Dataset Information

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China.

ABSTRACT:

Background

Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear.

Objective

To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China.

Methods

We performed a spatiotemporal analysis with prediction using time series analysis, Moran's I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors.

Results

The number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363-0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics, and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates.

Conclusion

The notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

SUBMITTER: Cui Z

PROVIDER: S-EPMC6497253 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China.

Cui Zhezhe Z Lin Dingwen D Chongsuvivatwong Virasakdi V Zhao Jinming J Lin Mei M Ou Jing J Zhao Jinghua J

PloS one 20190502 5

<h4>Background</h4>Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear.<h4>Objective</h4>To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China.<h4>Methods</h4>We performed ...[more]

PMID: 31048894

Similar Datasets

Project description:Tuberculosis (TB) is the leading cause of death from a bacterial pathogen worldwide. China has the third highest TB burden in the world, with a high reported burden in Hunan Province (amongst others). This study aimed to investigate the spatial distribution of TB and identify socioeconomic, demographic, and environmental drivers in Hunan Province, China. Numbers of reported cases of TB were obtained from the Tuberculosis Control Institute of Hunan Province, China. A wide range of covariates were collected from different sources, including from the Worldclim database, and the Hunan Bureau of Statistics. These variables were summarized at the county level and linked with TB notification data. Spatial clustering of TB was explored using Moran's I statistic and the Getis-Ord statistic. Poisson regression models were developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian approach with Markov chain Monte Carlo (MCMC) simulation. A total of 323,340 TB cases were reported to the Hunan TB Control Institute from 2013 to 2018. The mean age of patients was 51.7 years (SD + 17.6 years). The majority of the patients were male (72.6%, n = 234,682) and had pulmonary TB (97.5%, n = 315,350). Of 319,825 TB patients with registered treatment outcomes, 306,107 (95.7%) patients had a successful treatment outcome. The annual incidence of TB decreased over time from 85.5 per 100,000 population in 2013 to 76.9 per 100,000 population in 2018. TB case numbers have shown seasonal variation, with the highest number of cases reported during the end of spring and the beginning of summer. Spatial clustering of TB incidence was observed at the county level, with hotspot areas detected in the west part of Hunan Province. The spatial clustering of TB incidence was significantly associated with low sunshine exposure (RR: 0.86; 95% CrI: 0.74, 0.96) and a low prevalence of contraceptive use (RR: 0.88; 95% CrI: 0.79, 0.98). Substantial spatial clustering and seasonality of TB incidence were observed in Hunan Province, with spatial patterns associated with environmental and health care factors. This research suggests that interventions could be more efficiently targeted at locations and times of the year with the highest transmission risk.

Project description:BackgroundTuberculosis (TB) is an important public health issue worldwide. However, evidence concerning the impact of environmental factors on TB is sparse. We performed a retrospective analysis to determine the spatiotemporal trends and geographic variations of, and the factors associated with, the TB prevalence in Inner Mongolia.MethodsWe performed a retrospective analysis of the epidemiology of TB. A Bayesian spatiotemporal model was used to investigate the spatiotemporal distribution and trends of the TB prevalence. A spatial panel data model was used to identify factors associated with the TB prevalence in the 101 counties of Inner Mongolia, using county-level aggregated data collected by the Inner Mongolia Center for Disease Control and Prevention.ResultsFrom January 2010 to December 2014, 79,466 (6.36‱) incident TB cases were recorded. The TB prevalence ranged from 4.97‱ (12,515/25,167,547) in 2014 to 7.49‱ (18,406/ 24,578,678) in 2010; the majority of TB cases were in males, and in those aged 46-60 years; by occupation, farmers and herdsmen were the most frequently affected. The Bayesian spatiotemporal model showed that the overall TB prevalence decreased linearly from 2010 to 2014 and occupation-stratified analyses yielded similar results, corroborating the reliability of the findings. The decrease of TB prevalence in the central-western and eastern regions was more rapid than that in the overall TB prevalence. A spatial correlation analysis showed spatial clustering of the TB prevalence from 2011 to 2014 (Moran's index > 0, P < 0.05); in the spatial panel data model, rural residence, birth rate, number of beds, population density, precipitation, air pressure, and sunshine duration were associated with the TB prevalence.ConclusionsThe overall TB prevalence in Inner Mongolia decreased from 2010 to 2014; however, the incidence of TB was high throughout this period. The TB prevalence was influenced by a spatiotemporal interaction effect and was associated with epidemiological, healthcare, and environmental factors.

Project description:BACKGROUND:Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB. METHODS:Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model. RESULTS:From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0)12 model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0)12 model, we predicted the TB incidence in Guangxi from July 2019 to December 2020. CONCLUSIONS:This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0)12 model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0)12 model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China.

Project description:BackgroundDamming disrupts rivers and destroys neighboring terrestrial ecosystems through inundation, resulting in profound and long-lasting impacts on biodiversity and ecosystem processes far beyond the river system itself. Archipelagos formed by damming are often considered ideal systems for studying habitat fragmentation.MethodsHere we quantified the island attributes and landscape dynamics of the Thousand Island Lake (TIL) in China, which is one of the several long-term biodiversity/fragmentation research sites around the world. We also synthesized the major findings of relevant studies conducted in the region to further ecological understanding of damming and landscape fragmentation.ResultsOur results show that the vegetations on islands and the neighboring mainland were both recovering between 1985 and 2005 due to reforestation and natural succession, but the regeneration was partly interrupted after 2005 because of increasing human influences. While major changes in landscape composition occurred primarily in the lakefront areas and near-lakeshore islands, landscape patterns became structurally more complex and fragmented on both islands and mainland. About 80 studies from the TIL region show that the genetic, taxonomic, functional, and phylogenetic diversity on these islands were mainly influenced by island area at the patch scale, but fragmentation per se also affected species composition and related ecological processes at patch and landscape scales. In general, islands had lower species diversity but a steeper species-area relationship than the surrounding mainland. Fragmentation and edge effects substantially hindered ecological succession towards more densely vegetated forests on the islands. Environmental heterogeneity and filtering had a major impact on island biotic communities. We hypothesize that there are multiple mechanisms operating at different spatial scales that link landscape fragmentation and ecological dynamics in the TIL region, which beg for future studies. By focusing on an extensive spatiotemporal analysis of the island-mainland system and a synthesis of existing studies in the region, this study provides an important foundation and several promising directions for future studies.

Dataset Information

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China.

Background

Objective

Methods

Results

Conclusion

Publications

Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure