research study difference

Study vs. Research — What's the Difference?

Difference Between Study and Research

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Research vs. study

The confusion about these words is that they can both be either nouns or verbs. If you ask someone, "Does 'studies' mean the same as 'researches'?" you may hear "Yes," but it is only true if they are used as verbs. As nouns, they have subtly different meanings.

"This team has done a lot of good research. I just read their latest study, which they wrote about calcium in germinating soybeans. It described several interesting experiments."

research 1. to perform a systematic investigation

1. "What kind of scientist is he? He's a botanist. He researches plants."

study 1. to perform a systematic investigation; 2. to actively learn or memorize academic material

1. "What kind of scientist is he? He's a botanist. He studies plants."

2. "Mindy studies every day. That is why she gets such excellent grades. She wants to go to college to study math."

Some authors say "research" when they mean "study." "Research," as a verb, means "to perform a study or studies," but "research" as a noun refers to the sum of many studies. "Chemical research" means the sum of all chemical studies. If you find yourself writing "a research" or "in this research," change it to "a study" or "in this study."

research The act of performing research. Also, the results of research. Note that "research" is a mass noun. It is already plural in meaning but grammatically singular. If you want to indicate more than one type, say "bodies of research" or "pieces of research," not "researches."

"Dr. Lee was a prolific scientist. She performed a great deal of research over her long career."

study A single research project or paper.

"Dr. Lee was a prolific scientist. She performed a great many studies over her long career."

The noun "study" refers to a single paper or project. You can replace "paper" with "study" in almost all cases (but not always the other way around), to the point where you can say "I wrote a study." The noun "research" means more like a whole body of research including many individual studies: The research of a field. The lifetime achievements of a scientist or research team.

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Difference Between Research and Study

Research and study are terms often used interchangeably but have distinct meanings. Understanding the difference between research and study is important, as it can help you approach your academic work more effectively.

The study refers to learning and acquiring knowledge through reading, lectures, and other forms of instruction. It involves a systematic approach to learning and often requires a significant amount of time and effort. Students engage in study to gain a better understanding of a particular subject or topic and to prepare for exams or other assessments.

On the other hand, research involves systematically investigating a particular topic or problem. It involves collecting and analyzing data and drawing conclusions based on the results. Research is often conducted to answer specific questions or to test hypotheses, and it can be used to inform policy decisions or to advance scientific knowledge.

Definition of Research

Research is a systematic and scientific investigation of a particular subject matter or problem. It collects, analyzes, and interprets data to answer a research question or hypothesis. Research can be conducted in various fields, such as science, social science, business, and technology.

Research differs from studying because it involves a more rigorous and structured approach. While studying involves learning and understanding a particular topic, research requires a more in-depth and critical analysis. Research also involves various methods and techniques, such as surveys, experiments, case studies, and observations.

One of the primary objectives of the research is to contribute to the existing knowledge in a particular field. Research findings can be used to inform policy decisions, improve products and services, and advance scientific knowledge. Research can also identify gaps in knowledge and inform future research directions.

Definition of Study

Regarding academia, “study” is often used interchangeably with “research.” However, there is a subtle difference between the two. A study systematically examines and analyzes a particular subject or topic. It involves gathering and interpreting data in order to draw conclusions or make recommendations.

Studies can take many forms, depending on the discipline and the research question being addressed. Some common studies include case studies, observational studies, and experimental studies. In a case study, a single subject or group is examined in detail, often over an extended period. Researchers observe and record behavior or phenomena without intervening in an observational study. In an experimental study, researchers manipulate one or more variables to observe the effect on an outcome.

Regardless of the type of study, the goal is always to gain a deeper understanding of the subject being examined. This may involve developing new theories, refining existing ones, or identifying practical solutions to real-world problems. A study is a rigorous and systematic process that requires careful planning, execution, and analysis.

Purpose of Research

Research is a systematic investigation of a particular topic or issue to discover new knowledge or verify existing knowledge. The research aims to answer questions, solve problems, or improve understanding of a particular phenomenon. Research can be conducted in various fields, including science, social sciences, humanities, and business.

The main purpose of research is to contribute to the advancement of knowledge in a particular field. Research can help to identify new phenomena, explain existing phenomena, or develop new theories. It can also help identify knowledge gaps and suggest areas for further investigation.

Another important purpose of research is to provide evidence-based information for decision-making. Research can inform policy decisions, guide the development of new products or services, or support clinical practice. By providing reliable, valid, and objective data, research can reduce uncertainty and improve the quality of decision-making.

Research can also help to identify and solve practical problems. By identifying the causes of a problem and testing potential solutions, research can help to improve processes, products, or services. For example, research can be used to develop new drugs, improve educational programs, or design more efficient transportation systems.

Purpose of Study

One of the main differences between research and study is the purpose for which they are conducted. While both involve acquiring knowledge, they differ in terms of their objectives and goals.

Studies are typically conducted to understand a specific topic or phenomenon better. They are often exploratory and seek to answer questions such as “what is happening?” or “what are the characteristics of this phenomenon?” Studies may also be conducted to test hypotheses or theories, but their primary goal is to understand a particular subject better.

Studies can be qualitative or quantitative in nature. Qualitative studies are typically used to explore complex phenomena and gain a deeper understanding of human behavior, attitudes, and experiences. Quantitative studies, on the other hand, are used to measure and quantify data and test hypotheses.

Overall, the purpose of a study is to gain a deeper understanding of a specific phenomenon or topic. This can be achieved through various methods, including surveys, interviews, observations, and experiments. Studies can be conducted in various fields, including psychology, sociology, education, and business.

Research Methodology

Research methodology is the systematic process of collecting and analyzing data to answer research questions or test hypotheses. It involves a series of steps researchers follow to ensure their study is valid and reliable. The following are some of the common research methodologies used in academic research:

• Experimental research involves manipulating one or more variables to observe the effect on another variable. It is often used to test cause-and-effect relationships.
• Survey research involves collecting data from a sample of individuals using questionnaires or interviews. It is often used to gather information about attitudes, opinions, and behaviors.
• Case study research involves an in-depth analysis of a single case or a small group of cases. It is often used to gain a detailed understanding of a complex phenomenon.
• Observational research: This involves observing and recording the behavior of individuals or groups in their natural environment. It is often used to gather data on behaviors that cannot be manipulated in an experimental setting.

Each research methodology has its own strengths and weaknesses, and researchers must carefully choose the most appropriate methodology for their research question. They must also ensure that their study design is rigorous and that their data collection and analysis procedures are reliable and valid.

Study Methodology

When conducting a study, the methodology used can vary depending on the study’s type. Here are a few common study methodologies:

• Observational studies: In this study, researchers observe and record participants’ behavior without intervening or manipulating any variables.
• Experimental studies: In an experimental study, researchers manipulate one or more variables to see how they affect the outcome. Participants are randomly assigned to different groups, each receiving a different treatment or intervention.
• Survey studies: This study involves gathering data from a large group through questionnaires or interviews. Researchers analyze the responses to conclude the population being studied.

When designing a study, researchers must also consider the sample size or the number of participants included. A larger sample size generally yields more reliable results but can be more costly and time-consuming to recruit and analyze.

Additionally, researchers must consider potential biases that could affect the study results. For example, selection bias can occur if participants are not randomly selected, while response bias can occur if participants provide inaccurate or incomplete information.

Overall, the methodology used in a study is crucial for ensuring the validity and reliability of the results. By carefully designing and conducting a study, researchers can draw meaningful conclusions and contribute to the body of knowledge in their field.

Types of Research

Research is a systematic and scientific approach to collecting and analyzing data to answer a specific research question. There are several types of research, each with its purpose and methodology. Here are some of the most common types of research:

Descriptive research

This type of research describes the characteristics of a particular phenomenon or group of people. It is often used to generate hypotheses or to identify patterns and trends.

Exploratory research

 This type of research is conducted when the researcher needs to gain more knowledge about the subject of study. It is used to gain a better understanding of the research problem and to identify potential research questions.

Experimental research

 This type of research involves manipulating one or more variables to observe the effect on another variable. It is used to establish cause-and-effect relationships between variables.

Correlational research

 This type of research examines the relationship between two or more variables without manipulating them. It is used to identify the strength and direction of the relationship between variables.

Qualitative research

 This type of research is used to explore and understand the meaning and experiences of individuals or groups. It is often conducted using interviews, observations, and focus groups.

Quantitative research

 This type of research involves collecting and analyzing numerical data. It is often conducted using surveys, experiments, and statistical analysis.

Each type of research has its strengths and weaknesses, and the choice of research methodology depends on the research question, the nature of the research problem, and the available resources. Researchers must carefully consider the type of research that best suits their research question and design their study accordingly.

Types of Study

Studies can be classified into different types based on their objectives, design, and methodology. Here are some of the most common types of study:

Observational study

 This type of study involves observing and recording the behavior of subjects in their natural environment without any intervention or manipulation. Observational studies can be classified into cross-sectional, case-control, and cohort studies.

Experimental study

 This study involves manipulating one or more variables to observe the effect on the outcome of interest. Experimental studies can be classified into randomized controlled trials (RCTs), quasi-experimental studies, and single-subject designs.

Descriptive study

 This type of study aims to describe the characteristics of a population or phenomenon. Descriptive studies can be further classified into case reports, case series, and surveys.

Exploratory study

This study aims to explore a new or under-researched topic or phenomenon. Exploratory studies can be further classified into qualitative, pilot, and feasibility studies.

Diagnostic study

 This type of study aims to evaluate the accuracy of a diagnostic test or procedure. Diagnostic studies can be further classified into sensitivity and specificity studies, receiver operating characteristic (ROC) curve analysis, and likelihood ratio studies.

Each study type has its strengths and weaknesses, and the choice of study type depends on the research question, available resources, and ethical considerations. Researchers must carefully consider the study design and methodology to ensure the study is valid, reliable, and ethical.

Data Collection in Research

Data collection is a crucial aspect of research involving gathering information to answer research questions or test hypotheses. Data collection methods may vary depending on the research design, questions, and required data type. The most commonly used data collection methods in research include:

• Surveys/questionnaires
• Observations
• Experiments

Surveys or questionnaires are commonly used in research to collect data from many participants. Survey questions are usually closed-ended, and the responses are quantifiable. Surveys can be conducted online, by phone, or in person.

Conversely, interviews are more in-depth and can be conducted face-to-face or over the phone. Interviews are useful in collecting qualitative data, which is non-numerical data that provides insights into participants’ experiences, opinions, and attitudes.

Observations involve watching and recording the behavior of individuals or groups in their natural settings. Observations can be structured or unstructured and conducted in person or through video recordings.

Experiments are designed to test hypotheses and involve manipulating one or more variables to observe the effect on the outcome variable. Experiments can be conducted in a laboratory or the field.

Regardless of the data collection method used, it is essential to ensure that the data collected is valid and reliable. Validity refers to the extent to which the data collected measures what it is supposed to measure, while reliability refers to the consistency of the data collected.

Data Collection in Study

One of the most important aspects of a study is data collection. In a study, data is collected through various methods such as surveys, questionnaires, interviews, and observations. The collected data is then analyzed to draw conclusions and make recommendations.

Surveys and questionnaires are popular methods of data collection in studies. Surveys are used to gather information from a large group, while questionnaires are used to collect data from a smaller group. Both methods involve asking participants questions to gather information on a particular topic.

Interviews, on the other hand, are conducted on an individual basis. They are useful when in-depth information is required on a specific topic. The interviewer asks open-ended questions to gather information from the interviewee.

Observations are another method of data collection in studies. Observations involve watching and recording the behavior of individuals or groups. This method is useful when studying behavior or interactions between individuals or groups.

It is important to note that the quality of the data collected in a study depends on the accuracy of the data collection methods. Therefore, ensuring that the data collection methods used in a study are appropriate and reliable is crucial.

Data Analysis in Research

After collecting data through various methods, researchers analyze the data to derive meaningful conclusions. Data analysis is a crucial step in the research process, as it helps to identify patterns, relationships, and trends in the data.

Researchers can use different methods of data analysis, depending on the type of data collected and the research questions. Some common methods of data analysis in research include:

Descriptive statistics

 This method involves summarizing the data using measures such as mean, median, mode, and standard deviation. Descriptive statistics help to provide a general overview of the data and identify any outliers or anomalies.

Inferential statistics

This method involves using statistical tests to make inferences about the population based on the sample data. Inferential statistics help determine the findings’ significance and draw conclusions about the research questions.

Content analysis

 This method involves analyzing the content of written or verbal communication to identify themes, patterns, and meanings. Content analysis is commonly used in qualitative research to analyze data from interviews, focus groups, and open-ended survey questions.

Thematic analysis

 This method involves identifying and analyzing patterns and themes in qualitative data. Thematic analysis is commonly used in research to explore participants’ experiences, perspectives, and attitudes.

Regardless of the method used, data analysis in research is a systematic process that involves organizing, coding, and interpreting the data. Researchers must use appropriate data analysis methods to ensure the findings are valid, reliable, and meaningful.

Data Analysis in Study

Data analysis is a crucial part of any study, as it helps to draw conclusions and make sense of the data collected. The process of data analysis involves cleaning, transforming, and modeling data to extract useful information and insights. The following are some common methods of data analysis used in studies:

 This method involves summarizing and describing the main features of the data collected, such as mean, median, mode, and standard deviation.

 This method involves making inferences or predictions about a population based on a sample of the data collected. It uses techniques such as hypothesis testing and confidence intervals.

Qualitative analysis

 This method involves analyzing data that is not numerical, such as text or images. It is often used in social science research to understand the experiences and perspectives of participants.

Once the data has been analyzed, the researcher can conclude and make recommendations based on the findings. It is important to note that data analysis is not a one-size-fits-all process and may vary depending on the type of study being conducted.

Data analysis is critical to any study, as it helps ensure the findings are accurate and reliable. Using appropriate data analysis methods, researchers can draw meaningful conclusions and make informed decisions based on the results.

Research and study are two terms often used interchangeably, but they are not the same. While both involve acquiring knowledge, research is a more systematic and structured approach to gathering information. At the same time, study is a more general term that can refer to any learning or investigation.

Research involves using specific methods and techniques to collect and analyze data to answer a specific research question or hypothesis. The study, conversely, can refer to any type of learning or investigation, whether formal or informal, structured or unstructured.

Both research and study are important for advancing knowledge and understanding in a particular field, but they serve different purposes and require different approaches. Researchers must be skilled in designing research studies, collecting and analyzing data, and drawing valid conclusions. On the other hand, students must be able to absorb and retain information and apply it to their studies and future careers.

While research and study are different, they are both important for advancing knowledge and understanding in a particular field. By understanding the differences between these two terms, researchers and students can better appreciate the unique contributions that each makes to the pursuit of knowledge and understanding.

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Q. What's the difference between a research article (or research study) and a review article?

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Answered By: Priscilla Coulter Last Updated: Jul 29, 2022     Views: 231965

A research paper is a primary source ...that is, it reports the methods and results of an original study performed by the authors . The kind of study may vary (it could have been an experiment, survey, interview, etc.), but in all cases, raw data have been collected and analyzed by the authors , and conclusions drawn from the results of that analysis.

