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Competencies for Advanced Practice

Concepts and Competencies for Advanced Practice


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“…nicely integrates epidemiological concepts, evidence-based practice in population health, and program development and evaluation…Authors describe epidemiological research designs, research synthesis, and evidence assessment—knowledge essential for advanced practice nurses working with populations or in the community.”

—Journal of Community Health Nursing

In its third edition, Population-Based Nursing continues to be the only advanced practice nursing text to focus on core competencies in both epidemiology and population health. This comprehensive

resource delivers essential content for doctoral nursing practice (as outlined by the AACN) and encompasses the many changes in healthcare that affect population-based nursing. It describes the role of the advanced practice nurse in identifying and mitigating healthcare disparities at the local, national, and global levels and provides guidance on how to conduct community assessments. A strong foundation in epidemiologic methodology is provided, including coverage of mortality measures, testing validity and reliability, study designs, risk and casualty assessment, and data analysis and interpretation.

Updated throughout, the third edition includes new and expanded topics such as the role of accreditation in validating population-based practice and programs; value-based care; and the use of technology, data, and information systems in population health. The text includes links to online resources and a robust Instructor’s Manual with exercises and discussion questions. In addition to its value as a primary textbook in DNP and MSN programs, the text also serves as valuable resource for advanced practice nursing professionals.


• Includes a strong focus on both epidemiology and population-based nursing competencies

• Addresses the Essentials of Doctoral Education for Advanced Nursing Practice as outlined by the AACN

• Includes new and expanded topics such as the role of accreditation in validating population- based practice and programs; value-based care; and the use of technology, data, and information systems in population health

• Examines how technological innovation and social networking impact the development of interventions and population outcomes

• Provides links to online resources and a robust Instructor’s Manual with exercises and discussion questions

• Purchase includes access to the ebook for use on most mobile devices or computers









Concepts and Competencies for Advanced Practice

A N N L . C U P P C U R L E Y, PhD, RN, Editor







N -B






11 W. 42nd Street New York, NY 10036-8002 www.springerpub.com

9 780826 136732

ISBN 978-0-8261-3673-2






Ann L. Cupp Curley, PhD, RN, recently retired from her position as the Nurse Research Specialist at Capital Health in Trenton, New Jersey, where she was responsible for pro- moting and guiding the development of nursing research and evidence-based practice. She has an extensive background in nursing education at the undergraduate, graduate, and doctoral levels, and more than 10 years’ experience in community and public health nursing. Dr. Curley has been principal or co-principal investigator of many research proj- ects and continues to serve as an advisor on DNP project committees and a research con- sultant. She received a BSN from Boston College, an MSN in community health/clinical nurse specialist track from the University of Pennsylvania, and a PhD in urban planning and policy development from Rutgers, Th e State University of New Jersey. Dr. Curley has received many honors, including the Nurse.com Nursing Spectrum Excellence Award for Education and Mentorship.





Third Edition

Ann L. Cupp Curley, PhD, RN Editor



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In Memory of Patty Vitale

Good friends are hard to fi nd . . . and impossible to forget. —Anonymous





Contributors xi Foreword Patricia A. Polansky, RN, MS xiii Preface xv Acknowledgments xxi

1. Introduction to Population-Based Nursing 1 Ann L. Cupp Curley

Introduction 1 Background 2 Defi ning Populations 6 Using Data to Target Populations and Aggregates at Risk 7 Summary 17 Exercises and Discussion Questions 18 References 19 Internet Resources 22

2. Identifying Outcomes in Population-Based Nursing 25 Alyssa Erikson and Sonda M. Oppewal

Introduction 25 Identifying and Defi ning Population Outcomes 26 National Healthcare Objectives 37 Summary 45 Exercises and Discussion Questions 46 References 47 Internet Resources 50

3. Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part I 53 Patty A. Vitale and Ann L. Cupp Curley

Introduction 53 Th e Natural History of Disease 54 Prevention 55 Causation 57 Methods of Analysis 58 Descriptive Studies 72

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Analytic Epidemiology 74 Summary 81 Exercises and Discussion Questions 82 References 85 Internet Resources 86

4. Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part II 87 Patty A. Vitale and Ann L. Cupp Curley

Introduction 87 Errors in Measurement 87 Confounding 92 Interaction 93 Randomization 94 Data Collection 95 Causality 96 Scientifi c Misconduct (Fraud) 97 Study Designs 98 Databases 102 Summary 104 Exercises and Discussion Questions 104 References 106 Internet Resources 107

5. Applying Evidence at the Population Level 109 Vera Kunte and Ann L. Cupp Curley

Introduction 109 Asking the Clinical Question 110 Th e Literature Review 114 Assessing the Evidence 117 Integration of Evidence Into Practice 128 Summary 133 Exercises and Discussion Questions 134 References 134 Internet Resources 137

6. Using Information Technology to Improve Population Outcomes 139 Laura P. Rossi and Ann L. Cupp Curley

Introduction 139 Use of the Internet to Obtain Health Information 140 Using Technology to Improve Population Health 146 E-Resources Th at Support Population-Based Nursing 150




Summary 154 Exercises and Discussion Questions 155 References 155

7. Concepts in Program Design and Development 159 Laura P. Rossi and Ann L. Cupp Curley

Introduction 159 Sources of Data 159 Innovative Care Delivery Models 162 Program Development: Where to Start 163 Implementation 172 Overcoming Barriers and Challenges 178 Summary 180 Exercises and Discussion Questions 180 References 181 Internet Resources 182

8. Evaluation of Practice at the Population Level 183 Barbara A. Niedz

Introduction 183 Monitoring Healthcare Quality 184 Summary 210 Exercises and Discussion Questions 211 References 212 Internet Resources 216

9. Th e Role of Accreditation in Validating Population-Based Practice/Programs 219 Eileen M. Horton

Introduction 219 Governmental Programs 222 Non-Governmental Programs 227 Planning for Accreditation 231 Summary 233 Exercises and Discussion Questions 233 References 234 Internet Resources 235

10. Building Relationships and Engaging Communities Th rough Collaboration 237 Sonda M. Oppewal and Barbara A. Benjamin

Introduction 237 Foundation for Population-Focused Practice 237




Community Health Assessment 242 Assessment Tools and Methods 246 Building Relationships 254 Summary 257 Exercises and Discussion Questions 258 References 259 Internet Resources 261

11. Challenges in Program Implementation 263 Janna L. Dieckmann

Introduction 263 Lewin’s Stages of Change 264 Community Engagement 265 Summary 285 Exercises and Discussion Questions 285 References 286 Internet Resources 288

12. Implications of Global Health in Population-Based Nursing 289 Lucille A. Joel and Irina McKeehan Campbell

Core Competencies in Global Health 289 Summary 314 Exercises and Discussion Questions 314 References 315 Internet Resources 317

Index 319




Barbara A. Benjamin, EdD, RN, Adjunct Faculty, University of North Carolina, Chapel Hill

Irina McKeehan Campbell, PhD, MPH, Professor, Department of Health Sciences, School of Health and Human Services, National University, La Jolla, California

Ann L. Cupp Curley, PhD, RN, Research Consultant, Falmouth, Massachusetts

Janna L. Dieckmann, PhD, RN, Clinical Associate Professor, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

Alyssa Erikson, PhD, MSN, RN, Associate Professor and Chair, Department of Nursing, California State University Monterey Bay, Seaside, California

Eileen M. Horton, MSN, MSM, RN, NEA-BC, Senior Vice President, Hospital Adminstration and Chief Quality Offi cer (retired), Capital Health, Trenton, New Jersey

Lucille A. Joel, APN, EdD, FAAN, Distinguished Professor, Rutgers University School of Nursing, Newark, New Jersey

Vera Kunte, DNP, APN, Nurse Research Specialist, Capital Health, Trenton, New Jersey

Barbara A. Niedz, PhD, RN, CPHQ, Assistant Professor, Rutgers University, School of Nursing, Newark, New Jersey

Sonda M. Oppewal, PhD, RN, Professor, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

Laura P. Rossi, RN, PhD, Assistant Professor, Simmons University, College of Natural, Health and Behavioral Sciences, School of Nursing; Program Manager, Quality and Patient Safety, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, Massachusetts

Patty A. Vitale†, MD, MPH, FAAP

† Deceased





“What’s past is prologue,” wrote William Shakespeare in Th e Tempest. Why read Population-Based Nursing: Concepts and Competencies for Advanced Practice? In this book, the third edition, there is a lot more to know and learn. If the fi rst two decades of the 21st century have taught us anything it is that what we know as healthcare is changing at warp speed. And, as the anniversary of Florence Nightingale’s 200th birthday looms large, Shakespeare’s words echo in our ears and remind us of needed changes in the practice of the profession of nursing. We inherently know that what has happened in the past sets the scene for the really important morphs yet to be identifi ed and implemented. Th ose changes will determine nursing’s viability in the ever chang- ing national and global health marketplace.

While as in the fi rst and second editions successful strategies that nurses have used to improve population outcomes are paramount, the readers will discover new infor- mation in the third edition on how to identify healthcare needs at the population level and how to improve overall population outcomes. Voila. Not only that, but intro- duction of the most common study designs and successful program implementation strategies will lead you to correct design selection, successful implementation and most importantly overall success. Problem solved!

Th e third edition charts a path toward understanding how to successfully integrate new knowledge into practice. Th is, as experience teaches us, is no small task. Chapter 6 actually describes how technology can be used to truly enhance population-based nursing and describes the role and importance of APRNs in using data to make deci- sions that lead to new levels of program development and evaluation. While Clara Barton and Florence Nightingale did not have AI they understood that statistics are needed to measure outcomes. Chapter 8 identifi es ways and means to evaluate pop- ulation outcomes and systems changes. Th ese concepts and roles are explored within the competencies of the APRN. Th e healthcare marketplace is extremely competitive, and executives and managers are like radar screens looking to identify opportunities to distinguish and validate their organizations. Th is book helps the new APRN iden- tify the ways and means to achieve such validation.

Nurses need to be part of the highest level of care management and policy decision making in partnership with healthcare policy brokers and healthcare policy makers. In Chapter 10, the emphasis is placed on identifying community needs and assess- ment of resources. Chapter 11 rounds up by providing specifi c strategies for program implementation coupled with methods to empower the community to advocate for themselves. In the fi nal chapter global health and cultural issue for population-based




nursing theory and practice open one’s eyes to recent patterns in international interdis- ciplinary collaborations including the latest global health competencies. A primer for all practitioners whatever the setting.

Th is edition targets all of the important aspects in population-based care for the most trusted and recognized of all healthcare professions. Nursing remains, and should remain, a practice centered and caring profession, but current times mandate that nurses discover new and eff ective strategies for promoting health and providing care. Th is book gives nurses everything from A to Z describing the role of the APRN in the accreditation process to zeroing in how to eliminate health disparities. By looking back to the lessons and wisdom of the past and opening our minds to the new vistas and parameters of pop- ulations and the potential impact of a population-based approach to care it charts the ways and means toward the future. . ., which is now. Th e past is prologue.

Th ese are nursing’s new Tools of the Trade.

Patricia A. Polansky, RN, MS Director, Program Development and Implementation

Center to Champion Nursing in America RWJF/AARP

Washington, DC




My good friend, colleague, and co-editor Patty Vitale died shortly aft er we completed the planning for this, the third edition of our book. Patty had enormous energy and zest. Both fi guratively and literally she danced her way through life. Th e joy that she exuded on the dance fl oor was a refl ection of the joy that she had for living. She was dedicated to making the world a better place for children through her work as a pedi- atrician and an educator. Th is book is part of the enduring legacy that she left behind.

Th e original inspiration for this book grew out of our experience while co-teach- ing an epidemiology course for students enrolled in a doctorate in nursing practice (DNP) program. We found it diffi cult to fi nd a textbook that addressed the course objectives and was relevant to nursing practice. We decided a population-based nurs- ing textbook, targeted for use as a primary course textbook in a DNP program or as a supplement to other course materials in a graduate community health nursing pro- gram, would be of great benefi t and value to students enrolled in these programs. Th is book is the result of that vision. Th e chapters address the essential areas of content for a DNP program as recommended by the American Association of Colleges of Nursing (AACN), with a focus on the AACN core competencies for population-based nurs- ing. Th e primary audience for this text is nursing students enrolled in either a DNP program or a graduate community health nursing program. Each chapter includes discussion questions to help students use and apply their newly acquired skills from each chapter.

In this book, the third edition, our goals were to not only update the content of the existing chapters, but also add a chapter on accreditation of population-based programs. We were fortunate that a nurse with extensive experience in accreditation (Eileen Horton) agreed to write the chapter for us. In order to make it easier for read- ers to enhance their knowledge of the information that is covered in the book, we also decided to add a relevant list of Internet resources to each chapter.

Several events covering a wide range of issues in the healthcare fi eld have occurred over the past few years. Th ese include the attempts by the Trump administration to dismantle the Aff ordable Care Act and eff orts by the 116th Congress to expand the role of public programs in healthcare. Bills being introduced in the House range in scope from broad proposals to create a new national health insurance program for all residents (oft en referred to as “Medicare for All”) to more incremental approaches that would off er a public plan option in addition to current sources of coverage. It appears that the 2020 elections may well turn into a referendum on healthcare and how it should be paid for. States and local governments are increasingly turning to




legislation in an attempt to quell outbreaks of measles. As of April 2019, offi cials at the Centers for Disease Control and Prevention (CDC) had confi rmed 695 measles cases across 22 states for the current year, a record high since the disease was thought to have been eliminated in the United States in 2000. According to a UNICEF report, among high-income nations, the United States had the most children who went unvaccinated between 2010 and 2017 (CDC, 2019). Th ere is increasing concern over the usage of elec- tronic cigarettes and hookahs by children. Th ere is also increasing interest in the use of social media to address population health. Th is edition addresses these as well as other current issues in population-based nursing.

As in the fi rst and second editions, this textbook includes successful strategies that nurses have used to improve population outcomes and reinforces high-level applica- tion of activities that require the synthesis and integration of information learned. Th e goal is to provide readers with information that will help them to identify healthcare needs at the population level and improve population outcomes. In particular, Chapter 1, Introduction to Population-Based Nursing, introduces the concept of population-based nursing and discusses examples of successful approaches and interventions to improve population health. In this edition we use the title “advanced practice registered nurse” (APRN). APRN is the title used in Th e Consensus Model for APRN Regulation, Licensure, Accreditation, Certifi cation and Education (APRN Consensus Workgroup & National Council of State Boards of Nursing APRN Advisory Committee, 2008). Th is document is the product of the APRN Consensus Work Group and the National Council of State Boards of Nursing (NCSBN).

In order to design, implement, and evaluate interventions that improve the health of populations and aggregates, APRNs need to be able to identify and target outcome measures. Chapter 2, Identifying Outcomes in Population-Based Nursing, explains how to defi ne, categorize, and identify population outcomes using specifi c examples from practice settings. Th e identifi cation of outcomes or key health indicators is an essential fi rst step in planning eff ective interventions and is a requirement for evaluation. Th e chapter includes a discussion of nurse-sensitive indicators, Healthy People 2020, Healthy People 2030, national health objectives, and health disparities. Emphasis is on the identi- fi cation of healthcare disparities and approaches that can be used to eliminate or mitigate them. APRNs can advocate needed change at local, regional, state, or national levels by identifying areas for improvement in practice, by comparing evidence needed for eff ec- tive practice, and by better understanding health disparities. APRNs have an important collaborative role with professionals from other disciplines and community members to work toward eliminating health disparities.

Epidemiology is the basic science of prevention (Gordis, 2014). Evidence-based prac- tice, as it relates to population-based nursing, combines clinical practice and public health through the use of population health sciences in clinical practice (Heller & Page, 2002). Programs or interventions that are designed by APRNs should be evaluated and assessed for their eff ectiveness and ability to change or improve outcomes. Th is is true at an individual or population level. Data from these programs should be collected system- atically and in such a manner that can be replicated in future programs. Data collection




must be organized and analyzed using clearly defi ned outcomes developed early in the planning process. Best practice requires that data are not just collected; data must also be analyzed, interpreted correctly, and, if signifi cant, put into practice. Understanding how to interpret and report data accurately is critical as it sets up the foundation for evi- dence-based practice. With that said, it is important to understand the basics of how to measure disease or outcomes, how to present these measures, and to know what types of measures are needed to analyze a project or intervention.

Chapter 3, Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part I, describes the natural history of disease and concepts that are integral to the prevention and recognition (e.g., screening) of disease. Basic concepts that are neces- sary to understand how to measure disease, and design studies that are used in popula- tion-based research, are discussed. Disease measures, such as incidence, prevalence, and mortality rates, are covered, and their relevance to practice is discussed. Th is chapter also includes information on primary, secondary, and tertiary prevention, and the concept of causality is introduced. A section on survival and prognosis is included. Th is material broadens the knowledge of readers with information necessary for advanced practice and interpretation of survival data. Th e basics of data analysis, including the calculation of relative risk, attributable risk, and odds ratio, are presented with examples of how to use these measures. Study design selection is an important part of the planning pro- cess for implementing a program. A portion of Chapter 3, Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part I, is dedicated to introducing the most common study designs, because correct design selection is an essential part of sound methodology, successful program implementation, and overall success.

In order for APRNs to lead the fi eld of evidence-based practice, it is critical that they possess skills in analytic methods to identify population trends and evaluate outcomes and systems of care (American Association of Colleges of Nursing [AACN], 2006). Th ey need to carry out studies with strong methodology and be cognizant of factors that can aff ect study results. Identifi cation and early recognition of factors that can aff ect the results or outcomes of a study, such as systematic errors (e.g., bias), should be acknowl- edged because they cannot always be prevented. In Chapter 4, Epidemiological Methods and Measurements in Population-Based Nursing Practice: Part II, the APRN is intro- duced to the elements of bias with a comprehensive discussion of the complexities of data collection and the fundamentals of developing a database. More in-depth discus- sion of study designs is covered, as well as a comprehensive review of ways to report on randomized and nonrandomized studies. Critical components of data analysis are discussed, including causality, confounding, and interaction.

In order to provide care at an advanced level, nurses must incorporate the con- cepts and competencies of advanced practice into their daily practice. Th is requires that APRNs acquire the knowledge, tools, and resources to know when and how to integrate them into practice. In Chapter 5, Applying Evidence at the Population Level, the APRN learns how to integrate and synthesize information in order to design inter- ventions that are based on evidence to improve population outcomes. Nurses require several skills to become practitioners of evidence-based care. In this chapter, they learn




how to identify clinical problems, recognize patient safety issues, compose clinical questions that provide a clear direction for study, conduct a search of the literature, appraise and synthesize the available evidence, and successfully integrate new knowl- edge into practice.

Information technologies are transforming the way that information is learned and shared. Online communities provide a place for people to support each other and share information. Online databases contain knowledge that can be assessed for information on populations and aggregates, and many websites provide up-to-date information on health and healthcare. Chapter 6, Using Information Technology to Improve Population Outcomes, describes how technology can be used to enhance population-based nurs- ing. It identifi es websites that are available and how to evaluate them for quality. It also describes potential ways that technology can be used to improve population outcomes and how to incorporate technology into the development of new and creative interven- tions. APRNs use data to make decisions that lead to program development, implemen- tation, and evaluation. In Chapter 7, Concepts in Program Design and Development, the APRN learns how to design new programs using organizational theory. Nursing care delivery models that address organizational structure, process, and outcomes are described.

Oversight responsibilities for clinical outcomes at the population level are a critical part of advanced practice nursing. Th e purpose of Chapter 8, Evaluation of Practice at the Population Level, is to identify ways and means to evaluate population outcomes and systems changes, as well as to address issues of eff ectiveness and effi ciency and trends in care delivery across the continuum. Strategies to monitor healthcare quality are addressed, as are factors that lead to success. Th ese concepts are explored within the role and competencies of the APRN.

Th e healthcare marketplace is extremely competitive. Administrators are constantly on the look out to identify opportunities to diff erentiate and validate their organization. Achieving accreditation helps to validate programs and organizations in the context of national and professional standards. Developing programs and working toward program accreditation requires competence in each of the DNP essentials. Chapter 9, Th e Role of Accreditation in Validating Population-Based Practice/Programs, describes the role of the APRN in the accreditation process.

In order for APRNs to make decisions at the community level, APRNs who work in the community need to be part of the higher level of care management and policy-making and decision-making, in partnership with the community-based consortium of health- care policy makers. Chapter 10, Building Relationships and Engaging Communities Th rough Collaboration, describes the tools for successful community collaboration and project development. Emphasis is placed on identifying community needs and assess- ment of their resources. Specifi c examples are given to guide APRNs in developing their own community projects.

Chapter 11, Challenges in Program Implementation, identifi es barriers to change within communities and the importance of developing and sustaining community part- nerships. Specifi c strategies for program implementation are discussed, as well as the




methods to empower the community to advocate for themselves. Specifi c examples are given in order to guide APRNs in executing a project that has community acceptance and sustainability.

Finally, Chapter 12, Implications of Global Health in Population-Based Nursing, explores the implications of global health for the APRN. Th eories of global health, pop- ulation health, and public/community health are diff erentiated and compared, to further the understanding of how environmental conditions (e.g., poverty, housing, access to care) aff ect the health status of individuals and groups. Recent patterns in international interdisciplinary collaborations are reviewed, including the global health competencies developed by the Association of Schools of Public Health (ASPH) and the AACN.

Qualifi ed instructors may obtain access to an instructor’s manual for this title by contacting [email protected].

Ann L. Cupp Curley

REFERENCES American Association of Colleges of Nursing. (2006). Th e essentials of doctoral education for advanced prac-

tice nursing. Retrieved from http://www.aacn.nche.edu/DNP/pdf/Essentials.pdf APRN Consensus Workgroup & National Council of State Boards of Nursing APRN Advisory Committee.

(2008). Th e consensus model for APRN regulation, licensure, accreditation, certifi cation and education. Retrieved from http://www.aacn.nche.edu/education-resources/APRNReport.pdf

Centers for Disease Control and Prevention. (2019). CDC media statement: Measles cases in the U.S. are highest since measles was eliminated in 2000. Retrieved from https://www.cdc.gov/media/releases/2019/ s0424-highest-measles-cases-since-elimination.html

Gordis, L. (2014). Epidemiology (5th ed.). Canada: Elsevier Saunders. Heller, R., & Page, J. (2002). A population perspective to evidence based medicine: “Evidence for population

health.” Journal of Epidemiology & Community Health, 56(1), 45–47. doi:10.1136/jech.56.1.45





I wish to express my thanks to those professional colleagues who provided direction, guidance, and assistance in writing this book. Th ank you also to my family and friends for their support throughout this process. I want to give a very special thank you to my husband, Ed, for his unending patience and excellent advice. Th ank you to my publisher, Adrianne Brigido, for her valuable advice, assistance, and infi nite patience, and to Cindy Yoo and Kris Parrish at Springer Publishing Company for their assis- tance and expertise throughout the revision of this book. Finally, I could not have completed the text without the help of the library staff of the Health Services Library of Capital Health especially Erika Moncrief, MS, Director of Library Services.



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Some of the most signifi cant fi gures in the history of nursing made their reputa- tions by providing population-based care. Th eir infl uence on nursing has been such that their names live on and their achievements continue to be recognized because of their important contributions to nursing and to healthcare. A brief look at the stories of some of these nurses helps to provide a background for understanding population health.

Although she started her career as a teacher, Clarissa (Clara) Barton won her great- est acclaim as a nurse. Horrifi ed by the suff ering of wounded soldiers in the American Civil War (many of them were former neighbors and students) and struck by the lack of supplies needed to care for them, she worked to obtain various supplies and put herself at great risk by nursing soldiers on the front lines of several major battles. Her experience would eventually lead to her becoming the founder and fi rst president of the American Red Cross (Evans, 2003).

During the Crimean War, Florence Nightingale used statistical analysis to plot the incidence of preventable deaths among British soldiers. She used a diagram to drama- tize the unnecessary deaths of soldiers caused by unsanitary conditions and lobbied political and military leaders in London for the need to reform. She worked to pro- mote the idea that social phenomena could be objectively measured and subjected to mathematical analysis. Along with William Farr, she was one of the earliest healthcare practitioners to collect and analyze data in order to persuade people of the need for change in healthcare practices (Dossey, 2000; Lipsey, 1993).

Mary Breckinridge started the Frontier Nursing Service (FNS) in Kentucky in 1925 and remained its director until her death in 1965. Educated as a nurse and midwife,




she devoted her life to improving health in rural areas, especially among women and children. She believed in working with the communities that were served by the FNS and formed and worked with committees composed of community members to help plan and provide care. Similar to Florence Nightingale, she believed in the use of statistics to measure outcomes. From its onset, the FNS was so successful that there was an immedi- ate drop in infant and maternal deaths in the communities served by the FNS (Frontier Nursing University, 2019; January, 2009).

Th ese three nurses all worked to improve the health of at-risk populations. Th ey met with political leaders to advocate changes in polices to benefi t those populations, and both Nightingale and Breckinridge used statistical analysis to both support the need for change and to evaluate their interventions. Breckinridge was an early advocate of engag- ing communities to help address community health issues. Th ey were all pioneers of nursing and, although perhaps not in name, certainly in fact, among the fi rst nurses working in advanced practice.

For decades, community health nurses have recognized the importance and the impact of population-based care, but large segments of nursing practice focused primarily on caring for individual patients. Nursing remains, and should remain, a practice-based and caring profession, but nursing practice is changing. Th ere is an awareness of the need to provide evidence-based care and to design interventions that have a broad impact on the populations that nursing serves, no matter the setting. Population health obligates healthcare professionals to implement standard interventions, based on the best research evidence, to improve the health of targeted groups of people. It also obligates nurses to discover new and eff ective strategies for providing care and promoting health. Although clinical decision-making related to individual patients is important, it has little impact on overall health outcomes for populations. Interventions at the population level have the potential to improve overall health across communities.

Th is book addresses the essential areas of content for a doctorate in nursing practice (DNP) as recommended by the American Association of Colleges of Nursing (AACN), with a focus on the AACN core competencies for population-based nursing. Th e goal is to provide readers with information that will help them to identify healthcare needs at the population level and to improve population outcomes. Although the focus is on the essential components of a DNP program, the intent is to broadly address practice issues that should be the concern of any nurse in an advanced practice role.

