TY  - JOUR
SN  - 1936-7163 (Electronic) 0033-6572 (Linking)
AU  - Kuhr, Kathrin
AU  - Sasunna, Dominic
AU  - Frenzel Baudisch, Nicolas
AU  - Pitchika, Vinay
AU  - Zimmermann, Fabian
AU  - Ohm, Cristiana
AU  - Jordan, A. Rainer
T1  - 6th German Oral Health Study (DMS • 6): data processing and statistical methods
JF  - Quintessence International
SP  - S22-S29
VL  - 56 (Suppl.)
PY  - 2025
KW  - Cross-sectional analysis
KW  - Data processing
KW  - Deutsche Bevölkerung
KW  - Deutschland
KW  - DMS • 6
KW  - DMS 6
KW  - Epidemiologic study
KW  - Mundgesundheitsstudien
KW  - Projekt DMS 6
KW  - Statistical data analysis
KW  - Weighting
UR  - https://www.quintessence-publishing.com/deu/de/article/5981988/quintessence-international/2025/supplement/6th-german-oral-health-study-dms-6-data-processing-and-statistical-methods
L1  - \\kzbv-citavi\attachmentsidz$\attachments\IDZ\Kuhr, Sasunna et al 2025 - 6th German Oral Health Study.pdf
JA  - Quintessence Int
AB  - Introduction The Sixth German Oral Health Study (DMS • 6) is a combined cross-sectional and cohort study with the main objective to report on 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 To correct for deviations between the analysis set and the population structure in Germany, weighting factors were used as part of the statistical analysis. 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 weights, non-response weights, and calibration weights.

Processing of quantitative Variables Based on variables collected in dental examinations and social science interviews, the indices and transformed variables required for data analysis were defined. Dental characteristics were aggregated at the participant level.

Statistical methods For epidemiological 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, epidemiological descriptions from DMS • 6 and previous studies were compared.
DO  - 10.3290/j.qi.b5981988
M4  - Citavi
ER  -