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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

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Research Article

Recent quantitative research on determinants of health in high income countries: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

Roles Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

• Vladimira Varbanova, 
• Philippe Beutels

• Published: September 17, 2020
• https://doi.org/10.1371/journal.pone.0239031
• Peer Review
• Reader Comments

Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.

We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013—July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.

Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.

Conclusions

We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants’ influence.

Citation: Varbanova V, Beutels P (2020) Recent quantitative research on determinants of health in high income countries: A scoping review. PLoS ONE 15(9): e0239031. https://doi.org/10.1371/journal.pone.0239031

Editor: Amir Radfar, University of Central Florida, UNITED STATES

Received: November 14, 2019; Accepted: August 28, 2020; Published: September 17, 2020

Copyright: © 2020 Varbanova, Beutels. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study (and VV) is funded by the Research Foundation Flanders ( https://www.fwo.be/en/ ), FWO project number G0D5917N, award obtained by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Identifying the key drivers of population health is a core subject in public health and health economics research. Between-country comparative research on the topic is challenging. In order to be relevant for policy, it requires disentangling different interrelated drivers of “good health”, each having different degrees of importance in different contexts.

“Good health”–physical and psychological, subjective and objective–can be defined and measured using a variety of approaches, depending on which aspect of health is the focus. A major distinction can be made between health measurements at the individual level or some aggregate level, such as a neighborhood, a region or a country. In view of this, a great diversity of specific research topics exists on the drivers of what constitutes individual or aggregate “good health”, including those focusing on health inequalities, the gender gap in longevity, and regional mortality and longevity differences.

The current scoping review focuses on determinants of population health. Stated as such, this topic is quite broad. Indeed, we are interested in the very general question of what methods have been used to make the most of increasingly available region or country-specific databases to understand the drivers of population health through inter-country comparisons. Existing reviews indicate that researchers thus far tend to adopt a narrower focus. Usually, attention is given to only one health outcome at a time, with further geographical and/or population [ 1 , 2 ] restrictions. In some cases, the impact of one or more interventions is at the core of the review [ 3 – 7 ], while in others it is the relationship between health and just one particular predictor, e.g., income inequality, access to healthcare, government mechanisms [ 8 – 13 ]. Some relatively recent reviews on the subject of social determinants of health [ 4 – 6 , 14 – 17 ] have considered a number of indicators potentially influencing health as opposed to a single one. One review defines “social determinants” as “the social, economic, and political conditions that influence the health of individuals and populations” [ 17 ] while another refers even more broadly to “the factors apart from medical care” [ 15 ].

In the present work, we aimed to be more inclusive, setting no limitations on the nature of possible health correlates, as well as making use of a multitude of commonly accepted measures of general population health. The goal of this scoping review was to document the state of the art in the recent published literature on determinants of population health, with a particular focus on the types of determinants selected and the methodology used. In doing so, we also report the main characteristics of the results these studies found. The materials collected in this review are intended to inform our (and potentially other researchers’) future analyses on this topic. Since the production of health is subject to the law of diminishing marginal returns, we focused our review on those studies that included countries where a high standard of wealth has been achieved for some time, i.e., high-income countries belonging to the Organisation for Economic Co-operation and Development (OECD) or Europe. Adding similar reviews for other country income groups is of limited interest to the research we plan to do in this area.

In view of its focus on data and methods, rather than results, a formal protocol was not registered prior to undertaking this review, but the procedure followed the guidelines of the PRISMA statement for scoping reviews [ 18 ].

We focused on multi-country studies investigating the potential associations between any aggregate level (region/city/country) determinant and general measures of population health (e.g., life expectancy, mortality rate).

Within the query itself, we listed well-established population health indicators as well as the six world regions, as defined by the World Health Organization (WHO). We searched only in the publications’ titles in order to keep the number of hits manageable, and the ratio of broadly relevant abstracts over all abstracts in the order of magnitude of 10% (based on a series of time-focused trial runs). The search strategy was developed iteratively between the two authors and is presented in S1 Appendix . The search was performed by VV in PubMed and Web of Science on the 16 th of July, 2019, without any language restrictions, and with a start date set to the 1 st of January, 2013, as we were interested in the latest developments in this area of research.

Eligibility criteria

Records obtained via the search methods described above were screened independently by the two authors. Consistency between inclusion/exclusion decisions was approximately 90% and the 43 instances where uncertainty existed were judged through discussion. Articles were included subject to meeting the following requirements: (a) the paper was a full published report of an original empirical study investigating the impact of at least one aggregate level (city/region/country) factor on at least one health indicator (or self-reported health) of the general population (the only admissible “sub-populations” were those based on gender and/or age); (b) the study employed statistical techniques (calculating correlations, at the very least) and was not purely descriptive or theoretical in nature; (c) the analysis involved at least two countries or at least two regions or cities (or another aggregate level) in at least two different countries; (d) the health outcome was not differentiated according to some socio-economic factor and thus studied in terms of inequality (with the exception of gender and age differentiations); (e) mortality, in case it was one of the health indicators under investigation, was strictly “total” or “all-cause” (no cause-specific or determinant-attributable mortality).

Data extraction

The following pieces of information were extracted in an Excel table from the full text of each eligible study (primarily by VV, consulting with PB in case of doubt): health outcome(s), determinants, statistical methodology, level of analysis, results, type of data, data sources, time period, countries. The evidence is synthesized according to these extracted data (often directly reflected in the section headings), using a narrative form accompanied by a “summary-of-findings” table and a graph.

Search and selection

The initial yield contained 4583 records, reduced to 3686 after removal of duplicates ( Fig 1 ). Based on title and abstract screening, 3271 records were excluded because they focused on specific medical condition(s) or specific populations (based on morbidity or some other factor), dealt with intervention effectiveness, with theoretical or non-health related issues, or with animals or plants. Of the remaining 415 papers, roughly half were disqualified upon full-text consideration, mostly due to using an outcome not of interest to us (e.g., health inequality), measuring and analyzing determinants and outcomes exclusively at the individual level, performing analyses one country at a time, employing indices that are a mixture of both health indicators and health determinants, or not utilizing potential health determinants at all. After this second stage of the screening process, 202 papers were deemed eligible for inclusion. This group was further dichotomized according to level of economic development of the countries or regions under study, using membership of the OECD or Europe as a reference “cut-off” point. Sixty papers were judged to include high-income countries, and the remaining 142 included either low- or middle-income countries or a mix of both these levels of development. The rest of this report outlines findings in relation to high-income countries only, reflecting our own primary research interests. Nonetheless, we chose to report our search yield for the other income groups for two reasons. First, to gauge the relative interest in applied published research for these different income levels; and second, to enable other researchers with a focus on determinants of health in other countries to use the extraction we made here.

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https://doi.org/10.1371/journal.pone.0239031.g001

Health outcomes

The most frequent population health indicator, life expectancy (LE), was present in 24 of the 60 studies. Apart from “life expectancy at birth” (representing the average life-span a newborn is expected to have if current mortality rates remain constant), also called “period LE” by some [ 19 , 20 ], we encountered as well LE at 40 years of age [ 21 ], at 60 [ 22 ], and at 65 [ 21 , 23 , 24 ]. In two papers, the age-specificity of life expectancy (be it at birth or another age) was not stated [ 25 , 26 ].

Some studies considered male and female LE separately [ 21 , 24 , 25 , 27 – 33 ]. This consideration was also often observed with the second most commonly used health index [ 28 – 30 , 34 – 38 ]–termed “total”, or “overall”, or “all-cause”, mortality rate (MR)–included in 22 of the 60 studies. In addition to gender, this index was also sometimes broken down according to age group [ 30 , 39 , 40 ], as well as gender-age group [ 38 ].

While the majority of studies under review here focused on a single health indicator, 23 out of the 60 studies made use of multiple outcomes, although these outcomes were always considered one at a time, and sometimes not all of them fell within the scope of our review. An easily discernable group of indices that typically went together [ 25 , 37 , 41 ] was that of neonatal (deaths occurring within 28 days postpartum), perinatal (fetal or early neonatal / first-7-days deaths), and post-neonatal (deaths between the 29 th day and completion of one year of life) mortality. More often than not, these indices were also accompanied by “stand-alone” indicators, such as infant mortality (deaths within the first year of life; our third most common index found in 16 of the 60 studies), maternal mortality (deaths during pregnancy or within 42 days of termination of pregnancy), and child mortality rates. Child mortality has conventionally been defined as mortality within the first 5 years of life, thus often also called “under-5 mortality”. Nonetheless, Pritchard & Wallace used the term “child mortality” to denote deaths of children younger than 14 years [ 42 ].

As previously stated, inclusion criteria did allow for self-reported health status to be used as a general measure of population health. Within our final selection of studies, seven utilized some form of subjective health as an outcome variable [ 25 , 43 – 48 ]. Additionally, the Health Human Development Index [ 49 ], healthy life expectancy [ 50 ], old-age survival [ 51 ], potential years of life lost [ 52 ], and disability-adjusted life expectancy [ 25 ] were also used.

We note that while in most cases the indicators mentioned above (and/or the covariates considered, see below) were taken in their absolute or logarithmic form, as a—typically annual—number, sometimes they were used in the form of differences, change rates, averages over a given time period, or even z-scores of rankings [ 19 , 22 , 40 , 42 , 44 , 53 – 57 ].

Regions, countries, and populations

Despite our decision to confine this review to high-income countries, some variation in the countries and regions studied was still present. Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations–based on the instances where these were also explicitly listed—were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [ 30 , 45 , 54 ] or inconsistency [ 20 , 58 ], Gross Domestic Product (GDP) too low [ 40 ], differences in economic development and political stability with the rest of the sampled countries [ 59 ], and national population too small [ 24 , 40 ]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [ 60 ], similar healthcare systems [ 23 ], and being randomly drawn from a social spending category [ 61 ]. Some researchers were interested explicitly in a specific geographical region, such as Eastern Europe [ 50 ], Central and Eastern Europe [ 48 , 60 ], the Visegrad (V4) group [ 62 ], or the Asia/Pacific area [ 32 ]. In certain instances, national regions or cities, rather than countries, constituted the units of investigation instead [ 31 , 51 , 56 , 62 – 66 ]. In two particular cases, a mix of countries and cities was used [ 35 , 57 ]. In another two [ 28 , 29 ], due to the long time periods under study, some of the included countries no longer exist. Finally, besides “European” and “OECD”, the terms “developed”, “Western”, and “industrialized” were also used to describe the group of selected nations [ 30 , 42 , 52 , 53 , 67 ].

As stated above, it was the health status of the general population that we were interested in, and during screening we made a concerted effort to exclude research using data based on a more narrowly defined group of individuals. All studies included in this review adhere to this general rule, albeit with two caveats. First, as cities (even neighborhoods) were the unit of analysis in three of the studies that made the selection [ 56 , 64 , 65 ], the populations under investigation there can be more accurately described as general urban , instead of just general. Second, oftentimes health indicators were stratified based on gender and/or age, therefore we also admitted one study that, due to its specific research question, focused on men and women of early retirement age [ 35 ] and another that considered adult males only [ 68 ].

