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American Journal of Epidemiology Advance Access originally published online on April 11, 2008
American Journal of Epidemiology 2008 167(12):1476-1485; doi:10.1093/aje/kwn074
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

ORIGINAL CONTRIBUTIONS

Does Temperature Modify the Association between Air Pollution and Mortality? A Multicity Case-Crossover Analysis in Italy

M. Stafoggia1,2, J. Schwartz3, F. Forastiere1, C. A. Perucci1 the SISTI Group

1 Department of Epidemiology, Rome E Health Authority, Rome, Italy
2 Department of Biostatistics, Harvard School of Public Health, Boston, MA
3 Department of Environmental Health, Harvard School of Public Health, Boston, MA

Correspondence to Massimo Stafoggia, Department of Epidemiology, Rome E Health Authority, Via Santa Costanza 53, 00198 Rome, Italy (e-mail: stafoggia{at}asplazio.it).

Received for publication November 21, 2007. Accepted for publication March 6, 2008.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Adverse health effects of particulate matter <10 µm in aerodynamic diameter (PM10) and high temperatures are well known, but the extent of their interaction on mortality is less clear. This paper describes effect modification of temperature in the PM10–mortality association and tests the hypothesis that higher PM10 effects in summer are due to enhanced exposure to particles. All deaths of residents of nine Italian cities between 1997 and 2004 were selected. The case-crossover approach was adopted to estimate the effect of PM10 on mortality by season and temperature level. Three strata of temperature corresponding to low, medium, and high "ventilation" were identified, and the interaction between PM10 and temperature within each stratum was examined. Season and temperature levels strongly modified the PM10–mortality association: for a 10-µg/m3 variation in PM10, a 2.54% increase in risk of death in summer (95% confidence interval: 1.31, 3.78) compared with 0.20% (95% confidence interval: –0.08, 0.49) in winter. Analysis of the interaction between PM10 and temperature within temperature strata resulted in positive but, in most cases, nonstatistically significant coefficients. The authors found much higher PM10 effects on mortality during warmer days. The hypothesis that such an effect is attributable to enhanced exposure to particles in summer could not be rejected.

association; climate; effect modifiers (epidemiology); environmental exposure; mortality; particulate matter; seasons; temperature

Abbreviations: PM10, particulate matter <10 µm in aerodynamic diameter


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Numerous epidemiologic studies have reported associations between exposure to outdoor particulate matter and daily mortality (1–4). The association is consistent in many countries and concerns the overall population, but effects are higher in elderly people (5), less-affluent persons (6, 7), and those who are more vulnerable because of chronic conditions such as diabetes (5, 7, 8), heart failure (9), myocardial infarction (8), and chronic respiratory conditions (10).

A strong association between high summer temperatures and mortality also has been detected, with a generally J-shaped exposure response, immediate lags, and similar patterns in different countries (11–13), although the overall effect strongly depends on local characteristics (14), climatic conditions, and the availability of air-conditioning systems (15). The effect is more pronounced in the elderly (15), women (16), and people with chronic conditions such as psychoses (11), depression (11), cerebrovascular diseases (11), diabetes (15, 17), and chronic obstructive pulmonary disease (11).

It has been common practice in epidemiologic studies to adjust for temperature in the analyses of health effects of particulate matter and, to a lesser extent, to adjust for air pollutants when studying the effects of temperature on mortality. Surprisingly, the issue of effect modification that temperature may exert on the air pollution–mortality relation has been largely neglected, so that the extent of the interaction between the environmental exposures is less clear. Some papers have been published in the last few years on season-specific analyses of the association between particulate matter and mortality (7, 18–20), with some evidence that the higher effects are found in the warmer months. A few studies tried to further explore the interaction between particulate matter and temperature (21–23), identifying a significant enhanced effect for increasing values of both particulate matter and temperature. However, the form and the possible mechanisms of interaction are largely unknown. A recent scientific debate (24) indicates an increased level of exposure during the warm period due to open windows and outdoor activities or seasonal changes in the chemical composition of particulate matter as two alternative hypotheses that should be better evaluated.

