KISS Hospital Infection Surveillance System (Krankenhaus-Infektions-Surveillance-System)

Size: px
Start display at page:

Download "KISS Hospital Infection Surveillance System (Krankenhaus-Infektions-Surveillance-System)"

Transcription

1 (Krankenhaus-Infektions-Surveillance-System) NEO-KISS component Reference Data Prepared: 10 March 2010 National Reference Center for Nosocomial Infection Surveillance Hindenburgdamm Berlin Tel / Fax: / Last update: 10 March 2010

2 Birthweight class To 499 Total departments 140 Total patients 754 Total patient days 41,488 Average length of surveillance (days): Table 1: Device usage rate 1 Device Total device days Pooled average 25% quantile Median 75% quantile Vascular catheter 25, CVC 16, PVC 8, Mech. Ventilation 30, Intubation 14, CPAP 15, Antibiotics 19, Table 2: Incidence density 2 Type of infection Total infections Pooled average 25% quantile Median 75% quantile Severe HAI Pneumonia BSI NEC Device usage rate: Total device days / Total patient days x Incidence density: Total infections / Total patient days x Severe HAI (Hospital-associated infection): Total BSI and Reference data -1-

3 Table 3: Device-associated infection rates 4 Dev.-assoc. infection Total dev.-assoc. infections Pooled average 25% quantile Median 75% quantile Vascular catheterassoc. BSI 2, CVC-assoc. BSI 1, PVC-assoc. BSI Mech. ventilationassoc Intubation-assoc CPAP-assoc Device-associated infection rate: Total device-associated infection / Total device days x 1000 Reference data -2-

4 Birthweight class 500 to 999 Total departments 198 Total patients 9,226 Total patient days 454,978 Average length of surveillance (days): Table 1: Device usage rate 1 Device Total device days Pooled average 25% quantile Median 75% quantile Vascular catheter 241, CVC 138, PVC 103, Mech. Ventilation 264, Intubation 97, CPAP 166, Antibiotics 157, Table 2: Incidence density 2 Type of infection Total infections Pooled average 25% quantile Median 75% quantile Severe HAI 3 3, Pneumonia BSI 2, NEC Device usage rate: Total device days / Total patient days x Incidence density: Total infections / Total patient days x Severe HAI (Hospital-associated infection): Total BSI and Reference data -3-

5 Table 3: Device-associated infection rates 4 Dev.-assoc. infection Total dev.-assoc. infections Pooled average 25% quantile Median 75% quantile Vascular catheterassoc. BSI 2, CVC-assoc. BSI PVC-assoc. BSI Mech. Ventilationassoc Intubation-assoc CPAP-assoc Device-associated infection rate: Total device-associated infection / Total device days x 1000 Reference data -4-

6 Birthweight class 1000 to 1499 Total departments 207 Total patients 14,321 Total patient days 404,571 Average length of surveillance (days): Table 1: Device usage rate 1 Device Total device days Pooled average 25% quantile Median 75% quantile Vascular catheter 189, CVC 68, PVC 121, Mech. Ventilation 104, Intubation 26, CPAP 77, Antibiotics 93, Table 2: Incidence density 2 Type of infection Total infections Pooled average 25% quantile Median 75% quantile Severe HAI 3 1, Pneumonia BSI 1, NEC Device usage rate: Total device days / Total patient days x Incidence density: Total infections / Total patient days x Severe HAI (Hospital-associated infection): Total BSI and Reference data -5-

7 Table 3: Device-associated infection rates 4 Dev.-assoc. infection Total dev.-assoc. infections Pooled average 25% quantile Median 75% quantile Vascular catheterassoc. BSI 1, CVC-assoc. BSI PVC-assoc. BSI Mech. Ventilationassoc Intubation-assoc CPAP-assoc Device-associated infection rate: Total device-associated infection / Total device days x 1000 Reference data -6-

8 Pathogen distribution Table 1: Pneumonia pathogens (Total infections = 596) Pathogen Total infections with/out pathogen Total infections with/out pathogen per 100 infections (%) Total pathogens per 100 pathogens (%) No pathogen detected KNS URE ENT ENB ECO SAU KLE PAE CAN SON Pathogens in table Total pathogens Reference data -7-

9 Pathogen distribution Table 2: BSI pathogens (Total infections = 4,486) Pathogen Total infections with/out pathogen Total infections with/out pathogen per 100 infections (%) Total pathogens per 100 pathogens (%) No pathogen detected 2, KNS 1, SAU ENT ENB ECO SON KLE CAN SER ANB Total pathogens in table 2,199 91,13 Total pathogens 2, Reference data -8-

10 Reference data statistics Total neonatolgy departments that contributed at least one data set to reference data: 209 Distribution of neonatology levels of care (self-assigned, as defined by German law) Perinatal center LEVEL 1 (highest risk patients): 152 Perinatal center LEVEL 2 (wide spectrum intermediary care for high risk patients): 43 Centers with emphasis on perinatal care (wide spectrum postnatal care for infants in hospitals): 6 Maternity hospitals (centers only for mature births without significant risk): 8 Reference data -9-

Laboratory Enhancement Program HIV Laboratory, Public Health Ontario Updated analyses: January 2009 to December Background

Laboratory Enhancement Program HIV Laboratory, Public Health Ontario Updated analyses: January 2009 to December Background Laboratory Enhancement Program HIV Laboratory, Public Health Ontario Updated analyses: January 2009 to December 2012 Background To enhance laboratory-based HIV surveillance data and to better understand

