Use of multilevel frameworks and area-based socioeconomic measures (ABSMs) for public health monitoring can potentially overcome the absence of socioeconomic data in most US public health surveillance systems.

To assess whether ABSMs can meaningfully be used for diverse race/ethnicity–gender groups, we geocoded and linked public health surveillance data from Massachusetts and Rhode Island to 1990 block group, tract, and zip code ABSMs. Outcomes comprised death, birth, cancer incidence, tuberculosis, sexually transmitted infections, childhood lead poisoning, and nonfatal weapons-related injuries.

Among White, Black, and Hispanic women and men, measures of economic deprivation (e.g., percentage below poverty) were most sensitive to expected socioeconomic gradients in health, with the most consistent results and maximal geocoding linkage evident for tract-level analyses.

Despite growing interest in social inequalities in health,1–4 routine monitoring of socioeconomic gradients in health in the United States is hampered by a lack of socioeconomic data in most US public health surveillance systems.5,6 Notable exceptions include birth and death certificates, which since 1989 have included data on educational level,7 and also data obtained from specialized surveys, such as the National Health Interview Survey.8 However, surveys are incapable of monitoring important aspects of population health (e.g., cancer incidence, tuberculosis (TB), and childhood lead levels), nor can they readily provide the kinds of routine—and local—data required by health departments, especially on relatively small population subgroups, such as members of diverse racial/ethnic communities.5,9

Reflecting gaps created by unavailable socioeconomic data, the 2002 edition of Health, United States,10 the annual federal publication profiling the health of the nation, lacked socioeconomic data in 85.5% of its 71 tables on “health status and determinants”; virtually all tables, however, were stratified by “sex, race, and Hispanic origin.” Likewise, fully 70% of the 467 US public health objectives for the year 2010 had no socioeconomic targets, given a lack of baseline data.11,12 This absence of economic data from routine public health monitoring—equally evident in state health department publications—obscures socioeconomic gradients in health overall and within diverse race/ethnicity–gender groups, as well as the contribution of economic deprivation to racial/ethnic and gender inequalities in health.5,6,12–14

Fortunately, the methodology of geocoding residential addresses and using area-based socioeconomic measures (ABSMs) is a potential and relatively inexpensive solution to the problem of absent or limited socioeconomic data in US public health surveillance systems.6,12,15,16 In this approach, which draws on multilevel frameworks and area-based measures, both cases (numerators) and the catchment population (denominators) are classified by the socioeconomic characteristics of their residential area, thereby permitting calculation of rates stratified by the ABSMs.

Although this approach has been employed in US health research for over 75 years,17–20 to date there exists no consensus or standard as to which ABSMs, at which level of geography, are best suited for monitoring US socioeconomic inequalities in health.6,12,15,20 Nor, to our knowledge, have any investigations systematically assessed, empirically, whether specified ABSMs perform similarly or differently in diverse race/ethnicity–gender groups. Instead, published research has exhibited a remarkable eclecticism regarding choice of geographic level and types of ABSM used, both single variable and composite.6,12,21 Although such a plurality of measures may be useful for etiologic research, in the case of monitoring, such heterogeneity impedes comparing results across studies, outcomes, and regions and over time.

We accordingly launched the Public Health Disparities Geocoding Project to ascertain which ABSMs, at which geographic level (census block group, census tract, or zip code), would be most apt for monitoring US socioeconomic inequalities in health. To provide a robust evaluation, guided by ecosocial theory,22,23 we designed the study to encompass a wide variety of health outcomes, hypothesizing that some ABSMs and geographic levels might be more sensitive to socioeconomic gradients for some health outcomes than others.

Drawing on 1990 census data and public health surveillance systems of 2 New England states, Massachusetts and Rhode Island, we included 7 types of outcomes: mortality (all cause and cause specific), cancer incidence (all sites and site specific), low birth weight, childhood lead poisoning, sexually transmitted infections, TB, and nonfatal weaponsrelated injuries.24–26 Pertinent a priori considerations, derived in part from Rossi and Gilmartin’s criteria for valid and useful social indicators,27 included (a) 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?), (b) robustness (do the measures detect expected gradients across a wide range of outcomes?), (c) completeness (is the measure relatively unaffected by missing data?), and (d) user-friendliness (how easy is the measure to understand and explain?).

Our initial analyses focused on the total population of each state, with results suggesting that public health monitoring might be judiciously augmented by the use of census tract–level measures of economic deprivation, and specifically the measure “percentage of persons below poverty.”24–26 In this investigation, we extend our analyses by examining whether these conclusions hold for diverse race/ethnicity–gender groups.

Data Sources: Population and Health Outcomes

As described in our earlier publications,24–26 the study base comprised (a) populations and areas in Massachusetts and Rhode Island enumerated at the 1990 census and (b) health outcomes occurring in the period around the 1990 census. We obtained public health surveillance data from the Massachusetts Department of Public Health and the Rhode Island Department of Health for death, birth, cancer incidence, TB, sexually transmitted infections, childhood lead poisoning (Rhode Island only), and nonfatal weapons-related injuries (Massachusetts only).

Data for death, birth, cancer incidence, and childhood lead poisoning (among children 1 to 5 years old) were analyzed for persons. Data for TB, sexually transmitted infections, and nonfatal weapons-related injuries were analyzed for new cases only, since a given individual could experience the specified outcome more than once during the study period; data for lead poisoning were likewise analyzed only for a child’s first record in the study interval, not repeat follow-up tests. Slightly over 760 000 records were included in our final analytic data sets,24–26 restricted to records for in-state residents with health outcomes occurring during the specified study interval and not missing data on age, gender, or the specified outcome, plus additional restrictions described below. All records were geocoded to the census block group, census tract, and zip code levels by a commercial geocoding firm whose accuracy we validated (96%).28

With regard to outcomes, cause of death was categorized according to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and cancer type by standard Surveillance, Epidemiology, and End Results (SEER) site/histology definitions.24,29–34 Mortality outcomes analyzed included premature mortality (< 65 years old) and selected causes of death ranked among the top 5 causes of death in each state within one or more racial/ethnic groups,24 including mortality due to heart disease, neoplasm, diabetes, HIV, and homicide. Incidence of cancer was analyzed for all cancers combined and the 5 leading sites reported nationally34: breast, cervix, colon, lung, and prostate.24

We analyzed births to mothers aged 15 to 55 years; we report results only for singleton births, using the conventional definition of low birthweight as less than 2500 g.25,30,35 For Rhode Island’s mandatory universal childhood lead screening program, blood specimens were obtained 2 ways, at the screening physician’s discretion: venous and capillary/fingerstick.25,36 Because the latter may be subject to contamination (e.g., lead dust on the pricked finger),37 we analyzed the 2 sample types separately and present only the venous results. Following guidelines issued by the Centers for Disease Control and Prevention (CDC) in 1997,37 elevated blood lead levels were defined as 10 g/dL or above.

