research design journal pdf

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage

The book Research Design: Qualitative, Quantitative and Mixed Methods Approaches by Creswell (2014) covers three approaches-qualitative, quantitative and mixed methods. This educational book is informative and illustrative and is equally beneficial for students, teachers and researchers. Readers should have basic knowledge of research for better understanding of this book. There are two parts of the book. Part 1 (chapter 1-4) consists of steps for developing research proposal and part II (chapter 5-10) explains how to develop a research proposal or write a research report. A summary is given at the end of every chapter that helps the reader to recapitulate the ideas. Moreover, writing exercises and suggested readings at the end of every chapter are useful for the readers. Chapter 1 opens with-definition of research approaches and the author gives his opinion that selection of a research approach is based on the nature of the research problem, researchers' experience and the audience of the study. The author defines qualitative, quantitative and mixed methods research. A distinction is made between quantitative and qualitative research approaches. The author believes that interest in qualitative research increased in the latter half of the 20th century. The worldviews, Fraenkel, Wallen and Hyun (2012) and Onwuegbuzie and Leech (2005) call them paradigms, have been explained. Sometimes, the use of language becomes too philosophical and technical. This is probably because the author had to explain some technical terms.

Related Papers

Daniel Ortiz

Research Design: Qualitative, Quantitative and Mixed Methods

Dr. Esra Öztürk Çalık

Conducting a well-established research requires deep knowledge about the research designs. Doing research can be likened to jumping into the sea which may transform into a huge ocean if the researcher is not experienced. As a PhD candidate and a novice researcher, I believe that the book "Research Design: Qualitative, Quantitative and Mixed Methods Approaches" by J.W. Creswell is a true reference guide for novice researchers since it is the most comprehensive and informative source with its reader-friendly structure.

International Journal of Social Sciences Interdisciplinary Research

Prashant Astalin

rhoda taller

Yan-yi Chang

John W. Creswell was previously a professor in educational psychology in the University of Nebraska–Lincoln. He moved to the University of Michigan in 2015 as a professor in the Department of Family Medicine. He has published many articles and close to 27 books on mixed methods. Professor Creswell is also one of the founding members of the Journal of Mixed Methods Research. He was a Fulbright scholar in South Africa in 2008 and Thailand in 2012. In 2011, he served as a visiting professor in the School of Public Health of Harvard University. In 2014, he became the Chairman of the Mixed Methods International Research Association. Professor Creswell has a personal website called “Mixed Methods Research” at http://johnwcreswell.com/. The site contains the information about his background, his own blog, consulting works and published books. He also posted replies questions from academic researchers and practitioners in the blog.

Chisomo Mgunda

Gloria Thakane Leutle

Richard Baskas, Ed.D. Candidate

Nurul Gasmi

To understand educational research, you now have the map (the steps that exist in the process of research) and the different paths you can take (quantitative and qualitative). Now we will explore some distinguishing features along the qualitative research design. These features are the research designs you can use to collect, analyze, and interpret data using quantitative and qualitative research. Some of the research designs may be familiar; others may be new, such as how these paths can converge with two designs called mixed methods research and action research. The discussion of designs will provide a more advanced understanding of educational research on your journey.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

IRJET Journal

The SAGE Handbook of Applied Social Research Methods

Global Academic Journal of Linguistics and Literature

Dr. Lok R A J Sharma

IJIP Journal

Education for Health: Change in Learning & Practice

Kelly Devers

Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

ANA MARIA PINTO-LLORENTE

International Journal of Academic Multidisciplinary Research (IJAMR

Ramatu Ussif

Kathryn Pole

iosrjournals.org

Presented at Academic Business World International Conference. Nashville, Tennessee. May, 2007

Greg L Lowhorn

Changsong Wang

Mid-Western Educational Researcher

Mixed Methods Research for Improved Scientific Study

Mette Baran

Bostley Asenahabi

Studies in Comparative International Development

Gerardo Munck

International Research Journal of MMC

Umesh Raj Aryal

Villia Jefremovas

Seda Khadimally

shiroashi fullbuster

Nova Southeastern University

Addisalem Tadesse

Sarah Baloyi

koek Comfort

Dhea Savella Segui

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Grad Med Educ
• v.8(1); 2016 Feb

Research Design Considerations

Associated data.

Editor's Note: The online version of this article contains references and resources for further reading and the authors' professional information.

The Challenge

“I'd really like to do a survey” or “Let's conduct some interviews” might sound like reasonable starting points for a research project. However, it is crucial that researchers examine their philosophical assumptions and those underpinning their research questions before selecting data collection methods. Philosophical assumptions relate to ontology, or the nature of reality, and epistemology, the nature of knowledge. Alignment of the researcher's worldview (ie, ontology and epistemology) with methodology (research approach) and methods (specific data collection, analysis, and interpretation tools) is key to quality research design. This Rip Out will explain philosophical differences between quantitative and qualitative research designs and how they affect definitions of rigorous research.

What Is Known

Worldviews offer different beliefs about what can be known and how it can be known, thereby shaping the types of research questions that are asked, the research approach taken, and ultimately, the data collection and analytic methods used. Ontology refers to the question of “What can we know?” Ontological viewpoints can be placed on a continuum: researchers at one end believe that an observable reality exists independent of our knowledge of it, while at the other end, researchers believe that reality is subjective and constructed, with no universal “truth” to be discovered. 1,2 Epistemology refers to the question of “How can we know?” 3 Epistemological positions also can be placed on a continuum, influenced by the researcher's ontological viewpoint. For example, the positivist worldview is based on belief in an objective reality and a truth to be discovered. Therefore, knowledge is produced through objective measurements and the quantitative relationships between variables. 4 This might include measuring the difference in examination scores between groups of learners who have been exposed to 2 different teaching formats, in order to determine whether a particular teaching format influenced the resulting examination scores.

In contrast, subjectivists (also referred to as constructionists or constructivists ) are at the opposite end of the continuum, and believe there are multiple or situated realities that are constructed in particular social, cultural, institutional, and historical contexts. According to this view, knowledge is created through the exploration of beliefs, perceptions, and experiences of the world, often captured and interpreted through observation, interviews, and focus groups. A researcher with this worldview might be interested in exploring the perceptions of students exposed to the 2 teaching formats, to better understand how learning is experienced in the 2 settings. It is crucial that there is alignment between ontology (what can we know?), epistemology (how can we know it?), methodology (what approach should be used?), and data collection and analysis methods (what specific tools should be used?). 5

Key Differences in Qualitative and Quantitative Approaches

Use of theory.

Quantitative approaches generally test theory, while qualitative approaches either use theory as a lens that shapes the research design or generate new theories inductively from their data. 4

Use of Logic

Quantitative approaches often involve deductive logic, starting off with general arguments of theories and concepts that result in data points. 4 Qualitative approaches often use inductive logic or both inductive and deductive logic, start with the data, and build up to a description, theory, or explanatory model. 4

Purpose of Results

Quantitative approaches attempt to generalize findings; qualitative approaches pay specific attention to particular individuals, groups, contexts, or cultures to provide a deep understanding of a phenomenon in local context. 4

Establishing Rigor

Quantitative researchers must collect evidence of validity and reliability. Some qualitative researchers also aim to establish validity and reliability. They seek to be as objective as possible through techniques, including cross-checking and cross-validating sources during observations. 6 Other qualitative researchers have developed specific frameworks, terminology, and criteria on which qualitative research should be evaluated. 6,7 For example, the use of credibility, transferability, dependability, and confirmability as criteria for rigor seek to establish the accuracy, trustworthiness, and believability of the research, rather than its validity and reliability. 8 Thus, the framework of rigor you choose will depend on your chosen methodology (see “Choosing a Qualitative Research Approach” Rip Out).

View of Objectivity

A goal of quantitative research is to maintain objectivity, in other words, to reduce the influence of the researcher on data collection as much as possible. Some qualitative researchers also attempt to reduce their own influence on the research. However, others suggest that these approaches subscribe to positivistic ideals, which are inappropriate for qualitative research, 6,9,10 as researchers should not seek to eliminate the effects of their influence on the study but to understand them through reflexivity . 11 Reflexivity is an acknowledgement that, to make sense of the social world, a researcher will inevitably draw on his or her own values, norms, and concepts, which prevent a totally objective view of the social world. 12

Sampling Strategies

Quantitative research favors using large, randomly generated samples, especially if the intent of the research is to generalize to other populations. 6 Instead, qualitative research often focuses on participants who are likely to provide rich information about the study questions, known as purposive sampling . 6

How You Can Start TODAY

• Consider how you can best address your research problem and what philosophical assumptions you are making.
• Consider your ontological and epistemological stance by asking yourself: What can I know about the phenomenon of interest? How can I know what I want to know? W hat approach should I use and why? Answers to these questions might be relatively fixed but should be flexible enough to guide methodological choices that best suit different research problems under study. 5
• Select an appropriate sampling strategy. Purposive sampling is often used in qualitative research, with a goal of finding information-rich cases, not to generalize. 6
• Be reflexive: Examine the ways in which your history, education, experiences, and worldviews have affected the research questions you have selected and your data collection methods, analyses, and writing. 13

How You Can Start TODAY—An Example

Let's assume that you want to know about resident learning on a particular clinical rotation. Your initial thought is to use end-of-rotation assessment scores as a way to measure learning. However, these assessments cannot tell you how or why residents are learning. While you cannot know for sure that residents are learning, consider what you can know—resident perceptions of their learning experiences on this rotation.

