At a time when more than 16% of the population of the United States (more than 47 million people) lack insurance coverage for basic medical services, an important function of public health is to provide the underserved and people disproportionately affected by disease with access to preventive health services (1). To reach these people effectively, public health programs are best implemented locally, in counties or cities (2). Estimating the population eligible or targeted for specific services is often difficult at the local level, however, because of lack of data (3-7).

Because public health programs or interventions are usually tailored to improve the health of specific underserved or high-risk groups, an individual may have to meet particular criteria (e.g., be a woman aged 40 years or older with no health insurance) to be eligible for program services or may have to belong to a target group characterized by the intervention (e.g., women with low personal income at risk for pregnancy). Decennial census and intercensal population projections provide summary counts of local populations by various demographic characteristics, but these sources rarely contain data corresponding to the narrowly defined criteria that usually describe eligibility for public health programs. Public health surveys collect a wide range of information, but they, too, may not contain the necessary data for generating reliable estimates of local populations (3,4,6-10). In fact, many surveys conducted statewide have so few respondents that even state estimates of small eligible or target populations are unreliable.

Because an epidemiologic description of the eligible or target population is essential to developing and operating a public health program or intervention (11), the problem of insufficient data must be addressed. Reliably defining the service-eligible population tells program planners how many people are eligible for services, who these people are, and where they live and is central to such activities as projecting costs, preparing budget proposals, and justifying funding requests. Equally important, reliable estimates help planners determine the portion of the eligible population that the program cannot serve, given available resources. If funding is insufficient to reach all eligible people, estimates of subgroups of the eligible population, as defined by various demographic, geographic, or high-risk characteristics, enable a program to identify priority target groups, establish realistic enrollment goals, and request appropriate funding (12). Reliable estimates also provide evidence for program growth and infrastructure development and provide essential data for decision making about program policy and resource allocation and for monitoring and evaluating a program's effectiveness (13,14).

We demonstrate how the small-area estimation method was used by the California Department of Public Health to estimate the size of local populations that are eligible for free breast cancer screening and diagnostic services through the state's Cancer Detection Programs: Every Woman Counts (CDP:EWC). Although this method can be used in many ways, including to adjust for census undercounts and to estimate populations in political districts, it is presented here as a reliable approach to resolving the problem encountered by public health programs of precisely estimating local populations eligible for preventive health services and interventions when no single data source is adequate for the task (4,8,15).

The estimated county percentages of eligible women varied from a minimum of 5.5% (Marin County) to a maximum of 35.3% (Imperial County) (Table 2). The estimated percentage at the 25th percentile was 11.1%; at the 50th percentile (median), 13.6%; and at the 75th percentile, 15.9%. The mean of the estimates was 13.9%. The estimated proportions were not normally distributed, but skewed to the right.

The small-area estimation method yielded a wide range and considerable variability in the estimated proportions across counties (Figure). The estimated proportions of eligible Hispanic women varied more than did those of women of other races. Even so, the range of estimates across counties in each racial group was more than 10%. Imperial County, one of the outliers in the figure, had the highest proportion of eligible women of all races combined. In the second outlier, Del Norte County, an estimated 23.2% of black women aged 40 years or older were eligible for CDP:EWC services.

Estimated proportions of eligible women showed considerable variability by race within counties (data not displayed graphically). In every county, the estimated proportion of women aged 40 years or older who were eligible for CDP:EWC services was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, and American Indians/Alaska Natives. In Imperial County, an estimated 45.2% of Hispanic women aged 40 years or older were eligible for program services, compared with 18.6% of black women, 15.8% of American Indian/Alaska Native women, 12.1% of white women, and 10.2% of Asian/Pacific Islander women. In Los Angeles County, which is the most populous of the state's counties and had the fifth highest estimated proportion of eligible women (20.0%), an estimated 40.2% of Hispanic women, 19.2% of American Indian/Alaska Native women, 15.7% of black women, 13.3% of Asian/Pacific Islander women, and 8.4% of white women were eligible. These proportions were all reliable. Some of the estimated proportions of eligible Asian/Pacific Islander and American Indian/Alaska Native women were not reliable, however, because of the small sample sizes in the CWHS for women in these racial/ethnic groups.