More information

Quantifying the Predictive Accuracy of Time to Event Models in the Presence of Competing Risks

Quantifying the Predictive Accuracy of Time to Event Models in the Presence of Competing Risks Quantifying the Predictive Accuracy of Time to Event Models in the Presence of Competing Risks Rotraut Schoop, Jan Beyersmann, Martin Schumacher & Harald Binder Universität Freiburg i. Br. FDM-Preprint

More information

T test for two Independent Samples. Raja, BSc.N, DCHN, RN Nursing Instructor Acknowledgement: Ms. Saima Hirani June 07, 2016

T test for two Independent Samples. Raja, BSc.N, DCHN, RN Nursing Instructor Acknowledgement: Ms. Saima Hirani June 07, 2016 T test for two Independent Samples Raja, BSc.N, DCHN, RN Nursing Instructor Acknowledgement: Ms. Saima Hirani June 07, 2016 Q1. The mean serum creatinine level is measured in 36 patients after they received

More information

Using GIS to Brief New York City Public Officials after September 11

Using GIS to Brief New York City Public Officials after September 11 Using GIS to Brief New York City Public Officials after September 11 Presented by Zvia Segal Naphtali, Ph.D. and Leonard M. Naphtali, Ph.D. Presented at the ESRI International Health GIS Conference, May

More information

Hypothesis Testing, Power, Sample Size and Confidence Intervals (Part 2)

Hypothesis Testing, Power, Sample Size and Confidence Intervals (Part 2) Hypothesis Testing, Power, Sample Size and Confidence Intervals (Part 2) B.H. Robbins Scholars Series June 23, 2010 1 / 29 Outline Z-test χ 2 -test Confidence Interval Sample size and power Relative effect

More information

% G 4H! ; " =!*Q c KU >& (-+ AQ %+ :K >

% G 4H! ;  =!*Q c KU >& (-+ AQ %+ :K > -! "# $ 18 0 1,- '1390 '7 '1 1388/7/9 :89 0 1387/8/ :3 0 @E (Original Article) -60 BC#C 0 A0 @#0 >? =0 < $:; 3 1 > # G C ' = C ' 0 F ' ;C E " " #! 1 " " #! )*+ %&' ( " " # " # 1# 789 60 1#!3# + "++ /01

More information

Prolonged outbreak of Serratia marcescens in Tartu University Hospital: a case control study

Prolonged outbreak of Serratia marcescens in Tartu University Hospital: a case control study Adamson et al. BMC Infectious Diseases 2012, 12:281 RESEARCH ARTICLE Open Access Prolonged outbreak of Serratia marcescens in Tartu University Hospital: a case control study Vivika Adamson 1*, Piret Mitt

More information

David Rogers Health and Climate Foundation

David Rogers Health and Climate Foundation David Rogers Health and Climate Foundation Using environmental information Weather and Climate Informed Decisions Climate Information for Health Sector Decisions -

More information

Practice problems from chapters 2 and 3

Practice problems from chapters 2 and 3 Practice problems from chapters and 3 Question-1. For each of the following variables, indicate whether it is quantitative or qualitative and specify which of the four levels of measurement (nominal, ordinal,

More information

Implementation of Public Health Surveillance of Carbapenemase- Producing Enterobacteriaceae in Victoria, Australia

Implementation of Public Health Surveillance of Carbapenemase- Producing Enterobacteriaceae in Victoria, Australia Implementation of Public Health Surveillance of Carbapenemase- Producing Enterobacteriaceae in Victoria, Australia C.R. Lane*, J. Brett, M.B. Schultz, K. Stevens, A. van Diemen, S.A. Ballard, N.L. Sherry,

More information

Jun Tu. Department of Geography and Anthropology Kennesaw State University

Jun Tu. Department of Geography and Anthropology Kennesaw State University Examining Spatially Varying Relationships between Preterm Births and Ambient Air Pollution in Georgia using Geographically Weighted Logistic Regression Jun Tu Department of Geography and Anthropology Kennesaw

More information

Evaluation of building-scale heat-stress analysis system (BioCAS) based on mortality observation in Seoul

Evaluation of building-scale heat-stress analysis system (BioCAS) based on mortality observation in Seoul ICUC9 9th International Congress on Urban Climate 20 24 July, 2015, Toulouse France Evaluation of building-scale heat-stress analysis system (BioCAS) based on mortality observation in Seoul National Institute

More information

Section Comparing Two Proportions

Section Comparing Two Proportions Section 8.2 - Comparing Two Proportions Statistics 104 Autumn 2004 Copyright c 2004 by Mark E. Irwin Comparing Two Proportions Two-sample problems Want to compare the responses in two groups or treatments

More information

Disadvantages of using many pooled t procedures. The sampling distribution of the sample means. The variability between the sample means

Disadvantages of using many pooled t procedures. The sampling distribution of the sample means. The variability between the sample means Stat 529 (Winter 2011) Analysis of Variance (ANOVA) Reading: Sections 5.1 5.3. Introduction and notation Birthweight example Disadvantages of using many pooled t procedures The analysis of variance procedure

