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.

Geocoding and use of the CT poverty measure permit routine monitoring of US socioeconomic inequalities in health, using a common and accessible metric.

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

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Objectives. To assess if historical redlining, the US government’s 1930s racially discriminatory grading of neighborhoods’ mortgage credit-worthiness, implemented via the federally sponsored Home Owners’ Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation). Methods. We analyzed 2013–2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics. Results. The proportion of preterm births ranged from 5.0% in grade A (“best”—green) to 7.3% in grade D (“hazardous”—red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation. Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth. Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining’s impacts on present-day residential segregation and health outcomes.