindonald J. demography: Forecasting mortality, fertility, migration and population data. 2014. package version 1.18. https://CRAN.R-project.org/package=demography .Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). 2016. Available at www.mortality.org or www.humanmortality.de (data downloaded on 12th July 2016).Hatzopoulos P, Haberman S. Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data. Insurance Math Econ. 2013; 52(2):320–37.Iftimi A, Montes F, Santiyán AM, Martínez-Ruiz F. Space–time airborne disease mapping applied to detect specific behaviour of varicella in Valencia, Spain Spatial Spatio-Temporal Epidemiol. 2015; 14:33–44.Julious S, Nicholl J, George S. Why do we continue to use standardized mortality ratios for small area comparisons?. J Public Health. 2001; 23(1):40–6.Laurent T, Ruiz-Gazen A, Thomas-Agnan C. GeoXp: An R package for exploratory spatial data analysis. J Stat Softw. 2012; 47(2):1–23.Leon DA. Trends in European life expectancy: a salutary view. Int J Epidemiol. 2011; 40:271–7.Li H, Li L, Wu B, Xiong Y. The End of Cheap Chinese Labor. J Econ Perspect. 2013; 26(4):57–74.Mackenbach JP, Karanikolos M, McKee M. The unequal health of Europeans: successes and failures of policies. The Lancet. 2013; 381(9872):1125–34.Meslé F. Mortality in Central and Eastern Europe: Long-term trends and recent upturns. Demographic Res. 2004; 2:45–70.Meslé F, Vallin J. Mortality in Europe: The divergence between East and West. Population (English Edition). 2002; 57(1):157–97.Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950; 37(1-2):17–23.Moran PAP. A Test for the Serial Independence of Residuals. Biometrika. 1950; 37(1/2):178–81.Neuwirth E. RColorBrewer: ColorBrewer Palettes. R package version. 2014; 1:1–2. https://CRAN.R-project.org/package=RColorBrewer .Oleckno WA. Epidemiology: concepts and methods: Waveland Press, Inc.; 2008.Quah D. Galton’s Fallacy and Tests of the Convergence Hypothesis. Scand J Econ. 1993; 95(4):427–43.R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. 2015. https://www.R-project.org/ .Rey S. In: Fischer MM, Nijkamp P, (eds).Spatial Dynamics and Space-Time Data Analysis. Berlin, Heidelberg: Springer: Handbook of Regional Science; 2014, pp. 1365–83.Rey SJ. Spatial Empirics for Economic Growth and Convergence. Geogr Anal. 2001; 33(3):195–214.Riffe T. Reading Human Fertility Database and Human Mortality Database data into R. Technical Report TR-2015-004, MPIDR. 2015.Schofield R, Reher D, Bideau A. The Decline of Mortality in Europe. International studies in demography. Oxford: Clarendon Press; 1991.Shaw M, Orford S, Brimblecombe N, Dorling D. Widening inequality in mortality between 160 regions of 15 European countries in the early 1990s. Soc Sci Med. 2000; 50(7-8):1047–58.Spinakis A, Anastasiou G, Panousis V, Spiliopoulos K, Palaiologou S, Yfantopoulos J. Expert Review and Proposals for Measurement of Health Inequalities in the European Union. European Commission. Technical report,Luxembourg: European Commission Directorate General for Health and Consumers; 2011. http://ec.europa.eu/health/social_determinants/docs/full_quantos_en.pdf .Staehr K. Economic transition in Estonia. Background, reforms and results In: Rindzeviciute E, editor. Contemporary Change in Estonia. Baltic and East European Studies. Sodertorns hogskola: Baltic and East European Studies: 2004. p. 437–67.Trnka L, Dankova D, Zitova J, Cimprichova L, Migliori GB, Clancy L, Zellweger J. Survey of BCG vaccination policy in Europe: 1994-96. Bull World Health Organ. 1998; 76(1):85–91.United Nations Inter–agency Group for Child Mortality Estimation. Levels & Trends in Child Mortality: Report 2013. New York: Technical report, United Nations Children’s Fund; 2013. Avaliable at www.who.int/maternal_child_adolescent/documents/levels_trends_child_mortality_2013.pdf Accessed 27 Oct 2016.Vågerö D. The east–west health divide in Europe: Growing and shifting eastwards. Eur Rev. 2010; 18(01):23–34.Vaupel JW, Zhang Z, van Raalte AA, Vaupel JW, Zhang Z, van Raalte AA. Life expectancy and disparity: an international comparison of life table data. BMJ Open. 2011; 1:e000128.Wickham H, Chang W. devtools: Tools to Make Developing R Packages Easier. R package version 1.11.1. 2016. https://CRAN.R-project.org/package=devtools .Wilcox R. Introduction to robust estimation and hypothesis testing, 3rd Edition.San Diego: Academic Press; 2012

