objective for the health indicator set and is relevant to many of the other SUSA topic areas. The disparities section of this report details the types of comparisons that might be presented as disparities and how these and other domain comparisons might be presented as “drill-downs” linked to the main indicator displays.
Development of domain-level indicators for SUSA presents a variety of challenges based on the varying relevance of the indicators and availability of data to calculate them for various types of domains. Not every indicator can be defined meaningfully for every domain; for example, a measure of air quality might be defined for states but might not be meaningful for age groups.
The following discussion focuses on population-based measures for which domain estimates are conceptually meaningful, although not always practically obtainable with the data currently available. Two general issues affect the potential for developing domain estimates. First, the necessary variables might not be available from the same data source to both produce the indicator and define the domains of interest. For example, mortality reports used to estimate life expectancy typically include race but not income or education. A variant of this problem is that even if the data are collected in a suitable form, detailed domain-level linkages might not be publicly available due to concerns about confidentiality. For example, microdata (individual-level data) from some of the key federal health surveys, including the National Health Interview Survey (NHIS), are not generally released to the public with geographical detail below four regions, although more detailed geographical identifiers can be analyzed by researchers subject to restrictions on access.
Second, as data are broken down to smaller domains, the amount of data available for each domain also becomes smaller, reducing the statistical precision of estimates for those domains. With a simple random sampling survey design, the standard error (SE) of estimates, a measure of the typical size of errors due to random variation, is inversely proportional to the square root of the sample (or population size). Thus for a domain constituting a fourth of the population, the SE would be twice as large as for the entire population. With the more complicated designs typical of national surveys, the relationship of domain size to SE might be more complex; for example, the NHIS design collects no data at all from many counties.
The more or less gradual decline in precision for smaller domains suggests that some standards are required for precision and that domain measures should not be reported if they do not meet those standards. Such a procedure is followed in some standard tabular reports, where low reliability (or confidentiality concerns) might lead to suppression of measures for some small domains; criteria for suppression are set by