Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-3015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-3015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2022
Research article |
| 16 Sep 2022
| 16 Sep 2022
Machine learning models to predict myocardial infarctions from past climatic and environmental conditions
Lennart Marien
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Mahyar Valizadeh
Strategy and Digitalization, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
Wolfgang zu Castell
Department Geoinformation, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Christine Nam
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Diana Rechid
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Alexandra Schneider
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
Christine Meisinger
Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85754 Neuherberg, Germany
Jakob Linseisen
Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85754 Neuherberg, Germany
Kathrin Wolf
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
Laurens M. Bouwer
CORRESPONDING AUTHOR
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany
Related authors
No articles found.
Johannes Meuer, Laurens M. Bouwer, Frank Kaspar, Roman Lehmann, Wolfgang Karl, Thomas Ludwig, and Christopher Kadow
Hydrol. Earth Syst. Sci., 29, 3687–3701, https://doi.org/10.5194/hess-29-3687-2025, https://doi.org/10.5194/hess-29-3687-2025, 2025
Short summary
Short summary
Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data are scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.
Oakley Wagner, Verena Maleska, and Laurens M. Bouwer
EGUsphere, https://doi.org/10.5194/egusphere-2025-2943, https://doi.org/10.5194/egusphere-2025-2943, 2025
Short summary
Short summary
Convection-permitting regional climate models, such as ICON-CLM at 3 km resolution, have great potential for improved hydroclimatic simulations. The studied model run shows lower bias in summer air temperature and global radiation, as well as in the frequency of wind speed over the Weiße Elster catchment in East Central Germany. Due to a pronounced overestimation of the intensity and frequency of heavy rainfall, however the discharge estimates are skewed, with no apparent added value.
Joni-Pekka Pietikäinen, Kevin Sieck, Lars Buntemeyer, Thomas Frisius, Christine Nam, Peter Hoffmann, Christina Pop, Diana Rechid, and Daniela Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-1586, https://doi.org/10.5194/egusphere-2025-1586, 2025
Short summary
Short summary
This paper introduces REMO2020, a modernized version of the well-known and widely used REMO regional climate model. We demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities. It outperforms our previous model version in many analyzed areas and is the biggest update to REMO so far. It also supports climate service needs based developments through new more modular structure.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Mortimer M. Müller, Joni-Pekka Pietikäinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Sarah Veit, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
Short summary
Short summary
Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
Short summary
Short summary
We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
Short summary
Short summary
The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
Short summary
Short summary
We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Vanessa Reinhart, Peter Hoffmann, Diana Rechid, Jürgen Böhner, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, https://doi.org/10.5194/essd-14-1735-2022, 2022
Short summary
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
Short summary
Short summary
Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Kevin Sieck, Christine Nam, Laurens M. Bouwer, Diana Rechid, and Daniela Jacob
Earth Syst. Dynam., 12, 457–468, https://doi.org/10.5194/esd-12-457-2021, https://doi.org/10.5194/esd-12-457-2021, 2021
Short summary
Short summary
This paper presents new estimates of future extreme weather in Europe, including extreme heat, extreme rainfall and meteorological drought. These new estimates were achieved by repeating model calculations many times, thereby reducing uncertainties of these rare events at low levels of global warming at 1.5 and 2 °C above
pre-industrial temperature levels. These results are important, as they help to assess which weather extremes could increase at moderate warming levels and where.
Marcus Breil, Edouard L. Davin, and Diana Rechid
Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, https://doi.org/10.5194/bg-18-1499-2021, 2021
Short summary
Short summary
The physical processes behind varying evapotranspiration rates in forests and grasslands in Europe are investigated in a regional model study with idealized afforestation scenarios. The results show that the evapotranspiration response to afforestation depends on the interplay of two counteracting factors: the transpiration facilitating characteristics of a forest and the reduced saturation deficits of forests caused by an increased surface roughness and associated lower surface temperatures.
