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.


Introduction
Out-of-hospital cardiac arrest (OHCA) remains a major public health issue [1]. The high case fatality rate of OHCA (>90%) in most communities [2,3], indicates the importance of developing appropriate interventional strategies [4].
Efforts to improve out-of-hospital cardiac arrest outcomes focus on improving the chain of survival [5,6]. This includes early recognition of cardiac arrest symptoms, early CPR, early defibrillation and early advanced post-resuscitation care [7]. Of the interventions associated with survival, the provision of bystander CPR is one of the most important modifiable factors [8][9][10][11][12][13][14]. Improvements in bystander CPR rates through system changes, such as simplification of CPR instructions in the emergency call, have been reported [15]. However, whole-of-community interventions have shown mixed results [16], suggesting that such interventions might be better placed in communities with a high incidence of OHCA and a low prevalence of bystander CPR.
Research in the US has shown that these 'high-risk' census tracts can be identified by geocoding OHCA registry data [17,18]. However, it is unknown if such high-risk areas exist in the Australian community and whether these communities remain at constant high-risk over time. Therefore, the aim of this study is to identify census areas with high incidence of OHCA and low rates of bystander CPR in Victoria, Australia. In addition, this study aims to evaluate changes in these measures over time.

Study Design and Setting
We conducted an observational study using prospectively collected population-based OHCA data from the state of Victoria in Australia. Victoria has a current population of 5.6 million, 75% of whom reside in the metropolitan region of Melbourne. Ambulance Victoria (AV) is the sole provider of Emergency Medical Services (EMS) in the state. AV delivers a two-tiered EMS system, with Advanced Life Support Paramedics and Intensive Care Ambulance Paramedics. Fire fighters and volunteer Community Emergency Response Teams provide a first response in select areas of Victoria.

The Victorian Ambulance Cardiac Arrest Registry (VACAR)
AV maintains the Victorian Ambulance Cardiac Arrest Registry (VACAR), which registers and collects EMS clinical and outcome data for all OHCA attended by EMS in the state of Victoria [19]. Data collection is standardized using the Utstein definitions [20]. The Victorian Department of Health Human Research Ethics Committee (HREC) has approved VACAR (No. 08/02) data collection. Ethics approval for the current study was received from the Monash University Human Research Ethics Committee (CF12/3410-2012001638). aged greater than 20 years and the arrest was presumed to be of cardiac etiology based on EMS documentation (i.e. no other obvious cause recorded such as trauma, hanging, drowning, etc.).

Local Government Areas (LGAs) and Geospatial Mapping
Australia has a federal system of government under which state governments preside in each of the eight states and territories. Beneath this are local governments, for which there are 79 local government areas (LGAs) in the state of Victoria.
Using ArcGIS (ArcMap 10.0), we linked the location of the arrest using the dispatch coordinates (longitude and latitude) to Victorian LGAs.

Statistical Analysis
We restricted the calculation of bystander CPR rates to those arrests that were witnessed by a bystander (not a paramedic). We coded cases in which the patient received bystander chest compressions, even if 'stated as inadequate or poor', as having received bystander CPR. We defined the absence of bystander CPR as those cases that received no bystander chest compressions, or who received ventilation only. We excluded cases that were coded as 'unknown' or 'not stated' from estimates of bystander CPR rates (259, 4.1%).
We calculated the incidence of OHCA in Victorian LGAs using yearly population data from the Australian Bureau of Statistics, and bystander CPR rates for each LGA [21]. 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. We adjusted for relative socio-economic advantage and disadvantage using the 2011 Socio-Economic Indexes for Areas, 'SEIFA index', for each LGA [22]. The rates were modelled using a mixed-effects logit model for bystander CPR and a mixed-effects Poisson model for the OHCA incidence rate, where the random effect was assumed to be normally distributed with a mean of zero. These models estimate the 'shrunken' LGA rate as a weighted average of the pooled estimate (adjusted by SEIFA index) and the LGA-specific estimate [23]. The weights that contribute to this average are the inverse variances of the pooled estimate and the LGA-specific estimate. Thus an LGA with considerably fewer OHCA events will shrink toward the pooled estimate more than an LGA with many observed events. In this way, those LGAs with very few events are shrunken considerably toward the pooled estimate (or adjusted mean) regardless of their observed rates.
We considered two time periods to examine changes over time, 2008-2010 versus 2011-2013. These time periods also correspond with the introduction of the 2010 ILCOR guidelines. We plotted the shrunken estimates of the rates of bystander CPR and the incidence of OHCA in each LGA.

Results
Over the study period there were 31,019 adult OHCA attended, of which 21,436 (69.1%) cases were of presumed cardiac etiology. For those cases of presumed cardiac etiology, the overall incidence of OHCA was 64.6 cases per 100,000 person-years. The crude incidence declined from 66.6 cases per 100,000 person-years in 2008-2010 to 62.6 cases per 100,000 person-years in 2011-2013 (p<0.001).
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.
The map shown in Fig 1 provides the shrunken estimate of the incidence in each LGA. Significant variation among LGAs was observed with a 3 fold difference in the incidence rate between the lowest and highest LGAs. The highest incidence rates were seen in two neighboring rural LGAs (Central Goldfields LGA = 115.1 cases per 100,000 person-years; Loddon LGA = 110.6 cases per 100,000 person-years). The lowest incidence rates were generally in outer suburban areas surrounding Melbourne, with the lowest incidence rate found in the North-East of Melbourne in the Shire of Nillumbik (38.5 cases per 100,000 person-years).