More information

Modeling Prediction of the Nosocomial Pneumonia with a Multistate model

Modeling Prediction of the Nosocomial Pneumonia with a Multistate model Modeling Prediction of the Nosocomial Pneumonia with a Multistate model M.Nguile Makao 1 PHD student Director: J.F. Timsit 2 Co-Directors: B Liquet 3 & J.F. Coeurjolly 4 1 Team 11 Inserm U823-Joseph Fourier

More information

PubH 7405: REGRESSION ANALYSIS MLR: BIOMEDICAL APPLICATIONS

PubH 7405: REGRESSION ANALYSIS MLR: BIOMEDICAL APPLICATIONS PubH 7405: REGRESSION ANALYSIS MLR: BIOMEDICAL APPLICATIONS Multiple Regression allows us to get into two new areas that were not possible with Simple Linear Regression: (i) Interaction or Effect Modification,

More information

Solving related rates problems

Solving related rates problems Solving related rates problems 1 Draw a diagram 2 Assign symbols to all quantities that are functions of time 3 Express given information and the required rate in terms of derivatives 4 Write down a relation

More information

Title. Keywords Learning objectives. Abstract. Teaching methods. Specific recommendations for teachers. Assessment of students

Title. Keywords Learning objectives. Abstract. Teaching methods. Specific recommendations for teachers. Assessment of students Title METHODS AND TOOLS IN PUBLIC HEALTH A Handbook for Teachers, Researchers and Health Professionals ORGANIZING AND DESCRIBING DATA Module: 1.2.1 ECTS (suggested): 0.30 Author(s), degrees, Lijana Zaletel-Kragelj,

More information

Continuous random variables

Continuous random variables Continuous random variables A continuous random variable X takes all values in an interval of numbers. The probability distribution of X is described by a density curve. The total area under a density

More information

GE Healthcare. Vital Signs Devices. Supplies Accessories & Clinical Consumables

GE Healthcare. Vital Signs Devices. Supplies Accessories & Clinical Consumables GE Healthcare Vital Signs Devices Supplies Accessories & Clinical Consumables Vital Signs Devices A new GE healthcare company The acquisition of Vital Signs by GE Healthcare marks a distinct expansion

More information

Harvard University. Harvard University Biostatistics Working Paper Series

Harvard University. Harvard University Biostatistics Working Paper Series Harvard University Harvard University Biostatistics Working Paper Series Year 2014 Paper 176 A Simple Regression-based Approach to Account for Survival Bias in Birth Outcomes Research Eric J. Tchetgen

More information

THE ROYAL STATISTICAL SOCIETY 2015 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 3

THE ROYAL STATISTICAL SOCIETY 2015 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 3 THE ROYAL STATISTICAL SOCIETY 015 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 3 The Society is providing these solutions to assist candidates preparing for the examinations in 017. The solutions are

More information

BIOSTATISTICS METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH DAY #4: REGRESSION APPLICATIONS, PART C MULTILE REGRESSION APPLICATIONS

BIOSTATISTICS METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH DAY #4: REGRESSION APPLICATIONS, PART C MULTILE REGRESSION APPLICATIONS BIOSTATISTICS METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH DAY #4: REGRESSION APPLICATIONS, PART C MULTILE REGRESSION APPLICATIONS This last part is devoted to Multiple Regression applications; it covers

More information

Continuous Probability Distributions

Continuous Probability Distributions 1 Chapter 5 Continuous Probability Distributions 5.1 Probability density function Example 5.1.1. Revisit Example 3.1.1. 11 12 13 14 15 16 21 22 23 24 25 26 S = 31 32 33 34 35 36 41 42 43 44 45 46 (5.1.1)

More information

Avoidable Repeat Rate for June 2018 for Newborn Blood Spot Screening - Manchester Laboratory

Avoidable Repeat Rate for June 2018 for Newborn Blood Spot Screening - Manchester Laboratory Rate for June 2018 for Newborn Blood Spot Screening - Manchester Laboratory The avoidable repeat rate for samples received during June 2018 is displayed by Trust in Figure 1 and by current hospital (for

More information

Neonatal period is a period that susceptible

Neonatal period is a period that susceptible Paediatrica Indonesiana VOLUME 50 March NUMBER 2 Original Article Comparison of the accuracy of body temperature measurements with temporal artery thermometer and axillary mercury thermometer in term newborns

More information

Modelling spatio-temporal patterns of disease

Modelling spatio-temporal patterns of disease Modelling spatio-temporal patterns of disease Peter J Diggle CHICAS combining health information, computation and statistics References AEGISS Brix, A. and Diggle, P.J. (2001). Spatio-temporal prediction

More information

How is the Statistical Power of Hypothesis Tests affected by Dose Uncertainty?

How is the Statistical Power of Hypothesis Tests affected by Dose Uncertainty? How is the Statistical Power of Hypothesis Tests affected by Dose Uncertainty? by Eduard Hofer Workshop on Uncertainties in Radiation Dosimetry and their Impact on Risk Analysis, Washington, DC, May 2009

More information

Null Hypothesis Significance Testing p-values, significance level, power, t-tests

Null Hypothesis Significance Testing p-values, significance level, power, t-tests Null Hypothesis Significance Testing p-values, significance level, power, t-tests 18.05 Spring 2014 January 1, 2017 1 /22 Understand this figure f(x H 0 ) x reject H 0 don t reject H 0 reject H 0 x = test

