study vs research

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💡 Taught Masters vs Research Masters:

There are many types of Master’s degrees, and most of these are Taught Masters . In the United Kingdom, such programmes are also called postgraduate taught or PGT for short. They typically require completing a set number of modules and a thesis (also called dissertation), plus sometimes work experience through a placement. The type of dissertation you will undertake will depend on the type of Master’s degree you are enrolled in, and might for example include your own small research project. Most postgraduate taught degrees have these elements of independent work and research to varying extents.

In contrast, a Research Master ’ s degree will focus on, you guessed it, research. In the UK, such programmes are also called postgraduate research or PGR (although this term may also refer to doctorate programmes). Rather than attending classes every semester and completing module assignments, you will need to focus on an independent research project – under supervision, of course. The course will still include a few taught modules, most often on research methodologies, but it will require you to work independently for most of the time.

Master’s degrees in the UK are usually one year full-time or two years part-time, but in other countries the duration may differ.

Remember: A final dissertation will be compulsory for all Master ’ s degrees. However, a dissertation for an MRes will typically be longer than that for an MA or MSc.

Typically, Research Masters will lead to an MRes degree. At some universities, however, you'll instead be awarded an MPhil (Master of Philosophy) or MLitt (Master of Letters). For more information, have a look at our detailed glossary.

🤔 Is a Research Master’s the same as a PhD?

No, a Research Master’s degree is not the same as a PhD. Although for both degrees you will need to complete a dissertation based on an independent research project, there are notable differences:

• The first difference is the duration : A Master’s degree will typically last one to two years, while a PhD usually takes up about three to five years. The research project you’ll undertake during a doctorate degree will therefore be longer and broader than one you would pursue in a Master’s degree.
• As a PhD student, you’re expected to publish research papers in journals before you are awarded your degree. MRes students might occasionally do that during or after their studies, but it’s rarely obligatory.
• As a PhD student, you’ll most often be expected to take on other duties , such as teaching.

If you wish to pursue doctoral research and a career in academia, a research Master’s degree could be a great option for you as it will allow you to get to grips with and gather valuable experience and training on independent research early on in your studies.

👀 Overview: What’s the difference?

There are a few differences between Taught Masters and Research Masters , and not all of them are obvious.

The table below outlines some of the main elements to consider when choosing which of the two degrees to pursue after your Bachelor’s degree:

Study in Europe: Find your Master ’ s degrees

🏛️ Which should you choose?

The choice between a taught Master’s and a research Master’s depends on a few factors.

• First of all, do you enjoy research more than coursework? Then an MRes may be more suitable – but remember that any Master’s degree, especially an MSc, will have a research component.
• Then, it’s crucial to understand how you like to work and study. Do you particularly enjoy working independently? Perhaps then you can consider an MRes. In a taught Master’s, you’ll have a more solid structure, timetables and regular deadlines to keep you on track, but these may not be as readily available during an MRes, so consider which environment you are more likely to thrive in.  
• Another important consideration is what you want to do after your Master’s degree. If you want to enter the labour market immediately, and you are not particularly interested in focusing on research training, then perhaps a taught Master’s degree is more suited.

💸 Is there a difference in fees between Taught and Research Masters?

No, normally, you won’t find a huge difference in tuition fees between taught and research Master’s degrees. Only in some instances, Postgraduate Research Masters tend to be cheaper.

📝 Can I do a PhD after taking a Taught Master’s Degree?

Yes, you can pursue a PhD after any type of Master’s course, provided that you have a degree in a relevant subject. All taught postgraduate degrees involve some independent work and research, especially for your dissertation, which will prepare you for further research should you choose to pursue a PhD.

Some taught Masters require more independent research work than others, particularly when it comes to the dissertation after completing the taught modules. Consult the curriculum or ask admissions staff to get a better idea of what to expect.

While a taught Master’s degree won’t prevent you from doing a PhD further down the line, it’s vital that you have a good idea of what requirements you will have to fulfil in order to be admitted to the PhD, and how you can best prepare.

If you already have a clear idea of what field you’d like to conduct your doctoral research in, you could take advantage of the joint Master’s – PhD programmes on offer at some universities.

These four-year programmes – also called “combined” or “integrated” degrees – offer the chance to complete a Master’s degree in the first year and to progress seamlessly to PhD research in the next three.

Looking for Masters in Europe? Have a look at these English-taught degrees 👀

Author: Claudia Civinini

Claudia has many years of experience as a reporter and writer on international education and student mobility. Originally from Italy, she holds a BA in Communication and Media Studies from the University of Genova; a Graduate Diploma in Education, Secondary Education and Teaching from the Australian Catholic University; and a joint MSc in Educational Neuroscience from UCL and Birkbeck, University of London. Claudia has previously worked as Chief Reporter for the English Language Gazette, as Senior Reporter for the PIE News (Professionals in International Education), and as Reporter for Tes.

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study vs research

• Thread starter muskulpesent
• Start date May 20, 2020

muskulpesent

• May 20, 2020

Can study and research be used interchangeably? Is there any difference?  

Senior Member

Are you asking about the nouns or the verbs? Please give us a sentence in which you think that study and research are or possibly are interchangeable.  

Nouns. For example: This research aims to investigate studies about online counseling both in Turkey and USA. Can i use study instead of research in this sentence?  

You have already used studies in your sentence, so you would not use it twice. Research can sometimes mean a study. I think of study as a paper, i.e. the result of a study published in a journal. In this sense a study is part of research into something. Research is a broader term involving making investigations into a subject, and may include looking at various studies. A scientist may carry out research in an area and his or her research may encompass a number of studies.  

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Experimental vs Observational Studies: Differences & Examples

Understanding the differences between experimental vs observational studies is crucial for interpreting findings and drawing valid conclusions. Both methodologies are used extensively in various fields, including medicine, social sciences, and environmental studies. 

Researchers often use observational and experimental studies to gather comprehensive data and draw robust conclusions about their investigating phenomena. 

This blog post will explore what makes these two types of studies unique, their fundamental differences, and examples to illustrate their applications.

What is an Experimental Study?

An experimental study is a research design in which the investigator actively manipulates one or more variables to observe their effect on another variable. This type of study often takes place in a controlled environment, which allows researchers to establish cause-and-effect relationships.

Key Characteristics of Experimental Studies:

• Manipulation: Researchers manipulate the independent variable(s).
• Control: Other variables are kept constant to isolate the effect of the independent variable.
• Randomization: Subjects are randomly assigned to different groups to minimize bias.
• Replication: The study can be replicated to verify results.

Types of Experimental Study

• Laboratory Experiments: Conducted in a controlled environment where variables can be precisely controlled.
• Field Research : These are conducted in a natural setting but still involve manipulation and control of variables.
• Clinical Trials: Used in medical research and the healthcare industry to test the efficacy of new treatments or drugs.

Example of an Experimental Study:

Imagine a study to test the effectiveness of a new drug for reducing blood pressure. Researchers would:

• Randomly assign participants to two groups: receiving the drug and receiving a placebo.
• Ensure that participants do not know their group (double-blind procedure).
• Measure blood pressure before and after the intervention.
• Compare the changes in blood pressure between the two groups to determine the drug’s effectiveness.

What is an Observational Study?

An observational study is a research design in which the investigator observes subjects and measures variables without intervening or manipulating the study environment. This type of study is often used when manipulating impractical or unethical variables.

Key Characteristics of Observational Studies:

• No Manipulation: Researchers do not manipulate the independent variable.
• Natural Setting: Observations are made in a natural environment.
• Causation Limitations: It is difficult to establish cause-and-effect relationships due to the need for more control over variables.
• Descriptive: Often used to describe characteristics or outcomes.

Types of Observational Studies: 

• Cohort Studies : Follow a control group of people over time to observe the development of outcomes.
• Case-Control Studies: Compare individuals with a specific outcome (cases) to those without (controls) to identify factors that might contribute to the outcome.
• Cross-Sectional Studies : Collect data from a population at a single point to analyze the prevalence of an outcome or characteristic.

Example of an Observational Study:

Consider a study examining the relationship between smoking and lung cancer. Researchers would:

• Identify a cohort of smokers and non-smokers.
• Follow both groups over time to record incidences of lung cancer.
• Analyze the data to observe any differences in cancer rates between smokers and non-smokers.

Difference Between Experimental vs Observational Studies

Choosing Between Experimental and Observational Studies

The researchers relied on statistical analysis to interpret the results of randomized controlled trials, building upon the foundations established by prior research.

Use Experimental Studies When:

• Causality is Important: If determining a cause-and-effect relationship is crucial, experimental studies are the way to go.
• Variables Can Be Controlled: When you can manipulate and control the variables in a lab or controlled setting, experimental studies are suitable.
• Randomization is Possible: When random assignment of subjects is feasible and ethical, experimental designs are appropriate.

Use Observational Studies When:

• Ethical Concerns Exist: If manipulating variables is unethical, such as exposing individuals to harmful substances, observational studies are necessary.
• Practical Constraints Apply: When experimental studies are impractical due to cost or logistics, observational studies can be a viable alternative.
• Natural Settings Are Required: If studying phenomena in their natural environment is essential, observational studies are the right choice.

Strengths and Limitations

Experimental studies.

• Establish Causality: Experimental studies can establish causal relationships between variables by controlling and using randomization.
• Control Over Confounding Variables: The controlled environment allows researchers to minimize the influence of external variables that might skew results.
• Repeatability: Experiments can often be repeated to verify results and ensure consistency.

Limitations:

• Ethical Concerns: Manipulating variables may be unethical in certain situations, such as exposing individuals to harmful conditions.
• Artificial Environment: The controlled setting may not reflect real-world conditions, potentially affecting the generalizability of results.
• Cost and Complexity: Experimental studies can be costly and logistically complex, especially with large sample sizes.

Observational Studies

• Real-World Insights: Observational studies provide valuable insights into how variables interact in natural settings.
• Ethical and Practical: These studies avoid ethical concerns associated with manipulation and can be more practical regarding cost and time.
• Diverse Applications: Observational studies can be used in various fields and situations where experiments are not feasible.
• Lack of Causality: It’s easier to establish causation with manipulation, and results are limited to identifying correlations.
• Potential for Confounding: Uncontrolled external variables may influence the results, leading to biased conclusions.
• Observer Bias: Researchers may unintentionally influence outcomes through their expectations or interpretations of data.

Examples in Various Fields

• Experimental Study: Clinical trials testing the effectiveness of a new drug against a placebo to determine its impact on patient recovery.
• Observational Study: Studying the dietary habits of different populations to identify potential links between nutrition and disease prevalence.
• Experimental Study: Conducting a lab experiment to test the effect of sleep deprivation on cognitive performance by controlling sleep hours and measuring test scores.
• Observational Study: Observing social interactions in a public setting to explore natural communication patterns without intervention.

Environmental Science

• Experimental Study: Testing the impact of a specific pollutant on plant growth in a controlled greenhouse setting.
• Observational Study: Monitoring wildlife populations in a natural habitat to assess the effects of climate change on species distribution.

How QuestionPro Research Can Help in Experimental vs Observational Studies

Choosing between experimental and observational studies is a critical decision that can significantly impact the outcomes and interpretations of a study. QuestionPro Research offers powerful tools and features that can enhance both types of studies, giving researchers the flexibility and capability to gather, analyze, and interpret data effectively.

Enhancing Experimental Studies with QuestionPro

Experimental studies require a high degree of control over variables, randomization, and, often, repeated trials to establish causal relationships. QuestionPro excels in facilitating these requirements through several key features:

• Survey Design and Distribution: With QuestionPro, researchers can design intricate surveys tailored to their experimental needs. The platform supports random assignment of participants to different groups, ensuring unbiased distribution and enhancing the study’s validity.
• Data Collection and Management: Real-time data collection and management tools allow researchers to monitor responses as they come in. This is crucial for experimental studies where data collection timing and sequence can impact the results.
• Advanced Analytics: QuestionPro offers robust analytical tools that can handle complex data sets, enabling researchers to conduct in-depth statistical analyses to determine the effects of the experimental interventions.

Supporting Observational Studies with QuestionPro

Observational studies involve gathering data without manipulating variables, focusing on natural settings and real-world scenarios. QuestionPro’s capabilities are well-suited for these studies as well:

• Customizable Surveys: Researchers can create detailed surveys to capture a wide range of observational data. QuestionPro’s customizable templates and question types allow for flexibility in capturing nuanced information.
• Mobile Data Collection: For field research, QuestionPro’s mobile app enables data collection on the go, making it easier to conduct studies in diverse settings without internet connectivity.
• Longitudinal Data Tracking: Observational studies often require data collection over extended periods. QuestionPro’s platform supports longitudinal studies, allowing researchers to track changes and trends.

