Introduction to Epi Info ( Version 3.4.1) Analyze Data Module

Total Page:16

File Type:pdf, Size:1020Kb

Introduction to Epi Info ( Version 3.4.1) Analyze Data Module Introduction to Epi Info (Version 3.4.1) Analyze Data Module By Kevin M. Sullivan, PhD, MPH, MHA and Minn Minn Soe, MD, MCTM, MPH Department of Epidemiology Rollins School of Public Health of Emory University Figure 1. Epi Info Introductory Screen. Intro to Epi Info 3.4.1 Analysis.doc October 2007 i ii TABLE OF CONTENTS I. Introduction 1 II. Basic Commands 5 Reading Data and Writing Data 5 List Data 6 Display Variables 7 III. Simple Analytic Commands and Graphics 9 Frequencies 9 Means 10 Tables 12 Match 15 Summarize 18 Graph 20 Exercise 1 22 IV. Navigating and Managing the Output and the Program Editor windows 25 V. Data manipulation commands 29 Sort/Cancel Sort. 29 Select/Cancel Select 30 Define/Undefine 31 Assign 32 Recode 33 If 34 Exercise 2 38 VI. Setting System Defaults 39 VII. Advanced Statistics 41 Linear Regression 41 Simple linear regression 42 Multiple linear regression 44 Logistic Regression 45 Unconditional logistic regression 46 Conditional logistic regression 49 Survival Analysis 49 Kaplan-Meier 49 Cox Proportional Hazards 51 Complex sample commands 55 Complex Sample Frequencies 55 Complex Sample Tables 58 Complex Sample Means 59 Exercise 3 62 iii VIII. Statistics Command Options 63 Stratify by 63 Weight 70 IX. Advanced Data Management Topics 75 Write (Export) 75 Delete file/table 76 Delete/undelete records 77 Relate 78 Merge 80 Acknowledgments 83 References 83 APPENDICES 85 Appendix 1. Data Dictionaries 85 Appendix 2. Operators/ Functions 102 Appendix 3. Answers to Exercises 105 Appendix 4. Analysis Commands By Type of Variables 113 iv I. INTRODUCTION Epi Info is a program developed by the Centers for Disease Control and Prevention (CDC) that runs under the Microsoft Windows® operating system and provides programs for data entry and analysis. The Epi Info program and help information can be found at www.cdc.gov/epiinfo. The purpose of this document is to introduce the Analyze Data module, discussing commands in a sequence appropriate for learning the program. More detailed information on some topics is provided in the later chapters and details of the commands can be found in Epi Info’s on-line help. Also presented is the program OpenEpi (www.OpenEpi.com) and how it can be used to supplement Epi Info. Figure 1 on the front of this document presents the Epi Info (3.4.1) introductory screen (release date: July 3, 2007). To start the Analyze Data module, click on the Analyze Data button in the lower left of the screen or from the pull-down menu by Programs → Analyze Data (see Figure 1). The main Analyze Data screen is shown in Figure 2. The screen is composed of three windows. There is a narrow window of the left side of the screen labeled Analysis that presents the commands, i.e., the “command tree”; the largest window labeled Analysis Output comprises the upper right portion of the screen and is where the output is presented; and a smaller window in the bottom right of the screen labeled Program Editor where text commands appear. Figure 2. Analyze Data main screen, Epi Info. Before discussing the Analysis commands, let’s review some basics. To exit the Analyze Data module, you can click on the Exit button at the top of the left window; to minimize the module, click on the “_” button in the upper right-hand corner of the left window. The Help button at the bottom of the left window takes you to the Epi Info Help system. You can resize any of the three windows by placing the cursor where two windows meet, hold the left mouse button, and drag the border. 1 The commands listed in the narrow window on the left are grouped according to their general functions, such as those related to reading, relating, writing and merging data files (the Data section); those relating to creating and assigning values to variables (Variables and Select/If sections), and the analytic commands (Statistics and Advanced Statistics sections). A brief description of some of these commands is presented in Figure 3. In this document, the Analyze Data commands are described in the following Chapters in this order: ¾ II. Basic Commands o Read (Import) – usually the first command used; to “read” or “open” a data file o List – to view or update the data in a spreadsheet format o Display – to display variable names and types ¾ III. Simple Analytic commands and Graphics o Frequencies – for viewing the frequencies of values for a variable o Means – similar to Frequencies for a single variable except for numeric data where the Means command provides summary statistics; can also perform independent t-test, one-way ANOVA, and their nonparametric equivalents. o Tables – for single and stratified 2x2 tables (where the odds ratio, risk ratio, and other measures of association are provided) or any size r x c table o Match – for matched case-control data o Summarize – to create a new table