Cited articles
Achebak, H., Devolder, D., and Ballester, J.: Trends in temperature-related
age-specific and sex-specific mortality from cardiovascular diseases in
Spain: a national time-series analysis, Lancet Planet. Health, 3, e297–e306, https://doi.org/10.1016/S2542-5196(19)30090-7, 2019. a
Armstrong, B.: Models for the Relationship Between Ambient Temperature and Daily Mortality, Epidemiology, 17, 624–631, https://doi.org/10.1097/01.ede.0000239732.50999.8f, 2006. a
Ban, N., Caillaud, C., Coppola, E., et al.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021. a
Bhaskaran, K., Hajat, S., Haines, A., Herrett, E., Wilkinson, P., and Smeeth,
L.: Short term effects of temperature on risk of myocardial infarction in
England and Wales: time series regression analysis of the Myocardial Ischaemia National Audit Project (MINAP) registry, Brit. Med. J., 341, c3823,
https://doi.org/10.1136/bmj.c3823, 2010. a
Bhaskaran, K., Armstrong, B., Hajat, S., Haines, A., Wilkinson, P., and Smeeth, L.: Heat and risk of myocardial infarction: hourly level case-crossover analysis of MINAP database, Brit. Med. J., 345, e8050,
https://doi.org/10.1136/bmj.e8050, 2012. a
Bourdrel, T., Bind, M.-A., Béjot, Y., Morel, O., and Argacha, J.-F.:
Cardiovascular effects of air pollution, Arch. Cardiovascul. Diseas., 110, 634–642, https://doi.org/10.1016/j.acvd.2017.05.003, 2017. a
Breitner, S., Wolf, K., Devlin, R. B., Diaz-Sanchez, D., Peters, A., and
Schneider, A.: Short-term effects of air temperature on mortality and effect
modification by air pollution in three cities of Bavaria, Germany: A
time-series analysis, Sci. Total Environ., 485-486, 49–61,
https://doi.org/10.1016/j.scitotenv.2014.03.048, 2014. a
Cesaroni, G., Forastiere, F., Stafoggia, M., Andersen, Z. J., Badaloni, C.,
Beelen, R., Caracciolo, B., de Faire, U., Erbel, R., Eriksen, K. T.,
Fratiglioni, L., Galassi, C., Hampel, R., Heier, M., Hennig, F., Hilding, A.,
Hoffmann, B., Houthuijs, D., Jöckel, K.-H., Korek, M., Lanki, T., Leander, K., Magnusson, P. K. E., Migliore, E., Ostenson, C.-G., Overvad, K.,
Pedersen, N. L., Pekkanen J., J., Penell, J., Pershagen, G., Pyko, A., Raaschou-Nielsen, O., Ranzi, A., Ricceri, F., Sacerdote, C., Salomaa, V., Swart, W., Turunen, A. W., Vineis, P., Weinmayr, G., Wolf, K., de Hoogh, K., Hoek, G., Brunekreef, B., and Peters, A.: Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project, Brit. Med. J., 348, f7412, https://doi.org/10.1136/bmj.f7412, 2014. a
Chen, K., Wolf, K., Breitner, S., Gasparrini, A., Stafoggia, M., Samoli, E.,
Andersen, Z. J., Bero-Bedada, G., Bellander, T., Hennig, F., Jacquemin, B.,
Pekkanen, J., Hampel, R., Cyrys, J., Peters, A., and Schneider, A.: Two-way
effect modifications of air pollution and air temperature on total natural
and cardiovascular mortality in eight European urban areas, Environ, Int,, 116, 186–196, https://doi.org/10.1016/j.envint.2018.04.021, 2018. a, b
Chen, K., Breitner, S., Wolf, K., Hampel, R., Meisinger, C., Heier, M., von
Scheidt, W., Kuch, B., Peters, A., Schneider, A., Peters, A., Schulz, H., Schwettmann, L., Leidl, R., Heier, M., and Strauch, K.: Temporal variations in the triggering of myocardial infarction by air temperature in Augsburg, Germany, 1987–2014, Eur. Heart J., 40, 1600–1608, https://doi.org/10.1093/eurheartj/ehz116, 2019. a, b
Commandeur, F., Slomka, P. J., Goeller, M., Chen, X., Cadet, S., Razipour, A., McElhinney, P., Gransar, H., Cantu, S., Miller, R. J. H., Rozanski, A.,
Achenbach, S., Tamarappoo, B. K., Berman, D. S., and Dey, D.: Machine learning to predict the long-term risk of myocardial infarction and cardiac
death based on clinical risk, coronary calcium, and epicardial adipose