Experimental and observational studies are essential tools in the researcher’s toolkit. Each serves a unique purpose and offers distinct advantages and limitations. By understanding their differences, researchers can choose the most appropriate study design for their specific objectives, ensuring their findings are valid and applicable to real-world situations.

Whether establishing causality through experimental studies or exploring correlations with observational research designs, the insights gained from these methodologies continue to shape our understanding of the world around us. 

Whether conducting experimental or observational studies, QuestionPro Research provides a comprehensive suite of tools that enhance research efficiency, accuracy, and depth. By leveraging its advanced features, researchers can ensure that their studies are well-designed, their data is robustly analyzed, and their conclusions are reliable and impactful.

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Study vs research vs learn.

As I understood, study is trying to get the knowledge, learn is when you get the knowledge, and research is when you make new knowledge based of the existing knowledge. Am I right?

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Teen brains 'aged' during Covid lockdowns, new research suggests

Covid lockdowns, such as school closures, canceled sports activities and stay-at-home orders, prematurely aged teen brains by as much as four years, researchers from the University of Washington found. 

The new study, published Monday in the Proceedings of the National Academy of Sciences, is more evidence of how disruptions to daily routines may have contributed to behavioral problems, an increase in eating disorders , anxiety and depression in adolescent girls and boys . 

Scientists at the university's Institute for Learning & Brain Sciences  (I-LABS) began the study using MRIs in 2018 to see how the brain structure of 160 teens from the Seattle area developed over time. The participants, a nearly equal number of boys and girls, ranged in age from 9 to 19 at the start of the study. 

Lead researcher Patricia Kuhl, co-director of I-LABS, said that after Covid lockdowns began in 2020, they couldn’t do brain scan follow-ups until 2021. So they shifted the focus of the study to learn how the lockdowns had affected adolescent brain structure.

By measuring the thickness of the cerebral cortex — the outer layer of tissue in the brain which controls higher level functions of the brain like reasoning and decision-making — they discovered the brains of teen boys had prematurely aged 1.4 years. The brain scans of the girls showed accelerated aging of 4.2 years, according to the study.

The cerebral cortex naturally thins as we age. Chronic stress can also cause similar changes in the brain. But in the three-year period between the first scan and the follow-up, there was much more thinning than the researchers expected.

“As we age, the thinning of the cortex is associated with less fast-processing time, with less flexible thinking, with all of the things that we associate with aging,” Kuhl said. “All of the teens in general showed this accelerated aging.”

For teenage girls the aging was more pronounced. The thinning was found to be widespread throughout the female brain, occurring in 30 regions across both hemispheres and all lobes, the scans showed. In the male brain, the thinning was limited to only two regions, both in the occipital lobe, which affects distance and depth perception, face recognition and memory. 

The greater influence on girls could be due to differences in the importance of social interaction for girls versus boys, Kuhl said. Boys tend to gather for sports and physical activity. Adolescent girls may rely on personal relationships for emotional support and self-identity.

“When girls and women are stressed, there is a natural response to get together and talk about it, and we release oxytocin and other neurotransmitters that make us feel better, said Dr. Ellen Rome, head of adolescent medicine at Cleveland Clinic Children’s Hospital. Rome was not involved in the new research. 

What does the premature brain aging mean in the day-to-day lives of young people who navigated pandemic restrictions, often alone in their rooms, taking classes over Zoom or missing social connections? 

Are there long-term risks for ‘pandemic brains’?

The research doesn’t prove the lockdowns caused the brain changes — mental health disorders were rising among children even before Covid. It does suggest, however, that the thinning of the cortex can be connected to increased anxiety, depression and other behavioral disorders, Kuhl said. 

Another brain scan study in 2022 from Stanford University showed similar changes in cortical thickness in teen brains during Covid restrictions. The Stanford researchers compared the stress and disruptions of the pandemic to childhood trauma such as violence, neglect and family dysfunction. 

The pandemic was a traumatic time for everyone, Kuhl said. For young people — at a time in their lives when they’re already experiencing intense changes in their emotional and behavioral development — the isolation was even more damaging for their emotional health. 

“The pandemic was dramatic and unexpected, of course, but dramatic and catastrophic in a way, not only for physical health, but mental health,” she said.

Since 2021, several reports on youth mental health from the Centers for Disease Control and Prevention revealed unprecedented levels of hopelessness and suicidal thoughts among teenage girls and boys. In early August, a CDC survey released found a small improvement in teen mental health, although 53% of young women in high school say they are still feeling persistent sadness. 

In brain development there are periods of time when certain types of learning are most effective, said Dr. Jonathan Posner, professor of psychiatry at Duke University School of Medicine. For example, it’s much easier to learn language as a young child than as an adult.

“The teenage years are hugely important for social development,” said Posner, who was not part of the new study. “If you don’t have those social interactions, there’s just not the opportunity to have that social learning.” 

The cortex can’t regrow and continues to shrink throughout life. It’s not yet clear whether the young people’s prematurely aging “pandemic brains” could be at higher risk for disorders like ADHD and depression and, possibly, even diseases like Alzheimer and Parkinson, Kuhl said.

Seattle mom Karin Zaugg Black, 54, saw how the pandemic affected her two children during more than a year of remote school classes. Her daughter Delia, 14, was in seventh grade; Sam, 10, in fourth. 

Delia especially felt the loss of social interaction. 

“When she reflects back on that time, she was definitely like, 'Yeah, it was really hard. I felt like I had no friends, and that was really difficult,'” Black said about her daughter’s time during the pandemic.

“Their social skills are behind. You know, sort of they lost that ability to navigate in the social world with their peers,” she said.

The good news is Delia, now a senior in high school, has regained a lot of the social interaction muscle she lost during the pandemic. 

Experts say the loss doesn’t have to be permanent if young people’s social interactions and connections have recovered since the pandemic. 

“Fortunately, kids are really resilient, and we can get them back out there and help them catch up,” said Posner. “But we also don’t want to deceive ourselves that this was nothing. It had a significant impact on growth and development.”

Patrick Martin is a producer in the NBC News Health & Medical Unit. 

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Nature vs. nurture: the interplay between a child’s mind and environment

• Mental Health
• School of Education and Social Policy

While the mental health crisis has touched the lives of young people across a broad age spectrum, new Northwestern University research has found that the presence of difficult social environments and the absence of positive social environments predicted greater increases in depressive symptoms in youth, aged 9-11, over a two-year period. In addition to environment, left hippocampal volume amplified the social context effects, suggesting that youth with a larger left hippocampus experience greater increases in Major Depressive Disorder (MDD) symptoms in challenging social spaces.

“Our research has implications not only for future research, but we also hope it increases awareness among parents, educators, mental health professionals and policy makers,” said co-lead author Claudia Haase, associate professor of human development and social policy at Northwestern’s School of Education and Social Policy (SESP). “Over the years, the pendulum has swung back and forth between some researchers and practitioners emphasizing the role of nature and others emphasizing the role of nurture. And we have come to really appreciate that we need to look at both and their interplay together.”

The study , in Proceedings of the National Academy of Sciences, underscores the importance of families, peers and schools in the development of depression during adolescence, and how variation in neural structure can amplify or diminish sensitivity to their environment.

The study was first authored by Matías Martínez, doctoral student at SESP, with senior co-authors Haase and Yang Qu, associate professor of human development and social policy at SESP. Titled “Depressive symptoms during the transition to adolescence: Left hippocampal volume as a marker of social context sensitivity,” additional authors include Tianying Cai; Beiming Yang; Zexi Zhou; Stewart Shankman; and Vijay A. Mittal.

“Our study emphasizes the importance of paying attention to individual differences and how some people are more sensitive to social environments than others,” Qu said. “We should never assume that the same environment will have the same impact for everyone. There is no one size fits all.”

The findings

Since neuroscience has seen major developments over the past few years, the researchers focused on brain-based sensitivity in the development of depressive symptoms. “Previous studies have focused on physiological processes or genetic variants, but with the development of neuroscience, now we can look at how the brain can play a role in the sensitivity to environments,” Martinez said. “There's a longstanding debate on whether some individuals are more or less sensitive to environments and in this study, we focused on sensitivity to social experiences, both negative and positive.”

The results concluded that the left hippocampus — a region of the brain that is primarily associated with memory, learning and how humans experience the world around them — plays an important role in whether a person becomes depressed if they find themselves in a challenging social space. A larger hippocampus would result in an individual being better able to remember an experience or recall a memory.

“It is one of the most plastic regions of the brain,” Martinez said. “It's very responsive to the environment, especially in a person’s early years. Our findings show that this brain region is playing a role in making youth more sensitive to difficult environments and to the absence of positivity in their life experiences — leading to depression symptoms.”

That area of the brain being larger in a child could result in that child having more sensitivity to social experiences — family conflicts, primary caregiver’s depressive symptoms, peer victimization, parental warmth and prosocial school environment — into adolescence.

“Some people tend to assume that we are ‘born this way’ when it comes to the human brain. But the more we learn about the brain, the more scientists have come to understand how open and malleable our brains are, not just in infancy but across the life span,” Haase said. “Our brains can change in response to the environments we find ourselves in — and studies show that this is certainly the case for the hippocampus as a brain region.”

The researchers examined two-year longitudinal data from the Adolescent Brain Cognitive Development study. The study — one of the largest studies in the U.S. conducted by 21 research sites across the country — aims to follow a diverse sample of 11,800 kids aged 9-11 over a 10-year period to observe their cognitive, brain, social and emotional development over time.

“The ABCD study is phenomenal, and we are deeply indebted to the National Institutes of Health and all the researchers involved for making this possible, and, of course, to all the youth and their families who are participating,” Qu said. “It’s the largest long-term study of brain development and child health in the United States.”

The data revealed a stronger association between socio-experiential environments and MDD symptoms for youth with a larger left hippocampal volume and no differences in MDD symptoms between individuals with different sizes of left hippocampus at low levels of negative and high levels of positive context exposure.

What’s next

The researchers are hoping the study helps parents, teachers and policymakers better understand and support youth’s mental health during adolescence. Martinez is hoping their expanded research can better explain how children in difficult social environments adapt to their surroundings in the long term.

“The ABCD study is such a comprehensive project that will continue to follow youth development for many more years,” Martinez said. “It will be exciting to examine what the interplay between exposure to different environments, hippocampal volume and depressive symptoms looks like as our youth navigate their teenage years.”

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Research: When Bonuses Backfire

• Dirk Sliwka
• Timo Vogelsang

How to rethink your incentive strategy and reward employees in ways that actually motivate them.

Why do bonuses sometimes backfire? It’s because each incentive design choice both signals information about your own beliefs and intentions as an employer and shapes the signaling value of employee behavior within the organization. If you don’t think through these signals carefully, you may end up approving a bonus scheme with results that are the opposite of what you intend. This article offers a way to help you align the signals your incentive scheme sends with your performance goals.

More than 30 years ago, author and lecturer Alfie Kohn, in a rather controversial but often cited HBR article , claimed that “rewards typically undermine the very processes they are intended to enhance.” Yet until recently, nearly all scientific studies that have documented such “backfiring” effects have been confined to laboratory experiments or field settings outside of the firm. This may cause some to question whether these effects are really present in commercial contexts. Our new research, which consists of two large field experiments in retail organizations, demonstrates that they do indeed occur. One of the studies showed unequivocally that the provision of a monetary attendance bonus increased absence days. The other revealed that the added value of performance review conversations was wiped out when they were combined with a monetary bonus. In both cases, a well-intentioned financial reward ultimately had crucial and costly unintended effects, largely because of signals about expected behavior given by the incentives. To understand what was going on, let’s look at the two studies in turn.