Cases included in the sexually transmitted infection databases for both states were identified and reported to the state health department because they (a) were symptomatic patients, (b) sought testing because they were concerned about their exposure (i.e., after unsafe sex), (c) received a complete battery of sexually transmitted infection tests as part of seeking confidential HIV testing, (d) were sexual partners of persons identified as cases, or (e) received testing as part of a routine gynecologic examination.26,38,39 Cases in both states’ TB databases were identified and reported to the state health department via designated TB clinics and additional health care providers.40,41 Finally, data on nonfatal weapons-related injury (intentional and unintentional) were obtained from the Massachusetts Department of Public Health’s Weapons-Related Injury Surveillance System, which encompasses all Massachusetts acute care hospital emergency departments.42 Fully 97% of the nonfatal weapons-related injuries were intentional; data on whether the injury was intentional or not were obtained from the respondent, if conscious, and otherwise coded as “unknown.”

The 1990 census, the source of our denominators, used self-report data to classify race/ethnicity in accordance with censusdefined categories pertaining to “race” and Hispanic “ethnicity”; data on age and gender were also obtained by self-report.43 Data on race/ethnicity, gender, and age were reported by the next of kin or recorded by the funeral director for the death data and were abstracted by registry staff from medical records for the cancer data.24,29,30,32,33 For birth certificate data, mother’s race/ethnicity and age were obtained by self-report through use of closed-format questions; for childhood lead poisoning, race/ethnicity, gender, and age of the child were reported by the child’s parent (or adult guardian).25,30,35,36 Data on race/ethnicity, gender, and age for the sexually transmitted infection, TB, and injury cases were obtained by a mixture of self-report and observer report.26,38–42 Notably, the different databases employed different approaches to categorizing racial/ethnic data: some used separate fields for data on “race” and on “Hispanic origin” (or “ancestry”), permitting these data to be cross-classified, while others used only one field for these items.

To maximize the compatibility of racial/ethnic categories employed in the numerators (cases) and denominators (population) and to ensure adequate sample size to conduct meaningful analyses, our investigation thus focused primarily on 3 racial/ethnic groups: White, Black, and Hispanic. In these analyses, the White and Black populations were mutually exclusive and included all persons regardless of census-designated “ethnicity,” while the Hispanic population included persons of all census-designated “races.” According to the 1990 census,44 fully 98% of both the Massachusetts and Rhode Island White populations in 1990 identified themselves as “nonHispanic,” as did 92% of the Massachusetts and 89% of the Rhode Island Black population. Conversely, among the Hispanic population, 43% in Massachusetts and 48% in Rhode Island identified themselves as “White,” 8% and 9% as “Black,” and 47% and 41% as “other race.” Analyses for the American Indian and the Asian and Pacific Islander populations (respectively comprising, combined, 2.5% and 2.2% of the Massachusetts and Rhode Island populations) were limited to premature mortality and low birthweight, as they were the main outcomes with sufficient data to yield interpretable results.

Data Sources: ABSMs

As described in our prior investigations,24–26 we obtained 1990 census data for census tracts and block groups from US Census Bureau Summary Tape File 3A and zip code data from Summary Tape File 3B.43 According to the US Census Bureau, census tracts on average contain 4000 persons and are a “small, relatively permanent statistical subdivision of a county . . . designed to be relatively homogeneous with respect to population characteristics, economic status, and living conditions.45(pG-10,G-11) The census tract’s subdivision, the block group, contains on average 1000 persons and is the smallest geographic census unit for which census socioeconomic data are tabulated.45(pG-6)

Zip codes, in turn, have an average population of 30 000 and 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,” and they can range in size from large areas cutting across states to a single building or company with a high volume of mail.43(pA-13) Moreover, unlike with census tracts and block groups, zip code boundaries may overlap (since “carrier routes for one ZIP Code may intertwine with those of one or more ZIP Codes”),46(p22) and they can be added, eliminated, or have their codes changed or boundaries redefined in nondecennial years.47,48

Three considerations guided our development of ABSMs24–26: (a) a priori conceptual definitions of socioeconomic position (SEP) and social class,6 (b) US and UK evidence emphasizing the detrimental effects of material deprivation on health,1–4,49 and (c) the need for measures that can be meaningfully compared over time and space, so as to permit valid monitoring and contrasts in relation to time period and region.6,24–26,49 Our project generated, at each level of geography for each state, 11 single-variable and 8 composite ABSMs that met these criteria (Appendix 1; available from the first author upon request), which together reflected 6 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 SEP6,24–26.

Of note, one ABSM we included differs from the others: the Gini coefficient, which is a measure of within-area socioeconomic inequality rather than a measure of the average socioeconomic level of an area.50 We included this measure because of concerns about its uncritical use—for example, at the block group and census tract level—since realities of economic segregation imply that the Gini coefficient should be employed only for larger aggregations.51

Among the composite variables, 2 were US analogues of the UK Townsend21,49,52 and Carstairs21,49,53 deprivation indices, 1 used the algorithm for the CDC’s Index of Local Economic Resources (developed as a county-level measure),54 and the remainder were created exclusively for our study.24–26 Two of these latter composite variables, SEP1 and SEP2, were intended to mirror the skewed population distribution of socioeconomic resources and simultaneously combined categorical data on poverty, working class, and either wealth or high income. Finally, the “SEP index,” a summed z score akin to the Townsend index, was generated through inputs identified by factor analysis,55,56 as described for our prior analyses.24–26 Cutpoints for both the single-variable and composite categorical ABSMs were based on both their percentile distribution (e.g., quintiles) and a priori considerations (e.g., the federal definition of “poverty areas” as regions where 20% or more of the population is below the US poverty line)57,58 (Appendix 2; available from the first author upon request).

Data Analysis

Our analytic plan involved 4 steps, conducted separately for each race/ethnicity–gender group. In Step 1, we assessed the data distribution, including the extent of missing data. In Step 2, we calculated the relevant age-standardized average annual incidence rate or proportion (for low birthweight and childhood lead poisoning), stratified by each ABSM at each level of geography for each state.59,60 For age standardization, we employed the year 2000 standard million,61 using 5 age groups (birth–14, 15–24, 25–44, 45–64, ≥65 years). The numerators and denominators of the calculated rates and proportions consisted of persons residing in areas identified at the specified level of geography for which data on the specified ABSMs were available. Following standard practice for rates centered around a census,62,63 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.

