2025 Quintessence international (B…

6th German Oral Health Study (DMS * 6): data processing and statistical methods.

, , , , , ,

Quintessence international (Berlin, Germany : 1985) Vol. 56 (11) : S22-S29 • Mar 2025

OBJECTIVES: The 6th German Oral Health Study (DMS * 6) is a combined cross-sectional and cohort study with the main objective of reporting oral diseases in Germany. Based on cross-sectional data, current prevalence estimates and trend analyses on the development of oral health and care status in Germany were conducted using representative data. Associations between oral health and further participant characteristics were examined. The aim of this article is to provide details on data handling and statistical analysis of the cross-sectional data. Sample weighting: Weighting factors were used as part of the statistical analysis to correct for deviations between the analysis set and the population structure in Germany. The objective was to make nationwide representative statements for the age groups examined in the cross-sectional component of the DMS * 6. Different types of weights were calculated: design, non-response, and calibration weights. Processing of quantitative variables: The indices and transformed variables required for data analysis were defined based on variables collected in clinical examinations and social science interviews. Dental characteristics were aggregated at the participant level. STATISTICAL METHODS: For epidemiologic description, prevalence rates and means with associated 95% confidence intervals were calculated. Regression models were adjusted to estimate the strength of associations between participant characteristics of interest and oral health-related outcomes. To describe trends in the temporal development of oral health and dental care status in Germany, epidemiologic descriptions from DMS * 6 and previous studies were compared.

No clinical trial protocols linked to this paper

Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.
PICO Elements

No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.

Paper Details
MeSH Terms
+2 more
Associated Data

No associated datasets or code repositories found for this paper.

Related Papers

Related paper suggestions will be available in future updates.