Skip to main content

Propensity score weighted associations between financial strain and subsequent inflammatory biomarkers of aging among a representative sample of U.S. older adults



Despite known socioeconomic disparities in aging-related outcomes, the underlying physiologic mechanisms are understudied. This study applied propensity score weighting to estimate the effect of financial strain on inflammation-related aging biomarkers among a national sample of older adults.


Financial strain severe enough to lack money for housing, utilities, medical/prescription bills or food was measured among 4,593 community-dwelling National Health and Aging Trends Study participants aged ≥ 65 years in 2016. Inverse probability propensity score weights were generated based on 2015 background characteristics, including age, gender, race/ethnicity, income to poverty ratio, education, occupation, home ownership, retirement, Sect. 8 housing, Medicaid, food/energy assistance, childhood health, marital status, and U.S. region. Sampling weights additionally accounted for study design and non-response.


In propensity score-weighted analyses adjusting for age, gender, race/ethnicity, 2017 income to poverty ratio and education, those with 2016 financial strain had 15% higher IL-6 (p = 0.026) and 20% higher CRP levels (p = 0.002) in 2017 than those who were not strained, but did not differ with regard to hemoglobin A1c or CMV. In weighted comparisons, those with financial strain did not differ from those without with regard any 2015 background characteristics.


These results strengthen the etiologic evidence suggesting that financial strain increases inflammatory biomarkers among older adults. Importantly, inflammation is likely a key physiologic pathway contributing to socioeconomic disparities. Therefore, research is needed to address financial strain.

Peer Review reports


About one-third of older adults in the United States experience financial strain [1], or difficulty making ends meet. Financially strained older adults have a higher risk of physical disability, dementia, and earlier mortality [2,3,4]. However, the underlying mechanisms are poorly understood because financial strain cannot be randomly assigned for an experimental study. One study applying propensity score weighting found that financial strain specific to housing costs was associated with higher risk of poor health, hypertension, and arthritis, suggesting that lack of money for basic needs may cause poor health [5]. However, the specific underlying physiological pathways leading from financial strain to aging-related health outcomes are not well understood.

Inflammation is one physiologic pathway that likely is partly responsible for linking financial strain to aging-related outcomes. It is already known that aging itself is associated with increased production of pro-inflammatory cytokines like interleukin-6 (IL-6) and C-reactive protein (CRP) [6]. This perpetuates a chronic low-grade inflammatory phenotype that makes older adults more vulnerable to metabolic dysfunction [7], including increased hemoglobin A1c levels. Also, inflammation and latent cytomegalovirus (CMV) infection co-occur and may influence each other [8]. These biomarkers—IL-6, CRP, CMV, and hemoglobin A1c—predict earlier disability [9,10,11], dementia [12,13,14], and mortality [15,16,17]. This is likely due to fact that inflammatory biomarkers also predict the cardiometabolic conditions that contribute to risk for disability and dementia and that they are increased by other risk factors, such as smoking [18, 19]. Together, these studies suggest that inflammatory pathways contribute to these aging-related outcomes. However, not all older adults have the same risk for poor outcomes.

There is cross-sectional evidence showing relationships between financial strain and inflammation-related biomarkers. Studies conducted among a convenience sample [20], and a regional sample [21], have shown that financial strain is associated with higher levels of CRP and IL-6 [20, 21]. Among a national sample of U.S. older adults, financial strain was associated with higher hemoglobin A1c [22]. Together, these cross-sectional studies show correlation, but longitudinal studies with stronger causal methods among nationally representative samples are needed to elucidate underlying mechanisms.

Importantly, each of these inflammatory biomarkers capture distinct relationships between stress and health [23]. For example, secretion of CRP and IL-6 are triggered by the physiologic stress response, and may contribute to metabolic dysfunction including elevated hemoglobin A1c [24]. However, elevated hemoglobin A1c is also likely sensitive to environmental factors including food insecurity [25]. CMV may capture distinct relationships between social environmental characteristics and health because of socioeconomic and geographic differences in latent infection [26]. Determining the specific associations between financial strain and each of these individual biomarkers would allow for better understanding of how to mitigate the impact of financial strain on health.