K(2P)18.1 translates T cell receptor signals into thymic regulatory T cell development

It remains largely unclear how thymocytes translate relative differences in T cell receptor (TCR) signal strength into distinct developmental programs that drive the cell fate decisions towards conventional (Tconv) or regulatory T cells (Treg). Following TCR activation, intracellular calcium (Ca2+) is the most important second messenger, for which the potassium channel K(2P)18.1 is a relevant regulator. Here, we identify K(2P)18.1 as a central translator of the TCR signal into the thymus-derived Treg (tTreg) selection process. TCR signal was coupled to NF-kappa B-mediated K(2P)18.1 upregulation in tTreg progenitors. K(2P)18.1 provided the driving force for sustained Ca2+ influx that facilitated NF-kappa B- and NFAT-dependent expression of FoxP3, the master transcription factor for Treg development and function. Loss of K(2P)18.1 ion-current function induced a mild lymphoproliferative phenotype in mice, with reduced Treg numbers that led to aggravated experimental autoimmune encephalomyelitis, while a gain-of-function mutation in K(2P)18.1 resulted in increased Treg numbers in mice. Our findings in human thymus, recent thymic emigrants and multiple sclerosis patients with a dominant-negative missense K(2P)18.1 variant that is associated with poor clinical outcomes indicate that K(2P)18.1 also plays a role in human Treg development. Pharmacological modulation of K(2P)18.1 specifically modulated Treg numbers in vitro and in vivo. Finally, we identified nitroxoline as a K(2P)18.1 activator that led to rapid and reversible Treg increase in patients with urinary tract infections. Conclusively, our findings reveal how K(2P)18.1 translates TCR signals into thymic T cell fate decisions and Treg development, and provide a basis for the therapeutic utilization of Treg in several human disorders.Peer reviewe

Rivaroxaban versus standard anticoagulation for acute venous thromboembolism in childhood. Design of the EINSTEIN-Jr phase III study

Abstract Background Venous thromboembolism (VTE) is a relatively rare condition in childhood with treatment mainly based on extrapolation from studies in adults. Therefore, clinical trials of anticoagulation in children require novel approaches to deal with numerous challenges. The EINSTEIN-Jr program identified pediatric rivaroxaban regimens commencing with in vitro dose finding studies followed by evaluation of children of different ages through phase I and II studies using extensive modeling to determine bodyweight-related doses. Use of this approach resulted in drug exposure similar to that observed in young adults treated with rivaroxaban 20 mg once-daily. Methods EINSTEIN-Jr phase III is a randomized, open-label, study comparing the efficacy and safety of rivaroxaban 20 mg-equivalent dose regimens with those of standard anticoagulation for the treatment of any types of acute VTE in children aged 0–18 years. A total of approximately 500 children are expected to be included during the 4-year study window. Flexibility of treatment duration is allowed with study treatment to be given for 3 months with the option to continue treatment in 3-month increments, up to a total of 12 months. However, based on most common current practice, children younger than 2 years with catheter-related thrombosis will have a main treatment period of 1 month with the option to prolong treatment in 1-month increments, up to a total of 3 months. Conclusions EINSTEIN-Jr will compare previously established 20 mg-equivalent rivaroxaban dosing regimens with standard anticoagulation for the treatment of VTE in children. Demonstration of similarity of disease, as well as equivalent rivaroxaban exposure and exposure-response will enable extrapolation of efficacy from adult trials, which is critical given the challenges of enrollment in pediatric anticoagulation trials. Trial registration Clinicaltrials.gov NCT02234843, registered on 9 September 2014

K18.1 translates T cell receptor signals into thymic regulatory T cell development.

