Advancing youth tobacco surveillance with novel methods.

Tobacco surveillance in the United States is robust but cannot be quickly modified to capture newly identified tobacco products or behaviors. We present an example of a rapidly deployed nonprobability survey using social media recruitment that collected data on rapidly changing tobacco use behaviors. We recruited 15- to 17-year old current vapers from NY, USA, using targeted social media advertisements to complete the New York Adolescent Vaping Survey (NY AVS), which asked about vaping behaviors not addressed in existing probability surveillance surveys. We used the New York Youth Risk Behavior Survey (NY YRBS) to apply calibration weights to ensure that the distribution of the demographic characteristics accurately reflected the population distribution. We found systematic differences in demographic variable distributions between the probability-based NY YRBS and the convenience sample of the NY AVS that were reconciled in the weighting calibration. We found no statistically significant differences between the NY YRBS and NY AVS estimates after calibration for two outcome variables of interest. Recruiting a sample of adolescents using social media advertising to conduct a rapid survey on vaping provided valuable data that complemented traditional surveillance surveys; this approach could be used to fill future knowledge gaps in youth tobacco surveillance.

Multi-wavelength high-performance liquid chromatographic fingerprints and chemometrics to predict the antioxidant activity of Turnera diffusa as part of its quality control.

The determination of the antioxidant activity of Turnera diffusa using partial least squares regression (PLSR) on chromatographic data is presented. The chromatograms were recorded with a diode array detector and, for each sample, an enhanced fingerprint was constructed by compiling into a single data vector the chromatograms at four wavelengths (216, 238, 254 and 345 nm). The wavelengths were selected from a contour plot, in order to obtain the greater number of peaks at each of the wavelengths. A further pretreatment of the data that included baseline correction, scaling and correlation optimized warping was performed. Optimal values of the parameters used in the warping were found by means of simplex optimization. A PLSR model with four latent variables (LV) explained 52.5% of X variance and 98.4% of Y, with a root mean square error for cross validation of 6.02. To evaluate its reliability, it was applied to an external prediction set, retrieving a relative standard error for prediction of 7.8%. The study of the most important variables for the regression indicated the chromatographic peaks related to antioxidant activity at the used wavelengths.