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Table 2 Health-system-level effects of new diagnostic algorithms: mean per year in 10 years

From: Modeling the patient and health system impacts of alternative xpert® MTB/RIF algorithms for the diagnosis of pulmonary tuberculosis in Addis Ababa, Ethiopia

Diagnostic algorithms

Sputum samples tested for tuberculosis per year

Lab staff utilization (%)

Patients starting tuberculosis treatment per year including false positive

Missed TBa per year

Complete cures per year excluding false positive

Microscopy (000s)

Xpert (000s)

X-ray (000s)

Standard regimen

Treatment complete/cure

Treatment failure/MDR TB

ZN-Spot-Morning-Spot

113

0

12

30

6200

5919

59

963

3154

FN-Spot-Morning-Spot

112

0

12

22

5874

5608

59

893

3228

Targeted-Xpert-ZN-Negative-Spot-Morning-Spot

93

33

6

30

5257

4927

148

785

3332

Targeted-Xpert- MDR-HIV-ZN-Spot-Morning-Spot

98

7

11

28

6165

5831

118

859

3263

Full-Xpert

17

37

6

10

5588

5189

207

622

3500

FN-Spot-Spot

86

0

12

17

6269

5984

59

800

3322

FN-Spot-Morning

82

0

12

16

6200

5919

59

963

3154

Targeted-Xpert-MDR-HIV-FN-Spot-Spot

76

7

12

1

5874

5608

59

893

3228

  1. aMissed TB- Patient with TB but not diagnosed or lost to follow up