1. Introduction
As a result of climate change, extreme weather events, particularly heat waves, are expected to increase in intensity and frequency in the future (Revi et al. 2014). Because of the urban heat island effect, heat stress is a major challenge for urban society (Gobo et al. 2022; Wang et al. 2019; Muthers et al. 2017). A high population density and an ongoing urbanization process is further intensifying the pressure climate change places on cities as it exposes more people to severe heat stress and extreme weather events (Revi et al. 2014).
The 2019 Lancet Report underlines the fact that the pathophysiological effects of heat on humans and its effects are well understood and studied. In particular, the population over the age of 65, children, and adults engaged in intensive physical activities are especially vulnerable to heat stress (Watts et al. 2019; Székely et al. 2015). The set of symptoms can range from less severe heat syncope to more serious forms such as fatal heat strokes (Chen et al. 2019; Székely et al. 2015). Children especially suffer a greater susceptibility of renal and respiratory diseases as well as electrolyte depletion and fever (Xu et al. 2014). Furthermore, pronounced heat events have a significant negative effect on labor capacity as heat events are a significant threat to working people (Kjellstrom et al. 2018; Watts et al. 2019). Exposure to increased concentrations of the air pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter smaller than 10 μm in diameter (PM10) lead to adverse health effects across all age groups, predominantly on elderly and children [World Health Organization (WHO) European Centre for Environment and Health 2013; Cohen et al. 2017; Tripathi et al. 2021; Yang et al. 2021]. Cardiovascular (Cohen et al. 2017) and respiratory diseases like asthma, chronic obstructive pulmonary disease, and viral infections particularly seem to exacerbate the susceptibility with exposure to air pollutants (Ciencewicki and Jaspers 2007; Faustini et al. 2013).
An intensified heat stress during summer is already prominent in European cities and is linked with increased mortality (Muthers et al. 2017; Hoffmann et al. 2008; Chen et al. 2019). Furthermore, the concentration of air pollutants is especially high in cities as those pollutants are strongly associated with traffic and industries (WHO European Centre for Environment and Health 2013). Understanding heat- and air-pollution-related health impacts is important for urban development and adequate public health care interventions, as reviews show the strong relationship between built environment, accessible green spaces, and people’s health (Kent and Thompson 2014; Coppel and Wüstemann 2017). With time series data, the adverse effects of heat and air pollution on morbidity have been already examined for different cities and countries (Ashworth et al. 2021; Sherbakov et al. 2018; Wasem et al. 2018; Tong et al. 2012; Sun et al. 2014). Additionally, an increasing set of studies used emergency ambulance dispatch (EAD) data to analyze adverse health effects from severe heat (Cerutti et al. 2006; Cheng et al. 2016; Sun et al. 2014; Thornes et al. 2014; Turner et al. 2012; Guo 2017; Wong and Lai 2014) and a few focused additionally on spatial variability showing correlations with population and urban density (Bassil et al. 2009; Dolney and Sheridan 2006).
However, most studies with focus on EAD used data from metropolitan regions with population size above 1 million. In the European Union, however, the urban landscape is predominantly shaped by metropolitan regions with less than 1 million inhabitants and around 45% of the population live in regions with less than 500 000 inhabitants (Kotzeva et al. 2016). Management of health-related effects is required across all urban regions. To guarantee adequate management comprehensive and accessible sources of information for urban planning and decision-making are needed. The recently published WHO report on heat and health underlines the pivotal role of urban planning for the reduction of heat risks in cities (WHO Regional Office for Europe 2021). This paper examines the effects of temperature and air pollution on human health and identifies areas with increased occurrence of emergency ambulance dispatches in a medium-sized city. To this end, we used emergency ambulance dispatch data from Würzburg, Germany, and Open Street Map data to address the following research questions:
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What are the main effects of temperature and air pollution on the incidence of emergency ambulance dispatches?
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What are the effects of extreme temperature, the presence of heat waves and how long do the effects last?
