• Open Access

Higher energy prices are associated with diminished resources, performance and safety in Australian ambulance systems

Authors


Correspondence to: Lawrence H. Brown, Anton Breinl Centre for Public Health and Tropical Medicine, James Cook University, Townsville, QLD 4811; e-mail: Lawrence.Brown@my.jcu.edu.au

Abstract

Objective : To evaluate the impact of changing energy prices on Australian ambulance systems.

Methods : Generalised estimating equations were used to analyse contemporaneous and lagged relationships between changes in energy prices and ambulance system performance measures in all Australian State/Territory ambulance systems for the years 2000–2010. Measures included: expenditures per response; labour-to-total expenditure ratio; full-time equivalent employees (FTE) per 10,000 responses; average salary; median and 90th percentile response time; and injury compensation claims. Energy price data included State average diesel price, State average electricity price, and world crude oil price.

Results : Changes in diesel prices were inversely associated with changes in salaries, and positively associated with changes in ambulance response times; changes in oil prices were also inversely associated with changes in salaries, as well with staffing levels and expenditures per ambulance response. Changes in electricity prices were positively associated with changes in expenditures per response and changes in salaries; they were also positively associated with changes in injury compensation claims per 100 FTE.

Conclusion : Changes in energy prices are associated with changes in Australian ambulance systems’ resource, performance and safety characteristics in ways that could affect both patients and personnel. Further research is needed to explore the mechanisms of, and strategies for mitigating, these impacts. The impacts of energy prices on other aspects of the health system should also be investigated.

The potential adverse impacts of energy scarcity and rising energy prices on health services are the subject of much opinion, but little scientific evaluation. Three decades ago, noting that health facilities are dependent on energy, Bailey1 raised concerns about energy scarcity and energy costs in the context of the United States’ (US) dependence on imported oil. More recently, several authors have warned that energy is a critical input for all health services,2–8 and that there are several pathways through which energy scarcity and energy costs could affect health services. These include: the cost and availability of medical supplies and equipment; the cost and availability of health-related transport; the cost of lighting, heating and air conditioning health facilities; impacts on food security leading to increased demand on health services; and economic impacts that disrupt funding for health services.3,8 These concerns are shared by the general public,9 and there is some empirical basis for them. In a time series analysis of US petroleum and health care prices between 1973 and 2008, Hess et al.5 found a 1% increase in oil price inflation was associated with a 0.03% increase in medical care prices after an eight-month lag, although the effect was much stronger in the 1970s than in recent years.

Ambulance services are an important component of the health system, providing emergency medical care and/or medical transport to more than three million patients in Australia and New Zealand (NZ) each year.10 Recent reports in the lay media have anecdotally described the adverse effects of rising fuel prices on the operating budgets of ambulance services in the US, the United Kingdom and Canada,11–15 but the extent of those impacts and how they manifest have not been systematically studied in any setting. Increasing energy prices would be expected to lead to increased total operational costs, but ambulance services are complex systems involving vehicles, equipment, buildings, communication systems and personnel.16 A need to divert financial resources to meet increasing energy prices could conceivably have an impact on any or all of these system components, potentially in ways that ultimately affect system performance.

This study aimed to empirically evaluate, for the first time, the impact of changing energy prices on Australian ambulance systems. We used a panel data approach to test the null hypothesis that changes in ambulance service resource, performance and safety characteristics are not associated with changes in energy prices.

Methodology

This retrospective study was approved by the Human Research Ethics Committee at James Cook University, Australia (HREC #3982) with the understanding that the results would be aggregated and reported in a manner such that individual system performance could not be determined.

Setting

In Australia, responsibility for ambulance services rests with State and Territory governments. In New South Wales (NSW), South Australia (SA) and Tasmania (TAS) ambulance services are provided by the Department of Health. In Queensland (QLD) and the Australian Capital Territory (ACT) they are provided by the departments responsible for emergency services. St John Ambulance, an independent not-for-profit organisation, is contracted to provide services in the Northern Territory (NT) and Western Australia (WA). In Victoria (VIC), ambulance services are organised within the Department of Health, but prior to 2008 they were provided by two independent statutory authorities: the Metropolitan Ambulance Service (MAS) serving the greater Melbourne area, and Rural Ambulance Victoria (RAV) serving the outlying rural areas. In the 2009/10 financial year, Australian ambulance systems performed slightly more than 3.5 million emergency and non-emergency responses.10

Ambulance system data

Data on the resource, performance and safety characteristics of the ambulance systems in Australia's six states were obtained from publicly available annual reports published online at each agency's website, or its parent agency's (e.g. Department of Health) website, for 2000/01 through 2009/10. While Ambulance Victoria data for the entire state of VIC were available from 2008/09 forward, only annual reports for MAS were available for prior years. Because of differences in the structure and geography of these agencies, we treated them as individual systems in this analysis. Limited resource and performance data for the ambulance agencies serving Australia's two Territories (NT and ACT) were extracted from Australia's Council of Ambulance Authorities (CAA) annual reports, also available online. In some cases, prior year data were reported in the first available report, allowing collection (or calculation) of data for one additional year.

