Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study
Abstract BackgroundEarly diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. Active case-finding (ACF), a cornerstone of TB control programs, aims to achieve this by targeting sym...
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JMIR Publications
2025-04-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2025/1/e68355 |
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| author | Mauro Faccin Caspar Geenen Michiel Happaerts Sien Ombelet Patrick Migambi Emmanuel André |
| author_facet | Mauro Faccin Caspar Geenen Michiel Happaerts Sien Ombelet Patrick Migambi Emmanuel André |
| author_sort | Mauro Faccin |
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Abstract
BackgroundEarly diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. Active case-finding (ACF), a cornerstone of TB control programs, aims to achieve this by targeting symptom screening and laboratory testing for individuals at high risk of infection. However, its efficiency is dependent on the ability to accurately identify such high-risk individuals and communities. The socioeconomic determinants of TB include difficulties in accessing health care and high within-household contact rates. These two determinants are common in the poorest neighborhoods of many sub-Saharan cities, where household crowding and lack of health-care access often coincide with malnutrition and HIV infection, further contributing to the TB burden.
ObjectiveIn this study, we propose a new approach to enhance the efficacy of ACF with focused interventions that target subpopulations at high risk. In particular, we focus on densely inhabited urban areas, where the proximity of individuals represents a proxy for poorer neighborhoods with enhanced contact rates.
MethodsTo this end, we used satellite imagery of the city of Kigali, Rwanda, and computer-vision algorithms to identify areas with a high density of small residential buildings. We subsequently screened 10,423 people living in these areas for TB exposure and symptoms and referred patients with a higher risk score for polymerase chain reaction testing.
ResultsWe found autocorrelation in questionnaire scores for adjacent areas up to 782 meters. We removed the effects of this autocorrelation by aggregating the results based on H3 hexagons with a long diagonal of 1062 meters. Out of 324 people with high questionnaire scores, 202 underwent polymerase chain reaction testing, and 9 people had positive test results. We observed a weak but statistically significant correlation (r=P
ConclusionsNine previously undiagnosed individuals had positive test results through this screening program. This limited number may be due to low TB incidence in Kigali, Rwanda, during the study period. However, our results suggest that analyzing satellite imagery may allow the identification of urban areas where inhabitants are at higher risk of TB. These findings could be used to efficiently guide targeted ACF interventions. |
| format | Article |
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| institution | OA Journals |
| issn | 2369-2960 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
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| series | JMIR Public Health and Surveillance |
| spelling | doaj-art-0a0a32664f90471b9f8ffc8e55b911da2025-08-20T02:13:31ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602025-04-0111e68355e6835510.2196/68355Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot StudyMauro Faccinhttp://orcid.org/0000-0003-0706-3007Caspar Geenenhttp://orcid.org/0000-0002-2778-6344Michiel Happaertshttp://orcid.org/0000-0003-2690-7324Sien Ombelethttp://orcid.org/0000-0002-4943-0736Patrick Migambihttp://orcid.org/0000-0003-4344-2872Emmanuel Andréhttp://orcid.org/0000-0001-8321-3770 Abstract BackgroundEarly diagnosis and treatment initiation for tuberculosis (TB) not only improve individual patient outcomes but also reduce circulation within communities. Active case-finding (ACF), a cornerstone of TB control programs, aims to achieve this by targeting symptom screening and laboratory testing for individuals at high risk of infection. However, its efficiency is dependent on the ability to accurately identify such high-risk individuals and communities. The socioeconomic determinants of TB include difficulties in accessing health care and high within-household contact rates. These two determinants are common in the poorest neighborhoods of many sub-Saharan cities, where household crowding and lack of health-care access often coincide with malnutrition and HIV infection, further contributing to the TB burden. ObjectiveIn this study, we propose a new approach to enhance the efficacy of ACF with focused interventions that target subpopulations at high risk. In particular, we focus on densely inhabited urban areas, where the proximity of individuals represents a proxy for poorer neighborhoods with enhanced contact rates. MethodsTo this end, we used satellite imagery of the city of Kigali, Rwanda, and computer-vision algorithms to identify areas with a high density of small residential buildings. We subsequently screened 10,423 people living in these areas for TB exposure and symptoms and referred patients with a higher risk score for polymerase chain reaction testing. ResultsWe found autocorrelation in questionnaire scores for adjacent areas up to 782 meters. We removed the effects of this autocorrelation by aggregating the results based on H3 hexagons with a long diagonal of 1062 meters. Out of 324 people with high questionnaire scores, 202 underwent polymerase chain reaction testing, and 9 people had positive test results. We observed a weak but statistically significant correlation (r=P ConclusionsNine previously undiagnosed individuals had positive test results through this screening program. This limited number may be due to low TB incidence in Kigali, Rwanda, during the study period. However, our results suggest that analyzing satellite imagery may allow the identification of urban areas where inhabitants are at higher risk of TB. These findings could be used to efficiently guide targeted ACF interventions.https://publichealth.jmir.org/2025/1/e68355 |
| spellingShingle | Mauro Faccin Caspar Geenen Michiel Happaerts Sien Ombelet Patrick Migambi Emmanuel André Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study JMIR Public Health and Surveillance |
| title | Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study |
| title_full | Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study |
| title_fullStr | Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study |
| title_full_unstemmed | Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study |
| title_short | Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study |
| title_sort | analyzing satellite imagery to target tuberculosis control interventions in densely urbanized areas of kigali rwanda cross sectional pilot study |
| url | https://publichealth.jmir.org/2025/1/e68355 |
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