was previously out of bounds. Call presented several examples of data-based maps using the ACS to inform health policy questions, but one that particularly drew a point of comparison with the CPS is shown in Figure 2-1. Asking the general question “where should we allocate funds for community clinics, serving the uninsured?,” Call showed the type of analysis that SHADAC conducted for officials in the state of West Virginia, showing the percent of persons in the vulnerable population of interest: low income (defined as being at or under 200 percent of the federal poverty level), nonelderly, and uninsured. Trying to answer the question using 2011 CPS data (which covers calendar year 2010), Call said that the best estimates they could generate were for three regions of the state, based on core-based statistical areas—one of which (the Huntington-Ashland metropolitan area) includes areas of Kentucky and Ohio and so is not specific to West Virginia. Call said that SHADAC would not feel confident even giving the CPS-based analysis over to the state officials. By comparison, the ACS data for 2010 permit good estimates for 12 regions—the Public Use Microdata Areas (PUMAs) within the state, a considerably fuller picture of need within the state.
Call presented and discussed two further examples of ACS-based analysis, illustrating even finer detail and one intended to capture more macro-level trends. In one, 3-year estimates from the ACS were used to profile children not included in Colorado’s CHIP program at the county level. The same map was generated at the county level for children not covered under Medicaid, and Call said that the work helped propel policy debates about expanding insurance coverage for children. Call also presented a basic state-level map, shaded to indicate the percentage of persons who would be eligible for Medicaid under the PPACA.
With respect to access to the data—both by SHADAC and its clients—Call said that she had encountered a range of responses among state-level users concerning the Census Bureau’s American FactFinder interface. Some have loved the interface and appreciate that one does not have to be a computer programmer to use it; others find it overly cumbersome and not user friendly. The default tabulations available in American FactFinder reflect federal poverty thresholds but not (directly) the federal poverty guidelines promulgated by the U.S. Department of Health and Human Services.6 The FactFinder tabulations also do not provide direct results at the poverty cuts of particular policy interest—e.g., at or below 138 percent or 200 percent of the federal poverty threshold.7
In part to compensate for deficiencies in American FactFinder, SHADAC established its own online data center 2 years ago. The data center site—accessible at http://www.shadac.org/datacenter—acts as a table and chart generator using both ACS and CPS data, enhanced to describe health insurance units (as
7As of September 2012, the American FactFinder interface was modified to directly add a 138-percent-of-poverty-threshold tabulation.