The extracted annual data included: number of ambulance responses; total expenditures; labour-related expenditures; number of full time equivalent (FTE) employees; number of work-related injury compensation claims; median ambulance response time; and 90th percentile ambulance response time. When response time data were not included in the individual state ambulance agency annual reports, these indicators were instead extracted from the CAA annual reports.

From these data, eight system-related measures were identified or calculated: four resource indicators, two performance indicators, and two safety indicators (Table 1).

Table 1.  Ambulance system resource, performance and safety indicators extracted or calculated from the annual reports, total system-years of observations, and aggregate results for the entire study period.
IndicatorCalculationN System-Years
(max possible = 80)
Mean
(± Standard Deviation)
  1. $'000 – thousand dollars; min – minutes; FTE – full time equivalent employees; all financial data in 2009–10 Australian dollars.

Resource Indicators
Expenditures per Response ($)
Labour to Total Expenditure Ratio
FTE per 10,000 Responses
Average Salary ($’000)

Total Expenditures / Total Responses
Labour Expenditure / Total Expenditures
FTE / Total Responses × 10,000
Labour Expenditures / FTE

61
64
60
60

542.04 ± 125.99
0.65 ± 0.05
40.95 ± 5.32
85.57 ± 16.53
Performance Indicators
Median Response Time (min)
90th Percentile Response Time (min)

As Reported
As Reported

70
80

9.26 ± 0.90
17.56 ± 2.92
Safety Indicators
Compensation Claims per 10,000 Responses
Compensation Claims per 100 FTE

Compensation Claims / Total Responses × 10,000
Compensation Claims / FTE × 100

20

20

6.80 ± 2.93

17.75 ± 5.98

Energy price data

The Australian Institute of Petroleum (AIP) reports annual average terminal gate prices (TGP) for diesel fuel and petrol in Australia's capital cities, with the exception of Canberra, ACT. TGP reflects the wholesale price of diesel or petrol with goods and services tax added. TGP data for diesel fuel for financial years 2004/05 through 2009/10 were obtained from the AIP website,17 and the annual average TGP for diesel fuel in each capital city (as cents per litre) was used as a measure of vehicle fuel prices for each State or Territory. The annual average TGP for Sydney, NSW was used as a proxy measure for the cost of diesel in the ACT.

State average annual electricity prices (as dollars per megawatt hour) for financial years 2000/01 through 2009/10 were obtained from the Australian Energy Market Operator (AEMO).18 AEMO data for TAS were limited to the 2004/05 through 2009/10 financial years, and the AEMO data does not include electricity price data for WA, NT or ACT. Electricity prices for NSW were used as a proxy of electricity prices in ACT, but because of their remoteness, distinctive geography and unique economies, a proxy measure of electricity prices for NT and WA was not attempted.

Since TGP data were available for only six financial years, average annual world crude oil prices were used as an additional indirect measure of vehicle fuel prices with a longer historical record. Oil prices were obtained from the US Energy Information Administration (EIA) for financial years 2000/01 through 2009/10.19 EIA data are reported in US dollars, so annual average exchange rates reported by the Australian Tax Office were used to convert these prices into Australian dollar prices (as dollars per barrel).20

Model selection and specification

This panel data analysis followed the approach outlined by Markus,21 Hsiao,22 and Twisk.23 generalised estimating equation (GEE) modelling was used to evaluate the relationships between changes in the energy price measures and changes in the resource, performance and safety measures while accounting for the repeated-measures nature of the dependent variables. GEE models marginal expectations (or changes) in the dependent variables and, unlike conditional fixed effects models, does not require full specification of joint distribution of observations. In GEE, the relationships between the variables in the model at different time points are analysed simultaneously;23 conceptually it can be thought of as regression modelling with correction for the dependency of the serial observations.24 GEE generates robust and model-based standard errors, and produces consistent normal solutions even if the underlying correlation structure is incorrectly specified.23,25 These attributes make GEE well suited for unbalanced panel data analyses.

