Temporal and spatial dynamics of Cryptosporidium parvum infection on dairy farms in the New York City Watershed: a cluster analysis based on crude and Bayesian risk estimates

Investigation of multi-scale spatio-temporal pattern of oldest-old clusters in China on the basis of spatial scan statistics

Background

Ageing is becoming a considerable public health burden in China, which produces great societal development challenges. Healthy and active longevity could ease the ageing burden on families and communities. To date, most studies of the oldest-old distribution are focused on a simple scale from spatial perspective, and the multi-scale spatio-temporal clusters trend in the oldest-old population has not yet been determined. Thus, the objective in present study is to use a new method to evaluate the spatio-temporal pattern and detect the risk clusters in the oldest-old population from three scales.

Methods

Individuals aged 65 years or older and individuals aged 80 years or older on three scales in China from 2000 to 2010 were used. The exploratory spatial data analysis was performed using Moran’s I statistic, and the pattern of the oldest-old clusters among humans was examined by using the spatial scan statistical method. Then, spatial stratified heterogeneity was used to explore the factors affecting the spatial heterogeneity of the oldest-old population.

Results

The oldest-old index in the southeast coastal areas is higher than that in the northwest inland areas in China. A three-ladder terrain distribution of the oldest-old index from west to east is obvious. The overall pattern of the oldest-old index evolves from a “concave” shape to an “east-west uplift, and northern collapse” shape. Space-time analysis revealed that high-risk areas were concentrated in five regions: the Yangtze River Delta, the Pearl River Delta, the Southeast Coast, Sichuan and Chongqing, and the Central Plains. The oldest-old cluster at different scales shows a similar pattern, but local differences exist. The risk at the prefecture scale and county scale is greater than at the interprovincial scale; the sublevel can identify clusters that have not been identified at the previous level, especially the bordering areas of prefectures and counties; and more risk units and greater relative risk are found in urban areas than in rural areas.

Conclusions

The results emphasized that spatial scan statistics can be used to estimate the spatial clusters of the oldest-old people. The detection of these clusters might be highly useful in the surveillance of the ageing phenomenon, thus helping local public health authorities measure the population burden at all locations, identifying geographical areas that require more attention, and evaluating the impacts of intervention programs.

Dynamics of spatiotemporal distribution of schistosomiasis in Hubei Province, China

Schistosomiasis caused by parasitic flatworms of blood flukes, remains a major public health concern in China. The significant progress in controlling schistosomiasis in China over the past decades has resulted in the remarkable reduction in the prevalence and intensity of Schistosoma japonicum infection to an extremely low level. Therefore, the elimination of schistosomiasis has been promoted by the Chinese national government. Hubei Province is the major endemic area, that is, along the middle and low reaches of the Yangtze River in the lake and marshland regions of southern China. Eliminating the transmission of schistosomiasis in Hubei Province is challenging. The current issue is to determine the distributions and clusters of schistosomiasis transmission. In this study, we assessed the spatial distribution of schistosomiasis and the risk at the county level in Hubei Province from 2011 to 2015 to provide guidance on the elimination of schistosomiasis transmission in lake and marshland regions. Spatial database of human S.japonicum infection from 2011 to 2015 at the county level in the study area was built based on the annual schistosomias is surveillance data. Moran's I, the global spatial autocorrelation statistics, was utilized to describe the spatial autocorrelation of human S. japonicum infection. In addition, purely spatial scan statistics combined with space-time scan statistics were used to determine the epidemic clusters. Infection rates of S. japonicum decreased in each endemic county in Hubei from 2011 to 2015. Human S. japonicum infection rate showed statistical significance by global autocorrelation analysis during the study period (Moran's I > 0, P < 0.01). This result suggested that there were spatial clusters present in the distribution of S. japonicum infection for the five years. Purely spatial analysis of human S. japonicum infection showed one most likely cluster and one secondary cluster from 2011 to 2015, which covered four and one counties, respectively. Spatiotemporal clustering analysis determined one most likely cluster and one secondary cluster both in 2011-2012, which appeared in 4 and 5 counties, respectively. However, the number of clustering foci decreased with time, and no cluster was detected after 2013.The clustering foci were both located at the Jianghan Plain, along the middle reaches of the Yangtze River and its connecting branch Hanbei River. Spatial distribution of human S. japonicum infections did not change temporally at the county level in Hubei Province. A declining trend in spatiotemporal clustering was observed between 2011 and 2015. However, effective control strategies and integrated prevention should be continuously performed, especially at the Jianghan Plain area along the Yangtze and Hanbei River Basin. Multivariate statistical analysis was carried out to investigate the risk of missing examinations, missing treatment, and unstandardized treatment events. The results showed that age, education level and Sanitary latrines are risk factors for missing examinations (b > 0, OR >1), and treatment times in past and feeding cattle in village group are protective factors (b < 0, OR <1). We also found that age and education level are risk factors for missing treatment (b > 0, OR >1). Study of the risk for un-standardized treatment revealed that occupation is risk factors (b > 0, OR >1), though, education level is protective factors (b < 0, OR <1). Therefore, precise prevention and control should be mainly targeted at these special populations.

