Measuring national accessibility to cardiac services using geographic information systems

Applied Geography 34 (2012) 445e455

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Measuring national accessibility to cardiac services using geographic information systems Neil Coffee a, *,1, 2, Dorothy Turner b,1, Robyn A. Clark a, c,1, Kerena Eckert d,1, David Coombe e,1, Graeme Hugo f,1, Deborah van Gaans f,1, David Wilkinson g,1, Simon Stewart h,1, Andrew A. Tonkin i,1 a

Social Epidemiology and Evaluation Research, Sansom Institute, Division of Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia GIS and Environmental Modelling, School of Earth and Environmental Sciences, The University of Adelaide, Glen Osmond, South Australia, Australia School of Nursing and Midwifery and the Institute of Health and Biomedical Innovation (IBHI), Queensland University of Technology, Kelvin Grove, Queensland, Australia d Population Research and Outcome Studies, Discipline of Medicine, The University of Adelaide, Adelaide, South Australia, Australia e Department for Families and Communities, The Government of South Australia, South Australia, Australia f The Department of Geographical and Environmental Studies, The University of Adelaide, South Australia, Australia g School of Medicine, University of Queensland, Herston Road, Brisbane, Queensland, Australia h Baker Heart Research Institute, 75 Commercial Road, Melbourne, Victoria, Australia i Cardiovascular Research Unit, Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Victoria, Australia b c

a b s t r a c t Keywords: GIS GIS modelling Accessibility modelling Health modelling Cardiac accessibility

The Cardiac Access-Remoteness Index of Australia (Cardiac ARIA) used geographic information systems (GIS) to model population level, road network accessibility to cardiac services before and after a cardiac event for all (20,387) population localities in Australia., The index ranged from 1A (access to all cardiac services within 1 h driving time) to 8E (limited or no access). The methodology derived an objective geographic measure of accessibility to required cardiac services across Australia. Approximately 71% of the 2006 Australian population had very good access to acute hospital services and services after hospital discharge. This GIS model could be applied to other regions or health conditions where spatially enabled data were available. Ó 2012 Elsevier Ltd. All rights reserved.

Introduction Cardiovascular disease (CVD) remains Australia’s biggest killer, claiming the lives of approximately 50,000 people each year (34% of all deaths). It was also the second largest contributor to the burden of disease in Australia, after cancer (Australian Institute of Health and Welfare, 2010, p. 579). However, despite improvements in medical interventions over the past four decades, CVD continues to impose the major impact on health in terms of the associated prevalence, mortality, morbidity and healthcare costs in many countries. Timely access to healthcare services is a major determinant of outcomes after an acute cardiac event. Accessibility has been defined as the ease of access from one location to another. Remoteness has been defined as being distant or far away

* Corresponding author. Tel.: þ618 8302 2632; fax: þ618 8302 2603. E-mail address: [email protected] (N. Coffee). 1 On behalf of the Cardiac-ARIA project team. 2 Tel.: þ618 8302 2632; fax: þ618 8302 2603. 0143-6228/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2012.01.007

geographically or the ease of approach from one location to another location, measured in terms of distance travelled, the cost of travel, or the time taken (Higgs et al., 2003). These concepts are at the heart of geographic models of accessibility. These definitions refer to physical accessibility and do not include social accessibility which can be impacted by class structure, income, age, education, gender or ethnicity (Penchansky & Thomas, 1981). With increased life expectancy and an ageing population it is expected that 25% of Australians will have CVD by 2051 (Access Economics, 2005, p. 103). Similar trends are occurring in most industrialised countries (Mackay & Mensah, 2004). The progressive ageing of Australia’s population has also resulted in significant migration of retirees from metropolitan to nonmetropolitan areas, and a blurring of the sharp boundaries once drawn around major cities in countries such as Australia. Population growth in non-metropolitan areas has been variable, with growth occurring in the more accessible regions, such as the urban fringes and the “sea and tree” rural areas favoured by retirees, while the population in the more remote areas is declining (Clark et al., 2005, p. 443). While some areas have been experiencing population growth, services, especially health services, have not kept