tissue: a prospective study, Cardiovascul. Res., 116, 2216–2225,
https://doi.org/10.1093/cvr/cvz321, 2020. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Natl. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Davis, R. E., McGregor, G. R., and Enfield, K. B.: Humidity: A review and
primer on atmospheric moisture and human health, Environ. Res., 144, 106–116, https://doi.org/10.1016/j.envres.2015.10.014, 2016. a, b
Feng, H., Zhao, X., Chen, F., and Wu, L.: Using land use change trajectories to quantify the effects of urbanization on urban heat island, Adv. Space Res., 53, 463–473, https://doi.org/10.1016/j.asr.2013.11.028, 2014. a
Gabriel, K. M. A. and Endlicher, W. R.: Urban and rural mortality rates during heat waves in Berlin and Brandenburg, Germany, Environ. Pollut., 159, 2044–2050, https://doi.org/10.1016/j.envpol.2011.01.016, 2011. a
Havenith, G.: Temperature Regulation, Heat Balance and Climatic Stress, in: Extreme Weather Events and Public Health Responses, edited by: Kirch, W., Bertollini, R., and Menne, B., Springer, Berlin, Heidelberg, 69–80, https://doi.org/10.1007/3-540-28862-7_7, 2005. a
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in projections of regional precipitation change, Clim. Dynam., 37, 407–418,
https://doi.org/10.1007/s00382-010-0810-6, 2011. a
Holle, R., Happich, M., Löwel, H., and Wichmann, H.: KORA – A Research
Platform for Population Based Health Research, Gesundheitswesen, 67, 19–25, https://doi.org/10.1055/s-2005-858235, 2005. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A.,
Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A.,
García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a
Khraishah, H., Alahmad, B., Ostergard, R. L., AlAshqar, A., Albaghdadi, M.,
Vellanki, N., Chowdhury, M. M., Al-Kindi, S. G., Zanobetti, A., Gasparrini,
A., and Rajagopalan, S.: Climate change and cardiovascular disease: implications for global health, Nature Reviews Cardiology, Nature Publishing Group, 1–15, https://doi.org/10.1038/s41569-022-00720-x, 2022. a
Laverty, A. A., Goodman, A., and Aldred, R.: Low traffic neighbourhoods and
population health, Brit. Med. J., 372, n443, https://doi.org/10.1136/bmj.n443, 2021. a
Madrigano, J., Mittleman, M. A., Baccarelli, A., Goldberg, R., Melly, S., von Klot, S., and Schwartz, J.: Temperature, myocardial infarction, and
mortality: effect modification by individual- and area-level characteristics,
Epidemiology, 24, 439–446, https://doi.org/10.1097/EDE.0b013e3182878397, 2013. a
Merz, B., Kreibich, H., and Lall, U.: Multi-variate flood damage assessment: a tree-based data-mining approach, Nat. Hazards Earth Syst. Sci., 13, 53–64, https://doi.org/10.5194/nhess-13-53-2013, 2013. a
Mustafić, H., Jabre, P., Caussin, C., Murad, M. H., Escolano, S., Tafflet, M., Périer, M.-C., Marijon, E., Vernerey, D., Empana, J.-P., and Jouven, X.: Main Air Pollutants and Myocardial Infarction: A Systematic Review and Meta-analysis, JAMA-J. Am. Med. Assoc., 307, 713–721, https://doi.org/10.1001/jama.2012.126, 2012. a, b
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V.,
Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P.,
Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: software repository, https://scikit-learn.org/stable/, last access: 4 September 2022. a
Peters, A., von Klot, S., Heier, M., Trentinaglia, I., Hörmann, A., Wichmann, H. E., Löwel, H., and Cooperative Health Research in the Region of Augsburg Study Group: Exposure to traffic and the onset of myocardial infarction, N. Engl. J. Med., 351, 1721–1730, https://doi.org/10.1056/NEJMoa040203, 2004. a