In Step 3, we visually inspected and quantified socioeconomic gradients for each outcome, using each ABSM at each geographic level. Following standard US reporting practices,1,5 we computed the incidence rate ratio or odds ratio, as warranted, comparing people living in areas with the least and most resources. We also calculated the relative index of inequality, a measure that takes into account the proportion of the population in each stratum as well as the effect estimate for that stratum, thereby providing a single metric that can be meaningfully compared across diverse socioeconomic measures (whether using categories that emphasize the extremes or yield more equal distributions, e.g., quintiles).64–66 In Step 4, we summarized findings across ABSMs and geographic levels, in relation to our above-mentioned a priori considerations regarding external validity, robustness, completeness, and user-friendliness of each measure. All analyses were conducted in SAS.67

Finally, to consolidate our key findings, we devised a “scaled relative index of inequality plot,” in which we display the relative index of inequality for a given ABSM divided by the median value for all the ABSMs being compared. This plot facilitates determining which ABSMs were likely to detect relative indexes of inequality similar to, higher than, or lower than the median relative index of inequality, for each given outcome, at each geographic level, for each race/ethnicity–gender group. To address concerns pertaining to unreliable data, results for outcomes with less than 5 cases are suppressed, while those for outcomes with either 5 to 20 cases or 20 or more cases, and for which the width of the 95% confidence interval is 2 times or more the value of the relative index of inequality, are separately flagged.

Table 1 presents data on the study base, defined in terms of both population and areas (block group, census tract, and zip code), as enumerated in the 1990 census for Massachusetts and Rhode Island, which respectively included approximately 6 million and 1 million residents. As shown in Table 2, about half the White population (both women and men) in both states lived in areas where fewer than 5% of persons lived below the poverty line; by contrast, over half of the Black and Hispanic populations lived in areas where the poverty rate was 20% or more (same for women and men). Similar patterns were evident for the ABSM for low education and, more starkly, the Townsend index, with no notable difference by gender within racial/ethnic groups.

Table 3 provides data on the number of cases for each outcome, by race/ethnicity and gender, and the percentage geocoded to the block group, census tract, and zip code level, which displayed little variation by race/ethnicity or gender. Overall, 93.7% of records were geocoded to the block group level, 99.5% to the census tract level, and 99.5% to the zip code level; only 0.6% were not geocoded to any of these 3 levels. All records geocoded to the block group and census tract level were linked to the relevant censusdefined areas. By contrast, at the zip code level, for several outcomes (especially birth, cancer incidence, and TB), often 10% to 15% of cases could not be linked to census zip code areas, because either their zip codes were for nonresidential sites or the zip codes were created or changed after the 1990 census.

Table 4 presents results for premature mortality for the Massachusetts census tract–level ABSMs and offers an illustration of the data we generated for each outcome, at each geographic level, for each ABSM, for each race/ethnicity–gender group, for both states (Appendix 3; available from the first author upon request). For each economic stratum of each ABSM, the table (men only) displays the number of cases, the denominator, the computed age-standardized rate, and the relative index of inequality (summarizing the socioeconomic gradient across all the economic strata); the incidence rate ratio, comparing rates among persons living in areas with the least versus most resources, is available from the first author. Thus, in the case of premature mortality, the median relative index of inequality for the 11 census tract–level ABSMs typically was 2 or higher in every race/ethnicity–gender group, with measures of economic deprivation most frequently detecting the steepest gradients. Among men, reliable estimates of the relative index of inequality ranged from a low of 0.9 (Black men: wealth ABSM) to a high of 3.4 (Asian and Pacific Islander men: poverty ABSM). Among women, reliable estimates of the relative index of inequality ranged from a low of 1.2 (Black women: wealth and working class ABSM) to a high of 3.2 (Asian and Pacific Islander women: Townsend ABSM).

Building on these analyses, Figure 1 employs the scaled relative index of inequality plot to summarize our findings for the census tract–level ABSMs across all outcomes for the total population and for the different race/ethnicity–gender groups. Four results stand out, which held for analyses at each geographic level (plots at the block group and zip code level available from the first author upon request). First, within both the total population and each race/ethnicity–gender group, the relative index of inequality for most ABSMs was close to the median for virtually all outcomes, suggesting that the impact of socioeconomic position on a given health outcome is robust. Second, measures of economic deprivation—such as the percentage of persons below poverty (the dark green line) and the Townsend index (the bright red line)—were most sensitive to expected socioeconomic gradients in health, with their relative indexes of inequality routinely at or above the median. By contrast, relative indexes of inequality for measures of wealth and income inequality generally fell below the median, and those for measures of education hovered around the median. Third, these patterns were especially evident for HIV mortality, homicide, TB, and sexually transmitted infections, for which much larger (and expected24–26) gradients were detected by ABSMs for economic deprivation compared with other ABSMs. Fourth, for virtually all outcomes, the median relative index of inequality typically was largest for the White population and smallest for the Black population.

Two findings, however, differed by geographic level (data not shown; available from the first author upon request). First, in each race/ethnicity–gender group, the block group and census tract median relative indexes of inequality for most outcomes resembled each other (with the block group estimate often slightly larger), whereas the zip code median relative index of inequality variously was larger or smaller than the block group or the census tract median relative index of inequality. Second, as reported previously for the total population,48 in each of the race/ethnicity–gender groups, only zip code–level analyses yielded parameter estimates contrary to what has been reported in the literature (e.g., they detected a positive socioeconomic gradient when most research has reported a negative gradient).

Taken together, our results suggest that aptly chosen ABSMs can meaningfully augment US public health surveillance systems to permit routine monitoring of socioeconomic inequalities in health within diverse race/ethnicity–gender groups as well as the total population. Three key findings pertinent to selecting a particular ABSM at a particular level stand out. First, census tract–level analyses yielded the most consistent results with maximal geocoding linkage (i.e., the highest proportion of records both geocoded and linked to census-defined areas). Second, measures of economic deprivation were most sensitive to expected socioeconomic gradients in health. Third, the single-variable measure “percentage of persons below poverty” performed as well as more complex, composite measures of economic deprivation (e.g., the Townsend index). These findings, in conjunction with our a priori criteria pertaining to external validity, robustness, completeness, and user-friendliness, accordingly suggest that the census tract–level measure “percentage of persons below poverty” would be a plausible candidate variable. Also important would be use of a priori categorical cutpoints (including the policy-relevant 20% below poverty57,58) to facilitate meaningful comparisons across time and space.

