Table 3.

Linear regression estimates ($\mathrm{\beta }$) and 95% confidence interval for the association of proportion of all-cause deaths on extreme heat days on unsupervised HVI scores characterized by land cover type (continuous and equal interval categorizations), by geography for Detroit, Michigan, USA (2000–2009).

Unsupervised HVI type	Tract ($N=308$)				Block group ($N=913$)
	$N$	$\mathrm{\beta }$	95% CI	$R^2$	$N$	$\mathrm{\beta }$	95% CI	$R^2$
Impervious
Continuous	308	0.00	0.00, 0.00	0.00	913	0.00	0.00, 0.00	0.00
Categorical	—	—	—	0.00	—	—	—	0.00
0–3	0	0.00	Ref	—	0	0.00	Ref	—
4–6	5	0.57	0.27, 0.77	—	8	0.38	$-0.07$, 0.69	—
7–9	76	0.44	0.31, 0.56	—	203	0.37	0.29, 0.44	—
10–12	191	0.50	0.38, 0.61	—	600	0.40	0.34, 0.46	—
$\ge 13$	36	0.45	0.30, 0.57	—	102	0.41	0.32, 0.48	—
Trend $p$-value	0.46				0.58
Nontree canopy
Continuous	308	0.00	0.00, 0.00	0.00	913	0.00	0.00, 0.00	0.00
Categorical	—	—	—	0.01	—	—	—	0.00
0–3	0	0.00	Ref	—	0	0.00	Ref	—
4–6	3	0.35	$-0.58$, 0.88	—	17	0.46	0.23, 0.65	—
7–9	33	0.46	0.24, 0.64	—	76	0.37	0.27, 0.46	—
10–12	194	0.48	0.26, 0.66	—	584	0.40	0.35, 0.44	—
$\ge 13$	78	0.51	0.28, 0.68	—	236	0.41	0.35, 0.46	—
Trend $p$-value	0.05				0.75
Continuous	308	0.00	0.00, 0.00	0.00	913	0.00	0.00, 0.00	0.00
Categorical	—	—	—	0.00	—	—	—	0.00
0–3	0	0.00	Ref	—	0	0.00	Ref	—
4–6	5	0.44	$-0.43$, 0.89	—	14	0.43	0.15, 0.65	—
7–9	74	0.42	0.30, 0.54	—	204	0.37	0.30, 0.43	—
10–12	198	0.48	0.36, 0.58	—	590	0.40	0.35, 0.46	—
$\ge 13$	31	0.43	0.29, 0.56	—	105	0.41	0.33, 0.49	—
Trend $p$-value	0.99	0.66
Nontrees
Continuous	308	0.00	0.00, 0.00	0.00	913	0.00	0.00, 0.00	0.00
Categorical	—	—	—	0.00	—	—	—	0.00
0–3	0	0.00	Ref	—	0	0.00	Ref	—
4–6	6	0.32	$-0.28$, 0.74	—	28	0.39	0.18, 0.57	—
7–9	73	0.47	0.36, 0.56	—	182	0.39	0.32, 0.46	—
10–12	193	0.48	0.40, 0.56	—	608	0.41	0.36, 0.45	—
$\ge 13$	36	0.46	0.34, 0.56	—	95	0.41	0.32, 0.48	—
Trend $p$-value	0.42	0.91

Note: —, no data; CI, confidence interval; HS, high school; HVI, heat vulnerability index; NLCD, National Land Cover Database; Ref, reference. Regression estimates indicate how a one-unit increase of an unsupervised HVI is associated with an increase in the proportion of all-cause deaths occurring on an extreme heat day in Detroit, Michigan. Categorical cut-points were determined by creating approximately equal interval categories of HVI scores in ArcMap based on the number of census tracts and block groups, respectively. Tests for trend were assessed based on the categorical model’s $F$ statistic.