The financial data were adjusted to 2009/10 Australian dollars – standardising the values to most recent year of included data – using consumer price index (CPI) multipliers published by the Australia Bureau of Statistics (ABS).26 Changes in energy prices and changes in the dependent variables were calculated as the ‘first difference’, i.e. the difference between the value for any given year and the value for the prior year.

To avoid spurious regression, GEE requires that data are ‘stationary’, i.e. the mean and variance do not change over time or position. We used the Augmented Dickey Fuller test, allowing for a random walk and a drift, and the Phillips-Perron test to check for unit roots (or non-stationarity) in the data.

The analysis then evaluated both the contemporaneous (within the same financial year) and one-year lagged relationships between energy price changes and changes in each individual resource, performance and safety indicator within each State ambulance system. Both contemporaneous and lagged relationships must be considered to achieve unbiased statistical parameter estimations. System administrative structure (dichotomised as ‘health department based’ or ‘non health department based’) was included as a possible covariate, and time was modelled as a linear trend. The analysis used an auto-regressive GEE model with calculation of robust standard errors, assuming a Gaussian distribution and an identity link, as:

image

where ΔYit is the change in the resource, performance or safety indicator for the ambulance system in State or Territory i for year t; Admini is the administrative structure of each ambulance system; ΔDiesel$it is the change in the average terminal gate price for diesel in each State or Territory for year t; ΔDiesel$it-1 is the prior year change in average terminal gate price for diesel in each State or Territory; ΔOil$t is the change in average world crude oil price for year t; ΔOil$t-1 is the prior year change in average world crude oil price; ΔElect$it is the change in average electricity price in each State or Territory for year t; ΔElect$it-1 is the prior year change in average electricity price in each State or Territory; ΔYit-1 is the prior year change in the value of the dependent variable for each ambulance system; and ε is the error term. As recommended by Twisk, the autoregressive GEE modelling was conducted using an independent correlation structure.23

Finally, because the inclusion of electricity price in the models eliminated NT and WA from the analyses, both the contemporaneous and lagged relationships between diesel price and oil price changes and changes in the resource, performance and safety indicators for those two ambulance systems were modelled separately, omitting electricity price from the models described above.

Model fit was evaluated by calculating the explained variance (R2) in each model as “1 – (variance of the model / variance of the dependent variable)”. For all analyses, an alpha value of 0.05 was used to establish statistical significance.

Results

During the years included in this study, Australian ambulance systems performed more than 20 million ambulance responses, with annual median ambulance response times ranging between 7.2 and 11.0 minutes at an average (± standard deviation) CPI-adjusted expense of $616 ±$131 per response. The aggregate measures of all the performance indicators are shown in Table 1. None of the resource, performance or safety indicators were available for all ambulance agencies for all years, except for 90th percentile ambulance response time. Table 1 also shows the number of system-years (out of a maximum possible of 80) for which data were available. Safety-related measures were the most under-reported indicator, available for only four (out of eight) systems for a total of 20 system-years.

Figure 1 shows the evolution of CPI-adjusted energy prices and resource indicators (panel A), performance indicators (panel B) and safety indicators (panel C) over time. The presence of a unit root could be rejected for all of the first-difference data except for injury compensation claims per 10,000 responses (data not shown); thus that variable was excluded from the final analysis.

Figure 1.

Evolution of energy prices and Australian ambulance system resource (Panel A), performance (Panel B) and safety indicators (Panel C), 2000–2010.

Modelling diesel and electricity prices

Table 2 shows the results of the GEE models evaluating the associations between changes in diesel and electricity price and changes in the resource, performance and safety indicators in those systems for which electricity data were available. CPI-adjusted expenditures per response increased over time, as did the ratio of labour-related expenditures to total expenditures.

Table 2.  Associations between changes in oil and electricity costs and changes in Australian ambulance system resource, performance and safety indicators.
(Model)Dependent Variables – coefficient [standard error]
Independent VariableΔExp/RespΔL:TRatioΔFTE/RespΔAvgSalΔRTMedΔRT90ileΔComp/FTE
  1. p<0.05; Δ– change; Diesel – diesel price (¢/litre); Oil$– oil price ($/barrel); Elect$– electricity price ($/megawatt hour); Exp/Resp – expenditures per response ($); L:TRatio – labour-to-total expenditure ratio; FTE/Resp – full time equivalent employees per 10,000 responses; AvgSal – average salary ($1,000);
    RTMed – median response time (minutes); Comp/FTE – injury compensation claims per 100 full time equivalent employees; all financial data in 2009–10 Australian dollars; omitted – insufficient observations
    .

