Detecting high-risk neighborhoods and socioeconomic determinants for common oral diseases in Germany

• Published in: BMC oral health 2024-02, Vol.24 (1), p.205-20, Article 205
• Main Authors: Völker, Sebastian, van der Zee-Neuen, Antje, Rinnert, Alexander, Hanneken, Jessica, Johansson, Tim
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Chronic illnesses; Cluster analysis; Clustering; Data analysis; Dental care; Dental disorders; Dental health; Epidemiology; Financial services; Germany - epidemiology; Gum disease; Health care; Health care industry; Humans; Incidence; Incidence rate; Learning algorithms; Machine learning; Neighborhoods; Oral diseases; Oral hygiene; Patients; Periodontitis; Population; Population studies; Public health; Quality of life; Residence Characteristics; Retrospective Studies; Risk assessment; Socioeconomic determinants of health; Socioeconomic Factors; Socioeconomic status; Spatial Analysis; Spatial cluster analysis; Statistical analysis
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-024-03897-4

Ideally, health services and interventions to improve dental health should be tailored to local target populations. But this is not the standard. Little is known about risk clusters in dental health care and their evaluation based on small-scale, spatial data, particularly among under-represented groups in health surveys. Our study aims to investigate the incidence rates of major oral diseases among privately insured and self-paying individuals in Germany, explore the spatial clustering of these diseases, and evaluate the influence of social determinants on oral disease risk clusters using advanced data analysis techniques, i.e. machine learning. A retrospective cohort study was performed to calculate the age- and sex-standardized incidence rate of oral diseases in a study population of privately insured and self-pay patients in Germany who received dental treatment between 2016 and 2021. This was based on anonymized claims data from BFS health finance, Bertelsmann, Dortmund, Germany. The disease history of individuals was recorded and aggregated at the ZIP code 5 level (n = 8871). Statistically significant, spatially compact clusters and relative risks (RR) of incidence rates were identified. By linking disease and socioeconomic databases on the ZIP-5 level, local risk models for each disease were estimated based on spatial-neighborhood variables using different machine learning models. We found that dental diseases were spatially clustered among privately insured and self-payer patients in Germany. Incidence rates within clusters were significantly elevated compared to incidence rates outside clusters. The relative risks (RR) for a new dental disease in primary risk clusters were min = 1.3 (irreversible pulpitis; 95%-CI = 1.3-1.3) and max = 2.7 (periodontitis; 95%-CI = 2.6-2.8), depending on the disease. Despite some similarity in the importance of variables from machine learning models across different clusters, each cluster is unique and must be treated as such when addressing oral public health threats. Our study analyzed the incidence of major oral diseases in Germany and employed spatial methods to identify and characterize high-risk clusters for targeted interventions. We found that private claims data, combined with a network-based, data-driven approach, can effectively pinpoint areas and factors relevant to oral healthcare, including socioeconomic determinants like income and occupational status. The methodology presented here enables the identif