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How are emergency ambulance dispatches spatially distributed within the city?
In the discussion section we will contextualize the results within the urban planning context and sketch future applicability for medium- and small-sized cities.
2. Methods
a. Study area
This study was conducted in the city of Würzburg, in the south of Germany and the Free State of Bavaria (49°47′N, 9°56′E; see Fig. 1). Würzburg has a population of 127 934 inhabitants (as of 2019) and covers ∼87.63 km2. The city is located in the moderate climate zone and placed in a basin, which results in frequent temperature inversions, especially during winter.
(left) Map of the study area Würzburg and the three gauge stations for the environmental parameters. (right) Location of Würzburg in Europe.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
b. Data
EAD were obtained from the Würzburg emergency call center (ILS) for the time period from January 2011 to December 2019. Each record represents an emergency dispatch with related keywords, day, time, district, and street. For this study, the data were aggregated to daily counts of all dispatches. The keyword of each dispatch represents a complaint of the patient or the caller. These keywords were classified into four classes with the help of the International Classification of Diseases (ICD-10-GM, version 2020) and the Bavarian EAD protocols (Table 1).
Statistics for emergency ambulance dispatches for traumatic and nontraumatic cases and for cardiovascular (CVD) and respiratory (RD) diseases, both of which are subsets of nontraumatic dispatches; N = days with valid data.
For this study only the EAD for nontraumatic causes were selected, because it is assumed that traumatic causes may not be related to ambient temperature and air pollution exposure. Although almost all records and related keywords followed the guideline for emergency dispatches in Bavaria, the data are affected by uncertainty, because the complaint of the patient and consequently the keyword might not be aligned to the actual medical outcome. Literature suggests that, among all nontraumatic EAD, cardiovascular diseases (CVD) and respiratory diseases (RD) have the strongest association with ambient temperature and air pollution exposure, and therefore we extended our analysis to these subsets of nontraumatic EAD.
Meteorological data (Fig. 2) were retrieved from the German Weather Service (Deutscher Wetterdienst) (DWD), including daily mean, maximum, and minimum temperature measured at the station Würzburg (identifier 5705; 49°46′N, 9°57′E; see Fig. 1). The daily mean, maximum, and minimum concentration of ozone (O3), nitrogen dioxide (NO2), and PM10 were obtained from the Bavarian Agency for Environment (LfU). NO2 was measured at the station Würzburg Stadtring Süd (49°79′N, 9°94′E; see Fig. 1), and PM10 and O3 were measured at the station Würzburg Kopfklinik (49°48′N, 9°57′E; see Fig. 1). All stations are situated within the administrative boundaries of Würzburg (Fig. 1). The EAD dataset has valid data for all days. For the meteorological data, some days are missing because of presumed malfunctions of the stations.
Distribution of meteorological data. Observations are daily maximum, mean, and minimum values from 2011 to 2019. For temperature, N = 3285; for O3, N = 3284; for PM10, N = 3250; for NO2, N = 3266.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
c. Statistical analysis
The effects of extreme heat are not always limited to the day of exposure but can be delayed in time. This so-called lag of exposure is widely known for mortality after excessive heat events (Ashworth et al. 2021; Gasparrini 2016). As the prior model does not consider lagged effects and we aim to cover all possible effects, including delayed effects we applied our GAM model under the framework for penalized distributed lag nonlinear models (DLNM) as described by Gasparrini et al. (2017) following the external method.
We chose to analyze the lag response for daily maximum temperature and kept remaining environmental parameters at their daily mean values for lag periods up to 10 days to capture the potential lag period. Previous studies have shown that heat mortality and morbidity associations lasted for one to three days (Guo 2017; Guo et al. 2014; Sun et al. 2014; Gao et al. 2015; Turner et al. 2012). For the penalty on the lag structure, we chose P splines with 5 degrees of freedom. Reference temperatures were chosen based on the results and identified threshold temperatures from prior GAM analyses and led to the selection of 10°, 25°, and 18°C daily maximum temperature for nontraumatic, CVD, and RD, respectively.