More information

UNIVERSAL DEMULTIPLEXER/ DECODER

UNIVERSAL DEMULTIPLEXER/ DECODER UNIVERSAL DEULTIPLEXER/ DECODER FEATURES DESCRIPTION ax. propagation delay of 1200ps IEE min. of 92mA Industry standard 100K ECL levels Extended supply voltage option: VEE = 4.2V to 5.5V Voltage and temperature

More information

MATH4427 Notebook 4 Fall Semester 2017/2018

MATH4427 Notebook 4 Fall Semester 2017/2018 MATH4427 Notebook 4 Fall Semester 2017/2018 prepared by Professor Jenny Baglivo c Copyright 2009-2018 by Jenny A. Baglivo. All Rights Reserved. 4 MATH4427 Notebook 4 3 4.1 K th Order Statistics and Their

More information

CHAPTER 2 Description of Samples and Populations

CHAPTER 2 Description of Samples and Populations Chapter 2 27 CHAPTER 2 Description of Samples and Populations 2.1.1 (a) i) Molar width ii) Continuous variable iii) A molar iv) 36 (b) i) Birthweight, date of birth, and race ii) Birthweight is continuous,

More information

Continuous Probability Distributions

Continuous Probability Distributions 1 Chapter 5 Continuous Probability Distributions 5.1 Probability density function Example 5.1.1. Revisit Example 3.1.1. 11 12 13 14 15 16 21 22 23 24 25 26 S = 31 32 33 34 35 36 41 42 43 44 45 46 (5.1.1)

More information

Avoidable Repeat Rate for May 2018 for Newborn Blood Spot Screening - Manchester Laboratory

Avoidable Repeat Rate for May 2018 for Newborn Blood Spot Screening - Manchester Laboratory Rate for May 2018 for Newborn Blood Spot Screening - Manchester Laboratory The avoidable repeat rate for samples received during May 2018 is displayed by Trust in Figure 1 and by current hospital (for

More information

Introduction to Panel Data Analysis

Introduction to Panel Data Analysis Introduction to Panel Data Analysis Youngki Shin Department of Economics Email: yshin29@uwo.ca Statistics and Data Series at Western November 21, 2012 1 / 40 Motivation More observations mean more information.

More information

What is Statistics? Simple Summaries and Plots. Brian D. Ripley

What is Statistics? Simple Summaries and Plots. Brian D. Ripley What is Statistics? Simple Summaries and Plots Brian D. Ripley Statistics The name derives from state-istics, quantification of the state, an area now know as official statistics. Health statistics have

More information

BINF 702 SPRING Chapter 8 Hypothesis Testing: Two-Sample Inference. BINF702 SPRING 2014 Chapter 8 Hypothesis Testing: Two- Sample Inference 1

BINF 702 SPRING Chapter 8 Hypothesis Testing: Two-Sample Inference. BINF702 SPRING 2014 Chapter 8 Hypothesis Testing: Two- Sample Inference 1 BINF 702 SPRING 2014 Chapter 8 Hypothesis Testing: Two-Sample Inference Two- Sample Inference 1 A Poster Child for two-sample hypothesis testing Ex 8.1 Obstetrics In the birthweight data in Example 7.2,

More information

COOK ISLANDS TE MARAE ORA

COOK ISLANDS TE MARAE ORA COOK ISLANDS MINISTRY OF HEALTH TE MARAE ORA ANNUAL STATISTICAL TABLES HEALTH STATISTICAL TABLES 2008-2010 MEDICAL RECORDS UNIT Rarotonga Hospital It should be noted that information contained in this

More information

Why the CDS? The unique advantages of using an Australian antimicrobial susceptibility testing method

Why the CDS? The unique advantages of using an Australian antimicrobial susceptibility testing method Why the CDS? The unique advantages of using an Australian antimicrobial susceptibility testing method Peter Newton Medical Microbiologist Wollongong Hospital, Wollongong, NSW Where do I come from? SEALS

More information

2 Salmonella Typhimurium

2 Salmonella Typhimurium 96 2006 Salmonella Typhimurium 2 1) 1) 2) 1) 2) 18 1 10 18 4 27 2 Salmonella Typhimurium 1 7 2 7 (ciprofloxacin (CPFX) MIC 16 mg/ml) S. Typhimurium 2 fosfomycin (FOM) 1 PCR gyra parc RAPD-PCR DNA S. Typhimurium

More information

Lecture 8: The Metropolis-Hastings Algorithm

Lecture 8: The Metropolis-Hastings Algorithm 30.10.2008 What we have seen last time: Gibbs sampler Key idea: Generate a Markov chain by updating the component of (X 1,..., X p ) in turn by drawing from the full conditionals: X (t) j Two drawbacks:

More information

ANOVA: Analysis of Variation

ANOVA: Analysis of Variation ANOVA: Analysis of Variation The basic ANOVA situation Two variables: 1 Categorical, 1 Quantitative Main Question: Do the (means of) the quantitative variables depend on which group (given by categorical

More information

SARAWAK STATE HEALTH DEPARTMENT (Information and Documentation Unit)

SARAWAK STATE HEALTH DEPARTMENT (Information and Documentation Unit) SARAWAK STATE HEALTH DEPARTMENT (Information and Documentation Unit) VISION FOR HEALTH Malaysia is to be a nation of healthy individuals, families and communities, through a health system that is equitable,

More information

Floodlight Scorecard. Finance and Performance Committee Risk and Quality Committee. To improve the quality of all aspects of our services