In evaluating our results, it is important to consider several possible sources of bias and error, as well as issues regarding interpretation and use of ABSMs. First, bias could result if a person’s socioeconomic position were associated with being included in a given public health surveillance system, having an erroneous or ungeocodable address, or living in an area missing ABSM data.6,15 If, for example, these problems occurred more frequently among poorer persons, estimates of socioeconomic gradients would be deflated; if, less plausibly, these problems chiefly affected affluent persons, the estimate would be inflated. In our prior research, however, with the 2 databases containing individual-level socioeconomic data (birth and death), we found no variation by educational level in the proportion of records geocoded at each geographic level.24,25

Second, misclassification of race/ethnicity and differential census undercounts by race/ethnicity and socioeconomic position could also affect parameter estimates.14 In both cases, however, the resultant biases would be operative at each geographic level and thus would not invalidate comparisons of socioeconomic gradients across ABSMs or geographic levels. Moreover, only a tiny proportion of areas (typically under 1%) lacked data on ABSMs and, to minimize geocoding error, we used a geocoding firm whose accuracy we validated.28 Third, from a temporal standpoint, cross-sectional analysis cannot address issues of etiologic period; simultaneity of measurement of ABSMs and health outcomes, however, is appropriate for monitoring, given the goal of quantifying the population burden of ill health in relation to socioeconomic position.24–26

Use of ABSMs nevertheless does raise several concerns. First, associations between ABSMs and health status likely reflect a complex combination of 3 factors: (1) composition (people in poor areas have poor health because poor people, as individuals, have poor health), (2) context (people in poor areas also have poor health because a concentration of poverty creates or exacerbates harmful social interactions), and (3) location of public goods or environmental conditions (poor areas are less likely to have good supermarkets and are more likely to be situated next to industrial plants, thereby harming health of their residents).12,21,68–71

Ascertaining the relative contribution of each of these factors—a task relevant for etiologic research—would necessitate multilevel models with relevant individual-level and area-based data69–72 (i.e., precisely the data that most US public health surveillance systems lack). Germane to compositional effects, however, the handful of US studies comparing effect estimates using area-based and individual-level socioeconomic data in conjunction with individual health data have found effect estimates in the same direction, often with similar magnitude (at the block group and census tract levels, but not the zip code level).15,73–75 Multilevel models, moreover, additionally suggest that, for at least some health outcomes, area- and individual-level socioeconomic factors independently and jointly shape the population distributions of disease.68–72

A second concern pertains to “ecologic fallacy,” which occurs when both the dependent and independent variables are group-level data and confounding is introduced through the grouping process.69,76 This type of aggregation bias, however, is not germane to the method of appending ABSMs to individual records, because individuals constitute the unit of observation for both the dependent variable (health outcomes) and the independent variable (exposure to area-based socioeconomic conditions).15,24–26 Instead, as for any research using areal measures, at issue is whether the areas and contingent areal measures are meaningful entities51,71,77; in the case of monitoring, this translates to whether the areas and ABSMs are apt for characterizing the social contours of the population burden of disease.15,24–26 In the case of census tracts, not only are initial boundaries delineated to demarcate relatively economically homogeneous populations, as previously noted,45(pG-10,G-11) but these administrative areas also affect residents’ lives because they are used by federal, state, and local governments to plan programs and allocate resources—for example, to define “urban empowerment zones,” to designate “medically underserved areas,” and to delimit city neighborhoods served by public health agencies.

A third related concern is whether ABSMs meaningfully measure conditions experienced by all persons residing in the specified area, especially in areas marked by socioeconomic heterogeneity.21,49,71 Indeed, it was this concern which led us to investigate whether use of different geographic areas matters, and whether different patterns are seen for members of diverse race/ethnicity–gender groups. Our principal finding—that census tract and block group ABSMs yield similar parameter estimates, whereas zip code estimates are less consistent—importantly held, however, for both the total population and specified subpopulations. These results are consonant with other studies, especially in the United Kingdom, and support the view that one advantage of ABSMs is that they can be applied equally to all persons, regardless of age, gender, and employment status, thereby avoiding well-known problems associated with occupation- and education-based measures—that is, how to classify people not in the paid-labor force (children, housewives or househusbands, unemployed or retired persons) or who have not yet completed their education (school-age children and young adults).6,15,21,53,78

Concerns about ABSMs and the evidence addressing them thus suggests that while use of ABSMs for public health monitoring requires judicious interpretation, the information gained can usefully offset current gaps in knowledge due to the absence of socioeconomic data in most US public health surveillance systems. With these data, it becomes feasible to monitor socioeconomic gradients within diverse race/ethnicity–gender groups; by extension, the magnitude of race/ethnicity–gender inequality in health within specified economic strata could be assessed—as could the contribution of economic deprivation to racial/ethnic and gender disparities in health.12,15

For example, data in our study provided noteworthy evidence that economic gradients were steepest in the White population and shallowest in the Black population. Far from suggesting that economic inequality is less of a concern for African Americans’ health, these findings chiefly resulted from absolute rates being higher among Black compared with White Americans in areas with the most resources. In addition to underscoring that both absolute and relative rates must be considered when evaluating health disparities,65,79,80 these results emphasize why analyses of racial/ethnic inequalities in health need to take into account economic disparities and, conversely, why analyses of economic inequalities in health need to take into account racial/ethnic disparities.13,14,81

Relevant to future use of ABSMs are 2 additional considerations. The first is that starting with the year 2000 census, zip code–level data are no longer available and databases with only zip codes cannot be linked to census data absent geocoding to some other geographic level for which census data are available.48 Prompting this change were “difficulties in precisely defining the land area covered by each ZIP Code,”82 leading the US Census Bureau to create a new statistical entity built from census blocks: the 5-digit Zip Code Tabulation Area (ZCTA).83 Since ZCTAs and zip codes sharing the same 5-digit code may not necessarily cover the same area,83 zip codes obtained by self-report or from addresses in medical records cannot be assumed to correspond to census-defined ZCTAs.48

Second, regarding the timeliness of census data, pending authorization and funding by Congress, the decennial census long form (source of the socioeconomic data for the ABSMs) is scheduled to be replaced by the annual American Community Survey, which will provide yearly sociodemographic estimates at the national, state, and other geographic levels.84 Data at the census tract level are anticipated to be released starting in 2008, employing annual estimates based on 5-year rolling averages; less certainly, block group–level data may also be released starting in 2009 (C. Richard, senior program analyst, American Community Survey, oral communication, January 8, 2003). Presumably, a similar methodology, employing 5-year rolling averages, could also be used for health data among smaller population subgroups, thereby enabling the routine monitoring of socioeconomic inequalities in health among all race/ethnicity–gender groups, assuming that concerns about validity and consistency of racial/ethnic data across public health data systems could be addressed.14,85

In conclusion, results of our study highlight the importance of multilevel frameworks, including ecosocial theory, for public health research and practice.22,23,68–72,86–89 Tellingly, were data constrained only to the individual level, we would remain without any practical solution for improving routine monitoring of socioeconomic inequalities in the United States, other than continuing to advocate for inclusion of individual-level socioeconomic data in diverse public health surveillance systems. Even if it were possible to overcome resistance to including such measures5,90—let alone ensure use of identical measures across diverse databases to enable meaningful comparison—it would still not be possible to monitor secular trends in socioeconomic inequalities in health, owing to the absence of the individual-level socioeconomic data from previous years. By expanding the levels of analysis to include characteristics of areas in which people reside, it is instead possible to envision—and test—an alternative solution, that of geocoding and using ABSMs. By the same logic, further elaboration of multilevel frameworks and methods is likely to aid efforts to understand and address the persistent problem of social inequalities in health in the United States.22,23,68–72,86–91 One way to begin is by ensuring that the magnitude of these disparities is duly and routinely monitored, rather than hidden from view. We suggest that this can be accomplished by geocoding US public health surveillance systems and using the census tract–level measure “percentage of persons below poverty.”

