On some approaches to calculation of health risks caused by temperature waves
D.A. Shaposhnikov, B.A. Revich
National Economic Prediction Institute of the Russian Academy of Sciences, 47 Nakhimovsky Prospect, Moscow, 117418, Russian Federation
The paper dwells on techniques applied for assessing impacts exerted by environmental factors on population health which have become conventional all over the world over recent years. The greatest attention is paid to up-to-date approaches to calculating risks of additional mortality which occurs in big population groups during cold and hot temperature waves. The authors consider basic stages in direct epidemiologic research: temperature waves definition; statistics hypotheses formulation; models specification; statistical criteria sensitivity, and statistical validity of the obtained results. As per long-term research performed by us in various Russian cities, we constructed logistic curves which show probability of obtaining significant risk assessment results for small samplings. We recommend to apply percentiles of long-term average daily temperature distributions as temperature thresholds when identifying temperature waves; in our opinion, such thresholds correspond to perceptions of extreme (for this or that region) temperatures and provide comparable results in terms of expected waves quantity in different climatic zones. Poisson's generalized linear model for daily mortality is shown to be the most widely spread technique for calculating risks caused by hazardous environmental factors. It is advisable to allow for an apparent correlation between mortality and time and air contamination in any regression model. We can allow for meteorological conditions which influence heat balance (air humidity and wind speed) either via including them apparently into a model or via bioclimatic indexes application; research in this sphere is going on. When calculating risks, it is advisable to allow for time lags between extreme temperatures waves and changes in mortality. We revealed that minimal population of a typical city for which it is possible to obtain statistically significant assessment of risks caused by heat waves ensembles is about 200,000 people.
- Vtoroi otsenochnyi doklad Rosgidrometa ob izmeneniyakh klimata i ikh posledstviyakh na territorii Rossi-iskoi Federatsii. Razdel 1. Nablyudaemye izmeneniya klimata [The second assessment report by the Federal Ser-vice for Hydrometeorology and Environmental Monitoring on climatic changes and their consequences on the RF territory. Section 1. Observed climatic changes]. Moscow, Federal'naya sluzhba po gidrometeorologii i monitoringu okruzhayushchei sredy Publ., 2014, pp. 18–235 (in Russian).
- Kleshchenko L.K. Volny tepla i kholoda na territorii Rossii [Cold and heat waves on the RF territory]. Ana-liz izmenenii klimata i ikh posledstvii: sbornik trudov Vserossiiskogo nauchno-issledovatel'skogo instituta gidrometeorologicheskoi informatsii – Mirovogo tsentra dannykh [Analysis of climatic changes and their conse-quences: collection of works issued by the Russian Scientific-research Institute for Hydrometeorological Information – World data center]. In: V.N. Razuvaev, B.G. Sherstyukov, eds. 2010, no. 175, pp. 76–91 (in Russian).
- Guidelines on the definition and monitoring of extreme weather and climate events: Draft version – first re-view by TT-Dewce desember 2015. World Meteorological Organization, 2016, 62 p. Available at: https://www.wmo.int/pages/prog/wcp/ccl/opace/opace2/documents/Draftversi... (12.10.2017).
- Radinovic D., Curic M. Criteria for heat and cold wave duration. Theor. Appl. Climatol., 2012, vol. 97, no. 3–4, pp. 505–510.
- Revich B.A., Shaposhnikov D.A. Osobennosti vozdeistviya voln zhary i kholoda na smertnost' v gorodakh s rezko-kontinental'nym klimatom [Influence features of cold and heat waves to the population mortality – the city with sharply continental climate]. Sibirskoe meditsinskoe obozrenie, 2017, no. 2, pp. 84–90 (in Russian).
- Revich B.A., Shaposhnikov D.A. Volny kholoda v yuzhnykh gorodakh evropeiskoi chasti Rossii i prezhdevremennaya smertnost' naseleniya [Cold waves in southern cities of European Russia and premature mortality]. Problemy prognozirovaniya, 2016, no. 2, pp. 125–131 (in Russian).
- Revich B.A., Shaposhnikov D.A., Podol'naya M.A., Khar'kova T.L., Kvasha E.A. Volny zhary v yuzhnykh gorodakh evropeiskoi chasti Rossii kak faktor riska prezhdevremennoi smertnosti naseleniya [Heat waves in southern cities of European Russia as a risk factor for premature mortality]. Problemy prognozirovaniya, 2015, no. 2, pp. 56–67 (in Russian).
- Shaposhnikov D., Revich B. Towards meta-analysis of impacts of heat and cold waves on mortality in Russian North. Urban Climate, 2016, vol. 15, pp. 16–24. DOI: 10.1016/j.uclim.2015.11.007
- Anderson G.B., Bell M.L., Peng R.D. Methods to calculate the heat index as an expo¬sure metric in environmental health research. Environ. Health Perspect, 2013, vol. 121, no. 10, pp. 1111–1119. DOI: 10.1289/ehp.1206273
- Report on Wind Chill Temperature and Extreme Heat Indices: Evaluation and Improvement Projects. Washington, DC, Federal Coordinator for Meteorological Services and Supporting Research, Publ., 2003. Available at: http://solberg.snr.missouri.edu/gcc/OFCMWindchillReport.pdf (08.09.2017).
- Ryabenko E.A. Prikladnoi statisticheskii analiz dannykh. Obobshcheniya lineinoi regressii. [Applied sta-tistical data analysis. Linear regression generalizations]. 2016. Available at: http://www.machinelearning.ru/wiki/images/a/a6/Psad_otherreg.pdf (13.06.2017) (in Russian).
- Richiardi L., Bellocco R., Zugna D. Mediation analysis in epidemiology: methods, interpretation and bi-as. Int. J. Epidemiol., 2013, vol. 42, no. 5, pp. 1511–1519. DOI: 10.1093/ije/dyt127
- Gasparrini A., Armstrong B. The impact of heat waves on mortality. Epidemiology, 2011, vol. 22, no. 1, pp. 68–73. DOI: 10.1097/EDE.0b013e3181fdcd99
- Shaposhnikov D., Revich B., Bellander T. [et al.]. Mortality Related to Air Pollution with the Moscow Heat Wave and Wildfire of 2010. Epidemiology, 2014, vol. 25, no. 3, pp. 359–364. Available at: https://www.ncbi.nlm.nih.gov/pubmed/24598414 (08.09.2017).
- Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. Journal of Statistical Software, 2011, vol. 43, no. 8, pp. 1–20. DOI: 10.18637/jss.v043.i08
- Schwartz J. The distributed lag between air pollution and daily deaths. Epidemiology, 2000, vol. 11, pp. 320–326.
- R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2016. R. Core. Team. 2017. Available at: http://www.R-project.org/ (18.12.2017).
- Stafoggia M., Schwartz J., Forastiere F., Perucci C.A., and the SISTI Group. Does Temperature Modify the Association between Air Pollution and Mortality? A Multicity Case-Crossover Analysis in Italy. Am. J. Epi-demiol., 2008, vol. 167, no. 12, pp. 1476–1485. DOI: 10.1093/aje/kwn074
- Pope C.A. 3-rd, Hill R.W., Villegas G.M. Particulate air pollution and daily mortality on Utah’s Wasatch Front. Environ Health Perspect, 1999, vol. 107, no. 7, pp. 567–573.