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Published in: BMC Infectious Diseases 1/2022

Open Access 01-12-2022 | Tuberculosis | Research article

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China

Authors: Hao Li, Miao Ge, Mingxin Zhang

Published in: BMC Infectious Diseases | Issue 1/2022

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Abstract

Background

Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis.

Methods

Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence.

Results

In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM10, PM2.5, O3, NO2 and SO2 have impacts on TB incidence, and in different regions, the environmental factors show regional differences.

Conclusions

Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.
Literature
1.
go back to reference Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements AC, Trauer JM, et al. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med. 2018;16:193.CrossRef Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements AC, Trauer JM, et al. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med. 2018;16:193.CrossRef
2.
go back to reference Rao HX, Zhang X, Zhao L, Yu J, Ren W, Zhang XL, et al. Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis. Infect Dis Poverty. 2016;5(1):45.CrossRef Rao HX, Zhang X, Zhao L, Yu J, Ren W, Zhang XL, et al. Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis. Infect Dis Poverty. 2016;5(1):45.CrossRef
3.
go back to reference Rao HX, Shi XY, Zhang X. Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016. BMC Infect Dis. 2017;17(1):578.CrossRef Rao HX, Shi XY, Zhang X. Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016. BMC Infect Dis. 2017;17(1):578.CrossRef
4.
go back to reference Kiani B, Rahmati AR, Bergquist R, Hashtarkhani S, Firouragh N, Bagheri N, et al. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health. 2021;21:1093.CrossRef Kiani B, Rahmati AR, Bergquist R, Hashtarkhani S, Firouragh N, Bagheri N, et al. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health. 2021;21:1093.CrossRef
5.
go back to reference Wang T, Xue FZ, Chen YJ, Ma YB, Liu YX. The spatial epidemiology of tuberculosis in Linyi City, China, 2005–2010. BMC Public Health. 2012;12(1):885.CrossRef Wang T, Xue FZ, Chen YJ, Ma YB, Liu YX. The spatial epidemiology of tuberculosis in Linyi City, China, 2005–2010. BMC Public Health. 2012;12(1):885.CrossRef
6.
go back to reference Ge EJ, Zhang X, Wang XM, Wei XL. Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009–2012. Infect Dis Poverty. 2016;5:11.CrossRef Ge EJ, Zhang X, Wang XM, Wei XL. Spatial and temporal analysis of tuberculosis in Zhejiang Province, China, 2009–2012. Infect Dis Poverty. 2016;5:11.CrossRef
7.
go back to reference Sadeq M, Bourkadi JE. Spatiotemporal distribution and predictors of tuberculosis incidence in Morocco. Infect Dis Poverty. 2018;7(1):13.CrossRef Sadeq M, Bourkadi JE. Spatiotemporal distribution and predictors of tuberculosis incidence in Morocco. Infect Dis Poverty. 2018;7(1):13.CrossRef
8.
go back to reference Khaliq A, Batool SA, Chaudhry MN. Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013. J Epidemiol Global Health. 2015;5(4):397–403.CrossRef Khaliq A, Batool SA, Chaudhry MN. Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013. J Epidemiol Global Health. 2015;5(4):397–403.CrossRef
9.
go back to reference You SM, Tong YW, Neoh KG, Dai Y. On the association between outdoor PM2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong. Environ Pollut. 2016;218:1170–9.CrossRef You SM, Tong YW, Neoh KG, Dai Y. On the association between outdoor PM2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong. Environ Pollut. 2016;218:1170–9.CrossRef
10.
go back to reference Zhang X, Hao Y, Fei ZY, He J. Effect of meteorological factors on incidence of tuberculosis: a 15-year retrospective study based on Chinese medicine theory of five circuits and six qi. Chin J Integr Med. 2015;21:751–8.CrossRef Zhang X, Hao Y, Fei ZY, He J. Effect of meteorological factors on incidence of tuberculosis: a 15-year retrospective study based on Chinese medicine theory of five circuits and six qi. Chin J Integr Med. 2015;21:751–8.CrossRef