TABLE 1— Study Population, Public Health Disparities Geocoding Project—Adults, by Race/Ethnicity and Gender, and Areas, by Level of Geography: Massachusetts and Rhode Island, 1990
TABLE 1— Study Population, Public Health Disparities Geocoding Project—Adults, by Race/Ethnicity and Gender, and Areas, by Level of Geography: Massachusetts and Rhode Island, 1990
 MassachusettsRhode Island
Population (1990)6016425 312994828864771003464 522114481350
    American Indian125850.26620596542670.422042063
    Asian/Pacific Islander1407452.37054770198176151.889348681
Areas (1990)
    Block groups56031085.4665.25–100968971137.7670.87–5652
    Census tracts13314571.82080.018–154112354325.31810.926–9822
    Zip codes47412719.712244.114–650017014335.213234.863–53763
TABLE 2— Distribution of the Population for the Public Health Disparities Geocoding Project, Stratified by Block Group–, Census Tract–, and Zip Code–Level Area-Based Socioeconomic Measures, Race/Ethnicity, and Gender: Massachusetts and Rhode Island, 1990
TABLE 2— Distribution of the Population for the Public Health Disparities Geocoding Project, Stratified by Block Group–, Census Tract–, and Zip Code–Level Area-Based Socioeconomic Measures, Race/Ethnicity, and Gender: Massachusetts and Rhode Island, 1990
 MassachusettsRhode Island
 White BlackHispanicWhiteBlackHispanic
Poverty (categorical), %
        0–4.92 751 888(50.9)41 027(13.8)35 870(13.0)403 507(44.0)3 439(9.1)4 318(9.9)
        5.0–9.91 363 080(25.2)42 151(14.2)35 597(12.9)277 185(30.2)5 243(13.9)6 269(14.3)
        10.0–19.9838 315(15.5)70 156(23.6)55 316(20.1)138 953(15.1)6 822(18.1)6 851(15.6)
        20–100453 877(8.4)143 400(48.3)148 850(54.0)97 701(10.7)22 245(58.9)26 347(60.2)
        0–4.92 462 306(45.5)27 423(9.2)27 849(10.1)377 676(41.2)2 731(7.2)3 716(8.5)
        5.0–9.91 728 663(32.0)49 493(16.7)44 797(16.2)269 275(29.3)4 993(13.2)4 807(11.0)
        10.0–19.9798 485(14.8)70 404(23.7)57 020(20.7)195 495(21.3)8 915(23.6)10 861(24.8)
        20–100418 676(7.7)149 517(50.4)146 059(53.0)75 316(8.2)21 154(56.0)24 433(55.8)
        0–4.92 196 407(40.6)25 233(8.5)25 420(9.2)199 126(21.7)1 128(3.0)1 457(3.3)
        5.0–9.91 784 636(33.0)45 596(15.4)49 308(17.9)461 245(50.2)8 625(22.7)7 171(16.3)
        10.0–19.91 117 857(20.7)88 949(29.9)89 436(32.4)203 391(22.1)15 460(40.7)14 504(33.0)
        20–100312 867(5.8)137 228(46.2)111 695(40.5)55 311(6.0)12 773(33.6)20 800(47.3)
Low education (categorical), %
        0–14.92 488 369(46.0)49 370(16.6)42 416(15.4)212 623(23.1)4 193(11.0)3 331(7.6)
        15.0–24.91 525 764(28.2)61 190(20.6)40 161(14.6)252 207(27.4)3 180(8.4)2 479(5.6)
        25.0–39.9987 678(18.3)108 873(36.7)75 912(27.5)290 457(31.6)8 644(22.8)8 278(18.8)
        40–100409 851(7.6)77 450(26.1)117 332(42.5)163 786(17.8)21 969(57.8)29 844(67.9)
        0–14.92 261 944(41.8)42 569(14.3)36 822(13.3)142 306(15.5)3 660(9.6)2 670(6.1)
        15.0–24.91 839 913(34.0)68 539(23.1)50 295(18.2)313 258(34.1)6 127(16.1)3 476(7.9)
        25.0–39.9959 725(17.7)126 459(42.6)91 816(33.3)315 690(34.3)5 448(14.3)7 353(16.7)
        40–100350 080(6.5)59 316(20.0)96 888(35.1)147 819(16.1)22 751(59.9)30 433(69.3)
        0–14.92 042 089(37.7)33 138(11.2)33 863(12.3)150 152(16.3)4 629(12.2)2 235(5.1)
        15.0–24.92 075 209(38.3)71 227(24.0)71 401(25.9)243 270(26.5)4 598(12.1)2 568(5.8)
        25.0–39.91 020 759(18.9)157 593(53.1)108 260(39.2)368 287(40.1)11 756(30.9)12 998(29.6)
        40–100273 710(5.1)35 048(11.8)62 335(22.6)157 364(17.1)17 003(44.8)26 131(59.5)
Townsend index (categorical)
        1 (most resources)1 073 403(19.8)9 618(3.2)10 452(3.8)185 534(20.2)620(1.6)1 275(2.9)
        21 229 164(22.7)15 248(5.1)13 557(4.9)211 306(23.0)1 314(3.5)2 310(5.3)
        31 228 922(22.7)22 595(7.6)20 240(7.3)205 142(22.4)3 596(9.5)2 914(6.7)
        41 093 642(20.2)48 410(16.3)44 152(16.0)176 440(19.2)5 420(14.4)6 088(13.9)
        5 (least resources)784 731(14.5)201 047(67.7)187 425(68.0)138 924(15.1)26 799(71.0)31 198(71.3)
        1 (most resources)1 242 820(23.0)11 253(3.8)12 490(4.5)209 034(22.8)960(2.5)1 888(4.3)
        21 322 640(24.4)15 149(5.1)15 831(5.7)212 861(23.2)1 717(4.5)2 420(5.5)
        31 221 587(22.6)24 093(8.1)25 876(9.4)197 171(21.5)3 296(8.7)2 914(6.7)
        41041 083(19.2)55 712(18.8)55 520(20.1)188 484(20.5)6 925(18.3)6 291(14.4)
        5 (least resources)581 780(10.8)190 717(64.2)166 109(60.2)110 212(12.0)24 895(65.9)30 304(69.2)
        1 (most resources)605 276(11.2)6 107(2.1)6 225(2.3)48 066(5.2)141(0.4)230(0.5)
        2816 954(15.1)8 368(2.8)8 328(3.0)137 223(14.9)697(1.8)1 101(2.5)
        3946 108(17.5)11 304(3.8)12 011(4.4)160 402(17.5)1 019(2.7)1 470(3.3)
        41 336 631(24.7)23 974(8.1)31 806(11.5)262 883(28.6)3 313(8.7)4 155(9.5)
        5 (least resources)1 706 798(31.5)247 253(83.2)217 489(78.8)310 499(33.8)32 816(86.4)36 976(84.2)

Note. BG = block group; CT = census tract; ZC = zip code; ABSM = area-based socioeconomic measures.