containing a summary of descriptive statistics for the current dataset. o Graph – graphing data ¾ IV. Navigating and Managing the Output and the Program Editor Windows o Issues related to using and navigating the Output and the Program Editor windows ¾ V. Data manipulation commands o Sort/Cancel Sort – sort the data/cancel a sort o Select/Cancel Select – “select” or “unselect” a subset of records for analysis o Define/Undefine – “define” or “undefine” new variables Assign – “assign” values to a variable Recode – recode from one variable to another variable If – If commands for conditional logic, also Then and Else ¾ VI. Setting System Defaults o Set – choose default settings ¾ VII. Advanced Statistics o Linear Regression – simple linear and multiple linear regression o Logistic Regression – both unconditional and conditional logistic regression o Survival Analysis Kaplan-Meier – simple survival analysis Cox Proportional Hazards – advanced survival analysis o Complex sample commands – commands for use with cross-sectional data which include elements of cluster and/or stratification Complex Sample Frequencies Complex Sample Tables Complex Sample Means ¾ VIII. Statistics Command Options o Stratify by o Weight ¾ IX. Advanced Data Management Topics o Write (Export) o Delete File/Table o Merge o Relate 2 An introduction to these commands follows. Because the goal of this document is to provide an introduction to these commands, there will be some details of the commands that are not presented. For more detailed information on the commands, please consult the on-line help in Epi Info. In this document, for examples of the output of some commands, we have removed or slightly modified some of the output to reduce space. Figure 3. Short description of selected commands in Analyze Data ←The minimize button (“_”) on the Analysis commands window will minimize all 3 Analyze Data windows; the close button (“X”) closes all 3 windows, same as the Exit button described next. ←The Exit button will exit from the Analyze Data module. Data-Related commands Read – Read an Epi 2000 (i.e., Access 2000) file; can Import other file types, such as Epi6, dBase, and Excel Relate is for relating files, Write for writing new data files or Exporting to a different file (e.g., Epi Info 6, dBase, and others); and Merge to merge data files. Commands to Delete a file, table, records, or undelete records Variables – commands for creating variables & assigning values Define/Undefine to create/remove a temporary variable; Assign values to a variable; Recode to recode a variable (a short-hand version of If/Then/Else); and Display to display the variables by their variable names and types in the data set and any temporary defined variables. Select/If – commands for selecting a subset of records, If/Then/Else statements, and Sorting a file. Select/Cancel Select is for selecting/unselecting a subset of records. If/Then/Else commands Sort/Cancel Sort for sorting/unsorting the file on one or more variables. Statistics – commands for presenting data & statistical analyses List – data depicted as a spreadsheet (“grid”), allow data entry Frequencies – frequency, %, cum %, and confidence intervals Tables – cross-tabulations; stratified analysis Match – for matched case-control analysis; can have one or more controls for each case Means – frequency, descriptive statistics, independent t-test, one-way ANOVA, and nonparametric tests Summarize – Creates a new variable with descriptive statistics Graph – graphing module Map – mapping module Advanced Statistics Linear Regression – simple or multiple linear regression Logistic Regression – regression for dichotomous outcome variables Kaplan-Meier Survival analysis Cox Proportional Hazards survival analysis Complex Sample commands – for surveys using complex sample design, such as stratification and clusters, for Complex Sample Frequencies, Tables, and Means Help – to open the Epi Info Help program There are a number of commands lower in this window not described in this figure. 3 4 II. BASIC COMMANDS Reading and Writing Data One of the first things you will want to do in the Analyze Data module is to Read a data set, or, as usually stated in most Windows programs, to open a file. To Read a file, click on Read (Import) in the command window; a dialog box will be presented as shown in Figure 4. Figure 4. Dialog box for Read (Import), Epi Info. Epi Info uses the Microsoft Access data file format and in Epi Info referred to as Epi 2000 files. If you are not familiar with Access files, they can seem complicated because they may contain many “tables” which are the equivalent of data files such as Epi Info DOS .REC files and SPSS .SAV files. When Epi Info is installed on computer, the default Current Project (see top of dialog box in Figure 4) is usually on drive C:, the Epi_Info folder, and the Data Source drive, folder, and file name is C:\Epi_Info\Sample.mdb, a file distributed with Epi Info that contains a number of example files.