Research papers follow a particular format.  Look for:

• A brief introduction will often include a review of the existing literature on the topic studied, and explain the rationale of the author's study.  This is important because it demonstrates that the authors are aware of existing studies, and are planning to contribute to this existing body of research in a meaningful way (that is, they're not just doing what others have already done).
• A methods section, where authors describe how they collected and analyzed data.  Statistical analyses are included.  This section is quite detailed, as it's important that other researchers be able to verify and/or replicate these methods.
• A results section describes the outcomes of the data analysis.  Charts and graphs illustrating the results are typically included.
• In the discussion , authors will explain their interpretation of their results and theorize on their importance to existing and future research.
• References or works cited are always included.  These are the articles and books that the authors drew upon to plan their study and to support their discussion.

You can use the library's article databases to search for research articles:

• A research article will nearly always be published in a peer-reviewed journal; click here for instructions on limiting your searches to peer-reviewed articles.  
• If you have a particular type of study in mind, you can include keywords to describe it in your search .  For instance, if you would like to see studies that used surveys to collect data, you can add "survey" to your topic in the database's search box. See this example search in our EBSCO databases: " bullying and survey ".   
• Several of our databases have special limiting options that allow you to select specific methodologies.  See, for instance, the " Methodology " box in ProQuest's PsycARTICLES Advanced Search (scroll down a bit to see it).  It includes options like "Empirical Study" and "Qualitative Study", among many others.  

A review article is a secondary source ...it is written about other articles, and does not report original research of its own.  Review articles are very important, as they draw upon the articles that they review to suggest new research directions, to strengthen support for existing theories and/or identify patterns among exising research studies.  For student researchers, review articles provide a great overview of the existing literature on a topic.    If you find a literature review that fits your topic, take a look at its references/works cited list for leads on other relevant articles and books!

You can use the library's article databases to find literature reviews as well!  Click here for tips.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Case Study vs. Research

What's the difference.

Case study and research are both methods used in academic and professional settings to gather information and gain insights. However, they differ in their approach and purpose. A case study is an in-depth analysis of a specific individual, group, or situation, aiming to understand the unique characteristics and dynamics involved. It often involves qualitative data collection methods such as interviews, observations, and document analysis. On the other hand, research is a systematic investigation conducted to generate new knowledge or validate existing theories. It typically involves a larger sample size and employs quantitative data collection methods such as surveys, experiments, or statistical analysis. While case studies provide detailed and context-specific information, research aims to generalize findings to a broader population.

Further Detail

Introduction.

When it comes to conducting studies and gathering information, researchers have various methods at their disposal. Two commonly used approaches are case study and research. While both methods aim to explore and understand a particular subject, they differ in their approach, scope, and the type of data they collect. In this article, we will delve into the attributes of case study and research, highlighting their similarities and differences.

A case study is an in-depth analysis of a specific individual, group, event, or phenomenon. It involves a detailed examination of a particular case to gain insights into its unique characteristics, context, and dynamics. Case studies often employ multiple sources of data, such as interviews, observations, and documents, to provide a comprehensive understanding of the subject under investigation.

One of the key attributes of a case study is its focus on a specific case, which allows researchers to explore complex and nuanced aspects of the subject. By examining a single case in detail, researchers can uncover rich and detailed information that may not be possible with broader research methods. Case studies are particularly useful when studying rare or unique phenomena, as they provide an opportunity to deeply analyze and understand them.

Furthermore, case studies often employ qualitative research methods, emphasizing the collection of non-numerical data. This qualitative approach allows researchers to capture the subjective experiences, perspectives, and motivations of the individuals or groups involved in the case. By using open-ended interviews and observations, researchers can gather rich and detailed data that provides a holistic view of the subject.

However, it is important to note that case studies have limitations. Due to their focus on a specific case, the findings may not be easily generalized to a larger population or context. The small sample size and unique characteristics of the case may limit the generalizability of the results. Additionally, the subjective nature of qualitative data collection in case studies may introduce bias or interpretation challenges.

Research, on the other hand, is a systematic investigation aimed at discovering new knowledge or validating existing theories. It involves the collection, analysis, and interpretation of data to answer research questions or test hypotheses. Research can be conducted using various methods, including surveys, experiments, and statistical analysis, depending on the nature of the study.

One of the primary attributes of research is its emphasis on generating generalizable knowledge. By using representative samples and statistical techniques, researchers aim to draw conclusions that can be applied to a larger population or context. This allows for the identification of patterns, trends, and relationships that can inform theories, policies, or practices.

Research often employs quantitative methods, focusing on the collection of numerical data that can be analyzed using statistical techniques. Surveys, experiments, and statistical analysis allow researchers to measure variables, establish correlations, and test hypotheses. This objective approach provides a level of objectivity and replicability that is crucial for scientific inquiry.

However, research also has its limitations. The focus on generalizability may sometimes sacrifice the depth and richness of understanding that case studies offer. The reliance on quantitative data may overlook important qualitative aspects of the subject, such as individual experiences or contextual factors. Additionally, the controlled nature of research settings may not fully capture the complexity and dynamics of real-world situations.

Similarities

Despite their differences, case studies and research share some common attributes. Both methods aim to gather information and generate knowledge about a particular subject. They require careful planning, data collection, analysis, and interpretation. Both case studies and research contribute to the advancement of knowledge in their respective fields.

Furthermore, both case studies and research can be used in various disciplines, including social sciences, psychology, business, and healthcare. They provide valuable insights and contribute to evidence-based decision-making. Whether it is understanding the impact of a new treatment, exploring consumer behavior, or investigating social phenomena, both case studies and research play a crucial role in expanding our understanding of the world.

In conclusion, case study and research are two distinct yet valuable approaches to studying and understanding a subject. Case studies offer an in-depth analysis of a specific case, providing rich and detailed information that may not be possible with broader research methods. On the other hand, research aims to generate generalizable knowledge by using representative samples and quantitative methods. While case studies emphasize qualitative data collection, research focuses on quantitative analysis. Both methods have their strengths and limitations, and their choice depends on the research objectives, scope, and context. By utilizing the appropriate method, researchers can gain valuable insights and contribute to the advancement of knowledge in their respective fields.

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Differences in quality of anticoagulation care delivery according to ethnoracial group in the United States: A scoping review

• Open access
• Published: 11 May 2024

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• Sara R. Vazquez   ORCID: orcid.org/0000-0002-9267-8980 1 ,
• Naomi Y. Yates 2 ,
• Craig J. Beavers 3 , 4 ,
• Darren M. Triller 3 &
• Mary M. McFarland 5  

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Anticoagulation therapy is standard for conditions like atrial fibrillation, venous thromboembolism, and valvular heart disease, yet it is unclear if there are ethnoracial disparities in its quality and delivery in the United States. For this scoping review, electronic databases were searched for publications between January 1, 2011 – March 30, 2022. Eligible studies included all study designs, any setting within the United States, patients prescribed anticoagulation for any indication, outcomes reported for ≥ 2 distinct ethnoracial groups. The following four research questions were explored: Do ethnoracial differences exist in 1) access to guideline-based anticoagulation therapy, 2) quality of anticoagulation therapy management, 3) clinical outcomes related to anticoagulation care, 4) humanistic/educational outcomes related to anticoagulation therapy. A total of 5374 studies were screened, 570 studies received full-text review, and 96 studies were analyzed. The largest mapped focus was patients’ access to guideline-based anticoagulation therapy (88/96 articles, 91.7%). Seventy-eight articles made statistical outcomes comparisons among ethnoracial groups. Across all four research questions, 79 articles demonstrated favorable outcomes for White patients compared to non-White patients, 38 articles showed no difference between White and non-White groups, and 8 favored non-White groups (the total exceeds the 78 articles with statistical outcomes as many articles reported multiple outcomes). Disparities disadvantaging non-White patients were most pronounced in access to guideline-based anticoagulation therapy (43/66 articles analyzed) and quality of anticoagulation management (19/21 articles analyzed). Although treatment guidelines do not differentiate anticoagulant therapy by ethnoracial group, this scoping review found consistently favorable outcomes for White patients over non-White patients in the domains of access to anticoagulation therapy for guideline-based indications and quality of anticoagulation therapy management. No differences among groups were noted in clinical outcomes, and very few studies assessed humanistic or educational outcomes.

Graphical Abstract

Scoping Review: Differences in quality of United States anticoagulation care delivery by ethnoracial group. AF = atrial fibrillation; AMS = anticoagulation management service; DOACs = direct oral anticoagulants; INR = international normalized ratio; PSM = patient self-management; PST = patient self-testing

Avoid common mistakes on your manuscript.

Introduction

It is well-established that in the United States (US) ethnoracial disparities exist in various aspects of health care. Specifically, persons identifying with an ethnoracial minority group may have more challenging access to health care, worse clinical outcomes, and higher dissatisfaction with care compared to White persons [ 1 , 2 , 3 , 4 , 5 ]. There are differences by ethnoracial group in the prevalence of the three most common indications for which anticoagulants are prescribed, stroke prevention in atrial fibrillation (AF), treatment of venous thromboembolism (VTE), and valvular heart disease [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Specifically, VTE is most prevalent in Black patients compared to White and Asian patients, whereas AF is most prevalent in White patients compared to Black, Asian, and Hispanic patients [ 9 , 10 , 15 ]. Calcific heart valve disease has the most relevance to the US population, and epidemiologic data has shown that aortic stenosis is more prevalent in White patients compared to Black, Asian, and Hispanic patients [ 17 ]. Despite these epidemiologic differences, there is no evidence to suggest there should be any difference in treatment strategies across ethnoracial patient groups.

While studies have demonstrated genotypic differences that may result in different warfarin dose requirements[ 18 ], and early studies may indicate genotypic differences in direct oral anticoagulant (DOAC) response [ 19 ], no US-based labeling or guidelines recommend a difference in prescription or delivery of anticoagulation care based on race or ethnicity. However, it is unclear if there are in fact differences in the type and quality of anticoagulation therapy, which is standard of care for each of these conditions [ 20 , 21 , 22 , 23 , 24 ]. Anticoagulants remain in the top three classes of drugs causing adverse drug events (primarily bleeding) in the United States, according to the 2014 National Action Plan for Adverse Drug Event Prevention. One of the goals of the National Action Plan was to identify patient populations at higher risk for these adverse drug events to inform the development of targeted harm reduction strategies [ 25 ]. If ethnoracial minority patients are receiving sub-optimal anticoagulation therapy in certain measurable areas of anticoagulation quality, it is vital to highlight the areas of disparity so that these can be explored and care optimized. Anticoagulation providers often have high frequency contact with their patients and can be a reliable connection between disproportionately affected patients and a system in need of change. Systematic reviews of ethnoracial disparities in AF and VTE have been conducted. The AF review assessed AF prevalence among racial groups as well as differences in symptoms and management, including stroke prevention with warfarin or DOACs [ 9 ]. The VTE review specifically assessed VTE prevalence and racial differences in COVID-19 and did report the use of any prophylactic anticoagulation, but this was not part of the analysis [ 26 ]. No review of racial disparities in quality of anticoagulation therapy was found in search results conducted prior to protocol.

In this study we aimed to identify any potential ethnoracial disparities in anticoagulation care quality in the US. The decision to limit the study to a US population was based on our observation that the US has a unique history of interactions between racial and ethnic groups that may not necessarily be reflected by studies conducted in other countries. Additionally, health care delivery systems vary widely across the world, and we wanted to include the data most relevant to the potential racial disparities existing in the US health care system. The term “race” was used to identify a group of people with shared physical characteristics believed to be of common ancestry whereas the term “ethnicity” refers to a group of people with shared cultural traditions [ 27 ]. We recognize these terms may be far more complex. In order to encompass both the physical and cultural aspects of a patient’s identity we have chosen to use the term “ethnoracial” for this study [ 27 ]. Highlighting existing differences will serve as a stimulus for institutions and clinicians to assess current services, implement quality improvement measures, and inform future research efforts to deliver optimal anticoagulation care for all patients. The scoping review protocol was registered December 22, 2021 to Open Science Framework, https://doi.org/10.17605/OSF.IO/9SE7H [ 28 ].

We conducted this scoping review with guidance from the 2020 version of the JBI Manual for Evidence Synthesis and organized to Arksey's five stages: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data and 5) collating, summarizing and reporting the results [ 29 , 30 ]. For transparency and reproducibility, we followed the PRISMA-ScR and PRISMA-S reporting guidelines in reporting our results [ 31 ]. We used Covidence (Veritas Health Innovation,) an online systematic reviewing platform to screen and select studies. Citation management and duplicate detection and removal was accomplished with EndNote, version 19 (Clarivate Analytics.) Data was charted from our selected studies using REDCap, an electronic data capture tool hosted at the University of Utah [ 32 ].

Literature searching

An information specialist developed and translated search strategies for the online databases using a combination of keywords and controlled subject headings unique to each database along with team feedback. Peer review of the strategies was conducted by library colleagues using the PRESS guidelines. [ 33 ] Electronic databases searched included Medline (Ovid) 2011–2022, Embase (embase.com) 2011–2022, CINAHL Complete (Ebscohost) 2011–2022, Sociological Abstracts (ProQuest) 2011–2022, International Pharmaceutical Abstracts (Ovid) 2011–2022, Scopus (scopus.org) 2011–2022 and Web of Science Core Collection (Clarivate Analytics) 2011–2022. Limits included a date range from January 1, 2011 to March 30—April 19, 2022, as not all database results were exported on the same day. See Supplemental File 1 for detailed search strategies. A search of grey literature was not conducted due to time and resource constraints.

Study Selection

For inclusion, each study required two votes by independent reviewers for screening of titles and abstracts followed by full-text review. A third reviewer provided the deciding vote. Data extraction was performed by two independent reviewers, and consensus on any discrepancies was reached via discussion between the reviewers. The data form was piloted by two team members using sentinel articles prior to data extraction.

Eligible studies included all types of study designs in any setting with a population of patients of any age or gender located within the US who were prescribed anticoagulant therapy for any indication, published between January 1, 2011 – March 30, 2022 in order to capture contemporary and clinically relevant practices.

We defined the following research questions for this scoping review as described in Table  1 .

Studies must have reported any of these anticoagulation care delivery outcomes for at least 2 distinct racial or ethnic groups. We excluded genotyping studies and non-English language articles at full text review, as we had no funding for translation services. In checking references of included studies, no additional studies met inclusion criteria. In accordance with scoping review methodology, no quality assessment of included studies was conducted as our goal was to rapidly map the literature. As this is a scoping review of the literature, no aggregate or pooled analysis was performed; however, for ease of interpretation, when assessing for the directionality of the outcomes in the various studies, we categorized studies into Favoring White Group, Favoring Non-White Group, and No Differences Among Ethnoracial Groups. If studies had mixed outcomes of favoring one group for one outcome and no difference for another, then the study was categorized with the favoring group.

A PRISMA flow diagram in Fig.  1 depicts search results, exclusions, and inclusions. The search strategies retrieved 6900 results with 1526 duplicates removed. Following title and abstract screening of 5374 references, 570 articles received full-text review. The most common reason for the exclusion of 474 studies was that outcomes were not reported for two distinct ethnoracial groups (171 studies). Ninety-six studies underwent data extraction.

