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

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

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