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pace. Communities, many of which are on the fringes of major cities (50e100 km away), do not have sufficient accessibility to major health facilities, general practitioners and cardiac services (Clark et al., 2007; Wilkinson, 2000). Furthermore, there are vast rural and remote areas of Australia where access to services are limited due to the vast distances between population centres and lower population densities. Geographic information systems (GIS) software is a tool that uses location to integrate otherwise unrelated data and analyses, and to visualise these relationships. (Fig. 1) GIS has been used in many disciplines to help understand spatial relationships, especially in the environmental sciences and natural resource management. The use of GIS in social science and health applications is a growing area of research interest and there are many studies that have focused on health services and the use of GIS to objectively measure geographic access. A recent review (Nykiforuk & Flaman, 2011) identified four main areas of focus, disease surveillance, risk analysis, health access and planning and community profiling. A search of online sources in 2007 identified 621 journal articles and book chapters reporting health-related applications of GIS (Nykiforuk & Flaman, 2011). Of these articles and book chapters, 138 were access to health or health planning related. These studies were from a range of countries, both developed and developing and included access to a range of medical services. See for example, (Bamford et al., 1999; Brabyn & Beere, 2006; Brabyn & Skelly, 2002; Cinnamon et al., 2008; Dummer, 2003; Hare & Barcus, 2007; Haynes et al., 2006; Lovett et al., 2002; McLafferty, 2003; McLafferty & Grady, 2004; Ohta et al., 2007; Patel et al., 2007; Popick et al., 2009; Rosero-Bixby, 2004; Tsoka & le Sueur, 2004; Yang et al., 2006) This study adds to the growing interest in accessibility modelling and provides the basis for a national service GIS model. The aim of this project was to develop a simple, nationally consistent index of geographic accessibility to cardiac services to inform strategies to improve population level accessibility to cardiac services. The aims of this paper are to provide detail on the Cardiac ARIA GIS methodology, the issues associated with compiling data for a national accessibility model and important results. A more detailed report is available for download from www.qut.edu.au/research/cardiac-aria. Methods Theory The Cardiac Accessibility and Remoteness Index of Australia (Cardiac ARIA) project built upon earlier accessibility modelling by Bamford et al. in 1999 (Bamford et al., 1999; Commonwealth

Fig. 1. Location links data layers.

Department of Health and Aged Care, 2001). Cardiac ARIA used GIS to measure road network accessibility to cardiac services across Australia. In so doing it provided a simple objective tool for comparing geographic accessibility for those people residing in metropolitan, rural and remote Australia. Unlike the earlier Accessibility and Remoteness Index of Australia (ARIA) model which used population location size as a proxy for services provision (the larger the population the greater level of services available), Cardiac ARIA used the location of health services to model access. Cardiac ARIA measured access time to key medical services travelling by road ambulance in an acute cardiac event (Acute Cardiac ARIA) and by private vehicle after discharge from hospital (Aftercare Cardiac ARIA). Study design To achieve its objectives, the Cardiac ARIA project was modelled in three phases; 1. Phase 1: Consensus of an Expert panel to enable definition of the scope of a cardiac event, and generation of a master list of the necessary cardiac services; 2. Phase 2: Data acquisition and GIS modelling, and 3. Phase 3: Comparison between Cardiac ARIA and census-derived local population characteristics.

Phase 1 expert panel consensus process The expert panel of cardiologists and other key stakeholders (see Acknowledgements) defined a cardiac event to include: Cardiac arrest: a sudden cessation of cardiac function, resulting in loss of effective circulation. (American College of Cardiology ACC and American Heart Association AHA, 2001) Acute coronary syndrome (ACS): defined as a spectrum of clinical presentations ranging from those for ST-segment elevation myocardial infarction (STEMI) to presentations found in noneSTsegment elevation myocardial infarction (NSTEMI) or in unstable angina. In terms of pathology, ACS is almost always associated with rupture of an atherosclerotic plaque and partial or complete thrombosis of the infarct-related artery. (American College of Cardiology ACC and American Heart Association AHA, 2001) Acute decompensated heart failure; heart failure is a clinical syndrome characterized by systemic perfusion inadequate to meet the body’s metabolic demands as a result of impaired cardiac pump function. Patients with decompensated heart failure may be tachycardic and tachypneic, with bilateral inspiratory rales, jugular venous distention, and oedema. (American College of Cardiology ACC and American Heart Association AHA, 2001) Life-threatening arrhythmias or serious heart rhythm disturbances. (American College of Cardiology ACC and American Heart Association AHA, 2001) Current national and international guidelines (American College of Cardiology ACC and American Heart Association AHA, 2001; Australian Resuscitation Council, 2007; National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand, 2006; The American College of Cardiology et al., 2001) were then distilled into a single patient care pathway, which resulted in a list of national datasets of health services and resources which were considered necessary for care of patients during and after these events. Phase 2 data acquisition and GIS modelling Acquisition of quality and consistent data was essential to underpin this project. Due to differing management policies, incomplete coverage of national data, confidentiality, inconsistent