The objective of this study was to provide further insight into the subject, using data from nine Italian cities. We used a staged approach of 1) examining effects by season, 2) examining effects by strata defined by temperature, and 3) including interactions with temperature within those strata. Doing so addresses the hypothesis that the effect modification by temperature level on the relation between particulate matter <10 µm in aerodynamic diameter (PM10) and mortality is predominantly due to a higher exposure to air pollution during warmer months associated with open windows and more outdoor activities (the "ventilation" hypothesis). In particular, we postulate that the effect of PM10 on mortality would be higher in the high-ventilation days but that the interaction between PM10 and apparent temperature on those days would not be significant.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Study population
The study population consisted of inhabitants aged 35 years or older in nine Italian cities between 1997 and 2004. For each city, we selected all residents who died of natural causes (International Classification of Diseases, Ninth Revision, codes 1–799) in the city. Information on the underlying cause of death was available (for all but one city), and we divided them into three groups: cardiovascular diseases (codes 390–459), respiratory diseases (codes 460–519), and other natural diseases.

Environmental variables
Information on daily environmental variables was obtained from the Italian Air Force Meteorological Service, which provided data on air temperature (degrees Centigrade), dew point temperature (degrees Centigrade), and barometric pressure (hectopascals). Apparent temperature is a climatological index that takes into account the physical stress during warm days due to the combined effect of air temperature and humidity (25, 26).

Hourly PM10 data from city monitors located in residential areas were provided by the regional environmental protection agencies. Data were collected for each city according to standard procedures (27). The 24-hour mean daily value was calculated for each monitor by averaging hourly values, and a daily average for each city was estimated from the monitor-specific daily means. Missing data on the aggregate level were replaced by using a formula adapted from the Air Pollution and Health: A European Approach (APHEA) method (27).

The current- and the preceding-day PM10 means (lag 0–1) were averaged as the exposure variable, based on previous investigations (27).

Additional variables
We collected information on the following variables as potential confounders of the association between mortality and PM10: population decrease during the summer period, day of the week, holidays, barometric pressure, and influenza epidemics (defined as the annual 3-week period of maximum incidence of flulike illness based on estimates of weekly influenza incidence, as reported by the Italian National Health Service).

Analytic methods
The case-crossover design was used to study the association between PM10/apparent temperature and mortality (28). It is a variant of the case-control design, in which each subject is matched to himself or herself; controls are chosen as times (i.e., days) in which the event did not occur. It follows that all time-invariant individual characteristics are adjusted by design, while other time-dependent covariates can be controlled for by modeling.

We used the time-stratified approach (29) to select control days, with controls chosen every 3 days in the same month and year as the event day. Hence, for example, if one subject died on November, 20, 2000, his or her control days would have been the 2nd, 5th, 8th, 11th, 14th, 17th, 23rd, 26th, and 29th of November 2000. This approach controls for season by matching month and year. It also partly controls for other variables such as weather, since all comparisons are made within the same month.

We fit conditional logistic regression for each city, where the outcome variable was the indicator of case/control day and the exposure variable was PM10 (lag 0–1) (alone and in combination with apparent temperature (lag 0–1); refer to the information below), and we further controlled for barometric pressure (one linear term), cold apparent temperature at lag 1–6 (one linear term for values below 9°C), day of the week, decrease in population during summer, holidays, and influenza epidemics. All city-specific analyses were performed by using the function coxph( ) in the R statistical software package (30, 31).

Finally, the city-specific results were pooled with a random-effects meta-analysis, using maximum likelihood as the estimation method (32). Reported were p values of heterogeneity between cities. Refer to the online material for further details (this information is posted on the Journal's website (http://aje.oupjournals.org/)).

