The Poverty You Can’t See: Scarcity, Stress, and the Health Data That Miss Them (Part 1)
- vanessarodriguez22
- 3 days ago
- 6 min read
Measuring SES for Equity-Driven Modeling
Socioeconomic status (SES) is part of the default demographic toolkit in health research — right up there with age and sex. And much like those, it’s often included reflexively, adjusted for automatically, and measured using whatever data happen to be available.
In this two-part series, I examine what gets lost when SES is reduced to an income bracket or educational attainment, and what it would take to measure it as it’s actually lived.
Part 1 lays out the limits of income-based SES measures and introduces alternative ways to capture deprivation, including household-level metrics and geographic proxies.
Part 2, I turn to subjective economic strain and the lived experience of scarcity: how they shape health behavior and choices, and conclude with recommendations for how data science can reflect the lived realities of SES.
Part 1: From Income to Scarcity
Socioeconomic status (SES) is an established social determinant of health, traditionally conceived as a combination of income, occupation, and sometimes education.[1] Clinical and public health studies have shown correlations between SES and healthcare access and outcomes across the lifespan [2-4].
While we grasp social distinctions intuitively and on the fly in everyday life without a second thought, the contextual nature of these judgments and how they vary across social groups and cultures can be easy to miss. Indeed, it wasn’t until I arrived in the U.S. for graduate school — and encountered an entirely different system of social coordinates — that I could see, in retrospect, what the “hidden rules” of SES had been in Russia.
How Can People in the Same “Tier” Live So Differently?
In my grandmother’s village in the 1990s most residents were retired and were receiving government pensions calculated across bands and differing only little from person to person. Everybody living there would have fallen within the same SES tier by any common metric (“low”). However, this fact would come as a surprise to them.
My grandparents grew most of their food and raised chickens and pigs: to consume in-season, store, and barter. Garlic turned into cottage cheese, sauerkraut into a ride to the market, fatback into a jar of honey. These goods made my grandmother a sought-after exchange partner, but not as high in status as a neighbor with a cow (for milk) or a horse (for ploughing the land). Land use was strategic: every inch went to what would store or trade well, which meant flower beds were a luxury. A neighbor with more than a single peony bush? Clearly “well-off".
Despite these limitations, she would not have wanted to trade places with some of the younger members of their community, whose incomes were higher on paper but came at the cost of little time left over for farming or foraging, living in rented housing with no land, and buying items whose supply and quality were dictated by what the other families were willing to sell or trade. Some families were also navigating rotational shift work, addiction, instability, making their higher incomes a wash at best.
What Are the Blind Spots of Income-Based Measures?
A textbook approach that relies on income (even with occupation or education added) would have come up short in capturing the gradations I picked up on as a child. Income may work for analyses of macroeconomic, demographic, or labor trends, but when it comes to health, it may miss other factors impacting one’s financial security, such as:
debt, wealth, access to credit
cost of living variations
distribution of income within a household (which member controls the resources)
Income reporting is also prone to noise and error, especially when it comes to groups that equity-focused researchers are interested in characterizing: low-SES, marginalized, or displaced. In those contexts, informal labor, unstable employment, and non-monetary support systems are common and consequential.
Someone with $25,000 income in rural Ireland may live with very different health access and psychological strain than someone with the same income in suburban Boston.
Can Spending and Place Tell Us More Than Earnings?
To move beyond income, recent proposals have called for measuring material consumption instead. For example, the global effort to administer the Household Consumption and Expenditure Survey (HCES) tracks household expenditures on consumables, services, and durable goods. The UN’s Food Insecurity Experience Scale (FIES) asks about skipped meals, reduced amounts, and anxiety about future ability to obtain food.
