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Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People's Republic of China.

Li XX, Ren ZP, Wang LX, Zhang H, Jiang SW, Chen JX, Wang JF, Zhou XN - PLoS Negl Trop Dis (2016)

Bottom Line: There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies.Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association.Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China.

ABSTRACT
Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases' prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

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Spatial distributions of the specific component for active pulmonary tuberculosis across P. R. China (A. posterior medians of relative risks; B. posterior lower limits of 95% Bayesian credible intervals [CI] of relative risks; C. posterior upper limits of 95% Bayesian CI of relative risks)
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pntd.0004580.g006: Spatial distributions of the specific component for active pulmonary tuberculosis across P. R. China (A. posterior medians of relative risks; B. posterior lower limits of 95% Bayesian credible intervals [CI] of relative risks; C. posterior upper limits of 95% Bayesian CI of relative risks)

Mentions: The disease-specific components of RRs for active PTB and IHI derived from Bayesian shared component model are shown in Table 5 and Figs 6 and 7. One disease-specific term captured 71.2% (95% CI = 69.1–73.5%) of the total spatial variation in active PTB, among which 99.9% (95% CI = 99.8–100.0%) was spatially correlated. The other captured 30.1% (95% CI = 25.5–36.1%) of the total spatial variation in IHI, among which 83.7% (95% CI = 73.1–86.7%) was spatially correlated. The disease-specific component for active PTB had a distinct spatial pattern with higher estimation (RR > 1.2) in large areas of seven provinces including Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Qinghai and Xinjiang and the juncture of three provinces including Henan, Hunan and Shaanxi, as shown in Fig 6. The disease-specific component for IHI presented a different spatial pattern with higher estimation (RR > 1.0) in large areas of 12 provinces including Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou and Yunnan and sparse areas of the remaining provinces, as shown in Fig 7. The prediction uncertainties of disease-specific components can be seen in the additional files: S2B and S2C Fig.


Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People's Republic of China.

Li XX, Ren ZP, Wang LX, Zhang H, Jiang SW, Chen JX, Wang JF, Zhou XN - PLoS Negl Trop Dis (2016)

Spatial distributions of the specific component for active pulmonary tuberculosis across P. R. China (A. posterior medians of relative risks; B. posterior lower limits of 95% Bayesian credible intervals [CI] of relative risks; C. posterior upper limits of 95% Bayesian CI of relative risks)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835095&req=5

pntd.0004580.g006: Spatial distributions of the specific component for active pulmonary tuberculosis across P. R. China (A. posterior medians of relative risks; B. posterior lower limits of 95% Bayesian credible intervals [CI] of relative risks; C. posterior upper limits of 95% Bayesian CI of relative risks)
Mentions: The disease-specific components of RRs for active PTB and IHI derived from Bayesian shared component model are shown in Table 5 and Figs 6 and 7. One disease-specific term captured 71.2% (95% CI = 69.1–73.5%) of the total spatial variation in active PTB, among which 99.9% (95% CI = 99.8–100.0%) was spatially correlated. The other captured 30.1% (95% CI = 25.5–36.1%) of the total spatial variation in IHI, among which 83.7% (95% CI = 73.1–86.7%) was spatially correlated. The disease-specific component for active PTB had a distinct spatial pattern with higher estimation (RR > 1.2) in large areas of seven provinces including Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Qinghai and Xinjiang and the juncture of three provinces including Henan, Hunan and Shaanxi, as shown in Fig 6. The disease-specific component for IHI presented a different spatial pattern with higher estimation (RR > 1.0) in large areas of 12 provinces including Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou and Yunnan and sparse areas of the remaining provinces, as shown in Fig 7. The prediction uncertainties of disease-specific components can be seen in the additional files: S2B and S2C Fig.

Bottom Line: There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies.Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association.Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China.

ABSTRACT
Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases' prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

Show MeSH
Related in: MedlinePlus