Data types and sources

A great diversity of sources was utilized for data collection purposes. The accessible reference databases of the OECD ( https://www.oecd.org/ ), WHO ( https://www.who.int/ ), World Bank ( https://www.worldbank.org/ ), United Nations ( https://www.un.org/en/ ), and Eurostat ( https://ec.europa.eu/eurostat ) were among the top choices. The other international databases included Human Mortality [ 30 , 39 , 50 ], Transparency International [ 40 , 48 , 50 ], Quality of Government [ 28 , 69 ], World Income Inequality [ 30 ], International Labor Organization [ 41 ], International Monetary Fund [ 70 ]. A number of national databases were referred to as well, for example the US Bureau of Statistics [ 42 , 53 ], Korean Statistical Information Services [ 67 ], Statistics Canada [ 67 ], Australian Bureau of Statistics [ 67 ], and Health New Zealand Tobacco control and Health New Zealand Food and Nutrition [ 19 ]. Well-known surveys, such as the World Values Survey [ 25 , 55 ], the European Social Survey [ 25 , 39 , 44 ], the Eurobarometer [ 46 , 56 ], the European Value Survey [ 25 ], and the European Statistics of Income and Living Condition Survey [ 43 , 47 , 70 ] were used as data sources, too. Finally, in some cases [ 25 , 28 , 29 , 35 , 36 , 41 , 69 ], built-for-purpose datasets from previous studies were re-used.

In most of the studies, the level of the data (and analysis) was national. The exceptions were six papers that dealt with Nomenclature of Territorial Units of Statistics (NUTS2) regions [ 31 , 62 , 63 , 66 ], otherwise defined areas [ 51 ] or cities [ 56 ], and seven others that were multilevel designs and utilized both country- and region-level data [ 57 ], individual- and city- or country-level [ 35 ], individual- and country-level [ 44 , 45 , 48 ], individual- and neighborhood-level [ 64 ], and city-region- (NUTS3) and country-level data [ 65 ]. Parallel to that, the data type was predominantly longitudinal, with only a few studies using purely cross-sectional data [ 25 , 33 , 43 , 45 – 48 , 50 , 62 , 67 , 68 , 71 , 72 ], albeit in four of those [ 43 , 48 , 68 , 72 ] two separate points in time were taken (thus resulting in a kind of “double cross-section”), while in another the averages across survey waves were used [ 56 ].

In studies using longitudinal data, the length of the covered time periods varied greatly. Although this was almost always less than 40 years, in one study it covered the entire 20 th century [ 29 ]. Longitudinal data, typically in the form of annual records, was sometimes transformed before usage. For example, some researchers considered data points at 5- [ 34 , 36 , 49 ] or 10-year [ 27 , 29 , 35 ] intervals instead of the traditional 1, or took averages over 3-year periods [ 42 , 53 , 73 ]. In one study concerned with the effect of the Great Recession all data were in a “recession minus expansion change in trends”-form [ 57 ]. Furthermore, there were a few instances where two different time periods were compared to each other [ 42 , 53 ] or when data was divided into 2 to 4 (possibly overlapping) periods which were then analyzed separately [ 24 , 26 , 28 , 29 , 31 , 65 ]. Lastly, owing to data availability issues, discrepancies between the time points or periods of data on the different variables were occasionally observed [ 22 , 35 , 42 , 53 – 55 , 63 ].

Health determinants

Together with other essential details, Table 1 lists the health correlates considered in the selected studies. Several general categories for these correlates can be discerned, including health care, political stability, socio-economics, demographics, psychology, environment, fertility, life-style, culture, labor. All of these, directly or implicitly, have been recognized as holding importance for population health by existing theoretical models of (social) determinants of health [ 74 – 77 ].

https://doi.org/10.1371/journal.pone.0239031.t001

It is worth noting that in a few studies there was just a single aggregate-level covariate investigated in relation to a health outcome of interest to us. In one instance, this was life satisfaction [ 44 ], in another–welfare system typology [ 45 ], but also gender inequality [ 33 ], austerity level [ 70 , 78 ], and deprivation [ 51 ]. Most often though, attention went exclusively to GDP [ 27 , 29 , 46 , 57 , 65 , 71 ]. It was often the case that research had a more particular focus. Among others, minimum wages [ 79 ], hospital payment schemes [ 23 ], cigarette prices [ 63 ], social expenditure [ 20 ], residents’ dissatisfaction [ 56 ], income inequality [ 30 , 69 ], and work leave [ 41 , 58 ] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators.

We visualized the combinations in which the different determinants have been studied in Fig 2 , which was obtained via multidimensional scaling and a subsequent cluster analysis (details outlined in S2 Appendix ). It depicts the spatial positioning of each determinant relative to all others, based on the number of times the effects of each pair of determinants have been studied simultaneously. When interpreting Fig 2 , one should keep in mind that determinants marked with an asterisk represent, in fact, collectives of variables.

Groups of determinants are marked by asterisks (see S1 Table in S1 Appendix ). Diminishing color intensity reflects a decrease in the total number of “connections” for a given determinant. Noteworthy pairwise “connections” are emphasized via lines (solid-dashed-dotted indicates decreasing frequency). Grey contour lines encircle groups of variables that were identified via cluster analysis. Abbreviations: age = population age distribution, associations = membership in associations, AT-index = atherogenic-thrombogenic index, BR = birth rate, CAPB = Cyclically Adjusted Primary Balance, civilian-labor = civilian labor force, C-section = Cesarean delivery rate, credit-info = depth of credit information, dissatisf = residents’ dissatisfaction, distrib.orient = distributional orientation, EDU = education, eHealth = eHealth index at GP-level, exch.rate = exchange rate, fat = fat consumption, GDP = gross domestic product, GFCF = Gross Fixed Capital Formation/Creation, GH-gas = greenhouse gas, GII = gender inequality index, gov = governance index, gov.revenue = government revenues, HC-coverage = healthcare coverage, HE = health(care) expenditure, HHconsump = household consumption, hosp.beds = hospital beds, hosp.payment = hospital payment scheme, hosp.stay = length of hospital stay, IDI = ICT development index, inc.ineq = income inequality, industry-labor = industrial labor force, infant-sex = infant sex ratio, labor-product = labor production, LBW = low birth weight, leave = work leave, life-satisf = life satisfaction, M-age = maternal age, marginal-tax = marginal tax rate, MDs = physicians, mult.preg = multiple pregnancy, NHS = Nation Health System, NO = nitrous oxide emissions, PM10 = particulate matter (PM10) emissions, pop = population size, pop.density = population density, pre-term = pre-term birth rate, prison = prison population, researchE = research&development expenditure, school.ref = compulsory schooling reform, smoke-free = smoke-free places, SO = sulfur oxide emissions, soc.E = social expenditure, soc.workers = social workers, sugar = sugar consumption, terror = terrorism, union = union density, UR = unemployment rate, urban = urbanization, veg-fr = vegetable-and-fruit consumption, welfare = welfare regime, Wwater = wastewater treatment.

https://doi.org/10.1371/journal.pone.0239031.g002

Distances between determinants in Fig 2 are indicative of determinants’ “connectedness” with each other. While the statistical procedure called for higher dimensionality of the model, for demonstration purposes we show here a two-dimensional solution. This simplification unfortunately comes with a caveat. To use the factor smoking as an example, it would appear it stands at a much greater distance from GDP than it does from alcohol. In reality however, smoking was considered together with alcohol consumption [ 21 , 25 , 26 , 52 , 68 ] in just as many studies as it was with GDP [ 21 , 25 , 26 , 52 , 59 ], five. To aid with respect to this apparent shortcoming, we have emphasized the strongest pairwise links. Solid lines connect GDP with health expenditure (HE), unemployment rate (UR), and education (EDU), indicating that the effect of GDP on health, taking into account the effects of the other three determinants as well, was evaluated in between 12 to 16 studies of the 60 included in this review. Tracing the dashed lines, we can also tell that GDP appeared jointly with income inequality, and HE together with either EDU or UR, in anywhere between 8 to 10 of our selected studies. Finally, some weaker but still worth-mentioning “connections” between variables are displayed as well via the dotted lines.

The fact that all notable pairwise “connections” are concentrated within a relatively small region of the plot may be interpreted as low overall “connectedness” among the health indicators studied. GDP is the most widely investigated determinant in relation to general population health. Its total number of “connections” is disproportionately high (159) compared to its runner-up–HE (with 113 “connections”), and then subsequently EDU (with 90) and UR (with 86). In fact, all of these determinants could be thought of as outliers, given that none of the remaining factors have a total count of pairings above 52. This decrease in individual determinants’ overall “connectedness” can be tracked on the graph via the change of color intensity as we move outwards from the symbolic center of GDP and its closest “co-determinants”, to finally reach the other extreme of the ten indicators (welfare regime, household consumption, compulsory school reform, life satisfaction, government revenues, literacy, research expenditure, multiple pregnancy, Cyclically Adjusted Primary Balance, and residents’ dissatisfaction; in white) the effects on health of which were only studied in isolation.

Lastly, we point to the few small but stable clusters of covariates encircled by the grey bubbles on Fig 2 . These groups of determinants were identified as “close” by both statistical procedures used for the production of the graph (see details in S2 Appendix ).

Statistical methodology

There was great variation in the level of statistical detail reported. Some authors provided too vague a description of their analytical approach, necessitating some inference in this section.

The issue of missing data is a challenging reality in this field of research, but few of the studies under review (12/60) explain how they dealt with it. Among the ones that do, three general approaches to handling missingness can be identified, listed in increasing level of sophistication: case-wise deletion, i.e., removal of countries from the sample [ 20 , 45 , 48 , 58 , 59 ], (linear) interpolation [ 28 , 30 , 34 , 58 , 59 , 63 ], and multiple imputation [ 26 , 41 , 52 ].

Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [ 33 , 42 – 44 , 46 , 53 , 57 , 61 ]. Among the more advanced statistical methods, the family of regression models proved to be, by and large, predominant. Before examining this closer, we note the techniques that were, in a way, “unique” within this selection of studies: meta-analyses were performed (random and fixed effects, respectively) on the reduced form and 2-sample two stage least squares (2SLS) estimations done within countries [ 39 ]; difference-in-difference (DiD) analysis was applied in one case [ 23 ]; dynamic time-series methods, among which co-integration, impulse-response function (IRF), and panel vector autoregressive (VAR) modeling, were utilized in one study [ 80 ]; longitudinal generalized estimating equation (GEE) models were developed on two occasions [ 70 , 78 ]; hierarchical Bayesian spatial models [ 51 ] and special autoregressive regression [ 62 ] were also implemented.

Purely cross-sectional data analyses were performed in eight studies [ 25 , 45 , 47 , 50 , 55 , 56 , 67 , 71 ]. These consisted of linear regression (assumed ordinary least squares (OLS)), generalized least squares (GLS) regression, and multilevel analyses. However, six other studies that used longitudinal data in fact had a cross-sectional design, through which they applied regression at multiple time-points separately [ 27 , 29 , 36 , 48 , 68 , 72 ].

Apart from these “multi-point cross-sectional studies”, some other simplistic approaches to longitudinal data analysis were found, involving calculating and regressing 3-year averages of both the response and the predictor variables [ 54 ], taking the average of a few data-points (i.e., survey waves) [ 56 ] or using difference scores over 10-year [ 19 , 29 ] or unspecified time intervals [ 40 , 55 ].

Moving further in the direction of more sensible longitudinal data usage, we turn to the methods widely known among (health) economists as “panel data analysis” or “panel regression”. Most often seen were models with fixed effects for country/region and sometimes also time-point (occasionally including a country-specific trend as well), with robust standard errors for the parameter estimates to take into account correlations among clustered observations [ 20 , 21 , 24 , 28 , 30 , 32 , 34 , 37 , 38 , 41 , 52 , 59 , 60 , 63 , 66 , 69 , 73 , 79 , 81 , 82 ]. The Hausman test [ 83 ] was sometimes mentioned as the tool used to decide between fixed and random effects [ 26 , 49 , 63 , 66 , 73 , 82 ]. A few studies considered the latter more appropriate for their particular analyses, with some further specifying that (feasible) GLS estimation was employed [ 26 , 34 , 49 , 58 , 60 , 73 ]. Apart from these two types of models, the first differences method was encountered once as well [ 31 ]. Across all, the error terms were sometimes assumed to come from a first-order autoregressive process (AR(1)), i.e., they were allowed to be serially correlated [ 20 , 30 , 38 , 58 – 60 , 73 ], and lags of (typically) predictor variables were included in the model specification, too [ 20 , 21 , 37 , 38 , 48 , 69 , 81 ]. Lastly, a somewhat different approach to longitudinal data analysis was undertaken in four studies [ 22 , 35 , 48 , 65 ] in which multilevel–linear or Poisson–models were developed.

