Among 575 screening participants, 383 had 1 or more risk factors. Fifty-one glucose measurements were considered abnormal: 16 in patients without risk factors and 35 in patients with 1 or more risk factors. The likelihood ratio (LR) for a positive or “at risk” questionnaire (LR+) was 1.05; for a negative questionnaire (LR-), it was 0.93. Follow-up of only those patients with abnormal blood glucose results over 1.8 years revealed that 21 (41%) had confirmed diabetes: 15 with one or more risk factors and 6 with none. Performing blood glucose testing in only patients with 1 or more risk factors would have missed at least 6 of 21 or 29% of all cases of confirmed diabetes. As diabetes was not ruled out or confirmed in the patients without abnormal blood glucose concentrations, it is unknown how many cases were missed in that population.
The ADA has developed questionnaires13 from which risk of diabetes is calculated as a composite score. McGregor and colleagues14 studied the performance of 1 of these questionnaires in a screening program. This questionnaire combines assessment of risk factors with questions about diabetic symptoms, such as fatigue and thirst, to generate a total score. Questionnaires were mailed to and completed by 349 individuals aged older than 60 years in Everett, Washington. Only those individuals identified as “high risk” by the questionnaire were offered follow-up fasting blood glucose testing. This study also falls short of the validity criteria of the “Users’ Guide to the Medical Literature.” An independent nonblinded comparison was made between at-risk questionnaires and the widely accepted diagnostic standard of fasting blood glucose. The patients were older community residents who would likely be candidates for diabetes screening. The risk assessment instrument is widely available and easy to use. Unfortunately, blood glucose testing was not performed on all subjects regardless of their assessed risk. This makes it impossible to assess the likelihood of diabetes among those patients with negative questionnaire results.
One hundred eighty-one of the 349 completed questionnaires indicated patients as high risk. One hundred ten of these patients underwent fasting plasma glucose (FPG) testing. Eleven (10%) had FPG values that exceeded 6.38 mM (114.8 mg/dL); 7 (6.3%) had FPG levels that exceeded the higher reference standard of 7.77 mM (140 mg/dL).
Burden and Burden15 evaluated the performance of the same ADA questionnaire among 383 self-selected participants at a health fair in England. It is unknown whether the comparison of questionnaires and blood glucose results was done independently or blindly. A random glucose value of greater than 6.5 mM (117 mg/dL) was considered abnormal. Random blood glucose can be used as a diagnostic standard for diabetes only in patients with typical symptoms, such as polyuria and polydipsia.6 The investigators do not specify how many screening participants had symptoms. Burden and Burden, therefore, used a questionable reference standard for comparison. All patients underwent subsequent random blood glucose testing. One hundred fifty-eight of 383 participants who completed questionnaires were identified as high risk. Fifty elevated random blood glucose concentrations were found. Among these patients,23 were indicated as high risk by the questionnaires. The LR+ for this study was 1.15; LR-, 0.92.
Computerized Statistical Models
Three studies used statistical analysis to develop questionnaires to identify those subjects at high risk of diabetes. This method uses data from programs in which all participants undergo screening, and their diabetes status, risk factors, and other demographic variables are recorded. In the NHANES III, for example, known history of diabetes and a large number of demographic variables were recorded for each patient. All participants underwent blood glucose testing, and the proportion of previously undiagnosed diabetes was determined. Using this data, the risk factors for diabetes could be determined, and their relative contribution to the likelihood of a diagnosis of diabetes could be calculated. The resulting risk-factor-based model was tested on the same data to determine how effectively it detected the previously undiagnosed cases of diabetes. The obvious difficulty with this method is that the performance of the risk-factor-based model was not field tested in a population separate from that in which it was developed. Rather than comparing a diagnostic test with an accepted standard, this technique involves developing a diagnostic test and “fitting” it to results already obtained by the application of an accepted standard test.
Herman and coworkers16 used this technique to develop a risk-factor-based questionnaire using data from NHANES II,17 in which 164 people with previously undiagnosed diabetes and 3220 with neither previously known nor newly diagnosed diabetes were identified. Their questionnaire used older age, excess body weight, lower level of physical activity, family history of diabetes, and history of delivery of a macrosomic infant as risks for type 2 diabetes. It was then tested on the same NHANES II data. The comparison with the reference standard was, therefore, neither independent nor blind. A broad spectrum of patients, typical of those who might undergo diagnostic testing for diabetes in clinical practice, was included in NHANES II. As the reference standard was obtained before completion of the questionnaire, the question of whether the results of the test being evaluated influenced the decision to perform the reference standard is not applicable. Administration of the questionnaire is easy and reproducible.