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As a medical student in the early 1980s, I was rather scandalized to discover that my required textbook of medicine did not provide standard treatment protocols for even the most common of medical conditions.

As a medical student in the early 1980s, I was rather scandalized to discover that my required textbook of medicine did not provide standard treatment protocols for even the most common of medical conditions.

Public Health Informatics and Information Systems

J.A. Magnuson Paul C. Fu, Jr. Editors

2nd Edition

Health Informatics

 

 

Health Informatics

 

 

 

 

 

J.A. Magnuson • Paul C. Fu, Jr. Editors

Public Health Informatics and Information Systems

Second Edition

 

 

ISBN 978-1-4471-4236-2 ISBN 978-1-4471-4237-9 (eBook) DOI 10.1007/978-1-4471-4237-9 Springer London Heidelberg New York Dordrecht

Library of Congress Control Number: 2013954973

© Springer-Verlag London 2014

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifi cally for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

Editors J.A. Magnuson, PhD Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University Portland, OR USA

Paul C. Fu, Jr., MD, MPH Pediatrics Department Health and Policy and Management Los Angeles County Harbor-UCLA Medical Center David Geffen School of Medicine at UCLA UCLA Fielding School of Public Health Torrance , CA USA

 

 

As a medical student in the early 1980s, I was rather scandalized to discover that my required textbook of medicine did not provide standard treatment protocols for even the most common of medical conditions. What good is a textbook, I asked myself, if it does not provide even this most basic treatment information? The textbook in question was the (then) current edition of the Principles and Practices of Medicine , originally published by William Osler in 1892 and continually updated by Johns Hopkins University School of Medicine faculty in many editions to this day. In suc- ceeding years, of course, I came to realize that fi eld-encompassing textbooks cannot and should not be concerned with the specifi c treatments and protocols of the day, but rather – as Osler understood – the principles and practices that perennially defi ne the fi eld from generation to generation. This is similarly the essence and focus of this, the second edition of this public health informatics textbook: the principles and practices that defi ne and shape this growing and exciting discipline.

Having said that, there is a reason why Osler’s venerable textbook has been updated through dozens of editions and an ever-changing cast of editors: the chal- lenges and context for a discipline, whether medicine or public health informatics, are ever-changing, and textbooks that seek to guide, inform, and inspire new stu- dents of a given discipline must change likewise.

The fi rst edition of Public Health Informatics and Information Systems [1] was begun as a straightforward compendium of key public health–relevant information systems: mortality and natality data systems, survey-based systems (like the Behavioral Risk Factor Surveillance System), and so forth. But the editors quickly came to feel that a more comprehensive focus on informatics was needed, for two primary reasons: (1) the burgeoning information age presented the fi eld of public health with extraordi- nary and unprecedented opportunities to improve its effi ciency and effectiveness, and even to revolutionize the ways in which public health itself was practiced; and (2) an absence of familiarity with the basic tenets of informatics had led, and would inevita- bly lead in the future, to costly (and sadly predictable) failures to develop effective, integrated, and sustainable new information system applications for public health.

With this in mind, the project evolved into what would become the fi rst American public health informatics textbook, and its fi rst edition was expanded to include a broad presentation of the principals and practices, as well as the context and basic science, of

Foreword

 

 

vi

public health informatics. To be sure, the major information systems in general use by public health professionals were described and explained. But two concluding parts of the book were included, to describe then-emerging information systems and chal- lenges; and to illustrate through a diverse series of case studies the kinds of value that were being accrued through public health information system development, as well as the special challenges that the development of these systems often entailed. Through these case studies, undergirded by the material that preceded them, the essential prin- ciples and practices of public health informatics were illustrated in real-world terms.

This second edition, developed by JA Magnuson and Paul Fu, Jr., continues this focus and tradition. The basic sections of the original textbook have been preserved, providing the student with the context and science of public health informatics; descriptions of key public health information systems; overviews of new challenges and emerging systems; and a series of illustrative case studies. The material in every section has been enormously updated, however, to refl ect astonishingly rapid advances in information technology as well as profound changes in the societal and legislative context for both healthcare and public health.

By way of illustration, consider that when the fi rst edition was published in 2003, social media and social networking applications were essentially unknown. Facebook © , for example, was not launched until 2004. Yet as of September 2012, Facebook © had over one billion active users—roughly one-seventh of the entire global population (and a much higher proportion in developed countries). Consider also that the US Patient Protection and Affordable Care Act was only signed into law in March 2010 (roughly 3 years ago at this writing), and will not take full effect until 2014. Yet this game-changing legislation is already altering the landscape for healthcare in ways that powerfully promote truly health- oriented (as opposed to procedure-oriented) healthcare. By highlighting the importance of prevention—in fi nancial as well as ethical terms—the Act also promotes closer con- nections and collaboration between the healthcare and public health sectors.

These and many other rapid technological and societal developments present today’s informatics professionals with enormous, unprecedented opportunities to apply information science and technology in innovative ways to promote the pub- lic’s health. There has never been a better time to exert passionate and creative lead- ership to improve existing systems of prevention and public health, and to invent new and yet-undreamt-of approaches to promote human health and well-being.

With that, let me invite the student of public health informatics to take full advan- tage of the information and guidance in this textbook to ignite your passion and develop your creative informatics leadership; and let me congratulate the editors on this much-improved second edition.

Seattle, WA, USA Patrick W. O’Carroll, MD, MPH, FACPM, FACMI

Reference

1. O’Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, editors. Public health informatics and information systems. New York: Springer; 2003.

Foreword

 

 

When the fi rst edition of Public Health Informatics and Information Systems was published in 2002, Public Health Informatics was a relatively young fi eld. That fi rst edition was invaluable in helping to establish the fi eld of study and provide structure for the emerging discipline. A decade later, great progress has been made, but Public Health Informatics is still an emerging fi eld that needs continued focus in order to grow into its full potential.

This edition builds upon the foundation established by the fi rst edition. We have expanded into new areas that have become important due to changing technologies and needs, as well as updating and augmenting many of the original core tenets. The breadth of material included in this work makes it suitable for both undergraduate and graduate coursework in Public Health Informatics, enabling instructors to select chapters that best fi t their students’ needs.

Structure and Objective of This Book

The template for the chapters in this book contains learning objectives, an abstract or overview, the chapter content, review questions, and references. The book itself is organized into fi ve parts: • Part I. Context for Public Health Informatics provides a background for the text-

book. This part begins with an introduction to the subject of Public Health Informatics and a review of the history and signifi cance of information systems and public health. The context of biomedical informatics is discussed and the governmental and legislative context of informatics is reviewed.

• Part II. The Science of Public Health Informatics reviews the technology and science behind the fi eld of informatics. Informatics infrastructure and informa- tion architecture are discussed. This part examines data sources and tools, and the critical issue of information standards. The topics of privacy, confi dentiality, security, and ethics are explored. Electronic health records are examined, as well as project management and system evaluation.

Pref ace

 

 

viii

• Part III. Key Public Health Information Systems are studied in this part. The areas of disease prevention and epidemiology, and environmental health, are reviewed. Specifi c systems and instances for public health laboratories, risk factor informa- tion systems, the National Vital Statistics System, and immunization information systems are discussed.

• Part IV. New Challenges and Emerging Solutions addresses some of the newest challenges facing Public Health Informatics, as well as emerging solutions. Included are new means of data collection and accessibility, geographic informa- tion systems, health information exchange, decision support and expert systems, delivery of preventive medicine, and case-based learning.

• Part V. Case Studies: Information Systems and the Strata of Public Health high- lights informatics case studies from the different strata of public health. The case studies begin with local and regional public health, progressing to state examples for both high population and low population states. Then, national perspectives are represented by examples from the USA, Canada, and a collaborative chapter illustrating informatics experiences in Malawi and Rwanda.

Portland, OR, USA J.A. Magnuson, PhD Torrance, CA, USA Paul Fu, Jr., MD, MPH

Preface

 

 

This book refl ects the hard work and dedication of many people. As editors, we want to acknowledge the contributions of our chapter authors,

who generously managed to fi nd the time to share their wealth of knowledge and experience. Their contribution was absolutely critical to this effort, and we are grateful that so many leaders in the fi eld of Public Health Informatics were willing to participate in this project.

We are also grateful to the editors of the previous edition, whose hard work and inspiration pioneered a path for Public Health Informatics. The enthusiasm and encouragement given to us by that edition’s senior editor, Patrick O’Carroll, is espe- cially appreciated.

Finally, we would like to acknowledge the skill and support of our editor at Springer, Grant Weston, and our developmental editor Connie Walsh. Their encour- agement, guidance, and skills were invaluable.

