Direct Marketing Strategies Can
Enhance Enrollment of Low
Income Beneficiaries Into the Low
Income Subsidy (LIS)
Frank Funderburk, Christopher Koepke, Adam Burns,
Laura Salerno, Kevin Simpson,
Lisa Wilson & Thomas Kickham
Strategic Research & Campaign Management Group, CMS/OEA & Porter Novelli
Presentation to AcademyHealth Annual Research Conference Chicago Il, June 2009
Background
• The LIS assists low-income Medicare
beneficiaries by covering their prescription drug
costs and Part D premiums
• To qualify in 2008 beneficiaries must have
– Household Income < $15,600 ($21,000 if married)
– No significant assets (other than home, car)
• Despite intensive efforts, a substantial number of
eligible beneficiaries have not enrolled
Direct Marketing Study
• Gauge effectiveness of direct marketing
interventions
• Experimental conditions use best practices
and build on prior knowledge
• Designed to improve our understanding of
what works and why
• Designed to improve both targeting and
outreach efforts
Key Insights
• All direct marketing approaches tested were
significantly better than relying on current
efforts.
• Only the most intensive direct marketing
efforts were more effective than a standard
CMS letter.
• Commercially available income projections
can improve targeting efficiency.
• Most frequent reasons for not applying for
LIS included a belief that income was too
high and not wanting government support
Target Audience
Phyllis is 81 years old. She is a widow who
lives alone. She has worked hard most of her
life and is proud that she has never had to ask
for welfare or take a “handout” to raise her
family. She likes to garden, loves her
grandchildren, and appreciates getting a good
deal on the few things she buys with her
limited income. Her health is ok, but she has
some problems with arthritis and has been told
that her blood pressure is a bit too high.
Despite that, she rarely visits the doctor and
does not take any prescription drugs. She has
heard something about “government help” for
drugs, but has also been told (she’s not sure
by whom) that it can be pretty expensive. She
probably will not look into this further – she
has many other things to worry about!
Experimental Design
Using census block-level data and internal enrollment data, CMS
identified beneficiaries where median income was in the lowest 30%
who also had no known creditable coverage
CMS Database
≈1.66M
Database underwent cleaning procedures; selected individuals who
reside in states without an SPAP and in counties with active SHIPs
CMS Database
≈135K
CMS Standard List
C
1
2
3
n = 10,000
4
Attached income/age estimates from Experian;
randomly selected 20,000 study participants;
divided the database in two
Experian-refined list
C
1
2
3
n = 10,000
4
Refined list accepted only those with
predicted incomes of < $25K;
Standard list did not apply criterion
Participants randomly assigned
to experimental groups which
designate levels of outreach
Level 1: Official CMS Letter
Level 2: Self-mailer + BRC
Level 3: Invitation + BRC +
Sandwich Calls
Level 4: Invitation + BRC +
Personal Assistance
Evaluation, Phase 1:
Telephone Survey
•
Telephone survey developed to gauge outreach and list
effectiveness
•
Telephone numbers were matched to all individuals selected
to participate in the study
•
Telephone survey was 15 minutes in length
•
Advance letter sent to inform study participation
•
Surveys were initiated within 3 days of receipt of survey
letter and within 2 weeks after the end of the intervention
activity
•
Given this timeline, data in this presentation only capture
self-reported actions within the brief window from
intervention to survey
Survey Response Rate
•
The survey garnered a response rate of 25%, accounting for
bad numbers and survey ineligibility. Response or
cooperation rates did not vary by intervention group
•
Qualifying income questions had a non-response rate of
28%, so multiple imputation was used
•
The overall survey sample size was 4,208 people; the lowincome subgroup included an average of 2,400 people, with
a minimum of 2,079 people, in the separate imputations.
•
Analyses in this presentation focus on the subgroup of
people with household income < $21,000, as these
represent the primary target audience of these marketing
efforts.
