Authors: Antonella Zanobetti and Joel Schwartz

Transcription

1 Title: Mortality Displacement in the Association of Ozone with Mortality: An Analysis of 48 US Cities Authors: Antonella Zanobetti and Joel Schwartz ONLINE DATA SUPPLEMENT

2 Additional Information on Materials and Methods and Results Study population We obtained individual mortality data for forty-eight cities in the United States from the National Center for Health Statistics (NCHS) for the years 1989 to The chosen cities were a convenience sample of cities for which we had previously extracted data, enhanced by additional cities chosen to ensure a reasonable spread of cities with respect to climatic patterns and geography. The mortality files provided information on the exact date of death, and the underlying cause of death. For this study we selected all-cause daily mortality excluding any deaths from accidental causes (ICD- code 9 th revision: 1-799, ICD-code 10 th revision: V01-Y98); we also examined any cardiovascular disease (ICD-9: , ICD-10:I01-I60), any respiratory disease (ICD-9: , ICD-10:J01-J99), and stroke (ICD-9: 436, ICD-10: I64). City characteristics such as population density, percentage of population 65 years of age and older in poverty status, and Percent of Households with Householder 65+ and income > $50000 were obtained from the 2000 United States census and we calculated the mean and variance of the daily summer (June through August) apparent temperature using data from the National Weather Service Surface Station (E1). We calculated the percentage of households with central air conditioning (AC) using data from the American Housing Survey of the US Census Bureau (E2). 1

3 Environmental Data We obtained ozone (8-hour) data from US Environmental Protection Agency s Air Quality System Technology Transfer Network (USEPA Technology Transfer Network, 2005). When multiple monitors were present in a city we estimated an average daily value for the city, and we accounted for the impact of occasional missing values using an algorithm previously described (E3, E4). We obtained local meteorological data such as mean temperature, and dew point temperature, from the United States Surface Airways and Airways Solar Radiation hourly data (1). We also computed apparent temperature (AT), defined as an individual s perceived air temperature given the humidity. AT was calculated with the following formula (E5, E6). AT = (0.994*Ta) + (0.0153*Td 2 ) where Ta is mean temperature and Td is dew point temperature. Statistical Methods In each city we computed daily counts of mortality; the mortality data was then merged to the air pollution and weather daily data, and we computed lags for ozone and temperature. Since the ozone effect is either restricted to the warm season, or at least is substantially different outside that period, we wanted to ensure that any changes in effect estimate reflect the effects of lags, and not of seasonal effect modification. Since we wanted to examine the effect up to the previous 20 days, we want to ensure that the ozone levels of the previous 20 days are part of the warmer season. Mortality declines from 2

4 January, but is still noticeably higher in April and early May than in August; and in some cities there are still cold temperatures in the month of April; these could cause seasonal confounding in the longer lags. Therefore we analyzed daily deaths that occurred in June- August, so that all the lags were still in the warm season. Temperature also may affect daily deaths with a lag, however a previous examination of the pattern in US cities showed mortality effects of warm temperatures disappeared within seven days (E7). Previous studies have generally not included this many lags of temperature in examining the effects of ozone, which might have allowed residual confounding. We investigated the association between daily concentrations of air pollutants and mortality using a two stage approach. First, we fit a time series analysis in each city separately and then we combined the city-specific results using a maximum likelihood meta-analytic approach. The time series of daily counts of mortality and ozone were investigated with a generalized linear model, with a quasi-poisson link function to account for overdispersion. In the model we controlled for season using natural splines with 2 degrees of freedom for each 3-month summer season, day of the week with indicator variables, and an unconstrained distributed lag for apparent temperature to take into account the effect of temperature today and the previous 7 days. Each lag of apparent temperature was treated as linear in these analyses, which were restricted to the warm season. We examined the dependence of daily deaths on 8-hr ozone concentrations on the day of death to use as a baseline analysis. We then refit models using ozone on the day of 3

5 death, and up to the previous 20 days using an unconstrained distributed lag model. If the pollution-related deaths are only being advanced by a few days to a few weeks we would see this harvesting effect expressed by a negative associations between air pollution and deaths several days to weeks subsequently. The effect of air pollution, net of any such short term rebound up to 20 days is the sum of the positive and negative effect estimates for all 21 days. Hence if this second estimate were smaller than the estimate using only lag 0 ozone, this would support the harvesting hypothesis. For Poisson regression the unconstrained distributed lag model can be written as: Log( E[ Y ]) = covariates + b Z + b Z b Z (1) t 0 t 1 t- 1 q t- q Where Z t-p is the 8-hour ozone concentration p days prior to day t. The unconstrained distributed lag model is too noisy to provide any information about the shape of the effect versus lag, but it does give an unbiased estimate of the overall effect, computed as the sum of the ß j. Several methods have been used in order to study the shape of the distributed lag. One common approach is to constrain the β s to follow a flexible polynomial, such as cubic or quadratic. An extension of this idea, which aims for more flexibility than that afforded by a polynomial functions, is to subject the coefficients to non-parametric smoothing. As previously described (E8) we used a penalized quasi-likelihood to estimated the coefficient of the distributed lag. These models were fit in R (R Development Core Team (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN , URL 4