To examine the effects of heat waves, we used generalized linear model (GLM) following a quasi-Poisson and in case for RD dispatches a Poisson distribution to identify the relative risk (RR; Sistrom and Garvan 2004) for EAD during periods of heat waves. In this case, the relative risk describes the ratio of the probability of an EAD on a heat-wave day to the probability of an EAD on a non-heat-wave day. As results from our first analysis suggest a linear relationship between temperature and EAD after exceeding a daily mean temperature we analyzed only data during the warm season from May to September, to exclude possible overestimations from comparable lower daily EAD during seasons with lower mean temperatures. As we want to identify the extra burden on EAD associated with heat wave, we concluded the chosen time period to be suitable. Following Cheng et al. (2016), we have opted for a local definition of heat wave because no uniform definition of the term heat wave exists internationally. Definitions are often based on a combination of percentile-based thresholds and a minimum duration. In this case we chose a combination of the 95th, 97th, and 99th percentile and minimum durations of 2 and 3 days.
To visualize the geospatial distribution of the EAD, the data were analyzed with the help of GIS. Since information about the street for each dispatch was available, we combined it with street layer data from Open Street Map. Because no statements could be made about house numbers or exact geographical information for reasons of privacy protection, the number of dispatches were standardized on 1 km for each street with a minimum of one dispatch. Afterward we aggregated the data by using the line density function in QGIS with the standardized EAD data as weighting factor for each street per month and square kilometer and compared the amounts of EAD for each group. For CVD, a more in-depth comparison is provided by displaying the difference between 1) weekend and weekday, 2) meteorological summer and spring/autumn, and 3) summer and winter.
3. Results
A total of 103 605 nontraumatic and 34 017 traumatic EAD were registered during the study period (2011–19). As part of nontraumatic dispatches displayed in Table 1, CVD and RD account for 24 360 and 7884 EAD, respectively. Figure 2 displays the meteorological variables in Würzburg from 2011 to 2019. During this period, the maximum, mean, and minimum daily mean temperatures were 30.3°, 10.7°, and −11.9°C.
a. EAD as a function of environmental parameters
For nontraumatic EAD all parameters of daily temperature show a significant effect on the number of dispatches (Fig. 3). There is no effect of daily mean and maximum temperature up to a threshold temperature of around 5°C. Exceeding this threshold led to a significant linear increase with temperature to an expected amount of nontraumatic calls of around 37 EAD at a day with a mean temperature of 30°C as compared with 31 EAD at 5°C. In contrast, for daily minimum temperature the relationship is linear along the whole temperature range (Fig. 3, top row).
Effects of (top) temperature, (top middle) ozone, (bottom middle) particulate matter, and (bottom) nitrogen dioxide on emergency ambulance dispatches for (left) nontraumatic, (center) CVD, and (right) RD using a GAM. Red curves represent model outcome with daily maximum values, yellow curves represent outcome for daily mean values, and blue curves represent outcomes for daily minima.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
For CVD, the effect is strongest for daily mean and maximum temperature with a threshold around 18° and 26°C, respectively. CVD dispatches on a day with 30°C mean temperature are 37% higher relative to days with a mean of 18°C. Similar to nontraumatic dispatches, minimum daily temperature relationship follows a linear curve and is less sensitive. The effect of temperature on RD dispatches is significant, although the effect is of a lower magnitude and follows a linear curve for all three parameters of temperature.
