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Table 1.

Parameters used for the comparative cost-effectiveness analysis of the Febrile Antigen Brucella Agglutination Test and the Rose Bengal Test.

Parameters used for all scenarios unless otherwise indicated.

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Table 1 Expand

Fig 1.

The model structure used for the comparative cost-effectiveness analysis of the Febrile Antigen Brucella Agglutination Test and the Rose Bengal Test.

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Fig 1 Expand

Table 2.

Brucella test results obtained with the Febrile Antigen Brucella Agglutination Test (FBAT) performed at the hospital, and with the Rose Bengal Test (RBT) performed at the Busia field laboratory, for patients visiting 10 hospitals in western Kenya.

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Table 2 Expand

Table 3.

The running time and cost of the Febrile Antigen Brucella Agglutination Test (FBAT), the treatment prescribed to patients who tested positive for brucellosis and its cost in hospitals in western Kenya.

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Table 3 Expand

Fig 2.

The relative frequency of results for the cost-effectiveness, in terms of $/DALY averted, for the Febrile Antigen Brucella Agglutination Test and Rose Bengal Test in western Kenya (Scenario 1).

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Fig 2 Expand

Fig 3.

The relative frequency of results for the cost-effectiveness, in terms of $/DALY averted, for the Febrile Antigen Brucella Agglutination Test and Rose Bengal Test in northern Kenya (Scenario 2).

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Fig 3 Expand

Fig 4.

The relative frequency of results for the cost-effectiveness, in terms of $/DALY averted, for the Febrile Antigen Brucella Agglutination Test and Rose Bengal Test nationally (Scenario 3).

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Fig 4 Expand

Table 4.

Summary results of the cost-effectiveness model comparing the Febrile Antigen Brucella Agglutination Test (FBAT) with the Rose Bengal Test (RBT) in the three scenarios modelled.

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Table 4 Expand

Fig 5.

Tornado Graph of Spearman Rank Correlation Coefficient values for different input parameters illustrating those most influencing the cost-effectiveness of the Febrile Antigen Brucella Agglutination Test in western Kenya.

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Fig 5 Expand

Fig 6.

Tornado Graph of Spearman Rank Correlation Coefficient values for different input parameters illustrating those most influencing the cost-effectiveness of the Rose Bengal Test in western Kenya.

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Fig 6 Expand