A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan

Zaidi, Syed Mohammad Asad; Jamal, Wafa Zehra; Mergenthaler, Christina; Azeemi, Kiran Sohail; Berge, Nick Van Den; Creswell, Jacob; Khan, Aamir; Khowaja, Saira; Habib, Sadia

doi:10.1038/s41598-023-28529-9

Cited by 8 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However a small proportion of 5% events accounted for 40% of the TB cases diagnosed. The authors concluded that a more targeted approach for ACF in high population metropolitan cities can increase the yield and cost-effectiveness of interventions [30].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Alege,

Hashmi,

Eneogu

et al. 2024

Preprint

View full text Add to dashboard Cite

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. We evaluated if this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: We conducted a retrospective analysis of the data generated from an ACF intervention program in 4 south-western states in Nigeria. We subdivided wards (smallest administrative level in Nigeria) into further smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. We then combined this data 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 (Chi-Squared test p-value

show abstract

Section: Discussionmentioning

confidence: 99%

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Alege,

Hashmi,

Eneogu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, a small proportion of 5% events accounted for 40% of the TB cases diagnosed. The authors concluded that a more targeted approach for ACF in high population metropolitan cities can increase the yield and cost-effectiveness of interventions [34].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Alege,

Hashmi,

Eneogu

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Tuberculosis remains a major international health problem and the leading cause of avoidable death despite advances in radiological diagnosis and antituberculous therapy as people often do not seek care due to poor knowledge of the disease, stigma, or health access barriers [ 1 , 2 , 3 ]. Extrapulmonary tuberculosis cannot be ruled out with a negative tuberculin test or clear chest X-ray.…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis of the Lateral Condyle of the Femur: A Case Report

Mythilisri,

et al. 2023

Cureus

View full text Add to dashboard Cite

Tuberculosis in India has been a constant health issue. The revised national tuberculosis control program has suggested antitubercular drug regimens according to WHO guidelines for pulmonary and extrapulmonary tuberculosis. Here is an uncommon case of an adult presenting with a single lytic lesion of the lateral condyle of the femur; he had no history of primary foci of pulmonary tuberculosis and curettage of the lesion and histopathological examination of the bone tissue showed tubercular etiology. The patient, hence, was started on antitubercular drugs and at a six-month follow-up, resolution of the lesion was noticed.

show abstract

A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan

Cited by 8 publications

References 32 publications

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Effectiveness of Using AI Driven Hotspot Mapping for Active Case Finding of Tuberculosis in South-Western Nigeria

Tuberculosis of the Lateral Condyle of the Femur: A Case Report

Contact Info

Product

Resources

About