Demographic Profile
•
•
•
•
•
•
Comparable across groups
32% male
12% less than 65 years of age
82% completed high school
72% white, 15% African American
50% married, living with spouse
Analytical Approach: Logistic Regression
•
•
•
•
•
Outcome measures covered a range of behavioral change (awareness,
action, application for benefit)
Control group was used as an initial means of comparison for the
outreach strategies
Robust estimation procedures were employed to account for clustering
Multiple Imputation of household income (STATA 10, MICE procedure
[Royston, 2005], 5 imputations) to allow for more accurate model by
including cases who did not answer income questions
Odds ratios (OR) comparing the various intervention groups on key outcomes
– The OR quantifies the relationship between the predictor and the outcome
• OR = 1.0: variable has no effect on odds associated with predictor
• OR > 1.0: variable increases the odds
• OR < 1.0: variable decreases the odds
•
Model evaluated included awareness of LIS benefit, demographic, and
decision-making style (Williams & Heller, 2007) variables as well as
intervention group
Design and Key Outcomes
Intervention Activities
Control
Level 3
Level 4
Recorded pre-call from telephone marketing campaign
Yes
Yes
Invitation-style direct mail piece* with pre-populated
BRC and magnet picture frame
Yes
Yes
Second recorded call from telephone marketing
campaign after mail drop
Yes
Standard CMS letter
Level 1
Level 2
Yes
Self-mailer style direct mail piece* with pre-populated
BRC
Yes
Live enrollment assistance call from trained call center
after mail drop
Yes
Outcomes
Percent Reporting Recall of Campaign (Low-income)
4.3%
33.2%
15.6%
30.0%
34.4%
Percent Reporting Taking Some Action (Low-income)
1.0%
9.19%
5.24%
10.6%
13.9%
Percent Reporting Applying (Low-income)
0.2%
2.9%
1.3%
3.9%
3.6%
Recall
Low-Income Subgroup
Odds Ratio
Std. Error
t
p
Aware of LIS
1.90
.233
5.26
0.001
Age < 65
1.22
.203
1.23
0.221
Married
.86
.117
-1.09
0.279
High School or above
.92
.126
-0.62
0.535
Race White vs. Other
1.54
.309
2.17
0.031
Race Black vs. Other
1.45
.248
2.17
0.031
Male
1.05
.147
0.37
0.711
Active
1.26
.162
1.77
0.078
CMS Letter
11.10
3.022
8.84
0.001
Self-Mailer + BRC
3.77
1.090
4.58
0.001
Invite + BRC + Recorded Calls
9.48
2.532
8.42
0.001
10.99
2.974
8.86
0.001
All + Enrollment Assistance
FOR INTERNAL US ONLY
Reported Taking Action
Low-Income Subgroup
Odds Ratio
Std. Error
t
p
Aware of LIS
1.75
.304
3.21
0.001
Age < 65
1.56
.348
1.98
0.048
Married
0.62
.135
-2.18
0.030
High School or above
0.95
.197
-0.24
0.807
Race White vs. Other
1.73
.536
1.77
0.077
Race Black vs. Other
1.42
.397
1.24
0.215
Male
1.37
.263
1.65
0.100
Active
1.35
.254
1.59
0.112
CMS Letter
9.24
4.91
4.18
0.001
Self-Mailer + BRC
4.81
2.73
2.77
0.006
Invite + BRC + Recorded Calls
10.78
5.63
4.55
0.001
All + Enrollment Assistance
14.55
7.81
4.99
0.001
FOR INTERNAL US ONLY
Reported Applying
Low-Income Subgroup
Odds Ratio
Std. Error
t
p
Aware of LIS
2.38
.744
2.76
0.006
Age < 65
2.21
.796
2.20
0.028
Married
0.59
.228
-1.36
0.175
High School or above
1.05
.365
0.13
0.898
Race White vs. Other
1.76
.993
1.01
0.314
Race Black vs. Other
1.17
.613
0.30
0.761
Male
1.52
.513
1.26
0.209
Active
0.73
.261
-0.86
0.388
CMS Letter
11.30
11.86
2.31
0.021
Self-Mailer + BRC
4.67
5.10
1.41
0.159
Invite + BRC + Recorded Calls
14.91
15.43
2.61
0.009
All + Enrollment Assistance
13.55
14.08
2.51
0.012
FOR INTERNAL US ONLY
Conclusions
•
Direct marketing interventions like those used in this study can
have a significant impact in this target population
•
Interventions reached the target audience and led to increased
consumer activation in behavior related to LIS, including applying
for the benefit
•
The official agency letter worked as well as, if not better than, the
self-mailer on measures related to campaign exposure and
behavior related to LIS, including applying; it consistently
outperformed the control group on these measures
•
The most intensive interventions (involving telephone contacts
and enhanced mail materials) showed evidence of further impact
on measures of campaign exposure and most measures of
activation and LIS application
Conclusions (cont.)
•
For those exposed to the most intensive interventions, contact
from a State Health Insurance Program (SHIP)—initiated via return
of the BRC—appeared to be as effective in increasing LIS
application rates as involving an independent enrollment
assistance agent
•
Current targeting procedures can be improved significantly by
incorporating income estimates from list vendors
•
Despite the success of the interventions used, this study also
highlighted some barriers that remain to be addressed:
– Knowledge of the LIS is low among this audience
– Misperceptions exist in terms of income eligibility
– Some in this audience resist help from the government to pay for
prescription drugs
Next Steps
•
•
•
•
•
Conduct additional modeling of the factors affecting beneficiary
behavior and decision-making related to the LIS application and
enrollment process
Augment these analyses by incorporating administrative
enrollment and application data from the Social Security
Administration
– Objective evidence of application and disposition
– Longer time frame for response
– Preliminary data support survey results
Layer cost-effectiveness analyses onto these findings by looking
at the costs of each outreach strategy in relationship to its effect
on increasing applications the LIS
Further explore the impact targeting list refinement can have on
the efficiency of LIS outreach and subsequent application
Investigate improved messaging strategies to enhance future
outreach efforts
References
•
Holbrook A L, Krosnick JA, & Pfent, AM (2007). Response rates
in surveys by the news media and government contractor
survey research firms. In J. Lepkowski, B. Harris-Kojetin, P. J.
Lavrakas, C. Tucker, E. de Leeuw, M. Link, M. Brick, L. Japec, &
R. Sangster (Eds.), Telephone survey methodology. New York:
Wiley.
• Royston P (2005). Multiple imputation of missing values: Update
of ice. Stata Journal 5(4): 527-536
• Williams SS & Heller A (2007). Patient activation among
Medicare beneficiaries: Segmentation to promote informed
health care decision-making. International Journal of
Pharmaceutical and Healthcare Marketing, 1, 199-213.
Author Contact
• frank.funderburk@cms.hhs.gov
• 410-786-1820