A spatially filtered multilevel model to account for spatial dependency: application to selfrated health status in South Korea
 Yoo Min Park^{1} and
 Youngho Kim^{2}Email author
DOI: 10.1186/1476072X136
© Park and Kim; licensee BioMed Central Ltd. 2014
Received: 1 November 2013
Accepted: 7 February 2014
Published: 27 February 2014
Abstract
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 selfrated health status in South Korea. In many previous healthrelated studies, multilevel models and singlelevel spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in healthrelated 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 healthrelated 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 neighborhoodlevel 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 selfrated health status. Residents living in neighborhoods with low deprivation and a high doctortoresident 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 selfrated 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 outperform conventional models when it is used in other contexts.
Keywords
Selfrated health status Multilevel model Eigenvector spatial filtering Spatial dependencyBackground
To analyze both effects of individual and neighborhood factors on individual health outcomes, many previous healthrelated studies utilized multilevel models that can analyze two (or more) level independent variables in tandem [1–6]. These studies analyzed various health outcomes, such as infant mortality [1], a low birth weight [2], preterm birth [3], latestage breast cancer [4], children’s healthrelated quality of life [5], and tuberculosis incidence [6], using aggregated data in common, such as countylevel, census tractlevel, and postal codelevel data to represent neighborhoodlevel variables. The studies, however, do not take into account underlying spatial dependency across neighborhoods; thus their multilevel analyses results are potentially misleading in cases where data exhibit spatial dependency. Spatial dependency in healthrelated data indicates that health outcomes in nearby neighborhoods are more similar to each other than to those in distant neighborhoods. In other words, these studies only consider withinneighborhood correlation (i.e., correlation between individuals within the same neighborhood) using a hierarchical setting, but fail to account for potential betweenneighborhood correlation.
According to Jerrett et al. [7], spatial dependency of health outcomes among nearby neighborhoods may arise from similar socioeconomic (e.g., health facilities and services) and natural environmental conditions (e.g., air quality). For example, catchment areas for health facilities may encompass a broader area, thereby transcending localized administrative boundaries. In terms of local environment, disease risks from air pollution tend to be similar among closer neighborhoods because their local wind direction and/or road conditions (and environmental and traffic policies) are more likely to be similar; as a result, residents of those neighborhoods are exposed to similar types and concentrations of atmospheric pollutants [7–9]. However, the nonspatial multilevel model cannot address this spatial dependency because the method typically assumes that neighborhoods (i.e., spatial units) are statistically independent of each other [10]; thus multilevel models have been criticized as nonspatial and unrealistic [10–13].
Based on the notion of spatial dependency of health outcomes, some researchers used both a nonspatial singlelevel linear model ignoring spatial dependency (i.e., linear models estimated with ordinary least squares or weighted least squares) and a spatial autoregressive model (SAR) considering spatial dependency, and compared the two methods [9, 14]. The authors found that nonspatial singlelevel models and the SAR models provided different regression results depending on the presence of spatial dependency. These two studies, however, made limited attempts to model individual characteristics when using spatial models, because they used only aggregated variables. Studies that analyze health outcomes solely via aggregated data using a singlelevel spatial model cannot fully explain factors that truly influence individual health outcomes [15].
A few researchers have tried to incorporate a geographical perspective into the multilevel setting in various ways to take into account both the multilevel framework and spatial effects. Some studies attempting to address spatial dependency in residuals of multilevel models employed spatial lag regression model specifications [16, 17]. In the spatial lag regression model, the spatial autoregressive parameter is denoted as ρ, which indicates the intensity of spatial dependency. Another study [18] used multilevel models with geographically weighted regression (GWR) developed by Fotheringham et al. [13] to consider a spatially varying relationship between neighborhood factors and obesity. GWR allows researchers to estimate varying regression parameters over space. However, in some cases, there can still be spatial dependency after GWR is used, although this method may mitigate spatial dependency by considering spatial variation to some degree; this can influence the regression results considerably. In addition, according to Wheeler and Tiefelsdorf [19], GWR’s R^{2} goodness of fit tends to be high when residuals have high spatial dependency. Therefore, GWR should be used as an exploratory tool for understanding spatial variation rather than a statistically stable method for addressing spatial dependency.
As discussed above, limited attention has been paid within the literature to integrating multilevel models and spatial regression models. However, these two approaches should be used in combination because the objectives of both methods are important in healthrelated analyses. Thus, it is increasingly necessary to integrate multilevel models and spatial regression models, especially the eigenvector spatial filtering method, an advanced approach to addressing spatial dependency in datasets. Compared to spatial lag regression (or SAR) model specifications, which present only one parameter of global spatial component, the greatest advantage of eigenvector spatial filtering used in this paper is to visualize a spatial structure in a map form by decomposing it into smallerscale spatial patterns or local clusters with a set of eigenvectors [20, 21]. This trait could provide a better understanding of how health phenomena are distributed across the space. Additionally, because the spatial filtering technique can be applied to a generalized linear model specification based on the binomial or Poisson probability models, it is more flexible than the spatial lag regression (or SAR) model, which requires normalizing factor computation [22]. Compared with GWR, which has an inherent problem of multicollinearity among local regression coefficients [19], the spatial filtering method is more statistically reliable because eigenvectors generated in filtering procedure are mutually orthogonal, which indicates the absence of multicollinearity issues.
Griffith’s study [22] showed the possibility of combining hierarchical generalized linear models with spatial filtering method as a disease mapping technique. Based on this idea, the present study presents how multilevel modeling components can be linked to the spatial filtering framework by showing an integrated formulation and uses selfrated health status in South Korea to investigate whether an integrated “spatially filtered multilevel model” generates a more robust regression results than a conventional multilevel model.
This study first identifies whether spatial dependency exists within neighborhoodlevel residuals in the multilevel model. Where spatial dependency is detected, the eigenvector spatial filtering technique is applied to the multilevel model to control for spatial dependency. The study then compares the explanatory power of the models and the regression results between the conventional model and the spatially filtered model.
Methods
Data and variables
Descriptive statistics for a dependent variable and independent variables
N  %  