11.
go back to reference Keerqinfu, Zhang QM, Long Y, He J. Time series analysis of correlativity between pulmonary tuberculosis and seasonal meteorological factors based on theory of Human-Environmental Inter Relation. J Tradit Chin Med Sci. 2018;5(2):119–27. Keerqinfu, Zhang QM, Long Y, He J. Time series analysis of correlativity between pulmonary tuberculosis and seasonal meteorological factors based on theory of Human-Environmental Inter Relation. J Tradit Chin Med Sci. 2018;5(2):119–27.
12.
go back to reference Fernandes FMdC, Martins EdS, Pedrosa DMAS, Evangelista MdSN. Relationship between climatic factors and air quality with tuberculosis in the Federal District, Brazil, 2003–2012. Brazilian J Infect Dis. 2017;21(4):369–75.CrossRef Fernandes FMdC, Martins EdS, Pedrosa DMAS, Evangelista MdSN. Relationship between climatic factors and air quality with tuberculosis in the Federal District, Brazil, 2003–2012. Brazilian J Infect Dis. 2017;21(4):369–75.CrossRef
13.
go back to reference Zhu S, Xia L, Wu JL, Chen SB, Chen F, Zeng FF, et al. Ambient air pollutants are associated with newly diagnosed tuberculosis: a time-series study in Chengdu, China. Sci Total Environ. 2018;631–632:47–55.CrossRef Zhu S, Xia L, Wu JL, Chen SB, Chen F, Zeng FF, et al. Ambient air pollutants are associated with newly diagnosed tuberculosis: a time-series study in Chengdu, China. Sci Total Environ. 2018;631–632:47–55.CrossRef
14.
go back to reference Ge EJ, Fan M, Qiu H, Hu H, Tian LW, Wang XM, et al. Ambient sulfur dioxide levels associated with reduced risk of initial outpatient visits for tuberculosis: a population based time series analysis. Environ Pollut. 2017;228:408–15.CrossRef Ge EJ, Fan M, Qiu H, Hu H, Tian LW, Wang XM, et al. Ambient sulfur dioxide levels associated with reduced risk of initial outpatient visits for tuberculosis: a population based time series analysis. Environ Pollut. 2017;228:408–15.CrossRef
15.
go back to reference Wang JF, Xu CD, Geodetector. Principle and prospective. Acta Geogr Sin. 2017;72(01):116–34. Wang JF, Xu CD, Geodetector. Principle and prospective. Acta Geogr Sin. 2017;72(01):116–34.
16.
go back to reference Song Y, Wang JF, Ge Y, Xu CD. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GISci Remote Sens. 2020;57(5):593–610.CrossRef Song Y, Wang JF, Ge Y, Xu CD. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GISci Remote Sens. 2020;57(5):593–610.CrossRef
17.
go back to reference Li XX, Wang LX, Zhang H, Du X, Jiang SW, Shen T, et al. Seasonal variations in notification of active tuberculosis cases in China,2005–2012. PLoS ONE. 2013;8(7):e68102.CrossRef Li XX, Wang LX, Zhang H, Du X, Jiang SW, Shen T, et al. Seasonal variations in notification of active tuberculosis cases in China,2005–2012. PLoS ONE. 2013;8(7):e68102.CrossRef
18.
go back to reference Luo TY, Sumi A, Zhou D, Kobayashi N, Mise K, Yu B, et al. Seasonality of reported tuberculosis cases from 2006 to 2010 in Wuhan,China. Epidemiol Infect. 2013;142(10):2036–48.CrossRef Luo TY, Sumi A, Zhou D, Kobayashi N, Mise K, Yu B, et al. Seasonality of reported tuberculosis cases from 2006 to 2010 in Wuhan,China. Epidemiol Infect. 2013;142(10):2036–48.CrossRef
19.
go back to reference Yang JD, Zhang MX, Chen YG, Ma L, Yadikaer R, et al. A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in wulumuqi, China. Int J Infect Dis. 2020;96:42–7.CrossRef Yang JD, Zhang MX, Chen YG, Ma L, Yadikaer R, et al. A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in wulumuqi, China. Int J Infect Dis. 2020;96:42–7.CrossRef
20.
go back to reference Yang Z, Ye ZH, You AG. Application of multiple seasonal ARIMA model in prediction of tuberculosis incidence. Chin J Public Health. 2013;29(4):469 (in Chinese). Yang Z, Ye ZH, You AG. Application of multiple seasonal ARIMA model in prediction of tuberculosis incidence. Chin J Public Health. 2013;29(4):469 (in Chinese).