Floodlight Scorecard. Finance and Performance Committee Risk and Quality Committee. To improve the quality of all aspects of our services TRUST BOARD 28 th September 2011 Floodlight Scorecard abc Agenda Item: 12 PURPOSE: PREVIOUSLY CONSIDERED BY: To inform the Trust Board of performance against Trust Key Performance Indicators in a number

More information

Null Hypothesis Significance Testing p-values, significance level, power, t-tests Spring 2017

Null Hypothesis Significance Testing p-values, significance level, power, t-tests Spring 2017 Null Hypothesis Significance Testing p-values, significance level, power, t-tests 18.05 Spring 2017 Understand this figure f(x H 0 ) x reject H 0 don t reject H 0 reject H 0 x = test statistic f (x H 0

More information

Modeling the Transmission of Vancomycin-Resistant Enterococcus (VRE) in Hospitals: A Case Study

Modeling the Transmission of Vancomycin-Resistant Enterococcus (VRE) in Hospitals: A Case Study Modeling the Transmission of Vancomycin-Resistant Enterococcus (VRE) in Hospitals: A Case Study A. R. Ortiz 1, H. T. Banks 2, C. Castillo-Chavez 1, G. Chowell 1, C. Torres-Viera 3 and X. Wang 1 1 School

More information

1 ONE SAMPLE TEST FOR MEDIAN: THE SIGN TEST

1 ONE SAMPLE TEST FOR MEDIAN: THE SIGN TEST NON-PARAMETRIC STATISTICS ONE AND TWO SAMPLE TESTS Non-parametric tests are normally based on ranks of the data samples, and test hypotheses relating to quantiles of the probability distribution representing

More information

Hotspot detection using space-time scan statistics on children under five years of age in Depok

Hotspot detection using space-time scan statistics on children under five years of age in Depok Hotspot detection using space-time scan statistics on children under five years of age in Depok Miranti Verdiana, and Yekti Widyaningsih Citation: AIP Conference Proceedings 1827, 020018 (2017); View online:

More information

JOINT STRATEGIC NEEDS ASSESSMENT (JSNA) Key findings from the Leicestershire JSNA and Charnwood summary

JOINT STRATEGIC NEEDS ASSESSMENT (JSNA) Key findings from the Leicestershire JSNA and Charnwood summary JOINT STRATEGIC NEEDS ASSESSMENT (JSNA) Key findings from the Leicestershire JSNA and Charnwood summary 1 What is a JSNA? Joint Strategic Needs Assessment (JSNA) identifies the big picture in terms of

More information

This midterm covers Chapters 6 and 7 in WMS (and the notes). The following problems are stratified by chapter.

This midterm covers Chapters 6 and 7 in WMS (and the notes). The following problems are stratified by chapter. This midterm covers Chapters 6 and 7 in WMS (and the notes). The following problems are stratified by chapter. Chapter 6 Problems 1. Suppose that Y U(0, 2) so that the probability density function (pdf)

More information

Two Sample Problems. Two sample problems

Two Sample Problems. Two sample problems Two Sample Problems Two sample problems The goal of inference is to compare the responses in two groups. Each group is a sample from a different population. The responses in each group are independent

More information

Alternative Approaches to Thoracoscopic Lobectomy: Uniportal, Supxiphoid,

Alternative Approaches to Thoracoscopic Lobectomy: Uniportal, Supxiphoid, Alternative Approaches to Thoracoscopic Lobectomy: Uniportal, Supxiphoid, Thomas A. D Amico MD Gary Hock Endowed Professor Chief Thoracic Surgery Chief Medical Officer, Duke Cancer Institute Disclosures

More information

A multi-state model for the prognosis of non-mild acute pancreatitis

A multi-state model for the prognosis of non-mild acute pancreatitis A multi-state model for the prognosis of non-mild acute pancreatitis Lore Zumeta Olaskoaga 1, Felix Zubia Olaskoaga 2, Guadalupe Gómez Melis 1 1 Universitat Politècnica de Catalunya 2 Intensive Care Unit,

More information

Application of Exponential Smoothing for Nosocomial Infection Surveillance

Application of Exponential Smoothing for Nosocomial Infection Surveillance American Journal of Epidemiology Copyright 1996 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved Vol. 143, No. 6 Printed in U.S.A. Application of Exponential Smoothing

More information

Geozones: an area-based method for analysis of health outcomes

Geozones: an area-based method for analysis of health outcomes Statistics Canada www.statcan.gc.ca Geozones: an area-based method for analysis of health outcomes CIQSS Montreal, Quebec October 19, 2012 Paul A. Peters, PhD Health Analysis Division, Statistics Canada

More information

Avoidable Repeat Rate for August 2017 for Newborn Blood Spot Screening - Manchester Laboratory

Avoidable Repeat Rate for August 2017 for Newborn Blood Spot Screening - Manchester Laboratory Rate for August 2017 for Newborn Blood Spot Screening - Manchester Laboratory The avoidable repeat rate for samples received during August 2017 is displayed by Trust in Figure 1 and by current hospital

More information

ARIC Manuscript Proposal # PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2

ARIC Manuscript Proposal # PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2 ARIC Manuscript Proposal # 1186 PC Reviewed: _9/_25_/06 Status: A Priority: _2 SC Reviewed: _9/_25_/06 Status: A Priority: _2 1.a. Full Title: Comparing Methods of Incorporating Spatial Correlation in

