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.

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.

Tuberculosis (TB) remains a major global health crisis, with 10.8 million people diagnosed annually and 2.7 million cases going undetected in 2023. While TB digital health information systems offer promising solutions for detection and management in low- and middle-income countries, implementation effectiveness varies significantly across health system contexts. However, no studies have systematically examined implementation experiences across multiple high-burden countries using implementation science frameworks, leaving National TB Program (NTP) managers without evidence-based guidance for context-appropriate system selection and deployment strategies. We conducted semi-structured interviews with 15 TB program partners across 10 high TB burden countries to evaluate digital health system implementation in national TB programs. Semi-structured interviews were used. The data was analyzed using the Consolidated Framework for Implementation Research (CFIR). Systems were assessed for advantages, disadvantages, and implementation challenges, with findings organized across all five CFIR domains to identify policy-relevant implementation success factors. Across the countries, 29 distinct TB information systems were documented with varying implementation success. Key advantages included real-time data availability, patient-level tracking, and care cascade monitoring. Primary disadvantages included internet dependency, limited interoperability, and parallel paper-based requirements. Implementation success was determined by five policy-relevant factors: innovation adaptability, external policy drivers, organizational readiness (health system infrastructure availability), individual champions (tech-savvy staff), and implementation processes. For health policymakers and NTP managers, successful digital TB system implementation requires coordination across key implementation areas rather than technological sophistication alone. Countries should establish collaborative governance frameworks, prioritize contextual adaptation with offline capabilities, cultivate technical champions, and adopt user-centered design approaches. The study provides a practical framework for NTP managers to assess implementation readiness and develop context-appropriate digital health strategies that support health systems strengthening in low- and middle-income countries.

Providing tuberculosis preventive treatment (TPT) to close contacts of persons with TB is a core strategy recommended by WHO for the prevention and control of TB. Nigeria rolled out the 3-month Isoniazid-Rifampicin (3HR) shorter regimen TPT as a pilot for use among eligible adult and child contacts. This study assesses acceptance and completion rates of 3HR TPT among contacts and determines the perspectives of healthcare workers (HCWs) and contacts on acceptance and completion of 3HR TPT in Nigeria. In this cross-sectional descriptive study using mixed methods, records of TPT-eligible clients were retrospectively reviewed, while 18 purposely selected HCWs and 18 contacts on 3HR were interviewed. Of the 30,012 eligible contacts, 12,040 (40.1%) were initiated on TPT. Among these, 8213 (68%) were enrolled on 3HR, and 6972 (84.7%) of them completed treatment. Perceived facilitators include belief in its effectiveness, training among HCWs, and a good understanding of TPT from HCW counseling sessions. Barriers reported were linked to stockouts, misconceptions about side effects, non-disclosures, and disincentive follow-up strategies. The acceptance and completion rate for 3HR TPT was good. Scaling up 3HR TPT will require redesigning policies towards addressing identified barriers and utilizing interventions linked to capabilities, opportunities, and motivations among contacts of TB patients and HCWs.

This paper presents a formative assessment for the implementation of mobile portable chest X-ray (PDX) machines for tuberculosis (TB) contact tracing in the Oyo and Osun states, Nigeria. This descriptive qualitative study was carried out in eight local government areas, and 24 focus group discussions and 30 key informant interviews were conducted using a four-stage sampling technique to select participants. Validated transcribed notes were entered and analyzed using Nvivo. The respondents’ ages ranged from 17 to 85 years, with a mean age of 42.08 ± 14.9 years, and 4.0% had a postgraduate degree. The majority of government officials stated that deploying the PDX machine for screening at the community level is the best means for implementation because it will offer a level of proximity convenience to the TB contacts and assuage concerns about the cost of transportation to the health facility. In addition, it was suggested that TB community screening should be carried out with screening for other health conditions such as high blood pressure and diabetes. This portable X-ray machine intervention, therefore, should be implemented at the community level for contact tracing to allow more TB contacts living with the index patient to be screened without proximity and transportation constraints.

Background Nigeria is among the top 5 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 which could be otherwise missed. This work evaluated if 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 4 south-western states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were subdivided into further smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data was 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. Conclusion 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 efficiency of interventions.