Since 2010, the introduction of an effective serogroup A meningococcal conjugate vaccine has led to the near-elimination of invasive Neisseria meningitidis serogroup A disease in Africa’s meningitis belt. However, a significant burden of disease and epidemics due to other bacterial meningitis pathogens remain in the region. High-quality surveillance data with laboratory confirmation is important to monitor circulating bacterial meningitis pathogens and design appropriate interventions, but complete testing of all reported cases is often infeasible. Here, we use case-based surveillance data from 5 countries in the meningitis belt to determine how accurately estimates of the distribution of causative pathogens would represent the true distribution under different laboratory testing strategies. Detailed case-based surveillance data was collected by the MenAfriNet surveillance consortium in up to 3 seasons from participating districts in 5 countries. For each unique country-season pair, we simulated the accuracy of laboratory surveillance by repeatedly drawing subsets of tested cases and calculating the margin of error of the estimated proportion of cases caused by each pathogen (the greatest pathogen-specific absolute error in proportions between the subset and the full set of cases). Across the 12 country-season pairs analyzed, the 95% credible intervals around estimates of the proportion of cases caused by each pathogen had median widths of ±0.13, ±0.07, and ±0.05, respectively, when random samples of 25%, 50%, and 75% of cases were selected for testing. The level of geographic stratification in the sampling process did not meaningfully affect accuracy estimates. These findings can inform testing thresholds for laboratory surveillance programs in the meningitis belt.

中文翻译：

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模拟非洲细菌性脑膜炎监测的最佳实验室测试策略

自 2010 年以来，一种有效的血清群 A 脑膜炎球菌结合疫苗的引入导致非洲脑膜炎地带的侵袭性脑膜炎奈瑟菌血清群 A 疾病几乎被消灭。然而，该地区仍存在由其他细菌性脑膜炎病原体引起的重大疾病和流行病负担。具有实验室确认的高质量监测数据对于监测循环细菌性脑膜炎病原体和设计适当的干预措施很重要，但对所有报告病例进行完整检测通常是不可行的。在这里，我们使用来自脑膜炎地带 5 个国家的基于病例的监测数据来确定对致病病原体分布的估计如何准确地代表不同实验室检测策略下的真实分布。MenAfriNet 监测联盟在多达 3 个季节从 5 个国家的参与地区收集了详细的基于病例的监测数据。对于每个独特的国家-季节对，我们通过重复绘制测试病例的子集并计算由每种病原体引起的病例估计比例的误差幅度（最大病原体特异性绝对误差在子集和全部案例）。在分析的 12 个国家季节对中，当随机样本为 25%、50% 时，每种病原体引起的病例比例估计值的 95% 可信区间的中位数宽度分别为 ±0.13、±0.07 和 ±0.05 , 并选择了 75% 的案例进行测试。抽样过程中的地理分层水平对准确度估计没有显着影响。这些发现可以为脑膜炎地带实验室监测项目的检测阈值提供信息。