BMC Public Health 2007, 7:277 http://www.biomedcentral.com/1471-2458/7/277 a written survey. The completion of the questionnaire in the home setting allowed for thoughtful answers and for time to check records and medications for accurate report- ing. In addition, the respondents could work at their pace, and there was no interviewer bias. Any sampling bias due to demographics can be measured and adjusted for in the final analysis. Finally, the survey allowed the identifica- tion of respondents without a current diagnosis of the ill- ness of interest, and potential longitudinal evaluation of RFs for future diagnosis of that illness. SHIELD consists of a cross-sectional, and then 5-year lon- gitudinal observational study of individuals with or at risk for DM. A large number of the cross-sectional, screening questionnaires were sent and a high return rate was achieved, providing a sample that was generally represent- ative of the overall US population. The BL survey was designed to provide a more detailed view of DM and other health conditions in a large sample that will be followed over 4 subsequent annual surveys. The overall response rate for the BL survey (80%) was also quite high for a large, mailed survey. Demographically, the population sample was generally similar to US census data, indicating that the SHIELD results are representative and generalizable to the US pop- ulation. The population sample and the 0–2 RF groups were also similar to each other. One important result from this survey was the similarity of the 3–5 RF and the type 2 DM groups. Results from SHIELD reveal that the mean number of RFs increases with age, as does the likelihood of being diagnosed with DM. The proportion of individ- ual RFs was highest in the type 2 DM and 3–5 RF groups, with somewhat higher percentages for each of the five individual RFs in the 3–5 RF group as compared with the type 2 DM group. The 3–5 RF group has not been diag- nosed with diabetes but they appear similar to the type 2 DM group, which is not explained by age stratification. This finding may indicate the importance of managing the risk factors in the 3–5 RF group to delay or prevent diabe- tes. Importantly, in follow-up surveys, it may be possible to determine which RFs in those persons without DM best correlate to a future diagnosis of DM. The similarity in prevalence rates of DM between SHIELD and NHANES confirm that surveys like SHIELD, with self-reported diag- noses, acquire data approximating surveys that also include clinical and laboratory evaluations. The lower prevalence of dyslipidemia and abdominal obesity in SHIELD versus NHANES may be largely due to the use of laboratory data (cholesterol levels) and inter- viewer measurement in NHANES compared with only self-report in SHIELD. For diagnoses that are dependent on laboratory evaluations, particularly when more than one laboratory parameter is used to define a specific diag- nosis (such as dyslipidemia), self-report surveys may underestimate the true prevalence of these diagnoses. Multivariate analysis indicated that smaller household size, population size and the US South were independ- ently associated with a higher likelihood of type 2 DM diagnosis. These associations may be due to differences in diet, lifestyle habits, access to medical and/or diabetes care and limited family support. It should be noted that panel data have some limitations. For example, only a small percentage (5%–8%) of con- sumers invited to participate in the NFO panel elected to do so, leading to the possibility of bias. Household panels tend to under-represent the very wealthy and very poor segments of the population, and do not include military or institutionalized individuals. However, these limita- tions are true for most random sampling and clinically based methodologies as well. Another limitation is that data collected by self-reported surveys cannot always be directly compared to clinical and laboratory surveys, such as NHANES data. This is especially true in trying to assess clustering of CVD risk factors. Currently, further analyses of the screening and BL survey data are in progress, to investigate differences in health attitudes and behaviors between survey respondents with type 1 and type 2 DM and those with 3–5 RFs. It is antici- pated that the long-term, longitudinal data from SHIELD will allow for continued clarification of predictors of being tested for or being diagnosed with DM. In addition, it is possible that SHIELD data may help identify those RFs (and health attitudes and behaviors) that are most predictive of transitioning from one stage of DM to the next (disease progression) and from one stage of DM treatment to the next (treatment progression). Conclusion When comparing results from the SHIELD screening sur- vey to those from NHANES 1999–2002 (self-report and clinical and laboratory evaluations), the prevalence of DM was similar. In addition, the prevalence of DM observed in SHIELD increased with higher ages and lower income levels. Multivariable analyses of the SHIELD baseline sur- vey data found that abdominal obesity, higher BMI, and diagnosis of cholesterol problems, hypertension or CVD were each independently associated with higher likeli- hood of type 2 DM diagnosis. Other factors positively associated with type 2 DM diagnosis were increased age, black race, and decreased household income level. SHIELD is an ongoing self-reported survey study that began with a cross-sectional questionnaire, followed by targeted longitudinal surveys, and is the largest survey of Page 7 of 9 (page number not for citation purposes)