Background: Although several studies have examined associations between temperature and cardiovascular-disease-related mortality, fewer have investigated the association between temperature and the development of acute myocardial infarction (MI). Moreover, little is known about who is most susceptible to the effects of temperature. Methods: We analyzed data from the Worcester Heart Attack Study, a community-wide investigation of acute MI in residents of the Worcester (MA) metropolitan area. We used a case-crossover approach to examine the association of apparent temperature with acute MI occurrence and with all-cause in-hospital and postdischarge mortality. We examined effect modification by sociodemographic characteristics, medical history, clinical complications, and physical environment. Results: A decrease in an interquartile range in apparent temperature was associated with an increased risk of acute MI on the same day (hazard ratio = 1.15 [95% confidence interval = 1.01–1.31]). Extreme cold during the 2 days prior was associated with an increased risk of acute MI (1.36 [1.07–1.74]). Extreme heat during the 2 days prior was also associated with an increased risk of mortality (1.44 [1.06–1.96]). Persons living in areas with greater poverty were more susceptible to heat. Conclusions: Exposure to cold increased the risk of acute MI, and exposure to heat increased the risk of dying after an acute MI. Local area vulnerability should be accounted for as cities prepare to adapt to weather fluctuations as a result of climate change.

BACKGROUND:Prior studies have reported an association between traffic-related air pollution in urban areas and exacerbation of cardiovascular disease. We assess here whether time spent in different modes of transportation can trigger the onset of acute myocardial infarction (AMI).DESIGN:We performed a case-crossover study. We interviewed consecutive cases of AMI in the KORA Myocardial Infarction Registry in Augsburg, Southern Germany between February 1999 and December 2003 eliciting data on potential triggers in the four days preceding myocardial infarction onset.RESULTS:A total of 1459 cases with known date and time of AMI symptom onset, who had survived 24 hours after the onset, completed the registry's standard interview on potential triggers of AMI. An association between exposure to traffic and AMI onset 1 hour later was observed (odds ratio: 3.2; 95% confidence interval [CI]: 2.7-3.9, p < 0.001). Using a car was the most common source of traffic exposure; nevertheless, times spent in public transport or on a bicycle were similarly associated with AMI onset 1 hour later. While the highest risk for AMI onset was within 1 hour of exposure to traffic, the elevated risk persisted for up to 6 hours. Women, patients aged 65 years or older, patients not part of the workforce, and those with a history of angina or diabetes exhibited the largest associations between times spent in traffic and AMI onset 1 hour later.CONCLUSION:The data suggest that transient exposure to traffic regardless of the means of transportation may increase the risk of AMI transiently.

INTRODUCTION:In empirical studies it has repeatedly been shown that the socioeconomic status (SES) of a region could infl uence the health status of its inhabitants, even if measures of individual SES are controlled for. This research has just started in Germany, but most studies focus on large geographical areas such as rural districts. Taking the example of districts in the city of Augsburg, the analyses focus on the question if these associations can also be found in a small-scale regional comparison.METHODS:We included 1 888 participants of the KORA S4 Survey aged 25-74 years. The city districts were grouped according to the unemployment rate (low, medium, high). The dependent variables were self-rated health and 3 risk factors (obesity, high waist-hip ratio, hypertension). Additional individual variables included are age, sex, educational level and unemployment. The analyses were based on multilevel logistic regressions.RESULTS:After adjustment for individual level variables (age, sex, education, unemployment), the analyses show a signifi cantly increased risk of 'high waist-hip ratio' in the regions with the highest unemployment rate (OR 1.53; 95 % conf. interval 1.03-2.26). A similar association was found for obesity. No signifi cant association was observed between unemployment rate on the one hand and hypertension and self-rated health on the other.CONCLUSION:Some health risks seem to be especially high in city districts characterised by a high unemployment rate. It can be concluded that interventions aimed at reducing these risks should focus on districts with high unemployment rates. Further studies are needed for an understanding of the causes behind the social and regional inequalities shown here.

Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project.Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance.There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R-2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R-2 was less than 10% lower than the model R-2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R-2 compared to models in the same areas in which these variables were offered.Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies. (C) 2013 Elsevier Ltd. All rights reserved.