PRISMA Flow Diagram

Study characteristics-overall

Fifty of the 96 studies were published between 2011 and 2018 (an average of 6.25 articles per year that compared outcomes between two ethnoracial groups) and 43 of 96 studies were published in the years 2019–2021 (average 14.3 articles per year; 2022 excluded here because only 4 months of data was captured) (Fig.  2 ). Most studies analyzed an outpatient population (65.6%) for an indication of stroke prevention in AF (67.7%) in patients taking warfarin (71.9%) or DOACs (49.0%). Study population size was heterogenous, ranging from a study size of 24 patients to over 1.3 million patients (median 5,238 patients) in the 69 studies that reported population size by racial group. When stratified by size, 60.9% of the articles in the scoping review (42 articles) represented < 10,000 patients (Table  2 ).

Number of Articles by Publication Year. *2022 excluded from this figure since the search period did not capture the entire year

Study characteristics-by ethnoracial group

There were 50 studies (52.1%) where race or ethnicity was either mentioned in the title or objective of the article, with 24 of these published over the 7-year period 2011–2018 and 26 published over the 3-year period 2019 to first quarter 2022. The method for reporting race or ethnicity was unclear or unspecified in most studies (77.1%) and 16 articles (16.7%) utilized self-reporting of race or ethnicity. Most studies analyzed White or Caucasian racial groups (94.8%), followed by Black or African-American (80.2%), and many studies grouped all other racial groups into an “Other” category (41.7%) (Fig.  3 ).

Number of Articles by Ethnoracial Groups. *For study inclusion, a study had to compare outcomes for least two distinct ethnoracial groups 

White patients accounted for a median 77% of study populations, Black patients 9.5%, Hispanic/Latino patients 6.2%, “Other” racial groups 5.3%, and Asian patients 2.5%.

Study outcomes-overall

Of the 4 research questions, most studies included in this review analyzed patients’ access to guideline-based anticoagulation therapy (88/96 articles, 91.7%), clinical outcomes (42/96 articles, 43.8%), or quality of anticoagulation management (24/96 articles, 25.0%), while very few addressed humanistic or educational outcomes (5/96 articles, 5.2%) (Fig.  4 ). Many studies addressed multiple outcomes within the single study.

Number of Articles Mapped by Research Question

Seventy-eight of the 96 included studies provided statistical comparisons between ethnoracial groups, and these data are presented below.

Outcomes for research question 1: Do ethnoracial differences exist in access to guideline-based anticoagulation therapy?

Anticoagulation for a guideline-based indication.

This question focused on patients who had an indication for anticoagulation actually receiving an anticoagulant, specifically AF and VTE prophylaxis (based on risk stratification) and acute VTE. The majority of the AF studies (25/34 studies) demonstrated White patients receiving anticoagulation at significantly higher rates compared to non-White patients [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], while the six VTE studies largely demonstrated no difference among ethnoracial groups [ 61 , 62 , 63 , 64 , 65 , 66 ].

DOACs as first-line therapy for AF or VTE

Eighteen individual studies statistically assessed the outcome of DOAC as first-line therapy (compared to warfarin) for AF (15 studies), VTE treatment (2 studies), or both indications (1 study). Twelve of the 15 AF studies showed a significantly higher proportion of White patients received DOACs as first-line therapy compared to non-White patients [ 36 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 54 , 55 , 67 , 68 ]. Of those 12, 9 specifically compared White patients to Black patients. Both VTE treatment studies and the study that assessed both AF and VTE indications showed significantly higher DOAC prescribing rates for White patients compared to Black patients [ 69 , 70 , 71 ].

Anticoagulant therapy adherence/persistence

The eight studies that addressed anticoagulation therapy adherence/persistence showed variability in outcome directionality by ethnoracial group: 5 no difference [ 41 , 72 , 73 , 74 , 75 ], 2 showed better treatment adherence/persistence for White patients compared to Black patients[ 76 ] or non-White patients [ 77 ], and one showed better treatment adherence/persistence for White patients compared to Hispanic patients, but no difference in White versus Black patients [ 78 ].

Figure  5 summarizes the outcome directionality for Research Question 1 regarding access to guideline-based anticoagulation therapy. Overall, the areas of disparity identified included anticoagulation for atrial fibrillation and preferential use of DOAC therapy for AF and VTE treatment.

Outcome Directionality for the 4 Research Questions and their Subcategories. AC = anticoagulant; AMS = anticoagulation management service; INR = international normalized ratio; PST = patient self-testing; PSM = patient self-management

Research question 2: Do ethnoracial differences exist in the quality of anticoagulation therapy management?

A total of 21 studies assessed quality of anticoagulation therapy management: Warfarin time in therapeutic range (TTR)/INR (International Normalized Ratio) control 12 studies, appropriate anticoagulant dosing 3 studies, enrollment in an anticoagulation management service 5 studies, and PST/PSM one study.

In statistical comparisons of INR control in warfarin patients, all 12 studies (7 assessed mean or median TTR, 5 assessed other measures of INR control such as days spent above/below range, gaps in INR monitoring) showed White patients had favorable INR control compared to non-White patients (most comparisons included Black patients) [ 41 , 75 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ]. Enrollment in an anticoagulation management service was statistically compared among ethnoracial groups in 5 studies, and this opportunity favored White patients compared to other racial groups in four of the five [ 41 , 82 , 86 , 88 ]. Two of the three studies that statistically analyzed appropriate anticoagulant dosing showed a higher rate of appropriate DOAC dosing in White patients compared to non-White patients [ 41 , 89 ], and the third showed no difference among ethnoracial groups for enoxaparin dosing in the emergency department [ 90 ]. The one study assessing access to PST/PSM showed that more White patients used PST compared to Black or Hispanic patients[ 91 ] (Fig.  5 ).

Research question 3: Do ethnoracial differences exist in the clinical outcomes related to anticoagulation care?

Articles assessing clinical outcomes among ethnoracial groups primarily assessed bleeding (15 articles) or thrombosis (9 articles) outcomes, and 8 articles assessing anticoagulation related hospitalization or mortality. One article addressed a net clinical outcome including major bleeding, stroke or systemic embolism, and death from any cause. This was included in the bleeding outcomes category so that it was not double-counted in the other two outcome categories. Additional details about the 24 unique studies that statistically assessed clinical outcomes including the study design, population size, ethnoracial groups studied, anticoagulants used, and statistical outcomes measured can be found in Supplementary Tables 1 and 2 .

Sixteen studies statistically assessed bleeding outcomes of varying definitions (major bleeding 13 studies, clinically relevant non-major bleeding 3 studies, any bleeding 3 studies, bleeding otherwise defined 3 studies). Six studies demonstrated no difference in bleeding outcomes by ethnoracial group [ 55 , 92 , 93 , 94 , 95 , 96 ]9 reported that White patients had lower rates of bleeding compared to Black or Asian patients,[ 53 , 80 , 83 , 85 , 97 , 98 , 99 , 100 , 101 ]. In the remaining study, Asian patients had a more favorable net clinical outcome compared to non-Asian patients [ 102 ].

Nine studies statistically assessed thrombosis outcomes among ethnoracial groups, including stroke/systemic embolism (5 studies), recurrent VTE (3 studies), or any thrombosis (1 study). The stroke outcomes by racial group were heterogeneous, with 3 studies showing better outcomes for White patients compared to Black patients[ 103 , 104 , 105 ] and two studies showing no difference in outcomes when White patients were compared to Non-White patients [ 55 , 95 ]. In three of the four VTE studies there were no differences in outcomes by ethnoracial group [ 61 , 93 , 96 ], and in one study White patients had more favorable outcomes compared to Black patients [ 106 ].

Nine studies assessed anticoagulation-related hospitalizations or mortality by ethnoracial group. Outcomes were mixed, as four studies showed no difference in hospitalizations or mortality among ethnoracial groups,[ 89 , 95 , 96 , 107 ], three studies showed White patients had a lower rate of hospitalizations[ 85 , 105 ] or mortality[ 104 , 105 ] Another study showed lower rate of mortality or hospice after intracranial hemorrhage in Black and Other race patients [ 108 ].(Fig.  5 ).

Research question 4: Do ethnoracial differences exist in the humanistic/educational outcomes related to anticoagulation therapy?

The five studies reporting this category of outcomes were heterogeneous. Of the two studies assessing anticoagulation knowledge, one showed no difference by ethnoracial group [ 109 ], and the other favored the non-White group in appropriately estimating bleeding risk [ 110 ]. One study assessed an atrial fibrillation quality of life score at 2-year follow-up after AF diagnosis and found the outcomes favored White patients [ 79 ]. Another study assessed satisfaction with VTE care and found no difference among ethnoracial groups [ 111 ]. A third study found no difference in the percentage of racial groups having a cost conversation when initiating DOAC therapy (78% Whites, 72.2% non-Whites)[ 112 ] (Fig.  5 ).

Overall outcome directionality for all four research questions is shown in Fig.  6 . A total of 79 articles demonstrated favorable outcomes for White patients compared to non-White patients, 38 articles showed no difference between White and non-White groups, and 8 articles had outcomes favoring non-White groups (the total exceeds the 78 articles with statistical outcomes as many articles reported multiple outcomes). The biggest areas of disparity between White and non-White groups are access to guideline-based anticoagulation therapy and quality of anticoagulation therapy management. Clinical outcomes relating to anticoagulation care had the least difference among ethnoracial groups. Relatively few studies assessed potential ethnoracial disparities in humanistic and educational outcomes.

Outcome Directionality for All 4 Research Questions

This scoping review assessing ethnoracial differences in the quality of anticoagulation care and its delivery to patients in the United States encompassed eleven full years of literature and resulted in the inclusion of 96 studies, 78 of which contained statistical outcomes comparisons among ethnoracial groups. The most common reason for study exclusion was that outcomes were not reported for at least two distinct ethnoracial groups. We observed that beginning in 2019 and following the racial unrest of 2020, the density of articles addressing ethnoracial disparities in anticoagulation care more than doubled. During the entire study period, half of studies had race or ethnicity as the focus or objective of the paper, but this was largely driven by articles published after 2019.

Only 16% of included articles documented self-reporting of racial identity, with most of the remainder using an unspecified method for documenting racial identity. It is likely that many studies utilize demographic information extracted from an electronic medical record (EMR), but it is often unclear if that is truly self-reported race. A second element this scoping review identified was that many studies analyzed two or three ethnoracial groups and then categorized all others into a heterogenous “Other” category. For example, frequently studies would categorize patients as White, Black, and “Other.” It is unclear whether those in a racial category labeled as “Other” had an unknown or missing racial identity in the EMR, or intentionally chose not to disclose. It is also likely that study investigators decided to classify ethnoracial groups with lower population sizes into a miscellaneous category. There were few studies (15%) that specifically assessed patients identifying as Native American/Alaska Native, Native Hawaiian/Pacific Islander, and multiracial. While Hispanic/Latino is an ethnicity, most studies categorized it as a separate “race” category. Of the 37 studies that analyzed “Asian” patient populations, none specifically defined “Asian” beyond that. The US Census Bureau defines “Asian” race as a person having origins of the Far East, Southeast Asia, or the Indian subcontinent [ 113 ]. This broad definition encompasses many different ethnicities which could represent variability in health outcomes if better defined and more frequently analyzed. These may be opportunities for EMR systems to improve transparency for how race, ethnicity, and language preference are captured and for those designing research studies to be thoughtful and intentional about analyzing the ethnoracial identities of the study population, perhaps in alignment with the minimum 5 racial categories utilized by the US Census Bureau, the National Institutes of Health, and the Office of Management and Budget (White, Black, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, with permission for a “some other race” category and the option to select multiple races) [ 113 ]. Since 2017 Clinicaltrials.gov has required the reporting of race/ethnicity if collected, and there is good compliance with this requirement, but less so in publication of the work [ 114 ].

We examined the proportion of ethnoracial groups represented for each of the disease states in the studies included in this scoping review, relative to disease state prevalence and found a discrepancy. For AF, prevalence in White patients was 11.3%, in Black patients 6.6%, and in Hispanic patients 7.8% [ 15 ]. However, the representation in AF studies in this review were 74% White, 13% Black, and 8% Hispanic. Assessing VTE incidence by race is more difficult, as studies have shown regional and time variation, with Black patients typically having a higher incidence compared to other ethnoracial groups [ 16 ]. In this review, however, of the studies assessing VTE treatment or prophylaxis, only 16% of the patient population identified as Black, whereas 70% identified as White. There were only 3 studies that assessed a valvular heart disease population, making ethnoracial group representation difficult to assess.

The majority of studies captured in this review analyzed patients in the outpatient setting, for the anticoagulation indication of stroke prevention in AF, taking either warfarin or DOAC. Few studies involved the acute care setting or injectable anticoagulants, representing an area for future study of potential ethnoracial disparities.

Overall, the majority of studies in this scoping review addressed ethnoracial disparities in patients’ access to guideline-based anticoagulation therapy, clinical outcomes related to anticoagulation care, and quality of anticoagulation management. A research gap identified was more study is needed to assess gaps in educational outcomes such as anticoagulation and disease state knowledge, shared decision-making willingness and capability, and humanistic outcomes such as quality of life or satisfaction with anticoagulation therapy.

In analyzing the first research question regarding ethnoracial differences in access to guideline-based anticoagulation therapy, the majority of studies addressed use of any anticoagulation for stroke prevention in AF in patients above a threshold risk score and the preferential use of DOACs as first-line therapy instead of warfarin for AF. In both categories, patients in a non-White ethnoracial group (particularly Black patients) received recommended therapy less often than patients identified as White. It is unclear why this is the case. It could be on the patient, provider, and/or system level. It is possible that some studies more successfully adjusted for covariates than others. Sites or settings with systematic processes like order sets or clinical decision support systems in place for standard prescribing may be more successful in equitably prescribing indicated therapies. In one large study in the Veterans Affairs population of AF patients, even after adjusting for numerous variables that included clinical, demographic, socioeconomic, prescriber, and geographic site factors, DOAC prescribing remained lower in Asian and Black patients when compared with White patients. The authors in that study postulate that non-White populations may be less receptive to novel therapies due to historical mistrust of the health care system or have reduced access to education about the latest treatments, and they give the example of direct-to-consumer advertising [ 42 ]. It has also previously been demonstrated that prescribing of oral anticoagulation and particularly DOACs is lower in non-White patients [ 41 ]. These are difficult to capture as standard covariates, which is why further study is needed. We examined the publication dates for both access categories to see if perhaps there was a lack of contemporary data skewing the outcomes. However, for both anticoagulation for a guideline-based indication and DOACs as first-line therapy, the majority of articles came from the time period 2019–2021 (24 of 40 articles, and 15 of 18 articles, respectively), well after guideline updates preferentially recommended DOACs [ 34 , 35 ]. Also, there were relatively few studies addressing guideline-based therapy for VTE treatment and prophylaxis, making assessment of disparities difficult. Regarding access, it is well established that race and ethnicity often determine a patient’s socioeconomic status and that low socioeconomic status and its correlates (e.g., reduced education, income, and healthcare access) are associated with poorer health outcomes [ 115 ]. However, at each level of income or education, Black patients experience worse health outcomes than Whites [ 116 ]. So, low socioeconomic status does not fully explain poorer health outcomes for non-White individuals.

