Despite the promise of geocoding and use of area-based socioeconomic measures to overcome the paucity of socioeconomic data in US public health surveillance systems, no consensus exists as to which measures should be used or at which level of geography. The authors generated diverse single-variable and composite area-based socioeconomic measures at the census tract, block group, and zip code level for Massachusetts (1990 population: 6,016,425) and Rhode Island (1990 population: 1,003,464) to investigate their associations with mortality rates (1989-1991: 156,366 resident deaths in Massachusetts and 27,291 in Rhode Island) and incidence of primary invasive cancer (1988 ( -1992 -1992 resident cases in Rhode Island). Analyses of all-cause and cause-specific mortality rates and all-cause and sitespecific cancer incidence rates indicated that: 1) block group and tract socioeconomic measures performed comparably within and across both states, but zip code measures for several outcomes detected no gradients or gradients contrary to those observed with tract and block group measures; 2) similar gradients were detected with categories generated by quintiles and by a priori categorical cutpoints; and 3) measures including data on economic poverty were most robust and detected gradients that were unobserved using measures of only education and wealth. Am J Epidemiol 2002;156:471-82. censuses; geographic information system; geostatistics; mortality; neoplasms; population surveillance; poverty; socioeconomic factors Abbreviations: IRR, incidence rate ratio; RII, relative index of inequality; SEP, socioeconomic position. Despite growing recognition of the magnitude and persistence of socioeconomic inequalities in health and the need to address them (1-4), few or no socioeconomic data exist in most US public health surveillance databases (5, 6). Only in 1989 did collection of educational data on birth and death certificates become routine (7)-60 years after the last attempt, in 1930, to generate vital statistics stratified by occupational class (8, 9) . Even so, in 1997, only 7 percent, 4 percent, and 0 percent of US state registries for cancer, tuberculosis, and acquired immunodeficiency syndrome included data on education-and neither they nor birth and death databases included data on poverty, income, or other aspects of socioeconomic position (SEP) (5). This lack of data hampers meaningful monitoring of socioeconomic inequalities in public health databases. Reflecting the limitations of available data, the US National Center for Health Statistics' first-ever national chartbook on "Socioeconomic Status and Health," issued in 1998 (1), presented data based solely on birth and death records plus data from the National Health Interview Survey, but it could not include data on cancer incidence or survival, tuberculosis, human immunodeficiency virus/acquired immunodeficiency syndrome, and other health outcomes not assessed in the National Health Interview Survey. Relatedly, 70 percent of the 467 US public health objectives for the year 2010 lack quantitative targets for reducing socioeconomic disparities in health, given a lack of baseline data (10, 11). One possible solution to these gaps is to combine data from public health surveillance systems with socioeconomic data derived from the US Census. The basic approach is to classify people in public health databases and in the total population by the socioeconomic characteristics of their residential neighborhood, thereby permitting calculation of population-based rates stratified by area-based SEP (6, 12). These area-based geosocial measures-conceptualized as meaningful indicators of socioeconomic context in their own right and not merely "proxies" for individual-level datacan be validly applied to all persons, regardless of age, gender, and employment status (6, (12) (13) (14) (15) . First employed in US health studies in the 1930s (16) (17) (18) (19) (20) (21) (22) , the use of such geosocial measures-by which we mean empirically observable social and physical characteristics of areas whose spatial distribution is patterned by human activity-has been greatly facilitated by the past decade's rapid development of geographic information systems technology (23-25). Indeed, National Objective 23-3 of Healthy People 2010 sets the goal of geocoding, by the year 2010, 90 percent of "all major national, state, and local health data systems… to promote nationwide use of geographic information systems (GIS) at all levels" (10). Obstacles to the use of area-based socioeconomic measures are not only technical, however. They are also conceptual. To date, there exists no consensus in the United States regarding which area-based measures should be used, at which level of geography, to measure or monitor socioeconomic inequalities in health (6, 26) . Instead, studies on a variety of outcomes, spanning from birth to death (27-38), have employed markedly different single-variable and composite area-based measures, variously derived from three different geographic levels (figure 1): the census tract (average population = 4,000); the census block group, a subdivision of the census tract (average population = 1,000); and the US Postal Service zip code (average population = 30,000) (25). By contrast, in the United Kingdom, several well-established, theoretically conceived, and validated area-based deprivation measures, such as the Townsend index, permit meaningful comparisons and monitoring of national, regional, and local socioeconomic gradients in health over time (14, (39)(40)(41)(42). Accordingly, we designed the Public