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classifications and resourcing constraints affecting supplying agencies, not all data was of sufficient quality to be used in the modelling. Of the 20 national datasets determined after consensus of the expert panel, 9 were utilised in the final model of the index (Table 1). As Cardiac ARIA is a road distance\time-based geographic model, two of the key data sets were the road network for Australia and population locations. Roads and population locations were sourced from Pitney Bowes Business Insight (Pitney Bowes Business Insight, 2009a, 2009b, 2009c) and were part of a commercial data set designed for business applications and street navigation purposes. In addition to the road and population locations, the index also required the location of hospitals (plus rural and remote medical facilities), ambulance stations, general practitioners, retail pharmacies, pathology laboratories and cardiac rehabilitation services. Phase 3 comparison between cardiac ARIA and census-derived local population characteristics A critical component of this work was to describe implications for actual population groups associated with Cardiac ARIA index scores. The best source of population data in Australia is the five yearly census of population and housing, collected by the Australian Bureau of Statistics (ABS). As well as collecting the census, the ABS provide these data for a variety of spatial units from the Australian Standard Geographic Classification (ASGC). The ASCG is a hierarchical spatial classification, which starts at the national level and systematically sub-divides into smaller and smaller spatial units to ensure that Australia is covered without overlap or omission. The smallest of the ASCG units is the census collection district (CD) which equated to approximately 225 households and a quarter of a square kilometre per CD in an urban areas, and as low as a few households and as large as 200,000 square kilometres in

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remotes areas. At the 2006 census there were 38,704 CDs. As Cardiac ARIA is a spatial index, the population data can be spatially joined using the ASGC spatial units.

Calculation For modelling travel speeds, road type was used to set different travel speeds, and the ABS urban centre locations were used to differentiate urban from non-urban areas (based on a population of 1000 persons) (Australian Bureau of Statistics, 2006). The Australian Institute of Health and Welfare (AIHW) Public Hospital Peer Groups’ classification (Australian Institute of Health and Welfare, 2009) was the source for location and classification of public hospitals or other medical facilities based on the range of admitted patient activity. Remote area clinics were sourced from the National Aboriginal Community Controlled Health Organisation (NACCHO) (National Aboriginal Community Controlled Health Organisation, 2009). All Category one public hospitals (44 in total) had cardiac catheterisation services providing percutaneous coronary intervention, although not all were available 24 h a day seven days a week, and not all had a co-located cardiac surgery service. Private hospitals were excluded as they were generally co-located or proximal to public hospitals (81% were within 10 km and none were more than 50 km distant). Five categories of medical facilities/ hospitals were defined based on diminishing levels of access to cardiac services and increasing remoteness: 1. Principal Referral Hospital with Cardiac Catheter Laboratory; 2. Principal Referral Hospital without Cardiac Catheter Laboratory 3. Large Hospital, Major city, Regional Centre and Remote location.

Table 1 Datasets, cardiac ARIA project. Subset

Used

Essential facilities/Services

Source

Display

Y

Australian Bureau of Statistics -Australian Standard Geographic Classification 2006.

Model Model Model Display Analysis Model Model

Y Y Y Y Y N N

Australia land mass and state boundaries Localities Road networks Urban Areas Climate Classification Population Statistics Telecommunications Public Access Defibrillators

Model

N

First Responders

Model

N

Model

Y

Royal Flying Doctor Service Medical Chests Ambulance Station locations

Model

Y

Hospitals/Remote Area Clinic locations

Model Model Model Model

N N N Y

Air Ambulance Helicopter Bases Air Ambulance Fixed Wing Bases Fixed Wing Landing Grounds General Practitioners/Remote Area Clinics/Hospital locations

Model Model

Y Y

Model Model

Y N

Retail Pharmacies locations Cardiac Rehabilitation Facilities locations Pathology Laboratories locations Cardiologist Workplaces

