Associate Professor Federico Girosi gave an update on her research using the 45 and Up Study data at the Sax Institute's 45 and Up Study Collaborators' Meeting. This meeting is an annual event that offers our research partners, supporters and other interested parties the opportunity to receive a comprehensive update on the 45 and Up Study’s progress and updates on research projects that are using the Study resource. The meeting is also an opportunity for researchers, health decision makers and evaluators to engage and discuss the potential for maximising the Study’s value. For more information, visit www.saxinstitute.org.au.

1. Geographical Variation in
Medical Expenditures: What
Varies, How Much and Where
University of Western
Sydney
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•
•
•
Federico Girosi
Xiaoqi Feng
Louisa Jorm
Thomas Astell-Burt
Australian National
University
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•
•
•
Ian McRae
Soumya Mazumdar
Danielle Butler
Paul Konings

2. why study geographic cost variation?
variation may have
different sources
• unobservable
features
• access to care
• use of
guidelines/technolo
gies
• …
geographic variation
may point to inefficient
use of resources

3. first in a series of investigations
in geographic variation of costs
Today we focus on yearly total GP expenditures
We document variation in total expenditures at
individual and geographic level
We relate variation in expenditures to variation in visits
and price
We look at the role of remoteness in explaining
variation across Statistical Local Areas (SLAs)

4. data and methods
45 and Up data linked
to MBS data
• accessed through SURE
GP services: MBS
items representative
of primary care
• 85% of claims: consultation level B, C and A
yearly expenditures
and visits
• 6 months around interview date
• cost is expressed in constant 2012 $
All regression are OLS
• Ellis et al. (2013) already showed it is preferable
• our results are not dependent on specific method

5. definition of key variables
• Charge: how much was charged by the
physician
Ci: charge for visit i
n: number of visits in a year

6. Variation in charges across SLAs
Average per capita
yearly charges for
GP services
average NSW charge
Adjusted for:
• age
• sex
• SES
• health status
• risk factors

7. What does this figure suggest?
After controlling for individual
characteristics there is
significant variation in annual
GP charges across SLAs
• Ratio of 95th to 5th percentile
in charges is 1.6
Remoteness will play a role in
explaining the observed
pattern
• Charges in cities are 31%
larger than charges in outer
regions

8. what varies? Visits or Prices?
Log(Charge) = log(Price) + log(Visits)
We run three regressions at individual level:
Log(Charge) = βX
R2=0.23
Log(Charge) = log(Price) + βX
R2=0.30
Log(Charge) = βX + log(Visits)
R2=0.92
It is visits that drives variation in charges
this remains true even for specific MBS items

9. What explains the variation
at individual level?
Covariates:
• age
• sex
• SES
• health status
• risk factors
• SLA

10. What explains the variation
across SLAs at aggregate level?
Charge (R2 = 0.45)
Visits (R2 = 0.39)
Price (R2 = 0)
Estimate
t value
Estimate
t value
Estimate
t value
(Intercept)
394.7
107.3
8.1
86.6
46.9
140.5
Inner regional
-56.6
-9
-1.3
-8.2
0.2
0.3
-10.8
-2.1
-9.3
-0.2
-0.3
-1.7
-1.4
-1.9
0.9
0.3
Outer regional -94.9
Remote
-46.5

11. Summary
There is significant variation in GP expenditures
across SLAs unexplained by individual characteristics
The variation is due to variation in the number of GP
visits, rather than in the average price per visit
Observed individual characteristics explain 20% of
the variance in GP expenditures
Remoteness explains a large proportion of the
variance in aggregate SLA GP expenditures

12. Additional Material

13. Variation of SLA means
Charge
Mean
366
Ratio of 99th to 1st percentile
2.15
Ratio of 75th to 25th percentile
1.21
Coefficient of variation
0.14
R squared
0.20
Visits
7.5
2.18
1.27
0.17
0.24
Price
47
1.39
1.10
0.08
0.09

14. Focus on a specific item: 23
(level B consultation)
Log(Charge) = log(Price) + log(Visits)
We run three regressions:
Log(Charge) = βX
R2=0.16
Log(Charge) = log(Price) + βX
R2=0.18
Log(Charge) = βX + log(Visits)
R2=0.95
It is visits that drives variation in charges

15. Remoteness Is Likely to Play an
Important Role in the Analysis
Adjusted for:
• age
• sex
• SES
• health status
• risk factors