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Table 2 Final model parameter estimates for negative binomial regression models.

From: Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

Model

Parameter estimates

Country-wide analysis

Intercept

Urban (20)

Forest (20)

Mean elevation

Wetness index standard deviation

Random district effect ( σ d 2 MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaeq4Wdm3aa0baaSqaaiabdsgaKbqaaiabikdaYaaaaaa@3008@ )

 

0.76 (1.71)

-3.62 (1.96)

1.68 (0.88)

-0.85 (0.22)

3.06 (1.27)

0.55 (0.37)

Cluster 1

Intercept

Urban (0.5)

    
 

-6.05 (0.14)

-5.28 (1.81)

    
  1. Response variable is the number of Buruli ulcer cases in villages in Benin, West Africa. Models for two analyses are presented: a country-wide analysis (n = 327 villages), and an analysis for Buruli ulcer disease in cluster 1 (see methods; n = 100 villages). Cluster 1 had greater than expected prevalence (see Figure 3 for location of cluster). Parameter estimates are followed by standard errors in parentheses. Land use/cover types are percent in a buffer surrounding each village, the buffer width (km) follows the land use/cover type in parentheses.