Screening for diabetes in general practice: cross sectional population study

Lawrence, James; Bennett, P. H.; Young, Alan; Robinson, Anthony

doi:10.1136/bmj.323.7312.548

Cited by 86 publications

(84 citation statements)

References 11 publications

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“…On the other hand, the baseline predictors found in our study can easily be evaluated and used by physicians in treating patients in daily practice. It has been shown that screening for diabetes mellitus in general practice by measuring fasting blood glucose concentration is feasible, but has a very low yield in low risk population; 31 it could therefore be more beneficial in a high-risk patient group such as the present study population.…”

Section: Discussionmentioning

confidence: 92%

Predictors of new-onset diabetes mellitus in hypertensive patients: the VALUE trial

et al. 2008

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Diabetes mellitus often develops in patients with hypertension. We investigated predictors of diabetes mellitus development in hypertensives at risk of developing the disease in the VALUE trial population. Among the 9995 non-diabetic hypertensive patients at baseline, 1298 patients developed diabetes mellitus during the average follow-up of 4.2 years. New-onset diabetes mellitus was defined from adverse event reports, information about new antidiabetic medication and/or a fasting glucose X7.0 mmol l À1 at the end of trial. Twentyfive potential baseline predictors of new-onset diabetes mellitus were analysed by univariate logistic regression and 14 of 25 predictors were found to be statistically significant with a P-value o0.05. The predictors were in order of decreasing significance; glucose, body mass index (BMI), age, uric acid, non-Caucasian race, haemoglobin, heart rate, randomized study treatment, history of coronary heart disease (CHD), gender, total cholesterol, proteinuria, potassium and creatinine. Multivariate stepwise logistic regression analyses were used and potential baseline predictors of new-onset diabetes mellitus were considered significant by four different models (P-value o0.001). The final multivariate model selected included all patients, but not treatment group as a potential predictor, and the six significant predictors identified from this model were glucose, BMI, non-Caucasian race, age, heart rate and history of CHD. In conclusion, glucose and BMI were the most important predictors of new-onset diabetes mellitus in hypertensive patients at high cardiovascular risk, and easily accessible clinical characteristics strongly predict patients at risk of developing diabetes mellitus.

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Section: Discussionmentioning

confidence: 92%

Predictors of new-onset diabetes mellitus in hypertensive patients: the VALUE trial

et al. 2008

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“…In this section, studies that have assessed these costs and outcomes for different tests and test cut-off points are reviewed. 200 …”

Section: Studies Assessing the Costs And Short-term Outcomes Of Diabementioning

confidence: 99%

Screening for type 2 diabetes: literature review and economic modelling

Waugh¹,

Scotland²,

McNamee³

et al. 2007

Health Technol Assess

243

194

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“…The overall compliance with a screening programme for type 2 diabetes is unknown. In this paper, sensitivity analyses with levels of compliance ranging from 30-75% were performed [8,9].The untreated risk model uses the risk prediction engine derived from the UK Prospective Diabetes Study (UKPDS), calculating the 5-year cumulative risk of CHD events (fatal or non-fatal myocardial infarction or sudden death) in people with diabetes (Table 1) [10]. The relative risk reduction for treatment of individual cardiovascular risk factors used in the risk reduction model are taken from a meta-analysis of single agent clinical trials in people with diabetes [11].…”

Section: Methodsmentioning

confidence: 99%

What determines the cost-effectiveness of diabetes screening?

et al. 2006

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Aims/hypothesis: The cost-effectiveness of screening for diabetes is unknown but has been modelled previously. None of these models has taken account of uncertainty. We aimed to describe these uncertainties in a model where the outcome was CHD risk. Subjects and methods: Our model used population data from the Danish Inter99 study, and simulations were run in a theoretical population of 1,000,000 individuals. CHD risk was estimated using the UK Prospective Diabetes Study (UKPDS) risk engine, and risk reduction from published randomised clinical trials. Probabilistic sensitivity analysis was used to provide confidence intervals for modelled outputs. Uncertain parameter values were independently simulated from distributions derived from existing literature and deterministic sensitivity analysis performed using multiple model runs under different strategy choices and using extreme parameter estimates. Results: In the least conservative model (low costs and multiplicative risk reduction for combined treatments), the 95% confidence interval of the incremental cost-effectiveness ratio varied from £23,300-82,000. The major contributors to this uncertainty were treatment risk reduction model parameters: the risk reduction for hypertension treatment and UKPDS risk model intercept. Overall cost-effectiveness ratio was not sensitive to decisions about which groups to screen, nor the costs of screening or treatment. It was strongly affected by assumptions about how treatments combine to reduce risk. Conclusions/interpretation: Our model suggests that there is considerable uncertainty about whether or not screening for diabetes would be costeffective. The most important but uncertain parameter is the effect of treatment. In addition to directly influencing current policy decisions, health care modelling can identify important unknown or uncertain parameters that may be the target of future research.

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Screening for diabetes in general practice: cross sectional population study

Cited by 86 publications

References 11 publications

Predictors of new-onset diabetes mellitus in hypertensive patients: the VALUE trial

Predictors of new-onset diabetes mellitus in hypertensive patients: the VALUE trial

Screening for type 2 diabetes: literature review and economic modelling

What determines the cost-effectiveness of diabetes screening?

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