mortality data, similar to previous research (Knowlton et al. 2009). Data were obtained from the respective data stewards for each state. Mortality data come from the California Office of Vital Records, Center for Health Statistics (Sacramento, CA); Massachusetts Department of Public Health’s Registry of Vital Records and Statistics, Bureau of Vital Records and Health Statistics (Boston, MA); New Mexico Department of Health, Epidemiology and Response Division, State Center for Health Statistics (Santa Fe, NM); Oregon Health Authority, Public Health Division, Center for Health Statistic (Salem, OR); and Washington State Department of Health, Center for Health Statistics (Olympia, WA). Hospitalization data were obtained from the California Office of Statewide Health Planning and Development (Sacramento, CA); Massachusetts Division of Healthcare, Finance and Policy, Hospital Inpatient Discharge Database (Boston, MA); New Mexico Health Policy Commission, Office of Health Policy and Research, New Mexico Hospital Inpatient Discharge Data (Albuquerque, NM); Oregon Health Authority; and Washington State Department of Health (Olympia, WA), Center for Health Statistics, Comprehensive Hospitalization Abstract System (Salem, OR). These data were collected by the state partners and were not shared with the researchers at UCB. The development of the HVI was pre- viously described in Reid et al. (2009). Briefly, the HVI is composed of four factors derived from a principal components analysis (Table 1). Each factor has possible values of 1–6, with higher values denoting higher vul- nerability. The HVI is the sum of the four fac- tors (possible values of 4–24 for a given census tract). For this analysis, we calculated the HVI for each ZIP code as the area-weighted mean value of the census tracts that intersected with the ZIP code using the Geospatial Modeling Environment (Spatial Ecology LLC 2010); therefore, the HVI for each ZIP code is con- stant throughout our study. ZIP codes were assigned to weather stations based on both proximity and similarity of climate. First, we determined the 30-year (1971 through 2000) warm-season normal temperature for each Main componentsa Percentage of population below the poverty line Percentage of population of a race other than white Percentage of population with less than a high school diploma Percentage of nongreen space Percentage of population that live alone Percentage of population > 65 years of age that live alone Percentage of homes without central air conditioning Percentage of homes with no air conditioning of any kind Percentage of population diagnosed with diabetes Percentage of population > 65 years of age Reid et al. Environmental Public Health Tracking (EPHT) Network in which researchers at the University of California–Berkeley (UCB) collaborated with public health professionals from EPHT programs in several states. Materials and Methods Our study compared associations between the HVI and daily rates of morbidity and mortal- ity on abnormally hot days and other days in five states that joined the Academic Center of Excellence research project being run by UCB as part of the EPHT Network. Although the HVI was calculated only in areas where air conditioning prevalence data were available (Reid et al. 2009), the analysis presented here includes 1,205 ZIP codes in California, 392 in Massachusetts, 20 in New Mexico, 119 in Oregon, and 212 in Washington that corre- spond to approximately 71%, 79%, 7%, 30%, and 40% of ZIP codes in each state, respec- tively [see Supplemental Material, Figure 1 (http://dx.doi.org/10.1289/ehp.1103766)]. For the years 2000 through 2007, each participating state assembled daily counts for hospitalization due to electrolyte imbalance [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), code 276 (CDC 1979)], cardiovascular diseases (codes 390–398, 402, 404–429, 440–448), cerebrovascular disease (codes 430–438), respiratory illness (codes 460–519), nephritis and nephrotic syndrome (codes 580–589), acute renal failure (code 584), heat-related illness (code 992), and internal causes of hospitalization (all ICD-9-CM codes 0–799.9 except accidents, injuries, suicides, and homicides) and daily mortality counts, including all-cause mortality [all ICD-10 codes V01–Y98 (WHO 2007) except accidents, injuries, suicides, and homicides, excluding X30 (heat-related illness)], cardiovascular mortality (codes I00–I99), and respiratory mortality (codes J00–J99). These health outcomes have been analyzed in previous studies of extreme heat (Basu 2009; Knowlton et al. 2009; Ye et al. 2012). Daily counts for each outcome were calculated by searching all primary diagnoses and the first nine secondary diagnoses in hospital discharge and Table 1. Components of each factor in the HVI. Factor 1 Social/environmental 2 Social isolation 3 Air conditioning prevalence 4 Preexisting health conditions ZIP code centroid in the study area based on 30-year warm-season normal data from the Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) Climate Group (2010) normals product. Next, we obtained data on 30-year daily normals for maximum temperature for all weather stations in the study states and contiguous states from the National Climatic Data Center (2010) CLIM84 daily normals product. We then assigned each study area ZIP code to a weather station, such that ZIP codes that contained a single weather station (7.7%) were assigned to that weather station and ZIP codes that contained more than one weather station (0.5%) were assigned to the station whose 30-year warm-season normal was the closest to the ZIP code’s PRISM- derived 30-year warm-season normal. For ZIP codes with no weather station (91.9%), we assigned them to the weather station (out of the three closest stations) with the 30-year warm-season normal that was closest to that of the ZIP code’s PRISM-derived 30-year warm-season normal. ZIP code assignment to monitors was done using ArcGIS (version 9.3; ESRI, Redlands, CA). For each day from 1 May through 30 September, for 2000 through 2007, we calculated the deviation of the daily maxi- mum temperature from each weather station’s 30-year (1971 through 2000) daily normal maximum temperature. “Deviant days” were defined as days on which the deviation of maxi- mum temperature from the 30-year normal was in the upper 5th percentile of the deviations for the 8 years of this study (2000 through 2007) for that weather station. “Extreme days” were defined as days in the upper 5th percentile of the absolute maximum temperature for the 8 years of this study (2000 through 2007) for a given weather station (Figure 1). Deviant and extreme days whose maximum temperature was < 85°F (29.4°C) were reclassified as nondeviant or nonextreme because we wanted to focus on the health effects of higher temperatures. Each state partner calculated incidence rate ratios (RRs) for each hospitalization and mortality diagnosis using generalized esti- mating equation (GEE) Poisson regression clustering by ZIP code with an exchangeable working correlation matrix and the 2000 U.S. Census (population as an offset term) (U.S. Census Bureau 2002). GEE methods are robust to misspecification of the correlation matrix and to overdispersion. Daily counts of each health diagnosis category were regressed against HVI, deviant day, and the interaction between deviant day and the HVI while con- trolling for day of week, month, and daily maximum 8-hr ozone level. Similar models were run for extreme days. Separate models were also constructed to estimate the effects of HVI and deviant day or extreme day without aThe factors were created using principal components analysis. Listed here are the variables that loaded most heavily on each factor. Reid et al. (2009) explained how the factors were created. 716 ovlume 120 | number 5 | May 2012 • Environmental Health Perspectives