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Table 4 Multilevel models for malaria consultation rate change across community health centers and time, Mopti and Sévaré, 1998-2006

From: Trends in malaria morbidity among health care-seeking children under age five in Mopti and Sévaré, Mali between 1998 and 2006

 

Model A

Model B

Model C

Model D

Fixed Effects

Intercept

4·973****

(0·957)

6·720****

(1·691)

10·127***

(3·587)

9·824***

(3·659)

Bednet treatment kits

  

-8·146****

(1·649)

-8·163****

(1·654)

1999

  

-2·772*

(1·491)

-2·794*

(1·496)

2000

  

-7·192***

(2·246)

-7·227***

(2·247)

2001

  

-9·006***

(3·101)

-9·107***

(3·113)

2002

  

-13·223***

(4·229)

-13·255***

(4·231)

2003

  

-15·858

(14·984)

-15·978

(14·876)

2004

  

-17·922***

(5·981)

-18·117**

(8·736)

2005

  

-23·298***

(6·921)

-23·509***

(6·941)

2006

  

-27·765****

(7·898)

-28·023****

(7·929)

Monthly rainfall

  

0·374

(0·359)

0·348

(0·361)

Lagged monthly rainfall

   

0·154*

(0·092)

CSCOM water proximity during delta flooding

  

0·003

(0·034)

0·011*

(0·006)

CSCOM water proximity during non-flooding

  

-0·0003

(0·0004)

-0·001*

(0·0006)

Intercept

 

-0·027*

(0·016)

-0·204**

(0·096)

-0·231**

(0·110)

Bednet treatment kits

  

-0·164****

(0·037)

-0·162****

(0·036)

Variance Components

Within-CSCOM

38·615**

(19·652)

36·096**

(18·403)

32·175**

(12·295)

32·129**

(12·249)

In initial status

5·893*

(3·163)

14·320*

(7·698)

2·656*

(1·389)

2·651*

(1·386)

In rate of change

 

0·001

(0·006)

0·001

(0·015)

0·001

(0·015)

Covariance

 

-0·105

(0·086)

-0·033

(0·199)

-0·033

(0·197)

Goodness-of-Fit

AIC

3660·969

3644·288

3631·620

3629·451

  1. * p < .10; ** p < .05; *** p < .01; **** p < .001; standard deviation marked in ()
  2. Models A and B describe the variation in the malaria consultation rate over time between January 1998 and December 2006 without controlling for any predictors. Models C and D predict the malaria consultation rate change between January 1998 and December 2006 controlling for health care resource variables (CSCOM, nurses, doctors) and demographic variables (male, Bozo, Dogon, mean age).