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Sociodemographic disparities in chemotherapy and hematopoietic cell transplantation utilization among adult acute lymphoblastic and acute myeloid leukemia patients

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      Abstract

      Introduction

      Identifying sociodemographic disparities in chemotherapy and hematopoietic cell transplantation (HCT) utilization for acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) may improve survival for underserved populations. In this study, we incorporate neighborhood socioeconomic status (nSES), marital status, and distance from transplant center with previously studied factors to provide a comprehensive analysis of sociodemographic factors influencing treatments for ALL and AML.

      Methods

      Using the California Cancer Registry, we performed a retrospective, population-based study of patients ≥15 years old with ALL (n = 3,221) or AML (n = 10,029) from 2003 through 2012. The effect of age, sex, race/ethnicity, marital status, nSES, and distance from nearest transplant center on receiving no treatment, chemotherapy alone, or chemotherapy then HCT was analyzed.

      Results

      No treatment, chemotherapy alone, or chemotherapy then HCT were received by 11%, 75%, and 14% of ALL patients and 36%, 53%, and 11% of AML patients, respectively. For ALL patients ≥60 years old, HCT utilization increased from 5% in 2005 to 9% in 2012 (p = 0.03). For AML patients ≥60 years old, chemotherapy utilization increased from 39% to 58% (p<0.001) and HCT utilization from 5% to 9% from 2005 to 2012 (p<0.001). Covariate-adjusted analysis revealed decreasing relative risk (RR) of chemotherapy with increasing age for both ALL and AML (trend p <0.001). Relative to non-Hispanic whites, lower HCT utilization occurred in Hispanic [ALL, RR = 0.80 (95% CI = 0.65–0.98); AML, RR = 0.86 (95% CI = 0.75–0.99)] and non-Hispanic black patients [ALL, RR = 0.40 (95% CI = 0.18–0.89); AML, RR = 0.60 (95% CI = 0.44–0.83)]. Compared to married patients, never married patients had a lower RR of receiving chemotherapy [ALL, RR = 0.96 (95% CI = 0.92–0.99); AML, RR = 0.94 (95% CI = 0.90–0.98)] or HCT [ALL, RR = 0.58 (95% CI = 0.47–0.71); AML, RR = 0.80 (95% CI = 0.70–0.90)]. Lower nSES quintiles predicted lower chemotherapy and HCT utilization for both ALL and AML (trend p <0.001).

      Conclusions

      Older age, lower nSES, and being unmarried predicted lower utilization of chemotherapy and HCT among ALL and AML patients whereas having Hispanic or black race/ethnicity predicted lower rates of HCT. Addressing these disparities may increase utilization of curative therapies in underserved acute leukemia populations.

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      Most cited references 38

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      A modified poisson regression approach to prospective studies with binary data.

       Guangyong Zou (2004)
      Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
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        Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

        Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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          Marital status and survival in patients with cancer.

          To examine the impact of marital status on stage at diagnosis, use of definitive therapy, and cancer-specific mortality among each of the 10 leading causes of cancer-related death in the United States.
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            Author and article information

            Affiliations
            [1 ]Loma Linda University School of Public Health, Loma Linda, California, United States of America
            [2 ]University of California San Diego, Moores Cancer Center, La Jolla, California, United States of America
            University of Kentucky, UNITED STATES
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            • Conceptualization: MJW BJ JWM MEM.

            • Data curation: BJ JWM.

            • Formal analysis: BJ JWM MG.

            • Funding acquisition: JWM.

            • Investigation: BJ.

            • Methodology: BJ JWM MG MJW.

            • Project administration: BJ JWM MJW.

            • Resources: JWM.

            • Software: JWM.

            • Supervision: JWM MEM MJW.

            • Validation: BJ MG.

            • Visualization: BJ JWM MJW.

            • Writing – original draft: BJ JWM MJW.

            • Writing – review & editing: MEM MG.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            6 April 2017
            2017
            : 12
            : 4
            28384176 5383052 10.1371/journal.pone.0174760 PONE-D-16-42246
            © 2017 Jabo et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 3, Tables: 3, Pages: 18
            Product
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
            Award ID: N01-PC-35136
            Award Recipient :
            Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
            Award ID: N01-PC-35139
            Award Recipient :
            Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
            Award ID: N02-PC-15105
            Award Recipient :
            Funded by: funder-id http://dx.doi.org/10.13039/100000030, Centers for Disease Control and Prevention;
            Award ID: U58DP000807-01
            Award Recipient :
            This study was supported, in part, by contracts N01-PC-35136, N01-PC-35139 and N02-PC-15105 from the National Cancer Institute and the Surveillance Epidemiology and End Results (SEER) program; contract U58DP000807-01 from the Centers for Disease Control and Prevention and the National Program for Cancer Registries; and the California Department of Public Health Cancer Surveillance Branch. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Medicine and Health Sciences
            Oncology
            Cancers and Neoplasms
            Hematologic Cancers and Related Disorders
            Leukemias
            Myeloid Leukemia
            Acute Myeloid Leukemia
            Medicine and Health Sciences
            Hematology
            Hematologic Cancers and Related Disorders
            Leukemias
            Myeloid Leukemia
            Acute Myeloid Leukemia
            Medicine and Health Sciences
            Oncology
            Cancer Treatment
            Cancer Chemotherapy
            Medicine and Health Sciences
            Pharmaceutics
            Drug Therapy
            Chemotherapy
            Cancer Chemotherapy
            Medicine and Health Sciences
            Clinical Medicine
            Clinical Oncology
            Cancer Chemotherapy
            Medicine and Health Sciences
            Oncology
            Clinical Oncology
            Cancer Chemotherapy
            Medicine and Health Sciences
            Oncology
            Cancers and Neoplasms
            Hematologic Cancers and Related Disorders
            Leukemias
            Lymphoblastic Leukemia
            Acute Lymphoblastic Leukemia
            Medicine and Health Sciences
            Hematology
            Hematologic Cancers and Related Disorders
            Leukemias
            Lymphoblastic Leukemia
            Acute Lymphoblastic Leukemia
            Medicine and Health Sciences
            Oncology
            Cancers and Neoplasms
            Hematologic Cancers and Related Disorders
            Leukemias
            Medicine and Health Sciences
            Hematology
            Hematologic Cancers and Related Disorders
            Leukemias
            Medicine and Health Sciences
            Oncology
            Cancer Treatment
            Medicine and Health Sciences
            Pharmaceutics
            Drug Therapy
            Chemotherapy
            People and Places
            Population Groupings
            Ethnicities
            Hispanic People
            Social Sciences
            Sociology
            Social Stratification
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            All relevant data are within the paper.

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