J.A. Magnuson, PhD Paul Fu, Jr., MD, MPH

Acknowledgements

 

 

 

 

 

Part I Context for Public Health Informatics

1 Introduction to Public Health Informatics . . . . . . . . . . . . . . . . . . . . . 3 J.A. Magnuson and Patrick W. O’Carroll

2 History and Signifi cance of Information Systems and Public Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 John R. Lumpkin and J.A. Magnuson

3 Context and Value of Biomedical and Health Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 William R. Hersh

4 Governmental and Legislative Context of Informatics . . . . . . . . . . . 47 Margo Edmunds

Part II The Science of Public Health Informatics

5 Public Health Informatics Infrastructure . . . . . . . . . . . . . . . . . . . . . . 69 Brian E. Dixon and Shaun J. Grannis

6 Information Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Dina Dickerson and Patricia Yao

7 Data Sources and Data Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Edward Mensah and Johanna L. Goderre

8 Public Health Information Standards . . . . . . . . . . . . . . . . . . . . . . . . . 133 J.A. Magnuson, Riki Merrick, and James T. Case

9 Privacy, Confi dentiality, and Security of Public Health Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 William A. Yasnoff

Contents

 

 

xii

10 Electronic Health Records. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Stephen P. Julien

11 Ethics, Information Technology, and Public Health: Duties and Challenges in Computational Epidemiology . . . . . . . . . . . . . . . . 191 Kenneth W. Goodman and Eric M. Meslin

12 Project Management and Public Health Informatics . . . . . . . . . . . . . 211 James Aspevig

13 Evaluation for Public Health Informatics . . . . . . . . . . . . . . . . . . . . . . 233 Paul C. Fu, Jr., Herman Tolentino, and Laura H. Franzke

Part III Key Public Health Information Systems

14 Informatics in Disease Prevention and Epidemiology . . . . . . . . . . . . 257 Richard S. Hopkins and J.A. Magnuson

15 Informatics in Toxicology and Environmental Public Health . . . . . . 277 Edwin M. Kilbourne

16 Public Health Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Riki Merrick, Steven H. Hinrichs, and Michelle Meigs

17 The National Vital Statistics System. . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Charles J. Rothwell, Mary Anne Freedman, and James A. Weed

18 Risk Factor Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Alan Tomines

19 Setting National Policies and Standards for Immunization Information Systems. . . . . . . . . . . . . . . . . . . . . . . . 355 Nedra Y. Garrett

Part IV New Challenges and Emerging Solutions

20 New Means of Data Collection and Accessibility . . . . . . . . . . . . . . . . 375 I. Charie Faught, James Aspevig, and Rita Spear

21 Geographic Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Carol L. Hanchette

22 Public Health Informatics and Health Information Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 J.A. Magnuson and Paul C. Fu, Jr.

23 Decision Support and Expert Systems in Public Health . . . . . . . . . . 449 William A. Yasnoff and Perry L. Miller

Contents

 

 

xiii

24 Delivery of Preventive Medicine in Primary Care . . . . . . . . . . . . . . . 469 Paul C. Fu, Jr., Alan Tomines, and Larry L. Dickey

25 Case-Based Learning in Public Health Informatics . . . . . . . . . . . . . . 489 Herman Tolentino, Sridhar R. Papagari Sangareddy, Catherine Pepper, and J.A. Magnuson

Part V Case Studies: Information Systems and the Strata of Public Health

26 Local and Regional Public Health Informatics . . . . . . . . . . . . . . . . . . 513 Jeffrey M. Kriseman and Brian J. Labus

27 Public Health Informatics in High Population States: New York and Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Geraldine S. Johnson, Guthrie S. Birkhead, Rachel Block, Shannon Kelley, James Coates, Bob Campbell, and Brian Fowler

28 State Public Health Informatics: Perspective from a Low Population State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 James Aspevig

29 National Public Health Informatics, United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Seth Foldy

30 Public Health Informatics in Canada. . . . . . . . . . . . . . . . . . . . . . . . . . 603 Lawrence E. Frisch, Elizabeth M. Borycki, Alyse Capron, Abla Mawudeku, and Ronald St. John

31 Perspectives on Global Public Health Informatics . . . . . . . . . . . . . . . 619 Janise Richards, Gerry Douglas, and Hamish S.F. Fraser

Part VI Epilogue

32 Public Health Informatics: The Path Forward . . . . . . . . . . . . . . . . . . 647 J.A. Magnuson

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

Contents

 

 

 

 

 

Contributors

James Aspevig , MS, MPH Health Care Informatics , Montana Tech of the University of Montana , Butte , MT , USA

Guthrie S. Birkhead , MD, MPH New York State Department of Health , Public Health Informatics and Project Management Offi ce , Albany , NY , USA

Rachel Block New York State Department of Health , Offi ce of Health Information Technology Transformation , Albany , NY , USA

Elizabeth M. Borycki , RN, PhD School of Health Information Science, University of Victoria , Victoria , BC , Canada

Robert J. Campbell, PhD Ohio Department of Health

Alyse Capron , Masters of Nursing Clinical Strategy Unit, HealthLink BC , Victoria , BC , Canada

James T. Case , MS, DVM, PhD California Animal Health and Food Safety Laboratory , University of Calafornia, Davis , Davis , CA , USA

James Coates , MS, DVM, PhD Informatics Division , Explorys, Inc. , Cleveland , OH , USA

Dina Dickerson , MPH Maternal and Child Health Assessment, Evaluation and Informatics , Oregon Health Authority Center for Prevention and Health Promotion , Portland , OR , USA

Larry L. Dickey , MD, MPH California Department of Health Care Services , Health Information Technology , Sacramento , CA , USA

Brian E. Dixon , MPA, PhD Department of BioHealth Informatics, School of Informatics and Computing, Indiana University , Indianapolis , IN , USA

Center for Biomedical Informatics, Regenstrief Institute, Inc. , Indianapolis , IN , USA

Center of Excellence on Implementing Evidence-Based Practice, Health Services Research and Development Service, Department of Veterans Affairs, Veterans Health Administration , Indianapolis , IN , USA

 

 

xvi

Gerry Douglas , PhD Department of Biomedical Informatics , Center for Health Informatics for the Underserved, University of Pittsburgh , Pittsburgh , PA , USA

Margo Edmunds , PhD Evidence Generation and Translation, Academy Health , Washington , DC , USA

I. Charie Faught , PhD, MHA Health Care Informatics , Montana Tech of the University of Montana , Butte , MT , USA

Seth Foldy , MD, MPH, FAAFP Department of Family and Community Medicine , Medical College of Wisconsin , Milwaukee , WI , USA

Brian Fowler , MPH Division of Prevention and Health Promotion, Ohio Department of Health , Public Health Informatics and Vaccine-Preventable Disease Epidemiology , Columbus , OH , USA

Laura H. Franzke , PhD, MPH Scientifi c Education and Professional Development Program Offi ce , Centers for Disease Control and Prevention (CDC) , Atlanta , GA , USA

Hamish S. F. Fraser , MBChB, MRCP, MSc, FACMI Division of Global Health Equity, Department of Medicine , Brigham and Womens Hospital , Boston , MA , USA

Mary Anne Freedman , MA Poinciana Consulting, LLC , Venice , FL , USA

Lawrence E. Frisch , MD, MPH School of Population and Public Health, University of British Columbia; Vancouver Coastal Health Research Institute , Vancouver , BC , Canada

Paul C. Fu, Jr., MD, MPH David Geffen School of Medicine at UCLA and UCLA Fielding School of Public Health , Los Angeles County Harbor-UCLA Medical Center , Torrance , CA , USA

Nedra Y. Garrett , MS Division of Informatics Practice, Policy and Coordination , Centers for Disease Control and Prevention , Atlanta , GA , USA

Johanna L. Goderre , MPH Health Policy and Administration Division , School of Public Health, University of Illinois at Chicago , Chicago , IL , USA

Kenneth W. Goodman , PhD University of Miami Bioethics Program , Miami , FL , USA

Shaun J. Grannis , MD, MS Department of Family Medicine , Indiana University School of Medicine , Indianapolis , IN , USA

Center for Biomedical Informatics, Regenstrief Institute, Inc. , Indianapolis , IN , USA

Carol L. Hanchette , PhD Department of Geography , University of Louisville , Louisville , KY , USA

Contributors

 

 

xvii

William R. Hersh , MD Department of Medical Informatics and Clinical Epidemiology , Oregon Health & Science University , Portland , OR , USA

Steven H. Hinrichs , MD Department of Pathology and Microbiology , University of Nebraska Medical Center , Omaha , NE , USA

Richard S. Hopkins , MD, MSPH Department of Epidemiology , University of Florida College of Public Health and Health Professions and College of Medicine , Gainesville , FL , USA

Geraldine S. Johnson , MS New York State Department of Health , Public Health Informatics and Project Management Offi ce , Albany , NY , USA

Stephen P. Julien Department of Laboratory Medicine and Pathology , Mayo Clinic , Rochester , MN , USA

Shannon Kelley , MPH New York State Department of Health , Offi ce of Health Information Technology Transformation , Albany , NY , USA

Jeffrey M. Kriseman , PhD, MS Department of Informatics , Southern Nevada Health District , Las Vegas , NV , USA

Edwin M. Kilbourne , MD Health Solutions Group , Science Applications International Corporation (SAIC) , Atlanta , GA , USA

Brian J. Labus , MPH Health Solutions Group , Southern Nevada Health District , Las Vegas , NV , USA

John R. Lumpkin , MD, MPH Robert Wood Johnson Foundation , Princeton , NJ , USA

Abla Mawudeku , MPH Health Security and Infrastructure Branch , Public Health Agency of Canada , Ottawa , ON , Canada

J. A. Magnuson , PhD Department of Medical Informatics and Clinical Epidemiology , Oregon Health & Science University , Portland , OR , USA

Michelle Meigs Informatics Program , Association of Public Health Laboratories Silver Spring , MD , USA

Edward Mensah , PhD Health Policy and Administration Division , School of Public Health, University of Illinois at Chicago , Chicago , IL , USA