After examining access to appropriate and preferred anticoagulation therapy, the second research question of this scoping review examined potential ethnoracial disparities in the quality of anticoagulation therapy management. INR control measures such as time in therapeutic INR range are a surrogate measure of both thrombotic and bleeding outcomes and frequently used as a way to assess quality of warfarin therapy. The studies identified in this review showed clear disparity between White and non-White patient groups (especially Black patients), however all twelve studies comparing TTR among ethnoracial groups were published prior to 2019. This could be due to the decline in warfarin prescribing relative to increases in DOAC prescribing [ 117 , 118 , 119 ], but there remain patient populations that require or choose warfarin, so this marker of anticoagulation control remains relevant and requires continued reassessment. There were relatively few studies assessing other markers of anticoagulation management quality such as anticoagulation management service enrollment, appropriate DOAC dosing, and access to quality improvement strategies like PST or PSM. Few studies assessed educational outcomes, yet this may have relevance to the above anticoagulation care quality question. For those patients who remain on warfarin, dietary Vitamin K consistency is an example of a key educational point that links directly to INR control. It is unclear if there are disparities in this type of education among ethnoracial groups that may have more far-reaching effects.

Of note, clinical outcomes related to anticoagulant therapy seemed to have the fewest areas of disparity, although the number of articles was small. This suggests that if patients have access to high quality anticoagulation therapy, there is a promising sign that optimal clinical outcomes can be achieved for all ethnoracial groups.

There are some limitations of this scoping review that warrant consideration. First, we chose fairly broad inclusion criteria (all anticoagulants, all study types) because a review of this type had never been performed before. This resulted in a relatively large number of included articles for a scoping review. Second, there is likely a high degree of heterogeneity among patient populations and outcomes definitions. However, as this is a scoping review with the goal to present an overview of the literature and not report on composite outcomes, a risk of bias assessment was not performed. Third is our decision to group patients into White and non-White groups for assessment of outcome directionality. In doing so, we may have missed subtle differences in outcomes between various non-White ethnoracial groups. Fourth, in our main search we included all studies that reported outcomes, but due to scope, we only reported outcome directionality for studies that statistically compared outcomes between ethnoracial groups. Finally, due to the large number of studies that required review and analysis, this was a lengthy undertaking and we are certain that additional studies have been published since the closure of our search period.

In line with the 2014 National Action Plan for Adverse Drug Event Prevention’s goal of identifying patient populations at higher risk of adverse drug events, this scoping review highlights several areas where quality of anticoagulation care can be optimized for all patients. Future research opportunities in ethnoracial differences in the quality of anticoagulation care are summarized in Table  3 . While the scoping review focused exclusively on the evaluation of peer-reviewed manuscripts, the heterogeneity of terminology and methodologies identified in the published papers may have implications for national health policy relating to the quality and safety of care (e.g.the Medicare Quality Payment Program) [ 120 ]. To accurately and reliably quantify important disparities in AC-related care and support effective improvement initiatives, attention and effort will need to be invested across the full continuum of quality measure development [ 121 ], measure endorsement [ 122 ], measure selection, and status assignment within value-based payment programs (e.g., required/optional, measure weighting) [ 123 ]. The findings of the scoping review may be of utility to such efforts, and the development and implementation of suitable quality measures will likely be of value to future research efforts in this important therapeutic area.

Conclusions

Treatment guidelines do not recommend differentiating anticoagulant therapy by ethnoracial group, yet this scoping review of the literature demonstrates consistent directionality in favor of White patients over non-White patients in the domains of access to anticoagulation therapy for guideline-based indications, prescription of preferred anticoagulation therapies, and quality of anticoagulation therapy management. These data should serve as a stimulus for an assessment of current services, implementation of quality improvement measures, and inform future research to make anticoagulation care quality more equitable.

Data Availability

Data are available on request from the corresponding author.

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Acknowledgements

The authors wish to acknowledge the following individuals for their work in screening articles for this scoping review: April Allen, PharmD, CACP; Allison Burnett, PharmD, PhC, CACP; Stacy Ellsworth, RN, MSN, CCRC; Danielle Jenkins, MBA, RN, BSN, CRNI; Amanda Katz, MBA; Lea Kistenmacher, Julia Mulheman, PharmD; Surhabi Palkimas, PharmD, MBA; Terri Schnurr, RN, CCRC; Deborah Siegal, MD, MSc, FRCPC; Kimberly Terry, PharmD, BCPS, BCCCP; and Terri Wiggins, MS.

The authors wish to acknowledge the support of the Anticoagulation Forum in the development of this manuscript. The Anticoagulation Forum is a non-profit organization dedicated to improving the quality of care for patients taking antithrombotic medications.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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All authors contributed to the study conception and design. Material preparation was performed by Sara Vazquez, Naomi Yates, and Mary McFarland. Data collection and analysis were performed by Sara Vazquez, Naomi Yates, Craig Beavers, and Darren Triller. The first draft of the manuscript was written by Sara Vazquez and all authors edited subsequent drafts. All authors read and approved the final manuscript.

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Vazquez, S.R., Yates, N.Y., Beavers, C.J. et al. Differences in quality of anticoagulation care delivery according to ethnoracial group in the United States: A scoping review. J Thromb Thrombolysis (2024). https://doi.org/10.1007/s11239-024-02991-2

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Multiethnic refers to identification with multiple Asian American, Native Hawaiian, and/or Pacific Islander ethnic subgroups. Multiracial refers to identification with Asian American, Native Hawaiian, and/or Pacific Islander subgroups in combination with another racial or ethnic group (ie, American Indian or Alaska Native, Black, Hispanic or Latino, or White). Horizontal lines represent 95% CIs.

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• Pubertal Timing Variability Among Asian American, Native Hawaiian, and Pacific Islander Subgroups JAMA Network Open Invited Commentary May 13, 2024 Catherina T. Pinnaro, MD, MS; Vanessa A. Curtis, MD

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Kubo A , Acker J , Aghaee S, et al. Pubertal Timing Across Asian American, Native Hawaiian, and Pacific Islander Subgroups. JAMA Netw Open. 2024;7(5):e2410253. doi:10.1001/jamanetworkopen.2024.10253

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Pubertal Timing Across Asian American, Native Hawaiian, and Pacific Islander Subgroups

• 1 Kaiser Permanente Division of Research, Oakland, California
• 2 School of Public Health, University of California, Berkeley
• 3 Kaiser Permanente San Francisco Medical Center, San Francisco, California
• 4 Division of Pediatric Endocrinology, Department of Pediatrics, University of California, San Francisco
• 5 Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
• Invited Commentary Pubertal Timing Variability Among Asian American, Native Hawaiian, and Pacific Islander Subgroups Catherina T. Pinnaro, MD, MS; Vanessa A. Curtis, MD JAMA Network Open

Question   Does pubertal timing vary among Asian American, Native Hawaiian, and Pacific Islander youths when disaggregated by ethnic subgroups?

Findings   In this cohort study of 107 325 US children and adolescents, substantial variations in pubertal timing across Asian American, Native Hawaiian, and Pacific Islander ethnic subgroups were identified. Asian Indian, Native Hawaiian and Pacific Islander, and Other South Asian youths typically had earlier ages at pubertal onset, while Chinese and Korean youths exhibited later onset.

Meaning   These findings may provide insight into disparities in chronic diseases, such as type 2 diabetes and cardiovascular diseases, later in life.

Importance   Earlier puberty is associated with adverse health outcomes, such as mental health issues in adolescence and cardiometabolic diseases in adulthood. Despite rapid growth of the Asian American, Native Hawaiian, and Pacific Islander populations in the US, limited research exists on their pubertal timing, potentially masking health disparities.

Objective   To examine pubertal timing among Asian American, Native Hawaiian, and Pacific Islander children and adolescents by disaggregating ethnic subgroups.

Design, Setting, and Participants   This retrospective cohort study included Asian American, Native Hawaiian, and Pacific Islander youths aged 5 to 18 years assessed for pubertal development at Kaiser Permanente Northern California, a large, integrated health care delivery system. Follow-up occurred from March 2005, through December 31, 2019. Data were analyzed in October 2023.

Exposure   Race and ethnicity, categorized into 11 ethnic subgroups: Asian Indian, Chinese, Filipino, Japanese, Korean, Native Hawaiian and Pacific Islander, Other South Asian, Other Southeast Asian, Vietnamese, multiethnic, and multiracial.

Main Outcomes and Measures   Pubertal timing was determined using physician-assessed sexual maturity ratings (SMRs). Outcomes included the median age at transition from SMR 1 (prepubertal) to SMR 2 or higher (pubertal) for onset of genital development (gonadarche) in boys, breast development (thelarche) in girls, and pubic hair development (pubarche) in both boys and girls.

Results   In this cohort of 107 325 Asian American, Native Hawaiian, and Pacific Islander children and adolescents (54.61% boys; 12.96% Asian Indian, 22.24% Chinese, 26.46% Filipino, 1.80% Japanese, 1.66% Korean, 1.96% Native Hawaiian and Pacific Islander, 0.86% Other South Asian, 3.26% Other Southeast Asian, 5.99% Vietnamese, 0.74% multiethnic, and 22.05% multiracial), the overall median ages for girls’ pubarche and thelarche were 10.98 years (95% CI, 10.96-11.01 years) and 10.13 years (95% CI, 10.11-10.15 years), respectively. For boys’ pubarche and gonadarche, median ages were 12.08 years (95% CI, 12.06-12.10 years) and 11.54 years (95% CI, 11.52-11.56 years), respectively. Differences between subgroups with earliest and latest median age at onset were 14 months for girls’ pubarche, 8 months for thelarche, 8 months for boys’ pubarche, and 4 months for gonadarche. In general, Asian Indian, Native Hawaiian and Pacific Islander, and Other South Asian subgroups had the earliest ages at onset across pubertal markers, while East Asian youths exhibited the latest onset. Restricting to those with healthy body mass index did not substantially change the findings.

Conclusions and Relevance   In this cohort study of Asian American, Native Hawaiian, and Pacific Islander children and adolescents, pubertal timing varied considerably across ethnic subgroups. Further investigation is warranted to assess whether these differences contribute to observed health disparities in adulthood, such as type 2 diabetes and cardiovascular diseases.

Globally, girls are experiencing earlier pubertal onset today than in past generations. 1 This is a significant public health concern because early puberty in girls has been linked to a variety of behavioral and emotional problems in adolescence 2 - 6 and serious chronic conditions later in life, including cancers, 7 , 8 type 2 diabetes (T2D), 9 and cardiovascular diseases. 10 Fewer studies have been conducted among boys, although they too seem to be experiencing earlier pubertal timing, 11 , 12 with associated increased risks of externalizing behaviors during adolescence 2 , 4 , 6 and of cardiovascular diseases, diabetes, and prostate and testicular cancers later in life. 3 , 10 , 13 - 16

In the US, there are marked differences in pubertal timing across racial and ethnic groups, with studies consistently showing that non-Hispanic Black (hereafter, Black ) and Hispanic or Latino youths experience puberty significantly earlier than non-Hispanic White (hereafter, White ) youths. 11 , 17 Very little is known, however, regarding the pubertal timing among Asian American, Native Hawaiian, and Pacific Islander youths even though these populations are among the fastest-growing groups in the US. 18 Previous US studies either did not include these groups or, if they did, aggregated Asian American subgroups and Native Hawaiian and Pacific Islander youths into 1 group. Aggregation of ethnic groups that have distinct health-related characteristics may mask disparities in health outcomes. 19 Given the associations between early puberty and various health outcomes later in life, examining variations in the timing of puberty across Asian American, Native Hawaiian, and Pacific Islander subgroups could provide insights into early health indicators underlying disparities in chronic conditions.

To address these significant gaps in the literature, we assessed pubertal timing among Asian American, Native Hawaiian, and Pacific Islander youths by disaggregating ethnic subgroups using a large, diverse sample of boys and girls receiving care in an integrated health care delivery system. As obesity is one of the most robust factors associated with early pubertal timing, especially among girls, 17 , 20 , 21 we conducted analyses for the overall sample and, as a sensitivity analysis, only among individuals with healthy weight.

Kaiser Permanente Northern California (KPNC) is an integrated health care delivery system with over 4.4 million members and comprises approximately 29% of the child and adolescent (aged 10-19 years) northern California population. 22 The KPNC membership is racially and ethnically diverse and is similar sociodemographically to the overall population. 22 The KPNC institutional review board approved this study with a waiver of the requirement for informed consent because the study was based on data extracted from electronic health records (EHRs) with no participant contact. This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

The EHR was used to identify Asian American, Native Hawaiian, and Pacific Islander youths who had been assessed for pubertal development during routine pediatric primary care appointments at a KPNC facility. Data were also extracted from other KPNC clinical and administrative databases, including the KPNC Division of Research Virtual Data Warehouse, which harmonizes data across various data sources. 23 Follow-up for determining pubertal development began in March 2005 and continued through December 31, 2019. Data after 2019 were excluded due to disruptions in primary care from the COVID-19 pandemic as well as emerging evidence suggesting the pandemic may be associated with earlier onset of puberty in some populations. 24 Individuals with a pubertal assessment documented in the EHR before age 5 years or after age 18 years were excluded from the study. Additionally, youths with a nonspecific race and ethnicity designation (eg, Asian ) were excluded.

Race and ethnicity are typically documented in the EHR based on self-reported information from the KPNC member and may come from multiple source databases, including demographic data collected at clinic visits and during health plan enrollment. We categorized individuals into 9 ethnic subgroups: Asian Indian, Chinese, Filipino, Japanese, Korean, Native Hawaiian and Pacific Islander, Other South Asian, Other Southeast Asian, and Vietnamese. Additionally, youths identifying with more than 1 of these subgroups were categorized as multiethnic. Those identifying as Asian American, Native Hawaiian, and/or Pacific Islander in combination with another race (ie, American Indian or Alaska Native, Black, Hispanic or Latino, or White) were classified as multiracial. Because the focus of the analyses was to describe pubertal timing across Asian American, Native Hawaiian, and Pacific Islander individuals, we did not include other major racial and ethnic groups (eg, White, Black, or Hispanic or Latino). These groups also exhibit marked heterogeneity, and the undertaking to disaggregate each of these was beyond the scope of the current study.

Physician-assessed sexual maturity ratings (SMRs or Tanner stages) 25 are a routine part of KPNC pediatric appointments for children aged 6 years or older. SMRs use a 5-point ranking system to measure pubertal development from prepuberty (SMR 1) to full maturation (SMR 5). For girls, breast stage was assessed using visual inspection and palpation of breast tissue (to avoid miscategorization due to adiposity). For boys, testicular and genital stage was assessed using visual inspection and palpation of testicular tissue to estimate testicular volume. Pubic hair stage was assessed using visual inspection for both sexes. The accuracy of KPNC SMRs was validated and described in a previous study. 21 In this study, outcomes of interest were age at transition from SMR 1 (prepubertal) to SMR 2 or higher (pubertal) for genital development onset (gonadarche) in boys, breast development onset (thelarche) in girls, and pubic hair development onset (pubarche) in both boys and girls.

Child weight and height measurements were obtained from clinic visits between the ages of 5 and 6 years to estimate body mass index (BMI) prior to pubertal onset. We chose the weight and height closest to the 5th birthday to calculate BMI percentiles using age- and sex-specific Centers for Disease Control and Prevention year 2000 standard population distributions. 26 Body mass index was classified into underweight (less than 5th percentile), normal weight (5th to <85th percentile), overweight (85th to <95th percentile), and obese (≥95th percentile) categories.

