https://doi.org/10.4081/gh.2024.1255
Healthcare-seeking behavior and spatial variation of internal migrants with chronic diseases: a nationwide empirical study in China
Submitted: 27 November 2023
Accepted: 3 May 2024
Published: 28 May 2024
Accepted: 3 May 2024
Abstract Views: 1551
PDF: 131
HTML: 42
HTML: 42
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
Individuals migrating with chronic diseases often face substantial health risks, and their patterns of healthcare-seeking behavior are commonly influenced by mobility. However, to our knowledge, no research has used spatial statistics to verify this phenomenon. Utilizing data from the China Migrant Dynamic Survey of 2017, we conducted a geostatistical analysis to identify clusters of chronic disease patients among China’s internal migrants. Geographically weighted regressions were utilized to examine the driving factors behind the reasons why treatment was not sought by 711 individuals among a population sample of 9272 migrant people with chronic diseases. The results indicate that there is a spatial correlation in the clustering of internal migrants with chronic diseases in China. The prevalence is highly clustered in Zhejiang and Xinjiang in north-eastern China. Hotspots were found in the northeast (Jilin and Liaoning), the north (Hebei, Beijing, and Tianjin), and the east (Shandong) and also spread into surrounding provinces. The factors that affect the migrants with no treatment were found to be the number of hospital beds per thousand population, the per capita disposable income of medical care, and the number of participants receiving health education per 1000 Chinese population. To rectify this situation, the local government should “adapt measures to local conditions.” Popularizing health education and coordinating the deployment of high-quality medical facilities and medical workers are effective measures to encourage migrants to seek reasonable medical treatment.
Adhikari R, Sanou D, 2012. Risk factors of diabetes in Canadian immigrants: a synthesis of recent literature. Can J Diabetes 36:142-50.
DOI: https://doi.org/10.1016/j.jcjd.2012.06.001
Andersen RM, Davidson PL, 2014. Improving access to care in America: individual and contextual indicators. In: Andersen RM, Rice TH, Kominski GF, eds. Changing the U.S. health care system: Key issues in health services policy and management. Database: American Psychological Association.
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
Barcellos SH, Goldman DP, Smith JP, 2012. Undiagnosed disease, especially diabetes, casts doubt on some of reported health ‘advantage’ of recent Mexican immigrants. Health Aff (Milwood) 31:2727-37.
DOI: https://doi.org/10.1377/hlthaff.2011.0973
Braveman P, Egerter S, Williams DR, 2011. The social determinants of health: coming of age. Annu Rev Public Health 32:381-98.
DOI: https://doi.org/10.1146/annurev-publhealth-031210-101218
Castañeda H, Holme, SM, Madrigal DS, Young ME, Beyeler N, Quesada J, 2015. Immigration as a social determinant of health. Annu Rev Public Health 36:375-92.
DOI: https://doi.org/10.1146/annurev-publhealth-032013-182419
Charlton M, Brunsdon C, Fotheringham AS, 2002. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 288 pp.
Chen ZH, Zhang M, Li YC, Huang, ZJ, Wang, LM, 2017. Co-prevalence of chronic disease risk factors and influencing factors in floating population in China. Zhonghua Liu Xing Bing Xue Za Zhi 38:1226-30. [Article in Chinese].
Cui C, Yu C, Wang Q, 2022. Spatial patterns and underlying forces of talent migration and their implications on integrated high-quality development of the Yangtze river delta: an analysis of university graduates. J Nat Resour 37:1440-54. [Article in Chinese].
DOI: https://doi.org/10.31497/zrzyxb.20220605
Feng X, Sambamoorthi U, Wiener, RC, 2017. Dental workforce availability and dental services utilization in Appalachia: a geospatial analysis. Community Dent Oral Epidemiol 45:145-52.
DOI: https://doi.org/10.1111/cdoe.12270
Fotheringham AS, Kelly MH, Charlton M, 2013. The demographic impacts of the Irish famine: Towards a greater geographical understanding. Trans Inst Br Geogr 38:221-37.
DOI: https://doi.org/10.1111/j.1475-5661.2012.00517.x
Geary RC, 1954. The contiguity ratio and statistical mapping. Incorporated Statistician 5:115-45.
DOI: https://doi.org/10.2307/2986645
Getis A, Ord J, 1992. The analysis of spatial association by use of distance statistics. Geogr Anal 24:189-206.
DOI: https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Gu HY, Meng X, Shen TY, Cui NN, 2020. Spatial variation of the determinants of China’s urban floating population’s settlement intention. Acta Geogr Sin 75:240-54.
Gu HY, Qin XL, Shen TY, 2019. Spatial variation of migrants’s return intention and its determinants in China’s prefecture and provincial level cities. Geogr Res 38:1877-90.