(ΔDiesel$ & ΔElect$)        
Year76.354
[11.483]
0.036
[0.008]
1.154
[1.329]
5.066
[6.409]
0.107
[0.114]
0.288
[0.511]
insufficient
observations
Health-Based66.129
[42.158]
0.002
[0.016]
0.174
[0.944]
3.844
[3.802]
−0.215
[0.289]
−0.286
[0.361]
ΔDiesel$−1.634
[2.699]
0.0004
[0.0009]
0.120
[0.064]
−0.494
[0.325]
0.013
[0.013]
0.049
[0.019]
1L ΔDiesel$−0.968
[1.800]
0.0009
[0.0004]
0.044
[0.070]
−0.723
[0.204]
0.022
[0.006]
0.031
[0.014]
ΔElect$3.169
[1.595]
0.0009
[0.0004]
−0.029
[0.049]
0.662
[0.201]
−0.002
[0.007]
−0.006
[0.014]
1L ΔElect$2.720
[2.010]
0.0016
[0.0005]
−0.072
[0.040]
0.310
[0.182]
−0.003
[0.010]
−0.009
[0.005]
1L ΔDependent Variable−0.354
[0.486]
0.106
[0.235]
−0.173
[0.311]
−0.377
[0.339]
0.086
[0.318]
0.115
[0.155]
Explained Variance75.9%80.8%91.5%86.1%5.6%95.5%
(ΔOil$ & ΔElect$)        
Year−3.951
[10.583]
−0.0005
[0.001]
−0.149
[0.337]
0.769
[1.204]
−0.011
[0.019]
−0.028
[0.024]
−2.165
[0.948]
Health-Based6.958
[23.398]
0.004
[0.008]
−.0187
[0.307]
−0.701
[2.560]
−0.098
[0.101]
−0.024
[0.185]
2.571
[1.066]
ΔOil$−3.084
[1.741]
−0.0014
[0.0008]
−0.043
[0.043]
−0.264
[0.195]
−0.005
[0.006]
0.007
[0.009]
−0.050
[0.058]
1L ΔOil$−3.992
[1.949]
−0.0008
[0.0004]
−0.085
[0.042]
−0.680
[0.223]
0.010
[0.006]
0.009
[0.011]
−0.024
[0.060]
ΔElect$0.849
[0.937]
−0.0001
[0.0003]
−0.020
[0.044]
0.380
[0.151]
0.001
[0.005]
−0.006
[0.006]
0.305
[0.080]
1L ΔElect$0.704
[1.785]
0.0014
[0.0006]
−0.014
[0.043]
−0.050
[0.183]
0.003
[0.006]
0.008
[0.008]
0.644
[0.237]
1L ΔDependent Variable0.100
[0.139]
0.076
[0.052]
−0.048
[0.069]
0.031
[0.296]
0.220
[0.137]
0.155
[0.113]
−0.329
[0.395]
Explained Variance65.5%69.2%84.9%83.3%30.7%95.5%93.4%

There was a significant contemporaneous, positive association between changes in electricity price and changes in expenditures per ambulance response (coefficient [standard error] = 3.169 [1.595]); changes in electricity price also manifested in changes in the ratio of labour-related expenditures to total expenditures (0.0009 [0.0004]) and changes in average salary (0.662 [0.201]). The relationship between changes in energy price and changes in labour-related expenditures was also seen for lagged changes in both electricity price (0.0016[0.0005]) and diesel price (0.0009 [0.0004]).

There was a lagged positive association between changing diesel price and changes in median ambulance response time (0.022 [0.006]), but the explained variance for that model was low (5.6%). There was, however, both a contemporaneous (0.049 [0.019]) and a lagged (0.031 [0.014]) positive association between changes in diesel price and changes in 90th percentile ambulance response time, and the explained variance for that m odel exceeded 95%.

There were insufficient observations to model the associations between changes in diesel and electricity price and changes in workplace injury claims.

Modelling oil and electricity prices

Table 2 also shows the results of the GEE models evaluating changes in oil and electricity price over a longer historical record. In this analysis, workplace injury claims per 100 FTE diminished over time, but health-based systems had higher workplace injury claims when compared with non-health-based systems.