Regardless of the exact techniques used, most studies included in this review presented multiple model applications within their main analysis. None attempted to formally compare models in order to identify the “best”, even if goodness-of-fit statistics were occasionally reported. As indicated above, many studies investigated women’s and men’s health separately [ 19 , 21 , 22 , 27 – 29 , 31 , 33 , 35 , 36 , 38 , 39 , 45 , 50 , 51 , 64 , 65 , 69 , 82 ], and covariates were often tested one at a time, including other covariates only incrementally [ 20 , 25 , 28 , 36 , 40 , 50 , 55 , 67 , 73 ]. Furthermore, there were a few instances where analyses within countries were performed as well [ 32 , 39 , 51 ] or where the full time period of interest was divided into a few sub-periods [ 24 , 26 , 28 , 31 ]. There were also cases where different statistical techniques were applied in parallel [ 29 , 55 , 60 , 66 , 69 , 73 , 82 ], sometimes as a form of sensitivity analysis [ 24 , 26 , 30 , 58 , 73 ]. However, the most common approach to sensitivity analysis was to re-run models with somewhat different samples [ 39 , 50 , 59 , 67 , 69 , 80 , 82 ]. Other strategies included different categorization of variables or adding (more/other) controls [ 21 , 23 , 25 , 28 , 37 , 50 , 63 , 69 ], using an alternative main covariate measure [ 59 , 82 ], including lags for predictors or outcomes [ 28 , 30 , 58 , 63 , 65 , 79 ], using weights [ 24 , 67 ] or alternative data sources [ 37 , 69 ], or using non-imputed data [ 41 ].

As the methods and not the findings are the main focus of the current review, and because generic checklists cannot discern the underlying quality in this application field (see also below), we opted to pool all reported findings together, regardless of individual study characteristics or particular outcome(s) used, and speak generally of positive and negative effects on health. For this summary we have adopted the 0.05-significance level and only considered results from multivariate analyses. Strictly birth-related factors are omitted since these potentially only relate to the group of infant mortality indicators and not to any of the other general population health measures.

Starting with the determinants most often studied, higher GDP levels [ 21 , 26 , 27 , 29 , 30 , 32 , 43 , 48 , 52 , 58 , 60 , 66 , 67 , 73 , 79 , 81 , 82 ], higher health [ 21 , 37 , 47 , 49 , 52 , 58 , 59 , 68 , 72 , 82 ] and social [ 20 , 21 , 26 , 38 , 79 ] expenditures, higher education [ 26 , 39 , 52 , 62 , 72 , 73 ], lower unemployment [ 60 , 61 , 66 ], and lower income inequality [ 30 , 42 , 53 , 55 , 73 ] were found to be significantly associated with better population health on a number of occasions. In addition to that, there was also some evidence that democracy [ 36 ] and freedom [ 50 ], higher work compensation [ 43 , 79 ], distributional orientation [ 54 ], cigarette prices [ 63 ], gross national income [ 22 , 72 ], labor productivity [ 26 ], exchange rates [ 32 ], marginal tax rates [ 79 ], vaccination rates [ 52 ], total fertility [ 59 , 66 ], fruit and vegetable [ 68 ], fat [ 52 ] and sugar consumption [ 52 ], as well as bigger depth of credit information [ 22 ] and percentage of civilian labor force [ 79 ], longer work leaves [ 41 , 58 ], more physicians [ 37 , 52 , 72 ], nurses [ 72 ], and hospital beds [ 79 , 82 ], and also membership in associations, perceived corruption and societal trust [ 48 ] were beneficial to health. Higher nitrous oxide (NO) levels [ 52 ], longer average hospital stay [ 48 ], deprivation [ 51 ], dissatisfaction with healthcare and the social environment [ 56 ], corruption [ 40 , 50 ], smoking [ 19 , 26 , 52 , 68 ], alcohol consumption [ 26 , 52 , 68 ] and illegal drug use [ 68 ], poverty [ 64 ], higher percentage of industrial workers [ 26 ], Gross Fixed Capital creation [ 66 ] and older population [ 38 , 66 , 79 ], gender inequality [ 22 ], and fertility [ 26 , 66 ] were detrimental.

It is important to point out that the above-mentioned effects could not be considered stable either across or within studies. Very often, statistical significance of a given covariate fluctuated between the different model specifications tried out within the same study [ 20 , 49 , 59 , 66 , 68 , 69 , 73 , 80 , 82 ], testifying to the importance of control variables and multivariate research (i.e., analyzing multiple independent variables simultaneously) in general. Furthermore, conflicting results were observed even with regards to the “core” determinants given special attention, so to speak, throughout this text. Thus, some studies reported negative effects of health expenditure [ 32 , 82 ], social expenditure [ 58 ], GDP [ 49 , 66 ], and education [ 82 ], and positive effects of income inequality [ 82 ] and unemployment [ 24 , 31 , 32 , 52 , 66 , 68 ]. Interestingly, one study [ 34 ] differentiated between temporary and long-term effects of GDP and unemployment, alluding to possibly much greater complexity of the association with health. It is also worth noting that some gender differences were found, with determinants being more influential for males than for females, or only having statistically significant effects for male health [ 19 , 21 , 28 , 34 , 36 , 37 , 39 , 64 , 65 , 69 ].

The purpose of this scoping review was to examine recent quantitative work on the topic of multi-country analyses of determinants of population health in high-income countries.

Measuring population health via relatively simple mortality-based indicators still seems to be the state of the art. What is more, these indicators are routinely considered one at a time, instead of, for example, employing existing statistical procedures to devise a more general, composite, index of population health, or using some of the established indices, such as disability-adjusted life expectancy (DALE) or quality-adjusted life expectancy (QALE). Although strong arguments for their wider use were already voiced decades ago [ 84 ], such summary measures surface only rarely in this research field.

On a related note, the greater data availability and accessibility that we enjoy today does not automatically equate to data quality. Nonetheless, this is routinely assumed in aggregate level studies. We almost never encountered a discussion on the topic. The non-mundane issue of data missingness, too, goes largely underappreciated. With all recent methodological advancements in this area [ 85 – 88 ], there is no excuse for ignorance; and still, too few of the reviewed studies tackled the matter in any adequate fashion.

Much optimism can be gained considering the abundance of different determinants that have attracted researchers’ attention in relation to population health. We took on a visual approach with regards to these determinants and presented a graph that links spatial distances between determinants with frequencies of being studies together. To facilitate interpretation, we grouped some variables, which resulted in some loss of finer detail. Nevertheless, the graph is helpful in exemplifying how many effects continue to be studied in a very limited context, if any. Since in reality no factor acts in isolation, this oversimplification practice threatens to render the whole exercise meaningless from the outset. The importance of multivariate analysis cannot be stressed enough. While there is no “best method” to be recommended and appropriate techniques vary according to the specifics of the research question and the characteristics of the data at hand [ 89 – 93 ], in the future, in addition to abandoning simplistic univariate approaches, we hope to see a shift from the currently dominating fixed effects to the more flexible random/mixed effects models [ 94 ], as well as wider application of more sophisticated methods, such as principle component regression, partial least squares, covariance structure models (e.g., structural equations), canonical correlations, time-series, and generalized estimating equations.

Finally, there are some limitations of the current scoping review. We searched the two main databases for published research in medical and non-medical sciences (PubMed and Web of Science) since 2013, thus potentially excluding publications and reports that are not indexed in these databases, as well as older indexed publications. These choices were guided by our interest in the most recent (i.e., the current state-of-the-art) and arguably the highest-quality research (i.e., peer-reviewed articles, primarily in indexed non-predatory journals). Furthermore, despite holding a critical stance with regards to some aspects of how determinants-of-health research is currently conducted, we opted out of formally assessing the quality of the individual studies included. The reason for that is two-fold. On the one hand, we are unaware of the existence of a formal and standard tool for quality assessment of ecological designs. And on the other, we consider trying to score the quality of these diverse studies (in terms of regional setting, specific topic, outcome indices, and methodology) undesirable and misleading, particularly since we would sometimes have been rating the quality of only a (small) part of the original studies—the part that was relevant to our review’s goal.

Our aim was to investigate the current state of research on the very broad and general topic of population health, specifically, the way it has been examined in a multi-country context. We learned that data treatment and analytical approach were, in the majority of these recent studies, ill-equipped or insufficiently transparent to provide clarity regarding the underlying mechanisms of population health in high-income countries. Whether due to methodological shortcomings or the inherent complexity of the topic, research so far fails to provide any definitive answers. It is our sincere belief that with the application of more advanced analytical techniques this continuous quest could come to fruition sooner.

Supporting information

S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0239031.s001

S1 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s002

S2 Appendix.

https://doi.org/10.1371/journal.pone.0239031.s003

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• Research article
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• Published: 06 January 2021

Effects of the COVID-19 pandemic on medical students: a multicenter quantitative study

• Aaron J. Harries   ORCID: orcid.org/0000-0001-7107-0995 1 ,
• Carmen Lee 1 ,
• Lee Jones 2 ,
• Robert M. Rodriguez 1 ,
• John A. Davis 2 ,
• Megan Boysen-Osborn 3 ,
• Kathleen J. Kashima 4 ,
• N. Kevin Krane 5 ,
• Guenevere Rae 6 ,
• Nicholas Kman 7 ,
• Jodi M. Langsfeld 8 &
• Marianne Juarez 1  

BMC Medical Education volume  21 , Article number:  14 ( 2021 ) Cite this article

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The COVID-19 pandemic disrupted the United States (US) medical education system with the necessary, yet unprecedented Association of American Medical Colleges (AAMC) national recommendation to pause all student clinical rotations with in-person patient care. This study is a quantitative analysis investigating the educational and psychological effects of the pandemic on US medical students and their reactions to the AAMC recommendation in order to inform medical education policy.

The authors sent a cross-sectional survey via email to medical students in their clinical training years at six medical schools during the initial peak phase of the COVID-19 pandemic. Survey questions aimed to evaluate students’ perceptions of COVID-19’s impact on medical education; ethical obligations during a pandemic; infection risk; anxiety and burnout; willingness and needed preparations to return to clinical rotations.

Seven hundred forty-one (29.5%) students responded. Nearly all students (93.7%) were not involved in clinical rotations with in-person patient contact at the time the study was conducted. Reactions to being removed were mixed, with 75.8% feeling this was appropriate, 34.7% guilty, 33.5% disappointed, and 27.0% relieved.

Most students (74.7%) agreed the pandemic had significantly disrupted their medical education, and believed they should continue with normal clinical rotations during this pandemic (61.3%). When asked if they would accept the risk of infection with COVID-19 if they returned to the clinical setting, 83.4% agreed.

Students reported the pandemic had moderate effects on their stress and anxiety levels with 84.1% of respondents feeling at least somewhat anxious. Adequate personal protective equipment (PPE) (53.5%) was the most important factor to feel safe returning to clinical rotations, followed by adequate testing for infection (19.3%) and antibody testing (16.2%).

Conclusions

The COVID-19 pandemic disrupted the education of US medical students in their clinical training years. The majority of students wanted to return to clinical rotations and were willing to accept the risk of COVID-19 infection. Students were most concerned with having enough PPE if allowed to return to clinical activities.