To the Editor: Growing evidence indicates that elevated levels of the liver enzymes γ-glutamyltransferase (GGT), aspartate transaminase (AST), and alanine transaminase (ALT) are independently associated with increased risk of cardiovascular disease (CVD).1–4 GGT may also increase due to environmental pollution.1 Ambient particulate matter has been shown to induce oxidative stress and being linked to CVD5 and might potentially affect liver enzymes' levels. Therefore, we assessed the association between chronic ambient air pollution and serum liver enzymes, as a possible component in the mechanisms linking air pollution to CVD. We analyzed data collected in two KORA (Cooperative Health Research in the Region of Augsburg) surveys, conducted in Augsburg and two adjacent counties in southern Germany between 2004 and 2008. Blood was drawn from 5,892 adults aged 31 to 85 years, and the serum liver enzymes GGT, AST, and ALT were analyzed. Air pollution exposure was estimated within the ESCAPE study (European Study of Cohorts for Air Pollution Effects, http://www.escapeproject.eu/) between 2008 and 2009 by a combination of measurements and modeling. We estimated the annual average concentrations of particles below 2.5 µm (PM2.5), below 10 µm (PM10), coarse particles (PMcoarse), absorbance of PM2.5, nitrogen oxides (NOx), and nitrogen dioxide (NO2) at the residential address of each participant. We assessed the associations by multivariable linear models with log-transformed outcome variables. All models were adjusted for socioeconomic, lifestyle, and clinical covariates. For a detailed description of the outcome and exposure variables as well the covariates see the eAppendix (https://links.lww.com/EDE/A717). Percent changes of liver enzymes means associated with an increase in air pollutants from 5% of the distribution to 95% are shown in the Table. For GGT, elevated levels of pollutants were associated with increased mean serum level, most strongly for PM2.5. An increase of the annual average concentration of PM2.5 at residences of 2.77 µg/m3 (5%–95% range) increased mean serum concentration of GGT among the study participants by 5.1% (95% confidence interval = 0.1% to 10.4%). The association was stronger for participants with CVD (12.0% [4.4% to 20.2%]), whereas those without CVD showed no association. For AST and ALT, we observed no consistent patterns.TABLE: Percent Change (95% CI) of Mean Live Enzymes per 5%–95% Range Increase in Air Pollutants in the Augsburg Area, Germany (2004–2009)One biological insight into the association of serum GGT and CVD induction is a possible role of GGT in oxidative stress.1 GGT is present in atherosclerotic plaques and may catalyze oxidation of low-density lipoproteins, contributing to plaque evolution and rupture.1 Also, GGT acts as a protein catalyst in the catabolism of glutathione, the major thiol antioxidant in the body.1 The role of GGT in oxidative stress and in the progression of atherosclerosis has been supported by the association with carotid intima-media thickness.2,4,6 Moreover, GGT is more strongly associated with cardiovascular outcomes than ALT,1,7 which is considered to be a marker of liver injury but not of oxidative stress.1 Thus, our findings concerning GGT may strengthen the hypothesis that particulate air pollution affects the cardiovascular system through mechanisms related to systemic oxidative stress. As correlations between PM2.5 and other pollutants were weak, this suggests that the pathway of PM2.5 might differ from the pathways of other pollutants. For example, PM2.5 can penetrate deeper into the pulmonary tree compared with PM10 or PMcoarse because of its smaller aerodynamic diameter. Additionally, our finding regarding strong effect modification by CVD might indicate that people with CVD are more susceptible to air pollution. However, as our reported association has not been assessed previously, it has to be replicated in other studies. Iana Markevych Ludwig-Maximilians-Universität(LMU) Munich, Institute for Medical Informatics, Biometrics and Epidemiology, Munich, Germany, [email protected] Kathrin Wolf Regina Hampel Susanne Breitner Alexandra Schneider Stephanie von Klot Josef Cyrys Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany Joachim Heinrich Angela Döring Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology I, Neuherberg, Germany Rob Beelen Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands Wolfgang Koenig University of Ulm Medical Center, Department of Internal Medicine II-Cardiology, Ulm, Germany Annette Peters Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany

Aims: The potential influence of lunar phases on the occurrence of myocardial infarction is still controversial. The purpose of the present study was to investigate the association of the lunar cycle on the occurrence of fatal and non-fatal myocardial infarction based on a myocardial infarction registry.Methods and results: We studied 15,985 patients consecutively hospitalised with an acute myocardial infarction (AMI) between 1 January 1985 and 31 December 2007 with a known date of symptom onset who were recruited from a population-based myocardial infarction registry. The exact hour of AMI onset was known for 9813 events. Poisson regression analysis was performed to examine the relation between the lunar cycle and the occurrence of AMI.There was no association between new moon, full moon, waning moon and waxing moon and the occurrence of AMI. However, we observed that the three days after a new moon may be significantly protective for the occurrence of AMI, rate ratio (RR) 0.94 (95% CI 0.91-0.98), and the day before a new moon had a slightly negative effect (RR 1.06, 95% CI 1.00-1.12). Stratified analysis did not reveal any susceptible subgroups.Conclusion: The moon phases did not show any apparent association with AMI occurrence. However, there might be a 'cardioprotective' time three days after a new moon.

Background More people die in the winter from cardiac disease, and there are competing hypotheses to explain this. The authors conducted a study in 48 US cities to determine how much of the seasonal pattern in cardiac deaths could be explained by influenza epidemics, whether that allowed a more parsimonious control for season than traditional spline models, and whether such control changed the short term association with temperature. Methods The authors obtained counts of daily cardiac deaths and of emergency hospital admissions of the elderly for influenza during 1992–2000. Quasi-Poisson regression models were conducted estimating the association between daily cardiac mortality, and temperature. Results Controlling for influenza admissions provided a more parsimonious model with better Generalized Cross-Validation, lower residual serial correlation, and better captured Winter peaks. The temperature-response function was not greatly affected by adjusting for influenza. The pooled estimated increase in risk for a temperature decrease from 0 to −5°C was 1.6% (95% confidence interval (CI) 1.1-2.1%). Influenza accounted for 2.3% of cardiac deaths over this period. Conclusions The results suggest that including epidemic data explained most of the irregular seasonal pattern (about 18% of the total seasonal variation), allowing more parsimonious models than when adjusting for seasonality only with smooth functions of time. The effect of cold temperature is not confounded by epidemics.

In empirischen Studien wurde wiederholt gezeigt, dass der sozioökonomische Status (SES) einer Region den Gesundheitszustand der Bewohner beeinflussen kann, auch nach statistischer Kontrolle individueller Faktoren des SES. In Deutschland sind dazu erst wenige Arbeiten erschienen, zumeist beziehen sie sich auf relativ große Raumeinheiten wie Landkreise. Am Beispiel eines Vergleichs zwischen Stadtgebieten in Augsburg soll in der vorliegenden Arbeit untersucht werden, ob diese regionalen Effekte auch auf innerstädtischer Ebene vorhanden sind. In die Analyse eingeschlossen wurden 1 888 Probanden des KORA S4 Surveys im Alter von 25 bis 74 Jahren. Die Stadtgebiete wurden entsprechend der Arbeitslosenquote eingeteilt (niedrig, mittel, hoch). Als abhängige Variablen dienten Angaben zum selbst-eingeschätzten Gesundheitszustand sowie zu 3 Risikofaktoren (Adipositas, hohe Waist-Hip-Ratio und Hypertonie). Als unabhängige Variablen wurden auf individueller Ebene Alter, Geschlecht, Bildung und Arbeitslosigkeit eingeschlossen. Die Auswertung erfolgte mit logistischen Multilevel-Analysen. Nach statistischer Kontrolle der individuellen Variablen (Alter, Geschlecht, Bildung, Arbeitslosigkeit) zeigte sich ein signifikanter Zusammenhang zwischen hoher regionaler Arbeitslosenquote und ‚hoher Waist-Hip-Ratio‘ (OR 1,53; 95% Konf. Intervall 1,03–2,26). Für Adipositas ergab sich ein ähnliches Bild. Für Hypertonie und den selbst-eingeschätzten Gesundheitszustand konnten dagegen keine signifikanten Zusammenhänge mit der regionalen Arbeitslosenquote gefunden werden. Offenbar gibt es gesundheitliche Risiken, die in Stadtgebieten mit hoher Arbeitslosigkeit besonders groß sind. Eine praktische Folgerung wäre, dass sich die Maßnahmen zur Verringerung dieser Risiken auch und vor allem auf die Stadtgebiete konzentrieren sollten, in denen die Arbeitslosenquote besonders hoch ist. Weitere Untersuchungen sollten sich den Ursachen der hier gezeigten sozialen und räumlichen Ungleichheit widmen.