Th is chapter introduces the reader to the concept of population-based nursing. Th e reader learns how to identify population parameters, the potential impact of a population- based approach to care, and the importance of designing nursing interventions at the population level in advanced nursing practice.


For all the scare tactics out there, what’s truly scary—truly risky—is the prospect of doing nothing.

—President Barack Obama, Th e New York Times, August 16, 2009




Th e fi rst two decades of the 21st century have been witness to a growing and conten- tious debate on healthcare reforms. President Barack Obama’s stated goals in pushing for reforming health insurance were to extend healthcare coverage to the millions who lacked health insurance, stop the insurance industry’s practice of denying coverage on the basis of pre-existing conditions, and cut overall healthcare costs. Driven by a need for change in how healthcare is paid for, the Patient Protection and Aff ordable Care Act (ACA) was signed into law by President Obama in 2010. It went into eff ect over the span of 4 years beginning in 2011. Currently, there are three diff erent “markets” for insur- ance through the ACA. Th e federal marketplace is run solely by the federal government. Th e state marketplace is run solely by the state, and in partnership marketplaces, states run many of the important functions and make key decisions but the marketplace is operated by the federal government. Th e ACA includes an option that allows states to expand Medicaid eligibility to uninsured adults and children whose incomes are at or below 138% of the federal poverty level (there is also a provision for people living with mental illness).

One of candidate Donald Trump’s campaign promises was to repeal the ACA. Since his inauguration, the Trump administration has issued many regulations that have eff ec- tively undermined enrollment in the ACA by cutting funding for education, marketing, and outreach. While targeting enrollment, the Trump administration has, for the most part, enforced the law as written although the federal role in enforcement has decreased (Jost, 2018). In December 2018, the U.S. District Court in Fort Worth, Texas, ruled that the individual mandate requiring people to have health insurance is unconstitutional and that the remaining provisions of the ACA are also invalid. In March 2019 the Justice Department sent a letter to the 5th U.S. Circuit Court of Appeals in New Orleans to affi rm the judgment issued by the U.S. District Court in Fort Worth, Texas (Robson, 2019). Th is action signaled a revival of the current administration’s eff orts to repeal the entire ACA. Th ree days aft er this letter was issued an announcement was delivered from the White House that a Republican replacement for the ACA would not be introduced until aft er the 2020 elections (Pear & Haberman, 2019). As of this writing, the fate of the ACA remains uncertain and the contentious debate surrounding healthcare legislation and reform in the United States continues.

Th ere is ample evidence of a need for healthcare reform in the United States. Th e gross domestic product (GDP) is the total market value of the output of labor and property located in the United States. It refl ects the contribution of the healthcare sector relative to all other production in the United States. In 1960, the health sector’s proportion (NHE) of the GDP was 5% (i.e., $5 of every $100 spent in the United States went to pay for healthcare services). By 1990, this fi gure had grown to 12% and by 1996, 14%. A report issued by the Committee on the Budget of the U.S. Senate in 2008 warned that unless changes were made in how the United States provides care to its citizens, the GDP for the healthcare sector would grow to 25% by 2025 and 49% by 2089 (Orszag, 2008). In 2017, the NHE was $3.5 trillion and accounted for 17.9% of the GDP. Th e Centers for Medicare & Medicaid Services (CMS) published its forecast of healthcare costs for 2018 to 2027. It estimates that health spending will grow 0.8 percentage point faster than the GDP per




year over the 2018 to 2027 period and, as a result, the health share of GDP is expected to rise from 17.9% in 2017 to 19.4% by 2027. According to the CMS, income growth, the aging of the U.S. population, and the rising costs of medical goods and services are the three major factors driving healthcare costs at this time (CMS, 2019). Th e Organization for Economic Cooperation and Development (OECD) provides a global picture of healthcare spending. It reports that U.S. expenditures for healthcare as refl ected by the GDP is the highest among OECD countries and nearly double the average for OECD countries. One interesting fact that can be gleaned from that report is that the OECD attributes the diff erence in costs (U.S. costs as compared to other countries) is due to private health sector prices, primarily pharmaceuticals (OECD, 2019).

Th e rising cost of healthcare is refl ected in the insurance industry. According to the Henry J. Kaiser Family Foundation (2019a), the average annual premium for employer- sponsored family health coverage in 2017 was $18,687 and the average annual contribu- tion from employees was $5,218.

Unfortunately, although the United States ranks fi rst in spending on healthcare among industrialized nations, it ranks lower than most industrialized countries in important health indicators. Two commonly used indicators for measuring a country’s health are infant mortality and life expectancy at birth. Worldwide, the United States ranked 43rd for life expectancy at birth (life expectancy at birth in the United States is 80 years) and 55th for infant mortality (infant mortality rate in the United states is 5.87 per 1,000 live births) in 2017 (Central Intelligence Agency [CIA], n.d.). A report issued by the Institute of Medicine (IOM, 2010) argues that the system used in the United States for gathering and analyzing health measures is part of the problem. A second problem is the inad- equate system used in the United States for gathering, analyzing, and communicating information on the underlying factors that lead to chronic health conditions and other risk factors that contribute to poor health. Readers can refer to Chapter 12, Implications of Global Health in Population-Based Nursing, for a more detailed description of how the United States ranks among other countries in relation to health indicators.

Health insurance is an important factor in any discussion about healthcare. Th e United States is the only industrialized country in the world without universal care. In 2017, 20% of people in the United States who were uninsured went without needed medical care. People who are uninsured are less likely than those who are insured to receive preventive care (Th e Henry J. Kaiser Family Foundation, 2019b). Th e Commonwealth Fund, a pri- vate foundation whose stated mission is to promote a high-performing healthcare system, commissioned a survey of U.S. adults that was conducted by Princeton Survey Research Associates (Collins, Doty, Robertson, & Garber, 2011). Th e survey looked at the eff ect of health insurance coverage on healthcare-seeking behaviors. Th ey found that among unin- sured women aged 50 to 64, 48% say they did not see a doctor when they were sick, did not fi ll a prescription, or skipped a test, treatment, or follow-up visit because they could not aff ord it. Th e survey results also showed that only 67% of uninsured adult respondents had their blood pressure checked within the past year compared to 91% of insured adults. Additionally, only 31% of uninsured women aged 50 to 64 reported having a mammo- gram in the past 2 years, compared to 79% of women with health insurance.




Th e ACA has had an impacted on insurance rates. Th e number of uninsured adults decreased from 19.3% in 2013 to 18.4% during the fi rst quarter of 2014 and to 12.4% in 2018. Th e Commonwealth Fund’s Biennial Health Insurance Survey assesses the extent and quality of coverage for U.S. working-age adults. Th e 2018 results reveal that, for the most part, fewer adults are uninsured today compared to 2010, and the duration of coverage gaps people experience has shortened signifi cantly. Unfortunately, more people who have coverage are more underinsured now than they were in 2010, with the greatest increase occurring among those in employer plans. Although the ACA expanded and improved coverage options for people without access to a job-based health plan, it has had little impact on employer plans (Collins, Bhupal, & Doty, 2019). For complete results of the survey go to: www.commonwealthfund.org/publications/issue-briefs/2019/feb/health-insurance- coverage-eight-years-aft er-aca.

Diff erences in insurance rates are being observed based on the choices made by states as they relate to the ACA. In 2018, adults aged 18 to 64 in states with a federal market- place were more likely to be uninsured than those in states with a state-based market- place or states with a hybrid marketplace. In Medicaid expansion states, the percentage of uninsured adults decreased from 18.4% in 2013 to 8.7% in the fi rst 3 months of 2018. In nonexpansion states, the percentage of uninsured adults decreased, from 22.7% in 2013 to 17.5% in 2015. Th e percentage of people who were uninsured in these states increased from 17.5% in 2015 to 19.0% in 2017, and there was no signifi cant change between 2017 and the fi rst 3 months of 2018 (18.4%) (Cohen, Martinez, & Zammitti, 2018).

Enough time has passed since the enactment of the ACA that researchers have been able to examine the impact of Medicaid expansion on health outcomes. As of 2019, 37 states including the District of Columbia have opted to expand Medicaid (Massachusetts and Wisconsin, which are included in these numbers, expanded Medicaid before enact- ment of the ACA). Bhatt and Beck-Sagué (2018) compared infant mortality rates before and aft er the adoption of Medicaid expansion. Th ey found that the infant mortality rate in the United States declined nationally by 11.9% from 6.7 (2010) to 5.9 (2016) deaths per 1,000 live births. Th ese declines were more modest in non–Medicaid expansion (11.0%) than in Medicaid expansion (15.2%) states. Th e mean infant mortality rate in non-Medicaid expansion states rose slightly (6.4 to 6.5) from 2014 to 2016, whereas in Medicaid expansion states, it declined from 5.9 to 5.6 per 1,000 live births. Mazurenko, Balio, Agarwal, Carroll, and Menachemi (2018) analyzed 77 published, peer-reviewed studies. Th ey found expansion of Medicaid under the ACA was linked to increases in health coverage, use of health services and quality of care. Among their fi ndings was that health insurance gains were largest for adults without a college degree; use of primary care, mental health services, and preventive care among Medicaid enrollees went up; and reliance on emergency departments decreased. In another study, researchers found that counties in states where Medicaid expanded had 4 fewer deaths per 100,000 resi- dents each year from cardiovascular causes aft er expansion, compared with counties in non-expansion states (Khatana et al., 2019).

Our healthcare system is complex, and there is no simple solution to lowering costs and improving access. Th e goal of this textbook is not to provide an overarching solution




to the issues of cost, but to propose that nurses can contribute to improving the cost- eff ectiveness and effi ciency of care through the provision of evidence-based treatment guidelines to identifi ed populations with shared needs, and by advocating for policies that address the underlying factors that impact health and healthcare. To do this, we must change the way that we deliver healthcare and become politically active. In an ideal world, healthcare policies are created based on valid and reliable evidence and population need and demand. Th e ideal premise is that there is equitable distribution of healthcare services and that the appropriate care is given to the right people at the right time and at a reasonable cost. For 20 years, the American Nurses Association (ANA) has been advocating for healthcare reforms that would guarantee access to high-quality healthcare for all. Th e ANA supports the ACA and was an advocate for the public option. In 2016 the organization sent a letter to President Trump that advocated for “universal access to a standard package of essential healthcare services” (ANA, 2019, para 5). It is a function of individual choice to either support or not support health- care reforms. Th e actions of professional organizations are driven by membership. Regardless of your political alliance, involvement in professional organizations as well as in local, state, and national political activities (even if only minimally as a regis- tered and active voter) is part of the professional responsibility of advanced practice registered nurses (APRNs).


Th e AACN defi nition of advanced practice nursing includes recognizing the importance of identifying and managing health outcomes at the population level (AACN, 2004). In 2006, the AACN specifi ed that graduates of DNP programs have competency in meeting “the needs of a panel of patients, a target population, a set of populations, or a broad community” (AACN, 2006, p. 10). A core component of DNP education is clinical pre- vention (health promotion and disease prevention at individual and family levels) and population health (focus of care at aggregate and community levels and examination of environmental, occupational, cultural, and socioeconomic dimensions of health) (AACN, 2006; Allan et al., 2004). Regardless of whether DNP graduates practice with a focus on clinical prevention or population health, the ability to defi ne, identify, and analyze outcomes is imperative for improving the health status of individuals and popu- lations (AACN, 2006).

Th e goal of population-based nursing is to provide evidence-based care to targeted groups of people with similar needs in order to improve outcomes. Population-based nursing uses a defi ned population or aggregate as the organizing unit for care. Th e Merriam-Webster Dictionary (“Population,” 2019, para. 1) defi nes a population as “the whole number of people or inhabitants in a country or region.” A second defi nition is given as “a body of persons or individuals having a quality or characteristic in common” (para. 1). Subpopulations may be referred to as aggregates. Many diff erent parameters can be used to identify or categorize subpopulations or aggregates. Th ey may be defi ned by ethnicity (e.g., African American or Hispanic), religion (e.g., Roman Catholic or




Muslim), or geographical location (e.g., Boston or San Diego). Aggregates can also be defi ned by age or occupation. People with a shared diagnosis (e.g., diabetes) or a shared risk factor (e.g., smoking) comprise other identifi able aggregates. Sometimes people may choose to describe themselves as members of a particular group (e.g., conservative or liberal). One person may belong to more than one such group (e.g., White, younger than 18 years, current smoker).

A community is composed of multiple aggregates. Th e most common aggregate used in population-based nursing is the high-risk aggregate. A high-risk aggregate is a subgroup or subpopulation of a community that shares a high-risk factor among its members, such as a high-risk health condition (e.g., congestive heart failure) or a shared high-risk factor (e.g., smoking and sedentary behavior). Th e aggregate concept can be used to target interventions to specifi c aggregates or subpopulations within a community (Porche, 2004). Th e implementation of standard or proven (evidence-based) strategies to prevent illness and/or improve the health of targeted groups of people can have the eff ect of ameliorating health problems at the population and/or aggregate level. Making change at the population level may impact the health of a community not only in the present but for generations to come. As we learn how to approach and target populations using evidence, we improve our chance of long-term success and can strive to make lifelong changes in the health of a group of people.


Th e collection and analysis of data provide healthcare professionals and policy mak- ers with a starting point for identifying, selecting, and implementing interventions that target specifi c populations and aggregates. Many of the leading causes of death in the United States are preventable. One in three American adults has cardiovascular disease, and it is the leading cause of death among both men and women in the country, killing on average one American every 40 seconds (Boston Scientifi c, 2019). On the basis of data from 2016, the Centers for Disease Control and Prevention (CDC) has identifi ed, in descending order, the 10 leading causes of death in the United States. Th ey are heart dis- ease, cancer, accidents (unintentional injuries), chronic lower respiratory diseases, stroke (cerebral vascular diseases), Alzheimer’s disease, diabetes, infl uenza and pneumonia, renal diseases, and intentional self-harm (e.g., suicide) (CDC, 2017a). Several factors, such as the physical environment, healthcare systems, personal behaviors, and the social environment, can have a deleterious impact on individual and community health. Th e negative consequences of these factors are researched and well documented.

Smoking Life expectancy for smokers is at least 10 years shorter than for nonsmokers. It is a lead- ing cause of preventable morbidity and mortality, causing nearly one of every fi ve deaths annually in the United States. Th is fi gure includes heart attack deaths and lung cancer




deaths among nonsmokers who are exposed to secondhand smoke. It is estimated that smoking contributes $170 billion to healthcare costs in the United States (CDC, 2019a).

Th e CDC used the 2017 National Health Interview Survey (NHIS) to estimate adult smoking prevalence rates in the United States. Th e fi ndings indicate that 19.3% of U.S. adults use a tobacco product every day or some days. Smoking rates are higher among men, younger adults, non-Hispanic adults, those living in the Midwest and South, those with less education and income, and LGBT (lesbian, gay, bisexual, and transgender) adults (CDC, 2019b). Although higher rates are seen in younger adults, a reduction in smoking by school-age children should result in reductions in tobacco-related deaths in the future, but new data reveal that tobacco rates among American youth are increas- ing (CDC, 2019c). Th is is particularly bad news coming on the heels of the 2012‒2013 National Adult Tobacco Survey, which had revealed the lowest smoking rates for high school students since 1991. Th e current rise in smoking use among school age children is attributed to an increase in E-cigarette use (CDC, 2014, 2019c).

Technology has contributed to many positive advances in healthcare. E-cigarettes are not one of them. E-cigarettes are metal tubes that heat liquid into an inhalable vapor that contains nicotine. Between 2017 and 2018, E-cigarette use increased from 11.7% to 20.8% among high school students and from 3.3% to 4.9% among middle school students. During the same time period, no change was found in the use of other tobacco prod- ucts (including cigarettes) (CDC, 2019d). As of December 2018, 50 states have enacted laws that restrict the use of E-cigarettes by youth. Th e minimum legal age to purchase E-cigarettes in these 50 states varies but falls within a narrow range (19 to 21). Go to pub- lichealthlawcenter.org/sites/default/fi les/States-with-Laws-Restricting-Youth-Access- to-ECigarettes-Dec2018.pdf for specifi c information about youth access to E-cigarettes in the United States (Public Health Law Center, n.d.). Canada passed the Tobacco and Vaping Products Act in May, 2018. Th is act regulates the manufacture, sale, labeling and promotion of tobacco products and vaping products sold in Canada and includes restrictions on the sale of tobacco products to youth (Government of Canada, 2018). For more information on this Act go to www.canada.ca/en/health-canada/services/ health-concerns/tobacco/legislation/federal-laws/tobacco-act.html. APRNs need to keep abreast of new behaviors that can impact health. Being informed about risky behav- iors is of primary importance for APRNs to be eff ective in planning and delivering evi- dence-based education and in lobbying for changes to protect the public’s health.

Another very popular trend is the use of hookahs. Hookahs are water pipes used to smoke specially fl avored tobaccos. Youth are drawn toward this social trend in which groups of people share a hookah usually in a café setting. Although hookahs have been around for hundreds of years, they are not a safe alternative to smoking. Hookah use rates among youth remained relatively constant from 2011 to 2018. In 2018 1.2% of mid- dle schoolers (1% in 2011) and 4.1% of high school students (4.1% in 2011) reported that they had used a hookah within the past 30 days (CDC, 2018a). Th e tobacco and smoke from hookahs have toxic properties and have been linked to various cancers, including lung and oral cancers. Many of the same eff ects of cigarette smoking are found with smoking hookahs. As with any potential threat to health, education of our youth and




adult populations regarding the deleterious eff ects of hookahs is paramount to reducing the potential morbidity and mortality of long-term exposure to these fl avored tobac- cos. More recently, newer electronic forms of hookahs have been introduced, and little research has been conducted to determine the long-term health eff ects of these products. Regardless, the use of hookahs is another health behavior that an APRN can attempt to modify by evidence-based prevention education. For more on the eff ects of hookahs, refer to the CDC’s site (www.cdc.gov/tobacco/data_statistics/fact_sheets/tobacco_industry/ hookahs/index.htm#overview).

Th ere is huge potential for cost savings by preventing smoking-related illnesses, and one cannot overlook the eff ects of secondhand smoke on the health of family mem- bers and coworkers. It is well known that secondhand smoke has long-lasting eff ects on the unborn fetus, infant, and child. Th ese eff ects can manifest as preterm births (Been et al., 2014), increased respiratory infections and higher risk of asthma exacerbations (Abreo, Gebretsadik, Stone, & Hartert, 2018), sudden infant death (CDC, 2018b), and a lower intelligence quotient (Ling & Heff ernan, 2016). Th us, it is important to recognize not only the direct eff ects of smoking on health but also the indirect eff ects on fetuses, infants, children, and family members. Th e eff ects of secondhand smoke are not specifi c to smoking cigarettes. Exposure to hookah smoke is also associated with very similar eff ects on fetuses, infants, and family members. Education of pregnant mothers is just as important as with other family members as they may not realize the negative eff ects of secondhand exposure to hookah or cigarette smoke.

As with other smoking-related diseases, the cessation of smoking early on can reverse or ameliorate the potential long-term harmful eff ects of secondhand smoke exposure. Th ese data provide a starting point for targeting specifi c high-risk groups for interven- tion based on parameters such as age, education, income, and geographical location. Another pertinent fact is that many insurance companies are now charging higher pre- miums for smokers than for nonsmokers. Th is has led to increasing interest in cessation programs, but whether this will have a long-term impact on smoking rates is unknown. Th e recent increase in tobacco use by youth in the United States (largely attributed to the use of E-cigarettes) represents a troubling trend. Smoking cessation and smoking pre- vention programs are areas that off er opportunities for improving the health of people in the United States and for saving money. Other health problems, such as obesity, are also signifi cant public health concerns.

Obesity In 2009, researchers published their analysis of the cost of obesity in the United States, taking into account separate categories for inpatient, outpatient, and prescription drug spending. Th ey estimated that the medical costs of obesity may have been as high as $147 billion/year by 2008 (including $7 billion in Medicare prescription drug costs). According to their fi ndings, the annual medical costs for people who are obese were $1,429 higher than those for normal-weight people (Finkelstein, Trogdon, Cohen, & Dietz, 2009).




As part of Healthy People 2020, the United States set an objective to decrease the pro- portion of obese adult Americans (20 years of age or older) to 30.5%. Healthy People 2020 use the baseline of 33.9%, which was the percentage of persons aged 20 years and older who were obese in 2005 to 2008. Th e target objective for children (aged 2 to 19 years) is 14.5%. Th e baseline data for this objective is 16.1%, which was the percentage of children who were considered obese in 2005 to 2008 (HealthyPeople.gov, 2019). For the years 2015–2016 the prevalence of obesity among adults in the U.S. was 39.8%. Th is represents an increase from the Healthy People baseline. Th e highest rates were found among Hispanics (47.0%) and non-Hispanic Blacks (46.8%) followed by non-Hispanic Whites (37.9%) and non-Hispanic Asians (12.7%). Young adults had the lowest preva- lence rate (35.7%) and middle-aged adults had the highest (42.8%; CDC, 2018c). Th e CDC (2018d) reports that the prevalence of obesity among children (aged 2–19) in the United States is 18.5% (an increase over the Healthy People baseline data). Th e prevalence rate is lowest for children aged 2 to 5 (13.9%) and highest for children aged 12 to 19 (20.6%). It is most common for Hispanic (25.8%) and non-Hispanic Blacks (22.0%). It is lowest for non-Hispanic Whites (14.1%) and non-Hispanic Asians (11.0%).

Obesity is associated with increased morbidity and mortality rates. Abdelaal, le Roux, and Docherty (2017) have summarized the most important comorbidities of obesity. Th ey point out that obesity can cause both psychosocial (depression) and metabolic (dia- betes) dysfunction and identifi ed 13 specifi c domains that account for morbidity and mortality in obesity. Cardiovascular disease (CVD) and cancer account for the great- est mortality risk associated with obesity even when controlling for demographic and behavioral characteristics. Although people are familiar with the association between heart disease and obesity, many are just learning about the relationship between obe- sity and cancer. Obesity is associated with an increased risk for many cancers, including esophageal, pancreatic, colon and rectal, breast (aft er menopause), endometrial, kidney, thyroid, and gallbladder. It has been estimated that the percentage of cases attributed to obesity (although it varies) may be as high as 54% for some cancers (National Cancer Institute, 2017).

Jacobs et al. (2010) published a study that helps to illustrate the complexity of under- standing risk factors and their relationship to the development of poor health. Th ey stud- ied the association between waist circumference and mortality among 48,500 men and 56,343 women 50 years or older. Th ey determined that waist circumference as a measure of abdominal obesity is associated with higher mortality independent of body mass index (BMI). Th ey note that waist circumference is associated with higher circulating levels of infl ammatory markers, insulin resistance, type 2 diabetes, dyslipidemia, and coronary heart disease. In recent years, the constellation of these factors has been described as the metabolic syndrome. Metabolic syndrome is a complex syndrome that encompasses many conditions and risk factors, particularly abdominal obesity, high blood pressure, abnormal cholesterol and triglyceride levels, and insulin resistance, and is known to be associated with an increased risk of stroke, heart disease, and type 2 diabetes (Grundy, 2016). Th e increasing prevalence of metabolic syndrome is becoming a tremendous pub- lic health concern, and more evidence is appearing in the literature to better defi ne the




treatment as well as preventive measures needed to reduce the incidence. Although it is ill defi ned in children and adolescents, it is clear that early interventions to reduce obesity and sedentary behavior and to improve nutrition can have long-term eff ects and can improve overall life expectancy. Metabolic syndrome, similar to many conditions, demonstrates the complexity of interactions that occur in disease development and that no one factor in and of itself can be targeted alone. Our understanding of obesity is also becoming more complex, as new studies have identifi ed independent associations between sitting time/sedentary behaviors and increasing all-cause and cardiovascular disease mortality risk. Th is phenomenon highlights the importance of exercising and avoiding prolonged, uninterrupted periods of sitting time (Patel, Maliniak, Rees-Punia, Matthews, & Gapstur, 2018). Th e APRN needs to take into consideration the many facets of health and disease, genetics and environment, including human attitudes, attributes, and behavior when determining how to implement a population-based intervention.

Diabetes Mellitus One cannot talk about the epidemic of obesity and not mention its concomitant relation- ship to diabetes mellitus (DM). Th e number of American adults treated for DM more than doubled between 1996 and 2007 (from about 9 to 19 million). Th is includes an increase from 1.2 to 2.4 million among people aged 18 to 44 years. During this time period, the treatment costs for DM climbed from $18.5 to $40.8 billion (Soni, 2010). In 2017, the American Diabetes Association released the results of the National Diabetes Statistics report (CDC, 2017b). Th e report highlights the importance of tracking mor- bidity rates and the need to be aware of trends in order to target groups for interven- tions. Th e percentage of Americans with DM aged 65 and older is estimated to be as high as 25.2% (accounting for approximately 12 million seniors), which includes those who are undiagnosed. Approximately 24% of Americans under the age of 20 have been diagnosed with diabetes. Th e incidence rate of diabetes in 2015 was 6.7 per 1,000 or 1.5 million new cases. According to the American Diabetes Association (2018) the total costs of diagnosed diabetes in the United States in 2017 was $327 billion.

Th e rise in both incidence and prevalence rates for DM is closely tied to rising obe- sity levels, which is a preventable risk factor. Th is upward trend in the incidence rate for DM provides a clear direction for targeting prevention measures toward younger populations. Th ere is, in fact, a huge potential for improving the health of populations by targeting children using primary prevention measures that go well beyond reducing diabetes rates. Implications for early interventions beginning in pregnancy and continu- ing through infancy and early childhood are clear. Evidence is increasing that early feed- ing patterns (e.g., breastfeeding versus formula feeding) as well as parental obesity and parental eating patterns are linked to the increased likelihood of developing obesity in children, which puts them at an increased risk for type 2 DM (Owen, Martin, Whincup, Smith, & Cook, 2005). Th ere are many opportunities for APRNs to apply evidence-based, primary prevention interventions to improve the long-term outcomes of children at the beginning of pregnancy and at birth and thereaft er. Th is approach may include targeting




high-risk aggregates (e.g., parents with obesity and type 2 DM) and then expanding to communities through educational campaigns or changes in health policy.

