literature review level of evidence

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Evidence-Based Research: Levels of Evidence Pyramid

Introduction.

One way to organize the different types of evidence involved in evidence-based practice research is the levels of evidence pyramid. The pyramid includes a variety of evidence types and levels.

• systematic reviews
• critically-appraised topics
• critically-appraised individual articles
• randomized controlled trials
• cohort studies
• case-controlled studies, case series, and case reports
• Background information, expert opinion

Levels of evidence pyramid

The levels of evidence pyramid provides a way to visualize both the quality of evidence and the amount of evidence available. For example, systematic reviews are at the top of the pyramid, meaning they are both the highest level of evidence and the least common. As you go down the pyramid, the amount of evidence will increase as the quality of the evidence decreases.

Text alternative for Levels of Evidence Pyramid diagram

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Filtered Resources

Filtered resources appraise the quality of studies and often make recommendations for practice. The main types of filtered resources in evidence-based practice are:

Scroll down the page to the Systematic reviews , Critically-appraised topics , and Critically-appraised individual articles sections for links to resources where you can find each of these types of filtered information.

Systematic reviews

Authors of a systematic review ask a specific clinical question, perform a comprehensive literature review, eliminate the poorly done studies, and attempt to make practice recommendations based on the well-done studies. Systematic reviews include only experimental, or quantitative, studies, and often include only randomized controlled trials.

You can find systematic reviews in these filtered databases :

• Cochrane Database of Systematic Reviews Cochrane systematic reviews are considered the gold standard for systematic reviews. This database contains both systematic reviews and review protocols. To find only systematic reviews, select Cochrane Reviews in the Document Type box.
• JBI EBP Database (formerly Joanna Briggs Institute EBP Database) This database includes systematic reviews, evidence summaries, and best practice information sheets. To find only systematic reviews, click on Limits and then select Systematic Reviews in the Publication Types box. To see how to use the limit and find full text, please see our Joanna Briggs Institute Search Help page .

You can also find systematic reviews in this unfiltered database :

To learn more about finding systematic reviews, please see our guide:

• Filtered Resources: Systematic Reviews

Critically-appraised topics

Authors of critically-appraised topics evaluate and synthesize multiple research studies. Critically-appraised topics are like short systematic reviews focused on a particular topic.

You can find critically-appraised topics in these resources:

• Annual Reviews This collection offers comprehensive, timely collections of critical reviews written by leading scientists. To find reviews on your topic, use the search box in the upper-right corner.
• Guideline Central This free database offers quick-reference guideline summaries organized by a new non-profit initiative which will aim to fill the gap left by the sudden closure of AHRQ’s National Guideline Clearinghouse (NGC).
• JBI EBP Database (formerly Joanna Briggs Institute EBP Database) To find critically-appraised topics in JBI, click on Limits and then select Evidence Summaries from the Publication Types box. To see how to use the limit and find full text, please see our Joanna Briggs Institute Search Help page .
• National Institute for Health and Care Excellence (NICE) Evidence-based recommendations for health and care in England.
• Filtered Resources: Critically-Appraised Topics

Critically-appraised individual articles

Authors of critically-appraised individual articles evaluate and synopsize individual research studies.

You can find critically-appraised individual articles in these resources:

• EvidenceAlerts Quality articles from over 120 clinical journals are selected by research staff and then rated for clinical relevance and interest by an international group of physicians. Note: You must create a free account to search EvidenceAlerts.
• ACP Journal Club This journal publishes reviews of research on the care of adults and adolescents. You can either browse this journal or use the Search within this publication feature.
• Evidence-Based Nursing This journal reviews research studies that are relevant to best nursing practice. You can either browse individual issues or use the search box in the upper-right corner.

To learn more about finding critically-appraised individual articles, please see our guide:

• Filtered Resources: Critically-Appraised Individual Articles

Unfiltered resources

You may not always be able to find information on your topic in the filtered literature. When this happens, you'll need to search the primary or unfiltered literature. Keep in mind that with unfiltered resources, you take on the role of reviewing what you find to make sure it is valid and reliable.