Individuallevel variables (n = 61817)  
Sex  
Males  26116  42.2  
Females  35701  57.8  
Employment status  
Employed  24508  39.7  
Unemployed  37293  60.3  
Perceived levels of stress  
High level  13140  21.3  
Low level  48649  78.7  
Mean  Standard dev.  Range  
Monthly household income (US$)  1382.1  1988.4  0.0 – 99553.6 
Neighborhoodlevel variables (n = 223)  
Korean Deprivation Index (KDI)  0.3  0.9  1.5 – 1.7 
The number of doctors per 1000 people  2.2  2.0  0.6 – 20.7 
Degree of the Local Governments’ Financial Independence (LGFI)  65.1  9.5  33.7 – 91.4 
Dependent variable  
EQ5D index  0.783  0.261   0.229 – 1.000 
Figure 1 shows how selfrated health status is more similar to that in nearby neighborhood census tracts than that in distant neighborhoods. This is because nearby neighborhoods are likely to have similar demographic and socioeconomic characteristics (e.g., sex, age, race, income, language, and religion) and political resources within a larger citywide system [28, 29]. In South Korea, development policies have focused more on rapid economic growth than the distribution of accumulated wealth, resulting in serious regional disparities in health status across the country. For example, most districts in Seoul, Korea’s largest metropolitan area, show high selfrated health status (Figure 1). This is because the Seoul metropolitan area has sufficiently dense infrastructure provision for a healthy environment to ensure good accessibility to health services [30]. In contrast, many provincial cities in nonmetropolitan areas excluded from the benefits of economic development, such as Gangwon, Chungnam, and Gyeongbuk show low selfrated health status.
The CHS also provides individuallevel variables such as sex, employment status, perceived levels of stress, and monthly household income. Among these, sex (0 = males; 1 = females), employment status (0 = employed; 1 = unemployed), and perceived levels of stress (0 = people with high perceived levels of stress; 1 = people with low perceived levels of stress) are binary. Monthly household income is a continuous variable. Descriptive statistics for the independent variables are summarized in Table 1.
The neighborhoodlevel variables consist of the KDI [23], the doctortoresident ratio (number of doctors per 1,000 population), and the degree of the local governments’ financial independence (LGFI). The KDI is based on eight census indicators reflecting neighborhood socioeconomic levels, such as the proportions of households that are: without car ownership; in a low social class; comprised of elderly people, etc. The number of doctors per 1,000 population and LGFI were obtained from eRegional Indicators (2009). LGFI refers to the local government’s level of autonomy to raise and use financial funds. This ability facilitates implementation of welfare policy, such as providing healthy residential environment or enhancing health care services. The ratio of physicians to residents reflects accessibility to health care services. Descriptive statistics for neighborhoodlevel variables are provided in Table 1.
Multilevel model
When analyzing both individual and neighborhood variables in tandem, a multilevel model is generally more appropriate than an ordinary singlelevel regression model because it enables researchers to deal with hierarchical structure of variables [31]. The multilevel model assumes that individuals (i.e., lower hierarchy) belonging to a particular neighborhood (i.e., higher hierarchy) are not independent of each other because they are presumed to share the similar characteristics of that neighborhood; thus the model considers intraneighborhood correlation.
Model construction begins with analyzing a ‘null’ model, which is the simplest model and uses no independent variable. The null model includes distinct types of variance of the dependent variable, such as withinneighborhood and betweenneighborhood variances [32]. Based on this null model, an Intraclass Correlation Coefficient (ICC) is calculated, which guides how the null model should be extended further. The ICC is the ratio between the betweenneighborhood variance and the sum of both withinneighborhood and betweenneighborhood variances. A high ICC indicates that betweenneighborhood variance is not negligible, and thus a multilevel model should be employed to explain the interneighborhood dynamics.
The null model is then extended to a more advanced multilevel model by adding independent variables at the individual and neighborhoodlevels. The twolevel Equation 1 is expressed as follows [32]:
Here, Y_{ ij } represents the value of the dependent variable of the i th individual in neighborhood j, while X_{ ij } and Z_{ j } indicate the independent variables at different levels. In other words, X_{ ij } includes data about the individuals in neighborhood j; Z_{ j } contains data about the neighborhoods. β_{0 j} and β_{1j} are the individuallevel intercept and slope, respectively, in neighborhood j. r_{ ij } indicates the error term at the individuallevel (i.e., withinneighborhood variance). γ_{00} denotes the average of the dependent variable Y_{ ij }, controlling for the neighborhoodlevel variables Z_{ j }; γ_{01} is the slope of the neighborhoodlevel variables Z_{ j }; and γ_{10} indicates the overall value of slope at the individuallevel, controlling for the neighborhoodlevel variables Z_{ j }. Lastly, u_{0 j} and u_{1j} are error terms at the neighborhoodlevel (i.e., betweenneighborhood variance). In the framework of multilevel modeling, an intercept is assumed to be inconsistent if the neighborhood averages of a dependent variable differ between neighborhoods. Similarly, when effects of independent variables on the dependent variable vary across neighborhoods, the slopes of each neighborhood are expected to deviate from their average.