This study sought to elucidate mechanisms linking financial strain with inflammatory biomarkers of aging among a nationally representative sample of U.S. older adults. This study applies a propensity score approach to test the hypothesis that financial strain predicts higher subsequent levels of IL-6, CRP, hemoglobin A1c, and CMV antibodies.


Study design and sample

The National Health and Aging Trends Study (NHATS) recruited a cohort of U.S. Medicare beneficiaries aged 65 years and older using stratified random sampling in 2011 and replenished the sample in 2015 [27]. NHATS study design is described in detail elsewhere [28]. NHATS participants were interviewed at home annually by trained interviewers. Predictors of financial strain were measured in 2015, financial strain was measured in 2016, and outcomes were measured in 2017. In 2017, all self-responding participants (n = 5,266) were asked to provide a blood spot specimen and 93% (n = 4,903) consented to do so. Of those who consented, 95.7% (n = 4,691) were able to provide a blood specimen. Sampling weights from NHATS accounted for study design, attrition between 2015 and 2017, and non-participation in the blood spot portion of the study so that results can be generalized to the U.S. population of community-dwelling adults over age 67 years. Due to potential qualitative differences in financial strain, these analyses excluded 98 residential care participants, leaving an analytic sample of 4,593. Due to a high likelihood of active infection [29], 139 individuals with CRP levels higher than three standard deviations above the mean were also excluded from CRP and IL-6 analyses. NHATS was approved by the Johns Hopkins Bloomberg School of Public Health IRB (#2083) and participants provided informed consent. These analyses were deemed exempt by the local institutional review board because they were determined to not be human subjects research.

Exposure measures

The exposure of interest in this study was 2016 financial strain. Participants were classified as having financial strain if they experienced strain in any of four domains severe enough so they lacked money to pay the (1) rent/mortgage, (2) utility bills, or (3) medical/prescription bills in the past year or (4) skipped any meals because there was not enough money to buy food in the past month. Since compromise(s) of in these four domains of basic needs may influence health and because of evidence of trade-off decisions over time among low-income older adults [30, 31], we were interested in comparing older adults who experienced any type of financial strain to those with no financial strain. Additional participant characteristics were measured in 2015 including demographic characteristics, socioeconomic characteristics, utilization of public benefits, childhood health, U.S. region, and marital status. Demographic characteristics included age, gender, and race/ethnicity [White (ref.), Black, Hispanic, and other race]. Socioeconomic characteristics included income to poverty ratio, which was calculated as the ratio of household income to the relevant US Census Bureau poverty threshold for individuals aged ≥ 65 years based on household size and year, education [< high school, high school, some college, and ≥ Bachelor’s degree], longest occupation held using the U.S. Census classifications [management/professional occupation (ref.), service, sales/office, construction/farming, production, and homemaker], homeownership [rent (ref.), own with mortgage, own without payments], and retirement status (no/yes). Utilization of public benefits included measures for receipt in the past year of Sect. 8 housing, Medicaid, and food or energy assistance. Childhood health was classified as [excellent (ref.), very good, good, fair, or poor]. Marital status was classified as [married (ref.), separated/divorced, widowed, never married]. U.S. region was based on the Census classification [New England (ref.), Mid-Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific]. To account for time-variant exposures, the 2017 values for income to poverty ratio and diabetes status were also recorded. Since health conditions likely lie on the causal pathway between financial strain and inflammation-related biomarkers, health variables were not included in these analyses.

Outcome measures

Outcome biomarkers measured in 2017 included interleukin 6 (IL-6) in pg/ml, hemoglobin A1c in %, anti-cytomegalovirus IgG antibodies (CMV) in AU/ml, and high-sensitivity C-reactive protein (CRP) in mg/L. Blood spots were collected on a card, dried, frozen, and shipped to the University of Washington School of Medicine for processing and analyses as described elsewhere [32]. IL-6, CMV, and CRP were measured with sandwich ELISA and hemoglobin A1c was measured with a Variant II Hemoglobin Testing System (Bio-Rad Laboratories, Hercules, CA). Plasma-equivalent values were used in these analyses rather than raw values for dried blood specimens to aid clinical interpretability. Assay values were highly correlated with plasma equivalent values for IL-6 (r = 0.93), hemoglobin A1c (r = 0.98), CMV (r = 0.98), and CRP (r = 0.99).

