摘要：Objectives. We evaluated use of the Index of Concentration at the Extremes (ICE) for public health monitoring. Methods. We used New York City data centered around 2010 to assess cross-sectional associations at the census tract and community district levels, for (1) diverse ICE measures plus the US poverty rate, with (2) infant mortality, premature mortality (before age 65 years), and diabetes mortality. Results. Point estimates for rate ratios were consistently greatest for the novel ICE that jointly measured extreme concentrations of income and race/ethnicity. For example, the census tract–level rate ratio for infant mortality comparing the bottom versus top quintile for an ICE contrasting low-income Black versus high-income White equaled 2.93 (95% confidence interval [CI] = 2.11, 4.09), but was 2.19 (95% CI = 1.59, 3.02) for low versus high income, 2.77 (95% CI = 2.02, 3.81) for Black versus White, and 1.56 (95% CI = 1.19, 2.04) for census tracts with greater than or equal to 30% versus less than 10% below poverty. Conclusions. The ICE may be a useful metric for public health monitoring, as it simultaneously captures extremes of privilege and deprivation and can jointly measure economic and racial/ethnic segregation. Public health monitoring data need to be informative about not only health outcomes, but also their societal distribution and determinants, so that the data can be useful for policies, programs, and advocacy focused on improving population health and advancing health equity. 1–3 Both the global and US literature increasingly recognize the importance of assessing progress and setbacks in reducing health inequities (i.e., unfair, unnecessary, and preventable health differences between the groups at issue). 1–11 Adding to the urgency of using measures that illuminate inequitable health gaps is growing concern about 21st-century rising concentrations of income and wealth 12–19 and their implications for public health and health inequities. 12,20,21 Most public health monitoring systems, however, do not employ metrics that convey societal distributions of concentrations of privilege and deprivation. 1,2 Instead, the typical practice is to present health data in relation to characteristics measured at the individual or household level, such as income, educational level, and also, chiefly in the United States, race/ethnicity. Health outcomes are then compared across groups defined in relation to the chosen characteristics, which may be modeled either continuously or categorically. 1–3,22–24 Some analyses additionally employ variants of these measures aggregated to the neighborhood level (e.g., percentage of persons or households below poverty, percentage of persons with less than a high-school education, percentage of persons who are Black). 22–24 In either case, although gaps in health outcomes can be quantified by comparing groups with less versus more resources, distributional information on the extent to which the population is divided into the groups at issue is not part of the metric. The excess risk of societal groups that get the proverbial short end of the stick becomes the focus, and these groups effectively become characterized as the “problem”; by contrast, the societal groups holding the stick’s other, longer end simply stand as a referent group, and the problematic economic, political, and social relationships that produce health inequities are hidden from view. 11,12,25,26 A troubling feature of our era, however, is not a property of individuals or households but instead pertains to increasing spatial social polarization, part and parcel of growing concentrations of extreme income and wealth. 12–21,26,27 Memorably capturing this phenomenon is the title of Charles Dickens’ classic novel A Tale of Two Cities . 28,29 This novel, set amid the French Revolution of 1789 and its aftermath, vividly depicted the social and spatial relationships between vicious aristocrats and vengeful plebian citizens. The stark economic differences between neighborhoods, and between who literally held which stick, to beat or to protect whom, are a key theme of the book. We accordingly designed our study to assess the utility, for public health monitoring, of using a measure of spatial social polarization: the Index of Concentration at the Extremes (ICE). 30 Introduced into the social science literature in 2001 by Douglas Massey, a leading researcher on residential segregation, 13,14,31 the ICE has been used primarily in the social sciences, 32–34 as well as in a handful of etiological public health investigations. 35–45 To our knowledge, however, the ICE has not been used by any health department or agency with the responsibility of monitoring population health. The ICE is designed to reveal the extent to which an area’s residents are concentrated into groups at the extremes of deprivation and privilege: a value of −1 means that 100% of the population is concentrated in the most deprived group and a value of 1 means that 100% of the population is concentrated into the most privileged group; the formula is provided in the Methods section. 30 We chose to employ the ICE over 2 of the most commonly used population measures of economic and social inequality—the Gini coefficient (for income inequality) 46,47 and the Index of Dissimilarity (for residential racial segregation) 46,48–50 —because these latter measures, unlike the ICE, fail to be informative at the neighborhood level, precisely because of spatial social polarization. 3,30 For example, neighborhoods whose residents are either 100% low-income or 100% high-income have the same Gini coefficient (given perfect equality of income level within the neighborhood), and neighborhoods whose subunits (e.g., block groups) are either 100% White or 100% Black have the same Index of Dissimilarity for White–Black segregation (because everyone belongs to only 1 of the 2 groups at issue); by contrast, the ICE would appropriately assign these very different types of areas the values, respectively, of −1 and 1. Thus, a valuable feature of the ICE is that it can provide, at a glance, the directional tendency toward an extreme. To date, the ICE within the social science literature has been computed solely in relation to economic measures (e.g., income, education), 30,32–34 as is also true for 9 of the 11 published public health studies that have used the ICE. 35–43 Recognizing the importance of the entangled realities of socioeconomic and racial/ethnic inequities in the United States, 3,11,22,23,26,29–31 2 small public health studies, however, used a novel ICE measure pertaining to concentrations of low-income Black persons versus high-income White persons, 44,45 which are the 2 groups who, in Massey’s words, “continue to occupy opposite ends of the socioeconomic spectrum” in the United States. 51 (p324) To determine whether ICE measures might be useful for monitoring population health, we examined health outcomes in relation to 2 sets of comparisons. The first comparison examined use of the ICE measures computed for (1) city neighborhoods (i.e., relatively large political units relevant to health department planning and resource allocation) and (2) census tracts (relatively smaller US Census administrative units 52 ). The second set of comparisons, carried out at each level of geography, pertained to use of different ICE measures (i.e., ICE measures employing solely income data, solely racial/ethnic data, and also jointly integrating the socioeconomic and racial/ethnic data, in relation to each other), and also used the area-based poverty level. 3,24 To conduct our study, we analyzed data for New York City, which is the largest city in the United States and one whose population of 8.5 million 53 exceeds that of half the countries in the European Union. 54 We focused our analyses on 3 important public health outcomes for which notable health inequities exist: infant mortality, diabetes mortality (all ages), and premature mortality (all cause). 22–24,55–57