Guo R, 2021. Main factors influencing health outcomes and their mechanisms of action. Population and Health 291:124-6. [Article in Chinese]
He A, Yu Y, Zheng S, 2022. Influencing factors of medication seeking behaviours among adultmigrant population with chronic diseases: a hierarchical model-based analysis. Chin J Public Health 38:75-9.
Jephcote C, Chen H, 2012. Environmental injustices of children’s exposure to air pollution from road-transport within the model British multicultural city of Leicester: 2000-09. Sci Total Environ 414:140-51.
DOI: https://doi.org/10.1016/j.scitotenv.2011.11.040
McKeigue PM, Miller GJ, Marmot MG ,1989. Coronary heart disease in south Asians overseas: a review. Clin Epidemiol 42:597-609.
DOI: https://doi.org/10.1016/0895-4356(89)90002-4
Modesti PA, Perticone F, Parati G, Agabiti RE, Prisco D, 2016. Chronic disease in the ethnic minority and migrant groups: time for a paradigm shift in Europe. Intern Emerg Med 11:295-7.
DOI: https://doi.org/10.1007/s11739-016-1444-4
National Bureau of Statistics, 2021. Bulletin of the seventh national population census. Available from: https://www.stats.gov.cn/sj/tjgb/rkpcgb/qgrkpcgb/202302/t20230206_1902007.html. [Material in Chinese].
Oshan TM, Smith JP, Fotheringham AS, 2020. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. Int J Health Geogr 19:11.
DOI: https://doi.org/10.1186/s12942-020-00204-6
Qu WH, Zhi JY, 2015. The influence of environmental pollution, economic growth and health care services to public health based on China's provincial panel data. China Manag Sci 07:166-76. [Article in Chinese]
Song LM, Zhang MZ, 2022. Analysis of the influencing factors of population mobility decision-making, willingness to stay and willingness to settle in northeast China. Popul Dev 28:151- 160. [Article in Chinese]
Song YP, Zhang GY, 2021. Utilization of public health services and its influencing factors among migrant people with hypertension or diabetes in China. Chin J Public Health 37:198-202.
State Council of the People’s Republic of China, 2023. Notice from the Office of the National Social Security Administration on further improving the management of designated retail inclusion in outpatient coordination. Retrieved on: 11/04/2024.
Tang QM, Le X, 2014. Study on the influencing factors and regional differences of Chinese residents' medical healthcare expenditure. J Financial Res 2014:14. [Article in Chinese]
Testa R, Bonfigli AR, Genovese S, Ceriello A, 2016. Focus on migrants with type 2 diabetes mellitus in European Countries. Intern Emerg Med 11:319-26.
DOI: https://doi.org/10.1007/s11739-015-1350-1
Tobler WR, 1970. A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234-40.
DOI: https://doi.org/10.2307/143141
Vandenheede H, Deboosere P, Stirbu I, Agyemang CO, Harding S, Juel K, 2012. Migrant mortality from diabetes mellitus across Europe: the importance of socio-economic change. Eur J Epidemiol 27:109-17.
DOI: https://doi.org/10.1007/s10654-011-9638-6
Wang H, Zhang L, Fang X, Deng R, Yao J, 2022. Prevalence and spatial analysis of chronic comorbidity among Chinese middle-aged and elderly people. Chin Gen Pract 25:1186-90.
Xiang LC, Yamada M, Feng WM, Li D, Nie HS, 2023. Spatial variations and influencing factors of cumulative health deficit index of elderly in China. J Health Popul Nutr 42:66.
DOI: https://doi.org/10.1186/s41043-023-00403-4
Yang SF, Ge M, Li, XP, Pan, CQ, 2020. The spatial distribution of the normal reference values of the activated partial thromboplastin time based on ArcGIS and GeoDA. Int J Biometeorol 64: 779-90.
DOI: https://doi.org/10.1007/s00484-020-01868-2
Yao J, Zhao J, 2015. The health inequality outcomes of rural population mobility - from the perspective of labor force reproduction. Jiangsu Soc Sci 4:7. [Article in Chinese].
Zhang J, Cai JL, Huang YY, He ZC, Tang GZ, 2021. China’s floating population’s healthcare utilization choices and influencing factors. Chin Gen Pract 24:2008-14.
Zhang M, Wu J, Zhang X, Hu CH, Zhao ZP, Li C, Huang ZJ, Zhou MG, Wang LM, 2021. Prevalence and control of hypertension in adults in China, 2018. Zhonghua Liu Xing Bing Xue Za Zhi 42:1780-9. [Article in Chinese].
How to Cite
Li, D., Gao, D., Yamada, M., Chen, C., Xiang, L., & Nie, H. (2024). Healthcare-seeking behavior and spatial variation of internal migrants with chronic diseases: a nationwide empirical study in China. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1255
Copyright (c) 2024 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.
