Spatial pattern and variations in the prevalence of congenital heart disease in children aged 4-18 years in the Qinghai-Tibetan Plateau.

PURPOSE: This study aimed to investigate the spatial distribution pattern of the prevalence of congenital heart disease (CHD) in children in Qinghai-Tibetan Plateau (QTP), a high-altitude region in China. METHODS: Epidemiological data from a survey on the prevalence of CHD in Qinghai Province including 288,066 children (4-18 years) were used in this study. The prevalence and distribution pattern of CHD was determined by sex, CHD subtype, and nationality and altitude. Spatial pattern analysis using Getis-Ord Gi⁎ was used to identify the spatial distribution of CHD. Bayesian spatial binomial regression was performed to examine the relationship between the prevalence of CHD and environmental risk factors in the QTP. RESULTS: The prevalence of CHD showed a significant spatial clustering pattern. The Tibetan autonomous prefecture of Yushu (average altitude > 4000 m) and the Mongolian autonomous county of Henan (average altitude > 3600 m) in Huangnan had the highest prevalence of CHD. Univariate analysis showed that with ascending altitude, the total prevalence of CHD, that in girls and boys with CHD, and that of the subtypes PDA and ASD increasing accordingly. Thus, environmental factors greatly contributed to the prevalence of CHD. CONCLUSIONS: The prevalence of CHD shows significant spatial clustering pattern in the QTP. The CHD subtype prevalence clustering pattern has statistical regularity which would provide convenient clues of environmental risk factors. Our results may provide support to make strategies of CHD prevention, to reduce the incidence of CHD in high altitude regions of China.

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

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.

Spatio-temporal analysis of malaria vectors in national malaria surveillance sites in China.

BACKGROUND: To reveal the spatio-temporal distribution of malaria vectors in the national malaria surveillance sites from 2005 to 2010 and provide reference for the current National Malaria Elimination Programme (NMEP) in China. METHODS: A 6-year longitudinal surveillance on density of malaria vectors was carried out in the 62 national malaria surveillance sites. The spatial and temporal analyses of the four primary vectors distribution were conducted by the methods of kernel k-means and the cluster distribution of the most widely distribution vector of An.sinensis was identified using the empirical mode decomposition (EMD). RESULTS: Totally 4 species of Anopheles mosquitoes including An.sinensis, An.lesteri, An.dirus and An.minimus were captured with significant difference of distribution as well as density. An. sinensis was the most widely distributed, accounting for 96.25% of all collections, and its distribution was divided into three different clusters with a significant increase of density observed in the second cluster which located mostly in the central parts of China. CONCLUSION: This study first described the spatio-temporal distribution of malaria vectors based on the nationwide surveillance during 2005-2010, which served as a baseline for the ongoing national malaria elimination program.

Spatial patterns of the congenital heart disease prevalence among 0- to 14-year-old children in Sichuan Basin, P. R China, from 2004 to 2009.

BACKGROUND: Congenital heart disease (CHD) is the most common type of major birth defects in Sichuan, the most populous province in China. The detailed etiology of CHD is unknown but some environmental factors are suspected as the cause of this disease. However, the geographical variations in CHD prevalence would be highly valuable in providing a clue on the role of the environment in CHD etiology. Here, we investigate the spatial patterns and geographic differences in CHD prevalence among 0- to 14-year-old children, discuss the possible environmental risk factors that might be associated with CHD prevalence in Sichuan Basin from 2004 to 2009. METHODS: The hierarchical Bayesian model was used to estimate CHD prevalence at the township level. Spatial autocorrelation statistics were performed, and a hot-spot analysis with different distance thresholds was used to identify the spatial pattern of CHD prevalence. Distribution and clustering maps were drawn using geographic information system tools. RESULTS: CHD prevalence was significantly clustered in Sichuan Basin in different spatial scale. Typical hot/cold clusters were identified, and possible CHD causes were discussed. The association between selected hypothetical environmental factors of maternal exposure and CHD prevalence was evaluated. CONCLUSIONS: The largest hot-spot clustering phenomena and the CHD prevalence clustering trend among 0- to 14-year-old children in the study area showed a plausibly close similarity with those observed in the Tuojiang River Basin. The high ecological risk of heavy metal(Cd, As, and Pb)sediments in the middle and lower streams of the Tuojiang River watershed and ammonia-nitrogen pollution may have contribution to the high prevalence of CHD in this area.