Statistical analyses

Propensity score model

This study employed propensity score weighting to estimate the Average Treatment Effect (ATE) of financial strain on inflammation-related biomarkers using SAS 9.4 software. Since financially strained older adults differ from non-strained older adults with regard to numerous characteristics, propensity score weighting was used to create groups that are balanced with regard to these characteristics [33]. First, a propensity score logistic regression model (proc surveylogistic) used 2016 financial strain as an outcome and included 2015 values for participant characteristics that not only temporally precede but also theoretically predict both financial strain and biomarkers and do not lie on the causal pathway between them [33]. Characteristics included in the propensity score model were age, gender, race/ethnicity, income to poverty ratio, education, occupation, home ownership, retirement status, Sect. 8 housing receipt, Medicaid use, receipt of food or energy assistance, childhood health, marital status, and U.S. Census Region. To account for differential experiences of discrimination among Black vs. White older adults over the lifespan, the propensity score model also included interactions between Black race with childhood health, education, and home ownership. Balance was considered to have been achieved if the standardized mean difference across groups was reduced and there was no statistically significant differences in the weighted sample [33]. After developing a propensity score model that balanced covariates across financial strain groups, results from the propensity score model were used to generate inverse probability weights, which among those with financial strain was 1/propensity score and among those without financial strain was 1/(1-propensity score). To account for extreme values, inverse probability weights were truncated at the 95th percentile [33].

Outcome analyses

Due to skew, outcome values were ln-transformed prior to testing hypothesized relationships between 2016 financial strain and 2017 biomarkers using linear regression. Model 1 applied only sampling weights provided by NHATS to account for study design and attrition, not propensity-score weights, and adjusted for 2017 diabetes status in the hemoglobin A1c model. Model 2 additionally adjusted for age, gender, race/ethnicity, 2017 income to poverty ratio, and education in all models. Model 3 adjusted for the same covariates as in Model 2 but estimated the average treatment effect by using analytic weights, which had been calculated as the cross-product of the sampling weights and the inverse probability weights generated from the propensity score model. The analytic weights addresses confounding bias by improving balance (i.e. exchangeability) across financial strain groups. Therefore, Model 3 produces doubly robust estimates with regard to demographic and socioeconomic characteristics.

Two sensitivity analyses were conducted. First, we tried alternative weight truncation at the 90th and 99th percentiles. Second, due to skew in propensity score values for those without financial strain, participants with propensity scores > 0.5 were excluded to evaluate influential observations and address potential comparison of non-exchangeable groups.


About 6% of the sample had severe enough financial strain in 2016 so they lacked money for basic need(s), which translates into about 2,240,825 U.S. older adults (Table 1). Prior to propensity score weighting, participants who experienced financial strain tended to be younger, have a lower average income to poverty ratio, and were less likely to be White, have graduated high school, have had a professional occupation, or own a home. They were also less likely to have had excellent childhood health and more likely to be separated/divorced than married and to have received Sect. 8 housing, Medicaid, and food or energy assistance. Participants did not differ with regard to gender, retirement status, or U.S. region.

Table 1 Selected 2015 background characteristics of community-dwelling NHATS participants based on 2016 financial strain status before propensity score weighting (n = 4,335)

The standardized mean difference for 2015 background characteristics was reduced 87% overall after applying propensity score weights, from -0.31 to -0.038, and reduced 60% to 100% for each covariate; absolute standardized difference values ranged from 0.05 to 0.69 prior to weighting and 0 to 0.15 after weighting (Table 2). Improved covariate balance is depicted in Fig. 1. Five financially strained participants with extremely high propensity scores (weights exceeding 4,479,225) were excluded from outcomes analyses in propensity score weighted analyses to avoid off-support inferences. After excluding these individuals, there was considerable overlap in the propensity score distribution comparing financially strained to non-strained participants (Supplemental Fig. 1), suggesting that comparisons across the groups were appropriate.

