The estimates presented here are based on NHIS and BRFSS data and an underlying model based on the demographic profile of each geographic area. When there is sufficient NHIS and BRFSS data for a specific geographic area, the combined estimates depend mainly on the available data from that geographic area.
However, for areas with little or no NHIS and/or BRFSS sample, the estimates increasingly depend on using the demographic model to produce estimates for areas with "similar" profiles from across the country in terms of their covariates. These latter estimates cannot capture unique characteristics of the county not represented by the demographic profile, and also cannot capture specific cancer control programs that may have been implemented to increase screening rates or decrease smoking rates in that area. Based on model assumptions, the state model-based estimate generally improves with increasing NHIS state sample size. When the NHIS state sample is small, the model-based estimate depends heavily on the covariates (e.g., economic, educational, demographic, etc.). Additionally, the model assumes that the impact of the covariates upon outcomes is similar across all states. For example, since Alaska has the smallest state sample size, and because of how much it differs from states in terms of sample size, population density, remoteness, age distribution, and other factors, the impact of covariates on Alaska's estimates for health related outcomes may differ from those in the lower 48 states.
Model assumptions are necessary to correct for nonresponse and non-coverage biases and to smooth the estimates. The more the method smooths the estimates (to reduce variance) and corrects for biases, the more model assumptions are necessary. We feel that the assumptions made in our model-based approach are reasonable and are sufficient to address the two potential sources of bias and to reduce the variability in the "BRFSS direct" estimates.
Feedback is greatly appreciated, both in terms of the global utility of these estimates, as well as local anomalies.Provide feedback