Regions of High Out-Of-Hospital Cardiac Arrest Incidence and Low Bystander CPR Rates in Victoria, Australia

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) remains a major public health issue and research has shown that large regional variation in outcomes exists. Of the interventions associated with survival, the provision of bystander CPR is one of the most important modifiable factors. The aim of this study is to identify census areas with high incidence of OHCA and low rates of bystander CPR in Victoria, Australia. METHODS: We conducted an observational study using prospectively collected population-based OHCA data from the state of Victoria in Australia. Using ArcGIS (ArcMap 10.0), we linked the location of the arrest using the dispatch coordinates (longitude and latitude) to Victorian Local Government Areas (LGAs). We used Bayesian hierarchical models with random effects on each LGA to provide shrunken estimates of the rates of bystander CPR and the incidence rates. RESULTS: Over the study period there were 31,019 adult OHCA attended, of which 21,436 (69.1%) cases were of presumed cardiac etiology. Significant variation in the incidence of OHCA among LGAs was observed. There was a 3 fold difference in the incidence rate between the lowest and highest LGAs, ranging from 38.5 to 115.1 cases per 100,000 person-years. The overall rate of bystander CPR for bystander witnessed OHCAs was 62.4%, with the rate increasing from 56.4% in 2008-2010 to 68.6% in 2010-2013. There was a 25.1% absolute difference in bystander CPR rates between the highest and lowest LGAs. CONCLUSION: Significant regional variation in OHCA incidence and bystander CPR rates exists throughout Victoria. Regions with high incidence and low bystander CPR participation can be identified and would make suitable targets for interventions to improve CPR participation rates

Measuring efficiency in Australian and New Zealand paediatric intensive care units

Purpose: To develop a measure of paediatric intensive care unit (PICU) efficiency and compare the efficiency of PICUs in Australia and New Zealand. Methods: Separate outcome prediction models for estimating clinical performance and resource usage were constructed using patient data from 20,742 admissions between 2005 and 2007. A standardised mortality ratio was calculated using a recalibrated Paediatric Index of Mortality 2 model. A random effects length of stay (LoS) prediction model was used to provide an indicator of unit-level variation in resource use. A modified Rapoport-Teres plot of risk-adjusted mortality versus unit mean LoS provided a visual representation of efficiency. To account for potential differences in admission threshold, the calculation of performance measures was repeated on patients receiving mechanical respiratory support and compared to those estimated for all patients. Results: The modified plot provides a useful tool for visualising ICU efficiency. Two units were identified as potentially inefficient with higher SMR and risk-adjusted mean LoS at the 95% level. One unit had a significantly lower SMR and significantly higher risk-adjusted mean LoS. The measures for both SMR and risk-adjusted mean LoS showed good agreement between all patients and those who received mechanical respiratory support. Conclusion: There is significant variation in efficiency among PICUs in Australia and New Zealand. Two units were designated as inefficient and one unit was considered to be effective at the expense of high resource use. Application of these methods may help to identify ineffi-ciencies in units located in other countries or regions

First-stage GA-BW adjustment model for Early Neonatal Deaths among US births (1996–2006).

<p>First-stage GA-BW adjustment model for Early Neonatal Deaths among US births (1996–2006).</p

Univarite relationship between HSA-level covariates and GA-BW adjusted early neonatal mortality (2004–2006).

*<p>Refers to period 2004–2006.</p

Ratio of the GA-BW adjusted early neonatal mortality rate compared to national average by HSA, 2004–2006.

<p>Ratio of the GA-BW adjusted early neonatal mortality rate compared to national average by HSA, 2004–2006.</p

Ratio of the expected early neonatal mortality rate compared to national average by HSA, 2004–2006.

<p>Ratio of the expected early neonatal mortality rate compared to national average by HSA, 2004–2006.</p

Variance in GA-BW adjusted ENN mortality explained by HSA covariates (2004–2006).

<p>Variance in GA-BW adjusted ENN mortality explained by HSA covariates (2004–2006).</p

Health Service Area (HSA) covariates.

*<p>Preterm was defined as a birth before 34 weeks gestation.</p

Expected and Observed ENND rate 1996–2006.

<p>Expected and Observed ENND rate 1996–2006.</p