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Research Paper| Volume 14, ISSUE 2, 101415, March 2023

Association of individual low-income status and area deprivation with mortality in multiple myeloma

Published:February 09, 2023DOI:https://doi.org/10.1016/j.jgo.2022.12.003

      Abstract

      Introduction

      Lower individual-level socioeconomic status (SES) and area-level SES have each been associated with poor survival outcomes among patients with multiple myeloma (MM). A body of literature suggests that individual-level SES may be differentially associated with mortality depending on area-level SES, and vice versa. This study assessed the effect of the cross-level interaction between individual low-income status and area deprivation on mortality among patients with MM.

      Materials and Methods

      This retrospective cohort study used the Surveillance, Epidemiology, and End Results (SEER)—Medicare data (2006–2016). Individuals were defined as having low income if they were dually eligible for Medicare and Medicaid and/or if they received the Low-Income Subsidy. The county-level Social Deprivation Index (SDI) was linked to individual-level SEER-Medicare data and categorized into quintiles, from the least deprived (Quintile 1) to the most deprived (Quintile 5). Adjusted hazard ratios (HRs) for the associations between low-income status, area deprivation, and all-cause mortality were estimated from a mixed-effects Cox proportional-hazards (PH) model.

      Results

      The mortality hazard was higher for individuals with low income than individuals without low income in all quintiles of area deprivation, with the exception of Quintile 5 (Quintile 1: HR 1.53 [95% confidence interval [CI]: 1.32–1.77]; Quintile 2: HR 1.17 [95%CI: 1.01–1.36]; Quintile 3: HR 1.34 [95%CI: 1.18–1.53]; Quintile 4: HR 1.33 [95%CI: 1.17–1.52]; Quintile 5: HR 1.09 [95%CI: 0.96–1.23]). Among individuals without low income, individuals residing in the most deprived area had a higher mortality hazard than individuals residing in the least deprived area (HR: 1.22 [95%CI: 1.03–1.45]). In contrast, among individuals with low income, residing in a more deprived area, Quintile 2, was associated with a lower hazard of death than residing in the least deprived area, Quintile 1 (HR: 0.82 [95%CI: 0.67–0.99]), and there was no statistically significant difference between Quintile 1 and Quintiles 3, 4, and 5.

      Discussion

      In this analysis, there was a statistically significant cross-level interaction between individual low-income status and area deprivation on mortality. More research is needed to fully understand the mechanism behind these associations, but the findings show that patients and their health should be considered in the context of where they live.