Peer Review reports

The COVID-19 pandemic has tested the limits of healthcare systems and challenged conventional practices in medical education. The rapid evolution of the pandemic dictated that critical decisions regarding the training of medical students in the United States (US) be made expeditiously, without significant input or guidance from the students themselves. On March 17, 2020, for the first time in modern US history, the Association of American Medical Colleges (AAMC), the largest national governing body of US medical schools, released guidance recommending that medical students immediately pause all clinical rotations to allow time to obtain additional information about the risks of COVID-19 and prepare for safe participation in the future. This decisive action would also conserve scarce resources such as personal protective equipment (PPE) and testing kits; minimize exposure of healthcare workers (HCWs) and the general population; and protect students’ education and wellbeing [ 1 ].

A similar precedent was set outside of the US during the SARS-CoV1 epidemic in 2003, where an initial cluster of infection in medical students in Hong Kong resulted in students being removed from hospital systems where SARS surfaced, including Hong Kong, Singapore and Toronto [ 2 , 3 ]. Later, studies demonstrated that the exclusion of Canadian students from those clinical environments resulted in frustration at lost learning opportunities and students’ inability to help [ 3 ]. International evidence also suggests that medical students perceive an ethical obligation to participate in pandemic response, and are willing to participate in scenarios similar to the current COVID-19 crisis, even when they believe the risk of infection to themselves to be high [ 4 , 5 , 6 ].

The sudden removal of some US medical students from educational settings has occurred previously in the wake of local disasters, with significant academic and personal impacts. In 2005, it was estimated that one-third of medical students experienced some degree of depression or post-traumatic stress disorder (PTSD) after Hurricane Katrina resulted in the closure of Tulane University School of Medicine [ 7 ].

Prior to the current COVID-19 pandemic, we found no studies investigating the effects of pandemics on the US medical education system or its students. The limited pool of evidence on medical student perceptions comes from two earlier global coronavirus surges, SARS and MERS, and studies of student anxiety related to pandemics are also limited to non-US populations [ 3 , 8 , 9 ]. Given the unprecedented nature of the current COVID-19 pandemic, there is concern that students may be missing out on meaningful educational experiences and months of clinical training with unknown effects on their current well-being or professional trajectory [ 10 ].

Our study, conducted during the initial peak phase of the COVID-19 pandemic, reports students’ perceptions of COVID-19’s impact on: medical student education; ethical obligations during a pandemic; perceptions of infection risk; anxiety and burnout; willingness to return to clinical rotations; and needed preparations to return safely. This data may help inform policies regarding the roles of medical students in clinical training during the current pandemic and prepare for the possibility of future pandemics.

We conducted a cross-sectional survey during the initial peak phase of the COVID-19 pandemic in the United States, from 4/20/20 to 5/25/20, via email sent to all clinically rotating medical students at six US medical schools: University of California San Francisco School of Medicine (San Francisco, CA), University of California Irvine School of Medicine (Irvine, CA), Tulane University School of Medicine (New Orleans, LA), University of Illinois College of Medicine (Chicago, Peoria, Rockford, and Urbana, IL), Ohio State University College of Medicine (Columbus, OH), and Zucker School of Medicine at Hofstra/Northwell (Hempstead, NY). Traditional undergraduate medical education in the US comprises 4 years of medical school with 2 years of primarily pre-clinical classroom learning followed by 2 years of clinical training involving direct patient care. Study participants were defined as medical students involved in their clinical training years at whom the AAMC guidance statement was directed. Depending on the curricular schedule of each medical school, this included intended graduation class years of 2020 (graduating 4th year student), 2021 (rising 4th year student), and 2022 (rising 3rd year student), exclusive of planned time off. Participating schools were specifically chosen to represent a broad spectrum of students from different regions of the country (West, South, Midwest, East) with variable COVID-19 prevalence. We excluded medical students not yet involved in clinical rotations. This study was deemed exempt by the respective Institutional Review Boards.

We developed a survey instrument modeled after a survey used in a previously published peer reviewed study evaluating the effects of the COVID-19 pandemic on Emergency Physicians, which incorporated items from validated stress scales [ 11 ]. The survey was modified for use in medical students to assess perceptions of the following domains: perceived impact on medical student education; ethical beliefs surrounding obligations to participate clinically during the pandemic; perceptions of personal infection risk; anxiety and burnout related to the pandemic; willingness to return to clinical rotations; and preparation needed for students to feel safe in the clinical environment. Once created, the survey underwent an iterative process of input and review from our team of authors with experience in survey methodology and psychometric measures to allow for optimization of content and validity. We tested a pilot of our preliminary instrument on five medical students to ensure question clarity, and confirm completion of the survey in approximately 10 min. The final survey consisted of 29 Likert, yes/no, multiple choice, and free response questions. Both medical school deans and student class representatives distributed the survey via email, with three follow-up emails to increase response rates. Data was collected anonymously.

For example, to assess the impact on students’ anxiety, participants were asked, “How much has the COVID-19 pandemic affected your stress or anxiety levels?” using a unipolar 7-point scale (1 = not at all, 4 = somewhat, 7 = extremely). To assess willingness to return to clinical rotations, participants were asked to rate on a bipolar scale (1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = neither disagree nor agree, 5 = somewhat agree, 6 = agree, and 7 = strongly agree) their agreement with the statement: “to the extent possible, medical students should continue with normal clinical rotations during this pandemic.” (Survey Instrument, Supplemental Table  1 ).

Survey data was managed using Qualtrics hosted by the University of California, San Francisco. For data analysis we used STATA v15.1 (Stata Corp, College Station, TX). We summarized respondent characteristics and key responses as raw counts, frequency percent, medians and interquartile ranges (IQR). For responses to bipolar questions, we combined positive responses (somewhat agree, agree, or strongly agree) into an agreement percentage. To compare differences in medians we used a signed rank test with p value < 0.05 to show statistical difference. In a secondary analysis we stratified data to compare questions within key domains amongst the following sub-groups: female versus male, graduation year, local community COVID-19 prevalence (high, medium, low), and students on clinical rotations with in-person patient care. This secondary analysis used a chi square test with p value < 0.05 to show statistical difference between sub-group agreement percentages.

Of 2511 students contacted, we received 741 responses (29.5% response rate). Of these, 63.9% of respondents were female and 35.1% were male, with 1.0% reporting a different gender identity; 27.7% of responses came from the class of 2020, 53.5% from the class of 2021, and 18.7% from the class of 2022. (Demographics, Table 1 ).

Most student respondents (74.9%) had a clinical rotation that was cut short or canceled due to COVID-19 and 93.7% reported not being involved in clinical rotations with in-person patient contact at the time of the study. Regarding students’ perceptions of cancelled rotations (allowing for multiple reactions), 75.8% felt this was appropriate, 34.7% felt guilty for not being able to help patients and colleagues, 33.5% felt disappointed, and 27.0% felt relieved.

Most students (74.7%) agreed that their medical education had been significantly disrupted by the pandemic. Students also felt they were able to find meaningful learning experiences during the pandemic (72.1%). Free response examples included: taking a novel COVID-19 pandemic elective course, telehealth patient care, clinical rotations transitioned to virtual online courses, research or education electives, clinical and non-clinical COVID-19-related volunteering, and self-guided independent study electives. Students felt their medical schools were doing everything they could to help students adjust (72.7%). Overall, respondents felt the pandemic had interfered with their ability to develop skills needed to prepare for residency (61.4%), though fewer (45.7%) felt it had interfered with their ability to apply to residency. (Educational Impact, Fig.  1 ).

Perceived educational impacts of the COVID-19 pandemic on medical students

A majority of medical students agreed they should be allowed to continue with normal clinical rotations during this pandemic (61.3%). Most students agreed (83.4%) that they accepted the risk of being infected with COVID-19, if they returned. When asked if students should be allowed to volunteer in clinical settings even if there is not a healthcare worker (HCW) shortage, 63.5% agreed; however, in the case of a HCW shortage only 19.5% believed students should be required to volunteer clinically. (Willingness to Participate Clinically, Fig.  2 ).

Willingness to participate clinically during the COVID-19 pandemic

When asked if they perceived a moral, ethical, or professional obligation for medical students to help, 37.8% agreed that medical students have such an obligation during the current pandemic. This is in contrast to their perceptions of physicians: 87.1% of students agreed with a physician obligation to help during the COVID-19 pandemic. For both groups, students were asked if this obligation persisted without adequate PPE: only 10.9% of students believed medical students had this obligation, while 34.0% agreed physicians had this obligation. (Ethical Obligation, Fig.  3 ).

Ethical obligation to volunteer during the COVID-19 pandemic

Given the assumption that there will not be a COVID-19 vaccine until 2021, students felt the single most important factor in a safe return to clinical rotations was having access to adequate PPE (53.3%), followed by adequate testing for infection (19.3%) and antibody testing for possible immunity (16.2%). Few students (5%) stated that nothing would make them feel comfortable until a vaccine is available. On a 1–7 scale (1 = not at all, 4 = somewhat, 7 = extremely), students felt somewhat prepared to use PPE during this pandemic in the clinical setting, median = 4 (IQR 4,6), and somewhat confident identifying symptoms most concerning for COVID-19, median = 4 (IQR 4,5). Students preferred to learn about PPE via video demonstration (76.7%), online modules (47.7%), and in-person or Zoom style conferences (44.7%).

Students believed they were likely to contract COVID-19 in general (75.6%), independent of a return to the clinical environment. Most respondents believed that missing some school or work would be a likely outcome (90.5%), and only a minority of students believed that hospitalization (22.1%) or death (4.3%) was slightly, moderately, or extremely likely.

On a 1–7 scale (1 = not at all, 4 = somewhat, and 7 = extremely), the median (IQR) reported effect of the COVID-19 pandemic on students’ stress or anxiety level was 5 (4, 6) with 84.1% of respondents feeling at least somewhat anxious due to the pandemic. Students’ perceived emotional exhaustion and burnout before the pandemic was a median = 2 (IQR 2,4) and since the pandemic started a median = 4 (IQR 2,5) with a median difference Δ = 2, p value < 0.001.

Secondary analysis of key questions revealed statistical differences between sub-groups. Women were significantly more likely than men to agree that the pandemic had affected their anxiety. Several significant differences existed for the class of 2020 when compared to the classes of 2021 and 2022: they were less likely to report disruptions to their education, to prefer to return to rotations, and to report an effect on anxiety. There were no significant differences with students who were still involved with in-person patient care compared with those who were not. In comparing areas with high COVID-19 prevalence at the time of the survey (New York and Louisiana) with medium (Illinois and Ohio) and low prevalence (California), students were less likely to report that the pandemic had disrupted their education. Students in low prevalence areas were most likely to agree that medical students should return to rotations. There were no differences between prevalence groups in accepting the risk of infection to return, or subjective anxiety effects. (Stratification, Table  2 ).

The COVID-19 pandemic has fundamentally transformed education at all levels - from preschool to postgraduate. Although changes to K-12 and college education have been well documented [ 12 , 13 ], there have been very few studies to date investigating the effects of COVID-19 on undergraduate medical education [ 14 ]. To maintain the delicate balance between student safety and wellbeing, and the time-sensitive need to train future physicians, student input must guide decisions regarding their roles in the clinical arena. Student concerns related to the pandemic, paired with their desire to return to rotations despite the risks, suggest that medical students may take on emotional burdens as members of the patient care team even when not present in the clinical environment. This study offers insight into how best to support medical students as they return to clinical rotations, how to prepare them for successful careers ahead, and how to plan for their potential roles in future pandemics.

Previous international studies of medical student attitudes towards hypothetical influenza-like pandemics demonstrated a willingness (80%) [ 4 ] and a perceived ethical obligation to volunteer (77 and 70%), despite 40% of Canadian students in one study perceiving a high likelihood of becoming infected [ 5 , 6 ]. Amidst the current COVID-19 pandemic, our participants reported less agreement with a medical student ethical obligation to volunteer in the clinical setting at 37.8%, but believed in a higher likelihood of becoming infected at 75.6%. Their willingness to be allowed to volunteer freely (63.5%) may suggest that the stresses of an ongoing pandemic alter students’ perceptions of the ethical requirement more than their willingness to help. Students overwhelmingly agreed that physicians had an ethical obligation to provide care during the COVID-19 pandemic (87.1%), possibly reflecting how they view the ethical transition from student to physician, or differences between paid professionals and paying for an education.

