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

Glucose testing and insufficient follow-up of abnormal results: a cohort study

BACKGROUND: More than 6 million Americans have undiagnosed diabetes. Several national organizations endorse screening for diabetes by physicians, but actual practice is poorly understood. Our objectives were to measure the rate, the predictors and the results of glucose testing in primary care, including rates of follow-up for abnormal values. METHODS: We conducted a retrospective cohort study of 301 randomly selected patients with no known diabetes who received care at a large academic general internal medicine practice in New York City. Using medical records, we collected patients' baseline characteristics in 1999 and followed patients through the end of 2002 for all glucose tests ordered. We used multivariate logistic regression to measure associations between diabetes risk factors and the odds of glucose testing. RESULTS: Three-fourths of patients (78%) had at least 1 glucose test ordered. Patient age (≥45 vs. <45 years), non-white ethnicity, family history of diabetes and having more primary care visits were each independently associated with having at least 1 glucose test ordered (p < 0.05), whereas hypertension and hyperlipidemia were not. Fewer than half of abnormal glucose values were followed up by the patients' physicians. CONCLUSION: Although screening for diabetes appears to be common and informed by diabetes risk factors, abnormal values are frequently not followed up. Interventions are needed to trigger identification and further evaluation of abnormal glucose tests