Frequently Asked Questions (FAQs) for the Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors
- Will the model-based estimates be expanded to other screening or cancer-related variables?
- Are there factors other than noncoverage and nonresponse that contribute to the differences in the BRFSS and NHIS direct estimates?
- Why did you use a state level model to produce state estimates rather than aggregating the county estimates from the county level model to state level?
- Did you control the county estimates so that the aggregated county estimates equal to the state estimates?
- Who developed this methodology?
Will the model-based estimates be expanded to other screening or cancer-related variables?
For each new potential variable to be added, a careful evaluation must be conducted to determine if the questions are asked in an identical (or at least very similar) manner in the two surveys. Performing analyses when the questions are substantially different could lead to inappropriate estimates. We have added colorectal cancer screening variables starting from 2004 and prostate cancer screening variable starting from 2011.
Are there factors other than noncoverage and nonresponse that contribute to the differences in the BRFSS and NHIS direct estimates?
Differences in data collection time, proxy responses, question wording, response modes (telephone vs. in-person), sample design and weighting methodology could cause differences between the BRFSS and NHIS prevalence estimates. For small areas there is also larger sampling error and hence, estimates could differ by chance.
Both BRFSS and NHIS are designed to represent the adult population (18 years old or over) living in households. However, the NHIS is a complex, multistage area probability sample that incorporates stratification, clustering, and oversampling of some subpopulations (e.g., Black, Hispanic, and Asian in later years); while the BRFSS is a state-based random-digit-dial (RDD) probability sample that incorporates disproportionate stratified sampling in which listed residential telephone numbers are sampled at a higher rate than unlisted residential telephone numbers. Weighting adjustments are implemented in both surveys to compensate for design-imposed differential selection probabilities, nonrespondents, and under-covered population. Differences in the weighting adjustment process may cause some observed differences in the direct estimates from the two surveys.
Why did you use a state level model to produce state estimates rather than aggregating the county estimates from the county level model to state level?
We used a state level model mainly for two reasons. First, the direct estimates at the state level are more reliable than at the county level. Second, the relationship between the direct estimates and the auxiliary data at different geographical levels might be different.
Did you control the county estimates so that the aggregated county estimates equal to the state estimates?
We applied simple ratio adjustment to the state estimates so the aggregated final modeled state estimates are equal to the census regional direct estimates. We developed and applied a new adjustment factor to the county estimates so the aggregated final modeled county estimates are equal to the final benchmarked state estimates.
Who developed this methodology?
This work was undertaken as a collaboration of the Statistical Research & Applications Branch at the National Cancer Institute, the National Center for Health Statistics, and academic researchers from the Department of Biostatistics
and the Institute for Social Research at the University of Michigan, and the Center for Clinical Epidemiology and Biostatistics at the University of Pennsylvania School of Medicine. Both survey groups (NHIS at the National Center for Health Statistics, and BRFSS at the National Center for Chronic Disease Prevention and Health Promotion, both within the Centers for Disease Control and Prevention) have been involved in the development of this Web site, and are supportive of the development of methodologies of this type which takes advantage of the strengths of two complimentary government sponsored national health surveys.