Riki Merrick , MPH Consultant, Public Health Informatics , Carmichael , CA , USA

Eric M. Meslin , PhD Indiana University Center for Bioethics, Indiana University School of Medicine , Indianapolis , IN , USA

Perry L. Miller , MD, PhD VA Connecticut Healthcare System , West Haven , CT , USA

Center for Medical Informatics, Yale University, School of Medicine , New Haven , CT , USA

Contributors

 

 

xviii

Patrick W. O’Carroll , MD, MPH, FACPM, FACMI Offi ce of the Assistant Secretary for Health, US Department of Health and Human Services , Seattle , WA , USA

Sridhar R. Papagari Sangareddy , MS (EECS), MS (MIS) Public Health Informatics Fellowship Program , Centers for Disease Control and Prevention , Atlanta , GA , USA

Catherine Pepper , MLIS, MPH Medical Sciences Library , Texas A&M University , Round Rock , TX , USA

Janise Richards , PhD, MPH, MS Division of Global HIV/AIDS , Center for Global Health, Centers for Disease Control and Prevention , Atlanta , GA , USA

Charles J. Rothwell , MS, MBA National Center for Health Statistics, CDC, HHS , Hyattsville , MD , USA

Rita Spear , MS Health Care Informatics , Montana Tech of the University of Montana , Butte , MT , USA

Ronald St. John , MD, MPH St. John Public Health Consulting International , Manotick , ON , Canada

Herman Tolentino , MD Scientifi c Education and Professional Development Program Offi ce , Centers for Disease Control and Prevention (CDC) , Atlanta , GA , USA

Alan Tomines , MD Department of Pediatrics , Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA , Torrance , CA , USA

James A. Weed , PhD, National Center for Health Statistics, CDC, HHS (Retired) , Hyattsville , MD , USA

Patricia Yao , MSc (Medical Informatics) Department of Medical Informatics and Clinical Epidemiology , Oregon Health & Science University , Portland , OR , USA

William A. Yasnoff , MD, PhD NHII Advisors , Arlington , VA , USA

Contributors

 

 

Part I Context for Public Health Informatics

 

 

3J.A. Magnuson, P.C. Fu, Jr. (eds.), Public Health Informatics and Information Systems, Health Informatics, DOI 10.1007/978-1-4471-4237-9_1, © Springer-Verlag London 2014

Abstract The transformation of public health by informatics is still in the nascent stages. Thus far, informatics in public health generally has been relegated to “pushing the broom” at the end of the parade: public health has tended to bring in informaticists to help resolve systemic issues such as non-interoperability, rather than realizing the full potential benefi ts that would accrue from their involvement at the outset.

To facilitate the understanding of Public Health Informatics, this chapter includes a brief review of public health, discussing the purpose, history, structural organization, and challenges of public health. Once the context of public health has been reviewed, the principles of Public Health Informatics are described, including some history and background, and the challenges encountered, as well as the drivers for change.

Although the discipline of public health informatics has much in common with other informatics specialty areas, it differs from them in several ways. These include (a) a focus on applications of information science and technology that promote the health of populations, rather than of individuals, (b) a focus on disease prevention, rather than treatment, (c) a focus on preventive intervention at all vulnerable points in the causal chains leading to disease, injury, or disability, and (d) operation within a governmental, rather than a private, context.

Drivers of change forcing public health professionals to be conversant with the development, use, and strategic importance of computerized health information

Chapter 1 Introduction to Public Health Informatics

J. A. Magnuson and Patrick W. O’Carroll

J. A. Magnuson , PhD (*) Department of Medical Informatics and Clinical Epidemiology , Oregon Health & Science University , 5th Floor Biomedical Information Communication Center (BICC) , 3181 S.W. Sam Jackson Park Rd., Portland , OR 97239 , USA e-mail: jamagnuson@gmail.com

P. W. O’Carroll , MD, MPH, FACPM, FACMI Offi ce of the Assistant Secretary for Health, US Department of Health and Human Services , 2201 Sixth Avenue , Seattle , WA 98105 , USA e-mail: patrick.ocarroll@hhs.gov

 

 

4

systems include health reform, advances in information technology, the advent of Big Data, and continuation of disruptive innovation.

Keywords Big Data • Disruptive innovation • Electronic Health Record • Gene patenting • Healthy People • Informatician • Informaticist • Informatik • Informatique • Infrastructure • Meaningful use • Mobile technology • Open access • Personal health record • Personalized medicine • Prevalence • Preventability • Severity • Software as a Service • SaaS • Telehealth • Value • Variety • Velocity • Volume

Introducing Public Health Informatics

Karl Steinbuch (1917–2005) is often credited with creating the term informatik [ 1 ], for automatic information processing, a term which came to denote computer sci- ence in German. In 1962, Philippe Dreyfus [ 2 ] devised the French term informa- tique , and in 1966 Alexander Mikhailov et al. [ 3 ] promoted the Russian term informatika for the theory of scientifi c information. In the US, a public health infor- maticist or informatician (both are correct) is a professional in the “systematic application of information and computer science and technology to public health practice, research, and learning” [ 4 ], illustrating the relation but clear distinction between computer science and informatics in this usage.

The scope of public health informatics includes the conceptualization, design, development, deployment, refi nement, maintenance, and evaluation of communi- cation, surveillance, information, and learning systems relevant to public health. Public health informatics requires the application of knowledge from numerous disciplines, particularly information science, computer science, management, organizational theory, psychology, communications, political science, and law. Its practice must also incorporate knowledge from the other fi elds that contribute to public health, including epidemiology, microbiology, toxicology, and statistics.

Learning Objectives 1. Defi ne the concept of public health informatics and explain the aspects that

it has in common with medical informatics. 2. Understand the four principles that defi ne, guide, and provide the context

for the types of activities and challenges that comprise public health infor- matics and differentiate it from medical informatics.

3. Describe the history, organization, purpose, and challenges of public health in the US.

4. Explain how the four main drivers of change are affecting the future of public health informatics.

5. Discuss the major developments that have increased the importance and immediate relevance of informatics to public health.

J.A. Magnuson and P.W. O’Carroll

 

 

5

Although public health informatics draws from multiple scientifi c and practical domains, computer science and informatics science are its primary underlying dis- ciplines. Computer science, the theory and application of automatic data processing machines, includes hardware and software design, algorithm development, compu- tational complexity, networking and telecommunications, pattern recognition, and artifi cial intelligence. Informatics science encompasses the analysis of the structure, properties, and organization of information, information storage and retrieval, information system and database architecture and design, library science, project management, and organizational issues such as change management and business process reengineering.

An important distinction between medical and public health informatics is illu- minated by the difference between medicine and public health. Public health is concerned with the health of populations, whereas clinical medicine involves the health of the individual. The World Health Organization perspective of health as a “state of complete physical, mental and social well-being and not merely the absence of disease or infi rmity” [ 5 ] can be extrapolated to population health as well. Public health includes not only the often-spotlighted communicable disease pro- grams, but also chronic disease control, health and wellness promotion, environ- mental health, mental health, and other program areas.

Public health informatics differs from other informatics specialties in that it involves:

1. A focus on applications of information science and technology that promote the health of populations, rather than of individuals;

2. A focus on disease prevention, rather than treatment; 3. A focus on preventive intervention at all vulnerable points in the causal chains

leading to disease, injury, or disability; and 4. Operation typically within a governmental, rather than a private, context.

Principles of Public Health

In order to understand public health informatics, it is necessary to have a good intro- duction to public health. As referenced earlier in this chapter, public health is con- cerned with the health of populations. The key characteristics of public health as contrasted with medicine are presented in Table 1.1 .

History of Public Health

Data forms the foundation of public health, and has very early roots in that area. Some of the earliest known examples of public health data involve the pneumonic plague surveillance conducted by the Venetian Republic in the fourteenth century, and the recording of vital events in the sixteenth century in the London Bills of

1 Introduction to Public Health Informatics

 

 

6

Mortality [ 6 ]. As time passed, these rich sources of data came to be increasingly analyzed and studied for public health reasons. In the US, Massachusetts developed a postcard-based reporting system in 1874, which marks the beginning of US infec- tious disease reporting [ 7 ].

The Communicable Disease Center, precursor of the Centers for Disease Control and Prevention (CDC), was established in 1946 [ 8 ]. The new center was an exten- sion of the wartime agency MCWA (Malaria Control in War Areas), developed to combat malaria through mosquito control. From those DDT-drenched roots grew today’s CDC, with its emphases on working with states and other partners to moni- tor and prevent outbreaks; maintain national health statistics; and, as included in its very name (Disease Control and Prevention), to prevent and control infectious and chronic diseases, injuries, and environmental health hazards.

Public Health Strata in the United States

Public health in the US is a composite of agencies/responsibilities. Although some regions differ in their public health composition or have entirely different structures such as tribal health agencies, in general , public health agencies in the US are arranged into three strata – federal, state, and local.

