1. Pharmacy Benefit Design, Generic
Dispensing and Mail Service Utlization:
A Panel Analysis of Plan Sponsor Data
M. Christopher Roebuck, MBA and Joshua N. Liberman, PHD
CVS Caremark

2. Pharmacy Benefit Design, Generic Dispensing and Mail Service Utilization:
A Panel Analysis of Plan Sponsor Data
M. Christopher Roebuck, MBA and Joshua N. Liberman, PHD
oBjective data
To estimate the relationships between pharmacy benefit • Plan sponsor-level dataset of 367 employers and 27 health plans,
design, generic dispensing, and mail service utilization covering 11,273,851 members
by conducting a panel analysis of plan sponsor data. • Continuously managed by CVS Caremark from 1/1/2005 through
Although prior studies have investigated the impact 12/31/2006 (measured quarterly)
of patient cost-sharing on prescription drug utilization, • Having either a two-tier or three-tier pharmacy benefit
few have accounted for other common elements of design structure
pharmacy benefit design. Furthermore, trends in
• With both retail pharmacy and mail delivery options
pharmacy utilization due to demographics, brand and
generic drug pipelines, as well as prescription delivery • Without retail refill restrictions for maintenance medications
options have also been largely ignored, potentially (i.e., no “mandatory mail”)
leading to biased results. • Note: All variables were calculated as means, weighted by the
number of eligible participants within each plan sponsor-quarter
Methods
• Study design: A retrospective analysis of prescription claims • Detected and accounted for heteroskedasticity across panels
• Feasible generalized least squares models were estimated and first-order autoregressive serial correlation
for each of three dependent variables, by tier: • Independent variables included:
- Generic Dispensing Rate (GDR) = (# of Generic Rxs) ÷ - Proportion of plan members in each of 5 age groups, 5
(Total # of Rxs) geographic regions, and gender
- Generic Substitution Rate (GSR) = (# of Generic Rxs) ÷ - Indicator variable for employer versus health plan sponsor
(# of Generic Rxs + # of Multi-Source Brand Rxs) - Member out-of-pocket (OOP) costs for generic, preferred
- Mail Distribution Rate (MDR) = (# of Mail service Rxs) ÷ brands and non-prefered brands at retail (linear and squared)
(Total # of Rxs) - Mail-retail member out-of-pocket (OOP) cost ratios (linear
and squared)
table 1. descriptive statistics - Dichotomous variables for coinsurance, deductible,
two-tier three-tier
Pharmacy Benefit design elements (N=140) (N=254)
maximum allowable
Retail Coinsurance 0.54 (0.46) 0.24(0.40) benefit (MAB), maximum out-of-pocket (MOOP), and two
Retail Generic Copay ($) 7.52 (3.37) 8.70 (3.62)
Retail Brand Copay ($) 25.45 (9.42) – types of dispense-as-written (DAW) penalties
Retail Preferred Brand Copay ($) – 23.20 (7.36)
Retail Nonpreferred Brand Copay ($) – 38.02 (10.97) - A vector of 8 quarterly time dummies
Mail-Retail Generic Copay Ratio 2.03 (0.74) 2.25 (1.71)
Mail-Retail Brand Copay Ratio 1.82 (1.18) –
Mail-Retail Preferred Brand Copay Ratio – 1.84 (0.49)
Mail-Retail Nonpreferred Brand Copay Ratio – 1.84 (0.51)
Retail Deductible 0.13 (0.32) 0.14 (0.33)
Retail Dispense as Written Penalty (Physician) 0.17 (0.35) 0.23 (0.40)
Retail Dispense as Written Penalty (Patient) 0.21 (0.38) 0.34 (0.44)
Retail Maximum Allowable Benefit 0.09 (0.27) 0.04 (0.19)
Retail Maximum Out of Pocket 0.09 (0.28) 0.06 (0.22)
Notes: Mean values presented; standard deviations in parentheses.
All values are expressed as proportions unless otherwise indicated.

3. Results
• Most member cost-share variables were associated with changes in GDR, GSR, and MDR in their expected directions.
