Dataset Information

Forecasting the type 2 diabetes mellitus epidemic and the role of key risk factors in Oman up to 2050: Mathematical modeling analyses.

ABSTRACT:

Aims/introduction

To investigate and forecast type 2 diabetes mellitus epidemic, its related risk factors and cost in Oman by 2050.

Materials and methods

An age-structured mathematical model was used to characterize type 2 diabetes mellitus epidemiology and trends in Oman between 1990 and 2050. The model was parametrized using current and quality data, including six nationally representative population-based epidemiological surveys for type 2 diabetes mellitus and its key risk factors.

Results

The projected type 2 diabetes mellitus prevalence increased from 15.2% in 2020 to 23.8% in 2050. The prevalence increased from 16.8 and 13.8% in 2020 among women and men to 26.3 and 21.4% in 2050, respectively. In 2020, 190,489 Omanis were living with type 2 diabetes mellitus compared with 570,227 in 2050. The incidence rate per 1,000 person-years changed from 8.3 in 2020 to 12.1 in 2050. Type 2 diabetes mellitus' share of Oman's national health expenditure grew by 36% between 2020 and 2050 (from 21.2 to 28.8%). Obesity explained 56.7% of type 2 diabetes mellitus cases in 2020 and 71.4% in 2050, physical inactivity explained 4.3% in 2020 and 2.7% in 2050, whereas smoking accounted for <1% of type 2 diabetes mellitus cases throughout 2020-2050. Sensitivity and uncertainty analyses affirmed these predictions.

Conclusions

The type 2 diabetes mellitus epidemic in Oman is expected to increase significantly over the next three decades, consuming nearly one-third of the national health expenditure. The type 2 diabetes mellitus burden is heavily influenced by obesity. Interventions targeting this single risk factor should be a national priority to reduce and control the burden of type 2 diabetes mellitus in Oman.

SUBMITTER: Awad SF

PROVIDER: S-EPMC8264408 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSimple phenomenological growth models can be useful for estimating transmission parameters and forecasting epidemic trajectories. However, most existing phenomenological growth models only support single-peak outbreak dynamics whereas real epidemics often display more complex transmission trajectories.MethodsWe develop and apply a novel sub-epidemic modeling framework that supports a diversity of epidemic trajectories including stable incidence patterns with sustained or damped oscillations to better understand and forecast epidemic outbreaks. We describe how to forecast an epidemic based on the premise that the observed coarse-scale incidence can be decomposed into overlapping sub-epidemics at finer scales. We evaluate our modeling framework using three outbreak datasets: Severe Acute Respiratory Syndrome (SARS) in Singapore, plague in Madagascar, and the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC) and four performance metrics.ResultsThe sub-epidemic wave model outperforms simpler growth models in short-term forecasts based on performance metrics that account for the uncertainty of the predictions namely the mean interval score (MIS) and the coverage of the 95% prediction interval. For example, we demonstrate how the sub-epidemic wave model successfully captures the 2-peak pattern of the SARS outbreak in Singapore. Moreover, in short-term sequential forecasts, the sub-epidemic model was able to forecast the second surge in case incidence for this outbreak, which was not possible using the simple growth models. Furthermore, our findings support the view that the national incidence curve of the Ebola epidemic in DRC follows a stable incidence pattern with periodic behavior that can be decomposed into overlapping sub-epidemics.ConclusionsOur findings highlight how overlapping sub-epidemics can capture complex epidemic dynamics, including oscillatory behavior in the trajectory of the epidemic wave. This observation has significant implications for interpreting apparent noise in incidence data where the oscillations could be dismissed as a result of overdispersion, rather than an intrinsic part of the epidemic dynamics. Unless the oscillations are appropriately modeled, they could also give a false positive, or negative, impression of the impact from public health interventions. These preliminary results using sub-epidemic models can help guide future efforts to better understand the heterogenous spatial and social factors shaping sub-epidemic patterns for other infectious diseases.

Project description:BACKGROUND:Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis. METHODS:We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n?=?13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20?Gy or 40?Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient). RESULT:The selected model resulted in <?12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient?>?0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient?>?0.77 and?>?0.65, respectively). CONCLUSIONS:This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.