Next, you consider how to go about collecting this data—you could ask residents about their experiences in interviews or watch them in their natural settings. Since you would like to develop a theory of resident learning in clinical settings, you decide to use grounded theory as a methodology, as you believe asking residents about their experience using in-depth interviews is the best way for you to elicit the information you are seeking. You should also do more research on grounded theory by consulting related resources, and you will discover that grounded theory requires theoretical sampling. 14,15 You also decide to use the end-of-rotation assessment scores to help select your sample.

Since you want to know how and why students learn, you decide to sample extreme cases of students who have performed well and poorly on the end-of-rotation assessments. You think about how your background influences your standpoint about the research question: Were you ever a resident? How did you score on your end-of-rotation assessments? Did you feel this was an accurate representation of your learning? Are you a clinical faculty member now? Did your rotations prepare you well for this role? How does your history shape the way you view the problem? Seek to challenge, elaborate, and refine your assumptions throughout the research.

As you proceed with the interviews, they trigger further questions, and you then decide to conduct interviews with faculty members to get a more complete picture of the process of learning in this particular resident clinical rotation.

What You Can Do LONG TERM

• Familiarize yourself with published guides on conducting and evaluating qualitative research. 5,16–18 There is no one-size-fits-all formula for qualitative research. However, there are techniques for conducting your research in a way that stays true to the traditions of qualitative research.
• Consider the reporting style of your results. For some research approaches, it would be inappropriate to quantify results through frequency or numerical counts. 19 In this case, instead of saying “5 respondents reported X,” you might consider “respondents who reported X described Y.”
• Review the conventions and writing styles of articles published with a methodological approach similar to the one you are considering. If appropriate, consider using a reflexive writing style to demonstrate understanding of your own role in shaping the research. 6

Supplementary Material

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, what is your plagiarism score.

Economic Costs Attributed to Diagnosed Diabetes in Each U.S. State and the District of Columbia: 2021

• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Cite Icon Cite
• Get Permissions

Olga A. Khavjou , Minglu Sun , Sophia R. D’Angelo , Simon J. Neuwahl , Thomas J. Hoerger , Pyone Cho , Kristopher Myers , Ping Zhang; Economic Costs Attributed to Diagnosed Diabetes in Each U.S. State and the District of Columbia: 2021. Diabetes Care 2024; dc240832. https://doi.org/10.2337/dc24-0832

Download citation file:

• Ris (Zotero)
• Reference Manager

To update state-specific estimates of diabetes-attributable costs in the U.S. and assess changes in spending from 2013 to 2021.

We used an attributable fraction approach to estimate direct medical costs of diagnosed diabetes using the 2021 State Health Expenditure Accounts, the 2021 Behavioral Risk Factor Surveillance System, and the Centers for Medicare and Medicaid Services 2018–2019 Minimum Data Set. We estimated diabetes-attributable productivity losses from morbidity and mortality using the 2016–2021 National Health Interview Survey and the 2021 mortality data from the Centers for Disease Control and Prevention. Costs were adjusted to 2021 U.S. dollars.

Total diabetes-attributable cost in 2021 was $640 billion ($335 billion in direct medical costs and $305 billion in indirect costs). The median state-level total diabetes-attributable cost was $8.2 billion (range $842 million to $81 billion). The median state-level per-person cost was $21,082, ranging from $17,452 to $37,090. Total diabetes-attributable cost increased by a median of 33% between 2013 and 2021, ranging from 16 to 68% across states. Medical costs increased by 50% overall (range 33–79%) and by 27% (range 15–41%) for per person with diabetes. Costs paid by Medicaid experienced the highest increase between 2013 and 2021 (median 153%; range 41–483%).

State economic costs of diagnosed diabetes are substantial and increased over the last decade. These costs and their growth vary considerably across states. These findings may help state policy makers in developing evidenced-based public health interventions in their respective states to prevent and control the prevalence of diabetes.

Graphical Abstract

This article contains supplementary material online at https://doi.org/10.2337/figshare.26351743 .

Sign in via ADA

Sign in via your institution, email alerts.

• Online ISSN 1935-5548
• Print ISSN 0149-5992
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Intake of sugar...

Intake of sugar sweetened beverages among children and adolescents in 185 countries between 1990 and 2018: population based study

• Related content
• Peer review
• Renata Micha , professor 1 3 ,
• Frederick Cudhea , biostatistician 1 ,
• Victoria Miller , research fellow 1 4 5 ,
• Peilin Shi , biostatistician 1 ,
• Jianyi Zhang , biostatistician 6 ,
• Julia R Sharib , researcher 1 ,
• Josh Erndt-Marino , researcher 1 ,
• Sean B Cash , professor 7 ,
• Simon Barquera , director 8 ,
• Dariush Mozaffarian , professor 1 9 10
• on behalf of the Global Dietary Database
• 1 Food is Medicine Institute, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
• 2 Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
• 3 University of Thessaly, Volos, Greece
• 4 Department of Medicine, McMaster University, Hamilton, ON, Canada
• 5 Population Health Research Institute, Hamilton, ON, Canada
• 6 Center for Surgery and Public Health, Brigham and Women’s Hospital Boston, MA, USA
• 7 Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
• 8 Research Center on Nutrition and Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
• 9 Tufts University School of Medicine, Boston, MA, USA
• 10 Division of Cardiology, Tufts Medical Center, Boston, MA, USA
• Correspondence to: L Lara-Castor lauralac{at}uw.edu
• Accepted 11 June 2024

Objective To quantify global intakes of sugar sweetened beverages (SSBs) and trends over time among children and adolescents.

Design Population based study.

Setting Global Dietary Database.

Population Children and adolescents aged 3-19 years in 185 countries between 1990 and 2018, jointly stratified at subnational level by age, sex, parental education, and rural or urban residence.

Results In 2018, mean global SSB intake was 3.6 (standardized serving=248 g (8 oz)) servings/week (1.3 (95% uncertainly interval 1.0 to 1.9) in south Asia to 9.1 (8.3 to 10.1) in Latin America and the Caribbean). SSB intakes were higher in older versus younger children and adolescents, those resident in urban versus rural areas, and those of parents with higher versus lower education. Between 1990 and 2018, mean global SSB intakes increased by 0.68 servings/week (22.9%), with the largest increases in sub-Saharan Africa (2.17 servings/week; 106%). Of 185 countries included in the analysis, 56 (30.3%) had a mean SSB intake of ≥7 servings/week, representing 238 million children and adolescents, or 10.4% of the global population of young people.

Conclusion This study found that intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by 23% from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. SSB intakes showed large heterogeneity among children and adolescents worldwide and by age, parental level of education, and urbanicity. This research should help to inform policies to reduce SSB intake among young people, particularly those with larger intakes across all education levels in urban and rural areas in Latin America and the Caribbean, and the growing problem of SSBs for public health in sub-Saharan Africa.

• Download figure
• Open in new tab
• Download powerpoint

Introduction

In 2015, obesity was estimated to affect more than 100 million children and adolescents, in line with observed increases in body mass index among this population from 1975 to 2016 in most world regions. 1 43 Among the main risk factors for obesity, unhealthy diets play a crucial role. 2 In particular, intake of sugar sweetened beverages (SSBs) has been consistently reported to increase the risk of obesity among children and adolescents. 2 3 This is especially concerning because obesity in childhood tends to persist into adulthood, increasing the risk of type 2 diabetes, cardiovascular disease, and premature mortality. 4 Explanations for the increase in intake of SSBs include globalization of markets, transformation of food systems, aggressive marketing strategies directed at children and adolescents, and lack of (or poor) regulatory measures to limit intake. 5 6 In studies at national and subnational level, policies and strategies such as taxation on sugar sweetened drinks, restrictions on food marketing, regulations for front-of-package labeling, and restrictions at school level have proven to curb the intake of SSBs among children and adolescents. 6 7 8

Although quantifying the intake of SSBs among children and adolescents is critical to further evaluate the impact of these beverages on disease and the effectiveness of policies to control intake, recent national estimates among young people are unavailable for most countries. 6 The lack of such data prevents an analysis of the trends in SSB intake over time, as well as the role of key sociodemographic factors such as age, sex, education, and urbanicity to more accurately inform current and future policies. In this study we present SSB intakes among children and adolescents aged 3-19 years at global, regional, and national level and trends over time from 1990 to 2018, jointly stratified at subnational level by age, sex, parental level of education, and area of residence.

