Pharmaceuticals Policy and Law

There is an inherent disconnect between the global-level endeavor of innovating pharmacotherapeutics and the (still) national-level of decision-making about access to innovation. In this paper, we argue that rational pharmacoeconomic policy should not be at the national, neither at the supranational, but at the transnational level: based on the relative similarity of countries in terms of their per capita health care spending. The 2010 OECD Health database, complemented as necessary with secondary data sources to substitute for missing data, was used to compile an analysis sample of all but two OECD countries (Chile and Greece) with data through 2007. We first established the overall divergence of, and heterogeneity among, countries in terms of 2007 per capita total and pharmaceutical health care spending as relative proxies of pharmacoeconomic policy. Next, we applied cluster analytic methods to identify OECD member countries that are similar in terms of 2007 per capita pharmaceutical and total health care expenditures, and the ratio of both.

The scarcity of resources within both industrialized and developing nations has catalyzed the adoption of more formalized approaches to support decisionmaking, particularly within healthcare. In many instances, however, the unique aspects present within developing countries may not permit the direct translation of pharmacoeconomic guidelines or recommendations from high-income economies. The purpose of this paper is to delineate the key attributes that differentiate pharmacoeconomics within developing nations, including the potential barriers to implementation and policy considerations involved. Through its approach to comprehensively assess costs and outcomes of technologies, pharmacoeconomic methodologies may help foster the optimal allocation of resources to improve the effectiveness, efficiency, and equity of healthcare delivery for societies worldwide.

Recent health care legislation in the United States has turned considerable focus to comparative effectiveness research (CER) domestically, though it has been a topic of discussion internationally for many years. Without a fully comprehensive and consistent definition of CER developed, much uncertainty and confusion surrounds its utilization. In addition, contention exists regarding the incorporation of cost and economic considerations as a component of CER. This discussion includes various suggested definitions of CER, methodological considerations, legislation and utilization, and the role of cost-effectiveness evaluations in CER.

An increasing literature has focused upon investigating the relationships between an employee's health status and costs incurred by employers, including the specific impact of health status on productivity-related costs. Given the escalating costs of healthcare globally, attention has been drawn to better understand these associations using various analytic approaches. In the workplace setting, employers have also become more engaged in assessing the health of their employees and in improving the types of care that are being delivered. In this context, the purpose of this paper is to present the concepts of absenteeism and presenteeism and to describe the general approaches used to assess health-related work productivity.

Although more than 7000 diseases have been classified as rare in nature, estimates place fewer than five percent of these diseases as being a current focus of substantial drug development. Several challenges exist concerning the basic understanding of these conditions overall, including a lack of understanding surrounding natural history, epidemiology, and effective treatment options in the real-world. The purpose of this paper is to discuss issues concerning unmet needs of rare disease and adoption or reimbursement decisions, including ethical and policy considerations associated with the treatment of rare diseases. Elements that are often inadequately addressed by a standard economic calculus to evaluate rare disease such as distributive justice are also addressed. Overall, the opportunity costs of supporting orphan drug use to treat rare disease must be carefully balanced by the necessities of treating patients with more common diseases wherein cost-effective treatments may more readily exist.

Pharmacogenomics (PGx) has been recognized as a possible leader down the path of personalized medicine in the 21st century. While this field has been burgeoning, particularly after the completion of the sequencing of the human genome in 2001, the diffusion of this technology into clinical practice has been limited. Interest groups, including industry, payers, providers and patients, have a variety of concerns regarding the utilization of this technology. However, all groups have voiced concerns over possible economic impacts, with different concerns based on the perspective. In order to clear the proverbial ‘fourth hurdle', the use of PGx will have to prove not only to possess clear clinical utility, but positive or neutral economic consequences. The role health services researchers can play in facilitating research not only in economics, but also in clinical effectiveness and epidemiology, is critical. Public and private entities have begun taking steps towards guidance for use of this technology and the facilitation of communication and research across entities. A larger, robust evidence base will hopefully allow interest groups to make clear assessments as to the overall impact and utility of PGx.

This paper explores the US context of the opportunities and challenges of collecting and using patient-reported outcomes (PROs) in the post-approval environment. Firstly, an overarching goal of real-world comparisons of therapies based on their benefits and harms is borne in mind whilst placing PROs in the landscape of comparative effectiveness research. Secondly, a review of opportunities and challenges relating to the collection and use of real-world patient-reported outcome (PRO) data in clinical and community settings is presented, highlighting patient monitoring such as in post-marketing/post-approval (phase IV) studies, through formulary management and reimbursement systems, and the utilization of available technologies. The paper ends with a summary of policy considerations in optimizing these opportunities and addressing the related challenges. Throughout this paper, the use of the term ‘pre-approval environment’ is intended to refer to the time and efforts preceding the regulatory approval of a pharmaceutical product, and ‘post-approval environment’ on the other hand refers to the time and efforts proceeding the attainment of product approval and the respective labeling claims. ‘Real-world’ is intended to refer to settings other than clinical trials (i.e., clinical practice and community settings, and similar patient monitoring programs).

Critically ill patients are at the greatest risk of experiencing preventable and non-preventable adverse drug events (ADEs) compared to other patient populations. Information on ADEs concerning these patients is derived from a wide spectrum of sources such as surveillance studies involving large databases to landmark clinical investigations. Historically there has been a lack of consistency with regard to definitions of commonly used terms such as medication error, ADE, and preventability, which is a fundamental to appropriately conduct and interpret results of epidemiological investigations in this setting. However, attempts have been made to standardize this terminology by national organizations. The increased risk of ADEs in the critically ill patient population is due to a variety of reasons, which include the routine use of the intravenous route of drug administration, medications with a low therapeutic index and high complexity, ‘off-label’ use and an environment that may be conducive to errors. Reporting of ADEs is largely voluntary and is generally underreported and skewed towards the most serious events. Other methods for surveillance of ADEs in the critical care setting include medical record review and direct observation. Each of these methods has their own limitations and the data obtained can vary depending on the method used. A combination of data from all of these methods is likely to produce the most comprehensive information to improve patient safety.

Cost and outcomes data within pharmacoeconomic analyses often possess distributional properties that require advanced statistical approaches to yield robust findings. An analyst's failure to recognize and control for these characteristics may result in inappropriate evaluations of statistical associations or causal effects which may ultimately support incorrect policy decisionmaking. Given the importance of appropriate analysis and interpretation in pharmacoeconomics, the purpose of this paper is to address the more common statistical issues encountered in assessing healthcare costs or outcomes, emphasizing approaches that may be employed to analyze these data. More specifically, statistical methods used commonly with retrospective cohort analyses are presented including least squares (e.g., ordinary least squares, OLS), logarithmic transformations, log-plus-constant models, two-part models, maximum likelihood estimation (MLE), and generalized linear models (GLM) and extensions, among others.