Table 2 Selected propensity score weighted 2015 background characteristics of community-dwelling NHATS participants based on 2016 financial strain status (n = 4,335)
Fig. 1
figure 1

Balance in 2015 background characteristics based on the standardized mean difference between National Health and Aging Trends Study participants with financial strain in 2016 and those without financial strain, comparing estimates obtained before and after applying propensity score weights

In unadjusted models (Model 1, Table 3), financially strained older adults were estimated to have 22% higher IL-6 levels, 26% higher CMV antibody titers, and 28% higher CRP levels. Adjusting for diabetes diagnosis, they had 3% higher hemoglobin A1c levels. After additionally adjusting for age, gender, race/ethnicity, income to poverty ratio, and education (Model 2, Table 3), they had 18% higher expected IL-6 and 27% higher CRP, but did not differ with regard to hemoglobin A1c level or CMV antibodies.

Table 3 Associations between 2016 financial strain with 2017 inflammatory biomarkers of aging among NHATS participants

In propensity score weighted analyses, those with financial strain did not differ from non-strained older adults with regard to any measured background characteristics (Table 2). In adjusted models that apply sampling and propensity score weights, those with financial strain had 15% higher typical IL-6 and 20% higher typical CRP levels than those without financial strain, and did not differ with regard to hemoglobin A1c or CMV antibodies (Model 3, Table 3). Sensitivity analyses were performed as described in the methods section and inferences remained unchanged in those models.


Financial strain predicted subsequent IL-6 and CRP levels using a propensity-score approach among a nationally representative sample of U.S. older adults. These results build on those from prior studies reviewed earlier linking financial strain with inflammatory biomarkers [20,21,22] by providing relatively stronger evidence of an underlying causal relationship. Together with results elsewhere linking inflammatory cytokines to disability, dementia, and mortality in older adults, these results suggest that inflammatory cytokines may account for the disparities in these outcomes based on financial strain exposure.

These results are consistent with results from natural experimental studies that intervened to address low income, which contributes to financial strain. Although natural experimental studies have found evidence of increased smoking and drinking after receiving relatively large sums of money such as lottery winnings or annual casino profits [34, 35], there is also evidence of improvement in other health outcomes after receiving either larger or smaller sums of money, including improvements in IL-6, CRP,[21] cognitive function, heart rate, blood pressure levels [36], obesity, diabetes [37], and mental health [38]. Together, these results suggest that although improved access to socioeconomic resources may worsen some health behaviors, they often improve numerous other health outcomes related to stress, metabolism, and well-being. Additional work is needed to develop and test the health impact of policies and programs aimed at improving socioeconomic resources.

There are possible physiologic explanations for these results. Financial strain experienced chronically over the life course likely repeatedly activates stress response mechanisms, including triggering cortisol secretion in the hypothalamic pituitary adrenal axis, which, in turn, inhibits immune response and triggers IL-6 and CRP secretion [23, 39]. These results are important because there has been much attention given to the harmful impact of early life exposure to stressful experiences, which is a sensitive period [23]. However, results from this study suggest that stressful events may also provoke immune responses among older adults, and this could explain the accumulation of disease and disability burden among socioeconomically disadvantaged older adults and the widening disparities documented over the adult lifespan comparing rich and poor [40]. Importantly, evidence of the ongoing accumulation of physiologic wear and tear during older adulthood suggest that it is not too late to address financial strain in late life. Interventions to attenuate financial strain among older adults may prevent health declines.