Rai, M., Breitner, S., Wolf, K., Peters, A., Schneider, A., and Chen, K.:
Impact of climate and population change on temperature-related mortality burden in Bavaria, Germany, Environ. Res. Lett., 14, 124080, https://doi.org/10.1088/1748-9326/ab5ca6, 2019. a
Rajagopalan, S., Al-Kindi, S. G., and Brook, R. D.: Air Pollution and
Cardiovascular Disease: JACC State-of-the-Art Review, J. Am. College Cardiol., 72, 2054–2070, https://doi.org/10.1016/j.jacc.2018.07.099, 2018. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Roth, G. A.: Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019, J. Am. Coll. Cardiol., 76, 2982-–3021, 2020. a
Schmidt, S., Hendricks, V., Griebenow, R., and Riedel, R.: Demographic change
and its impact on the health-care budget for heart failure inpatients in
Germany during 1995–2025, Herz, 38, 862–867, https://doi.org/10.1007/s00059-013-3955-3, 2013. a
Schwartz, J., Samet, J. M., and Patz, J. A.: Hospital Admissions for Heart
Disease: The Effects of Temperature and Humidity, Epidemiology, 15, 755–761, https://doi.org/10.1097/01.ede.0000134875.15919.0f, 2004. a, b
Sewe, M. O., Tozan, Y., Ahlm, C., and Rocklöv, J.: Using remote sensing
environmental data to forecast malaria incidence at a rural district hospital
in Western Kenya, Scient. Rep., 7, 2589, https://doi.org/10.1038/s41598-017-02560-z, 2017. a, b
Sieck, K., Nam, C., Bouwer, L. M., Rechid, D., and Jacob, D.: Weather extremes over Europe under 1.5 and 2.0 ∘C global warming from HAPPI regional climate ensemble simulations, Earth Syst. Dynam., 12, 457–468, https://doi.org/10.5194/esd-12-457-2021, 2021. a
Statistisches Bundesamt: 14. Koordinierte Bevölkerungsvorausberechnung –
Basis 2018,
https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsvorausberechnung/aktualisierung-bevoelkerungsvorausberechnung.html, last access: 4 September 2022. a
Sun, Z., Chen, C., Xu, D., and Li, T.: Effects of ambient temperature on
myocardial infarction: A systematic review and meta-analysis, Environ. Pollut., 241, 1106–1114, https://doi.org/10.1016/j.envpol.2018.06.045, 2018. a, b
Tamarappoo, B. K., Lin, A., Commandeur, F., McElhinney, P. A., Cadet, S.,
Goeller, M., Razipour, A., Chen, X., Gransar, H., Cantu, S., Miller, R. J.,
Achenbach, S., Friedman, J., Hayes, S., Thomson, L., Wong, N. D., Rozanski, A., Slomka, P. J., Berman, D. S., and Dey, D.: Machine learning integration
of circulating and imaging biomarkers for explainable patient-specific
prediction of cardiac events: A prospective study, Atherosclerosis, 318,
76–82, https://doi.org/10.1016/j.atherosclerosis.2020.11.008, 2021. a
The Eurowinter Group: Cold exposure and winter mortality from ischaemic heart
disease, cerebrovascular disease, respiratory disease, and all causes in warm
and cold regions of Europe, Lancet, 349, 1341–1346, https://doi.org/10.1016/S0140-6736(96)12338-2, 1997. a
Tunstall-Pedoe, H., Kuulasmaa, K., Amouyel, P., Arveiler, D., Rajakangas, A. M., and Pajak, A.: Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents, Circulation, 90, 583–612, https://doi.org/10.1161/01.CIR.90.1.583, 1994. a
Turnock, S. T., Allen, R. J., Andrews, M., Bauer, S. E., Deushi, M., Emmons,
L., Good, P., Horowitz, L., John, J. G., Michou, M., Nabat, P., Naik, V.,
Neubauer, D., O'Connor, F. M., Olivié, D., Oshima, N., Schulz, M., Sellar, A., Shim, S., Takemura, T., Tilmes, S., Tsigaridis, K., Wu, T., and Zhang, J.: Historical and future changes in air pollutants from CMIP6 models, Atmos. Chem. Phys., 20, 14547–14579, https://doi.org/10.5194/acp-20-14547-2020, 2020. a
Vanos, J. K., Baldwin, J. W., Jay, O., and Ebi, K. L.: Simplicity lacks
robustness when projecting heat-health outcomes in a changing climate, Nat.
