Identifying optimal survey-based algorithms to distinguish diabetes type among adults with diabetes.

OBJECTIVES: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. RESEARCH DESIGN AND METHODS: We collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents' report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases. RESULTS: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as "other diabetes type" all respondents who reported "other," and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations. CONCLUSIONS: The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

Seroepidemiology of human papillomavirus type 11 in the United States: results from the third National Health And Nutrition Examination Survey, 1991--1994.

OBJECTIVES: The national seroprevalence of the nononcogenic human papillomavirus (HPV) type 11, one of the types targeted by the quadrivalent HPV vaccine, has not been evaluated in the United States. The objectives of this study were to estimate the national seroprevalence and evaluate predictors of HPV-11 seropositivity. STUDY DESIGN: We tested serum samples for HPV-11 antibodies and analyzed questionnaire data from the second phase of the National Health and Nutrition Examination Survey III, 1991--1994. Seroprevalence estimates were weighted to represent the US population. RESULTS: : Overall seroprevalence of HPV-11 infection was 4.7%. Seroprevalence was significantly higher among females (5.7%) than among males (3.6%). Independent predictors of HPV-11 seropositivity included sex, race/ethnicity, lifetime number of sex partners, education, and HPV-16 seropositivity. CONCLUSION: This study represents the most comprehensive picture of HPV-11 infection in the United States to date, and provides baseline data on the prevalence of HPV-11 before availability of the quadrivalent HPV vaccine.

Effect of storage temperatures on the stability of cytokines in cervical mucous.

Cytokines have progressively come to serve as indicators for the presence or severity of a disease. But accurate measurement of cytokine levels can be deterred by lack of proper handling and storage of the samples. In this study, we attempted to measure the effect of snap-freezing and refrigeration at the time of collection on cervical mucous. Luminex analyses of the frozen and refrigerated pairs exhibited no significant differences in levels for 7 out of the 10 cytokines measured simultaneously. The cytokines TNF-alpha, IFN-gamma and IL-1beta, were significantly different between the pairs with the refrigerated samples showing higher levels for each of these cytokines. The results suggest that refrigeration of mucous samples immediately after collection would allow for better conservation of the cytokines in cervical mucous.