THE INFLUENCE OF ECOLOGICAL RISK FACTORS ON MORTALITY IN MOSCOW POPULATION. RISK EVALUATION AND PREDICTION - S. A. Boytsov; M. M. Lukyanov; A. D. Deev; V. G. Klyashtorny; A. V. Ivanenko; N. S. Volkova; A. S. Kuznetsov; A. S. Skvortsov; D. V. Solovyev (Российский кардиологический журнал)
Aim. To assess the influence of temperature, humidity, air pollution on the mortality ofMoscowinhabitants in 2007-2014, to evaluate the possibility of mortality prediction by mathematic modelling.Material and methods. For each day of 2007-2014 years, the data was analyzed forMoscowinhabitants mortality, temperature, humidity and air pollution (РМ10). Number of deaths was 956436 (48,1% of men and 51,9% women), of those from cardiovascular disorders (CVD) — 536625. Multifactorial regression analysis was done with generalized logistic regression, evaluation of risks ratios (RR), 95%, confidence analysis. The prognostic modelling applied, based on non-linear multifactorial analysis.Results. The factor “heat wave” increased RR for the death from all cases and from CVD by 1,43 and 1,65 times. Heat waves mostly increased all-cause mortality, and from CVD in women (p<0,005), the increase of air pollution (РМ10) — in men (р<0,005). The highest increase of death risk was found for those 70 years and older, higher humidity worsened it. For the summer 2010 prediction was finer than for 2014 (correlation 90,1% and 45,1%), e. g. for the period of more significant anomalies of temperature and air pollution. Applying multifactorial prediction model for 2007-2014 period with additional evaluation of weather factors influence for last 4 days and all-cause mortality for the last month, correlation was higher for death risk prediction for 1-3 days (88,0%), than for month (45,2%).Conclusion. Heat waves, increase of air temperature and pollution do increase the risk of all-cause mortality, especially from CVD, in those 70 y. o. and older. Anomal heat increases death risk for women higher than for men. Prediction of mortality by 1-3 days with multifactorial model show better correlation than prediction for month.