Department of Epidemiology of Microbial Diseases, Yale University, New Haven, CT, United States of America.
Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States of America.
PLoS One. 2023 Oct 30;18(10):e0293519. doi: 10.1371/journal.pone.0293519. eCollection 2023.
Mathematical models have suggested that spatially-targeted screening interventions for tuberculosis may efficiently accelerate disease control, but empirical data supporting these findings are limited. Previous models demonstrating substantial impacts of these interventions have typically simulated large-scale screening efforts and have not attempted to capture the spatial distribution of tuberculosis in households and communities at a high resolution. Here, we calibrate an individual-based model to the locations of case notifications in one district of Lima, Peru. We estimate the incremental efficiency and impact of a spatially-targeted interventions used in combination with household contact tracing (HHCT). Our analysis reveals that HHCT is relatively efficient with a median of 40 (Interquartile Range: 31.7 to 49.9) household contacts required to be screened to detect a single case of active tuberculosis. However, HHCT has limited population impact, producing a median incidence reduction of only 3.7% (Interquartile Range: 5.8% to 1.9%) over 5 years. In comparison, spatially targeted screening (which we modeled as active case finding within high tuberculosis prevalence areas 100 m2 grid cell) is far less efficient, requiring evaluation of ≈12 times the number of individuals as HHCT to find a single individual with active tuberculosis. Furthermore, the addition of the spatially targeted screening effort produced only modest additional reductions in tuberculosis incidence over the 5 year period (≈1.3%) in tuberculosis incidence. In summary, we found that HHCT is an efficient approach for tuberculosis case finding, but has limited population impact. Other screening approaches which target areas of high tuberculosis prevalence are less efficient, and may have limited impact unless very large numbers of individuals can be screened.
数学模型表明,针对结核病的空间靶向筛查干预措施可能能够有效地加速疾病控制,但支持这些发现的经验数据有限。以前的模型表明,这些干预措施具有重大影响,但通常模拟的是大规模的筛查工作,并且没有试图以高分辨率捕捉家庭和社区中结核病的空间分布。在这里,我们根据秘鲁利马一个区的病例报告位置对基于个体的模型进行了校准。我们估计了结合家庭接触者追踪(HHCT)使用的空间靶向干预措施的增量效率和影响。我们的分析表明,HHCT 相对有效,中位数需要筛查 40 个(四分位距:31.7 至 49.9)个家庭接触者才能发现一例活动性结核病。然而,HHCT 的人口影响有限,仅在 5 年内将发病率降低中位数 3.7%(四分位距:5.8%至 1.9%)。相比之下,空间靶向筛查(我们将其建模为在高结核病流行地区 100 m2 网格单元内进行主动病例发现)效率要低得多,需要评估大约 12 倍于 HHCT 的人数才能发现一例活动性结核病患者。此外,在 5 年内,添加空间靶向筛查工作仅使结核病发病率略有进一步降低(约 1.3%)。总之,我们发现 HHCT 是一种有效的结核病病例发现方法,但人口影响有限。其他针对高结核病流行地区的筛查方法效率较低,除非能够对大量人群进行筛查,否则可能影响有限。