• DS Dirk Sliwka is a professor of management in the Faculty of Management, Economics, and Social Sciences at the University of Cologne in Germany
• TV Timo Vogelsang is an Associate Professor of Managerial Accounting at the Frankfurt School of Finance and Management in Germany

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• Open access
• Published: 02 September 2024

Green spaces provide substantial but unequal urban cooling globally

• Yuxiang Li 1 ,
• Jens-Christian Svenning   ORCID: orcid.org/0000-0002-3415-0862 2 ,
• Weiqi Zhou   ORCID: orcid.org/0000-0001-7323-4906 3 , 4 , 5 ,
• Kai Zhu   ORCID: orcid.org/0000-0003-1587-3317 6 ,
• Jesse F. Abrams   ORCID: orcid.org/0000-0003-0411-8519 7 ,
• Timothy M. Lenton   ORCID: orcid.org/0000-0002-6725-7498 7 ,
• William J. Ripple 8 ,
• Zhaowu Yu   ORCID: orcid.org/0000-0003-4576-4541 9 ,
• Shuqing N. Teng 1 ,
• Robert R. Dunn 10 &
• Chi Xu   ORCID: orcid.org/0000-0002-1841-9032 1  

Nature Communications volume  15 , Article number:  7108 ( 2024 ) Cite this article

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• Climate-change mitigation
• Urban ecology

Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

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Introduction.

Heat extremes are projected to be substantially intensified by global warming 1 , 2 , imposing a major threat to human mortality and morbidity in the coming decades 3 , 4 , 5 , 6 . This threat is particularly concerning as a majority of people now live in cities 7 , including those cities suffering some of the hottest climate extremes. Cities face two forms of warming: warming due to climate change and warming due to the urban heat island effect 8 , 9 , 10 . These two forms of warming have the potential to be additive, or even multiplicative. Climate change in itself is projected to result in rising maximum temperatures above 50 °C for a considerable fraction of the world if 2 °C global warming is exceeded 2 ; the urban heat island effect will cause up to >10 °C additional (surface) warming 11 . Exposures to temperatures above 35 °C with high humidity or above 40 °C with low humidity can lead to lethal heat stress for humans 12 . Even before such lethal temperatures are reached, worker productivity 13 and general health and well-being 14 can suffer. Heat extremes are especially risky for people living in the Global South 15 , 16 due to warmer climates at low latitudes. Climate models project that the lethal temperature thresholds will be exceeded with increasing frequencies and durations, and such extreme conditions will be concentrated in low-latitude regions 17 , 18 , 19 . These low-latitude regions overlap with the major parts of the Global South where population densities are already high and where population growth rates are also high. Consequently, the number of people exposed to extreme heat will likely increase even further, all things being equal 16 , 20 . That population growth will be accompanied by expanded urbanization and intensified urban heat island effects 21 , 22 , potentially exacerbating future Global North-Global South heat stress exposure inequalities.

Fortunately, we know that heat stress can be buffered, in part, by urban vegetation 23 . Urban green spaces, and especially urban forests, have proven an effective means through which to ameliorate heat stress through shading 24 , 25 and transpirational cooling 26 , 27 . The buffering effect of urban green spaces is influenced by their area (relative to the area of the city) and their spatial configuration 28 . In this context, green spaces become a kind of infrastructure that can and should be actively managed. At broad spatial scales, the effect of this urban green infrastructure is also mediated by differences among regions, whether in their background climate 29 , composition of green spaces 30 , or other factors 31 , 32 , 33 , 34 . The geographic patterns of the buffering effects of green spaces, whether due to geographic patterns in their areal extent or region-specific effects, have so far been poorly characterized.

On their own, the effects of climate change and urban heat islands on human health are likely to become severe. However, these effects will become even worse if they fall disproportionately in cities or countries with less economic ability to invest in green space 35 or in other forms of cooling 36 , 37 . A number of studies have now documented the so-called ‘luxury effect,’ wherein lower-income parts of cities tend to have less green space and, as a result, reduced biodiversity 38 , 39 . Where the luxury effect exists, green space and its benefits become, in essence, a luxury good 40 . If the luxury effect holds among cities, and lower-income cities also have smaller green spaces, the Global South may have the least potential to mitigate the combined effects of climate warming and urban heat islands, leading to exacerbated and rising inequalities in heat exposure 41 .

Here, we assess the global inequalities in the cooling capability of existing urban green infrastructure across urban areas worldwide. To this end, we use remotely sensed data to quantify three key variables, i.e., (1) cooling efficiency, (2) cooling capacity, and (3) cooling benefit of existing urban green infrastructure for ~500 major cities across the world. Urban green infrastructure and temperature are generally negatively and relatively linearly correlated at landscape scales, i.e., higher quantities of urban green infrastructure yield lower temperatures 42 , 43 . Cooling efficiency is widely used as a measure of the extent to which a given proportional increase in the area of urban green infrastructure leads to a decrease in temperature, i.e., the slope of the urban green infrastructure-temperature relationship 42 , 44 , 45 (see Methods for details). This simple metric allows quantifying the quality of urban green infrastructure in terms of ameliorating the urban heat island effect. Meanwhile, the extent to which existing urban green infrastructure cools down an entire city’s surface temperatures (compared to the non-vegetated built-up areas) is referred to as cooling capacity. Hence, cooling capacity is a function of the total quantity of urban green infrastructure and its cooling efficiency (see Methods).

As a third step, we account for the spatial distributions of urban green infrastructure and populations to quantify the benefit of cooling mitigation received by an average urban inhabitant in each city given their location. This cooling benefit is a more direct measure of the cooling realized by people, after accounting for the within-city geography of urban green infrastructure and population density. We focus on cooling capacity and cooling benefit as the measures of the cooling capability of individual cities for assessing their global inequalities. We are particularly interested in linking cooling adaptation inequality with income inequality 40 , 46 . While this can be achieved using existing income metrics for country classifications 47 , here we use the traditional Global North/South classification due to its historical ties to geography which is influential in climate research.

Results and discussion

Our analyses indicate that existing green infrastructure of an average city has a capability of cooling down surface temperatures by ~3 °C during warm seasons. However, a concerning disparity is evident; on average Global South cities have only two-thirds the cooling capacity and cooling benefit compared to Global North cities. This inequality is attributable to the differences in both quantity and quality of existing urban green infrastructure among cities. Importantly, we find that there exists considerable potential for many cities to enhance the cooling capability of their green infrastructure; achieving this potential could dramatically reduce global inequalities in adaptation to outdoor heat stress.

Quantifying cooling inequality

Our analyses showed that both the quantity and quality of the existing urban green infrastructure vary greatly among the world’s ~500 most populated cities (see Methods for details, and Fig.  1 for examples). The quantity of urban green infrastructure measured based on remotely sensed indicators of spectral greenness (Normalized Difference Vegetation Index, NDVI, see Methods) had a coefficient of variation (CV) of 35%. Similarly, the quality of urban green infrastructure in terms of cooling efficiency (daytime land surface temperatures during peak summer) had a CV of 37% (Supplementary Figs.  1 , 2 ). The global mean value of cooling capacity is 2.9 °C; existing urban green infrastructure ameliorates warm-season heat stress by 2.9 °C of surface temperature in an average city. In truth, however, the variation in cooling capacity was great (global CV in cooling capacity as large as ~50%), such that few cities were average. This variation is strongly geographically structured. Cities closer to the equator - tropical and subtropical cities - tend to have relatively weak cooling capacities (Fig.  2a, b ). As Global South countries are predominantly located at low latitudes, this pattern leads to a situation in which Global South cities, which tend to be hotter and relatively lower-income, have, on average, approximately two-thirds the cooling capacity of the Global North cities (2.5 ± 1.0 vs. 3.6 ± 1.7°C, Wilcoxon test, p  = 2.7e-12; Fig.  2c ). The cities that most need to rely on green infrastructure are, at present, those that are least able to do so.

a , e , i , m , q Los Angeles, US. b , f , j , n , r Paris, France. c , g , k , o , s Shanghai, China. d , h , l , p , t Cairo, Egypt. Local cooling efficiency is calculated for different local climate zone types to account for within-city heterogeneity. In densely populated parts of cities, local cooling capacity tends to be lower due to reduced green space area, whereas local cooling benefit (local cooling capacity multiplied by a weight term of local population density relative to city mean) tends to be higher as more urban residents can receive cooling amelioration.

a Global distribution of cooling capacity for the 468 major urbanized areas. b Latitudinal pattern of cooling capacity. c Cooling capacity difference between the Global North and South cities. The cooling capacity offered by urban green infrastructure evinces a latitudinal pattern wherein lower-latitude cities have weaker cooling capacity ( b , cubic-spline fitting of cooling capacity with 95% confidence interval is shown), representing a significant inequality between Global North and South countries: city-level cooling capacity for Global North cities are about 1.5-fold higher than in Global South cities ( c ). Data are presented as box plots, where median values (center black lines), 25th percentiles (box lower bounds), 75th percentiles (box upper bounds), whiskers extending to 1.5-fold of the interquartile range (IQR), and outliers are shown. The tails of the cooling capacity distributions are truncated at zero as all cities have positive values of cooling capacity. Notice that no cities in the Global South have a cooling capacity greater than 5.5 °C ( c ). This is because no cities in the Global South have proportional green space areas as great as those seen in the Global North (see also Fig.  4b ). A similar pattern is found for cooling benefit (Supplementary Fig.  3 ). The two-sided non-parametric Wilcoxon test was used for statistical comparisons.

When we account for the locations of urban green infrastructure relative to humans within cities, the cooling benefit of urban green infrastructure realized by an average urban resident generally becomes slightly lower than suggested by cooling capacity (see Methods; Supplementary Fig.  3 ). Urban residents tend to be densest in the parts of cities with less green infrastructure. As a result, the average urban resident experiences less cooling amelioration than expected. However, this heterogeneity has only a minor effect on global-scale inequality. As a result, the geographic trends in cooling capacity and cooling benefit are similar: mean cooling benefit for an average urban resident also presents a 1.5-fold gap between Global South and North cities (2.2 ± 0.9 vs. 3.4 ± 1.7 °C, Wilcoxon test, p  = 3.2e-13; Supplementary Fig.  3c ). Urban green infrastructure is a public good that has the potential to help even the most marginalized populations stay cool; unfortunately, this public benefit is least available in the Global South. When walking outdoors, the average person in an average Global South city receives only two-thirds the cooling amelioration from urban green infrastructure experienced by a person in an average Global North city. The high cooling amelioration capacity and benefit of the Global North cities is heavily influenced by North America (specifically, Canada and the US), which have both the highest cooling efficiency and the largest area of green infrastructure, followed by Europe (Supplementary Fig.  4 ).

One way to illustrate the global inequality of cooling capacity or benefit is to separately look at the cities that are most and least effective in ameliorating outdoor heat stress. Our results showed that ~85% of the 50 most effective cities (with highest cooling capacity or cooling benefit) are located in the Global North, while ~80% of the 50 least effective are Global South cities (Fig.  3 , Supplementary Fig.  5 ). This is true without taking into account the differences in the background temperatures and climate warming of these cities, which will exacerbate the effects on human health; cities in the Global South are likely to be closer to the limits of human thermal comfort and even, increasingly, the limits of the temperatures and humidities (wet-bulb temperatures) at which humans can safely work or even walk, such that the ineffectiveness of green spaces in those cities in cooling will lead to greater negative effects on human health 48 , work 14 , and gross domestic product (GDP) 49 . In addition, Global South cities commonly have higher population densities (Fig.  3 , Supplementary Fig.  5 ) and are projected to have faster population growth 50 . This situation will plausibly intensify the urban heat island effect because of the need of those populations for housing (and hence tensions between the need for buildings and the need for green spaces). It will also increase the number of people exposed to extreme urban heat island effects. Therefore, it is critical to increase cooling benefit via expanding urban green spaces, so that more people can receive the cooling mitigation from a given new neighboring green space if they live closer to each other. Doing so will require policies that incentivize urban green spaces as well as architectural innovations that make innovations such as plant-covered buildings easier and cheaper to implement.

The axes on the right are an order of magnitude greater than those on the left, such that the cooling capacity of Charlotte in the United States is about 37-fold greater than that of Mogadishu (Somalia) and 29-fold greater than that of Sana’a (Yemen). The cities presenting lowest cooling capacities are most associated with Global South cities at higher population densities.

Of course, cities differ even within the Global North or within the Global South. For example, some Global South cities have high green space areas (or relatively high cooling efficiency in combination with moderate green space areas) and hence high cooling capacity. These cities, such as Pune (India), will be important to study in more detail, to shed light on the mechanistic details of their cooling abilities as well as the sociopolitical and other factors that facilitated their high green area coverage and cooling capabilities (Supplementary Figs.  6 , 7 ).

We conducted our primary analyses using a spatial grain of 100-m grid cells and Landsat NDVI data for quantifying spectral greenness. Our results, however, were robust at the coarser spatial grain of 1 km. We find a slightly larger global cooling inequality (~2-fold gap between Global South and North cities) at the 1-km grain using MODIS data (see Methods and Supplementary Fig.  17 ). MODIS data have been frequently used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . Our results reinforce its robustness for comparing urban thermal environments between cities across broad scales.

