We evaluated the association of demographic and clinical characteristics with participation in an epidemiologic study of diabetes mellitus among youth.
SEARCH for Diabetes in Youth is a multicenter study of physician-diagnosed diabetes in youth under the age of 20 comprising a surveillance and a cohort component. At each center, we enumerated all prevalent cases of diabetes in 2001 (n=6266) and all incident cases between 2002 and 2004 (n=3668). After confirmation of eligibility and validation, we invited each case to complete a survey and participate in a study visit. Here we evaluate how age, sex, race, and diabetes type are associated with participation in the survey and study visit.
Among prevalent cases, participation in the survey was 68% and 41% in the study visit. Among 2002 to 2004 incident cases, participation varied for the survey (76%, 81%, and 82%) and study visit (52%, 60%, and 60%). In multivariate logistic regression analyses among all incident cases, older age was associated with a lower odds of participation in the study visit (15-17 vs. <10 years: OR 0.5, 95% CI 0.4-0.7; 18-19 vs. <10 years: OR 0.3, 95% CI 0.2-0.5), as was having type 2 diabetes vs. type 1 diabetes (OR 0.5, 95% CI 0.4-0.7) and being of African American race vs. non-Hispanic White (OR 0.6, 95% CI 0.4-0.8). Results were very similar among prevalent cases.
Elucidating the relationship between individual characteristics and participation is essential for evaluating nonresponse bias, correcting for it, and for planning and implementing recruitment strategies.
"Age has also been associated with non-response and attrition. Adolescents and older adults are generally harder to include than children and young to middle-aged adults [4,6,11,17]. However, very little is known about how the effect of extensive recruitment efforts relate to sample and survey characteristics. "
[Show abstract][Hide abstract] ABSTRACT: Background
Extensive recruitment effort at baseline increases representativeness of study populations by decreasing non-response and associated bias. First, it is not known to what extent increased attrition occurs during subsequent measurement waves among subjects who were hard-to-recruit at baseline and what characteristics the hard-to-recruit dropouts have compared to the hard-to-recruit retainers. Second, it is unknown whether characteristics of hard-to-recruit responders in a prospective population based cohort study are similar across age group and survey method.
First, we compared first wave (T1) easy-to-recruit with hard-to-recruit responders of the TRacking Adolescents’ Individual Lives Survey (TRAILS), a prospective population based cohort study of Dutch (pre)adolescents (at first wave: n = 2230, mean age = 11.09 (SD 0.56), 50.8% girls), with regard to response rates at subsequent measurement waves. Second, easy-to-recruit and hard-to-recruit participants at the fourth TRAILS measurement wave (n = 1881, mean age = 19.1 (SD 0.60), 52.3% girls) were compared with fourth wave non-responders and earlier stage drop-outs on family composition, socioeconomic position (SEP), intelligence (IQ), education, sociometric status, substance use, and psychopathology.
First, over 60% of the hard-to-recruit responders at the first wave were retained in the sample eight years later at the fourth measurement wave. Hard-to-recruit dropouts did not differ from hard-to-recruit retainers. Second, extensive recruitment efforts for the web based survey convinced a population of nineteen year olds with similar characteristics as the hard-to-recruit eleven year olds that were persuaded to participate in a school-based survey. Some characteristics associated with being hard-to-recruit (as compared to being easy-to-recruit) were more pronounced among non-responders, resembling the baseline situation (De Winter et al.2005).
First, extensive recruitment effort at the first assessment wave of a prospective population based cohort study has long lasting positive effects. Second, characteristics of hard-to-recruit responders are largely consistent across age groups and survey methods.
BMC Medical Research Methodology 07/2012; 12(1):93. DOI:10.1186/1471-2288-12-93 · 2.27 Impact Factor
"Basic demographic and clinical information was available for virtually all cases and generally available from a variety of data sources such as administrative data, medical record or selfreport . A hierarchical approach was used to classify case characteristics (Liese et al., 2008). "
[Show abstract][Hide abstract] ABSTRACT: We evaluated geographic variation in type 1 and type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002-03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10-19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small-area variation within study region was observed for T1DM and T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (r(SC)=0.31) and CO non-Hispanic Whites (r(CO)=0.40) and CO Hispanics (r(CO)=0.72). At the tract level, no evidence for meaningful joint spatial correlation was observed (r(SC)=-0.02; r(CO)=-0.02; r(OH)=0.03; and r(WA=)0.09). Our study provides evidence for the presence of both regional and small area, localized variation in type 1 and type 2 incidence among youth aged 10-19 years in the United States.
Health & Place 05/2010; 16(3):547-56. DOI:10.1016/j.healthplace.2009.12.015 · 2.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper discusses an adaptive nonlinear learning algorithm for direct-sequence code division multiple access (DS-CDMA) system. The algorithm is based on the least square support vector machine (LS-SVM), a nonlinear kernel based machine. The LS-SVM detectors have advantages in that they have moderate complexity, can realize nonlinear decision regions, can be implemented adaptively, and require only training sequence data from the desired user. Through simulations, the performance of bit error rate (BER) of the designed LS-SVM receiver is compared to other conventional CDMA receivers and observes that the LS-SVM detector's performance approaches that of the Bayesian receiver. The simulation results also show that the proposed adaptive LS-SVM receiver can track data in time varying environment.
Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on; 12/2002
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