Note: You can also find systematic reviews and other filtered resources in these unfiltered databases.

The Levels of Evidence Pyramid includes unfiltered study types in this order of evidence from higher to lower:

You can search for each of these types of evidence in the following databases:

TRIP database

Background information & expert opinion.

Background information and expert opinions are not necessarily backed by research studies. They include point-of-care resources, textbooks, conference proceedings, etc.

• Family Physicians Inquiries Network: Clinical Inquiries Provide the ideal answers to clinical questions using a structured search, critical appraisal, authoritative recommendations, clinical perspective, and rigorous peer review. Clinical Inquiries deliver best evidence for point-of-care use.
• Harrison, T. R., & Fauci, A. S. (2009). Harrison's Manual of Medicine . New York: McGraw-Hill Professional. Contains the clinical portions of Harrison's Principles of Internal Medicine .
• Lippincott manual of nursing practice (8th ed.). (2006). Philadelphia, PA: Lippincott Williams & Wilkins. Provides background information on clinical nursing practice.
• Medscape: Drugs & Diseases An open-access, point-of-care medical reference that includes clinical information from top physicians and pharmacists in the United States and worldwide.
• Virginia Henderson Global Nursing e-Repository An open-access repository that contains works by nurses and is sponsored by Sigma Theta Tau International, the Honor Society of Nursing. Note: This resource contains both expert opinion and evidence-based practice articles.
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• Next Page: Evidence Types
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Systematic Reviews

• Levels of Evidence
• Evidence Pyramid
• Joanna Briggs Institute

The evidence pyramid is often used to illustrate the development of evidence. At the base of the pyramid is animal research and laboratory studies – this is where ideas are first developed. As you progress up the pyramid the amount of information available decreases in volume, but increases in relevance to the clinical setting.

Meta Analysis  – systematic review that uses quantitative methods to synthesize and summarize the results.

Systematic Review  – summary of the medical literature that uses explicit methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate st atistical techniques to combine these valid studies.

Randomized Controlled Trial – Participants are randomly allocated into an experimental group or a control group and followed over time for the variables/outcomes of interest.

Cohort Study – Involves identification of two groups (cohorts) of patients, one which received the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.

Case Control Study – study which involves identifying patients who have the outcome of interest (cases) and patients without the same outcome (controls), and looking back to see if they had the exposure of interest.

Case Series   – report on a series of patients with an outcome of interest. No control group is involved.

• Levels of Evidence from The Centre for Evidence-Based Medicine
• The JBI Model of Evidence Based Healthcare
• How to Use the Evidence: Assessment and Application of Scientific Evidence From the National Health and Medical Research Council (NHMRC) of Australia. Book must be downloaded; not available to read online.

When searching for evidence to answer clinical questions, aim to identify the highest level of available evidence. Evidence hierarchies can help you strategically identify which resources to use for finding evidence, as well as which search results are most likely to be "best".                                             

Image source: Evidence-Based Practice: Study Design from Duke University Medical Center Library & Archives. This work is licensed under a Creativ e Commons Attribution-ShareAlike 4.0 International License .

The hierarchy of evidence (also known as the evidence-based pyramid) is depicted as a triangular representation of the levels of evidence with the strongest evidence at the top which progresses down through evidence with decreasing strength. At the top of the pyramid are research syntheses, such as Meta-Analyses and Systematic Reviews, the strongest forms of evidence. Below research syntheses are primary research studies progressing from experimental studies, such as Randomized Controlled Trials, to observational studies, such as Cohort Studies, Case-Control Studies, Cross-Sectional Studies, Case Series, and Case Reports. Non-Human Animal Studies and Laboratory Studies occupy the lowest level of evidence at the base of the pyramid.

