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.

Background: Undiagnosed tuberculosis (TB) cases are the major challenge to TB control in Nigeria. An early warning outbreak recognition system (EWORS) is a system that is primarily used to detect infectious disease outbreaks; this system can be used as a case-based geospatial tool for the real-time identification of hot spot areas with clusters of TB patients. TB screening targeted at such hot spots should yield more TB cases than screening targeted at non–hot spots. Objective: We aimed to demonstrate the effectiveness of an EWORS for TB hot spot mapping as a tool for detecting areas with increased TB case yields in high TB-burden states of Nigeria. Methods: KNCV Tuberculosis Foundation Nigeria deployed an EWORS to 14 high-burden states in Nigeria. The system used an advanced surveillance mechanism to identify TB patients’ residences in clusters, enabling it to predict areas with elevated disease spread (ie, hot spots) at the ward level. TB screening outreach using the World Health Organization 4-symptom screening method was conducted in 121 hot spot wards and 213 non–hot spot wards selected from the same communities. Presumptive cases identified were evaluated for TB using the GeneXpert instrument or chest X-ray. Confirmed TB cases from both areas were linked to treatment. Data from the hot spot and non–hot spot wards were analyzed retrospectively for this study. Results: During the 16-month intervention, a total of 1,962,042 persons (n=734,384, 37.4% male, n=1,227,658, 62.6% female) and 2,025,286 persons (n=701,103, 34.6% male, n=1,324,183, 65.4% female) participated in the community TB screening outreaches in the hot spot and non–hot spot areas, respectively. Presumptive cases among all patients screened were 268,264 (N=3,987,328, 6.7%) and confirmed TB cases were 22,618 (N=222,270, 10.1%). The number needed to screen to diagnose a TB case in the hot spot and non–hot spot areas was 146 and 193 per 10,000 people, respectively. Conclusions: Active TB case finding in EWORS-mapped hot spot areas yielded higher TB cases than the non–hot spot areas in the 14 high-burden states of Nigeria. With the application of EWORS, the precision of diagnosing TB among presumptive cases increased from 0.077 to 0.103, and the number of presumptive cases needed to diagnose a TB case decreased from 14.047 to 10.255 per 10,000 people.

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.

Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.

Background: In Nigeria, most children with tuberculosis (TB) present at primary health clinics where there are limited personnel skilled in collecting appropriate respiratory specimens from those who cannot produce sputum. KNCV Nigeria, in collaboration with the National Tuberculosis Control Program, implemented a modified simple, one-step (SOS), stool-based Xpert MTB/RIF method for diagnosis of TB in children who cannot expectorate sputum. We evaluated the impact of its implementation on childhood TB diagnosis. Method: A cross-sectional study was conducted across 14 selected states using secondary data of children presumed to have TB. Stool was collected from children presumed to have TB and processed using Xpert. Result: Out of 52,117 presumptive TB cases, 52% were male and 59.7% were under 5 years old. A total of 2440 (5%) cases were diagnosed with TB, and 2307 (95%) were placed on treatment. Annual TB notifications increased significantly after the introduction of the stool-based Xpert test when compared to those in the pre-implementation period. Increasing contributions from stool testing were observed throughout the implementation period, except in 2020 during the COVID-19 era. Overall, stool Xpert testing improved childhood TB notification in the studied states. Interventions aimed at awareness creation, capacity building, and active case finding improved the performance of the test.

Tuberculosis (TB) remains a significant public health challenge in Nigeria, with high rates of transmission and low case detection rates. This paper presents the challenges of screening and investigation of contacts of patients with TB in Oyo and Osun State, Nigeria. This descriptive-qualitative study was conducted in eight Local Government Areas with high TB burdens. Twenty-four focus group discussions and 30 key informant interviews were conducted among TB patients, household TB contacts, and government TB staff, among others. Respondents ages ranged from 17–85 years with a mean of 42.08 ± 14.9 years, and (4.0%) had a postgraduate degree. This study identified that the majority of TB contacts who tested negative for TB were unwilling to be placed on TB preventive therapy because of the belief that only a sick person should take drugs. Also, hostility from the TB contacts to the contact tracers during the house-to-house screening of presumptive TB cases due to community stigma associated with TB was another existing gap reported in TB contact investigations. The findings emphasise the importance of tailored approaches in TB prevention and control, addressing challenges in testing and contact investigations; this necessitates investments in community engagement strategies to enhance the cooperation of TB contacts.