The associations between changes in energy price and resource indicators differed when modelling oil price over ten years instead of diesel price over six years: there was still a positive association between lagged changes in electricity price and changes in the ratio of labour-related expenditures to total expenditures (0.0014 [0.0006]), but changes in electricity price were not associated with changes in expenditures per response. Lagged changes in oil price, however, were negatively associated with changes in expenditures per response (−3.992 [1.949]) and FTE per 10,000 responses (−0.085 [0.042]). There was a lagged negative association between changes in oil price and changes in average salary (−0.633 [0.255]), but there was a contemporaneous positive association between changes in electricity price and changes in average salary (0.380 [0.151]).

When modelling changes in oil and electricity price, there was a significant positive association between changes in electricity price and changes in workplace injury claims per 100 FTE that was seen for both contemporaneous (0.305 [0.080]) and lagged (0.644 [0.237]) changes in electricity price.

Results for systems without electricity data

The only significant associations between changes in diesel price and the outcome measures in the systems without electricity data were both a contemporaneous (−0.001 [0.0002]) and lagged (−0.0006 [0.0001]) negative association between changes in diesel price and changes in the ratio of labour expenditures to total expenditures, and a significant positive association between changes in diesel price and changes in median response time (0.024 [0.003]). When modelling changes in oil price in these States, there was a significant negative association between lagged changes in oil prices and changes in expenditures per response (−3.992 [1.949]), and between lagged changes in oil prices and labour-related expenditures (−0.0003 [<0.0001]). There was a contemporaneous positive association between changes in oil prices and changes in FTE per 10,000 responses (0.086 [0.037]). Finally, increasing oil price was also contemporaneously associated with changes in median ambulance response time (0.010 [0.0002]), although lagged changes in oil price were inversely associated with changes in 90th percentile ambulance response time (−0.025 [0.006]).

Discussion

This study establishes, for the first time, that there is an association between changes in energy prices and changes in the resource, performance and safety characteristics of Australian ambulance systems. While there are some differences in the associations that achieve significance when modelling diesel price versus oil price, the results are not contradictory. The same is mostly true of our analyses for the systems without electricity data, where changes in diesel and oil price affect some of the labour-related measures differently than in the other States, but otherwise the results do not contradict the primary analysis. This relative consistency of the results adds to our confidence in the findings.

Table 3 translates the coefficients for the main findings of this study into practical terms, demonstrating that energy prices are more than simply a financial concern for Australian ambulance agencies: they are associated with distinct resource, performance and safety implications that potentially affect both patients and personnel.

Table 3.  Practical interpretation of the effects of changing energy prices on Australian Ambulance agencies.
Change in Energy PriceAssociated Change in Indicator*Timing of Effect
  1. * Compared with indicator value if energy price did not change. Diesel$– diesel price; Oil$– oil price; Elect$– electricity price; FTE – full time equivalent employee; L – litre; kWh – kilowatt hour; all financial data in 2009–10 Australian dollars. Note: 1¢/kWh =$10/MWh.

10¢/L higher Diesel$1% higher labour to total expenditure ratio
$7,200 lower average salary
12 second longer median response time
29 second longer 90th percentile response time
18 second longer 90th percentile response time
1 year lag
1year lag
1 year lag
contemporaneous
1 year lag
1¢/kWh higher Elect$$32 higher expenditure / ambulance response
1% higher labour to total expenditure ratio
1.4–1.6% higher labour to total expenditure ratio
$3,800-$6,600 higher average salary
3 more injury claims / 100 FTE
6 more injury claims / 100 FTE
contemporaneous
contemporaneous
1 year lag
contemporaneous
contemporaneous
1 year lag
$10/barrel higher Oil$$40 lower expenditure / ambulance response
∼1 fewer FTE / 10,000 ambulance responses
$6,800 lower average salary
1year lag
1year lag
1 year lag

The positive association between changes in electricity price and changes in expenditures per response is not surprising – rising energy costs would be expected to contribute to rising total costs. What is somewhat surprising is the counter-intuitive inverse relationship seen between lagged changes in oil price and changes in expenditures per response, along with an inverse association between lagged changes in oil price and changes in both FTE per 10,000 responses and average salary. This might simply reflect broader economic pressures leading to improved efficiency; or it might suggest that, over the longer run, ambulance agency budgets and staffing levels suffer when energy prices increase and State and Territory governments are faced with difficult decisions about how to apportion limited resources.

