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for England. Hence, the gap between delivered and
theoretical maximum demand can be calculated. With the
gap quantified, adjustments can be made to the delivered
radiotherapy treatments in different age groups, or to
access rates, to estimate the increase in demand for
services.
Results
Figure 1. shows the difference between modelled demand
and delivered treatment. In the age bands there is a small
fall-off in the number of fractions prescribed per
incidence at the ages above 75 and a more marked drop-
off above 80, shown in Table 1. However, the access rate
appears to be declining steadily from an earlier age. If the
average attendance per episode was to increase to a more
steady decline of a few fractions per age band (80-84:
12.5#, 85+:10#) then demand would increase by 41,000
fractions per year. If access rate was to increase to
account for a greater number of elderly being treated (80-
84:32%, 85+:28%) then demand would increase by 44,000
fractions per year. If both the access rate and
fractionation increased then demand would increase by
97,000 fractions per year. The effects on local populations
will vary, considering Malthus predicts demand across
England to vary between 19,000 fractions per million to
80,000 fractions per million.
Conclusion
Even with relatively minor increases to access rate and
fractionation, the modelled fraction burden increases
significantly enough to require extra investment in
services. The numbers presented here are for England as
a whole, however considerable regional differences are to
be expected. A non-urban retirement area could expect a
much greater increase in fraction burden with a change in
the paradigm for treatment of the elderly, compared to
inner-city hospitals that have a much younger population.
Additionally, machine throughput would have to be
studied closely as the elderly often take longer to treat
due to patient factors such as decreased mobility.
PV-0092 Criterion-Based Benchmarking approach of
the appropriate use of radiotherapy in NSW-ACT,
Australia
G. Gabriel
1
, G. Delaney
1
, M. Barton
1
1
Ingham Institute for Applied Medical Research,
Collaboration for Cancer Outcomes Research and
Evaluation CCORE and University of NSW, Liverpool-
NSW, Australia
Purpose or Objective
Planning for radiotherapy (RT) services requires
information on the proportion of patients who should be
given RT during their cancer journey. CCORE has
previously estimated optimal rates of radiotherapy
utilization (RTU) based on the development of decision-
trees using evidence-based treatment guidelines and
epidemiological data. Mackillop and colleagues in Ontario
established a Criterion-Based Benchmarking (CBB)
approach to estimate the proportion of cancer patients
who should be treated with RT. Aims:
1.
Calculate actual RTU rates for NSW-ACT patients
2.
Identify benchmark communities
3.
Calculate RTU rates for the benchmark
communities
4.
Compare actual and CBB RTU with the estimated
optimal RTU rates
Material and Methods
RT data were collected from all RT centers in NSW and
ACT for Jan-2004 to Jun-2007 and were linked to Central
Cancer Registry records. Road distance between patient
residence and the nearest RT center was calculated.
Cancer patients who lived nearer to RT center outside NSW
or ACT were excluded. Non NSW-ACT residents who were
treated in NSW or ACT were also excluded. Adjacent Local
Government Areas (LGAs) with <500 patients in each LGA
were merged to form larger geographical areas with
number of patients equivalent approximately to the
average number of patients in other LGAs. LGAs with
public RT center that satisfy the following RT
benchmarking criteria were selected, where:
1.
Patients make no direct payment for RT
2.
All RT is provided by site-specialized radiation
oncologists in multi-disciplinary centers
3.
Radiation oncologist receive a salary for their
service
4.
>75% of patients live within 30 km from the
nearest RT, and
5.
Patients waiting times were <4 weeks
Results
Overall, 25.4% of patients received radiotherapy as part of
their initial treatment (within 1-year of diagnosis) in the
CBB LGAs compared to 22.1% in all LGAs. For patients
diagnosed with cancer of breast, prostate, lung, rectum or
cervix, the proportions of patients who received RT within
1-year were 60%, 22%, 40%, 26% and 53% in the CBB LGAs
compared to 51%, 19%, 36%, 24% and 49% in all LGAs,
respectively. The corresponding optimal RTU were 82%,
55%, 70%, 63% & 71%, respectively. Table-1 shows a
comparison between our data and Ontario, Canada. Figure
1 shows RTU rates for all LGAs in NSW and ACT.