Eigenvector spatial filtering
Proposed by Griffith [33], an eigenvector spatial filtering technique handles spatial dependency within ordinary singlelevel regression by utilizing a linear combination of eigenvectors. Eigenvectors function as synthetic explanatory variables expressing underlying spatial structures of the regression model [20]. This method allows one to visualize local spatial clusters in a map form. Because eigenvectors are always independent of each other, the associated spatial structures are thus regarded as being distinct.
where X β refers to the systematic trend, while ϵ^{*} is the nby1 spatially autocorrelated error vector. X denotes the nbyk data matrix (i.e., n number of observations and k number of independent variables); β indicates the kby1 parameter vector corresponding to the independent variables. E γ is the spatial signal captured by selected eigenvectors E. The dimension of E is nbyp (i.e., n number of observations and p number of selected eigenvectors), and γ is the pby1 parameter vector corresponding to the selected eigenvectors. Lastly, ξ is the nby1 spatiallyindependent error vector.
When generating eigenvectors, two different spatial processes are considered: (1) simultaneous autoregressive (SAR); and (2) spatial lag [35]. These processes may generate different analytical results due to their differing model structures; for further details, see the study by Tiefelsdorf and Griffith [35]. The present study deals only with eigenvectors for the SAR process.
where a projection matrix M(x) ≡ I  X(X^{T}X)^{1}X^{T}; I represents an n  byn identity matrix; X is an n  byk matrix including n number of objectives and k number of independent variables. A subset of {e_{1}, e_{2}, ⋯, e_{n}}_{SAR} is denoted by E_{SAR}, which contains only selected eigenvectors. This set of eigenvectors can be introduced in a model as spatial proxies to ‘filter out’ spatial dependency [35].
Eigenvectors are selected in a stepwise manner, and the selection procedure is repeated until the value of Moran’s I^{b} (an indicator of a strength of spatial dependency) approaches a predetermined threshold (e.g., z(Moran’s I) < 0.1). Each eigenvector, owing to their mutual orthogonality, shows its unique spatial patterns and different degrees of spatial dependency. The first selected eigenvector has the highest Moran’s I value and therefore accounts for the largest proportion of the overall spatial dependency. The second eigenvector has the secondhighest Moran’s I value, and is uncorrelated with the first one [20]; similarly, the n th eigenvector is considered to have the n thhighest Moran’s I value, expressing the n thlargest proportion of the spatial dependency.
Spatially filtered multilevel model
This integrated model, entitled ‘spatially filtered multilevel model,’ regards the fixed effects in the multilevel model as identical to the systematic trend X β in the framework of eigenvector spatial filtering. In this model, a linear combination of eigenvectors E γ is included as a spatial proxy to separate the spatial signal from the spatially autocorrelated random effects at the neighborhoodlevel (u_{0j} + u_{1j}X_{ ij }), leaving only a whitenoise ${\mathit{u}}_{0\mathit{j}}^{\text{'}}+{\mathit{u}}_{1\mathit{j}}^{\text{'}}{\mathit{X}}_{\mathit{ij}}$ within them. This filtering process results in unbiased regression results that improve the explanatory power of the model.
All analyses are conducted in the R environment. The ‘lme4’ package [37] is used for the multilevel model run, and the ‘spdep’ package [38] is employed for the ‘SpatialFiltering’ function for the eigenvector spatial filtering.
Results
Results of the conventional multilevel model
The null model finds that the variance at neighborhoodlevel is 2.3% (ICC = 0.023). This indicates that 2.3% of the total variance in selfrated health status arises from interneighborhood dynamics. Given that a health outcome itself is generally influenced more by individual factors than by neighborhood characteristics, it is reasonable that variance at individuallevel is much larger than that at neighborhoodlevel. The 2.3% variance at neighborhoodlevel should be regarded with some caution, because Kreft and de Leeuw pointed out that for a sufficiently large number of samples, even a small ICC (for example, 1%) could considerably affect the degree of significance [31].
Estimation results for the conventional multilevel model and the spatially filtered multilevel model
Variables  Null model  Level1 multilevel model  Level2 multilevel model  Spatially filtered multilevel model 

