Geographical Variation in Medical Expenditures: What Varies, How Much and Where

University of Western Sydney

• Federico Girosi

• Xiaoqi Feng

• Louisa Jorm

• Thomas Astell-Burt

Australian National University

• Ian McRae

• Soumya Mazumdar

• Danielle Butler

• Paul Konings

why study geographic cost variation?variation may have different sources

• unobservable features

• access to care

• use of guidelines/technologies

• …

geographic variation may point to inefficient use of resources

first in a series of investigationsin 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)

data and methods

• accessed through SURE45 and Up data linked

to MBS data

• 85% of claims: consultation level B, C and A

GP services: MBS items representative

of primary care

• 6 months around interview date

• cost is expressed in constant 2012 $

yearly expenditures and visits

• Ellis et al. (2013) already showed it is preferable

• our results are not dependent on specific methodAll regression are OLS

definition of key variables

• Charge: how much was charged by the physician

Ci: charge for visit i

n: number of visits in a year

Variation in charges across SLAs

Average per capitayearly charges for GP services

Adjusted for:• age• sex• SES• health status• risk factors

average NSW charge

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

what varies? Visits or Prices?

Log(Charge) = log(Price) + log(Visits)

We run three regressions at individual level:

Log(Charge) = log(Price) + βX

Log(Charge) = βX + log(Visits)

Log(Charge) = βX R2=0.23

R2=0.30

R2=0.92

It is visits that drives variation in charges

this remains true even for specific MBS items

What explains the variation at individual level?

Covariates:• age• sex• SES• health status• risk factors• SLA

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

Outer regional -94.9 -10.8 -2.1 -9.3 -0.2 -0.3

Remote -46.5 -1.7 -1.4 -1.9 0.9 0.3

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

Additional Material

Variation of SLA means

Charge Visits Price

Mean 366 7.5 47

Ratio of 99th to 1st percentile 2.15 2.18 1.39

Ratio of 75th to 25th percentile 1.21 1.27 1.10

Coefficient of variation 0.14 0.17 0.08

R squared 0.20 0.24 0.09

Focus on a specific item: 23(level B consultation)

Log(Charge) = log(Price) + log(Visits)

We run three regressions:

Log(Charge) = log(Price) + βX

Log(Charge) = βX + log(Visits)

Log(Charge) = βX R2=0.16

R2=0.18

R2=0.95

It is visits that drives variation in charges

Remoteness Is Likely to Play an Important Role in the Analysis

Adjusted for:• age• sex• SES• health status• risk factors