21.
go back to reference Li YS, Zhu LM, Lu W, Chen C, Yang HT. Seasonal variation in notified tuberculosis cases from 2014 to 2018 in eastern China. J Int Med Res. 2020;48(8):030006052094903.CrossRef Li YS, Zhu LM, Lu W, Chen C, Yang HT. Seasonal variation in notified tuberculosis cases from 2014 to 2018 in eastern China. J Int Med Res. 2020;48(8):030006052094903.CrossRef
22.
go back to reference Wubuli A, Xue F, Jiang DB, Yao XM, Upur H, Wushouer Q. Socio-demographic predictors and distribution of pulmonary tuberculosis (TB) in Xinjiang, China: a spatial analysis. PLoS ONE. 2015;10(12):e0144010.CrossRef Wubuli A, Xue F, Jiang DB, Yao XM, Upur H, Wushouer Q. Socio-demographic predictors and distribution of pulmonary tuberculosis (TB) in Xinjiang, China: a spatial analysis. PLoS ONE. 2015;10(12):e0144010.CrossRef
23.
go back to reference Kang W, Li P, Zheng S. Study on spatial clustering of tuberculosis in China. Chin J Health Stat. 2008;25(3):3. Kang W, Li P, Zheng S. Study on spatial clustering of tuberculosis in China. Chin J Health Stat. 2008;25(3):3.
24.
go back to reference Xia L, Zhu S, Chen C, Rao ZY, Wu JL. Spatio-temporal analysis of socio-economic characteristics for pulmonary tuberculosis in Sichuan province of China, 2006–2015. BMC Infect Dis. 2020;20(1):433.CrossRef Xia L, Zhu S, Chen C, Rao ZY, Wu JL. Spatio-temporal analysis of socio-economic characteristics for pulmonary tuberculosis in Sichuan province of China, 2006–2015. BMC Infect Dis. 2020;20(1):433.CrossRef
25.
go back to reference Kiani B, Rahmati AR, Bergquist R, Hashtarkhani S, Mohammadi A. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health. 2021;21(1):1093.CrossRef Kiani B, Rahmati AR, Bergquist R, Hashtarkhani S, Mohammadi A. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health. 2021;21(1):1093.CrossRef
26.
go back to reference Li QH, Liu MY, Zhang YJ, Wu SW, Yang Y, Liu Y, et al. The spatio-temporal analysis of the incidence of tuberculosis and the associated factors in mainland China, 2009–2015. Infect Genet Evol. 2019;75:103949.CrossRef Li QH, Liu MY, Zhang YJ, Wu SW, Yang Y, Liu Y, et al. The spatio-temporal analysis of the incidence of tuberculosis and the associated factors in mainland China, 2009–2015. Infect Genet Evol. 2019;75:103949.CrossRef
27.
go back to reference Pinto CT, Nano FE. Stable, temperature-sensitive recombinant strain of Mycobacterium smegmatis generated through the substitution of a psychrophilic ligA gene. FEMS Microbiol Lett. 2015;362(18):fnv152.CrossRef Pinto CT, Nano FE. Stable, temperature-sensitive recombinant strain of Mycobacterium smegmatis generated through the substitution of a psychrophilic ligA gene. FEMS Microbiol Lett. 2015;362(18):fnv152.CrossRef
28.
go back to reference Baughman A, Arens EA. Indoor humidity and human health—Part 1: literature review of health effects of humidity-influenced indoor pollutants. ASHRAE Trans. 1996;102(1):193–211. Baughman A, Arens EA. Indoor humidity and human health—Part 1: literature review of health effects of humidity-influenced indoor pollutants. ASHRAE Trans. 1996;102(1):193–211.
29.
go back to reference Lin YJ, Liao CM. Seasonal dynamics of tuberculosis epidemics and implications for multidrug-resistant infection risk assessment. Epidemiol Infect. 2014;142(2):358–70.CrossRef Lin YJ, Liao CM. Seasonal dynamics of tuberculosis epidemics and implications for multidrug-resistant infection risk assessment. Epidemiol Infect. 2014;142(2):358–70.CrossRef
30.
go back to reference Omonijo AG, Oguntoke O, Matzarakis A, Adeofun CO. A study of weather related respiratory diseases in eco-climatic zones. Afr Rev Phys. 2011;5:41–56. Omonijo AG, Oguntoke O, Matzarakis A, Adeofun CO. A study of weather related respiratory diseases in eco-climatic zones. Afr Rev Phys. 2011;5:41–56.
31.