Influencing factors

The global inequality of cooling amelioration could have a number of proximate causes. To understand their relative influence, we first separately examined the effects of quality (cooling efficiency) and quantity (NDVI as a proxy indicator of urban green space area) of urban green infrastructure. The simplest null model is one in which cooling capacity (at the city scale) and cooling benefit (at the human scale) are driven primarily by the proportional area in a city dedicated to green spaces. Indeed, we found that both cooling capacity and cooling benefit were strongly correlated with urban green space area (Fig.  4 , Supplementary Fig.  8 ). This finding is useful with regards to practical interventions. In general, cities that invest in saving or restoring more green spaces will receive more cooling benefits from those green spaces. By contrast, differences among cities in cooling efficiency played a more minor role in determining the cooling capacity and benefit of cities (Fig.  4 , Supplementary Fig.  8 ).

a Relationship between cooling efficiency and cooling capacity. b Relationship between green space area (measured by mean Landsat NDVI in the hottest month of 2018) and cooling capacity. Note that the highest level of urban green space area in the Global South cities is much lower than that in the Global North (dashed line in b ). Gray bands indicate 95% confidence intervals. Two-sided t-tests were conducted. c A piecewise structural equation model based on assumed direct and indirect (through influencing cooling efficiency and urban green space area) effects of essential natural and socioeconomic factors on cooling capacity. Mean annual temperature and precipitation, and topographic variation (elevation range) are selected to represent basic background natural conditions; GDP per capita is selected to represent basic socioeconomic conditions. The spatial extent of built-up areas is included to correct for city size. A bi-directional relationship (correlation) is fitted between mean annual temperature and precipitation. Red and blue solid arrows indicate significantly negative and positive coefficients with p  ≤ 0.05, respectively. Gray dashed arrows indicate p  > 0.05. The arrow width illustrates the effect size. Similar relationships are found for cooling benefits realized by an average urban resident (see Supplementary Fig.  8 ).

A further question is what shapes the quality and quantity of urban green infrastructure (which in turn are driving cooling capacity)? Many inter-correlated factors are possibly operating at multiple scales, making it difficult to disentangle their effects, especially since experiment-based causal inference is usually not feasible for large-scale urban systems. From a macroscopic perspective, we test the simple hypothesis that the background natural and socioeconomic conditions of cities jointly affect their cooling capacity and benefit in both direct and indirect ways. To this end, we constructed a minimal structural equation model including only the most essential variables reflecting background climate (mean annual temperature and precipitation), topographic variation (elevation range), as well as gross domestic product (GDP) per capita and city area (see Methods; Fig.  4c ).

We found that the quantity of green spaces in a city (again, in proportion to its size) was positively correlated with GDP per capita and city area; wealthier cities have more green spaces. It is well known that wealth and green spaces are positively correlated within cities (the luxury effect) 40 , 46 ; our analysis shows that a similar luxury effect occurs among them at a global scale. In addition, larger cities often have proportionally more green spaces, an effect that may be due to the tendency for large cities (particularly in the US and Canada) to have lower population densities. Cities that were hotter and had more topographic variation tended to have fewer green spaces and those that were more humid tended to have more green spaces. Given that temperature and humidity are highly correlated with the geography of the Global South and Global North, it is difficult to know whether these effects are due to the direct effects of temperature and precipitation, for example, on the growth rate of vegetation and hence the transition of abandoned lots into green spaces, or are associated with historical, cultural and political differences that via various mechanisms correlate to climate. Our structural equation model explained only a small fraction of variation among cities in their cooling efficiency, which is to say the quality of their green space. Cooling efficiency was modestly influenced by background temperature and precipitation—the warmer a city, the greater the cooling efficiency in that city; conversely, the more humid a city the less the cooling efficiency of that city.

Our analyses suggested that the lower cooling adaptation capabilities of Global South cities can be explained by their lower quantity of green infrastructure and, to a much lesser extent, their weaker cooling efficiency (quality; Supplementary Fig.  2 ). These patterns appear to be in part structured by GDP, but are also associated with climatic conditions 39 , and other factors. A key question, unresolved by our work, is whether the climatic correlates of the size of green spaces in cities are due to the effects of climate per se or if they, instead, reflect correlates between contemporary climate and the social, cultural, and political histories of cities in the Global South 52 . Since urban planning has much inertia, especially in big cities, those choices might be correlated with climate because of the climatic correlates of political histories. It is also possible that these dynamics relate, in part, to the ways in which climate influences vegetation structure. However, this seems less likely given that under non-urban conditions vegetation cover (and hence cooling capacity) is normally positively correlated with mean annual temperature across the globe, opposite to our observed negative relationships for urban systems (Supplementary Fig.  9g ). Still, it is possible that increased temperatures in cities due to the urban heat island effects may lead to temperature-vegetation cover-cooling capacity relationships that differ from those in natural environments 53 , 54 . Indeed, a recent study found that climate warming will put urban forests at risk, and the risk is disproportionately higher in the Global South 55 .

Our model serves as a starting point for unraveling the mechanisms underlying global cooling inequality. We cannot rule out the possibility that other unconsidered factors correlated with the studied variables play important roles. We invite systematic studies incorporating detailed sociocultural and ecological variables to address this question across scales.

Potential of enhancing cooling and reducing inequality

Can we reduce the inequality in cooling capacity and benefits that we have discovered among the world’s largest cities? Nuanced assessments of the potential to improve cooling mitigation require comprehensive considerations of socioeconomic, cultural, and technological aspects of urban management and policy. It is likely that cities differ greatly in their capacity to implement cooling through green infrastructure, whether as a function of culture, governance, policy or some mix thereof. However, any practical attempts to achieve greater cooling will occur in the context of the realities of climate and existing land use. To understand these realities, we modeled the maximum additional cooling capacity that is possible in cities, given existing constraints. We assume that this capacity depends on the quality (cooling efficiency) and quantity of urban green infrastructure. Our approach provides a straightforward metric of the cooling that could be achieved if all parts of a city’s green infrastructure were to be enhanced systematically.

The positive outlook is that our analyses suggest a considerable potential of improving cooling capacity by optimizing urban green infrastructure. An obvious way is through increases in urban green infrastructure quantity. We employ an approach in which we consider each local climate zone 56 to have a maximum NDVI and cooling efficiency (see Methods). For a given local climate zone, the city with the largest NDVI values or cooling efficiency sets the regional upper bounds for urban green infrastructure quantities or quality that can be achieved. Notably, these maxima are below the maxima for forests or other non-urban spaces for the simple reason that, as currently imagined, cities must contain gray (non-green) spaces in the form of roads and buildings. In this context, we conduct a thought experiment. What if we could systematically increase NDVI of all grid cells in each city, per local climate zone type, to a level corresponding to the median NDVI of grid cells in that upper bound city while keeping cooling efficiency unchanged (see Methods). If we were able to achieve this goal, the cooling capacity of cities would increase by ~2.4 °C worldwide. The increase would be even greater, ~3.8°C, if the 90th percentile (within the reference maximum city) was reached (Fig.  5a ). The potential for cooling benefit to the average urban resident is similar to that of cooling capacity (Supplementary Fig.  10a ). There is also potential to reduce urban temperatures if we can enhance cooling efficiency. However, the benefits of increases in cooling efficiency are modest (~1.5 °C increases at the 90th percentile of regional upper bounds) when holding urban green infrastructure quantity constant. In theory, if we could maximize both quantity and cooling efficiency of urban green infrastructure (to 90th percentiles of their regional upper bounds respectively), we would yield increases in cooling capacity and benefit up to ~10 °C, much higher than enhancing green space area or cooling efficiency alone (Fig.  5a , Supplementary Fig.  10a ). Notably, such co-maximization of green space area and cooling efficiency would substantially reduce global inequality to Gini <0.1 (Fig.  5b , Supplementary Fig.  10b ). Our analyses thus provide an important suggestion that enhancing both green space quantity and quality can yield a synergistic effect leading to much larger gains than any single aspect alone.

a The potential of enhancing cooling capacity via either enhancing urban green infrastructure quality (i.e., cooling efficiency) while holding quantity (i.e., green space area) fixed (yellow), or enhancing quantity while holding quality fixed (blue) is much lower than that of enhancing both quantity and quality (green). The x-axis indicates the targets of enhancing urban green infrastructure quantity and/or quality relative to the 50–90th percentiles of NDVI or cooling efficiency, see Methods). The dashed horizontal lines indicate the median cooling capacity of current cities. Data are presented as median values with the colored bands corresponding to 25–75th percentiles. b The potential of reducing cooling capacity inequality is also higher when enhancing both urban green infrastructure quantity and quality. The Gini index weighted by population density is used to measure inequality. Similar results were found for cooling benefit (Supplementary Fig.  10 ).

Different estimates of cooling capacity potential may be reached based on varying estimates and assumptions regarding the maximum possible quantity and quality of urban green infrastructure. There is no single, simple way to make these estimates, especially considering the huge between-city differences in society, culture, and structure across the globe. Our example case (above) begins from the upper bound city’s median NDVI, taking into account different local climate zone types and background climate regions (regional upper bounds). This is based on the assumption that for cities within the same climate regions, their average green space quantity may serve as an attainable target. Still, urban planning is often made at the level of individual cities, often only implemented to a limited extent and made with limited consideration of cities in other regions and countries. A potentially more realistic reference may be taken from the existing green infrastructure (again, per local climate zone type) within each particular city itself (see Methods): if a city’s sparsely vegetated areas was systematically elevated to the levels of 50–90th percentiles of NDVI within their corresponding local climate zones within the city, cooling capacity would still increase, but only by 0.5–1.5 °C and with only slightly reduced inequalities among cities (Supplementary Fig.  11 ). This highlights that ambitious policies, inspired by the greener cities worldwide, are necessary to realize the large cooling potential in urban green infrastructure.

In summary, our results demonstrate clear inequality in the extent to which urban green infrastructure cools cities and their denizens between the Global North and South. Much attention has been paid to the global inequality of indoor heat adaptation arising from the inequality of resources (e.g., less affordable air conditioning and more frequent power shortages in the Global South) 36 , 57 , 58 , 59 . Our results suggest that the inequality in outdoor adaptation is particularly concerning, especially as urban populations in the Global South are growing rapidly and are likely to face the most severe future temperature extremes 60 .

Previous studies have been focusing on characterizing urban heat island effects, urban vegetation patterns, resident exposure, and cooling effects in particular cities 26 , 28 , 34 , 61 , regions 22 , 25 , 62 , or continents 32 , 44 , 63 . Recent studies start looking at global patterns with respect to cooling efficiency or green space exposure 35 , 45 , 64 , 65 . Our approach is one drawn from the fields of large-scale ecology and macroecology. This approach is complementary to and, indeed, can, in the future, be combined with (1) mechanism driven biophysical models 66 , 67 to predict the influence of the composition and climate of green spaces on their cooling efficiency, (2) social theory aimed at understanding the factors that govern the amount of green space in cities as well as the disparity among cities 68 , (3) economic models of the effects of policy changes on the amount of greenspace and even (4) artist-driven projects that seek to understand the ways in which we might reimagine future cities 69 . Our simple explanatory model is, ultimately, one lens on a complex, global phenomenon.

Our results convey some positive outlook in that there is considerable potential to strengthen the cooling capability of cities and to reduce inequalities in cooling capacities at the same time. Realizing this nature-based solution, however, will be challenging. First, enhancing urban green infrastructure requires massive investments, which are more difficult to achieve in Global South cities. Second, it also requires smart planning strategies and advanced urban design and greening technologies 37 , 70 , 71 , 72 . Spatial planning of urban green spaces needs to consider not only the cooling amelioration effect, but also their multifunctional aspects that involve multiple ecosystem services, mental health benefits, accessibility, and security 73 . In theory, a city can maximize its cooling while also maximizing density through the combination of high-density living, ground-level green spaces, and vertical and rooftop gardens (or even forests). In practice, the current cities with the most green spaces tend to be lower-density cities 74 (Supplementary Fig.  12 ). Still, innovation and implementation of new technologies that allow green spaces and high-density living to be combined have the potential to reduce or disconnect the negative relationship between green space area and population density 71 , 75 . However, this development has yet to be realized. Another dimension of green spaces that deserves more attention is the geography of green spaces relative to where people are concentrated within cities. A critical question is how best should we distribute green spaces within cities to maximize cooling efficiency 76 and minimize within-city cooling inequality towards social equity 77 ? Last but not least, it is crucial to design and manage urban green spaces to be as resilient as possible to future climate stress 78 . For many cities, green infrastructure is likely to remain the primary means people will have to rely on to mitigate the escalating urban outdoor heat stress in the coming decades 79 .

