30 research outputs found
Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China.
No full textObjectivesXinjiang is one of the highest TB-burdened provinces of China. A time-series analysis was conducted to evaluate the trend, seasonality of active TB in Xinjiang, and explore the underlying mechanism of TB seasonality by comparing the seasonal variations of different subgroups.MethodsMonthly active TB cases from 2005 to 2014 in Xinjiang were analyzed by the X-12-ARIMA seasonal adjustment program. Seasonal amplitude (SA) was calculated and compared within the subgroups.ResultsA total of 277,300 confirmed active TB cases were notified from 2005 to 2014 in Xinjiang, China, with a monthly average of 2311±577. The seasonality of active TB notification was peaked in March and troughed in October, with a decreasing SA trend. The annual 77.31% SA indicated an annual mean of additional TB cases diagnosed in March as compared to October. The 0-14-year-old group had significantly higher SA than 15-44-year-old group (P0.05).ConclusionTB notification in Xinjiang shows an apparent seasonal variation with a peak in March and trough in October. For the underlying mechanism of TB seasonality, our results hypothesize that winter indoor crowding increases the risk of TB transmission, and seasonality was mainly influenced by the recent exogenous infection rather than the endogenous reactivation
Impact of collateral circulation status on favorable outcomes in thrombolysis treatment: A systematic review and meta-analysis
No full textSocio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis.
No full textXinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.Numbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran's I statistic. Anselin's local Moran's I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.Incidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin's Local Moran's I identified the "hotspots" which were consistently located in the southwest regions composed of 20 to 28 districts, and the "coldspots" which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of "hotspots" and "coldspots" with different intensity; 30 county/districts clustered as "hotspots", while 47 county/districts clustered as "coldspots". OLS regression model included the "proportion of minorities" and the "per capita GDP" as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting "proportion of minorities" to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R2 = 0.68, AIC = 871), which revealed that "proportion of minorities" was a strong predictor in the south central regions while "per capita GDP" was a strong predictor for the southwest regions.The SS+TB incidence of Xinjiang had a decreasing trend during 2005-2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters-the "hotspots", which were consistently located in the southwest regions, and the "coldspots", which were consistently located in the north central regions. The exploration of socio-demographic predictors identified the "proportion of minorities" and the "per capita GDP" as predictors and may help to guide TB control programs and targeting intervention
The trend of Global Moran`s I value for pulmonary TB incidence from 2005 to 2013.
No full textThe trend of Global Moran`s I value for pulmonary TB incidence from 2005 to 2013.</p
Hotspot Analysis with Getis-Ord Gi* statistic, from 2010–2013.
No full textHotspot Analysis with Getis-Ord Gi* statistic, from 2010–2013.</p
Coefficients of predictors and standard residuals of GWR.
No full text(A) Coefficients of proportion of minorities. (B) Coefficients of per capita GDP. (C) Standard residual of GWR.</p
Results of the global spatial autocorrelation analysis of pulmonary TB incidence from 2005–2013.
No full textResults of the global spatial autocorrelation analysis of pulmonary TB incidence from 2005–2013.</p
Summary of hotspot/coldspot clusters of annualized average incidence of pulmonary TB in Xinjiang.
No full textSummary of hotspot/coldspot clusters of annualized average incidence of pulmonary TB in Xinjiang.</p
The incidence of pulmonary TB cases in Xinjiang, from 2005–2009.
No full textThe incidence of pulmonary TB cases in Xinjiang, from 2005–2009.</p
