Acute referral of patients from general practitioners: should the hospital doctor or a nurse receive the call?

<p>Abstract</p> <p>Background</p> <p>Surprisingly little is known about the most efficient organization of admissions to an emergency hospital. It is important to know, who should be in front when the GP requests an acute admission. The aim of the study was to analyse how experienced ED nurses perform when assessing requests for admissions, compared with hospital physicians.</p> <p>Methods</p> <p>Before- and after ED nurse assessment study, in which two cohorts of patients were followed from the time of request for admission until one month later. The first cohort of patients was included by the physicians on duty in October 2008. The admitting physicians were employed in the one of the specialized departments and only received request for admission within their speciality. The second cohort of patients was included by the ED in May 2009. They received all request from the GPs for admission, independent of the speciality in question.</p> <p>Results</p> <p>A total of 944 requests for admission were recorded. There was a non-significant trend towards the nurses admitting a smaller fraction of patients than the physicians (68 versus 74%). While the nurses almost never rejected an admission, the physicians did this in 7% of the requests. The nurses redirected 8% of the patients to another hospital, significantly more than the physicians with only 1%. (p < 0.0001). The nurses referred significantly more patients to the correct hospital than the doctors (78% vs. 70% p: 0.03). There were no differences in the frequency of unnecessary admissions between the groups. The self-reported use of time for assessment was twice as long for the physicians as for the nurses. (p < 0.0001).</p> <p>Conclusions</p> <p>We found no differences in the frequency of admitted patients or unnecessary admissions, but the nurses redirected significantly more patients to the right hospital according to the catchment area, and used only half the time for the assessment. We find, that nurses, trained for the assignment, are able to handle referrals for emergency admissions, but also advise the subject to be explored in further studies including other assessment models and GP satisfaction.</p

Distinctive effects of allochthonous and autochthonous organic matter on CDOM spectra in a tropical lake

Despite the increasing understanding about differences in carbon cycling between temperate and tropical freshwater systems, our knowledge on the importance of organic matter (OM) pools on light absorption properties in tropical lakes is very scarce. We performed a factorial mesocosm experiment in a tropical lake (Minas Gerais, Brazil) to evaluate the effects of increased concentrations of al-lochthonous and autochthonous OM, and differences in light availability on the light absorption characteristics of chromophoric dissolved organic matter (CDOM). Autochthonous OM deriving from phytoplankton (similar to Chl a) was stimulated by addition of nutrients, while OM from degradation of terrestrial leaves increased allochthonous OM, and neutral shading was used to manipulate light availability. Effects of the additions and shading on DOC, Chl a, nutrients, total suspended solid concentrations (TSM) and spectral CDOM absorption were monitored every 3 days. CDOM quality was characterized by spectral indices (S250-450, S275-295, S350-450, S-R and SUVA(254)). Effects of carbon sources and shading on the spectral CDOM absorption was investigated through principal component (PCA) and redundancy (RDA) analyses. The two different OM sources affected CDOM quality very differently and shading had minor effects on OM levels, but significant effects on OM quality, especially in combination with nutrient additions. Spectral indices (S250-450 and S-R) were mostly affected by allochthonous OM addition. The PCA showed that enrichment by allochthonous carbon had a strong effect on the CDOM spectra in the range between 300 and 400 nm, while the increase in autochthonous carbon increased absorption at wavelengths below 350 nm. Our study shows that small inputs of allochthonous OM can have large effects on the spectral light absorption compared to large production of autochthonous OM, with important implications for carbon cycling in tropical lakes.Peer reviewe

Linking shifts in bacterial community with changes in dissolved organic matter pool in a tropical lake

