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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& 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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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)

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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

%

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2004-05

Distribution of Dependent Variable (Treatment Gp)

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number of PBS drugs taken

%

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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

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number of PBS drugs taken

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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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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

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Thresholds

neg bin

poisson

Possible Distributions (Treatment Group) – mid-year

mean = 15.25

0%2%4%6%8%

10%12%

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Thresholds

neg bin

poisson

Possible Distns (Treatment Group) – throughout year

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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

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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