OBJECTIVE: To develop a simple risk score to identify high-risk individuals for diabetes screening in Thailand. MATERIAL AND METHOD: The authors analyzed data from 75-g oral glucose tolerance tests performed in 159 males and 270 females, aged 48.4 +/- 10.9 years. RESULTS: The independent variables associated with diabetes included age (p < 0.001), BMI (p < 0.01) and known history of hypertension (HHT) (p < 0.01). The risk equation was Y = 3 age + 5 BMI + 50 HHT. At the cut-off Y value of 240, the sensitivity and specificity for having diabetes were 96.8% and 24.0%, respectively. The positive predictive value was 17.8% and the negative predictive value was 97.8%. Using the equation in a validation group comprising 1617 subjects, it was found that 560 (34.6%) diabetes screenings could be saved while 28 subjects (12.8%) with diabetes would be missed. CONCLUSION: The authors have developed a simple risk scoring method that should be helpful in decreasing the number of unnecessary screening and optimizing the costs associated with diabetes screening.
OBJECTIVE: To develop a simple risk score to identify high-risk individuals for diabetes screening in Thailand. MATERIAL AND METHOD: The authors analyzed data from 75-g oral glucose tolerance tests performed in 159 males and 270 females, aged 48.4 +/- 10.9 years. RESULTS: The independent variables associated with diabetes included age (p < 0.001), BMI (p < 0.01) and known history of hypertension (HHT) (p < 0.01). The risk equation was Y = 3 age + 5 BMI + 50 HHT. At the cut-off Y value of 240, the sensitivity and specificity for having diabetes were 96.8% and 24.0%, respectively. The positive predictive value was 17.8% and the negative predictive value was 97.8%. Using the equation in a validation group comprising 1617 subjects, it was found that 560 (34.6%) diabetes screenings could be saved while 28 subjects (12.8%) with diabetes would be missed. CONCLUSION: The authors have developed a simple risk scoring method that should be helpful in decreasing the number of unnecessary screening and optimizing the costs associated with diabetes screening.
Authors: Katya L Masconi; Tandi E Matsha; Justin B Echouffo-Tcheugui; Rajiv T Erasmus; Andre P Kengne Journal: EPMA J Date: 2015-03-11 Impact factor: 6.543
Authors: Laura M Koehly; Bronwyn A Morris; Kaley Skapinsky; Andrea Goergen; Amanda Ludden Journal: BMC Public Health Date: 2015-11-13 Impact factor: 3.295