E N C Tong

Royal Brisbane Hospital, Brisbane, Queensland, Australia

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Publications (5)10.22 Total impact

  • A Barker · A Morton · M Gatton · E Tong · A Clements
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    ABSTRACT: To evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation. Retrospective audit. The Northern Hospital, Melbourne, Australia. 171,059 patients admitted between January 2001 and December 2006. Measurements: The analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit. The data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation. Charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data.
    Quality and Safety in Health Care 12/2009; 18(6):473-7. DOI:10.1136/qshc.2007.025601 · 2.16 Impact Factor
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    E N C Tong · A C A Clements · MA Haynes · MA Jones · AP Morton · M Whitby
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    ABSTRACT: To allow direct comparison of bloodstream infection (BSI) rates between hospitals for performance measurement, observed rates need to be risk adjusted according to the types of patients cared for by the hospital. However, attribute data on all individual patients are often unavailable and hospital-level risk adjustment needs to be done using indirect indicator variables of patient case mix, such as hospital level. We aimed to identify medical services associated with high or low BSI rates, and to evaluate the services provided by the hospital as indicators that can be used for more objective hospital-level risk adjustment. From February 2001-December 2007, 1719 monthly BSI counts were available from 18 hospitals in Queensland, Australia. BSI outcomes were stratified into four groups: overall BSI (OBSI), Staphylococcus aureus BSI (STAPH), intravascular device-related S. aureus BSI (IVD-STAPH) and methicillin-resistant S. aureus BSI (MRSA). Twelve services were considered as candidate risk-adjustment variables. For OBSI, STAPH and IVD-STAPH, we developed generalized estimating equation Poisson regression models that accounted for autocorrelation in longitudinal counts. Due to a lack of autocorrelation, a standard logistic regression model was specified for MRSA. Four risk services were identified for OBSI: AIDS (IRR 2.14, 95% CI 1.20 to 3.82), infectious diseases (IRR 2.72, 95% CI 1.97 to 3.76), oncology (IRR 1.60, 95% CI 1.29 to 1.98) and bone marrow transplants (IRR 1.52, 95% CI 1.14 to 2.03). Four protective services were also found. A similar but smaller group of risk and protective services were found for the other outcomes. Acceptable agreement between observed and fitted values was found for the OBSI and STAPH models but not for the IVD-STAPH and MRSA models. However, the IVD-STAPH and MRSA models successfully discriminated between hospitals with higher and lower BSI rates. The high model goodness-of-fit and the higher frequency of OBSI and STAPH outcomes indicated that hospital-specific risk adjustment based on medical services provided would be useful for these outcomes in Queensland. The low frequency of IVD-STAPH and MRSA outcomes indicated that development of a hospital-level risk score was a more valid method of risk adjustment for these outcomes.
    BMC Infectious Diseases 10/2009; 9(1):145. DOI:10.1186/1471-2334-9-145 · 2.61 Impact Factor
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    ABSTRACT: Non-multiresistant methicillin-resistant Staphylococcus aureus (nmMRSA) infections are emerging worldwide and are often community-associated. This prospective case-cohort study compares features of 96 nmMRSA clinical isolates with 96 matched multiresistant MRSA (mMRSA) and 192 matched methicillin-susceptible S. aureus (MSSA) clinical isolates. Seventy-four percent of nmMRSA infections were healthcare-associated. nmMRSA infections were much more likely to involve skin and soft tissue (skin and soft tissue infections; SSTIs) and were much less likely to be treated appropriately with antibiotics than MSSA or mMRSA infections. Panton-Valentine leukocidin (PVL) genes were detected in 55% of nmMRSA, 16% of MSSA and 2% of mMRSA isolates. Independent of the methicillin-resistance phenotype, 59% of PVL-positive SSTIs presented as furunculosis compared to only 10% of PVL-negative SSTIs. Patients with PVL-positive infections were much younger than patients with PVL-negative infections. The proportion of PVL-positive infections peaked in the 10-29 years old age group, followed by a linear decline.
    European Journal of Clinical Microbiology 06/2008; 27(5):355-64. DOI:10.1007/s10096-007-0449-3 · 2.67 Impact Factor
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    A C A Clements · E N C Tong · A P Morton · M Whitby
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    ABSTRACT: This study evaluated the US National Nosocomial Infection Surveillance (NNIS) risk index (RI) in Australia for different surgical site infection (SSI) outcomes (overall, in-hospital, post-discharge, deep-incisional and superficial-incisional infection) and investigated local risk factors for SSI. A SSI surveillance dataset containing 43 611 records for 13 common surgical procedures, conducted in 23 hospitals between February 2001 and June 2005, was used for the analysis. The NNIS RI was evaluated against the observed SSI data using diagnostic test evaluation statistics (sensitivity, specificity, positive predictive value, negative predictive value). Sensitivity was low for all SSI outcomes (ranging from 0.47 to 0.69 and from 0.09 to 0.20 using RI thresholds of 1 and 2 respectively), while specificity varied depending on the RI threshold (0.55 and 0.93 with thresholds of 1 and 2 respectively). Mixed-effects logistic regression models were developed for the five SSI outcomes using a range of available potential risk factors. American Society of Anaesthesiologists (ASA) physical status score >2, duration of surgery, absence of antibiotic prophylaxis and type of surgical procedure were significant risk factors for one or more SSI outcomes, and risk factors varied for different SSI outcomes. The discriminatory ability of the NNIS RI was insufficient for its use as an accurate risk stratification tool for SSI surveillance in Australia and its sensitivity was too low for it to be appropriately used as a prognostic indicator.
    Journal of Hospital Infection 06/2007; 66(2):148-55. DOI:10.1016/j.jhin.2007.02.019 · 2.78 Impact Factor
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    ABSTRACT: Routine surveillance of colonisations with multiple antibiotic resistant organisms (MROs) is now widespread and these data are increasingly summarised in control charts. The purpose of their analysis in this manner is to provide early warning of outbreaks or to judge the response to system changes designed to reduce colonisation rates. Conventional statistical process control (SPC) charts assume independence of observations. In addition, there needs to be a run of stable, non-trended (stationary) data values to obtain accurate control limits. Colonisation with an MRO is not an independent event as it must involve transmission from a carrier and this can lead to excessive variation. In addition, non-linear trends are often present and MRO prevalence data display temporal correlation. The latter occurs when data at particular times are more like data at related, usually contiguous times, than data from more distant times; thus they are not temporally independent. These characteristics make it difficult to implement conventional SPC charts with MRO data. To overcome these problems, we suggest the use of generalised additive models (GAMs) when there is no temporal correlation, as with new colonisations, and generalised additive mixed models (GAMMs) when temporal correlation is present; as occurs commonly with prevalence data. We illustrate their use with multi-resistant methicillin-resistant Staphylococcus aureus (mMRSA) prevalence and new colonisation data. These methods are able to deal with excess variability, trends and temporal correlation. They are easily implemented in the freely available R software package. Our analysis demonstrates an upward non-linear trend in mMRSA prevalence between January 2004 and October 2006. The mMRSA new colonisation data also display an upward trend between September 2005 and May 2006. Monthly new colonisation rates exceeded the upper control limit in April 2005 and equalled it in May 2006. There was a modest downward trend in the new colonisation rate in the latter part of 2006. (author abstract)
    Healthcare Infection 01/2007; DOI:10.1071/HI07014