A Probabilistic Transmission Model to Assess Infection Risk from<i>Mycobacterium Tuberculosis</i>in Commercial Passenger Trains

Chen, Szu-Chieh; Liao, Chung‐Min; Li, Sih-Syuan; You, Shu-Han

doi:10.1111/j.1539-6924.2010.01552.x

Cited by 23 publications

(12 citation statements)

References 25 publications

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“…The study explained that staying indoors during high ambient air pollution episodes would lead to an increase in human contact and thus the increased risk of TB transmission. The same hypothesis was given elsewhere as well [43]. Therefore, the real mechanism is not yet clear and warrants more related research.…”

Section: Discussionmentioning

confidence: 71%

Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis

Huang

Xiang

Yang

et al. 2020

IJERPH

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Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015–2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ground-level ozone (O3). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May–October) and cold (November–April) seasons. In the single-pollutant model, for a 10 μg/m3 increase in PM2.5, PM10, and O3 at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.

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Section: Discussionmentioning

confidence: 71%

Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis

Huang

Xiang

Yang

et al. 2020

IJERPH

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“…Many bacteria have been identified as infectious agents disseminated through ventilation systems. Among them, there are pathogens causing tuberculosis ( Mycobacterium tuberculosis ) [ 110 - 112 ], pneumonia ( Streptococcus pneumoniae ) [ 113 ], scarlet fever ( Streptococcus pyogenes ) [ 112 , 113 ], diphtheria ( Corynebacterium diphtheriae ) [ 112 ] or whooping cough ( Bordetella pertussis ) [ 114 ]. To purify air in closed areas, high efficiency particulate air (HEPA) filters are applied [ 115 ].…”

Section: Reviewmentioning

confidence: 99%

Chances and limitations of nanosized titanium dioxide practical application in view of its physicochemical properties

et al. 2015

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Nanotechnology is a field of science that is nowadays developing in a dynamic way. It seems to offer almost endless opportunities of contribution to many areas of economy and human activity, in general. Thanks to nanotechnology, the so-called nanomaterials can be designed. They present structurally altered materials, with their physical, chemical and biological properties entirely differing from properties of the same materials manufactured in microtechnology. Nanotechnology creates a unique opportunity to modify the matter at the level of atoms and particles. Therefore, it has become possible to obtain items displaying new, useful properties, i.e. self-disinfecting and self-cleaning surfaces. Those surfaces are usually covered by a thin layer of a photocatalyst. The role of the photocatalyst is most of the time performed by the nanosized titanium dioxide (nano-TiO2). Excitation of nano-TiO2 by ultraviolet radiation initiates advanced oxidation processes and reactions leading to the creation of oxygen vacancies that bind water particles. As a result, photocatalytic surfaces are given new properties. Those properties can then be applied in a variety of disciplines, such as medicine, food hygiene, environmental protection or building industry. Practically, the applications include inactivation of microorganisms, degradation of toxins, removing pollutants from buildings and manufacturing of fog-free windows or mirrors.

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“…Applications to water-borne and food-borne infectious microbes are too numerous to cite, and have enhanced public health decision making through exploration of: scenarios that cannot be directly observed [ 1 ], the effectiveness of interventions [ 2 ], and causes of infectious disease outbreaks [ 3 ]. In the context of microbes capable of causing respiratory infections, quantitative microbial risk assessment research has focused on influenza [ 4 - 8 ] and tuberculosis, often in the context of transportation [ 5 , 9 - 11 ]. One reason for limited application of QMRA to microbes capable of causing respiratory infections may be the limited availability of dose-response functions, which are complicated by uncertainty about mechanisms of disease transmission.…”

Section: Introductionmentioning

confidence: 99%

Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus

Jones

2015

BMC Infect Dis

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BackgroundDose-response assessment is one step in quantitative microbial risk assessment (QMRA). Four infectious microbes capable of causing respiratory diseases important to public health, and for which dose-response functions have not been available are: Bordetella pertussis (whooping cough), group A Streptococcus (pharyngitis), rhinovirus (common cold) and respiratory syncytial virus (common cold). The objective of this study was to fit dose-response functions for these microbes to published experimental data.MethodsExperimental infectivity data in human subjects and/or animal models were identified from the peer-reviewed literature. The exponential and beta-Poisson dose-response functions were fitted using the method of maximum likelihood, and models compared by Akaike’s Information Criterion.ResultsDose-response functions were identified for each appropriate data set for the four infectious microbes. Statistical and graphical measures of fit are presented.ConclusionsWith the fitted dose-response functions it will be possible to perform QMRA for these microbes. The dose-response functions, however, have a number of limitations associated with the route of exposure, use of animal hosts, and quality of fit. As a result, thoughtfulness must be used in selecting one dose-response function for a QMRA, and the function should be recognized as a significant source of uncertainty. Nonetheless, QMRA offers a transparent, systematic framework within which to understand the mechanisms of disease transmission, and evaluate interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-015-0832-0) contains supplementary material, which is available to authorized users.

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A Probabilistic Transmission Model to Assess Infection Risk fromMycobacterium Tuberculosisin Commercial Passenger Trains

Cited by 23 publications

References 25 publications

Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis

Short-Term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-Series Analysis

Chances and limitations of nanosized titanium dioxide practical application in view of its physicochemical properties

Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus

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