• Finding Evidence-Based Answers to Clinical Questions – Quickly & Effectively A tip sheet from the health sciences librarians at UC Davis Libraries to help you get started with selecting resources for finding evidence, based on type of question.
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• Next: Locating Systematic Reviews >>
• Getting Started
• What is a Systematic Review?
• Locating Systematic Reviews
• Searching Systematically
• Developing Answerable Questions
• Identifying Synonyms & Related Terms
• Using Truncation and Wildcards
• Identifying Search Limits/Exclusion Criteria
• Keyword vs. Subject Searching
• Where to Search
• Search Filters
• Sensitivity vs. Precision
• Core Databases
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• Clinical Trial Registries
• Conference Presentations
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• Citation Indexes
• Documenting the Search Process
• Managing your Review

Research Support

• Last Updated: Apr 8, 2024 3:33 PM
• URL: https://guides.library.ucdavis.edu/systematic-reviews

• Research Process

Levels of evidence in research

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Table of Contents

Level of evidence hierarchy

When carrying out a project you might have noticed that while searching for information, there seems to be different levels of credibility given to different types of scientific results. For example, it is not the same to use a systematic review or an expert opinion as a basis for an argument. It’s almost common sense that the first will demonstrate more accurate results than the latter, which ultimately derives from a personal opinion.

In the medical and health care area, for example, it is very important that professionals not only have access to information but also have instruments to determine which evidence is stronger and more trustworthy, building up the confidence to diagnose and treat their patients.

5 levels of evidence

With the increasing need from physicians – as well as scientists of different fields of study-, to know from which kind of research they can expect the best clinical evidence, experts decided to rank this evidence to help them identify the best sources of information to answer their questions. The criteria for ranking evidence is based on the design, methodology, validity and applicability of the different types of studies. The outcome is called “levels of evidence” or “levels of evidence hierarchy”. By organizing a well-defined hierarchy of evidence, academia experts were aiming to help scientists feel confident in using findings from high-ranked evidence in their own work or practice. For Physicians, whose daily activity depends on available clinical evidence to support decision-making, this really helps them to know which evidence to trust the most.

So, by now you know that research can be graded according to the evidential strength determined by different study designs. But how many grades are there? Which evidence should be high-ranked and low-ranked?

There are five levels of evidence in the hierarchy of evidence – being 1 (or in some cases A) for strong and high-quality evidence and 5 (or E) for evidence with effectiveness not established, as you can see in the pyramidal scheme below:

Level 1: (higher quality of evidence) – High-quality randomized trial or prospective study; testing of previously developed diagnostic criteria on consecutive patients; sensible costs and alternatives; values obtained from many studies with multiway sensitivity analyses; systematic review of Level I RCTs and Level I studies.

Level 2: Lesser quality RCT; prospective comparative study; retrospective study; untreated controls from an RCT; lesser quality prospective study; development of diagnostic criteria on consecutive patients; sensible costs and alternatives; values obtained from limited stud- ies; with multiway sensitivity analyses; systematic review of Level II studies or Level I studies with inconsistent results.

Level 3: Case-control study (therapeutic and prognostic studies); retrospective comparative study; study of nonconsecutive patients without consistently applied reference “gold” standard; analyses based on limited alternatives and costs and poor estimates; systematic review of Level III studies.

Level 4: Case series; case-control study (diagnostic studies); poor reference standard; analyses with no sensitivity analyses.

Level 5: (lower quality of evidence) – Expert opinion.

By looking at the pyramid, you can roughly distinguish what type of research gives you the highest quality of evidence and which gives you the lowest. Basically, level 1 and level 2 are filtered information – that means an author has gathered evidence from well-designed studies, with credible results, and has produced findings and conclusions appraised by renowned experts, who consider them valid and strong enough to serve researchers and scientists. Levels 3, 4 and 5 include evidence coming from unfiltered information. Because this evidence hasn’t been appraised by experts, it might be questionable, but not necessarily false or wrong.

Examples of levels of evidence

As you move up the pyramid, you will surely find higher-quality evidence. However, you will notice there is also less research available. So, if there are no resources for you available at the top, you may have to start moving down in order to find the answers you are looking for.