Recommended publications
  • Patient Safety Culture and Associated Factors Among Health Care Providers in Bale Zone Hospitals, Southeast Ethiopia: an Institutional Based Cross-Sectional Study
    Drug, Healthcare and Patient Safety Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH Patient Safety Culture and Associated Factors Among Health Care Providers in Bale Zone Hospitals, Southeast Ethiopia: An Institutional Based Cross-Sectional Study This article was published in the following Dove Press journal: Drug, Healthcare and Patient Safety Musa Kumbi1 Introduction: Patient safety is a serious global public health issue and a critical component of Abduljewad Hussen 1 health care quality. Unsafe patient care is associated with significant morbidity and mortality Abate Lette1 throughout the world. In Ethiopia health system delivery, there is little practical evidence of Shemsu Nuriye2 patient safety culture and associated factors. Therefore, this study aims to assess patient safety Geroma Morka3 culture and associated factors among health care providers in Bale Zone hospitals. Methods: A facility-based cross-sectional study was undertaken using the “Hospital Survey 1 Department of Public Health, Goba on Patient Safety Culture (HSOPSC)” questionnaire. A total of 518 health care providers Referral Hospital, Madda Walabu University, Goba, Ethiopia; 2Department were interviewed. Analysis of variance (ANOVA) was employed to examine statistical of Public Health, College of Health differences between hospitals and patient safety culture dimensions. We also computed Science and Medicine, Wolayta Sodo internal consistency coefficients and exploratory factor analysis. Bivariate and multivariate University, Sodo, Ethiopia; 3Department of Nursing, Goba Referral Hospital, linear regression analyses were performed using SPSS version 20. The level of significance Madda Walabu University, Goba, Ethiopia was established using 95% confidence intervals and a p-value of <0.05. Results: The overall level of patient safety culture was 44% (95% CI: 43.3–44.6) with a response rate of 93.2%.
    [Show full text]
  • Association Between Indoor Air Pollution and Cognitive Impairment Among Adults in Rural Puducherry, South India Yuvaraj Krishnamoorthy, Gokul Sarveswaran, K
    Published online: 2019-09-02 Original Article Association between Indoor Air Pollution and Cognitive Impairment among Adults in Rural Puducherry, South India Yuvaraj Krishnamoorthy, Gokul Sarveswaran, K. Sivaranjini, Manikandanesan Sakthivel, Marie Gilbert Majella, S. Ganesh Kumar Department of Preventive and Background: Recent evidences showed that outdoor air pollution had significant Social Medicine, Jawaharlal influence on cognitive functioning of adults. However, little is known regarding the Institute of Postgraduate Medical Education and association of indoor air pollution with cognitive dysfunction. Hence, the current study was done to assess the association between indoor air pollution and cognitive Research, Puducherry, India Abstract impairment among adults in rural Puducherry. Methodology: A community‑based cross‑sectional study was done among 295 adults residing in rural field practice area of tertiary care institute in Puducherry during February and March 2018. Information regarding sociodemographic profile and household was collected using pretested semi‑structured questionnaire. Mini‑Mental State Examination was done to assess cognitive function. We calculated adjusted prevalence ratios (aPR) to identify the factors associated with cognitive impairment. Results: Among 295 participants, 173 (58.6) were in 30–59 years; 154 (52.2%) were female; and 59 (20.0%) were exposed to indoor air pollution. Prevalence of cognitive impairment in the general population was 11.9% (95% confidence interval [CI]: 8.7–16.1). Prevalence of cognitive impairment among those who were exposed to indoor air pollution was 27.1% (95% CI: 17.4–39.6). Individuals exposed to indoor air pollution (aPR = 2.18, P = 0.003) were found to have two times more chance of having cognitive impairment.