Bacterioplankton communities have a pivotal role in the global carbon cycle. Still the interaction between microbial community and dissolved organic matter (DOM) in freshwater ecosystems remains poorly understood. Here, we report results from a 12-day mesocosm study performed in the epilimnion of a tropical lake, in which inorganic nutrients and allochthonous DOM were supplemented under full light and shading. Although the production of autochthonous DOM triggered by nutrient addition was the dominant driver of changes in bacterial community structure, temporal covariations between DOM optical proxies and bacterial community structure revealed a strong influence of community shifts on DOM fate. Community shifts were coupled to a successional stepwise alteration of the DOM pool, with different fractions being selectively consumed by specific taxa Typical freshwater clades as Limnohabitans and Sporichthyaceae were associated with consumption of low molecular weight carbon, whereas Gammaproteobacteria and Flavobacteria utilized higher molecular weight carbon, indicating differences in DOM preference among Glades. Importantly. Verrucomicrobiaceae were important in the turnover of freshly produced autochthonous DOM, ultimately affecting light availability and dissolved organic carbon concentrations. Our findings suggest that taxonomically defined bacterial assemblages play definite roles when influencing DOM fate, either by changing specific fractions of the DOM pool or by regulating light availability and DOC levels. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

Habitat Model of Eelgrass in Danish Coastal Waters: Development, Validation and Management Perspectives

During the last century, eutrophication significantly reduced the depth distribution and density of the habitat forming eelgrass meadows (Zostera marina) in Danish coastal waters. Despite large reductions in nutrient loadings and improved water quality, Danish eelgrass meadows are currently not as widely distributed as expected from improvements in water clarity alone. This point to the importance of other environmental conditions such as sediment quality, wave exposure, oxygen conditions and water temperature that may limit eelgrass growth and contribute to constraining current distributions. Recently, detailed local models have been set up to evaluate the importance of such regulating factors in selected Danish coastal areas, but nationwide maps of eelgrass distribution and large-scale evaluations of regulating factors are still lacking. To provide such nationwide information, we applied a spatial habitat GIS modeling approach, which combines information on six key eelgrass habitat requirements (light availability, water temperature, salinity, frequency of low oxygen concentration, wave exposure, and sediment type) for which we were able to obtain national coverage. The modeled potential current distribution area of Danish eelgrass meadows was 2204 km2 compared to historical estimates of around 7000 km2, indicating a great potential for further distribution. While validating the modeled eelgrass distribution area in three areas (83–111 km2) that hold large eelgrass meadows, we found an agreement of 67% with in situ monitoring data and 77% for eelgrass areas as identified from summer orthophotos. The GIS model predicted higher coverage especially in shallow waters and near the depth limits. Areas of disagreement between GIS-modeled and observed coverage generally exhibited higher exposure level, mean summer temperature and salinity compared to areas of agreement. A sensitivity analysis showed that the modeled area distribution of eelgrass was highly sensitive to light conditions, with 18–38% increase in coverage following an increase in light availability of 20%. Modeled coverage of eelgrass was also sensitive to wave exposure and temperature conditions while less sensitive to changes in oxygen and salinity conditions. Large regional differences in habitat conditions suggest spatial variation in the factors currently limiting the recovery of eelgrass and, hence, variations in actions required for sustainable management

Wind and trophic status explain within and among-lake variability of algal biomass

Phytoplankton biomass and production regulates key aspects of freshwater ecosystems yet its variability and subsequent predictability is poorly understood. We estimated within-lake variation in biomass using high-frequency chlorophyll fluorescence data from 18 globally distributed lakes. We tested how variation in fluorescence at monthly, daily, and hourly scales was related to high-frequency variability of wind, water temperature, and radiation within lakes as well as productivity and physical attributes among lakes. Within lakes, monthly variation dominated, but combined daily and hourly variation were equivalent to that expressed monthly. Among lakes, biomass variability increased with trophic status while, within-lake biomass variation increased with increasing variability in wind speed. Our results highlight the benefits of high-frequency chlorophyll monitoring and suggest that predicted changes associated with climate, as well as ongoing cultural eutrophication, are likely to substantially increase the temporal variability of algal biomass and thus the predictability of the services it provides.Peer reviewe

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

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer diel ranges in their near-surface temperatures of between 4 and 7°C. Large diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored