Temperature, relative humidity and sunshine may be the effective predictors for occurrence of malaria in Guangzhou, southern China, 2006–2012

Table 2 Negative binomial regression model of meteorological factors associated with risk of malaria inciedence

	B	STD	P	(e^β − 1) = RR (%)	95% CI for RR (%)
					Lower bound	Upper bound
(A)
Intercept	−350.15	16.80	0.02	-	-	-
Average atmospheric pressure	−0.01	0.01	0.60	−0.76	−3.60	2.15
Average relative humidity	0.02	0.01	0.05	2.42	−0.04	4.94
Average wind velocity	0.14	0.10	0.17	14.48	−5.62	38.86
Aggregate rainfall	0.00	0.00	0.93	0.00	−0.08	0.07
Aggregate Sunshine	0.01	0.00	0.00	0.52	0.24	0.80
year	0.02	0.03	0.54	2.11	−4.48	9.16
(B)
Intercept	−560.76	21.02	0.01	-	-	-
Average temperature	0.01	0.06	0.03	0.80	0.40	3.77
Average relative humidity	0.02	0.01	0.04	2.31	0.15	4.53
Average wind velocity	0.17	0.10	0.11	18.03	−3.72	44.69
Aggregate rainfall	0.00	0.00	0.84	−0.01	−0.08	0.07
Aggregate Sunshine	0.00	0.00	0.00	0.48	0.19	0.77
year	0.03	0.04	0.44	2.81	−4.12	10.24
(C)
Intercept	−11.69	0.52	0.00	-	-	-
Average temperature	0.01	0.01	0.03	0.90	0.60	1.10
Average relative humidity	0.04	0.01	0.00	3.99	2.53	5.48
Aggregate sunshine	0.01	0.00	0.00	0.68	0.47	0.88

Note .*Negative binomial regression model for monthly malaria incidence without average temperature (A) and without atmospheric pressure (B). Final models (C).
*RR, relative risk; CI, confidence Interval.

ISSN: 1756-3305