StreetPro Localities Layer 2009-Pitney Bowes Business Insight\Tonkin Consulting Pty Ltd. StreetPro 2009 - Pitney Bowes Business Insight\Tonkin Consulting Pty Ltd Australian Bureau of Statistics -Australian Standard Geographic Classification 2006. Bureau of Meteorology. Australian Bureau of Statistics, Census of Population and Housing, 2006. Not supplied (Commercial in confidence) There are several commercial suppliers and each provided some data, but incomplete. There is no national register of locations or numbers. State ambulance services provided some data, but as it was not clearly defined across jurisdictions it could not be used. Three states provided generalised location of medical chests in remote areas, but these data were not consistent and the coverage was incomplete State Ambulance Services; St John Ambulance NT; Royal Flying Doctor Service, Remote Area Nurses JPM Media’s Hospital and Health Services Yearbook 2009; Department of Health and Ageing; Australian Institute of Health and welfare Peer Group Classification; Aboriginal Community Controlled Health Services State Ambulance Services State Ambulance Services,Royal Flying Doctor Service Royal Flying Doctor Service, Geoscience Australia. Business Points 2009 - Pitney Bowes Business Insight\Tonkin Consulting Pty Ltd; JPM Media’s Hospital and Health Services Yearbook 2009; Department of Health and Ageing; Australian Institute of Health and welfare Peer Group Classification 2009; Aboriginal Community Controlled Health Services Business Points 2009 - Pitney Bowes Business Insight\Tonkin Consulting Pty Ltd Australian Cardiovascular Health & Rehabilitation Association 2009 National Association of Testing Authorities e Accredited laborities 2009. Cardiac Society Australia New Zealand membership survey e too few responses.

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4. Medium Hospital Major city, Regional Centre and Remote location. 5. Other Hospital, Regional Centre and Remote location, which included (Small Acute and Small Non-Acute Hospitals, MultiPurpose Centres, Other Non-Acute Hospitals and Remote Clinics). General practitioners and retail pharmacies were sourced from a commercially available business locations database (Pitney Bowes Business Insight, 2009a, 2009b, 2009c) and pathology services from the National Association of Testing Authorities Australia (2010). Cardiac rehabilitation services and all rural, remote and Aboriginal cardiac rehabilitation and secondary prevention programs were acquired from the Australian Cardiovascular Health & Rehabilitation Association (ACRA) and a National Health and Medical Research Council report (National Health and Medical Research Council, 2005). GIS modelling The GIS software used for this project was Environmental Systems Research Institute (ESRI) Arc Map, version 9.3.1(ESRI, 2010; ESRI Arcview, 2006) The Cardiac ARIA index was operationalised as a two digit numeric-alpha categorization. The first digit (numeric) categorised accessibility to services for an acute cardiac event, and the second digit (alpha) categorised accessibility to the services required for care following discharge after the event. The road network and population locations provided the base for all of the GIS modelling. The road network provided the travel potential layer and the population locations provided the travel distances layer (time). The quality of the road network was vital for this project, as any break in the road network (such as missing bridges) would provide spurious results. Population locations were included with StreetPro and upon comparison with other available population location data proved to be more complete, up-to-date and importantly, spatially related to the road network. In total there were 20,387 population locations used in the modelling and the distances were calculated from the centroid of each population location. These centroids were calculated using the minimum bounding rectangle method commonly employed in GIS. The minimum bounding method used the smallest possible rectangle to completely contain the population location area and then located the centroid at the centre of the rectangle. This is then used to represent the spatial centre of the population location. Most of the population centres and facilities data were supplied as geocoded point data. For rural and remote facilities, these points were shifted to the population centre, for urban locations the actual geolocations were used. For data that required geocoding, the facilities were geocoded to the population location centroid. As these locations were based on the same national road and address data file, all georeferenced data were consistent. Raster based cost-distance analysis was used to generate costdistance layers for each input layer and the raster calculator was used to combine the layers into the final ARIA indices for both the Acute and Aftercare Cardiac ARIA. Cost-distance analysis was used to calculate the cost (in this case cost equals distance) of a single feature along a pathway (road network) and calculated a single value for each cell in the raster layer. For Cardiac ARIA the cell size was set at 200 m. The 200 m cell size was selected for two reasons, it provided a resolution that enabled the zonal process to work with the vector CD layer in urban areas (a larger resolution would not work for the smaller urban CDs) and a smaller cell size was not practical for processing the model (the software could not process