The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM2.5, PM2.5 absorbance, PM10, and PMcoarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R-2) was 71% for PM2.5 (range across study areas 35-94%). Model R-2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32-81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R-2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R-2 results were on average 8-11% lower than model R-2. Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

BACKGROUND:Associations between air temperature and mortality have been consistently observed in Europe and the United States; however, there is a lack of studies for Asian countries. Our study investigated the association between air temperature and cardio-respiratory mortality in the urban area of Beijing, China.METHODS:Death counts for cardiovascular and respiratory diseases for adult residents (≥15 years), meteorological parameters and concentrations of particulate air pollution were obtained from January 2003 to August 2005. The effects of two-day and 15-day average temperatures were estimated by Poisson regression models, controlling for time trend, relative humidity and other confounders if necessary. Effects were explored for warm (April to September) and cold periods (October to March) separately. The lagged effects of daily temperature were investigated by polynomial distributed lag (PDL) models.RESULTS:We observed a J-shaped exposure-response function only for 15-day average temperature and respiratory mortality in the warm period, with 21.3°C as the threshold temperature. All other exposure-response functions could be considered as linear. In the warm period, a 5°C increase of two-day average temperature was associated with a RR of 1.098 (95% confidence interval (95%CI): 1.057-1.140) for cardiovascular and 1.134 (95%CI: 1.050-1.224) for respiratory mortality; a 5°C decrease of 15-day average temperature was associated with a RR of 1.040 (95%CI: 0.990-1.093) for cardiovascular mortality. In the cold period, a 5°C increase of two-day average temperature was associated with a RR of 1.149 (95%CI: 1.078-1.224) for respiratory mortality; a 5°C decrease of 15-day average temperature was associated with a RR of 1.057 (95%CI: 1.022-1.094) for cardiovascular mortality. The effects remained robust after considering particles as additional confounders.CONCLUSIONS:Both increases and decreases in air temperature are associated with an increased risk of cardiovascular mortality. The effects of heat were immediate while the ones of cold became predominant with longer time lags. Increases in air temperature are also associated with an immediate increased risk of respiratory mortality.

O-29A9-3 Background/Aims: Long distance travel by plane, car, or bus can be accompanied by long periods of sitting, lack of movement, and an oxygen undersupply which may precipitate an acute myocardial infarction (AMI). No epidemiological study so far has examined long distance travel as possible trigger of AMI. Methods: We assessed the association of long-distance travel, defined as journeys by car, train, ship, or bus of at least 2 hours, or flights of any length, with the onset of non-fatal AMI using the case-crossover design. Cases of age 25–74 years were recruited from the KORA (Collaborative Health Research in the Region of Augsburg) Coronary Event Registry Germany, from February 1999 through December 2003. In personal interviews, detailed information was collected on activities and location for the 4 days preceding the onset of AMI. For each case, we compared exposure during the hazard period preceding the onset of AMI with the same subjects' exposure during 2 control periods 24 and 48 hours earlier in conditional logistic regression models. Results: We included 1265 cases with confirmed AMI and complete activity data in the analyses. Of these, 125 mentioned at least 1 long distance travel in the diary. The patients reporting travelling were on average younger, more often male, employed, and rarely suffering from chronic diseases. In total, we identified 150 car travels, 12 flights, and 42 journeys with other means of transportation. The case-crossover analyses showed an association of travelling by plane with AMI within 24 hours (relative risk [RR[ 5.9, 95% confidence interval [CI[: 1.2–28.8). For long-distance car travelling, we estimated a RR of 1.3 (95% CI: 0.7–2.4) for the onset of AMI within 24 hours. Conclusion: This study suggests that long distance travel especially by plane may be associated with a transiently increased risk of nonfatal AMI in susceptible populations.