At the time our study was conducted, there were widespread concerns for possible HCW shortages. It was unclear whether medical students would be called to volunteer when residents became ill, or even graduate early to start residency training immediately (as occurred at half of schools surveyed). This timing allowed us to capture a truly unique perspective amongst medical students, a majority of whom reported increased anxiety and burnout due to the pandemic. At the same time, students felt that their medical schools were doing everything possible to support them, perhaps driven by virtual town halls and daily communication updates.

Trends in secondary analysis show important differences in the impacts of the pandemic. Women were more likely to report increased anxiety as compared to men, which may reflect broader gender differences in medical student anxiety [ 15 ] but requires more study to rule out different pandemic stresses by gender. Graduating medical students (class of 2020) overall described less impact on medical education and anxiety, a decreased desire to return to rotations, but equal acceptance of the risk of infection in clinical settings, possibly reflecting a focus on their upcoming intern year rather than the remaining months of undergraduate medical education. Since this class’s responses decreased overall agreement on these questions, educational impacts and anxiety effects may have been even greater had they been assessed further from graduation. Interestingly, students from areas with high local COVID-19 prevalence (New York and Louisiana) reported a less significant effect of the pandemic on their education, a paradoxical result that may indicate that medical student tolerance for the disruptions was greater in high-prevalence areas, as these students were removed at the same, if not higher, rates as their peers. Our results suggest that in future waves of the current pandemic or other disasters, students may be more patient with educational impacts when they have more immediate awareness of strains on the healthcare system.

A limitation of our study was the survey response rate, which was anticipated given the challenges students were facing. Some may not have been living near campus; others may have stopped reading emails due to early graduation or limited access to email; and some would likely be dealing with additional personal challenges related to the pandemic. We attempted to increase response rates by having the study sent directly from medical school deans and leadership, as well as respective class representatives, and by sending reminders for completion. The survey was not incentivized, and a higher response rate in the class of 2021 across all schools may indicate that students who felt their education was most affected were most likely to respond. We addressed this potential source of bias in the secondary analysis, which showed no differences between 2021 and 2022 respondents. Another limitation was the inherent issue with survey data collection of missing responses for some questions that occurred in a small number of surveys. This resulted in slight variability in the total responses received for certain questions, which were not statistically significant. To be transparent about this limitation, we presented our data by stating each total response and denominator in the Tables.

This initial study lays the groundwork for future investigations and next steps. With 72.1% of students agreeing that they were able to find meaningful learning in spite of the pandemic, future research should investigate novel learning modalities that were successful during this time. Educators should consider additional training on PPE use, given only moderate levels of student comfort in this area, which may be best received via video. It is also important to study the long-term effects of missing several months of essential clinical training and identifying competencies that may not have been achieved, since students perceived a significant disruption to their ability to prepare skills for residency. Next steps could be to study curriculum interventions, such as capstone boot camps and targeted didactic skills training, to help students feel more comfortable as they transition into residency. Educators must also acknowledge that some students may not feel comfortable returning to the clinical environment until a vaccine becomes available (5%) and ensure they are equally supported. Lastly, it is vital to further investigate the mental health effects of the pandemic on medical students, identifying subgroups with additional stressors, needs related to anxiety or possible PTSD, and ways to minimize these negative effects.

In this cross-sectional survey, conducted during the initial peak phase of the COVID-19 pandemic, we capture a snapshot of the effects of the pandemic on US medical students and gain insight into their reactions to the unprecedented AAMC national recommendation for removal from clinical rotations. Student respondents from across the US similarly recognized a significant disruption to their medical education, shared a desire to continue with in-person rotations, and were willing to accept the risk of infection with COVID-19. Our novel results provide a solid foundation to help shape medical student roles in the clinical environment during this pandemic and future outbreaks.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

The authors wish to thank Newton Addo, UCSF Statistician.

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Contributions

All authors made substantial contributions to the study and met the specific conditions listed in the BMC Medical Education editorial policy for authorship. All authors have read and approved the manuscript. AH as principal investigator contributed to study design, survey instrument creation, IRB submission for his respective medical school, acquisition of data and recruitment of other participating medical schools, data analysis, writing and editing the manuscript. CL contributed to background literature review, study design, survey instrument creation, acquisition of data, data analysis, writing and editing the manuscript. LJ contributed to study design, survey instrument creation, acquisition of data from his respective medical school and recruitment of other participating medical schools, data analysis, and editing the manuscript. RR contributed to study design, survey instrument creation, data analysis, writing and editing the manuscript. JD contributed to study design, survey instrument creation, recruitment of other participating medical schools, data analysis, and editing the manuscript. MBO contributed as individual site principal investigator obtaining IRB exemption acceptance and acquisition of data from her respective medical school along with editing the manuscript. KK contributed as individual site principal investigator obtaining IRB exemption acceptance and acquisition of data from her respective medical school along with editing the manuscript. NKK contributed as individual site co-principal investigator obtaining IRB exemption acceptance and acquisition of data from his respective medical school along with editing the manuscript. GR contributed as individual site co-principal investigator obtaining IRB exemption acceptance and acquisition of data from her respective medical school along with editing the manuscript. NK contributed as individual site principal investigator obtaining IRB exemption acceptance and acquisition of data from his respective medical school along with editing the manuscript. JL contributed as individual site principal investigator obtaining IRB exemption acceptance and acquisition of data from her respective medical school along with editing the manuscript. MJ contributed to study design, survey instrument creation, data analysis, writing and editing the manuscript.

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Correspondence to Aaron J. Harries or Marianne Juarez .

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This study was reviewed and deemed exempt by each participating medical school’s Institutional Review Board (IRB): University of California San Francisco School of Medicine, IRB# 20–30712, Reference# 280106, Tulane University School of Medicine, Reference # 2020–331, University of Illinois College of Medicine), IRB Protocol # 2012–0783, Ohio State University College of Medicine, Study ID# 2020E0463, Zucker School of Medicine at Hofstra/Northwell, Reference # 20200527-SOM-LAN-1, University of California Irvine School of Medicine, submitted self-exemption IRB form. In accordance with the IRB exemption approval, each survey participant received an email consent describing the study and their optional participation.

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Supplementary Information

Additional file 1: table s1..

Survey Instrument

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Harries, A.J., Lee, C., Jones, L. et al. Effects of the COVID-19 pandemic on medical students: a multicenter quantitative study. BMC Med Educ 21 , 14 (2021). https://doi.org/10.1186/s12909-020-02462-1

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Received : 29 July 2020

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DOI : https://doi.org/10.1186/s12909-020-02462-1

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Quantitative and Qualitative Research

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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• portal: Libraries and the Academy

Approaches for Improving Validity in Quantitative Research Articles

• Andrew D. Asher
• Johns Hopkins University Press
• Volume 24, Number 2, April 2024
• pp. 209-215
• 10.1353/pla.2024.a923703
• View Citation

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• Open access
• Published: 11 April 2024

METTL3 recruiting M2-type immunosuppressed macrophages by targeting m6A-SNAIL-CXCL2 axis to promote colorectal cancer pulmonary metastasis

• Peng Ouyang 1   na1 ,
• Kang Li 1   na1 ,
• Wei Xu 1   na1 ,
• Caiyun Chen 1 ,
• Yangdong Shi 1 ,
• Yao Tian 1 ,
• Jin Gong 1 &
• Zhen Bao 1  

Journal of Experimental & Clinical Cancer Research volume  43 , Article number:  111 ( 2024 ) Cite this article

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The regulatory role of N6-methyladenosine (m6A) modification in the onset and progression of cancer has garnered increasing attention in recent years. However, the specific role of m6A modification in pulmonary metastasis of colorectal cancer remains unclear.

This study identified differential m6A gene expression between  primary colorectal cancer and its pulmonary metastases using transcriptome sequencing and immunohistochemistry. We investigated the biological function of METTL3 gene both in vitro and in vivo using assays such as CCK-8, colony formation, wound healing, EDU, transwell, and apoptosis, along with a BALB/c nude mouse model. The regulatory mechanisms of METTL3 in colorectal cancer pulmonary metastasis were studied using methods like methylated RNA immunoprecipitation quantitative reverse transcription PCR, RNA stability analysis, luciferase reporter gene assay, Enzyme-Linked Immunosorbent Assay, and quantitative reverse transcription PCR.

The study revealed high expression of METTL3 and YTHDF1 in the tumors of patients with pulmonary metastasis of colorectal cancer. METTL3 promotes epithelial-mesenchymal transition in colorectal cancer by m6A modification of SNAIL mRNA, where SNAIL enhances the secretion of CXCL2 through the NF-κB pathway. Additionally, colorectal cancer cells expressing METTL3 recruit M2-type macrophages by secreting CXCL2.

METTL3 facilitates pulmonary metastasis of colorectal cancer by targeting the m6A-Snail-CXCL2 axis to recruit M2-type immunosuppressive macrophages. This finding offers new research directions and potential therapeutic targets for colorectal cancer treatment.

In patients with colorectal cancer (CRC), cancer metastasis is the primary cause of cancer-related deaths, with the liver and lungs being the most common target organs for metastasis [ 1 , 2 ]. While the majority of research has traditionally focused on liver metastases, colorectal cancer pulmonary metastasis (CRPM) has received relatively less attention. This gap in research is particularly significant given that the lungs are the second most common site of metastasis for CRC. Epidemiological studies estimate that pulmonary metastases occur in approximately 10–18% of rectal cancer patients and 5–6% of colon cancer patients [ 3 ]. The mechanisms of CRPM are multifaceted, encompassing intracellular factors such as genetic and epigenetic abnormalities, tumor cell heterogeneity, and the process of epithelial-mesenchymal transition (EMT). Furthermore, the influence of the tumor microenvironment cannot be underestimated [ 4 ]. Given its significance in cancer progression and treatment, in-depth research on CRPM is imperative. This not only contributes to a better understanding of the metastatic mechanisms in CRC but is also crucial for the development of novel therapeutic strategies.

In the formation process of CRPM, the EMT plays a crucial role [ 3 ]. EMT is a critical mechanism in the process of tumor initiation and development, and its activation is typically regulated by transcription factors that induce EMT. These factors include Snail family transcriptional repressor 1 (SNAIL) protein, Zinc finger E-box-binding homeobox 1 (ZEB) protein, and Twist family bHLH transcription factor 1 (TWIST1) protein [ 5 , 6 , 7 , 8 ]. This activation process leads to a series of changes in cells, including the loss of cell apical-basal polarity, disruption of cell–cell junctions, degradation of the basement membrane, and remodeling of the extracellular matrix (ECM) [ 9 , 10 ]. These changes transform epithelial cells from their original cobblestone-like appearance into spindle-shaped mesenchymal cells [ 11 ]. At the molecular level, the characteristics of EMT include a decrease in epithelial markers such as E-cadherin and an increase in mesenchymal markers such as vimentin, N-cadherin, and fibronectin [ 12 ]. Clinically, these changes enhance the mobility and invasive capacity of tumor cells, thereby promoting the distant metastasis of colorectal cancer cells [ 13 ]. Therefore, in-depth research into the EMT process is crucial for understanding the progression mechanisms of colorectal cancer and developing effective personalized treatment strategies.