Notably, financial strain did not predict levels of CMV antibodies or hemoglobin A1c in adjusted analyses, despite evidence of an association with hemoglobin A1c found in another study [22] and evidence that older adults compromise basic necessities including food when experiencing financial challenges [30, 31, 41]. There are several potential reasons for this. First, it is possible that financial strain does not directly influence hemoglobin A1c or CMV infection. As examples, financial strain tends to co-occur with food insecurity and cost-related medication non-adherence [42] and these other social determinants of health may influence hemoglobin A1c. Second, low income households have been shown to have increased risk of hypoglycemia at the end of the month when food budgets tend to run low [43] but hemoglobin A1c may not be sensitive to temporal glucose fluctuations because it measures average blood sugar over three months. However, it also possible that the mechanisms linking financial strain to hemoglobin A1c and CMV infection occur earlier in the lifespan than older age because CMV infection tends to occur in early life [26] and diabetes tends to occur in mid-life [44]. Future studies in younger populations and using repeated outcome measures are needed to investigate causal mechanisms further. Future studies should consider using methods to strengthen causal inference, such as propensity score approaches.


Although this study was strengthened by accounting for multiple factors which likely capture multiple confounding pathways across the life-course, including childhood health, educational achievement, lifetime occupation, and a large set of late-life exposures, older adults likely have accumulated a large set of exposures earlier in their life which may predict both financial strain and inflammation-related biomarkers but are difficulty to quantify and this study had limited life-course measures. Another limitation is lack of baseline measurement for inflammation-related biomarkers. This study was not able to examine specific indicators of financial strain to estimate their effects on health outcomes. Strengths of this study include the inclusion of a nationally representative sample of U.S. older adults and temporal ordering of exposure and outcome.


These results have important clinical, public health, and policy implications. Growing attention to the need to screen for social determinants of health in clinical practice and public health surveillance has led to new guidance. The CMS Accountable Health Communities Health-Related Social Needs Screening Tool [45] can identify financial strain and other social determinants of health in clinical settings. Increased screening for financial strain is important because financial strain is modifiable. Because financial strain is the balance between income and need, it is possible to impact this strain by increasing resources or decreasing expenses. As examples of potential policy solutions to increase resources among older Americans, monthly benefit amounts for Social Security and Supplemental Security Income should be updated to account for the fact that the official poverty threshold has not kept pace with cost of living changes over time [46]. Also, many older adults are not able to utilize public benefits such as the Supplemental Nutrition Assistance Program and affordable housing options because of cumbersome enrollment processes and long wait lists, despite good evidence that participation in the programs is related to better self-rated health, less distress and less health care utilization [47,48,49,50,51,52]. Efforts to streamline access to these programs may reduce financial strain. Also, lowering prescription drug prices or improving access to generic medications could lower expenses for older people. Each of these efforts may impact subsequent biomarkers and future health.


This study applied a propensity-score approach to compare financially strained and non-strained older adults. Financially strained older adults had higher levels of subsequent IL-6 and CRP compared with non-strained older adults, but not hemoglobin A1c or CMV antibodies. These results build on those of prior studies by providing stronger evidence that financial strain may provoke an inflammatory response. Together with results from other studies, these results suggest that inflammatory pathways may partly explain socioeconomic disparities in aging-related health outcomes.

Availability of data and materials

The NHATS data analyzed in the current study are available for research purposes at



United States


National Health and Aging Trends Study




C-reactive protein




  1. Alley D, Kahn JR. Demographic and Psychosocial Predictors of Financial Strain in Older Adults. San Francisco: Annual Meeting of the Population Association of America; 2012.

    Google Scholar 

  2. Tucker-Seeley RD, Li Y, Subramanian SV, Sorensen G. Financial hardship and mortality among older adults using the 1996–2004 Health and Retirement Study. Ann Epidemiol. 2009;19(12):850–7.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Samuel LJ, Szanton SL, Wolff JL, Ornstein KA, Parker LJ, Gitlin LN. Socioeconomic disparities in six-year incident dementia in a nationally representative cohort of U.S. older adults: an examination of financial resources. BMC Geriatr. 2020;20(1):156.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Szanton SL, Thorpe RJ, Whitfield K. Life-course financial strain and health in African-Americans. Soc Sci Med. 2010;71(2):259–65.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pollack CE, Griffin BA, Lynch J. Housing affordability and health among homeowners and renters. Am J Prev Med. 2010;39(6):515–21.

