Early Identification of Type 2 Diabetes: Policy should be aligned with health systems strengthening

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Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data

Multi-state models of chronic disease are becoming increasingly important in medical research to describe the progression of complicated diseases. However, studies seldom observe health outcomes over long time periods. Therefore, current clinical research focuses on the secondary data analysis of the published literature to estimate a single transition probability within the entire model. Unfortunately, there are many difficulties when using secondary data, especially since the states and transitions of published studies may not be consistent with the proposed multi-state model. Early approaches to reconciling published studies with the theoretical framework of a multi-state model have been limited to data available as cumulative counts of progression. This paper presents an approach that allows the use of published regression data in a multi-state model when the published study may have ignored intermediary states in the multi-state model. Colloquially, we call this approach the Lemonade Method since when study data give you lemons, make lemonade. The approach uses maximum likelihood estimation. An example is provided for the progression of heart disease in people with diabetes. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63056/1/3599_ftp.pd

Estimated morbidity and mortality in adolescents and young adults diagnosed with Type 2 diabetes mellitus

Aims  To estimate remaining life expectancy (RLE), quality‐adjusted life expectancy (QALE), causes of death and lifetime cumulative incidence of microvascular/macrovascular complications of diabetes for youths diagnosed with Type 2 diabetes. Methods  A Markov‐like computer model simulated the life course for a hypothetical cohort of adolescents/young adults in the USA, aged 15–24 years, newly diagnosed with Type 2 diabetes following either conventional or intensive treatment based on the UK Prospective Diabetes Study. Outcomes included RLE, discounted QALE in quality‐adjusted life years (QALYs), cumulative incidence of microvascular/macrovascular complications and causes of death. Results  Compared with a mean RLE of 58.6 years for a 20‐year‐old in the USA without diabetes, conventional treatment produced an average RLE of 43.09 years and 22.44 discounted QALYs. Intensive treatment afforded an incremental 0.98 years and 0.44 discounted QALYs. Intensive treatment led to lower lifetime cumulative incidence of all microvascular complications and lower mortality from microvascular complications (e.g. end‐stage renal disease (ESRD) death 19.4% vs. 25.2%). Approximately 5% with both treatments had ESRD within 25 years. Lifetime cumulative incidence of coronary heart disease (CHD) increased with longer RLE and greater severity of CHD risk factors. Incorporating disutility (loss in health‐related quality of life) of intensive treatment resulted in net loss of QALYs. Conclusions  Adolescents/young adults with Type 2 diabetes lose approximately 15 years from average RLE and may experience severe, chronic complications of Type 2 diabetes by their 40s. The net clinical benefit of intensive treatment may be sensitive to preferences for treatment. A comprehensive management plan that includes early and aggressive control of cardiovascular risk factors is likely needed to reduce lifetime risk of CHD.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90578/1/j.1464-5491.2011.03542.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/90578/2/DME_3542_sm_Technical_ReportS1.pd

Cost-Effectiveness of Intensified Versus Conventional Multifactorial Intervention in Type 2 Diabetes: Results and projections from the Steno-2 study

OBJECTIVE—To assess the cost-effectiveness of intensive versus conventional therapy for 8 years as applied in the Steno-2 study in patients with type 2 diabetes and microalbuminuria

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

Background: Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these tests varies substantially. Approach: An expert committee compiled evidence-based recommendations for the use of laboratory testing for patients with diabetes. A new system was developed to grade the overall quality of the evidence and the strength of the recommendations. Draft guidelines were posted on the Internet and presented at the 2007 Arnold O. Beckman Conference. The document was modified in response to oral and written comments, and a revised draft was posted in 2010 and again modified in response to written comments. The National Academy of Clinical Biochemistry and the Evidence-Based Laboratory Medicine Committee of the American Association for Clinical Chemistry jointly reviewed the guidelines, which were accepted after revisions by the Professional Practice Committee and subsequently approved by the Executive Committee of the American Diabetes Association. Content: In addition to long-standing criteria based on measurement of plasma glucose, diabetes can be diagnosed by demonstrating increased blood hemoglobin A$_{1c}$ (HbA$_{1c}$) concentrations. Monitoring of glycemic control is performed by self-monitoring of plasma or blood glucose with meters and by laboratory analysis of HbA$_{1c}$. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of autoantibodies, urine albumin, insulin, proinsulin, C-peptide, and other analytes are addressed. Summary: The guidelines provide specific recommendations that are based on published data or derived from expert consensus. Several analytes have minimal clinical value at present, and their measurement is not recommended