The daily levels of ozone have no effect on EAD across all groups (Fig. 3, top middle row). PM10 is similarly not a significant and strong contributor for EAD (Fig. 3, bottom middle row). Nitrogen dioxide is not a significant contributor for CVD and RD dispatches and can be neglected in comparison with temperature. For nontraumatic dispatches nitrogen dioxide is significant for daily mean values and leads to increased dispatches with higher concentrations (Fig. 3, bottom row).
b. Exposure–response and lag effects for EAD
The risk of nontraumatic EAD increased with temperature by up to 40% relative to the reference temperature of 10°C (Fig. 4). The risk for CVD dispatches was almost 2 times as high on a day with a maximum temperature of 40°C relative to its reference temperature (25°C), although with high uncertainty. The risk for RD dispatches at maximum temperature (40°C) is 1.5 times as high relative to the reference temperature at 18°C but lacks certainty, resulting in wide confidence intervals (Fig. 4).
The (a) overall and (b) cumulative exposure response of EAD for (left) nontraumatic, (center) CVD, and (right) RD causes associated with the (c) 95th (29.7°C) and (d) 99th (33.8°C) percentile of daily maximum temperature in comparison with the reference temperature (10°, 25°, and 18°C for nontraumatic, CVD, and RD, respectively) along lags of up to 10 days.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
The risk of EAD for all types of dispatches was the largest at the day of exposure and slightly increased with higher temperature threshold for CVD dispatches (Figs. 4c,d). Relative risks rising from 1.09 for the 95th percentile of daily maximum temperature to 1.1 for the 99th percentile. For CVD, relative increase in risk is higher than for all nontraumatic EAD for the 95th percentile (+6%) but rises up to 15% for the 99th percentile (with maximum temperature of 33.8°C). For RD, relative risk does not respond as strongly, with +4% for the 95th percentile and +6% for the 99th percentile. These results also indicate that exposure to extreme heat affects EAD for up to two days afterward.
c. Effect estimates of heat waves
The results in Fig. 5 indicate a strong association between a heat wave and EAD outcomes for CVD. Depending on the intensity and duration of a heat wave, the risk varies for all three types of EAD. For instance, on heat-wave days with an intensity above the 97th percentile and a minimum duration of 2 days, the RR of a CVD dispatch is 17.4% [95% confidence interval (CI): 5.9%, 30.0%] higher relative to a non-heat-wave day in this time period (May–September). For CVD, heat-wave definitions with an intensity above the 95th and 97th percentile and a duration of longer than 2 days show the strongest association between EAD outcome and the presence of a heat wave. Although the association seems higher for heat waves with an intensity above the 99th percentile and a duration of more than 3 days (21.2%), note that the uncertainty is higher (95% CI: 1.2%, 44.0%). For nontraumatic and RD dispatches, no heat-wave definition indicates a significant link.
Effect estimates of heat waves on emergency ambulance dispatches under different definitions in Würzburg for May–September (2011–19). Heat-wave definitions are based on percentiles of daily mean temperature and minimum duration.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
d. Spatial pattern
The greatest dispatch density is located within the urban center, where mean EAD density peaks at nearly 0.078 EAD (month × km2)−1 for nontraumatic dispatches (Fig. 6). The main hotspot for nontraumatic dispatches is near the train station and the university hospital west of the urban center. CVD dispatches peak with 0.020 EAD (month × km2)−1 close to the train station and the central marketplace within the urban center. RD dispatches peak at 0.005 EAD (month × km2)−1 close to the train station and south of the historic center.
Mean monthly distribution of emergency ambulance dispatches for nontraumatic, CVD, and RD, with population density provided by the Urban Atlas 2012 (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
The biggest changes between weekday and weekend are revealed within the historic center close to the train station with 0.008 EAD (month × km2)−1 more on weekdays than on weekends and holidays (Fig. 7). For the seasonal changes, however, the biggest difference between summer and spring/autumn occurs within the historic center close to the central marketplace with on average 0.004 EAD (month × km2)−1 more during summer.