More information

Credit at (circle one): UNB-Fredericton UNB-Saint John UNIVERSITY OF NEW BRUNSWICK DEPARTMENT OF MATHEMATICS & STATISTICS

Credit at (circle one): UNB-Fredericton UNB-Saint John UNIVERSITY OF NEW BRUNSWICK DEPARTMENT OF MATHEMATICS & STATISTICS Last name: First name: Middle initial(s): Date of birth: High school: Teacher: Credit at (circle one): UNB-Fredericton UNB-Saint John UNIVERSITY OF NEW BRUNSWICK DEPARTMENT OF MATHEMATICS & STATISTICS

More information

Full file at

Full file at IV SOLUTIONS TO EXERCISES Note: Exercises whose answers are given in the back of the textbook are denoted by the symbol. CHAPTER Description of Samples and Populations Note: Exercises whose answers are

More information

More Statistics tutorial at Logistic Regression and the new:

More Statistics tutorial at  Logistic Regression and the new: Logistic Regression and the new: Residual Logistic Regression 1 Outline 1. Logistic Regression 2. Confounding Variables 3. Controlling for Confounding Variables 4. Residual Linear Regression 5. Residual

More information

Do patients die from or with infection?

Do patients die from or with infection? FACULTY OF SCIENCES Do patients die from or with infection? Finding the answer through causal analysis of longitudinal intensive care unit data. Maarten Bekaert Hieronder volgt een overzicht van de informatie,

More information

2016 ANNUAL REPORT HSAG: The Florida ESRD Network (Network 7)

2016 ANNUAL REPORT HSAG: The Florida ESRD Network (Network 7) 2016 ANNUAL REPORT HSAG: The Florida ESRD Network (Network 7) Together we can spread positive change to make healthcare better. Centers for Medicare & Medicaid Contract Number: HHSM-500-2016-0007C HSAG:

More information

A broad spectrum vaccine to prevent invasive Salmonella Infections for sub- Saharan Africa

A broad spectrum vaccine to prevent invasive Salmonella Infections for sub- Saharan Africa A broad spectrum vaccine to prevent invasive Salmonella Infections for sub- Saharan Africa Myron M Levine, Raphael Simon, Sharon Tennant, James Galen, Andrew Lees, Velupillai Puvanesarajah, Ellen Higginson,

More information

NI - INTEGRATED PUBLIC PROVISION OF HEALTH CARE SERVICES (P164452)

NI - INTEGRATED PUBLIC PROVISION OF HEALTH CARE SERVICES (P164452) LATIN AMERICA AND CARIBBEAN Nicaragua Health, Nutrition & Population Global Practice IBRD/IDA Investment Project Financing FY 2018 Seq No: 2 ARCHIVED on 26-Dec-2018 ISR35016 Implementing Agencies: Ministry

More information

Performance of Number-Between g-type Statistical Control Charts for Monitoring Adverse Events

Performance of Number-Between g-type Statistical Control Charts for Monitoring Adverse Events To Appear in Health Care Management Science, 200 Performance of Number-Between g-type Statistical Control Charts for Monitoring Adverse Events Last Revised: August 9, 2000 James C. Benneyan * 334 Snell

More information

9 One-Way Analysis of Variance

9 One-Way Analysis of Variance 9 One-Way Analysis of Variance SW Chapter 11 - all sections except 6. The one-way analysis of variance (ANOVA) is a generalization of the two sample t test to k 2 groups. Assume that the populations of

More information

Sample Date: March 30, 2018 Date Received: March 31, 2018 Date of Report: April 9, 2018 (877) Fax: (877)

Sample Date: March 30, 2018 Date Received: March 31, 2018 Date of Report: April 9, 2018 (877) Fax: (877) U.S. Micro-Solutions, Inc. * 075 South Main Street, Suite 04 * Greensburg, PA 560 Phone: (877) 876-4276 Fax: (724) 853-4049 AIHA-LAP, LLC EMLAP #03009 075 South Main Street, Suite 04 Greensburg, PA 560

More information

Agricultural Operator Exposure Model (AOEM)

Agricultural Operator Exposure Model (AOEM) Agricultural Operator Exposure Model (AOEM) Claudia Großkopf BUNDESINSTITUT FÜR RISIKOBEWERTUNG Introduction Current situation: different models used in risk assessment for PPPs mainly based on data for

More information

Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants

Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Near/Far Matching Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Joint research: Mike Baiocchi, Dylan Small, Scott Lorch and Paul Rosenbaum What this talk

More information

Example: Data from the Child Health and Development Study

Example: Data from the Child Health and Development Study Example: Data from the Child Health and Development Study Can we use linear regression to examine how well length of gesta:onal period predicts birth weight? First look at the sca@erplot: Does a linear

More information

Responsibilities: Effective Date: November Revision Date: February 8, VP, Facilities and Construction Management. Issuing Authority:

Responsibilities: Effective Date: November Revision Date: February 8, VP, Facilities and Construction Management. Issuing Authority: Title: Chemical Hygiene Written Program Effective Date: November 2005 Revision Date: February 8, 2017 Issuing Authority: Responsible Officer: VP, Facilities and Construction Management Director Environmental

More information

Statistical Experiment A statistical experiment is any process by which measurements are obtained.