Interval-censored survival analysis was used to estimate the timing of pubertal onset, which accounts for the transitions that occur in SMR stages between pediatric checkups. Youths were considered interval-censored if examined at SMR 1 and then at SMR 2 or higher at a subsequent visit. Youths were left-censored if they had already transitioned to SMR 2 or higher at the time of their first examination with an SMR assessment and right-censored if they had not transitioned to SMR 2 or higher by the end of follow-up.

Maximum likelihood estimates of the median age at pubertal onset were estimated overall and separately for each ethnic subgroup by sex, assuming a Weibull distribution for time to event. Monte Carlo 95% CIs for the median ages were estimated based on 4000 random samples from the asymptotic normal distribution of the maximum likelihood estimators of the Weibull distribution shape and scale parameters. We also conducted sensitivity analyses restricting to only those with BMIs in the 5th to less than 85th percentiles to describe the differences in pubertal timing across ethnic subgroups independent of the prevalence of childhood overweight and obesity. Analyses were conducted in October 2023 using the icenReg package, version 2.0.15, in R, version 4.2.2 (R Project for Statistical Computing). 27

The analytic cohort included 107 325 youths, consisting of 58 613 boys (54.61%) and 48 712 girls (45.39%). A total of 12.96% were Asian Indian; 22.24%, Chinese; 26.46%, Filipino; 1.80%, Japanese; 1.66%, Korean; 1.96%, Native Hawaiian and Pacific Islander; 0.86%, Other South Asian; 3.26%, Other Southeast Asian; 5.99%, Vietnamese; 0.74%, multiethnic; and 22.05%, multiracial. We initially identified 113 399 KPNC patients aged 5 to 18 years who had an SMR and had a disaggregated Asian American or Native Hawaiian and Pacific Islander subgroup specified. Among them, 5559 were excluded due to medical conditions that may affect pubertal development (eg, congenital adrenal hyperplasia). Additionally, 515 were excluded due to regression in SMR stages over time, meaning that individuals went from pubertal at one visit to prepubertal at a later visit. The distribution of ethnic subgroups in the final sample is described in the Table .

The pubarche analysis included 47 500 girls. The overall median age at pubarche among aggregated Asian American, Native Hawaiian, and Pacific Islander groups was 10.98 years (95% CI, 10.96-11.01 years). When we disaggregated the study population into ethnic subgroups, we observed stark variations in the timing of pubarche across ethnic subgroups ( Figure 1 ). Notably, Other South Asian (10.30 years; 95% CI, 10.12-10.48 years), Asian Indian (10.38 years; 95% CI, 10.33-10.43 years), and Native Hawaiian and Pacific Islander (10.45 years; 95% CI, 10.30-10.60 years) girls exhibited the earliest median ages at pubarche. In contrast, Korean girls had a substantially later median pubarche at 11.49 years (95% CI, 11.32-11.67 years); the difference in the median age at pubarche between Other South Asian and Korean girls was 14 months.

The overall median age at thelarche (n = 47 592) was 10.13 years (95% CI, 10.11-10.15 years), with significant variations in the timing across subgroups ( Figure 2 ). Native Hawaiian and Pacific Islander girls had the earliest median age at thelarche at 9.80 years (95% CI, 9.67-9.94 years) followed by Other South Asian girls at 9.87 years (95% CI, 9.70-10.06 years). In contrast, Korean girls had the latest median onset at 10.47 years (95% CI, 10.31-10.64 years), which was 8 months later than their Native Hawaiian and Pacific Islander counterparts.

The overall median age at pubarche for boys among aggregated Asian American, Native Hawaiian, and Pacific Islander groups (n = 58 295) was 12.08 years (95% CI, 12.06-12.10 years). Subgroup variations were observed when data were disaggregated ( Figure 3 ); pubarche in boys was earliest among Native Hawaiian and Pacific Islander (11.72 years; 95% CI, 11.62-11.83 years), Other South Asian (11.75 years; 95% CI, 11.61-11.91 years), and Asian Indian (11.77 years; 95% CI, 11.72-11.81 years) boys. In contrast, Chinese boys had the latest median pubarche at 12.37 years (95% CI, 12.33-12.40 years). The difference in median age at pubarche between Native Hawaiian and Pacific Islander boys and Chinese boys was 8 months.

The overall median age at gonadarche in boys (n = 57 373) was 11.54 years (95% CI, 11.52-11.56 years). In contrast to the other pubertal markers, gonadarche in boys displayed substantially less variability across the ethnic subgroups ( Figure 4 ). The difference between the earliest- and latest-onset groups was 4 months (Native Hawaiian and Pacific Islander boys at 11.31 years [95% CI, 11.20-11.43 years] compared with Chinese boys at 11.66 years [95% CI, 11.62-11.70 years]).

When we restricted the cohort to those who were in the healthy weight category (5th to <85th BMI percentile) at age 5 to 6 years, the significant variations in the timing of pubertal onset across subgroups persisted, although the 95% CIs widened slightly due to the smaller sample sizes. Asian Indian, Native Hawaiian and Pacific Islander, and Other South Asian girls continued to have the earliest onset of pubarche and thelarche, while Chinese, Japanese, Korean, and Vietnamese girls had substantially later onset. For instance, the difference between the median ages of pubarche between Asian Indian girls and Chinese girls was 14 months. Among boys, Native Hawaiian and Pacific Islander boys continued to have the earliest pubarche and gonadarche among all the subgroups, while Chinese boys had the latest onset.

To our knowledge, this is the first population-based study to describe pubertal timing among Asian American, Native Hawaiian, and Pacific Islander boys and girls in the US and the only one to compare differences in timing by disaggregating the population by several Asian American subgroups. We found substantial differences in the timing of pubertal onset across ethnic subgroups; Asian Indian, Native Hawaiian and Pacific Islander, and Other South Asian boys and girls tended to experience earlier pubertal onset than their East Asian counterparts. The variability of pubertal timing across ethnic subgroups was greater for girls than for boys. These differences persisted even when we restricted the samples to those with a healthy BMI. These findings suggest that other factors in addition to BMI likely contribute to disparities in pubertal timing, such as stress, environment, and lifestyle factors (eg, diet, physical activity).

About 7.2% of US individuals, or 24 million people, identify as being of Asian descent, and another 0.5% are of Native Hawaiian and Pacific Islander origin. 28 Asian American, Native Hawaiian, and Pacific Islander groups are among the most rapidly growing populations in the US 18 ; the Asian American population is projected to surpass 46 million by 2060. 18 Although many studies have described secular trends toward earlier timing of puberty among US children and adolescents, few studies included Asian American, Native Hawaiian, and Pacific Islander populations. For instance, the landmark 1997 study by Herman-Giddens et al, 29 often used as baseline data when describing contemporary trends in pubertal timing in the US, included only Black and White girls. A follow-up study from this group described pubertal timing among boys 11 and included Black, Hispanic, and White boys but not Asian American or Native Hawaiian and Pacific Islander boys. One of the few studies of pubertal timing that included Asian American children was conducted by Biro and colleagues, 17 who concluded that Asian American and White girls had later onset of breast development compared with their Black or Hispanic counterparts. However, only 57 girls from this study (5%) were Asian American, with few Native Hawaiian and Pacific Islander girls; no disaggregated data for Asian American subgroups or specific rates for the Native Hawaiian and Pacific Islander population were reported.

Additional research specifically addressing pubertal timing among Asian American, Native Hawaiian, and Pacific Islander children and adolescents is limited to menarche data with no examination by ethnic subgroup. For instance, findings from the National Longitudinal Study of Adolescent Health suggested that Asian American girls are less likely to experience early menarche (≤11 years) and more likely to have late menarche (≥14 years) compared with their peers of other ethnicities. 30 The authors noted that Chinese and Filipino girls constituted a significant portion of the Asian American sample but did not specify whether Native Hawaiian and Pacific Islander girls were included. Conversely, a cohort study of 1386 girls from Catholic schools in Los Angeles reported that Asian American, Native Hawaiian, and Pacific Islander girls (n = 164) reached menarche at a median age of 12.2 years compared with 12.8 years for White girls. 31 However, the generalizability of this study is questionable due to its specific population.

Our study extends the existing literature by demonstrating marked variability across Asian American, Native Hawaiian, and Pacific Islander subgroups. It is known that aggregation of heterogeneous subgroups that have distinct health-related cultural and lifestyle differences likely results in masking of risk estimation. 19 This study highlights the importance of disaggregating data to potentially unmask important social and health differences to better inform health policies and resource allocation that are tailored to the needs of specific subgroups. 32 , 33 Further investigation to better understand the sources of health disparities across Asian American, Native Hawaiian, and Pacific Islander subgroups is critical and may elucidate targets that are subgroup specific for preventive interventions to address certain disease outcomes.

Health disparities in chronic conditions are well documented among Asian American, Native Hawaiian, and Pacific Islander subgroups. Some disparities in pubertal development may correspond to disparities in chronic diseases later in life, such as T2D, cardiovascular diseases, 34 and cancer. 35 For example, a study on adult T2D revealed that Filipino, Native Hawaiian and Pacific Islander, and South Asian individuals had the highest prevalence and incidence among all racial and ethnic groups. 36 Similar results have been reported on gestational diabetes. 37 Our results are in line with these disparities. Early pubarche has been found to be associated with increased risks of cardiometabolic diseases, including T2D and gestational diabetes. 9 , 38 Further research is needed to determine whether the disparities in pubertal timing observed in this study correspond to the disparities in T2D and gestational diabetes among these subgroups in adult populations.

Marked strengths of our study are the inclusion of boys and the use of objective measures of puberty assessed by pediatricians. Most previous studies of pubertal timing only included girls and relied solely on menarche data. Using menarche data as the only marker of puberty poses methodologic problems. Pubertal transitions take place over several years: among girls, for instance, pubertal characteristics often start with thelarche followed by pubarche, acne, a growth spurt, and then menarche. Other biological maturation events subsequently occur (eg, full or adult breasts or pubic hair growth). In addition, boys have remained vastly understudied because there is no similar hallmark of puberty that can be measured reliably. The observable signs of puberty in boys often begin with gonadarche (testicular enlargement and penile growth) followed by spermarche, pubarche, and later, the growth spurt, acne, voice deepening, and facial hair. 39 Previous studies have used recalled age at shaving initiation, 40 age at first nocturnal emission, 41 , 42 and age at voice change or cracking 16 , 41 - 43 to assess onset. These are far from the best proxies for age at initiation or completion of the pubertal process and are subject to recall bias. Because of the availability of pediatrician-assessed SMRs, we were able to include boys, and results demonstrated that Asian American, Native Hawaiian, and Pacific Islander boys had less variability in pubertal timing across ethnic subgroups compared with girls.

There are also some study limitations. First, because our data were collected through routine pediatric visits and not for research purposes, we did not have information on diet, exercise, other lifestyle factors, and family history that may have contributed to differences in pubertal timing. However, availability of EHR data and a large and diverse cohort of Asian American, Native Hawaiian, and Pacific Islander boys and girls provided us with a unique opportunity to disaggregate the data by ethnic subgroups, enabling the study to be the first to focus on pubertal timing among these populations, to our knowledge. Second, we used SMRs assessed primarily by pediatricians rather than more highly trained endocrinologists; thus, staging may be less accurate. However, our group conducted a validation study (described elsewhere 21 ), which demonstrated high correlation between pediatrician- and pediatric endocrinologist–assessed SMRs, suggesting that the data are relatively reliable. Many previous studies assessed pubertal timing by self- or parental report; objective measures of pubertal assessment by clinicians is thus an important strength of this study. Third, we were unable to further disaggregate participants in the multiethnic or multiracial groups. There are many combinations of race and ethnicity comprising these groups, making it difficult to determine how to disaggregate the groups in a way that would permit us to draw meaningful conclusions from the analyses. However, we included these groups in our analyses to better represent the population of northern California.

In this cohort study of 107 325 Asian American, Native Hawaiian, and Pacific Islander children and adolescents from northern California, the median age at pubertal onset varied substantially across ethnic subgroups, even among youths with healthy weight. Further investigation is warranted to assess whether these subgroup differences in pubertal timing correspond to disparities observed in adult chronic conditions, such as T2D.

Accepted for Publication: March 2, 2024.

Published: May 13, 2024. doi:10.1001/jamanetworkopen.2024.10253

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

Corresponding Author: Ai Kubo, PhD, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 ( [email protected] ).

Author Contributions: Mss Acker and Aghaee 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: Kubo, Acker, Aghaee, Kushi, Greenspan, Deardorff.

Acquisition, analysis, or interpretation of data: Kubo, Acker, Aghaee, Kushi, Quesenberry, Srinivasan, Kanaya, Deardorff.

Drafting of the manuscript: Kubo, Acker, Deardorff.

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

Statistical analysis: Acker, Quesenberry.

Obtained funding: Kubo.

Administrative, technical, or material support: Kubo, Aghaee, Greenspan, Srinivasan, Deardorff.

Supervision: Kubo, Deardorff.

Conflict of Interest Disclosures: Dr Kubo reported receiving grants from the National Institute of Mental Health, National Institutes of Health (NIH), during the conduct of the study. Ms Aghaee reported receiving grants from the NIH during the conduct of the study. Dr Kushi reported receiving grants from the NIH during the conduct of the study. Dr Quesenberry reported receiving grants from the NIH during the conduct of the study. Dr Kanaya reported receiving grants from the NIH during the conduct of the study. Dr. Deardorff reported receiving grants from the NIH during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was funded by grant R01HD098220 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Dr Kubo).

Role of the Funder/Sponsor: The Eunice Kennedy Shriver National Institute of Child Health and Human Development 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.

Data Sharing Statement: See the Supplement .

Additional Contributions: Madeline Kim, University of California, Los Angeles, assisted with the literature review without compensation.

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• Roland Tillmann 4 &
• Christopher Jones 1 , 5  

BMC Health Services Research volume  24 , Article number:  589 ( 2024 ) Cite this article

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Previous studies have identified substantial regional variations in outpatient antibiotic prescribing in Germany, both in the paediatric and adult population. This indicates inappropriate antibiotic prescribing in some regions, which should be avoided to reduce antimicrobial resistance and potential side effects. The reasons for regional variations in outpatient antibiotic prescribing are not yet completely understood; socioeconomic and health care density differences between regions do not fully explain such differences. Here, we apply a behavioural perspective by adapting the Theoretical Domains Framework (TDF) to examine regional factors deemed relevant for outpatient antibiotic prescriptions by paediatricians and general practitioners.

Qualitative study with guideline-based telephone interviews of 40 prescribers (paediatricians and general practitioners) in outpatient settings from regions with high and low rates of antibiotic prescriptions, stratified by urbanity. TDF domains formed the basis of an interview guide to assess region-level resources and barriers to rational antibiotic prescription behaviour. Interviews lasted 30–61 min (M = 45 min). Thematic analysis was used to identify thematic clusters, and relationships between themes were explored through proximity estimation.

Both paediatricians and general practitioners in low-prescribing regions reported supporting contextual factors (in particular good collegial networks, good collaboration with laboratories) and social factors (collegial support and low patient demand for antibiotics) as important resources. In high-prescribing regions, poor coordination between in-patient and ambulatory health services, lack of region-level information on antimicrobial resistance, few professional development opportunities, and regional variations in patient expectations were identified as barriers to rational prescribing behaviour.