We performed the analysis in four stages. First, we evaluated the association of PM10 (lag 0–1) with all natural-cause mortality by season while adjusting for apparent temperature within each season stratum. In both the full-year and the season-specific models, apparent temperature (lag 0–1) was controlled for by using a penalized spline (33) with an effective number of degrees of freedom chosen by minimizing Akaike's Information Criterion (34). In addition, apparent temperature (lag 1–6) below 9°C was adjusted for with a linear term to control for potential confounding of low temperatures. Refer to the online supplement for further details.

Second, we defined three strata of daily apparent temperature (lag 0–1) according to city-specific percentiles of the apparent temperature distributions: below the 50th percentile (relatively low temperature, heat on, windows closed, resulting in low ventilation), between the 50th and 75th percentiles (transient period with intermediate temperature and mid-ventilation levels), and above the 75th percentile (relatively high temperature, windows open, high ventilation). Air-conditioning prevalence was very low in Italy during the study period, which enabled us to classify days by temperature instead of season, thus capturing warm days in the spring and fall into our high-temperature (and hence high-ventilation) category. We thus analyzed the PM10–mortality association stratifying by the new categories while adjusting for apparent temperature within each stratum to take into account potential residual confounding.

Third, we explored potential nonlinearities in the joint association of PM10/apparent temperature with natural mortality for the two largest cities in the study: Milan and Rome. They were chosen because they comprise 65 percent of the studied population and are representative of the different climate conditions in Italy, namely, continental in northern Italy versus Mediterranean in central-southern Italy. Three-dimensional surfaces were obtained by using thin-plate splines (35). This step was performed by fitting a Poisson generalized additive model as an alternative to conditional logistic regression because previous investigations have shown that the two methods are equivalent as long as time trend is suitably controlled for (36). Again, refer to the online supplement for further details.

Finally, we added an interaction term between PM10 and apparent temperature within each stratum from the previous temperature-stratified analysis to check whether the greater effect of particulate matter on warmer days was due to higher exposure to PM10 (absence of linear interaction on a log scale) or the interactive effect of the two exposures was still present even after having stratified by "ventilation." We analyzed the "interaction term" again by shifting the second cutoff point of apparent temperature to higher values, from the 75th percentile to the 90th, to determine whether a potential interaction between PM10 and apparent temperature on natural mortality became evident on the warmest days.

All previous analyses were repeated by considering cause-specific mortality as alternative outcomes.

Sensitivity analysis
Several sensitivity analyses were performed to check the robustness of results to model specifications, dose-response shapes of confounders, and study periods. Specifically, an alternative strategy for selecting the control days within the time-stratified approach was applied, choosing as control days the same days of the week within the same month and year as the event day. Second, the confounding effect of cold temperatures was adjusted for by using penalized splines of apparent temperature (lag 1–6) or linear splines with one inner knot chosen differently for each city. Third, the analyses were rerun excluding 2003–2004, a period characterized by extremely high temperatures and increased use of air-conditioning. More details are reported in the online Appendix.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Table 1 displays the study population by city and cause of death. A total of 321,024 residents aged 35 years or older died from natural causes: 41.2 percent of the deaths were caused by cardiovascular diseases and 6.9 percent by respiratory diseases.


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  		TABLE 1. Study population: numbers and percentages of deaths of people aged ≥35 years and residing in nine Italian cities, by cause of death, 1997–2004

 
The distribution of death events by season and apparent temperature is displayed, for each city, in table 2. There were no meaningful differences across cities.


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  		TABLE 2. Study population: distribution of death events (numbers and percentages) among people aged ≥35 years and residing in nine Italian cities, by season, level of apparent temperature, and city, 1997–2004

 
The main characteristics of the two environmental exposures, PM10 and apparent temperature, are displayed in table 3, as well as their Pearson correlation coefficients (and corresponding p values). Daily mean PM10 ranged from 35.1 µg/m3 in Pisa to 71.5 µg/m3 in Turin. There was a clear north-south gradient in terms of daily temperatures, with the coldest cities located in northern Italy (Turin, Milan, Mestre, Bologna) and the warmest in central (Pisa, Florence, Rome) and southern (Taranto and Palermo) Italy. The cutoff points chosen to define apparent temperature strata reflect this trend. The correlation between PM10 and apparent temperature was somewhat heterogeneous across the cities and was always negative, with the exception of Palermo.


