Health benefits from ozone reduction in the United States current NAAQS ozone standard of 75 ppb, and two more stringent standards of 70 and 60 ppb. We used recent ozone monitor data and current concentration–response (C-R) function estimates to estimate changes in ozone-related premature mortality and mor- bidity that would have been observed during 2005–2007 instead of projecting data into the future. We hypothesized that ozone-related premature mortality and morbidity would have decreased across the United States, with regional variability across the nation. Methods Data sources: air pollution and risk estimates. We acquired ozone monitoring data for 2005– 2007, the most recent data years available in BenMAP, from the U.S. EPA’s Air Quality System (AQS) regulatory air pollution moni- tor network (U.S. EPA 2012a). Monitoring data were supplied by federal, state, local, and tribal air pollution control agencies and audited by the U.S. EPA for quality assurance, including sampler performance, precision, bias, and accuracy (U.S. EPA 2008a). We examined epidemiological literature for ozone-attributed C-R functions for mortality- and respiratory-related morbidity to summarize the association between ozone concentration and health (Table 1). Because our study focuses on regulatory policy with ozone, we used guidance from a recent U.S. EPA ozone regulatory impact assessment to select studies that maximize national applicability (U.S. EPA 2008b). The selection criteria included an array of statistical designs and broad geographic coverage. We chose time-series studies (Bell et al. 2004; Huang et al. 2005), meta-analyses (Bell et al. 2005; Ito et al. 2005; Levy et al. 2005), a case-crossover study (Schwartz 2005), and a cohort study of long-term effects (Jerrett et al. 2009). Morbidity studies were primarily time-series analyses although they included fewer cities and years than mortality studies. Except for ED visits, the morbidity estimates were limited to age-specific populations. We felt that of the substantial ozone literature available, these selected C-R functions represent a small but well-cited and diverse subsection of critical ozone health effects estimates. Air pollution monitoring, rollback, and interpolation. We selected air monitors according to the NAAQS criteria (U.S. EPA 2008c). Data were restricted to the 153-day ozone season (1 May–30 September) and monitors with at least nine hourly observa- tions for > 50% data days. Because monitors are sometimes collocated and measure multi- ple pollutants, the U.S. EPA uses parameter occurrence codes (POCs) to distinguish pri- mary devices, so we chose only monitors with a POC ≤ 4 for inclusion (U.S. EPA 2008a). We assumed a policy-relevant back- ground level for non-anthropogenic ozone of 40 ppb, which is the upper limit of aver- age background ozone during the warm sea- son based on a GEOS-Chem model of the United States (Wang et al. 2009). To assess the impact of regulatory changes, we “rolled back” (i.e., reduced) air monitor concentra- tions to achieve the hypothetical ozone standards. Specifically, we used a quadratic rollback approach that proportionally reduced ozone levels such that simulated concentra- tions at any one monitor were reduced more during high ozone days than lower ozone days. This approach affects concentrations substantially above the regulatory limit more than those near compliance (Rizzo 2005). Rounding to two significant digits, we added 0.9 ppb to each standard while initiating compliance at the fourth highest daily ozone value to mitigate the influence of anomalously high measurements. We interpolated ozone to a 36 × 36-km grid using the deterministic Voronoi Neighbor Averaging (VNA) tech- nique, a default BenMAP option (Chen 2004; Hubbell et al. 2005: U.S. EPA 2010a). In addition to quadratic ozone rollbacks, we performed sensitivity analyses assuming a “peak shaving rollback” in which ozone con- centrations above the systemic compliance cap were truncated, but ozone levels below the cap were left at measured levels. We also performed sensitivity analyses assuming alternative back- ground concentrations (e.g., 20 and 30 ppb) and evaluated the impact of altered ozone levels when applied across a full year, instead of limit- ing the analysis to a warm season. Estimation of health impacts. Health impact functions enable the quantification of health outcomes (e.g., ozone-related mortality Table 1. Epidemiological studies used as a source of C-R function data. Morbidities were pooled to estimate health effects. Health end point Nonaccidental mortality All-cause mortality Cardiopulmonary mortality Respiratory mortality Hospital admissions (respiratory disease) Schwartz 1995 Hospital admission [chronic lung disease, pneumonia, lung disease (minus asthma)] School-loss days Acute respiratory symptoms (restricted activity) Asthma-related ED visits Reference Bell et al. 2004 Ito et al. 2005 Schwartz 2005 Bell et al. 2005 Levy et al. 2005 Huang et al. 2005 Jerrett et al. 2009 Burnett et al. 2001 Schwartz 1994 Chen et al. 2000 Gilliland et al. 2001 Ostro and Rothschild 1989 Jaffe et al. 2003 Peel et al. 2005 Wilson et al. 2005 Study design Time-series of short-term ozone over 14 years; 95 U.S. cities (NMMAPS); all ages. Distributed lag model (community rates); hierarchical model (national rate). Meta-analysis (43 studies) of short-term ozone (global); additional U.S. city time-series (n = 7); all ages. Case-crossover of short-term ozone over 8 years; 14 U.S. cities; all ages. City-specific regression (community rates); iterative MLE (national rate). Meta-analyses (39 studies) of short-term ozone; NMMAPS time-series; all ages. Two-stage Bayesian hierarchical model. Meta-analyses (71 time-series reviewed; 28 selected) of short-term ozone; all ages. Hierarchical linear model. Time-series of short-term ozone; 19 U.S. cities (NNMAPS); all ages. Hierarchical distributed lag model. Cohort study of long-term ozone effects for 96 MSAs over 18 years; > 30 years of age. Multilevel Cox regression models. Time-series of short-term ozone over 3 years. 2 U.S. cities; elderly (> 65 years of age). Poisson regression model. Time-series of short-term ozone exposure over 3 years; Toronto, Canada; children < 2 years of age. Log relative risks estimated with an exponential function. Time-series short-term ozone exposure over 3 years; Detroit; elderly (> 65 years of age). Poisson regression model. Time-series of short-term ozone over 3 years; children (5–17 years of age). Time-series of short-term ozone over 5 years; Urban United States; adults (18–65 years of age). Poisson regression model. Time-series of short-term ozone over 5 years; 3 Ohio cities; young people (5–34 years of age). Poisson regression model. Time-series of short-term ozone over 7 years; Atlanta; all ages. Poisson generalized estimating equation model. Time-series of short-term ozone over 3 years;2 U.S. cities; all ages. Generalized additive model. Abbreviations: MLE, maximum likelihood estimation; NMMAPS, National Morbidity, Mortality, and Air Pollution Study. Environmental Health Perspectives • volume 120 | number 10 | October 2012 1405