https://doi.org/10.4081/gh.2022.1078
Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province
Submitted: 1 February 2022
Accepted: 29 April 2022
Published: 17 May 2022
Accepted: 29 April 2022
Abstract Views: 1103
PDF: 657
HTML: 105
HTML: 105
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.
Al-Ahmadi K, Al-Zahrani A, 2013. Spatial autocorrelation of cancer incidence in Saudi Arabia. Int J Env Res Pub He 10:7207-28.
DOI: https://doi.org/10.3390/ijerph10127207
Anselin L, 1995. Local indicators of spatial association-LISA. Geogr Anal 27:93-115.
DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A, 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 394-424.
DOI: https://doi.org/10.3322/caac.21492
Chen W, Sun K, Zheng R, Zeng H, Zhang S, Xia C, Yang Z, Li H, Zou X, He J, 2018. Cancer incidence and mortality in China, 2014. Chin J Cancer Res 30:1-12.
DOI: https://doi.org/10.21147/j.issn.1000-9604.2018.01.01
Chen W, Zheng R, 2015. Incidence, mortality and survival analysis of breast cancer in China. Chin J Clin Oncol 668-74.
Chu J, Zhou C, Guo X, Sun J, Xue F, Zhang J, Lu Z, Fu Z, Xu A, 2017. Female breast cancer mortality clusters in Shandong Province, China: a spatial analysis. Sci Rep-Uk 7:1-08.
DOI: https://doi.org/10.1038/s41598-017-00179-8
Chung S, Imberger J, Hipsey MR, Lee HS, 2014. The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir. Ecol Model 289:133-49.
DOI: https://doi.org/10.1016/j.ecolmodel.2014.07.010
Debarsy N, Dossougoin C, Ertur C, Gnabo J, 2018. Measuring sovereign risk spillovers and assessing the role of transmission channels: a spatial econometrics approach. J Econ Dyn Control 87:21-45.
DOI: https://doi.org/10.1016/j.jedc.2017.11.005
Elhorst JP, 2010. Applied spatial econometrics: raising the bar. Spat Econ Anal 5:9-28.
DOI: https://doi.org/10.1080/17421770903541772
Fan C, Cai T, Gai Z, Wu Y, 2020. The relationship between the migrant population’s migration network and the risk of COVID-19 transmission in China - Empirical analysis and prediction in prefecture-level cities. Int J Env Res Pub He 17:2630.
DOI: https://doi.org/10.3390/ijerph17082630
Fan H, Gao B, Xu R, Wang J, 2017. Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity. Acta Oceanol Sin 36:111-21.
DOI: https://doi.org/10.1007/s13131-017-0969-3
Fan L, Strasser-Weippl K, Li J, St Louis J, Finkelstein DM, Yu K, Chen W, Shao Z, Goss PE, 2014. Breast cancer in China. Lancet Oncol 15:e279-89.
DOI: https://doi.org/10.1016/S1470-2045(13)70567-9
Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F, 2021. Cancer statistics for the year 2020: An overview. Int J Cancer 149:778-89.
DOI: https://doi.org/10.1002/ijc.33588
Fu J, Jiang D, Huang Y, 2014. China km grid population distribution data set. Glob Change Sci Res Data 1:1-04.
Ge E, Zhang X, Wang X, Wei X, 2016. Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009-2012. Infect Dis Poverty 5:11-20.
DOI: https://doi.org/10.1186/s40249-016-0104-2
Ghoncheh M, Pournamdar Z, Salehiniya H, 2016. Incidence and mortality and epidemiology of breast cancer in the world. Asian Pac J Cancer Prev 17:43-46.
DOI: https://doi.org/10.7314/APJCP.2016.17.S3.43
Graif C, Sampson RJ, 2009. Spatial heterogeneity in the effects of immigration and diversity on neighborhood homicide rates. Homicide Stud 13:242-60.
DOI: https://doi.org/10.1177/1088767909336728
Halunga AG, Orme CD, Yamagata T, 2017. A heteroskedasticity robust Breusch - Pagan test for Contemporaneous correlation in dynamic panel data models. J Econometrics 198: 209-30.
DOI: https://doi.org/10.1016/j.jeconom.2016.12.005
Huang Y, Jiang D, Fu J, 2014. China km grid GDP distribution data set. Glob Change Sci Res Data 1:5-08.
