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Medicare Prescription Drug Coverage: Who Knew? (who sought and who intended) Presented at Academy Health Seattle WA June 25, 2006 Christopher Koepke, PhD.

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Presentation on theme: "Medicare Prescription Drug Coverage: Who Knew? (who sought and who intended) Presented at Academy Health Seattle WA June 25, 2006 Christopher Koepke, PhD."— Presentation transcript:

1 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

2 Medicare Prescription Drug Coverage Open enrollment from November 15, 2005 to May 15, –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.

3 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.

4 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)

5 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?

6 Outcomes Knowledge –Scale of 9 items (see next slide) –Alpha =.87 –One principal components factor – eigen value = 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.

7 Knowledge Items (% “this is accurate” in all 4 combined waves) Coverage is insurance34% You have to sign up58% There is a deadline51% Monthly fee55% Co-pay52% Available to everyone54% Choice of plans42% Extra help available49% Is coverage as good as49%

8 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

9 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

10 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

11 All Outcomes Increased Significantly Over Time Aug Sep 27-2 Oct Nov Mean Know % Sought25%41%40%61% % Intent25%26%28%41%

12 Knowledge and Information Seeking Increased Significantly with Exposure Know. Mean (P<.01) % Sought (P<.01) % Intent (N.S.)

13 Outcomes by Attitudes

14 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.

15 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

16 Exposure Still Predictive of Knowledge After Controls Exposure (B=.394) predicted knowledge above the effects of time (B=.218). –Adj. R 2 =.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. R 2 =.388 # drugs, marital status, and gender were no longer predictive

17 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. R 2 =.21 (lower than exposure equation) # drugs and gender were no longer predictive

18 Predicting Information Seeking note: a = p<.01 b=p<.05 Adj OR Time1.23 a - Exposure1.78 a - Too Costly-1.81 a Education1.34 b 1.37 a Agens.97 b Marriedns

19 Attitudes and Exposure Predict Intent note: a = p<.01 b=p<.05 Adj OR Timen.s. Exposure1.24 b Costly.53 a Safety Net3.31 a Favorable3.39 a # Drugs1.24 b

20 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.


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