Health and the Social Environment Most of the information discussed earlier exemplifi es the biological and environmental factors that contribute to poor health in adults. However, it is becoming more appar- ent that social (e.g., psychological) factors starting as early as conception (e.g., maternal stress) may play a more signifi cant role in adult health than was once thought. Having a comprehensive understanding of the underlying causes of adult diseases (includ- ing social, psychological, biological, and environmental) is necessary to successfully approach the problems seen in populations. Without this comprehensive understanding, it may be diffi cult to successfully implement a primary prevention program (the goal of which is to prevent disease before it occurs).

Stress is a regular part of day-to-day life, and small amounts of stress are normal and necessary for developing coping skills. However, exposure to prolonged and severe stress- ors, such as abuse, neglect, or being a witness to or victim of violence, can lead to changes that occur in the brain and can lead to short-term and even long-term poor health out- comes. Th is type of stress is termed toxic stress. Th e eff ects of toxic stress are being rig- orously studied, and in particular, studies looking at adverse childhood events (ACE) were some of the fi rst to show a correlation between toxic stress exposures and high-risk behaviors and poor health outcomes in adults. Th e ACE study is an ongoing, joint project of the CDC and Kaiser Permanente that looks retrospectively at the relationships among several categories of childhood trauma. Childhood trauma exposures were broken down into three categories: abuse (e.g., physical or sexual), neglect (e.g., emotional or physi- cal), and household dysfunction (e.g., having an incarcerated household member, family member with mental health issue and/or drug and alcohol problems, domestic violence, or parental divorce or separation). An ACE score is calculated based on past exposures to the subparts of each of the aforementioned categories. Th e higher the ACE score, the stronger the relationship to high-risk behaviors or poor health outcomes (CDC, 2016). In one widely cited ACE study (Felitti et al., 1998), people who experienced a score of four or more categories of ACEs, compared with those who had no history of exposure, had a four- to 12-fold increased risk for alcoholism, drug abuse, depression, and suicide attempts. Th ey also experienced a two- to four-fold increase in smoking and self- reported poor health. Subsequent research provides additional evidence to support the link between childhood trauma and adverse events and poor health outcomes. For additional information on the eff ects of childhood stress, refer to the CDC publication at www.cdc .gov/violenceprevention/childabuseandneglect/acestudy/index.html.

Many additional studies have been conducted that demonstrate the destructive eff ects of exposure to toxic stress. Smyth, Heron, Wonderlich, Crosby, and Th ompson (2008) completed a study of students entering college directly from high school to investigate the association between adverse events in childhood and eating distur- bances. Th ey found that childhood adverse events predicted eating disturbances in col- lege. Childhood adverse events have also been linked to drug abuse and dependence




(Messina et al., 2008) and greater use of healthcare and mental health services (Cannon, Bonomi, Anderson, Rivara, & Th ompson, 2010). Building on earlier studies that linked smoking in adulthood with ACEs, Brown et al. (2010) discovered a relationship between a history of ACEs and the risk of dying from lung cancer. Researchers have identifi ed similar outcomes in studies carried out with populations in other countries. A study conducted in Saudi Arabia, where beating and insults are oft en an acceptable parenting style, identifi ed a correlation between beating and insults (once or more per month) and an increased risk for cancer, cardiac disease, and asthma (Hyland, Alkhalaf, & Whalley, 2012). Scott, Smith, and Ellis (2012) completed a study in New Zealand, which found that adults who had a history of child protection involvement had increased odds of a diagnosis of asthma. McKelvey, Saccente, and Swindle (2019) examined the associations between ACEs in infancy and toddlerhood and obesity and related health indicators in middle childhood. Across all outcomes examined, children with four or more ACEs had the poorest health and were more likely to be obese when compared to children with no ACE exposure.

More and more studies are being conducted to look at the relationship of sustained exposure to toxic stress to a variety of poor health outcomes and high-risk behaviors. Th ese behaviors include such things as cutting, hypervigilance, promiscuity, eating dis- orders, poor school performance, depression, violence, suicidal ideation/attempts, and justice system involvement. Th ese are just a few of the many behaviors found to be asso- ciated with sustained exposure to toxic stress. Studies such as these illustrate the impor- tance of understanding the social determinants of poor health and the potential for doing good and preventing harm to aggregates and populations by targeting exposures to such things as child abuse and neglect for prevention, early recognition, and intervention.

Population Strategies in Acute Care Targeting evidence-based interventions toward aggregates in the acute care population also has the potential to improve health outcomes broadly. How can we improve the quality of care for our acute care patients by taking a population-based approach? When nurses apply evidence-based interventions to identifi ed aggregates, they can improve outcomes more eff ectively than when interventions are designed on a case-by-case (indi- vidualized) basis. Th e following examples illustrate this point.

Several organizations, including the Association for Professionals in Infection Control and Epidemiology and the CDC, proposed a call to action to move toward elimination of healthcare–associated infections. Th e CDC and Agency for Healthcare Research and Quality (AHRQ) have published evidence-based recommendations for preventing cen- tral venous catheter-related bloodstream infections (CR-BSIs). Th ese recommendations include hand hygiene, use of maximal barrier precautions, use of chlorhexidine gluco- nate for insertion site preparation, and avoidance of catheter changes. Catheters impreg- nated with antimicrobial agents are recommended when infection rates are high and/ or catheters will be in place for a long time. Using these guidelines, hospitals have made good progress in reducing the incidence rate of CR-BSIs. In a report released by the CDC, CR-BSIs fell 50% between 2008 and 2014 (Th ompson, 2018).




Another intervention that uses standard, evidence-based protocols to improve long- term outcomes addresses the treatment of stroke in an acute care setting. It was found that stroke patients taken to hospitals that follow specifi c treatment protocols have a bet- ter chance of surviving than patients taken to hospitals without specifi c stroke treatment protocols. A study evaluated the outcomes of the fi rst 1 million stroke patients treated at hospitals enrolled in the Get With the Guidelines (GWTG-S) stroke program that was started by the American Heart Association (AHA) in 2003. Th e American Stroke Association (ASA) guidelines require that hospitals follow seven specifi c evidence-based steps for treating stroke patients. Between 2003 and 2009, hospitals that followed these protocols lowered the risk of death by 10% for patients with ischemic stroke (Fonarow et al., 2011). Th e program grew from 24 participating hospitals in 2003 to more than 2,000 hospitals by the end of 2017. It is estimated that 80% of all ischemic stroke patients are now discharged from hospitals that participate in the GWTG-S program. It is notable that stroke declined from the nation’s third to the fourth leading cause of death in 2008 and continued to decline to the fi ft h leading cause of death in 2013 where it remained in 2017 (AHA & ASA, 2018). For more information on stroke centers see Chapter 9, Th e Role of Accreditation in Validating Population-Based Practice/Programs.

Surveillance of poor health outcomes in acute care facilities is one way in which APRNs can identify causative factors and design interventions to reduce costs and improve care. For example, recognizing the causative factors that lead to increased rehospitalization rates and superutilization of emergency departments could be the fi rst step in designing an intervention. Th e AHRQ analyzed data from the Healthcare Cost and Utilization Project from 2010 to examine 30-day rehospitalization rates for specifi c diseases and procedures. Approximately 25% of all U.S. hospital patients are readmitted within 1 year for the same conditions that led to their original hospitalization. Although superutilizers constitute just 2.6% to 6.1% of all patients seen in an emergency department, they account for 10.5% to 26.2% of all visits. Medicare and Medicaid patients have both higher 30-day readmission rates and higher emergency department superutilization rates than both uninsured and privately insured patients. Some specifi c diagnoses and procedures have higher rates for these two events also. For example, among Medicare patients diagnosed with congestive heart failure, 25% were readmitted to hospitals within 30 days and among privately insured the rate was 20% (AHRQ, 2018).

Readmissions and superutilization of services are costly in dollars to both consum- ers and hospitals and negatively impact the quality of life for patients. Better outpatient care could prevent unnecessary repeat hospital admissions and reduce superutilization of services. Identifying and targeting populations who are at high risk for either event off ers great return on investment.

Chronic Conditions Noncommunicable diseases (NCDs) are the main cause of illness and disability in the United States and are responsible for the greater part of healthcare costs according to the CDC. About 60% of U.S. adults have at least one chronic condition, and 40% have two




or more chronic conditions. Most chronic conditions result from preventable risk fac- tors such as smoking, poor diet, sedentary behavior, and excessive alcohol consumption (CDC, 2019e). Th e U.S. Department of Health and Human Services (DHHS) created and supports the Initiative on Multiple Chronic Conditions. Its strategic framework includes goals to foster healthcare and public health system changes to improve the health of those with multiple chronic conditions (MCC). Two additional goals are to equip care providers with tools, information, and other interventions to help people with MCC and to support targeted research about individuals with MCC and eff ective interventions. Th e intention of the framework is to create change in how chronic illnesses are addressed in the United States. from an individual approach to one that uses a population-focused approach (DHHS, 2015).

Th e problem of chronic diseases is not restricted to the United States. Th e World Health Organization (WHO) has published a report that documents the global prob- lem of NCDs. NCDs now account for more deaths than infectious diseases even in poor countries. Director-General Dr. Margaret Chan of WHO is quoted as saying, “[F]or some countries, it is no exaggeration to describe the situation is an impend- ing disaster; a disaster for health, society, most of all for national economies” (WHO, 2011, p.  ii). Chronic diseases, such as heart disease, stroke, cancer, chronic respira- tory diseases, and diabetes, are the leading causes of global mortality. WHO estimates that NCDs kill 40 million people each year and account for 70% of deaths worldwide. Nearly 40% of these deaths occur in people younger than 70 (WHO, 2019). Millions of people die each year as a result of modifi able risk factors that underlie the major NCDs. Th e writers of a report by WHO contend that 8% of premature heart disease, stroke, and diabetes can be prevented. Ten action points, including banning smoking in public places, enforcing tobacco advertising bans, restricting access to alcohol, and reducing salt in food, are listed. All of these actions require a population approach to be eff ective (WHO, 2019).

A survey conducted by the AHA lends an interesting perspective to this argument. Th ey surveyed 1,000 people in the United States. Th e AHA found that only 30% of respondents knew the AHA’s recommended limits for daily wine consumption. Drinking too much alcohol of any kind can increase blood pressure and lead to heart failure. Th e survey results also found that most respondents do not know the source of sodium content in their diets and are confused by low-sodium food choices. A majority of the respondents (61%) believe that sea salt is a low-sodium alternative to table salt when in fact it is chemically the same (AHA, 2011). A poll conducted in 2017 by the American Society of Clinical Oncology (ASCO) revealed that only 31% of Americans are aware that obesity is a risk factor for cancer and just 30% recognize alcohol consumption as a risk factor (ASCO, 2017). Th ese surveys reinforce the idea that people require more understanding of nutrition and the relationship between nutrition and health. It also reinforces the argument that interventions to improve health must be addressed at the community or population level.

Interventions that are evidence-based and population appropriate can reduce the underlying causes of chronic disease. Th is approach has the potential to lower the mean




level of risk factors and shift outcomes in a favorable direction. An example that receives a lot of attention is sodium intake. Excess sodium in the diet can put people at risk for stroke and heart disease. Th e CDC has reported that 9 of 10 Americans consume more salt than is recommended. Only 5.5% of adults follow the recommendation to limit sodium intake to less than 2,300 mg a day. Most sodium does not come from salt added to foods at the table but from processed foods. Th ese foods include grain-based frozen meals, soup, and processed meat. Sodium content can vary across brands, making it diffi cult to monitor intake. A cheeseburger from a fast food restaurant for example can have between 370 mg and 730 mg of sodium. Reducing dietary salt could greatly reduce the yearly number of U.S. cases of coronary heart disease, stroke, and heart attacks, with a savings of up to $18 billion in healthcare costs each year (CDC, 2019f).

Th is discussion illustrates the need to promulgate laws and develop policies that can eff ect positive health outcomes. It also illustrates the diffi culty involved in planning inter- ventions when evidence is sometimes contradictory, and causation is not only multifac- torial but sometimes outside of the control of the people whose health is compromised. Changing individual behavior is diffi cult and has little impact on population health. Using the power of legislation and regulation to make changes in the environment, such as banning smoking in public places, improving air quality, and reducing the amount of sodium in processed foods, has enormous potential for improving the overall health of populations.

Th e basic sciences of public health (particularly epidemiology and biostatistics) pro- vide tools for the APRN working with specialized populations and the means to fi nd evi- dence for eff ective and effi cient interventions. Th e care of specialized groups is the core of advanced practice. Evidence-based practice is defi ned as “a life-long problem-solving approach to the delivery of healthcare that integrates the best evidence from well-de- signed studies (i.e., external evidence) and integrates it with a patient’s preferences and values and a clinician’s expertise, which includes internal evidence gathered from patient data” (Melnyk, Gallagher-Ford, Long, & Fineout-Overholt, 2014, p. 5).

Population-based nursing requires APRNs to plan, implement, and evaluate care in the population of interest. Th e evaluation of outcome measures in populations begins with an identifi cation of the health problems, the needs of defi ned populations, and the diff erences among groups. Th e rates calculated from these numbers can help the APRN to identify risk factors, target populations at risk, and lay the foundation for design- ing interventions. Prevention is best carried out at the population level, whether at the level of direct care or through the support and promotion of policies. For example, an evidence-based program to prevent hospital readmissions for congestive heart failure can lead to improved health and decreased health-related costs. Th e promulgation of policies and regulations to support primary prevention measures, such as decreased sodium in prepared foods, could potentially lead to decreased rates of hypertension and heart disease. Interventions that are appropriate at the individual level and applied at the population level can result in a far-reaching eff ect.

Outcomes measurement refers to collecting and analyzing data using predetermined outcomes indicators for the purposes of making decisions about healthcare (ANA, 2015).




Outcomes research in APRN practice is research that focuses on the eff ectiveness of nursing interventions. Outcomes measurement in population-based care begins with the identifi cation of the population and the problem, followed by the generation of a clinical question related to outcomes. It is a measure of the process of care. An outcomes measure should be clearly quantifi able, be relatively easy to defi ne, and lend itself to standardization.

In outcomes measurement, the APRN is ultimately concerned with whether a pop- ulation benefi ts from an intervention. Th e APRN also needs to be concerned with the question of quality, effi cacy (Does the intervention work under ideal conditions?), and eff ectiveness (Does it work under real-life situations?). Other important considerations are effi ciency (cost benefi t), aff ordability, accessibility, and acceptability.


Th e Robert Wood Johnson Foundation and the IOM issued a report to respond to the need to transform the nursing profession. Th e committee developed four key messages:

1. Nurses should practice to the full extent of their education and should achieve higher levels of education and training.

2. Th e education system for nurses should be improved so that it provides seam- less academic progression.

3. Nurses should be full partners with physicians and other healthcare professionals.

4. Healthcare in the United States should be redesigned for eff ective workforce planning and policy making (IOM, 2011).

To improve population health, APRNs need to practice to the full extent of their edu- cation, be active in the political arena, and work collaboratively with other healthcare professionals. To promote health, APRNs can use epidemiological methods to identify aggregates at risk, analyze problems of highest priority, design evidence-based interven- tions, and evaluate the results. An important concept in the fi eld of population health is attention to the multiple determinants of health outcomes and the identifi cation of their distribution throughout the population. Th ese determinants include medical care, public health interventions, characteristics of the social environment (e.g., income, education, employment, social support, culture), physical environment (e.g., housing, air, and water quality), genetics, and individual behavior. A fi nal note about the use of “APRN” in this book: Th e Consensus Model for APRN Regulation: Licensure, Accreditation, Certifi cation & Education was completed and published in 2008 by the APRN Consensus Work Group and the National Council of State Boards of Nursing APRN Advisory Committee. Th e title “APRN” is used throughout this book to refer to certifi ed nurse anesthetists, certifi ed nurse midwives, clinical nurse specialists, and certifi ed nurse practitioners. Th e model was created through a collaborative eff ort of more than 40 organizations in order to “align the interrelationships among licensure, accreditation, certifi cation, and education




to create a more uniform practice across the country” (American Nurses Credentialing Center [ANCC], 2014). Th e goal for implementation of the model was 2015. As of 2019 many states have adopted portions of the model but there are variations from state to state. APRNs can check the status of the model in their state by going to: www.ncsbn. org/5397.htm (NCSBN, 2019).


Exercise 1.1 Using the following table as an example, list the parameters that describe the population(s) to whom you provide care.


Patients who have been diagnosed with congestive heart failure (CHF) and who live in the community

Adult (18 years of age and older) patients discharged from an urban medical center with a primary diagnosis of CHF

P1 = diagnosis P2 = age P3 = location/service area

Population of New Jersey All permanent residents of New Jersey

P1 = geographical location P2 = permanent residency

Exercise 1.2 PolitiFact is a website created by the St. Petersburg Times (now the Tampa Bay Times) and a winner of the 2009 Pulitzer Prize (www.politifact.com). It was cre- ated to help people fi nd the truth in American politics. Reporters and editors from the newspaper check statements by members of Congress, the White House, lobbyists, and interest groups and rate them on a Truth-O-Meter. Find a statement that is being circu- lated about the ACA, and then check the Truth-O-Meter to determine the veracity of the statement.

Exercise 1.3 Identify two or three population-based and health-related interventions at your institution or in your community. Determine whether the approach has been suc- cessful in changing outcomes and/or reducing health-related costs. Identify the aggregate population and what parameters were used in this intervention. Identify any changes in policy associated with these interventions.

Exercise 1.4 Th is chapter includes a brief description of the health eff ects of E-cigarettes. Design an educational program for a school-age aggregate that addresses the negative health eff ects of E-cigarettes. First, research the health eff ects of E-cigarettes and select three to fi ve health eff ects to address. Identify your target population and design an educational intervention for your target population. What outcomes will you look at to determine whether your intervention works? What approach will you use to engage your target population? Does your state regulate the purchase of E-cigarettes by youth?

Exercise 1.5 Th e relationship between obesity and cancer is described and discussed in this chapter. Conduct a search to answer the following questions. Th e incidence rates for




six cancers associated with obesity are increasing in young Americans. Identify them. What is the prevalence rate of obesity in people younger than 18 in your state? Which children are at highest risk for obesity in your state? Are there any prevention programs in your state that address this issue? Are they eff ective? Has your state passed and enacted any laws designed to decrease obesity? Are they eff ective? If they are not eff ective, explain why you believe they are not working.

REFERENCES Abdelaal, M., le Roux, C. W., & Docherty, N. G. (2017). Morbidity and mortality associated with obesity.

Annals of Translational Medicine, 5(7), 161. doi:10.21037/atm.2017.03.107 Abreo, A., Gebretsadik, T., Stone, C. A., & Hartert, T. V. (2018). Th e impact of modifi able risk factor

reduction on childhood asthma development. Clinical & Translational Medicine, 7(1), 1. doi:10.1186/ s40169-018-0195-4

Agency for Healthcare Research and Quality. (2018). 30-day readmission rates to U.S. hospitals. Retrieved from https://www.ahrq.gov/data/infographics/readmission-rates.html

Allan, J., Barwick, T., Cashman, S., Cawley, J. F., Day, C., Douglass, C. W., . . . Wood, D. (2004). Clinical prevention and population health: Curriculum framework for health professions. American Journal of Preventive Medicine, 27(5), 471–476. doi:10.1016/s0749-3797(04)00206-5

American Association of Colleges of Nursing. (2004). AACN position statement on the practice doctorate in nursing. Washington, DC: Author.

American Association of Colleges of Nursing. (2006). Th e essentials of doctoral education for advanced practice nursing. Washington, DC: Author. Retrieved from https://www.aacnnursing.org/DNP/DNP-Essentials

American Diabetes Association. (2018). Statistics about diabetes. Retrieved from http://www.diabetes.org/ diabetes-basics/statistics

American Heart Association. (2011). Most Americans don’t understand health eff ects of wine and sea salt, survey fi nds. Dallas, TX: Author. Retrieved from https://www.prnewswire.com/news-releases/most- americans-dont-understand-health-eff ects-of-wine-and-sea-salt-survey-fi nds-120595304.html

American Heart Association & American Stroke Association. (2018). Twenty years of progress. Retrieved from https://www.strokeassociation.org/-/media/stroke-files/about-the-asa/asa-20th-anniv-report-ucm_ 498858.pdf?la=en

American Nurses Association. (2015). Nursing: Scope and standards of practice (3rd ed.). Washington, DC: Author.

American Nurses Association. (2019). Health system reform. Retrieved from https://www.nursingworld .org/practice-policy/health-policy/health-system-reform

American Nurses Credentialing Center. (2018). APRN Faculty Tool Kit. Retrieved from https://www.nurs- ingworld.org/~48cd5b/globalassets/docs/ancc/faculty-toolkit-presentation.pdf

American Society of Clinical Oncology. (2017). National survey reveals most Americans are unaware of key cancer risk factors. Retrieved from https://www.asco.org/about-asco/press-center/news-releases/ national-survey-reveals-most-americans-are-unaware-key-cancer

APRN Consensus Work Group & the National Council of State Boards of Nursing APRN Advisory Committee. (2008). Consensus model for APRN regulation: Licensure, accreditation, certifi cation & educa- tion. Retrieved from https://www.ncsbn.org/Consensus_Model_for_APRN_Regulation_July_2008.pdf

Been, J. V., Nurmatov, U. B., Cox, B., Nawrot, T. S., van Schayck, C. P., & Sheikh, A. (2014). Eff ect of smoke- free legislation on perinatal and child health: A systematic review and meta-analysis. Lancet (London, England), 383(9928), 1549–1560. doi:10.1016/S0140-6736(14)60082-9

Bhatt, C. B., & Beck-Sagué, C. M. (2018). Medicaid expansion and infant mortality in the United States. American Journal of Public Health, 108(4), 565–567. doi:10.2105/AJPH.2017.304218

Boston Scientifi c. (2019). Heart disease facts. Retrieved from http://www.your-heart-health.com/content/ close-the-gap/en-US/heart-disease-facts.html

Brown, D., Anda, R., Felitti, V., Edwards, V., Malarcher, A., Croft , J., & Giles, W. (2010). Adverse childhood experiences are associated with the risk of lung cancer: A prospective cohort study. BMC Public Health, 10, 20. Retrieved from EBSCOhost.




Cannon, E., Bonomi, A., Anderson, M., Rivara, F., & Th ompson, R. (2010). Adult health and relationship outcomes among women with abuse experiences during childhood. Violence and Victims, 25(3), 291– 305. Retrieved from EBSCOhost.

Centers for Disease Control and Prevention. (2014). Cigarette smoking among U.S. high school students at lowest level in 22 years [Press Release]. Retrieved from http://www.cdc.gov/media/releases/2014/p0612- YRBS.html?s_cid=ostltsdyk_cs_506

Centers for Disease Control and Prevention. (2016). Adverse childhood experiences (ACE). Retrieved from https://www.cdc.gov/violenceprevention/childabuseandneglect/acestudy/index.html

Centers for Disease Control and Prevention. (2017a). Leading causes of death. Retrieved from https://www .cdc.gov/nchs/fastats/leading-causes-of-death.htm

Centers for Disease Control and Prevention. (2017b). National diabetes statistics report, 2017. Retrieved from https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf

Centers for Disease Control and Prevention. (2018a). Smoking and tobacco use–hookahs. Retrieved from http://www.cdc.gov/tobacco/data_statistics/fact_sheets/tobacco_industry/hookahs/index.htm#overview

Centers for Disease Control and Prevention. (2018b). Health eff ects of secondhand smoke. Retrieved from https://www.cdc.gov/tobacco/data_statistics/fact_sheets/secondhand_smoke/health_eff ects/index.htm

Centers for Disease Control and Prevention. (2018c). Adult obesity facts. Retrieved from https://www.cdc .gov/obesity/data/adult.html

Centers for Disease Control and Prevention. (2018d). Childhood obesity facts. Retrieved from https://www .cdc.gov/obesity/data/childhood.html

Centers for Disease Control and Prevention. (2019a). Smoking and tobacco use. Retrieved from https://www .cdc.gov/tobacco/data_statistics/fact_sheets/index.htm

Centers for Disease Control and Prevention. (2019b). Smoking and tobacco use: Current cigarette smoking among adults in the United States. Retrieved from https://www.cdc.gov/tobacco/data_statistics/fact_ sheets/adult_data/cig_smoking/index.htm

Centers for Disease Control and Prevention. (2019c). Tobacco use by youth is rising. Vital Signs. Retrieved from https://www.cdc.gov/vitalsigns/youth-tobacco-use/?s_cid=osh-stu-home-slider-005

Centers for Disease and Control and Prevention. (2019d). Youth and tobacco use. Retrieved from https:// www.cdc.gov/tobacco/data_statistics/fact_sheets/youth_data/tobacco_use/index.htm

Centers for Disease Control and Prevention. (2019e). About chronic diseases. Retrieved from https://www .cdc.gov/chronicdisease/about/index.htm

Centers for Disease Control and Prevention. (2019f). Salt. Retrieved from https://www.cdc.gov/salt/index .htm

Centers for Medicare & Medicaid Services. (2019). NHE fact sheet. Retrieved from https://www.cms.gov/ research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact -sheet.html

Central Intelligence Agency. (n.d.). Th e world factbook. Retrieved from https://www.cia.gov/library/ publications/the-world-factbook/rankorder/2091rank.html

Cohen, R. A., Martinez, M. E., & Zammitti, E. P. (2018). Health insurance coverage: Early release of estimates from the National Health Interview Survey, January-March 2018. Retrieved from https://www.cdc.gov/ nchs/data/nhis/earlyrelease/Insur201808.pdf

Collins, S., Bhupal, H., & Doty, M. (2019). Health insurance coverage eight years aft er the ACA. Retrieved from https://www.commonwealthfund.org/publications/issue-briefs/2019/feb/health- insurance-coverage-eight-years-aft er-aca

Collins, S., Doty, M., Robertson, R., & Garber, T. (2011). How the recession has left millions of workers without health insurance, and how health reform will bring relief: Findings from the Commonwealth Fund Biennial Health Insurance Survey of 2010. Retrieved from https://www.commonwealthfund.org/publications/ fund-reports/2011/mar/help-horizon-how-recession-has-left -millions-workers-without

Dossey, B. (2000). Florence Nightingale: Mystic, visionary, healer. Springhouse, PA: Springhouse Corporation. Evans, G. D. (2003). Clara Barton: Teacher, nurse, Civil War heroine, founder of the American Red Cross.