Commun., 11, 6079, https://doi.org/10.1038/s41467-020-19994-1, 2020. a
Vos, T., Allen, C., Arora, M., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet, 388, 1545–1602, https://doi.org/10.1016/S0140-6736(16)31678-6, 2016. a
Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage
modelling with limited data using supervised learning approaches, Nat.
Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, 2017. a
Wagenaar, D., Hermawan, T., Homberg, M. J. C., Aerts, J. C. J. H., Kreibich,
H., Moel, H., and Bouwer, L. M.: Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction, Risk Anal., 41, 37–55, https://doi.org/10.1111/risa.13575, 2021. a
Wolf, K., Schneider, A., Breitner, S., von Klot, S., Meisinger, C., Cyrys, J., Hymer, H., Wichmann, H. E., and Peters, A.: Air Temperature and the
Occurrence of Myocardial Infarction in Augsburg, Germany, Circulation, 120, 735–742, https://doi.org/10.1161/CIRCULATIONAHA.108.815860, 2009. a, b
Wolf, K., Hoffmann, B., Andersen, Z. J., Atkinson, R. W., Bauwelinck, M.,
Bellander, T., Brandt, J., Brunekreef, B., Cesaroni, G., Chen, J., Faire, U. D., Hoogh, K. D., Fecht, D., Forastiere, F., Gulliver, J., Hertel, O.,
Hvidtfeldt, U. A., Janssen, N. A. H., Jørgensen, J. T., Katsouyanni, K.,
Ketzel, M., Klompmaker, J. O., Lager, A., Liu, S., MacDonald, C. J., Magnusson, P. K. E., Mehta, A. J., Nagel, G., Oftedal, B., Pedersen, N. L.,
Pershagen, G., Raaschou-Nielsen, O., Renzi, M., Rizzuto, D., Rodopoulou, S.,
Samoli, E., v. d. Schouw, Y. T., Schramm, S., Schwarze, P., Sigsgaard, T.,
Sørensen, M., Stafoggia, M., Strak, M., Tjønneland, A., Verschuren, W.
M. M., Vienneau, D., Weinmayr, G., Hoek, G., Peters, A., and Ljungman, P. L. S.: Long-term exposure to low-level ambient air pollution and incidence of
stroke and coronary heart disease: a pooled analysis of six European cohorts within the ELAPSE project, Lancet Planet. Health, 5, e620–e632, https://doi.org/10.1016/S2542-5196(21)00195-9, 2021. a
Zhang, D.-L., Shou, Y.-X., and Dickerson, R. R.: Upstream urbanization
exacerbates urban heat island effects, Geophys. Res. Lett., 36, L24401, https://doi.org/10.1029/2009GL041082, 2009. a, b
Zhang, K., Li, Y., Schwartz, J. D., and O'Neill, M. S.: What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods, Environ. Res., 132, 350–359,
https://doi.org/10.1016/j.envres.2014.04.004, 2014. a
Zinszer, K., Verma, A. D., Charland, K., Brewer, T. F., Brownstein, J. S., Sun, Z., and Buckeridge, D. L.: A scoping review of malaria forecasting: past work and future directions, Brit. Med. J. Open, 2, e001992,
https://doi.org/10.1136/bmjopen-2012-001992, 2012. a
Short summary
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air...
Special issue
Altmetrics
Final-revised paper
Preprint