• Systematic Reviews: -Exhaustive summaries of all the existent literature about a certain topic. When drafting a systematic review, authors are expected to deliver a critical assessment and evaluation of all this literature rather than a simple list. Researchers that produce systematic reviews have their own criteria to locate, assemble and evaluate a body of literature.
• Meta-Analysis: Uses quantitative methods to synthesize a combination of results from independent studies. Normally, they function as an overview of clinical trials. Read more: Systematic review vs meta-analysis .
• Critically Appraised Topic: Evaluation of several research studies.
• Critically Appraised Article: Evaluation of individual research studies.
• Randomized Controlled Trial: a clinical trial in which participants or subjects (people that agree to participate in the trial) are randomly divided into groups. Placebo (control) is given to one of the groups whereas the other is treated with medication. This kind of research is key to learning about a treatment’s effectiveness.
• Cohort studies: A longitudinal study design, in which one or more samples called cohorts (individuals sharing a defining characteristic, like a disease) are exposed to an event and monitored prospectively and evaluated in predefined time intervals. They are commonly used to correlate diseases with risk factors and health outcomes.
• Case-Control Study: Selects patients with an outcome of interest (cases) and looks for an exposure factor of interest.
• Background Information/Expert Opinion: Information you can find in encyclopedias, textbooks and handbooks. This kind of evidence just serves as a good foundation for further research – or clinical practice – for it is usually too generalized.

Of course, it is recommended to use level A and/or 1 evidence for more accurate results but that doesn’t mean that all other study designs are unhelpful or useless. It all depends on your research question. Focusing once more on the healthcare and medical field, see how different study designs fit into particular questions, that are not necessarily located at the tip of the pyramid:

• Questions concerning therapy: “Which is the most efficient treatment for my patient?” >> RCT | Cohort studies | Case-Control | Case Studies
• Questions concerning diagnosis: “Which diagnose method should I use?” >> Prospective blind comparison
• Questions concerning prognosis: “How will the patient’s disease will develop over time?” >> Cohort Studies | Case Studies
• Questions concerning etiology: “What are the causes for this disease?” >> RCT | Cohort Studies | Case Studies
• Questions concerning costs: “What is the most cost-effective but safe option for my patient?” >> Economic evaluation
• Questions concerning meaning/quality of life: “What’s the quality of life of my patient going to be like?” >> Qualitative study

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Evidence based practice toolkit.

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Levels of Evidence / Evidence Hierarchy

Evidence pyramid (levels of evidence), definitions, research designs in the hierarchy, clinical questions --- research designs.

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Levels of evidence (sometimes called hierarchy of evidence) are assigned to studies based on the research design, quality of the study, and applicability to patient care. Higher levels of evidence have less risk of bias . 

Levels of Evidence (Melnyk & Fineout-Overholt 2023)

*Adapted from: Melnyk, & Fineout-Overholt, E. (2023).  Evidence-based practice in nursing & healthcare: A guide to best practice   (Fifth edition.). Wolters Kluwer.

Levels of Evidence (LoBiondo-Wood & Haber 2022)

Adapted from LoBiondo-Wood, G. & Haber, J. (2022). Nursing research: Methods and critical appraisal for evidence-based practice (10th ed.). Elsevier.

" Evidence Pyramid " is a product of Tufts University and is licensed under BY-NC-SA license 4.0

Tufts' "Evidence Pyramid" is based in part on the  Oxford Centre for Evidence-Based Medicine: Levels of Evidence (2009)

• Oxford Centre for Evidence Based Medicine Glossary

Different types of clinical questions are best answered by different types of research studies.  You might not always find the highest level of evidence (i.e., systematic review or meta-analysis) to answer your question. When this happens, work your way down to the next highest level of evidence.

This table suggests study designs best suited to answer each type of clinical question.

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Levels of Evidence

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Levels of evidence (sometimes called hierarchy of evidence) are assigned to studies based on the methodological quality of their design, validity, and applicability to patient care. These decisions gives the grade (or strength) of recommendation. Just because something is lower on the pyramid doesn't mean that the study itself is lower-quality, it just means that the methods used may not be as clinically rigorous as higher levels of the pyramid. In nursing, the system for assigning levels of evidence is often from Melnyk & Fineout-Overholt's 2011 book,  Evidence-based Practice in Nursing and Healthcare: A Guide to Best Practice .  The Levels of Evidence below are adapted from Melnyk & Fineout-Overholt's (2011) model.  

