Projected number of diabetic renal disease patients among insulin-dependent diabetes mellitus children in Japan using a Markov model with probabilistic sensitivity analysis

Projecting the Burden of Chronic Kidney Disease in a Developed Country and Its Implications on Public Health

BACKGROUND

Chronic Kidney Disease (CKD) is a major public health problem worldwide. There is limited literature on a model to project the number of people with CKD. This study projects the number of residents with CKD in Singapore by 2035 using a Markov model.

METHODS

A Markov model with nine mutually exclusive health states was developed according to the clinical course of CKD, based on a discrete time interval of 1 year. The model simulated the transition of cohorts across different health states from 2007 to 2035 using prevalence, incidence, mortality, disease transition, and disease detection rates.

RESULTS

From 2007 to 2035, the number of residents with CKD is projected to increase from 316,521 to 887,870 and the prevalence from 12.2% to 24.3%. Patients with CKD stages 1-2 constituted the largest proportion. The proportion of undiagnosed cases will decline from 72.1% to 56.4%, resulting from faster progression to higher CKD stages and its eventual detection.

CONCLUSION

By 2035, about one-quarter of the Singapore residents are expected to have CKD. National policies need to focus on primary disease prevention and early disease detection to avoid delayed treatment of CKD which eventually leads to end-stage renal disease.

Projection of prediabetes and diabetes population size in Singapore using a dynamic Markov model

BACKGROUND

The aim of the present study was to forecast the prevalence and number of adult Singapore residents with prediabetes and diabetes in 2035.

METHODS

A dynamic Markov model with nine mutually exclusive states was developed based on the clinical course of diabetes using time-dependent rates and probabilities. A 1-year cycle over a 25-year time horizon from 2010 to 2035 was used in the model. With publicly available data and a chronic disease register, the model forecast annual disease burden by simulating transition of cohorts across different health states using prevalence rates, incidence rates, mortality rates, disease transition, disease detection, and complication rates. An aging index was used in the model in anticipation of population aging to minimize risks of underestimating disease burden.

RESULTS

From 2010 to 2035, the number of Singapore residents with prediabetes and diabetes is projected to more than double, from 434 685 to 903 596 and from 373 104 to 823 802, respectively. The prevalence of prediabetes and diabetes will rise steadily from 15.5 % to 24.9 % and from 13.3 % to 22.7 %, respectively. By 2035, a further estimate of 733 174 and 100 250 patients with prediabetes and uncomplicated diabetes, respectively, will remain undiagnosed. The prevalence of detected and undetected complications is forecast to rise from 60.0 % in 2010 to 70.2 % by 2035.

CONCLUSION

By 2035, the prevalence of prediabetes and diabetes among Singapore residents aged 21+ years is expected to be one in four and one in five, respectively. There is an impetus to adopt more aggressive interventions to contain disease progression.

Large-Sample Bayesian Posterior Distributions for Probabilistic Sensitivity Analysis

In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both controlled and uncontrolled studies, and discuss how to use these posterior distributions in a probabilistic sensitivity analysis. These results draw on and extend procedures from the literature on large-sample Bayesian posterior distributions and Bayesian random effects meta-analysis. They improve on standard approaches to probabilistic sensitivity analysis by allowing a proper accounting for heterogeneity across studies as well as dependence between control and treatment parameters, while still being simple enough to be carried out on a spreadsheet. The authors apply these methods to conduct a probabilistic sensitivity analysis for a recently published analysis of zidovudine prophylaxis following rapid HIV testing in labor to prevent vertical HIV transmission in pregnant women.