Table 2 Interactions of Social Factors and Infrastructure on heat mortality: linear regression for 3051 counties between 2010 and 2020.

a. Linear Regression Model
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Number of Extreme Heat Events	3.453***	3.112***	2.757***	3.383***	3.210***	2.842***	3.096***
	(0.360)	(0.323)	(0.346)	(0.360)	(0.388)	(0.346)	(0.389)
Air Conditioning Uninstallation Rate	0.842**			0.537	0.787*		0.458
	(0.305)			(0.318)	(0.306)		(0.320)
Normalized Difference Vegetation Index		−1.012***		−0.886**		−1.040***	−0.928***
		(0.254)		(0.265)		(0.254)	(0.266)
Below Poverty Line Rate			0.805*		0.692	0.882*	0.808*
			(0.409)		(0.411)	(0.409)	(0.412)
Constant	−8.114***	−7.038***	−7.903***	−7.343***	−8.193***	−7.147***	−7.398***
	(0.201)	(0.247)	(0.172)	(0.306)	(0.206)	(0.252)	(0.307)
R-sq	0.030	0.033	0.029	0.034	0.031	0.034	0.035
Adj. R-sq	0.029	0.032	0.028	0.033	0.030	0.033	0.034
N	3051	3051	3051	3051	3051	3051	3051


	AC	NDVI	Poverty	Poverty_AC	Poverty_NDVI
Heat Death	−0.09***	−0.18***	0.01	0.00	−0.06**
	(4.25)	(4.26)	(6.91)	(0.95)	(0.87)

a is the linear regression model results. We examine the impacts of extreme heat frequency, three leading decisive factors (social factor: Poverty; and infrastructure: NDVI, AC), and Aging on heat mortality in seven linear regression models. We use the county-level heat-related mortality rate as the dependent variable, with EHE (Extreme Heat Events), and social or infrastructure factors as independent variables, to quantify the interaction between social factors and infrastructure. The differences in coefficients of factors between models help identify the role of infrastructure in shaping heat-related mortality. Structural equation modeling b shows the results of SEM. Specifically, Poverty_AC and Poverty_NDVI mean the interaction between poverty and AC, poverty and NDVI.

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