Background and Aims. It has been suggested that long-term exposure to air pollution may lead to chronic increase in arterial blood pressure possibly resulting in hypertension. In this study, we investigated the influence of long-term residential traffic exposure on the prevalence of hypertension. Methods. We used data on 9116 individuals aged 25 to 74 years who participated in the cross-sectional MONICA S3 (1994/95) and the KORA S4 (1999-2001) surveys conducted in the region of Augsburg, Germany. The residential traffic exposure was assessed by specifying daily traffic intensity of and distance to nearest street, and of daily traffic load (sum of product of traffic intensity and length of all segments divided by area) and total road length within buffers of 50, 100 and 300 meters around the residences. Hypertension was defined according to the AHA definition as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg. We used logistic regression adjusting for age, BMI, education, alcohol consumption, LDL-cholesterol and smoking status. Additionally, sex was examined as effect modifier. Results. Traffic load within a radius of 100 meters of a person's residence was associated with an elevated risk for the prevalence of hypertension with an odds ratio of 1.05 (95% CI, 1.01 to 1.10) per increase in 36.2 vehicles/(day*m). The odds ratio for participants living near major roads within 100 meters compared to other persons was 1.14 (95% CI, 1.01 to 1.28). Results for 50 meter buffers were similar. Traffic intensity of the nearest road and inverse distance to nearest road showed positive but not significant effects. Almost 60% of participants with hypertension were male. However, in stratified analyses significant associations were found in women only. Conclusions. Long-term residential exposure to high traffic may induce hypertension. An influence only in women has to be verified by other studies.

Although ambient particulate matter contributes to atherosclerosis in animal models, its role in atherogenesis in humans needs to be established. This article discusses concepts, study design, and choice of health outcomes to efficiently investigate the atherogenic role of ambient air pollution, with an emphasis on early preclinical biomarkers of atherosclerosis that are unaffected by short-term exposure to air pollution (eg, carotid intima-media thickness [CIMT] and functional performance of the vessel). Air pollution studies using these end points are summarized. The CIMT is currently the most frequently used outcome in this field (6 studies). The continuous nature of CIMT, the lack of short-term variation, its relationship to atherosclerotic changes in the artery wall, its predictive value for coronary heart disease, and the noninvasiveness of the assessment make it a useful candidate for cross-sectional and longitudinal studies investigating the role of air pollution in atherogenesis.

Although ambient particulate matter contributes to atherosclerosis in animal models, its role in atherogenesis in humans needs to be established. This article discusses concepts, study design, and choice of health outcomes to efficiently investigate the atherogenic role of ambient air pollution, with an emphasis on early preclinical biomarkers of atherosclerosis that are unaffected by short-term exposure to air pollution (eg, carotid intima-media thickness [CIMT] and functional performance of the vessel). Air pollution studies using these end points are summarized. The CIMT is currently the most frequently used outcome in this field (6 studies). The continuous nature of CIMT, the lack of short-term variation, its relationship to atherosclerotic changes in the artery wall, its predictive value for coronary heart disease, and the noninvasiveness of the assessment make it a useful candidate for cross-sectional and longitudinal studies investigating the role of air pollution in atherogenesis.

