Elucidating the spatially varying relation between cervical cancer and socio-economic conditions in England

International Journal of Health Geographics

Arjan K Shahani¹  Peter M Atkinson¹  Edith MY Cheng² 

[1] Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield, Southampton, UK;Faculty of Medicine, University of Southampton, Highfield, Southampton, UK

关键词: disease mapping;    screening;    cervical cancer;    Geographically weighted regression;   

Others  :  822472
DOI  :  10.1186/1476-072X-10-51

 received in 2011-04-15, accepted in 2011-09-26,  发布年份 2011

【 摘 要 】

Background

Geographically weighted Poisson regression (GWPR) was applied to the relation between cervical cancer disease incidence rates in England and socio-economic deprivation, social status and family structure covariates. Local parameters were estimated which describe the spatial variation in the relations between incidence and socio-economic covariates.

Results

A global (stationary) regression model revealed a significant correlation between cervical cancer incidence rates and social status. However, a local (non-stationary) GWPR model provided a better fit with less spatial correlation (positive autocorrelation) in the residuals. Moreover, the GWPR model was able to represent local variation in the relations between cervical cancer incidence and socio-economic covariates across space, whereas the global model represented only the overall (or average) relation for the whole of England. The global model could lead to misinterpretation of the relations between cervical cancer incidence and socio-economic covariates locally.

Conclusions

Cervical cancer incidence was shown to have a non-stationary relationship with spatially varying covariates that are available through national datasets. As a result, it was shown that if low social status sectors of the population are to be targeted preferentially, this targeting should be done on a region-by-region basis such as to optimize health outcomes. While such a strategy may be difficult to implement in practice, the research does highlight the inequalities inherent in a uniform intervention approach.