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  		TABLE 3. Environmental variables: mean (SD*) of PM10*; mean (SD), 50th percentile, and 75th percentile of apparent temperature; and Pearson correlation coefficient between PM10 and apparent temperature for nine Italian cities, 1997–2004

 
The pooled association between PM10 and mortality, by cause of death, season, and apparent temperature strata, is reported in table 4, where results are expressed as percent increases in risk, and 95 percent confidence intervals, corresponding to a 10-µg/m3 variation in PM10. Reported are p values for heterogeneity among city-specific estimates. There was strong effect modification by season, with a general pattern of a stronger effect in summer (2.54 percent, 95 percent confidence interval: 1.31, 3.78 for all natural mortality) and a lower effect in winter (0.20 percent, 95 percent confidence interval: –0.08, 0.49). This pattern was similar for all, cardiovascular, and respiratory mortality. Table 4 also reports results stratified by apparent temperature strata. Again, the effect of PM10 was considerably higher as temperature increased. Results were homogeneous between centers in the low-temperature and winter/spring strata, but some heterogeneity was present for higher temperatures, although with different patterns according to cause of death: greater differences between cities in the mid-temperature and fall season strata for natural and cardiovascular mortality, greater heterogeneity in the high-temperature and summer season strata for respiratory mortality.


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  		TABLE 4. Pooled results: association between PM10* (lag 0–1) and mortality among people aged ≥35 years and residing in nine Italian cities, by cause of death, season, and apparent temperature level on the day of death, including percent increases in risk (%) and 95% confidence intervals corresponding to a 10-µg/m3 variation in the pollutant as well as pH*{dagger} 1997–2004

 
Table 5 shows the regression coefficients, standard errors, and p values for the interaction terms PM10 x apparent temperature, by apparent temperature strata and causes of death. Also reported are p values for heterogeneity among city-specific estimates. There was a suggestion of a positive linear interaction (on a log scale) within the highest stratum of apparent temperature for all groups of causes of death (p of about 0.15) and in the mid-stratum of apparent temperature for cardiovascular mortality (p = 0.014). Results were homogeneous among centers (p for heterogeneity was always >0.10).


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  		TABLE 5. Pooled results: linear interaction between PM10* (lag 0–1, 10 µg/m3) and apparent temperature (lag 0–1, 1°C) against mortality among people aged ≥35 years and residing in nine Italian cities, by cause of death and apparent temperature level on the day of death, including regression coefficients (beta), p values, and pH*{dagger}

 
The above model assumes a linear interaction between the two exposures. To determine whether we were missing a more complex interaction between PM10 and apparent temperature, we fit a nonlinear interaction by using thin plate splines, and the results are shown in figure 1 as three-dimensional exposure-response surfaces and contour plots for Milan and Rome. Although the patterns were different in the two cities, the estimated numbers of deaths were lowest for low values of PM10 and apparent temperature and increased exponentially for increasing temperatures and PM10, without any sign of a threshold.


Figure 1
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  		FIGURE 1. City-specific results: exposure-response surfaces relative to the joint association of particulate matter <10 µm in aerodynamic diameter (PM10; lag 0–1) and apparent temperature (lag 0–1) with all natural mortality among Italian residents of Milan, 1999–2004 (left), and Rome, 1998–2004 (right), aged ≥35 years. Three-dimensional plots in the upper half, contour plots in the lower half.

 
Figure 2 reports the coefficients of the interaction terms between PM10 and apparent temperature on all natural mortality in the third stratum of apparent temperature defined on the basis of a city-specific cutoff point ranging from the 75th to the 90th percentile of apparent temperature. The interaction term never reached statistical significance (with the exception of the 79th percentile), and it decreased to zero for the warmest cutoff points, indicating a lack of (linear) interaction between the two environmental exposures on the warmest days.


