Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions
Angela D. Liese,Andrew B. Lawson,Hae Ryoung Song,James Hibbert,Dwayne E. Porter,Michele Nichols,Archana P. Lamichhane,Dana Dabelea,Elizabeth J. Mayer-Davis,Debra Standiford,Lenna Liu,Richard F. Hamman,Ralph B. D'Agostino +12 more
TLDR
This 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.About:
This article is published in Health & Place.The article was published on 2010-05-01 and is currently open access. It has received 54 citations till now.read more
Citations
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The SEARCH for Diabetes in Youth study: rationale, findings, and future directions.
Richard F. Hamman,Ronny A. Bell,Dana Dabelea,Ralph B. D'Agostino,Lawrence M. Dolan,Giuseppina Imperatore,Jean M. Lawrence,Barbara Linder,Santica M. Marcovina,Elizabeth J. Mayer-Davis,Catherine Pihoker,Beatriz L. Rodriguez,Beatriz L. Rodriguez,Sharon Saydah +13 more
TL;DR: Markers of micro- and macrovascular complications are evident in youth with either type 1 and type 2 diabetes, highlighting the seriousness of diabetes in this contemporary cohort of youth.
Journal ArticleDOI
Young-onset type 2 diabetes mellitus - implications for morbidity and mortality.
Dianna J. Magliano,Julian W. Sacre,Jessica L. Harding,Edward W. Gregg,Paul Zimmet,Jonathan E. Shaw,Jonathan E. Shaw +6 more
TL;DR: The evidence pertaining to young-onset T2DM and its current and future burden of disease in terms of incidence and prevalence in both developed and developing nations is reviewed, highlighting the need for prevention initiatives in these populations.
Journal ArticleDOI
Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study
Angela D. Liese,Robin C. Puett,Archana P. Lamichhane,Michele Nichols,Dana Dabelea,Andrew B. Lawson,Dwayne E. Porter,James Hibbert,Ralph B. D'Agostino,Elizabeth J. Mayer-Davis +9 more
TL;DR: This study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk.
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The epidemic of type 1 diabetes: what is it telling us?
TL;DR: Investigation into the source of the current epidemic of T1D has shed light on several possible causes, but has not provided definitive answers, yet.
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Prevalence of Diagnosed and Undiagnosed Type 2 Diabetes Mellitus Among US Adolescents: Results from the Continuous NHANES, 1999–2010
TL;DR: T2DM accounts for approximately half of adolescent diabetes in the United States, and one-third of these cases are undiagnosed, indicating that T2DM accounted for 43% of all cases.
References
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Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
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General methods for monitoring convergence of iterative simulations
Stephen P. Brooks,Andrew Gelman +1 more
TL;DR: This work generalizes the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence.
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Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions
Alan Agresti,Brent A. Coull +1 more
TL;DR: For example, this paper showed that using the adjusted Wald test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes, and that the 95% score interval has similar behavior as the adjusted-Wald interval obtained after adding two "successes" and two "failures" to the sample.
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Bayesian image restoration, with two applications in spatial statistics
TL;DR: There has been much recent interest in Bayesian image analysis, including such topics as removal of blur and noise, detection of object boundaries, classification of textures, and reconstruction of two- or three-dimensional scenes from noisy lower-dimensional views as mentioned in this paper.
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Rejoinder (Bayesian image restoration,with two applications in spatial statistics)
TL;DR: The present paper argues that many problems in the analysis of spatial data can be interpreted as problems of image restoration, since the amounts of data involved allow routine use of computer intensive methods, such as the Gibbs sampler, that are not yet practicable for conventional images.