| Summary: | 博士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 102 === Tuberculosis (TB), one of the most important infectious diseases worldwide, is a notifiable communicable disease with the highest morbidity and mortality. Recently, multidrug-resistant tuberculosis (MDR TB) and particularly high TB incidence observed in the senior care facilities make TB more difficult to control. TB patients do not stick (non-adherence) to the treatment regimen that is thought to be the main cause for the emergence of MDR TB. Moreover, statistical data on TB cases shows seasonal fluctuations in many countries, however, the seasonal transmission and population dynamics of TB/MDR TB in Taiwan are still poorly understood. Thus, the purpose of this dissertation was threefold: (i) to characterize the seasonal population transmission dynamics of TB/MDR TB and to assess the potential infection risks among Taiwan regions, (ii) to evaluate indoor TB population transmission dynamics and the efficacy of control measures in the senior care facilities, and (iii) to quantify the impact of different multidrug combination regimens with non-adherence on treatment efficacy and drug resistance probability.
This dissertation developed an integrated seasonal transmission dynamic model by linking the population transmission dynamic models and seasonal regression model to understand how seasonality influences the transmission dynamics of TB/MDRTB in Taiwan. The integrated seasonal transmission dynamic model was also used to predict TB/MDR TB incidence trends in the future. A probabilistic risk model was developed to estimate TB/MDR TB infection risks. This study conducted investigations for the senior care facilities to obtain the underlying characteristics including total population size, volume of airspace, ventilation condition, and elderly daily life behavior. A mathematical multiple control model combined with the population transmission dynamic model was used to evaluate the efficacy of various control strategies for reducing indoor TB transmission in senior care facilities. A pharmacokinetic/pharmacodynamic (PK/PD) based drug treatment model was constructed to explore the population and resistance evolution dynamics of TB bacilli during multidrug combination treatment taking into account non-adherence. This study further used a simple time-dependent bacterial population size based probabilistic function to quantify the probability of resistance to multiple drugs.
The results of model validation demonstrated that the proposed integrated seasonal transmission dynamic model was capable of describing the patterns of TB/MDR TB incidences in Taiwan. Simulation results showed that the TB epidemic in the future will finally be dominated by latently infected TB cases as a result of reactivation and reinfection. This study also found that, in the highest disease burden area, the incidences of TB/MDR TB had a gradually decreasing trend that might be attributable to the effective control TB programmes. The risk assessment indicated that high TB incidence areas had nearly 52 – 65% probabilities exceeding 50% of the total proportion of infected population. The results also indicated that latently infected TB cases were likely to pose the relatively high TB infection risk among populations. Additionally, there was only ~4% probability of having exceeded 10% of the population infected attributable to MDR TB for selected study sites of Taiwan. Although MDR TB seems unlikely to result in an emergency, MDR TB remains alarming from a conservative point of view.
Given the analytical results from the integrated mathematical control model, the investigated senior care facilities had a potentially higher risk of TB exposure. Nevertheless, the proposed combinations of engineering control measures along with personal protection could effectively reduce indoor transmission of TB bacilli in the senior care facilities in that the efficacies ranged from 65 – 97%. On the other hand, results from the drug treatment model showed that the duration of treatment would be increased 1.6 – 3.4 times for patients who were non-adherent to the therapeutic regimen relative to compliance. Non-adherence also led to treatment failure and accelerated the resistant mutants to grow and evolve, resulting in a much higher probability of resistance to multiple drugs. Overall, the determination of optimal treatment regimens depended on the different types of medication adherence.
This dissertation provided an integrated seasonal transmission dynamic model not only to examine the underlying mechanisms of TB seasonal transmission but also to predict the incidence patterns of TB/MDR TB and associated potential infection risks. A better understanding of the mechanisms of TB seasonality is helpful for establishing an early warning system and designing more effective public control programmes. The developed mathematical multiple control model can be applied to determine the optimal control strategies indoors for other infectious diseases. This dissertation hopes that the proposed drug treatment model can be used to improve the treatment protocols for TB or certain of disease that are needed to be treated with multiple drugs.
|
|---|