Epigenetic abnormalities play a crucial role in the occurrence of CRPM [ 3 ]. Indeed, N6-methyladenosine (m6A) modification is one of the most common forms of epigenetic modifications found in messenger RNA (mRNA) [ 14 , 15 ]. Recent research has indicated that m6A plays a crucial role in various aspects of mRNA, including splicing, nuclear export, translation, and stability. Its significance extends to the development of many human diseases, including cancer [ 16 , 17 ]. This modification is controlled by a group of specialized enzymes, including "writers" that add methyl groups and "erasers" that remove methyl groups [ 18 ]. In this context, the 'writers' complex comprises enzymes such as methyltransferase-like 3 (METTL3) and methyltransferase-like 14 (METTL14), which are responsible for transferring methyl groups onto RNA [ 19 , 20 , 21 , 22 ]. The demethylation of m6A is facilitated by enzymes such as obesity-related protein (FTO) and alkB homologue 5 (ALKBH5) [ 23 , 24 ]. These modifications are recognized and interpreted by a group of proteins known as 'readers,' which include YTH domain family proteins and insulin-like growth factor 2 mRNA-binding proteins (IGF2BP) [ 25 , 26 ]. These intricate interactions influence the behavior and function of RNA, thereby playing crucial roles in the development of CRPM.

During the development of tumors, m6A often exerts its influence on the expression of key genes associated with EMT, either directly or indirectly, leading to the promotion of tumor cell EMT and facilitating their progression and metastasis. [ 27 , 28 , 29 , 30 ]. The relationship between m6A and EMT in cancer cells has been extensively studied, but its role in CRPM remains unclear. In our study, we conducted transcriptome sequencing, quantitative PCR (qPCR), Western blotting (WB), and immunohistochemical (IHC) analysis on clinical specimens of colorectal primary tumors and their lung metastatic tumors. Additionally, we integrated data from The Cancer Genome Atlas (TCGA) database and observed an increased expression of m6A-related genes, such as METTL3 and YTHDF1. These observations prompted us to further explore the potential link between these changes and colorectal cancer lung metastasis. Through in vivo and in vitro experiments, we found that METTL3 could enhance the expression of SNAIL protein through m6A modification, thereby promoting EMT in tumor cells. Furthermore, we observed that tumor cells overexpressing SNAIL could promote the secretion of CXCL2 by activating the NF-κB pathway. Secreted CXCL2, upon binding to CXCR2 on the surface of M2-type macrophages, facilitated the infiltration of these immune cells into the tumor center. These findings provide a new perspective on understanding the molecular mechanisms underlying colorectal cancer lung metastasis and may have significant implications for the development of novel therapeutic strategies.

Materials and methods

Specimens, cell culture and treatment.

The transcriptome sequencing data for CRC in this study were sourced from The Cancer Genome Atlas (TCGA) data portal (accessible at https://portal.gdc.cancer.gov/ ). Chromatin immunoprecipitation (ChIP) sequencing data were obtained from the Gene Expression Omnibus (GEO) database (see https://www.ncbi.nlm.nih.gov/gds/ ). In situ carcinoma and lung metastatic carcinoma tissue specimens from colorectal cancer lung metastasis patients were provided by the First Affiliated Hospital of Jinan University. Collection of all specimens received approval from the ethics committee of the First Affiliated Hospital of Jinan University, and written informed consent was obtained from the patients before collection.

CRC cell lines used in this study, including RKO and SW480, as well as the human monocytic cell line THP-1, were all obtained from the American Type Culture Collection (ATCC, Manassas, USA) (details available at https://www.atcc.org/ ). THP-1 cells were treated with 100 ng/ml phorbol 12-myristate 13-acetate (PMA, Sigma, Darmstadt, Germany) for 48 h to induce their differentiation into mature macrophages. Subsequently, the cells were incubated for over 48 h under conditions containing 20 ng/ml interleukin-4 (IL-4, Sigma, Darmstadt, Germany) and 20 ng/ml interleukin-13 (IL-13, Sigma, Darmstadt, Germany) to promote their polarization towards the M2 phenotype. All cell lines were cultured following the guidelines recommended by ATCC.

Production of lentiviruses

The lentiviral transfection vectors used in this study were obtained from GenePharma (Shanghai, China). Experimental groups included sh-METTL3 (METTL3 knockdown group), sh-YTHDF1 (YTHDF1 knockdown group), sh-SNAIL (SNAIL knockdown group), and Ov-SNAIL (SNAIL overexpression group), along with their corresponding negative control group sh-NC. To establish stable colorectal cancer cell lines, cell selection was performed using 400 μg/ml of neomycin.

Wound-healing assays

In this experiment, cells were initially seeded in 6-well plates. When the cells reached approximately 80% confluence, sterile 200 μl pipette tips were gently used to create scratches in the cell monolayer. Subsequently, these treated cells were incubated for an additional 48 h in serum-free culture medium. The healing process of cell injuries was observed using an inverted microscope produced by Olympus Corporation, Japan. Finally, quantitative analysis of the wound area was performed using ImageJ software to assess cell migration and healing ability.

Transwell invasion assay

In our cell invasion experiments, we utilized Transwell chambers and Matrigel from Corning, USA. Initially, a layer of extracellular matrix gel was coated in the upper chamber of the Transwell, and medium containing 20% FBS was added to the lower chamber. Once the gel had completely solidified, we added 200 µL of medium containing 1 × 10^5 cells to the upper chamber. Subsequently, the cells were incubated at 37 °C with 5% CO2 for 48 h. After the incubation period, we fixed the cells in the upper chamber with 4% paraformaldehyde and then stained them with 1% crystal violet. Finally, we observed the cells that had invaded from the upper chamber to the lower chamber using an inverted microscope produced by Olympus Corporation, Japan, and conducted quantitative analysis using ImageJ software.

Cell counting kit-8 (CCK-8) and EDU assay

In the CCK-8 experiment, cells were evenly distributed in a 96-well plate and cultured for 1, 2, 3, 4, and 5 days. Subsequently, 10 µL of CCK-8 solution was added to each well. To assess cell viability, the optical density (OD450) of each well was measured using a microplate reader from Thermo Fisher, USA.

For the EDU experiment, cell suspensions were evenly distributed in a 96-well plate to achieve a cell count of 1 × 10^4 cells per well. The culture dishes were then incubated overnight in a CO2 incubator to allow the cells to adhere to the surface. After a 2-h incubation with 10 µM EDU (Beyotime, Shanghai, China), the culture medium was removed, and 1 mL of fixation solution (Beyotime, Shanghai, China) was added to each well. The fixation solution was left at room temperature for 15 min and then removed. The cells were subsequently washed three times with 1 mL of washing solution. After removing the washing solution, 1 mL of Triton X-100 (Beyotime, Shanghai, China) was added to each well and incubated at room temperature for 10–15 min. Next, the Click reaction solution (Beyotime, Shanghai, China) was added and incubated in the dark at room temperature for 30 min. Finally, Hoechst 33,342 (Beyotime, Shanghai, China) was used for nuclear staining. Cell counting and photography were performed using a fluorescence inverted microscope, and quantitative analysis was conducted using ImageJ software.

Clone formation assays

To collect cells during the logarithmic growth phase, a cell suspension was prepared using standard digestion and centrifugation methods. After cell counting, the cells were seeded into a 6-well plate at a concentration of 500 cells per well. Once the cells reached the 6th generation, culturing was stopped. The culture medium was discarded, and cells were washed twice with PBS solution. Subsequently, the cells were fixed in methanol for 15 min and stained with 1% crystal violet solution for 10 min. After capturing photographs with a camera, quantitative analysis was performed using ImageJ software.

Apoptosis assays

In the apoptosis experiment, the detection of cell apoptosis was performed using the FITC Annexin V Cell Apoptosis Detection Kit (provided by BD Biosciences). The experimental steps were as follows: After 48 h of shRNA viral infection, the cells were seeded into 6 cm culture dishes. Cells were then cultured in medium containing 1% fetal bovine serum for 5 days. Following this, cells were digested using trypsin and mixed with the culture supernatant. After digestion was completed, cells were centrifuged, and the cell pellet was washed with PBS buffer to remove trypsin. Subsequently, the cells were resuspended and subjected to Annexin V/PI double staining. Finally, flow cytometry analysis of the samples was performed using the BD LSRII flow cytometer (BD Biosciences). Throughout the entire experiment, precautions should be taken to avoid exposure to light, and strict adherence to laboratory safety regulations is essential.

M2-type macrophage migration assay

M2-type macrophage (1 × 10^5 cells per well) were plated in the upper compartment of a transwell with a pore size of 0.8 mm, in 100 mL of 1640 medium. The lower compartment was filled with 600 mL of conditioned medium collected from RKO or SW480 cells, excluding fetal bovine serum. Following a 4-h incubation period, the migrated cells in the lower chamber were quantified.

RNA stability

Add Act-D to the cell culture medium at a concentration of 5 µg/mL when the cells have reached a confluence of 70–80%. Collect cell samples at different time points after Act-D treatment. Use TRIzol reagent (Invitrogen) to extract total RNA from the collected samples. Quantify the RNA and assess its purity using a NanoDrop spectrophotometer. Convert the extracted total RNA into cDNA using a reverse transcription reagent kit (Vazyme, Nanjing, China) following the manufacturer's instructions. Perform quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis to detect the mRNA levels of specific genes (please specify the genes of interest). Normalize the data using GAPDH as the reference gene. Calculate the relative expression levels using the 2^-ΔΔCT method. Create line graphs illustrating the change in RNA levels over time to reflect RNA stability. Ensure that you follow the manufacturer's protocols for reagent usage and experimental procedures.

Western blotting and immunohistochemistry

Cell and tissue samples were processed using RIPA buffer from Servicebio Technology, Wuhan, China. Protein extraction was carried out following the instructions provided in the radio-immunoprecipitation assay (RIPA) kit. Protein concentrations were quantified using the bicinchoninic acid (BCA) assay kit also provided by Servicebio Technology, Wuhan, China. The protein samples were subsequently separated by electrophoresis and transferred onto membranes. After washing the membranes three times with tris-buffered saline containing Tween (TBST), they were incubated with the primary antibody overnight at 4 °C. Following incubation, the membranes were washed again and incubated with the secondary antibody. Finally, protein bands were visualized using the enhanced chemiluminescence (ECL) assay kit provided by Servicebio Technology, Wuhan, China.

Immunohistochemistry and immunofluorescence as previously described [ 31 ]. In our study, immunohistochemistry was conducted using a range of primary antibodies with the following detailed information: 1. METTL3, Vimentin, E-Cadherin, YTHDF1, YTHDF2, SNAIL, and ARG1 antibodies were all obtained from Abcam (Cambridge, UK), each with the following product codes: METTL3 (ab195352), Vimentin (ab92547), E-Cadherin (ab40772), YTHDF1 (ab252346), SNAIL (ab180714), and CD163 (ab316218). 2. P65, Phospho-P65, GAPDH, and Tubulin antibodies were sourced from Proteintech Group (Chicago, USA) and were identified by the specific product codes: P65 (80,979–1-RR), Phospho-P65 (82,335–1-RR), GAPDH (10,494–1-AP), and Tubulin (11,224–1-AP). 3. Goat anti-rabbit IgG was purchased from Biosharp Life Sciences (Beijing, China) and had the product code BL052A. The quantification of all proteins and determination of average optical density were performed using Image J software.

Luciferase reporter assays

The dual-luciferase reporter gene assay was conducted using the Dual-Luciferase® Reporter Gene Assay System provided by Promega Corporation. To standardize transfection efficiency, a co-transfection approach with the pRL-TK reporter gene (also supplied by Promega) was employed. Relative luciferase activity was calculated based on the activity of Renilla luciferase.

mRNA-sequencing and methylated RNA immunoprecipitation quantitative reverse transcription polymerase chain reaction (MeRIP qRT-PCR)

Total RNA was extracted from colorectal cancer cell lines using the TRIzol reagent provided by Invitrogen Corporation (Carlsbad, CA, USA). Subsequently, mRNA sequencing and analysis were carried out by the HaploX Genomics Center in Shenzhen, China.

