Exposure Risk to Public Health and a Method for Assessing Its Increase

BACKGROUND: The combined impact of climatic and socio-economic factors, along with chemical and radiation pollution of the environment, is a defining feature of modern life. The current challenges stem from both insufficient knowledge of the molecular and cellular mechanisms of individual and combined factor effects, and the lack of methodological approaches to studying potential synergistic interactions between factors of different nature.

AIM: To develop an algorithm for detecting an increase in exposure risk to public health in a specific city or territory.

METHODS: Since a significant proportion is made up of small towns, one of the requirements for the developed algorithm was its applicability (robustness) in analyzing data from large, medium, and small cities or territories. The core of the developed algorithm was a comparative analysis of age-specific mortality rates. To detect a statistically significant (non-random) increase in mortality, a comparison was made between mortality data recorded during the observation period and those from the reference interval. To test the adequacy of the proposed algorithm, a comparative analysis of mortality data was performed for 10 cities (Ufa, Kursk, Penza, Kirov, Kaluga, Vologda, Kostroma, Kolomna, Obninsk, Dimitrovgrad) in the European part of the Russian Federation. These cities are located in a temperate continental climate zone and experienced extreme weather conditions in 2010 (low temperatures in January and high temperatures in summer), whereas weather conditions in the following nine years remained within climatic norms. (In Kirov, abnormally low temperatures were recorded in February 2011, so the 2011 mortality data for Kirov were excluded from the analysis.) The year 2010 was designated as the observation period, and 2011–2019 as the reference interval.

RESULTS: In all cities included in the study, an increase in exposure risk was identified in 2010 according to the proposed algorithm. At the same time, in none of the years from 2011 to 2019 was the condition of the algorithm met that would allow for a conclusion about an increase in exposure risk. This further supports the adequacy of the proposed algorithm, as no conditions were presented in the studied cities during the reference interval (2011–2019) that could have led to an increase in exposure risk.

CONCLUSION: The proposed algorithm has been shown to be effective for identifying increased exposure risk to public health in cities with varying population sizes.