Individuallevel variables  
Sex (male:0; female:1)    – 49.88***  – 49.65***  – 49.69*** 
Monthly household income  –  0.10***  0.10***  0.09*** 
Employment status (employed:0; unemployed:1)  –  –134.10***  –134.90***  –135.30*** 
Perceived levels of stress (high:0; low:1)  –  154.60***  155.60***  155.70*** 
Neighborhood–level variables  
Korean Deprivation Index (KDI)  –  –  –23.82*  –15.51* 
The number of doctors per 1000 people  –  –  4.85*  2.60* 
Degree of the Local Governments’ Financial Independence (LGFI)  –  –  0.98  0.16 
Random effects  
Variance at individual–level  66725  52226  52225  56013 
Between monthly household income variance  –  0.0039  0.0036  0.0011 
Variance at neighborhood–level  1591  1102  1062  555 
Constant  785.31***  761.40***  747.00***  770.70*** 
Eigenvector selection  –  –  –  8 eigenvectors 
Moran’s I of neighborhood–level residuals  –  –  0.101*  0.005 
AIC  861942  830665  830650  830549 
Log–likelihood  – 447328  – 415324  – 415314  – 415254 
For the next step, both individuallevel and neighborhoodlevel variables are added together in the neighborhoodlevel model (hereafter, Level2 model). By introducing neighborhoodlevel variables, a further 2% of variance at neighborhoodlevel is explained compared with the Level1 model. This suggests that neighborhood factors explicitly influence the individuals’ selfrated health status. The Level2 model shows the lowest AIC and the highest explanatory power among the three models. Like the Level1 model, all individuallevel variables remain significant (p < 0.001). Of the three neighborhoodlevel variables, only two variables, KDI and the doctortoresident ratio, are statistically significant (p < 0.05) (Table 2).
Results of applying eigenvector spatial filtering
Before applying the eigenvector spatial filtering method, we tested for spatial dependency between neighborhoodlevel residuals in the multilevel model and found this to be significant (Moran’s I = 0.101; p < 0.05). Hence, it is necessary to eliminate this spatial dependency by applying the eigenvector spatial filtering.
Eigenvectors in this study are extracted from a transformed spatial weight matrix based on topological adjacency, socalled a “Queen” criterion— if two areas share a boundary or a vertex, the entity of the spatial weight matrix is coded as 1, and otherwise, 0. As an eigenvector selection algorithm, this study uses a Moran’s I minimization scheme [35].
Discussion
The spatially filtered multilevel model presents unbiased regression results and yields a lower AIC than the conventional multilevel model. Both analyses present similar regression parameters and the same parameter signs (Table 2). In this study, addressing spatial dependency has little effect on the fixed effects, whereas it improves the independence of the random effects. With eigenvector spatial filtering, the Moran’s I of the neighborhoodlevel residual declines from 0.101 to 0.005 and becomes nonsignificant (p = 0.824).
According to the regression results, selfrated health status is significantly higher for respondents meeting the following conditions: male; employed; higher monthly household income; lower stress level; living in a neighborhood with lower KDI and proportionally more physicians. These findings are similar to those of previous studies [23, 40–45]. For the doctortoresident ratio variable, however, Matteson et al. reported conversely that counties with more family practitioners per capita have higher infant mortality [1]. However, they also found that more hospital beds per capita predicted lower risk of infant death. These results are somewhat contradictory because it is generally considered that the numbers of physicians and hospital beds tend to have a strong positive relationship [46, 47]. There does not appear to be a clear and consistent effect of the doctortoresident ratio on individual health outcomes; further studies are therefore needed. The present study finds no significant relationship between health status and LGFI, whereas some previous domestic studies reported positive relationship between LGFI and health outcomes [48, 49].