For RNA immunoprecipitation (MeRIP) analysis of m6A modification, we initially subjected the RNA to fragmentation using the riboMIP™ m6A Transcriptome Analysis Kit (Ribobio, Guangzhou, China) and then performed immunoprecipitation with m6A antibodies. The RNA isolated after immunoprecipitation was analyzed using qRT-PCR to investigate the m6A modification status of RNA.

RNA Extraction and quantitative reverse transcription polymerase chain reaction (qRT-PCR)

Total RNA was extracted using TRIzol (Invitrogen, Carlsbad, USA), while cytoplasmic and nuclear RNA were extracted using cytoplasmic & nuclear RNA purification (Amyjet Scientific, Wuhan, China). qRT-PCR was performed following the protocol (Vazyme, Nanjing, China).

The cell culture medium was transferred to a sterile centrifuge tube and centrifuged at 1000 × g for 10 min at 4 °C. Aliquots of the supernatant were then dispensed into EP tubes for further use. The CXCL2 protein was detected using the manufacturer's protocols for the CXCL2 ELISA kit (Solarbio, Beijing, China).

Animal studies

The experiments conducted in this study received ethical approval from the Ethics Committee of South China Agricultural University. Female nude mice aged 4 to 5 weeks were procured from the Animal Experiment Center of Southern Medical University for the experimental procedures. During the experimentation, a total of 10 nude mice were randomly divided into two groups, each consisting of 5 mice (n = 5). they were subcutaneous injected with 2 × 10^6 RKO/SW480 cells stably silencing METTL3 or control cells. Additionally, six nude mice were randomly assigned to two groups (n = 3), and they were intravenously injected with 2 × 10^6 RKO/SW480 cells stably silencing METTL3 or control cells. These nude mice were housed under standard laboratory conditions with access to ample food and water. Tumor size and volume were monitored weekly throughout the study. Before sample collection, humane euthanasia was administered to all mice using a 2% pentobarbital sodium solution at a dose of 150 mg/kg. After a 2-week interval, mice from the subcutaneous tumor group were euthanized due to tumor development. Similarly, mice from the intravenous tumor group were euthanized at the 4-week mark. The tumors were documented through photographic imaging and weighed. Subsequently, all collected tumor specimens were stored either in liquid nitrogen or fixed using a 4% formaldehyde solution for subsequent analysis.

Statistical analysis

In this study, all statistical analyses were performed using SPSS 19.0 software and R language (version 4.2.1). Graphs and data visualization were carried out using GraphPad Prism 9.5 software. We defined a p -value of less than 0.05 as indicating statistically significant differences.

METTL3 and YTHDF1 are highly expressed in CRPM

To assess the expression profiles of m6A WERs (writers, erasers, and readers) in colon adenocarcinoma (COAD), this study analyzed data from The Cancer Genome Atlas (TCGA) database. The analysis results revealed that several m6A WERs exhibited abnormal expression in CRC (Fig.  1 a). To further investigate the status of these differentially expressed genes in colorectal cancer in situ and lung metastatic cancer, we conducted sequencing on both types of cancer samples. The sequencing results indicated a significant increase in the expression of METTL3, wilms tumor 1 associated protein (WTAP), METTL14, and YTHDF1 in lung metastatic cancer. It's noteworthy that METTL14 did not show expression differences in the TCGA database (Fig.  1 b).

METTL3 and YTHDF1 are highly expressed in CRPM. a Expression profile of WERs in COAD based on TCGA database. b Volcano plot of differential gene sequencing WERs in colorectal cancer in situ and lung metastatic cancer. c qPCR Analysis of METTL3, WTAP, METTL14, and YTHDF1 Gene Expression in colorectal In Situ Carcinoma and Pulmonary Metastatic Carcinoma. d-e Western blotting analysis of METTL3 and YTHDF1 protein expression in colorectal in situ carcinoma and pulmonary metastatic carcinoma. T: colorectal In Situ Carcinoma. M: colorectal pulmonary metastatic carcinoma. f Immunohistochemical Analysis of METTL3 and YTHDF1 Protein Expression in colorectal In Situ Carcinoma and Pulmonary Metastatic Carcinoma. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

To further validate these findings, we conducted qPCR analysis on colorectal cancer in situ and lung metastatic cancer samples. The qPCR results demonstrated that the expression of METTL3 and YTHDF1 was significantly higher in colorectal cancer lung metastases compared to in situ cancer, while WTAP expression showed no significant difference (Fig.  1 c). Additionally, through WB and IHC experiments, we further confirmed the high expression of METTL3 and YTHDF1 in colorectal cancer lung metastases (Fig.  1 d-f).

Silencing the METTL3 gene inhibited the proliferation, invasion, and migration abilities of CRC cells while promoting apoptosis

In the initial stages of this study, we successfully silenced the METTL3 gene in RKO and SW480 cell lines using lentiviral infection technology. To assess the silencing effect, we performed Western blot analysis (Fig.  2 g). Subsequent cell proliferation experiments, including CCK-8, EDU staining, and plate colony assays, showed that after METTL3 knockdown, the OD450 values significantly decreased, the number of EDU-positive cells and cell colony formation were reduced (Fig.  2 c-e). These results indicate that downregulation of METTL3 weakened the proliferation ability of RKO and SW480 cells. We further investigated the impact of METTL3 on apoptosis in CRC cells. Through Annexin V-APC/PI staining experiments, we observed a significant increase in cell apoptosis rate after METTL3 inhibition (Fig.  2 f). Additionally, in vivo experiments using a nude mouse model showed that silencing METTL3 significantly reduced the size of subcutaneous tumors (Fig.  3 l). In wound healing and Transwell experiments, we found that METTL3 knockdown in CRC cell lines resulted in weakened migration and invasion abilities (Fig.  2 a-b). Another in vivo experiment also confirmed that inhibiting METTL3 significantly reduced the number of lung metastases in nude mice (Fig.  3 b). To assess whether cells underwent EMT, we conducted Western blot analysis, which revealed that after METTL3 knockdown, the expression of VIM and Snail proteins significantly decreased, while E-cadherin protein expression significantly increased (Fig.  2 g). Immunohistochemistry on tumor specimens from in vivo experiments also suggested that in the low-expression METTL3 group, both subcutaneous tumors and lung metastases showed significantly reduced expression of VIM and SNAIL proteins (Fig.  3 c-d).

Silencing the METTL3 gene inhibited the proliferation, invasion, and migration abilities of CRC cells while promoting apoptosis. a Representative images and quantitative analysis of CRC cell migration based on wound healing assay. b Decreased expression of METTL3 restrained CRC cell invasion ability based on transwell assay. c The colony formation ability of CRC cells silencing METTL3 or not was measured by colony formation assay. d EDU assay in CRC cells was performed the to measure the proliferation level. e CCK-8 assay was applied to measure the proliferation level of CRC cells after transfected with sh-METTL3 or not. f Flow cytometry analysis the proportion of apoptosis. g Western blotting analysis was applied to measure the protein expression level of E-cadherin, METTL3, SNAIL and Vimentin. GAPDH was used as a loading control. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

Decreased expression of METTL3 inhibits tumor growth in vivo. a Comparison of subcutaneous tumor size in nude mice after injecting RKO/SW480 CRC cells stably transfected with Sh-NC/Sh-METTL3. b The tail vein-lung metastasis model. Cells were injected into the tail vein to produce tumor cell lung metastasis. c-d IHC analysis of METTL3, SNAIL and Vimentin for tissues of subcutaneous tumor and lung metastasis tumor. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

METTL3 regulates tumor EMT process via m6A-Snail

According to previous studies, SNAIL plays a crucial role in the tumor microenvironment [ 31 ]. Furthermore, existing research suggests a mutual regulatory relationship between METTL3 and Snail in various types of cancer [ 28 ]. Based on this background, our study aimed to investigate whether METTL3 in colorectal cancer (CRC) exhibits a similar regulatory relationship with Snail. Our hypothesis is that METTL3 controls epithelial-mesenchymal transition (EMT) in tumors through the m6A-Snail pathway. To validate this hypothesis, we conducted Snail overexpression experiments (ov-Snail) in sh-NC (control group) and sh-METTL3 (METTL3 silenced group) cells. Western blot analysis showed that silencing METTL3 attenuated Snail expression, and the EMT process could be reversed, while overexpressing Snail could restore EMT (Fig.  4 a). This finding strongly supports the hypothesis that METTL3 regulates the EMT process through Snail. Additionally, we conducted SNAIL overexpression experiments in sh-METTL3 RKO and SW480 cell lines. The results showed that SNAIL overexpression reversed the inhibitory effect of silenced METTL3 on the proliferation, migration and invasion of CRC cells and its promotion of apoptosis (Fig.  4 b-g).

METTL3 regulates tumor process via Snail. a Western blotting analysis was applied to measure the protein expression level of E-cadherin, METTL3, SNAIL and Vimentin. GAPDH was used as a loading control. b Representative images and quantitative analysis of CRC cell migration based on wound healing assay. c Increased expression of SNAIL in sh-METTL3 CRC cell promoted CRC cell invasion ability based on transwell assay. d The colony formation ability of CRC cells overexpressing SNAIL or not was measured by colony formation assay in sh-METTL3 CRC cell. e EDU assay in CRC cells was performed the to measure the proliferation level. f CCK-8 assay was applied to measure the proliferation level of CRC cells after transfected with ov-SNAIL or not in sh-METTL3 CRC cell. g Flow cytometry analysis the proportion of apoptosis. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

To further explore how METTL3 regulates Snail, we first observed that silencing METTL3 led to a decrease in m6A modification levels on SNAIL mRNA using m6A qPCR technology (Fig.  5 a). Subsequently, to investigate the impact of m6A modification on SNAIL RNA, we detected the expression levels of SNAIL precursor mRNA (pre-mRNA) and mature mRNA in sh-METTL3-treated cells. The results showed that both pre-mRNA and mature mRNA levels of SNAI1 were significantly higher in the sh-METTL3 group than in the control group (Fig.  5 b). Furthermore, subcellular localization analysis of SNAI1 mRNA in sh-METTL3 and sh-NC cells revealed no significant differences between the two groups (Fig.  5 c).

METTL3 regulates tumor EMT process via m6A-Snail. a The MeRIP qRT-PCR analysis of SNAIL mRNA in sh-NC and sh-METTL3 CRC cells. b Precursor and mature mRNA of SNAI1 in sh-NC and sh-METTL3 CRC cells. c The relative levels of the nuclear versus cytoplasmic SNAI1 mRNA in sh-NC and sh-METTL3 CRC cells. d sh-NC and sh-METTL3 CRC cells were pretreated with Act-D for 90 min, then precursor (right) or mature (left) SNAIL mRNA were analyzed at indicated times. e sh-NC and sh-YTHDF2 CRC cells were pretreated with Act-D for 90 min, then mature SNAI1 mRNA were analyzed at indicated times. f Western blotting analysis was applied to measure the protein expression level of SNAIL and YTHDF1. GAPDH was used as a loading control. g GSEA analysis of SNAIL high-expression and low-expression groups utilizing TCGA COAD data. h Western blotting analysis was applied to measure the protein expression level of SNAIL, P65 and P-P65. Tubulin was used as a loading control. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

To assess the stability of SNAI1 pre-mRNA and mature mRNA, we used Act-D to inhibit new RNA transcription. The experimental results showed that the half-life of pre-mRNA and mature mRNA in the sh-METTL3 group was significantly longer than that in the control group (Fig.  5 d). Given that YTHDF2 is known to mediate the degradation of m6A-modified mRNA, we hypothesized that the observed phenomenon might be related to YTHDF2-mediated mRNA degradation. Therefore, we silenced YTHDF2 in colorectal cancer cell lines and observed an increase in the stability of SNAIL mRNA (Fig.  5 e).