Igene H, 2008. Global health inequalities and breast cancer: an impending public health problem for developing countries. Breast J 14:428-34.
DOI: https://doi.org/10.1111/j.1524-4741.2008.00618.x
Ji P, Gong Y, Jiang CC, Hu X, Di GH, Shao ZM, 2020. Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study. Cancer Med-US 9:1922-36.
DOI: https://doi.org/10.1002/cam4.2842
Kloog I, Haim A, Portnov BA, 2009. Using kernel density function as an urban analysis tool: Investigating the association between nightlight exposure and the incidence of breast cancer in Haifa, Israel. Comput Enviro Urban Syst 33:55-63.
DOI: https://doi.org/10.1016/j.compenvurbsys.2008.09.006
Lantz PM, Mujahid M, Schwartz K, Janz NK, Fagerlin A, Salem B, Liu L, Deapen D, Katz SJ, 2006. The influence of race, ethnicity, and individual socioeconomic factors on breast cancer stage at diagnosis. Am J Public Health 96:2173-78.
DOI: https://doi.org/10.2105/AJPH.2005.072132
Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, Zeng H, Zhou J, Wei W, 2021. Global patterns of breast cancer incidence and mortality: a population-based cancer registry data analysis from 2000 to 2020. Cancer Commun 41:1183-94.
DOI: https://doi.org/10.1002/cac2.12207
Li J, Zhang B, Fan J, Pang Y, Zhang P, Wang S, Zheng S, Zhang B, Yang H, Xie X, 2011. A nation-wide multicenter 10-year (1999-2008) retrospective clinical epidemiological study of female breast cancer in China. BMC Cancer 11:364.
DOI: https://doi.org/10.1186/1471-2407-11-364
Liao Y, Li D, Zhang N, Xia C, Zheng R, Zeng H, Zhang S, Wang J, Chen W, 2019. Application of sandwich spatial estimation method in cancer mapping: A case study for breast cancer mortality in the Chinese mainland, 2005. Stat Methods Med Res 28:3609-26.
DOI: https://doi.org/10.1177/0962280218811344
Liao Y, Wang J, Wu J, Driskell L, Wang W, Zhang T, Xue G, Zheng X, 2010. Spatial analysis of neural tube defects in a rural coal mining area. Int J Environ Health Res 20:439-50.
DOI: https://doi.org/10.1080/09603123.2010.491854
Liu W, Song Z, Liu Z, 2016. Progress of economic geography in China’s mainland since 2000. J Geogr Sci 26:1019-40.
DOI: https://doi.org/10.1007/s11442-016-1313-0
Macquillan EL, Curtis AB, Baker KM, Paul R, Back YO, 2017. Using GIS mapping to target public health interventions: examining birth outcomes across GIS techniques. J Commun Health 42:633-38.
DOI: https://doi.org/10.1007/s10900-016-0298-z
Nelson JK, Brewer CA, 2017. Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartogr Geogr Inf Sci 44:35-50.
DOI: https://doi.org/10.1080/15230406.2015.1093431
Nordstokke DW, Zumbo BD, 2010. A new nonparametric Levene test for equal variances. Psicológica 31:401-30.
Qi F, Du F, 2013. Tracking and visualization of space-time activities for a micro-scale flu transmission study. Int J Health Geogr 12:6.
DOI: https://doi.org/10.1186/1476-072X-12-6
Qi Y, Yang Y, Jin F, 2013. China’s economic development stage and its spatio-temporal evolution: A prefectural-level analysis. J Geogr Sci 23:297-314.
DOI: https://doi.org/10.1007/s11442-013-1011-0
Ramadan AAB, Jacksonthompson J, Boren SA, 2017. Geographic information systems: usability, perception, and preferences of public health professionals. Online J Public Health Inform 9:1-16.
DOI: https://doi.org/10.5210/ojphi.v9i2.7437
Ren J, Gong Y, Ling H, Hu X, Shao Z, 2019. Racial/ethnic differences in the outcomes of patients with metastatic breast cancer: contributions of demographic, socioeconomic, tumor and metastatic characteristics. Breast Cancer Res Treat 173:225-37.