Statistical Experiment A statistical experiment is any process by which measurements are obtained. (التوزيعات الا حتمالية ( Distributions Probability Statistical Experiment A statistical experiment is any process by which measurements are obtained. Examples of Statistical Experiments Counting the number

More information

GeoHealth Applications Platform ESRI Health GIS Conference 2013

GeoHealth Applications Platform ESRI Health GIS Conference 2013 GeoHealth Applications Platform ESRI Health GIS Conference 2013 Authors Thomas A. Horan, Ph.D. Professor, CISAT Director April Moreno Health GeoInformatics Ph.D. Student Brian N. Hilton, Ph.D. Clinical

More information

Chapter 18. Sampling Distribution Models. Bin Zou STAT 141 University of Alberta Winter / 10

Chapter 18. Sampling Distribution Models. Bin Zou STAT 141 University of Alberta Winter / 10 Chapter 18 Sampling Distribution Models Bin Zou (bzou@ualberta.ca) STAT 141 University of Alberta Winter 2015 1 / 10 Population VS Sample Example 18.1 Suppose a total of 10,000 patients in a hospital and

More information

Genesis Hospital. Surgery Simulation. Curtis Theel, MBA, CSSBB, PMP 2016 ASQ Columbus Spring Conference March 7 th, 2016

Genesis Hospital. Surgery Simulation. Curtis Theel, MBA, CSSBB, PMP 2016 ASQ Columbus Spring Conference March 7 th, 2016 Genesis Hospital Surgery Simulation Curtis Theel, MBA, CSSBB, PMP 2016 ASQ Columbus Spring Conference March 7 th, 2016 Background information In 2011, Genesis Healthcare System decided to combine 2 separate

More information

Serratia marcescens (

Serratia marcescens ( 103 Serratia marcescens 1 1,2 1,3 1,4 1,5 1 2 3 4 5 Serratia marcescens 2017 5 8.54 4 4.59 ( ) df = 1, 2 = 1.474, P < 0.000 5 S. marcescens 3 2018:28:103-111 Serratia marcescens Serratia marcescens ( )

More information

GIS and Health Geography. What is epidemiology?

GIS and Health Geography. What is epidemiology? GIS and Health Geography { What is epidemiology? TOC GIS and health geography Major applications for GIS Epidemiology What is health (and how location matters) What is a disease (and how to identify one)

More information

Chapter 2 Exercise Solutions. Chapter 2

Chapter 2 Exercise Solutions. Chapter 2 Chapter 2 Exercise Solutions Chapter 2 2.3.1. (a) Cumulative Class Cumulative Relative relative interval Frequency frequency frequency frequency 0-0.49 3 3 3.33 3.33.5-0.99 3 6 3.33 6.67 1.0-1.49 15 21

More information

Approximate and Fiducial Confidence Intervals for the Difference Between Two Binomial Proportions

Approximate and Fiducial Confidence Intervals for the Difference Between Two Binomial Proportions Approximate and Fiducial Confidence Intervals for the Difference Between Two Binomial Proportions K. Krishnamoorthy 1 and Dan Zhang University of Louisiana at Lafayette, Lafayette, LA 70504, USA SUMMARY

More information

Chapter 7 Fall Chapter 7 Hypothesis testing Hypotheses of interest: (A) 1-sample

Chapter 7 Fall Chapter 7 Hypothesis testing Hypotheses of interest: (A) 1-sample Bios 323: Applied Survival Analysis Qingxia (Cindy) Chen Chapter 7 Fall 2012 Chapter 7 Hypothesis testing Hypotheses of interest: (A) 1-sample H 0 : S(t) = S 0 (t), where S 0 ( ) is known survival function,

More information

A mathematical and computational model of necrotizing enterocolitis

A mathematical and computational model of necrotizing enterocolitis A mathematical and computational model of necrotizing enterocolitis Ivan Yotov Department of Mathematics, University of Pittsburgh McGowan Institute Scientific Retreat March 10-12, 2008 Acknowledgment:

More information

1-Way ANOVA MATH 143. Spring Department of Mathematics and Statistics Calvin College

1-Way ANOVA MATH 143. Spring Department of Mathematics and Statistics Calvin College 1-Way ANOVA MATH 143 Department of Mathematics and Statistics Calvin College Spring 2010 The basic ANOVA situation Two variables: 1 Categorical, 1 Quantitative Main Question: Do the (means of) the quantitative

More information

A is one of the categories into which qualitative data can be classified.

A is one of the categories into which qualitative data can be classified. Chapter 2 Methods for Describing Sets of Data 2.1 Describing qualitative data Recall qualitative data: non-numerical or categorical data Basic definitions: A is one of the categories into which qualitative

More information

BAYESIAN PROCESSOR OF ENSEMBLE (BPE): PRIOR DISTRIBUTION FUNCTION

BAYESIAN PROCESSOR OF ENSEMBLE (BPE): PRIOR DISTRIBUTION FUNCTION BAYESIAN PROCESSOR OF ENSEMBLE (BPE): PRIOR DISTRIBUTION FUNCTION Parametric Models and Estimation Procedures Tested on Temperature Data By Roman Krzysztofowicz and Nah Youn Lee University of Virginia

More information

Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants

Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Near/Far Matching Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Joint research: Mike Baiocchi, Dylan Small, Scott Lorch and Paul Rosenbaum Classic set