    [Show full text]
  • Antiretroviral Adverse Drug Reactions Pharmacovigilance in Harare City
    bioRxiv preprint doi: https://doi.org/10.1101/358069; this version posted June 28, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Original Manuscript 2 Title: Antiretroviral Adverse Drug Reactions Pharmacovigilance in Harare City, 3 Zimbabwe, 2017 4 Hamufare Mugauri1, Owen Mugurungi2, Tsitsi Juru1, Notion Gombe1, Gerald 5 Shambira1 ,Mufuta Tshimanga1 6 7 1. Department of Community Medicine, University of Zimbabwe 8 2. Ministry of Health and Child Care, Zimbabwe 9 10 Corresponding author: 11 Tsitsi Juru [email protected] 12 Office 3-66 Kaguvi Building, Cnr 4th/Central Avenue 13 University of Zimbabwe, Harare, Zimbabwe 14 Phone: +263 4 792157, Mobile: +263 772 647 465 1 bioRxiv preprint doi: https://doi.org/10.1101/358069; this version posted June 28, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 15 Abstract 16 Introduction: Key to pharmacovigilance is spontaneously reporting all Adverse Drug 17 Reactions (ADR) during post-market surveillance. This facilitates identification and 18 evaluation of previously unreported ADR’s, acknowledging the trade-off between 19 benefits and potential harm of medications. Only 41% ADR’s documented in Harare 20 city clinical records for January to December 2016 were reported to Medicines 21 Control Authority of Zimbabwe (MCAZ).
    [Show full text]
  • Communicable Disease Control in Emergencies: a Field Manual Edited by M
    Communicable disease control in emergencies A field manual Communicable disease control in emergencies A field manual Edited by M.A. Connolly WHO Library Cataloguing-in-Publication Data Communicable disease control in emergencies: a field manual edited by M. A. Connolly. 1.Communicable disease control–methods 2.Emergencies 3.Disease outbreaks–prevention and control 4.Manuals I.Connolly, Máire A. ISBN 92 4 154616 6 (NLM Classification: WA 110) WHO/CDS/2005.27 © World Health Organization, 2005 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the pert of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on map represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify he information contained in this publication. However, the published material is being distributed without warranty of any kind, either express or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising in its use.
    [Show full text]
  • CDC CSELS Division of Public Health
    Center for Surveillance, Epidemiology and Laboratory Services (CSELS) Division of Public Health Information Dissemination (DPHID) The primary mission for the Center for Surveillance, Epidemiology and Laboratory Services (CSELS) is to provide scientific service, expertise, skills, and tools in support of CDC's national efforts to promote health; prevent disease, injury and disability; and prepare for emerging health threats. CSELS has four divisions which represent the tactical arm of CSELS, executing upon CSELS strategic objectives. They are critical to CSELS ability to deliver public health value to CDC in areas such as science, public health practice, education, and data. The four Divisions are: Division of Health Informatics and Surveillance Division of Laboratory Systems Division of Public Health Information Dissemination Division of Scientific Education and Professional Development Applied Public Health Advanced Laboratory Epidemiology The mission of the Division of Public Health Information Dissemination (DPHID) is to serve as a hub for scientific publications, information and library sciences, systematic reviews and recommendations, and public health genomics, thereby collaborating with CDC CIOs and the public health community in producing, disseminating, and implementing evidence-based and actionable information to strengthen public health science and improve public health decision- making. Major Products or Services provided by DEALS include: The American Hospital Association (AHA): AHA Annual Survey and AHA Healthcare IT Survey Data. Contact [email protected] Centers for Medicare & Medicaid Services (CMS) health data coordination - The Centers for Medicare and Medicaid Services (CMS) Virtual Research Data Center (VRDC) is CDC’s Gateway to CMS Data. CMS has developed a new data access model as an option for requesting Medicare and Medicaid data for a broad range of analytic studies.
