Yunnan Center for Disease Control and Prevention, Kunming, China.
Kunming Medical University, Kunming, China.
BMC Public Health. 2024 May 25;24(1):1397. doi: 10.1186/s12889-024-18794-2.
The real-world tuberculosis (TB) surveillance data was generally incomplete due to underreporting and underdiagnosis. The inventory study aimed to assess and quantify the incompletion of surveillance systems in southwestern China.
The inventory study was conducted at randomly selected health facilities (HF) by multi-stage stratified cluster sampling. The participants were included in the period between August of 2020 in province-level and prefecture-level HF, and in the period between June to December of 2020 in other categories of HF respectively. The clinical committee confirmed medical records were matched to the National Notifiable Disease Reporting System (NNDRS) and the Tuberculosis Information Management System (TBIMS) to define the report and register status. The underreporting and under-register rates were evaluated based on the matched data, and factors associated with underreport and under-register were assessed by the 2-level logistic multilevel model (MLM).
We enrolled 7,749 confirmed TB cases in the analysis. The province representative overall underreport rate to NNDRS was 1.6% (95% confidence interval, 95% CI, 1.3 - 1.9), and the overall under-register rate to TBIMS was 9.6% (95% CI, 8.9-10.3). The various underreport and under-register rates were displayed in different stratifications of background TB disease burden, HF level, HF category, and data source of the medical record in HF among prefectures of the province. The intraclass correlation coefficient (ICC) was 0.57 for the underreporting null MLM, indicating the facility-level cluster effect contributes a great share of variation in total variance. The two-level logistic MLM showed the data source of medical records in HF, diagnostic category of TB, and type of TB were associated with underreporting by adjusting other factors (p < 0.05). The ICC for under-register was 0.42, and the HF level, HF category, data source of medical records in HF, diagnostic category of TB and type of TB were associated with under-register by adjusting other factors (p < 0.05).
The inventory study depicted incomplete TB reporting and registering to NNDRS and TBIMS in southwestern China. It implied that surveillance quality improvement would help advance the TB prevention and control strategy.
由于漏报和漏诊，真实世界的结核病（TB）监测数据通常不完整。本研究旨在评估和量化中国西南部监测系统的不完整性。
采用多阶段分层聚类抽样，在随机选择的卫生机构（HF）中进行清查研究。参与者包括省级和市级 HF 分别在 2020 年 8 月和其他类别的 HF 在 2020 年 6 月至 12 月期间纳入研究。临床委员会确认的病历与国家法定传染病报告系统（NNDRS）和结核病信息管理系统（TBIMS）相匹配，以确定报告和登记情况。根据匹配数据评估漏报率和漏登率，并通过 2 级逻辑多层模型（MLM）评估与漏报和漏登相关的因素。
我们共纳入 7749 例确诊 TB 病例进行分析。NNDRS 报告的省级代表性总漏报率为 1.6%（95%置信区间，95%CI，1.3-1.9），TBIMS 的总漏登率为 9.6%（95%CI，8.9-10.3）。不同的漏报和漏登率在省级 HF 不同的背景 TB 疾病负担、HF 级别、HF 类别和 HF 中病历数据来源的分层中显示。NNDRS 漏报率的零 MLM 内类相关系数（ICC）为 0.57，表明设施级聚类效应对总方差的变异贡献很大。两水平逻辑 MLM 表明，HF 中病历的数据来源、TB 的诊断类别和 TB 的类型与调整其他因素后的漏报相关（p<0.05）。TBIMS 漏登率的 ICC 为 0.42，HF 级别、HF 类别、HF 中病历的数据来源、TB 的诊断类别和 TB 的类型与调整其他因素后的漏登相关（p<0.05）。
清查研究描述了中国西南部 NNDRS 和 TBIMS 对 TB 报告和登记的不完整。这意味着监测质量的提高将有助于推进结核病防控策略。