TABLE 3— Number of Cases for the Public Health Disparities Geocoding Project: Percentage Geocoded and Percentage Matched to Census Areas, by Level of Geography and Race/Ethnicity: Massachusetts and Rhode Island , ca. 1990
TABLE 3— Number of Cases for the Public Health Disparities Geocoding Project: Percentage Geocoded and Percentage Matched to Census Areas, by Level of Geography and Race/Ethnicity: Massachusetts and Rhode Island , ca. 1990
   % Geocoded and Matched to Census Areasa    
   BG LevelCT LevelZC Level    
   MassRIMassRIMassRINot Geocoded
 Total Cases        MassRI
Data Source: PopulationMassRIGeo + MatchGeo + MatchGeo + MatchGeo + MatchGeoGeo + MatchGeoGeo + MatchN(%)N(%)
Mortality (Mass, 1989–1991; RI, 1989–1991)
    White women77 54313 56793.689.899.994.699.996.494.092.5116(0.1)737(5.4)
    White men69 99412 68293.892.299.895.899.996.595.393.5141(0.2)528(4.2)
    Black women2 44837295.894.199.897.099.898.197.096.86(0.2)11(3.0)
    Black men3 02550196.494.499.697.299.697.396.495.613(0.4)14(2.8)
    Hispanic women6827395.597.398.898.699.396.698.697.38(1.2)1(1.4)
    Hispanic men1 15017295.495.998.996.599.196.796.595.313(1.1)6(3.5)
    American Indian women312880.689.3100.092.9100.096.889.389.30(0.0)2(7.1)
    American Indian men674888.187.598.591.798.595.591.789.61(1.5)4(8.3)
    Asian/Pacific Islander women3464095.497.598.0100.098.396.0100.097.57(2.0)0(0.0)
    Asian/Pacific Islander men4564493.9100.097.1100.097.193.6100.0100.013(2.9)0(0.0)
Low birthweight (Mass, 1989–1991; RI, 1987–1993)
    White girls92 35436 24594.496.6100.099.2100.091.699.497.50(0.0)275(0.8)
    White boys97 75838 19294.296.5100.099.3100.091.799.497.50(0.0)271(0.7)
    Black girls9 5623 40596.997.7100.099.0100.092.499.798.20(0.0)33(1.0)
    Black boys9 8103 57796.597.5100.098.8100.092.599.797.80(0.0)44(1.2)
    Hispanic girls12 5682 65996.898.5100.099.7100.093.599.899.10(0.0)8(0.3)
    Hispanic boys13 2152 72696.798.7100.099.7100.093.799.998.80(0.0)9(0.3)
    American Indian girls23241192.794.9100.098.1100.094.098.398.10(0.0)8(1.9)
    American Indian boys22838890.896.6100.098.7100.090.899.098.20(0.0)5(1.3)
    Asian/Pacific Islander girls4 4551 45896.596.8100.097.9100.089.598.796.90(0.0)30(2.1)
    Asian/Pacific Islander boys4 8421 54196.797.0100.098.4100.090.699.197.50(0.0)25(1.6)
Cancer incidence (Mass, 1988–1992; RI, 1989–1992)
    White women79 2529 81792.191.3100.099.7100.089.399.899.17(0.0)25(0.3)
    White men76 2099 48692.091.6100.099.8100.089.699.899.04(0.0)21(0.2)
    Black women2 04421194.796.2100.0100.0100.093.6100.0100.00(0.0)0(0.0)
    Black men2 29219292.892.7100.0100.0100.094.9100.099.00(0.0)0(0.0)
    Hispanic womenNA62 91.9 100.0  100.096.8  0(0.0)
    Hispanic menNA67 94.0 98.5  98.598.5  1(1.5)
Childhood lead poisoning (RI, 1994–1996)
    White girls 7 145 93.4 94.2  93.991.3  414(5.8)
    White boys 7 550 93.5 94.7  94.592.2  402(5.3)
    Black girls 1 032 97.3 97.9  97.997.9  22(2.1)
    Black boys 1 056 97.7 98.3  98.097.3  18(1.7)
    Hispanic girls 2 571 97.7 97.9  97.897.7  55(2.1)
    Hispanic boys 2 578 98.1 98.3  98.398.2  45(1.7)
Tuberculosis (Mass, 1993–1998; RI, 1985–1994)
    White women28414590.190.3100.093.1100.084.291.790.30(0.0)10(6.9)
    White men49022389.887.9100.090.699.883.989.787.90(0.0)21(9.4)
    Black women2083490.4100.0100.0100.0100.088.9100.097.10(0.0)0(0.0)
    Black men2735886.491.4100.096.6100.087.996.696.60(0.0)2(3.4)
    Hispanic women933488.297.1100.0100.0100.083.997.197.10(0.0)0(0.0)
    Hispanic men1566385.990.5100.095.2100.073.193.793.70(0.0)3(4.8)
Syphilis (Mass, 1994–1998; RI, 1994–1996)
    White women1282783.688.9100.088.9100.098.488.988.90(0.0)3(11.1)
    White men1983181.390.3100.096.8100.095.596.893.50(0.0)1(3.2)
    Black women3942087.660.0100.090.0100.
    Black men5345184.362.7100.080.4100.097.280.476.50(0.0)10(19.6)
    Hispanic women3135091.188.0100.094.0100.095.894.092.00(0.0)3(6.0)
    Hispanic men5338674.767.4100.081.4100.097.281.480.20(0.0)16(18.6)
Gonorrhea (Mass, 1994–1998; RI, 1994–1996)
    White women70125990.394.6100.095.4100.096.995.494.20(0.0)12(4.6)
    White men96617789.292.1100.094.4100.095.994.493.20(0.0)10(5.6)
    Black women99123690.394.9100.095.8100.097.895.893.60(0.0)10(4.2)
    Black men1 21135091.792.6100.093.499.998.093.492.30(0.0)23(6.6)
    Hispanic women4799490.892.6100.093.6100.097.592.692.60(0.0)6(6.4)
    Hispanic men55810889.694.4100.094.499.897.094.494.40(0.0)6(5.6)
Chlamydia (Mass, 1994–1998; RI, 1994–1996)
    White women5 8162 09987.291.8100.093.1100.095.993.090.90(0.0)144(6.9)
    White men73426987.983.6100.085.1100.095.885.182.50(0.0)40(14.9)
    Black women3 10486490.792.4100.093.9100.097.593.891.40(0.0)53(6.1)
    Black men1 06227688.890.2100.091.3100.096.291.389.10(0.0)24(8.7)
    Hispanic women3 89292391.896.0100.096.7100.096.796.596.00(0.0)30(3.3)
    Hispanic men69018584.993.5100.095.1100.095.795.194.60(0.0)9(4.9)
Nonfatal weapon-related injury (Mass, 1995–1997)
    White women283 89.0 97.9 100.095.1  6(2.1)  
    White men1 812 87.7 96.9 100.093.8  57(3.1)  
    Black women262 87.0 97.7 100.095.0  6(2.3)  
    Black men1 413 84.1 94.6 100.091.8  77(5.4)  
    Hispanic women132 90.2 97.7 100.095.5  3(2.3)  
    Hispanic men1 091 86.8 96.4 100.093.8  39(3.6)  