When calculating reliable estimates directly from survey or population data is not possible, the ability to combine multiple sources of data, each with different facets of the necessary information, is a strength of the small-area estimation method. In our example, available survey data contained information corresponding to CDP:EWC eligibility criteria, but they were appropriate only for state estimates. With the small-area estimation method, we were able to supplement statewide survey data with community census data by means of statistical modeling and produce reliable estimates for each California county.

Although the term small-area estimation suggests that this method is used to estimate populations living in small geographic areas, this method is also useful in identifying sparse target populations. Describing the distribution of a narrowly defined characteristic in racial/ethnic groups is a common problem because of the small number of people in some of these groups. With small-area estimation, however, we were able to calculate reliable estimates of service-eligible women in five racial/ethnic groups for most California counties.

Public health professionals have synthetically calculated local estimates when data with an adequate sample size to directly calculate local estimates are unavailable (3,4,9,10). In our demonstration, for example, we could have calculated a direct estimate of the proportion of eligible women in California from CWHS data and then multiplied each county's census population by this proportion to estimate the local numbers of eligible women. The resulting estimates would be based on the assumption that the demographic characteristics that define program eligibility are present in every county in the same proportion as they are in the state (4,8). This would be a poor assumption, however, because the synthetic method would estimate that 16.3% of women aged 40 years or older in each county were eligible for CDP:EWC services, whereas the small-area estimation method that we used yielded widely varying estimated proportions by county.

Another benefit of the small-area estimation method is that variability and reliability can be measured, and these statistics are informative. Although the standard error and confidence interval can be calculated for each synthetically generated point estimate (i.e., proportion of eligible women), these measures are not meaningful because the estimates themselves are limited by the flawed assumption we have described.

As with any means of estimation, however, obtaining statistically reliable results depends on factors such as sample size. When generating local estimates in the absence of sufficient local data, the small-area estimation method allows the researcher to borrow strength from available data (9,20). For some sparse local populations, however, no amount of supplemental information can compensate for the small number of survey respondents sampled, and model-based estimates for these populations will be unreliable.

A major limitation of small-area estimation statistics is that diagnostics for checking nonlinear models are few and not well-developed (8). Even so, comparing model-based with directly calculated survey-based estimates of the target population in the large area (i.e., the aggregate of local areas) can provide some indication of the performance of a model (28). For example, our method estimated that 15.3% of California women aged 40 years or older were service-eligible, whereas the direct method yielded an estimate of 16.3%. For practical purposes, the two estimates are similar, and without a gold standard, observing similar values resulting from two different methods can be a qualitative confirmation of methods and analysis. Although the statistical method that we used has been validated (4,8), a model-based overall estimate that was vastly different from the survey-based direct estimate would be a signal to the researcher to reassess the analysis.

California's CDP:EWC program has benefited by knowing of the wide variation in numbers and percentages of eligible women in the state's counties. For instance, the county estimates inform decisions related to the dissemination of resources and funds to the community partnerships that assist the program with public education, outreach, and clinical quality assurance measures. The estimates by racial/ethnic group are useful in developing culturally appropriate messages and educational materials and in improving access to high quality screening services.

Other public health programs that have difficulty describing the distribution of their target populations because of a general lack of local data on health insurance status may also benefit from applying the method we have described. For example, other states that participate in the National Breast and Cervical Cancer Early Detection Program (NBCCEDP) (http://www.cdc.gov/cancer/nbccedp/) have eligibility criteria similar to those of California's CDP:EWC program and could produce meaningful estimates of eligible local populations by racial/ethnic and age groups by applying the small-area estimation method using a state survey or the Current Population Survey (a national survey that contains health insurance information [www.census.gov/cps/]) and census data (29). WISEWOMAN (Well-Integrated Screening and Evaluation for Women Across the Nation [www.cdc.gov/wisewoman/]), a state-based program offering NBCCEDP-enrolled women free or low-cost risk-factor screening, lifestyle interventions, and referral services aimed at preventing cardiovascular and other chronic diseases (30), could use this method to determine local estimates of the eligible population by demographic group to help identify provider sites and to determine the number of potential WISEWOMAN recruits.

One might think that in this age of information, data to describe any population of interest would be easy to obtain. This is not always the case, however, particularly when a population is narrowly defined, either by residence in a small geographic area or by specific characteristics. Small-area estimation statistics, as applied in our example, give public health programs a means of obtaining reliable estimates of their local or sparse target populations, even when no data seem to be available.