. Author manuscript; available in PMC: 2022 Feb 1.

Published in final edited form as: Glob Chang Biol. 2020 Nov 22;27(4):738–754. doi: 10.1111/gcb.15435

Table 2.

Effect of climate and non-climate variables on Lyme disease incidence by region. Only variables included in the best fit model, as determined by variable selection, are shown. The scaled coefficient estimates (Coef.) shown here reflect the standard deviation change in Lyme disease incidence for a one standard deviation change in the climate variable. The coefficients are scaled so that the effects of different variables are directly comparable. The standard errors (SE) shown are clustered by the agricultural statistics district (see Methods: Statistical analysis). Statistically significant (p < 0.05) coefficients are denoted with *.

	Northeast		Midwest		Pacific		Pacific Southwest		Southwest		Southeast
Variable	Coef.	SE	Coef.	SE	Coef.	SE	Coef.	SE	Coef.	SE	Coef.	SE
Avg. winter temp.			−0.073	0.237	−0.967	1.039	0.119	0.172
Avg. winter temp,²			0.381	0.253	1.268	0.894	0.391	0.403
Avg. spring precip.	0.067	0.129	−0.051	0.041			0.089	0.089	−0.998	0.836
Avg. spring precip,²	−0.094	0.083
Hot, dry days	−0.302*	0.128	−0.264*	0.099					0.151	0.137	−0.029	0.022
Hot, dry days²	0.106	0.062	0.121*	0.055
Cumulative temp.	1.034*	0.468							1.589	1.429	1.928	1.657
Cumulative temp,²									−2.127	1.620	−2.405	1.811
Total annual precip.	−0.141	0.283	−0.046	0.176					1.192	0.981
Total annual precip,²	0.183	0.229	−0.010	0.115
Temp. variability	0.365	0.596					0.112	0.954			0.813*	0.310
Temp. variability,²	0.131	0.483					0.224	0.488			−0.473*	0.241
Precip. variability			0.040	0.048					−0.220	0.176
Precip. variability²			0.012	0.019
Lag ‘ticks’ search	0.168*	0.075	0.016	0.017	0.014	0.036	0.049	0.059	0.020	0.069	−0.016	0.019
Poverty	−0.055	0.087	0.046	0.072					0.210	0.133
Percent insured											−0.009	0.039
Forest cover	1.988	1.283	−3.966	3.896	−1.515*	0.763	−0.365	0.513			0.663	0.383
Mixed dev. cover									1.447	1.650	1.441*	0.686
R²	0.728		0.829		0.405		0.327		0.309		0.330
Model with only climate and dummy variables
R²	0.681		0.768		0.230		0.137		0.112		0.146
Model with only non-climate and dummy variables
R²	0.712		0.820		0.400		0.308		0.258		0.320
Model with only county dummy variable
R²	0.606		0.700		0.156		0.114		0.090		0.149
Model with only year dummy variable
R²	0.045		0.018		0.028		0.014		0.007		0.010