The effect of increases to concession card income eligibility thresholds on pharmaceutical consumption
Work in Progress
Peter Siminski, PhD student, UNSW
Presented at CAER Summer Workshop in Health Economics 1st Feb 2007
Outline
A ‘natural experiment’ – change in eligibility rules for Commonwealth Seniors Health Card (CSHC)
Methods– Diff-in-diff to measure effect on consumption
using National Health Surveys
– Average price decreased by at least 66%
Results: very low estimated price elasticity
Commonwealth Seniors Health Card (CSHC)
CSHC -> concession price for PBS drugs for people of age pension age
Income eligibility threshold for CSHC almost doubled in 1999
What was impact on PBS consumption for the affected?
CSHC income eligibility threshold, couples $2001 $’000s p.a.
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A ‘Natural Experiment’
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Treatment group
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& 4th compgroup
Income Thresholds Used in the Analysis
Couples
0200400
600800
100012001400
160018002000
actual 1995proxy
2001proxy
2004-05proxy
Comp Gp 2
Treat Gp
Comp Gp
Singles
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600
800
1000
1200
actual 1995proxy
2001proxy
2004-05proxy
Sample Size
1995 2001 2004-05 Total
Treatment Group 166 189 203 558
Comp Gp 1 (low income) 3897 2205 2995 9097
Comp Gp 2 (high income) 111 64 92 267
Comp Gp 3 (M 50-64; F 50-59) 5116 2588 3890 11594
Total 9290 5046 7180 21516
0%
20%
40%
60%
80%
100%
1995 2001 2004-05
no card
card
A Complication – Treatment Group is Contaminated
Concession card coverage recorded in each year, but CSHC not distinguishable from other cards
Contaminated Treatment Group
0%
20%
40%
60%
80%
100%
1995 2001 2004-05
no card '01 & '05(contaminators)
no card in '01 & '05(non-take up)
no card 1995; cardlater
card in 1995 (safetynet)
card in 1995(contaminators)
Accounting for the contamination
Contaminators are present in each year and are unaffected by the intervention
If estimated treatment effect is a change of x%, it can be shown that the effect on the affected is
Where p2 is the proportion of contaminators,
p1 =1-p2 and
d is mean PBS consumption
11
2211 )(
dp
dpdpx
Dependent Variable
Number of PBS drugs taken for selected conditions in previous two weeks
As reported by respondent Drug data collected inconsistently between
years– NHS 2001 & 2004-05 recorded if taken for selected
conditions
– NHS 1995 recorded all drugs taken
– ABS classifies drugs into types commonly used for specific conditions
Generic drug names checked against PBS Schedule
Dependent Variable (Cont.)
Conditions for which drug data are available in all three surveys– Heart and circulatory conditions
– Asthma
– Diabetes and high sugar levels
These account for 41% of all PBS drug consumption in 2001
NHS 1995 and 2004-05 also have data for – Arthritis and osteoporosis
– Mental wellbeing
Mean PBS Consumption by Group and Year (excl 04-05)
0
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'95 '01 '95 '01 '95 '01 '95 '01
Treatment group Comparison group(low income)
Comparison group(high income)
Comparison group(middle aged)
Distribution of Dependent Variable (all groups)
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number of PBS drugs taken
%
1995
2001
2004-05
Distribution of Dependent Variable (Treatment Gp)
0
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0 1 2 3 4 5 6 7 8
number of PBS drugs taken
%
1995
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Modelling Approach
Count data models– Poisson regression model
Where
– Negative Binomial regression model (NegBin)
– Negative Binomial / Logit Hurdle model (not yet implemented)
Model each combination of year and comparison group separately and then pooled
)exp(~3210 XIntYrGr
)exp( 3210 XIntYrGr
),,,|( XIntYrGryE
Goodness of Fit (NegBin)
Formally, NegBin model rejected by Pearson Chi-square test (p < 0.001), suggesting misspecification
Hurdle model might be more appropriate
0%
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0 1 2 3 4 5 6 7 8 9 10
number of PBS drugs taken
%
Actual
NegBin fitted
Main results
Negbin:3= -0.007 in pooled model -1% change in PBS consumption after adjusting
for contaminators (95% CI: -54%, 51%) Poisson: -2% change (95% CI: -53%, 49%)
Out-of-pocket Price of PBS Drugs ($1995)
$0$2$4$6$8
$10$12$14$16
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Treatment group 1995 Treatment group 2001
What is the average marginal price change?
Depends on distn of the number of scripts purchased in calendar year prior to interview
For treatment group average annual consumption estimated to be 30.5 scripts
Assume over-dispersed count variable distribution (negative binomial)
Consider a range of possible distributions
Possible Distributions (Treatment Group) – end of year
mean = 30.5
0%
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10%
1 10 19 28 37 46 55 64 73 82
Thresholds
neg bin
poisson
Possible Distributions (Treatment Group) – mid-year
mean = 15.25
0%2%4%6%8%
10%12%
1 10 19 28 37 46 55 64 73 82
Thresholds
neg bin
poisson
Possible Distns (Treatment Group) – throughout year
0%
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thresholds
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Price Change (Cont.)
A maximum of 12.8% of treatment group experienced price increase
Average marginal price fall was at least 66% Similarly, average marginal price increased by
at least– 15% for comparison group 1
– 17% for comparison groups 2 & 3
Modelled price change = -71%or –119% using midpoint formula
Price Change (Cont.)
Calculate price elasticity using midpoint formula
How important is the price change?– Average person in treatment group purchases 30
scripts per year -> saves $360 p.a. = 1% income
– Person purchasing 10 times average saves $1083 p.a. = 3% income
– consider non-monetary costs + cost of seeing GP
2)(
)(
2)(
)(
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PP
PP
CC
CCE
Estimated Price Elasticity
-1.0-0.8-0.6-0.4-0.20.00.20.40.60.81.01.21.41.6
2001 2004-05 2001 2004-05 2001 2004-05
Comp 1 Comp 2 Comp 3 POOLED
Threats to Validity
Quantity taken in fortnight might not be proportional to drugs purchased. Price change could lead to:– Changes in frequency or dosage of consumption
– Using out-of-date or substitute medications
– leads to underestimation of elasticity
Endogeneity of Health Care Card Status– People may reduce income to qualify for card, but unlikely
given low relative value of concession
– leads to overestimation of elasticity
Possible overestimation of price change:– PBS premiums not accounted for
– Non-monetary costs + costs of GP consultation
Conclusion
Results are preliminary, next step is implementation of hurdle model.
In the interim: Policy is efficient. Does not seem to induce
excess consumption Recipient value might be higher than govt cost
(on average) due to insurance value Middle income older people do not need
concessions to purchase sufficient quantity of pharmaceuticals
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