Paediatrics (RCH) - Research Publications
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ItemNo Preview AvailableAssessment of ambulance dispatch data for surveillance of influenza-like illness in Melbourne, Australia(W B SAUNDERS CO LTD, 2009-02)OBJECTIVES: Ambulance dispatch data are collated electronically in many jurisdictions and have a wide reach into the community. They may therefore be useful for syndromic surveillance and early recognition of emerging infectious diseases. This study assessed whether ambulance dispatch data are suitable for influenza surveillance. STUDY DESIGN: Comparison of a time series of ambulance dispatch data from Melbourne, Australia for the years 1997-2005 with locum service and general practice (GP) sentinel surveillance data for influenza-like illness (ILI). METHODS: All data were aggregated into 1-week periods, corresponding to the data collection period used in the GP sentinel surveillance system, which was used as the reference system. Rates of ambulance dispatches classified to respiratory or breathing problems per 1000 total dispatches were compared with rates of callouts for flu or influenza per 1000 locum calls, and rates of ILI per 1000 patients from the sentinel GPs. Signals from the ambulance data were generated using the log likelihood ratio CUSUM, a method of continuous monitoring suitable for surveillance. RESULTS: The ambulance dispatch data displayed seasonal trends that were similar to those observed in locum service surveillance and GP sentinel systems, and identified the years with higher-than-expected seasonal ILI activity (1998 and 2003) and the epidemic year (1997). However, there was a high baseline rate of ambulance calls classified to respiratory or breathing problems (90-100 per 1000 calls) in months where there was minimal influenza activity. CONCLUSION: Ambulance dispatch data have potential for syndromic surveillance, but because of the high background noise are not definitive and would need to be calibrated to suit particular local circumstances.
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ItemBayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department(BMC, 2009-05-11)BACKGROUND: The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. METHODS: This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. RESULTS: Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. CONCLUSION: In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.
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ItemUtility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines(BMC, 2009-05-27)BACKGROUND: Not all cancer patients receive state-of-the-art care and providing regular feedback to clinicians might reduce this problem. The purpose of this study was to assess the utility of various data sources in providing feedback on the quality of cancer care. METHODS: Published clinical practice guidelines were used to obtain a list of processes-of-care of interest to clinicians. These were assigned to one of four data categories according to their availability and the marginal cost of using them for feedback. RESULTS: Only 8 (3%) of 243 processes-of-care could be measured using population-based registry or administrative inpatient data (lowest cost). A further 119 (49%) could be measured using a core clinical registry, which contains information on important prognostic factors (e.g., clinical stage, physiological reserve, hormone-receptor status). Another 88 (36%) required an expanded clinical registry or medical record review; mainly because they concerned long-term management of disease progression (recurrences and metastases) and 28 (11.5%) required patient interview or audio-taping of consultations because they involved information sharing between clinician and patient. CONCLUSION: The advantages of population-based cancer registries and administrative inpatient data are wide coverage and low cost. The disadvantage is that they currently contain information on only a few processes-of-care. In most jurisdictions, clinical cancer registries, which can be used to report on many more processes-of-care, do not cover smaller hospitals. If we are to provide feedback about all patients, not just those in larger academic hospitals with the most developed data systems, then we need to develop sustainable population-based data systems that capture information on prognostic factors at the time of initial diagnosis and information on management of disease progression.
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ItemUsing routine inpatient data to identify patients at risk of hospital readmission(BIOMED CENTRAL LTD, 2009-06-09)BACKGROUND: A relatively small percentage of patients with chronic medical conditions account for a much larger percentage of inpatient costs. There is some evidence that case-management can improve health and quality-of-life and reduce the number of times these patients are readmitted. To assess whether a statistical algorithm, based on routine inpatient data, can be used to identify patients at risk of readmission and who would therefore benefit from case-management. METHODS: Queensland database study of public-hospital patients, who had at least one emergency admission for a chronic medical condition (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes or dementia) during 2005/2006. Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months. The performance of the algorithm was tested against recorded readmissions using sensitivity, specificity, and Likelihood Ratios (positive and negative). RESULTS: Several factors were identified that predicted readmission (i.e., age, co-morbidities, economic disadvantage, number of previous admissions). The discriminatory power of the model was modest as determined by area under the receiver operating characteristic (ROC) curve (c = 0.65). At a risk score threshold of 50, the algorithm identified only 44.7% (95% CI: 42.5%, 46.9%) of patients admitted with a reference condition who had an admission in the next 12 months; 37.5% (95% CI: 35.0%, 40.0%) of patients were flagged incorrectly (they did not have a subsequent admission). CONCLUSION: A statistical algorithm based on Queensland hospital inpatient data, performed only moderately in identifying patients at risk of readmission. The main problem is that there are too many false negatives, which means that many patients who might benefit would not be offered case-management.