Registration & Inspection Units 2003 (pdf 620 kb)

Summary of Statistical Results

Registration & Inspection Units 2002 (pdf 308 kb)

Summary of Statistical Results

Registration & Inspection Units 2001 (pdf 332 kb)

Summary of Statistical Results

Registration & Inspection Units 2000 (pdf 428 kb)

Summary of Statistical Results

Local Distance-Based Generalized Linear Models using the dbstats package for R

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly with the generalized linear model concept, then by localizing. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. Models can be fitted and analysed with the R package dbstats, which implements several distancebased prediction methods.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

Hepatitis C and hepatitis B virus infection in different hemodialysis units in Belo Horizonte, Minas Gerais, Brazil

The prevalence, virological and epidemilogical aspects of the hepatitis C virus (HCV) and the hepatitis B virus (HBV) infections vary among hemodialysis patients in different countries. Aiming at analyzing these aspects of HCV and HBV infections in hemodialysis patients in Belo Horizonte, MG, Brazil, we studied three hemodialysis units including 434 patients. Serology was used to detect anti-HCV and HBsAg. Reverse trancriptase nested polymerase chain reaction (RT-nested-PCR) of the 5'-noncoding region was used to detect circulating HCV RNA and restriction fragment length polymorphism analysis for genotyping. Seroprevalence varied from 26.5% to 11.1% for hepatitis C and from 5.9% to 0% for hepatitis B. Risk factors observed for HBV and/or HCV infections were the number of patients per dialysis unit, duration of treatment, number of clinics attended, number of blood units transfused, and lower level scholarity. Alanine aminotransferase levels were altered with a higher frequency in HBV or HCV seropositive patients. Half of ten patients, negative for anti-HCV, had detectable viremia by RT-nested-PCR, indicating that this technique should be used to confirm infections in this group of patients. The HCV genotype 1 was the most frequently observed, followed by the genotype 2, but no correlation was detected between genotype and clinical or epidemiological data.

Dementia Special Care Units in Residential Care Communities: United States, 2010

Assisted living and similar residential care communities provide an alternative to nursing homes for individuals with dementia who can no longer live independently .Individuals with dementia can live in residential care communities that have dementia special care units, or in a more traditional setting where these residents are integrated with residents without dementia. This report compares residential care communities with and without dementia special care units. Read more.

Estimating attributable mortality due to nosocomial infections acquired in intensive care units.

BACKGROUND: The strength of the association between intensive care unit (ICU)-acquired nosocomial infections (NIs) and mortality might differ according to the methodological approach taken. OBJECTIVE: To assess the association between ICU-acquired NIs and mortality using the concept of population-attributable fraction (PAF) for patient deaths caused by ICU-acquired NIs in a large cohort of critically ill patients. SETTING: Eleven ICUs of a French university hospital. DESIGN: We analyzed surveillance data on ICU-acquired NIs collected prospectively during the period from 1995 through 2003. The primary outcome was mortality from ICU-acquired NI stratified by site of infection. A matched-pair, case-control study was performed. Each patient who died before ICU discharge was defined as a case patient, and each patient who survived to ICU discharge was defined as a control patient. The PAF was calculated after adjustment for confounders by use of conditional logistic regression analysis. RESULTS: Among 8,068 ICU patients, a total of 1,725 deceased patients were successfully matched with 1,725 control patients. The adjusted PAF due to ICU-acquired NI for patients who died before ICU discharge was 14.6% (95% confidence interval [CI], 14.4%-14.8%). Stratified by the type of infection, the PAF was 6.1% (95% CI, 5.7%-6.5%) for pulmonary infection, 3.2% (95% CI, 2.8%-3.5%) for central venous catheter infection, 1.7% (95% CI, 0.9%-2.5%) for bloodstream infection, and 0.0% (95% CI, -0.4% to 0.4%) for urinary tract infection. CONCLUSIONS: ICU-acquired NI had an important effect on mortality. However, the statistical association between ICU-acquired NI and mortality tended to be less pronounced in findings based on the PAF than in study findings based on estimates of relative risk. Therefore, the choice of methods does matter when the burden of NI needs to be assessed.

Early-onset ventilator-associated pneumonia incidence in intensive care units: a surveillance-based study.