the model). A separate raster layer was calculated for distance from ambulance stations, distance to each hospital peer classification, and to the location of the nearest general practitioners, pharmacies, rehabilitation services and pathology laboratories. These layers were then combined using the ESRI raster calculator. Dispatch time and time on scene were added during this process to create the single ARIA index (Fig. 1). Acute Cardiac ARIA The Acute Cardiac ARIA Index was calculated based on travel time by ambulance along the road network as opposed to helicopter and fixed wing aircraft as road ambulance transport is the most common response to a cardiac event (South Australian Ambulance Service, 2010). Travel time included the components of dispatch time, travel time to the scene, time at the scene and subsequent travel to the closest medical service. Dispatch time was set at 3 min, and time at the scene was set at 15 min in urban locations and 19 min for all other locations. Dispatch time was based on the South Australian Ambulance Service 2008-09 chest pain statistics (South Australian Ambulance Service, 2010). Time at scene was based on data from the South Australian Ambulance Service (South Australian Ambulance Service, 2010) 2008-09 chest pain statistics and from the 28,298 chest pain calls received by the Queensland Ambulance Service (QAS) in 2003 (both urban and rural) (Scuffham & Tippett, 2008) (Table 2). Although Cardiac ARIA was based on road distance (and derived time), the state ambulance services did not supply information on average urban and rural travel speeds. While Ambulance drivers are allowed to travel above the legal speed limit when travelling to an emergency, it is often difficult to travel much faster than the normal traffic flow as ambulance drivers must drive with due diligence, and reduce speed when driving through traffic lights and towns. The Victorian Transport Department (VicRoads) performance bulletin show that average travel speeds in the City of Melbourne arterial road network have been approximately 42 kph (26 mph) in recent times (Saggers, 2005). A project in New Zealand (Begnell, 2004) that modelled population access to public hospitals, calculated mean ambulance speed on sealed urban roads at 30 kph (19 mph), urban motorways at 80 kph (50 mph), non-urban sealed roads at 40e80 kph (25e50 mph) depending on the type and number of bends in the road and non-sealed country road at 30e50 kph (19e31 mph) (Table 2). Consequently, the travel times used in Cardiac ARIA modelling were deliberately conservative, and were based on these reported average speeds. The speeds used in the model were 40kph (25 mph) for urban roads, 80kph (50 mph) for non-urban roads and 50kph (31 mph) for unsealed roads. Using ESRI Spatial Analyst cost-distance modelling, the distance from each hospital class was calculated along the road to each population location. The calculated time component of the index is based on the need to obtain medical assistance as quickly as possible. Synthesis of the national and international cardiac guidelines and a model for a cardiac “golden hour” (Boersma et al., 1996) were used to derive the time thresholds. Boersma’s research showed that the best cardiac outcomes (lives and cardiac muscle saved) occurred in patients who reached medical care within

Table 2 Travel speed and time used to calculate Cardiac ARIA. Terrain

Dispatch time

Travel speed

Time on scene

Metro (Capitals) Other urban Non urban road Non urban off-road

3 3 3 3

40 40 80 50

15 19 19 19

min min min min

kph kph kph kph

min min min min

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Fig. 2. Acute cardiac ARIA.

60 min of ambulance retrieval. In this model the highest-category medical facility within 1 h road travel via ambulance always overrode a lower category facility, even if a lower category was closer.The Acute Index scores are provided below:

1. An index score of 1 represented access to a category 1 public hospital within 1 h by road ambulance; 2. A score of 2 represented 1 h road ambulance access to a category 2 public hospital;

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Fig. 3. Aftercare cardiac ARIA.

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3. Scores of 3e5 represented 1 h road ambulance access to a category 3 to 5 public hospital\medical clinic; 4. A score of 6 represented all locations which are between 1 and 3 h from any public hospital or clinic by road ambulance; 5. An index score of 7 represented those locations that do not have ambulance services but could access a remote clinic by private vehicle within 30 min; and, 6. A score of 8 represented location more than 3 h by road ambulance from any medical facility (Fig. 2). Aftercare Cardiac ARIA The minimal services required for a patient after discharge following a cardiac event were based on the evidence-based guidelines for the services required for cardiac rehabilitation and secondary prevention of subsequent cardiac events. The model included access within 1 h by private vehicle along the road network to a general practitioner/nurse (GP), retail pharmacy, cardiac rehabilitation program and pathology laboratory. These services were selected as the minimum required ensuring that post-hospital recovery could be monitored and managed. The 1 h access time was based on research that indicated that compliance with appropriate care was reduced with increased travel times to services such as general practices and pharmacies (Chan et al., 2006; Hiscock et al., 2008; National Health and Medical Research

