AI 中文总结
针对被动监测易低估疾病负担及主动监测与被动监测数据空间错位问题，受波多黎各医院钩端螺旋体病监测项目启发，开发贝叶斯时空框架，扩展泊松 - 逻辑框架，纳入空间异质性与检测情况，能更好整合数据估计疾病负担。
AI 中文摘要
被动监测系统利用医疗机构常规收集的数据监测传染病病例数，实施相对简单，但常因诊断不足和检测不完善导致疾病负担低估。有针对性的主动监测可纠正病例数。然而，主动监测在部分医院开展，被动监测数据在区域层面汇总上报时，需协调空间错位问题以估计空间区域层面的实际疾病发生率。受波多黎各四家医院近期钩端螺旋体病主动监测项目启发，我们应对这一挑战，开发了一种新颖的贝叶斯时空框架，以更好反映患该疾病且到医院就诊的真实人数。该方法扩展了泊松 - 逻辑框架，纳入研究区域内到各医院就诊概率的空间异质性，还考虑了主动监测数据中不完善的诊断检测情况。通过模拟评估该模型，然后应用于钩端螺旋体病数据。我们的方法为整合空间错位的被动和主动监测数据提供了全面框架，能更好估计真实疾病负担。
英文摘要
Passive surveillance systems, in which data routinely collected by medical facilities are used to monitor the caseload of infectious diseases, are relatively straightforward to implement but often result in underestimation of the burden of disease due to under-diagnosis and imperfect testing. Targeted active surveillance can be used to correct these case counts to better reflect the true burden of disease. However, when the active surveillance effort is performed at a subset of hospitals and passive surveillance data is reported at an aggregated regional level, the resulting spatial misalignment must be reconciled to estimate the true rate of hospital-presenting disease at the spatial region level. Motivated by a recent active surveillance project for leptospirosis in four Puerto Rican hospitals, we address this challenge and develop a novel Bayesian spatio-temporal framework to better reflect the true number of hospital-presenting individuals with the disease. In particular, our method extends the Poisson-logistic framework to incorporate spatial heterogeneity in the probability of presenting to the hospitals across the study region. Our framework also accounts for imperfect diagnostic testing within the active surveillance data, addressing a common challenge for infectious diseases, particularly for neglected ones like leptospirosis. The model is assessed via simulation under various scenarios and then applied to the motivating leptospirosis data. Our approach offers a comprehensive framework for integrating spatially misaligned passive and active surveillance data, enabling better estimation of true disease burden.
