摘要：Objectives. To aid state and local policymakers, program planners, and community advocates, we created estimates of the percentage of the population lacking health insurance in small geographic areas of California. Methods. Finally, calibration ensured the consistency and stability of the estimates when they were aggregated. Results. Health insurance coverage among nonelderly persons varied widely across assembly districts, from 10% to 44%. The utility of local-level estimates was most apparent when the variations in subcounty uninsured rates in Los Angeles County (19%–44%) were examined. Conclusions. Stable and useful estimates of health insurance rates for small areas such as legislative districts can be created through use of multiple sources of publicly available data. Lack of health insurance is a chronic public health problem for over 45 million children and nonelderly adults in the United States. 1 California, the most populous state, 2 , 3 has the third highest uninsured rate nationally. 4 – 6 An estimated 19% of California’s population, 6.3 million adults and children, went without health insurance coverage in 2001. 7 Although California is a large and diverse state, its uninsured population is unevenly distributed. Having no health insurance has been associated with socioeconomic status, race/ ethnicity, age, and area of residence. 8 – 12 Expanding health insurance coverage has been a priority in many state legislatures. Federal funds for the State Children’s Health Insurance Program have been used to create many new state initiatives. In the past decade, there have been other state health policy initiatives to further expand health insurance to children, as well as to reduce the number of uninsured adults. 13 Legislators want to know how their districts will be affected by these initiatives; however, current sources of data on health insurance do not provide information at the legislative district level. Nationally, the primary source of health insurance information is the March Current Population Survey (CPS) of the US Census Bureau, which provides annual estimates of the uninsured population. Its ability to generate substate estimates is limited, however, especially for metropolitan areas with populations under 500 000. 14 In addition, California legislative districts do not follow administrative boundaries, such as counties or metropolitan statistical areas (MSAs), that public data sources identify. Estimates are therefore needed for California Assembly districts, which have approximately 400 000 residents each, and geographic areas with meandering census tract boundaries. (In 2000, legislative districts were redrawn with census blocks used as boundaries.) In response to the demand for local-level data on health insurance, we developed a small-area estimation procedure to calculate the numbers and percentages of the population without health insurance in California’s legislative districts in 2000, including all 80 assembly districts. Rao 15 has reviewed the methodology and application of small-area estimation in health-related research. Small-area estimation has been used to produce estimates for the prevalence of overweight adults, 16 substance abuse, 17 , 18 and physician visits. 19 The US Census Bureau’s Small Area Income and Poverty Estimates program provides intercensual data of selected income and poverty statistics for states, counties, and school districts. 20 , 21 However, small-area methodology has only recently been applied to health insurance coverage. The US Census Bureau’s Small-Area Health Insurance Estimates project developed model-based estimates for the numbers of the uninsured population for states and counties. 22 However, the Small-Area Health Insurance Estimates project is limited in its ability to derive estimates for areas smaller than the county level. Using regression models and data from multiple sources, we developed a small-area methodology to derive synthetic estimates of uninsured rates at subcounty levels. Our method, which is similar to another approach independently developed by Twigg et al., 23 is flexible and can be replicated to develop similar estimates at any geographic level of interest that is compatible with public data sources.