• Federal level – There are numerous so-called “operating divisions” within the US Department of Health and Human Services (HHS) that comprise the federal public health family: CDC, US Food and Drug Administration (FDA), National Institutes of Health (NIH), Indian Health Service (IHS), Substance Abuse and Mental Health Services Administration (SAMHSA), and Health Resources and Services Administration (HRSA) foremost among them. However, as regards the day-to-day practice of public health, the CDC [ 9 ] may be considered HHS’s

Table 1.1 Some critical differences between public health and medicine

Attribute Medicine Public health

Source Clinicians, health practitioners Agencies and organizations Primary

focus Persons with disease, injuries,

other health problems Populations (in communities, states,

the nation) Primary

strategy Treatment of persons with disease,

injury, or disability; secondary emphasis on prevention

Prevention of disease, injury, and disability

Timing of action

Usually taken after illness/injury occurs

Both before illness/injury (e.g., prevention) and after (e.g., surveillance)

Intervention context

Clinical and surgical encounters and treatment

Any vulnerable points in the causal chain. Modes include education, policy, research, monitoring, assurance

Operational context

Private practices, clinics, hospitals Governmental context, requiring responsiveness to legislative, regulatory, policy directives, and political context

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primary federal public health agency. It has many important responsibilities, including but not limited to:

– Development and dissemination of prevention guidelines and policies. – Distribution of federal funds to states (and, to a lesser degree, directly to local

health departments) for specifi c public health programs (e.g., immunization, HIV-AIDS, preparedness). Many state initiatives and program areas rely almost exclusively on federal funding.

– Collaboration, representation, and leadership in the public health arena – Assistance to other public health organizations, at their request. In 2011, for

example, CDC sent Epi-Aid assistance (Epi-Aids are requests to the CDC for epidemiological assistance) to US states (Wisconsin, Arkansas, Louisiana, and Georgia), and Ethiopia [ 10 ].

• State and Territory level – State health departments coordinate public health at the state level. Responsibilities include:

– Assisting local health departments (LHDs) with investigations such as out- break investigations

– Coordinating statewide initiatives and programs, such as statewide electronic laboratory reporting, vital statistics, immunization registries, etc.

– Setting state policy and legislation, such as state notifi able conditions. The Council of State and Territorial Epidemiologists (CSTE) maintains a State Reportable Conditions Assessment (SRCA) that represents an annual assess- ment of reporting requirements by state and territory [ 11 ].

– Distributing funds (often federal funds) to LHDs.

• Local level – The local level includes county health departments, metropolitan area health organizations, tribal public health, and regional collaboration organizations.

– LHDs often have the primary responsibility for investigating cases and outbreaks.

– Not all states have LHDs; some may perform all investigations at a state level. – Many large metropolitan areas have health organizations that function at the

level of an LHD. For example, the New York City Department of Health and Mental Hygiene gathers data and provides information on residents of New York City [ 12 ].

– The National Indian Health Board (NIHB) works with a variety of partners, including the Indian Health Service (IHS) and CDC, on public health projects such as the recent Traditional Foods Project and the Methamphetamine and Suicide Prevention Initiative (MSPI) [ 13 ].

– Regional public health initiatives may adhere to the ten HHS-designated regions of the US [ 14 ] or may constitute a response to local needs, such as Alaska’s public health centers [ 15 ].

In addition to governmental structure, public health is arranged into program areas based on activity and purpose. The Public Health Accreditation Board (PHAB) offers public health department accreditation options to tribal, state, local, and

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territorial public health departments in the US. The core public health programs and activities covered under PHAB [ 16 ] include:

• Access to clinical services • Chronic disease prevention and control • Communicable disease • Community health • Environmental public health • Governance • Health education • Health promotion • Injury prevention • Management/administration of public health programs and activities • Maternal and child health • Public health emergency preparedness • Public health laboratory services

The CDC is arranged into centers, institutes, and offi ces that refl ect focus on dif- ferent public health concerns [ 17 ]. These include such examples as the Offi ce of Infectious Diseases, National Institute for Occupational Safety and Health (NIOSH), National Center for Environmental Health/Agency for Toxic Substances and Disease Registry, and Offi ce of Surveillance, Epidemiology, and Laboratory Services.

The Purpose of Public Health

The Institute of Medicine’s 1988 report on public specifi es that the “core functions of public health agencies at all levels of government are assessment, policy devel- opment, and assurance” [ 18 ]. The CDC National Public Health Performance Standards Program (NPHPSP) determined ten Essential Public Health Services [ 19 ] essential to all communities, listed as:

1. Monitor health status to identify and solve community health problems. 2. Diagnose and investigate health problems and health hazards in the community. 3. Inform, educate, and empower people about health issues. 4. Mobilize community partnerships and action to identify and solve health

problems. 5. Develop policies and plans that support individual and community health efforts. 6. Enforce laws and regulations that protect health and ensure safety. 7. Link people to needed personal health services and assure the provision of

healthcare when otherwise unavailable. 8. Assure competent public and personal healthcare workforce. 9. Evaluate effectiveness, accessibility, and quality of personal and population-

based health services. 10. Research for new insights and innovative solutions to health problems.

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These ten essential services of public health harmonize well with the IOM’s three core functions (assessment, policy development, and assurance), and all are improved by the application of informatics. Assessment includes collection and analysis of health data, as well as the critical step of distribution of information gained to the community: informatics can advance the accuracy and security of health data collection, and increase the value of knowledge distribution. In addition, informatics-enhanced data improves the effi cacy of both policy development and assurance, including enactment of regulations or provision of services.

Public Health has achieved tremendous accomplishments in the twentieth cen- tury. From the Morbidity and Mortality Weekly Report (MMWR) list of ten highly- signifi cant public health achievements in the US, it is easy to see that the principles of PHI must have been involved [ 20 ]. The unordered list below includes some selected highlights of those achievements:

• Vaccination – worldwide eradication of smallpox, and elimination of poliomy- elitis in the US

• Motor – vehicle safety – such as seat belt implementation, reduction in drunk driving

• Safer workplaces – reduction in occupational injuries and unsafe working conditions

• Control of infectious diseases – improved sanitation, improved therapies • Decrease in coronary heart disease / stroke deaths – smoking cessation programs,

improved treatment and detection • Safer and healthier foods – food fortifi cation, reduction in contamination • Healthier mothers and babies – improvements in nutrition and healthcare access • Family planning – contraception, STD prevention, and treatment • Fluoridation of drinking water – reduced tooth decay • Recognition of tobacco as health hazard – antismoking campaigns

Public health has signifi cantly increased life expectancy. Since 1900, the average life expectancy in the US has increased 30 years, and a startling 25 of those years are attributed to public health initiatives. In the twentieth century alone, smallpox killed around 300 million people [ 21 ]. In 1977, a dedicated public health initiative brought about worldwide eradication of this disease [ 22 ]. And in the 1970s, a huge majority (88 %) of US children had elevated levels of blood lead, but by 1994, pub- lic health had reduced that percentage to only 4.4 % [ 23 ].

Public Health’s Unique Challenges and the Promise of Public Health Informatics

Public health usually operates in a resource-scarce environment, dependent upon inconstant but always inadequate public funding. Additionally, the public health workforce is impacted by detrimental factors including: between 1980 and 2000, the number of public health workers per 100,000 Americans declined from 220 to

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158; around half of the public health workforce is nearing retirement age; and four out of fi ve public health employees lack formal public health training [ 24 ]. Given these and other challenges, public health must be cautious about committing resources to a program. In order for a condition to realistically be of interest to pub- lic health, it usually needs to match some degree of each of the following criteria: severity – the condition/disease must be severe enough in its effects to warrant some type of intervention/monitoring; preventability – the condition must be preventable or at least able to be mitigated by health interventions, behavioral modifi cations, etc.; and prevalence – the condition must be prevalent enough in the population to warrant some type of intervention/monitoring (Fig. 1.1 ). In this environment of scarcity, public health is beginning to realize the benefi ts that can accrue from appli- cation of informatics.

Principles of Public Health Informatics

History and Background

Public health informatics is related to medical informatics in several respects [ 25 ]. Both disciplines seek to use information science and technology to improve human health, and there are subject matter areas of common concern (e.g., standards for vocabulary and information exchange). Moreover, lessons learned in medical infor- matics often apply to public health informatics. Further, there are informatics appli- cations for which there is no real distinction between public health and medical informatics. Examples of such applications include systems for accessing public health data from electronic medical record systems or for providing patient-specifi c prevention guidance at the clinical encounter.

Nevertheless, we believe that public health informatics is a distinct specialty area within the broader discipline of informatics, a specialty area defi ned by a specifi c set of principles and challenges.

Our view is that the various informatics specialty areas – for instance, nurs- ing informatics and medical informatics – are distinguished from one another by

Severity

Preventability Prevalence

Fig. 1.1 Diagram illustrating the intersection of qualifying conditions for a public health response

 

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the principles underlying their respective application domains (i.e., nursing and medicine), as well as by the differing nature and challenges of their informatics applications. In the case of public health informatics, there are four such prin- ciples, fl owing directly from the scope and nature of public health, that distin- guish it from other informatics specialty areas. These four principles defi ne, guide, and provide the context for the types of activities and challenges that comprise this fi eld:

1. The focus of public health informatics is on applications of information science and technology that promote the health of populations as opposed to the health of specifi c individuals.

2. Another focus of public health informatics is on applications of informatics sci- ence and technology that prevent disease and injury by altering the conditions or the environment that put populations of individuals at risk. Although notable exceptions exist, traditional healthcare largely treats individuals who already have a disease or high-risk condition, whereas public health practice seeks to avoid the conditions that led to the disease in the fi rst place. This difference in focus has direct implications for the ways in which informatics technology might be deployed.