We used the world population data from the World’s Cities in 2018 Data Booklet 80 to select 502 major cities with population over 1 million people (see Supplementary Data  1 for the complete list of the studied cities). Cities are divided into the Global North and Global South based on the Human Development Index (HDI) from the Human Development Report 2019 81 . For each selected city, we used the 2018 Global Artificial Impervious Area (GAIA) data at 30 m resolution 82 to determine its geographic extent. The derived urban boundary polygons thus encompass a majority of the built-up areas and urban residents. In using this approach, rather than urban administrative boundaries, we can focus on the relatively densely populated areas where cooling mitigation is most needed, and exclude areas dominated by (semi) natural landscapes that may bias the subsequent quantifications of the cooling effect. Our analyses on the cooling effect were conducted at the 100 m spatial resolution using Landsat data and WorldPop Global Project Population Data of 2018 83 . In order to test for the robustness of the results to coarser spatial scales, we also repeated the analyses at 1 km resolution using MODIS data, which have been extensively used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . We discarded the five cities with sizes <30 km 2 as they were too small for us to estimate their cooling efficiency based on linear regression (see section below for details). We combined closely located cities that form contiguous urban areas or urban agglomerations, if their urban boundary polygons from GAIA merged (e.g., Phoenix and Mesa in the United States were combined). Our approach yielded 468 polygons, each representing a major urbanized area that were the basis for all subsequent analyses. Because large water bodies can exert substantial and confounding cooling effects, we excluded permanent water bodies including lakes, reservoirs, rivers, and oceans using the Copernicus Global Land Service (CGLS) Land Cover data for 2018 at 10 m resolution 84 .

Quantifying the cooling effect

As a first step, we calculated cooling efficiency for each studied city within the GAIA-derived urban boundary. Cooling efficiency quantifies the extent to which a given area of green spaces in a city can reduce temperatures. It is a measure of the effectiveness (quality) of urban green spaces in terms of heat amelioration. Cooling efficiency is typically measured by calculating the slope of the relationship between remotely-sensed land surface temperature (LST) and vegetation cover through ordinary least square regression 42 , 44 , 45 . It is known that cooling efficiency varies between cities. Influencing factors might include background climate 29 , species composition 30 , 85 , landscape configuration 28 , topography 86 , proximity to large water bodies 33 , 87 , urban morphology 88 , and city management practices 31 . However, the mechanism underlying the global pattern of cooling efficiency remains unclear.

We used Landsat satellite data provided by the United States Geological Survey (USGS) to calculate the cooling efficiency of each studied city. We used the cloud-free Landsat 8 Level 2 LST and NDVI data. For each city we calculated the mean LST in each month of 2018 to identify the hottest month, and then derived the hottest month LST; we used the cloud-free Landsat 8 data to calculate the mean NDVI for the hottest month correspondingly.

We quantified cooling efficiency for different local climate zones 56 separately for each city, to account for within-city variability of thermal environments. To this end, we used the Copernicus Global Land Service data (CGLS) 84 and Global Human Settlement Layers (GHSL) Built-up height data 89 of 2018 at the 100 m resolution to identify five types of local climate zones: non-tree vegetation (shrubs, herbaceous vegetation, and cultivated vegetation according to the CGLS classification system), low-rise buildings (built up and bare according to the CGLS classification system, with building heights ≤10 m according to the GHSL data), medium-high-rise buildings (built up and bare areas with building heights >10 m), open tree cover (open forest with tree cover 15–70% according to the CGLS system), and closed tree cover (closed forest with tree cover >70%).

For each local climate zone type in each city, we constructed a regression model with NDVI as the predictor variable and LST as the response variable (using the ordinary least square method). We took into account the potential confounding factors including topographic elevation (derived from MERIT DEM dataset 90 ), building height (derived from the GHSL dataset 89 ), and distance to water bodies (derived from the GSHHG dataset 91 ), the model thus became: LST ~ NDVI + topography + building height + distance to water. Cooling efficiency was calculated as the absolute value of the regression coefficient of NDVI, after correcting for those confounding factors. To account for the multi-collinearity issue, we conducted variable selection based on the variance inflation factor (VIF) to achieve VIF < 5. Before the analysis, we discarded low-quality Landsat pixels, and filtered out the pixels with NDVI < 0 (normally less than 1% in a single city). Cooling efficiency is known to be influenced by within-city heterogeneity 92 , 93 , and, as a result, might sometimes better fit non-linear relationships at local scales 65 , 76 . However, our central aim is to assess global cooling inequality based on generalized relationships that fit the majority of global cities. Previous studies have shown that linear relationships can do this job 42 , 44 , 45 , therefore, here we used linear models to assess cooling efficiency.

As a second step, we calculated the cooling capacity of each city. Cooling capacity is a positive function of the magnitude of cooling efficiency and the proportional area of green spaces in a city and is calculated based on NDVI and the derived cooling efficiency (Eq.  1 , Supplementary Fig.  13 ):

where CC lcz and CE lcz are the cooling capacity and cooling efficiency for a given local climate zone type in a city, respectively; NDVI i is the mean NDVI for 100-m grid cell i ; NDVI min is the minimum NDVI across the city; and n is the total number of grid cells within the local climate zone. Local cooling capacity for each grid cell i (Fig.  1 , Supplementary Fig.  7 ) can be derived in this way as well (Supplementary Fig.  13 ). For a particular city, cooling capacity may be dependent on the spatial configuration of its land use/cover 28 , 94 , but here we condensed cooling capacity to city average (Eq.  2 ), thus did not take into account these local-scale factors.

where CC is the average cooling capacity of a city; n lcz is the number of grid cells of the local climate zone; m is the total number of grid cells within the whole city.

As a third step, we calculated the cooling benefit realized by an average urban resident (cooling benefit in short) in each city. Cooling benefit depends not only on the cooling capacity of a city, but also on where people live within a city relative to greener or grayer areas of the city. For example, cooling benefits in a city might be low even if the cooling capacity is high if the green parts and the dense-population parts of a city are inversely correlated. Here, we are calculating these averages while aware that in any particular city the exposure of a particular person will depend on the distribution of green spaces in a city, and the occupation, movement trajectories of a person, etc. On the scale of a city, we calculated cooling benefit following a previous study 35 , that is, simply adding a weight term of population size per 100-m grid cell into cooling capacity in Eq. ( 1 ):

Where CB lcz is the cooling benefit of a given local climate zone type in a specific city, pop i is the number of people within grid cell i , \(\overline{{pop}}\) is the mean population of the city.

Where CB is the average cooling benefit of a city. The population data were obtained from the 100-m resolution WorldPop Global Project Population Data of 2018 83 . Local cooling benefit for a given grid cell i can be calculated in a similar way, i.e., local cooling capacity multiplied by a weight term of local population density relative to mean population density. Local cooling benefits were mapped for example cities for the purpose of illustrating the effect of population spatial distribution (Fig.  1 , Supplementary Fig.  7 ), but their patterns were not examined here.

Based on the aforementioned three key variables quantified at 100 m grid cells, we conducted multivariate analyses to examine if and to what extent cooling efficiency and cooling benefit are shaped by essential natural and socioeconomic factors, including background climate (mean annual temperature from ECMWF ERA5 dataset 95 and precipitation from TerraClimate dataset 96 ), topography (elevation range 90 ), and GDP per capita 97 , with city size (geographic extent) corrected for. We did not include humidity because it is strongly correlated with temperature and precipitation, causing serious multi-collinearity problems. We used piecewise structural equation modeling to test the direct effects of these factors and indirect effects via influencing cooling efficiency and vegetation cover (Fig.  4c , Supplementary Fig.  8c ). To account for the potential influence of spatial autocorrelation, we used spatially autoregressive models (SAR) to test for the robustness of the observed effects of natural and socioeconomic factors on cooling capacity and benefit (Supplementary Fig.  14 ).

Testing for robustness

We conducted the following additional analyses to test for robustness. We obtained consistent results from these robustness analyses.

(1) We looked at the mean hottest-month LST and NDVI within 3 years (2017-2019) to check the consistency between the results based on relatively short (1 year) vs. long (3-year average) time periods (Supplementary Fig.  15 ).

(2) We carried out the approach at a coarser spatial scale of 1 km, using MODIS-derived NDVI and LST, as well as the population data 83 in the hottest month of 2018. In line with our finer-scale analysis of Landsat data, we selected the hottest month and excluded low-quality grids affected by cloud cover and water bodies 98 (water cover > 20% in 1 × 1 km 2 grid cells) of MODIS LST, and calculated the mean NDVI for the hottest month. We ultimately obtained 441 cities (or urban agglomerations) for analysis. At the 1 km resolution, some local climate zone types would yield insufficient samples for constructing cooling efficiency models. Therefore, instead of identifying local climate zone explicitly, we took an indirect approach to account for local climate confounding factors, that is, we constructed a multiple regression model for a whole city incorporating the hottest-month local temperature 95 , precipitation 96 , and humidity (based on NASA FLDAS dataset 99 ), albedo (derived from the MODIS MCD43A3 product 100 ), aerosol loading (derived from the MODIS MCD19A2 product 101 ), wind speed (based on TerraClimate dataset 96 ), topography elevation 90 , distance to water 91 , urban morphology (building height 102 ), and human activity intensity (VIIRS nighttime light data as a proxy indicator 103 ). We used the absolute value of the linear regression coefficient of NDVI as the cooling efficiency of the whole city (model: LST ~ NDVI + temperature + precipitation + humidity + distance to water + topography + building height + albedo + aerosol + wind speed + nighttime light), and calculated cooling capacity and cooling benefit based on the same method. Variable selection was conducted using the criterion of VIF < 5.

Our results indicated that MODIS-based cooling capacity and cooling benefit are significantly correlated with the Landsat-based counterparts (Supplementary Fig.  16 ); importantly, the gap between the Global South and North cities is around two-fold, close to the result from the Landsat-based result (Supplementary Fig.  17 ).

(3) For the calculation of cooling benefit, we considered different spatial scales of human accessibility to green spaces: assuming the population in each 100 × 100 m 2 grid cell could access to green spaces within neighborhoods of certain extents, we calculated cooling benefit by replacing NDVI i in Eq. ( 3 ) with mean NDVI within the 300 × 300 m 2 and 500 × 500 m 2 extents centered at the focal grid cell (Supplementary Fig.  18 ).

(4) Considering cities may vary in minimum NDVI, we assessed if this variation could affect resulting cooling capacity patterns. To this end, we calculated the cooling capacity for each studied city using NDVI = 0 as the reference (i.e., using NDVI = 0 instead of minimum NDVI in Supplementary Fig.  13b ), and correlated it with that using minimum NDVI as the reference (Supplementary Fig.  19 ).

Quantifying between-city inequality

Inequalities in access to the benefits of green spaces in cities exist within cities, as is increasingly well-documented 104 . Here, we focus instead on the inequalities among cities. We used the Gini coefficient to measure the inequality in cooling capacity and cooling benefit between all studied cities across the globe as well as between Global North or South cities. We calculated Gini using the population-density weighted method (Fig.  5b ), as well as the unweighted and population-size weighted methods (Supplementary Fig.  20 ).

Estimating the potential for more effective and equal cooling amelioration

We estimated the potential of enhancing cooling amelioration based on the assumptions that urban green space quality (cooling efficiency) and quantity (NDVI) can be increased to different levels, and that relative spatial distributions of green spaces and population can be idealized (so that their spatial matches can maximize cooling benefit). We assumed that macro-climate conditions act as the constraints of vegetation cover and cooling efficiency. We calculated the 50th, 60th, 70th, 80th, and 90th percentiles of NDVI within each type of local climate zone of each city. For a given local climate zone type, we obtained the city with the highest NDVI per percentile value as the regional upper bounds of urban green infrastructure quantity. The regional upper bounds of cooling efficiency are derived in a similar way. For each local climate zone in a city, we generated a potential NDVI distribution where all grid cells reach the regional upper bound values for the 50th, 60th, 70th, 80th, or 90th percentile of urban green space quantity or quality, respectively. NDVI values below these percentiles were increased, whereas those above these percentiles remained unchanged. The potential estimates are essentially dependent on the references, i.e., the optimal cooling efficiency and NDVI that a given city can reach. However, such references are obviously difficult to determine, because complex natural and socioeconomic conditions could play important roles in determining those cooling optima, and the dominant factors are unknown at a global scale. We employed the simplifying assumption that background climate could act as an essential constraint according to our results. We therefore used the Köppen climate classification system 105 to determine the reference separately in each climate region (tropical, arid, temperate, and continental climate regions were involved for all studied cities).

