Dental diseases and tooth loss result in various health, psychological, and even social problems. The objective of the study was determination of the number of missing teeth among adult rural and urban inhabitants of the Lublin Region, and whether or not there is a relationship between missing teeth and place of residence, and other socio-economic factors, such as: gender, age, education level and the occupation performed (farmer/non-farmer). Data concerning the number of missing teeth were collected from 3,388 individuals. The mean number of missing teeth among the respondents in the study was 13.6. This mean value was significantly higher among the rural than urban inhabitants. Tooth loss was significantly more often found among females than males, this relationship being statistically significant only in the subpopulation of rural inhabitants. According to expectations, the largest number of missing teeth was found in respondents aged over 60, among those aged 31–60 this number was nearly 2.5- fold smaller, while the smallest number of missing teeth was observed among respondents aged 18–30. The largest number of missing teeth was noted among respondents who possessed incomplete elementary or elementary education, followed by those with elementary vocational and secondary school/post-secondary school education, whereas this number was the smallest among respondents who had university education level. Farmers had a significantly larger number of missing teeth, compared to respondents who performed non-agricultural occupations. Using an analysis of regression, the relationship was confirmed between the number of missing teeth, and the respondents’ gender, age, education level, place of residence, and occupation performed. Discrimination analysis was applied to show the relationship between the occurrence of total edentulism and the respondents’ age, gender, education level and place of residence. It was observed that age was the variable which most strongly discriminated the occurrence of this characteristic, followed by education level, as well as gender and place of residence, which were the weakest discriminatory variables.
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