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Council, 2005). Travel time was calculated along the road network by private car. As per the Acute Cardiac ARIA Index, time to facilities was calculated using travel speeds of 40 kph (25 mph) for urban roads, 80 kph (50 mph) for non-urban roads and 50 kph (31 mph) for unsealed roads. The aftercare component of Cardiac ARIA was a five character alpha category denoting access to all services within 1 h (category A) through to zero services within 1 h (category E) (Fig. 3). The Cardiac ARIA Model The complete Cardiac ARIA model combined the Acute and the Aftercare indices to form a numeric-alpha value for each locality. There were eight categories of acute care access and five categories of cardiac aftercare access. When the criteria were modelled using GIS for all population locations, only 19 of the possible 40 combinations resulted (Fig. 4). Comparison between cardiac ARIA and census-derived local population characteristics A critical component of this work was to provide actual population outcomes associated with Cardiac ARIA index scores. For the population-Cardiac ARIA comparison, population data were sourced from the ABS 2006 census at the CD spatial unit level. The ABS CD is a vector spatial unit while Cardiac ARIA is a regular grid with 200 m by 200 m cells. The CD is not a standard

Fig. 4. Cardiac ARIA index.

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Fig. 5. Calculating collection district cardiac ARIA scores.

size or shape and varies considerably in urban areas where it is quite small (100  100 m) to remote areas where it is very large (up to 400 km wide). The CD is generally larger than the 200 m cell and therefore, one CD will contain many raster grid cells, especially in rural and remote Australia. To calculate the CD ARIA values, the raster zonal methodology was applied. This methodology overlays the CD on the raster grid, creates a spatial join between each raster cell and the bounding CD and then calculates the average value for each CD (sum of cell values divided by number of cells per CD) (Fig. 5). Fig. 5 displays a zoom extent to part of the cardiac ARIA grid with overlying CD boundaries. This highlights how existing spatial boundaries can mask the more detailed ARIA scores with small areas of higher access being lost through the averaging process as smaller areas of higher access are overwhelmed by larger areas of lower access. The ESRI zonal method averages the cells completely or intersected by the vector layer. It does not weight for complete over partial containment nor does it take account of proportions of different raster cell values. This is at the heart of integrating raster and vector data and highlights one of the key issues, which is more to do with the way vector boundaries are constructed than how this impacts on raster and vector integration. The zonal process works well in the urban regions because the resolution was set to enable a close match between cell size and CD size. The problem is in the vast rural and remote CDs where a single CD is 400 km wide and how well the zonal average value represents the differing distances per CD. The same problem occurs when using CD centroids to extract the raster values, these will be relevant in the urban areas but will not reflect the many different raster cell values within large CDs. In the absence of a raster population base and no other unit for population data, this is the only option and provides the best possible compromise for associating population with the accessibility model. Despite these shortcomings in this methodology,

there were no other means of joining population data based on CD boundaries with raster based indices such as Cardiac ARIA.

Results The results of the Cardiac ARIA model are demonstrated in Table 3 and Figs. 3e5. Cardiac ARIA provided an index score for all

Table 3 Cardiac ARIA summary results. Cardiac ARIA

Locality

Persons

Persons age 55 or older

Index

Number

Percent

Number

Percent

Number

Percent

1A 2A 3A 4A 4B 4C 5A 5B 5C 5D 6A 6B 6C 6D 6E 7D 8C 8D 8E NA Total

3648 1238 1083 1852 14 165 2476 347 953 342 3360 608 1457 943 460 180 2 185 1074 0 20387

17.9 6.1 5.3 9.1 0.1 0.8 12.1 1.7 4.7 1.7 16.5 3.0 7.1 4.6 2.3 0.9 0.0 0.9 5.3 0.0 100.0

13,983,696 1,645,086 1,100,338 1,127,226 7183 89,497 669981 101,629 223,851 102,898 486,069 44,293 79,455 40,411 16,139 40,809 2332 3757 29,764 18,666 19,813,080

70.6 8.3 5.6 5.7 0.0 0.5 3.4 0.5 1.1 0.5 2.5 0.2 0.4 0.2 0.1 0.2 0.0 0.0 0.2 0.1 100.0

3,257,449 415,277 303,527 323,185 1787 24,873 196,465 30,469 56,556 17,391 1,39,819 11,939 20,800 7751 3523 4079 1056 509 5379 5175 4,827,009

