BackgroundTuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB.MethodsOur TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation.ResultsThe Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran’s I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively.ConclusionsHigh TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences.Electronic supplementary materialThe online version of this article (doi:10.1186/s40249-016-0139-4) contains supplementary material, which is available to authorized users.

BackgroundAlthough the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures.MethodsWe extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff’s retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai.ResultsA total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff’s scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016.ConclusionThis study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2643-y) contains supplementary material, which is available to authorized users.

This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.

Background Both coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) are caused by coronaviruses and have infected people in China and worldwide. We aimed to investigate whether COVID-19 and SARS exhibited similar spatial and temporal features at provincial level in mainland China. Methods The number of people infected by COVID-19 and SARS were extracted from daily briefings on newly confirmed cases during the epidemics, as of Mar. 4, 2020 and Aug. 3, 2003, respectively. We depicted spatiotemporal patterns of the COVID-19 and SARS epidemics using spatial statistics such as Moran’s I and the local indicators of spatial association (LISA). Results Compared to SARS, COVID-19 had a higher overall incidence. We identified 3 clusters (predominantly located in south-central China; the highest RR = 135.08, 95% CI: 128.36–142.08) for COVID-19 and 4 clusters (mainly in Northern China; the highest RR = 423.51, 95% CI: 240.96–722.32) for SARS. Fewer secondary clusters were identified after the “Wuhan lockdown”. The LISA cluster map detected a significantly high-low (Hubei) and low-high spatial clustering (Anhui, Hunan, and Jiangxi, in Central China) for COVID-19. Two significant high-high (Beijing and Tianjin) and low-high (Hebei) clusters were detected for SARS. Conclusions COVID-19 and SARS outbreaks exhibited distinct spatiotemporal clustering patterns at the provincial levels in mainland China, which may be attributable to changes in social and demographic factors, local government containment strategies or differences in transmission mechanisms.

Investigation of the prevalence and diversity of Bartonella infections in small mammals in the Qaidam Basin, western China, could provide a scientific basis for the control and prevention of Bartonella infections in humans. Accordingly, in this study, small mammals were captured using snap traps in Wulan County and Ge’ermu City, Qaidam Basin, China. Spleen and brain tissues were collected and cultured to isolate Bartonella strains. The suspected positive colonies were detected with polymerase chain reaction amplification and sequencing of gltA, ftsZ, RNA polymerase beta subunit (rpoB) and ribC genes. Among 101 small mammals, 39 were positive for Bartonella, with the infection rate of 38.61%. The infection rate in different tissues (spleens and brains) (χ2 = 0.112, P = 0.738) and gender (χ2 = 1.927, P = 0.165) of small mammals did not have statistical difference, but that in different habitats had statistical difference (χ2 = 10.361, P = 0.016). Through genetic evolution analysis, 40 Bartonella strains were identified (two different Bartonella species were detected in one small mammal), including B. grahamii (30), B. jaculi (3), B. krasnovii (3) and Candidatus B. gerbillinarum (4), which showed rodent-specific characteristics. B. grahamii was the dominant epidemic strain (accounted for 75.0%). Furthermore, phylogenetic analysis showed that B. grahamii in the Qaidam Basin, might be close to the strains isolated from Japan and China. Overall, we observed a high prevalence of Bartonella infection in small mammals in the Qaidam Basin. B. grahamii may cause human disease, and the pathogenicity of the others Bartonella species needs further study, the corresponding prevention and control measures should be taken into consideration.