Regional Bystander CPR
The median estimate of bystander CPR among LGAs was 63.2% (IQR: 60.5%-65.7%) over the entire study period. Fig 2 provides a map of Melbourne City and surrounding local government areas. Even across the metropolitan regions, large disparities in the rate of bystander CPR can be seen. There was a 25.1% absolute difference in bystander CPR rates between the highest and lowest LGAs. The highest rate of bystander CPR was in Melbourne city with a rate of 78.1%. In contrast the rate of bystander CPR was lowest in Greater Dandenong, South-East of Melbourne with a rate of 53.0%. Fig 3 provides the observed OHCA events, the crude rate of bystander CPR and the shrunken estimate in each LGA during the study period. While crude rates varied from 43.8% to 100%, these were in LGAs with a very small number of OHCA events.
In all but one LGA the rates of bystander CPR improved during the study period. LGA. The lower right quadrant indicates those regions with an incidence rate higher than the median with bystander CPR rates that are lower than the median.   Tables 1 and 2 provide a complete list of the incidence (Table 1) and bystander CPR rates ( Table 2) for each LGA for the entire period, as well as split into the first half and second half of the study period.

Discussion
Our study, using geocoding of Victorian OHCA registry data, found significant regional variation in both the incidence of OHCA and rates of bystander CPR rates for witnessed arrests. This variation was seen across the entire state, with differences seen in neighboring communities in both metropolitan Melbourne and regional areas. Areas with high incidence and low bystander CPR were able to be identified.
Reported OHCA incidence rates are known to vary both within and across countries [3], including in previous reports from Australia [24,25]. Our data suggests some of this variation may be explained by the regions examined and in the periods of time studied. We found significant sub-State variation in OHCA incidence rates, which is consistent with reports across census tracts in the United States [26][27][28]. To our knowledge, ours is the first study to include rural regions with low and very low density populations (<200/km2), some of which had the highest incidence rates across the state. Our study also noted a decline in the overall incidence over the study period, which was also reported in another Australian study [29], but this decline was not consistently seen across the state. The fluctuations in incidence rates over time in our study is contrary to a previous US study [26], which found incidence rates across census tracts to be stable between two consecutive 2-year periods. However, it's worth noting that we compared periods of different length (2 years vs 3 years), and different years (2005-2009 vs 2008-2013) to the Semple study. Similar to our study though, Semple et al. [26] did report changes in bystander CPR rates across census tracks over time [26].
Our study restricted the examination of bystander CPR rates to bystander witnessed OHCAs. We did this as witnessed arrests are strongly associated with bystander CPR [30], and rates of witnessed arrests have fluctuated over time with our region [19,31]. Increases in bystander CPR rates occurred in all but one region over the study period. This increase is difficult to explain as telephone-CPR instructions remained constant over the study period [15]. Rural regions changed from a manual system of emergency call taking to an electronic emergency call taking algorithm in 2010-11 [19]. This change may have improved the recognition of OHCA in the emergency call [32] or possibly the compliance with the protocols, but does not explain the increase seen in metropolitan regions. Alternatively, it's possible that there were changes in CPR training following the 2010 guidelines or shifts in the underlying demographics of the regions which may impact rates.
Studies based in the US and Asia have identified demographics factors that differ between low and high-risk regions. These areas tend to have specific racial compositions, and lower levels of education and income [18,33,34]. The next phase of our study intends to explore the demographic factors seen in our high risk regions and whether shifts in the population are responsible for the changes seen over time in our study. However, that these high-risk areas can change over time is an important consideration in the development and testing of  community-based interventions targeting these regions. Thus interventions may choose to focus on those areas at persistently high-risk over time [26], and consider changes in relevant underlying characteristics during any evaluation period. This study has a number of limitations. Firstly, we assigned arrests to the regions in which they occurred. This means that estimates in the regions where people commute to work may over estimate that incidence. This is particularly likely to affect the Melbourne central business Regions of High Out-Of-Hospital Cardiac Arrest Incidence and Low CPR district; however it's worth noting that bystander CPR rates in this region are the highest in the state and thus unlikely to be targeted for interventions to improve bystander CPR participation. Secondly, it's possible that the incidence of OHCA and bystander CPR rates may be correlated with population density. Thus it's possible that estimates in rural areas may have been biased too heavily toward the mean. Although ours is a conservative approach, small-area estimation models which better explain the heterogeneity may improve estimates for data sparse regions [35].

Conclusion
Our data supports reports of regional variation in OHCA incidence and bystander CPR rateswhich in our case occurred even across a large metropolitan city and among bystander witnessed OHCAs. Identifying lower bystander CPR rates in the context of higher OHCA incidence identifies those regions where there is the greatest potential to improve survivalparticularly in regions where high-risk status persists.