International History of Nursing Journal, 7(3), 75–82. Felitti, V., Anda, R., Nordenberg, D., Williamson, D., Spitz, A., Edwards, V., . . . Marks, J. (1998). Relationship

of childhood abuse and household dysfunction to many of the leading causes of death in adults: Th e adverse childhood experiences (ACE) study. American Journal of Preventative Medicine, 14, 245–258. doi:10.1016/s0749-3797(98)00017-8




Finkelstein, E., Trogdon, J., Cohen, J., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer-and service-specifi c estimates. Health Aff airs, 28(5). Retrieved from http://content.healthaff airs .org/content/28/5/w822.full.pdf

Fonarow, G., Smith, E., Saver, J., Reeves, M., Bhatt, D., Grau-Sepulveda, M., . . . Schwamm, L. (2011). Timeliness of tissue-type plasminogen activator therapy in acute ischemic stroke: Patient characteris- tics, hospital factors, and outcomes associated with door-to-needle times within 60 minutes. Circulation, 123(7), 750–758. Retrieved from EBSCOhost.

Frontier Nursing University. (2019). Historical timeline. Retrieved from https://frontier.edu/about-frontier/ history

Government of Canada. (2018). Tobacco and Vaping Products Act. Retrieved from https://www.canada.ca/ en/health-canada/services/health-concerns/tobacco/legislation/federal-laws/tobacco-act.html

Grundy, S. M. (2016). Metabolic syndrome update. Trends in Cardiovascular Medicine, 26(4), 364–373. doi:10.1016/j.tcm.2015.10.004

HealthyPeople.gov. (2019). Nutrition and weight status (Healthy People 2020). Retrieved from https://www .healthypeople.gov/2020/topics-objectives/topic/nutrition-and-weight-status/objectives

Hyland, M. E., Alkhalaf, A. M., & Whalley, B. (2012). Beating and insulting children as a risk for adult cancer, cardiac disease and asthma. Journal of Behavioral Medicine, 36(6), 632–640. doi:10.1007/ s10865-012-9457-6

Institute of Medicine. (2010). For the public’s health: Th e role of measurement in action and accountability. Retrieved from http://www.nationalacademies.org/hmd/Reports/2010/For-the-Publics-Health-Th e-Role -of-Measurement-in-Action-and-Accountability.aspx

Institute of Medicine. (2011). Initiative on the future of nursing. Retrieved from http://www.thefutureofnurs- ing.org/recommendations

Jacobs, E., Newton, C., Wang, Y., Patel, A., McCullough, M., Campbell, P., . . . Gapster, S. (2010). Waist cir- cumference and all-cause mortality in a large U.S. cohort. Archives of Internal Medicine, 170(15), 1293– 1301. doi:10.1001/archinternmed.2010.201

January, A. M. (2009). Friday at Frontier Nursing Service. Public Health Nursing, 26(2), 202–203. doi:10.1111/j.1525-1446.2009.00771.x

Jost, T. S. (2018). Th e Aff ordable Care Act under the Trump administration. Retrieved from Th e Commonwealth Fund website https://www.commonwealthfund.org/blog/2018/aff ordable-care-act- under-trump-administration

Khatana, S. A., Bhatla, A., Nathan, A. S., Giri, J., Shen, C., Kazi, D. S., & Groeneveld, P. W. (April, 2019). 3— Association of medicaid expansion with cardiovascular mortality—A quasi-experimental analysis. Paper presented at American Heart Association, Quality of Care & Outcomes Research, Arlington, VA.

Ling, J., & Heff ernan, T. (2016). Th e cognitive defi cits associated with second-hand smoking. Frontiers in Psychiatry, 7, 46. doi:10.3389/fpsyt.2016.00046

Lipsey, S. (1993). Mathematical education in the life of Florence Nightingale. Retrieved from http://www .agnesscott.edu/lriddle/women/night_educ.htm

Mazurenko, O., Balio, C. P., Agarwal, R., Carroll, A. E., & Menachemi, N. (2018). Th e eff ects of Medicaid expansion under the ACA: A systematic review. Health Aff airs (Project Hope), 37(6), 944–950. doi:10.1377/ hlthaff .2017.1491

McKelvey, L. M., Saccente, J. E., & Swindle, T. M. (2019). Adverse childhood experiences in infancy and tod- dlerhood predict obesity and health outcomes in middle childhood. Childhood Obesity, 15(3), 206–215. doi:10.1089/chi.2018.0225

Melnyk, B. M., Gallagher-Ford, L., Long, L. E., & Fineout-Overholt, E. (2014). Th e establishment of evi- dence-based practice competencies for practicing registered nurses and advanced practice nurses in real-world clinical settings: Profi ciencies to improve healthcare quality, reliability, patient outcomes, and costs. Worldviews on Evidence-Based Nursing, 11(1), 5–15. doi:10.1111/wvn.12021

Messina, N., Marinelli-Casey, P., Hillhouse, M., Rawson, R., Hunter, J., & Ang, A. (2008). Childhood adverse events and methamphetamine use among men and women. Journal of Psychoactive Drugs, 40 (Suppl. 5), 399–409. Retrieved from EBSCOhost.

National Cancer Institute. (2017). Obesity and cancer. Retrieved from http://www.cancer.gov/cancertopics/ factsheet/Risk/obesity

NCSBN. (2019). APRN collaboration consensus status. Retrieved from https://www.ncsbn.org/5397.htm Obama, B. (2009, August 16). Why we need healthcare reform. Th e New York Times, WK9.




Organisation for Economic Co-operation and Development. (2019). Health expenditure. Retrieved from http://www.oecd.org/els/health-systems/health-expenditure.htm

Orszag, P. R. (2008). Growth in healthcare costs. Statement before the Committee on the Budget United States Senate. Retrieved from http://www.cbo.gov/ft pdocs/89xx/doc8948/01-31-HealthTestimony.pdf

Owen, C. G., Martin, R. M., Whincup, P. H., Smith, G. D., & Cook, D. G. (2005). Eff ect of infant feeding on the risk of obesity across the life course: A quantitative review of published evidence. Pediatrics, 115(5), 1367–1377.

Patel, A. V., Maliniak, M. L., Rees-Punia, E., Matthews, C. E., & Gapstur, S. M. (2018). Prolonged leisure time spent sitting in relation to cause-specifi c mortality in a large U.S. cohort. American Journal of Epidemiology, 187(10), 2151–2158. doi:10.1093/aje/kwy125

Pear, R., & Haberman, M. (2019, April 2). Trump retreats on health care aft er McConnell warns it won’t happen. Th e New York Times. Retrieved from https://www.nytimes.com/2019/04/02/us/politics/ obamacare-donald-trump.html

Population. (2019). Merriam Webster Dictionary. Boston, MA: Houghton Miffl in. Retrieved from https:// www.merriam-webster.com/dictionary/population

Porche, D. J. (2004). Public and community health nursing practice. Th ousand Oaks, CA: Sage. Public Health Law Center. (n.d.). Youth access to e-cigarettes. Retrieved from https://publichealthlawcenter

.org/sites/default/fi les/States-with-Laws-Restricting-Youth-Access-to-ECigarettes-Dec2018.pdf Robson, N. (2019). In reversal, DOJ now says whole ACA unconstitutional. Retrieved from Th e National

Law Journal website https://www.law.com/nationallawjournal/2019/03/25/in-reversal-doj-now-says -whole-aca-unconstitutional/?slreturn=20190305094315

Scott, K., Smith, D., & Ellis, P. (2012). A population study of childhood maltreatment and asthma diagno- sis: Diff erential associations between child protection database versus retrospective self-reported data. Psychosomatic Medicine, 74(8), 817–823. doi:10.1097/PSY.0b013e3182648de4

Smyth, J., Heron, K., Wonderlich, S., Crosby, R., & Th ompson, K. (2008). Th e infl uence of reported trauma and adverse events on eating disturbance in young adults. International Journal of Eating Disorders, 41(3), 195–202. Retrieved from EBSCOhost.

Soni, A. (2010, December). Trends in use and expenditures for diabetes among adults 18 and older, U.S. civil- ian noninstitutionalized population, 1996 and 2007. Agency for Healthcare Research and Policy. Retrieved from http://www.meps.ahrq.gov/mepsweb/data_fi les/publications/st304/stat304.pdf

Th e Henry J. Kaiser Family Foundation. (2019a). Average annual family premium per enrolled employee for employer based health insurance. Retrieved from https://www.kff .org/other/state-indicator/family -coverage/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22 asc%22%7D

Th e Henry J. Kaiser Family Foundation. (2019b). Key facts about the uninsured population. Retrieved from https://www.kff .org/uninsured/fact-sheet/key-facts-about-the-uninsured-population

Th ompson, W. H. (2018). Management of catheter-related bloodstream infections. Critical Care Alert, 26(2), 9–13.

U.S. Department of Health and Human Services. (2015). HHS initiative on multiple chronic conditions. Retrieved from https://www.hhs.gov/ash/about-ash/multiple-chronic-conditions/index.html

World Health Organization. (2011). WHO report: Deaths from noncommunicable diseases rise, hitting developing countries hard. Retrieved from https://www.who.int/pmnch/media/news/2011/20110427_who_ncd_rise/en

World Health Organization. (2019). Noncommunicable diseases and their risk factors. Retrieved from http:// www.who.int/chp/en

INTERNET RESOURCES Centers for Disease Control and Prevention, ACESs https://www.cdc.gov/violenceprevention/childabuseandneglect/acestudy/index.html

Centers for Disease Control and Prevention, Smoking and Tobacco Use https://www.cdc.gov/tobacco/data_statistics/fact_sheets/index.htm

Centers for Disease Control and Prevention, Smoking and Tobacco Use / Hookahs https://www.cdc.gov/tobacco/data_statistics/fact_sheets/tobacco_industry/hookahs/index.htm#overview




Canada / Tobacco and Vaping Products Act https://www.canada.ca/en/health-canada/services/health-concerns/tobacco/legislation/federal-laws/


Findings: Commonwealth Fund’s Biennial Health Insurance Survey https://mail.yahoo.com/d/folders/1/messages/AFZhsg8SqrtiXK3xhQOlmKQk0ws

Public Health Law Center, Youth Access to E-cigarettes https://publichealthlawcenter.org/sites/default/files/States-with-Laws-Restricting-Youth-Access-to-


U.S. Department of Health and Human Services (DHHS) / DHHS initiative on multiple chronic conditions https://www.hhs.gov/ash/about-ash/multiple-chronic-conditions/index.html







Nurses have a long and rich history of wanting to do the most good for the most people. Today, it is imperative that advanced practice registered nurses (APRNs) con- tinue that tradition by delivering care that improves the health of populations. By assessing community, aggregate, family, and individual factors and conditions that have a strong infl uence on health, APRNs are better equipped to deliver eff ective and evidence-based care. Identifying population-level healthcare needs and healthcare disparities can improve equity in health outcomes at all levels.

Th e American Association of Colleges of Nursing’s (AACN) defi nition of advanced practice nursing includes the importance of identifying and managing health outcomes at the population level (AACN, 2004). In 2006, the AACN specifi ed in Essential II: Organizational and Systems Leadership for Quality Improvement and Systems Th inking that graduates of doctorate in nursing practice (DNP) programs have competency in meeting “the needs of a panel of patients, a target population, a set of populations, or a broad community” (p. 10). A core component of DNP edu- cation is clinical prevention (health promotion and disease prevention at individual and family levels) and population health (focus of care at aggregate and community levels and examination of environmental, occupational, cultural, and socioeconomic dimensions of health) (AACN, 2006; Zenzano et al. 2011). Regardless of whether DNP graduates practice with a focus on clinical prevention or population health, the ability to defi ne, identify, and analyze outcomes is imperative for improving the health status of individuals and populations (AACN, 2006; U.S. Department of Health and Human Services [DHHS], 2018a).




Th e purpose of this chapter is to explore how APRNs can identify determinants of health and defi ne population outcomes. Specifi c examples from various settings, such as acute care, primary care, long-term care, and the community, are given, as well as outcomes related to health disparities and national health objectives. Th e identifi cation of factors that lead to certain outcomes or key health indicators is an essential fi rst step in planning eff ective interventions and is used later in the evaluation process. By com- paring outcomes, APRNs can advocate for needed resources and changes in policies at local, regional, state, and/or national levels by identifying areas for improvement in prac- tice, by comparing evidence needed for eff ective practice, and by better understanding health disparities.

Health disparities are not fair or socially just. Th ey are preventable. Th ey refl ect an uneven distribution of social determinants and environmental, economic, and political factors. Health disparities can be defi ned as the diff erences identifi ed in incidence or prevalence of illness, health outcomes, mortality, injury, or violence, or diff erences in opportunities to reach optimal health equity due to disadvantages based on ethnicity, socioeconomic status, gender, sexual orientation, geographic location, or other reasons (Penman-Aguilar, Bouye, & Liburd, 2016). Health equity is both a process and an out- come that is defi ned as “reducing and ultimately eliminating disparities in health and its determinants that adversely aff ect excluded or marginalized groups” (Braverman, Arkin, Orleans, Proctor, & Plough, 2017). Along with professionals from other disciplines and community members, APRNs play an important collaborative role in the work required to eliminate health inequities and healthcare disparities.


Background One of the earliest records of observed outcomes by nurses dates back to 1854 during the Crimean War at the Scutari Hospital in Turkey under Florence Nightingale’s lead- ership and pioneering work. Nightingale, credited as the founder of modern nursing, documented a decrease in mortality among the British soldiers aft er providing more nutritious food, cleaning up the environment, and improving the sewage system (Fee & Garofalo, 2010). Despite these exemplary nursing outcomes in the 1850s, variation and challenges with outcome documentation persisted as the nursing profession matured. By the mid-1990s, documentation of nursing outcomes started to improve (Griffi ths, 1995; Hill, 1999; Lang & Marek, 1991; van Maanen, 1979). Early work in nursing outcomes focused on costs, and it was clear that a more comprehensive model that included other types of outcomes was needed to advance healthcare and refl ect the various outcomes that result from nursing interventions (Nelson, Batalden, Plume, & Mohr, 1996). Today, nursing interventions are based on evidence using models of practice that include stan- dards and synchronization with other systems to deliver quality of care, patient safety, and optimal population health outcomes (Institute of Medicine [IOM], 2015; Patrician et al., 2013; Xiao, Widger, Tourangeau, & Berta, 2017). Health reform eff orts to improve




quality and access to care and to reduce costs spurred more work to examine outcomes while also examining their relationship to indicators of structure and process. Th e Patient-Centered Outcomes Research Institute (PCORI), for example, developed out of the Patient Protection and Aff ordable Care Act of 2010 (ACA) and uniquely engages patients and the healthcare community on research projects (Newhouse, Barksdale, & Miller, 2015).

Defi ning, Categorizing, and Identifying Outcomes Health outcomes are usually defi ned as an end result that follows some kind of health- care provision, treatment, or intervention and may describe a patient’s condition or health status (Jones, 2016; Kleinpell, 2007; Kleinpell & Gawlinski, 2005). Using a popula- tion perspective, a health outcome can be measured using public health metrics, such as mortality and life expectancies, that are used to demonstrate the contribution of certain diseases to population mortality. New trends also emphasize the inclusion of qualitative metrics that are based on subjective data, such as self-perceived health status, psycholog- ical state, or ability to function, that can illustrate collective social well-being (Boothe, Sinha, Bohm, & Yoon, 2013; Parrish, 2010).

Evaluating population-based outcomes and their impact on population health involves looking at what to assess and how to assess it. Establishing the impact takes time and requires using an evaluation that is able to link interventions to long-term outcomes such as reducing disease morbidity and mortality at the population level. APRNs can best determine the eff ectiveness of an intervention and long-term impact by focusing on an accurate assessment and interpretation of data that are generated or collected using indi- vidual, population, and community health indicators (Anderson & McFarlane, 2015).

Classifying and categorizing outcomes can be done in several ways. Outcomes may be classifi ed into categories by describing “who” is measured, such as individuals, aggregates, communities, populations, or organizations; by identifying the “what” or the type of out- come, such as care, patient, or performance-related outcomes (Kapu, Sicoutris, Broyhill, D’Agostino, & Kleinpell, 2017; Kleinpell & Gawlinski, 2005); and by determining the “when” or the time it takes to achieve an outcome, such as short-term, intermediate, or long-term outcomes (Rich, 2015). Table 2.1 provides examples of various outcomes using these diff erent classifi cation systems. Each outcome type is listed by benefi ciary and has a related example of the type of measurement, the potential outcome, and the potential impact of that outcome. Many of them also include a time frame for the outcome.

Th e Donabedian (1980) framework is frequently used in nursing and healthcare to evaluate quality of care and relies on the examination of three components: structure, process, and outcome. Structure refers to healthcare resources, such as the number and type of health and social service agencies, and can also include utilization indicators. Process describes how the healthcare is delivered. Outcome refers to the change in health status related to the intervention provided (Donabedian, 1980). Th is framework is par- ticularly useful in describing the health of a community. It is based on the concept of community as client and focuses on the health of the collective or population instead of the individual (Gibson & Th atcher, 2016).




TABLE 2.1 Examples of Outcomes, Measures, and Impact by Benefi ciary, Type, and Time Frame




Individual outcomes

BP measurement Decreased BP The degree to which perceived health status is improved by BP management

Aggregate outcomes

Weekly weights of participants in an exercise class

Reduced mean weight for exercise class members each week

Sustained weight maintenance using BMI parameters

Community outcomes

A town’s seat belt usage per 100 drivers ≥18 years of age computed yearly

Increased yearly rate of a town’s seat belt usage per 100 drivers ≥18 years of age

Decrease in the town’s percentage of automobile accident injuries/fatalities in drivers ≥18 years of age

Population outcomes

Reported number of infant deaths within 1 year of birth per 1,000 infants

Decreased infant mortality rate compared to previous year

Five-year decrease in infant mortality rate




Care-related outcomes

Annual rate of hospital- acquired infections determined from hospital infectious disease reports

Decreased hospital- acquired infections rate from previous year

Decreased length of stay and decreased mortality in patients with hospital- acquired infections

Patient-related outcomes

Observation of insulin injection administration technique

Correct demonstration by patient of safe insulin administration technique

Decreased hemoglobin A1C and decreased incidence of microvascular complications

Performance- related outcomes

Chart review for completed checklist of asthma best- practices protocol

Nursing staff adherence to asthma best- practices protocol

Decreased hospital readmissions due to asthma




Short-term outcomes

Self-report of nipple discomfort among fi rst-time breast-feeding mothers in a postpartum unit

Absence of nipple discomfort among fi rst-time breast-feeding mothers 1 week after hospital discharge from a postpartum unit

Improved breast-feeding rates among women discharged from a postpartum unit

Intermediate outcomes

Self-report of tobacco usage by fi rst-time outpatient clinic users during the calendar year

An increase in smoking- cessation rates among outpatient clinic users during the calendar year

Decrease in smoking- related illnesses among outpatient clinic users during the calendar year

Long-term outcomes

Incidence rate of HIV in an urban African American population

Annual reduction of incidence rate of HIV in an urban African American population

Annual reduction of morbidity and mortality related to HIV in an urban African American population

BMI, body mass index; BP, blood pressure.




Using Donabedian’s framework, a community’s health can be described in terms of its structure by the number and type of health and social agencies present, its healthcare workforce, health services utilization indicators, and the community’s educational and socioeconomic levels in relation to demographic measures of ethnicity, gender, and age. A community’s health process can measure healthcare delivery methods and how well community members work together to build capacity and solve their problems, which refl ects the ability to share power and resources and to respond to needs and changes (Minkler & Wallerstein, 2012). Community health outcomes can include measures associated with vital statistics (e.g., births, deaths, marriages, divorces, fetal deaths, and induced termination of pregnancies); morbidity or illness data and trends; social deter- minants of health such as housing, unemployment, and poverty rates; health risk profi les of aggregates by specifi c areas, neighborhood safety, access to fresh fruits and vegetables, as well as physical activity venues such as parks, playgrounds, and neighborhood sports fi elds (Anderson & McFarlane, 2015). Other indicators of a community’s health status may include the number of premature deaths, quality of life, disabilities, risk factors, and injuries. Community health outcomes models are used to assess the interaction between the physical and the social environments (the built environment) and the impact on health at the individual, population, and community levels (DeGuzman & Kulbok, 2012). Guided by these models of practice and research, APRNs can work in partnership with community members to identify what they see as relevant and important, build social capital, use outcome data to advocate for changes in policy, and then continue to work in partnership to identify strategies to intervene, monitor, and improve those outcomes (Bigbee & Issel, 2012; Loyo et al., 2012; Payán et al., 2017).

Vital Statistics Vital statistics provide important outcome measures that APRNs can monitor and com- pare over time and analyze by demographic variables to detect such things as health dis- parities. In the United States, the National Center for Health Statistics (NCHS) located within the Centers for Disease Control and Prevention (CDC) collects information from a variety of sources, such as birth and death certifi cates, health records, surveys, physical exams, and laboratory testing (Rothwell, 2015). Personnel from local health departments review the data from death certifi cates, including demographic data, look- ing at the immediate cause of death and any contributing factors of death, and record- ing multiple causes of death. Local data are sent to a state offi ce for collation and then sent to the NCHS, which provides this information to the public on its website (www. cdc.gov/nchs) and in an annual publication, Vital Statistics of the United States (Friis & Sellers, 2015). APRNs can access national and global health statistics from multiple agency sources, including government agencies, to identify health trends and patterns. However, due to the lack of agencies and/or resources in certain populations or regions, health information might not be available or might be limited in scope. Partners in Information Access for the Public Health Workforce is a collaborative project among U.S. government agencies, public health organizations, and health science libraries that pro- vides a list of extensive web links for sources of data that healthcare providers can use to




identify local issues and develop interventions to improve health (U.S. National Library of Medicine [NLM], 2018).

Behavioral Risk Factor Surveillance System (BRFSS) In the early 1980s, personal health behaviors became a key source of information that paved the way in understanding risk behavior and its impact on morbidity and mortality. Th e Behavioral Risk Factor Surveillance System (BRFSS; www.cdc.gov/brfss), a system established to collect state-level data, also allows states to estimate prevalence for regions that can be compared across states (CDC, 2014). Th e data generated by this surveillance system have been pivotal in assessing and addressing urgent or emerging health issues. Examples of emergent health issues include man-made and natural disasters, infl uenza vaccine shortages, and increasing incidence of preventable diseases such as infl uenza or measles. Th e ability to reach cell phone users has expanded BRFSS’s accessibility to populations that were not accessible by prior data-collection methods. Th is ability has increased representation and generated higher quality information.

Social Determinants of Health Social determinants of health and disparities data are areas that APRNs can also use to inform and guide their practice to develop socioculturally appropriate interventions. Social determinants that lead to health disparities are recognized situations related to where people are born, grow up, work, live, and the systems of care available to them to deal with illness and disease (DHHS, 2018a; World Health Organization [WHO], 2011). Examples of social determinants that are related to health inequalities include poverty, educational level, racism, income, and poor housing. Th ese inequalities can lead to poor quality of life, poor self-rated health, multiple morbidities, limited access to resources, unnecessary risks and vulnerabilities, and premature death. To expand our understanding of the association between social determinants and health outcomes, theoretical models are being tested to examine the interaction between the social environment (physical, chemical, biological, behavioral, and/or life events) and genetics and its application to population health, such as examining the interplay of social environments, genetics and Black-White disparities and their contribution to infant mortality (El-Sayed, Paczkowski, Rutherford, Keyes, & Galea, 2015).

Another example of social determinants of health and inequalities is that people liv- ing at or below 100% of the federal poverty level (FPL) have decreased access to health- care compared to those at or above 400% of the FPL, which can then negatively impact health status (Agency for Healthcare Research and Quality [AHRQ], 2017). Th ese social and economic conditions may limit a person’s ability to be employed, access health- care services, and receive timely quality care. Th is is evident in rural populations who experience place-based health disparities due to limited local healthcare services, lack of technological infrastructure to support health promotion interventions, and poten- tial job-associated exposures (e.g., chemicals in agriculture work; Weinstein, Geller, Negussie, & Baciu, 2017).




A problem encountered repeatedly by healthcare practitioners is the lack of available census data and statistics about key issues in the health and healthcare of people with unauthorized status. APRNs may be able to access health information needed by work- ing together with other sectors outside of health, such as housing, labor, education, and community-based or faith-based organizations that off er services to immigrant commu- nities. Th is involves the collection, documentation, and use of data that can be used to monitor health inequalities in exposures, opportunities, and outcomes.

Morbidity and Mortality Data APRNs are oft en responsible for reviewing morbidity and mortality trends and can use this information to advocate for improved health policy and additional resources, or to develop innovative interventions. Provisional weekly updates of reportable diseases can be accessed electronically through the Morbidity and Mortality Weekly Report (MMWR), published by the CDC. Morbidity data are less standardized in general than mortality data because state legislatures and local agencies decide what illnesses must be reported to the CDC. Reporting of cases of infectious diseases and related conditions is an import- ant step in controlling and preventing the spread of communicable disease. Th e list of reportable or notifi able diseases can change as some diseases may become eradicated and other, new diseases and conditions are discovered such as the 2014-2016 Ebola outbreak in the United States. Th e accuracy of morbidity data is diminished if healthcare providers fail to report a disease or illness for fear of violating an individual’s privacy or because they may not be aware of reporting requirements or because the healthcare provider mis- diagnosed the illness (Macha & McDonough, 2012). It is imperative that APRNs educate themselves on the reporting requirements in their state. Certain diseases with easy and/ or rapid transmission are more likely to harm a population’s health. Infectious or com- municable diseases, such as certain sexually transmitted infections (STIs) or other dis- eases, such as rabies, rubella, plague, measles, tetanus, and food-borne illnesses, can lead to signifi cant morbidity and mortality if not reported promptly (Friis & Sellers, 2015).

Another way to evaluate population morbidity is derived from population surveys that are conducted to determine the frequency of acute and chronic illnesses and disabil- ity as well as other population characteristics. Th e U.S. National Health Interview Survey (NHIS) is an example of a morbidity survey that was fi rst authorized by Congress in 1956 for the purpose of informing the U.S. population about various health measures and indicators. In an eff ort obtain a representative national sample, the NHIS continuously surveys households throughout the year on a variety of health topics, such as physical and mental health status, chronic conditions, and access to and use of healthcare ser- vices. One fi nding from the 2017 data indicated a disparity in health insurance coverage, with Hispanic persons more likely to be without coverage than non-Hispanic Black and non-Hispanic White persons (Schiller, Clark, & Norris, 2018). Th e NHIS debuted a rede- sign in 2019 to improve its content and structure. Th e redesign emphasized content with a strong link to public health, such as intermediate health outcomes for leading causes of morbidity/mortality. It also targeted major federal health promotion initiatives, and healthcare access and utilization (CDC, 2018).