We calculated potential cooling capacity and cooling benefit based on these potential NDVI maps (Fixed cooling efficiency in Fig.  5 ). We then calculated the potentials if cooling efficiency of each city can be enhanced to 50–90th percentile across all urban local climate zones within the corresponding biogeographic region (Fixed green space area in Fig.  5 ). We also calculated the potentials if both NDVI and cooling efficiency were enhanced (Enhancing both in Fig.  5) to a certain corresponding level (i.e., i th percentile NDVI +  i th percentile cooling efficiency). We examined if there are additional effects of idealizing relative spatial distributions of urban green spaces and humans on cooling benefits. To this end, the pixel values of NDVI or population amount remained unchanged, but their one-to-one correspondences were based on their ranking: the largest population corresponds to the highest NDVI, and so forth. Under each scenario, we calculated cooling capacity and cooling benefit for each city, and the between-city inequality was measured by the Gini coefficient.

We used the Google Earth Engine to process the spatial data. The statistical analyses were conducted using R v4.3.3 106 , with car v3.1-2 107 , piecewiseSEM v2.1.2 108 , and ineq v0.2-13 109 packages. The global maps of cooling were created using the ArcGIS v10.3 software.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

City population statistics data is collected from the Population Division of the Department of Economic and Social Affairs of the United Nations ( https://www.un.org/development/desa/pd/content/worlds-cities-2018-data-booklet ). Global North-South division is based on Human Development Report 2019 which from United Nations Development Programme ( https://hdr.undp.org/content/human-development-report-2019 ). Global urban boundaries from GAIA data are available from Star Cloud Data Service Platform ( https://data-starcloud.pcl.ac.cn/resource/14 ) . Global water data is derived from 2018 Copernicus Global Land Service (CGLS 100-m) data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), European Space Agency (ESA) WorldCover 10 m 2020 product ( https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100 ), and GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) at https://www.soest.hawaii.edu/pwessel/gshhg/ . Landsat 8 LST and NDVI data with 30 m resolution are available at  https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 . Land surface temperature (LST) data with 1 km from MODIS Aqua product (MYD11A1) is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 . NDVI (1 km) dataset from MYD13A2 is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2 . Population data (100 m) is derived from WorldPop ( https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop ). Local climate zones are also based on 2018 CGLS data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), and built-up height data is available from Global Human Settlement Layers (GHSL, 100 m) ( https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H ). Temperature data is calculated from ERA5-Land Monthly Aggregated dataset ( https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR ). Precipitation and wind data are calculated from TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho) ( https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE ). Humidity data is calculated from Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System ( https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 ). Topography data from MERIT DEM (Multi-Error-Removed Improved-Terrain DEM) product is available at https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3 . GDP from Gross Domestic Product and Human Development Index dataset is available at https://doi.org/10.5061/dryad.dk1j0 . VIIRS nighttime light data is available at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG . City building volume data from Global 3D Building Structure (1 km) is available at https://doi.org/10.34894/4QAGYL . Albedo data is derived from the MODIS MCD43A3 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 ), and aerosol data is derived from the MODIS MCD19A2 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES ). All data used for generating the results are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

Code availability

The codes used for data collection and analyses are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

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Acknowledgements

We thank all the data providers. We thank Marten Scheffer for valuable discussion. C.X. is supported by the National Natural Science Foundation of China (Grant No. 32061143014). J.-C.S. was supported by Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173), and his VILLUM Investigator project “Biodiversity Dynamics in a Changing World”, funded by VILLUM FONDEN (grant 16549). W.Z. was supported by the National Science Foundation of China through Grant No. 42225104. T.M.L. and J.F.A. are supported by the Open Society Foundations (OR2021-82956). W.J.R. is supported by the funding received from Roger Worthington.

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Yuxiang Li, Shuqing N. Teng & Chi Xu

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Jens-Christian Svenning

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences, Beijing, China

Beijing Urban Ecosystem Research Station, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

Global Systems Institute, University of Exeter, Exeter, UK

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Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA

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Y.L., S.N.T., R.R.D., and C.X. designed the study. Y.L. collected the data, generated the code, performed the analyses, and produced the figures with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., Z.Y., S.N.T., R.R.D. and C.X. Y.L., S.N.T., R.R.D. and C.X. wrote the first draft with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., and Z.Y. All coauthors interpreted the results and revised the manuscript.

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Correspondence to Shuqing N. Teng , Robert R. Dunn or Chi Xu .

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Li, Y., Svenning, JC., Zhou, W. et al. Green spaces provide substantial but unequal urban cooling globally. Nat Commun 15 , 7108 (2024). https://doi.org/10.1038/s41467-024-51355-0

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• Introduction
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CT indicates computed tomography.

a Reasons cited in detail in primary study report by Rowell et al. 24

GFAP levels were calculated using Youden Index for identifying patients with intracranial lesions on CT.

eTable 1. Description of 3 Patients With an Initially Unremarkable CT Scan Who Required Subsequent Neurosurgical Intervention

eTable 2. Classification Performance of GFAP Concentration Cutoff at 30pg/mL Measured for the 241 Patients Who Had Samples Later Than 60 Minutes of Injury

eTable 3. Threshold Concentrations of GFAP for the Outcome Measures

eFigure 1. Boxplots of GFAP, Concentrations Measured Within 30 and 60 Minutes of Injury Relative to the Categories of Marshall Classification of CT Scan Lesions

eFigure 2. Boxplots of UCH-L1 Concentrations Measured Within 30 and 60 Minutes of Injury Relative to the Categories of Marshall Classification of CT Scan Lesions

eFigure 3. Boxplots of MAP-2 Concentrations Measured Within 30 and 60 Minutes of Injury Relative to the Categories of Marshall Classification of CT Scan Lesions

eFigure 4. Area Under the ROC (AUROC) Curve for GFAP, UCH-L1, and MAP-2 Measured Within 30 and 60 Minutes of Injury for Detecting Intracranial Lesions on CT Scan (Primary Outcome)

eFigure 5. Area Under the ROC (AUROC) Curve for GFAP, UCH-L1, and MAP-2 Measured Within 30 and 60 Minutes of Injury for Predicting Neurosurgical Intervention Within 24 Hours of Injury (Secondary Outcome)

eFigure 6. Area Under the ROC (AUROC) Curve for GFAP, UCH-L1, and MAP-2 Measured Within 30 and 60 Minutes of Injury for Predicting Clinical Important Early Outcomes (Tertiary Outcome)

eFigure 7. Scatterplot Exploring Cutoff Thresholds of GFAP for Having Neurosurgical Intervention

eFigure 8. Scatterplot Exploring Cutoff Thresholds of GFAP for Clinically Important Early Outcome Within 7 Days of Injury

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• Performance of Diagnostic Biomarkers for TBI Within the First Hour JAMA Network Open Invited Commentary September 4, 2024 Ramon Diaz-Arrastia, MD, PhD

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Papa L , McKinley WI , Valadka AB, et al. Diagnostic Performance of GFAP, UCH-L1, and MAP-2 Within 30 and 60 Minutes of Traumatic Brain Injury. JAMA Netw Open. 2024;7(9):e2431115. doi:10.1001/jamanetworkopen.2024.31115

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Diagnostic Performance of GFAP, UCH-L1, and MAP-2 Within 30 and 60 Minutes of Traumatic Brain Injury

• 1 Department of Emergency Medicine, Orlando Regional Medical Center, Orlando, Florida
• 2 Department of Surgery, The University of Chicago Medicine, Chicago, Illinois
• 3 Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas
• 4 Pritzker School of Medicine, University of Chicago, Chicago, Illinois
• 5 Department of Public Health Sciences, University of Chicago, Chicago, Illinois
• 6 Department of Social Sciences and Psychology, University of Chicago, Chicago, Illinois
• 7 Department of Surgery, Oregon Health and Science University, Portland
• 8 Department of Neurology, University of California, San Francisco
• Invited Commentary Performance of Diagnostic Biomarkers for TBI Within the First Hour Ramon Diaz-Arrastia, MD, PhD JAMA Network Open

Question   How do biomarkers glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1, and microtubule-associated protein 2 measured within 30 and 60 minutes perform in identifying intracranial lesions on computed tomography, need for neurosurgical intervention, and clinically important early outcomes among patients with traumatic brain injury (TBI)?

Findings   In this cohort study including 804 patients with TBI, all 3 biomarkers were significantly elevated in blood within 30 and 60 minutes of TBI and were associated with CT lesions, diffuse injury severity on CT scan, 24-hour neurosurgical intervention, and clinically important early outcomes, including early death, neurosurgery, and prolonged mechanical ventilation over 7 days. GFAP had the strongest independent association, with sensitivities of 98%-99% and specificities of 18%-36% for estimating outcomes.

Meaning   This cohort study found that GFAP was associated with acute outcomes with high diagnostic accuracy within 30 minutes of TBI and sets the precedent for early use of GFAP in future clinical and research efforts.

Importance   Data on the performance of traumatic brain injury (TBI) biomarkers within minutes of injury are lacking.

Objectives   To examine the performance of glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and microtubule-associated protein 2 (MAP-2) within 30 and 60 minutes of TBI in identifying intracranial lesions on computed tomography (CT) scan, need for neurosurgical intervention (NSI), and clinically important early outcomes (CIEO).

Design, Setting, and Participants   This cohort study is a biomarker analysis of a multicenter prehospital TBI cohort from the Prehospital Tranexamic Acid Use for TBI clinical trial conducted across 20 centers and 39 emergency medical systems in North America from May 2015 to March 2017. Prehospital hemodynamically stable adult patients with traumatic injury and suspected moderate to severe TBI were included. Blood samples were measured for GFAP, UCH-L1, and MAP-2. Data were analyzed from December 1, 2023, to March 15, 2024.

Main Outcomes and Measures   The presence of CT lesions, diffuse injury severity on CT, NSI within 24 hours of injury, and CIEO (composite outcome including early death, neurosurgery, or prolonged mechanical ventilation ≥7 days) within 7 days of injury.

Results   Of 966 patients enrolled, 804 patients (mean [SD] age, 41 [19] years; 418 [74.2%] male) had blood samples, including 563 within 60 minutes and 375 within 30 minutes of injury. Among patients with blood drawn within 30 minutes of injury, 212 patients (56.5%) had CT lesions, 61 patients (16.3%) had NSI, and 112 patients (30.0%) had CIEO. Among those with blood drawn within 60 minutes, 316 patients (56.1%) had CT lesions, 95 patients (16.9%) had NSI, and 172 patients (30.6%) had CIEO. All biomarkers showed significant elevations with worsening diffuse injury on CT within 30 and 60 minutes of injury. Among blood samples taken within 30 minutes, GFAP had the highest area under the receiver operating characteristic curve (AUC) to detect CT lesions, at 0.88 (95% CI, 0.85-0.92), followed by MAP-2 (AUC, 0.78; 95% CI, 0.73-0.83) and UCH-L1 (AUC, 0.75; 95% CI, 0.70-0.80). Among blood samples taken within 60 minutes, AUCs for CT lesions were 0.89 (95% CI, 0.86-0.92) for GFAP, 0.76 (95% CI, 0.72-0.80) for MAP-2, and 0.73 (95% CI, 0.69-0.77) for UCH-L1. Among blood samples taken within 30 minutes, AUCs for NSI were 0.78 (95% CI, 0.72-0.84) for GFAP, 0.75 (95% CI, 0.68-0.81) for MAP-2, and 0.69 (95% CI, 0.63-0.75) for UCH-L1; and for CIEO, AUCs were 0.89 (95% CI, 0.85-0.93) for GFAP, 0.83 (95% CI, 0.78-0.87) for MAP-2, and 0.77 (95% CI, 0.72-0.82) for UCH-L1. Combining the biomarkers was no better than GFAP alone for all outcomes. At GFAP of 30 pg/mL within 30 minutes, sensitivity for CT lesions was 98.1% (95% CI, 94.9%-99.4%) and specificity was 34.4% (95% CI, 27.2%-42.2%). GFAP levels greater than 6200 pg/mL were associated with high risk of NSI and CIEO.