Conclusions

Interventions targeting professional development, better collaboration structures with laboratories and clearer and user-friendly guidelines could potentially support rational antibiotic prescribing behaviour. In addition, better networking and social support among physicians could support lower prescription rates.

Peer Review reports

Antimicrobial resistance is a major threat to global health systems [ 1 ]. Despite improvements in international surveillance programs [ 2 ], for example in the WHO European region in 2019 alone, around 541,000 deaths were associated with and 133,000 deaths were directly attributable to antimicrobial resistance [ 3 ]. One of the key drivers of antimicrobial resistance in humans is previous exposure to antibiotics [ 4 ]. To reduce the development of antimicrobial resistance, improving rational antibiotic prescription practices (i.e. avoiding unnecessary prescriptions) is crucial [ 5 , 6 ]. Most antibiotic prescriptions in outpatient settings in Europe are for respiratory and urinary tract infections [ 7 , 8 ]. In Germany, the setting of this study, most outpatient prescriptions for antibiotics are issued by general practitioners and paediatricians [ 8 , 9 ].

Germany consistently ranks among the European countries with the lowest community consumption of antibiotics, for example, in the 2021 surveillance report of the European Centre for Disease Prevention and Control [ 10 ], Germany has the 3rd lowest community consumption of antibiotics for systemic use. Still, there is considerable regional variation in outpatient prescription rates across regions in Germany [ 11 ]. International research suggests that such regional differences in outpatient prescriptions cannot be fully explained by regional differences in infectious disease prevalence [ 12 ]. Instead, socioeconomic, demographic and cultural differences have been highlighted as additional key determinants [ 13 ].

A recent small-area analysis based on health insurance claims data [ 11 ], breaking down differences between the 401 administrative districts in Germany, found up to 4-fold differences in outpatient prescription rates for children (between 188 and 710 age- and sex-standardized outpatient prescriptions per 1000 persons/year), and more than 2-fold differences in adults (between 300 and 693 prescriptions per 1000 persons/year). These substantial regional variations in prescription rates continue to raise concern about the appropriateness of antibiotic prescribing practices in Germany [ 8 ].

At the same time, reasons for the observed regional differences in outpatient antibiotic prescription rates are not fully understood. On the one hand, urban-rural differences in prescription patterns might be due to differences in health care access and socioeconomic differences in populations such as age or deprivation status [ 14 ]. Proximity to animal breeding or fattening farms has also been associated with variations in antibiotic prescriptions [ 15 ]. Further regional differences exist in the quality and accessibility of out-of-hours emergency primary care settings, which have both been associated with an increase in antibiotic prescriptions [ 16 ].

On the other hand, non-clinical factors such as demographic and socioeconomic differences [ 13 ], or differences in patient demand and prescription practices have been suggested to underlie regional variations [ 17 ], and the influence of patient demand on inappropriate antibiotic prescriptions is well documented [ 18 , 19 ]. Supporting small-area differences, calls have been made to take into account small-area regional factors in devising targeted interventions to support rational prescription practices [ 20 ].

Together, this suggests that a better understanding of the reasons underlying regional variations in outpatient antibiotic prescriptions is vital, especially for the development and implementation of better interventions to avoid inappropriate antibiotic prescriptions. The present study is based on a mixed methods research project commissioned by the German Federal Ministry of Health (SARA; “Studie zur Analyse der Regionalen Unterschiede bei der Antibiotika-Verordnung” [Study to analyse regional variations in antibiotic prescriptions]). Previous publications from this research project include the abovementioned small-area analysis of health insurance claims data [ 11 ] and a conference presentation containing some of the present data [ 21 ]. The current study focuses on the qualitative part of the project and reports results from interviews with prescribers in outpatient settings.

To this end, it builds on the patterns of regional differences identified in the previous quantitative study [ 11 ] to better understand the drivers of these regional differences in prescription behaviour based on perceptions of prescribers (general practitioners and paediatricians) in districts differing by outpatient antibiotic prescription rates.

In order to do so, an established framework of determinants of health care professional behaviours, the Theoretical Domains Framework (TDF; [ 22 , 23 ]) was used to guide qualitative interviews with prescribers.

The TDF is a psychological model developed for healthcare and behaviour change research and is based on comprehensive reviews of behavioural theories [ 22 , 24 ]. It comprises 14 key individual, social and contextual domains influencing human behaviour: knowledge, skills, social/professional role/identity, beliefs about capabilities, optimism, beliefs about consequences, reinforcement, intentions, goals, memory/attention/decision processes, environmental context/resources, social influences, emotion, and behavioural regulation. Both main effects of and interactions between domains are possible.

The TDF has been instrumental in examining individual determinants of antibiotic prescribing behaviour [ 25 , 26 , 27 , 28 ], and most studies show the domain of environmental context and resources to be influential for antibiotic prescriptions. However, which contextual aspects are particularly relevant is poorly understood to date.

The degree to which contextual resources and barriers as well as their interactions are specific to small-area districts and regions is vital to understand the observed variations in prescription rates and improve future intervention efforts. This study will therefore apply the TDF to understand differences in contextual determinants of antibiotic prescriptions and map these onto established small-area differences in paediatricians and general practitioners in Germany.

Participants and procedure

To identify region-level determinants of differences in outpatient antibiotic prescribing, semi-structured interviews were conducted with general practitioners and paediatricians working in outpatient settings. The protocol for this study was approved by the University of Bremen Ethics committee (AZ 2021-03).

Data collection materials

An interview guide (supplementary file 1 ) based on the Theoretical Domains Framework (TDF) [ 22 , 23 ] and previous studies using the TDF in antibiotic prescription contexts [ 25 , 28 ] was designed with input from a paediatrician (RT) and pharmacoepidemiologists (UH, OS) and was pilot-tested with GP representatives known to the researchers. The interview guide started with informing participants about the status of their district as high-or low-prescribing and subsequently asked an open question on prescribers’ ideas on reasons for this. Following this, we asked prescribers for their perceptions on regional levels of TDF domains relevant for prescribing antibiotics [ 25 , 28 ]; (i) knowledge, (ii) social support, (iii) environmental context and resources (and perceived differences to other districts), (iv) social and professional role, (v) social influences (patients), (vi) goals, (vii) beliefs about capabilities (patient expectation management), (viii) beliefs about consequences, (ix) optimism, (x) intentions, (xi) memory and attention processes.

Recruitment

We employed purposive sampling and stratified potential participants based on our previous quantitative analysis of regional differences in medical claims data of outpatient antimicrobial prescriptions in Germany [ 11 ]. Here, differences in prescriptions were compared between administrative districts (“Landkreise” or “kreisfreie Städte”; Nomenclature of Territorial Units for Statistics NUTS level-3 subdivision [ 29 ]).

In order to compare and contrast health care providers’ perspectives on regional differences, we selected, separately for paediatricians and GPs, 5 districts each that were within the 5% highest antibiotic prescription rates per 1,000 insured persons, and 5 districts that were within the 5% lowest antibiotic prescription rates. Within each district group, we further selected rural and urban districts (classification based on official regional statistics in Germany; [ 30 ]) to account for potential differences in settlement structure.

Contact information for paediatrician and GP practices in the respective districts were obtained through the regional representations of the respective medical councils, and were contacted through email and phone calls. Snowball recruitment was used during which participants recommended further colleagues within the respective districts, and a total of 1,444 contact attempts were made. Participants received €75 (approximately US$80) for their participation.

Prescribers who had expressed interest in the study were emailed a participant information sheet and were asked to suggest a date and time for a phone interview. Semi-structured telephone interviews were subsequently conducted by experienced female and male qualitative researchers (CJ, BS, PK), audio recorded and were transcribed verbatim. Interviews lasted a mean of 45 min (range 30–61 min) and started with an introduction, brief overview of the study goals, and verbal informed consent was obtained prior to interview commencement. The interviewed prescribers had no personal or professional connection to the researchers before the interviews.

Data analysis

Starting with the TDF domains in the interview guide, data analysis utilized an deductive approach and was based on thematic analysis [ 31 ]. Two researchers (CJ, BS) independently coded the material using MaxQDA data management software. Initial codes were reviewed between the two researchers, and saturation was achieved with both the paediatrician and GP interviews. All codes were mapped onto at least one of the TDF domains. Relationships between codes were examined looking at code overlaps in coded segments and analysing the relative proximity of coded segments in the transcribed text. The more frequently two codes appear in the same segment or in relative proximity, the more substantial overlaps between the codes are assumed. The relative positions of codes in this two-dimensional space were operationalized using multidimensional scaling implemented in MaxQDA. Here, a solution is estimated which replicates the distance between elements in the two-dimensional space between codes as well as possible relationships. Assigning of a code to a cluster of codes is estimated using the Unweighted Average Linkage method [ 32 ]. Disagreements were resolved through discussion between the researchers.

Results of the thematic analyses are presented separately for GPs and paediatricians.

Participants

A total of 40 interviews (17 paediatricians; 10 from high-prescription and 7 from low-prescription districts, 23 GPs; 10 from high-prescription and 13 from low-prescription districts) were conducted. Participants had between 1 and 35 years of experience in their current positions (mean 13.4 years, SD 9.9 years). Interviews lasted an average of 44.8 min (SD 7.1 min, range 30–61 min).

Paediatricians

TDF domains on region levels mentioned as influencing paediatricians’ prescribing behaviour (Fig.  1 ) included context and resources (86 mentions), social influences (56 mentions), knowledge (36 mentions), skills (22 mentions), social/professional role (15 mentions), beliefs about consequences (15 mentions), beliefs about capabilities (9 mentions), goals (9 mentions), behavioural regulation (6 mentions), optimism (3 mentions) and emotions (2 mentions).

TDF domains mentioned as barriers (red) or resources (blue) by paediatricians

Context and resources

Regional context and resources can affect prescribing behaviour through multiple, direct and indirect pathways, according to the participating paediatricians. The distinction between contextual (i.e., factors specific to the region) and composition effects (i.e., factors resulting from the composition of the population within a region; [ 33 ]) is particularly relevant.

Paediatricians mainly mentioned contextual factors, e.g., air pollution as a risk factor:

This area here is a former working-class area, air quality is poor, and this means we have more respiratory illnesses which are the most frequent reasons for antimicrobial prescriptions.

(A, paediatrician, urban area, high prescription rate)

Similar direct contextual effects are evident in the density of paediatricians:

…This means service provision for children in an emergency is limited, and they are rather seen by GPs. And the GPs are fantastic, […], but they don’t have our special training and might be a bit more anxious if they see a child with a high fever….

(B, paediatrician, rural area, high prescription rate)

This low density then results in overload of the paediatricians, which in turn can increase antimicrobial prescriptions:

I mean on a Monday in February I have seen about 200 children, or thereabouts. And then I can’t start discussing for ages, this just doesn’t work.

(C, paediatrician, rural area, high prescription rates)

Suboptimal transition from in-patient to out-patient care were also seen to increase antimicrobial prescriptions in districts with higher prescription rates:

…in the hospitals, they prescribe broad-spectrum antibiotics. And I have to say, after we have sat down together a year ago and have talked about outpatient antibiotic therapies, we had agreed on not prescribing some particular antibiotics. And now I see that these exact antibiotics are still being used in the hospital.

(D, paediatrician, rural area, high prescription rates)

Contextual effects however also can constitute resources for lower prescription rates, for example in high-quality laboratories and quick turnaround times:

This means we can get samples to them three or four times a day and are not dependent on pickups once a day like in the practices out there. This really is a resource I think .

(E, paediatrician, rural area, low prescription rates)

Social influences

Social influences have been mentioned frequently, both as social influences through patients and through other health care providers. In particular where patient characteristics are being discussed, such influences could also be classified as compositional context resources (see above). However, as most of the quotes illustrate, these compositional factors also contain social influences.

Social influences as factors affecting high prescription rates are mainly located on patient level, illustrated in the following quote referring to patients with Middle-Eastern migration history:

This is a totally different culture, also affecting ideas about illnesses. Their ideas are totally different, and antibiotics are seen as miracle drugs – they are over the moon if they can get an antibiotic.

(F, paediatrician, urban area, high prescription rates)

However, the demand by patients is also being attributed to context effects such as dominating agricultural influences:

I think that there are lots of expectations for antibiotics by patients. For example, I do have a mother who generally insists on getting an antibiotic for her child, and I wouldn’t prescribe it. And I tell you how she says it: ‘I also give this to my pigs, so it can’t be bad for my kids’. So I think that antibiotic practices in the farms around here, I think that this means they (antibiotics) are applied liberally and happily, and the parents have experience and want them for their kids as well.

At the same time, social influences are seen as malleable influences, in particular in combination with skills and knowledge which can then contribute to improvements in prescription practice:

It has become much better, yes. They (patients) now understand it, they have gotten used to it. And now we have, when the doctor says, you don’t need an antimicrobial, then more than half of them don’t go and see another doctor immediately and say ‘I need an antibiotic’.

(G, paediatrician, urban area, low prescription rates)

Knowledge included both information on current recommendations for antimicrobial prescribing, information on local resistance prevalence, information on local and personal prescription rates, and training content relevant to prescribing antimicrobials.

Participants from low-prescription districts mentioned knowledge on current recommendations as a resource and linked this knowledge to lower prescription rates within their districts:

We feel quite well informed. And everyone builds on that through individual research, further training and talking to colleagues. And I think, else we wouldn’t see these numbers.

(H, paediatrician, rural area, low prescription rates)

In contrast, paediatricians from high-prescription areas mentioned increased effort in obtaining relevant information:

[…] There is no information in the district, you always have to look after this yourself.

(I, paediatrician, rural area, high prescription rates).

In districts that had employed a paediatrician-initiated education programme (AnTiB; [ 34 ]), this programme was mentioned as an explicit resource:

We used to have this little informal guideline here in (city), which is also lying around in out-of-hours paediatric services and which every paediatrician here is likely to have in their practice. It is very useful and if you are doing emergency shifts, you pull it out of the drawer, look at the dosage and then prescribe.

(J, paediatrician, urban area, low prescription rate).

In contrast, the lack of specific knowledge in paediatric emergency services is seen as a barrier to effective prescribing:

We live in one of the areas with the most children in Germany, and, you can’t make this stuff up, we don’t have a paediatric out-of-hours service. This means out-of-hours is staffed by colleagues, e.g., urologists who have no clue, who start googling first – and then quickly prescribe an antibiotic.

Skills as mentioned by the paediatricians include both discipline-specific and generic skills such as language skills or interpersonal skills.

Lack of specific treatment skills are mentioned as barriers to lower prescription rates by paediatricians in high-prescribing districts:

Perhaps the experience that as a urologist, you might not have that much experience with these really high fever temperatures in toddlers under two years.

(K, paediatrician, rural area, high prescription rate).

Similarly, a lack of language skills both on the side of the prescribers and patients is being seen as a barrier, both to non-prescribing and to instructing parents to monitor their children’s health:

… there is such a large language barrier which prevents you from explaining what the parents have to look out for, what are the signs of deteriorations, when do they need to come back, well, that this is a problem overall.