Spatiotemporal patterns of clinical bovine dermatophilosis in Zimbabwe 1995-2014

A retrospective study of clinical bovine dermatophilosis outbreaks and cases for the period 1995-2014 was conducted, using data obtained from the Division of Veterinary Services (DVS). A total of 3856 outbreaks and 26 659 cases of dermatophilosis were reported countrywide during this period. The post rainy season accounted for 37.9% of the outbreaks followed by the rainy season (26.7%), cold dry season (22.1%) and the hot dry season (13.2%). A retrospective space-time scan statistic in SaTScanTM was used to detect clusters. From this study, it was evident that dermatophilosis was spreading from the north-west of Zimbabwe through the central to the north-east during the period 2010-2014. Five clusters were identified mainly in the central and north-western regions of Zimbabwe. The primary cluster was centred at Ungwe, Gokwe district in Midlands; the second, third, fourth and fifth likely clusters were centred at Bonga (Mashonaland Central), ARDA (Mashonaland West), Nsenga (Matabeleland North) and Zanda in Gokwe, respectively. The findings of this study suggest the continued spread of dermatophilosis across the country; as such the Department of Livestock and Veterinary Services are advised to develop measures aimed at managing this spread such as dipping, quarantine, movement control and raising farmer awareness.

Spatial analysis of Schistosomiasis in Hubei Province, China: a GIS-based analysis of Schistosomiasis from 2009 to 2013

BACKGROUND

Schistosomiasis remains a major public health problem in China. The major endemic areas are located in the lake and marshland regions of southern China, particularly in areas along the middle and low reach of the Yangtze River. Spatial analytical techniques are often used in epidemiology to identify spatial clusters in disease regions. This study assesses the spatial distribution of schistosomiasis and explores high-risk regions in Hubei Province, China to provide guidance on schistosomiasis control in marshland regions.

METHODS

In this study, spatial autocorrelation methodologies, including global Moran's I and local Getis-Ord statistics, were utilized to describe and map spatial clusters and areas where human Schistosoma japonicum infection is prevalent at the county level in Hubei province. In addition, linear logistic regression model was used to determine the characteristics of spatial autocorrelation with time.

RESULTS

The infection rates of S. japonicum decreased from 2009 to 2013. The global autocorrelation analysis results on the infection rate of S. japonicum for five years showed statistical significance (Moran's I > 0, P < 0.01), which suggested that spatial clusters were present in the distribution of S. japonicum infection from 2009 to 2013. Local autocorrelation analysis results showed that the number of highly aggregated areas ranged from eight to eleven within the five-year analysis period. The highly aggregated areas were mainly distributed in eight counties.

CONCLUSIONS

The spatial distribution of human S. japonicum infections did not exhibit a temporal change at the county level in Hubei Province. The risk factors that influence human S. japonicum transmission may not have changed after achieving the national criterion of infection control. The findings indicated that spatial-temporal surveillance of S. japonicum transmission plays a significant role on schistosomiasis control. Timely and integrated prevention should be continued, especially in the Yangtze River Basin of Jianghan Plain area.