Study design

This investigation is based on a serial cross sectional analysis of SSB intakes from the Global Dietary Database 2018 for 185 countries. Details on the methods and standardized data collection protocol are described in detail elsewhere. 9 10 11 12 13 Compared with the Global Dietary Database 2010, innovations include major expansion of individual level dietary surveys and global coverage up to 2018; inclusion of new data jointly stratified at subnational level by age, sex, education level, and urban or rural residence; and updated modeling methods, covariates, and validation to improve prediction of stratum specific mean intakes and uncertainty. This present analysis focused on children and adolescents aged 3-19 years.

Data sources

The approach and results of our survey search strategy by dietary factor, time, and region are reported in detail elsewhere. 11 We performed systematic online searches for individual level dietary surveys in global and regional databases: PubMed, Embase, Web of science, LILACS, African Index Medicus, and the South-east Asia Index Medicus, using search terms “nutrition” or “diet” or “food habits” or “nutrition surveys” or “diet surveys” or “food habits”[mesh] or “diet”[mesh] or “nutrition surveys”[mesh] or “diet surveys”[mesh] and (“country of interest”). Additionally, we identified surveys through extensive personal communications with researchers and government authorities throughout the world, inviting them to be corresponding members of the Global Dietary Database. The search included surveys that collected data on at least one of 54 foods, beverages, nutrients, or dietary indices, including SSBs. A single reviewer screened identified studies by title and abstract, a random subset of articles was screened by a second reviewer to ensure consistency and accuracy, and a third reviewer screened studies to ensure that survey inclusion criteria were met. Surveys were prioritized if they were performed at national or subnational level and used individual level dietary assessments with standardized 24 hour recalls, food frequency questionnaires, or short standardized questionnaires (eg, Demographic Health Survey questionnaires). When national or subnational surveys at individual level were not identified for a country, we searched for individual level surveys from large cohorts, the World Health Organization (WHO) Global Infobase, and the WHO Stepwise Approach to Surveillance database. When individual level dietary surveys were not identified for a particular country, we considered household budget surveys. We excluded surveys focused on special populations (eg, exclusively pregnant or nursing mothers, individuals with a specific disease) or cohorts (eg, specific occupations or dietary patterns). Supplementary methods 1-3, supplementary tables 1-2, and supplementary figure 1 provide additional details on the methods. The final Global Dietary Database model incorporated 1224 dietary surveys from 185 countries, with 89% representative at national or subnational level, thus covering about 99.0% of the global population in 2018. Among these, 450 surveys reported data on SSBs, 85% of which provided individual level data. These 450 originated from 118 countries and surveyed a total of 2.9 million individuals, with 94% being representative at national or subnational level (see supplementary tables 4 and 5). Supplementary data 1 provides details on the characteristics of the survey.

Data extraction

For each survey, we used standardized methods to extract data on survey characteristics and dietary metrics, units, and mean and standard deviation of intake by age, sex, education level, and urban or rural residence (see supplementary methods 1). 12 All intakes are reported adjusted to 5439 kilojoules (kJ) daily (1300 kilocalories (kcal) daily) for ages 3-5 years, 7113 kJ/day (1700 kcal/day) for ages 6-10 years, and 8368 kJ/day (2000 kcal/day) for ages 11-19 years. SSBs were defined as any beverages with added sugars and ≥209 kJ (50 kcal) for each 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excluded 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. All included surveys used this definition.

Data modeling

Our model estimates intakes of SSBs for years for which we have survey data available. To incorporate and deal with differences in data comparability and sampling uncertainty, we used a bayesian model with a nested hierarchical structure (with random effects by country and region) to estimate the mean consumption of SSBs and its statistical uncertainty for each of 264 population strata across 185 countries for 1990, 1995, 2000, 2005, 2010, 2015, and 2018. Our model incorporated seven world regions: central and eastern Europe and central Asia, high income countries, Latin America and the Caribbean, the Middle East and north Africa, south Asia, southeast and east Asia, and sub-Saharan Africa. Our team and others (eg, the Global Burden of Disease study) have previously used this (or similar) classification for world regions, which aims to group nations by general similarities in risk profiles and disease outcomes. Although the current analysis only focuses on children and adolescents aged 3-19 years, the model used all age data to generate the strata predictions. Modeling all age groups jointly allows the use of the full set of available data and covariates to inform estimates, including age patterns, relationships between predictors and SSB intakes, and influence of covariates (eg, dietary assessment methods).

Primary inputs were the survey level quantitative data on SSB intakes (by country, time, age, sex, education level, and urban or rural residence), survey characteristics (dietary assessment method, type of dietary metric), and country-year specific covariates (see supplementary methods 2). The model included overdispersion of survey level variance for surveys that were not nationally representative or not stratified by smaller age groups (≤10 years), sex, education level, or urbanicity. Survey level covariates addressed potential survey bias, and the overdispersion parameter non-sampling variation due to survey level error (from imperfect study design and quality). The model then estimated intakes jointly stratified by age (<1, 1-2, 3-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, ≥95 years), sex, education (≤6 years, >6-12 years, >12 years), and urbanicity (urban, rural). For children and adolescents (age <20 years) the stratification by education refers to parental education.

The uncertainty of each stratum specific estimate was quantified using 4000 Monte Carlo iterations to determine posterior predictive distributions of mean intake jointly by country, year, and sociodemographic subgroup. We computed the median intake and the 95% uncertainty interval (UI) for each stratum as the 50th, 2.5th, and 97.5th percentiles of the 4000 draws, respectively. For model selection and validation, we compared results from fivefold cross validation (randomly omitting 20% of the survey data at the stratum level and using that to evaluate predictive ability, run five times), compared predicted country intakes with survey observed intakes, assessed implausible estimates (see supplementary table 2), and visually assessed global and national mean intakes using heat maps.

A second bayesian model was used to strengthen time trend estimates for dietary factors (including SSBs) with corresponding available date on food or nutrients from the Food and Agriculture Organization’s food balance sheets 14 or the Global Expanded Nutrient Supply dataset. 15 No time component was formally included in the model; rather, time was captured by the underlying time variation in the model covariates. This second model incorporated country level intercepts and slopes, along with their correlation estimated across countries. The model is commonly referred to as a varying slopes model structure, and it leverages two dimensional partial pooling between intercepts and slopes to regularize all parameters and minimize the risk of overfitting. 16 17 The final presented results are a combination of these two bayesian models, as detailed in supplementary methods 3.

Statistical analysis

Global, regional, national, and within country population subgroup intakes of SSBs and their uncertainty were calculated as population weighted averages using all 4000 posterior predictions for each of the 264 demographic strata in each country-year. Population weights for each year were derived from the United Nations (UN) Population Division, 18 supplemented with data for education and urban or rural status from Barro and Lee 19 and the UN. 20

Intakes were calculated as 248 g (8 oz) servings weekly, or two thirds of a common 355 mL (12 oz) can of a sugar sweetened drink weekly. Absolute changes and percentage changes in consumption between 1990 and 2005, 2005 and 2018, and 1990 and 2018 were calculated at the stratum specific prediction level to account for the full spectrum of uncertainty and standardized to the proportion of individuals within each stratum in 2018 to account for changes in population characteristics over time. Stratum specific predictions were summed to calculate the differences in intake between all children and adolescents aged 3-19 years, high and low parental education (>12 years and ≤6 years, respectively), and urban and rural residence, further stratified by sex, age, parental education, and area of residence, as appropriate.

National intakes of SSBs and trends were assessed by sociodemographic development index, including trends over time between 1990 and 2005, 2005 and 2018, and 1990 and 2018. The sociodemographic development index is a measure of the development of a country or region, ranging from 0 to 1, with 0 representing the minimum level and 1 the maximum level of development of a given nation, and it is based on income per capita, average educational attainment, and fertility rates. 21 Our UIs are derived from a bayesian model and can be interpreted as at least 95% probability that the true mean is contained within the interval. For comparisons between groups (or over time), if the 95% UI of the difference (or change over time) does not include zero, this can be interpreted as at least 95% probability of a true difference. No hypothesis testing was conducted, as estimation with uncertainty has been recognized as a more informative approach. 22

Patient and public involvement

No patients or members of the public were involved in the study as we did not collect data directly from individuals, the funding source did not provide support for direct patient and public involvement, and the study was initiated before patient and public involvement was common. The present analysis used modeled data derived from dietary data that had been previously collected, and we engaged with a diverse set of 320 corresponding members in nations around the world.