    [Show full text]
  • Exposure and Body Burden of Environmental Pollution and Risk Of
    Ingela Helmfrid Ingela Linköping University medical dissertations, No. 1699 Exposure and body burden of environmental pollutants and risk of cancer in historically contaminated areas Ingela Helmfrid Exposure and body burden of FACULTY OF MEDICINE AND HEALTH SCIENCES environmental pollutants and risk Linköping University medical dissertations, No. 1699, 2019 of cancer in historically Occupational and Environmental Medicine Center and Department of Clinical and Experimental Medicine contaminated areas Linköping University SE-581 83 Linköping, Sweden www.liu.se 2019 Linköping University Medical Dissertation No. 1699 Exposure and body burden of environmental pollutants and risk of cancer in historically contaminated areas Ingela Helmfrid Occupational and Environmental Medicine Center, and Department of Clinical and Experimental Medicine Linköpings universitet, SE-581 83 Linköping, Sweden Linköping 2019 © Ingela Helmfrid, 2019 Printed in Sweden by LiU-Tryck, Linköping University, 2019 Linköping University medical dissertations, No. 1699 ISSN 0345-0082 ISBN 978-91-7685-006-0 Exposure and body burden of environmental pollutants and risk of cancer in historically contaminated areas By Ingela Helmfrid November 2019 ISBN 978-91-7685-006-0 Linköping University medical dissertations, No. 1699 ISSN 0345-0082 Keywords: Contaminated area, cancer, exposure, metals, POPs, Consumption of local food Occupational and Environmental Medicine Center, and Department of Clinical and Experimental Medicine Linköping University SE-581 83 Linköping, Sweden Preface This thesis is transdisciplinary, integrating toxicology, epidemiology and risk assessment, and involving academic institutions as well as national, regional and local authorities. Systematic and transparent methods for the characterization of human environmental exposure to site-specific pollutants in populations living in historically contaminated areas were used.
    [Show full text]
  • The First Line in the Above Code Defines a New Variable Called “Anemic”; the Second Line Assigns Everyone a Value of (-) Or No Meaning They Are Not Anemic
    The first line in the above code Defines a new variable called “anemic”; the second line assigns everyone a value of (-) or No meaning they are not anemic. The first and second If commands recodes the “anemic” variable to (+) or Yes if they meet the specified criteria for hemoglobin level and sex. A Note to Epi Info DOS users: Using the DOS version of Epi you had to be very careful about using the Recode and If/Then commands to avoid recoding a missing value in the original variable to a code in the new variable. In the Windows version, if the original value is missing, then the new variable will also usually be missing, but always verify this. Always Verify Coding It is recommended that you List the original variable(s) and newly defined variables to make sure the coding worked as you expected. You can also use the Tables and Means command for double-checking the accuracy of the new coding. Use of Else … The Else part of the If command is usually used for categorizing into two groups. An example of the code is below which separates the individuals in the viewEvansCounty data into “younger” and “older” age categories: DEFINE agegroup3 IF AGE<50 THEN agegroup3="Younger" ELSE agegroup3="Older" END Use of Parentheses ( ) For the Assign and If/Then/Else commands, for multiple mathematical signs, you may need to use parentheses. In a command, the order of mathematical operations performed is as follows: Exponentiation (“^”), multiplication (“*”), division (“/”), addition (“+”), and subtraction (“-“). For example, the following command ASSIGNs a value to a new variable called calc_age based on an original variable AGE (in years): ASSIGN calc_age = AGE * 10 / 2 + 20 For example, a 14 year old would have the following calculation: 14 * 10 / 2 + 20 = 90 First, the multiplication is performed (14 * 10 = 140), followed by the division (140 / 2 = 70), and then the addition (70 + 20 = 90).
