ArticleJMIR public health and surveillance2023
Identifying Hot Spots of Tuberculosis in Nigeria Using an Early Warning Outbreak Recognition System: Retrospective Analysis of Implications for Active Case Finding Interventions.
Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 16 citations in OpenAlex.
- Prevalence and determinants of tuberculosis and COVID-19 among patients with presumptive tuberculosis in a Nigerian tertiary hospital.BMC infectious diseases · 2026Article
- Tuberculosis: An Ongoing Global Threat.Advances in experimental medicine and biology · 2026Review
- Cost-effectiveness of different tuberculosis diagnostic approaches in Nigeria based on decision analytical modelling.BMJ global health · 2025Article
- Gender disaggregation of the TB care cascade in Nigeria: a four-year retrospective study 2018-2021.Public health action · 2025Article
- Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study.JMIR public health and surveillance · 2025Article
- Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria.Public health action · 2025Article
- Geographical location and genotyping analysis of pulmonary tuberculosis in the state of Nuevo Leon, Mexico.Scientific reports · 2025Article
- Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: a systematic analysis for the Global Burden of Disease Study 2021.The Lancet. Infectious diseases · 2024Article
- Effectiveness of Using AI-Driven Hotspot Mapping for Active Case Finding of Tuberculosis in Southwestern Nigeria.Tropical medicine and infectious disease · 2024Article
- Improving TB control: efficiencies of case-finding interventions in Nigeria.Public health action · 2023Article
- Exploring the role of artificial intelligence toward management of HIV and TB co-infection in Nigeria: a comprehensive narrative review.Therapeutic advances in infectious diseaseReview
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20 authors at 6 institutions in 3 countries.
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Abstract
backgroundUndiagnosed 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.
objectiveWe 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.
methodsKNCV 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.
resultsDuring 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.
conclusionsActive 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.
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