Technologies for calculating and visualizing statistics on prevalence and incidence on the example of information about polypous rhinosinusitis in the Samara region

Abstract

Aim to statistically determine the distribution of chronic diseases in the chronology of observations; to show the specifics of methods for testing hypotheses in the quantitative and probabilistic prediction of the prevalence of polypous rhinosinusitis. Material and methods. The outpatient data for the period of 20172021 and quantitative information about the cases with polypous rhinosinusitis as main or concomitant diagnosis registered by medical organizations of 25 districts of the Samara region were used in the study. Results. The synthesis of the initial data statistics, which amounted to the volume of the numerical expansion of primary indicators in the following ratio: categories 15.8%, counting data 26.3%; quantitative values 21.1%; 26.7% relative incidence and prevalence data. The rest of the data is the descriptive statistics and indicators in the form of tables of correlation coefficients. For extensions of the synthesized data, distributions were evaluated and hypotheses tested using statistical criteria. Conclusion. The count of the number of chronic diseases is approximated by the density of atypical distributions. Approximately 58% of samples for diagnoses are not confirmed as obeying the law of distribution. In such a situation, when preparing a forecast for the transition to a time series, it is necessary to solve the problem of obtaining sequences with stationary characteristics. In machine learning, data in predictive calculations must be checked for probabilistic confirmation of the coincidence of related sample parameter distributions. The results of the forecast should be taken as a probabilistic conclusion at the level of an unrejected hypothesis.

Keywords

• descriptive statistics
• quantitative correlations
• statistical tests
• statistical hypothesis testing
• regression
• time series forecasting
• p-values