Figure 2
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  		FIGURE 2. Pooled results: linear interaction between particulate matter <10 µm in aerodynamic diameter (PM10; lag 0–1) and apparent temperature (lag 0–1) and total natural mortality among residents of nine Italian cities aged ≥35 years, by percentile of apparent temperature. Each diamond represents the regression coefficient (and the vertical line the corresponding 95% confidence interval (CI)) of the linear interaction term between PM10 and apparent temperature on mortality, within the subset of days when the apparent temperature was above the relative percentile.

 
Results from the sensitivity analyses (table E1 in the online Appendix) did not alter the main conclusions.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
The present study was designed to investigate the interactive effect of particulate matter and apparent temperature on mortality in nine Italian cities. The increasing effect of PM10 by temperature level is suggestive of a synergism between the two exposures. The three-dimensional exposure-response surfaces for Milan and Rome (figure 1) showed that the interactive patterns are quite complex; the expected numbers of deaths tended to increase steeply in Milan as temperature and PM10 increased, while the joint relation was more irregular in Rome. We evaluated the hypothesis that the effect modification was predominantly due to greater exposure to air pollution during warm months by examining effects within strata of daily temperature and by adding linear interaction terms between PM10 and apparent temperature within strata of temperature itself. We found limited evidence to reject the "ventilation" hypothesis.

Numerous studies have investigated the role of season as an effect modifier in the air pollution–mortality association, finding consistent results in both North America and Europe. Katsouyanni et al. (37) identified higher effects of PM10, black smoke, and sulfur dioxide on total mortality in warmer months, studying 12 European cities within the Air Pollution and Health: A European Approach project; Michelozzi et al. (18) found a significant effect of nitrogen dioxide and PM10 on mortality in Rome in the warm season only; more recently, Peng et al. (19), using data from 100 US cities within the National Morbidity, Mortality, and Air Pollution Study, found a significant association between PM10 and mortality in summer (lag 0) and spring (lag 1) only; a study conducted in Belgium (20) identified a strong interaction between PM10 and season for both overall and cause-specific mortality. On the other hand, studies conducted in Asia gave contrasting results (38, 39).

The interaction between air pollution and temperature has been explored to a lesser extent in the epidemiologic literature. One of the first papers that shed light on the subject (21) identified a strong interaction between sulfur dioxide and temperature (<30°C vs. ≥30°C) in Athens, Greece, while the main effect of sulfur dioxide was not significant. Samet et al. (40) studied the effect of total suspended particles and sulfur dioxide on mortality in Philadelphia, Pennsylvania, while adjusting for weather in four different models and testing for potential effect modification; they found little evidence of an interaction, regardless of the approach used to model weather. In 2001, Katsouyanni et al. (2) addressed the issue of effect modification within the Air Pollution and Health: A European Approach project by performing a meta-analysis of city-specific results regarding the association between PM10/black smoke and mortality in 14 European centers and adding mean daily temperature in the meta-regression stage: temperature contributed to explain residual variation in city-specific results in a highly significant way, and the estimated increases in number of deaths for a 10-µg/m3 variation in PM10/black smoke were much higher in the 75th percentile of temperature than in the 25th percentile.

In recent years, a few studies (21–23, 41) have explored the interactive patterns of PM10 and temperature on mortality in more detail by using more flexible models. In all cases, a consistent increased risk of death for increasing values of both environmental variables has been confirmed.

Several explanations have been proposed for the effect modification of temperature in the air pollution–human health association. Gordon (42) observed that a synergism between the two exposures is biologically plausible because the thermoregulatory system responds to heat stress by activating three key mechanisms to dissipate excess heat: cardiovascular, respiratory, and sudomotor (sweating). Activation of these thermoeffector systems can have direct or indirect effects on the entry of toxicants into the body, thus augmenting total intake of airborne pollutants. On the other hand, it is possible that the higher PM10 effects during summer are due merely to greater exposure in that period or simply to a better exposure measurement, since people are more likely to keep the windows open or spend time outdoors, and outdoor monitoring-based exposure better reflects actual individual exposure. Personal exposure studies have shown substantially higher personal/outdoor particulate matter slopes during periods when windows are open compared with periods when windows are closed (43). Finally, particulate matter composition may be different by season, with the most toxic components at higher concentrations during warmer periods (19).

