Purebrush Antibacterial Toothbrush Purifier

Subjects and methods Subjects. We recruited a convenience sample of 320 veterans who were at least 60 years of age from the dental outpatient clinic of the Veterans Affairs Hospital, Ann Arbor, Mich. (n = 206), and from a long-term care facility (nursing home) associated with the hospital (n = 114). Patients who use the Veterans Affairs medical and dental facilities usually have a service-connected injury or disability, or have met a financial hardship qualification. There were no age, sex, racial, income or educational differences among the subjects from the dental outpatient clinic (that is, the independent living group) and the long-term care facility (that is, the dependent living group), which led us to initially combine all the subjects into a single group for statistical analysis.⁹ The group from the long-term care facility had significantly more edentulous subjects than did the outpatient group⁹; because of this, a separate variable indicating whether the participant entered from the independent living or dependent living situation was included in the multivariate analysis to account for any confounders that might have been introduced as a result of entry site. Dental variables. One of us (B.L.D.) performed a clinical examination to determine the number of teeth and the number of restorations and amount of decay on all tooth surfaces in each subject. The presence and type of dentures and other prosthetic devices were recorded. We stratified the subjects into two groups: the first was composed of subjects with one to 14 teeth and the second was composed of subjects with 15 to 28 teeth. This division was done because subjects with one to 14 teeth could have been wearing a full denture, whereas subjects with 15 to 28 teeth could not wear a full denture. Third molars, which were rarely present in these older subjects, were omitted from all analyses. We measured periodontal pocket depths, attachment levels and gingival recession for all teeth with an automated pressure-sensitive periodontal probe (Dental Probe Inc.), and recorded the results electronically. Oral hygiene was assessed with the plaque index, or PlI,¹⁰ and gingivitis was assessed with the papillary bleeding score, or PBS.^11,12 We calculated the mean PlI and PBS for each subject by summing the PlIs and PBSs of the individual teeth, and then dividing the totals by the number of teeth. These mean PlIs and PBSs were then used in the statistical analyses. The subjects were asked how often they brushed and flossed their teeth and how often they visited their dentist or hygienist. A complaint of xerostomia was elicited by asking questions about perceived dryness of the mouth.¹³ Bacteriologic variables. We used stimulated saliva to determine the number of colony-forming units, or CFU, of selected bacterial types. The media used, the dispersal procedures and the culturing conditions have been described elsewhere.¹⁴ A curette was used to obtain plaque samples from the mesial surface of the first molars or, if they were missing, from the most posterior tooth in each quadrant. The four plaque samples were individually applied to the lower reagent strip on the BANA test card. The cards were incubated at chairside for five minutes at 55 C,¹⁵ and the resultant blue color for each plaque sample was scored and the numbers averaged to give a single BANA score for each subject. Medical variables. For this study, CHD had to be a medically established diagnosis in the patient�s medical record based on the International Classification of Diseases, 9th Revision, coding system used by the Veterans Affairs Hospital for CHD. This diagnosis was supplemented with a review of the patient�s medical records and documentation of established myocardial infarction; bypass surgery; clinical angina; electrocardiogram readings; serum enzyme levels, if available; angiography; and a positive response to treatment for heart disease. Systolic and diastolic blood pressures and blood cholesterol values were obtained from the patient�s medical records. We also obtained the subject�s diabetic status from the medical records; if diabetes was present, the medical records indicated whether it was controlled by insulin, diet or medication. Two of the authors (B.L.D. and N.G.) obtained the patient�s weight and height during the dental examination, and these measurements were used to calculate the body mass index, or BMI. We determined the number of medications used by the subjects through interviews (performed by B.L.D. and N.G.) and by examining a computerized record of medications maintained by the Department of Veteran�s Affairs. Statistical methods. Summary statistics are presented as means (± standard deviations) and frequencies, with percentages as appropriate. Initial statistical tests consisted of X² analyses and/or multiple logistic models with Bonferroni-adjusted (P < .05) pairwise comparisons for categorical data. After extensive investigation of transformations of the continuous measures, we concluded that normality was not achievable, so nonparametric Wilcoxon-Rank sum or Kruskal-Wallis tests with Bonferroni-adjusted (P < .05) pairwise comparisons were used. Based on these initial analyses, variables with P < .25 were entered into multiple logistic analyses. These models consisted of predictors with dental relevance and/or statistical significance. Only significant (P < .05) overall models were retained, and significant (P < .05) predictors were noted. Two models were developed, one that included all subjects, but excluded the dental variables (that is, the all-subjects model) and one that was restricted to the dentate subjects, but included the dental variables (that is, the dentate subjects model). In the all-subjects model, we would be able to observe the effect of variables such as salivary flow and swallowing, independent of the tooth-related variables, whereas in the dentate subjects model, any effect of being edentulous and wearing complete dentures would not be considered. These models consisted of predictors with dental relevance and/or statistical significance, as well as many of the recognized risk factors for CHD (that is, diabetes, smoking history, current use of alcohol, age, BMI, blood pressure and serum cholesterol levels). Based on analyses of these models, variables with P < .25 were entered into reduced models until a model was obtained that combined the highest likelihood ratio with the fewest degrees of freedom. In this reduced model, the nonsignificant variables were then added back, one at a time, to the model containing the significant variables, to determine their individual effect on the model.

Introduction	Results

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