go back to reference Jassal MS, Bakman I, Jones B. Correlation of ambient pollution levels and heavily-trafficked roadway proximity on the prevalence of smear-positive tuberculosis. Public Health. 2013;127(3):268–74.CrossRef Jassal MS, Bakman I, Jones B. Correlation of ambient pollution levels and heavily-trafficked roadway proximity on the prevalence of smear-positive tuberculosis. Public Health. 2013;127(3):268–74.CrossRef
32.
go back to reference Lai TC, Chiang CY, Wu CF, Yang SL, Liu DP, Chan CC, et al. Ambient air pollution and risk of tuberculosis: a cohort study. Occup Environ Med. 2015;73(1):56–61.CrossRef Lai TC, Chiang CY, Wu CF, Yang SL, Liu DP, Chan CC, et al. Ambient air pollution and risk of tuberculosis: a cohort study. Occup Environ Med. 2015;73(1):56–61.CrossRef
33.
go back to reference Xiang K, Xu Z, Hu YQ, He YS, Pan HF. Association between ambient air pollution and tuberculosis risk: a systematic review and meta-analysis. Chemosphere. 2021;11:130342.CrossRef Xiang K, Xu Z, Hu YQ, He YS, Pan HF. Association between ambient air pollution and tuberculosis risk: a systematic review and meta-analysis. Chemosphere. 2021;11:130342.CrossRef
34.
go back to reference Yang CX, Yang HB, Shu G, Wang ZS, Xu XH, Duan XL, et al. Alternative ozone metrics and daily mortality in Suzhou: the China Air Pollution and Health Effects Study (CAPES). Sci Total Environ. 2012;426:83–9.CrossRef Yang CX, Yang HB, Shu G, Wang ZS, Xu XH, Duan XL, et al. Alternative ozone metrics and daily mortality in Suzhou: the China Air Pollution and Health Effects Study (CAPES). Sci Total Environ. 2012;426:83–9.CrossRef
35.
go back to reference Yan ML, Liu ZR, Liu XT, Duan HY, Li TT. Meta-analysis of the Chinese studies of the association between ambient ozone and mortality. Chemosphere. 2013;93(6):899–905.CrossRef Yan ML, Liu ZR, Liu XT, Duan HY, Li TT. Meta-analysis of the Chinese studies of the association between ambient ozone and mortality. Chemosphere. 2013;93(6):899–905.CrossRef
36.
go back to reference Xu M, Liao JQ, Yin P, Hou J. Association of air pollution with the risk of initial outpatient visits for tuberculosis in Wuhan, China. Occup Environ Med. 2019;76(8):560–6.CrossRef Xu M, Liao JQ, Yin P, Hou J. Association of air pollution with the risk of initial outpatient visits for tuberculosis in Wuhan, China. Occup Environ Med. 2019;76(8):560–6.CrossRef
37.
go back to reference Tian L, Yang C, Zhou ZJ, Wu ZT, Pan XC, Clements ACA. Spatial patterns and effects of air pollution and meteorological factors on hospitalization for chronic lung diseases in Beijing, China. Sci China Life Sci. 2019;62(10):1381–8.CrossRef Tian L, Yang C, Zhou ZJ, Wu ZT, Pan XC, Clements ACA. Spatial patterns and effects of air pollution and meteorological factors on hospitalization for chronic lung diseases in Beijing, China. Sci China Life Sci. 2019;62(10):1381–8.CrossRef
38.
go back to reference Kan HD, Wong CM, Vadakan NV, Qian ZM, PAPA Project Teams. Short-term association between sulfur dioxide and daily mortality: the Public Health and Air Pollution in Asia (PAPA) study. Environ Res. 2010;110(3):258–64.CrossRef Kan HD, Wong CM, Vadakan NV, Qian ZM, PAPA Project Teams. Short-term association between sulfur dioxide and daily mortality: the Public Health and Air Pollution in Asia (PAPA) study. Environ Res. 2010;110(3):258–64.CrossRef
39.
go back to reference Niu ZC, Qi YJ, Zhao PQ, Li YD. Short-term effects of ambient air pollution and meteorological factors on tuberculosis in semi-arid area, northwest China: a case study in Lanzhou. Environ Sci Pollut Res. 2021;28(48):1–10. Niu ZC, Qi YJ, Zhao PQ, Li YD. Short-term effects of ambient air pollution and meteorological factors on tuberculosis in semi-arid area, northwest China: a case study in Lanzhou. Environ Sci Pollut Res. 2021;28(48):1–10.
Metadata
Title
Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China
Authors
Hao Li
Miao Ge
Mingxin Zhang
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Infectious Diseases / Issue 1/2022
Electronic ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07539-4

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