    Article  PubMed  Google Scholar 

  6. Nikolich-Zugich J. The twilight of immunity: emerging concepts in aging of the immune system. Nat Immunol. 2018;19(1):10–9.

    CAS  Article  PubMed  Google Scholar 

  7. Liu SQ, Hempe JM, McCarter RJ, Li SX, Fonseca VA. Association between Inflammation and Biological Variation in Hemoglobin A1c in US Nondiabetic Adults. J Clin Endocr Metab. 2015;100(6):2364–71.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Leng SX, Margolick JB. Aging, sex, inflammation, frailty, and CMV and HIV infections. Cell Immunol. 2020;348:104024.

    CAS  Article  PubMed  Google Scholar 

  9. Aiello AE, Haan MN, Pierce CM, Simanek AM, Liang J. Persistent infection, inflammation, and functional impairment in older Latinos. J Gerontol A-Biol Sci Med Sci. 2008;63(6):610–8.

    Article  PubMed  Google Scholar 

  10. Ferrucci L, Harris TB, Guralnik JM, et al. Serum IL-6 level and the development of disability in older persons. J Am Geriatr Soc. 1999;47(6):639–46.

    CAS  Article  PubMed  Google Scholar 

  11. Wray LA, Ofstedal MB, Langa KM, Blaum CS. The effect of diabetes on disability in middle-aged and older adults. J Gerontol A Biol Sci Med Sci. 2005;60(9):1206–11.

    Article  PubMed  Google Scholar 

  12. Schmidt R, Schmidt H, Curb JD, Masaki K, White LR, Launer LJ. Early inflammation and dementia: a 25-year follow-up of the Honolulu-Asia Aging Study. Ann Neurol. 2002;52(2):168–74.

    Article  PubMed  Google Scholar 

  13. Barnes LL, Capuano AW, Aiello AE, et al. Cytomegalovirus Infection and Risk of Alzheimer Disease in Older Black and White Individuals. J Infect Dis. 2015;211(2):230–7.

    CAS  Article  PubMed  Google Scholar 

  14. Crane PK, Walker R, Hubbard RA, et al. Glucose Levels and Risk of Dementia. New Engl J Med. 2013;369(6):540–8.

    CAS  Article  PubMed  Google Scholar 

  15. Varadhan R, Yao W, Matteini A, et al. Simple biologically informed inflammatory index of two serum cytokines predicts 10 year all-cause mortality in older adults. J Gerontol A Biol Sci Med Sci. 2014;69(2):165–73.

    CAS  Article  PubMed  Google Scholar 

  16. Simanek AM, Dowd JB, Pawelec G, Melzer D, Dutta A, Aiello AE. Seropositivity to cytomegalovirus, inflammation, all-cause and cardiovascular disease-related mortality in the United States. PLoS ONE. 2011;6(2):e16103.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Wei M, Gaskill SP, Haffner SM, Stern MP. Effects of diabetes and level of glycemia on all-cause and cardiovascular mortality. The San Antonio Heart Study. Diabetes Care. 1998;21(7):1167–72.

    CAS  Article  PubMed  Google Scholar 

  18. Friedman E, Shorey C. Inflammation in Multimorbidity and Disability: An Integrative Review. Health Psychol. 2019;38(9):791–801.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet. 2017;390(10113):2673–734.

    Article  PubMed  Google Scholar 

  20. Gemes K, Ahnve S, Janszky I. Inflammation a possible link between economical stress and coronary heart disease. Eur J Epidemiol. 2008;23(2):95–103.

    Article  PubMed  Google Scholar 

  21. Samuel LJ, Szanton SL, Fedarko NS, Simonsick EM. Leveraging naturally occurring variation in financial stress to examine associations with inflammatory burden among older adults. J Epidemiol Community Health. 2020.

