Literature DB >> 23316251

Healthcare access, socioeconomic factors and late-stage cancer diagnosis: an exploratory spatial analysis and public policy implication.

Fahui Wang1, Lan Luo, Sara McLafferty.   

Abstract

Patients diagnosed with late-stage cancer have lower survival rates than those with early-stage cancer. This paper examines possible associations between several risk factors and late-stage diagnosis for four types of cancer in Illinois: breast cancer, prostate cancer, colorectal cancer, and lung cancer. Potential risk factors are composed of spatial factors and nonspatial factors. The spatial factors include accessibility to primary healthcare and distance or travel time to the nearest cancer screening facility. A set of demographic and socioeconomic variables are consolidated into three nonspatial factors by factor analysis. The Bayesian model with convolution priors is utilised to analyse the relationship between the above risk factors and each type of late-stage cancer while controlling for spatial autocorrelation. The results for breast cancer suggest that people living in neighbourhoods with socioeconomic disadvantages and cultural barriers are more likely to be diagnosed at a late stage. In regard to prostate cancer, people in regions with low socioeconomic status are also more likely to be diagnosed at a late stage. Diagnosis of late-stage colorectal or lung cancer is not significantly associated with any of the abovementioned risk factors. The results have important implications in public policy.

Entities:  

Year:  2009        PMID: 23316251      PMCID: PMC3540777          DOI: 10.1504/IJPP.2010.030606

Source DB:  PubMed          Journal:  Int J Public Pol        ISSN: 1740-0600


  80 in total

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9.  Effect of age and race on the survival of men with prostate cancer in the Metropolitan Detroit tricounty area, 1973 to 1987.

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  15 in total

1.  Using spatially adaptive floating catchments to measure the geographic availability of a health care service: Pulmonary rehabilitation in the southeastern United States.

Authors:  Kevin A Matthews; Anne H Gaglioti; James B Holt; Anne G Wheaton; Janet B Croft
Journal:  Health Place       Date:  2019-02-16       Impact factor: 4.078

2.  Influence of geographic access and socioeconomic characteristics on breast cancer outcomes: A systematic review.

Authors:  Benoit Conti; Audrey Bochaton; Hélène Charreire; Hélène Kitzis-Bonsang; Caroline Desprès; Sandrine Baffert; Charlotte Ngô
Journal:  PLoS One       Date:  2022-07-19       Impact factor: 3.752

3.  Medicaid Expansion Associated With Reduction in Geospatial Breast Cancer Stage at Diagnosis Disparities.

Authors:  Weichuan Dong; Johnie Rose; Uriel Kim; Gregory S Cooper; Jennifer Tsui; Siran M Koroukian
Journal:  J Public Health Manag Pract       Date:  2022-04-05

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Authors:  Fahui Wang
Journal:  Ann Assoc Am Geogr       Date:  2012-03-27

5.  Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach.

Authors:  Lan Luo; Sara McLafferty; Fahui Wang
Journal:  Int J Health Geogr       Date:  2010-10-19       Impact factor: 3.918

6.  Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.

Authors:  Weichuan Dong; Wyatt P Bensken; Uriel Kim; Johnie Rose; Nathan A Berger; Siran M Koroukian
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2021-10-25       Impact factor: 4.090

7.  General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare.

Authors:  Soumya Mazumdar; Paul Konings; Danielle Butler; Ian Stewart McRae
Journal:  BMC Health Serv Res       Date:  2013-09-03       Impact factor: 2.655

8.  Coping Strategies of Southern Italian Women Predict Distress Following Breast Cancer Surgery.

Authors:  Rossana De Feudis; Tiziana Lanciano; Stefano Rinaldi
Journal:  Eur J Psychol       Date:  2015-05-29

9.  Exploring neighborhood inequality in female breast cancer incidence in Tehran using Bayesian spatial models and a spatial scan statistic.

Authors:  Erfan Ayubi; Mohammad Ali Mansournia; Ali Ghanbari Motlagh; Alireza Mosavi-Jarrahi; Ali Hosseini; Kamran Yazdani
Journal:  Epidemiol Health       Date:  2017-05-17

10.  Estimating health service utilization potential using the supply-concentric demand-accumulation spatial availability index: a pulmonary rehabilitation case study.

Authors:  Kevin A Matthews; Anne H Gaglioti; James B Holt; Anne G Wheaton; Janet B Croft
Journal:  Int J Health Geogr       Date:  2020-08-03       Impact factor: 3.918

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