3. Public health informatics applications explore the potential for prevention at all vulnerable points in the causal chains leading to disease, injury, or dis- ability; applications are not restricted to particular social, behavioral, or environmental contexts. In public health, the nature of a given preventive intervention is not predetermined by professional discipline, but rather by the effectiveness, expediency, cost, and social acceptability of intervening at various potentially vulnerable points in a causal chain. Public health inter- ventions have included, for example, legislatively mandated housing and building codes, solid waste disposal and wastewater treatment systems, smoke alarms, fl uoridation of municipal water supplies, redesign of automo- biles, development of inspection systems to ensure food safety, and removal of lead from gasoline. Contrast this approach with the approach of the mod- ern healthcare system, which generally accomplishes its mission through direct patient care services such as clinical and surgical encounters. Although some of these healthcare system encounters can properly be considered pub- lic health measures (e.g., vaccination), public health action is not limited to the clinical encounter.

4. As a discipline, public health informatics refl ects the governmental context in which public health is practiced. Much of public health operates through gov- ernment agencies that require direct responsiveness to legislative, regulatory, and policy directives; careful balancing of competing priorities; and open dis- closure of all activities. In addition, some public health actions involve author- ity to take specifi c (sometimes coercive) measures to protect the community in an emergency. Examples include medication or food recalls, closing down a restaurant or a contaminated pool or lake, and making changes to immunization policy.

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Challenges of Public Health Informatics

In addition to these principles, the nature of public health also defi nes a special set of informatics challenges. For example, in order for public health practitioners to assess a population’s health and risk status, they must obtain data from multiple disparate sources, such as hospitals, social service agencies, police departments, departments of labor and industry, population surveys, on-site inspections, etc. Data from these various sources about particular individuals must be accurately com- bined. Then, individual-level data must be compiled into usable, aggregate form at the population level. This information must be presented in clear and compelling ways to legislators and other policymakers, scientists, advocacy groups, and the general public. At the same time, the public health practitioner must insure that the confi dentiality of the health information about specifi c individuals is not compromised.

Together with the four principles that distinguish public health informatics from other informatics specialty areas, then, these and other special challenges defi ne public health informatics as a distinct specialty area.

Change Is a Constant: The Future of Public Health Informatics

There are many drivers mediating the rapid advances and changes in Public Health Informatics. The escalating power and speed of these factors make it increasingly critical that public health professionals be conversant with the development, use, and strategic importance of computerized health information systems and resources. Some of these drivers are discussed briefl y in this chapter; many will be covered in detail in the following chapters.

Driver for Change: Health Reform

Both clinical care and public health are undergoing massive changes. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 was enacted as part of the American Recovery and Reinvestment Act (ARRA) to foster the adoption and meaningful use of health information technology. In 2010, the Patient Protection and Affordable Care Act (PPACA, or commonly, ACA) was signed; it seeks to change the very nature of clinical practice, in part by changing fi nancial incentives that promote health and wellness versus pay-for-procedure reimbursement. In this new context, healthcare entities can potentially increase reimbursement by keeping their patients healthier – potentiating a new focus on prevention and new partnerships with public health agencies.

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Public health is (still) eagerly anticipating the bonanza of information it expects to accrue from the HITECH Act [ 26 ]. Electronic Health Records ( EHRs ) have tra- ditionally elicited an almost Pavlovian response from public health workers as they anticipate a cornucopia of surveillance and research data, but in truth, public health is only just starting to realize the full extent of the confi dentiality and data access problems involved.

The HITECH Act incentivizes adoption of EHR technology by offering Medicare and Medicaid payment to healthcare providers and hospitals that use certifi ed EHR systems to achieve meaningful use , a set of standards specifi ed by the Centers for Medicare & Medicaid Services (CMS) [ 27 ]. And the incentives are at an unprece- dented level – a total of US$27 billion over 10 years, on a per clinician basis of up to US$44,000 (Medicare) and US$63,750 (Medicaid) per clinician. Now at the beginning of 2013, US Healthcare IT News reports that “Medicare and Medicaid electronic health record payments are estimated to have blasted through [US]$10.3 billion to a total of 180,200 physicians and hospitals through December [2012] since the program’s inception” [ 28 ].

Meaningful Use is planned to develop in three stages, as described on the HealthIT.gov site referenced above:

• Stage 1, 2011–2012: Data capture and sharing. This stage concentrates on cap- turing data electronically and in standardized format and reporting clinical qual- ity measures and public health information.

• Stage 2, 2014: Advance clinical processes. This stage emphasizes increased health information exchange (HIE) and e-prescribing, and incorporation of labo- ratory results.

• Stage 3, 2016: Improved outcomes. This stage is planned to lead to better out- comes through elevated quality, safety, and effi ciency, and to improved popula- tion health.

EHRs are expected or hoped to produce three general benefi ts for patients, and to a lesser degree, to public health. First, more complete and accurate information should lead to better patient care. Second, providers will have better access to infor- mation. Third, patients will be empowered by increased access to their medical information, including the ability to download and share (if desired) their medical records.

Realizing the benefi ts of EHRs is not an easy task. Many of the factors needed for effectiveness of an EHR system, such as acceptance by partners (including the pub- lic), interoperability, implementation of coding systems and standard formats, and utilization of a unique health identifi er (UHI), are also barriers to implementation.

Driver for Change: Advances in Information Technology

The information technology revolution continues unabated. Today’s computer sys- tems are both faster and less expensive than ever before, and prices are continuing

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to decrease rapidly. In fact, computer hardware is no longer the major cost it once was in information system development projects.

More important, the Internet has emerged as both a universal communications medium and the source of a universal graphical user interface – the World Wide Web, accessed with Internet browser software. In fact, the growth in use of the Internet has been little short of phenomenal in recent years.

The broad deployment of the Web has provided a powerful paradigm for stan- dardized implementation of the communication capabilities that are central to all information systems. A Web browser interface allows broad access without the necessity for development or deployment of specifi c software or communications protocols for potential users. Updating information systems is greatly simplifi ed, since new versions of Web-based applications are immediately available to users without distribution of new end-user-installed software. Most system development has utilized this paradigm, with the resultant creation of many new and powerful tools to streamline and simplify the process. Consequently, information system development is now faster and easier than ever before, with collaborative develop- ment, interactive Web experiences, and explosive growth of social media continuing to unlock new opportunities. In this environment, the benefi ts of public health infor- mation systems are more obvious and more easily achievable, and thus much more compelling.

However, along with advances in capabilities come parallel advances in system hacking, identity theft, and other malicious intent. The goals of privacy, confi denti- ality, and security have never before been so challenging or so critical. While public health is accustomed to handling sensitive data, handling those data in electronic form introduces new and continually evolving spheres of ethical and security concerns.

Driver for Change: Big Data

Advances in medicine and public health, such as the explosion of genomic data and the implementation of EHR systems, are rapidly bringing attention to the topic of Big Data in health fi elds. As noted by IBM recently, “Every day, we create 2.5 quin- tillion bytes of data – so much that 90 % of the data in the world today has been created in the last 2 years alone” [ 29 ].

Health data is rapidly exceeding conventional database capacities. The over- whelming volume of data and its rapid accumulation are further complicated by the inherent variability of the data; health data can be structured, such as data from monitoring equipment and laboratory results, or unstructured, such as medical transcription and imaging. The traditional Three V’s of Big Data – volume, velocity, and variety – can and should be supplemented by a fourth V, value [ 30 ]. This applies to any kind of data, and especially to public health data – the resources invested in accumulating and analyzing data must be offset by the value to the population. The ultimate goals for all health data sources and

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tools, both public and private, should be to improve cost, increase effi ciency, and improve health.

Driver: Disruptive Innovation

Disruptive innovation, which creates new markets/fi elds and displaces existing technologies, has become the norm for technological advances. For example, today’s (2013) smart phones have more computing power than was used for the NASA moon landing in 1969 [ 31 ].

Public Health Informatics, undergirded as it is by information technology, will experience the same disruptive changes. Ten years into the future, today’s public health informatics students will be working at jobs that are not even visualized yet. Therefore, it is absolutely critical that public health today embrace rather than resist (futilely) the turbulence of disruptive innovation.

Many of the disruptive innovations taking place in healthcare also will affect public health. A few examples of these innovations include:

• Mobile technology : increasingly utilized by private health clinicians for purposes such as data access and entry during hospital rounds, mobile technology can similarly be used by public health professionals in clinics or for surveillance and tracking purposes, such as mapping wells or disease outbreaks using GPS.

• Telehealth : both public and private health consultation and diagnostic services can be provided to remote districts using telecommunication technologies or telehealth .

• Personalized medicine : private health can provide treatment that is customized or tailored to an individual being, based on detailed knowledge gained from spe- cialized testing such as genetic screening. Genetic data are just beginning to be used by public health, usually for purposes such as HIV genotype research and tracking, but these usages are destined to expand greatly as genetic screening technologies simultaneously expand in value and decrease in price.

• Personal health record ( PHR ): a PHR is maintained by the patient, as opposed to an electronic health record (EHR) that is maintained by an institution. Public health should work to develop ways to add value to PHRs, in order to increase engagement with the public and foster prevention of adverse health conditions.

• Open Access ( OA ): OA publishing offers the potential to enable greater access to research articles, which would benefi t both private and public health researchers.

