A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea

International Journal of Health Geographics

Youngho Kim¹  Yoo Min Park² 

[1] Department of Geography Education, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea;Department of Geography, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea

关键词: Spatial dependency;    Eigenvector spatial filtering;    Multilevel model;    Self-rated health status;   

Others  :  806100
DOI  :  10.1186/1476-072X-13-6

 received in 2013-11-01, accepted in 2014-02-07,  发布年份 2014

【 摘 要 】

Background

This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets.

Methods

In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs.

Results

The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study.

Conclusions

The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts.

【 授权许可】

   
2014 Park and Kim; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Matteson DW, Burr JA, Marshall JR: Infant mortality: a multi-level analysis of individual and community risk factors. Soc Sci Med 1998, 47:1841-1854.
• [2]O'Campo P, Xue X, Wang MC, Caughy M: Neighborhood risk factors for low birthweight in Baltimore: a multilevel analysis. Am J Public Health 1997, 87:1113-1118.
• [3]Ahern J, Pickett KE, Selvin S, Abrams B: Preterm birth among African American and white women: a multilevel analysis of socioeconomic characteristics and cigarette smoking. J Epidemiol Community Health 2003, 57:606-611.
• [4]McLafferty S, Wang F, Luo L, Butler J: Rural–urban inequalities in late-stage breast cancer: spatial and social dimensions of risk and access. Environment and Planning B: Planning and Design 2011, 38:726-740.
• [5]Drukker M, Kaplan C, Feron F, van Os J: Children's health-related quality of life, neighbourhood socio-economic deprivation and social capital: a contextual analysis. Soc Sci Med 2003, 57:825-841.
• [6]Oren E, Koepsell T: Area-based socio-economic disadvantage and tuberculosis incidence. Int J Tuberc Lung Dis 2012, 16:880-885.
• [7]Jerrett M, Gale S, Kontgis C: Spatial modeling in environmental and public health research. Int J Environ Res Public Health 2010, 7:1302-1329.
• [8]Cakmak S, Burnett R: Spatial regression models for large-cohort studies linking community air pollution and health. Journal of Toxicology and Environmental Health, Part A: Current Issues 2003, 66:1811-1824.
• [9]Chakraborty J: Revisiting Tobler’s first law of geography: spatial regression models for assessing environmental justice and health risk disparities. In Geospatial analysis of environmental health. Volume 4. 2011 edition. Edited by Maantay J, McLafferty S. Dordrecht: Springer; 2011::337-356.
• [10]Corrado L, Fingleton B: Multilevel modelling with spatial effect. Glasgow: University of Strathclyde press; 2011.
• [11]Xu H: Compare spatial and multilevel regression models for binary outcomes in neighborhood studies. Sociological Methodology, forthcoming
• [12]Langford IH, Leyland AH, Rasbash J, Goldstein H: Multilevel modelling of the geographical distributions of diseases. J R Stat Soc: Ser C: Appl Stat 1999, 48:253-268.
• [13]Fotheringham AS, Brunsdon C, Charlton M: Geographically Weighted Regression: the analysis of spatially varying relationships. England: John Wiley & Sons; 2002.
• [14]Lorant V, Thomas I, Deliege D, Tonglet R: Deprivation and mortality: the implications ofspatial autocorrelation for health resources allocation. Soc Sci Med 2001, 53:1711-1719.
• [15]Pickett KE: Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 2001, 55:111-122.
• [16]Morenoff JD: Neighborhood mechanisms and the spatial dynamics of birth weight. Am J Sociol 2003, 108:976-1017.
• [17]Chen DR, Wen TH: Elucidating the changing socio-spatial dynamics of neighborhood effects on adult obesity risk in Taiwan from 2001 to 2005. Health & Place 2010, 16:1248-1258.
• [18]Chen DR, Truong K: Using multilevel modeling and geographically weighted regression to identify spatial variations in the relationship between place-level disadvantages and obesity in Taiwan. Appl Geogr 2012, 32:737-745.
• [19]Wheeler D, Tiefelsdorf M: Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 2005, 7:161-187.
• [20]Griffith DA: Spatial autocorrelation and spatial filtering. New York: Springer; 2003.
• [21]Patuelli R, Schanne N, Griffith DA, Nijkamp P: Persistence of regional unemployment: application of a spatial filtering approach to local labor markets in Germany. J Reg Sci 2012, 52:300-323.
• [22]Griffith DA: A comparison of four analytical disease mapping techniques as applied to West Nile Virus in the coterminous Uited States. Int J Health Geogr 2005, 4:18. BioMed Central Full Text