67.5 8.6 6.3 6.7 0.0 0.5 4.1 0.6 1.2 0.4 2.9 0.2 0.4 0.2 0.1 0.1 0.0 0.0 0.1 0.1 100.00

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20,387 localities in Australia, ranging from 1A (state capital city) to 8E (very remote isolated locations). Approximately 71% of the 2006 Australian population had very good access to acute and after care cardiac services. Conversely, this equated to 18% of localities, reflecting the concentration of the population in the larger population locations. This can be compared with the 14% of localities with poor access to cardiac services (6D8E) which was home to less than 1% of the population. It should be noted that while the 6D to 8E locations represented less than 1% of the Australian population, these locations were largely populated by Indigenous Australians who have lower life expectancies and higher rates of CVD. Therefore while such people represent a small proportion of the total Australian population, this is an area with significant health access issues and Cardiac ARIA helps identify both the extent of this problem and can inform policy directed towards solutions. Cardiac ARIA provided a simple nationally consistent index for accessibility from any location in Australia. When spatially joined to the ABS census CD, Cardiac ARIA can be used to analyse any of the approximately 3000 population characteristics resulting from the Australian five yearly census. The majority of the 2006 Australian population (71%) had 1 h access to the best cardiac care and therefore, the potential for improved outcomes and survival. To highlight the further potential of Cardiac ARIA, the results for the population aged 55 years or older are also provided. Increasing age is associated with a higher risk for CVD so an understanding of the match between this group and cardiac services is important. Similar to the total population, the majority (67%) of older Australians had 1 h access to acute and after care cardiac services. However, approximately 140,000 older Australians (3%) resided more than 1 h from any medical service and were therefore at increased risk and less likely to survive a critical cardiac event. Tools such as Cardiac ARIA provide the opportunity for service planning to identify the populations most at risk, assess the adequacy of the services provided and put in place strategies to manage high risk populations in low services regions. For more detailed population results of the Cardiac ARIA project see the public report at www.qut.edu.au/research/cardiac-aria. Discussion The purpose of this paper was to provide detail on the GIS methodology and the aspects associated with compiling data for a national model of accessibility to healthcare services for one of the major public health problem for many countries. Cross-disciplinary research The first phase in the project was to condense complex clinical guidelines into minimum services, data sets and timeframes. This process took several meetings and teleconferences to reach consensus between clinicians, and also to align the needs of clinicians and geographers and represented an excellent example of cross-disciplinary research. The research team included geographers and health professionals and this provided the expertise to understand the complex medical issues as well as the spatial issues. The most challenging aspect of the cross-disciplinary research was synthesising the many (and complex) heart management guidelines into a geographic outcome. Early meetings between the Cardiac Specialists and geographers were divided along subject lines. The Cardiologists maintained that the guidelines provided all the information and the geographers insisted on some form of geographic location and relationship to the road network. The process took a number of meetings and several presentations from each side before both the

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medical and spatial aspects were understood by both the medical and geographers on the team. What was most useful during this process was that a project such as this could not succeed without medical and geographical expertise and the combination provided an outcome that was credible for cardiac clinicians, allied health, public health practitioners and geographers. Innovative methodology The methodology outlined in this paper highlights the potential of GIS for modelling geographic healthcare service accessibility and for linking with population data and analysing the relationship between service coverage and the population. The utility of Cardiac ARIA will be proven over time as the results are used to help guide policy for both acute and after care services to those less accessible regions of Australia. Limitations and data issues Arguably the most difficult and most underestimated phase of this project was data capture. As was evident from Table 1, not all of the data sets on the initial list were used for the final modelling. This occurred for several reasons, most notably refusals, but also because of multiple jurisdictions, and inconsistent classifications. Most of the data required for this project was potentially available, with a few notable exceptions. Data that were available, but not provided, came from both the private and public sectors. The private sector data was withheld due to “commercial in confidence” arguments in the belief that competitors would access the data and gain an advantage through data entering the public domain. This was the case for telephone coverage, which represented an important data set that would highlight regions where an emergency call could or could not be made. The best available advice from the major telecommunications companies was that emergency calls could be made from anywhere in Australia, and this was taken as the basis for the modelling. The other major data set that was not supplied was the Federal Government (Medicare Australia) data concerning the location of doctors, cardiologists, pharmacies and pathology laboratories. These data would have provided the most accurate and up-to-date data on the location of key medical providers. The model would have been improved with the Medicare data, and in its absence alternative data were used. Later versions of Cardiac ARIA would be improved if both the telecommunications and Medicare data were utilised. Australia is a federation of eight states and territories and each jurisdiction manages key health care services. This creates issues of data consistency across the country. The many agencies, such as the state ambulance services, were very cooperative and supplied data, most of which could be compared nationally, but some data were either incomplete or the underlying classifications differed too much to enable comparison. For example, the concept of “first responder”, the service activated to first reach the scene of an emergency, was classified differently across the eight agencies. Even though first responder data were important for emergency considerations the differences could not be reconciled and therefore these data were not used. Another key data set was the location of defibrillators. Although two of the major commercial suppliers of defibrillators provided data, this was incomplete and could not be used. Although there are many defibrillators in the community, in airports, shopping centres, sporting clubs, sporting venues, and other public and private sector locations, these locations were not systematically recorded or maintained Therefore the value of defibrillators for emergency cardiac events is very much negated

