Medicare Prescription Drug
Coverage: Who Knew?
(who sought and who intended)
Presented at Academy Health
Seattle WA
June 25, 2006
Christopher Koepke, PhD
Centers for Medicare & Medicaid Services
Office of External Affairs
Thanks to Beth Simon, Ph.D. CMS,
Sandra Tirey and Jan van Lohouizen, Ph.D.
Voter Consumer Research
Medicare Prescription
Drug Coverage
• Open enrollment from November 15, 2005 to
May 15, 2006.
– Must enroll to receive benefit.
• Many people with drug coverage had creditable
coverage.
– “As good as”
• So, an important target is those without drug
coverage.
Three Goals of Medicare
Prescription Drug Outreach
• Provide clear, accessible, accurate
information.
• Increase information seeking.
• Encourage enrollment.
• NOTE: Conceptual goals for purpose of paper, not necessarily CMS
publicly stated goals.
Methods
• Monthly Tracking Surveys
– Telephone using listed sample to increase
65+ incidence
– August through November 2005
– N=400 per wave
– Today’s analysis limited to those without
coverage.
• ~ 135 per wave (total n=547)
Overall Research Questions
•
Outcomes: Knowledge, Info Seeking, Intent
1. Did outcomes increase over time?
2. Was campaign exposure related with
outcomes?
3. Who, demographically, were higher on
outcomes?
4. Was knowledge related with intent?
5. Were attitudes related with outcomes?
Outcomes
• Knowledge
– Scale of 9 items (see next slide)
– Alpha = .87
– One principal components factor – eigen value =
4.416 explaining 49% of the variance
• Information seeking
– Yes to “have tried to find out more… to see if it would
work for you…”
• Intent
– Definitely or Probably will sign up.
– In November measure includes those who did sign
up.
Knowledge Items
(% “this is accurate”
in all 4 combined waves)
Coverage is insurance
You have to sign up
There is a deadline
Monthly fee
Co-pay
Available to everyone
Choice of plans
Extra help available
Is coverage as good as
34%
58%
51%
55%
52%
54%
42%
49%
49%
Predictor Variables
• Time
– Survey wave, August, September, October,
November.
• Information exposure
– Additive scale (alpha=.53, 1 factor 41% var)
•
•
•
•
•
Range from 0 to 4
Recall TV ad
Recall print ad
Recall direct mail
Discussed with someone
Predictor Variables: Attitudes
(% across all four waves)
• Confusing (49%)
– This is all very confusing (Strongly agree)
• Too costly (31%)
– Costs too much and covers too little (SA)
– Return on investment concept
• Safety net (34%)
– Should sign up for safety net (SA)
• Overall favorability (29%)
– Very and somewhat favorable
Control Variables
• Number prescription drugs currently taken
– Mean = 4.5
• Education
– 22% < HS
• Age
– Mean = 75
• Income
– 23% <$20K, 24% missing
• Marriage status
– 53% married (35% widowed, 12% single/div)
• Gender
– 60% Female
All Outcomes Increased
Significantly Over Time
Aug
11-17
Sep
27-2
Oct
22-26
Nov
27-30
Mean
Know.
2.61
4.18
4.79
5.98
% Sought
25%
41%
40%
61%
% Intent
25%
26%
28%
41%
Knowledge and Information Seeking
Increased Significantly with Exposure
Know.
Mean
0
1
2
3
4
2.03
3.50
4.66
5.77
6.55
14
26
44
62
68
28
24
29
34
40
(P<.01)
%
Sought
(P<.01)
%
Intent
(N.S.)
Outcomes by Attitudes
Mean
Knowledge
4.11 b
SA Confusing
else
4.76
a
5.36
SA Too costly
else
3.97
c
4.76
SA Safety Net
else
4.23
4.43
Favorable Overall
Not favorable
4.40
a
= p<.01, b = p<.05, c = p=.058
% Sought Info
41
44
53a
37
45
41
42
43
% Intend to
Enroll
28
33
19a
35
a
51
20
53a
21
Demographics Overview
• Intent was only related with number of drugs –
no other demographics.
• Conversely, knowledge was related with every
demographic.
– Oddly enough, it was negatively related with number
of drugs taken.
• Seeking information was related with education,
being married, and age.
• Older people were less knowledgeable and less
likely to seek information.
• Married people were more knowledgeable and
more likely to seek information.
Multivariate Analysis
• To assess whether bi-variate observed
relationships where upheld after controls.
• Only variables that had significant bi-variate
relationships were used in analysis.
• Linear regression used to predict knowledge.
• Logistic regression used to predict information
seeking and intent
Exposure Still Predictive of
Knowledge After Controls
• Exposure (B=.394) predicted knowledge above
the effects of time (B=.218).
– Adj. R2=.26
• Exposure (B=.276), info seeking (B=.259), and
time (B=.182) predicted knowledge above
controls.
– only education (B=.230) and age (B=-.11) remained
significant.
– Adj. R2=.388
• # drugs, marital status, and gender were no longer predictive
Costly and Safety Net Still Predictive of
Knowledge After Controls
• Confusing was no longer predictive after
controls.
• Costly (B=.194) and safety net (B=.128).
– Education (B=.278), age (B=-.177), Married
(B=.124) remained predictive.
– Adj. R2=.21 (lower than exposure equation)
• # drugs and gender were no longer predictive
Predicting Information Seeking
note: a = p<.01 b=p<.05
Adj OR
Adj OR
Time
1.23a
-
Exposure
1.78a
-
Too Costly
-
1.81a
Education
1.34b
1.37a
Age
ns
.97b
Married
ns
ns
Attitudes and Exposure Predict Intent
note: a = p<.01 b=p<.05
Adj OR
Time
n.s.
Exposure
1.24b
Costly
.53a
Safety Net
3.31a
Favorable
3.39a
# Drugs
1.24b
Conclusions
• Exposure, and by extrapolation the campaign
activities, predicted outcomes more than time or
demographics.
– Predicted knowledge more than attitudes.
• Beliefs, far more than knowledge, predicted
intent.
• Education predicted knowledge and information
seeking, but not intent
• Confusion belief, while strong, did not predict
outcomes.