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      Geographic disparities in colorectal cancer survival

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          Abstract

          Background

          Examining geographic variation in cancer patient survival can help identify important prognostic factors that are linked by geography and generate hypotheses about the underlying causes of survival disparities. In this study, we apply a recently developed spatial scan statistic method, designed for time-to-event data, to determine whether colorectal cancer (CRC) patient survival varies by place of residence after adjusting survival times for several prognostic factors.

          Methods

          Using data from a population-based, statewide cancer registry, we examined a cohort of 25,040 men and women from New Jersey who were newly diagnosed with local or regional stage colorectal cancer from 1996 through 2003 and followed to the end of 2006. Survival times were adjusted for significant prognostic factors (sex, age, stage at diagnosis, race/ethnicity and census tract socioeconomic deprivation) and evaluated using a spatial scan statistic to identify places where CRC survival was significantly longer or shorter than the statewide experience.

          Results

          Age, sex and stage adjusted survival times revealed several areas in the northern part of the state where CRC survival was significantly different than expected. The shortest and longest survival areas had an adjusted 5-year survival rate of 73.1% (95% CI 71.5, 74.9) and 88.3% (95% CI 85.4, 91.3) respectively, compared with the state average of 80.0% (95% CI 79.4, 80.5). Analysis of survival times adjusted for age, sex and stage as well as race/ethnicity and area socioeconomic deprivation attenuated the risk of death from CRC in several areas, but survival disparities persisted.

          Conclusion

          The results suggest that in areas where additional adjustments for race/ethnicity and area socioeconomic deprivation changed the geographic survival patterns and reduced the risk of death from CRC, the adjustment factors may be contributing causes of the disparities. Further studies should focus on specific and modifiable individual and neighborhood factors in the high risk areas that may affect a person's chance of surviving cancer.

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          Most cited references64

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          Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project.

          N Krieger (2002)
          Despite the promise of geocoding and use of area-based socioeconomic measures to overcome the paucity of socioeconomic data in US public health surveillance systems, no consensus exists as to which measures should be used or at which level of geography. The authors generated diverse single-variable and composite area-based socioeconomic measures at the census tract, block group, and zip code level for Massachusetts (1990 population: 6,016,425) and Rhode Island (1990 population: 1,003,464) to investigate their associations with mortality rates (1989-1991: 156,366 resident deaths in Massachusetts and 27,291 in Rhode Island) and incidence of primary invasive cancer (1988-1992: 140,610 resident cases in Massachusetts; 1989-1992: 19,808 resident cases in Rhode Island). Analyses of all-cause and cause-specific mortality rates and all-cause and site-specific cancer incidence rates indicated that: 1) block group and tract socioeconomic measures performed comparably within and across both states, but zip code measures for several outcomes detected no gradients or gradients contrary to those observed with tract and block group measures; 2) similar gradients were detected with categories generated by quintiles and by a priori categorical cutpoints; and 3) measures including data on economic poverty were most robust and detected gradients that were unobserved using measures of only education and wealth.
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            Bringing context back into epidemiology: variables and fallacies in multilevel analysis.

            A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group- or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes. These types of analyses, which have been called contextual or multi-level analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, multilevel analysis holds potential for reemphasizing the role of macro-level variables in shaping health and disease in populations.
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              Measurement of socioeconomic status in health disparities research.

              Socioeconomic status (SES) is frequently implicated as a contributor to the disparate health observed among racial/ ethnic minorities, women and elderly populations. Findings from studies that examine the role of SES and health disparities, however, have provided inconsistent results. This is due in part to the: 1) lack of precision and reliability of measures; 2) difficulty with the collection of individual SES data; 3) the dynamic nature of SES over a lifetime; 4) the classification of women, children, retired and unemployed persons; 5) lack of or poor correlation between individual SES measures; and 6) and inaccurate or misleading interpretation of study results. Choosing the best variable or approach for measuring SES is dependent in part on its relevance to the population and outcomes under study. Many of the commonly used compositional and contextual SES measures are limited in terms of their usefulness for examining the effect of SES on outcomes in analyses of data that include population subgroups known to experience health disparities. This article describes SES measures, strengths and limitations of specific approaches and methodological issues related to the analysis and interpretation of studies that examine SES and health disparities.
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                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central
                1476-072X
                2009
                23 July 2009
                : 8
                : 48
                Affiliations
                [1 ]New Jersey Department of Health & Senior Services, New Jersey State Cancer Registry, Cancer Epidemiology Services, Trenton, New Jersey, USA
                [2 ]New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
                Article
                1476-072X-8-48
                10.1186/1476-072X-8-48
                2724436
                19627576
                2b2bcdfe-2f6d-4537-aa4b-bf9a19203fd3
                Copyright © 2009 Henry et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 April 2009
                : 23 July 2009
                Categories
                Research

                Public health
                Public health

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