Public Health Monitoring of Privilege and Deprivation With the Index of Concentration at the Extremes

Abstract

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 P ublic 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][2][3][4][5][6][7][8][9][10][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][13][14][15][16][17][18][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. 1-3,22-24Some 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 highschool education, percentage of persons who are Black). [22][23][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][13][14][15][16][17][18][19][20][21]26,27

Local Residential Segregation Matters: Stronger Association of Census Tract Compared to Conventional City-Level Measures with Fatal and Non-Fatal Assaults (Total and Firearm Related), Using the Index of Concentration at the Extremes (ICE) for Racial, Economic, and Racialized Economic Segregation, Massachusetts (US), 1995–2010

“…We used the US Census data [40][41][42], to generate our three types of area-based measures at the census tract and city/town levels: the Index of Concentrations at the Extremes (ICE) [14,[19][20][21], the Index of Dissimilarity [7], and the poverty measure [43,44]. In Table 1, we provide the formulas and conceptual and operational definitions of the variables employed.…”

“…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. Primarily employed in social science research [14,[55][56][57], the ICE has begun to be used in public health research [15-21, 45, 58-60], including our novel extension to compute ICE measures for both racial segregation and racialized economic segregation [19][20][21]. The Index of Dissimilarity, one of the most widely used measures of racial segregation [7][8][9][10][11], in turn measures the extent to which a region's population (e.g., in a city) would need to be redistributed across sub-units (e.g., census tracts) to achieve a uniform distribution.…”

“…b) Although a value of 0 theoretically could arise from two different scenarios-(1) the plausible scenario in which no persons are in either of the extremes, and (2) the implausible scenario in which an equal number of persons are in solely the two extreme categories (highly unlikely because of how spatial social polarization occurs), analyses of ICE values for census tracts within two US states (Massachusetts and New York) and their largest cities (Boston and New York City) demonstrated that only the first scenario was operative [19,21]. c) Conceptual and operational features of the ICE:…”

“…Our concern, building on recent work on extreme concentrations of privilege and deprivation [14][15][16][17][18][19][20][21] and on local segregation [22][23][24][25], is that health outcomes are more likely to exhibit stronger associations with segregation measures that (1) are computed at the local, compared to city level, and (2) are computed jointly in relation to income and race/ethnicity, rather than only one or the other. To test our hypothesis, we selected an outcome welldocumented to be positively associated with adverse racial residential segregation and economic deprivation: fatal and non-fatal assaults, especially those involving firearms [26][27][28][29][30].…”

At the intersection of place, race, and health in Brazil: Residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

“…The link between segregation and health has been explored for a number of health outcomes including cardio-metabolic risk factors (Kershaw and Albrecht, 2015) with most studies documenting stark inequalities along dimensions of race and class-based measures of segregation (Kershaw and Albrecht, 2015;Krieger, 2014;Krieger et al, 2015).…”