Global, regional, and national SSB intakes in 2018

In 2018, the mean global intake of SSBs among children and adolescents was 3.6 (standardized serving=248 g (8 oz)) servings/week (95% UI 3.3 to 4.0), with wide (sevenfold) variation across world regions, from 1.3 servings/week (1.0 to 1.9) in south Asia to 9.1 (8.3 to 10.1) in Latin America and the Caribbean ( table 1 ). Among the 25 countries with the largest population of children and adolescents worldwide, mean highest intakes were in Mexico (10.1 (9.1 to 11.3)), followed by Uganda (6.9 (4.5 to 10.6)), Pakistan (6.4 (4.3 to 9.7)), South Africa (6.2 (4.7 to 8.1)), and the US (6.2 (5.9 to 6.6)); while the lowest intakes were in India and Bangladesh (0.3 servings/week each) ( fig 1 , also see supplementary figure 9). Of the 185 countries included in the analysis, 56 (30.3%) had mean SSB intakes of ≥7 servings/week, representing 238 million young people aged 3-19 years, or 10.4% of the global population for this age group.

Global and regional mean intake of SSBs (248 g (8 oz) serving/week) in children and adolescents aged 3-19 years, by age, sex, parental education, and area of residence across 185 countries in 2018

• View inline

National mean intakes of SSBs (standardized 248 g (8 oz) serving/week for this analysis) in children and adolescents aged 3-19 years across 185 countries in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. For this visual representation, values were truncated at 21 servings/week to better reflect the distribution of intakes globally. The figure was created using the rworldmap package (v1.3-6). SSB=sugar sweetened beverage

SSB intake by sex and age in 2018

Globally, regionally, and nationally, SSB intakes between male and female children and adolescents aged 3-19 years did not differ noticeably, as observed by the 95% UI of the differences including zero ( table 1 , also see supplementary tables 7 and 8). Intake of SSBs in young people was greater with increasing age globally and regionally, although with varying magnitude of these differences by region ( table 1 and fig 2 ). For instance, intakes of SSBs exceeded 9 servings/week among children aged ≥10 years in Latin America and the Caribbean and in the Middle East and north Africa but were just over 1 serving/week among young people of the same age in south Asia. Regionally, patterns of intake by age were similar between young people (see supplementary figure 2). Considering both age and region, the highest weekly intakes of SSBs were in Latin America and the Caribbean in 15-19 year olds (11.5 servings/week) and lowest in southeast and east Asia in 3-4 year olds (0.9 servings/week) ( table 1 ). Among the 25 most populous countries, the highest intakes of SSBs were in Mexico among 10-14 year olds (11.9 servings/week) and 15-19 year olds (12.8 servings/week) and lowest in Kenya and China among 3-4 year olds (0.2 servings/week each) (supplementary table 6).

Global and regional intakes of SSBs (standardized 248 g (8 oz) serving/week for this analysis) by age in children and adolescents aged 3-19 years in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. The filled circles represent the mean SSBs intake (248 g (8 oz) serving/week) and the error bars the 95% UIs. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

SSB intake by parental education and urbanicity in 2018

Intakes of SSBs were greater in children and adolescents from urban areas than those from rural areas (4.6 servings/week (4.2 to 5.0) v 2.7 servings/week (2.4 to 3.1); table 1 ). When parental education and area of residence was assessed jointly, globally the highest intakes of SSBs were among children and adolescents of parents with high education in urban areas (5.15 servings/week (4.76 to 5.64)), representing 11.2% of the global population of children and adolescents ( fig 3 ). Regionally, a similar pattern was observed in Latin America and the Caribbean, south Asia, and sub-Saharan Africa, with the largest intakes of SSBs in children and adolescents of parents with high and medium education in urban and rural areas in Latin America and the Caribbean (≥9 servings/week each), representing 56% of the population of children and adolescents in that region. Intakes of SSBs by area of residence and education were inverted in the Middle East and north Africa, with larger intakes among children and adolescents from rural areas and of parents with lower education, and little variation was observed in other world regions. See supplementary tables 7, 9, and 10, supplementary figures 3 and 4, and supplementary results for further details on SSB intakes by parental education and area of residence.

Global and regional mean SSB intakes (standardized 248 g (8 oz) serving/week for this analysis) in children and adolescents aged 3-19 years by area of residence and parental education level in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. Error bars represent 95% UIs. Values were truncated at 11.5 servings/week to better reflect the distribution of intakes. Upper 95% UIs above that value are shown with a dashed line. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

Trends in SSB intake during 1990-2005, 2005-18, and 1990-2018

Supplementary tables 11-14 and supplementary figures 5-8 show absolute global, regional, and national intakes of SSBs for 1990 and 2005. Globally, from 1990 to 2018, intakes among children and adolescents increased by 0.68 servings/week (95% UI 0.54 to 0.85; 22.9%) ( fig 4 , also see supplementary data 2). The magnitude of global increase was similar from 1990 to 2005 (0.33 (0.25 to 0.43); 11.0%) and from 2005 to 2018 (0.35 (0.26 to 0.47); 10.7%). However, regionally, changes did not follow the same global pattern. Between 1990 and 2005, increases in intakes of SSBs were observed in most regions, with the largest increase in high income countries (1.48 (1.37 to 1.60); 29.1%), little change in central and eastern Europe and central Asia and in south Asia, and a decrease in Latin America and the Caribbean (−1.20 (−1.54 to −0.88); −12.7%). More recently, from 2005 to 2018, increases continued in most regions, with the largest in sub-Saharan Africa (1.38 (1.01 to 1.85); 49.2%), except for south Asia where little change was evident and high income countries where intakes decreased (−1.59 (−1.71 to −1.47); −24.1%). In the overall period from 1990 to 2018, the largest regional increase was in sub-Saharan Africa (2.17 (1.60 to 2.88); 106%), with other world regions showing steady, more modest increases over time. Exceptions were high income countries and Latin America and the Caribbean, where intakes increased after 1990 and then decreased close to 1990 levels by 2018. The supplementary results and supplementary table 15 describe regional trends over time by age, sex, parental education, and urbanicity.

(Top panel) Mean SSB intakes (standardized 248 g (8 oz) serving/week for this analysis) by world region in 1990, 2005, and 2018, and absolute changes from 1990 to 2005, 2005-18, and 1990-2018 in children and adolescents aged 3-19 years. (Bottom panel) Absolute changes in SSB intakes from 1990-2005, 2005-18, and 1990-2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. Error bars represent 95% UIs. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

Among the 25 most populous countries, the largest increase from 1990 to 2005 was in the US (2.95 (2.73 to 3.17); 43.2%) and the largest decrease was in Brazil (−3.42 (−3.95 to −2.97); −40.6%) (see supplementary data 2 and supplementary figure 9). From 2005 to 2018, the largest increase was in Uganda (4.30 (2.31 to 7.39); 173%), and the largest decrease was in the US (−3.55 (−3.81 to −3.30); −36.4%). Overall, between 1990 and 2018, the largest increased was in Uganda (6.73 (4.38 to 10.39); 5573%) and the largest decrease was in Brazil (−3.29 (−3.79 to −2.86); −39.0%) (see supplementary data 2 and supplementary figure 10). The supplementary results and supplementary tables 16-19 show trends over time within the 25 most populous countries by age, sex, parental education, and urbanicity.

SSB intakes and trends by sociodemographic development index and obesity

In 1990 and 2005 a positive correlation was evident between national intakes of SSBs and sociodemographic development index, with greater intakes observed in countries with a higher sociodemographic development index (see supplementary figures 11 and 12). However, this correlation was no longer present in 2018 (r=−0.001, P=0.99). Intakes of SSBs and prevalence of obesity were positively correlated in both 1990 (r=0.28, P<0.001) and 2018 (r=0.23, P<0.001) (see supplementary figure 13).