Our results seem to be consistent with the "ventilation" hypothesis, even though other explanations cannot be ruled out. We found limited evidence of linear interaction, within temperature strata, for total mortality (the interaction term was statistically significant only when we shifted the cutoff point of temperature at percentile 79th). No interaction of the two environmental exposures emerged for respiratory mortality and other causes of death, whereas the linear interaction term was highly significant for cardiovascular mortality in the third quartile of apparent temperature and suggestive of a departure from a multiplicative model in the upper quartile.

Several strengths of the present study deserve consideration. First, it included more than 300,000 deaths from nine cities evenly spread across Italy, with detailed information on causes of death: the large quantity of data enabled us to explore several components of the PM10–temperature interaction. Second, to our knowledge, it is the first study to explore the subject by using case-crossover methodology. Third, we explored the PM10–temperature interaction two different ways: by stratifying for apparent temperature and assuming a linear relation between PM10 and mortality and by allowing both environmental exposures to affect mortality nonparametrically. Fourth, we explicitly addressed the issue about possible causes of the effect modification.

Some limitations should be mentioned. First, given the observational nature of the study, we could not identify any causal effect of environmental exposures, only suggestive associations and interactions. In this sense, residual bias due to unmeasured confounding is still possible, and none of the above explanations could actually be verified (or falsified) by the present study. Nonetheless, the approach used controls for potential individual confounders by design, and strong residual confounding due to unmeasured time-dependent covariates is unlikely. Second, measurement error in the exposures cannot be excluded: daily PM10 and temperature values were estimated from central monitoring stations, so that personal exposure may have differed substantially from estimated exposure. However, it is unlikely to be differential. On the contrary, misclassification of the outcome is doubtful in this study because of the broad categorization of causes of death. Third, all analyses were not adjusted for ozone, which is known to be highly correlated with temperature and positively associated with mortality in warmer periods. However, the correlation between PM10 and ozone is generally low; therefore, it is unlikely to be a confounder of the PM10–mortality association (but it can affect the extent of the effect modification by temperature). Finally, we did not know the ventilation status of the homes of the decedents in our study, and we assumed that there were more open windows on warm days, an assumption clearly subject to error.

In conclusion, we identified strong effect modification by season and temperature in the association between PM10 and natural and cause-specific mortality. The linear interaction terms within temperature strata were in most cases not statistically significant, and the hypothesis of higher effects of PM10 during warmer months due to higher exposure to air pollution cannot be rejected. However, there was a suggestion of a more than multiplicative pattern within the third and fourth quartiles of apparent temperature for cardiovascular mortality that deserves further research. Because air pollution and high temperatures are strong predictors of mortality, correct identification of their joint effect on health outcomes is of primary interest from a public health perspective, especially considering the current debate over the potential effects of climate changes.


   ACKNOWLEDGMENTS
 
This study was funded by the Lazio Region Health Authority.

The authors thank Margaret Becker for her help in editing the manuscript.

SISTI Group (Italian Study on Susceptibility to Temperature and Air Pollution) members—Bologna: D. Agostini, S. De Lisio, R. Miglio, P. Pandolfi, and C. Scarnato; Firenze: A. Biggeri, E. Chellini, and S. Mallone; Mestre: L. Simonato and R. Tessari; Milano: L. Bisanti, M. Rognoni, and A. Russo; Palermo: A. Cernigliaro and S. Scondotto; Pisa: M. Vigotti; Roma: V. Belleudi, F. de'Donato, F. Forastiere, P. Michelozzi, C. A. Perucci, S. Picciotto, and M. Stafoggia; Taranto: R. Primerano and M. Serinelli; Torino: G. Berti, E. Cadum, N. Caranci, M. Chiusolo, and M. Demaria.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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