Table 2.

Cross-sectional models (equations 1 and 2) and pooled models (equation 3) of the county population share living in the wildland-urban interface^*

Variable	Coefficient	SE	P	Variable	Coefficient	SE	P
Eastern counties (large sample)				Eastern counties (small sample)
2000				2000
LDI_00	0.232	0.047	< 0.001	LDI_00	0.188	0.048	< 0.001
Constant	364.2	10.951	< 0.001	Constant	407.1	16.693	< 0.001
Adjusted R2 = 0.025				Adjusted R2 = 0.034
2010				2010
LDI_10	0.372	0.046	< 0.001	LDI_10	0.356	0.051	< 0.001
Constant	341.7	11.790	< 0.001	Constant	347.6	19.417	< 0.001
Adjusted R2 = 0.068				Adjusted R2 = 0.107
2000 and 2010 pooled				2000 and 2010 pooled
LDI_lagged	0.211	0.033	< 0.001	LDI_lagged	0.170	0.034	< 0.001
Year_2010	−1.718	15.258	0.910	Year_2010	−13.131	22.581	0.561
Constant	368.4	10.819	< 0.001	Constant	414.9	16.049	< 0.001
Adjusted R2 = 0.022				Adjusted R2 = 0.028
Dependent variable = WUIpop × 1,000

^*Cross-sectional and pooled models with contemporaneous (LDI_00 or LDI_10) and lagged (LDI_lagged) measure of Lyme disease incidence in the small sample and large sample of Eastern counties. LDI is the number of confirmed cases of Lyme disease in a county per 100,000 population and WUIpop is the share of the county population living in the wildland–urban interface. In both samples and both years, we found a counterintuitive positive relationship that was significant (P < 0.05) when contemporaneous LDI was used. Using lagged LDI did not change the sign or statistical significance of the result.