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American Diabetes Association (ADA) Risk Calculator

The American Diabetes Association (ADA) risk calculator predicts the risk of undiagnosed diabetes to determine who should receive screening.
<40 0
40-49 1
50-59 2
>=60 3
Female 0
Male 1
Past gestational diabetes
No 0
Yes 1
First degree relative with diabetes
No 0
Yes 1
High blood pressure
Reported past of high blood pressure, recommend antihypertensive medication, and/or BP ≥140/90
No 0
Yes 1
Physically active
Reports by the patient
No 1
Yes 0
BMI (Body mass index)
<25 0
25 to <30 1
30 to <40 2
>=40 3


Measured Factor
Risk of undiagnosed diabetes to determine screening eligibility.
Measured Factor Disease
  • Diabetes
Measured Factor Detail
The ADA risk calculator evaluates a patient's risk for undiagnosed diabetes and whether or not the patient should be regularly screened for diabetes. The calculator takes into consideration age, gender, gestational diabetes, family history of diabetes, level of physical activity, body mass index and hypertension. Each factor is given a numerical factor and points are added or subtracted depending on which factors the patient has. ADA scores ≥5 should be formally screened for diabtes, while scores ≤ 4 are not at high risk and screening is not recommended.
General Physician
Body System
Measured Factor Low Impact
  • ADA scores ≤ 4 indicates the patient is not at high risk and screening for diabetes is not recommended.
Measured Factor High Impact
  • ADA scores ≥5 indicates the patient is at high risk and screening for diabetes is recommended.

Result Interpretation

Ranges Ranges
  • Critical High: ≥5
  • Normal: 0-4
  • Normal Adult Male: 0-4
  • Normal Adult Female: 0-4
  • Normal Pediatric: 0-4
  • Normal Geriatric Male: 0-4
  • Normal Geriatric Female: 0-4
Result High Conditions
  • Diabetes


Study Validation 1
Current prevention of type-2 diabetes mellitus (DM) focuses on identify individuals at high risk for preDM. Paper comparing two different screening scores for the utility of preDM screening from the American Diabetes Association (ADA) and Centers for Diasea Control and Prevention (CDC).  The ADA score consists of 7 questions with scores between 0 to 11 points on age, sex, gestational DM, family history of DM, hypertension, physical activity and obesity based on body mass index. The CDC calculator consists of 7 questions with scores between 0 to 18 points based on age, having delivered a baby weighing more than 9 lbs, sibling's DM, parent's DM, physical activity and obesity. Scores ≥5 indicate DM and 4 indicate preDM in the ADA score, while scores ≥10 indicate DM and 9 indicate preDM in the CDC score. Restrospective data collected consisted of adult patients aged 20 years or older that had no previous DM diagnosis. Odds ratio (OR) with 95% condifence interval (CI), p-values, and aure aunder the receiver operating characteristic curve (AUC) were calculated for assessing discrimination ability. Additionally, percent of high risk individuals, sensitivity, specificity, positive and negative predictive values were also calculated (PPV and NPV, respectively). The ADA score performed better with AUC=0.77 (compared to CDC AUC=0.73-0.74) for DM and 0.72-0.74 (vs. CDC AUC = 0.70-0.71) for preDM. In conclusion the ADA and CDC risk scores could be further implemented as a tool for differentiating patients who should be screened for preDM.
References: 2
Study Validation 2
Study evaluating the performance of the American Diabetes Association (ADA) diabetes screening score calculator. Data was collected from a prospective study consisting of patients that were being followed for development of cardiovascular risk factors in Hong Kong. Diabetes was defined as a fasting blood glucose (FBG) ≥ 7 mmol/L or a 2-hour post oral glucose tolerance test glucose ≥ 11.1 mmol/L. The ADA score is based in seven paramenters with possible scores ranging from 0 to 11 points; a score ≥ 5 is considered to have a high risk of having diabetes. A total of 1415 patients with an average age of 58 years were evaluated, of which 95 (6.7%) were found to have diabetes. Data was presented using mean ± SD or as median with interquartile ranges (IQR). The area under the receiver operating characteristic (ROC) curve (AUROC) was calculated to assess the performance in identifying patients with diabetes. The ADA risk score showed good accuracy (AUC = 0.725) in screening for diabetes a cut-off score of 5. When compared with the screening criteria, the ADA test also had better specificity (0.57 vs. 0.41, p<0.001), positive predictive value PPV (0.12 vs. 0.09; p<0.001) and positive diagnostic likelihood ratio (1.85 vs. 1.37, p<0.001). In conclusion, the risk test appeared to be more effective as a screening tool.
References: 3
Study Validation 3
Study comparing the risk estimation performance between four different diabetes risk calculators and two predictive models from data collected between 1999-2012. The four online diabtes risk calculators were from national diabetes associations in the US (American Diabetes Associaiton or ADA), UK (Leicester Risk Assessment or LRA), Australia (Australian Type 2 Diabetes Risk Assessment Tool or AUSDRISK) and Canada (Canadian Diabetes Risk Questionnaire or CANRISK). To define patients with diabetes a fasting plasma glucose level  ≥126 mg/dL was selected, for patients with pre-diabetes a level  ≥ 100 mg/dL was used. Simple scoring models were used for all four online calculators and Cohen's Kappa (k) coefficient was calculated. Kappa values were defined as followed; values < 0 as no agreement, 0-0.20 as slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial and 0.81-1 as almost perfect in agreement. In the results, the ADA calculator achieved the best predictive performance with an Area Under the ROC Curve (AUC) of 0.699 for undiagnosed diabetes and 0.662 for pre-diabetes. However, the best AUC was obtained in diabetes risk predicting statistical methods using logistic regression (AUC of 0.775 and average of 34% of people selected for screening). Overall, results demonstrated a serious lack of predictive performance in the foud major online calculators when compared to the predictive models.
References: 4


Heejung Bang, PhD, is a professor of biostatistics at the University of California, Davis. Dr. Bang completed her Ph.D in statistics from the North Carolina State University in 1999 in addition to a fellowship at the Harvard School of Public Health from 1999 to 2001. Her reasearch focuses on clinical and observational studies, medical cost and cost-effectiveness analysis, prediction and screening models.


  1. Bang H, Edwards AM, Bomback AS, Ballantyne CM, Brillon D, Callahan MA, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med. 2009 Dec 1;151(11):775-83.
  2. Poltavskiy E, Kim DJ, Bang H. Comparison of screening scores for diabetes and prediabetes. Diabetes Res Clin Pract. 2016 Aug;118:146-53.
  3. Woo YC, Lee CH, Fong CHY, Tso AWK, Cheung BMY, Lam KSL. Validation of the diabetes screening tools proposed by the American Diabetes Association in an aging Chinese population. PLoS One. 2017 Sep 14;12(9):e0184840.
  4. Stiglic G, Pajnkihar M. Evaluation of Major Online Diabetes Risk Calculators and Computerized Predictive Models. PLoS One. 2015; 10(11): e0142827. PMCID: PMC4641713. PMID: 26560153

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