Background: While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time.
Methods:Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan’s spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time.
Results: Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others.
Conclusion: Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures. 相似文献
Summary In adults hyperinsulinaemia is associated with an atherogenic risk profile including obesity, low levels of HDL-cholesterol, high levels of triglycerides and elevated blood pressure. To examine these associations in the young we studied the cross-sectional relationships of insulin with obesity indices (body mass index, subscapular skinfold thickness), serum lipids and blood pressure in 1,865 children, adolescents and young adults aged 6–24 years. We also used longitudinal data to study the value of a single insulin measurement to predict high risk factor levels and clustering of multiple risk factors after a 6-year follow-up. In cross-sectional analyses the levels of triglycerides, HDL-cholesterol, systolic blood pressure and obesity indices were usually significantly different across the quartiles of fasting insulin in both sexes among children, adolescents and young adults. In general, no associations were seen with total cholesterol or LDL-cholesterol. In prospective analysis elevated baseline insulin was related to the incidence of hypertriglyceridaemia (95th percentile) at the follow-up. This relationship persisted even after adjustments for baseline obesity or 6-year change in obesity status. Moreover, baseline insulin concentration was higher in subjects who subsequently showed clustering of high triglycerides, low HDL-cholesterol and high systolic blood pressure levels at the follow-up. We conclude that high fasting insulin levels measured in children and adolescents predict the development of hypertriglyceridaemia years later. In addition, high insulin levels seem to precede the development of a potentially atherogenic risk factor profile including low HDL-cholesterol, high triglycerides and high systolic blood pressure.Abbreviations SBP
Systolic blood pressure
- DBP
diastolic blood pressure
- BMI
body mass index 相似文献
BACKGROUND: While higher smoking prevalences have been better described for adults and adolescents in the mountainous areas than in the plain area in Taiwan, no studies have previously examined whether this disparity begins with children in elementary schools. The purpose of this study was thus designed to explore clustering in smoking behavior among elementary school children attending mountain schools compared to those attending city schools. METHODS: This study analyzed data obtained by a survey on smoking behavior collected during the School Smoking Survey Project performed in 13 elementary schools of Taoyuan County, Taiwan. Overall, 1585 third and fourth graders (mean age 8.9 years) participated in the study. A multilevel logistic regression analysis was performed to explore the effects of school location on individual smoking behavior among elementary school children while controlling for individual-level characteristics. RESULTS: Overall, 34.9% of the elementary school students in the mountain schools reported having tried cigarette smoking compared to only 9.6% of students from city schools. Students attending mountain schools had a greater likelihood of reporting smoking than students attending city schools after controlling for individual-level characteristics (OR = 2.57, 95% CI = 1.10-5.99). CONCLUSIONS: A significant individual clustering in smoking behavior was found among third- and fourth-grade children attending mountain schools. The new findings suggest that the adult geographic smoking disparity begins in elementary school. Interventions aimed at reducing smoking disparity in adults need to target elementary schools in high-risk locations. 相似文献