Setting: Nigeria, a high tuberculosis (TB) burden country. Objective: To study the rate, distribution and causes of unsuccessful Xpert ® MTB/RIF test outcomes, with the aim of identifying key areas that need to be strengthened for optimal performance of the assay. Design: This was a retrospective analysis of data uploaded between January and December 2015 from Xpert facilities to the central server using GXAlert. Result: Of 52 219 test results uploaded from 176 Xpert machines, 22.5% were positive for Mycobacterium tuberculosis, 10.8% of which were rifampicin-resistant; 4.7% of the total number of results were invalid, 4.2% had error results and 2.1% no result outcomes. Technical errors were most frequent (69%); these were non-seasonal and occurred in all geopolitical regions and at all health facility levels. Temperature-related errors were more prevalent in the North-West Region, with peaks in April to June. Peak periods for temperature and machine malfunction errors coincided with the periods of low utilisation of the assay. Conclusion:The key challenge affecting performance was poor adherence to standard operating procedures. Periodic refresher training courses, regular supervision, preventive maintenance of Xpert machines and proper storage of cartridges are strategies that could improve Xpert performance.

Introduction: Gene-Xpert MTBRIF, rapid tuberculosis and rifampicin resistance diagnostic technology is implemented in Nigeria to enhance public health response to tuberculosis diagnosis in HIV patients with presumed tuberculosis (TB), and presumed cases of drug resistant TB. The aim of the paper is to share experience on programmatic issues on Xpert MTB RIF roll-out.

Setting: Nigeria adopted GeneXpert MTB Rif as a primary diagnostic tool were available and accessible since 2016. The current geographical coverage of GeneXpert machines by LGAs stands at 48%, with a varied access and utilization. Objectives: To assess the association between the type and level of health facilities implementing GeneXpert MTB/Rif and performance outcome of the machines in Nigeria. Study Design: Retrospective secondary data analysis of GeneXpert performance for 2017 from GXAlert database. The independent variables were type and levels of health care facilities, and dependent variables were GeneXpert performance (utilization, successful test, error rates, MTB detected, and Rifampicin resistance detected). Results: Only 366 health care facilities are currently implementing and reporting GeneXpert performance, the distribution is 86.9% and 13.1% public and private health care facilities respectively, and only 6.3% of the facilities are primary health care. Of 354,321 test conducted in 2017, 91.5% were successful, and among unsuccessful test 6.8% were errors. The yield was 16.8% MTB detected (54,713) among which 6.8% had Rif resistance. The GeneXpert utilization rate was higher among private health care facilities (55.8%) compared to 33.3% among public health care facilities. There was a statistically significant difference in the number of successful test between public and private health facility-based machines as determined by one-way ANOVA (F(1,2) = 21.81, P = 0.02) and between primary, secondary and tertiary level health facility-based machines (F(1,2) = 41.24, P < 0.01). Conclusion: Nigeria with very low TB coverage should rapidly scale-up and decentralize GeneXpert services to the private sector.

Information, Communication Technology (ICT) has become the order of the day. Globally, there is increasing quest for use of ICT in various spheres of life. The Health care sector is not left out: Computer based diagnosis is the hope of fast and accurate diagnostic process. GeneXpert machines for rapid diagnosis of Tuberculosis (TB) and drug resistant tuberculosis (DR-TB), work with GeneXpert (GX) software and computer programs. This study was carried out to assess Knowledge, Attitude and Practice of Laboratory staff on computer with the view to unraveling its role in scaling up Xpert MTB/Rif in Nigeria. The survey was done using a structured, closed-ended questionnaire administered to laboratory staff operating GeneXpert machine, who participated in the study. A total of 76 GeneXpert machine operators (56.7%) out of 134 laboratory staff trained from 31 Xpert sites in Nigeria were interviewed. These included 49 Laboratory Scientists, 15 laboratory technicians and 12 other laboratory staff that operate the machine. Majority, 55 (72.4%) of the respondents had good knowledge of computer; 43 (78.2%), 4 (7.3%) and 8 (14.5%) of these were laboratory scientists, technicians and other laboratory staff respectively. Good computer knowledge was highest among scientists and lowest among technicians. These differences were statistically significant (df = 1 P < 0.01). Age, gender, owning a personal computer and formal computer training significantly influenced computing knowledge. Most Xpert MTB/RIF users 45 (64.5%) had positive attitude towards computing and this was significantly influenced by respondent's age and formal computer training. Only 38 (50%) had good computing practice; this was significantly associated with owning a personal computer (P < 0.01) and formal computer training. The major computer operation challenges observed among the laboratory staff included; Xpert calibration; completion of electronic recording tool and software operations like importing of assay definition file; plunger maintenance; generating system and error log reports as well as archiving/retrieving of tests. Introduction of basic computer training module into the Xpert training curriculum, strict adherence to SOP, continuous supportive supervision and mentorship training are recommended in Nigeria to boost efficiency of laboratory staff.

Wellness on Wheels (WoW) provided mobile systematic TB screening of high-risk populations combining digital chest radiography with computer-aided automated interpretation and chronic cough screening to identify beneficiaries of GeneXpert MTB/RIF testing in communities and prisons. We piloted and refined approaches in phased evaluations, recalibrating CAD4TB thresholds adjusting to balance TB yield and feasibility. Iterative data monitoring of screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and HIV testing are required. Given pre-selection of highest risk individuals via an accurate screening test, inability to collect or test samples impacts yield and cost-per-case. Linkage to care and treatment outcomes improved overtime. Short conclusion: Mobile computer-assisted digital chest x-ray and chronic cough screening with GeneXpert MTB/RIF testing is feasible, acceptable, efficient and high-yield when highest risk groups are engaged, and operations evolve in response to monitoring data.