    Article  PubMed  Google Scholar 

  22. Walker RJ, Garacci E, Campbell JA, Harris M, Mosley-Johnson E, Egede LE. Relationship Between Multiple Measures of Financial Hardship and Glycemic Control in Older Adults With Diabetes. J Appl Gerontol. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Charmandari E, Tsigos C, Chrousos G. Endocrinology of the stress response. Annu Rev Physiol. 2005;67:259–84.

    CAS  Article  PubMed  Google Scholar 

  24. Black PH. The inflammatory response is an integral part of the stress response: Implications for atherosclerosis, insulin resistance, type II diabetes and metabolic syndrome X. Brain Behav Immun. 2003;17(5):350–64.

    CAS  Article  PubMed  Google Scholar 

  25. Seligman HK, Bindman AB, Vittinghoff E, Kanaya AM, Kushel MB. Food insecurity is associated with diabetes mellitus: results from the National Health Examination and Nutrition Examination Survey (NHANES) 1999–2002. J Gen Intern Med. 2007;22(7):1018–23.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dowd JB, Aiello AE, Alley DE. Socioeconomic disparities in the seroprevalence of cytomegalovirus infection in the US population: NHANES III. Epidemiol Infect. 2009;137(1):58–65.

    CAS  Article  PubMed  Google Scholar 

  27. DeMatteis J, Freedman VA, Kasper JD. National Health and Aging Trends Study Round 5 Sample Design and Selection. Baltimore, US: Johns Hopkins University School of Public Health, 2016. NHATS Technical Paper #16.

  28. Kasper JD, Freedman VA. National Health and Aging Trends Study User Guide: Rounds 1–10 Beta Release. Baltimore: Johns Hopkins University School of Public Health; 2021.

    Google Scholar 

  29. Vanderschueren S, Deeren D, Knockaert DC, Bobbaers H, Bossuyt X, Peetermans W. Extremely elevated C-reactive protein. Eur J Intern Med. 2006;17(6):430–3.

    CAS  Article  PubMed  Google Scholar 

  30. Samuel LJ, Wright R, Granbom M, et al. Community-dwelling older adults who are low-income and disabled weathering financial challenges. Geriatr Nurs. 2021;42(4):901–7.

    Article  PubMed  Google Scholar 

  31. Samuel LJ, Wright R, Taylor J, Roberts Lavigne LC, Szanton SL. Social Norms about Handling Financial Challenges in relation to Health-Protective Capacity among Low-Income Older Adults. The Gerontologist. In press.

  32. Kasper JD, Skehan ME, Seeman T, Freedman VA. Dried Blood Spot (DBS) Based Biomarkers in the National Health and Aging Trends Study User Guide: Final Release. Baltimore: Johns Hopkins University Bloomberg School of Public Health; 2019.

    Google Scholar 

  33. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661–79.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Bruckner TA, Brown RA, Margerison-Zilko C. Positive income shocks and accidental deaths among Cherokee Indians: a natural experiment. Int J Epidemiol. 2011;40(4):1083–90.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Apouey B, Clark AE. Winning big but feeling no better? The effect of lottery prizes on physical and mental health. Health Econ. 2015;24:516–38.

    Article  PubMed  Google Scholar 

  36. Mani A, Mullainathan S, Shafir E, Zhao J. Poverty Impedes Cognitive Function. Science. 2013;341:976–80.

    CAS  Article  PubMed  Google Scholar 

  37. Wolfe B, Jakubowski J, Haveman R, Courey M. The income and health effects of tribal casino gaming on American Indians. Demography. 2012;49(2):499–524.

    Article  PubMed  Google Scholar 

  38. Costello EJ, Erkanli A, Copeland W, Angold A. Association of Family Income Supplements in Adolescence With Development of Psychiatric and Substance Use Disorders in Adulthood Among an American Indian Population. Jama-J Am Med Assoc. 2010;303(19):1954–60.

    CAS  Article  Google Scholar 

  39. Darcy J, Tseng YH. The Link between Stress and IL-6 Is Heating Up. Cell Metab. 2020;32(2):152–3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Crimmins EM, Kim JK, Seeman TE. Poverty and biological risk: the earlier “aging” of the poor. J Gerontol A Biol Sci Med Sci. 2009;64(2):286–92.