ABSTRACT: BACKGROUND: The incidence of ventilator-associated pneumonia (VAP) within the first 48 hours of intensive care unit (ICU) stay has been poorly investigated. The objective was to estimate early-onset VAP occurrence in ICUs within 48 hours after admission. METHODS: We analyzed data from prospective surveillance between 01/01/2001 and 31/12/2009 in 11 ICUs of Lyon hospitals (France). The inclusion criteria were: first ICU admission, not hospitalized before admission, invasive mechanical ventilation during first ICU day, free of antibiotics at admission, and ICU stay >=48 hours. VAP was defined according to a national protocol. Its incidence was the number of events per 1,000 invasive mechanical ventilation-days. The Poisson regression model was fitted from day 2 (D2) to D8 to incident VAP to estimate the expected VAP incidence from D0 to D1 of ICU stay. RESULTS: Totally, 367 (10.8%) of 3,387 patients in 45,760 patient-days developed VAP within the first 9 days. The predicted cumulative VAP incidence at D0 and D1 was 5.3 (2.6-9.8) and 8.3 (6.1-11.1), respectively. The predicted cumulative VAP incidence was 23.0 (20.8-25.3) at D8. The proportion of missed VAP within 48 hours from admission was 11% (9%-17%). CONCLUSIONS: Our study indicates underestimation of early-onset VAP incidence in ICUs, if only VAP occurring [greater than or equal to]48 hours is considered to be hospital-acquired. Clinicians should be encouraged to develop a strategy for early detection after ICU admission.

Colonization with multiple Staphylococcus aureus strains among patients in European intensive care units.

Patients and healthy individuals intermittently and inconsistently carry different methicillin-resistant Staphylococcus aureus (MRSA) subtypes. In the present study, we assessed the clonality of methicillin-susceptible S. aureus (MSSA) and MRSA strains in patients admitted to 1 of 6 intensive care units (ICUs), using spa typing and multilocus variable number of tandem repeats analysis (MLVA).

Acquisition and cross-transmission of Staphylococcus aureus in European intensive care units

Objective. To study the acquisition and cross-transmission of Staphylococcus aureus in different intensive care units (ICUs). Methods. We performed a multicenter cohort study. Six ICUs in 6 countries participated. During a 3-month period at each ICU, all patients had nasal and perineal swab specimens obtained at ICU admission and during their stay. All S. aureus isolates that were collected were genotyped by spa typing and multilocus variable-number tandem-repeat analysis typing for cross-transmission analysis. A total of 629 patients were admitted to ICUs, and 224 of these patients were found to be colonized with S. aureus at least once during ICU stay (22% were found to be colonized with methicillin-resistant S. aureus [MRSA]). A total of 316 patients who had test results negative for S. aureus at ICU admission and had at least 1 follow-up swab sample obtained for culture were eligible for acquisition analysis. Results. A total of 45 patients acquired S. aureus during ICU stay (31 acquired methicillin-susceptible S. aureus [MSSA], and 14 acquired MRSA). Several factors that were believed to affect the rate of acquisition of S. aureus were analyzed in univariate and multivariate analyses, including the amount of hand disinfectant used, colonization pressure, number of beds per nurse, antibiotic use, length of stay, and ICU setting (private room versus open ICU treatment). Greater colonization pressure and a greater number of beds per nurse correlated with a higher rate of acquisition for both MSSA and MRSA. The type of ICU setting was related to MRSA acquisition only, and the amount of hand disinfectant used was related to MSSA acquisition only. In 18 (40%) of the cases of S. aureus acquisition, cross-transmission from another patient was possible. Conclusions. Colonization pressure, the number of beds per nurse, and the treatment of all patients in private rooms correlated with the number of S. aureus acquisitions on an ICU. The amount of hand disinfectant used was correlated with the number of cases of MSSA acquisition but not with the number of cases of MRSA acquisition. The number of cases of patient-to-patient cross-transmission was comparable for MSSA and MRSA.

A compositional data analysis package for R providing multiple approaches

”compositions” is a new R-package for the analysis of compositional and positive data.It contains four classes corresponding to the four different types of compositional andpositive geometry (including the Aitchison geometry). It provides means for computation,plotting and high-level multivariate statistical analysis in all four geometries.These geometries are treated in an fully analogous way, based on the principle of workingin coordinates, and the object-oriented programming paradigm of R. In this way,called functions automatically select the most appropriate type of analysis as a functionof the geometry. The graphical capabilities include ternary diagrams and tetrahedrons,various compositional plots (boxplots, barplots, piecharts) and extensive graphical toolsfor principal components. Afterwards, ortion and proportion lines, straight lines andellipses in all geometries can be added to plots. The package is accompanied by ahands-on-introduction, documentation for every function, demos of the graphical capabilitiesand plenty of usage examples. It allows direct and parallel computation inall four vector spaces and provides the beginner with a copy-and-paste style of dataanalysis, while letting advanced users keep the functionality and customizability theydemand of R, as well as all necessary tools to add own analysis routines. A completeexample is included in the appendix

New Features of CoDaPack. An Userfriendly Compositional Data Package

The statistical analysis of compositional data is commonly used in geological studies.As is well-known, compositions should be treated using logratios of parts, which aredifficult to use correctly in standard statistical packages. In this paper we describe thenew features of our freeware package, named CoDaPack, which implements most of thebasic statistical methods suitable for compositional data. An example using real data ispresented to illustrate the use of the package