Applying the small-area estimation method to estimate a population eligible for breast cancer detection services

Abstract

Introduction: Populations eligible for public health programs are often narrowly defined and, therefore, difficult to describe quantitatively, particularly at the local level, because of lack of data. This information, however, is vital for program planning and evaluation. We demonstrate the application of a statistical method using multiple sources of data to generate county estimates of women eligible for free breast cancer screening and diagnostic services through California's Cancer Detection Programs: Every Woman Counts.

Methods: We used the small-area estimation method to determine the proportion of eligible women by county and racial/ethnic group. To do so, we included individual and community data in a generalized, linear, mixed-effect model.

Results: Our method yielded widely varied estimated proportions of service-eligible women at the county level. In all counties, the estimated proportion of eligible women was higher for Hispanics than for whites, blacks, Asian/Pacific Islanders, or American Indian/Alaska Natives. Across counties, the estimated proportions of eligible Hispanic women varied more than did those of women of other races.

Conclusion: The small-area estimation method is a powerful tool for approximating narrowly defined eligible or target populations that are not represented fully in any one data source. The variability and reliability of the estimates are measurable and meaningful. Public health programs can use this method to estimate the size of local populations eligible for, or in need of, preventive health services and interventions.

Figure

Estimated proportions of women aged 40 years or older in California counties eligible for breast cancer screening and diagnostic services through Cancer Detection Programs: Every Woman Counts, by racial/ethnic group, 1998–2003. Asterisk (*) indicates value suspected as outlier; plus (+), mean of county proportions. Note. The bottom line of each box represents the 25th percentile of the estimated proportions; the middle line, the 50th percentile (median), and the top line, the 75th percentile. The endpoints of the whiskers are the most extreme values not identified as suspected outliers. We identified as suspected outliers county proportions exceeding the 75th percentile plus 1.5 times the interquartile range, or falling short of the 25th percentile minus 1.5 times the interquartile range.