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Trends in Breast Cancer Stage and Mortality in Michigan (1992–2009) by Race, Socioeconomic Status, and Area Healthcare Resources

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      Abstract

      The long-term effect of socioeconomic status (SES) and healthcare resources availability (HCA) on breast cancer stage of presentation and mortality rates among patients in Michigan is unclear. Using data from the Michigan Department of Community Health (MDCH) between 1992 and 2009, we calculated annual proportions of late-stage diagnosis and age-adjusted breast cancer mortality rates by race and zip code in Michigan. SES and HCA were defined at the zip-code level. Joinpoint regression was used to compare the Average Annual Percent Change (AAPC) in the median zip-code level percent late stage diagnosis and mortality rate for blacks and whites and for each level of SES and HCA. Between 1992 and 2009, the proportion of late stage diagnosis increased among white women [AAPC = 1.0 (0.4, 1.6)], but was statistically unchanged among black women [AAPC = −0.5 (−1.9, 0.8)]. The breast cancer mortality rate declined among whites [AAPC = −1.3% (−1.8,−0.8)], but remained statistically unchanged among blacks [AAPC = −0.3% (−0.3, 1.0)]. In all SES and HCA area types, disparities in percent late stage between blacks and whites appeared to narrow over time, while the differences in breast cancer mortality rates between blacks and whites appeared to increase over time.

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      The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.
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        Constructing socio-economic status indices: how to use principal components analysis.

        Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.
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          Cancer Statistics, 2017

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            Author and article information

            Affiliations
            [1 ]Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, United States of America
            [2 ]Department of Epidemiology, University of Nebraska School of Public Health, Omaha, Nebraska, United States of America
            [3 ]Michigan Cancer Surveillance Program, Michigan Department of Community Health, Lansing, Michigan, United States of America
            [4 ]Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
            [5 ]Department of Family Medicine and Public Health Sciences and Barbara Ann Karmanos Institute, Wayne State University School of Medicine, Detroit, Michigan, United States of America
            [6 ]Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
            [7 ]University of Michigan Center for Global Health, Ann Arbor, Michigan, United States of America
            Baylor College of Medicine, United States of America
            Author notes

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

            Helped draft the manuscript: AS GC MB KS SM. Read and approved the manuscript: TA AS GC MB KS SM. Conceived and designed the experiments: TA AS GC MB KS SM. Analyzed the data: TA. Wrote the paper: TA.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2013
            29 April 2013
            : 8
            : 4
            23637921
            3639257
            PONE-D-12-27308
            10.1371/journal.pone.0061879
            (Editor)

            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.

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            Pages: 9
            Funding
            Funding for this research was provided by the University of Michigan Graduate School and the University of Michigan Department of Epidemiology block grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology
            Medicine
            Epidemiology
            Cancer Epidemiology
            Non-Clinical Medicine
            Health Care Policy
            Ethnic Differences
            Geographic and National Differences
            Health Care Quality
            Obstetrics and Gynecology
            Breast Cancer
            Oncology
            Cancers and Neoplasms
            Breast Tumors
            Cancer Detection and Diagnosis
            Public Health
            Socioeconomic Aspects of Health

            Uncategorized

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