    [Show full text]
  • Diagnostic Value of Alpha-Fetoprotein Combined with Neutrophil-To
    Hu et al. BMC Gastroenterology (2018) 18:186 https://doi.org/10.1186/s12876-018-0908-6 RESEARCH ARTICLE Open Access Diagnostic value of alpha-fetoprotein combined with neutrophil-to-lymphocyte ratio for hepatocellular carcinoma Jian Hu1†, Nianyue Wang2†, Yongfeng Yang2,LiMa2, Ruilin Han3, Wei Zhang4, Cunling Yan3*, Yijie Zheng5* and Xiaoqin Wang1* Abstract Background: To investigate the diagnostic performance of alpha-fetoprotein (AFP) and neutrophil-to-lymphocyte ratio (NLR) as well as their combinations with other markers. Methods: Serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), AFP and levels as well as the numbers of neutrophils and lymphocytes of all enrolled patients were collected. The NLR was calculated by dividing the number of neutrophils by the number of lymphocytes. Receiver operating characteristic (ROC) curve analysis was conducted to determine the ability of each marker and combination of markers to distinguish HCC and liver disease patients. Results: In total, 545 patients were included in this study. The area under the ROC curve (AUC) values for AFP, ALT, AST, and NLR were 0.775 (0.738–0.810), 0.504 (0.461–0.547), 0.660 (0.618–0.699), and 0.738 (0.699–0.774) with optimal cut-off values of 24.6 ng/mL, 111 IU/mL, 27 IU/mL, and 2.979, respectively. Of the four biomarkers, AFP and NLR showed comparable specificity (0.881 and 0.858) and sensitivity (0.561 and 0.539). The combination of AFP and NLR showed the highest AUC (0.769) with a significantly higher sensitivity (0.767) and a lower specificity (0.773) compared to AFP or NLR alone, and it had the highest sum of sensitivity and specificity (1.54) among all combinations.
    [Show full text]
  • Epi Info Guide
    Epi Info Guide Data Management and Analysis A PLACE Manual Guide for Using Epi Info Software This guide was made possible by support from the U.S. Agency for International Development (USAID) under terms of Cooperative Agreement GPO-A-00-03-00003-00. The authors’ views expressed in this publication do not necessarily reflect the views of USAID or the United States Government. November 2005 MS-05-13 Epi Info Guide Introduction This technical document provides information you will need to know in order to manage and analyze the data collected during the PLACE assessment, as well as guidance in preparing the data tables used in a PLACE report. It begins with step-by-step instructions for preparing customized data entry screens in Epi Info for each questionnaire, so that data from the Community Informant Questionnaire (Form A), the Venue Verification Form (Form C), and the Questionnaire for Individuals Socializing at Venues (Form D) can be entered, stored, and analyzed in Epi Info. (The Venue and Event Report [Form B] does not require the use of Epi Info.) Instructions are also provided for modifying or creating a simple checking program to ensure that values entered during data entry fall within the parameters of “allowed” response codes. Lastly, instructions are provided to help you prepare the data and perform the analysis necessary to complete a PLACE report, which will summarize the PLACE findings in your priority prevention areas. This document is presented in the same sequence of data management and analysis activities that are used in a PLACE study. Each of the four sections begins with a summary of the instructions that will follow.
