Faccin Mauro, Geenen Caspar, Happaerts Michiel, Ombelet Sien, Migambi Patrick, André Emmanuel
Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 9, Bologna, 40126, Italy, 39 051 2143578.
Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Bologna, Italy.
JMIR Public Health Surveill. 2025 Apr 24;11:e68355. doi: 10.2196/68355.
Early 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.
In 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.
To 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.
We 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=0.28; P=.04) between the mean questionnaire score and the mean urban density of each hexagonal area.
Nine 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.
结核病（TB）的早期诊断和治疗启动不仅能改善个体患者的治疗效果，还能减少社区内的传播。主动病例发现（ACF）是结核病控制项目的基石，旨在通过针对感染高危个体的症状筛查和实验室检测来实现这一目标。然而，其效率取决于准确识别此类高危个体和社区的能力。结核病的社会经济决定因素包括获得医疗保健的困难以及家庭内部高接触率。这两个决定因素在许多撒哈拉以南城市最贫困的社区很常见，在这些社区，家庭拥挤和缺乏医疗保健往往与营养不良和艾滋病毒感染同时存在，进一步加重了结核病负担。
在本研究中，我们提出一种新方法，通过针对高危亚人群的重点干预措施来提高ACF的效果。特别是，我们关注人口密集的城市地区，在这些地区，个体之间的近距离代表了接触率较高的较贫困社区。
为此，我们使用了卢旺达基加利市的卫星图像和计算机视觉算法来识别小型住宅楼密集的区域。随后，我们对居住在这些区域的10423人进行了结核病暴露和症状筛查，并将风险评分较高的患者转诊进行聚合酶链反应检测。
我们发现相邻区域问卷得分的自相关性高达782米。我们通过基于长对角线为1062米的H3六边形汇总结果，消除了这种自相关性的影响。在324名问卷得分较高的人中，202人接受了聚合酶链反应检测，9人检测结果呈阳性。我们观察到每个六边形区域的平均问卷得分与平均城市密度之间存在微弱但具有统计学意义的相关性（r = 0.28；P = 0.04）。
通过该筛查项目，9名先前未被诊断的个体检测结果呈阳性。这个数量有限可能是由于研究期间卢旺达基加利市的结核病发病率较低。然而，我们的结果表明，分析卫星图像可能有助于识别居民结核病风险较高的城市地区。这些发现可用于有效地指导有针对性的ACF干预措施。
