Incorporating severity and risk as factors to the Fardal cost-effectiveness model to create a cost-benefit model for periodontal treatment.

BACKGROUND: A previously described economic model was based on average values for patients diagnosed with chronic periodontitis (CP). However, tooth loss varies among treated patients and factors for tooth loss include CP severity and risk. The model was refined to incorporate CP severity and risk to determine the cost of treating a specific level of CP severity and risk that is associated with the benefit of tooth preservation. METHODS: A population that received and another that did not receive periodontal treatment were used to determine treatment costs and tooth loss. The number of teeth preserved was the difference of the number of teeth lost between the two populations. The cost of periodontal treatment was divided by the number of teeth preserved for combinations of CP severity and risk. RESULTS: The cost of periodontal treatment divided by the number of teeth preserved ranged from (US) $1,405 to $4,895 for high or moderate risk combined with any severity of CP and was more than $8,639 for low risk combined with mild CP. The cost of a three-unit bridge was $3,416, and the cost of a single-tooth replacement was $4,787. CONCLUSION: Periodontal treatment could be justified on the sole basis of tooth preservation when CP risk is moderate or high regardless of disease severity.

Reduction of tooth loss associated with periodontal treatment.

Comparing tooth loss for populations comprising subjects with periodontal disease has been limited by broad and different definitions of disease severity. Numeric scores for periodontal disease severity and risk were used to enhance the precision of comparing tooth loss for two populations. Both populations received routine dental care, but only one received comprehensive periodontal treatment. The analysis provides evidence that adding periodontal treatment to routine dental care is associated with less tooth loss and more patients who do not lose any teeth. Furthermore, it may be possible to nearly eliminate tooth loss associated with periodontal disease.

Tooth loss in 776 treated periodontal patients.

BACKGROUND: The most common form of periodontitis is a variably progressive dynamic pathologic process that causes attachment loss, destroys the alveolar bone supporting a tooth, and terminates with tooth loss. We evaluated the loss of teeth of treated periodontal patients categorized by severity and risk. METHODS: Each of nine periodontists evaluated 100 consecutive periodontal maintenance patients. The disease severity and risk level were determined from data at the initial examination. The number of teeth lost was determined from data at the initial and maintenance visits. RESULTS: A stepwise regression analysis showed that disease (P = 0.0000478) and risk (P = 0.00129) scores predicted the mean tooth loss rate. The adjusted R(2) statistic was 88.56%. The ordinal logistic regression model showed that only the disease score (P <0.0005) was significantly associated with the probability of patients losing a specific number of teeth. CONCLUSIONS: Categorizing a patient by severity may be beneficial in the management of the periodontal patient. The disease score can be used to establish a criterion and target for care. For example, treatment can result in nearly no lost teeth when the severity is low, and this benefit is lost when the severity is high. The disease score provides an objective means to quickly determine severity. An increase in the disease score provides evidence that a new treatment plan is needed. Therefore, the effect of the routine use of the disease score could result in fewer patients with severe disease and reduce the number of teeth lost.

Periodontitis severity plus risk as a tooth loss predictor.

BACKGROUND: Tooth loss can be a consequence of the natural history of periodontitis. Stratification of periodontitis severity, risk, and tooth loss exists within the United States adult population, and tooth loss correlates to severity and risk. We evaluated the loss of teeth for a periodontitis-affected population categorized by the combination of severity and risk in which the subjects predominantly did not receive periodontal treatment. METHODS: The clinical records of 523 subjects enrolled in the Veterans Affairs Dental Longitudinal Study, covering a period of 15 years, were used. Disease severity, risk level, and the number of teeth lost for each subject were determined. RESULTS: A stepwise regression analysis showed that disease and risk scores predicted mean tooth loss rate. The P value for the disease score was <0.0005, and the P value for the risk score was 0.001. The ordinal logistic regression model showed that disease (P = 0.002) and risk scores (P = 0.000) were significantly associated with the probability of subjects losing a specific number of teeth. CONCLUSIONS: Tooth loss is more precisely and accurately predicted by the combination of risk score and periodontal disease score than by either score alone. The combined scores may be a surrogate variable for periodontal status. Because the scores are derived from routine clinical measurements, they may be useful for population surveillance and dynamics, practice management, patient care decisions, practice-based research, and the determination of treatment effectiveness and the factors required for successful treatment, resulting in improved oral health and higher clinician productivity and income.

The Oral Health Information Suite (OHIS): its use in the management of periodontal disease.

Health care costs continue to increase at a rapid rate. Dental costs alone have risen from $31.5 billion in 1990 to $70.3 billion in 2002, outpacing inflation by 160 percent. Payers for health care services have no means to evaluate the value of these large expenditures. Quantified information is not available regarding a patient's condition prior to and after treatment nor on the probability of future disease. The absence of this information prevents dentists from responding effectively to challenges by payers and patients, and specifically prevents dentists from effectively influencing the quality of periodontal care. We have developed a user-friendly Internet-based technology that quantifies risk for periodontitis and periodontal disease severity and extent and generates recommended treatments and interventions. A caries risk assessment tool has also been developed, and an oral cancer assessment tool is being developed. This technology, designated the Oral Health Information Suite (OHIS), provides quantitative information to the clinician and patient as an aid to diagnosis and to facilitate individual, needs-based treatment planning. OHIS enables successful application of the wellness model of oral health care, which may be expected to result in more uniform and accurate clinical decision making, improved oral health, reduction in the need for complex periodontal therapy, reduction in oral health care costs, and improved clinician productivity and income. It also will permit patients to become more involved in their oral health care, payers to quantify and predict their health care expenditures, dentists to experience an increase in trust and respect, and periodontists to be more properly consulted regarding periodontal care.

Use of risk assessment in attaining and maintaining oral health.

The cost of health care is increasing rapidly. A transition from the "repair" to a "wellness" model of oral health care that minimizes the occurrence of disease and the need for complex treatment may reduce costs and improve oral health. Quantification of risk is essential for successful use of the wellness model. Subjective risk assessment by expert clinicians is too variable to be useful in clinical decision making. However, user-friendly Internet-based technology has been developed that provides a consistently accurate and valid quantified risk assessment for periodontitis, as well as a quantified measure of disease severity and extent. The numeric information helps clinicians and patients make diagnoses and generate individual, needs-based treatment plans. This technology enables successful application of the wellness model of care in day-to-day dental practice. Use of the wellness model may result in more uniform and accurate periodontal clinical decision making, improved oral health, less need for complex periodontal therapy, lower oral health care costs, and improved clinician productivity and income.