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by 23% (0.68 servings/week (0.54 to 0.85)) from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. 23 We found a positive correlation between intake of SSBs and prevalence of obesity among children and adolescents in all years. This finding needs particular attention given the incremental economic costs associated with overweight and obesity globally, which are projected to increase from about $2.0tn (£1.6tn; €1.9tn) in 2020 to $18tn by 2060, exceeding 3% of the world’s gross domestic product. 24 Chronic diet related conditions such as obesity have been recognized as part of a global syndemic along with undernutrition given their interaction and shared underlying societal drivers. 25 Tackling drivers of obesity and other diet related diseases among children and adolescents is also critical to be better equipped for potential future pandemics, as cardiometabolic conditions such as obesity, diabetes, and hypertension were top drivers of increased risk of hospital admission and death with covid-19. 26 The increase in intakes of SSBs among children and adolescents corresponded to nearly twice the absolute increase in intake observed among the adult population from 1990 to 2018, for which policies targeting specifically children and adolescents are critical. 13 Young people are particularly appealing to the food industry as they are easily influenced by food marketing, having an effect on not only their current intakes but also their preferences as they develop into adulthood. 27 Their susceptibility to marketing, rising trends in obesity, and accelerated increases in intakes of SSBs underline the necessity for interventions such as taxes, regulations on front-of-package labeling, and regulations in the school environment to curb intakes of SSBs. 6 8 27 28

Changes in intakes of SSBs in children and adolescents from 1990 to 2018 varied substantially by world region. As with the adult population, the largest increase from 1990 to 2018 was in sub-Saharan Africa, emphasizing the need for prompt interventions in this region. Young people in the Middle East and north Africa and in southeast and east Asia showed a more accelerated increase in SSB consumption compared with adults, underlining the importance of policies targeting young people in these regions. The Middle East and north Africa had the second highest intakes of SSBs among children and adolescents in 2018, which differed from our findings among adults, where the Middle East and north Africa occupied third place after sub-Saharan Africa.

Latin America and the Caribbean experienced an overall decrease in intakes of SSBs from 1990 to 2005, which could be attributed to the economic crisis experienced among most of the major economies in the region during this period, 29 in addition to potential greater health awareness as a result of healthy eating campaigns in several countries in the region. 30 In contrast, the increases in intakes in this region from 2005 to 2018 may relate to economic recovery, increased marketing campaigns, and industry opposition to public policies to reduce the intake of SSBs. 31 These findings align with findings in the adult population of this region. 13 Over the past 30 years, Latin America and the Caribbean has undergone an accelerated transformation in the food systems, resulting in wider availability of unhealthy foods, including SSBs, that could explain the large intakes in this region. 7 Moreover, the influence of multinational corporations responsible for ultra-processed foods, marketing strategies targeted at young people, lack of (or poor) regulatory measures to limit the intake of SSBs have also been consistently observed in Latin America and other regions with improving economies. 1 6 7 The use of social media and TV to target advertising at young people has been identified as being especially high in Latin America as well as in the Middle East. 6 27

High income countries experienced an overall decrease in intakes of SSBs from 2005 to 2018. This might be explained by the increasing scientific and public health attention on the harms of SSBs as well as obesity in these nations during this period, which may have led to increased media and public awareness about the harms to health associated with SSBs, wider formulation, promotion, and availability of non-caloric sweetened beverage substitutes, and, more recently, taxation on SSBs in several of these nations. 32

The potential role of sociodemographic factors on intakes of SSBs was evidenced by the large variations in intake by parental education and urbanicity, particularly in south Asia and sub-Saharan Africa, evidencing the need to account for these factors in the design of policies and interventions. At national level, the correlation between intake of SSBs and sociodemographic development index changed from positive in 1990 to null in 2018 (see supplementary figure 11), suggesting that the association between the two might be reversing. This is similar to what was observed in adults, where the association between intake of SSBs and sociodemographic index changed from null to negative from 1990 to 2018. 13 Our new findings show similar directional trends in national and subnational intakes of SSBs among young people compared with adults, 13 although with generally higher absolute intakes among young people, suggesting nation specific influences on SSB intakes are at least partly shared across the lifespan. Further efforts are needed to incorporate data on other social determinants of health, such as income, access to water, access to healthcare, and race/ethnicity to elucidate additional potential heterogeneities.

Strengths and limitations of this study

Our study has several strengths. We assessed and reported global, regional, and national estimates of SSB intakes jointly stratified by age, sex, parental education, and urbanicity among children and adolescents. Compared with previous estimates, our current model included a larger number of dietary surveys, additional demographic subgroups, and years of assessment. Our updated bayesian hierarchical model better incorporated survey and country level covariates—and addressed heterogeneity and uncertainty about sampling and modeling. 13 33 Intakes were estimated from 450 surveys—mostly representative at national and subnational levels and collected at individual level—and represented 87.1% of the world’s population. Other recent estimates for global intakes of SSBs relied mostly on national per capita estimates of food availability (eg, Food and Agriculture Organization food balance sheets) or sales data. 34 Such estimates can substantially overestimate and underestimate intake compared with individual level data 35 and are less robust for characterizing differences across population subgroups. Our estimates are informed by dietary data at individual level collected from both 24 hour recalls (24% of surveys), considered the ideal method for assessing nutritional intakes of populations), and food frequency questionnaires (61% of surveys), a validated approach for measuring intakes of SSBs 36 (see supplementary table 4).

Overall, our findings should be taken as the best currently available, but nonetheless imperfect, estimates of SSB intakes worldwide. Even with systematic searches for all relevant surveys, we identified limited availability of data for several countries (particularly lower income nations) and time periods. 11 Thus, estimated findings in countries with no primary individual level surveys have higher corresponding uncertainty, informing surveillance needs to assess SSBs nationally and in populations at subnational level. Particularly, we identified limited surveys for south Asia (n=9) and sub-Saharan Africa (n=22), which might have affected the accuracy of our estimates in those regions (see supplementary table 4). This finding emphasizes the critical need for further efforts in data collection and surveillance, particularly in these regions. Categorization by age, parental education, and urbanicity were in groups rather than in more nuanced classifications, balancing the interest in subgroup detail versus the realities required from a global demographic effort of de novo harmonized analyses of individual level dietary data from hundreds of different dietary surveys and corresponding members globally. All types of dietary assessments include some errors, whether from individual level surveys, national food availability estimates, or other sources. Our model’s incorporation of multiple types and sources of dietary assessments provided the best available estimates of global diets, as well as the uncertainty of these estimates. For instance, self-reported data rely on the memory and personal biases of the respondents, thus introducing potential bias from underreporting or overreporting of actual intakes. Furthermore, assumptions relating to standardization of serving sizes, SSB definitions, energy adjustment, and disaggregation at household level, as well as of no interaction between sociodemographic variables in our model, could have impacted our estimates. To minimize these limitations, we used standardized approaches and carefully documented each survey’s methods and standardization processes to maximize transparency.

Our definition and data collection on SSBs excluded 100% fruit juice, sugar sweetened milk, tea, and coffee, given that evidence for health effects of these beverages is inconsistent and does not achieve at least probable evidence for causal harms. 37 38 These differences may relate to additional nutrients, such as calcium, vitamin D, fats, and protein in milk, caffeine and polyphenols in coffee and tea, and fiber and vitamins in 100% juice; or to differences in rapidity of consumption and drinking patterns. Each of these beverages is generally also excluded in policy and surveillance efforts around SSBs. A recent meta-analysis suggested a modest positive association between 100% fruit juices and body mass index in children (0.03 units higher for each daily serving), 39 highlighting the need for more research on the health impacts of these and other beverages in children. Sweetened milks are mostly targeted at children and adolescents, and in some regions are mostly consumed by the youngest children. 40 Given that our SSBs definition excluded sweetened milk, this could partially explain the low intakes observed in our study among the youngest age categories. Future studies should also look into characterizing intakes of sweetened milks, especially in countries such as the US, Australia, Pakistan, and Chile where high intakes among children and adolescents have been reported. 40 41 Home sweetened teas and coffees were not explicitly excluded from the definition of SSBs at the time of data collection, but tea and coffee were collected as separate variables and thus most likely excluded by data owners from the SSBs category. SSBs were defined as beverages with added sugars and ≥209 kJ (50 kcal) per 237g serving, capturing most of the SSBs during the time period of our investigation that typically contained about 418 kJ (100 kcal) per serving. More recently, some SSBs with slightly less than 10 g of added sugar have entered the market. As these are a relatively recent addition, their exclusion is unlikely to meaningfully alter our findings, but future research should focus on more refined surveillance of SSBs to allow flexibility in beverage group definitions—for example, similar to the data harmonized in our collaboration with the FAO/WHO GIFT food consumption data tool. 42 Our current definition leveraging product name and caloric content to identify beverages with added sugar across the world ensures consistency in reporting.

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by almost a quarter from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. Policies and approaches at both a national level and a more targeted level are needed to reduce intakes of SSBs among young people worldwide, highlighting the larger intakes across all education levels in urban and rural areas in Latin America and the Caribbean, and the growing problem of SSBs for public health in sub-Saharan Africa. Our findings are intended to inform current and future policies to curb SSB intakes, adding to the UN’s 2030 Agenda for Sustainable Development for improving health and wellbeing, reducing inequities, responsible consumption, poverty, and access to clean water.