Furthermore, deprivation at the level of communities and neighborhoods [5-8] is now being tracked by instruments such as:
Index of Multiple Deprivation (IMD, UK) includes indicators such as crime, barriers to services, and living environment
Area Deprivation Index (ADI); Social Vulnerability Index (SVI); specific to health issues: Congressional District Health Dashboard (US)
French Deprivation Index (FDep, France)
Research using these measures shows worse health outcomes for residents of the most deprived neighborhoods compared to the least-deprived ones, across chronic diseases and mental health disorders [9]. Crucially, there are independent effects of neighborhood-level deprivation on health outcomes: someone with a lot of personal resources living in a deprived neighborhood will still experience adversity. Conversely, individual-level resources may either exacerbate or buffer someone from experiences at the neighborhood level.
These newer frameworks bring us closer to capturing structural constraints and barriers. But they still leave out how poverty feels and what it means to navigate the tradeoffs it poses every day. That’s where Part 2 picks up.
Thank you for reading this blog post! What did you find interesting? What questions do you have? Leave your comments down below!
Statistical Methods at DSxHE: Blog Series on Methods for Health Equity
The Statistical Methods section of Data Science for Health Equity (DSxHE) is launching a public-facing blog series that demystifies methods and showcases their real-world impact on health equity. Each post will blend clear, accessible explanation with concrete applications, from study design and causal inference to measurement, modelling, and evaluation, highlighting how methods can drive fairer health outcomes. We invite authors to bring not only their technical expertise but also their personal insights, and encourage anyone interested to volunteer their perspective. Submissions can be tutorials, case studies, opinion articles, or "what we learned" stories.
Want to contribute? Send a brief pitch or draft to info@datascienceforhealthequity.com.
Glossary
Independent effect
The effect of a variable on an outcome that is not influenced by a potential confounding variable.
Confounding variable
A variable that is correlated with both the independent and dependent (outcome) variables and can distort the relationship between them.
References
Braveman, P., Cubbin, C., Egerter, S., Chideya, S., Marchi, K., Metzler, M., & Posner, S. (2005). Socioeconomic status in health research: One size does not fit all. JAMA, 294(22), 2879–2888.
Clark, A. M., DesMeules, M., Luo, W., Duncan, A. S., & Wielgosz, A. (2009). Socioeconomic status and cardiovascular disease: risks and implications for care. Nature Reviews Cardiology, 6(11), 712-722.
McMaughman, D.J., Oloruntoba, O., Smith, M.L. (2020). Socioeconomic status and access to healthcare: Interrelated drivers for healthy aging. Frontiers in Public Health, 8, 231.
Goodman, E., Slap, G. B., & Huang, B. (2003). The public health impact of socioeconomic status on adolescent depression and obesity. American Journal of Public Health, 93(11), 1844-1850.
Valenzuela, S., Peak, K.D., Huguet, N., Marino, M., Schmidt, T.D., Voss, R., et al. Social deprivation and multimorbidity among community-based Health Center patients in the United States. (2024). Preventing Chronic Disease, 21, 1-18. https://doi.org/10.5888/pcd21.240060
Bhavsar, N. A., Gao, A., Phelan, M., Pagidipati, N. J., & Goldstein, B. A. (2018). Value of neighborhood socioeconomic status in predicting risk of outcomes in studies that use electronic health record data. JAMA Network Open, 1(5), e182716. https://doi.org/10.1001/jamanetworkopen.2018.2716
Belau, M. H. (2024). Material and social deprivation associated with public health actual causes of death among older people in Europe: longitudinal and multilevel results from the Survey of Health, Ageing and Retirement in Europe (SHARE). Frontiers in Public Health, 12, 1469203.
Papageorgiou, V., Davies, B., Cooper, E., Singer, A., & Ward, H. (2022). Influence of material deprivation on clinical outcomes among people living with HIV in high-income countries: a systematic review and meta-analysis. AIDS and Behavior, 26(6), 2026-2054.
Trinidad, S., Brokamp, C., Mor Huertas, A., Beck, A. F., Riley, C. L., Rasnick, E., ... & Kotagal, M. (2022). Use of area-based socioeconomic deprivation indices: a scoping review and qualitative analysis: study examines socioeconomic deprivation indices. Health Affairs, 41(12), 1804-1811.