However, we also found some contradictory phenomena. Despite the increased expression of SNAIL mRNA in the sh-METTL3 group, previous research indicated a decrease in SNAIL protein expression in this group. We speculated that this difference might be due to the influence of m6A on SNAIL mRNA translation efficiency. Therefore, we investigated YTHDF1, which serves as a "reader" of m6A and can recognize m6A-modified mRNA, enhancing its translation [ 32 ]. To further explore this mechanism, we conducted experiments with si-YTHDF1 in colorectal cancer cell lines. The results showed that knocking down YTHDF1 significantly reduced SNAIL protein expression (Fig.  5 f).

SNAIL induces CXCL2 expression via the NF-κΒ pathway

Our previous research has revealed a regulatory relationship between SNAIL and CXCL2, but the specific regulatory mechanism remains unclear. In previous sequencing analyses, we noticed significant changes in the NF-κB pathway in the context of lung metastasis cancer (CRPM) compared to colorectal cancer (CRC). Furthermore, through gene set enrichment analysis (GSEA) of CRC data from the TCGA database, we found that in samples with high SNAIL expression, the NF-κB pathway and chemokine signaling pathway were activated, accompanied by epithelial-mesenchymal transition (EMT) (Fig.  5 g). Based on these preliminary findings, we proposed a hypothesis: Snail may regulate the expression of CXCL2 through the NF-κB pathway. To validate this hypothesis, we conducted silencing experiments using sh-Snail in SW480 and RKO cell lines and observed a significant decrease in phosphorylated P65 (Phospho-P65) levels (Fig.  5 h). Additionally, we treated control group cells with the NF-κB inhibitor BAY11-7082 and found that CXCL2 expression also significantly decreased (Fig.  6 c). Furthermore, we analyzed CHIP-seq data (GSE61198) from the Gene Expression Omnibus (GEO) database and found that Snail could bind to the transcription start site (TSS) upstream of CXCL2 (Fig.  6 a). To further explore the direct regulatory relationship between SNAIL and CXCL2, we constructed luciferase reporter gene vectors for different lengths of the CXCL2 promoter (pGL-CXCL2-1606 bp, 942 bp, and 457 bp, respectively). The experimental results showed that silencing Snail significantly reduced the activity of the CXCL2-1606 bp promoter, while it had no significant effect on the activity of the CXCL2-942 bp and CXCL2-457 bp promoters (Fig.  6 b).

METTL3-expressing CRC Cells Recruits M2-type macrophage Via Secreting CXCL2. a Chromatin immunoprecipitation sequence data show that Snail might directly bind to Cxcl2 proximal promoters. b (Left) Schematic representation of CXCL2 promoter organization, and the luciferase reporter constructs pGL-CXCL2 (1606 bp: − 1606 to + 104 bp, 942 bp: − 942 to + 104 bp, 457 bp: − 457 to + 104 bp). TSS: transcriptional start site, E1: exon1, and Luc: luciferase. The green bars indicate E-boxes (CANNTG), which are the binding sites of Snail. (Right) Relative luciferase activities are shown. c ELISA to analyze the expression of CXCL2 in sh-NC/sh-SNAIL RKO cells (left), and the expression of CXCL2 in sh-NC/sh-SNAIL RKO cells (right), treated with or without BAY11-7082 (NF-κB inhibitor) at 10 μM for 24 h. d M2-type macrophage were seeded in the top chamber of the transwell containing 100 mL 1640 medium with or without CXCR2 inhibitor (SB265610, 10 mM). On the other hand, the bottom chamber contained 600 mL of CRC cell conditioned medium (no fetal bovine serum) with or without recombinant CXCL2 protein (1 ng/mL). After 4-h incubation, cells that have completely migrated to the bottom chamber were counted. e Immunofluorescence analysis of CD163 protein expression in colorectal in nude mice specimen. f Immunofluorescence analysis of CD163 protein expression in colorectal in situ carcinoma and pulmonary metastatic carcinoma. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001

METTL3-expressing CRC cells recruits M2-type macrophage via secreting CXCL2

Immunohistochemical analysis of subcutaneous tumors and lung metastatic tumors in mice from the sh-METTL3 and sh-NC groups indicated that silencing METTL3 significantly reduced the expression of the M2 macrophage marker CD163 protein (Fig.  6 e). Similarly, in clinical samples, the protein expression of the M2 macrophage marker CD163 was significantly higher in lung metastatic tumors compared to primary tumors (Fig.  6 f). Next, we evaluated the role of the METTL3-CXCL2 axis in the chemotaxis of M2 macrophages in an in vitro M2 macrophage migration assay. METTL3 knockout significantly reduced the migration of M2 macrophages toward conditioned media derived from RKO and SW480 cells. The addition of recombinant CXCL2 protein rescued M2 macrophage migration in METTL3 knockout cells. On the other hand, blocking the CXCL2 receptor CXCR2 eliminated the difference in mediating M2 macrophage migration between the control and METTL3 knockout conditioned media (Fig.  6 d).

METTL3, as a core component of the multifaceted m6A methyltransferase complex (MTC), has been reported to play critical roles in various cancer types [ 33 , 34 , 35 , 36 , 37 , 38 ]. While alternative views have been proposed by other studies [ 39 , 40 , 41 , 42 , 43 ], our research findings reveal a significant oncogenic role for METTL3 in the process of tumorigenesis. Previous research has suggested that in certain cancers, METTL3 may function as a tumor suppressor gene. Given the controversies surrounding the roles of m6A modification and METTL3 in different cancer types, our study underscores the potential involvement of METTL3 in colorectal cancer. Our findings demonstrate that METTL3 promotes proliferation, invasion, migration, and inhibits apoptosis in colorectal cancer, highlighting its oncogenic potential. This further emphasizes the widespread impact of METTL3 and m6A methylation in cancer development and precision therapy.

In the field of oncology, epithelial-mesenchymal transition transcription factors (EMT-TFs) such as SNAIL play pivotal roles. SNAIL suppresses the expression of E-cadherin by binding to the e-boxes in the CDH1 promoter and recruiting the multisubunit repressor complex, a crucial process in tumor cells [ 44 , 45 ]. Aberrant Snail expression is closely associated with EMT, which, in turn, is linked to the invasion, migration, metastasis, settlement, and growth capabilities of tumors, ultimately promoting the formation of CRPM [ 46 ]. In the field of colorectal cancer research, prior investigations have already elucidated the regulatory relationship between METTL3 and SNAIL [ 47 ]. However, these studies were limited to in vitro research, leaving unanswered the question of whether METTL3 continues to exert a similar influence in the context of colorectal cancer lung metastasis. Consequently, our study is focused on elucidating the m6A modification of SNAIL mRNA by METTL3 and its subsequent ramifications in this specific context. We found that m6A modification enhances the degradation of SNAIL mRNA, which may be related to YTHDF2 specifically recognizing m6A-modified SNAIL mRNA and promoting its degradation [ 48 ]. However, this finding contradicts our experimental results, where m6A modification of SNAIL is associated with increased protein expression, raising our concern. Additionally, through sequencing and clinical sample testing, we observed elevated expression levels of METTL3 and YTHDF1 in samples of colorectal cancer lung metastasis. YTHDF1, another m6A "reader," has been found to recognize m6A-modified mRNA and enhance the translation of its targets [ 32 ]. To validate the role of YTHDF1, we silenced YTHDF1, and the results showed a significant reduction in SNAIL protein expression, suggesting that m6A methylation regulation of SNAIL is a dynamic process involving multiple factors that require further discussion. Furthermore, our research has also revealed the role of SNAIL in regulating the tumor microenvironment [ 31 ]. SNAIL not only recruits M2 macrophages to infiltrate the tumor center by promoting the secretion of CXCL2 but also indirectly regulates the expression of CXCL2 through the NF-κB pathway and directly modulates CXCL2 expression by binding to its promoter region. Moreover, colorectal cancer cells expressing METTL3 recruit M2 macrophages by secreting CXCL2, a finding confirmed through in vitro experiments and in vivo experiments with METTL3-silenced colorectal cancer cells. These findings provide a new perspective for clinical treatment, such as disrupting this malignant cycle using CXCR2 inhibitors.

In summary, our study aimed to elucidate the role and mechanism of RNA m6A methyltransferase METTL3 in CRPM. The results indicate that METTL3 plays a critical role in promoting the development of CRPM by targeting the m6A-Snail-CXCL2 axis, which is involved in recruiting M2 immunosuppressive macrophages. It is worth noting that we also observed a significant reduction in tumor migration upon inhibiting METTL3, suggesting that therapeutic strategies targeting METTL3 may be an effective approach for treating CRPM.

Availability of data and materials

The analyzed data sets generated during the present study are available from the corresponding author on reasonable request.

Abbreviations

AlkB homologue 5

American Type Culture Collection

Chromatin immunoprecipitation

Colon adenocarcinoma

Colorectal cancer

Colorectal cancer pulmonary metastasis

Extracellular matrix

Enzyme linked immunosorbent assay

Epithelial-mesenchymal transition

Fat mass and obesity-related protein

Gene Expression Omnibus

Heterogeneous nuclear ribonucleoprotein

Insulin-like growth factor 2 mRNA-binding proteins

Immunohistochemistry

Methyltransferase-like 3

Methylated RNA immunoprecipitation quantitative reverse transcription polymerase chain reaction

Messenger RNA

N6-methyladenosine

Phorbol 12-myristate 13-acetate

Quantitative reverse transcription PCR

Snail family transcriptional repressor 1

The Cancer Genome Atlas

Twist family bHLH transcription factor 1

Western blotting

Writers, erasers, and readers

Wilms tumor 1 associated protein

Zinc finger E-box-binding homeobox 1

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This study was supported by the Fundamental Research Funds for the Central Universities (21623305,21623409), Guangdong Medical Science and Technology Research Fund Project(A2023398), Guangzhou Science and Technology Plan Basic and Applied Basic Research Funding Project(2024A04J3707) and Guangzhou Science and Technology Plan City-School Joint Funding Project (202201020304).

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Peng Ouyang, Kang Li and Wei Xu contributed equally to this work.

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Department of General Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, 510632, Guangdong, China

Peng Ouyang, Kang Li, Wei Xu, Caiyun Chen, Yangdong Shi, Yao Tian, Jin Gong & Zhen Bao

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POY: As the first author, POY was responsible for the primary study design, experimental procedures, data collection and analysis, as well as the writing and revision of the manuscript. KL (co-first author): KL collaborated with the first author in study design, experimental procedures, participated in data analysis, and contributed to the writing of the manuscript. WX (co-first author): WX worked in collaboration with the first author on experimental design and data analysis, making significant contributions to the writing of the manuscript. CYC, YDS, YT: These three authors were involved in some experimental procedures, contributed to the collection and preliminary analysis of experimental data. JG (co-corresponding author): Jin Gong, as a co-corresponding author, participated in guiding the research, contributed to the writing, and provided final review to ensure the quality and accuracy of the study. ZB (corresponding author): As the corresponding author, Zhen Bao was responsible for overseeing the entire research project, obtaining funding, and overseeing the final review and submission of the manuscript. All authors read and approved the final manuscript.

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Ouyang, P., Li, K., Xu, W. et al. METTL3 recruiting M2-type immunosuppressed macrophages by targeting m6A-SNAIL-CXCL2 axis to promote colorectal cancer pulmonary metastasis. J Exp Clin Cancer Res 43 , 111 (2024). https://doi.org/10.1186/s13046-024-03035-6

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Published : 11 April 2024

DOI : https://doi.org/10.1186/s13046-024-03035-6

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