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

Abstract

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.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

“…However, evidence described in the literature confirmed the importance of conducting a study to better understand how air quality and weather can influence the cases of TB. Environmental factors such as air quality index [ 15 ], concentration of carbon monoxide [ 16 ], nitrogen dioxide [ 17 ] and sulphur dioxide [ 18 ], particulate matter [ 19 ] and weather factors such as rainfall [ 20 ], humidity [ 21 ], temperature [ 22 ], wind speed [ 23 ], and atmospheric pressure [ 24 ], can enhance the reproduction and growth of Mycobacterium tuberculosis to a certain extent.…”

Integrated Human Exposure to Air Pollution

“…The use of statistical models can also be employed to explore potential associations between exposure to air pollutants and health outcomes to highlight potential causal relationships. A study conducted in Wuhan city (China) evaluated the time-series of air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 ) and focused on the tuberculosis’ incidence in order to identify its potential association with short-term exposure to air pollution, based on kriged data and single and multi-pollutant models [ 13 ]. Single pollutant models showed that an increase of 10 μg/m 3 in concentrations of PM 2.5 , PM 10 and O 3 promoted an increase of the associated tuberculosis risk, while in the multi-pollutant model, only PM 2.5 showed a statistically significant effect on tuberculosis incidence.…”

Isotopic biomonitoring of anthropic carbon emissions in a megalopolis

“…This allows the estimation of parameters of interest in relatively large geographical regions (Liao, Li & Zhang, 2017), where only a sparse sampling is available (Oliver & Webster, 2014). While interpolation protocols have been developed that improve on Ordinary Kriging, this model is among the most utilized methods in environmental studies, including those considering atmospheric pollution (Liao, Li & Zhang, 2017;Gupta et al, 2018;Gómez-Losada et al, 2019;Huang et al, 2020).…”