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Table 4 Negative binomial regression univariable analyses (P ≤ 0.1) for cases diagnosed by community health workers

From: Temporal variation in confirmed diagnosis of fever-related malarial cases among children under-5 years by community health workers and in health facilities between years 2013 and 2015 in Siaya County, Kenya

Variable

Variable category

Coefficient

95% confidence interval

P > |z|

Likelihood ratio test P*

α

Likelihood-ratio test of α = 0

Village

Marenyo

0.52

[0.30, 0.74]

0.000

0.000

0.21

0.000

Nyawara

0.06

[− 0.17, 0.28]

0.608

   

Nyandiwa

− 0.21

[− 0.44, 0.01]

0.064

   

Gongo

0.26

[0.04, 0.49]

0.021

   

Ramula

0.83

[0.61, 1.06]

0.000

   

Nyamninia

0.01

[− 0.21, 0.24]

0.896

   

Jina

− 0.13

[− 0.36, 0.08]

0.234

   

Uranga

0.32

[0.10, 0.54]

0.005

   

Lihanda

0.23

[0.01, 0.46]

0.040

   

Year

2014

0.52

[0.39, 0.64]

0.000

0.000

0.22

0.000

2015

0.73

[0.61, 0.86]

0.000

   

Month

February

0.15

[− 0.13, 0.42]

0.301

0.001

0.28

0.000

March

0.35

[0.07, 0.63]

0.012

   

April

0.26

[− 0.01, 0.54]

0.063

   

May

0.45

[0.18, 0.73]

0.001

   

June

0.45

[0.18, 0.73]

0.001

   

July

0.59

[0.31, 0.87]

0.000

   

August

0.34

[0.06, 0.62]

0.015

   

September

0.18

[− 0.09, 0.46]

0.193

   

October

0.12

[− 0.15, 0.40]

0.374

   

November

0.15

[− 0.12, 0.43]

0.284

   

December

0.24

[− 0.03, 0.52]

0.088

   

Rainfall

Current

0.00042

[− 0.0006, 0.0015]

0.436

0.436

0.30

0.000

1 month lag

− 0.0004

[− 0.0006, 0.0015]

0.410

0.411

0.30

0.000

2 month lag

0.00014

[− 0.002, 0.002]

0.896

0.896

  

2 month cumulative

− 0.00001

[− 0.0006, 0.0006]

0.964

0.964

0.30

0.000

3 month cumulative

0.00006

[− 0.0004, 0.0006]

0.825

0.825

0.30

0.000

4 month cumulative

− 0.00005

[− 0.0005, 0.0004]

0.847

0.847

0.30

0.000

  1. P used to test the statistical significance (P ≤ 0.1) of the contribution of the variable to the univariable model; P used to test the statistical significance of α (the overdispersion parameter). When the statistical significance of α is significant (P ≤ 0.05), it suggests that the variance in the data is higher than would be expected for a Poisson regression