• Gene patenting : fully as controversial as the patenting of genetically modifi ed organisms, gene patenting is (currently) allowed in the US. Although gene pat- ents do not apply to naturally-occurring genes, the repercussions and legal issues are guaranteed to affect medical research and testing, making them important to both private and public health.

• Software as a Service ( SaaS ): software delivery over a network, rather than through individually purchased installations, has the potential to greatly reduce IT support costs for both private and public health.

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Conclusion

Informatics has become something of a buzzword, which has the potential damage of diluting the power of the fi eld. When a popular term is co-opted, there is a danger of devaluation. Currently, examples of this incorrect usage include IT professionals and web designers often self-identifying as informaticists. While many of the skills held by these professions can and indeed should be part of an informaticist’s toolbox, the possession of those skills does not automatically bestow the title of informaticist.

In the context of the challenges discussed in this chapter, familiarity with at least the basic principles and practices of informatics is becoming essential. This may not be a welcome development for many public health practitioners, who already must be conversant with such wide-ranging fi elds as epidemiology and statistics, risk communication, community organization, legislative development, behavioral mod- ifi cation, emergency response, and of course program management. Nevertheless, facility in at least the use of key information technologies for public health (e.g., the Web, social media tools, web conferencing, secure communications, and epidemio- logic databases) is already a requirement for state-of-the-art public health practice. And more advanced informatics expertise is undeniably critical for the develop- ment of future information systems such as immunization registries, improved dis- ease and epidemic surveillance, and so forth. Like it or not, informatics has already joined the long list of disciplines with which public health practitioners must be conversant.

Public health informatics has often found itself in the position of “pushing the broom” at the end of the parade, being brought in to solve problems such as non- interoperability or poor data quality. But as informatics continues to grow as a fi eld, public health will begin to realize the full potential benefi ts of public health infor- matics when it becomes routine to involve informaticists at the outset or ground level of project planning and system improvement.

Review Questions 1. What are the main differences between public health informatics and other

informatics fi elds? 2. Discuss the history of public health in the US. What do you think has

been the most important factor in developing today’s public health infrastructure?

3. Of the top achievements of public health in the US, which do you think is most closely dependent upon informatics, and why?

4. Compare and contrast the functions performed by public health profession- als and practitioners of traditional healthcare. How do they differ in their approach to (1) the individual, and (2) the community? To what parties are these two categories of professionals accountable for their actions, and how?

5. Discuss the drivers of change in public health informatics. Which do you think will have the greatest impact, and why?

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J.A. Magnuson and P.W. O’Carroll

 

 

19J.A. Magnuson, P.C. Fu, Jr. (eds.), Public Health Informatics and Information Systems, Health Informatics, DOI 10.1007/978-1-4471-4237-9_2, © Springer-Verlag London 2014

Abstract From the earliest development of counting and counting machines to today’s sophisticated public health systems, a fundamental problem of public health practice has been the development of systems that can collect and analyze data, then convert it to useful forms. The development of modern mechanical measuring devices was a quantum leap toward solving the problem, but even after the invention of the computer in the twentieth century, there was a continuing need for systems that would maximize integration of system components and minimize duplication of data entry. A review of the three waves of modern federal-state public health system development reveals the progression toward the optimization goal. In general, today’s systems to manage public health data and information have evolved in step with the scientifi c basis underlying public health practice, a practice that integrates fi ndings in the biomedical fi eld with the sciences of epidemiology and biostatistics.

Keywords Data • Information • Knowledge • Age of observation • Age of analysis • Software reuse • Public health data collection • Federal-state system development • Public health information system development

Chapter 2 History and Signifi cance of Information Systems and Public Health

John R. Lumpkin and J. A. Magnuson

J. R. Lumpkin , MD, MPH (*) Health Care Group, Robert Wood Johnson Foundation , Route 1 & College Road East , Princeton , NJ 08543 , USA e-mail: jlumpki@rwjf.org

J. A. Magnuson , PhD Department of Medical Informatics and Clinical Epidemiology , Oregon Health & Science University , 5th Floor, Biomedical Information Communication Center (BICC) , 3181 S.W. Sam Jackson Park Rd., Portland , OR 97239-3098 , USA e-mail: jamagnuson@gmail.com

 

 

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Introduction

Today’s systems to manage public health data and information have evolved in step with the evolution of the scientifi c basis underlying public health practice. Public health practice now integrates fi ndings in the biomedical fi eld with the sci- ences of epidemiology and biostatistics. As the need for knowledge integration has become more complex, so has the nature of the information systems necessary for acquisition and understanding of larger amounts of data, along with the ana- lytical systems necessary for processing those data. Technological advances have allowed the automation of the systems that are now required for the practice of public health.

In this chapter, we will trace the history and evolution of the science of public health informatics. We will begin by tracing the development of counting and count- ing machines in the human experience. In a brief examination of public health infor- mation management in the pre-computer era, we will discuss the developments that created the need for increasingly complex data collection and analysis systems. The chapter concludes with a review of the three waves of federal-state public health systems development, beginning with the fi rst wave in the late 1960s and closing with an examination of the third wave now underway.

Data, Information, and Knowledge

The terms data, information, and knowledge are often misused in discussions of public health informatics. This misuse can lead to confusion, so our fi rst task is to defi ne these terms in the context of public health informatics. The term data is used to designate a measurement or characteristic of the entities (such as persons, things, measurements) that are the focus of a public health information system. The term

Learning Objectives 1. Clearly differentiate among the terms data, information, and knowledge,

and provide an example of each. 2. Briefl y trace the evolution of information systems, from the development

of counting and counting machines to the development of computers. 3. Explain and distinguish between the three stages in development of public

health information management systems. 4. List and discuss the nineteenth century developments in Europe and the

United States that contributed to the development of modern public health data collection and analysis.

5. List and describe the characteristics of the three waves of federal-state public health information system development.

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‘data’ can be used as a singular noun (as for an abstract mass, such as “public health data is complex”) or as a plural noun (as in, “these data are lacking standards”), and both usages are correct and standard. This term can encompass clinical measure- ments, laboratory values, medication dosages, clinical or diagnostic fi ndings, and treatment options, to name only a few examples. In isolation, data have little mean- ing. Consider, for example, the components of data in a vital records system used as part of a mission to monitor the health status of a nation. Each record in the system includes a notation of the deceased individual’s age, race, and other demographic features. It also typically includes a description of the cause of death by a physician, a medical examiner, or a coroner. All of these data are the raw material of the vital records system. However, without context or analysis, these isolated bits do not convey much meaning.

In contrast, information refers to data placed in context with analysis. Extending our previous example of the vital records system, the data element indicating cause of death may lack meaning in isolation. But if a public health offi cial correlates this data element and generates a table categorizing the frequency of numerous causes of death, then context has been applied and this has led to the creation of informa- tion. A user of the public health table can identify the leading causes of death, as well as the distribution of those causes in the jurisdiction under study.

Finally, knowledge in a public health system is the application of information by the use of rules. In our vital records system example, suppose that one leading cause of death identifi ed in a locality is lead poisoning. In that locality, a toxicologist can review results of blood lead tests administered to the population and compare the outcomes to areas with normal blood lead values. This process in itself yields infor- mation. At the same time, the toxicologist has access to action levels developed by experts working with the CDC. These action levels represent rules for action for managing blood lead levels in the affected population. The action levels, then, are an example of knowledge; they prescribe the rules to be used in the application of information. Table 2.1 summarizes the distinction among these three terms.

Table 2.1 Data information and knowledge

Term Defi nition Example

Data A measurement or characteristic of the person or the thing that is the focus of an information system

A public health assessor records the levels of thallium at various locations at a toxic waste site.

Information Data placed in context with analysis

A public health assessor creates a table showing the proportion of the locations exceeding the appropriate maximum contaminant level for thallium at the site.

Knowledge The application of information by the use of rules

The public health assessor consults the action levels for thallium as published by CDC/ ATSDR and determines the appropriate remedial actions to be taken at the contaminated site.

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The Development of Counting and Counting Machines

As the scientist William Thomson, Lord Kelvin, stated in the late 1800s, “When you can measure what you are speaking about and express it in numbers, you know something about it, but when you cannot measure it, when you cannot express it in number, your knowledge is of a meagre and unsatisfactory kind” [ 1 ]. Indeed, the history of information systems is in one sense a history of mea- surement. From the earliest known artifact associated with counting – a fibula of a baboon, with 29 clearly defined notches, dated approximately 35,000 BCE and found in a cave in the Lebombo Mountains in southern Africa [ 2 ] – to the present day, information systems have concentrated on measurement. In addi- tion, of course, they now perform sophisticated analytical work on large sets of data.

The earliest counting systems refl ect the fact that the human brain has inherent limitations in its ability to comprehend quantity. The eye is not a very precise count- ing tool, particularly in comprehending quantities above four or fi ve. Societies that entered the twentieth century isolated from the rest of the world rarely had words for numbers greater than four. You can verify the limitations of the eye in counting with a simple experiment: Look at a number of marbles in a bowl very briefl y, starting with one or two marbles and then adding a few marbles to the bowl. As you add marbles, try to determine the number without counting. If your visual limits are typical, you will have diffi culty in determining the exact number of marbles without counting them once the actual number exceeds four or fi ve.

