Data Questions

There will be one estimate per measure for the entire population of each census tract and of each city.

We are exploring the different formats through which the data will be downloadable. It is a priority for CDC to ensure that the data are made available in a manner that facilitates local use.

For the static maps that CDC will make available, we based the data classifications for the maps on the entire dataset and chose to represent 9 classes of data, helping to ensure that there is perceived geographic variations in the maps of any particular city.  For some cities where the range of estimated prevalence at the census tract is narrow, the resulting map may only have two or three classes.  For other cities with a greater range in data estimates at the tract level, there will be more classes mapped.  If we had chosen fewer classes, overall, it would be possible for an individual city map to have had only one data class.  The data class breaks will be the same across all maps, thus facilitating map-to-map comparisons.

Confidence intervals will be presented alongside the prevalence estimates.

Details on the validation are available in the 2015 AJE paper, cited on the 500 Cities website.  Sensitivity and specificity analyses were not applicable to this type of modeling procedure and thus were not conducted.

Yes

There will be no individual-level data. The data estimates will be aggregated to the census tract and the city levels.

The SAE for each city is dependent mainly upon the demographic characteristics of that city, but they also are affected by the county- and state-level context that was included as random effects in the modeling procedure.

We cannot include census tract as a random effect. However, we do not assume that there is no variation across census tracts. In the prediction step,  we incorporate tract level poverty; in addition, differences in the population demographics of the blocks that make up the census tracts are also considered in the prediction step.

County-level poverty was used in the first step of the modeling procedure, because that is the smallest geographic level that corresponds to the geocode available for the BRFSS survey respondent.  In the prediction step of the modeling process we do use census tract poverty estimates.

We hope that individual cities, more familiar with local definitions and conceptualizations of neighborhoods, may make use of these data in their own public health and outreach efforts.  It would be technically possible, for instance, for a city to download their data from 500 Cities and incorporate the data in a local website – perhaps even GIS-enable maps that include overlays of the boundaries of local neighborhoods as defined by the community.

The preventive measures and core unhealthy behaviors were selected based on the following factors:

• Amenable to public health intervention
• Reflect public health priorities to address leading causes of morbidity and mortality
• Preventive services are consistent with U.S. Preventive Services Task Force recommendations
• Exhibit substantial, meaningful variation at the city and census tract level
• Can be estimated for small area levels from existing, regularly-collected surveillance data–BRFSS
• Fills a niche for health data at the city and census tract level, which are not presently available, while not duplicating health-related data that are available elsewhere
• Compliments similar state-level measures that are available elsewhere