Conclusions and Relevance   In this cohort study of prehospital patients with TBI, GFAP, UCH-L1, and MAP-2 measured within 30 and 60 minutes of injury were significantly associated with traumatic intracranial lesions and diffuse injury severity on CT scan, 24-hour NSI, and 7-day CIEO. GFAP was the strongest independent marker associated with all outcomes. This study sets a precedent for the early utility of GFAP in the first 30 minutes from injury in future clinical and research endeavors.

Despite the volume of traumatic brain injury (TBI) biomarker research over the last 2 decades, researchers have yet to examine the performance of biomarkers at very early time points after injury. Several large-scale studies have measured TBI biomarkers over the first 24 hours of injury, 1 - 4 but only a handful of studies have examined them within 2 to 4 hours of TBI, particularly for identifying intracranial lesions on computed tomography (CT) scans and need for neurosurgical intervention. 5 - 14 In a study examining the time course of glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) within 4 hours of injury, both GFAP and UCH-L1 were elevated within an hour of injury, even as early as 15 minutes. 8 Biomarker information obtained this early after injury could inform decision-making in various settings, including prehospital transport decisions to trauma centers, military transport to medical facilities from theater, sports-related brain injuries on the sidelines, and triaging patients for future therapeutic clinical trials for TBI.

Blood-based biomarkers GFAP and UCH-L1 were approved by the US Food and Drug Administration (FDA) for clinical use in adult patients with mild TBI with Glasgow Coma Scale (GCS) score of 13 to 15 to help determine the need for CT scan within 12 hours of injury in 2018 and 2021, respectively. 15 , 16 GFAP is an astroglial biomarker of injury and is found in the astroglial skeleton of both white and gray brain matter. 17 UCH-L1 is a neuronal brain injury biomarker found in high abundance in neurons. 18 Microtubule-associated protein 2 (MAP-2) is a relatively novel biomarker of human TBI that is localized mainly in neuronal cell bodies and dendrites and is a component of the neuron microtubule system that modulates microtubule organization and stabilizes the cytoskeleton. 19 - 21 Following severe TBI, MAP-2 measured within 6 hours of injury is associated with TBI severity and early 2-week mortality 22 and has been shown to improve the 6-month neuroprognostic performance of validated clinical models. 23

The Prehospital Tranexamic Acid (TXA) for TBI trial was a large multicenter trial that obtained blood samples from participants with suspected TBI with GCS score 3 to 12 within 4 hours of injury and provided an opportunity to examine blood-based biomarkers at very early time points after injury. 24 Our current study sought to examine the performance of GFAP, UCH-L1, and MAP-2 within 30 minutes and 60 minutes of injury in (1) identifying patients with traumatic intracranial lesions on initial CT scan and examining the association with CT injury severity, (2) identifying patients requiring neurosurgical intervention within 24 hours of injury, (3) identifying patients having clinically important early outcomes (CIEO) from TBI within 7 days of injury, and (4) examining threshold concentrations of biomarkers to inform TBI risk stratification for potential clinical and research purposes.

This cohort study was approved by the institutional review board for the Resuscitation Outcomes Consortium Clinical Trials Center at the University of Washington, with contingent approval provided by individual sites. Participants were enrolled under US regulations for Exception From Informed Consent Requirements for Emergency Research (21CFR50.24) and the Canadian Tri-Council Policy Statement 2 (Ethical Conduct for Research Involving Humans). Consent for continued participation was obtained from the participant or their legally authorized representative at the earliest feasible opportunity. An opportunity for opt out was provided according to the requirements set forth by each site’s local regulatory board. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

This is a biomarker analysis of a multicenter prehospital TBI cohort from the Prehospital TXA Use for Traumatic Brain Injury clinical trial, a multicenter double-blinded, randomized clinical trial performed across 20 centers and 39 emergency medical systems in the United States and Canada (ClinicalTrials.gov identifier: NCT01990768 ). The trial was designed to examine the efficacy and safety of prehospital administration of TXA compared with placebo in patients with suspected moderate to severe TBI. The full trial protocol has been published elsewhere. 24

Between May 2015 and March 2017, hemodynamically stable (systolic blood pressure >90 mm Hg) patients aged older than 15 years with suspected TBI with a prehospital GCS score of 3 to 12 (prior to sedatives) and at least 1 reactive pupil were eligible and were randomized to 1 of 3 groups: placebo, 2 g TXA bolus plus a placebo infusion over 8 hours, or 1 g TXA bolus plus 1 g TXA infusion over 8 hours. Although eligible patients were randomized to 1 of 3 groups, our group recently demonstrated that TXA had no effect on the levels of TBI-related biomarkers GFAP, UCH-L1, or MAP-2, 25 so the entire cohort of patients in the trial was included in this analysis.

Race and ethnicity were identified through the electronic medical record. Classification was in accordance with National Institute of Neurological Disorders and Stroke definitions to ensure research is inclusive of racial and ethnic minority groups. Race was categorized as American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, more than 1 race, and unknown; ethnicity was categorized as Hispanic or not Hispanic. Race and ethnicity were included in analysis per National Institutes of Health guidelines.

Initial blood samples were obtained from each patient with suspected TBI as soon as possible on arrival at the emergency department within 4 hours of injury. Samples were collected in serum separator tubes and then centrifuged, aliquoted for serum, placed in bar-coded aliquot containers, and frozen at −80 °C. They were batch shipped by the clinical research staff to the Donald D. Trunkey for Civilian and Combat Casualty Care, Oregon Health & Science University, for storage until they were transported to a central laboratory for biomarker analysis.

The primary outcome measure was the presence of an acute traumatic intracranial lesion visualized on CT scan and included such findings as extra-axial lesions (epidural or subdural hematomas), subarachnoid hemorrhage, intra-axial lesions (cortical and noncortical parenchymal contusions or hemorrhages), cerebral edema, traumatic axonal injury, midline shift of intracranial contents, or any signs of brain herniation. Only initial head CT scans performed on arrival at the hospital were used for this analysis. Deidentified digital images of head CTs were transferred to the Oregon Health & Science University image repository and reviewed centrally by a neuroradiologist-trained technician and audited by a neuroradiologist; 10% of scans were audited to verify accurate and consistent measurements. Marshall classification was used to group CT lesion into 6 categories based on morphological anomalies (Diffuse Injury I-IV, evacuated mass lesions, and nonevacuated mass lesions). 26

The secondary outcome measure was the need for neurosurgical intervention within 24 hours of injury, including craniotomy, craniectomy, and placement of intracranial neuromonitoring or drainage devices. Early identification of patients requiring neurosurgical intervention is imperative so patients can be quickly transported or transferred to facilities that provide definitive neurosurgical care. A delay in this care can be detrimental.

The tertiary outcome measure was CIEO within 7 days of injury, a composite outcome of early death within 7 days of injury secondary to TBI, the need for neurosurgical intervention within 7 days of injury, and mechanical ventilation for more than 7 days in patients with TBI. 27 Having 1 or any combination of these 3 outcomes would constitute a clinically important 7-day outcome. This end point was adapted from seminal work done in patients with TBI and GCS 13 to 15 at risk for clinically significant TBI and includes early death, intubation, and neurosurgical intervention. 28 - 30 Not only are these outcomes applicable to the full spectrum of TBI, they are particularly relevant very early after injury for risk stratification when TBI severity is not always clinically apparent and the GCS score can be inexact. These outcomes were analyzed as a together as a composite and individually. Threshold concentrations of biomarkers were explored to examine levels of biomarkers as very early indicators of injury severity to improve injury classification and inform clinical management and as potential inclusion criteria for future clinical trials very early after injury.

Serum GFAP, UCH-L1, and MAP-2 levels were measured in duplicate using a validated enzyme-linked immunosorbent assay platform (Banyan Biomarkers). Serum MAP-2 levels were measured using a standard sandwich enzyme-linked immunosorbent assay protocol reported previously. 22 For GFAP, the lower limit of quantification (LLOQ) was 3 pg/mL, the upper limit of quantification (ULOQ) was 320 pg/mL, and lower limit of detection (LoD) was 3 pg/mL. For UCH-L1, the LLOQ was 14 pg/mL, the ULOQ was 2560 pg/mL, and the LoD was 6 pg/mL. For MAP-2, the LLOQ was 26 pg/mL, the ULOQ was 1846 pg/mL, and the LoD was 9 pg/mL. Samples below the reportable range were analyzed as half of the LoD. Any samples yielding a signal over the quantification or calibrator range were diluted and reassayed.

Descriptive statistics included means, medians, and proportions along with 95% CIs and IQRs. Biomarker data were expressed as medians and IQRs. Data were assessed for homoscedasticity and distribution and logarithmic transformations conducted accordingly. Group comparisons were performed using analysis of variance, and the χ 2 test. Receiver operating characteristics curves were created to explore the ability of the biomarkers to identify intracranial lesions on CT scan, 24-hour neurosurgical intervention, and 7-day CIEO. Combinations of the biomarkers were assessed using logistic regression analysis. Estimates of the areas under these curves (AUCs) were obtained. Classification performance was assessed by sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% CIs. 31 Optimal thresholds for biomarker levels were selected using Youden Index. Analyses were performed using the statistical software package SPSS version 29.0 (IBM). Sample size was based on available data from the trial and the biomarkers and provided by the Data Coordinating Center. P values were 2-sided, and statistical significance was set at P  ≤ .05. Data were analyzed from December 1, 2023, to March 15, 2024.

There were 966 patients enrolled in the original study, and 804 patients (mean [SD] age, 41 [19] years; 418 [74.2%] male) had blood samples drawn and biomarker data available for analysis. Patient characteristics are shown in Table 1 . By race and ethnicity, 3 patients (0.5%) were American Indian or Alaskan Native, 18 patients (3.2%) were Asian, 68 patients (12.1%) were Black or African American, 2 patients (0.4%) were Native Hawaiian or Pacific Islander, 400 patients (71%) were White, 2 patients (0.4%) were more than 1 race, and 70 patients (12.4%) had unknown race; 78 patients (13.9%) were of Hispanic ethnicity. The median (IQR) injury severity score was 17 (6-26), and 546 injuries (97.0%) were from blunt mechanisms. There were 563 patients who had samples drawn within 60 minutes of injury and a subset of 375 patients who had samples drawn within 30 minutes of injury ( Figure 1 ). Of patients with samples drawn within 60 minutes, 316 patients (56.1%) had traumatic intracranial lesions on their initial CT scan, 95 patients (16.9%) had neurosurgical intervention within 24 hours of injury, and 172 patients (30.6%) had CIEO within 7 days of injury. Of patients with samples drawn within 30 minutes of injury, 212 patients (56.5%) had CT lesions, 61 patients (16.3%) had neurosurgical intervention within 24 hours, and 112 patients (30.0%) had CIEO ( Figure 1 ). Three patients with no lesions on initial CT scan underwent a neurosurgical intervention within 24 hours (eTable 1 in Supplement 1 ).

Concentrations of GFAP, UCH-L1, and MAP-2 were plotted against the Marshall CT Classification system and all 3 biomarkers showed significant incremental elevations with worsening diffuse injury on CT within both 30 and 60 minutes of injury (eFigures 1-3 in Supplement 1 ). There were no evacuated mass lesions represented, as patients were within 30 to 60 minutes of injury.

AUCs were calculated for GFAP, UCH-L1, and MAP-2 for identifying traumatic intracranial lesions on CT scan in samples taken within 30 and 60 minutes of injury ( Table 2 ; eFigure 4 in Supplement 1 ). GFAP demonstrated the best performance for samples taken within 30 minutes of injury, with an AUC of 0.88 (95% CI, 0.85-0.92), followed by MAP-2 (AUC, 0.78; 95% CI, 0.73-0.83) and UCH-L1 (AUC, 0.75; 95% CI, 0.70-0.80). Various combinations of the 3 biomarkers were no better than GFAP alone ( Table 2 ) (eFigure 4 in Supplement 1 ). Results were similar for samples taken within 60 minutes of injury (eFigure 4 in Supplement 1 ), and GFAP remained an independent marker of CT lesions ( Table 2 ).

For samples taken within 30 minutes of injury, GFAP alone had the highest association with having 24-hour neurosurgical intervention, with an AUC of 0.78 (95% CI, 0.72-0.84), followed by MAP-2 (AUC, 0.75; 95% CI, 0.68-0.81) and UCH-L1 (AUC, 0.69; 95% CI, 0.63-0.75) ( Table 2 ) (eFigure 5 in Supplement 1 ). GFAP also had the strongest independent performance for samples taken within 60 minutes of injury (eFigure 5 in Supplement 1 ). There was no added benefit from incorporating MAP-2 or UCH-L1 to GFAP alone at either 30 or 60 minutes ( Table 2 ).

