Small area analysis has a long history in public health.  The first well-documented use of the methodology was by Glover (1938, as cited in Goodman & Green, 1996), who found substantial variation in the rates of tonsillectomy among different school districts in England.  Glover demonstrated that the rates were not associated with incidence of disease, poverty, or medical services, and suggested that differences in the medical practice preferences of the attending physicians were largely responsible for the variation in rates.  More recently, several authors (Paul-Shaheen, Clark, & Williams, 1987; Wennberg, 1987; Wennberg & Gittelsohn, 1973; Wilson & Tedeschi, 1984) have used small area analysis to characterize health care services within specific geographic areas, and to explore the effects of supply factors versus population need on the distribution of health services.  For instance, Wennberg (1987) reported that hospitalization rates were more clearly related to the supply of hospital beds than to the morbidity of the population, especially for medical conditions where hospitalization was not standard practice.  Small area analysis has also been used to demonstrate that the socio-economic status of residential areas was associated with mortality (Waitzman & Smith, 1998; Gould, Davey and LeRoy, 1989) and with primary cesarean section rates (Gould, Davey & Stafford, 1989).  Other applications of small area analysis in public health have included the examination of patterns of disease in relation to environmental conditions (Lang & Polansky, 1994), the production of synthetic estimates of disease or risk factor prevalence (Lafata, Koch & Weissert, 1994; Spasoff, Strike, Nair et al., 1996), and identification of areas with excess morbidity or mortality in order to target preventive or curative services at population groups with special needs (Andrews, Kerner, Zauber et al., 1994; Kleinman, 1977).

Public health, as is true of public policy in general, has increasingly emphasized local, or community health, assessment and planning (American Public Health Association, 1991;  APEXPH Steering Committee, 1991).  Those efforts are often inhibited by a paucity of relevant and meaningful information about the current status and needs of local populations.  Although the information needs of community planners cannot all be met by the results of small area analysis, understanding community health status can only improve community public health planning.

Information from different datasets can be combined using small area analysis so that previously unexplored relationships can be examined.  Although this is sometimes possible with individual-level data, matching individuals across datasets is typically not possible because the same individuals are seldom represented in multiple datasets.  Matching small areas across datasets, on the other hand, is often easily accomplished.  For instance, economic data from the U.S. Census can be combined with vital statistics data, such as infant mortality; disease data can be compared to information on environmental exposures; survey data on risk factors can be compared to information on hospitalizations, and so forth.

In this paper, methods to specify small areas in Utah are described and discussed.  The small areas that are specified by these methods are then used to examine health status in Utah.  Four health measures were selected as examples because they represented variables from a variety of data sources that yielded geographic differences:  Motor vehicle crash death rates, percentages of persons without health insurance, percentages of women giving birth who did not have early prenatal care, and percentages of adults who smoked cigarettes.  Several issues surrounding the design and use of small areas are also discussed.