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) is a global health challenge, particularly in the Central African Republic (CAR), which is classified as a high TB burden country. In the CAR, factors like poverty, limited healthcare access, high HIV prevalence, malnutrition, inadequate sanitation, low measles vaccination coverage, and conflict-driven crowded living conditions elevate TB risk. Improved AI-driven surveillance is hypothesized to address under-reporting and underdiagnosis. Therefore, we created an epidemiological digital representation of TB in Bangui by employing passive data collection, spatial analysis using a 100 × 100 m grid, and mapping TB treatment services. Our approach included estimating undiagnosed TB cases through the integration of TB incidence, notification rates, and diagnostic data. High-resolution predictions are achieved by subdividing the area into smaller units while considering influencing variables within the Bayesian model. By designating moderate and high-risk hotspots, the model highlighted the potential for precise resource allocation in TB control. The strength of our model lies in its adaptability to overcome challenges, although this may have been to the detriment of precision in some areas. Research is envisioned to evaluate the model’s accuracy, and future research should consider exploring the integration of multidrug-resistant TB within the model.

INTRODUCTION: Hookah smoking is becoming increasingly popular among the youth. Evidence shows that exposure to marketing of the unhealthy products through social media platforms may impact adolescent health behaviors. The aim of the study was to perform a content analysis of online portals selling hookah products. MATERIALS AND METHODS: A content analysis of online retail market was conducted on Google India using three keywords hookah, hookah products, and shisha. Retail websites popular in India that were selling hookah products were randomly selected and explored. A total of 15 themes were developed and used to describe various promotional strategies for hookah products. RESULTS: In all, 41 (19.2%) products claimed to be tobacco/nicotine-free and only 14 products (6.5%) displayed age/health-specific warnings. About 86% of products were available at discounted rates; glamorizing words for describing products in form of superior, premium, and legendary were found on 189 (88.3%) products. Phrases such as “ultimate way to celebrate,” “perfect excuse to chill with your friends,” and “now enjoy the world of smoking without any doubt of harm” are commonly used to promote hookah products. CONCLUSION: Easy availability of newer forms of smoking at online markets could play a role in promoting the use of hookah among the youth. Most products are being sold without any warnings and there is no means to control the selling of the products to minors. There is a need to raise the issue of hookah products in the same tune as done for other forms of tobacco.

Tuberculosis remains a major public health concern in Uganda, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-control platform, an AI-driven predictive modelling tool, to predict community-level hotspots and support data-driven active case-finding (ACF). Using retrospective chest X-ray screening data, we integrated demographic, environmental, and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the model identified significantly higher TB yields in hotspot areas (risk ratio = 1.69, 95% CI 1.41–2.02; p < 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact.

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.