What is already known in this topic

The intake of sugar sweetened beverages (SSBs) has been consistently reported to increase the risk of obesity among children and adolescents

This is especially concerning given that obesity in childhood tends to persist into adulthood, increasing the risk of type 2 diabetes, cardiovascular disease, and premature mortality

Quantification of SSB intakes among children and adolescents is therefore critical, yet recent estimates among children and adolescents are unavailable for most nations

What this study adds

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by almost a quarter from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally

Larger intakes were identified across all education levels in urban and rural areas in Latin America and the Caribbean, along with the growing problem of SSBs for public health in sub-Saharan Africa

Intake of SSBs among children and adolescents showed large heterogeneity by region and population characteristics, informing the need for national and targeted policies and approaches to reduce SSB intake among this population worldwide

Ethics statements

Ethical approval.

This investigation was exempt from ethical review board approval because it was based on published deidentified nationally representative data, without personally identifiable information. Individual surveys underwent ethical review board approval required for the applicable local context.

Data availability statement

The individual SSB intake estimate distribution data used in this as means and uncertainty (SE) for each strata in the analysis are available freely online at the Global Dietary Database (Download 2018 Final Estimates: https://www.globaldietarydatabase.org/data-download ). Global Dietary Database data were utilized in agreement with the database guidelines. Absolute and relative differences by strata and by year presented in this analysis were calculated using the 4000 simulations corresponding to the stratum level intake data derived from the bayesian model. The 4000 simulations files can be made available to researchers upon request. Eligibility criteria for such requests include utilization for non-profit purposes only, for appropriate scientific use based on a robust research plan, and by investigators from an academic institution. If you are interested in requesting access to the data, please submit the following documents: (1) proposed research plan (please download and complete the proposed research plan form: https://www.globaldietarydatabase.org/sites/default/files/manual_upload/research-proposal-template.pdf ), (2) data-sharing agreement (please download this form https://www.globaldietarydatabase.org/sites/default/files/manual_upload/tufts-gdd-data-sharing-agreement.docx and complete the highlighted fields, have someone who is authorized to enter your institution into a binding legal agreement with outside institutions sign the document. Note that this agreement does not apply when protected health information or personally identifiable information are shared), (3) email items (1) and (2) [email protected]. Please use the subject line “GDD Code Access Request.” Once all documents have been received, the Global Dietary Database team will be in contact with you within 2-4 weeks about subsequent steps. Data will be shared as .csv or .xlsx files, using a compressed format when appropriate. Population weights for each strata and year were derived from the United Nations Population Division ( https://population.un.org/wpp/ ), supplemented with data for education and urban or rural status from Barro and Lee (doi: 10.3386/w15902 ) and the United Nations ( https://population.un.org/wup/Download/ ).

Acknowledgments

We acknowledge the Tufts University High Performance Computing Cluster ( https://it.tufts.edu/high-performance-computing ), which was used for the research reported in this paper.

Members of the Global Dietary Database (see supplementary text 1 for affiliations)

Antonia Trichopoulou, Murat Bas, Jemal Haidar Ali, Tatyana El-Kour, Anand Krishnan, Puneet Misra, Nahla Hwalla, Chandrashekar Janakiram, Nur Indrawaty Lipoeto, Abdulrahman Musaiger, Farhad Pourfarzi, Iftikhar Alam, Celine Termote, Anjum Memon, Marieke Vossenaar, Paramita Mazumdar, Ingrid Rached, Alicia Rovirosa, María Elisa Zapata, Roya Kelishadi, Tamene Taye Asayehu, Francis Oduor, Julia Boedecker, Lilian Aluso, Emanuele Marconi, Laura D’Addezio, Raffaela Piccinelli, Stefania Sette, Johana Ortiz-Ulloa, J V Meenakshi, Giuseppe Grosso, Anna Waskiewicz, Umber S Khan, Kenneth Brown, Lene Frost Andersen, Anastasia Thanopoulou, Reza Malekzadeh, Neville Calleja, Anca Ioana Nicolau, Cornelia Tudorie, Marga Ocke, Zohreh Etemad, Mohannad Al Nsour, Lydiah M Waswa, Maryam Hashemian, Eha Nurk, Joanne Arsenault, Patricio Lopez-Jaramillo, Abla Mehio Sibai, Albertino Damasceno, Pulani Lanerolle, Carukshi Arambepola, Carla Lopes, Milton Severo, Nuno Lunet, Duarte Torres, Heli Tapanainen, Jaana Lindstrom, Suvi Virtanen, Cristina Palacios, Noel Barengo, Eva Roos, Irmgard Jordan, Charmaine Duante, Corazon Cerdena (retired), Imelda Angeles-Agdeppa (retired), Josie Desnacido, Mario Capanzana (retired), Anoop Misra, Ilse Khouw, Swee Ai Ng, Edna Gamboa Delgado, Mauricio T Caballero, Johanna Otero, Hae-Jeung Lee, Eda Koksal, Idris Guessous, Carl Lachat, Stefaan De Henauw, Ali Reza Rahbar, Alison Tedstone, Annie Ling, Beth Hopping, Catherine Leclercq, Christian Haerpfer, Christine Hotz, Christos Pitsavos, Coline van Oosterhout, Debbie Bradshaw, Dimitrios Trichopoulos, Dorothy Gauci, Dulitha Fernando, Elzbieta Sygnowska, Erkki Vartiainen, Farshad Farzadfar, Gabor Zajkas, Gillian Swan, Guansheng Ma, Hajah Masni Ibrahim, Harri Sinkko, Isabelle Sioen, Jean-Michel Gaspoz, Jillian Odenkirk, Kanitta Bundhamcharoen, Keiu Nelis, Khairul Zarina, Lajos Biro, Lars Johansson, Leanne Riley, Mabel Yap, Manami Inoue, Maria Szabo, Marja-Leena Ovaskainen, Meei-Shyuan Lee, Mei Fen Chan, Melanie Cowan, Mirnalini Kandiah, Ola Kally, Olof Jonsdottir, Pam Palmer, Philippos Orfanos, Renzo Asciak, Robert Templeton, Rokiah Don, Roseyati Yaakub, Rusidah Selamat, Safiah Yusof, Sameer Al-Zenki, Shu-Yi Hung, Sigrid Beer-Borst, Suh Wu, Widjaja Lukito, Wilbur Hadden, Xia Cao, Yi Ma, Yuen Lai, Zaiton Hjdaud, Jennifer Ali, Ron Gravel, Tina Tao, Jacob Lennert Veerman, Mustafa Arici, Demosthenes Panagiotakos, Yanping Li, Gülden Pekcan, Karim Anzid, Anuradha Khadilkar, Veena Ekbote, Irina Kovalskys, Arlappa Nimmathota, Avula Laxmaiah, Balakrishna Nagalla, Brahmam Ginnela, Hemalatha Rajkumar, Indrapal Meshram, Kalpagam Polasa, Licia Iacoviello, Marialaura Bonaccio, Simona Costanzo, Yves Martin-Prevel, Nattinee Jitnarin, Wen-Harn Pan, Yao-Te Hsieh, Sonia Olivares, Gabriela Tejeda, Aida Hadziomeragic, Le Tran Ngoan, Amanda de Moura Souza, Daniel Illescas-Zarate, Inge Huybrechts, Alan de Brauw, Mourad Moursi, Augustin Nawidimbasba Zeba, Maryam Maghroun, Nizal Sarrafzadegan, Noushin Mohammadifard, Lital Keinan-Boker, Rebecca Goldsmith, Tal Shimony, Gudrun B Keding, Shivanand C Mastiholi, Moses Mwangi, Yeri Kombe, Zipporah Bukania, Eman Alissa, Nasser Al-Daghri, Shaun Sabico, Rajesh Jeewon, Martin Gulliford, Tshilenge S Diba, Kyungwon Oh, Sihyun Park, Sungha Yun, Yoonsu Cho, Suad Al-Hooti, Chanthaly Luangphaxay, Daovieng Douangvichit, Latsamy Siengsounthone, Pedro Marques-Vidal, Peter Vollenweider, Constance Rybak, Amy Luke, Nipa Rojroongwasinkul, Noppawan Piaseu, Kalyana Sundram, Jeremy Koster, Donka Baykova, Parvin Abedi, Sandjaja Sandjaja, Fariza Fadzil, Noriklil Bukhary Ismail Bukhary, Pascal Bovet, Yu Chen, Norie Sawada, Shoichiro Tsugane, Lalka Rangelova, Stefka Petrova, Vesselka Duleva, Ward Siamusantu, Lucjan Szponar, Hsing-Yi Chang, Makiko Sekiyama, Khanh Le Nguyen Bao, Sesikeran Boindala, Jalila El Ati, Ivonne Ramirez Silva, Juan Rivera Dommarco, Luz Maria Sanchez-Romero, Simon Barquera, Sonia Rodríguez-Ramírez, Nayu Ikeda, Sahar Zaghloul, Anahita Houshiar-rad, Fatemeh Mohammadi-Nasrabadi, Morteza Abdollahi, Khun-Aik Chuah, Zaleha Abdullah Mahdy, Alison Eldridge, Eric L Ding, Herculina Kruger, Sigrun Henjum, Milton Fabian Suarez-Ortegon, Nawal Al-Hamad, Veronika Janská, Reema Tayyem, Bemnet Tedla, Parvin Mirmiran, Almut Richter, Gert Mensink, Lothar Wieler, Daniel Hoffman, Benoit Salanave, Shashi Chiplonkar, Anne Fernandez, Androniki Naska, Karin De Ridder, Cho-il Kim, Rebecca Kuriyan, Sumathi Swaminathan, Didier Garriguet, Anna Karin Lindroos, Eva Warensjo Lemming, Jessica Petrelius Sipinen, Lotta Moraeus, Saeed Dastgiri, Sirje Vaask, Tilakavati Karupaiah, Fatemeh Vida Zohoori, Alireza Esteghamati, Sina Noshad, Suhad Abumweis, Elizabeth Mwaniki, Simon G Anderson, Justin Chileshe, Sydney Mwanza, Lydia Lera Marques, Samuel Duran Aguero, Mariana Oleas, Luz Posada, Angelica Ochoa, Alan Martin Preston, Khadijah Shamsuddin, Zalilah Mohd Shariff, Hamid Jan Bin Jan Mohamed, Wan Manan, Bee Koon Poh, Pamela Abbott, Mohammadreza Pakseresht, Sangita Sharma, Tor Strand, Ute Alexy, Ute Nöthlings, Indu Waidyatilaka, Ranil Jayawardena, Julie M Long, K Michael Hambidge, Nancy F Krebs, Aminul Haque, Liisa Korkalo, Maijaliisa Erkkola, Riitta Freese, Laila Eleraky, Wolfgang Stuetz, Laufey Steingrimsdottir, Inga Thorsdottir, Ingibjorg Gunnarsdottir, Lluis Serra-Majem, Foong Ming Moy, Corina Aurelia Zugravu, Elizabeth Yakes Jimenez, Linda Adair, Shu Wen Ng, Sheila Skeaff, Regina Fisberg, Carol Henry, Getahun Ersino, Gordon Zello, Alexa Meyer, Ibrahim Elmadfa, Claudette Mitchell, David Balfour, Johanna M Geleijnse, Mark Manary, Laetitia Nikiema, Masoud Mirzaei, Rubina Hakeem