    Article  PubMed  Google Scholar 

  41. Samuel L, Dwivedi P, Hladek M, et al. The effect of COVID-19 pandemic-related financial challenges on mental health and well-being among U.S. older adults Journal of the American Geriatrics Society. In Press.

  42. Berkowitz SA, Seligman HK, Choudhry NK. Treat or eat: food insecurity, cost-related medication underuse, and unmet needs. Am J Med. 2014;127(4):303–10 e3. doi:

  43. Seligman HK, Bolger AF, Guzman D, Lopez A, Bibbins-Domingo K. Exhaustion of food budgets at month’s end and hospital admissions for hypoglycemia. Health Aff (Millwood). 2014;33(1):116–23.

    Article  Google Scholar 

  44. Bullard KM, Cowie CC, Lessem SE, et al. Prevalence of Diagnosed Diabetes in Adults by Diabetes Type - United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(12):359–61.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Billioux A, Verlander K, Anthony S, Alley D. Standardized Screening for Health-Related Social Needs in Clinical Settings: The Accountable Health Communities Screening Tool. Washington, DC: National Academy of Medicine; 2017.

    Google Scholar 

  46. Mutchler JE, Li Y, Xu P. How strong is the Social Security safety net? Using the Elder Index to assess gaps in economic security. J Aging Soc Policy. 2019;31(2):123–37.

    Article  PubMed  Google Scholar 

  47. Fenelon A, Mayne P, Simon AE, et al. Housing Assistance Programs and Adult Health in the United States. Am J Public Health. 2017;107(4):571–8.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Denary W, Fenelon A, Schlesinger P, Purtle J, Blankenship KM, Keene DE. Does rental assistance improve mental health? Insights from a longitudinal cohort study. Soc Sci Med. 2021;282:114100.

    Article  PubMed  Google Scholar 

  49. Keene DE, Niccolai L, Rosenberg A, Schlesinger P, Blankenship KM. Rental Assistance and Adult Self-Rated Health. J Health Care Poor U. 2020;31(1):325–39.

    Article  Google Scholar 

  50. Berkowitz SA, Seligman HK, Rigdon J, Meigs JB, Basu S. Supplemental Nutrition Assistance Program (SNAP) Participation and Health Care Expenditures Among Low-Income Adults. JAMA Intern Med. 2017;177(11):1642–9.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Samuel LJ, Szanton SL, Cahill R, et al. Does the Supplemental Nutrition Assistance Program Affect Hospital Utilization Among Older Adults? The Case of Maryland. Popul Health Manag. 2018;21(2):88–95.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Szanton SL, Samuel LJ, Cahill R, et al. Food assistance is associated with decreased nursing home admissions for Maryland’s dually eligible older adults. BMC Geriatr. 2017;17(1):162.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


This work was supported by the National Institute on Aging (K01AG054751) and the National Heart Lung and Blood Institute (F31HL146080). The National Health and Aging Trends Study (NHATS) is sponsored by the National Institute on Aging (U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health

Author information

Authors and Affiliations



LJS and LRL conceptualized the research question with input from MH, SL and SLS; JT led analyses in consultation with LJS and LRL; all authors contributed to writing this manuscript.

Corresponding author

Correspondence to Laura J. Samuel.

Ethics declarations

Ethics approval and consent to participate

The National Health and Aging Trends Study was approved by the Johns Hopkins Bloomberg School of Public Health IRB (#2083). Participants provided written informed consent. The current analyses were deemed exempt by the Johns Hopkins School of Medicine IRB.

Consent for publication


Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1.

Propensity score frequency distributions showing overlap comparing National Health and Aging Trends Study participants with financial strain in 2016 to those without.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Samuel, L.J., Hladek, M., Tian, J. et al. Propensity score weighted associations between financial strain and subsequent inflammatory biomarkers of aging among a representative sample of U.S. older adults. BMC Geriatr 22, 467 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Socioeconomic factors
  • Inflammation
  • Metabolic function
  • Health equity