On the evaluation of drug benefits policy changes with longitudinal claims data: the policy maker's versus the clinician's perspective
Introduction
Cost containment in pharmaceutical benefit plans are almost universal but often controversially debated for their potential of unintended consequences on health and overall expenditures. With the increasing availability of detailed claims databases the need for thorough evaluation of drug-benefit changes becomes less expensive and less time consuming. Longitudinal data on health-care utilization for individual patients are readily available in computerized form allowing time series analyses over a long period of time. Time series analyses of health outcomes and healthcare utilization data covering periods before and after a policy intervention are considered powerful quasi-experimental designs and can be conducted with standard software tools [1], [2].
However, the evaluation of cost-sharing policies with time-series designs requires careful formulation of the research hypotheses. Similar to cost-effectiveness analyses, hypotheses and analysis are mainly driven by the evaluation perspective, which can be that of a policy maker or an individual patient [3]. In many cost-containment measures for medications, including cost sharing and formularies, where patients have the choice not to follow the intention of the policy depending on their willingness and ability to pay increasing copayments, it is important to understand the consequences of complying with the policy on health outcomes, utilization, and costs. Earlier evaluations of cost-sharing policies realized that such findings can only be useful for clinicians and policymakers when selection bias can be controlled completely [4], [5]. The fact that patients can easily switch between alternative drug treatments and, thus, have low compliance with the policy, further complicates the evaluation.
A most recent example of drug-cost containment is differential cost sharing (DCS). DCS occurs when patients must pay a prescription copayment, which is higher for more expensive medications. The different forms of DCS are all based on the assumption that substances within a medication class are interchangeable and that a common reimbursement level can be established. Reference pricing allows selected substances within a class to be reimbursed fully by the drug-benefit plan, but for a higher priced substance, patients have to pay the price difference out of pocket [6]. DCS systems differ by the relation between drug price and cost-sharing. They include ‘three-tier’ copayments with a stepwise increase of cost-sharing and proportional cost-sharing which requires an up to 20% copay of the medication price (Fig. 1) [7], [8]. In contrast, fixed cost-sharing policies require a copayment independent of the medication price.
A large scale natural experiment began in British Columbia when reference pricing was introduced for angiotensin converting enzyme (ACE) inhibitors on January 1, 1997. Costs for the least expensive preparations of Captopril, Quinapril and Ramipril were covered under the policy without any cost-sharing. The amount of cost-sharing ranged from $2 to $62 per monthly supply [9].
The main concerns of cost-containment measures like DCS are restricted access to medications forcing vulnerable populations with low income to switch to a less effective, low cost treatment in critical circumstances. It is debated whether this may cause less favorable health outcomes and increased overall health-care expenditures [10], [11], [12], [13], [14], [15].
The objective of this report is to clarify terminology and methodological issues in longitudinal designs to evaluate drug-benefit-policy changes with particular reference to the policy maker's and the physician's perspective, their hypotheses, analyses and assumptions.
Section snippets
Policy compliance
Changes in drug-benefit policies may be followed by a variety of different reactions by physicians and/or patients based on an appraisal of financial consequences, health benefits, and risks of adverse events or drug–drug interactions. Particularly elderly patients, who consume over 30% of all medications in the US, [16] must carefully consider risks and benefits of medication changes due to economical disincentives, since drug treatment in the elderly is complicated by changes in
Hypotheses in the evaluation of DCS and their interpretations
The primary research question in an evaluation of a DCS policy is to determine whether or not it has reduced overall health-care costs without adversely affecting patients. An important secondary aim is to test whether patients who comply with the DCS policy have different health and cost outcomes than they would have had if they had remained on the higher priced medication they were accustomed to taking. After further reflection, it is clear that there are several ways of specifying research
Integration of the policy model and clinical model
The policy model and the clinical model provide different qualities of information. The choice of model names reflect the extent to which evaluation results are useful for different levels of decision making in health care. However, the clinical model is also important for policy makers in the actual implementation or adaptation of new policies because, through better understanding of the policy effects, they can prospectively design preventive strategies. The interpretation of the clinical
Acknowledgements
We thank Dr Dennis Ross-Degnan and an anonymous reviewer for their insightful comments. Dr Schneeweiss was supported by the Deutsche Forschungsgemeinschaft (German Research Council, SCHN 527/3 and SCHN 527/4) and the Pharmacoepidemiology Teaching and Research Fund of the Harvard School of Public Health.
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