We developed a sampling frame for a probability-based household survey by purchasing an exhaustive list of over 818,000 residential mailing addresses in Dallas County, Texas. The addresses were obtained from the Delivery Sequence File (DSF) offered by the US Postal Service (USPS) through a nonexclusive license agreement with private companies. The DSF is a computerized file that contains all delivery point addresses serviced by the USPS, with the exception of general delivery. We used the geographic coordinates of the addresses to construct digital maps of the immediate vicinity around each selected address to help the field interviewers locate the selected address. To evaluate the coverage of the mailing addresses, we selected a sub-sample of 2,498 addresses and used the Half-Open Interval (HOI) procedure (Kish 1965) to search for missed housing units in the interval between the selected address and the next address in delivery sequence order. A total of 46 missed addresses were found with the HOI procedure. Also, we found that the vast majority of persons who maintain a residential P.O. Box also have mail delivered to their street address. The 90 percent occupancy rate is consistent with other metropolitan household surveys that use traditional on-site enumeration methods.
This study attempts to extend what is known about adolescent substance abusers in adolescent-oriented substance abuse treatment by describing and comparing background and pretreatment characteristics and posttreatment outcomes of African American (n = 213), Hispanic (n = 108), and White adolescent (n = 773) substance abusers who participated in the Drug Abuse Treatment Outcome Studies for Adolescents (DATOS-A). The pretreatment data indicated that patients in each group were similar only with respect to basic demographics. Posttreatment comparisons revealed racial/ ethnic differences in serious illegal activity only. Logistic regression results indicated that African American adolescents had a lower likelihood of engaging in serious illegal activity as compared to White adolescents during the posttreatment period. The results of this study provide a mechanism for more comprehensive understanding of adolescent substance abusers, their treatment needs, and their treatment outcomes.
BackgroundNuclear magnetic resonance spectroscopy is one of the primary tools in metabolomics analyses, where it is used to track and quantify changes in metabolite concentrations or profiles in response to perturbation through disease, toxicants or drugs. The spectra generated through such analyses are typically confounded by noise of various types, obscuring the signals and hindering downstream statistical analysis. Such issues are becoming increasingly significant as greater numbers of large-scale systems or longitudinal studies are being performed, in which many spectra from different conditions need to be compared simultaneously.ResultsWe describe a novel approach, termed Progressive Consensus Alignment of Nmr Spectra (PCANS), for the alignment of NMR spectra. Through the progressive integration of many pairwise comparisons, this approach generates a single consensus spectrum as an output that is then used to adjust the chemical shift positions of the peaks from the original input spectra to their final aligned positions. We characterize the performance of PCANS by aligning simulated NMR spectra, which have been provided with user-defined amounts of chemical shift variation as well as inter-group differences as would be observed in control-treatment applications. Moreover, we demonstrate how our method provides better performance than either template-based alignment or binning. Finally, we further evaluate this approach in the alignment of real mouse urine spectra and demonstrate its ability to improve downstream PCA and PLS analyses.ConclusionsBy avoiding the use of a template or reference spectrum, PCANS allows for the creation of a consensus spectrum that enhances the signals within the spectra while maintaining sample-specific features. This approach is of greatest benefit when complex samples are being analyzed and where it is expected that there will be spectral features unique and/or strongly different between subgroups within the samples. Furthermore, this approach can be potentially applied to the alignment of any data having spectra-like properties.
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