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Assessing the cost effectiveness of a new health intervention often requires modelling to estimate the impact of the intervention on cost, survival and quality of life over the lifetime of a cohort of patients. Markov modelling is a methodology that is commonly employed to estimate these long-term costs and benefits. As commonly used, these models assume that the patients continue to get the treatments assigned regardless of the change in health states. In this paper, we describe an extension to the Markov modelling approach, called Markov decision modelling. Such a model starts with a set of health states and treatments and optimally assigns treatments to each of the health states.A Markov decision model can be used to identify the optimal treatment strategy not just for the initial disease state, but also as the disease state changes over time. We present a dynamic programming approach to identifying the optimal assignment of treatments, and illustrate this methodology using an example.The Markov decision modelling approach provides an efficient way of identifying optimal assignment of treatments to health states, but, like the standard Markov model, may be of limited use when probabilities of future events depend on past history in a complex fashion. Even with its limitations, Markov decision models offer an opportunity for health economists to inform healthcare decision-makers on how to modify current treatment pathways to incorporate new treatments as they become available. [ABSTRACT FROM AUTHOR]

Aim: We aimed to explore the association between South Asian ethnicity and complications of type 1 diabetes, and whether this is affected by migration., Methods: In this retrospective cohort study, data on diabetes control and complications were obtained for South Asians in India (South Asians India , n = 2592) and the UK (South Asians UK , n = 221) and white Europeans in the UK (n = 1431). Multivariable logistic regression was used to identify associations between ethnicity and diabetic kidney disease, retinopathy and neuropathy adjusting for age, sex, BMI, disease duration, HbA 1c , blood pressure (BP) and cholesterol., Results: South Asians India had significantly greater adjusted odds of diabetic kidney disease [odds ratio (OR) 5.0, 95% confidence intervals (CI) 3.6-7.1] and retinopathy (OR 1.8, 95% CI 1.2-2.5), but lower odds of neuropathy (OR 0.5, 95% CI 0.4-0.6) than white Europeans. South Asians India had significantly greater adjusted odds of diabetic kidney disease (OR 3.0, 95% 1.8-5.3) than South Asians UK , but there was no significant difference in the odds of other complications., Conclusions: In this hypothesis-generating study, we report that South Asian ethnicity is associated with greater risk of diabetic kidney disease and retinopathy, and lower risk of neuropathy than white European ethnicity. Part of the excess diabetic kidney disease risk is reduced in South Asians UK . These associations cannot be accounted for by differences in vascular risk factors. Our findings in South Asians with type 1 diabetes mirror previous findings in type 2 diabetes and now need to be validated in a study of the effect of ethnicity on type 1 diabetes complications where healthcare is provided in the same setting., (© 2019 Diabetes UK.)

OBJECTIVE To compare screen-detected diabetes prevalence and the degree of diagnostic agreement by ethnicity with the current oral glucose tolerance test (OGTT)-based and newly proposed A1C-based diagnostic criteria. RESEARCH DESIGN AND METHODS Six studies (1999-2009) from Denmark, the U.K., Australia, Greenland, Kenya, and India were tested for the probability of an A1C > or =6.5% among diabetic case subjects based on an OGTT. The difference in probability between centers was analyzed by logistic regression adjusting for relevant confounders. RESULTS Diabetes prevalence was lower with the A1C-based diagnostic criteria in four of six studies. The probability of an A1C > or =6.5% among OGTT-diagnosed case subjects ranged widely (17.0-78.0%) by study center. Differences in diagnostic agreement between ethnic subgroups in the U.K. study were of the same magnitude as between-country comparisons. CONCLUSIONS A shift to an A1C-based diagnosis for diabetes will have substantially different consequences for diabetes prevalence across ethnic groups and populations.

The aim of this study was to ascertain whether international glycaemic index (GI) values, predominantly developed using peoples living in Europe, North America or Australia, are applicable to Asian Indians resident in their own country. Thirty-four Caucasian subjects were recruited in Oxford, UK and thirteen Asian Indian subjects in Chennai, India. Two types of sweet biscuits and three breakfast cereals were tested for glycaemic response in each group. Subjects were served equivalent available carbohydrate amounts (50 g) of test foods and a reference food (glucose), on separate occasions. Capillary blood glucose was measured from finger-prick samples in fasted subjects ( - 5 and 0 min) and at 15, 30, 45, 60, 90 and 120 min after starting to eat. For each test food, the incremental area under the curve (IAUC) and GI values were determined. Although the glycaemic response to the reference food was higher in Asian Indian subjects compared with UK Caucasian subjects (IAUC 219 mmol/min per litre v. 157 mmol/min per litre, respectively; P < 0.01), there was no significant difference in GI values of the five test foods between the two groups. This is the first study known to the authors to examine the role of ethnicity on GI when the subjects are resident in their own countries. The findings from this study have important implications for the use of the GI concept worldwide and support the application of international values to different ethnic groups. The higher glycaemic response to all foods in Asian Indians may represent another mechanism for increased diabetes susceptibility among Asian Indians.

We have investigated the distribution of genotypes of a restriction fragment length polymorphism of the T-cell receptor beta-subunit gene in Caucasoid controls and patients with insulin-dependent diabetes mellitus, celiac disease, dermatitis herpetiformis, and idiopathic membranous nephropathy and also in South Indian controls and diabetics. We found no significant differences between the controls and patients with any disease in either ethnic group, a result which contrasts with previous reports of associations with both insulin-dependent diabetes mellitus and idiopathic membranous nephropathy. However, the most striking finding was a marked disparity between the genotype distribution in our Caucasoid control population and that previously reported by other investigators.

A genetic analysis of diabetic and non-diabetic Punjabi Sikhs (n = 164) was made for markers of non-insulin-dependent diabetes mellitus using insulin receptor, insulin, and HLA-D alpha chain gene probes. Additionally British Caucasoids (n = 163) were studied using the insulin receptor probe. Insulin receptor gene restriction fragment length polymorphisms were defined using Southern blot techniques and the restriction enzyme Bgl II and BAm Hl. In Punjabi Sikhs and British Caucasoids neither of the restriction fragment length polymorphisms distinguished non-insulin-dependent diabetes mellitus subjects from controls. In the Sikhs no association with non-insulin-dependent diabetes mellitus was seen with the hypervariable region of the insulin gene or with HLA-DR/DQ/DX alpha chain restriction fragment length polymorphisms. We therefore conclude that despite the high prevalence of non-insulin-dependent diabetes mellitus in Asians we were unable to find any genetic markers for this disease using the available cloned gene probes.