• [23]Yoon TH: Regional health inequalities in Korea: the status and policy tasks. J Crit Soc Policy 2010, 30:49-77.
• [24]EuroQol - Home http://www.euroqol.org/ webcite
• [25]Group EQ: EuroQol - a new facility for the measurement of health related quality of life. Health Policy 1990, 16:199-208.
• [26]Kind P: The EuroQol instrument: an index of Health-related Quality of Life. In Quality of Life and Pharmacoeconomics in Clinical Trials. 2nd edition. Edited by Spilker B. Philadelphia: Lippincott-Raven; 1996:191-201.
• [27]Kang E, Shin H, Park H, Jo M, Kim N: A valuation of health status using EQ-5D. Korean J Health Econ Policy 2006, 12:19-43.
• [28]Schelling TC: Models of segregation. Am Econ Rev 1969, 59:488-493.
• [29]Schelling TC: Dynamic models of segregation. J Math Sociol 1971, 1:143-186.
• [30]Elio & Company: Health Ranking. Seoul: Elio & Company; 2011.
• [31]Kreft IGG, de Leeuw J: Introducing multilevel modeling. London: Sage; 1998.
• [32]Luke DA: Multilevel modeling. Thousand Oaks: Sage; 2004.
• [33]Griffith DA: A linear regression solution to the spatial autocorrelation problem. J Geogr Syst 2000, 2:141-156.
• [34]Haining R: Spatial data analysis: theory and practice. Cambridge: Cambridge University press; 2003.
• [35]Tiefelsdorf M, Griffith DA: Semiparametric filtering of spatial autocorrelation: the eigenvector approach. Environment and Planning A 2007, 39:1193-1221.
• [36]Chun Y: Modeling network autocorrelation within migration flows by eigenvector spatial filtering. J Geogr Syst 2008, 10:317-344.
• [37]Bates D, Maechler M, Bolker B: lme4: Linear mixed-effects models using S4 Classes. 2013. http://cran.r-project.org/web/packages/lme4/index.html webcite. [R package version 0.999999-2
• [38]Bivand R, et al.: spdep: Spatial dependence: weighting schemes, statistics and models. 2013. http://cran.r-project.org/web/packages/spdep/index.html webcite. [R package version 0.5-57
• [39]Akaike H: Factor analysis and AIC. Psychometrika 1987, 52:317-332.
• [40]Hanyang University Industry Academic Cooperation Foundation: Management center for health promotion: Health promotion strategies and programmes development for health inequalities alleviation. Seoul: Ministry of Health and Welfare; 2009.
• [41]Carstairs V, Morris R: Deprivation: explaining differences in mortality between Scotland and England and Wales. Biritish Med J 1989, 299:886-889.
• [42]Sloggett A, Joshi H: Higher mortality in deprived areas: community or personal disadvangate? BMJ 1994, 309:1470-1474.
• [43]Davey Smith G, Hart CL, Watt G, Hole DJ, Hawthorne VM: Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley study. J Epidemiol Community Health 1998, 52:399-405.
• [44]Byun YC: Regional differences in health expectancy in Korea and policy suggestions. The Korea Institute for Health and Social Affairs: Seoul; 2011.
• [45]Han MA, Ryu SY, Park J, Kang MG, Park JK, Kim KS: Health-related Quality of Life assessment by the EuroQol-5D in some rural adults. J Prev Med Public Health 2008, 41:173-180.
• [46]Goodman DC, Fisher ES, Bronner KK: Hospital and physician capacity update: a brief report from the Dartmouth Atlas of health care. Dartmouth Institute for Health Policy and Clinical Practice: Hanover; 2009.
• [47]Leu RE, Rutten FFH, Brouwer W, Matter P, Rütschi C: The Swiss and Dutch health insurance systems: universal coverage and regulated competitive insurance markets. http://www.commonwealthfund.org/Publications/Fund-Reports/2009/Jan/The-Swiss-and-Dutch-Health-Insurance-Systems--Universal-Coverage-and-Regulated-Competitive-Insurance.aspx webcite
• [48]Jo DG: A spatial analysis of sociodemographic correlates of Health related Quality of Life. Korean J Popul Stud 2009, 32:1-20.
• [49]Han JY, Na BJ, Lee MS, Hong JY, Lim NG: The relationship between local fiscal indices and standardized mortality rate. In Proceedings of the KAIS 2010 Fall conference: 12-13 November 2010; Jeju. Cheonan: The Korea Academia-Industrial cooperation Society; 2010:1072-1076.
• [50]Root E, Emch M: Regional environmental patterns of diarrheal disease in Bangladesh: a spatial analytical and multilevel approach. In Geospatial analysis of environmental health. Volume 4. 2011 edition. Edited by Maantay J, McLafferty S. Dordrecht: Springer; 2011:191-204.
• [51]Kwan M-P: The uncertain geographic context problem. Annals of the Association of American Geographers 2012, 102:958-968.
• [52]Gatrell AC: Mobilities and health. Aldershot: Ashgate; 2011.
• [53]Moran PAP: Notes on continuous stochastic phenomena. Biometrika 1950, 37:17-23.