O-30A3-4 Background/Aims: Associations between air temperature and mortality have been consistently observed in studies conducted in Europe and the United States; however, there is still a lack of studies for Asian countries. Our study aimed at investigating the association between daily air temperature and daily cardiovascular as well as respiratory mortality in the urban area of Beijing, China. Methods: Daily death counts for cardiovascular and respiratory diseases for adult residents (≥15 years) and meteorological parameters were obtained from local authorities from January 2003 to August 2005. Confounder-adjusted semiparametric Poisson regression models were used to estimate the effects of 2-day and 15-day temperature averages. Time trend and relative humidity were forced in every model. Exposure-response curves for temperature were estimated using penalized regression splines. Moreover, we analyzed the associations between temperature and mortality for the potentially more susceptible subgroup of elderly people (≥65 years). Effects are presented as relative risk (RR) for mortality per 5°C change in the whole temperature range if the shape of the exposure-response curve was linear, or, if non-linear, for the slopes above a temperature threshold. Results: We observed J-shaped exposure-response relationships between 2-day average temperature and cardiovascular mortality with a temperature threshold of 23°C. For respiratory mortality, the relationships were considered linear. Overall, a 5°C increase of the 2-day average temperature was associated with a RR of 1.017 (95% confidence-interval [CI]: 1.008–1.027) and 1.143 (95% CI: 1.089–1.199) for cardiovascular and respiratory mortality, respectively. For elderly people, the associations were weaker for respiratory mortality, but stronger for cardiovascular mortality. Regarding cold effects, a 5°C decrease of the 15-day average temperature was associated with a RR of 1.036 (95% CI: 1.001–1.071) for cardiovascular mortality. Elderly people showed again a similar effect. Conclusion: Heat as well as cold effects were found for the association of air temperature with cardiovascular and respiratory mortality. Thereby, heat effects were immediate, while with longer time lags cold effects became predominant.

BACKGROUND AND OBJECTIVESself-reported road traffic noise annoyance is commonly used in epidemiological studies for assessment of potential health effects. Alternatively, some studies have used geographic information system (GIS) modelled exposure to road traffic noise as an objective parameter. The aim of this study was to analyse the association between noise exposure due to neighbouring road traffic and the noise annoyance of adults, taking other determinants into consideration.METHODSparents of 951 Munich children from the two German birth cohorts GINIplus and LISAplus reported their annoyance due to road traffic noise at home. GIS modelled road traffic noise exposure (L(den), maximum within a 50 m buffer) from the noise map of the city of Munich was available for all families. GIS-based calculated distance to the closest major road (≥10,000 vehicles per day) and questionnaire based-information about family income, parental education and the type of the street of residence were explored for their potential influence. An ordered logit regression model was applied. The noise levels (L(den)) and the reported noise annoyance were compared with an established exposure-response function.RESULTSthe correlation between noise annoyance and noise exposure (L(den)) was fair (Spearman correlation r(s) = 0.37). The distance to a major road and the type of street were strong predictors for the noise annoyance. The annoyance modelled by the established exposure-response function and that estimated by the ordered logit model were moderately associated (Pearson's correlation r(p) = 0.50).CONCLUSIONSroad traffic noise annoyance was associated with GIS modelled neighbouring road traffic noise exposure (L(den)). The distance to a major road and the type of street were additional explanatory factors of the noise annoyance appraisal.

The current study investigates the association of estimated personal exposure to traffic-related air pollution and acute myocardial infarction (AMI). Cases of AMI were interviewed in the Augsburg KORA Myocardial Infarction Registry from February 1999 through December 2003, and 960 AMI survivors were included in the analyses. The time-varying component of daily personal soot exposure (the temporally variable contribution due to the daily area level of exposure and daily personal activities) was estimated using a linear combination of estimated mean ambient soot concentration, time spent outdoors, and time spent in traffic. The association of soot exposure with AMI onset was estimated in a case-crossover analysis controlling for temperature and day of the week using conditional logistic regression analyses. Estimated personal soot exposure was associated with AMI (relative risk, 1.30 per 1.1 m−1 × 10−5 [95% confidence interval, 1.09-1.55]). Estimated ambient soot and measured ambient PM2.5 particulate matter 2.5 µm and smaller in aerodynamic diameter were not significantly associated with AMI onset. Our results suggest that an increase in risk of AMI in association with personal soot exposure may be in great part due to the contribution of personal soot from individual times spent in traffic and individual times spent outdoors. As a consequence, estimates calculated based on measurements at urban background stations may be underestimations. Health effects of traffic-related air pollution may need to be updated, taking into account individual time spent in traffic and outdoors, to adequately protect the public.