However, support for the nonlinearities of these relationships emerged.
• Marginal effects were generally greater in magnitude for brand OOP costs than for generic OOP costs
• Decreases in mail-retail OOP cost ratios were related to increases in MDR, but did not necessarily lead to increases in GDR or GSR
• GDR and GSR were 1 to 2 percentage points higher in:
- Two-tier plans with a deductible, DAW, or MAB
- Three-tier plans with a DAW or MAB
• MDR was 1 to 4 percentage points higher in three-tier plans with a retail deductible, DAW, or MAB
• GDR, GSR, MDR all increased significantly with time by 7, 2, and 2 percentage points, respectively
table 2. selected Results from FGls Models of Generic dispensing Rate by tier table 3. selected Results from FGls Models of Generic substitution Rate by tier table 4. selected Results from FGls Models of Mail distribution Rate by tier
Pharmacy Benefit design elements two-tier three-tier Pharmacy Benefit design elements two-tier three-tier Pharmacy Benefit design elements two-tier three-tier
0.01046*** -0.00650*** 0.00254** -0.00663*** 0.00254** -0.00663***
Retail Coinsurance Retail Coinsurance Retail Coinsurance
(0.00205) (0.00218) (0.00108) (0.00131) (0.00108) (0.00131)
-0.00049 -0.00221*** -0.00089* -0.00109*** -0.00089* -0.00109***
Retail Generic Copay Retail Generic Copay Retail Generic Copay
(0.00084) (0.00058) (0.00051) (0.00032) (0.00051) (0.00032)
0.00004 0.00010*** 0.00006** 0.00005*** 0.00006** 0.00005***
Retail Generic Copay Squared Retail Generic Copay Squared Retail Generic Copay Squared
(0.00005) (0.00002) (0.00003) (0.00001) (0.00003) (0.00001)
0.00204*** 0.00050** 0.00050**
Retail Brand Copay – Retail Brand Copay – Retail Brand Copay –
(0.00042) (0.00026) (0.00026)
-0.00001* 0.00000 0.00000
Retail Brand Copay Squared – Retail Brand Copay Squared – Retail Brand Copay Squared –
(0.00001) 0.00000 0.00000
0.00090 0.00014 0.00014
Retail Preferred Brand Copay – Retail Preferred Brand Copay – Retail Preferred Brand Copay –
(0.00062) (0.00033) (0.00033)
-0.00002 -0.00001 -0.00001
Retail Preferred Brand Copay Squared – Retail Preferred Brand Copay Squared – Retail Preferred Brand Copay Squared –
(0.00001) (0.00001) (0.00001)
0.00275*** 0.00110*** 0.00110***
Retail Nonpreferred Brand Copay – Retail Nonpreferred Brand Copay – Retail Nonpreferred Brand Copay –
(0.00036) (0.00018) (0.00018)
-0.00002*** -0.00001*** -0.00001***
Retail Nonpreferred Brand Copay Squared – Retail Nonpreferred Brand Copay Squared – Retail Nonpreferred Brand Copay Squared –
0.00000 0.00000 0.00000
0.00643* -0.00171 0.00713*** -0.00025 0.00713*** -0.00025
Mail-Retail Generic Copay Ratio Mail-Retail Generic Copay Ratio Mail-Retail Generic Copay Ratio
(0.00390) (0.00139) (0.00267) (0.00084) (0.00267) (0.00084)
-0.00193** 0.00011 -0.00189*** -0.00009 -0.00189*** -0.00009
Mail-Retail Generic Copay Ratio Squared Mail-Retail Generic Copay Ratio Squared Mail-Retail Generic Copay Ratio Squared
(0.00077) (0.00010) (0.00061) (0.00006) (0.00061) (0.00006)
0.00177 0.00133 0.00133
Mail-Retail Brand Copay Ratio – Mail-Retail Brand Copay Ratio – Mail-Retail Brand Copay Ratio –
(0.00187) (0.00125) (0.00125)
-0.00016 -0.00008 -0.00008
Mail-Retail Brand Copay Ratio Squared – Mail-Retail Brand Copay Ratio Squared – Mail-Retail Brand Copay Ratio Squared –
