BACKGROUND: The cluster detection of health care-associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages.
OBJECTIVE: We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters.
METHODS: We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters.
RESULTS: The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination-only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens-only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens-only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5.
CONCLUSIONS: The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.

OBJECTIVE: Gonorrhea remains a major public health concern worldwide. This study aims to explore the spatiotemporal distribution and sociodemographic determinants of gonorrhea rates during 2004-2014 in mainland China.
METHODS: Space-time scan statistics and spatial panel regression model.
METHODS: The gonorrhea infection data and sociodemographic data during 2004-2014 at the provincial level in mainland China were extracted from the China Public Health Science Data Center and China Statistical Yearbooks, respectively. The space-time scan statistics were used to identify the high-risk clusters of gonorrhea, and the spatial panel regression model was adopted to examine the sociodemographic determinants.
RESULTS: One most likely and five secondary high-risk clusters of gonorrhea rates were identified, which were mainly located in southern and eastern China. The regions with higher GDP per capita, larger floating population, less access to healthcare, higher male-female ratio, and higher divorce rate were more likely to become high-risk areas of gonorrhea.
CONCLUSIONS: Gonorrhea rates were distributed unevenly through space and time and affected by various sociodemographic variables. The space-time scan statistics and spatial panel regression are viable tools for identifying clusters and examining determinants of gonorrhea rates. The findings provide valuable implications for developing targeted prevention and control programs in public health practice.

BACKGROUND: Detection of healthcare-associated infection (HCAI) clusters is crucial in limiting disease transmission.
OBJECTIVE: To investigate whether data on antibiotic use can be an alternative indicator for the identification of HCAI clusters caused by multidrug-resistant organisms (MDROs).
METHODS: We retrospectively analysed MDRO-related HCAIs and the 10 indicators of antibiotic use from four independent high-risk units at a tertiary hospital in China from January 2014 to January 2017. Spearman\'s correlation test was used to evaluate the correlations between the variables, and Shewhart chart algorithm was used to evaluate the performances of cluster identification.
RESULTS: We identified 856 MDRO-related HCAI cases. All indicators of antibiotic use were positively correlated with the incidence of MDRO-related HCAIs (r = 0.2-0.5; P < 0.05), except for the antibiotics utilization rate (AUR) for single-agent use (r = -0.191; P = 0.017) and the AUR of unrestricted drugs (r = -0.042, P = 0.601). Shewhart chart algorithm identified 22 clusters of MDRO-related HCAI. The AUR of special-grade antibiotics, the AUR for three agents used in combination, and the number of antibiotic varieties per patient displayed the optimal predictive values for detecting these 22 MDRO-related HCAI clusters. At an acceptable specific level of 75%, these three indicators were considered as the optimal surveillance indicators for detecting MDRO-related HCAI clusters, with sensitivities from 80.00% to 95.00%, and positive predictive values from 71.05% to 77.50%.
CONCLUSIONS: The use of data on antibiotic use is a sensitive method for identifying clusters of MDRO-related HCAIs in high-risk units and may be a useful adjunctive method for HCAI surveillance.

OBJECTIVE: China has experienced a sharply increasing rate of human brucellosis in recent years. Effective spatial monitoring of human brucellosis incidence is very important for successful implementation of control and prevention programmes. The purpose of this paper is to apply exploratory spatial data analysis (ESDA) methods and the empirical Bayes (EB) smoothing technique to monitor county-level incidence rates for human brucellosis in mainland China from 2004 to 2010 by examining spatial patterns.
METHODS: ESDA methods were used to characterise spatial patterns of EB smoothed incidence rates for human brucellosis based on county-level data obtained from the China Information System for Disease Control and Prevention (CISDCP) in mainland China from 2004 to 2010.
RESULTS: EB smoothed incidence rates for human brucellosis were spatially dependent during 2004-2010. The local Moran test identified significantly high-risk clusters of human brucellosis (all p values <0.01), which persisted during the 7-year study period. High-risk counties were centred in the Inner Mongolia Autonomous Region and other Northern provinces (ie, Hebei, Shanxi, Jilin and Heilongjiang provinces) around the border with the Inner Mongolia Autonomous Region where animal husbandry was highly developed. The number of high-risk counties increased from 25 in 2004 to 54 in 2010.
CONCLUSIONS: ESDA methods and the EB smoothing technique can assist public health officials in identifying high-risk areas. Allocating more resources to high-risk areas is an effective way to reduce human brucellosis incidence.