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J Clin Pediatr Dent ; 22(2): 125-31, 1998.
Article in English | MEDLINE | ID: mdl-9643186

ABSTRACT

Approval for state sponsored funding of orthodontic treatment is often decided using an index of malocclusion. The purpose of this study was to determine whether two indices used for prioritizing patients would identify different groups of individuals qualifying for orthodontic treatment funding approval. In addition, the characteristics of patients approved using different indices were compared. The records of 40 patients previously submitted for state medicaid funding approval were evaluated by three study examiners using two orthodontic treatment priority indices, the Salzmann Handicapping Malocclusion Assessment (Salzmann) and the Index of Orthodontic Treatment Need (IOTN). Comparisons were made between state medicaid and study examiner Salzmann scores, rankings, and funding decisions, and between study examiner Salzmann rankings, IOTN rankings, and funding decisions. Correlation and rank correlation coefficients between the state and study examiners' Salzman scores were high (r = 0.74; p < 0.001, and R = 0.77; p < 0.001). Rank correlation analysis of the study examiners' Salzmann and IOTN values demonstrated a weaker relationship (R = 0.40; p < 0.02). Agreement on funding decisions, evaluated by the Kappa statistic, was greater between the two Salzmann evaluations (K = 0.57) than between the study examiners' Salzmann and IOTN evaluations (K = 0.14). As expected, depending on the method used to determine orthodontic treatment funding priority, different patients were likely to be identified for treatment approval. The characteristics of patients whose treatment was approved was closely related to the criteria defined by the method employed.


Subject(s)
Malocclusion/diagnosis , Malocclusion/therapy , Orthodontics, Corrective , Severity of Illness Index , Health Care Rationing , Health Priorities , Health Services Needs and Demand , Humans , Medicaid , Orthodontics, Corrective/economics , Patient Care Planning , Statistics, Nonparametric , United States
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