Health Disparities Geocoding Project to determine which area-based socioeconomic measures, at which level of geography, would be most appropriate for US public health surveillance systems and research. Considerations pertained to 1) external validity (do the measures find gradients in the direction reported in the literature, i.e., positive, negative, or none, and across the full range of the distribution?); 2) robustness (do the measures detect expected gradients across a wide range of outcomes?); 3) completeness (is the measure relatively unaffected by missing data?); and 4) user-friendliness (how easy is the measure to understand and explain?). Guided by an ecosocial framework (43), we deliberately included data from multiple public health surveillance systems to maximize our ability to assess associations and geosocial health disparities observed for diverse health outcomes manifested at different ages. In this paper, we report our results for mortality rates and cancer incidence.  MATERIALS AND METHODS The study base comprised populations and areas in Massachusetts and Rhode Island enumerated at or within 2 years of the 1990 US Census (44, 45). Mortality data and cancer incidence data (table 1) were provided by the Massachusetts Department of Public Health and the Rhode Island Department of Health. Use of these data was approved by all relevant institutional review boards/human subjects committees at the Harvard School of Public Health, the Massachusetts Department of Public Health, and the Rhode Island Department of Health. Cause of death was categorized according to the International Classification of Diseases, Ninth Revision, Clinical Modification (46), cancer type according to the standard site/histology definitions of the Surveillance, Epidemiology, and End Results Program (47), and gender and race/ethnicity (plus educational level, death only) as reported by next of kin, and/or as recorded by the funeral director (for death data) or abstracted by registry staff from medical records (for cancer data). Mortality outcomes analyzed included all-cause mortality and the top five causes of death in each state by race/ethnicity, yielding nine specific causes of death: heart disease, malignant neoplasm, cerebrovascular disease, pneumonia and influenza, chronic obstructive pulmonary disease, unintentional injury, diabetes mellitus, human immunodeficiency virus, and homicide and legal intervention. Incidence of cancer was analyzed for all cancers combined and for five leading sites: the breast, cervix, colon, lung, and prostate (27, 47). We obtained 1990 Census data for census tracts and block groups from US Bureau of the Census Summary Tape File 3A and zip code data from Summary Tape File 3B (48). The Census Bureau defines a census tract as a "small, relatively permanent statistical subdivision of a county… designed to be relatively homogeneous with respect to population characteristics, economic status, and living conditions" (25, pp. G-10, G-11); its subdivision, the block group, is the smallest geographic census unit for which census socioeconomic data are tabulated (25, p. G-6). By contrast, zip codes are "administrative units established by the United States Postal Service… for the most efficient delivery of mail, and therefore generally do not respect political or census statistical area boundaries" (48, p. A-13). Spanning from large areas cutting across states to a single building or company with a high volume of mail, "carrier routes for one zip code may intertwine with those of one or more zip codes" such that "this area is more conceptual than geographic" (49, p. 22). To geocode data to the census tract, block group, and zip code levels, we submitted residential addresses from the mortality and cancer databases to a commercial geocoding firm selected for its accuracy (50). Two criteria central to formulating apt area-based measures of SEP are that they 1) meaningfully summarize important aspects of the specified area's socioeconomic conditions and 2) employ socioeconomic data that can legitimately be compared over time and across regions (6, 11, 14, 26, (39)(40)(41)(42). On the basis of a priori conceptual definitions of SEP and social class (6) and evidence from both the United States and the United Kingdom emphasizing the detrimental effects of material deprivation on health (1-4, 51), we developed area-based socioeconomic measures for six domains of SEP-occupational class, income, poverty, wealth, education, and crowding-premised on the understanding that social class, as a social relationship, fundamentally drives the distribution of these manifest aspects of SEP (6). Table 2 provides information on the 11 single-variable measures and eight composite measures we generated for each state at each level of geography. Among the composite variables, two were US analogs of the United Kingdom * Created using data from the 1990 US Census (40). † Variables employed in the factor analysis: percentage working class, unemployment, percentage below poverty line, home ownership, car ownership, no telephone, expensive homes, low education (less than high school), high education (≥4 years of college), household crowding, households with only one room, no kitchen, no private plumbing, median household income, and proportion of total income in the area derived from interest, dividends, and net rent. ‡ Values for "expensive homes" and "median household income" were reversed before the z score was computed so that a higher score on the SEP index would correspond to a higher degree of deprivation. Operational A "summary index" based on "white-collar employment, unemployment, and family income" (52). P78, P71, P107A SEP1 A composite categorical variable based on percentage below the US poverty line, working class, and expensive homes. A composite categorical variable based on percentage below the US poverty line, working class, and high income. Factor 1 † A factor pertaining to economic resources. Highly correlated with poverty, median household income, home ownership, and car ownership. - † Factor 2 † A factor pertaining to occupation and education. Highly correlated with percentage working class, low education (less than high school), and high education (≥4 years of college). - † SEP index A summary deprivation measure consisting of a standardized z score combining data on percentage working class, unemployment, percentage below the US poverty line, low education (less than high school), expensive homes, and median household income ‡. P78, P71, P117, P57, P80 (52), and five were created exclusively for our study. To mirror the skewed population distribution of socioeconomic resources, we created the variables "SEP1" and "SEP2" to combine simultaneously categorical data on poverty, working class, and either wealth or high income. We generated "factor 1" and "factor 2" by factor analysis with a maximum likelihood approach (53, 54) applied to inputs listed in table 2 (see second footnote in table) , using rank values of the census data, rather than impose arbitrary transformations to normalize their often considerably skewed distributions; tied values were assigned an average rank. We selected the two-factor model as the most appropriate description of the underlying factor structure. Correlations between the factors ranged from 0.420 to 0.564 after oblique rotation. Finally, we generated the "SEP index," a standardized z score akin to the Townsend index, using inputs identified by the factor analysis. Our analytical plan involved five steps. Step 1 was to assess the distribution and missingness of data. Step 2 was to calculate age-standardized average annual mortality rates and cancer incidence rates stratified by the area-based socioeconomic measures at each level of geography for each state (55, p. 54; 56, p. 263). We standardized for age using the year 2000 standard million (57) and age-specific rates generated for 11 age groups (<1, 1-4, 5-14, 15-24, …, 75-84, and ≥85 years). The numerators and denominators of these rates consisted of persons residing in areas identified at the specified geographic level for which data on the specified areabased socioeconomic measure were available. Following standard practice for rates centered around a census (58, 59), we set the total number of person-years in the denominator equal to the population in that socioeconomic stratum enumerated in the 1990 Census multiplied by the relevant number of years of observation. Cutpoints for categorical area-based socioeconomic measures (see Appendix at http:// www.aje.oupjournals.org) were based on both their percentile distributions (e.g., quintiles) and a priori considerations (e.g., the federal definition of "poverty areas" as regions where ≥20 percent of the population lives below the US poverty line (60, 61)). In step 3, we visually inspected and quantified socioeconomic gradients for each outcome using each area-based socioeconomic measure at each geographic level, excluding persons who were geocoded to areas with no population (e.g., geocoded to a zip code not included in the 1990 Census). Based on clear evidence of linear trends (data not shown; available upon request), we followed standard US reporting practices (1) and computed the mortality rate ratio, incidence rate ratio (IRR), and incidence rate difference, comparing rates for people living in areas with the least resources with rates for people living in areas with the most resources; given similar patterns, we report only the IRR. To take into account both the population distribution of the exposure and the magnitude of the rate ratio detected in each socioeconomic stratum, we also calculated the relative index of inequality (RII), a measure of effect that consequently permits meaningful comparison of gradients across different socioeconomic measures (62-64). In step 4, we further restricted analyses to persons geocoded to all three levels of geography. In step 5, we summarized findings across socioeconomic measures and geographic levels, in relation to our a priori considerations regarding external validity, robustness, and completeness of each measure. As a further check on internal validity, we also analyzed mortality using individual-level educational data. All analyses were conducted in SAS (65). Fully 92.8 percent of the 370,196 mortality and cancer records for Massachusetts and Rhode Island were successfully geocoded to the census block group level, and 99.6 percent were geocoded to both the census tract level and the zip code level. These results were independent of gender, age, race/ethnicity, and, for the mortality data, educational level (table 3) . The proportion of areas without the specified socioeconomic measures was also low (typically <1 percent), considering all measures across all levels of geography in both states (data not shown; available upon request). Among the total 368,530 records geocoded to the zip code level, 23,350 (6.3 percent) could not be linked to 1990 Census data because their zip codes either were for nonresidential areas (e.g., government agencies, businesses with a high mail volume, or post offices and post office boxes) or were created or changed after the 1990 Census. Table 4 (the full