(L, paediatrician, urban area, high prescription rate)

Social and professional role

Social and professional role are mainly seen as a resource for low prescription rates. The main effects are seen to be indirect, via social norms and better professional networks. In some areas, this professional role is a relevant part of paediatricians’ identity which is used to be a role model to other paediatricians.

I think there are these lighthouse or role model practices here, the bigger ones. And they do this on purpose, to set standards and blaze a trail, and the younger colleagues or others then orient themselves on them.

(M, paediatrician, urban area, low prescription rates)

In addition, the social influence through networks is being seen as strengthened through social and professional roles and identity:

So we do have quite a number of colleagues who are really well connected. They always participate in our quality groups, participate very reliably, and have good contact amongst themselves.

Beliefs about consequences

Beliefs about consequences tend to be related to contextual and environmental resources or barriers as well as regional outcomes. A particularly strong motive seems to be using antibiotics to prevent potential risks.

Paediatricians from districts with high prescription rates discuss avoiding consequences in particular with regards to patient overload:

My personal record in winter was 209 children a day. […] I have briefly checked them and then prescribed an antibiotic, because even if most of it is viral, you have children with whooping cough and I tend to be generous, because the hospitals are full of pneumonia.

Paediatricians from districts with low prescription rates on the other hand discuss low beliefs about negative consequences such as patients changing doctors due to low competition pressure:

So we don’t really have a competitive mindset here, because changes from one paediatrician to the other are really, really rare.

Interestingly, beliefs about consequences in terms of developing resistant microbes differ between paediatricians from low- and high-prescribing districts. Whereas those from high-prescribing districts argue that the responsibility for resistances is mainly located in the agricultural sector:

I think that resistant microbes develop if the farms in the area use lots of antibiotics […] So the kids who have MRSA here, they are all from farms. So they didn’t get MRSA because we gave them antibiotics but because the farms at home use lots of antibiotics.

(C, paediatrician, rural area, high prescription rates),

Those from low-prescription districts tend to attribute resistance development to health care professional behaviour:

The less antibiotics one prescribes, and if this happens everywhere, then we can expect, that the development of resistances will be less bad than elsewhere.

Beliefs about competences

Beliefs about competences mainly revolved around perceptions of competence to influence local resistance developments and largely mirror those exemplified in the beliefs about consequences section.

Both paediatricians from low- and high-prescribing districts explicitly mentioned goals to prescribe less antimicrobials, and mention that these goals are also shared by colleagues in the respective districts. Differences exist in the context within goals are mentioned – paediatricians from low-prescription districts mention the goal of lower prescriptions as part of a combinations of goals (e.g., optimal therapy or limiting resistance development), paediatricians from high-prescription districts concentrate on potentially more relevant goals than lower prescription rates:

…I think I can speak for most of my colleagues here, one tries to prescribe as little as possible. But if they really all read the reports, do they change their prescription behaviour, I doubt that. There are quite some other problems here that need solving as well.

Behavioural regulation

Behavioural regulation had only six mentions, but these were mainly together with contextual factors in districts with high prescription prevalence to highlight that contextual factors can pose barriers which also affect the low likelihood to change through impeding behavioural regulation:

And I think that these are basically deeply rooted, historic, ritualized prescription patterns, which then manifest regionally such that it is really difficult to change this.

General Practitioners (GPs)

TDF domains on district level that affected GP prescribing behaviour (Fig.  2 ) included context and resources (159 mentions), social influence (60 mentions), knowledge (41 mentions), beliefs about consequences (29 mentions), social/professional role (16 mentions), skills (16 mentions), goals (6 mentions), and behavioural regulation (4 mentions).

TDF domains mentioned as barriers (red) or resources (blue) by GPs

Similar to the paediatric participants, GPs reported on a range of regional contextual factors that influenced prescribing behaviour. These can also be differentiated along contextual and compositional factors [ 33 ].

A combination of contextual (main industry in the region) and compositional (migrant workers in the main industry) is a good example for these influences:

With the (migrant) workers in the meat industry, we do have a lot of people who might have potentially problems in dental hygiene, infections due to cuts for example. This happens a lot, and then increases the prescription of (antimicrobials).

(N, GP, rural area, high prescription rates).

GPs also report on regional differences in the influence of pharmaceutical representatives in their practices. For example, a GP from a low-prescription rural district mentioned that their local quality circles “will not invite pharmaceutical representatives if possible”.

Social influence

Social influences differ between districts, according to GP participants, and similar to paediatricians, these influences come through colleagues and patients.

One example for a local social influence could be long established GPs who influence local quality circles:

…as a young and newly arrived doctor, I quit going to the quality circles because the old guard was so present and influenced communication, work and thinking about practices. However, we do have now a new generation of GPs and things change.

(O, GP, rural area, low prescription rates)

Patient-level influences are also perceived to differ between districts, with some of the differences in expectations to be prescribed antibiotics being attributed to cultural factors:

There is a group of patients who are really eager to get antibiotics and who are incredibly demanding. Germans from the former Soviet Republics, and we do have many of them in this district. For them, it (not being prescribed antibiotics) is not a real therapy, even if it is viral….

(P, GP, rural area, high prescription rates)

Similar to cultural factors, the age distribution in a district is perceived to affect prescription, with more older adults in a district being associated with higher antibiotic demand.

Similar to this influence on higher prescriptions, specific regional social influences are also perceived as being influential for low prescription rates:

I mean, (city) is a very special city. It’s an administrative centre, a big university city, so I think there are a lot of people with a relatively high educational attainment, relatively little industry and I guess it’s also related to the fact that people have a bit of a different attitude. .

(Q, GP, urban area, low prescription rates)

Similar to the results in paediatricians, knowledge about current recommendations, information on local resistance, and training content relevant to prescribing antimicrobials were seen as relevant resources. One particular additional factor was that in one of the participating districts, the local university was seen as influential for particularly rational prescribing behaviour:

I think that this is due to the fact that here in (city) there are many doctors who have studied in (city). And I remember from my studies that antibiotic prescriptions were an important topic, and that in microbiology et cetera we were always being reminded that one does not just prescribe antibiotics but needs to justify this really well.

(R, GP, rural area, low prescription rates)

At the same time, similar to the paediatricians, a lack of knowledge in out-of-hours services is seen as a relevant factor for high prescription rates:

But there are many colleagues working in the out-of-hours primary care and doing GP tasks who have for example an anesthesia background, or something else from the hospital, they don’t know it any better.

(N, GP, rural area, high prescription rates)

These knowledge factors interact with resources and barriers on context level.

Similar to paediatricians, beliefs about consequences include beliefs about having to avoid liabilities, which are often mentioned in combination with structural and contextual factors:

And if something does go wrong, and that’s always a problem in outpatient settings, you are the one who screwed it up. That’s what all the colleagues are afraid of. So the fear of making a mistake and not prescribing the antibiotic is always bigger than the fear of damaging something with the antibiotic.

(S, GP, rural area, high prescription rates)

Losing patients to other practices in situations with strong competition was a strong belief about consequences in districts with high prescriptions:

You can say, No I am not going to prescribe this, but then you lose the patient, they are just going somewhere else.

(T, GP, urban area, high prescription rates).

At the same time, a lack of such perceived consequences has been perceived as a resource for lower prescriptions:

…at least we don’t have to bow to patient demands too much. It is very different here compared to (city) where I was before, in the inner city, where there was a lot of competition due to too many GPs. You are much more likely to give in to irrational demands then.

(U, GP, rural area, low prescription rates)

Social / professional role

Specific regional ideas on the professional roles are perceived to influence prescription behaviour, in particular in combination with specific aspects of rurality that could affect the composition of the local GP structure:

I just see what kind of colleagues – to say it cautiously – are coming to this region, who take over old practices or establish new ones. They are not necessarily the most committed doctors.

(V, GP, rural area, high prescription rates)

In districts with low prescription rates, skills were mainly being mentioned with regards to interpersonal skills regarding expectation management with patients, which were perceived to be higher in the respective districts:

…in fact, skills training in multiple areas. General communication skills, difficult patients, bad prognosis, diagnosis, or making the patients understand why a particular therapy is indicated – these are all key skills and have always been emphasized during our studies.

Similar to paediatricians, GPs from both low- and high-prescription districts mention the goal of low prescription rates, and assume that their colleagues in the district have similar goals. GPs in low-prescription districts mention this goal as part of multiple goals (ideal therapy, avoid resistance) in low-prescription districts, GPs in high-prescription districts mention this goal as having lower priority compared to competing demands.

Similar to the paediatricians, a lack of behavioural regulation in combination with contextual measures such as relatively old GPs in the district was seen as a risk factor for higher prescriptions:

Prescription behaviour by older colleagues plays a role I think. You can see this when you look at the age structure of the GPs here. They tend to prescribe antibiotics quickly whenever there are respiratory infections.

(W, GP, rural area, high prescription rates)

This study examined prescribers’ perceptions of region-specific drivers of outpatient antibiotic prescriptions. We conducted 40 interviews in districts stratified by antibiotic prescription rates, and mapped these perceptions on dimensions of the Theoretical Domains Framework [ 22 , 23 ]. A total of 11 domains were identified, and these served as, partially interacting, barriers against and resources for low antibiotic prescription rates. Most barriers and facilitators were similar between paediatricians and GPs. However, while GPs mentioned the age and workforce structure in districts as additional barrier, paediatricians emphasized a lack of skills and knowledge of GP colleagues treating young children as a barrier in districts with only few paediatricians.

We could link differences in the perception of TDF domains and their interactions to differences in prescribing behaviour in the districts to identify overarching barriers and resources for appropriate prescription practices.

Overarching barriers to low prescription rates

Both paediatric and GPs mentioned a lack of knowledge on district-level resistance developments as particular barrier to rational prescribing. This knowledge factor overlaps with a lack of contextual and environmental resources which could provide this information such as routine information flows between laboratories and health care providers.

Similarly, a lack of collaboration and coordination of knowledge in out-of-hours services was perceived to be associated with higher prescription rates – partly also due to a perception to avoid liabilities if prescribing antibiotics.

Lower health care provider density as contextual factor has been associated with higher prescription rates in previous international studies [ 35 , 36 ]. In the present study, lower prescriber density has only indirectly been associated with higher prescription rates – in the cases where lower density correlates with suboptimal emergency services prescription guidelines [ 16 ].

Participants also associated specific regional industries in rural districts (pig farming and meat factories) with higher patient demands due to either antibiotic practices in farming [ 15 ] or an increased demand for antibiotics by migrant workers and due to cuts in meat factories.

Social influences included culture-specific expectations about the effectiveness of antibiotics leading to higher patient demand for antibiotics, which together with time pressure from high patient load increased pressure on prescribers during consultations. This finding replicates findings from other European studies on antibiotic prescribing behaviour [ 18 , 19 ].

Overarching resources for low prescription rates

Overarching resources for low prescription rates that were mentioned by both paediatricians and GPs included environmental context and resources . Here in particular existing local networks supporting quality control were perceived as supportive of appropriate prescribing, both through the provision of information, best-practice examples and social norms. This replicates an earlier study suggesting that well-functioning local or regional primary care networks in Germany are associated with more appropriate antibiotic prescribing [ 37 ]. In addition, laboratories routinely providing information on local resistance data were perceived as resources for rational prescribing, which is in line with previous studies in Germany outlining the lack of local resistance information as a barrier to appropriate prescribing [ 38 ] and, similarly, showing that practitioners perceive information on local resistance as beneficial [ 39 ]. Low local population demand for antibiotics was also perceived as resource, as participants reported this to positively impact their prescription practice.

Implications

Most barriers and resources to rational outpatient prescribing in this study were contextual factors. However, contextual factors such as the local population, the main local branches of industry or (at least in Germany), or the free choice of practitioners to open practices anywhere within a district are not directly modifiable. This means that interventions should in particular target local collaboration structures and the availability of locally adapted guidelines.

If collaborations between local medical councils and laboratories can be improved to routinely provide local antimicrobial resistance data to prescribers, this information can readily be included into the prescription decision-making process [ 39 ]. In particular since both German and international studies [ 40 ] show that there is substantial variation in the degree to which individual practices take local resistance data into consideration, routine approaches are warranted. Germany has implemented a standardized surveillance program for multiresistant microbes such as MSRA [ 2 ], but the degree to which these surveillance findings are broken down locally and are available to practices varies considerably between districts, suggesting policies to standardise practice. If these findings are then included into local prescription guidelines such as the AnTiB guidelines [ 34 ], local prescription practices can be improved.

Routine antibiotic stewardship programmes that support paediatric and general practices could also help facilitating such closer collaborations and in turn build on some of the networking aspects mentioned as resources in the interviews. At the moment, antibiotic stewardship programs for outpatient settings in Germany are supported through national professional and scientific associations and are eligible for training credits, but implementation depends on local initiatives [ 5 ]. National policies to mandate such programmes would help to reduce the current regional disparities in antibiotic prescription practices, and the current antibiotic strategy of the German government DART 2030 [ 41 ] plans to explore compulsory training.

In terms of knowledge resources, participants mentioned that easy-to-use recommendations for emergency practice services are an important resource in particular if there is no paediatric emergency service, and children are seen by non-paediatrician practitioners. In Germany, initiatives such as Antibiotic Therapy in Bielefeld (AnTiB; [ 34 ]) provide such guidelines, but a systems-wide implementation of easy-to-follow guidelines such as e.g., NICE guidelines for upper respiratory tract infections [ 42 ] is currently lacking and would likely improve prescription practices in Germany.

Patient information such as leaflets might lead to increased patient knowledge about the role of antibiotics in managing infections and lower patient demand [ 43 ] without increased reconsultations [ 44 ]. At the same time, the role of involving audiences in the design of such leaflets and ensuring their understandability is crucial [ 45 ].

Strengths and limitations

A particular strength of the study lies in using the TDF to examine district-level differences in prescription behaviour, which allowed us to identify and interpret the impact of the factors mentioned by GPs and paediatricians. This deductive approach allowed mapping key themes on an established framework, which can in turn be used to determine and develop potential intervention applications. Our study complements previous work applying the TDF to understand antibiotic prescribing behaviour [ 25 ] by extending the perspective of the TDF on individual determinants onto characteristics of the district.

At the same time, the perceptions of participants regarding district-level TDF-based characteristics are subjective perceptions and do not necessarily correspond to the actual level of resources and barriers in the districts. Compared to face-to-face interviews, telephone interviews miss out on nonverbal information, but have allowed us to accommodate prescribers’ schedules. Due to the self-report nature of interviews, demand characteristics might affect responses such that participants exaggerate or downplay relevant factors.

Saturation in that no new codes emerged was achieved in all study cells (defined by practitioner group, urban/rural practice site and prescription rates) apart from GPs from high-prescribing urban areas, where only one interview could be realised. It is thus possible that additional interviews could have provided additional barriers and resources.

Substantial district-level differences in outpatient antibiotic prescriptions in paediatric and general practices can be mapped on differences in prescriber perceptions of district-level barriers and resources to rational prescribing. Given the regional variation in underlying reasons for inappropriate prescribing of antibiotics, similar qualitative studies in all districts in Germany with high prescription rates could be a promising approach to design targeted interventions. According to the results of interviews conducted in this study, routine provision of local antibiotic resistance data, better and clearer guidelines for paediatric patients in ambulatory emergency services, patient information and a wider implementation of standardised antibiotic stewardship programs could be promising targets for interventions.