Mean monthly differences of emergency ambulance dispatches for CVD for different time periods and seasons, with population density provided by the Urban Atlas 2012 (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-22-0046.1
4. Discussion
In this study, we analyzed the relations between temperature, air pollution, and three groups of diseases derived from EAD and their spatial distribution within the city of Würzburg, Germany. The relationship between temperature and EAD is significant for all chosen parameters of temperature, that is, daily minimum, mean, and maximum values. The strong increase in EAD with higher mean and maximum temperatures after exceeding specific thresholds is comparable to studies on mortality and morbidity.
For Germany a significantly increased utilization of health care services during heat periods was determined (Wasem et al. 2018) and for Ticino, Switzerland, the number of ambulance service interventions during the summer of 2003 was larger than in previous years (Cerutti et al. 2006). Results from Toronto, Ontario, Canada, showed that for every 1°C increase in maximum temperature a 30% increase in heat-related EAD was observed during the summer period of 2005 (Bassil et al. 2011).
However, for daily minimum temperature the effect is linear without threshold. A possible explanation is the nature of EAD data, which, in addition to the environmental parameters, are also heavily dependent on people’s activity: research has shown the strong diurnal cycle and the dependence of EAD on human activity (Guo 2017). In particular, daily minimum temperature covers days with high nocturnal heat load, this includes days with low maximum temperature, in which fewer EAD might occur as a result of less human activity relative to days with high maximum temperature.
Despite the negative effects described in the literature, air pollution parameters were not found to be significant drivers of EAD. On the one hand, the single measurement sites for particulate matter PM10, O3, and NO2 are not suitable for statements about real concentration on the emergency site at the time of the alarm and on the other hand it can be assumed that the fluctuations of concentration of air pollutants within the city fluctuates more than that of temperature. Traffic and wind circulation are well-known influencing factors for air pollution (Kim and Guldmann 2011; Sun et al. 2021; Reiminger et al. 2020).
Although respiratory diseases are known to be associated with high temperatures (Wasem et al. 2018), EAD in our case are not as sensitive as CVD, which is potentially linked to a general low case count and potentially false diagnosis during the dispatch procedure.
Immediate effects of temperature were found with the greatest effect on the day of exposure and significant until two days after. Similar exposure–response relationships were found in several studies (Sun et al. 2014; Guo 2017; Gao et al. 2015), although the lag effect can persist up to seven days after exposure as was observed in the city of Huainan, China (Cheng et al. 2016).
Additionally, we also estimated heat-wave effects with different definitions considering the duration and intensity, using a subset of the data of the warm season (May–September) to investigate how the risk of EAD changes. Our results suggest a significantly higher relative risk of CVD dispatches on days defined as heat waves with the 97th percentile of daily maximum temperature and a duration of more than two days. The results are associated with increasing confidence intervals when heat-wave definitions are narrowed to higher temperatures, which implies decreasing sample size. Nevertheless, our results, especially for cardiovascular diseases, align with results from Huainan, China (Cheng et al. 2016); Brisbane, Australia (Turner et al. 2012); and a nationwide study in Japan (Onozuka and Hagihara 2016).
The spatial distribution of EAD concentrates generally in areas with a high population density. The historic city center and central station form two important hotspots for CVD dispatches. This analysis does not allow us to draw conclusions about the effect of temperature on spatial distribution of EAD in general but reveals spatial patterns for CVD. The historic center with the market square represents an area with increased dispatches in summer when compared with spring, autumn, and winter. This might be due to an increased number of tourists, residents and commuters and associated activities but might be linked to the combination of increased overheating because of high urban building densities as well. Results from Toronto show that parts of the city with a high burden of heat-related EAD include areas with high rates of populated streets, often synonymous with the urban core and areas that include summer outdoor recreational activities (Bassil et al. 2009; Dolney and Sheridan 2006). Similarly, mortality risk factors can be seen in residential areas with high building density and surface temperatures (Dolney and Sheridan 2006), although these results do not allow us to draw a final conclusion about the effects of building density on EAD in the city center, as many confounding factors, especially activities, are unknown. The latter could explain differences between weekdays and weekends. Still, it provides important information on where public health interventions during periods with severe heat could be reasonable, for example, by establishing drinking fountains or shading or by informing vulnerable groups.