Th e NHIS is one sector of the data collection program at the NCHS, housed within the CDC. Th e NCHS works with public and private partners to collect data that provide reliable and valid evidence on a population’s health status, infl uences on health, and health outcomes (CDC, 2017). APRNs can review these data to identify health dispari- ties among subgroups based on ethnicity and/or socioeconomic status, monitor trends with health status and with healthcare delivery systems, support research endeavors, identify health problems, evaluate health policies, and access important information that can be used to improve policies and health services.

In addition to population surveys such as the NHIS, the NCHS collects data using other surveys with each method yielding information that is readily available on the Internet for use by healthcare providers, researchers, and educators. First, the National Vital Statistics System provides information about state and local vital statistics, includ- ing teen birth rates, prenatal care, birth weights, risk factors related to poor pregnancy outcomes, infant mortality rates, life expectancy, and leading causes of death (www. cdc.gov/nchs/nvss). Second, the National Health and Nutrition Examination Survey (NHANES) is conducted through mobile examination centers held at randomly selected sites throughout the United States. Data are obtained from interviews (e.g., environmental exposures, risk factors), and additional data are collected from physi- cal examinations, diagnostic procedures, laboratory tests, and indicators of growth and development, including weight, diet, and nutrition (www.cdc.gov/nchs/nhanes.htm). Th ird, Th e National Healthcare Surveys obtain data using a collection of surveys targeted toward various healthcare providers and healthcare settings (www.cdc.gov/nchs/dhcs). A variety of data is collected, including information regarding patient safety and safety indicators, clinical management of specifi c health conditions, disparities in healthcare utilization and health quality, and information about the use of healthcare innovations. All these survey data are collated and made available for policy makers, practitioners, and researchers and all provide useful outcome information for APRNs. Additional surveys can be found on the NCHS website, but the aforementioned surveys are most useful for analyzing outcomes data.

Morbidity and mortality trends and other health-related survey data can be used by APRNs to advocate for improved health policy and additional resources, or to develop innovative interventions. For example, if an APRN notices an increase over the past year of closed head injuries in teenagers because of motor vehicle crashes (MVCs), the APRN can design a plan of care that targets risk factors associated with teenage driving and MVCs. Th e APRN may review emergency department (ED) records of teenage drivers in car accidents to assess factors such as seat belt usage, distracted driving behaviors, blood alcohol levels, drug screening, prior ED visits for accidents, and age at the time of the incident. Th e APRN may also approach high schools in order to collaborate with school nurses for the purpose of developing peer training programs. High school students could be trained as peer teachers to encourage classmates to wear seat belts, avoid entering a car with an impaired driver, say no to drug and alcohol usage, and eliminate use of electronic devices, such as cellular phones, while driving. Nurse educators could encourage teach- ers to integrate the importance of wearing seat belts in their classes by discussing the




potential for traumatic brain injury in MVCs, especially in unrestrained drivers. Review of the biomechanics of accidents in a physics or science class might provide teens with knowledge that is both benefi cial and relatable and may reinforce the dangers of unre- strained driving. Additionally, the hospital could partner with a high school and pilot an educational program with adolescents to reduce texting while driving (Unni et al., 2017). Or school nurses could implement an online educational tool, like Let’s Choose Ourselves, to address adolescent drivers’ inattention and distraction (McDonald, Brawner, Fargo, Swope, & Sommers, 2017).

Aft er developing and implementing appropriate interventions, the APRN should reassess (e.g., in 6 months, 1 year) seat belt usage and repeat ED visits for MVCs, and revaluate whether there have been any positive drug screens, elevated blood alcohol concentrations, or documented distracted driving related to MVCs. Re-evaluation of these data can help to identify those interventions that work. It may also lead to changes in school policy and/or curriculum to modify behavior, thereby reducing MVCs among teens. Statewide policy changes that the APRN can advocate for include legislation whereby any detectable blood alcohol concentration is illegal, more strin- gent and enforced driving fi nes for unrestrained passengers and drivers, and graduated driving license laws that increase driving supervision time, restrict passengers, and limits nighttime driving (Williams, 2017). Although this is just one example of how surveillance by an APRN could lead to the development of interventions to improve outcomes in a population, one can see the potential value of community collaboration and the use of such outcomes to identify a need for change and to evaluate the impact of interventions.

Identifying Outcomes How do APRNs decide what outcomes to study? Th ere are a variety of outcomes that exist in relation to cost, clinical and functional data, social conditions, and community and environmental indicators. Oft en, outcomes will refl ect the desired or anticipated eff ects of the intervention that are related to the problem or population of interest. Another way to select outcomes is by reviewing available epidemiological and social epidemi- ological data for outcomes that may be of interest or relevance to an APRN’s interven- tion or study (Galea & Link, 2013; Macha & McDonough, 2012; Minkler & Wallerstein, 2012). Using the earlier example of designing an intervention to reduce teenage MVCs, an APRN could seek out epidemiological data from the National Highway Traffi c Safety Administration’s (NHTSA) Fatality Analysis Reporting System and review annual data and trends for fatalities in drivers age 15 to 19 (crashstats.nhtsa.dot.gov).

Outcomes can also be identifi ed using County Health Rankings & Roadmaps (CHR&R), a user-friendly web-based data source. It allows an APRN to compare sim- ilarly sized counties on various measures, such as premature death, low birthweight, or drug overdose deaths (www.countyhealthrankings.org). McCullough and Leider (2017) used data from the CHR&R and U.S. Census Bureau to examine the relationship among county wealth, health, and social services spending, and health outcomes.




Th ere is no shortage of usable resources for identifying outcomes. Th e Community Guide is a helpful resource (available at www.thecommunityguide.org). It provides evi- dence-based recommendations for public health interventions, analyses from system- atic reviews to determine program and policy eff ectiveness, information on whether an intervention might work in one’s community, and information about the intervention’s costs and benefi ts. APRNs can review topics or areas of focus and strategies that work for various outcomes. For example, systematic reviews are available on adolescent health. By spending a few minutes exploring the website, one can fi nd numerous outcomes such as number of self-reported risk behaviors, including engagement in any sexual activity, fre- quency of sexual activity, number of partners, frequency of unprotected sexual activity, use of protection to prevent STIs, use of protection to prevent pregnancy, and self-reported or clinically documented STIs. Other community-guide topics are listed in Table 2.2 with example outcomes adapted from the website.

Trust for America’s Health (TFAH) is another resource to inform APRNs’ outcome identifi cation (www.tfah.org). Th e TFAH’s work is centered on three main principles (prevention, protection, and communities) and is a resource for issues such as public health funding, obesity, substance abuse and misuse, and health disparities. It also allows an APRN to examine state level data and rankings on key health indicators (e.g., percent- age of the population with hypertension, the number of cases of tuberculosis, and the percentage of asthma in high school students).

Outcome Monitoring Aft er identifying outcomes, monitoring of measures to assess eff ectiveness of inter- ventions is increasingly important and, in many cases, is a necessity to justify program implementation or program funding. For example, outcome monitoring is used to assess quality of healthcare by examining the association between the level of improved health services and the desired health outcomes of individuals and populations (IOM, 2015). Th is is best done by having a quality-improvement (QI) plan that systematically and consistently implements improvement strategies to address areas that are defi cient and not meeting benchmarks. Th e Institute for Healthcare Improvement (IHI) is a useful resource for determining a QI plan and provides a toolkit with 10 tools (e.g., Plan Study Do Act [PDSA] worksheet) to guide QI projects (www.ihi.org/resources/Pages/Tools/ Quality-Improvement-Essentials-Toolkit.aspx).

Outcomes are an expected part of what APRNs must collect when their focus is on populations. When combined with an evidence-based practice approach, outcomes can help provide standards or parameters for developing innovative interventions, insti- tuting approaches more likely to impact the problem, and/or developing new practice guidelines or protocols. Th rough working with populations, APRNs contribute to meet- ing the IHI (2018) Triple Aim: (a) improve the patient experience, (b) improve the health of the population, and (c) reduce the per capita cost of health. For example, an APRN working in a community-based clinic with a Hispanic population may gather informa- tion on factors related to an increased rate of type 2 diabetes mellitus, such as hyperten- sion, obesity or acculturation. An assessment can be made to determine if diff erences in




TABLE 2.2 Community Guide Topics and Outcome Examples


Adolescent health Alcohol, tobacco, and drug usage; injury, violence, and suicide rates; BMI, physical activity, and educational attainment

Asthma Symptom-free days, quality-of-life scores, school absenteeism, environmental mold remediation, medication usage, hospital admissions

Birth defects Folic acid daily intake, daily alcohol consumption, medications, vaccinations

Cancer Cigarette smoking, physical activity, nutrition, screening test results

Cardiovascular disease

Blood pressure, physical activity, cholesterol levels, BMI

Diabetes Hemoglobin A1C, incidence of skin infections, obesity, peripheral neuropathy, renal insuffi ciency

Health communication and health information technology

Use of reliable digital and mobile technology for HI or appointment reminders, health literacy level, communication by provider of understandable HI, diffi culty using HI

Health equity Use of school-based health centers, use of interpreter services or number of bilingual providers, access to healthcare, employment rates

HIV/AIDS, STIs, and pregnancy

Abstinence, condom use, incidence of STIs or pregnancy

Mental health Depression scale scores, hospital admissions, attendance at school or work, suicidal ideation or attempts

Motor vehicle crashes

Use of child safety seats, use of seat belts, blood alcohol concentration, use of phone while driving, moving violations

Nutrition Daily intake of fruits and vegetables, BMI, soda intake, fat intake, fi ber intake

Obesity Daily physical activity; sedentary time in front of the TV, computer, or electronic screen; weight loss; BMI

Oral health Dental caries; incidence of oral or throat cancer; use of helmets, face masks, and mouth guards in contact sports; reduced or discontinued use of chewing tobacco

Physical activity Muscle strength and endurance activities, moderate- or vigorous-intensity aerobic physical activity

Tobacco Out-of-pocket costs for cessation therapies, creation of smoke-free policies, retail tobacco sales to youth

Vaccination Number of infectious cases, hospitalizations, deaths from vaccine-preventable disease, immunization rates, immunization failures

Violence Number of violence-related hospitalizations and deaths, participation in therapeutic foster care, school-based violence prevention programs, reduction of nonaccidental trauma in infants and toddlers

Worksite health Stair usage by employees, gym membership by employees, use of weight management counseling by employees

BMI, body mass index; HI, health information.

Source: Adapted from U.S. Department of Health and Human Services, Community Preventive Services Task Force (2019). The community guide. Retrieved from https://www.thecommunityguide.org




health outcomes exist based on social support, family structures, barriers to obtaining medications or durable medical equipment, or other variables of interest. Once these outcomes are assessed, actions can be taken to address the issues that may contribute to poor health outcomes or increased incidence, such as implementing a community health worker (CHW) intervention (Chang et al., 2018). A reassessment of outcomes is neces- sary aft er an intervention to determine if a change has occurred.

Outcomes can also be used to measure quality of care in an outpatient setting. APRNs in an outpatient pediatric oncology practice who administer chemotherapy through a central venous catheter may set a goal to reduce catheter-related bloodstream infections by employing a before and aft er hands-on simulation education program for parents and nurses emphasizing aseptic techniques before, during, and aft er infusion. Th e success of the intervention can be measured by comparing outcomes such as number of positive cultures and prolonged hospitalizations, and rates of bloodstream infection before and aft er implementation of the educational intervention.

Nurse-Sensitive Quality Indicators As documented evidence of patient safety concerns grew in the United States and at a time when healthcare costs were increasing and healthcare quality was being questioned, various nursing organizations started to focus on establishing a coordinated system for evaluating patient safety. In 1994, the American Nurses Association (ANA) developed Nursing’s Safety and Quality Initiative, which initiated studies of patient safety with the goal of advocating healthy change. It was clear that nurse managers and administrators needed sound data for comparing their hospital units with similar units across the nation as a means of improving quality by developing and refi ning quality-improvement initia- tives and monitoring progress. Th e indicators needed to be specifi c or sensitive to nurs- ing care rather than ones that refl ected medical care or institutional care. Th e indicators would have to be highly correlated with nursing quality and be measurable with a high degree of reliability and validity. Furthermore, the indicator must not pose undue hard- ships on personnel tasked with collecting the data. Donabedian’s (1982) framework of focusing on structure, process, and patient-centered outcomes was used for identifying and honing the indicators. Structure indicators included staff mix and nursing care hours per patient day; process indicators included maintenance of skin integrity and nurse satisfaction; and patient-focused outcomes included nosocomial infections, patient fall rates, patient satisfaction with pain management, patient education, nursing care, and overall care (Montalvo, 2007).

Th e National Database of Nursing Quality Indicators® (NDNQI®) was created in 1998 by the ANA as part of the initiative to make changes to improve safety and quality of care, to help educate nurses about measurement, and to invest in research studies that exam- ined safe and high-quality patient care. Th e NDNQI helped standardize information that was submitted by hospital units throughout the United States on indicators related to nursing structure (staffi ng level, educational level), process measures, and outcome measures. Hospitals use these results to compare their performance with those of other hospitals with similar demographic makeup and patient population. Originally housed




and managed by the University of Kansas Medical Center (KUMC) School of Nursing through a contractual agreement with the American Nurses Credentialing Center (ANCC), NDNQI was purchased by Press Ganey Associates in 2014. Technical assis- tance and continuing education are provided by liaisons to ensure that reliable and valid data-collection methods are used by hospital personnel. Th is database provides a wealth of information on a quarterly and annual basis of more than 2,000 facilities in the United States (Press Ganey Associates, n.d.). In addition to hospital indicators, nurse-sensitive indicators for community-based healthcare settings also exist. Th e ability to collect and compare data on nurse-sensitive indicators and the ability to develop new indicators over time enhance the NDNQI initiative and provide APRNs with important informa- tion to help measure, compare, and improve the health and safety of populations.

Standardized Language in Nursing Th e use of standardized language is important in any fi eld to ensure a level of communi- cation that is both consistent and eff ective in ensuring quality outcomes. Specifi cally, in nursing and other health professions, standardized language is critical for patient safety and quality of care. By establishing a uniform nursing language in electronic health records, research, and the development of evidence-based practice, APRNs have a stron- ger foundation to communicate and improve patient outcomes and standards of care. Th e North American Nursing Diagnosis Association (NANDA) was developed in the 1970s to classify and standardize nursing diagnoses. Now referred to as NANDA International or NANDA-I, the nursing diagnoses include a name or label, signs and symptoms or defi ning characteristics, and risk factors associated with the diagnosis. Th e NANDA-I defi nitions and classifi cations have been recently updated to refl ect new trends in nursing healthcare (www.nanda.org). Members of NANDA-I worked with nursing researchers at the University of Iowa to develop the Nursing Interventions Classifi cation (NIC) and the Nursing Outcomes Classifi cation (NOC). NANDA-I, NIC, and NOC, now referred to as NNN, collectively refl ect a standardized way of communicating with defi ned terms within and across various national and international settings (Smith & Craft -Rosenberg, 2010). As APRNs contribute to the body of evidence-based practice and collaborate with others to generate more evidence of eff ective practice, their work may benefi t from reviewing and using the NNN language for diagnoses, nursing interventions, and patient outcomes (Kautz & Van Horn, 2008). It is imperative that APRNs use standardized lan- guage in their research and in their practice so that outcomes can be compared in similar ways with larger databases for evaluation and research purposes.


AHRQ’s National Healthcare Quality Report Since 2003, the AHRQ has partnered with members of the DHHS to report on health- care quality improvement by publishing the National Healthcare Quality and Disparities Report (QDR). Th e intent of this report is to respond to the status of healthcare quality in the United States, identify where improvement is most needed, and describe how the




quality of healthcare that is given to Americans changes over time. Th is report includes more than 250 measures of quality and disparities and uses the Th ree Aims for Improving Healthcare as its framework, which are (a) better care, (b) healthy people/healthy com- munity, and (c) aff ordable care (AHRQ, 2017). Findings from the 2016 report reveal that healthcare for Americans is improving in some areas and worsening in others. Th e QDR examines quality through six priority areas; person-centered care, patient safety, healthy living, eff ective treatment, care coordination, and care aff ordability. Th e priority area of person-centered care saw the largest number of measures improve and the priority area of care aff ordability saw the least. Some areas of care that improved included a decrease in uninsured rates, a decrease in hospital admissions with central venous catheter-related blood infections, and an improvement in provider-patient communication. Examples of areas where the quality of care worsened include a lower percentage of women ages 21 to 65 that received a Pap smear in the last 3 years and an increase in the percentage of children ages 12 to 19 who are obese.

A key fi nding related to disparities is that some disparities narrrowed between 2000 and 2015, but still existed. On many measures, poor and low-income house- holds demonstrated worse care than high-income households. A second key fi nding is in the diff erences in quality-of-care outcomes based on geography, with Southern and Southwestern states, several Western states, and one Midwestern state performing poorly in delivering overall quality care compared to Mideast and Northeast states. Hospital care has been improving since the Centers for Medicare & Medicaid Services (CMS) started reporting on quality measures. Th ese measures can be found on the Hospital Compare website (www.medicare.gov/hospitalcompare/search.html). A third key fi nding is a diff erence in quality of care for Blacks, Hispanics, and Asians compared with Whites among states. Th e report details initiatives to address sur- vey fi ndings, such as Project ECHO and the Language Access Portal (AHRQ, 2017). In addition to the annual QDR report, AHRQ’s website has useful information that APRNs can use to identify and monitor outcomes (www.qualityindicators.ahrq.gov). Other tools, referred to as indicators, can be used by APRNs to identify outcomes or measures of the quality of healthcare. Th e inpatient quality indicators were designed to help hospitals identify possible issues and problems in need of quality improvement by using hospital administrative data to analyze morbidity and mortality rates for spe- cifi c conditions and procedures, hospital- and area-level procedure utilization rates, and number of procedures (for select procedures). In addition to the inpatient quality indicators, other sets of quality indicators are available, including preventive quality indicators, patient safety indicators, and pediatric quality indicators.

Healthy People 2020 Healthy People 2020, released by the DHHS in early December 2010, serves as a blueprint or road map for the United States to achieve health promotion and disease prevention objectives that are designed to improve the health of all Americans. Th e Healthy People initiative started in 1979 when the surgeon general released a report that focused on




promoting health and preventing disease for all Americans. It was followed by Healthy People 2000 in 1989 and, 10 years later, Healthy People 2010. With leadership provided by the DHHS, an appointed advisory committee and numerous public and private groups, local and state policy makers and offi cials, and numerous organizations (voluntary, advocacy, faith-based, and for-profi t businesses), input is solicited regionally, statewide, and nationally to help craft the vision, mission, and overarching goals. Th ese groups and organizations also develop strategies to improve health and prevent disease with the ulti- mate goal of helping Americans live longer and healthier lives. Th e resulting objectives, whether on the county, state, or national level, are intended for use by broad audiences and stakeholders to help motivate, guide, and focus action for a healthier nation.

Compared to previous national health promotion blueprints, the Healthy People 2020 framework emphasizes the importance of a variety of infl uences on health, such as per- sonal (e.g., genetic, biological, psychological), organizational or institutional (e.g., Head Start or employee health programs), environmental (e.g., social and physical), and pol- icy level (e.g., smoking bans in public places, seat belt laws). It moves beyond an indi- vidual-level approach to interventions and guides the creation of policies to promote the social and physical environments that are conducive to health. Another change in the 2020 version is the reorganization of objectives so that they can be retrieved by three broad categories: interventions, determinants, and objectives and information (with a feature for users to be able to retrieve information by local, state, or national level). Some of the 2020 objectives were retained from Healthy People 2010 because they were not met, some objectives were modifi ed, and some were entirely new to Healthy People 2020. Improvements to Healthy People 2020 made it more web user-friendly, such that users can easily retrieve, search, and interact with the database. Hence, APRNs and other users are able to tailor information available from Healthy People 2020 for their specifi c use and needs.

Table 2.3 provides a summary of the Healthy People 2020 initiatives with its vision, mission, goals, foundation health measures, and topic areas. Each topic area has a list of objectives with data sources, baseline, and target measures to achieve. Th e Healthy People website is continually improved to ease access of the data and to make sense of the fi ndings. Features of Healthy People 2020 are additional topic-related clinical rec- ommendations, evidence-based interventions, a program planning tool, eLearning, and other resources and links with consumer health information. Social determinants of health is extensively described, along with related objectives and an interventions and resources webpage. Information about Healthy People 2020 can be found at www .healthypeople.gov.

Healthy People 2030 is under development by the Secretary’s Advisory Committee with considerable input from stakeholders. Its framework is publicly available and includes an updated vision, mission, foundational principles, overarching goals, and plan of action. New objectives are being identifi ed and will be available for public com- ment before its launch in 2020. (For more information on the development of Healthy People 2030 go to www.healthypeople.gov/2020/About-Healthy-People/Development- Healthy-People-2030.)




TABLE 2.3 Vision, Mission, Goals, Foundation Health Measures, and Topic Areas of Healthy People 2020


Mission Healthy People 2020 strives to: ■ Identify nationwide health-improvement priorities ■ Increase public awareness and understanding of the determinants of

health, disease, and disability and the opportunities for progress ■ Provide measurable objectives and goals that can be used at the

national, state, and local levels ■ Engage multiple sectors to take actions to strengthen policies and

improve practices that are driven by the best available evidence and knowledge

■ Identify critical research, evaluation, and data-collection needs

Overarching goals Attain high-quality, longer lives free of preventable disease, disability, injury, and premature death

■ Achieve health equity, eliminate disparities, and improve the health of all groups

■ Create social and physical environments that promote good health for all

■ Promote quality of life, healthy development, and healthy behaviors across all life stages

Foundation Health Measures

General health status ■ Life expectancy ■ Healthy life expectancy ■ Physical and mental unhealthy days ■ Limitation of activity ■ Chronic disease prevalence ■ International comparison (where available)

Disparities and inequity Disparities/inequity to be assessed by the following: ■ Race/ethnicity ■ Gender ■ Socioeconomic status ■ Disability status ■ LGBT status ■ Geography

Social determinants of health

Determinants can include the following: ■ Social and economic factors ■ Natural and built environments ■ Policies and programs

Health-related quality of life and well-being

Well-being/satisfaction ■ Physical, mental, and social health-related quality of life ■ Participation in common activities category

Healthy People 2020 Topic Areas

■ Access to health services ■ Adolescent health ■ Arthritis, osteoporosis, and chronic back conditions ■ Blood disorders and blood safety ■ Cancer ■ Chronic kidney diseases ■ Dementias, including Alzheimer’s disease

(continued )




TABLE 2.3 Vision, Mission, Goals, Foundation Health Measures, and Topic Areas of Healthy People 2020 (continued )

■ Diabetes ■ Disability and health ■ Early and middle childhood ■ Educational and community-based programs ■ Environmental health ■ Family planning ■ Food safety ■ Genomics ■ Global health ■ Healthcare-associated infections ■ Health communication and health information technology ■ Health-related quality of life and well-being ■ Hearing and other sensory or communication disorders ■ Heart disease and stroke ■ HIV ■ Immunization and infectious diseases ■ Injury and violence prevention ■ LGBT health ■ Maternal, infant, and child health ■ Medical product safety ■ Mental health and mental disorders ■ Nutrition and weight status ■ Occupational safety and health ■ Older adults ■ Oral health ■ Physical activity ■ Preparedness ■ Public health infrastructure ■ Respiratory diseases ■ Sexually transmitted diseases ■ Sleep health ■ Social determinants of health ■ Substance abuse ■ Tobacco use ■ Vision

LGBT, lesbian, gay, bisexual, and transgender

Source: Adapted from the U.S. Department of Health and Human Services. (2018b). Healthy People 2020: Disparities. Retrieved from https://www.healthypeople.gov/2020/about/foundation-health-measures/Disparities

Health Disparities

Healthy People 2010 included two overarching goals: to increase years of healthy living and to eliminate health disparities. Th ese goals were retained for Healthy People 2020 as evi- dence continues to mount that the United States has issues related to equity of healthcare access, quality of care, and health status. Th e midcourse review of Healthy People 2020 as well as many governmental and nongovernmental reports and independent studies docu- ment these disparities (AHRQ, 2017; Armstrong et al., 2018; IOM, 2015; DHHS, 2018b).

By monitoring potential diff erences among groups, health professionals will have the tools to recognize why and where population disparities are occurring. Th is in turn will (one hopes) lead to creative strategies to reduce health disparities and improve equity in health and the delivery of healthcare.




Th ere are numerous dimensions of disparities or diff erences related to health that can adversely aff ect groups of people because of specifi c characteristics or obstacles. It is widely recognized now that the social determinants of health, such as housing, education, access to public transportation, access to safe water, access to fresh food, and the built environment, are all related to a population’s health. In addition to ethnicity, other characteristics also contribute to the presence of disparities or the achievement of good health such as gender; sexual orientation; geographic location; working environment; cognitive, sensory, or physical disability; and socioeconomic status. Th e outcomes identifi ed in the objectives of Healthy People 2020 are intended to improve the health of all groups of people and bridge those gaps. Healthy People 2020 and Healthy People 2030 will assess health disparities in U.S. populations by tracking morbidity and mortality outcomes in relation to factors found to be associ- ated with disparities.

Online Resources APRNs have numerous online resources they can access to improve quality and timely access to healthcare, and decrease health disparities. Kleinpell and Kapu (2017) describe 13 resources for quality measures that are relevant to APRN practice, such as the Nurse Practitioner Outcomes Toolkit through the American Association of Nurse Practitioners (AANP). Th e National Partnership for Action (NPA) to End Health Disparities (minori- tyhealth.hhs.gov/npa/) was started by the Offi ce of Minority Health to mobilize individ- uals and groups to work to improve quality and eliminate health disparities. Established in 1988, the Offi ce of Minority Health and Health Disparities (OMHD) is housed within the CDC. Resources available from this offi ce can be used by APRNs to determine how minority populations compare with the U.S. population as a whole. Such disparities are complicated to analyze and explain as they go beyond diff erences in genetics, biologi- cal characteristics, and health behaviors (microlevel properties). Racist and discrimina- tory behaviors and policies, cultural barriers, lack of access to care, and interaction with the environment (macrolevel properties) play a major role in creating the problem (see Chapter 11, Challenges in Program Implementation).

