Exploration of the Impact of Preferred Drug Lists on Hospital and Physician Visits and the Costs to Medicaid

Published Online: January 15, 2005
Matthew M. Murawski, RPh, PhD; and Tamer Abdelgawad, MA

Objective: To conduct an exploratory investigation of the possible effects of the implementation of a state Medicaid preferred drug list (PDL) on the average number of visits by Medicaid patients to hospitals and physicians, and to provide preliminary estimates of the Medicaid reimbursement costs of these additional visits.

Study Design: A regression-based, difference-in-differences retrospective analysis using anonymized patient-level data on cardiovascular-related inpatient and outpatient hospital visits and procedures, and physician visits and procedures.

Methods: The impact of the implementation of a state Medicaid PDL on a test group of Medicaid cardiovascular patients was examined. A contemporaneous group of non-Medicaid cardiovascular patients from the same state were used as controls.

Results: There was a statistically significant increase in the number of outpatient hospital visits and physician visits for the test group compared with the control group in the first 6 months after PDL implementation. There was a positive but statistically insignificant increase in the number of inpatient hospital visits. All increases in visits for the test group compared with the control group in the second 6 months after PDL implementation were positive but statistically insignificant. As a result, estimated average Medicaid reimbursement costs for cardiovascular patients in the state increased during that year.

Conclusion: The observed range of increases in hospital and physician visits is evidence for the possible existence of an unintended consequence of PDL implementation by state Medicaid programs. Precautionary research in this area is clearly called for.

(Am J Manag Care. 2005;11:SP35-SP42)

State Medicaid programs, facing substantial budgetary shortfalls, are aggressively implementing various cost-containment measures. One popular approach is the preferred drug list (PDL), which functions by encouraging the use of less expensive drugs considered equal in efficacy to more expensive alternatives in any given therapeutic class, and by soliciting additional manufacturer rebates for products included on the PDL beyond those rebates mandated by federal law. A prior-authorization mechanism is included to permit off-list prescribing, but in practice, the more onerous prior-authorization requirements become, the more the PDL resembles a closed formulary.

Adoption of PDL programs is widespread, with 29 states having instituted some form of PDL by 2003.1 Although these programs have been adopted with the expectation that they will result in significant reductions in drug acquisition costs, there is some evidence that similar restrictive-access mechanisms have been associated with increased utilization (and costs) in other components of the Medicaid budget.2-6 This cost-shifting may largely offset any achieved prescription savings, or even result in a net increase in costs. Because of the high rate of PDL adoption, the overall impact of these possible unintended effects on Medicaid costs could prove substantial. Over time, several models have been offered to explain potential increases in nonprescription healthcare utilization and costs associated with restrictive-access mechanisms. The literature suggests that substitution of less expensive generics in some therapeutic areas could lead to problems.7-9 Forced shifts from newer, more effective products to older-brand medications have been reported to result in higher rates of side effects and adverse reactions.10,11 Also, the use of front-line medications has been shown to result in significant cost avoidance.12,13 These explanations, which parallel early, anecdotal reports evaluating PDL programs,14 suggest at least 3 plausible mechanisms through which PDL-induced cost-shifting could occur: "administrative" physician visits to make or clarify the switch to PDL-approved drugs; additional physician (and sometimes hospital) visits to deal with side effects and adverse reactions to new therapies; and physician or hospital visits to address outright treatment failure.

A recent literature survey by Soumerai concluded, in part, that additional research is required to address the question: "What are the economic and clinical consequences of ...policies that promote use of lower-cost preferred drugs for major medical illnesses (such as cardiovascular disease, chronic mental illnesses and neurologic disorders), especially in low-income Medicaid populations?"15 (p142) To our knowledge, extensive research has not yet been done, possibly because of the formidable methodologic challenges involved in investigating this question. In this paper, we attempt to provide a preliminary quantitative exploration of this question by isolating the impact of PDL implementation on inpatient and outpatient hospital services and physician visits by cardiovascular patients. We also provide exploratory estimates of the costs to Medicaid of these PDL-related visits.


