Assessing the public health benefits of reduced ozone concentrations

In this paper we examine scientific evidence and related uncertainties in two steps of benefit-cost analyses of ozone reduction: estimating the health improvements attributable to reductions in ozone and determining the appropriate monetary values of these improvements. Although substantial evidence exists on molecular and physiologic impacts, the evidence needed to establish concentration-response functions is somewhat limited. Furthermore, because exposure to ozone depends on factors such as air conditioning use, past epidemiologic studies may not be directly applicable in unstudied settings. To evaluate the evidence likely to contribute significantly to benefits, we focus on four health outcomes: premature mortality, chronic asthma, respiratory hospital admissions, and minor restricted activity days. We determine concentration-response functions for these health outcomes for a hypothetical case study in Houston, Texas, using probabilistic weighting reflecting our judgment of the strength of the evidence and the possibility of confounding. We make a similar presentation for valuation, where uncertainty is due primarily to the lack of willingness-to-pay data for the population affected by ozone. We estimate that the annual monetary value of health benefits from reducing ozone concentrations in Houston is approximately $10 per person per microgram per cubic meter (24-hr average) reduced (95% confidence interval, $0.70-$40). The central estimate exceeds past estimates by approximately a factor of five, driven by the inclusion of mortality. We discuss the implications of our findings for future analyses and determine areas of research that might help reduce the uncertainties in benefit estimation.

Assessing the impact of differential measurement error on estimates of fine particle mortality

In air pollution epidemiology, error in measurements of correlated pollutants has been advanced as a reason to distrust regressions that find statistically significant weak associations. Much of the related debate in the literature and elsewhere has been qualitative. To promote quantitative evaluation of such errors, this paper develops an air pollution time-series model based on correlations among unit-normal variables. Assuming there are no other sources of bias present, the model shows the expected amount of relative bias in the regression coefficients of a bivariate regression of coarse and fine particulate matter measurements on daily mortality. The model only requires information on instrumental error and spatial variability, along with the observed regression coefficients and information on the true fine-course correlation. Analytical results show that if one pollutant is truly more harmful than the other, then it must be measured more precisely than the other in order not to bias the ratio of the fine and course regression coefficients. Utilizing published data, a case study of the Harvard Six-Cities study illustrates use of the model and emphasizes the need for data on spatial variability across the study area. Current epidemiology time-series regressions can use this model to address the general concern of correlated pollutants with differing measurement errors.