National Quality Partners™ (www.qualityforum.org/National_Quality_Partners. aspx) includes key private and public stakeholders who have agreed to work on major health priorities of patients and families, palliative and end-of-life care, care coordination, patient safety, and population health. Another excellent resource can be found on the site of the Association of American Medical Colleges (AAMC). It has compiled the report Th e State of Health Equity Research: Closing Knowledge Gaps to Address Inequities (avail- able at www.aamc.org/initiatives/research/healthequity/402654/closingknowledgegaps. html). Th e AAMC and AcademyHealth worked together to review all U.S. health dis- parities–focused health services research funded during the 5-year period between 2007 and 2011. Th eir purpose was to determine where such research is taking place and who is funding it, identify gaps in populations and health outcomes, and assess trends in the funding of solution-focused health equity research (AAMC, 2014).




Established in 2000 and housed within the National Institute of Health (NIH), is the National Institute on Minority Health and Health Disparities (NIMHD). It sup- ports researchers who address issues of health inequity. Its website highlights success- ful initiatives and programs (www.nimhd.nih.gov/programs/edu-training), such as the Transdisciplinary Collaborative Center for Health Disparities Research Program, which funds regional coalitions. APRNs can contact the principal investigators or other research staff to obtain information, to explore collaborative endeavors with researchers, to participate with community-based participatory research, to assist with translational research studies, and to share expertise with the aim of decreasing health disparities among vulnerable populations.

Also within the NIH, is the National Cancer Institute’s Division of Cancer Control and Population Sciences (DCCPS), which has a variety of resources available, includ- ing funding opportunities, reports and health surveys, data sets, tool kits for research projects, and cross-cutting areas such as health disparities, patient-centered com- munication, and care coordination (cancercontrol.cancer.gov). Th ere is also a health disparities calculator (HD*Calc). HD*Calc is statistical soft ware that can be down- loaded and used to generate and calculate 11 disparity measurements (seer.cancer .gov/hdcalc).

Examples of Health Disparities Even a cursory review of reports and studies available through government agencies and various other organizations as well as peer-reviewed journals reveals many examples of healthcare disparities in the United States. Th e following are just a few examples.

Despite advances in diabetes prevention and treatment, American Indian/Alaska Natives (AI/AN) have a higher age-adjusted percentage of people with diabetes mellitus (as compared to Whites), followed by non-Hispanic Blacks and Hispanics (CDC, 2017; Subica, Agarwal, Sullivan, & Link, 2017). African Americans 65 years and younger continue to have a higher death rate than Whites (851.9 per 100,000 compared to 735.0 per 100,000) for all-cause mortality (CDC, 2017). Gu, Yue, Desai, and Argulian (2017) found poorer hypertension control in African Americans and Hispanics compared to Whites in analyzing data from 2003 to 2012. Even though the infant mortality rate in the United States has declined in recent years, and even when controlling for socioeconomic factors, the African American infant mortality rate is more than double that of rates for Whites or Hispanics (El-Sayed et al., 2015; Gregory, Drake, & Martin, 2018).

Examples of studies that explore gender inequities include a literature review of 11 studies on acute coronary syndromes in older adults that found that women had a higher in-hospital mortality rate than men (Gillis, Arslanian-Engoren, & Struble, 2014). Researchers in the area of health literacy have found disparities based on the lit- eracy level of the client and the ability of the provider to facilitate patient understand- ing of treatment and management of a disease. Studies on the impact of health literacy on health outcomes have found that there is poor access to care, lower health-related




quality of life, and lower health knowledge among people with low health litreracy (Cajita, Cajita, & Hae-Ra, 2016; Hälleberg, Nilsson, Dahlberg, & Jaensson, 2018; Levy & Janke, 2016).

Evidence-based nursing interventions can be successful in addressing such dispar- ities (AHRQ, 2017; Schneiderman et al., 2014). One research team successfully inte- grated a diabetes education program within a faith-based framework for use in African American churches to support health behavior changes (Whitney et al., 2017). To address the higher prevalence of diabetes in Hispanic/Latino populations, researchers in the Diabetes Among Latinos Best Practices Trial (DIALBEST) randomly assigned a group of Latinos with type 2 diabetes to community-health workers (CHWs). Th ey received culturally and linguistically appropriate services (CLAS) through education sessions on nutrition, blood glucose monitoring, medication adherence and other top- ics. Th e researchers found that subjects in the CHWs group had improved HbA1C at 3, 6, 12, and 18 months post intervention, but there was no change in serum lipid levels, hypertension, or weight (Pérez-Escamilla et al., 2015). Similar fi ndings were found across 53 studies in a scoping review on CHWs’ role in diabetes management (Egbujie et al., 2018). Policies are being written and adopted at all levels of government to address healthcare disparities. Some of these policies can be reviewed on the National Conference of State Legislatures (NCSL) website at www.ncsl.org/research/health /population-groups/health-disparities.aspx.

In summary, health disparities are deplorable, and eff ective strategies to reverse this trend are urgently needed. Although much research has been done to better under- stand healthcare disparities, researchers suggest that a multidimensional approach is needed; a history of institutionalized racism and individual racism that is embedded in every aspect of life of ethnic minorities must be recognized and properly addressed (Hardeman, Murphy, Karbeah, & Kozhimannil, 2018). For the purpose of eliminating health disparities, the National Stakeholder Strategy for Achieving Health Equity, a product of the National Partnership for Action, established guidelines for the develop- ment and continuous assessment of the impact of policies and programs to improve the health of vulnerable populations and achieve health equity. Healthcare workers need cultural competency training, communication needs to improve between providers and patients, strategies to improve community relations are needed, and adherence to non- discriminatory health policies is also necessary to bridge the gaps in providing equity and quality care to eliminate health disparities. (See report at www.minorityhealth.hhs. gov/npa/fi les/Plans/NSS/CompleteNSS.pdf.)

It is critical that APRNs advocate for the elimination of health disparities, as this work is of vital importance and urgently needed. And from an ethical standpoint, working to eliminate health disparities is the right thing to do. By recognizing health disparities and developing a better understanding of how process and status impact the outcome of interest, APRNs are better prepared to develop eff ective interventions to eliminate or reduce health disparities. Such strategies may include advocating better health insur- ance coverage for poor and immigrant populations; incorporating social determinants of health into a broader framework of care delivery; assessing the interaction among




social environments, genetics, and population health; encouraging minority participa- tion in research studies with community-based participatory research and specifi cally with practice-based research networks; using linguistically and culturally appropriate communication and written handouts; promoting and facilitating community partner- ships; and implementing strategies to graduate a diverse nursing workforce (Gates, 2018; Quinones, Talavera, Castaneda, & Saha, 2015; Sentell et al., 2014; Williams & Purdie- Vaughns, 2016).

APRNs have successfully tested interventions to decrease health disparities, and by careful and thorough review of the current literature and resources available, they have the tools to develop additional eff ective and culturally sensitive interventions and to identify outcomes in order to achieve better and more equitable health outcomes.


APRNs have a critical role in improving population health by intervening at every level from the individual to the community. Before an eff ective intervention can take place, it is imperative that realistic and measurable outcomes are fi rst identifi ed and defi ned so that they can be measured and analyzed. Outcomes may be classifi ed by the bene- fi ciary of the health intervention and by type such as care, patient, or performance-re- lated, and also by time frame of achievement. Outcomes may also be categorized by clinical or disease-specifi c outcomes, function, cost-eff ectiveness, self-perception health status, and satisfaction outcomes. A commonly used framework to classify outcomes is Donabedian’s framework of structure, process, and outcomes. Th is framework has been used for describing a community’s health as well as for classifying nurse-sensitive indica- tors. Outcomes are an important part of the standardized language that nursing leaders and researchers continue to refi ne and operationalize as a means of improving healthcare.

Th e national healthcare objectives in Healthy People 2020 and updated in Healthy People 2030 provide a blueprint of health promotion and disease prevention objectives that are designed to improve the health of all people in the United States. Building on the goal of Healthy People, APRNs can use these web-based resources to identify outcomes and compare them with national and state data that can be further analyzed by stratify- ing for a population’s ethnicity, race, income, education, and/or gender. Federal agencies, such as the AHRQ, the CDC Offi ce of Minority Health and Health Disparities, and the NIH’s NIMHD, provide ready access to a plethora of information and resources that can be used to identify and defi ne outcomes.

APRNs have a tremendous opportunity to access and use available data to contrib- ute to the current body of knowledge that forms the basis for evidence-based practice. By selecting and using well-defi ned indicators and comparing those to national norms, APRNs can provide important information on trends or patterns of quality of care. Th is information has the potential to stimulate the development of creative and innovative programs or interventions to improve health outcomes. Evidence of improved outcomes will help APRNs to justify and advocate for change through policy, practice, and research, with the ultimate goal of providing quality care for all.





Exercise 2.1 An APRN is working in a community clinic providing postnatal care to a diverse population of families. Th e APRN knows that there is an ethnic disparity for infant mortality.

1. Where could the APRN go to fi nd information on infant mortality disparities? 2. What is the ethnic disparity in infant mortality? 3. What social determinants of health are associated with infant mortality? 4. How might an APRN participate in local eff orts to reduce infant mortality

rates on a population level?

Exercise 2.2 An APRN who is interested in reducing opiate-related overdoses in high schools develops an online training program to teach all school employees to administer Narcan® (naxolone).

1. What are related Leading Health Indicators found in Healthy People 2020? 2. In examining the Community Guide topics, which ones are most relevant to

this scenario? 3. What outcomes might the APRN monitor for eff ectiveness of the program? 4. What other population level strategies could the APRN implement to address

the issue?

Exercise 2.3 APRNs should not only recognize health disparities, they should also make it part of their practice to develop strategies to reduce or eliminate them. Review infor- mation from Healthy People 2020 and the CDC Offi ce of Minority Health and Health Disparities websites.

1. What health disparities can you fi nd that are relevant to your county or state? 2. What culturally and linguistically appropriate services (CLAS) interventions

could an APRN implement in his or her specifi c practice that are consistent with the National CLAS Standards?

3. What outcomes could an APRN monitor related to the health disparities/state issue?

4. Which objectives in Healthy People 2020/2030 could help this eff ort?

Exercise 2.4 Diabetes aff ects a growing number of Americans. An APRN working in a local hospital is part of a collaborative of community agencies strategically addressing diabetes from a community perspective.

1. What social determinants of health should the community look at in relation to risk or incidence of diabetes?

2. What resources could the APRN use to identify diff erent outcomes related to diabetes?




3. What outcomes related to diabetes are of most interest to community members?

4. Using the AHRQ’s Healthcare Quality and Disparities Report Data Query (nhqrnet.ahrq.gov/inhqrdr/data/submit), what related national and state level data are available to the APRN?

REFERENCES Agency for Healthcare Research and Quality. (2017). National healthcare quality and disparities reports.

Retrieved from https://www.ahrq.gov/research/fi ndings/nhqrdr/index.html American Association of Colleges of Nursing. (2004). AACN position statement on the practice doctorate in

nursing. Washington, DC: Author. American Association of Colleges of Nursing. (2006). Th e essentials of doctoral education for advanced nurs-

ing practice. Retrieved from http://www.aacn.nche.edu/DNP/pdf/Essentials.pdf Anderson, E. T., & McFarlane, J. (2015). Community as partner: Th eory and practice in nursing (7th ed.).

New York, NY: Lippincott Williams & Wilkins. Armstrong, S., Wong, C. A., Perrin, E., Page, S., Sibley, L., & Skinner, A. (2018). Association of physical

activity with income, race/ethnicity, and sex among adolescents and young adults in the United States: Findings from the National Health and Nutrition Examination Survey, 200–2016. JAMA Pediatrics, 172(8), 732–740. doi:10.1001/jamapediatrics.2018.1273

Association of American Medical Colleges. (2014). Th e state of health equity research: Closing knowledge gaps to address inequities. Retrieved from https://www.aamc.org/initiatives/research/healthequity/402654/ closingknowledgegaps.html

Bigbee, J. L., & Issel, L. M. (2012). Conceptual models for population-focused public health nursing interventions and outcomes: Th e state of the art. Public Health Nursing, 29(4), 370–379. doi:10.1111/ j.1525-1446.2011.01006.x

Boothe, V. L., Sinha, D., Bohm, M., & Yoon, P. W. (2013). Community health assessment for population health improvement; resource of most frequently recommended health outcomes and determinants. CDC Stacks Public Health Publications. Retrieved from http://stacks.cdc.gov/view/cdc/20707

Braverman, P., Arkin, E., Orleans, T., Proctor, D., & Plough, A. (2017). What is health equity? And what dif- ference does a defi nition make? Princeton, NJ: Robert Wood Johnson Foundation. Retrieved from https:// www.rwjf.org/content/dam/farm/reports/issue_briefs/2017/rwjf437393

Cajita, M. I., Cajita, T. R., & Hae-Ra, H. (2016). Health literacy and heart failure. Journal of Cardiovascular Nursing, 31(2), 121–130. doi:10.1097/JCN.0000000000000229

Centers for Disease Control and Prevention. (2014). Behavioral Risk Factor Surveillance System (BRFSS). Retrieved from http://www.cdc.gov/brfss/about/about_brfss.htm

Centers for Disease Control and Prevention. (2017). National health care surveys. Retrieved from http:// www.cdc.gov/nchs/dhcs.htm

Centers for Disease Control and Prevention. (2018). 2019 questionnaire redesign. Retrieved from https:// www.cdc.gov/nchs/nhis/2019_quest_redesign.htm

Chang, A., Patberg, E., Cueto, V., Hua, L., Singh, B., Kenya, S., & . . . Carrasquillo, O. (2018). Community health workers, access to care, and service utilization among Florida Latinos: A randomized controlled trial. American Journal of Public Health, 108(9), 1249–1251. doi:10.2105/AJPH.2018.304542

DeGuzman, P. B., & Kulbok, P. A. (2012). Changing health outcomes of vulnerable populations through nursing’s infl uence on neighborhood built environment: A framework for nursing research. Journal of Nursing Scholarship, 44(4), 341–348. doi:10.1111/ j.1547-5069.2012.01470.x

Donabedian, A. (1980). Explorations in quality assessment and monitoring. Ann Arbor, MI: Health Administration Press.

Donabedian, A. (1982). Th e criteria and standards of quality. Ann Arbor, MI: Health Administration Press. Egbujie, B. A., Delobell, P. A., Levitt, N., Puone, T., Sanders, D., & van Wyk, B. (2018). Role of commu-

nity health workers in type 2 diabetes mellitus self-management: a scoping review. PLOS One, 13(6), e0198424. doi:10.1371/journal.pone.0198424




El‐Sayed, A. M., Paczkowski, M., Rutherford, C. G., Keyes, K. M., & Galea, S. (2015). Social environments, genetics, and Black-White disparities in infant mortality. Paediatric & Perinatal Epidemiology, 29(6), 546–551. doi:10.1111/ppe.12227

Fee, E., & Garofalo, M. E. (2010). Florence Nightingale and the Crimean war. American Journal of Public Health, 100(9), 1591. doi:10.2105/AJPH.2009.188607

Friis, R. H., & Sellers, T. A. (2015). Epidemiology for public health practice (5th ed.). Boston, MA: Jones & Bartlett.

Galea, S., & Link, B. G. (2013). Six paths for the future of social epidemiology. American Journal of Epidemiology, 178(6), 843–849. doi:10.1093/aje/kwt148

Gates, S. A. (2018). What works in promoting and maintaining diversity in nursing programs. Nursing Forum, 53(2), 190–196. doi:10.1111/nuf.12242

Gibson, M. E., & Th atcher, E. J. (2016). Community as client: Assessment and analysis. In M. Stanhope & J. Lancaster (Eds.), Public health nursing: Population-centered health care in the community (9th ed., pp. 396–421). St. Louis, MO: Mosby.

Gillis, N. K., Arslanian-Engoren, C., & Struble, L. M. (2014). Acute coronary syndromes in older adults: A review of the literature. Journal of Emergency Nursing, 40(3), 270–275. doi:10.1016/j.jen.2013.03.003

Gregory, E. C. W., Drake, P., & Martin, J. A. (2018). Lack of change in perinatal mortality in the United States, 2014-2016. NCHS Data Brief, No 316. Retrieved from https://www.cdc.gov/nchs/data/databriefs/db316 .pdf

Griffi ths, P. (1995). Progress in measuring nursing outcomes. Journal of Advanced Nursing, 21(6), 1092– 1100. doi:10.1046/j.1365-2648.1995.21061092.x

Gu, A., Yue, Y., Desai, R. P., & Argulian, E. (2017). Racial and ethnic diff erences in antihypertensive med- ication use and blood pressure control among U.S. adults with hypertension: Th e National Health and Nutrition Examination Survey, 2003 to 2012. Circulation: Cardiovascular Quality and Outcomes, 10(1), doi:10.1161/circoutcomes.116.003166

Hälleberg, N. M., Nilsson, U., Dahlberg, K., & Jaensson, M. (2018). Association between functional health literacy and postoperative recovery, health care contacts, and health-related quality of life among patients undergoing day surgery: Secondary analysis of a randomized clinical trial. JAMA Surgery, 153(8), 738– 745. doi:10.1001/jamasurg.2018.0672

Hardeman, R. R., Murphy, K. A., Karbeah, J., & Kozhimannil, K. B. (2018). Naming institutionalized racism in the public health literature: A systematic literature review. Public Health Reports, 133(3), 240–249. doi:10.1177/0033354918760574

Hill, M. (1999). Outcomes measurement requires nursing to shift to outcome-based practice. Nursing Administration Quarterly, 24(1), 1–16. doi:10.1097/00006216-199910000-00003

Institute for Healthcare Improvement. (2018). Triple Aim for Populations. Retrieved from http://www.ihi .org/Topics/TripleAim/Pages/default.aspx

Institute of Medicine. (2015). Vital signs: Core metrics for health and health care progress. Washington, DC: National Academies Press.

Jones, T. (2016). Outcome measurement in nursing: Imperatives, ideals, history, and challenges. Online Journal of Issues in Nursing, 21(2), 1. doi:10.3912/OJIN.Vol21No02Man01

Kapu, A. N., Sicoutris, C., Broyhill, B. S., D’Agostino, R., & Kleinpell, R. M. (2017). Measuring outcomes in advanced practice nursing: Practice-specifi c quality metrics. In R. Kleinpell (Ed.), Outcome assessment in advanced practice nursing (4th ed., pp. 1-18). New York, NY: Springer Publishing Company.

Kautz, D. D., & Van Horn, E. R. (2008). An exemplar of the use of NNN language in developing evi- dence-based practice guidelines. International Journal of Nursing Terminologies and Classifi cations, 19(1), 14–19. doi:10.1111/j.1744-618X.2007.00074.x

Kleinpell, R. M. (2007). APRNs: Invisible champions? Nursing Management, 38(5), 18–22. doi:10.1097/01. LPN.0000269815.74178.de

Kleinpell, R., & Gawlinski, A. (2005). Assessing outcomes in advanced practice nursing practice: Th e use of quality indicators and evidence-based practice. AACN Clinical Issues, 16(1), 43–57. doi:10.1097/00044067-200501000-00006

Kleinpell, R., & Kapu, A. N. (2017). Quality measures for nurse practitioner practice evaluation. Journal of Th e American Association Of Nurse Practitioners, 29(8), 446–451. doi:10.1002/2327-6924.12474

Lang, N. M., & Marek, K. D. (1991). Th e policy and politics of patient outcomes. Journal of Nursing Quality Assurance, 5(2), 7–12.




Levy, H., & Janke, A. (2016). Health literacy and access to care. Journal of Health Communication, 21(Suppl 1), 43–50. doi:10.1080/10810730.2015.1131776

Loyo, H. K., Batcher, C., Wile, K., Huang, P., Orenstein, D., & Milstein, B. (2012). From model to action: Using a system dynamics model of chronic disease risks to align community action. Health Promotion Practice, 14(1), 53–61. doi:10.1177/1524839910390305

Macha, K., & McDonough, P. (Eds.). (2012). Epidemiology for advanced nursing practice. Sudbury, MA: Jones & Bartlett.

McDonald, C. C., Brawner, B. M., Fargo, J., Swope, J., & Sommers, M. S. (2017). Development of a theo- retically grounded, web-based intervention to reduce adolescent driver attention. Th e Journal of School Nursing, 34(4), 270–280. doi:10.1177/1059840517711157

McCullough, J. M., & Leider, J. P. (2017). Associations between county wealth, health and social service spending, and health outcomes. American Journal of Preventive Medicine, 53(5), 592–598. doi:10.1016/j. amepre.2017.05.005

Minkler, M., & Wallerstein, N. (2012). Improving health through community organization and community building: Perspectives from health education and social work. In M. Minkler (Ed.), Community orga- nizing and community building for health and welfare (3rd ed., pp. 37–58). New Brunswick, NJ: Rutgers University Press.

Montalvo, I. (2007). Th e National Database of Nursing Quality Indicators® (NDNQI®). Online Journal of Issues in Nursing, 12(3), doi:10.3912/OJIN.Vol12No03Man02

Nelson, E. C., Batalden, P. B., Plume, S. K., & Mohr, J. J. (1996). Improving health care, Part 2: A clinical improvement worksheet and users’ manual. Joint Commission Journal on Quality Improvement, 22(8), 531–548. doi:10.1016/s1070-3241(16)30254-1

Newhouse, R., Barksdale, D. J., & Miller, J. A. (2015). Th e Patient-Centered Outcomes Research Institute: Research done diff erently. Nursing Research, 64(1), 72–77. doi:10.1097/NNR.0000000000000070

Parrish, R. G. (2010). Measuring population health outcomes. Preventing Chronic Disease, 7(4), A71. Retrieved from https://www.cdc.gov/pcd/issues/2010/jul/10_0005.htm

Patrician, P. A., Dolansky, M. A., Pair, V., Bates, M., Moore, S. M., Splaine, M., & Gilman, S. C. (2013). Th e Veterans Aff airs National Quality Scholars program: A model for interprofessional education in quality and safety. Journal Nursing Care Quality, 28(1), 24–32. doi:10.1097/NCQ.0b013e3182678f41

Payán, D. D., Sloane, D. C., Illum, J., Vargas, R. B., Lee, D., Galloway-Gilliam, L., & Lewis, L. B. (2017). Catalyzing implementation of evidence-based interventions in safety net settings: A clinical–community partner- ship in south Los Angeles. Health Promotion Practice, 18(4), 586–597. doi:10.1177/1524839917705418

Penman-Aguilar, A., Bouye, K., & Liburd, L. (2016). Strategies for reducing health disparities – Selected CDC-sponsored interventions, United States, 2016. Morbidity and Mortality Weekly Report, 65(1), 2–3. doi:10.15585/mmwr.su6501a2

Pérez-Escamilla, R., Damio, G., Chhabra, J., Fernandez, M. L., Segura-Pérez, S., Vega-López, S., . . . D’Agostino, D. (2015). Impact of a community health workers-led structured program on blood glu- cose control among Latinos with type 2 diabetes: Th e DIALBEST trial. Diabetes Care, 38(2), 197–205. doi:10.2337/dc14-0327

Press Ganey Associates. (n.d.). National Database of Nursing Quality Indicators® (NDNQI®). Retrieved from http://www.nursingquality.org/#intro

Quinones, A. R., Talavera, G. A., Castaneda, S. F., & Saha, S. (2015). Interventions that reach into communi- ties – promising directions for reducing racial and ethnic disparities in healthcare. Journal of Racial and Ethnic Health Disparities, 2(3), 336–40. doi:10.1007/s40615-014-0078-3

Rich, K. A. (2015). Evaluating outcomes of innovations. In N. A. Schmidt & J. M. Brown (Eds.), Evidence- based practice: Appraisal and application of research (3rd ed., pp. 484–503). Sudbury, MA; Jones & Bartlett.