More information

Chapter 7: Hypothesis testing

Chapter 7: Hypothesis testing Chapter 7: Hypothesis testing Hypothesis testing is typically done based on the cumulative hazard function. Here we ll use the Nelson-Aalen estimate of the cumulative hazard. The survival function is used

More information

Bayesian Use of Likelihood Ratios in Biostatistics

Bayesian Use of Likelihood Ratios in Biostatistics Bayesian Use of Likelihood Ratios in Biostatistics David Draper Department of Applied Mathematics and Statistics University of California, Santa Cruz, USA draper@ams.ucsc.edu www.ams.ucsc.edu/ draper JSM

More information

Neisseria meningitidis and Haemophilus influenzae Survey Brief

Neisseria meningitidis and Haemophilus influenzae Survey Brief N. MENINGITIDIS AND H. INFLUENZAE SURVEY BRIEF APRIL Neisseria meningitidis and Haemophilus influenzae Survey Brief BACKGROUND Neisseria meningitidis and Haemophilus influenzae are contagious vaccine preventable

More information

Biology 8 Learning Outcomes

Biology 8 Learning Outcomes Biology 8 Learning Outcomes CELLS (Bio 8-1) I can connect the names, diagrams, and functions of organelles in a cell I know the major differences between plant and animal cells I can explain cell theory

More information

Prepared by: The Center for Health Services and Outcomes Research

Prepared by: The Center for Health Services and Outcomes Research . WEST VIRGINIA PALLIATIVE CARE TEAM REPORT Prepared by: The Center for Health Services and Outcomes Research January December 2013 Mary Emmett, Ph.D. Director Suzanne E. Kemper, MPH Research Associate

More information

Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products

Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products Introduction Egg products refer to products made by adding other types of food or food additives to eggs

More information

A Comparison of Computational Efficiencies of Stochastic Algorithms in Terms of Two Infection Models

A Comparison of Computational Efficiencies of Stochastic Algorithms in Terms of Two Infection Models A Comparison of Computational Efficiencies of Stochastic Algorithms in Terms of Two Infection Models H.T. Banks 1, Shuhua Hu 1, Michele Joyner 3 Anna Broido 2, Brandi Canter 3, Kaitlyn Gayvert 4, Kathryn

More information

TIME SERIES MODELS ON MEDICAL RESEARCH

TIME SERIES MODELS ON MEDICAL RESEARCH PERIODICA POLYTECHNICA SER. EL. ENG. VOL. 49, NO. 3 4, PP. 175 181 (2005) TIME SERIES MODELS ON MEDICAL RESEARCH Mária FAZEKAS Department of Economic- and Agroinformatics, University of Debrecen e-mail:

More information

In its most basic terms, the theory of evolution states that species CHANGE over time.

In its most basic terms, the theory of evolution states that species CHANGE over time. In its most basic terms, the theory of evolution states that species CHANGE over time. Lamark Use Disuse Hypothesis or Passing on of Acquired Characteristics Summarize how Lamark believes the giraffe got

More information

The Genetic Epidemiology of Antibiotic Resistance

The Genetic Epidemiology of Antibiotic Resistance The Genetic Epidemiology of Antibiotic Resistance Professor Neil Woodford Antimicrobial Resistance & Healthcare Associated Infections (AMRHAI) Reference Unit Crown copyright The forensics of AMR Resistance

More information

Statistics 262: Intermediate Biostatistics Regression & Survival Analysis

Statistics 262: Intermediate Biostatistics Regression & Survival Analysis Statistics 262: Intermediate Biostatistics Regression & Survival Analysis Jonathan Taylor & Kristin Cobb Statistics 262: Intermediate Biostatistics p.1/?? Introduction This course is an applied course,

More information

3. Tests in the Bernoulli Model

3. Tests in the Bernoulli Model 1 of 5 7/29/2009 3:15 PM Virtual Laboratories > 9. Hy pothesis Testing > 1 2 3 4 5 6 7 3. Tests in the Bernoulli Model Preliminaries Suppose that X = (X 1, X 2,..., X n ) is a random sample from the Bernoulli

More information

Marginal Survival Modeling through Spatial Copulas

Marginal Survival Modeling through Spatial Copulas 1 / 53 Marginal Survival Modeling through Spatial Copulas Tim Hanson Department of Statistics University of South Carolina, U.S.A. University of Michigan Department of Biostatistics March 31, 2016 2 /

More information

2015 Annual Report. November 2016 Prepared by: IPRO ESRD Network of the Ohio River Valley esrd.ipro.org. Brandywine Creek Falls, Cleveland, OH

2015 Annual Report. November 2016 Prepared by: IPRO ESRD Network of the Ohio River Valley esrd.ipro.org. Brandywine Creek Falls, Cleveland, OH 2015 Annual Report Brandywine Creek Falls, Cleveland, OH November 2016 Prepared by: IPRO ESRD Network of the Ohio River Valley esrd.ipro.org Submitted to: U.S. Department of Health and Human Centers for

More information

CONTACT DETAILS. Cape Town BIOSTATISTICIANS. Esmé Jordaan Specialist Statistician Tel:

CONTACT DETAILS. Cape Town BIOSTATISTICIANS. Esmé Jordaan Specialist Statistician   Tel: CONTACT DETAILS Cape Town Esmé Jordaan Specialist E-mail: esme.jordaan@mrc.ac.za Tel: +27-21-9380922 Interest areas: Finite Mixture models with a focus on Latent Class Models; Growth mixture modelling

More information