Note. BG = block group; CT = census tract; ZC = zip code; NA = not available.

a“Geo + Match” = geocoded and matched to census area; “Geo” = geocoded but not able to be matched to census area.

TABLE 4— Premature Mortality (<65 Years Old, Per 100 000a) Among Men, by Race/Ethnicity, Stratified by Census Tract–Level Area-Based Socioeconomic Measures, Massachusetts, 1989–1991: Public Health Disparities Geocoding Project
TABLE 4— Premature Mortality (<65 Years Old, Per 100 000a) Among Men, by Race/Ethnicity, Stratified by Census Tract–Level Area-Based Socioeconomic Measures, Massachusetts, 1989–1991: Public Health Disparities Geocoding Project
 WhiteBlackHispanicAsian/Pacific IslanderAmerican Indian
ABSM and CategoryCasesPopRateRII (95% CI)CasesPopRateRII (95% CI)CasesPopRateRII (95% CI)CasesPopRateRII (95% CI)CasesPopRateRII (95% CI)
Working class, %
    0.0–49.92 4681 137 351187.72.1 (2.0, 2.3)16838 739475.31.4 (1.2, 1.6)4630 192196.72.1 (1.7, 2.7)5153 667113.02.4 (1.6, 3.7)41 749211.01.3 (0.4, 4.4)
    50.0–65.98 6603 227 388255.5 451108 090448.8 167100 566200.2 7576 779116.2 116 639165.9 
    66.0–74.95 9011 837 386311.1 705159 258487.8 271108 252276.3 4438 745140.3 105 316212.0 
    75.0–100.02 569669 279402.1 485100 686573.2 389155 535329.6 5533 732231.0 63 171208.6 
Median household income, $
    > 47 1253 7141 736 397186.92.6 (2.5, 2.8)6424 648262.62.7 (2.3, 3.2)2224 234112.52.8 (2.2, 3.5)4139 291101.92.7 (1.8, 4.2)32 142129.97.1 (1.9, 27.2)
    39 287–47 1244 2991 663 815242.4 10833 093351.7 4127 369158.3 1628 49469.8 02 7720.0 
    33 163–39 2864 3591 466 310286.0 21664 140363.8 7343 749195.0 4736 156173.3 83 081230.2 
    26 472–33 1623 9131 183 224328.1 516120 399471.4 19896 201263.5 4142 144124.5 54 311162.2 
    4 999–26 4713 313818 049446.0 905162 303648.9 539202 362333.6 8056 775207.1 154 524372.3 
Poverty, %
    0.0–4.97 5633 203 058212.72.6 (2.5, 2.8)10742 777280.92.7 (2.2, 3.2)5241 931134.62.8 (2.2, 3.6)4454 79884.63.4 (2.2, 5.2)44 31486.46.3 (1.7, 22.8)
    5.0–9.96 3972 145 999285.3 21769 978342.2 10064 854181.2 5148 780129.7 85 220166.6 
    10.0–19.93 485983 442356.7 42296 048478.2 18281 999281.9 4638 985149.3 73 318234.3 
    20.0–1002 153537 120484.8 1063197 895610.6 539205 635332.4 8460 312211.8 123 978371.7 
Gini index
    0.009–0.3484 0191 708 737227.51.6 (1.5, 1.7)11443 965284.72.9 (2.5, 3.5)3531 692123.82.6 (2.1, 3.3)2626 082111.42.3 (1.5, 3.6)13 29431.76.8 (1.8, 25.6)
    0.349–0.3714 1731 535 217255.3 10540 929288.6 6240 221193.9 3033 39096.5 62 490244.6 
    0.372–0.3954 1941 490 193260.2 20051 504413.2 11458 449205.3 3636 339109.8 33 171115.1 
    0.396–0.4284 0561 257 159307.4 39896 168469.2 222100 536282.7 4440 950160.1 84 143211.8 
    0.429–0.6503 156876 489347.1 992172 017642.6 440163 017340.0 8966 099182.7 133 732380.4 
Wealth, %
    20.0–1002 046905 262191.51.9 (1.8, 2.0)17530 165623.00.9 (0.7, 1.2)5424 726255.61.4 (1.0, 2.0)5242 669130.41.1 (0.7, 1.8)61 488392.20.2 (0.1, 1.0)
    10.0–19.91 440576 903223.8 5916 953340.2 3217 166233.9 1411 277152.0 41 539230.6 
    5.0–9.91 964707 640251.4 14333 015455.1 2818 831168.7 2116 884120.6 21 314109.2 
    0.0–4.91 3 9194 643 028295.5 1 366317 397484.6 694318 000267.4 133126 078138.0 1412 243130.2 
Crowding, %
    0.0–4.917 5046 324 546258.23.3 (3.0, 3.5)640172 611410.61.6 (1.4, 1.9)337176 184219.51.7 (1.3, 2.1)131132 219112.33.0 (1.9, 4.6)2212 753175.10.9 (0.2, 5.3)
    5.0–9.91 422383 601437.0 590115 011579.7 25899 579338.7 5141 889179.6 22 28072.7 
    10.0–19.9572147 078497.3 508103 203546.0 22999 378295.8 2520 700196.1 41 683265.5 
    20.0–10010012 570911.8 7113 758539.6 4918 774294.9 188 052262.3 31141 987.9 
Low education, %
    0.0–14.96 5252 920 797204.02.4 (2.3, 2.6)20263 957344.12.3 (2.0, 2.8)7553 625187.02.4 (1.9, 3.0)6582 70195.13.3 (2.1, 5.0)115 739178.72.8 (0.8, 9.7)
    15.0–24.97 0742 333 658291.9 34095 100397.0 11973 434172.4 6053 778135.4 34 68658.1 
    25.0–39.94 3231 198 464357.9 812168 933528.6 310129 690294.1 5742 762172.6 124 125318.4 
    40.0–1001 677422 184402.1 45578 759687.7 369137 799337.3 4323 766241.7 52 340282.5 
Townsend index
    −8.123 to −2.7973 5541 609 386190.72.7 (2,6, 2.9)5018 390287.42.7 (2.3, 3.3)2718 618187.72.7 (2.0, 3.4)2327 17180.93.0 (1.8, 4.6)42 274144.04.8 (1.3, 17.2)
    −2.796 to −1.5964 2871 711 941234.2 5822 704256.1 2522 956123.2 2223 496111.2 33 02482.3 
    −1.595 to −0.0514 3681 521 165273.9 9933 333319.5 5036 825146.9 2726 673112.9 63 579219.8 
    −0.050 to 2.8604 2811 282 509339.1 27378 378427.2 17879 506261.6 5249 983135.6 33 42995.4 
    2.861 to 11.2233 108746 451470.1 1 329253 968581.3 593236 640319.5 10175 600195.9 154 569383.3 
    20–263 9951 799 544193.52.4 (2.3, 2.6)12033 924387.72.2 (1.9, 2.6)5031 869190.82.8 (2.2, 3.6)4754 42994.53.4 (2.2, 5.1)32 460100.04.4 (1.3, 15.5)
    16–193 7091 450 092242.5 10932 022359.2 2727 087136.2 3332 310131.2 32 394100.5 
    11–1555151 839 060289.3 16354 567361.0 8255 227157.5 3742 588100.8 63 330182.9 
    6–1034911 091 622325.3 475107 718459.3 18476 107283.5 3632 700151.1 63 942231.6 
    0–52888691 134437.5 942178 542613.4 530204 255332.9 7240 896246.8 134 749317.9 
    1 (most resources)1 991937 200179.52.4 (2.3, 2.6)12927 288500.61.8 (1.5, 2.1)3421 852185.22.1 (1.7, 2.6)3937 794113.93.0 (2.0, 4.7)41 146308.81.3 (0.3, 4.5)
    21 373524 109235.4 8616 938516.6 2712 294325.4 1110 965114.9 61 656412.4 
    3438198 441220.2 2611 160263.1 87 710189.1 914 69189.7 05790.0 
    412 3534 318 989273.7 518151 362371.1 218127 887189.6 8482 077117.1 88 60488.7 
    51 460416 673351.7 6714 532581.6 6827 045316.8 67 61457.4 21 062217.3 
    6697188 562440.2 50692 481610.2 15960 735310.8 2218 114194.4 31 560602.4 
    7 (least resources)1 051243 006491.6 41083 487575.4 294120 975325.7 4925 521282.2 31 917152.1 
SEP index
    −13.768 to −3.2653 2641 523 331184.92.5 (2.5, 2.7)12131 938411.02.2 (1.8, 2.6)3527 054151.22.8 (2.2, 3.6)4447 403100.93.2 (2.1, 5.0)41 908190.01.5 (0.4, 5.4)
    −3.264 to −1.1533 9481 612 971231.6 9731 716326.1 4127 429226.2 2126 14896.2 63 168161.3 
    −1.152 to 0.3964 6411 571 037284.7 15740 029456.5 6341 865161.9 4240 602120.1 53 381163.6 
    0.397 to 3.0064 3771 345 047321.7 30290 357365.1 13672 450215.5 3837 884135.1 23 60090.3 
    3.007 to 20.6053 133772 827427.7 1 065201 093597.0 533209 196325.9 7544 724234.2 94 467248.3 
    Median   2.4   2.2   2.6   3.0   2.8