That limitation of the human brain to readily accommodate larger numbers led to the use of objects to implement one-to-one correspondence in measurement, and to reliance on the property of mapping. We can see this human tendency to grasp the principle of one-to-one correspondence and to utilize the property of mapping in an infant who, at 15 or 16 months, has gone beyond simple observa- tion of the environment. If we give such a child an equal number of dolls and little chairs, the infant will probably try to fi t a doll on each seat. This kind of play is nothing other than mapping the elements of one set (dolls) onto the elements of a second set (chairs). But if we set out more dolls than chairs (or more chairs than dolls), after a time the child will begin to fret: it will realize that the mapping is not working [ 3 ].

Application of the principle of one-to-one correspondence led early humankind to the use of objects to record the association of one thing to another. We have already mentioned the fi bula of the baboon dated to approximately 35,000 BCE; it is marked with 29 clearly defi ned notches, and it resembles calendar sticks still in use by Bushmen clans in Namibia [ 4 ]. In a similar fashion, cave drawings with clear counting marks beneath the depicted animals may have represented an account of success at a hunt. One-to-one correspondence is also demonstrated by the earliest tally sticks used for counting and for accounting, and other historic devices,

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including counting pebbles and molded, unbaked clay tokens. Another example is an early form of an abacus used in Sumer (lower Mesopotamia).

It is believed that the earliest counting tool was the human body, and specifi cally the hand. In fact, the earliest device used for calculation was the fi ngers of the hand. This counting system would seem to have led to the development of numbering systems with a base of fi ve in many locations throughout the world. Funerary paint- ings from an Egyptian tomb at Beni Hassan dating from the Middle Kingdom (2,100–1,600 BCE) depict people playing the game of morra , a game that uses fi nger- based calculations to determine the winner [ 5 ].

The Egyptians were noted for their early adoption of a written numerical sys- tem. A document carved on the Palermo Stone (circa 2,925–2,325 BCE) listed the current census of livestock, as well as a 600-year history of the cycle of fl ood- ing of the Nile [ 6 ]. The Egyptian civilization was dependent upon the water from the Nile River that fertilized the fi elds when it fl ooded once per year. However, if the fl ooding was too great, the damage to irrigation systems (and homes) would lead to poor crops. The government stored grain to abate any shortfall of grain production. By measuring the height of the fl ood, they were able to calculate the expected size of the crop and project any shortfalls [ 7 ]. The Egyptians of the Middle Kingdom were early users of numbers and counting to do more than just document their environment; they also used counting to predict and plan for the future.

Development of Mechanical Counting Devices

The success of the abacus, fi nger-based calculation, and other similar methods pre- dominated until the 1600s CE. These counting methods were used primarily in commerce. It was the measurement of time, of the motion of stars, and of distance that sparked the development of mechanical calculating devices. Egyptians were among the fi rst to use mechanical devices to measure the passage of time. They invented the water clock to mark the hours of the night (early fourteenth century BCE). The water clock used the passage of water from a carefully designed vessel to divide the night into 12 equal hours. This device had adjustments for the seasons, when the length of night and day varied. This water clock is one of the earliest known mechanical calculation devices [ 8 ]. In approximately 150 BCE, Hipparchus developed a device, called an astrolabe, to calculate the position of the stars [ 9 ]. Other Greek mechanical artifacts from the time indicated the use of gears and wheels to calculate the positions of the planets and stars [ 8 ]. In the same period, Roman documents indicated the development of a geared device to measure dis- tance [ 8 ]. Such devices were also developed in China in the third century CE. In 723 CE, I-Hsing, a Buddhist monk and mathematician, developed a water-driven mechanical clock [ 8 ].

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The Development of Modern Mechanical Measuring Devices

Mechanical devices for arithmetic or other mathematical calculations were not developed until 1622 CE, when English mathematician William Oughtred invented the rectilinear logarithmic slide rule. His student Richard Delamain developed the circular slide rule in 1630 CE [ 10 ]. These devices used logarithmic theory to approximate complex mathematical calculations. Slide rules were used until the 1970s, when they were replaced by electronic calculators.

The fi rst truly mechanical calculating device was developed in 1623, when German scientist Wilhelm Schickard developed a machine that used sprocket wheels to add numbers. Multiplication and division was possible with the use of logarithm tables [ 11 ]. In 1642, Blaise Pascal developed the fi rst adding and sub- tracting device; it was able to carry or borrow digits from column to column auto- matically [ 3 , 10 ]. Over the next 240 years, the fundamental principles developed by Oughtred, Schickard, and Pascal formed the basis of calculation machines (calculators).

Although these calculating machines and their increasingly sophisticated descen- dants were able to perform basic arithmetic functions accurately, they were unable to perform more sophisticated analytical work on large sets of data. In 1820, British mathematician Charles Babbage began construction of a machine for calculating mathematical tables. He secured aid from the Royal Society and the British govern- ment to continue his work, but ran out of funding in 1856 without completing his device [ 10 ]. However, many of his concepts have formed the foundation of elec- tronic computers in use today [ 12 ].

Early mechanical calculators were effective for accounting purposes in the busi- ness setting, but as mentioned, they were less effective when working with large data sets. It was the 1880 United States (US) census that served as a catalyst for the development of the fi rst machine capable of performing analysis of such large data sets. By 1880, the increased population of the US created signifi cant obstacles for the decennial census, and in fact, it took 8 years to complete. Under direction of Dr. John Shaw Billings, from the US Surgeon General’s offi ce, Herman Hollerith bor- rowed technology from Joseph-Marie Jacquard, the developer of the automated loom. Jacquard’s loom was controlled by a series of cards with holes punched in them, corresponding to the weave pattern. Hollerith developed a system that read holes punched into a card. Each dollar bill-sized card was able to hold a large amount of data. The card was read in a rapid fashion by a machine designed by Hollerith. The 1890 census was completed in half the time required for the 1880 census, with savings of US$500,000 (US 1890 dollars) [ 13 ]. This innovation was the basis of many electric business and scientifi c machines, well into the second half of the twentieth century.

The military challenges of World War I led to a greater focus on automated cal- culation. To hit the faster targets on the mechanized battlefi eld, gunnery offi cers had to make quick adjustments for speed of the target, weight of the shell, and wind speed and direction. To assist the gunnery offi cers, the US Army sought to prepare

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fi ring tables. Those tables allowed the gunnery offi ce to determine quickly the angu- lation and direction for the guns. However, the time-consuming computations nec- essary for developing the tables completely overwhelmed the Ballistic Research Laboratory. Through a contract with the University of Pennsylvania, more than 100 students began working on the project, but failed to eliminate the backlog [ 14 ].

In response to the need to speed up this process, the Army funded the creation of ENIAC (Electronic Numerical Integrator and Computer). The project was started in 1943 and completed in 1945. When completed, it weighed 30 tons, contained 18,000 vacuum tubes, and was capable of 360 multiplications per second [ 13 , 15 ]. The ENIAC, along with the Mark I, developed by Howard Aiken, were the fi rst modern programmable computers [ 11 ].

Although ENIAC was not the only computer of its time – the British computer Colossus, for example, had been designed to crack Nazi codes – it was the fi rst multipurpose computer. It could be programmed to perform different functions, and it was also fast (at the time). For example, it could add 5,000 numbers or do 14 10-digit multiplications in a second. Although these feats are slow by modern stan- dards, they were incredible for the 1940s. ENIAC was the brainchild of Professor John Mauchly, a physics teacher, and graduate student J. Presper Eckert, both of the University of Pennsylvania. Although the purpose of the design of ENIAC was to assist the army in performing the calculations necessary for gunnery charts, it was completed too late to be of use for that purpose during WWII. In fact, ENIAC began its fi rst task even before it was dedicated in 1945: performing millions of calcula- tions associated with top-secret studies of nuclear chain reactions in connection with the eventual development of the hydrogen bomb.

Later, Dr. John von Neumann, of the Institute for Advanced Study in Princeton, contributed an enhancement to ENIAC. Before his work with ENIAC, reprogram- ming the computer involved manually rewiring it. Dr. von Neumann suggested that code selection be made with switches, so that cable connections could remain fi xed. This innovation saved considerable time in reprogramming ENIAC [ 15 ].

Stages in Development of Public Health Information Management Systems

Public health information management systems have their roots in antiquity. The fi rst phase of these systems refl ected public health observations according to indi- vidual experience (Age of Observation). A second phase refl ected a movement beyond observation to analysis of the root causes of public health disturbances (Age of Analysis). Finally, a third phase, leading to the rise of modern public health infor- matics, featured advanced methods of data collection and analysis in public health practice (Modern Public Health Informatics). 1

1 Melnick D. Building Robust Statistical Systems for Health. Report to the National Committee on Vital and Health Statistics; 1999. Unpublished. Available from author: danmelnick1008@gmail.com.

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The Age of Observation

Observations based upon individual experience marked the fi rst phase of data-based public health practice. Observations by the great physicians of their times in China, Egypt, India, Greece, and Rome provided the foundations for preventive and cura- tive practice; the practice of vaccination is known to have existed as early as the fi rst century BCE in China [ 16 ]. Of course, one of the most famous pre-computer era public health practitioners was Hippocrates, whose teachings refl ect the way early health practitioners used observation to understand the relationship of health to liv- ing conditions. The observations of such practitioners led to the development and implementation of public health interventions. For example, the public health importance of sanitation was discovered early in the rise of civilization. Eventually, the age of observation in public health gave way to the age of analysis.

