Applicability of Moyer’s Method of Mixed Dentition Analysis in a Tanzanian Population

Introduction: Space analysis is the process of evaluating whether the available space in a dental arch is enough to accommodate all the erupting permanent teeth. In mixed dentition, where some permanent teeth have not yet erupted, tooth size prediction is required to compute the analysis. For accurate prediction and analysis, customized data is needed since tooth size varies between populations. Therefore, the present study aimed to assess the applicability of Moyer’s method of mixed dentition analysis, and to determine the tooth groups with the best correlation in predicting the sum of unerupted canine and premolars in the Tanzanian population. Materials & Methods: The study involved 204 study casts of patients aged between 12 and 30 years who sought orthodontic treatment at the Muhimbili University Dental Clinic. The mesiodistal width of permanent teeth was measured using a digital vernier caliper. Comparison between actual and predicted measurements was performed using a paired sample t-test. Correlation analysis was conducted to assess the relationship between different tooth groups, and linear regression was used to derive a new prediction formula. Results: There was no statistically significant difference observed between the right and left sides of the jaws, however, males had greater tooth width compared to females. Moyer’s method significantly underestimated the width of canine and premolars at the 50th and 75th percentiles and overestimated the same measurements at the 90th percentile. The combined width of lower incisors and the first lower permanent molars displayed the highest correlation in predicting the size of unerupted canines and premolars. Therefore, they were used to formulate population-specific prediction formulas. Conclusion: Moyer’s method of mixed dentition analysis was not a reliable method for predicting the size of unerupted canines and premolars in the Tanzanian population. The use of the newly generated prediction formulas is recommended.