      Keywords

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      References

        • Braunlin M.
        • Belani R.
        • Buchanan J.
        • Wheeling T.
        • Kim C.
        Trends in the multiple myeloma treatment landscape and survival: a U.S. analysis using 2011–2019 oncology clinic electronic health record data.
        62(2). 2020: 377-386https://doi.org/10.1080/10428194.2020.1827253
        • Fonseca R.
        • Abouzaid S.
        • Bonafede M.
        • et al.
        Trends in overall survival and costs of multiple myeloma, 2000-2014.
        Leukemia. 2017; 31: 1915-1921https://doi.org/10.1038/leu.2016.380
        • Maiese E.M.
        • Evans K.A.
        • Chu B.-C.
        • Irwin D.E.
        Temporal trends in survival and healthcare costs in patients with multiple myeloma in the United States.
        Am Heal Drug Benefits. 2018; 11 (/pmc/articles/PMC5902764/. [Accessed August 12, 2021]): 39
        • Costa L.J.
        • Brill I.K.
        • Brown E.E.
        Impact of marital status, insurance status, income, and race/ethnicity on the survival of younger patients diagnosed with multiple myeloma in the United States.
        Cancer. 2016; 122: 3183-3190https://doi.org/10.1002/cncr.30183
        • Fiala M.A.
        • Finney J.D.
        • Liu J.
        • et al.
        Socioeconomic status is independently associated with overall survival in patients with multiple myeloma.
        Leuk Lymphoma. 2015; 56: 2643-2649https://doi.org/10.3109/10428194.2015.1011156
        • Winkleby M.
        • Cubbin C.
        • Ahn D.
        Effect of cross-level interaction between individual and neighborhood socioeconomic status on adult mortality rates.
        Am J Public Health. 2006; 96: 2145-2153https://doi.org/10.2105/AJPH.2004.060970
        • Yen I.H.
        • Kaplan G.A.
        Neighborhood social environment and risk of death: multilevel evidence from the Alameda County study.
        Am J Epidemiol. 1999; 149: 898-907https://doi.org/10.1093/oxfordjournals.aje.a009733
        • Shin J.
        • Choi Y.
        • Kim S.W.
        • Lee S.G.
        • Park E.C.
        Cross-level interaction between individual socioeconomic status and regional deprivation on overall survival after onset of ischemic stroke: national health insurance cohort sample data from 2002 to 2013.
        J Epidemiol. 2017; 27: 381-388https://doi.org/10.1016/J.JE.2016.08.020
        • Robert Graham Center. Social Deprivation Index (SDI)
        • Cheng E.
        • Soulos P.R.
        • Irwin M.L.
        • et al.
        Neighborhood and individual socioeconomic disadvantage and survival among patients with nonmetastatic common cancers.
        JAMA Netw Open. 2021; 4https://doi.org/10.1001/JAMANETWORKOPEN.2021.39593
        • National Council on Aging
        Part D LIS eligibility and benefits.
        • National Cancer Institute
        SEER-medicare linked data resource.
        (Accessed November 6, 2022)
        • Goto D.
        • Khairnar R.
        • Yared J.A.
        • et al.
        Utilization of novel systemic therapies for multiple myeloma: a retrospective study of front-line regimens using the SEER-Medicare data.
        Cancer Med. 2020; 9: 626-639https://doi.org/10.1002/cam4.2698
        • Butler D.C.
        • Petterson S.
        • Phillips R.L.
        • Bazemore A.W.
        Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery.
        Health Serv Res. 2013; 48: 539-559https://doi.org/10.1111/j.1475-6773.2012.01449.x
        • National Cancer Institute
        NCI comorbidity index overview.
        • Albarmawi H.
        • Nagarajan M.
        • Sun K.
        • et al.
        Costs associated with follicular lymphoma among individuals diagnosed with non-Hodgkin lymphoma: a longitudinal analysis using SEER-Medicare data.
        Leuk Lymphoma. 2020; 61: 75-83https://doi.org/10.1080/10428194.2019.1648804
        • Davidoff A.J.
        • Zuckerman I.H.
        • Pandya N.
        • et al.
        A novel approach to improve health status measurement in observational claims-based studies of cancer treatment and outcomes.
        J Geriatr Oncol. 2013; 4: 157-165https://doi.org/10.1016/j.jgo.2012.12.005
        • Olszewski A.J.
        • Dusetzina S.B.
        • Trivedi A.N.
        • Davidoff A.J.
        Prescription drug coverage and outcomes of myeloma therapy among medicare beneficiaries.
        in: Journal of clinical oncology. vol. 36. American Society of Clinical Oncology, 2018: 2879-2886https://doi.org/10.1200/JCO.2018.77.8894
        • Kyle R.A.
        • Gertz M.A.
        • Witzig T.E.
        • et al.
        Review of 1027 patients with newly diagnosed multiple myeloma.
        Mayo Clin Proc. 2003; 78: 21-33https://doi.org/10.4065/78.1.21
        • Sigurdardottir E.E.
        • Turesson I.
        • Lund S.H.
        • et al.
        The role of diagnosis and clinical follow-up of monoclonal gammopathy of undetermined significance on survival in multiple myeloma.
        JAMA Oncol. 2015; 1: 168-174https://doi.org/10.1001/jamaoncol.2015.23
        • Brookhart M.A.
        • Schneeweiss S.
        • Rothman K.J.
        • Glynn R.J.
        • Avorn J.
        • Stürmer T.
        Variable selection for propensity score models.
        Am J Epidemiol. 2006; 163: 1149-1156https://doi.org/10.1093/aje/kwj149
        • Zhang Z.
        • Kim H.J.
        • Lonjon G.
        • Zhu Y.
        Balance diagnostics after propensity score matching.
        Ann Transl Med. 2019; 7 (16–16)https://doi.org/10.21037/atm.2018.12.10
        • National Cancer Institute
        SEER*explorer application. Myeloma SEER relative survival rates by time since diagnosis, 2000–2018.
        • Pezzi T.A.
        • Schwartz D.L.
        • Pisters K.M.W.
        • et al.
        Association of Medicaid Insurance with Survival among Patients with Small Cell Lung Cancer.
        JAMA Netw Open. 2020; 3 (e203277)https://doi.org/10.1001/jamanetworkopen.2020.3277
        • Chang C.M.
        • Su Y.C.
        • Lai N.S.
        • et al.
        The combined effect of individual and Neighborhood socioeconomic status on cancer survival rates.
        PLoS One. 2012; 7https://doi.org/10.1371/journal.pone.0044325
        • Bradley C.J.
        • Gardiner J.
        • Given C.W.
        • Roberts C.
        Cancer, medicaid enrollment, and survival disparities.
        Cancer. 2005; 103: 1712-1718https://doi.org/10.1002/cncr.20954
        • Waitzman N.J.
        • Smith K.R.
        Phantom of the area: poverty-area residence and mortality in the United States.
        Am J Public Health. 1998; 88: 973-976https://doi.org/10.2105/AJPH.88.6.973
        • Elstad J.I.
        The psycho-social perspective on social inequalities in health.
        Sociol Heal Illn. 1998; 20: 598-618https://doi.org/10.1111/1467-9566.00121
        • Pearce N.
        • Davey Smith G.
        Is social capital the key to inequalities in health?.
        Am J Public Health. 2003; 93: 122-129https://doi.org/10.2105/AJPH.93.1.122
        • Samantha Shoemaker J.
        • Davidoff A.J.
        • Stuart B.
        • Zuckerman I.H.
        • Onukwugha E.
        • Powers C.
        Eligibility and Take-up of the medicare part D low-income subsidy.
        49(3). 2012: 214-230https://doi.org/10.5034/INQUIRYJRNL_49.03.04