It remains largely unclear how thymocytes translate relative differences in T cell receptor (TCR) signal strength into distinct developmental programs that drive the cell fate decisions towards conventional (Tconv) or regulatory T cells (Treg). Following TCR activation, intracellular calcium (Ca2+) is the most important second messenger, for which the potassium channel K2P18.1 is a relevant regulator. Here, we identify K2P18.1 as a central translator of the TCR signal into the thymus-derived Treg (tTreg) selection process. TCR signal was coupled to NF-κB-mediated K2P18.1 upregulation in tTreg progenitors. K2P18.1 provided the driving force for sustained Ca2+ influx that facilitated NF-κB- and NFAT-dependent expression of FoxP3, the master transcription factor for Treg development and function. Loss of K2P18.1 ion-current function induced a mild lymphoproliferative phenotype in mice, with reduced Treg numbers that led to aggravated experimental autoimmune encephalomyelitis, while a gain-of-function mutation in K2P18.1 resulted in increased Treg numbers in mice. Our findings in human thymus, recent thymic emigrants and multiple sclerosis patients with a dominant-negative missense K2P18.1 variant that is associated with poor clinical outcomes indicate that K2P18.1 also plays a role in human Treg development. Pharmacological modulation of K2P18.1 specifically modulated Treg numbers in vitro and in vivo. Finally, we identified nitroxoline as a K2P18.1 activator that led to rapid and reversible Treg increase in patients with urinary tract infections. Conclusively, our findings reveal how K2P18.1 translates TCR signals into thymic T cell fate decisions and Treg development, and provide a basis for the therapeutic utilization of Treg in several human disorders

Rivaroxaban compared with standard anticoagulants for the treatment of acute venous thromboembolism in children: a randomised, controlled, phase 3 trial.

BACKGROUND Treatment of venous thromboembolism in children is based on data obtained in adults with little direct documentation of its efficacy and safety in children. The aim of our study was to compare the efficacy and safety of rivaroxaban versus standard anticoagulants in children with venous thromboembolism. METHODS In a multicentre, parallel-group, open-label, randomised study, children (aged 0-17 years) attending 107 paediatric hospitals in 28 countries with documented acute venous thromboembolism who had started heparinisation were assigned (2:1) to bodyweight-adjusted rivaroxaban (tablets or suspension) in a 20-mg equivalent dose or standard anticoagulants (heparin or switched to vitamin K antagonist). Randomisation was stratified by age and venous thromboembolism site. The main treatment period was 3 months (1 month in children <2 years of age with catheter-related venous thromboembolism). The primary efficacy outcome, symptomatic recurrent venous thromboembolism (assessed by intention-to-treat), and the principal safety outcome, major or clinically relevant non-major bleeding (assessed in participants who received ≥1 dose), were centrally assessed by investigators who were unaware of treatment assignment. Repeat imaging was obtained at the end of the main treatment period and compared with baseline imaging tests. This trial is registered with ClinicalTrials.gov, number NCT02234843 and has been completed. FINDINGS From Nov 14, 2014, to Sept 28, 2018, 500 (96%) of the 520 children screened for eligibility were enrolled. After a median follow-up of 91 days (IQR 87-95) in children who had a study treatment period of 3 months (n=463) and 31 days (IQR 29-35) in children who had a study treatment period of 1 month (n=37), symptomatic recurrent venous thromboembolism occurred in four (1%) of 335 children receiving rivaroxaban and five (3%) of 165 receiving standard anticoagulants (hazard ratio [HR] 0·40, 95% CI 0·11-1·41). Repeat imaging showed an improved effect of rivaroxaban on thrombotic burden as compared with standard anticoagulants (p=0·012). Major or clinically relevant non-major bleeding in participants who received ≥1 dose occurred in ten (3%) of 329 children (all non-major) receiving rivaroxaban and in three (2%) of 162 children (two major and one non-major) receiving standard anticoagulants (HR 1·58, 95% CI 0·51-6·27). Absolute and relative efficacy and safety estimates of rivaroxaban versus standard anticoagulation estimates were similar to those in rivaroxaban studies in adults. There were no treatment-related deaths. INTERPRETATION In children with acute venous thromboembolism, treatment with rivaroxaban resulted in a similarly low recurrence risk and reduced thrombotic burden without increased bleeding, as compared with standard anticoagulants. FUNDING Bayer AG and Janssen Research & Development