Rothwell, C. J. (2015). About the national center for health statistics. Retrieved from https://www.cdc.gov/ nchs/about/index.htm

Schiller, J. S., Clarke, T. C., & Norris, T. (2018). Early release of selected estimates based on data from the January-September 2017 National Health Interview Survey. Retrieved from https://www.cdc.gov/nchs/ data/nhis/earlyrelease/EarlyRelease201803.pdf

Schneiderman, N., Llabre, M., Cowie, C. C., Barnhart, J., Carnethon, M., Gallo, L. C., . . . Avilés-Santa, M. L. (2014). Prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic community health study/study of Latinos (HCHS/SOL). Diabetes Care, 37(8), 2233–2239. doi:10.2337/ dc13-2939




Sentell, T., Zhang, W., Davis, J., Baker, K. K., & Braun, K. L. (2014). Th e infl uence of community and indi- vidual health literacy on self-reported health status. Journal of General Internal Medicine, 29(2), 298–304. doi:10.1007/s11606-013-2638-3

Smith, K. J., & Craft -Rosenberg, M. (2010). Using NANDA, NIC, and NOC in an undergraduate nursing practicum. Nurse Educator, 35(4), 162–166. doi:10.1097/NNE.0b013 e3181e33953

Subica, A. M., Agarwal, N., Sullivan, G., & Link, B. G. (2017). Obesity and associated health disparities among understudied multiracial, Pacifi c Islander, and American Indian adults. Obesity, 25(12), 2128– 2136. doi:10.1002/oby.21954

Unni, P., Estrade, C. M., Chung, D. H., Riley, E. B., Worsely-Hynd, L., & Stinson, N. (2017). A multiyear assessment of a hospital program to promote teen motor vehicle safety. Th e Journal of Trauma and Acute Care Surgery, 83(5S), S190–S196. doi:10.1097/TA.0000000000001521

U.S. Department of Health and Human Services. (2018a). Healthy People 2020. Retrieved from http://www .healthypeople.gov

U.S. Department of Health and Human Services. (2018b). Healthy People 2020: Disparities. Retrieved from https://www.healthypeople.gov/2020/about/foundation-health-measures/Disparities

U.S. Department of Health and Human Services, Community Preventive Services Task Force (2019). Th e community guide. Retrieved from https://www.thecommunityguide.org

U.S. National Library of Medicine. (2018). About partners in information access for the public health workforce. Retrieved from https://www.nlm.nih.gov/nichsr/partners.html?_ga=2.260215309 .129585028.1534454264-809822319.1534454264

van Maanen, H. M. T. (1979). Perspectives and problems on quality of nursing care: An overview of con- tributions from North America and recent developments in Europe. Journal of Advanced Nursing, 4(4), 377–389. doi:10.1111/j.1365-2648.1979.tb00872.x

Williams, A. F. (2017). Graduated driving licensing (GDL) in the United States in 2016: A literature review and commentary. Journal of Safety Research, 63, 29–41. doi:10.1016/j.jsr.2017.08.010

Williams, D. R., & Purdie-Vaughns, V. (2016). Needed interventions to reduce racial/ethnic disparities in health. Journal of Health Politics, Policy and Law, 41(4), 627–651. doi:10.1215/03616878-3620857

Whitney, E., Kindred, E., Pratt, A., O’Neal, Y., Harrison, R. C. P., & Peek, M. E. (2017). Culturally tailor- ing a patient empowerment and diabetes education curriculum for the African-American church. Th e Diabetes Educator, 43(5), 441–448. doi:10.1177/0145721717725280

Weinstein, J. N., Geller, A., Negussie, Y., & Baciu, A. (2017). Communities in action: Pathways to health equity. Washington (DC): Th e National Academies Press. Retrieved from https://www.ncbi.nlm.nih.gov/ books/NBK425844

World Health Organization. (2011). Rio political declaration on social determinants of health. Retrieved from http://www.who.int/sdhconference/declaration/Rio_political_declaration.pdf?ua=1

Xiao, S., Widger, K., Tourangeau, A., & Berta, W. (2017). Nursing process health care indicators. Journal of Nursing Care Quality, 32(1), 32–39. doi:10.1097/ncq.0000000000000207

Zenzano, T., Allan, J. D., Bigley, M. B., Bushardt, R. L., Garr, D. R., Johnson, K., . . . Stanley, J. M. (2011). Th e roles of healthcare professionals in implementing clinical prevention and population health. American Journal of Preventative Medicine, 40(2), 261–267. doi:10.1016/j.amepre.2010.10.023

INTERNET RESOURCES Agency for Healthcare Research and Quality (AHRQ): www.qualityindicators.ahrq.gov Association of American Medical Colleges (AAMC) “Th e State of Health Equity Research: Closing

Knowledge Gaps to Address Inequities”: www.aamc.org/initiatives/research/healthequity/402654/closing knowledgegaps.html

CMS, “Hospital Compare”: https://www.medicare.gov/hospitalcompare/search.html County Health Rankings and Roadmaps: http://www.countyhealthrankings.org Healthy People 2020: https://www.healthypeople.gov Healthy People 2030 Development Plan: https://www.healthypeople.gov/2020/About-Healthy-People/

Development-Healthy-People-2030 Institute for Healthcare Improvement/Quality Improvement Essentials Toolkit: http://www.ihi.org/





NANDA International: www.NANDA.org National Cancer Institute, Division of Cancer Control & Population Sciences: http://cancercontrol.cancer.gov National Cancer Institute, Health Disparities Calculator: http://seer.cancer.gov/hdcalc National Center for Health Statistics (NCHS): http://www.cdc.gov/nchs National Center for Health Statistics / National Vital Statistics Program: www.cdc.gov/nchs/nvss National Center for Statistics and Analysis (NCSA) Motor Vehicle Traffi c Crash Data Resource Page: https://

crashstats.nhtsa.dot.gov National Conference of State Legislature (NCSL): http://www.ncsl.org/research/health/population-groups/

health-disparities.aspx National Health and Nutrition Examination Survey (NHNES): www.cdc.gov/nchs/nhanes.htm National Quality Partners’: https://www.qualityforum.org/National_Quality_Partners.aspx National Stakeholder Strategy for Achieving Health Equity: https://www.minorityhealth.hhs.gov/npa/fi les/

Plans/NSS/CompleteNSS.pdf NIH, National Institute on Minority Health and Health Disparities: https://www.nimhd.nih.gov/programs/

edu-training Th e Behavioral Risk Factor Surveillance System (BRFSS): http://www.cdc.gov/brfss Th e Community Guide: www.thecommunityguide.org Th e National Healthcare Surveys: www.cdc.gov/nchs/dhcs Th e National Partnership for Action (NPA) to End Health Disparities: https://minorityhealth.hhs.gov/npa Trust for America’s Health: https://www.tfah.org







Evidence-based practice as it relates to population-based nursing combines clinical practice and public health through the use of population health sciences in clinical practice (Heller & Page, 2002). Epidemiology is the science of public health. It is concerned with the study of the factors determining and infl uencing the frequency and distribution of disease, injury, and other health-related events and their causes (Gordis, 2014). In addition to epidemiology, an understanding of other scientifi c disciplines, such as biology and biostatistics, is also important for identifying asso- ciations and determining causation when looking at exposures and outcomes as they relate to population health.

Population-based care focuses on populations at risk, analysis of aggregate data, evaluation of demographic factors, and recognition of health disparities. It is con- cerned with the patterns of delivery of care and outcome measurements at the popula- tion or subpopulation level. Th e purpose of this chapter is to provide readers with an understanding of the natural history of disease and the approaches that are integral for the prevention of disease. It addresses the Doctor of Nursing Practice competencies specifi ed in Essential III: Clinical Scholarship and Analytical Methods for Evidence- Based Practice (American Association of Colleges of Nursing [AACN], 2006). We introduce basic concepts that are necessary to understand how to measure disease outcomes and select study designs that are best suited for population-based research. Emphasis is placed on measuring disease occurrence with a fundamental discussion of how to calculate incidence, prevalence, and mortality rates. Successful advanced




practice nursing in population health depends upon the ability to recognize the diff er- ence between the individual and population approaches to the collection and use of data and the ability to assess needs and evaluate outcomes at the population level. Concepts surrounding survival data are also discussed along with strategies to guide advanced practice registered nurses (APRNs) on how to calculate and interpret survival data.


Th e natural history of disease refers to the progression of a disease from its preclinical state (prior to symptoms) to its clinical state (from onset of symptoms to cure, control, disability, or death). Disease is not something that occurs suddenly, but rather it is a multifactorial process that is dynamic and occurs over time. It evolves and changes and is sometimes initiated by events that take place years, even decades, before symptoms fi rst appear. Many diseases have a natural life history that can extend over a very long period of time. Th e natural history of disease is described in stages. Understanding the diff erent stages allows for a better understanding of the approach to the prevention and control of disease.

Stage of Susceptibility Th e stage of susceptibility refers to the time prior to disease development. In the pres- ence of certain risk factors, genetics, or environment, disease may develop and the severity can vary among individuals. Risk factors are those factors that are associated with an increased likelihood of disease developing over time. Th e idea that individuals could modify “risk factors” tied to heart disease, stroke, and other diseases is one of the key fi ndings of the Framingham Heart Study (National Heart, Lung, and Blood Institute and Boston University, 2018). Started in 1948 and still in operation, this study is one of the most important population studies ever carried out in the United States. Before Framingham, for example, most healthcare providers believed that atherosclerosis was an inevitable part of the aging process. Although not all risk factors are amenable to change (e.g., genetic factors), the identifi cation of risk factors is important and funda- mental to disease prevention.

Preclinical Stage of Disease During the preclinical phase, the disease process has begun but there are no obvious symptoms. Although there is no clear manifestation of disease, because of the interac- tion of biological factors, changes have started to occur. During this stage, however, the changes are not always detectable. Screening technologies have been developed to detect the presence of some diseases before clinical symptoms appear. Th e Papanicolaou (Pap) smear is an example of an eff ective screening method for detecting cancer in a prema- lignant state to improve mortality related to cervical cancer. Th e use of the Pap smear as a screening tool facilitates early detection and treatment of premalignant changes of the cervix prior to development of malignancy.




Clinical Stage of Disease In the clinical stage of disease, suffi cient physiologic and/or functional changes occur, lead- ing to the development of recognizable symptoms of disease. It might also be accurately referred to as the treatment stage. For some people, the disease may completely resolve (either spontaneously or with medical intervention), whereas for some it will lead to dis- ability and/or death. It is for this reason that the clinical stage of disease is sometimes sub- divided for better medical management. Staging systems used in malignancies to better defi ne the extent of disease involvement are an example of a system that can help guide the type of treatment modality selected based on the stage. In many cases, staging can provide an estimate of prognosis. Another example is the identifi cation of disability as a specifi c subcategory of the treatment stage. Disability occurs when a clinical disease leaves a person either temporarily or permanently disabled. When people become disabled, the goal of the treatment is to mitigate the eff ects of disease and to help these individuals to function to their optimal abilities. Th is is very diff erent from the goal for someone who can be treated and restored to the level of functioning that he or she enjoyed prior to the illness.

The Nonclinical Disease Stage Th is nonclinical or unapparent disease stage can be broken into four subparts. Th e fi rst subpart is the preclinical stage, which, as mentioned earlier, is the acquisition of disease prior to development of symptoms and is destined to become disease. Th e second subpart is the subclinical stage that occurs when someone has the disease but it is not destined to develop clinically. Th e third subpart is the chronic or persistent stage of disease, which is disease that persists over time. And fi nally, there is the fourth subpart or latent stage in which one has disease with no active multiplication of the biologic agent (Gordis, 2014).

The Iceberg Phenomenon For most health problems, the number of identifi ed cases is exceeded by the number of unidentifi ed cases. Th is occurrence, referred to as the “iceberg phenomenon,” makes it diffi cult to assess the true burden of disease. Many diseases do not have obvious symp- toms, as stated earlier, and may go unrecognized for many years. Unrecognized dis- eases, such as diabetes, hypertension, and mental illness, create a signifi cant problem with identifying populations at risk and estimating service needs. Complications also arise when patients are not recognized or treated during an early stage of a disease when interventions are most eff ective. Additionally, patients who do not have symptoms or do not recognize their symptoms do not seek medical care and, in many cases, even if they do have a diagnosis, do not take their medications as they perceive that they are healthy when they are asymptomatic.


Understanding the natural history of disease is as important as understanding the causal factors of disease because it provides the APRN with the knowledge that is required to




design programs or interventions that target populations at risk. Understanding how disease develops is fundamental to the concept of prevention and provides a framework for disease prevention and control. Th e primary goal of prevention is to prevent disease before it occurs. Th e concept of prevention has evolved to include measures taken to interrupt or slow the progression of disease or to lessen its impact. Th ere are three levels of prevention.

Primary Prevention Primary prevention refers to the process of altering susceptibility or reducing exposure to susceptible individuals and includes general health promotion and specifi c mea- sures designed to prevent disease prior to a person getting the disease. Interventions designed for primary prevention are carried out during the stage of susceptibility and can include things such as providing immunizations to change a person’s susceptibility. Actions taken to prevent tobacco usage are another example of primary prevention. Tobacco use is one of the 12 leading health indicators used by Healthy People 2020 to measure health. Cigarette smoking is the leading cause of preventable mortality in the United States (Centers for Disease Control and Prevention [CDC], 2018a), and prevention or cessation of smoking can reduce the development of many smoking-re- lated diseases. Taxes on cigarettes, education programs, and support groups to help people stop smoking and the creation of smoke-free zones are all examples of primary prevention measures. Th e CDC linked a series of tobacco control eff orts by Minnesota to a decrease in adult smoking prevalence rates. From 1999 to 2010, Minnesota imple- mented a series of antismoking initiatives, including a statewide smoke-free law, cig- arette tax increases, media campaigns, and statewide cessation eff orts. Adult smoking prevalence decreased from 22.1% in 1999 to 16.1% in 2010 (CDC, 2011). In 2013, Minnesota increased the tax on a pack of cigarettes an additional $1.60. Following this increase, smoking decreased by 33% among Minnesota’s 11th graders and by 10% among adult residents. Smokers reported that the tax increase did infl uence their smoking behaviors (Minnesota Department of Health, 2018). Th is is an excellent example of a successful statewide primary prevention eff ort to reduce smoking preva- lence through a variety of initiatives.

Secondary Prevention Th e early detection and prompt treatment of a disease at the earliest possible stage are referred to as secondary prevention. Th e goals of secondary prevention are to either identify and cure a disease at a very early stage or slow its progression to prevent com- plications and limit disability. Secondary prevention measures are carried out during the preclinical or presymptomatic stage of disease. Screening programs are designed to detect specifi c diseases in their early stages while they are curable and to prevent or reduce morbidity and mortality related to a later diagnosis of disease. Examples of sec- ondary prevention include the Pap smear, mentioned earlier, as well as annual testing of cholesterol levels, mammography, and rapid HIV testing of asymptomatic individuals.




Tertiary Prevention Tertiary prevention strategies are implemented during the middle or late stages of clinical disease and refer to measures taken to alleviate disability and restore eff ective functioning. Attempts are made to slow the progression or to cure the disease. In cases in which permanent changes have taken place, interventions are planned and designed to help people lead a productive and satisfying life by maximizing the use of remaining capabilities (rehabilitation). Cardiac rehabilitation programs that provide physical and occupational therapies to postoperative cardiac patients are an example of tertiary prevention.


The Epidemiological Triangle Th e relationship between risk factors and disease is complex. Research studies may describe a relationship between a risk factor and disease, but how do we know that this relationship is causal? An understanding of causation is important if APRNs want to eff ectively impact the health of populations. Th e epidemiological triangle is a model that has historically been used to explain causation. Th e model consists of three interactive factors: the causative agent (those factors for which presence or absence cause disease— biologic, chemical, physical, nutritional), a susceptible host (things such as age, gender, race, immune status, genetics), and the environment (including diverse elements such as water, food, neighborhood, pollution). A change in the agent, host, and environmen- tal balance can lead to disease (Harkness, 1995). Th e underlying assumptions of this model are that causative factors can be both intrinsic and extrinsic to the host and that the cause of disease is related to interaction among these three factors. Th is model was developed initially to explain the transmission of infectious diseases and was particu- larly useful when the focus of epidemiology was on acute diseases. It is less helpful for understanding and explaining the more complicated processes associated with chronic disease. With the rise of chronic diseases as the primary cause of morbidity and mortal- ity, a model that recognizes multiple causative factors was needed to better understand this complex interaction.

The Web of Causation Th e dynamic nature of chronic diseases calls for a more sophisticated model for explain- ing causation than the epidemiological triangle. Introduction of the web of causation concept fi rst appeared in the 1960s when chronic diseases overtook infectious diseases as the leading cause of morbidity and mortality in the United States. Th e foundation of this concept is that disease develops as the result of many antecedent factors and not as a result of a single, isolated cause. Each factor is itself the result of a complex pattern of events that can be best perceived as interrelated in the complex confi guration of a web. Th e use of a web is helpful for visualizing how diffi cult it is to untangle the many events that can precede the onset of a chronic illness.




Critics have argued that this model places too much emphasis on epidemiological methods and too little on theories of disease causation. As theories evolved about the relationship between smoking and cancer, the U.S. Surgeon General appointed a com- mittee to review the evidence. Th is committee developed a set of guidelines for judging whether an observed association is causal. Th ese guidelines include temporal relation- ship, strength of the association, dose–response relationship, replication of the fi ndings, biologic plausibility, consideration of alternative explanation, cessation of exposure, con- sistency with other knowledge, and specifi city of the association (Gordis, 2014). For a more detailed discussion on this model, see Chapter 4, Epidemiological Methods and Measurements in Population-Based Nursing Practice.


Successful population-based approaches depend on the ability to recognize the diff er- ence between the collection and use of data from individuals and populations and the ability to assess needs and evaluate outcomes at the population level. Several of the more recent theories of causation can be helpful in determining whether an exposure is caus- ally related to the development of disease. In particular, calculating the strength of asso- ciation using statistics is one of several criteria that can be used to determine causality. However, statistics must be used with caution. Health is a multidimensional variable: Factors that aff ect health, and that interact to aff ect health, are numerous. Many rela- tionships are possible. Th ere are problems inherent in the use of statistics to explain dif- ferences among groups. Although statistics can describe disparities, they cannot explain them. It is left to the researchers to explain the diff erences. In addition to statistics, one must also be aware of the validity and reliability of the data. Th ere are problems asso- ciated with the categorizing and gathering of statistics that can have an eff ect on how the data should be interpreted. In order to be successful in research, one must do more than just collect data: One must look at the theoretical issues associated with explaining the relationship among the variables. Additionally, even if a relationship is found to be statistically signifi cant, that does not ensure that it is clinically signifi cant. Recognizing limitations in research and in practice are the most important steps prior to making conclusions in any setting. Th erefore, it is important that APRNs have a commitment to higher standards with an emphasis placed on adherence to careful and thorough proce- dural and ethical practice.

Methods derived from epidemiology can be useful in identifying the etiology or the cause of a disease. Among the important steps in this process are the identifi cation of risk factors and their impact in a population, determining the extent of a disease and/or adverse events found in a population, and evaluating both existing and new preventive and therapeutic measures and modes of healthcare delivery. Applying strong epidemio- logic methods with a sound application and interpretation of statistics are the foundation for evidence-based practice. Th e integration of evidence can lead to the creation of good public policy and regulatory decisions.




Descriptive Epidemiology Rates

Knowledge of how illness and injury are distributed within a population can provide valuable information on disease etiology and can lay the foundation for the introduction of new prevention programs. It is important to know how to measure disease in popu- lations, and rates are a useful method for measuring attributes over time such as disease and injury in any population. Rates can also be used to identify trends and evaluate outcomes and can allow for comparisons within and between groups. Th e Morbidity and Mortality Weekly Report (MMWR; located at www.cdc.gov/mmwr) is a publication of the CDC and contains updated information on incidence and prevalence of many diseases and conditions. Th ese rates provide healthcare providers with up-to-date information on the risks and burdens of various diseases and conditions (CDC, 2018b). Th e information obtained from the MMWR can be used to identify trends and provide policy makers with information for designating resources. Th e following is an example of such information:

Drinking sugar sweetened beverages (SSBs) is associated with several adverse health consequences including obesity, type 2 diabetes, and cardiovascular dis- ease. In 2013 the Behavioral Risk Factor Surveillance System (a telephone survey) investigated self-reported SSB intake in the United States.In this survey of adults aged 18 years of age and older an SSB was identifi ed as regular soda, fruit drink, sweet tea, and sports or energy drink intake. Th e results revealed that the overall age- adjusted prevalence of SSB intake once or more per day is 30% and ranges from 18% in Vermont to 47.5% in Mississippi. It is most prevalent among adults aged 18 to 24 years (43.3%), men (34.1%), non-Hispanic Blacks (39.9%), unem- ployed adults (34.4%) and persons with less than a high school education (42.4%). (Th is excerpt is adapted from an issue of MMWR published on February 26, 2016 [Park, Xu, Town, & Blanck, 2016].)

By publishing rates in percentages and comparing those rates among groups, it highlights the disparity between diff erent demographic profi les related to SSB intake. Information such as this can be useful to both clinicians and policy makers who make decisions about interventions and services.

When calculating rates, the numerator is the number of events that occur during a specifi ed period of time and is divided by the denominator, which is the average popula- tion at risk during that specifi ed time period. Th is number is multiplied by a constant— either 100, 1,000, 10,000, or 100,000—and is expressed per that number. Th e purpose of expressing rates per 100,000, for example, is to have a constant denominator, and it allows investigators to compare rates among groups with diff erent population sizes. To put it simply, the rate is calculated as follows:

Rate = Numerator/denominator × Constant multiplier

In order to calculate rates, the APRN must fi rst have a clear and explicit defi nition of the patient population and of the event. An important consideration when calculating




rates is that anyone represented in the denominator must have the potential to enter the group in the numerator, and all persons represented in the numerator must come from the denominator.

Rates can be either crude or specifi c. Crude rates apply to an entire population with- out any reference to any characteristics of the individuals within it. For example, to cal- culate the crude mortality rate, the numerator is the total number of deaths during a specifi c period of time divided by the denominator, which is the average number of peo- ple in the population during that specifi ed period of time (including those who have died). Typically, the population value for a 1-year period is determined using the mid- year population.

Specifi c rates can also be calculated for a population that has been categorized into groups. Suppose that an APRN wants to calculate the number of new mothers who initi- ate breastfeeding in a specifi c hospital in 2018. Th e formula would be:

Total number of breastfeeding infants in community hospital in 2018

Total number of live births in the same community hhospital in 2018

Constant multiplier×

In order to compare rates in two or more groups, the events in the numerator must be defi ned in the same way, the time intervals must be the same, and the constant multiplier must be the same. Rates can be used to compare two diff erent groups, or one group during two diff erent time periods. Returning to the example about breastfeeding, the breastfeed- ing rates could be compared in the same hospital, but at two diff erent times, before and aft er implementation of a planned intervention to increase breastfeeding rates.

Formulae for the rates discussed in this chapter can be found in Exhibit 3.1.



Calculating Rates Incidence rate describes the occurrence of new disease cases in a community over a period of time relative to the size of the population at risk.

Incidence rate

Number of new during a specified perio=

cases dd

Population at risk during the same specified period

Const× aant multiplier

(continued )





Prevalence rate is the number of all existing cases of a specifi c disease in a population at a given point in time relative to the population at risk.

Prevalence rate

Number of cases at a specified per=

existing iiod

Population at risk at the same specified period

Constan× tt multiplier

Crude rates summarize the occurrence of births (crude birth rate) or deaths (crude death rate). The numerator is the number of events and the denominator is the average population size (usually estimated as a midyear population).

Crude death rate

Number of deaths in a population during a = sspecified period

Population estimate during same specified pperiod

Constant multiplier×

Specifi c rates are used to overcome some of the biases seen with crude rates. They are used to control for variables such as age, race, gender, and disease.


death rate

Number of deaths for a specified a= gge group during a specified time

Population estimate for thhe specified age group during same specified time

Constant× multiplier

Case fatality rate is used to measure the percentage of people who die from a certain disease. This rate tells you how fatal or severe a disease is compared to other diseases.

Case fatality rate

Number of individuals dying after diseas= ee onset or diagnosis

Number of individuals with the specifieed disease

× 100

Proportionate mortality ratio is useful for determining the leading causes of death.

Proportionate mortality ratio

Number of deaths from a spec


iified cause during specified time period

Total deaths duringg the same period × 100

(continued )




Calculations Used in Health Impact Assessment Number needed to treat (NNT) is the number of patients needed to receive a treatment to pre- vent one bad outcome. The NNT calculated should be rounded up to the next highest number. Before the NNT can be calculated, the absolute risk reduction (ARR) must be identifi ed.

ARR = Incidence in exposed − Incidence in nonexposed NNT = 1/ARR

The NNT can also be calculated in randomized trials using mortality rates:

NNT = 1/(Mortality rate in untreated group − Mortality rate in treated group)

Disease impact number (DIN) is the number of those with the disease in question among whom one event will be prevented by the intervention.


ARR Proportion of people with the disease who are exposed

× tto the intervention

⎛ ⎝⎜

⎞ ⎠⎟

Population impact number (PIN) is the number of those in the whole population among whom one event will be prevented by the intervention.


ARR Proportion of people with

the disease who are exposed× tto the intervention

Proportion of the total population wit× hh the disease

of interest

⎝ ⎜ ⎜

⎠ ⎟ ⎟

Years of potential life lost (YPLL) is used for setting heath priorities. Predetermined standard age at death in the United States is 75 years.

YPLL (75) = 75 − Age at death from a specifi c cause

Add the years of life lost for each individual for specifi c cause of death = YPLL

Calculations Used in Screening Programs Sensitivity is the ability of a screening test to identify accurately those persons with the disease.

Sensitivity = TP/(TP + FN)

Specifi city refl ects the extent to which it excludes the persons who do not have the disease.

Specifi city = TN/(TN + FP)


(continued )





+ Test True positive (TP) False positive (FP)

– Test False negative (FN) True negative (TN)

Source: Adapted from Fulton, J. S., Lyon, B. L., & Goudreau, K. A., (2014). Foundations of clinical nurse specialist practice (2nd ed.). New York, NY: Springer Publishing Company.


Incidence and Prevalence Incidence rates describe the occurrence of new events in a population over a period of time relative to the size of the population at risk. Prevalence rates describe the number of all cases of a specifi c disease or attribute in a population at a given point in time relative to the size of the population at risk. Incidence provides information about the rate at which new cases occur and is a measure of risk. For example, the formula for the incidence rate for HIV is:

Total number of people who are diagnosed with HIV in a commuunity during 2018

Population in that community at midyear oof 2018 Rate per × =1 000 1 000, ,

Incidence rates provide us with a direct measure of how oft en new cases occur within a particular population and provide some basis on which to assess risk. By comparing inci- dence rates among population groups that vary in one or more risk factors, the APRN can begin to get some idea of the association between risk factors and disease. If, in the earlier example of breastfeeding, the APRN discovers breastfeeding rates are signifi – cantly diff erent among diff erent ethnic groups, the characteristics of the groups can be compared and the causes for this disparity can be hypothesized and tested.

Period prevalence measures the number of cases of disease during a specifi c period of time and is a measure of burden. Th e formula for the period prevalence rate for HIV in 2018 is:

Total number of people who are HIV positive in a community dduring 2018

Population in that community at midyear of 20188 Rate per × =1 000 1 000, ,

In the formula given here, all newly diagnosed cases for the year plus existing cases are included. Point prevalence is defi ned as the number of cases of disease at a specifi c point in time divided by the number of people at risk at that specifi c point in time multiplied by a constant multiplier. An example of the use of point prevalence would be the information




gathered from a survey in which an investigator asks questions such as who has diabe- tes, hypertension, epilepsy, or any other disease or event at that specifi c point in time. Prevalence, whether point or period, cannot give us an estimate of the risk of disease; it can only tell us about the burden of disease for a specifi ed period of time. Prevalence is useful when comparing rates between populations but should be interpreted with cau- tion. Diseases that are chronic will have a higher prevalence because at any given time, those with chronic disease will always have that disease. Th is can make it challenging to interpret prevalence rates as they do not tell us the risk of developing disease but they can be helpful when trying to determine resource needs for chronic diseases. With diseases that are short in duration, prevalence may not capture the true burden of disease for that population. Additionally, it is important to note that unidentifi ed cases are not captured in either prevalence rates or incidence rates. Rates can only estimate the burden of disease, but they are the best way to draw comparisons using a common denominator.