6 In a second stage of the analysis, the city specific results were combined using the meta-regression technique of Berkey and coworkers (E9). To be conservative we report the results incorporating a random effect, whether or not there was a significant heterogeneity. The I 2 statistic is a generalization of the Χ 2 or Q test for heterogeneity, and express the proportion of variance explained. We used the I 2 statistic to assess the proportion of total variation in effect estimates that was due to between-cities heterogeneity (E10). We used the following formula: I 2 = [Q / (k - 1)] - 1 / [Q / (k - 1)], where Q is the Q-test for heterogeneity and k is the number of cities. We examined effect modification by city characteristics by entering them as predictor variables in the meta-regression. These included measures of socio-economic condition (Percent of Households with Householder 65+ and income > $50000, Percent population aged 65+ in poverty status), exposure related measures (Mean and Variance of summer apparent temperature), general social factors (population density), and percent population with central air conditioning (AC). 5

7 Results The cities analyzed in this study are listed in Table E1 together with the total population and the count of total and cause-specific summer time deaths per city. Figure E1 shows the city specific distribution of the daily all cause mortality during the June- August months; New York City, Los Angeles and Chicago are the biggest cities. Overall, we examined 1,614,124 deaths. Table E2 presents descriptive statistics of apparent temperature and ozone showing a wide range of ozone exposures and accompanying temperatures across the 48 cities. For example, the 75 th percentile of 8-hour ozone ranged from 19.8 ppb in Honolulu to 75.9 ppb in Los Angeles, while the 75 th percentile for apparent temperature ranged from 20.6 C in Spokane to 34.6 C in Houston. Figure 2 shows boxplots of the daily ozone distribution in each city, sorted by level of concentration of ozone. Figure E3 presents the city-specific distribution of apparent temperature during the summer months. 6

8 References E1. National Environmental Satellite, and a. I. S. Data. TD-3280 U.S. Surface Airways and Airways Solar Radiation Hourly E2. US Census Bureau. American Housing Survey for the United States: U.S. Department of Commerce/U.S. Department of Housing and Urban Development, Washington, DC E3. Zanobetti A, Schwartz J, and Dockery DW. Airborne particles are a risk factor for hospital admissions for heart and lung disease. Environ Health Perspect 2000; 108(11): E4. Schwartz J. The distributed lag between air pollution and daily deaths. Epidemiology 2000; 11(3): E5. Steadman RG The assessment of sultriness. Part II: Effects of wind, extra radiation and barometric pressure on apparent temperature. Journal of Applied Meteorology 1979; 18: E6. Kalkstein LS, Valimont KM. An evaluation of summer discomfort in the United States using a relative climatological index. Bulletin American Meteorological Society 1986; 67(7): E7. Braga AL, Zanobetti A, Schwartz J. The time course of weather-related deaths. Epidemiology 2001; 12(6): E8. Zanobetti A, Wand MP, Schwartz J, Ryan LM. Generalized additive distributed lag models: quantifying mortality displacement. Biostatistics 2000; 1(3):

9 E9. Berkey CS, Hoaglin DC, Antczak-Bouckoms A, Mosteller F, Colditz GA. Metaanalysis of multiple outcomes by regression with random effects. Stat Med 1998; 17(22): E10. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21( ):

10 FIGURE LEGENDS Figure E1: City-specific distribution of the daily counts of all cause mortality for the June-August period for the years Figure E2: City-specific distribution of the daily levels of concentrations of ozone during the June-August period for the years Figure E3: City-specific distribution of the daily levels of apparent temperature during the June-August period for the years

