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      Racial/Ethnic Disparities in Cervical Cancer Stage at Diagnosis: Mediating Effects of Neighborhood-level Socioeconomic Deprivation

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          Abstract

          Background:

          Mortality from cervical cancer has declined steadily in the United States over the past several decades due to widespread screening for precancerous and early-stage cervical cancer (ECC), which are significantly easier to treat compared with late-stage cervical cancer (LCC). Unequal screening access continues to cause significant racial/ethnic disparities in cervical cancer diagnosis stage. This study examined the underlying role of neighborhood-level socioeconomic disadvantage as a potential mediator of the association between race/ethnicity and cervical cancer diagnosis stage.

          Methods:

          We analyzed Texas Cancer Registry data for cervical cancer cases diagnosed among women ages 18 or older from 2010 to 2018. We performed causal mediation analyses of the association between race/ethnicity and cervical cancer stage at diagnosis mediated by neighborhood-level socioeconomic disadvantage.

          Results:

          Of the 9,192 women with cervical cancer, 4,720 (51.3%) had LCC at diagnosis. Compared with non-Hispanic white (NHW) women (106.13, standard deviation (SD) = 13.32), non-Hispanic Black (NHB; 111.46, SD = 9.55) and Hispanic (112.32, SD = 9.42) women had higher area deprivation index (ADI) and had greater odds of LCC diagnosis [total effects: adjusted odds ratios (AOR) = 1.29 (95% CI, 1.11–1.46) and AOR 1.14 (95% CI, 1.03–1.25), respectively]. Approximately 34.7% and 71.6% of the disparity in LCC diagnosis were attributable to higher neighborhood socioeconomic disadvantage among NHB and Hispanic women, respectively.

          Conclusions:

          LCC disparity varied by race/ethnicity and was partly attributable to neighborhood disadvantage. The disparity among Hispanic women due to neighborhood deprivation was twice as high among NHB women.

          Impact:

          Findings may be used to develop targeted race- and place-specific interventions to improve cancer care equity.

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          Most cited references39

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros.

            Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this article are several-fold. First we seek to bring the developments in mediation analysis for nonlinear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically, and we compare the types of inferences about mediation that are allowed by a variety of software macros. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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              Identifiability and Exchangeability for Direct and Indirect Effects

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                Author and article information

                Journal
                Cancer Epidemiol Biomarkers Prev
                Cancer Epidemiol Biomarkers Prev
                Cancer Epidemiology, Biomarkers & Prevention
                American Association for Cancer Research
                1055-9965
                1538-7755
                01 June 2023
                03 April 2023
                : 32
                : 6
                : 818-824
                Affiliations
                [1 ]Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, Texas.
                [2 ]Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas.
                [3 ]Department of Pediatrics, Baylor College of Medicine, Houston, Texas.
                Author notes
                [* ] Corresponding Author: Itunu O. Sokale, Department of Medicine, Baylor College of Medicine, One Baylor Plaza, MS: BCM 307, Room 613D, Houston, TX 77030-3498. Phone: 713-798-2972; E-mail: Itunu.sokale@ 123456bcm.edu

                Cancer Epidemiol Biomarkers Prev 2023;32:818–24

                Author information
                https://orcid.org/0000-0003-4673-2998
                https://orcid.org/0000-0002-6603-6941
                https://orcid.org/0000-0002-0840-9316
                https://orcid.org/0000-0002-0084-5308
                Article
                EPI-23-0038
                10.1158/1055-9965.EPI-23-0038
                10233349
                37067295
                9c4e10bd-541c-4892-bb1f-091bdd5eefb4
                ©2023 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 13 January 2023
                : 10 March 2023
                : 30 March 2023
                Page count
                Pages: 7
                Funding
                Funded by: Cancer Prevention and Research Institute of Texas (CPRIT), https://doi.org/10.13039/100004917;
                Award ID: RP210037
                Award Recipient :
                Funded by: National Institute on Minority Health and Health Disparities (NIMHD), https://doi.org/10.13039/100006545;
                Award ID: R01MD01375
                Award Recipient :
                Funded by: National Institutes of Health (NIH), https://doi.org/10.13039/100000002;
                Award ID: P30 CA125123
                Award Recipient :
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                Research Articles

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