Статистические связи с дозой облучения и оценка радиационных рисков неонкологических заболеваний эндокринной системы ликвидаторов чернобыльской аварии с учётом возможных ошибок в постановке и регистрации диагнозов

Abstract

The paper provides research on statistical relationship between endocrine diseases registered in the Chernobyl liquidators and radiation dose they received. Data of clinical and epidemiological observations accumulated at the National Radiation Epidemiological Register (NRER) for the period from 1986 to 2020 were used for analysis. The cohort contained 41,979 liquidators with 107,713 established diagnoses included in the ICD-10 Е00-E90 blocks. Statistical relationship between radiation doses and endocrine diseases was investigated with the use of statistical methods. Statistical Data Mining methods free of apriori guesses for probable doses and diagnoses distribution, models of dose-effect (disease) relationship were used for analysis of statistical relationship between radiation dose and the diagnoses structure. Statistically significant relationship between radiation doses and diagnoses were found in the blocks E01 (iodine deficiency), E04 (goiter), E06 (thyroiditis), E11 (type II diabetes mellitus). Relationships between radiation dose and diagnosis were also found in the E01.2 block (endemic goiter, caused due to iodine deficiency) and in E04.0 block (non-toxic diffuse goiter). Existence of identified statistical relationship is supported with the estimates of excess relative risk (ERR) and the relative risk (RR). The threshold relationship between RR and radiation dose with threshold of 0.3 Gy shows that radiation effect on human endocrine system is a deterministic effect (tissue reactions). Quantitative risks estimates for endocrine diseases in the liquidators are stable in relation to the diagnostic specificity index up to 90%. The general statistic Data Mining method to search relationship between a disease diagnosis and radiation dose is useful for the diseases identification in order to estimate radiation risk with conventual radiation epidemiology approaches.