11 Table E1: city-specific descriptive: population, and counts of all causes and cause-specific deaths deaths during the June-August months Population Number of deaths by 1000 All cause CVD Respiratory Stroke City State by 1000 Albuquerque NM Atlanta GA Austin TX Baltimore MD Birmingham AL Boston MA Boulder CO Broward FL Canton OH Charlotte NC Chicago IL Cincinnati OH Cleveland OH Colorado Springs CO Columbus OH Dallas TX Denver CO Detroit MI Greensborough NC Honolulu HI Houston TX Jersy city NJ Kansas City KS Los Angeles CA Miami FL Milwaukee WI Nashville TN New Haven CT New Orleans LA NYC NY Oklahoma City OK Orlando FL Philidelphia PA Phoenix AZ Pittsburg PA Provo/Orem UT Sacramento CA St. Louis MO Salt Lake City UT San Diego CA San Francisco CA Seattle WA Spokane WA Tampa FL Terra Haute IN Tulsa OK Washington DC DC Youngstown OH

12 Table E2: City-specific descriptive for apparent temperature and ozone during the June-August months Apparent temperature ( C) 8-hr Ozone (ppb) City State min 25th p mean 75th p max min 25th p mean 75th p max Albuquerque NM Atlanta GA Austin TX Baltimore MD Birmingham AL Boston MA Boulder CO Broward FL Canton OH Charlotte NC Chicago IL Cincinnati OH Cleveland OH Colorado Springs CO Columbus OH Dallas TX Denver CO Detroit MI Greensborough NC Honolulu HI Houston TX Jersy city NJ Kansas City KS Los Angeles CA Miami FL Milwaukee WI Nashville TN New Haven CT New Orleans LA NYC NY Oklahoma City OK Orlando FL Philidelphia PA Phoenix AZ Pittsburg PA Provo/Orem UT Sacramento CA St. Louis MO Salt Lake City UT San Diego CA San Francisco CA Seattle WA Spokane WA Tampa FL Terra Haute IN Tulsa OK Washington DC DC Youngstown OH

13 FIG E1 New York City, NY Los Angeles, LA Chicago, IL Boston, MA Houston, TX Detroit, MI Miam i, FL Phoenix, AZ Philadelphia, PA San Diego, CA Pittsburgh, PA Baltimore, MD Saint Louis, MO Cleveland, OH Atlanta, GA Broward, FL Dallas, TX Seattle, WA Milwaukee, WI Cincinnati, OH Kansas City, KS Columbus, OH Birmingham, AL Tampa, FL Sacramento, CA Washington DC New Haven, CT San Francisco, CA Oklahoma City, OK Nashville, TN Orlando, FL New Orleans, LA Honolulu, HI Tulsa, OK Denver, CO Salt Lake City, UT Jersey City, NJ Charlotte, NC Albuquerque, NM Youngstown, OH Spokane, WA Canton, OH Aus tin, TX Greensborough, NC Colorado Springs, CO Terra Haute, IN Provo, UT Boulder, CO All cause mortality

14 FIG E2 Charlotte, NC Los Angeles, LA Atlanta, GA Greensborough, NC Provo, UT Baltimore, MD Phoenix, AZ Canton, OH Salt Lake City, UT Washington DC Tulsa, OK Boulder, CO Columbus, OH Jersey City, NJ Terra Haute, IN Sacramento, CA Cincinnati, OH Oklahoma City, OK Pittsburgh, PA Albuquerque, NM Philadelphia, PA Saint Louis, MO Youngstown, OH Birmingham, AL Kansas City, KS Nashville, TN Cleveland, OH Colorado Springs, CO Denver, CO Dallas, TX Milwaukee, WI New Haven, CT San Diego, CA Spokane, WA Detroit, MI Boston, MA New York City, NY Chicago, IL Orlando, FL Aus tin, TX Tampa, FL New Orleans, LA Houston, TX Seattle, WA Miam i, FL Broward, FL San Francisco, CA Honolulu, HI Ozone (ppb)

15 FIG E3 Phoenix, AZ Miam i, FL Broward, FL Houston, TX Dallas, TX New Orleans, LA Tampa, FL Aus tin, TX Orlando, FL Tulsa, OK Oklahoma City, OK Birmingham, AL Honolulu, HI Atlanta, GA Nashville, TN Charlotte, NC Saint Louis, MO Washington DC Greensborough, NC Kansas City, KS Philadelphia, PA Baltimore, MD Jersey City, NJ New York City, NY Cincinnati, OH Terra Haute, IN Columbus, OH Chicago, IL Salt Lake City, UT Provo, UT Albuquerque, NM Pittsburgh, PA Cleveland, OH Detroit, MI New Haven, CT Boston, MA Canton, OH Milwaukee, WI Sacramento, CA Youngstown, OH San Diego, CA Los Angeles, LA Denver, CO Boulder, CO Colorado Springs, CO Spokane, WA Seattle, WA San Francisco, CA Apparent Temperature ( C)