Note. RII = relative index of inequality; CI = confidence interval; Pop = population; CDCILER = Centers for Disease Control and Prevention’s Index of Economic Resources; CT = census tract; ABSM = area-based socioeconomic measures. For SEP1 and SEP index, see “Methods” section.

aAge-standardized to the year 2000 standard million.

bSEP1: 1 = any poverty, < 50% working class, ≥10% wealth; 2 = any poverty, 51%–74% working class, ≥10% wealth; 3 = any poverty, < 50% working class, < 10% wealth; 4 = < 20% poverty, 51%–74% working class, < 10% wealth; 5 = ≤20% poverty, ≥75% working class, any wealth; 6 = ≥20% poverty, 51%–74% working class, < 10% wealth; 7 = ≥20% poverty, ≥75% working class, any wealth.

This work was funded by the National Institutes of Health’s National Institute of Child Health and Human Development (1 R01 HD36865–01) and Office of Behavioral and Social Science Research (principal investigator, N. Krieger, PhD).

We thank Dr Daniel Friedman (assistant commissioner, Bureau of Health Statistics, Research and Evaluation, Massachusetts Department of Health) and Dr Jay Buechner (chief, Office of Health Statistics, Rhode Island Department of Health) for facilitating the conduct of this study using data from their respective health departments and for their helpful comments on our manuscript. We likewise thank the following health department staff for their contributions to accessing the study’s data:

Massachusetts Department of Public Health: Alice Mroszczyk, program coordinator for 24A/B/111B Review Committee; Cancer Registry: Dr Susan Gershman, director, Mary Mroszczyk, geocoding/special projects coordinator, and Ann R. MacMillan, data analyst; Registry of Vital Records and Statistics: Elaine Trudeau, registrar of vital records, and Charlene Zion, Public Information Office; Infectious Diseases: Alfred DeMaria, assistant commissioner, Yuren Tang, chief of Surveillance Program, and Sharon Sharnprapai, TB epidemiologist; Weapons-Related Injury Surveillance System: Victoria Vespe Ozonoff, program director, Beth Hume, data manager/analyst, Patrice Cummins, epidemiologist, and Laurie Janelli, site coordinator;

Rhode Island Department of Health: Vital Statistics: Roberta Chevoya, state registrar of vital records; Division of Disease Prevention and Control: Dr John Fulton, associate director, Ted Donnelly, senior public health epidemiologist, and Michael Goscminksi, epidemiologist; Environmental Health Risk Assessment: Susan Feeley, public health epidemiologist; Childhood Lead Poisoning Prevention: Magaly Angeloni, program manager.

Human Participant Protection Use of the data employed in this study 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.


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Nancy Krieger, PhD, Jarvis T. Chen, ScD, Pamela D. Waterman, MPH, David H. Rehkopf, MPH, and S. V. Subramanian, PhD The authors are with the Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, Mass. “Race/Ethnicity, Gender, and Monitoring Socioeconomic Gradients in Health: A Comparison of Area-Based Socioeconomic Measures—The Public Health Disparities Geocoding Project”, American Journal of Public Health 93, no. 10 (October 1, 2003): pp. 1655-1671.

PMID: 14534218