Data availability

The qualitative data collected for this study was de-identified before analysis. Consent was not obtained to use or publish individual-level data from the participants and therefore may not be shared publicly. The de-identified (German) data can be obtained from the corresponding author upon reasonable request.

Change history

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Acknowledgements

The support of Paula Kinzel during data assessment is gratefully acknowledged.

Open Access funding enabled and organized by Projekt DEAL.

The SARA project—on which this publication is based—was commissioned by the Federal Ministry of Health (grant number ZMVI1-2519FSB115).

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Contributions

BS conceived of the study, analysed and interpreted data and wrote the first draft of the manuscript. OS contributed to design of the study, interpretation of data. UH contributed to acquisition, concept and design of the study as well as interpretation of data. RT contributed to design and acquisition of the study as well as interpretation of data. CJ contributed to design and concept of the study as well as assessment, analysis and interpretation of the data. All authors critically reviewed the manuscript.

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Ethics approval was obtained through the University of Bremen ethics committee (AZ 2021-03). All methods and procedures were performed in accordance with the relevant guidelines. All participants provided informed consent including before being interviewed for this study.

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OS and UH are working at an independent, non-profit research institute, the Leibniz Institute for Prevention Research and Epidemiology – BIPS. Unrelated to this study, BIPS occasionally conducts studies financed by the pharmaceutical industry. These are post-authorization safety studies (PASS) requested by health authorities. The design and conduct of these studies as well as the interpretation and publication are not influenced by the pharmaceutical industry. The study presented was not funded by the pharmaceutical industry. The Federal Ministry of Health specified the research question and the main content of the study concept and regularly participated in discussions on the implementation of the study. The authors were independent in the specific design, execution, interpretation, and writing of the study. The Federal Ministry of Health has authorized the publication of the results. BS, RT and CJ declare no conflicts of interest.

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Schüz, B., Scholle, O., Haug, U. et al. Drivers of district-level differences in outpatient antibiotic prescribing in Germany: a qualitative study with prescribers. BMC Health Serv Res 24 , 589 (2024). https://doi.org/10.1186/s12913-024-11059-z

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APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease

• Juan Fortea   ORCID: orcid.org/0000-0002-1340-638X 1 , 2 , 3   na1 ,
• Jordi Pegueroles   ORCID: orcid.org/0000-0002-3554-2446 1 , 2 ,
• Daniel Alcolea   ORCID: orcid.org/0000-0002-3819-3245 1 , 2 ,
• Olivia Belbin   ORCID: orcid.org/0000-0002-6109-6371 1 , 2 ,
• Oriol Dols-Icardo   ORCID: orcid.org/0000-0003-2656-8748 1 , 2 ,
• Lídia Vaqué-Alcázar 1 , 4 ,
• Laura Videla   ORCID: orcid.org/0000-0002-9748-8465 1 , 2 , 3 ,
• Juan Domingo Gispert 5 , 6 , 7 , 8 , 9 ,
• Marc Suárez-Calvet   ORCID: orcid.org/0000-0002-2993-569X 5 , 6 , 7 , 8 , 9 ,
• Sterling C. Johnson   ORCID: orcid.org/0000-0002-8501-545X 10 ,
• Reisa Sperling   ORCID: orcid.org/0000-0003-1535-6133 11 ,
• Alexandre Bejanin   ORCID: orcid.org/0000-0002-9958-0951 1 , 2 ,
• Alberto Lleó   ORCID: orcid.org/0000-0002-2568-5478 1 , 2 &
• Víctor Montal   ORCID: orcid.org/0000-0002-5714-9282 1 , 2 , 12   na1  

Nature Medicine ( 2024 ) Cite this article

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• Alzheimer's disease
• Predictive markers

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

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Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study

Association of APOE e2 genotype with Alzheimer’s and non-Alzheimer’s neurodegenerative pathologies

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Data availability.

Access to tabular data from ADNI ( https://adni.loni.usc.edu/ ), OASIS ( https://oasis-brains.org/ ), A4 ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp ) and NACC ( https://naccdata.org/ ) can be requested online, as publicly available databases. All requests will be reviewed by each studyʼs scientific board. Concrete inquiries to access the WRAP ( https://wrap.wisc.edu/data-requests-2/ ) and ALFA + ( https://www.barcelonabeta.org/en/alfa-study/about-the-alfa-study ) cohort data can be directed to each study team for concept approval and feasibility consultation. Requests will be reviewed to verify whether the request is subject to any intellectual property.

Code availability

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Acknowledgements

We acknowledge the contributions of several consortia that provided data for this study. We extend our appreciation to the NACC, the Alzheimer’s Disease Neuroimaging Initiative, The A4 Study, the ALFA Study, the Wisconsin Register for Alzheimer’s Prevention and the OASIS3 Project. Without their dedication to advancing Alzheimer’s disease research and their commitment to data sharing, this study would not have been possible. We also thank all the participants and investigators involved in these consortia for their tireless efforts and invaluable contributions to the field. We also thank the institutions that funded this study, the Fondo de Investigaciones Sanitario, Carlos III Health Institute, the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas and the Generalitat de Catalunya and La Caixa Foundation, as well as the NIH, Horizon 2020 and the Alzheimer’s Association, which was crucial for this research. Funding: National Institute on Aging. This study was supported by the Fondo de Investigaciones Sanitario, Carlos III Health Institute (INT21/00073, PI20/01473 and PI23/01786 to J.F., CP20/00038, PI22/00307 to A.B., PI22/00456 to M.S.-C., PI18/00435 to D.A., PI20/01330 to A.L.) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas Program 1, partly jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, Una Manera de Hacer Europa. This work was also supported by the National Institutes of Health grants (R01 AG056850; R21 AG056974, R01 AG061566, R01 AG081394 and R61AG066543 to J.F., S10 OD025245, P30 AG062715, U54 HD090256, UL1 TR002373, P01 AG036694 and P50 AG005134 to R.S.; R01 AG027161, R01 AG021155, R01 AG037639, R01 AG054059; P50 AG033514 and P30 AG062715 to S.J.) and ADNI (U01 AG024904), the Department de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca I Innovació en Salut (SLT006/17/00119 to J.F.; SLT002/16/00408 to A.L.) and the A4 Study (R01 AG063689, U24 AG057437 to R.A.S). It was also supported by Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151 o J.F.) and Horizon 2020–Research and Innovation Framework Programme from the European Union (H2020-SC1-BHC-2018-2020 to J.F.; 948677 and 847648 to M.S.-C.). La Caixa Foundation (LCF/PR/GN17/50300004 to M.S.-C.) and EIT Digital (Grant 2021 to J.D.G.) also supported this work. The Alzheimer Association also participated in the funding of this work (AARG-22-923680 to A.B.) and A4/LEARN Study AA15-338729 to R.A.S.). O.D.-I. receives funding from the Alzheimer’s Association (AARF-22-924456) and the Jerome Lejeune Foundation postdoctoral fellowship.

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These authors contributed equally: Juan Fortea, Víctor Montal.

Authors and Affiliations

Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Lídia Vaqué-Alcázar, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain

Juan Fortea & Laura Videla

Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

Lídia Vaqué-Alcázar

Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

Juan Domingo Gispert & Marc Suárez-Calvet

Neurosciences Programme, IMIM - Hospital del Mar Medical Research Institute, Barcelona, Spain

Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina. Instituto de Salud carlos III, Madrid, Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA

Sterling C. Johnson

Brigham and Women’s Hospital Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Reisa Sperling

Barcelona Supercomputing Center, Barcelona, Spain

Víctor Montal

You can also search for this author in PubMed   Google Scholar

Contributions

J.F. and V.M. conceptualized the research project and drafted the initial manuscript. V.M., J.P. and J.F. conducted data analysis, interpreted statistical findings and created visual representations of the data. O.B. and O.D.-I. provided valuable insights into the genetics of APOE. L.V., A.B. and L.V.-A. meticulously reviewed and edited the manuscript for clarity, accuracy and coherence. J.D.G., M.S.-C., S.J. and R.S. played pivotal roles in data acquisition and securing funding. A.L. and D.A. contributed to the study design, offering guidance and feedback on statistical analyses, and provided critical review of the paper. All authors carefully reviewed the manuscript, offering pertinent feedback that enhanced the study’s quality, and ultimately approved the final version.

Corresponding authors

Correspondence to Juan Fortea or Víctor Montal .

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Competing interests.

S.C.J. has served at scientific advisory boards for ALZPath, Enigma and Roche Diagnostics. M.S.-C. has given lectures in symposia sponsored by Almirall, Eli Lilly, Novo Nordisk, Roche Diagnostics and Roche Farma, received consultancy fees (paid to the institution) from Roche Diagnostics and served on advisory boards of Roche Diagnostics and Grifols. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development and Roche Diagnostics. J.D.G. has served as consultant for Roche Diagnostics, receives research funding from Hoffmann–La Roche, Roche Diagnostics and GE Healthcare, has given lectures in symposia sponsored by Biogen, Philips Nederlands, Esteve and Life Molecular Imaging and serves on an advisory board for Prothena Biosciences. R.S. has received personal consulting fees from Abbvie, AC Immune, Acumen, Alector, Bristol Myers Squibb, Janssen, Genentech, Ionis and Vaxxinity outside the submitted work. O.B. reported receiving personal fees from Adx NeuroSciences outside the submitted work. D.A. reported receiving personal fees for advisory board services and/or speaker honoraria from Fujirebio-Europe, Roche, Nutricia, Krka Farmacéutica and Esteve, outside the submitted work. A.L. has served as a consultant or on advisory boards for Almirall, Fujirebio-Europe, Grifols, Eisai, Lilly, Novartis, Roche, Biogen and Nutricia, outside the submitted work. J.F. reported receiving personal fees for service on the advisory boards, adjudication committees or speaker honoraria from AC Immune, Adamed, Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, Laboratorios Carnot, Life Molecular Imaging, Lilly, Lundbeck, Perha, Roche and outside the submitted work. O.B., D.A., A.L. and J.F. report holding a patent for markers of synaptopathy in neurodegenerative disease (licensed to Adx, EPI8382175.0). The remaining authors declare no competing interests.

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Fortea, J., Pegueroles, J., Alcolea, D. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat Med (2024). https://doi.org/10.1038/s41591-024-02931-w

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

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Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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New study warns parents not to overdo attunement with their children.

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More parent child attunement is not always better.

Since the advent of the helicopter parent, trending parenting advice has pushed the idea that parents should stay in sync with their kids at all times. In contrast, a new study finds that higher parent-child synchrony may be a signal that something is not working in the interaction. More parental attunement does not imply a better relationship, but may instead reflect interaction issues.

Researchers from the University of Essex in the U.K. studied 140 families in order to measure parent-child dyad synchrony and its relationship to the attachment styles of the parents and children.

More Parent-Child Synchrony Is Not Better

The study has important implications for current parenting practices, which emphasize parental attunement—meaning a parent tunes in to what their child is feeling and needing, trying to get on their wavelength and achieve synchrony. For example, if a child is upset, a parent might comfort them in just the right way without the child even having to ask. High attunement is promoted as a way to ensure the child forms a secure attachment to their parent.

On social media, Vrtička notices a very strong emphasis on seeking high attunement under any circumstance in some parenting approaches. “This is in stark contrast to the optimal mid range model that was suggested fourteen years ago,” says Vrtička. According to the science-supported model, too much synchrony can lead to problems in parent-child interaction and child development in the same way that too little synchrony can. When parents are too attuned to their children, it becomes intrusive, inappropriate or overstimulating to the child. High synchrony may also be related to stress, as found in one study that measured salivary cortisol between interaction partners.

Parents And Children Solve Puzzles Together

For the study, children from Eastern Germany between ages 5 and 6 years old were paired with their biologically related mothers or fathers. The pairs (or dyads) were either instructed to solve a puzzle cooperatively or to relax and close their eyes.

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The researchers scored the dyads for both behavioral and brain synchrony. Functional near infrared spectroscopy measured brain activity from both parents and children in areas related to effortful attention regulation and perspective taking.

Regarding behavior, the dyads were scored on “how much turn-taking there was during the interaction,” says says study author Pascal Vrtička, an associate professor of psychology at the University of Essex, “which reflects how well the parents take their children's perspective into consideration.”

Attachment Styles

Attachment style refers to the way we connect to the people in relationships and impacts how comfortable we are getting close to others or how we react when they are not around. Based on our childhood experiences with our own parents, we form either a secure of an insecure attachment style. And insecure style correlated with mental health problems throughout adult life.

In order to understand how the attachment styles impacted the puzzle-solving scenario, the researchers used validated tools to assess the attachment styles of the parents, and the attachment representations of the children. By attachment representation, Vrtička explains that he means children’s thinking about the availability and responsiveness of their parents and their own capacity to elicit help when needed.

How Attachment Styles Impacted Synchrony

The team found no relationship between attachment styles and behavioral synchrony: parents and children did equally well with turn-taking while solving the puzzle regardless of whether their attachment representations were secure or insecure.

However, when mothers had an insecure attachment style, they had higher levels of brain-to-brain synchrony with their child. Meanwhile, these mothers did just as well with behavioral reciprocity as other mothers. “We think that where mothers are insecurely attached, their brains, together with their children’s brains might need to work harder to get to the same level of behavioral synchrony, especially in the regulatory attention area,” says Vrtička. “It required more attention, effort and regulatory effort from both of them.”

Differences Between Mothers And Fathers

Mother-child dyads scored higher than father-child dyads for behavioral reciprocity. “With fathers there was less of a give and take. We saw more of one of them taking the lead for extended amounts of time and one being rather disengaged,” says Vrtička.

The team concluded that when one element of the pair is less in sync, another element must compensate by showing higher synchrony. So father-child dyads had higher brain synchrony as compared to mother-child dyads because their behavior was less in sync. “Overall, there is an optimum amount of synchrony that enables the interaction to to actually happen and to function efficiently,” says Vrtička.

Optimum parent child synchrony is context dependent

Children thrive when parents are comfortable enough to both attune to their emotions and needs, and to give them autonomy. “Optimum synchrony needs to be context dependent, and tailor to the relationship and to the interaction,” says Vrtička. “What really contributes to positive child development and a secure attachment is when the parent can take the needs of the child into perspective and act upon that.”

In one example, Vrtička explains that the popular trend of baby wearing in order to promote attachment can backfire. When parents are told they should be very close to baby all the time, even co-sleeping, it pushes their behavioral synchrony with the baby to a very high level. But is this actually appropriate for the baby? "To have high synchrony all the time, regardless of the circumstances, might be detrimental and actually lead to insecure attachment and relationship problems,” he says.

The danger of parenting styles that emphasize high synchrony at all times is that they leave no room for one of the most important learning experiences a child can have: rupture and repair. “No interaction is perfect, right? Many interactions consist of rupture and repair cycles. And that's exactly where children learn the most because that's when they need the parent as an external co-regulator, which has been shown to be very important for the development of a secure attachment,” says Vrtička. Healthy relationships show a fluctuation of engagement, disengagement, high synchrony, low synchrony, mistakes and apologies. And these cycles give children space to learn.

Future Research

In their next investigations, the research team will look synchrony within families with neurodivergent children as well as in adopted children. Ultimately, their hope is to further clarify what an optimal range of synchrony looks like, in order to help parents form healthy relationships with their children.

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