This review reports the main results concerning two of the principal research topics of the Centre of Epidemiology, Biostatics and medical Information technology: epidemiology of type 1 diabetes, and statistical methods of age estimation. Our studies investigated the disease incidence, the time trend, the geographical patterns, the role of environmental factors, contributing to increase the knowledge of type 1 diabetes occurrence. Studying the determinants of disease management, the use of new technologies, patients’ quality of life and disease awareness, have provided useful information improving disease management. Statistical methods of age estimation were studied evaluating the performance of regression-based models assessing age in children. To overcome bias in age estimation of these models, a full Bayesian calibration method was developed. On-going research is developing aspects related to dietary intake, glycaemic variability, evaluation of therapeutic diagnostic paths and appropriateness of care in type 1 diabetes. Future studies will assess ethnic and racial differences in skeletal growth pattern and incorporation of multiple age predictors in the Bayesian calibration method to improve accuracy.

Methods in childhood health: Chronic disease epidemiology and age estimation / Gesuita, R.; Ferrante, L.; Skrami, E.; Carle, F.. - (2020), pp. 389-403. [10.1007/978-3-030-33832-9_26]

Methods in childhood health: Chronic disease epidemiology and age estimation

Gesuita R.;Ferrante L.
;
Skrami E.;Carle F.
2020-01-01

Abstract

This review reports the main results concerning two of the principal research topics of the Centre of Epidemiology, Biostatics and medical Information technology: epidemiology of type 1 diabetes, and statistical methods of age estimation. Our studies investigated the disease incidence, the time trend, the geographical patterns, the role of environmental factors, contributing to increase the knowledge of type 1 diabetes occurrence. Studying the determinants of disease management, the use of new technologies, patients’ quality of life and disease awareness, have provided useful information improving disease management. Statistical methods of age estimation were studied evaluating the performance of regression-based models assessing age in children. To overcome bias in age estimation of these models, a full Bayesian calibration method was developed. On-going research is developing aspects related to dietary intake, glycaemic variability, evaluation of therapeutic diagnostic paths and appropriateness of care in type 1 diabetes. Future studies will assess ethnic and racial differences in skeletal growth pattern and incorporation of multiple age predictors in the Bayesian calibration method to improve accuracy.
2020
The First Outstanding 50 Years of "Universita Politecnica delle Marche": Research Achievements in Life Sciences
978-3-030-33831-2
978-3-030-33832-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/290255
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