An example of how prevalence rates are used in the literature is as follows:

Th e CDC (2018c) reported that the prevalence of obesity was 39.8% for U.S. adults in 2015–2016. Hispanics (47.0%) and non-Hispanic Blacks (46.8%) had the highest age-adjusted prevalence of obesity, followed by non-Hispanic Whites (37.9%) and non-Hispanic Asians (12.7%). Th e prevalence of obesity was 35.7% among young adults aged 20 to 39 years, 42.8% among middle-aged adults aged 40 to 59 years, and 41.0% among older adults aged 60 and over.

Information on the prevalence of adult obesity in the United States has led to increased attention to factors that cause obesity (especially in children). Th is has led to the devel- opment of new programs aimed at primary and secondary prevention.

Mortality Rates Mortality rates, also known as death rates, can be useful when evaluating and comparing populations. As stated earlier, there are many factors that can aff ect the natural history of disease, and measuring mortality allows investigators to compare death rates among and within populations. Th e formula for mortality rate is:

Number of deaths in a population during a specified time

Averrage population estimate during the specified time

Constan× tt multiplier

Mortality rates can be specifi c or broad in defi nition and can include any qualifi ers for time, age, or disease type. It is important to include those specifi cs in your denominator to ensure that the population value used is the best estimate of the population at risk. For example, to look at the number of deaths in 2018 due to breast cancer in women aged 18 to 40, the denominator should only include the midyear population of women aged 18 to 40 in 2018. It is also important to include those women who died during that year in the denominator. Again, it is impossible to know exactly how many women in that




age group are at risk using a midyear population, but the key is to use similar sources of measurement so that comparisons can be made assuming similar sources are used to estimate the denominator.

Standardization of crude rates is an important consideration when comparing mortality rates among populations. Standardization is used to control the eff ects of age and other char- acteristics in order to make valid comparisons between groups. Age adjustment is an exam- ple of rate standardization and perhaps the most important one. No other factor has a larger eff ect on mortality than age. Consider the problem of comparing two communities with very diff erent age distributions. One community has a much higher mortality rate for colon cancer than the other, leading investigators to consider a possible environmental hazard in that community, when in fact, that community’s population is older, which could account for the higher mortality. Direct age adjustment or standardization allows a researcher to eliminate the age disparities between two populations by using a standardized population. Th is allows the researcher to compare mortality or death rates between groups by eliminat- ing age diff erences between populations and comparing actual age-adjusted mortality rates to determine whether age truly plays a role in the crude unadjusted mortality rates.

Th ere are two methods of age adjustment: direct, as mentioned earlier, and indirect. Th e direct method applies observed age-specifi c mortality or death rates to a standard- ized population. Th e indirect method applies the age-specifi c rates of a standardized population to the age distribution of an observed population and is used to determine whether one population has a greater mortality because of an occupational hazard or risk compared to the general population. (To learn how to perform age adjustment, refer to an advanced epidemiology text.)

Th e case fatality rate (CFR) is a measure of the severity of disease (such as infectious diseases) and can be helpful when designing programs to reduce the rate or disparity in the population. It should be noted that CFR is not a true rate as it has no explicit time implication but rather is a proportion of persons with disease who died from that disease aft er diagnosis. It is a measure of the probability of death among diagnosed cases. Its use- fulness for chronic diseases is limited because the length of time from diagnosis to death can be long. CFR is also useful in determining when to use a screening test. Screening tests identify disease early so that an intervention or treatment can be initiated in the hopes of lessening the morbidity or mortality of that disease. Th ose diseases that are rapidly fatal may not necessarily be benefi cial to screen unless the screening will allow for a cure or treatment to change the overall outcome or to prevent unnecessary spread of the disease. Screening is useful in identifying disease in asymptomatic individuals in whom further transmission of disease can be prevented or reduced, such as in HIV. CFRs, therefore, can be helpful for comparisons between study populations and can pro- vide useful information that could help determine whether an intervention or treatment is working. Th e formula for CFR is as follows:

Case fatality rate %

Number of individuals with the specif


iied disease after disease onset or diagnosis Number of casess of that specific disease





CFR is usually expressed as a percentage; so in this case, one would multiply this rate by a constant multiplier of 100 to obtain the percentage of disease that is fatal. It is important in all of these rates to include those who have died from the disease in the denominator. Removing those who have died from the denominator falsely increases the CFR, making the disease appear more fatal or severe (Gordis, 2014).

Th e proportionate mortality ratio is useful for determining the leading causes of death. Th e formula for proportionate mortality ratio is as follows:

Number of deaths from a specified cause during specified timme period Total deaths from all causes

during the same speciified time period


Again, this measure is usually reported as a percentage and refl ects the burden of death due to a particular disease. Th is information is useful for policy makers who make deci- sions about the allocation of resources. (See Exhibit 3.1 for a list of these formulae.)

Survival and Prognosis Mortality rates are very helpful when comparing groups and looking at disparities among populations. One cannot discuss mortality without having an understanding of survival and prognosis. Many diseases, particularly cancer, are studied over time, with attention placed on survival. Ideally, survival should be measured from the onset of disease until death, but the true onset of disease is generally unknown. Survival rates are usually cal- culated at various intervals from diagnosis or initiation of treatment. Prognosis is cal- culated using collected data to estimate the risk of dying or surviving aft er diagnosis or treatment begins. As mentioned earlier, CFRs give a good estimate of prognosis or severity of disease. However, they are best suited for acute diseases in which death occurs relatively soon aft er diagnosis. Survival analysis is better suited for chronic diseases or those diseases that take time to progress.

Survival time is generally calculated from the time of diagnosis or from the start of treatment. Th is can vary from patient to patient, as some patients may seek care imme- diately aft er symptoms present or may wait months to seek care. Some patients are diagnosed prior to symptom presentation aft er they screened positive on a screening test. Some may obtain a diagnosis immediately, whereas others may have poor access to care, and diagnosis is delayed by weeks to months or even years. Once a diagnosis is made, treatment may or may not occur immediately. Additionally, some patients may die before diagnosis or treatment. Because these individuals are not represented in survival analysis, this can lead to a falsely increased survival time. With that said, one can see how diffi cult it is to establish a true survival time aft er diagnosis. However, we can estimate survival if we use a common denominator and consistent criteria for measurement.

Before we discuss how to calculate and interpret survival data, we must touch on two important concepts, lead time bias and overdiagnosis bias. Lead time bias is a phenom- enon whereby a patient is diagnosed earlier by screening and appears to have increased




survival due to screening but rather dies at the same time he or she would regardless of screening. In other words, the time from which a patient is diagnosed earlier from screening is the lead time, and the bias is the error that occurs as a result of concluding that screening leads to a longer survival aft er diagnosis. As can be seen in the follow- ing timeline (Figure 3.1), the survival time is longer when screening is implemented, but the ultimate time of death is unchanged. Although this is not true for all screen- ing tests, it is important to recognize the phenomenon of lead time bias as it can aff ect the conclusions that are made regarding survival, which ultimately can aff ect a patient’s perceived prognosis.

Overdiagnosis bias occurs as a result of making a diagnosis from screening for a dis- ease or cancer that would not have manifested clinically or has a slow progression, such that the person dies from another etiology. Th is type of bias has the potential to increase undue stress in individuals and can also falsely increase survival times, especially for diseases with slow progression. In both these types of biases, there is no diff erence in overall mortality in those screened versus those who were not screened. With that said, considerations must also be made for those screening tests in which a false-negative test reassures a patient who may not seek care and ultimately develops cancer and potentially has decreased survival due to delay in diagnosis. All of these biases need to be taken into consideration when interpreting survival data.

Prognosis is calculated using survival rates. Th ere are two methods of conducting survival analysis and estimating prognosis that are discussed. Th e fi rst is the actuarial

FIGURE 3.1 Timeline illustrating lead time bias.





Time of diagnosis




Survival time

Lead time

Survival time


















method, which measures the likelihood of surviving aft er each year of treatment (or a predetermined interval). Th is is calculated as follows: the probability of surviving 2 years if one survived 1 year, or the probability of surviving 3 years if one survived 2 years, and so on. Prognosis is most commonly described in the literature as the probability of sur- viving 1, 2, 3, or more years. Generally, survival is calculated as a probability P1, P2, P3, and so on. Th e survival aft er 1 year is designated as P1; if patients survived 1 year aft er treatment, those who survived to 2 years = P2; if patients survived 2 years aft er treat- ment, those who survived to 3 years = P3; and so on. To calculate P1, divide the number of survivors over the number of patients with the disease at the start of the study or treat- ment. It is important to note that those who are lost to follow-up (also known as with- drawals) or who are no longer studied must be removed from the denominator. When a study ends or is terminated, those patients are no longer followed and must be taken into consideration in your analysis, and this is called censorship. For simplicity, the fol- lowing examples will assume no losses to follow-up, but it is important to recognize that those who are lost to follow-up or those who are censored must be taken into account in your calculations. Of note, in a more advanced epidemiology textbook, you will fi nd that those who are lost to follow-up will be subtracted out of the denominator and multiplied by 1/2 to account for the chance they were at risk for half the interval. Again, for the purposes of this text, we will use a hypothetical example in which no patients are lost to follow-up. By defi nition, here is how to calculate P1, P2, and so on:

■ P1 = (Number alive aft er 1 year of treatment)/(Number who started treatment) ■ P2 = (Number alive aft er 2 years of treatment)/(Number who survived fi rst

year of treatment − Th ose who dropped out or were lost to follow-up) ■ P3 = (Number alive aft er 3 years of treatment)/(Number who survived second

year of treatment − Th ose who dropped out or were lost to follow-up)

To calculate the probability of surviving 1, 2, 3, or more years, the calculation is as follows:

P1 = probability of surviving 1 year P1 × P2 = probability of surviving 2 years P1 × P2 × P3 = probability of surviving 3 years P1 × P2 × P3 × P4 = probability of surviving 4 years P1 × P2 × P3 × P4 × P5 = probability of surviving 5 years

Using data from Table 3.1, we can calculate and interpret these probabilities.

TABLE 3.1 Survival Rates After Treatment (Hypothetical Life Table of 100 Patients With No Patients Lost to Follow-Up)



Cohort (N = 100)

88 76 55 47 33




In this example:

P1 = 88/100 = 0.88

P2 = 76/88 = 0.86

P3 = 55/76 = 0.72

P4 = 47/55 = 0.85

P5 = 33/47 = 0.70

Probability of surviving 1 year = 0.88

Probability of surviving 2 years = 0.88 × 0.86 = 0.76

Probability of surviving 3 years = 0.88 × 0.86 × 0.72 = 0.54

Probability of surviving 4 years = 0.88 × 0.86 × 0.72 × 0.85 = 0.46

Probability of surviving 5 years = 0.88 × 0.86 × 0.72 × 0.85 × 0.70 = 0.32

It is important to distinguish between the probability of surviving 5 years and the prob- ability of surviving 5 years given that someone survived 4 years. Generally, the longer someone survives aft er treatment, the more likely that person will make it to the next year. Overall survival aft er 5 years is always a smaller number as the probability of sur- viving each year is multiplied against each year (Gordis, 2014).

Note that the actuarial method can be used to look at outcomes other than survival or death as it can estimate probabilities of an outcome or event occurring such as a treat- ment side eff ect (e.g., vomiting, headache) or recurrence of disease. Another import- ant consideration is survival over time. When looking at survival rates measured over years, it is important that an APRN take into account the improvements and advances in treatments over time. APRNs should consider comparing survival rates for earlier treatment regimens with those for newer regimens, as this can aff ect the validity of the overall survival if not taken into consideration. In addition, certain confounders (e.g., age, gender, ethnicity, socioeconomic status) may contribute to diff erences in survival rates and should be examined when performing a survival analysis (see Chapter 4, “Epidemiological Methods and Measurements in Population-Based Nursing Practice,” for more on confounding). Recognition of these diff erences is a critical step for the eval- uation of potential health disparities and is a perfect opportunity for an APRN to develop strategies to address the underlying issue causing those disparities.

In the literature, survival analysis using the actuarial method is plotted on a curve in which the x-axis represents time and the y-axis represents the number of survivors at each time interval. Th is is called a survival curve and represents the pattern of survival over predetermined time intervals. Using the data from the earlier example, the proba- bilities are plotted in a standardized survival curve (Figure 3.2).

Th e second type of survival analysis is the Kaplan–Meier method. Th is method is com- monly used in medicine and is well suited for analyses of small and large populations, as well as comparisons between treatments or interventions. Although beyond the scope of this book, statistical analyses can be performed to compare treatments or interventions




using tests of signifi cance (log-rank test) and logistic regression (proportional hazard models [Cox models]). As with any comparison trial, it is important to take into con- sideration the characteristics of those patients who are lost to follow-up because if they occur more frequently in one treatment group compared to another, this can aff ect the results. For example, if the majority of patients lost to follow-up are receiving treatment A and most of them can be characterized as impoverished with poor access to care, then this could skew the results of the remaining patients receiving treatment A. Th us, minimizing loss to follow-up or censored patients and/or maintaining similar losses with similar characteristics in each group is paramount to reducing bias and improving the strength of the study conclusions. Th is reiterates the importance of randomization, which will be discussed more thoroughly in Chapter 4, Epidemiological Methods and Measurements in Population-Based Nursing Practice.

Kaplan–Meier curves are used to plot survival, and these plots represent a stepwise pattern of survival in which the increments of time are not standardized (e.g., 1 year, 5 years), but rather each step represents an event (e.g., time to death or an outcome of interest). Kaplan–Meier curves are seen more commonly in the literature and are a better estimate of survival as they also take into consideration patients who are lost to follow-up or are censored. Th ese curves also allow for comparisons between diff erent treatment regimens (Figure 3.3).

Kaplan–Meier curves are diff erent from traditional survival curves in that they do not slope downward aft er each event but rather maintain a horizontal line until the next event (e.g., death) occurs, and then a downward vertical line is drawn until the new cumulative survival is reached and the steps are continued until the study is completed. At the time in which no deaths are occurring (also known as the death-free period), the cumulative survival is maintained; however, hatch marks can be seen in these plots, which represent those lost to follow-up or censored during that interval (Jekel, Katz, Elmore, & Wild, 2007).

Th e importance of having the knowledge and skills to interpret and calculate survival data cannot be understated. APRNs can use survival data or outcome data in various ways. Most importantly, the evidence obtained from survival or outcome data can help

FIGURE 3.2 Hypothetical example of a survival curve using data from the earlier example.

Survival %

6543210 0







Survival %

S ur

vi va

l %




APRNs to design and justify interventions to improve the quality of life for diseases such as cancer. Comparisons to other groups can be made by addressing outcomes of interest to determine whether certain interventions make a diff erence in the quality of life and ultimately impact the survival of those involved.

Health Impact Assessment As mentioned previously, rates can be used to describe the distribution of disease and other health-related states and events, but sometimes the APRN may be more concerned with knowing how data can be used to describe the relevancy of clinical practice. Health impact assessment (HIA) is the assessment of the potential health eff ects, positive or nega- tive, of a particular intervention on a population. HIAs can evaluate population-directed programs or interventions before they are implemented and can provide recommenda- tions on how those programs can potentially aff ect the health of a population irrespective of whether positive or negative. Certain calculations can be performed to determine the effi cacy of a treatment or intervention. Th e number needed to treat (NNT), the dis- ease impact number (DIN), and the population impact number (PIN) are formulae that are used in HIAs. NNT is the number of patients needed to receive a treatment to prevent one bad outcome, and the lower the NNT, the better for assessing superiority

FIGURE 3.3 Hypothetical example of a Kaplan–Meier curve—comparison of treatment A to treatment B.

Treatment group

Treatment A Treatment B


% C

u m

u la

ti ve

s u

rv iv


0 10 20 30 40 50 60










of treatments. However, the NNT takes into account only those patients being treated rather than all those with disease in the population. Th e DIN, on the other hand, uses the number of those with the disease in question among whom one event will be prevented by the intervention. Similarly, the PIN is the number of those in the whole population among whom one event will be prevented by the intervention (Heller & Dobson, 2000). Another calculation commonly used is the YPLL, which measures premature mortality, and the productive years that are lost related to early death (Merrill, 2017). Information on years of potential life lost (YPLL) helps to magnify the importance of primary pre- vention measures designed to address diseases such as obesity and other risk factors such as smoking. Each of these measurements is helpful in determining the benefi ts or risks of new interventions or treatments. Specifi cally, the DIN and the PIN provide a better population-based estimate of treatment or intervention impacts on the population as a whole. (See Exhibit 3.1 for a list of these formulae.)

It is important for the APRN who is involved in population-based evaluation to be aware of these concepts. More extensive information on HIA formulae and standardiza- tion can be found in most advanced epidemiology texts and on the CDC’s Health Impact Assessment Resources page at https://www.cdc.gov/healthyplaces/hiaresources.htm.


Descriptive epidemiology is used to describe the distribution of disease and other health-related states and events in terms of personal characteristics, geographical dis- tribution, and time. Th ere are four types of descriptive studies: case reports, case series, cross-sectional studies, and correlation or ecologic studies. Th e data used in descriptive studies are oft en readily available and can be retrieved from such sources as hospital records, census data, or vital statistics records.

Case Reports and Case Series Case reports are succinct written accounts of generally rare or unusual cases in which the treatment or management of the disease or condition is worth reporting. Th ese are usually published to assist healthcare providers in the management of rare, unre- searched, or undocumented cases. A case series is merely a report of a series of patients with similar diseases or conditions that describes their management or treatment in order to identify new strategies that may be helpful to treat patients with similar con- ditions. Th ey also lead to future studies and can be helpful for APRNs as they can use these cases to build a case for future research of treatments or interventions that have not yet been rigorously studied.

Correlation Studies Correlation studies are also referred to as ecologic studies and are used to conduct studies of aggregate or population characteristics. In ecologic studies, rates are calculated for characteristics that describe populations and are used to compare frequencies between diff erent groups at the same time or the same group at diff erent times. Th ey are useful




for identifying long-term trends, seasonal patterns, and event-related clusters. Because data are collected on populations instead of individuals, an event cannot be linked to an exposure in individuals, and the investigator cannot control for the eff ect of other vari- ables. Th ese types of studies lead to more rigorous studies that can control for variables of interest and look at individual data to determine whether an association truly exists. Correlational studies can only report that a correlation exists and cannot show an asso- ciation exists as they compare population or aggregate data. An example of a correlation study would be one that shows a correlation between high fat content and breast cancer. Countries with a high fat content correlate to countries with higher rates of breast cancer. Without knowing individual data, one cannot determine whether women with breast cancer actually also have a high fat consumption (Gordis, 2014).

A study by Pillai, Maleku, and Wei (2013) provides an example of a correlational study. Th e authors used data from 143 countries to study the relationship between female literacy and maternal mortality. Th eir analysis reveals a signifi cant negative relationship between female literacy rates and maternal mortality. Populations with a higher prevalence of liter- acy have lower maternal mortality rates and populations with a lower prevalence of liter- acy have higher maternal mortality rates. Th e authors point out limitations to their study, most importantly the diffi culty of controlling for known correlates with maternal mortal- ity (such as access to healthcare services) due to a scarcity of cross-national data. Th ese data show that a correlation exists between the variables, but not necessarily a causal one. Th ere are many possible explanations for the relationship, including (but not exclusively) demographic and economic diff erences among the countries. Correlation studies must be interpreted with caution, but important information can be obtained from the trends that could identify disparities and lead to further studies and hypothesis testing.

Cross-Sectional Studies In cross-sectional studies, also known as prevalence studies, both exposures and out- comes are collected simultaneously. Th ese studies provide a “snapshot” at one point in time and thus exclude people who have died or who chose not to participate, which can introduce bias. Temporal relationships are diffi cult to determine in these studies as only prevalence can be determined and the risk of developing disease cannot be estimated. Many cross-sectional studies are surveys that sample a population and its various charac- teristics. Th ey can be inexpensive and can provide timely descriptive data about a group under study, but again, they do not tell us about causality or the true risk of developing a certain outcome such as disease.

Spoelstra, Given, von Eye, and Given (2010) conducted a cross-sectional study to deter- mine whether individuals with a history of cancer fall at a higher rate than those without cancer. Th ey also examined whether or not the occurrence of falls in the elderly was infl uenced by individual characteristics. Th e study population consisted of 7,448 com- munity-dwelling elderly who were 65 years or older living in one state in the Midwestern United States. Th e analysis of the data revealed that having cancer was not a predictor of falls in this study. Further analysis revealed that predictors of falls in this population included race, sex, activities of daily living, incontinence, depression, and pain. Although




cancer was not found to be a predictor of falls, the authors did fi nd a high frequency of falls in that study population. Th e fi ndings led the authors to conclude that it is import- ant to develop a predictive model for fall risk in the community-dwelling elderly.

Th is study serves to illustrate both the advantages and disadvantages of cross- sectional studies. Th e study was carried out at one point in time using an existing data set (the minimum data set). One limitation of the study was that it missed people whose falls were not reported. Another limitation the authors cited was that they could not deter- mine whether a specifi c cancer diagnosis, stage, or treatment was a risk factor for falls. Finally, they were unable to determine whether or not comorbidities may have placed individuals at a higher risk for falls. Th e inability to control for or identify the signifi – cance of potentially important variables is a disadvantage of using a cross-sectional study design. With that said, a cross-sectional study is a fairly quick method to obtain descrip- tive data and can be useful in identifying prevalence rates for specifi ed populations.


Analytic epidemiology looks at the origins and causal factors of diseases and other health-related events. Analytic designs are oft en carried out to test hypotheses formu- lated from a descriptive study. Th e goal of analytic epidemiology is to identify factors that increase or decrease risk. Risk is the probability that an event will occur. For exam- ple, a patient who is obese might ask, “What is the likelihood that I will develop diabetes if I do not lose weight?”

Although descriptive studies allow a basis for comparison and can provide the APRN with data to identify potential risk factors and diff erences among groups, study designs, such as a prospective cohort, need to be carried out in order to determine whether there is an association between an exposure and a disease and to determine the strength of that association. To do this, the APRN can compare exposed and nonexposed groups and follow them over time to see who develops an outcome (such as a specifi c disease) and who does not. Comparison is an essential component of population studies. Case–con- trol studies can also allow for comparisons by retrospectively looking back in time to see what exposure or risk factors are associated with being a case or a control. Comparisons can also be made by following a group using treatment A compared to treatment B or treatment A can be compared to no treatment at all. Th ere are multiple study designs, but we will focus only on the most common study designs and discuss the advantages and disadvantages that each one poses in practice.

Cohort Studies Cohort designs can be either prospective or retrospective. In a prospective cohort design, the investigator begins with a defi ned population and then follows a group of individ- uals who were either exposed or nonexposed to a factor of interest and then follows both groups to compare the incidence of an outcome or disease. In a cohort study, one can look at multiple outcomes that develop from an exposure. In a retrospective cohort design, exposure is ascertained from past records and outcome is ascertained at the time




the study begins. If an association exists between the exposure and the outcome, then the incidence rate in the exposed group will be greater than that in the nonexposed group. Th e ratio of these is the relative risk (RR), which is the incidence rate in the exposed group divided by the incidence rate in the nonexposed group. RR is a measure of the strength of an association between an exposure and an outcome or disease (Table 3.2).

If the RR = 1 (the numerator equals the denominator), then the risk to the two groups is equal. If the RR >1 (the numerator is greater than the denominator), then the risk in the exposed group is greater than the risk in the nonexposed group and can be consid- ered a positive association. If the RR <1 (the denominator is greater than the numerator), then the risk in the exposed group is less than the risk in the nonexposed group and can be considered protective. An example of a protective association may be the association between exercise and heart disease. Exercise can actually reduce the risk of heart disease and has an RR <1. Th us, it is considered a protective exposure.

Attributable risk (AR), absolute risk, or risk diff erence is the amount of risk that can be attributed to an exposure. For example, it is well known that smoking can cause lung cancer, but lung cancer can also occur in nonsmokers. Th e amount of disease that is asso- ciated with risks/exposures other than smoking is called the background risk. In order to calculate the risk attributable to a particular exposure, subtract the incidence of disease (lung cancer) in the exposed group (smokers) minus the incidence of disease (lung can- cer) in the nonexposed group (background risk). Th is value is considered the AR due to exposure (see Table 3.2). If an APRN wants to know how much risk of disease can be reduced by removing a risk factor, one can calculate the ARR, which is synonymous with the AR. Th e relative risk reduction (RRR) is calculated the same as the AR proportion. Th is can be confusing as these terms are interchanged in medicine and epidemiology, but it is important to recognize and understand how these terms are used and interpreted. An example of RRR would be described as: What percentage of motor vehicle deaths could be reduced if we could eliminate texting while driving? Th is RRR percentage is what is commonly reported in the news and can be very helpful for policy makers and for

TABLE 3.2 Calculation of RR and Attributable Risk in a Cohort Study


Exposure a b a + b Incidence in the exposed (Inc exp) = a/a + b

No Exposure c d c + d Incidence in the nonexposed (Inc nonexp) = c/c + d

Relative risk (RR) = Inc exp/Inc nonexp

Attributable risk (AR) = Inc exp − Inc nonexp

AR proportion in the exposed population = Inc exp – Inc nonexp

Inc exp

AR proportion in the total population = Incidence in total population – Inc nonexp

Incidence in total population




justifi cation of funding. Th e AR can also be calculated as a proportion of the total popu- lation. For example, to determine the amount of lung cancer attributable to smoking in the total population (AR proportion), one would have to know the incidence in the total population (to review how to calculate the incidence in the total population, refer to an advanced epidemiology textbook). APRNs should be familiar with how to calculate and interpret RR and AR, as these values are reported commonly in the literature and reports such as the MMWR.

Cohort studies are best carried out when the investigator has good evidence that links an exposure to an outcome, when the time interval between exposure and the outcome is short, and when the outcome occurs relatively oft en. One of the major problems with cohort studies is that they can be time-consuming and expensive, especially if the cohort needs to be followed for a prolonged length of time. Diseases that are rare or that take many years to develop may be better suited for a case–control study as it can be diffi cult to follow participants for many years, especially if the outcome of interest is rare. Th e longer the time period, the more likely participants will be lost to follow-up, and multiple exposures can potentially confound the relationship.

A cohort study was carried out in Norway to ascertain characteristics that woul

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