    [Show full text]
  • Research a Retrospective Comparison of Dental Hygiene Supervision Changes from 2001 to 2011
    Research A Retrospective Comparison of Dental Hygiene Supervision Changes from 2001 to 2011 April V. Catlett, RDH, BHSA, MDH; Robert Greenlee, PhD Introduction Abstract Several factors contribute to the Purpose: The purpose of this study is to evaluate the extent of poor dental health of low-income change in the professional practice environment for dental hy- populations in the U.S. Some of the gienists in the 50 states and District of Columbia by comparing most significant factors that contrib- the state supervision requirements for dental hygienists during ute to this lack of access to care are a 2001 to 2011 to the previous 7 year period, 1993 to 2000. shortage of dentists, poor participa- Methods: A retrospective comparison evaluation was conduct- tion of dentists in public assistance ed using the 2 tables entitled “Tasks Permitted and Mandat- programs and dental hygiene prac- ed Supervision of Dental Hygienists by State, 1993, 1998 and tice acts.1 The dental hygiene prac- 2000” and “Dental Hygiene Practice Act Overview: Permitted tice act supervision requirements, Functions and Supervision Levels by State.” To score the net dictated by state dental boards, lim- change in supervision, a numerical score was assigned to each it the dental workforce conditions. level of alteration in supervision with a +1 or -1 for each level In 2006, the dentist-to-population of change. ratio in the U.S. was 5.8 dentists per 10,000 residents.1 In May 2010, Results: With a 95% confidence level, the mean change in there were over 25% more dental dental hygiene supervision from 2001 to 2011 was 6.57 with a hygienists as general dentists in the standard deviation of 5.70 (p-value=0.002).
    [Show full text]
  • An Examination of Epidemiological Study Designs in Veterinary Science Jonah Nelson Cullen Iowa State University
    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2016 An examination of epidemiological study designs in veterinary science Jonah Nelson Cullen Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Epidemiology Commons, and the Veterinary Medicine Commons Recommended Citation Cullen, Jonah Nelson, "An examination of epidemiological study designs in veterinary science" (2016). Graduate Theses and Dissertations. 15687. https://lib.dr.iastate.edu/etd/15687 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. An examination of epidemiological study designs in veterinary science by Jonah N. Cullen A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Veterinary Preventive Medicine Program of Study Committee: Annette M. O’Connor, Major Professor Duck-chul Lee Kenneth J. Koehler Jeffrey J. Zimmerman Iowa State University Ames, Iowa 2016 Copyright © Jonah N. Cullen, 2016. All rights reserved. ii DEDICATION This thesis is dedicated to my amazing wife Kelly, and always-supportive parents Dan and Jeanne. iii TABLE OF CONTENTS Page ACKNOWLEDGMENTS .......................................................................................
    [Show full text]
  • Epi Info™ 7 User Guide – Chapter 8 - Visual Dashboard
    Epi Info™ 7 User Guide – Chapter 8 - Visual Dashboard Visual Dashboard: Performing Statistical Analyses with Visual Tools Introduction Visual Dashboard is one of the Epi Info™ 7 analysis modules. The Visual Dashboard is designed to be intuitive and simple to use. With the use of gadgets, the need for programming code is minimized. Data can be selected, sorted, listed, or manipulated using the various gadgets in Visual Dashboard. The statistical analyses tools available in Visual Dashboard include frequencies, means, and more advanced statistical calculations (e.g., linear regression and logistic regression). Visual Dashboard has graphing functionality to display data as an Epi Curve, Pareto Chart, and several other bar and column charts. Users can access Visual Dashboard from the Epi Info™ 7 main menu by clicking the Visual Dashboard button, or by selecting Dashboard from the main page menu of Enter once an Epi Info project has been loaded. Figure 8.1: Epi Info™ 7 main menu, with Visual Dashboard button highlighted 8-1 Epi Info™ 7 User Guide – Chapter 8 - Visual Dashboard Navigate the Visual Dashboard Workspace Figure 8.2: Visual Dashboard canvas The Visual Dashboard contains seven areas: the Canvas, the Canvas Display Mode Selector, the Data Source and Record Count Display, the Canvas Output Controls, the Canvas Tools Toolbar, the Defined Variables gadget, and the Data Filters Gadget. 1. The Canvas is the blank space in the center of the Visual Dashboard. It displays output generated by the multiple gadgets available in the tool. The canvas has a function dialog box that appears when a blank space is right-clicked.
    [Show full text]