Defining & Measuring SES
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PEACH to hold seminar on May 13
Despite its importance for understanding health inequity, there is little consensus on how socioeconomic status (SES) should be defined, measured, and used in pediatric research and practice.i As a concept, it captures different kinds of social difference (e.g., education, occupation, income, and wealth). Each comes with its own strengths and limitations for representing differences between people that can matter in terms of how care might be delivered.
However, critically, determining how to incorporate SES into research requires understanding what SES data is available. Below we provide an overview of basic measures of SES available at Michigan Medicine and why geographic proxies for SES such as the Area Deprivation Index may be useful for your research.
Address is a useful SES proxy given that there is considerable social inequality across neighborhoods in terms of economic (and racial) segregation.
SES data in the EMR
Many key indicators of SES are not reliably tracked (if at all) in patient data. Frequently, researchers using large-scale patient datasets turn to insurance status as a proxy for SES, which has significant limitations for most equity research.ii
Unmet needs surveys
In recent years, health systems (including Michigan Medicine) have increasingly incorporated the practice of screening patients for unmet needs that can influence their health and health care, like housing and food insecurity. These screening data can play a critical role in tracking differences in care that are generated around at-risk populations and will be used by PEACH. However, these data do not adequately characterize SES gradations among the entire patient population.
Geographic proxies
Another way to capture SES is through using address as a proxy. Address is a useful SES proxy given that there is considerable social inequality across neighborhoods in terms of economic (and racial) segregation. Because SES often clusters geographically, it becomes possible to profile geographic units and, with some limitations, apply those profiles to residents. Moreover, such measures can capture the complex, interacting components of SES that can materialize at the neighborhood level, such as concentration of poverty and isolation from economic opportunities.
However, key limitations stem from the assumption that geographic units are relatively homogenous and stable, which is not necessarily the case. The best way to address this potential limitation is to use smaller geographic units, like census block groups and tracts, versus larger geographic units, such as zip codes. Larger geographic units are typically not homogenous in their sociodemographic characteristics; smaller units are better at demarcating relatively homogenous populations. See the table below for more information.
Census Data and Area-Based Socioeconomic Deprivation Indices
While address can help locate patients within a geographic unit, the next question is how we should assign a profile to that unit. There are two primary ways we will do this at PEACH.
First, we can compare geographic units in terms of single area-level measures, such as Census measures of income and poverty (e.g., “Ratio of Income to Poverty”). In some cases, it may make sense to take this approach. Single area-level measures often can be simpler to interpret (e.g., it can be easier to understand the difference in poverty rates between two census tracts versus differences in an index that combines multiple variables). Single area-level measures also can be useful for evaluating the impact of a specific aspect of socioeconomic deprivation, like education.iv
However, in other cases, we will rely on area-based socioeconomic deprivation indices like the Area Deprivation Index and Social Deprivation Index. These indices combine a set of metrics to tabulate neighborhood disadvantage. Such indices can provide a more comprehensive representation of the ways in which deprivation materializes at the neighborhood-level. As a result, they have been shown to have a stronger relationship with health outcomes compared to many single-variable measures.v Still, these indices also come with limitations.vi
Because SES often clusters geographically, it becomes possible to profile geographic units and, with some limitations, apply those profiles to residents.
Ultimately, whatever index or measure is chosen will depend on the specifics of the research. If you have questions, please ask!
Appendix
Appendix
i For a discussion of research that looks at SES and inequity, see: Gengler AM, Jarrell MV. What Difference Does Difference Make? The Persistence of Inequalities in Healthcare Delivery. Sociology Compass. 2015;9(8):718-730. For a discussion of the challenges of measuring SES, see: Kachmar AG, Connolly CA, Wolf S, Curley MAQ. Socioeconomic Status in Pediatric Health Research: A Scoping Review. The Journal of Pediatrics. 2019;213:163-170.
ii Insurance status is an imperfect proxy for capturing differences in SES across patient populations. Per Kachmar et al (2019): “Insurance status types are simple to categorize and can be found in hospital health records, but significant variability exists within a public program like Medicaid, with income eligibility varying by state and age (133%-375% of the federal poverty level) and medically needy children qualifying regardless of income. Furthermore, private insurance options widely differ in terms of costs and coverage.” Hence, trying to discern whether patients may be treated differently at different SES gradations becomes particularly challenging with insurance data. Insurance data, however, can be very useful for examining whether insurance status specifically shapes health care practices (e.g., see: Bisgaier J, Rhodes KV. Auditing Access to Specialty Care for Children with Public Insurance. New England Journal of Medicine.)
iii The text from this table is drawn directly from: N. Krieger, D. R. Williams, N. E. Moss. “Measuring Social Class in US Public Health Research: Concepts, Methodologies, and Guidelines” Annual Review of Public Health 1997 18:1, 341-378.
iv Trinidad S, Brokamp C, Mor Huertas A, et al. Use Of Area-Based Socioeconomic Deprivation Indices: A Scoping Review And Qualitative Analysis. Health Affairs. 2022;41(12):1804-1811.
v Ibid
vi Hannan EL, Wu Y, Cozzens K, Anderson B. The Neighborhood Atlas Area Deprivation Index For Measuring Socioeconomic Status: An Overemphasis On Home Value: Study examines the Neighborhood Atlas Area Deprivation Index as a tool to measure socioeconomic status. Health Affairs. 2023;42(5):702-709.
vii Specific variables for the Child Opportunity Index: # of ECE centers within a 5-mile radius, # of NAEYC accredited centers within a 5-mile radius, % 3- and 4-year-olds enrolled in nursery school, % third graders scoring proficient on standardized reading tests, % third graders scoring proficient on standardized math tests, % ninth graders graduating from high school on time, Ratio of students enrolled in at least one AP course to the number of 11th and 12th graders, % 18-24 year-olds enrolled in college within 25-mile radius, % students in elementary schools eligible for free or reduced-price lunches, reversed, % teachers in their first and second year, reversed, % adults ages 25 and over with a college degree or higher, % households without a car located further than a half-mile from the nearest supermarket, reversed, % impenetrable surface areas such as rooftops, roads or parking lots, reversed, EPA Walkability Index, % housing units that are vacant, reversed, Average number of Superfund sites within a 2-mile radius, reversed, Index of toxic chemicals released by industrial facilities, reversed, Mean estimated microparticle (PM2.5) concentration, reversed, Mean estimated 8-hour average ozone concentration, reversed, Summer days with maximum temperature above 90F, reversed, % individuals ages 0-64 with health insurance coverage, % adults ages 25-54 who are employed, % workers commuting more than one hour one way, reversed, % individuals living in households with incomes below 100% of the federal poverty threshold, reversed, % households receiving cash public assistance or Food Stamps/Supplemental Nutrition Assistance Program, reversed, % owner-occupied housing units, % individuals ages 16 and over employed in management, business, financial, computer, engineering, science, education, legal, community service, health care practitioner, health technology, arts and media occupations, Median income of all households, % family households that are single-parent headed, reversed
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