Kamau Everlyn, Gass Katherine, Harding-Esch Emma M, Saboyá Díaz Martha Idalí, Ante-Testard Pearl Anne, Kello Amir B, Bailey Robin L, Fornace Kimberly, Solomon Anthony W, Nash Scott D, Arnold Benjamin F
Francis I. Proctor Foundation, University of California San Francisco, San Francisco, USA.
Neglected Tropical Diseases Support Center, Task Force for Global Health, Atlanta, USA.
medRxiv. 2025 Sep 7:2025.09.05.25334960. doi: 10.1101/2025.09.05.25334960.
Serology is increasingly used to monitor disease transmission and elimination. Embedding dried blood spot collection in trachoma surveys allows transmission intensity inference through population-level seroconversion rates (SCR) estimation, but there is no formal assessment of the required sample size. Using data from 40 prevalence surveys, we estimated intra-cluster correlation coefficient, a key design parameter, and assessed survey design considerations for incorporating serological monitoring. Design scenarios focused on recent proposed operational SCR thresholds (2.2 [no action needed] and 4.5 [action needed] per 100 child years) for interpretation of serological data in low-transmission and post-elimination settings. We evaluated SCR estimation in 42 two-stage designs by calculating precision (confidence interval width around an SCR value) and power (the measure of deviation of suggested thresholds to the SCR value). When the underlying SCR is ≤1.5 and >5.7 per 100 person-years, sample sizes between 300-2000 allowed good precision of SCR estimation. The same sample range would correctly classify areas as above or below the thresholds with >80% power when the underlying SCR is <1.7 or >5 per 100 person-years in lower and higher endemicity settings, respectively. Our results support estimation of serological data via the recommended population-based survey design for trachoma monitoring.
血清学越来越多地用于监测疾病传播和消除情况。在沙眼调查中采用干血斑采集法,可通过估计人群血清转化率(SCR)来推断传播强度,但尚未对所需样本量进行正式评估。我们利用40项患病率调查的数据,估计了关键设计参数——组内相关系数,并评估了纳入血清学监测的调查设计考量因素。设计方案聚焦于近期提出的操作性SCR阈值(每100儿童年2.2[无需采取行动]和4.5[需采取行动]),用于在低传播和消除后环境中解释血清学数据。我们通过计算精度(SCR值周围的置信区间宽度)和效能(建议阈值与SCR值的偏差度量),评估了42种两阶段设计中的SCR估计情况。当潜在SCR≤1.5且>5.7每100人年时,300 - 2000之间的样本量可实现良好的SCR估计精度。当潜在SCR在低流行和高流行环境中分别<1.7或>5每100人年时,相同的样本范围将以>80%的效能正确地将区域分类为高于或低于阈值。我们的结果支持通过推荐的基于人群的调查设计来估计血清学数据,以用于沙眼监测。