This study has several limitations that should be considered in future research. First, even after introducing neighborhoodlevel variables into the model, variance at neighborhoodlevel still remains. This may be because some of the key determinants of selfrated health status are omitted. In future research, other neighborhood socioeconomic and environmental factors should be considered to explain the remaining variance. For environmental factors such as air pollution, it is possible to use the interpolated map data in multilevel modeling by integrating it with survey datasets via geographic information science (GIS) [50]. Second, given that the respondents in this study are elderly (aged 60 and over), the employment status variable used in this study can be problematic, because people in their 70s or older are more likely to retire than people in their 60s. In other words, it is possible that the regression result could be confounded by an ‘age’ factor. Third, although census tract data are the only viable option in this study, it could be unclear whether census tracts accurately represent the geographical areas where healthrelated activities actually occur [21, 51]. If they do not, then the estimation of neighborhood effects via these administrative units would be unclear. Due to human mobility, individual health outcomes may be influenced by more complex geographical and temporal contexts beyond their residential environment [52]. However, it is actually difficult to delineate these complex contexts because there is a lack of spatial and temporal information in many cases [51]. Kwan defined this as the uncertain geographic context problem (UGCoP) [51]. To obtain more realistic results, future studies should attempt to identify the actual contexts influencing individual health and mitigate UGCoP. Lastly, some recent studies notice that an approach of removing spatial dependency should practice caution in some cases where neighborhood characteristics change abruptly across a study area. Some researchers have begun to examine this issue; so it must be left to future research.
Conclusion
This study explores the effects of individual and neighborhoodlevel factors on selfrated health status of people over the age of 60 via an approach that combines a multilevel model and an eigenvector spatial filtering technique. The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with selfrated health status. In addition, residents living in neighborhoods with low deprivation and a high doctortoresident ratio tend to report higher health status. There are no changes in the signs of parameters or the significance level between the two models used in this case study. Nevertheless, the proposed spatially filtered multilevel model provides unbiased and robust estimations and has greater explanatory power than conventional multilevel models. The spatially filtered approach is a useful tool for understanding the spatial dynamics of selfrated 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. The inherent modeling complexities of the eigenvector spatial filtering method mean this approach has only recently been put to practical use despite its advantage of visualizing underlying spatial structures. This study hopes that applied models using the eigenvector spatial filtering might be developed in many future studies. Finally, it is hoped that the present findings might inform policy interventions to mitigate health inequality in South Korea.
Endnote
^{a}See the study by Kang et al. [27].
where n is the number of spatial units; y_{ i } and y_{ j } are attribute values at spatial units i and j; $\overline{\mathit{y}}$ is the average of y; and v_{ ij } is an entity of a spatial weight matrix. If attribute values at i and j are both higher (or both lower) than the average, Moran’s I is a positive value between 0 and 1. When the Moran’s I is 1, the attribute values of i and j are assumed to be perfectly correlated. On the other hand, if the attribute value at i is higher than the average, but the value at j is lower than the average, the Moran’s I is negative. If attribute values of spatial units are perfectly dispersed, Moran’s I is 1. A Moran’s I of zero indicates that there is no spatial dependency and thus observations are randomly distributed.
Abbreviations
 SAR:

Simultaneous autoregressive
 GWR:

Geographically weighted regression
 CHS:

Community Health Survey
 KDI:

Korean Deprivation Index
 EQ5D:

EuroQol5 Dimension
 LGFI:

Degree of the Local Governments’ Financial Independence
 ICC:

Intraclass Correlation Coefficient
 AIC:

Akaike Information Criterion
 GIS:

Geographic information system
 UGCoP:

The uncertain geographic context problem.
Declarations
Acknowledgements
This research was supported by the National Research Scholarship (#20120101082546) from Korea Student Aid Foundation (KOSAF).
Authors’ Affiliations
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