Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis.

AIMS: Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. METHODS: We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). RESULTS: In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained. CONCLUSIONS: Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.

Predictors of dependency on nursing care after stroke: results from the Dortmund and Münster stroke registry.

BACKGROUND: The long-term effects of stroke have been inadequately studied. We identified social and clinical factors that were associated with application for insurance payments for long-term care within 3.6 years after stroke. METHODS: In a quality-assurance project called "Stroke Northwest Germany," information was obtained from 2286 stroke patients on their socio-demographic background, type of stroke, comorbidities, and degree of physical impairment during their hospital stay, as measured on the Rankin Scale, the Barthel Index, and the Neurological Symptom Scale. We used logistic regression models to identify possible associations between these factors and application for insurance payments for long-term care within 3.6 years after stroke. We developed an appropriate prognostic model by means of backward selection. RESULTS: 734 (32.1%) of the patients participated in follow-up and reported whether they had applied for insurance payments for long-term care. 22.5% had submitted an application. The rate of application was positively correlated with age, female sex, the number of comorbidities and complications during hospitalization, and the degree of physical impairment. CONCLUSION: Stroke has major long-term effects. The probability that a stroke patient will apply for insurance payments for long-term care is a function of the patient's age, sex, previous stroke history, and physical impairment as measured on the Rankin Scale and the Barthel Index.