version is available on the World Wide Web at http://www.aje.oupjournals.org) presents results of selected analyses generating and comparing all-cause and cause-specific mortality rates and cancer incidence rates, stratified by each area-based socioeconomic measure at each level of geography, for each state. Given the similar findings, we present data for the categorical version of the poverty variable but not the quintile version, for SEP1 but not SEP2, for the SEP index but not factor 1 or factor 2, and for death due to diabetes but not death due to unintentional injury (data not shown; available upon request). Patterns of association were equivalent for analyses restricted to persons geocoded to all three levels of geography (data not shown; available upon request). As table 4, section a, illustrates, depending on the type of mortality and the area-based socioeconomic measure chosen, estimates of effect comparing Massachusetts mortality rates for persons living in areas with the least resources versus persons living in areas with the most resources ranged from no effect to a substantial effect; similar patterns were observed at each level of geography. For example, across levels of geography, the median value of both the IRR and the RII for all-cause mortality was 1.3-1.4, with most measures performing similarly in detecting (as expected) associations between higher mortality and fewer economic resources (1); the exception was the Gini coefficient, a measure of income inequality (no gradient detected). Similar patterns were evident for Massachusetts mortality due to heart disease, malignant neoplasm (albeit with a weaker gradient), and diabetes (with a stronger gradient). By contrast, for mortality due to human immunodeficiency virus and to homicide and legal intervention, measures intended to reflect poverty detected notably larger gradients. For human immunodeficiency virus, estimates ranged from no effect (wealth) to a >20-fold effect (RII for poverty, Townsend index, and crowding), with a median IRR between 3 and 4 and a median RII between 5 and 7. For homicide and legal intervention, estimates ranged from a twofold effect (IRR for wealth) to a >30-fold effect (RII for poverty, crowding, and Townsend index), with a median IRR between 9 and 11 and a median RII between 22 and 24. For all outcomes, the precision of the effect estimates was greater for the RII than for the IRR. Analysis of the Rhode Island mortality data (table 4, section c) yielded similar patterns, except that somewhat stronger socioeconomic gradients were apparent both for median household income and for all outcomes except homicide and legal intervention. For both states, analyses of mortality and individual-level education data comparing persons with a high school education or less with persons with more than a high school education showed gradients pointing in the same direction (data not shown; available upon request). Alternatively, for cancer incidence, level of geography mattered for several of the sites but not all (table 4, sections b and d). For example, census block and tract-level measures detected expected socioeconomic gradients (27, 28, 66) for three cancer sites not captured by zip code measures (breast and prostate cancer in Massachusetts and lung cancer in Rhode Island). In Massachusetts, gradients detected using zip code data were in the direction opposite that observed using block group and tract socioeconomic data for colon cancer (IRR and RII) and for all sites combined (RII only). Visually summarizing key results, figure 2 depicts socioeconomic gradients in all-cause mortality for Massachusetts by employing the three block group measures that most consistently detected socioeconomic gradients in health while differently delimiting the population at risk: poverty (single-variable, categorical), SEP1 (composite, categorical), and the Townsend index (composite, quintile). DISCUSSION This study-which to our knowledge was the first systematic US investigation of area-based socioeconomic measures suitable for monitoring population health and the first that simultaneously compared diverse area-based socioeconomic measures within and across levels of geography-provided empirical evidence that both choice of measure and level of geography matter. Specifically, examining mortality and cancer incidence for two New England states during the period around 1990 in conjunction with 1990 US Census data, we obtained three findings. First, measures designed to detect economic deprivation were most robust, consistently detecting socioeconomic gradients not only for the leading causes of death and cancer, as did the other measures, but also for deaths due to human immunodeficiency virus and homicide and legal intervention, whose gradients were detected less well or missed by measures of education and wealth. Second, census block group and census tract TABLE 4. Rates of all-cause mortality according to area-based socioeconomic measures* (census block group, census tract, and zip code) for persons residing in areas with the least and the most socioeconomic resources, Massachusetts, 1989Massachusetts, -1991
REF compared different geographic units of observation such as ZIP Codes, census tracts and blocks and concluded that the ZIP Code is the least accurate geographic unit.
Geocoding and Monitoring of US Socioeconomic Inequalities in Mortality and Cancer Incidence: Does the Choice of Area-based Measure and Geographic Level Matter?: The Public Health Disparities Geocoding Project
American journal of epidemiology
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