Modeling Cessation Lag: How quickly does risk fall once regulation is implemented? Tammy Barlow MURPHY, Ph.D. 2004-2005 AAAS Science and Technology Policy Environmental Fellow, U.S. EPA Assistant Professor, Dept. of Economics, University of Massachusetts-Boston Email: firstname.lastname@example.org ABSTRACT: This paper examines the implications of cessation lag for estimating the benefits of regulation that reduces the risk of exposure to contaminants with adverse health effects. Several approaches to modeling cessation lag and the implications for the present discounted value of benefits are addressed. Recently the U.S. Office of Management and Budget (OMB) and the U.S. Environmental Protection Agency’s Science Advisory Board (SAB) have recommended that EPA include a model of cessation lag in its economic analysis of regulatory actions that reduce the risk of exposure to contaminants with adverse health effects. The term “cessation lag” refers to the amount of time it takes, once exposure to a risk is eliminated or reduced, for the risk level of individuals that were previously exposed at a given level to decrease to the risk level associated with a lifetime of exposure to the lower level. The cessation lag is not necessarily equal to latency, which is the time period between the start of exposure and an increase in risk. For example, the latency period for smoking and increased lung cancer risk is about 20 years, but cessation lag is considerable shorter. The issue of the appropriate cessation lag model has direct implications for the time profile of benefits resulting from a policy that lowers the population’s exposure to a particular environmental risk, thereby affecting the present discounted value (PDV) of benefits from the proposed policy. All else held constant, the longer the cessation lag is, the lower the PDV of the benefits of a reduction in exposure to a contaminant. Thus, the manner in which cessation lag is modeled may determine whether or not a proposed regulation passes the benefit-cost analysis test. The existing research on cessation lag modeling has overwhelmingly focused on the relationship between smoking and cancer risk. EPA first began wrestling with cessation lag in the context of the Clean Air Act (1999) and the Arsenic Rule (2001, 2003), using studies from smoking cessation as the foundation of their analysis, even though smoking is not the contaminant targeted by either of these two regulatory actions. Despite further debate in 2004 in the context of reduction in exposure to particulate matter through CAA regulations, it remains unclear how to model cessation lag in the absence of data on the specific disease processes involved in many of EPA’s current proposed regulations (e.g. the relationship between chlorination dis-infection by-products (DBPs) and bladder cancer risk in EPA’s proposed Stage 2 DBP Rule fro drinking water). A valid cessation lag model must recognize that for many contaminants, risk reduction will have an effect on acute, medium-term and chronic health conditions. The cessation lag modeling efforts of Chen and Gibb (2003) are commendable; however, their approach requires considerable data (e.g. hazard functions). Such data is often unavailable for environmental contaminants and their suspected adverse health effects. The mode of action of a contaminant as a cancer promoter vs. cancer initiator can sometimes be used to approximate cessation lag; however, for many contaminants (e.g. DBPs), we cannot be certain whether the contaminants acts primarily as cancer promoters or initiators, or as both (e.g. cigarette smoke). This paper will explore alternatives for modeling cessation lag in the face of considerable uncertainty. EPA’s proposed Stage 2 Dis-infection By-Products Rule, which is slated to be final in 2005, is used as a case study of a regulation where the maximum annual benefit possible is not likely to be realized in the time period immediately following regulation implementation (i.e., where cessation lag is not expected to be zero). The paper also explores the possibility that cessation lag may depend on exposure duration to the status quo level of the contaminant. Sensitivity analysis of the regulation’s estimated economic benefits to cessation lag model choice is conducted. The paper also offers recommendations for future data collection and empirical research that will allow for improved estimation of cessation lag, and therefore, improved estimation of the economic benefits of environmental health risk regulation.
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