DOI: https://doi.org/10.1007/s10549-018-4956-y
Salinas GD, Whitworth L, Merwin P, Emarine J, 2018. Pancreatic cancer management and treatment landscape awareness of gastroenterologists: results from US physician surveys conducted in 2013 and 2015. J Gastrointest Cancer 49:41-9.
DOI: https://doi.org/10.1007/s12029-016-9906-5
Seidman H, Stellman SD, Mushinski MH, 1982. A different perspective on breast cancer risk factors: some implications of the non-attributable risk. Cancer J Clin 32:301-13.
DOI: https://doi.org/10.3322/canjclin.32.5.301
Shrestha A, Luo W, 2018. Assessment of groundwater nitrate pollution potential in Central Valley Aquifer using geodetector-based frequency ratio (GFR) and optimized-DRASTIC methods. ISPRIS Int J Geoinf 7:211.
DOI: https://doi.org/10.3390/ijgi7060211
Siegel RL, Miller KD, Jemal A, 2015. Cancer statistics, 2015. Cancer J Clin 65:5-29.
DOI: https://doi.org/10.3322/caac.21254
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F, 2021. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 71:209-49.
DOI: https://doi.org/10.3322/caac.21660
Sun X, Zhao D, Liu Y, Liu Y, Yuan Z, Wang J, Xue F, 2017. The long-term spatial-temporal trends and burden of esophageal cancer in one high-risk area: a population-registered study in Feicheng, China. PLos One 12:e173211.
DOI: https://doi.org/10.1371/journal.pone.0173211
Tan J, Wang X, Pan J, 2021. The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: a case study from China. Geospat Health 16:32-45.
DOI: https://doi.org/10.4081/gh.2021.936
Wang J, Haining R, Liu T, Li L, Jiang C, 2013. Sandwich estimation for multi-unit reporting on a stratified heterogeneous surface. Environ Plann 45:2515-34.
DOI: https://doi.org/10.1068/a44710
Wang J, McMichael AJ, Meng B, Becker NG, Han W, Glass K, Wu J, Liu X, Liu J, Li X, 2006. Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area. Bull World Health Organ 84:965-68.
DOI: https://doi.org/10.2471/BLT.06.030247
Wang J, Stein A, Gao B, Ge Y, 2012. A review of spatial sampling. Spat Stat-Neth 2:1-14.
DOI: https://doi.org/10.1016/j.spasta.2012.08.001
Wang J, Zhang T, Fu B, 2016. A measure of spatial stratified heterogeneity. Ecol Indic 67:250-6.
DOI: https://doi.org/10.1016/j.ecolind.2016.02.052
Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY, 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci 24:107-27.
DOI: https://doi.org/10.1080/13658810802443457
Wang Q, Li J, Zheng S, Li J, Pang Y, Huang R, Zhang B, Zhang B, Yang H, Xie X, 2012. Breast cancer stage at diagnosis and area-based socioeconomic status: a multicenter 10-year retrospective clinical epidemiological study in China. BMC Cancer 12:122.
DOI: https://doi.org/10.1186/1471-2407-12-122
Xia C, Kahn C, Wang J, Liao Y, Chen W, Yu XQ, 2016. Temporal trends in geographical variation in breast cancer mortality in China, 1973-2005: an analysis of nationwide surveys on cause of death. Int J Env Res Pub Health 13:963.
DOI: https://doi.org/10.3390/ijerph13100963
Xu X, Zhao Y, Xia S, Zhang X, 2019. Investigation of multi-scale spatio-temporal pattern of oldest-old clusters in China on the basis of spatial scan statistics. PLoS One 14:e219695.
DOI: https://doi.org/10.1371/journal.pone.0219695
Xu X, Zhao Y, Zhang X, Xia S, 2018. Identifying the impacts of social, economic, and environmental factors on population aging in the Yangtze River Delta using the geographical detector technique. Sustainability-Basel 10:1528.
DOI: https://doi.org/10.3390/su10051528
Zhou W, Zhang Z, Bi Y, Wang L, Liu X, Yu C, 2018. Joinpoint regression analysis for the mortality trend for breast cancer in Chinese female from 1987 to 2014. J Cent South Univ 43:210-5.
How to Cite
Wang, Y., & Wan, Z. (2022). Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province. Geospatial Health, 17(1). https://doi.org/10.4081/gh.2022.1078
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
