good indicator of overall health vulnerabil- ity, independent of exposure to heat. Given that the HVI incorporates many variables that are known to be strong determinants of ill health, this is not surprising. However, in at least some of the states, health outcomes were more strongly associated with HVI on abnor- mally hot days than on other days, especially for heat-related illness. We found stronger associations between HVI and cardiovascular mortality on non- extreme days than on extreme days and between HVI and all-cause mortality on nondeviant days than on deviant days in Washington. This could be due to residual seasonal confounding because cardiovascular mortality is lowest in the middle of the sum- mer, when deviant and extreme days were most likely to occur for Washington. The finding of a similar effect for heat-related hos- pitalizations in New Mexico could be due to small numbers. No significant interactions were found in Oregon, which could imply that the HVI is not predictive of increased risk of adverse health effects on abnormally hot days in all parts of the country. Oregon had the second fewest ZIP codes, and, for some health out- comes, daily counts were low, so find- ings might have been due to lower statistical power. However, the general pattern of higher RRs on deviant compared to nondeviant days for most health outcomes is consistent with our other findings. Most previous studies of heat-related health effects have used time-series methods to determine whether day-to-day changes in temperature are associated with changes in health effects. To have sufficient power to test their hypotheses, these studies have aggre- gated health outcome data over larger spatial areas (e.g., county or populated metropolitan areas). The purpose of the HVI, however, is to identify the most vulnerable neighbor- hoods within a metropolitan area. Therefore, spatial aggregation beyond the ZIP code level was not appropriate for our analysis. Our recalculation of the HVI from census tracts to ZIP codes could have slightly changed the relationships observed, had health data been available at the census tract level. This study is, to our knowledge, the first to investigate deviation of maximum temperature from the daily 30-year normal maximum temperature as a heat exposure metric. One of the primary benefits of this heat metric is that it captures the health effects of abnormally hot days earlier in the warm season that would have been missed by investigating the effect of the hottest days using absolute temperature. Temperature thresholds above which adverse health outcomes increase have been used in previous studies; some of these thresholds were determined statistically, whereas other studies used an a priori cut-point of the 95th or 99th percentile of temperature (Basu 2009). Our analysis used the 95th percentile of deviant maximum temperature for 8 years of warm seasons, 2000 through 2007, by weather station to attain an estimate of a local threshold temperature for each location. We found some differences between estimated effects of HVI on deviant days versus extreme days, but results were consistent overall. The reason for introducing the concept of a “deviant” day was to allay a concern that our analysis could have residual seasonal confounding if we used only extreme days because they are most likely to fall in July and August (Table 3), when outcomes such as cardiovascular hospitalizations are lowest and extreme days are most likely. Deviant days are less likely to follow the temporal pattern; therefore, we were less concerned about temporal confounding than with extreme days. Time-series analysis of these exposure metrics could shed more light on whether deviance from normal temperature is a valid or more precise way to estimate the effect of high temperatures on health. Previous efforts to create heat vulnerabil- ity maps (e.g., Johnson et al. 2009; Loughnan et al. 2009) have differed in the variables selected, the methods used to combine the variables, and whether they have been evalu- ated for their ability to predict spatial variabil- ity in health risks. Further analysis is needed to determine whether cities differ with regard to important predictors of heat-related mor- bidity or mortality, or if variation in results among studies is due to methodological dif- ferences. Over time, patterns of heat vul- nerability are likely to change, for example, because of shifts in aging population distri- butions or changing patterns of underlying disease, necessitating periodic reevaluation of vulnerability maps. The HVI was created using a principal components analysis for all areas in the United States for which data on component variables were available. If the HVI were created instead based on data from a single metropolitan loca- tion or state, the resulting HVI values might change because of different variable loadings on the factors. Indeed, a study conducted in Phoenix, Arizona, found that although the factors had different combinations of the same 10 vulnerability variables used in the national HVI, this locally derived HVI did predict locations with heat-related deaths (Harlan 2010). Although evidence exists of local dif- ferences in vulnerability to heat (Basu 2009), future research should investigate whether specific subcomponents of the HVI are more important in some regions of the country than in others. It is likely unreasonable to expect every local health department to create its own heat vulnerability map, and therefore, Environmental Health Perspectives • volume 120 | number 5 | May 2012 Evaluation of the heat vulnerability index a national HVI created through freely avail- able national data sets is useful. Our study was limited by the number of states that participated in this project. The states involved in this study do not represent the diversity of climates or vulnerability patterns that exist throughout the United States. Additionally, because of confidentiality concerns on the part of data providers, health data were not able to be shared with UCB, and thus we could not combine the data into one “national” analysis. Our research was also limited by the few ZIP codes for which we could analyze data in each state because of where the original HVI had been calculated. Further research that includes more states and allows for sharing of data could further the understanding of spatial vulnerability to heat and the usefulness of a national HVI for climate change adaptation purposes. Conclusions Climate change will likely exacerbate health risks that populations already experience, including, but not limited to, health effects related to exposure to extreme heat. Mapping the locations of populations that are vulnerable to climate-change–related risks facilitates plan- ning of interventions to prevent adverse health events. Our results suggest that the HVI can be used to identify areas with increased risks of adverse health outcomes in general, and that it may identify areas at increased risk of heat- related illness and possibly other heat-related outcomes on abnormally hot days. Heat may exacerbate preexisting health disparities associ- ated with the HVI. Deviant days and extreme days were found to capture health effects asso- ciated with heat similarly well. Further investi- gation is warranted to assess whether the HVI indicates increased risk of health effects in asso- ciation with different heat exposure metrics and whether the HVI should be modified for differ- ent regions of the country. Targeting resources toward decreasing inequities in vulnerability now may increase communities’ resilience to multiple hazards to health in the future. Ref Rencs Anderson G, Bell M. 2011. Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities. Environ Health Perspect 119:210–218. Balbus J, Ebi KL, Finzer L, Malina C, Chadwick A, McBride D, et al. 2008. Are We Ready? Preparing for the Public Health Challenges of Climate Change. Available: http://www. edf.org/documents/7846_AreWeReady_April2008.pdf [accessed 23 December 2010]. Bassil KL, Cole DC. 2010. 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