The importance of health and urban planning continues to grow with ongoing climate change, illustrated as more research is focusing on the assessment of the nexus of heat, health, and urban green and blue infrastructure (Barton and Grant 2013; Coppel and Wüstemann 2017; Jagarnath et al. 2020; Dong et al. 2020). Decision-makers and urban planners show additionally increased interest after the experiences of the last hot summers in Germany, for example, 2015 and 2018, to deal with those topics of human health and severe heat (Blättner et al. 2020). This study was able to show that with EAD data, in this form available for every city or municipality, the relationship between morbidity and temperature and their spatial distribution can be analyzed. Transferring these results and methods into urban planning can help to derive and justify specific measures mitigating adverse effects of severe heat in all urban regions, even in smaller cities. If collected and analyzed on a regular basis EAD might as well serve as useful tool to monitor the efficacy of adaptation strategies and measures. Thus, EAD represents a new additional tool for all cities to assess and monitor their own vulnerability and to support measures to improve their resilience toward heat. Future research could resume on this study by estimations of potential costs associated with severe heat.
This study design has some limitations. First, EAD protocols are designed to determine the resources that should be dispatched in a rapid and efficient way. Therefore, an accurate medical diagnosis is not guaranteed and can lead to cases that have been originally coded as a CVD dispatch but turned out to be of a different kind and vice versa. A study from the United Kingdom found that only 4.2% of identified pediatric cardiac arrest turned out to be the correct diagnosis when the rescue services arrived. In 28.7% of the cases the query protocol missed a pediatric cardiac arrest (Deakin et al. 2017). A study in Amsterdam, the Netherlands, blamed missing questions on breathing as a contributory cause of misdiagnosis in myocardial infarction (Berdowski et al. 2009), and a study from Australia identifies linguistic variations in the dispatcher’s queries as a factor that can have an influence on the precision and efficiency with which the information from the call is processed (Riou et al. 2017). Therefore, the uncertainties associated with this type of data should be taken into consideration when interpreting the results. Second, our study is based on environmental data from a single measurement site and, therefore, does not account for spatial variability and might underestimate the effect on EAD, as recent research suggests (Thomas et al. 2021).
5. Conclusions
This analysis illustrates the effects of temperature and air pollution on emergency ambulance dispatches in Würzburg. Significant effects of temperature and heat waves on EAD, especially for cardiovascular diseases, were detected. Air pollution parameters were not revealed as significant drivers for EAD. During the summer period, the presence of a heat wave increases the relative risk for CVD dispatches significantly and exposure to extreme heat can affect EAD up to two days. Additionally, the analyses could identify hotspots of heat-related EAD in areas with increased population and building density, mainly in the city center. Therefore, health care measures and urban planning should preferably focus in these areas to mitigate adverse heat effects.
This study provides baseline information about the adverse short-term effects of heat on health and depicts the ability of EAD data to support decision-making in future urban planning and public health care management for many cities. Limitations arise from the quality of diagnosis in the data. Therefore, the introduction of a new category of EAD for heat-related calls could improve forecasting methods and hotspot analysis. Future research should further test the transferability and replicability of the results in other cities.
Acknowledgments.
This research was conducted in the context of one project: ExTrass–Urbane Resilienz gegenüber extremen Wetterereignissen–Typologien und Transfer von Anpassungsstrategien in kleinen Großstädten und Mittelstädten; this project is funded by the Federal Ministry of Education and Research, Germany (Grant 01LR1709A1). The authors declare that they have no competing interests. Author Schneider analyzed the data and wrote the paper. Schnieder and authors Thieken and Walz interpreted the data. Thieken and Walz were contributors in writing the paper. All authors read and approved the final version of the paper.
Data availability statement.
The dataset and R script can be found in a repository online (https://doi.org/10.5281/zenodo.6426825).
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