For samples taken within 30 minutes of injury, GFAP alone had the highest association with CIEO, with an AUC of 0.89 (95% CI, 0.85-0.93), followed by MAP-2 (AUC, 0.83; 95% CI, 0.78-0.87) and UCH-L1 (AUC, 0.77; 95% CI, 0.72-0.82) ( Table 2 ; eFigure 6 in Supplement 1 ). GFAP also had the strongest association compared with the other biomarkers collected within 60 minutes (eFigure 6 in Supplement 1 ). There was no added benefit to adding MAP-2 or UCH-L1 to GFAP ( Table 2 ). Individually, early mortality from TBI within 7 days, neurosurgery within 7 days, and mechanical ventilation in TBI more than 7 days each had significant associations with the biomarkers ( Table 2 ), especially GFAP. Early mortality had a particularly significant association, with AUCs of 0.95 (95% CI, 0.93-0.98) for GFAP, 0.73 (95% CI, 0.63-0.82) for UCH-L1, and 0.85 (95% CI, 0.78-0.93) for MAP-2 in samples taken within 30 minutes and similarly within 60 minutes ( Table 2 ).

Based on FDA-approved GFAP cutoff level for CT lesion detection of 30 pg/mL, 16 the sensitivity and specificity for detecting CT lesions were 98.1%(95% CI, 94.9%-99.4%) and 34.4% (95% CI, 27.2%-42.2%) for samples from within 30 minutes and 98.7% (95% CI, 96.6%-100%) and 36.4% (95% CI, 30.5%-42.8%) for samples from within 60 minutes, respectively ( Table 3 ). For 24-hour neurosurgical intervention, the sensitivity and specificity were 98.4% (95% CI, 90.0%-99.9%) and 18.8% (95% CI, 14.7%-23.6%) for samples from within 30 minutes and 97.9% (95% CI, 91.9%-99.6%) and 19.7% (16.2-23.6) for samples from within 60 minutes ( Table 3 ). For CIEO, the sensitivity and specificity were 99.1% (95% CI, 94.4%-100%) and 22.4% (95% CI, 17.6%-28.1%) for samples from within 30 minutes and 98.8% (95% CI, 95.4%-99.8%) and 23.5% (95% CI, 19.5%-28.1%) for samples from within 60 minutes ( Table 3 ). Similar results were found in later samples from 60 minutes (eTable 2 in Supplement 1 ).

The optimal threshold calculated for GFAP using Youden Index for identifying patients with intracranial lesions on CT ( Figure 2 ) was consistent with the FDA-approved level of 30 pg/mL. This level optimized the sensitivity and NPV (as a rule-out test) so patients below the threshold were highly unlikely to have lesions on CT (NPV, 95.7%; 95% CI, 88.8%-98.6%) (eTable 3 in Supplement 1 ). A threshold of 110 pg/mL optimized both sensitivity and specificity (eTable 3 in Supplement 1 ). A threshold of 2300 pg/mL optimized the specificity and PPV of GFAP (as a rule-in test) so that patients with GFAP levels above this threshold were highly likely to have lesions on CT scan (PPV, 97.1%; 95% CI, 91.3%-99.2%) (eTable 3 in Supplement 1 ). These thresholds yielded similar performances for samples drawn within 30 and 60 minutes of injury (eTable 3 in Supplement 1 ).

For 24-hour neurosurgical intervention, a threshold of 55 pg/mL optimized GFAP sensitivity, 500 pg/mL optimized both sensitivity and specificity, and 6200 pg/mL optimized the specificity for samples from within both 30 and 60 minutes of injury (eTable 3 and eFigure 7 in Supplement 1 ). For CIEO, cutoffs were similar with thresholds of 55 pg/mL, 485 pg/mL, and 6200 pg/mL (eTable 3 and eFigure 8 in Supplement 1 ), indicating that patients with concentrations of GFAP greater than 6200 pg/mL were at highest risk of CIEO within 7 days of injury. For early mortality from TBI, the thresholds were 60 pg/mL, 1000 pg/mL, and 15 000 pg/mL, respectively (eTable 3 in Supplement 1 ), indicating that patients with GFAP levels greater than 15000 pg/mL were at highest risk of early mortality.

Given the lack of data on the performance and accuracy of brain injury biomarkers very early after injury, this cohort study addresses a critical gap in the literature by using a large cohort of patients with TBI enrolled in a multicenter prehospital TBI clinical trial. Studies evaluating TBI biomarkers very early after injury are very challenging to conduct and the prehospital TXA trial provided a unique opportunity to obtain samples within minutes of injury. This study confirmed that validated biomarkers GFAP and UCH-L1 and the novel biomarker MAP-2 were significantly elevated within 30 and 60 minutes following TBI and were associated with presence of traumatic intracranial lesions on initial CT scan, need for neurosurgical intervention within 24 hours of injury, and CIEOs within 7 days of injury with very good performance characteristics. Additionally, biomarker concentrations were significantly associated with the severity of diffuse injury on initial CT and provided valuable information on the severity of the TBI much sooner after injury than previously reported. GFAP was a strong independent biomarker associated with injury, followed by MAP-2 and UCH-L1. Notably, MAP-2 showed considerable promise as a novel biomarker.

It is standard practice in patients with suspected TBI with initially low GCS scores (3-12) to perform CT scan of the head as part of their initial clinical management. Although TBI biomarkers may not be necessary to decide whether a CT scan should be performed, there are reasons and opportunities to use TBI biomarkers in this population. Although GCS score is an important triage tool in determining TBI severity, its accuracy is limited in situations where neurologic function may be impaired by alcohol, illicit drugs, medications (eg, sedatives or neuromuscular blockers), emotional trauma, distraction by other injuries, or physiological compromise from hypoxia or hypotension. Although the patients enrolled in this study were all suspected of having a moderate to severe TBI, more than 40% had no evidence of traumatic intracranial injuries on initial CT scan of the head, and only 16% had a neurosurgical intervention within 24 hours. Thus, there is an opportunity for TBI biomarkers to impact risk stratification of patients with suspected TBI very early after injury, even in those with more severe injury. Moreover, there are many settings in which access to CT scans may be limited and clinical decision-making could benefit from a point of care test. An objective measure of injury available so early after injury could also inform early in-hospital care, such intensive care unit admission, need for neuromonitoring, and early treatment.

Given that GFAP had the strongest independent association of the 3 biomarkers evaluated, GFAP thresholds within 30 and 60 minutes of injury were explored to gauge the classification performance for the 3 outcomes. Creating cutoffs served to risk stratify patients by GFAP concentrations. Clinically, GFAP thresholds have the potential to drive prehospital transport decisions to trauma centers with neurosurgical capabilities, military transport to medical facilities located far from theater, sideline decisions at organized sports events, and rapid emergency department triage and treatment prior to neuroimaging.

From a research perspective, there is tremendous potential for GFAP to guide selection and inclusion of patients with TBI into future therapeutic clinical trials. For example, if a trial aimed to include only patients with TBI with intracranial lesions on CT (rule-in), levels of GFAP greater than 2300 pg/mL (with a specificity of 99% and PPV of 97%) could select for patients most likely to have traumatic intracranial lesions. Conversely, if a clinical trial aimed to include only patients with TBI without intracranial lesions (rule-out), a GFAP threshold of less than 30 pg/mL (with a sensitivity of 99% and NPV of 99%) could potentially select patients highly unlikely to have intracranial lesions on CT scan. This premise holds for selection of patients with need for neurosurgical intervention within 24 hours and CIEO using cutoff thresholds of 55 pg/mL and 6200 pg/mL as rule-out and rule-in criteria, respectively. If a treatment were aimed at patients with TBI at risk for neurosurgical intervention, early death, or prolonged mechanical ventilation within 7 days of injury, a minimum GFAP threshold of 6200 pg/mL could potentially identify these patients (rule-in) with a specificity of 99% and a PPV 90% within 30 minutes of injury. Moreover, patients with GFAP levels greater than 15 000 pg/mL were at the highest risk of early mortality.

These results are consistent with other studies that have measured biomarkers in subsets of milder TBI cohorts early after injury. 8 , 9 , 11 , 12 , 32 Both GFAP and UCH-L1 have been shown to be detectible within an hour of injury in patients with mild TBI. 8 It appears that the presence of these TBI biomarkers in blood within the first 30 minutes of injury is consistent across different severities of TBI, including those with GCS scores of 13 to 15 who are less likely to have intracranial lesions on CT. One caveat is that the prevalence of TBI in a cohort can affect the NPV and PPV of a diagnostic test and must be accounted for when interpreting results. In a cohort of patients with TBI and GCS of 3 to 12 in which the prevalence of CT lesions is 40% to 70%, the sensitivity and NPV of the biomarkers will likely be better than in cohorts with GCS of 13 to 15 (prevalence CT lesions of 10%-20%). Results from this cohort are therefore likely to exhibit better performance characteristics than cohorts with milder injuries.

Despite the important findings of this study, we recognize that there are limitations to the analysis. A substantial proportion of the patients in this study was male (74%). Although the incidence of TBI is higher in males than females, 33 the limited representation of females needs to be recognized, as biomarker concentrations may differ in females, warranting further study. 34

Although neurosurgical intervention is an important outcome for identifying patients needing neurosurgical care, we did not examine the specific clinical circumstances surrounding ICP monitoring, drainage devices, or specific neurosurgical procedures. Withdrawal from life-sustaining measures was not accounted for.

Although this trial had 3 treatment groups, our study group previously demonstrated that the 3 TXA treatment groups had no association with any of the levels of TBI-related biomarkers GFAP, UCH-L1, or MAP-2. 25 The assigned study infusion was initiated by emergency medical services in the field, and the blood draw was obtained immediately on hospital arrival.

In this cohort study of prehospital patients with TBI, concentrations of GFAP, UCH-L1, and MAP-2 measured within 30 and 60 minutes of injury were significantly associated with the presence of traumatic intracranial lesions on CT scan, degree of diffuse injury on CT scan, neurosurgical intervention within 24 hours of injury, and CIEO within 7 days of injury. GFAP had the strongest independent associations of the 3 biomarkers measured, followed by MAP-2 and UCH-L1. GFAP thresholds to optimize sensitivity and specificity provided an important overview of how GFAP could be applied clinically and in future clinical trials. These findings support the clinical value of TBI biomarkers measured in the first 30 minutes from injury and set the precedent for early use of these biomarkers in future clinical and research endeavors.

Accepted for Publication: June 24, 2024.

Published: September 4, 2024. doi:10.1001/jamanetworkopen.2024.31115

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License . © 2024 Papa L et al. JAMA Network Open .

Corresponding Author: Linda Papa, MDCM, MSc, Department of Emergency Medicine, Orlando Regional Medical Center, 1335 Sligh Blvd, 5th Floor, Orlando, FL 32806 ( [email protected] ).

Author Contributions: Drs Papa and Rowell had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Papa, McKinley, Hinson, Rowell.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Papa, Newman.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Papa, Nordgren.

Obtained funding: Papa, Schreiber, Rowell.

Administrative, technical, or material support: Papa, McKinley, Loss, Tinoco-Garcia, Schreiber, Rowell.

Supervision: Valadka, Brito, Hinson, Schreiber, Rowell.

Conflict of Interest Disclosures: Dr McKinley reported receiving personal fees from Intuitive Surgical, Medical Device Business Services, and Zimmer Biomet Holdings outside the submitted work. Dr Schreiber reported receiving grants from CSL Behring and Haemonetics and serving on advisory boards for CSL Behring, Haemonetics, Velico Medical, Tricol, and Octapharma outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by Award “Blood Based Biomarkers of Injury and Outcome in the Prehospital TXA for TBI Trial” from the National Heart, Lung, and Blood Institute (grant No. 1RO1HL126585). This study was also supported by Award “Prehospital Tranexamic Acid Use for Traumatic Brain Injury” Department of Defense Emergency Management Response System (grant No.13335004) as part of the Resuscitations Outcome Consortium.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, Department of Defense, or National Heart, Lung and Blood Institute, and should not be construed as an official Department of Defense or US Army policy unless designated by other documentation. No official endorsement should be made.

Data Sharing Statement: See Supplement 2 .

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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 September 5, 2024 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.

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

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.

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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.

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.

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