Data Description

We obtained data for prescriptions, hospital visits, and physician visits from Verispan, LLC (Yardley, Pa). The prescription dataset covers anonymous patient-level cardiovascular prescriptions, and includes the patient's age group and sex, zip code of the pharmacy where the prescription was filled, the prescriber's medical specialty, and payer information for each prescription (Medicaid, cash, or third party). The data come from electronic pharmacy records and benefit claims, and cover 50% to 55% of all prescriptions filled in the United States during the study's time frame. We used the pharmacy zip code in conjunction with zip-3 level US census 2000 data to assign an urban/rural designation to each patient as a demographic control.

The hospital and physician datasets cover physician and hospital visits where the primary diagnosis was hypertensive or heart disease (the following International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] diagnosis code subheadings were covered: 394-398, 402, 404, 410-416, and 420-429). We will refer to these as cardiovascular-related visits. In addition to the diagnosis codes, the data also contain all the codes for procedures performed during the visit. The data come from electronic billing systems installed in hospitals and physicians' offices, and cover about 20% and 10%, respectively, of all US hospital and physician transactions. Table 1 contains a listing of all variables used in this study and their definitions.


The state under study implemented a Medicaid PDL in June 2002. The 3-month period immediately after the PDL was implemented was the index period (June 2002 to August 2002), and we used it for initial sample classification (described below). The prescription data span a 2-year period from June 2001 to May 2003, while the data for hospital and physician visits span a shorter, 18-month period from December 2001 to May 2003. We used the data for the 6-month period before the PDL implementation (December 2001 to May 2002) to establish baselines for our variables of interest. Then we examined changes in these variables in the two 6-month periods after June 2002.

Sample Selection and Classification

Although none of the datasets covers 100% of its target population, we are not aware of any sample selection problems or systematic omissions that would bias the direction of our results. In addition, since we conducted a difference-in-differences analysis, we needed only to make the weaker assumption that even if bias or systematic omissions were present, they affected the test and control patient groups in the same way.

Each patient in the data was uniquely identified by an anonymous patient_ ID variable that linked all of the patient's records over time and across datasets. The initial patient sample was selected from the prescription data based on a pre-PDL history of antihypertensive prescriptions, 1 or more prescriptions filled after May 2002 in a drug class restricted by the PDL (angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, and calcium channel blocker classes were restricted), and at least 1 antihypertensive prescription filled during the index period. These criteria were intended to select patients who potentially experienced the PDL process at its inception (the index period), and whose cardiovascular-prescription claims history implied they would be most affected should the state's PDL restrictions be applied to them. We will refer to these patients as CV-PDL patients.

A CV-PDL patient was classified as a Medicaid patient if Medicaid paid for 75% or more of his/her antihypertensive prescriptions in the 1-year post-PDL period, and classified as a non-Medicaid patient otherwise. A total of 3250 CV-PDL patients satisfied the criterion for classification as Medicaid patients and served as the basis for the selection of our (retrospective) experimental test group. Of the much larger number of non- Medicaid CV-PDL patients, we randomly selected 3788 patients (1.5%) to serve as a basis for the selection of our control group.

To select our hospital test and control samples, CVPDL patients were matched against the hospital dataset to assign inpatient and outpatient hospital visits, diagnoses, and procedure data to each patient. During the period from December 2001 to May 2003, 935 of the 3250 Medicaid CV-PDL patients appeared in the hospital dataset with 1 or more hospital visits, but only 269 had 1 or more hospital visits that were actually cardiovascular related. We took the 935 Medicaid CV-PDL patients as our test group, and those without cardiovascular-related visits were assumed to have zero cardiovascular-related visits in our quantitative analysis. As a control, we selected the 1114 out of 3788 non-Medicaid CV-PDL patients who appeared in the hospital dataset with 1 or more hospital visits (only 274 had cardiovascular-related hospital visits, the rest were assumed to have zero cardiovascular-related visits).

Selection of the physician sample was done similarly. Here, 1421 Medicaid CV-PDL patients (364 with cardiovascular-related visits) and 1668 non-Medicaid CV-PDL patients (457 with cardiovascular-related visits) had physician visits during our study's time horizon, and were selected as test and control groups, respectively.


Our main goal was to examine the change in the number and overall cost of hospital and physician visits per patient when the PDL was implemented. Specifically, we estimated the differences in visits and cost changes between Medicaid and non-Medicaid CV-PDL patients by regressing the change variables (delta1_visits, delta2_visits, delta1_cost, and delta2_cost) on the medicaid dummy, while using the demographic and behavioral variables available to us as controls. The following is a typical visits regression equation from our model for patient i:


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