In this study, changes in diesel price were also positively associated with changes in ambulance response time. While there are very few emergencies for which an association between rapid ambulance response and decreased mortality has been demonstrated,27–31 most ambulance systems are still expected to meet rigid response-time standards. This requires considerable resources in the form of vehicles, stations and personnel. The relationship between higher diesel prices and longer response times could be due to broader unmeasured forces on ambulance system performance, or it too might be an early indication of an erosion of ambulance service financial and human resources that diminishes system performance. Certainly ambulance agencies would be unlikely to electively reduce salaries, institute layoffs or reduce service quality in response to energy price rises, but they might be prevented from filling staff vacancies that result from natural attrition or from adding additional employees despite increasing response volumes.

Finally, when modelling changes in oil and electricity price over a 10-year period, changes in electricity price were positively associated with changes in injury compensation claims per 100 FTE. Although safety performance indicators were only available for four systems and a small subset of the system-years represented in this study, this model for injury compensation claims per 100 FTE was strong, with an explained variance (R2) exceeding 90%. To our knowledge, this is the first evaluation to report a link between energy prices and workplace injury claims. There are a number of theoretical mechanisms that might explain this finding. For example, paramedics would individually face the same increases in electricity prices, and they might choose to work additional overtime shifts or take on second jobs to bolster their household budgets. Extra work hours – whether in their primary job or a second job – could lead to worker fatigue, which has been demonstrated to increase the risk of workplace injury among ambulance personnel.32

Limitations and future studies

A potential limitation is that this analysis depends on summary data extracted from annual reports of the ambulance agencies or their parent organisations, rather than data specifically collected for this study. Also, data for ambulance-related activities that are not delivered by the States and Territories, most notably the Royal Flying Doctor Service and also other small independent agencies, are not included in this analysis. Although these services represent a very small proportion of total Australian ambulance responses, it is possible that changing energy prices manifest differently in these organisations, particularly those that rely heavily on air transport. We encourage further research in those settings.

We used the first difference for all of our variables in order to minimise the potential influence of omitted, time-invariant factors on our models. As a result, our models are limited to within-subject, or longitudinal, effects. If there are cross-sectional effects of energy prices on ambulance systems – that is, if there are effects related to some systems facing consistently higher energy prices while others face consistently lower prices – those would not be captured in our analysis. Also, we modelled time as a continuous linear variable. While modelling time as a categorical variable can provide better information about time effects in GEE models, it is not appropriate when the data set contains many missing observations,23 as does ours.

These models evaluate associations, not cause-and-effect. We did not set out to determine all the factors that drive ambulance system resources, operational performance and safety, nor have we explored all of the possible effects of energy prices on ambulance services. Where we have found associations, these should not be interpreted to mean that only energy prices are associated with those measures, or that only those characteristics of ambulance services are potentially influenced by energy prices. The indicators we evaluated might well change even when energy prices do not; what we have modelled are the additional (or marginal) changes in the indicators that occur in association with changing energy prices. Further, energy prices are driven by a complex interplay between production levels, world events, consumer demand, political sensitivities and tax policies.33–35 Some of those same factors could also directly or indirectly affect the measures we explored as well as other aspects of ambulance services and/or their parent agencies. Considerably more research will be required to untangle these complex relationships.

Lastly, these are ecological data on an annual scale; they cannot be extrapolated to individual ambulance responses, or to shorter-run (for example, weekly or monthly) changes in energy prices.

Future studies might seek to explain the exact mechanisms of the effects observed in this study. Ambulance systems involve vehicles, equipment, buildings, communication systems, and personnel, as well as information technology systems, clinical training activities, physician oversight, and administrative structures.16 Fluctuating energy prices could conceivably affect any or all of these inter-related components. It is also possible that ambulance services are able to achieve efficiencies in the face of rising energy prices so that the resource, performance and safety implications identified in this study do not diminish patient care, but additional research is needed to specifically explore the effects of rising energy prices on patient outcomes. The effects of energy prices on ambulance services in other countries, particularly those where ambulance agencies operate as free-market enterprises, remain to be determined. Finally, significant research is needed to evaluate the impacts of energy costs on other aspects of the health system, and to determine how best to mitigate those effects.

Conclusion

There is an association between changes in energy prices and some of the resource, performance and safety characteristics of Australian ambulance systems. Rising electricity prices are associated with higher operational costs, but rising oil prices have a counter-intuitive inverse association with expenditures per response and staffing levels. Increasing diesel price is associated with slower ambulance response times. Finally, there is also an association between rising electricity prices and higher rates of injury compensation claims.

Presented at the National Association of EMS Physicians annual meeting, Tucson, AZ, January 2012.

Ancillary