【 授权许可】

   
2011 Cheng et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Chandler R: On the use of generalized linear models for interpreting climate variability. Environmetrics 2005, 16:699-715.
• [2]Green P, Richardson S: Hidden Markov models and disease mapping. Journal of the American statistical association 2002, 460:1-16.
• [3]Jackson C, Best N, Richardson S: Improving ecological inference using individual-level data. Statistics in medicine 2006, 25:2136-2159.
• [4]Fotheringham A, Brunsdon C, Charlton M: Geographically weighted regression: the analysis of the spatially varying relationships. Chichester; John Wiley & Sons; 2002.
• [5]Nakaya T: Local spatial interaction modelling based on the geographically weighted regression approach. Geo Journal 2001, 53:347-358.
• [6]Nakaya T, Fotheringham A, Brunsdon C, Charlton M: Geographically weighted Poisson regression for disease association mapping. Statistics In Medicine 2005, 24:2695-2717.
• [7]Best N, Ickstant K, Wolpert R: Spatial Poisson regression for health and exposure data measured at disparate resolutions. Journal of the American statistical association 2000, 95:1076-1088.
• [8]Lawson A, Clark A: Spatial mixture relative risk models applied to disease mapping. Statistics in medicine 2002, 21:359-370.
• [9]Richardson S, Thomson A, Best N, Elliott P: Interpreting posterior relative risk estimates in disease-mapping studies. Environmental health perspective 2004, 112:1016-1025.
• [10]Fotheringham A, Brundson C, Charlton M: Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A 1998, 30:1905-1927.
• [11]Abellan J, Fecht D, Best N, Richardson S, Briggs D: Bayesian analysis of the multivariate geographical distribution of the socio-economic environment in England. Environmetrics 2007, 18:745-758.
• [12]Carstairs V: Socio-economic factors at areal level and their relationship with health. In Spatial Epidemiology: methods and applications. Edited by Elliott P, Wakefield JC, Best NG, Briggs DJ. Oxford: Oxford University Press; 2000:51-67.
• [13]Jarup L, Best N, Toledano M, Wakefield J, Elliott P: Geographical epidemiology of prostate cancer in Great Britain. International journal of cancer 2002, 97:695-699.
• [14]Smith J: The relationship between maternal work and other socioeconomic factors and child health in Bangladesh. Public health 1999, 113:299-302.
• [15]Wakefield JC, Best NG, Richardson S, Bernardinelli L, Staines A: Statistical issues in the analysis of disease mapping data. Statistics in Medicine 2000, 19:2493-2519.
• [16]Goddard M, Smith P: Equity of access to health care services: Theory and evidence from the UK. Social Science and Medicine 2001, 53:1149-1162.
• [17]Townsend P, Davidson N, Black D, Morris JN, Smith C: Inequalities in health: the black report. 1st edition. Penguins: Pelican; 1982.
• [18]Townsend P, Phillimore P, Beattie A: Health and Deprivation: inequalities and the North. London: Croom helm; 1988.
• [19]Judge A, Welton N, Sandhu J, Ben-Shlomo Y: Equity in access to total joint replacement of the hip and knee in England: cross sectional study. British Medical Journal 2010, 341:c4092.
• [20]Raine R, Mclvor M: 9 years on: what progress has been made on achieving UK health-care equity? Lance 2006, 368:1542-1545.
• [21]Whynes DK, Philips Z, Avis M: Why do women participate in the English cervical cancer screening programme. Journal of Health Economics 2007, 26:306-325.
• [22]World Health Organization, Cervical cancer [http://www.who.int/reproductivehealth/topics/cancers/en/] webcite
• [23]NHS Cervical Screening Programme: A pocket Guide Sheffield 2004.
• [24]World Health Organization, Cancer [http://www.who.int/mediacentre/factsheets/fs297/en/] webcite
• [25]Office for National Statistics, Incidence of Health of the Nation cancers by social class [http:/ / info.cancerresearchuk.org/ cancerstats/ types/ cervix/ riskfactors/ cervical-cancer-risk-factors] webcite
• [26]England cervical cancer incidence rate [http://info.cancerresearchuk.org/cancerstats/types/cervix/incidence/] webcite
• [27]Sing A, Monaghan J: Lower genital tract precancer: colpocopy, pathology and treatment. Second edition. Oxford: Wiley-Blackwell; 2000.
• [28]Arias-Pulido H, Joste NE, Vargas H, Wheeler CM: Human papillomavirus type 16 integration in cervical carcinoma in situ and in invasive cervical cancer. Journal of clinical microbiology 2006, 44:1755-1762.
• [29]Goldie SJ, Grima D, Kohli M, Wright TC, Weinstein M, Franco E: A comprehensive natural history model of HPV infection and cervical cancer to estimate the clinical impact of a prophylactic HPV 16/18 vaccine. International Journal of cancer 2003, 106:896-904.
• [30]Jenkins D, Sherlaw-Johnson C, Gallivan S: Can papilloma virus testing be used to improve cervical canceer screening? International Journal of cancer 1996, 65:768-773.
• [31]England cervical cancer survival rate [http://info.cancerresearchuk.org/cancerstats/types/cervix/survival/?a=5441] webcite
• [32]NHS Cervical Cancer Screening Programmes: Annual Review Report Sheffield 2008.
• [33]NHS Cervical Screening Programme: A pocket Guide Sheffield 2009.
• [34]NHS Cervical Cancer Screening Programmes: Celebrating 20 years of Screening Sheffield 2008.
• [35]Gelman A, Carlin J, Stem H, Rubin D: Bayesian data analysis. Chapman and Hall, CRC; 2003.
• [36]Lawson A, Browne W, Rodeiro C: Disease mapping with WinBUGS and MLwiN. Chichester; Wiley; 2003.
• [37]Waller LA, Gotway CA: Applied Spatial Statistics for Public Health Data. John Wiley & Sons, Inc; 2004.
• [38]Loader C: Local regression and likelihood. New York; Springer; 1999.
• [39]Akaike H: A new look at the statistical model identification. Anal Biochem 1987, 162:156-159.
• [40]Akaike information criterion statistics: KTK Scientific Publishers. Tokyo 1986.
• [41]Sugiura N: Further analysis of the data by Akaike's information criterion and the finite corrections. Communications in Statistics, theory and methods 1978, A7:13-26.
• [42]Hurvich C, Tsai C: Regression and time series model selection in small samples. Biometrika 1989, 76:297-307.
• [43]Lunn D, Whittaker J, Best N: A Bayesian toolkit for genetic association studies. Genetic Epidemiology 2006, 30:231-247.
• [44]The English Indices of Deprivation: Technical report, Office of the Deputy Prime Minister (ODPM) Neighbourhood Renewal Unit, London; 2004.
• [45]Wright G, Noble M: The South African Index of Multiple Deprivation 2007 at Municipality level. Department: Social Development Republic of South Africa; 2009.
• [46]Lavori P, Dawson R, Shera D: A multiple imputations strategy for clinical-trials with truncation of patient data. Statistics in medicine 1995, 14:1913-1925.
• [47]Jackson C, Best N, Richardson S: Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors. Journal of the royal statistical society series A 2008, 171:159-178.
• [48]Herbert A, Smith J: Cervical screening - Women under 25 should be offered screening. British medical journal 2007, 334:273-273.
• [49]Raffle AE: Cervical screening: recent changes in policy regarding age and frequency are a poor use of resources. British Medical Journal 2004, 328:1272-1273.
• [50]Ferko N, Debicki D, Barnfi F, Marocco A, Mantovani K: Estimating the long-term health and economic impact of a prophylactic cervical cancer vaccine on the burden of cervical disease in Italy. Value in health 2007, 10:A440-A441.
• [51]Hammerschmidt T, Siebery U, Schwarz T, Schneider A, Rogoza R, Ferko N, Welte R: A cost-effectiveness analysis of a prophylactic cervical cancer vaccine in Germany: results from a health economic model. Value in health 2007, 10:A441.