The Body Mass Index (BMI) is a well-known tool for measuring normal, healthy weight. Nowadays, we can also use it as a prognostic factor for various diseases. Purpose: Our study aims to confirm or decline the correlation between body mass index and the onset of different diseases. Methods: The study was conducted among a random sample of 550 people (324 women and 226 men) from the district of Stara Zagora, Republic of Bulgaria. The participants in the study were aged between 18 and 65. The connection between BMI and the occurrence of diseases of different systems has been researched. A statistically significant relationship was found at p<0.05. The statistically significant influence of BMI on the occurrence of diseases is proved by the results of the ANOVA procedure. Results: The results give us reason to believe that not only obesity but also overweight is a risk of cardiovascular disease. This is evidenced by our study, according to which people with the highest average BMI (BMI = 28.72 kg/m2) have diseases of the cardiovascular system. People with a BMI around and above 25.87 kg / m2 often suffer from diseases of the endocrine system, and those with a BMI around 25.60 kg / m2 are at risk for diseases of the musculoskeletal system. Conclusions: BMI would find its daily implementation in the activities of every physician working in every field of medicine. BMI can be used as a tool to predict the onset of disease and a regulator of prevention.

AIM:This paper aims to create a mathematical model for forecasting the morbidity of the population in the Republic of Bulgaria and the Stara Zagora Municipality in particular as a consequence of the atmospheric pollution.SUBJECTS AND METHODS:This model is based on a formula which determines the correlation between the average annual concentrations of atmospheric pollutants SO2, PM10, Pb aerosols, NO2 and H2S) and the morbidity of the population based on the number of people who visited their GPs in a relation with a chronic health problem or emergency condition and the number of hospitalisations in two age groups (newborn to 17 years olds and 18 and older) as well as for the entire population in the period 2009-2013, making it possible to predict morbidity levels.RESULTS:The expected morbidity level predictions based on the number of people who visited their GPs in Municipality are lower, while hospitalisation level predictions are higher. This model has been created and tested and is applicable in all residential areas.CONCLUSIONS:A new, very sensitive, mathematical model has been created and tested (average margin of error from 0.61% to 2.59%) and is applicable in all residential areas.