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Methods and Limitations


The prevalence of risk factors for complications among adults with diabetes in the United States was determined by using data from the Behavioral Risk Factor Surveillance System (BRFSS). An ongoing, yearly, state-based telephone survey of the noninstitutionalized adult population in each state, the BRFSS provides state-specific information on behavioral risk factors for disease and on preventive health practices. Respondents who reported that a physician told them they had diabetes (other than during pregnancy) were considered to have that condition. Respondents who reported that they smoked at least 100 cigarettes in their lifetime and currently smoke were considered current smokers. Those who report no physical activity in the past 30 days were considered physically inactive. Self reported weight and height were used to calculate body mass index (BMI): weight in kilograms divided by the square of height in meters. A BMI greater than or equal to 25 was considered to be overweight or obese, a BMI of greater than or equal to 30 was considered to be obese. Respondents who reported that a physician told them they had high blood pressure or high cholesterol were considered to have those conditions. The physical activity questions before 2000, as well as the hypertension and high cholesterol questions, are included on the survey every other year. Estimates were age-adjusted using the 2000 U.S. Standard Population.

Data Limitations

Persons residing in nursing homes and in households without telephones are not included in this survey; therefore, these results cannot be generalized to those segments of the population. All data in the BRFSS are obtained by self-report and are subject to recall bias or may be underreported or overreported. Self-report of diabetes and self-report of sociodemographic characteristics are highly accurate.1,2  Self-report of cigarette smoking status and physical activity have been shown to have high accuracy as well.3-5  Reliance on self-reported heights and weights to calculate the BMI is likely to underestimate average BMI and the proportion of the population in higher BMI categories in population surveys.6  Self-report of high blood pressure has been shown to have a moderate sensitivity 7, while self-report of hyperlipidemia has been shown to have a high sensitivity and low specificity.8 Further investigation of the reliability and validity of self-reported Cardiovascular Disease risk factors is needed.



  1. Bowlin SJ, Morrill BD, Nafziger AN, Lewis C, Pearson TA. Reliability and changes in validity of self-reported cardiovascular disease risk factors using dual response: the Behavioral Risk Factor Survey. Journal of Clinical Epidemiology 1996;49:511–517.
  2. Stein AD, Courval JM, Lederman RI, Shea S. Reproducibility of responses to telephone interviews: demographic predictors of discordance in risk factor status. American Journal of Epidemiology 1996;141:1097–1106.
  3. Associated with Discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons aged 17 years or older. Third National Health and Nutrition Examination Survey, 1988–1994.
  4. Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: a review and meta-analysis. American Journal of Public Health 1994;84:1086–1093.
  5. Harada ND, Chiu V, King AC, Stewart AL. An evaluation of three self-report physical activity instruments for older adults. Medicine & Science in Sports & Exercise. 2001;33(6):962–970.
  6. Cameron R, Evers SE. Self-report issues in obesity and weight management: State of the art and future directions. Behavioral Assessment 1990;12(1):91–106
  7. Tormo M-J, Navarro C, Chirlaque MD, Barber X. Validation of self diagnosis of high blood pressure in a sample of the Spanish EPIC cohort: overall agreement and predictive values. Journal of Epidemiology & Community Health 2000;54(3):221–226.
  8. Johansson J, Hellenius M-L, Elofsson S, Krakau I. Self-report as a selection instrument in screening for cardiovascular disease risk. American Journal of Preventive Medicine 1999;16(4):322–324.

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