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for most of the population. A national register of defibrillators is highly recommended as an outcome of this project. Despite these data issues, this project still created a nationally consistent data set and used this with GIS to model accessibility to cardiac services across Australia. This method provided an objective model for geographic access to healthcare services, and even though cardiac events were the focus for the present research, the methodology could be equally applied for any specialist medical care (or indeed any service) for which georeferenced data are available. Therefore, Cardiac ARIA provides a simple, but robust methodology to measure geographic accessibility to healthcare services and facilities, in any country with a spatially enabled road network and services data. Further research is planned using CVD mortality and morbidity data to enable a detailed assessment of the location of cardiac services in relation to the populations at risk.

We wish to especially thank David McDonald, Spatial Information Manager, Tonkin Consulting, Nigel Lester, Channel Sales Manager Australia & New Zealand, Pitney Bowes Business Insight, for supporting the project by providing strategic datasets such as Tonkin Street Pro. The project acknowledges the contribution to Cardiac ARIA from all collaborators including Prof Graeme Hugo, Ms Maria Fugaro, Carmel Sutcliff, Ms Louise Moylan, Mr. Chris Moyan, Prof Mark Daniel, Prof Kerin O’Dea, Prof Esther May, A/Prof Annette Raynor, Ms Marian Milligan, Mr. Greg Pearce, Mr. Justin Lawrence, Ms Tricia Smail, Ms Jacqui Howard, The Heart Foundation (Australia) and our expert panellists: Professor Derek Chew, Professor Hugh Grantham, Professor Peter Mc Donald, Professor Andrew Mac Isaac, Professor Peter Thompson, Professor Andrew Tonkin (Chair) Professor Warren Walsh, Professor Phil Tideman, Rosy Tirimacco, Wendy Keech and Vanessa Poulsen, Associate Professor Matt Hooper.

Conclusion References The Cardiac ARIA project methodology was used to create a novel, simple objective geographic measure of accessibility to cardiac services. These results support the concept of using GIS to measure geographic accessibility and indicate that the existing system is providing timely access for the majority of Australians. Additionally, Cardiac ARIA provides a basis for development of new strategies for managing patients in regions that fall outside the cut-off point of 1 h access for an acute event, and provides a service portfolio which can be used to maximise the survival and improve other outcomes in patients after acute cardiac events. GIS is a tool that is well suited to this form of accessibility modelling and through the use of location to link with population data provided the capability to assess population and demography associated with each index score. The potential of this methodology linked with spatially referenced data, such as the population, provides a policy and management tool that can assess need (demand) to inform appropriate service delivery (supply). Ethics Ethics approval for this project was provided by the Human Research Ethics Committee of the University of South Australia approval number P136/09. Authors’ contributions Primary writing of this report was by Neil Coffee, Dorothy Turner and Robyn Clark. Data analysis was conducted by co-authors NC, DT, RC, KE, and AT. All authors contributed to the project work and commented upon and approved the final manuscript (See acknowledgements). Competing interests This project was funded by an Australian Research Council Linkage Grant LP0775217 with Linkage partner Alphapharm Pty Ltd. Strategic datasets such as Tonkin Street Pro were provided without charge by Pitney Bowes Business Insight. Acknowledgements We acknowledge Errol Bamford for his contribution to the original conceptualization of the index and Mr. Peter Astles, a passionate advocate who is acknowledged for establishing the partnership for the linkage funding.

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