Objective This study examined individual and contextual factors which predict the dental care received by patients in a state-funded primary dental care training facility in England. Methods Routine clinical and demographic data were extracted from a live dental patient management system in a state-funded facility using novel methods. The data, spanning a four-year period [2008–2012] were cleaned, validated, linked by means of postcode to deprivation status, and analysed to identify factors which predict dental treatment need. The predictive relationship between patients’ individual characteristics (demography, smoking, payment status) and contextual experience (deprivation based on area of residence), with common dental treatments received was examined using unadjusted analysis and adjusted logistic regression. Additionally, multilevel modelling was used to establish the isolated influence of area of residence on treatments. Results Data on 6,351 dental patients extracted comprised of 147,417 treatment procedures delivered across 10,371 courses of care. Individual level factors associated with the treatments were age, sex, payment exemption and smoking status and deprivation associated with area of residence was a contextual predictor of treatment. More than 50% of children (<18 years) and older adults (≥65 years) received preventive care in the form of ‘instruction and advice’, compared with 46% of working age adults (18–64 years); p = 0.001. The odds of receiving treatment increased with each increasing year of age amongst adults (p = 0.001): ‘partial dentures’ (7%); ‘scale and polish’ (3.7%); ‘tooth extraction’ (3%; p = 0.001), and ‘instruction and advice’ (3%; p = 0.001). Smokers had a higher likelihood of receiving all treatments; and were notably over four times more likely to receive ‘instruction and advice’ than non-smokers (OR 4.124; 95% CI: 3.088–5.508; p = 0.01). A further new finding from the multilevel models was a significant difference in treatment related to area of residence; adults from the most deprived quintile were more likely to receive ‘tooth extraction’ when compared with least deprived, and less likely to receive preventive ‘instruction and advice’ (p = 0.01). Conclusion This is the first study to model patient management data from a state-funded dental service and show that individual and contextual factors predict common treatments received. Implications of this research include the importance of making provision for our aging population and ensuring that preventative care is available to all. Further research is required to explain the interaction of organisational and system policies, practitioner and patient perspectives on care and, thus, inform effective commissioning and provision of dental services.