Contributors: LLC, RM, and DM conceived the study. FC, PS, JZ, JRS, JEM, VM, LLC, RM, DM curated the data. FC, LLC, RM, and DM were responsible for the methodology. LLC, JRS, VM, and RM collected the data. FC, PS, JZ, JEM, VM, and LLC developed the software. FC, PS, JZ, VM, LLC, RM, and DM validated the data. LLC, SBC, SB, RM, and DM performed the formal analysis. LLC prepared the original draft of the manuscript. LLC, FC, PS, JZ, JRS, JEM, VM, SBC, SB, RM, and DM wrote, reviewed, and edited the manuscript. LLC generated the original figures and tables; SBC, SB, RM, and DM supervised the analysis, manuscript draft, and generation of figures and tables. LLC, RM, and DM acquired funding. They are the guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This research was supported by the Bill & Melinda Gates Foundation (grant OPP1176682 to DM), the American Heart Association (grant 903679 to LLC), and Consejo Nacional de Ciencia y Tecnología in Mexico (to LLC). This material is based upon work supported by the National Science Foundation under grant number 2018149. The computational resource is under active development by Research Technology, Tufts Technology Services. The funding agencies had no role in the design of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare the following: support from the Bill & Melinda Gates Foundation, American Heart Association, and Consejo Nacional de Ciencia y Tecnología in Mexico. LLC reports research funding from the Bill & Melinda Gates Foundation, the American Heart Association, and Consejo Nacional de Ciencia y Tecnología in Mexico (CONACyT), outside of the submitted work. RM reports research funding from the Bill & Melinda Gates Foundation; and (ended) the US National Institutes of Health, Danone, and Nestle. She also reports consulting from Development Initiatives and as IEG chair for the Global Nutrition Report, outside of the submitted work. FC, JZ, and PS report research funding from the Bill & Melinda Gates Foundation, as well as the National Institutes of Health, outside of the submitted work. VM reports research funding the Canadian Institutes of Health Research and from the American Heart Association, outside the submitted work. JRS reports research funding from the Bill & Melinda Gates Foundation, as well as the National Institutes of Health, Nestlé, Rockefeller Foundation, and Kaiser Permanent Fund at East Bay Community Foundation, outside of the submitted work. SBC reports research funding from the US. National Institutes of Health, US. Department of Agriculture, the Rockefeller Foundation, US. Agency for International Development, and the Kaiser Permanente Fund at East Bay Community Foundation, outside the submitted work. SB reports funding from Bloomberg Philanthropies, CONACyT, United Nations International Children’s Emergency Fund (Unicef), and Fundación Rio Arronte, outside the submitted work. DM reports research funding from the US National Institutes of Health, the Bill & Melinda Gates Foundation, the Rockefeller Foundation, Vail Innovative Global Research, and the Kaiser Permanente Fund at East Bay Community Foundation; personal fees from Acasti Pharma, Barilla, Danone, and Motif FoodWorks; is on the scientific advisory board for Beren Therapeutics, Brightseed, Calibrate, Elysium Health, Filtricine, HumanCo, Instacart, January, Perfect Day, Tiny Organics, and (ended) Day Two, Discern Dx, and Season Health; has stock ownership in Calibrate and HumanCo; and receives chapter royalties from UpToDate, outside the submitted work. The investigators did not receive funding from a pharmaceutical company or other agency to write this report, and declare no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (LLC) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: Our research will be disseminated to the scientific community in a scientific conference and scientific publications; to the public through our website and social media; and to funders and interested ministries in various nations through presentations and brief reports.

Provenance and peer review: Not commissioned; externally peer reviewed.

Publisher’s note: Published maps are provided without any warranty of any kind, either express or implied. BMJ remains neutral with regard to jurisdictional claims in published maps.

Editor’s note: The visual abstract was included in this article on 9 August 2024 post-publication.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

• Mahumud RA ,
• Owusu-Addo E ,
• Morton RL ,
• Renzaho AMN
• Yoshida Y ,
• Weihrauch-Blüher S ,
• Potvin Kent M ,
• de Billy N ,
• Neufeld LM ,
• Andrade EB ,
• Ballonoff Suleiman A ,
• Popkin BM ,
• Corvalán C ,
• Khatibzadeh S ,
• Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE)
• Andrews KG ,
• Engell RE ,
• Mozaffarian D ,
• Global Dietary Database
• Lara-Castor L ,
• ↵ Food and Agriculture Organization of the United Nations. Food balances. 2021. https://www.fao.org/faostat/en/#data (accessed 5 July 2022).
• Golden CD ,
• Diefenbach DR ,
• Christensen S ,
• ↵ United Nations Population Division. Total population by sex (thousands). 2019. https://population.un.org/wpp/
• ↵ United Nations Population Division. Urban population (% of total population). World Urbanization Prospects, 2018. https://population.un.org/wup/Download/
• ↵ Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Socio-Demographic Index (SDI) 1950-2019. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME); 2020. https://ghdx.healthdata.org/record/ihme-data/gbd-2019-socio-demographic-index-sdi-1950-2019
• Kruschke JK ,
• NCD Risk Factor Collaboration (NCD-RisC)
• ↵ World Obesity Federation. The Economic Impact of Overweight & Obesity 2nd Edition with Estimates for 161 Countries, 2022. https://data.worldobesity.org/publications/
• Swinburn BA ,
• Allender S ,
• O’Hearn M ,
• Mozaffarian D
• ↵ Lobstein T. Reducing consumption of sugar-sweetened beverages to reduce the risk of childhood overweight and obesity. 2014. https://www.who.int/elena/bbc/ssbs_childhood_obesity/en/2021 ).
• ↵ Murphy K. Causes of Chaos: Understanding Economic Crises in Latin America [dissertation]. University of New Hampshire; 2020.
• Carriedo A ,
• Encarnación LM ,
• Herforth A ,
• Sundberg S ,
• Del Gobbo LC ,
• Imamura F ,
• ↵ Willett W. Nutritional Epidemiology, 3rd edn, Monographs in Epidemiology and Biostatistics (2012; online edn, Oxford Academic, 24 Jan 2013). doi: https://doi.org/10.1093/acprof:oso/9780199754038.001.0001
• O’Connor L ,
• Karageorgou D ,
• Mozaffarian D.
• Jarvis SE ,
• Chiavaroli L ,
• Fayet-Moore F
• Lara Castor L ,
• Padula de Quadros V ,
• ↵ NCD Risk Factor Collaboration (NCD-RisC) . Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults . Lancet 2017 ; 390 : 2627 - 42 . OpenUrl CrossRef PubMed

