Table 2 Predicting poor health with greenspace only.

From: Quantifying the Impact of Scenic Environments on Health

 

All areas

Urban

Suburban

Rural

Greenspace

−0.019***

−0.011

0.014

−0.008

Income Deprivation

1.696***

1.797***

1.418***

1.024***

Employment Deprivation

3.181***

3.107***

3.301***

4.015***

Education Deprivation

0.003***

0.003***

0.004***

0.006***

Housing Deprivation

−0.001***

0.000

−0.001***

−0.001**

Crime

0.010***

−0.003

0.007*

0.015***

Living Deprivation

0.000***

0.001**

0.000*

−0.001*

AIC

−10904

−1301

−5033

−5443

No of observations

16907

3944

7781

5182

  1. A correlation analysis indicates that scenicness is significantly correlated with greenspace (τ  = 0.2, p < 0.001, N = 128,213). We therefore build another four CAR models to predict standardized rates of reports of poor health, using greenspace only. Here, we present the regression coefficients. As in Table 1, models are built for England as a whole and for urban, suburban and rural areas separately. A range of socioeconomic deprivation variables are controlled for and the analysis is carried out at the level of Lower Layer Super Output Areas. In this revised model, while less greenspace is significantly associated with reports of worse health, this effect no longer holds when the analysis is broken down into urban, suburban and rural areas. *p < 0.05, **p < 0.01, ***p < 0.001.
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