(0.00012) (0.00008) (0.00008)
-0.00028 -0.01458*** -0.01458***
Mail-Retail Preferred Brand Copay Ratio – Mail-Retail Preferred Brand Copay Ratio – Mail-Retail Preferred Brand Copay Ratio –
(0.00916) (0.00475) (0.00475)
-0.00173 0.00365*** 0.00365***
Mail-Retail Preferred Brand Copay Ratio Squared – Mail-Retail Preferred Brand Copay Ratio Squared – Mail-Retail Preferred Brand Copay Ratio Squared –
(0.00208) (0.00112) (0.00112)
-0.01521** -0.00107 -0.00107
Mail-Retail Nonpreferred Brand Copay Ratio – Mail-Retail Nonpreferred Brand Copay Ratio – Mail-Retail Nonpreferred Brand Copay Ratio –
(0.00648) (0.00352) (0.00352)
0.00555*** 0.00113** 0.00113**
Mail-Retail Nonpreferred Brand Copay Ratio Squared – Mail-Retail Nonpreferred Brand Copay Ratio Squared – Mail-Retail Nonpreferred Brand Copay Ratio Squared –
(0.00117) (0.00053) (0.00053)
0.01272*** 0.00293 0.00648*** 0.00202** 0.00648*** 0.00202**
Retail Deductible Retail Deductible Retail Deductible
(0.00310) (0.00205) (0.00126) (0.00090) (0.00126) (0.00090)
0.01932*** 0.01859*** 0.01668*** 0.01334*** 0.01668*** 0.01334***
Retail Dispense as Written Penalty (Physician) Retail Dispense as Written Penalty (Physician) Retail Dispense as Written Penalty (Physician)
(0.00242) (0.00183) (0.00133) (0.00093) (0.00133) (0.00093)
0.01423*** 0.00498*** 0.01085*** 0.00576*** 0.01085*** 0.00576***
Retail Dispense as Written Penalty (Patient) Retail Dispense as Written Penalty (Patient) Retail Dispense as Written Penalty (Patient)
(0.00206) (0.00171) (0.00130) (0.00087) (0.00130) (0.00087)
0.01004** 0.02450*** -0.00098 0.00622*** -0.00098 0.00622***
Retail Maximum Allowable Benefit Retail Maximum Allowable Benefit Retail Maximum Allowable Benefit
(0.00418) (0.00333) (0.00156) (0.00152) (0.00156) (0.00152)
-0.03043*** 0.00393 -0.01567*** 0.01118*** -0.01567*** 0.01118***
Retail Maximum Out of Pocket Retail Maximum Out of Pocket Retail Maximum Out of Pocket
(0.00323) (0.00285) (0.00176) (0.00156) (0.00176) (0.00156)
Notes: FGLS = Feasable generalized least squares regression with heteroskedastic panels and Notes: FGLS = Feasable generalized least squares regression with heteroskedastic panels and Notes: FGLS = Feasable generalized least squares regression with heteroskedastic panels and
common AR(1) autocorrelation. Statistical significance denoted as: *** p<0.01; ** p<0.05; * common AR(1) autocorrelation. Statistical significance denoted as: *** p<0.01; ** p<0.05; * common AR(1) autocorrelation. Statistical significance denoted as: *** p<0.01; ** p<0.05;
p<0.10. p<0.10. * p<0.10.
coNclusioNs
Brand and generic member OOP costs likely have differential and non-linear effects on the relative utilization of prescription
drugs. Prior studies of the effect of member cost-sharing on pharmacy utilization may be biased if other benefit design
characteristics, mail service prescription fulfillment, and time trends in generic and brand use are omitted.
liMitatioNs & FutuRe diRectioNs
Despite the cross-sectional time series, random effects-based approach, the potential endogeneity of pharmacy benefit design
variables still exists. Therefore, causality should not be inferred. The analytical dataset is being expanded to include additional
clients and time periods to allow for fixed effects estimation.

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