Effect of coal-fired power plant flexible operating method on boiler header fatigue life

• Original Article
• Published: 12 August 2024

Cite this article

• Jeong Myun Kim 1 ,
• Karam Han 1 ,
• Byeong Seon Choi 1 ,
• Seung Heon Song 1 ,
• Mingyu Park 1 &
• Myung Hwan Choi 2  

Due to the increase in renewable power generation sources, flexible operation is being required for large-capacity coal-fired power plants. Fatigue needs to be considered because the possibility of fatigue damage to equipment increases compared with rated operation. Existing studies qualitatively analyzed the impact on power generation facilities, and quantitative comparison studies were not conducted. Accordingly, this study comparatively analyzed the impact of flexible operation methods on the lifetime of boiler headers. Finite element analysis models were created to analyze stress in transient operation conditions, and fatigue rupture life was derived using fatigue test data from previous research. Then, as a result of assessing the fatigue life according to design criteria, the low load operation mode was confirmed to have a fatigue life 2.57 to 4.61 times longer than the start & stop mode. This study is intended to provide technical information for decision-making on flexible operation methods of power plants.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Abbreviations

Convection heat transfer

Convection coefficient

Temperature of inner area

Temperature of outer area

Conduction heat transfer

Conduction coefficient

Inner surface temperature of header

Outer surface temperature of header

Wall thickness of header

Nusselt number

Reynolds number

Prandtl number

Pipe internal diameter

Thermal conductivity

Specific heat

Fatigue strength coefficient

Fatigue strength exponent

Fatigue ductility coefficient

Fatigue ductility exponent

Cycles to failure

Damage fraction

Number of cycles at the time of assessment

Total fatigue life

D. Paul, O. Matthew, B. Gregory and J. Jennie, Overgeneration from Solar Energy in California: A Field Guide to the Duck Chart , National Renewable Energy Laboratory, U.S.A. (2015) 3–4.

Google Scholar  

C. Chitic Foldi, Steam Turbine Flexible Operations Update , Electric Power Research Institute, U.S.A. (2020) 19.

R. Roberts, Impact of Cycling on the Operation and Maintenance Cost of Conventional and Combined-Cycle Power Plants , Electric Power Research Institute, U.S.A. (2013) 60–84.

Y. Zhao, P. Fan, M. Liu, D. Chong and J. Yan, Fatigue lifetime estimation of a heater in coal-fired power plants under a flexible operational framework-regulating extraction steam of high-pressure heaters, 10 th International Conference on Applied Energy (ICAE 2018) , Hong-Kong, China (2018) 5225–5230.

H. Oesterlin and G. Maier, Numerical assessment of thick walled power plant components under creep-fatigue load with advanced models for deformation and lifetime, Materials at High Temperatures , 31 (4) (2014) 364–369.

Article   Google Scholar  

S. Storm, Flexible Operations Cost Management Tool , Electric Power Research Institute, U.S.A (2021).

J. Shi, W. Bell and A. M. Laird, Fatigue life assessment of boiler components under cyclic operating conditions, The Eleventh International Conference on Pressure Vessel Technology (ICPVT-11) , Vancouver, Canada (2006).

BS EN 12952-3:2021, Water-tube Boilers and Auxiliary Installations - Part 3: Design and Calculation for Pressure Parts of the Boiler , European Committee for Standardization, UK (2021) 99–115.

BS EN 12952-4:2011, Water-tube Boilers and Auxiliary Installations - Part 4: In-service Boiler Life Expectancy Calculations , European Committee for Standardization, UK (2011) 11–18.

R. Roberts, Flexible Operations Readiness Guide , Electric Power Research Institute, U.S.A. (2013).

S. Rosinski, Effect of Flexible Operation on Boiler Components–Theory and Practice, Vol. 3, Steam Touched Components , Electric Power Research Institute, U.S.A. (2018) 119–120.

B. Dogan, Plant Component Assessment for Creep-Fatigue Damage: Component Assessment Methodologies , Electric Power Research Institute, U.S.A. (2009).

G. J. Nakoneczny and C. C. Schultz, Life assessment of high temperature headers, Proceedings of the American Power Conference , Chicago, U.S.A., 57-III (1995) 3–4.

K. I. Shin and K. B. Yoon, Thermal stress evaluation by elastic-creep analysis during start-up of boiler header, Journal of the KOSOS , 24 (2) (2009) 17–22.

D. Richardot and J. C. Vailant, The T91/P92 Book , Vallourec & Mannesmann Tubes, U.S.A. (2000) 9–27.

M. Caravaggio, Handbook for the Economic Flexible Operation of Gas and Coal-Fired Power Plants , Electric Power Research Institute, U.S.A. (2020).

J. S. Park and H. H. Cho, Prediction of surface and steam temperature in internal heat exchanger of usc boiler according to inlet temperature distribution, KSFM Journal of Fluid Machinery , Korea, 20 (6) (2017) 49–56.

J. Okrajni and M. Twardawa, Description of the thermo-mechanical fatigue of thick-walled pressure vessels, 20 th European Conference on Fracture (ECF20) (2014) 918–923.

ASM International, ASM Handbook Volume 11: Failure Analysis and Prevention , ASM International, U.S.A. (2021).

H. Y. Lee and Y. S. Joo, A method on integrity evaluation with high reliability for superheater structure in a supercritical thermal power plant, Transaction of the Korean Society of Pressure Vessels and Piping , 16 (1) (2020) 65–73.

J. A. Bannantine and J. J. Comer, Fundamentals of Metal Fatigue Analysis , Prentice-Hall, U.S.A. (1990) 40–72.

S. S. Manson, Fatigue: A Complex Subject – Some Simple Approximations , Lewis Research Center, NASA, Cleveland, Ohio, U.S.A. (1964) 12–20.

U. Muralidharan and S. S. Manson, A modified universal slopes equation for estimation of fatigue characteristics of metals, Journal of Engineering Materials & Technology , 110 (1) (1988) 3595–3601.

E. Zhao, Q. Zhou, W. Qu and W. Wang, Fatigue properties estimation and life prediction for steels under axial, torsional, and in-phase loading, Advances in Materials Science and Engineering (2020) 1–8.

A. Baumel Jr. and T. Seeger, Materials Data for Cyclic Loading , Elsevier Science Publishers, Amsterdam, Netherland (1990).

J. C. McMahon and F. V. Lawrence, Predicting Fatigue Properties through Hardness Measurements , University of Illinois at Urbana-Champaign, Illinois, U.S.A. (1984).

M. Roessle and A. Fatemi, Strain-controlled fatigue properties of steels and some simple approximations, International Journal of Fatigue , 22 (6) (2000) 495–511.

D. Gandy and J. Parker, Program on Technology Innovation: Evaluation of the Creep-Fatigue Behavior of Grade 92 Steel and Its Predictability , Electric Power Research Institute, U.S.A. (2011) 52–70.

M. Jürgens and J. Olbricht, Low cycle fatigue and relaxation performance of ferritic-martensitic grade P92 steel, Metals , 9 (1) (2019) 99.

Z. Zhang and H. Tu, Experimental and numerical investigation of low-cycle fatigue behavior of 9Cr ferritic-martensitic steel at room temperature, Mat Design Process Comm. , 2 (1) (2020).

O. K. Chopra and W. J. Shack, Review of the Margins for ASME Code Fatigue Design Curve – Effects of Surface Roughness and Material Variability , Argonne National Laboratory, U.S.A. (2003) 11–43.

Book   Google Scholar  

Download references

Author information

Authors and affiliations.

Korea Electric Power Research Institute, 105, Munji-ro, Yuseong-gu, Daejeon, Korea

Jeong Myun Kim, Karam Han, Byeong Seon Choi, Seung Heon Song & Mingyu Park

Korea Electric Power Corporation, 55, Jeollyeok-ro, Naju-si, Jeollanam-do, Korea

Myung Hwan Choi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jeong Myun Kim .

Additional information

Jeong Myun Kim is a Senior Researcher with the KEPCO Research Institute. He received an M.D. in Mechanical Design Engineering from Chungnam National University. His research interests includes diagnostics and life assessment of pressure vessels for thermal power plant.

Rights and permissions

Reprints and permissions

About this article

Kim, J.M., Han, K., Choi, B.S. et al. Effect of coal-fired power plant flexible operating method on boiler header fatigue life. J Mech Sci Technol (2024). https://doi.org/10.1007/s12206-024-0844-z

Download citation

Received : 14 December 2023

Revised : 12 May 2024

Accepted : 20 May 2024

Published : 12 August 2024

DOI : https://doi.org/10.1007/s12206-024-0844-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Flexible operation
• Daily start & stop
• Low load operation
• Fatigue life
• Find a journal
• Publish with us
• Track your research