Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

for the ANZICS Centre for Outcome and Resource Evaluation (CORE) of the Australian and New Zealand Intensive Care Society (ANZICS)BACKGROUND Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. METHODS Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. RESULTS The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag ₄₀ and 35% had autocorrelation through to lag ₄₀; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. CONCLUSIONS The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.John L Moran, Patricia J Solomo

Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study

Contaminated water resources have important implications on health and the environment. Nitrate contamination of the groundwater is a serious problem in the European Union. A method based on the statistical process control (SPC) and time series analysis is developed to monitoring and to predict the concentration evolution of nitrate (NO 3 -) in groundwater. In many pumping wells the NO 3 -concentration ([NO 3 -]) increases and approaches or even passes the European Community standard of 50 mg l -1. The objective of this paper is to show the application of statistical process control as a monitoring tool for groundwater pollution from agricultural practices. We propose the autoregressive integrated moving average (ARIMA) model as a management tool to monitoring and reduction of the intrusion of nitrate into the groundwater. This tool should help in setting up useful guidelines for evaluating actual environmental performance against the firm's environmental objectives and targets and regulatory requirements. We concluded that the statistical process control method may be a potentially important way of monitoring groundwater quality that also permits rapid response to serious increases in pollutants concentrations. In doing so, the paper fills an important gap in the water pollution standards and emerging polices (Water Framework directives). © 2010 Springer-Verlag.The author is grateful to the anonymous referees and the editor for several constructive comments that have improved this paper. The author acknowledge the financial support of Programa de Apoyo a la Investigacion y Desarrollo (PAID-06-08) of the Universidad Politecnica de Valencia.García-Díaz, JC. (2011). Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study. 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Off J Eur Union L 372:19–31Chistensen OF, Cassiani G, Diggle PJ, Ribero P, Andreotti G (2004) Statistical estimation of the relative efficiency of natural attenuation mechanisms in contaminated aquifers. Stoch Environ Res Risk Assess 18:339–350Cook DF, Zobel CW, Wolfe ML (2006) Environmental statistical process control using an augmented neural network classification approach. Eur J Oper Res 174:1631–1642De Paz JM, Ramos C (2004) Simulation of nitrate leaching for different nitrogen fertilization rates in a region of Valencia (Spain) using a GIS–GLEAMS system. Agric Ecosyst Environ 103(1):59–73European Commission (1998) The Implementation of Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources. Office for Official Publications of the European Communities, LuxembourgGeneralitat Valenciana (2000) Decreto 13/2000 del 25-enero de 2000, del gobierno valenciano por el que se designan, en el ámbito de la Comunidad Valenciana, determinados municipios como zonas vulnerables a la contaminación de las aguas por nitratos procedentes de fuentes agrarias. DOGV (Diario Oficial de la Generalitat Valenciana) No. 3677. Valencia, Spain, pp 1511–1515Harris TJ, Ross WH (1991) Statistical process control procedures for correlated observations. Can J Chem Eng 69:48–57ITGME (1998) Mapa de contenido en nitrato de las aguas subterráneas en España. Instituto Tecnológico GeoMinero de España. Ministerio de Medio Ambiente, MadridLi N, Liang X, Li X, Wang C, Wu DD (2009) Network environment and financial risk using machine learning and sentiment analysis. Hum Ecol Risk Assess 15(2):227–252Militino AF, Ugarte MD, Ibáñez B (2008) Longitudinal analysis of spatially correlated data. 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Cosmology with the Laser Interferometer Space Antenna

The Laser Interferometer Space Antenna (LISA) has two scientific objectives of cosmological focus: to probe the expansion rate of the universe, and to understand stochastic gravitational-wave backgrounds and their implications for early universe and particle physics, from the MeV to the Planck scale. However, the range of potential cosmological applications of gravitational wave observations extends well beyond these two objectives. This publication presents a summary of the state of the art in LISA cosmology, theory and methods, and identifies new opportunities to use gravitational wave observations by LISA to probe the universe