Mahawan Nithinan, Rattananupong Thanapoom, Sri-Uam Puchong, Jiamjarasrangsi Wiroj
School of Nursing, the Excellence Center of Community Health Promotion, Walailak University, Nakhon Si Thammarat, Thailand.
Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
PLoS One. 2025 Jan 24;20(1):e0318089. doi: 10.1371/journal.pone.0318089. eCollection 2025.
This study examined the ability of the following five dynamic models for predicting pulmonary tuberculosis (PTB) incidence in a prison setting: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour and liters per second per person, the Issarow et al. model, and the applied susceptible-exposed-infected-recovered (SEIR) tuberculosis (TB) transmission model. This 1-year prospective cohort study employed 985 cells from three Thai prisons (one prison with 652 cells as the in-sample, and two prisons with 333 cells as the out-of-sample). The baseline risk of TB transmission for each cell was assessed using the five dynamic models, and the future PTB incidence was calculated as the number of new PTB cases per cell and the number of new PTB cases per 1,000 person-years (incidence rate). The performance of the dynamic models was assessed by a four-step standard assessment procedure (including model specification tests, in-sample model fitting, internal validation, and external validation) based on the Negative Binomial Regression model. A 1% increase in baseline TB transmission probability was associated with a 3%-7% increase in future PTB incidence rate, depending on the dynamic model. The Wells-Riley model exhibited the best performance in terms of both internal and external validity. Poor goodness-of-fit was observed in all dynamic models (chi-squared goodness-of-fit tests of 70.75-305.1, 8 degrees of freedom, p < .001). In conclusion, the Wells-Riley model was the most appropriate dynamic model, especially for large-scale investigations, due to its fewer parameter requirements. Further research is needed to confirm our findings and gather more data to improve these dynamic models.
本研究考察了以下五种动态模型在监狱环境中预测肺结核（PTB）发病率的能力：威尔斯 - 莱利方程、基于每小时换气次数和每人每秒升数的鲁德尼克与米尔顿提出的两个模型、伊萨罗等人的模型，以及应用的易感 - 暴露 - 感染 - 恢复（SEIR）结核病（TB）传播模型。这项为期1年的前瞻性队列研究使用了来自泰国三所监狱的985个牢房（一所监狱的652个牢房作为样本内，两所监狱的333个牢房作为样本外）。使用这五种动态模型评估每个牢房的结核病传播基线风险，并将未来PTB发病率计算为每个牢房的新PTB病例数和每1000人年的新PTB病例数（发病率）。基于负二项回归模型，通过四步标准评估程序（包括模型设定检验、样本内模型拟合、内部验证和外部验证）评估动态模型的性能。根据动态模型的不同，基线结核病传播概率增加1%与未来PTB发病率增加3% - 7%相关。威尔斯 - 莱利模型在内部和外部有效性方面表现最佳。在所有动态模型中均观察到拟合优度较差（卡方拟合优度检验值为70.75 - 305.1，自由度为8，p < .001）。总之，威尔斯 - 莱利模型是最合适的动态模型，特别是对于大规模调查，因为其所需参数较少。需要进一步研究以证实我们的发现并收集更多数据来改进这些动态模型。
