# Empirical Evidences for Urban Influences on Public Health in Hamburg
## 3 Results: Identifying Urban Influences on Health
### Multiple Linear Regression Analyses  
For the regression analysis, ECOVAS was used as the dependent variable. As examined by [PERSON] et al. [94], the EQ-5D components collectively produce results very similar to the ones produced by the visual analogue scale alone. However, the EQ VAS scale offers the advantage of a high resolution continuous scale and is therefore more suitable as a target value. Another reason to use the visual analogue scale as the dependent variable was the fact that it can be derived by the empirical data collected in the questionnaire alone, while the EQ-5D calculation requires the use of a EuroQol tool involving external and undisclosed data [95]. All variables from factor analysis that load only somewhat on the health factor are defined as independent variables. In the first step, backward elimination was used. This is advantageous relative to forward selection, because a group of variables can have considerable predictive ability even if any subset of them does not, something which cannot be ascertained by means of forward selection. Backward elimination begins with all variables in the model so that their common predictive ability is revealed. Missing data were removed using listwise case exclusion. Backward regression analysis excluded 23 variables (out of 35) in the first iteration. These did not contribute enough to clarify the variance; \(r^{2}\) did not decrease significantly due to their exclusion. Due to covariance, four further variables were subsequently excluded. In addition, outliers were also excluded from the analysis (nine cases identified by casewise diagnostics in SPSS; SD of standardized  
Figure 2: Factors identified by principal component analysis and their relation to UrbWellth. residuals \(>\pm 3\)). In four iterations, a total of 22 cases (outliers) were excluded. Together with the reduction due to \"listwise case exclusion\" during analyses, this resulted in an \(n\) of 811 used for LR. Of the remaining eight variables, three were identified as confounders: income (INC), age (AGE) and smoking behavior (SMO). Additionally, these factors are known to be the most important medical variables to access general health [36]. The remaining five variables that were identified as significant variables in the prognosis of health (EQVAS) were rating of one's social network (SOC), rating of neighborhood air quality (QCA), rating of neighborhood health infrastructure (NHI), heat stress (day/summer/outdoors) (HDO), and cold stress (night/winter/indoors) (CNI). The main influencing variables determined thus represent only some of the urban components (factors 1, 4, 5, and 8). Nonetheless, variables from all sectors contributed to the elucidation of the dependent variable (Figure 3). For the final model, a separate block was formed with the confounders in the regression analysis in order to be able to quantify the effect of these three variables alone. Here, the method chosen in SPSS was \"Enter.\" The goal was to predict EQVAS based on SOC, QCA, NHI, HDO, and CNI, while taking into account the effect of INC, AGE, and SMO. Table 3 shows the coefficients for the two resulting models in SPSS. The resulting regression Equation (1) for only the confounding variables was significant with (\(F\)(3, 807 = 45.973, \(p<0.001\)), with an \(r^{2}\) of 0.15. Participants predicted  
\[\text{EQVAS}=79.787+0.831\text{ (INC)}-0.241\text{ (AGE)}+1.055\text{ (SMO)}. \tag{1}\]  
Figure 3: Urban variables identified with significant influence on UrbWellth. The resulting regression Equation (2) for all variables was significant with (\(F\)(8, 802 = 36.365, \(p\) < 0.001), with an \(r^{2}\) of 0.27. Participants predicted  
\[ \begin{split}\text{EQVAS}&=73.331-1.379\ (\text{SOC})-0.726\ (\text{QCA})-2.488\ (\text{NHI})+3.641\ (\text{HDO})\\ &+2.389\ (\text{CNI})+0.831\ (\text{INC})-0.241\ (\text{AGE})+1.055\ (\text{SMO}). \end{split}  \tag{2}\]  
All variables were significant predictors of EQVAS (see Table 3).