The Age of Analysis

The fall of the Roman Empire, during the late 400s of the Common Era, marked the end of an exchange of scientifi c learning between the hemispheres. For the next 1,000 years, social and political forces led to the isolation of Europe from many of the cultural and scientifi c developments in Africa, Asia, and other parts of the world. Many of the writings and knowledge acquired during the Observation Era were lost. However, the Arab cultures of the Mediterranean preserved it to some extent, and reintroduced it to the peoples of Europe during trade and the Moorish occupation. The European rediscovery of the Americas and the subsequent colonization resulted in a Eurocentric New World scientifi c community. The scientifi c and health systems that developed in the colonial and nineteenth century US was dependent on the state of the art in Europe.

Certain events occurring during the Age of Analysis had profound implications for public health practice. These events and developments included:

• Plague epidemics . The breakout of bubonic plague in Messina, Sicily, in October 1347, with the subsequent spread of the deadly disease to other parts of Europe, resulted in social upheaval.

• The Renaissance . A great explosion in knowledge and learning accompanied the Renaissance in Europe. An important resulting enhancement to the evolution of public health practice was the adoption of the scientifi c method, a systematic approach that laid the foundation for collection and analysis of health-related data.

• Concept of population health . General recognition of the importance of a healthy population to the national wealth and power was established. The philosopher William Perry, who invented the term political arithmetic , argued that the analy- sis of data could throw light on matters of national interest and policy. He sug- gested that the control of communicable disease and the reduction of infant

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mortality would contribute the most to preventing impairment of the population. Perry was one of the fi rst to calculate the economic loss caused by disease [ 17 ].

• Concept of Data analysis . The basic principles for analysis of data and determi- nation of data reliability were established by John Graunt, who in 1662 analyzed over 30 years of vital statistics and social data. Graunt’s work demonstrated a method of developing useful information through the careful and logical inter- pretation of imperfect data.

• Mortality tables precursor . Huygens developed a precursor to mortality tables, work that was based on the fi ndings of Graunt and his own earlier work on probability.

• First mortality tables . Edmond Haley merged these concepts and developed the fi rst mortality tables to predict life expectancy in 1693. Haley’s merger of data collection and probabilistic analysis established modern principles for the management and analysis of public health data.

• Roots of epidemiology . Scientists such as Laplace and Bernoulli applied mathematical principles to public health issues, work that set the stage for the major advances in data and information management that led to the development of the modern epidemiological approach.

The Origin of Modern Public Health Informatics

During the nineteenth century and the fi rst half of the twentieth century, develop- ments in both England and the US created the necessity for advanced methods of data collection and analysis in public health practice. Some of these developments are discussed in detail in the following sections.

The Cholera Outbreaks in England

In England, the nineteenth century cholera epidemics led to major changes in the practice of public health. The cholera epidemics of 1831 and 1832 highlighted the role of neglected sanitation among the poor in imperiling the health of all. The Poor Law was passed in 1834 [ 18 ] and the Poor Law Commission was formed in response. Dr. Edwin Chadwick was appointed the secretary of the commission and became one of the leading forces in the sanitation movement. He proposed the for- mation of the Bureau of Medical Statistics in the Poor Law Offi ce. Under his leader- ship, Dr. William Farr began to use data that became available under the 1836 Births and Deaths Act. Chadwick proposed that this act would lead to registration of the causes of disease, with a view to devising remedies or means of prevention [ 19 ]. A vast amount of data was collected under these two acts. Analysis of these data by Farr led to a better understanding of the role of sanitation and health. Farr’s analysis represented one of the earliest examples of the presentation of a plausible epidemio- logical theory to fi t known facts and collected data.

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In 1859, Florence Nightingale, working with William Farr, confi rmed the connection between sanitation and mortality by studying the horrendous death rate in the British Army in the Crimea. Not only did these public health workers com- pare death rates for non-combat-related illness in the army to rates in a reference population, they also published one of the fi rst uses of graphics to present public health data. Also at this time, Adolphe Quetelet consolidated current statistical developments and applied them to the analysis of community health data compiled by observation and enumeration. He noted that variation was a characteristic of biological and social phenomenon, and that such variation occurred around a mean of a number of observations. Further, he demonstrated that the distribution of obser- vations around a mean corresponded to the distribution of probabilities on a proba- bility curve. This work helped form the foundation of biostatistics as applied to the health of the public.

In 1854, cholera again struck London. Dr. John Snow conducted an investigation of this outbreak in the Soho section of London. He carefully mapped the location of each of the victims, which revealed a pattern centered on the Broad Street pump. He then proceeded to convince local authorities to remove the handle from the pump, thereby stopping the outbreak. He continued the analysis of the outbreak and was able to associate the location of the water intake that supplied the Broad Street pump with other water companies and sewage outfl ows in the Thames River. His work led to future regulation of water supply intakes. The methodology that he used has become the foundation of all modern epidemiological investigations of disease outbreaks. He also was one of the fi rst to use a rudimentary manual geographical information system (GIS), his tools basically consisting of a map and a pencil [ 20 , 21 ]. Thus, the application of scientifi c learning began to have a positive impact on the health of the English population. In 1866, it was noted that cities without a system for monitoring and combating cholera fared far worse in the epidemic of that year [ 22 ].

Public Health Data Collection in the United States

In the US, independence fostered the development of strong state and local governments. These organizations began to incorporate current scientifi c knowl- edge into protecting the health of their populations. The fi rst local health department was formed in 1798 in Baltimore, Maryland [ 23 ]. In the early 1800s, local health departments collected health data only sporadically. In Illinois, for example, spo- radic data were collected in the City of Chicago starting in 1833, with the formation of the Chicago Department of Health.

Data collection problems in the seventh decennial census in 1850, however, inspired more comprehensive public health data collection and analysis in the US. The seventh census included gross death and birth rates that many considered inac- curate, due to defects in the collection of this data. Changes in the methods of data collection were implemented for the eighth decennial census in 1860, and more reliable data were collected [ 24 ].

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One of the most profoundly infl uential nineteenth century data collection developments in the US was the publication in 1850 of Lemuel Shattuck’s Report of the Sanitary Commission of Massachusetts . This report provided the basic blueprint for the development of a public health system in the US. It outlined many elements of the modern public health infrastructure, including a recommendation for establishing state and local health boards [ 25 ].

By 1900, many state and local health departments had formed in the US. An important role of these departments was the collection and analysis of reports of communicable diseases and vital statistics. In the early 1900s, the vital records sys- tem was still struggling. The Census Bureau worked with many states to encourage the recording and reporting of birth and death data. During the Depression and the Second World War, the importance of enumerating and documenting births became evident as more people needed to prove citizenship, for eligibility for relief and other programs. In fact, during World War II, laws prohibited the employment of noncitizens in essential defense projects; for many job seekers, proof of citizenship through birth or naturalization became essential.

In 1933, Texas became the last state to begin reporting vital statistics to the fed- eral government. Even so, in 1940, it was estimated that as many as 55 million native-born persons did not have birth records [ 26 ]. In response, the US Bureau of the Budget recommended moving the vital statistics offi ce to the Public Health Service. In the 1960s, the vital statistics function became a part of the new National Center for Health Statistics, and the current cooperative system with states was put into place [ 27 , 28 ].

In the fi rst part of the twentieth century, the system for collecting birth and death records was being established and standardized. However, data about nonfatal ill- nesses was diffi cult to obtain and therefore sparsely available. An early attempt at a survey-based assessment of the health status of the US population was conducted by the US Public Health Service in the 1930s, using Work Projects Administration funds. The survey incorporated data from 750,000 households in 84 cities and sev- eral rural areas. It was conducted with the time’s accepted methodology, which did not include probability sampling or standardized questionnaires. These data became the reference for policy development until the National Health Interview Survey (NHIS) reported its fi rst results in 1957 [ 27 ]. The design of the NHIS was one of the early tasks of the National Committee on Vital and Health Statistics (NCVHS) in 1953 [ 28 ].

The scientifi c discoveries of the nineteenth century laid the basis for substantial progress in the control of infectious disease. The nature of public health challenges changed as the importance of data in policy and program decision-making became better understood, both by organized public health agencies and researchers. Advances in immunizations, sanitation, and nutrition led to substantial improve- ments in the health of the public. By the middle of the twentieth century, the leading causes of death had changed to heart disease, cancer, and stroke. The increasing importance of these chronic illnesses in public health practice mandated a disease model capable of handling numerous factors, including longer intervals between cause and effect. As interventions became more complex and long–term, new

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approaches had to be developed that involved data collected about individuals over time and space. In turn, the need to analyze data in different locations and times led to the concept of data linkage [ 29 ]. Initially, attempts were made to develop a paper- based cross-index, but the complexity of such a task became daunting and led to frustration and failure.

Better surveillance systems and enhancements to national and local vital statis- tics systems increased the amount of data available to public health agencies, enabling programmatic decisions for the prevention and treatment of disease to be driven by data and information. The increasing volumes of data, along with the increasing need to analyze that data, created conditions that were ripe for techno- logical advancement. In fact, many tasks, including record linkage on a large scale, were impossible, given the state of technology in the mid-twentieth century. The newly emerging automated information systems were a perfectly-timed match with the need for public health entities to manage large volumes of data and information.

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