TY - GEN
T1 - Multi-scale simulation modeling for prevention and public health management of diabetes in pregnancy and sequelae
AU - Qin, Yang
AU - Freebairn, Louise
AU - Atkinson, Jo-An
AU - Qian, Weicheng
AU - Safarishahrbijari, Anahita
AU - Osgood, Nathaniel D.
PY - 2019
Y1 - 2019
N2 - ![CDATA[Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.]]
AB - ![CDATA[Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.]]
KW - complications
KW - diabetes in pregnancy
KW - discrete-time systems
KW - intelligent agents (computer software)
KW - prevention
KW - risk factors
UR - http://hdl.handle.net/1959.7/uws:52846
U2 - 10.1007/978-3-030-21741-9_26
DO - 10.1007/978-3-030-21741-9_26
M3 - Conference Paper
SN - 9783030217402
SP - 256
EP - 265
BT - Social, Cultural, and Behavioral Modeling: Proceedings of the 12th International Conference, SBP-BRiMS 2019, Washington, DC, USA, July 9-12, 2019
PB - Springer Nature
T2 - International Conference on Social Computing_Behavioral-Cultural Modeling_and Prediction and Behavior Representation in Modeling and Simulation
Y2 - 9 July 2019
ER -
