Gold et al. The TEOM sample filter is heated to 50°C, leading to season-speciﬁc temperature-related loss of semivolatile mass. Season-speciﬁc cali- bration factors were used to correct for the losses of mass (Allen et al. 1997). The calibra- tion factors were obtained by regressing contin- uous PM2.5 concentrations averaged over 24-hr periods on the corresponding collocated inte- grated 24-hr Harvard Impactor (Air Diagnostics Environmental Inc., Harrison, ME, USA) low- volume Teﬂon ﬁlter gravimetric measurements. In the summer in Boston, BC measure- ments are surrogates for carbonaceous parti- cles, components of PM2.5, many of which derive from trafﬁc (local or transported). BC data from this instrument, using the internal empirically determined conversion factor, have correlated well with elemental carbon (Hansen and Rosen 1984). BC was measured using a model AE-14 aethalometer (Magee Scientiﬁc Inc., Berkeley, CA). CO was meas- ured continuously with a gas analyzer (model 48; ThermoEnvironmental, Franklin, MA) using a U.S. Environmental Protection Agency (EPA) reference method (Automated Reference Method: RFCA-0981-054). Statistical analyses. For each portion of the protocol, we analyzed the effect of pollution on Table 1. Participant characteristics [n (%)]. Characteristic Sex Male Female Race/ethnicity Black, non-Hispanic White Other Cigarette smoking Never Former Current Ever asthmac Coronary artery disease (ever angina or heart attack) Ever congestive heart failure Ever hypertensionc Medication use Beta-blocker Calcium channel blocker Angiotensin-converting enzyme inhibitor Age [median years (range)] Heart rate (beats/min) ST-segment level, modiﬁed V5 lead (mm) ST-segment level, modiﬁed aVF lead (mm) 884 Entire cohort (n = 28) 7 (25) 21 (75) 8 (29) 19 (68) 1 (4) 11 (39) 16 (57) 1 (4) 1 (4) 5 (18) 2 (7) 11 (39) 5 (18) 3 (11) 7 (25) 73 (60–89) First rest 65b –0.13 0.12 Blood pressure — –0.10 0.12 Standing Exercise 78 –0.08 0.11 86 –0.29 0.10 Second rest 67 –0.27 0.05 Paced breathing 65 –0.17 0.10 Percentages may not add up to 100 because of rounding. aAnalyses assess the association of pollution with ST-segment level. bAnalyses assess the association of pollution with ST-segment depression ≥ 0.5 mm. cReport of doctor’s diagnosis of disease. Table 2. Mediana heart rate and ST-segment level for six protocol periods. aMedian of the mean values for each part of the protocol, for observations included in analyses. Based on 269 observa- tions on the 24 subjects in analyses using either the continuous or dichotomous outcomes. bMedian heart rate for the period that includes both ﬁrst rest and blood pressure portions of the protocol. between-visit, within-subject changes in mean ST-segment level. A standard model for ana- lyzing repeated measures on the same individ- ual is the linear mixed model, which accounts for residual correlation among observations taken on the same subject by including nor- mally distributed random intercepts and pol- lutant slopes in a linear regression model. Descriptive statistics for ST-segment values, however, revealed skewness in the subjects’ baseline values, making the normality assump- tion on the random intercepts untenable. As a result, we used two alternative approaches to analyzing the data from each portion of the protocol. First, treating ST-segment level as a continuous outcome, we used a conditional linear mixed model (Verbeke and Molenberghs 2000), which estimates the within-subject effect of a pollutant after conditioning out each subject’s baseline value. This corresponds to putting subject into the linear model as a ﬁxed effect, while specifying the linear slope of pollutant as a random effect (Verbeke and Molenberghs 2000). The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) study has demonstrated the impor- tance of selecting a vulnerable population ST-segment analysis Continuous Dichotomous outcomea outcomeb (n = 23) (n = 13) 5 (22) 18 (78) 6 (26) 16 (70) 1 (4) 10 (43) 13 (57) 0 1 (4) 5 (22) 2 (9) 10 (43) 4 (17) 3 (13) 7 (30) 71 (61–88) 3 (23) 10 (77) 3 (23) 9 (69) 1 (8) 4 (31) 8 (62) 1 (8) 1 (8) 4 (31) 1 (8) 5 (38) 1 (8) 2 (15) 4 (31) 76 (62–88) when seeking to investigate whether pollution influences ECG changes consistent with ischemia (Pekkanen et al. 2002). Although we did not, as in the ULTRA study, have a cohort selected for coronary artery disease, our aim was to evaluate particle pollution effects on elderly individuals with a tendency to develop ST-segment depression, with some ECG evidence for vulnerability to the out- come of interest. Therefore, a priori, for each part of the protocol for analyses treating ST- segment level as a continuous outcome, we included only vulnerable subjects, deﬁned as those whose mean ST-segment values for that part of the protocol were negative at least two times during the study (23 of 28 study par- ticipants). Analyses were repeated including all study participants to assess the sensitivity of results to the exclusion criteria and to the presence of outliers. In addition to analyses evaluating ST- segment level as a continuous outcome, we analyzed the binary response “ST-segment depression ≥ 0.5 mm,” deﬁned as a mean ST- segment level for a given portion of the proto- col of at least –0.5 mm (i.e., mean ST-segment level ≤ –0.5 mm compared with ST-segment level > –0.5 mm). This definition differed from that of classic ischemia in that it did not require within-test or within-portion of the protocol reversibility. For this secondary analy- sis, we ﬁt a logistic regression model with ran- dom intercepts to data from those subjects having at least one response of each type (depressed and nondepressed ST-segment) during that particular protocol (13 of 28 study participants contributed data to at least one portion of the protocol). Twenty-four study participants with 269 observations were included in analyses either with continuous or with binary (dichotomous) ST-segment outcomes. We had sufficient observations to evaluate the effects of between- test increases in pollution levels on between-test depression in the mean ST-level for each por- tion of the protocol. However, we were unable to assess the effect of between-test changes in pollution on the risk of within-test reversible ST-segment depression that fit criteria for ischemia because of the rarity and lack of vari- ability of such events. During the study, only 5 of 28 study participants had ischemic ECG events (deﬁned above as within-test reversible horizontal or down-sloping ST-segment depression ≥ 0.5 mm). Each regression model included an indica- tor variable for each subject, pollutant con- centration, a cubic effect of the mean of the current hour temperature, and a linear trend of time. Other confounders considered included day of week and time of day, which were both highly correlated with the subject indicator vari- ables and were thus dropped from the model. Separate models were ﬁt using lags of 1–24 hr, VOLUME 113 | NUMBER 7 | July 2005 • Environmental Health Perspectives