Melnyk & Fineout-Overholt (2011)

• Meta-Analysis:  A systematic review that uses quantitative methods to summarize the results. (Level 1)
• Systematic Review:  A comprehensive review that authors have systematically searched for, appraised, and summarized all of the medical literature for a specific topic (Level 1)
• Randomized Controlled Trials:  RCT's include a randomized group of patients in an experimental group and a control group. These groups are followed up for the variables/outcomes of interest. Examples of RCTs are clinical trials that compare the effects of drugs, surgical techniques, medical devices, diagnostic procedures, diets or other medical treatments. (can be Level 2 or Level 4, depending on how expansive the study)
• Non-Randomized Controlled Trials:  A clinical trial in which the participants are not assigned by chance to different treatment groups. Participants may choose which group they want to be in, or they may be assigned to the groups by the researchers.
• Cohort Study:  Identifies two groups (cohorts) of patients, one which did receive the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest. ( Level 5)
• Case-Control Study:  Involves identifying patients who have the outcome of interest (cases) and control patients without the same outcome, and looking to see if they had the exposure of interest.
• Background Information/Expert Opinion:  Handbooks, encyclopedias, and textbooks often provide a good foundation or introduction and often include generalized information about a condition.  While background information presents a convenient summary, often it takes about three years for this type of literature to be published. (Level 7)
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Critically Appraised Individual Articles

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Grades of Recommendation

Critically-appraised individual articles and synopses include:

Filtered evidence:

• Level I: Evidence from a systematic review of all relevant randomized controlled trials.
• Level II: Evidence from a meta-analysis of all relevant randomized controlled trials.
• Level III: Evidence from evidence summaries developed from systematic reviews
• Level IV: Evidence from guidelines developed from systematic reviews
• Level V: Evidence from meta-syntheses of a group of descriptive or qualitative studies
• Level VI: Evidence from evidence summaries of individual studies
• Level VII: Evidence from one properly designed randomized controlled trial

Unfiltered evidence:

• Level VIII: Evidence from nonrandomized controlled clinical trials, nonrandomized clinical trials, cohort studies, case series, case reports, and individual qualitative studies.
• Level IX: Evidence from opinion of authorities and/or reports of expert committee

Two things to remember:

1. Studies in which randomization occurs represent a higher level of evidence than those in which subject selection is not random.

2. Controlled studies carry a higher level of evidence than those in which control groups are not used.

Strength of Recommendation Taxonomy (SORT)

• SORT The American Academy of Family Physicians uses the Strength of Recommendation Taxonomy (SORT) to label key recommendations in clinical review articles. In general, only key recommendations are given a Strength-of-Recommendation grade. Grades are assigned on the basis of the quality and consistency of available evidence.
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Explanation

"Determining what constitutes the best evidence requires an ability to identify, critique and categorize literature, placing it into a so-called hierarchy of evidence or, rank-order, with randomized controlled trials (RCT's) and meta-analyses of RCT's at the top and uncontrolled studies or opinion at the bottom. This is a necessary first step as the ability to infer a recommendation or establish a grade of recommendation for a treatment or intervention is directly related to the quality of evidence that is available for review. These steps then provide the basis for the development of clinical practice guidelines, to not replace clinical decision making but augment it. There have been a number of systems developed to try to categorize studies into their respective levels of evidence."

Source: B.A. Petrisor, J. Keating, E. Schemitsch. Grading the evidence: Levels of evidence and grades of recommendation, Injury, Volume 37, Issue 4, 2006, Pages 321-327, ISSN 0020-1383, https://doi.org/10.1016/j.injury.2006.02.001 .

Source: Merlin, T. , Weston, A. , Tooher, R., (2009). Extending An Evidence Hierarchy To Include Topics Other Than Treatement: Revisting The Australian 'Levels of Evidence'. BMC Medical Research Methodology 2009, 9:34.   doi:10.1186/1471-2288-9-34

Multimedia Resources

A general overview of the concept of levels of evidence and how it is applied in the medical field. Source ( https://youtu.be/OaOzXEWIXY4 )

Provides a more in-depth look at the different levels of evidence as reported in the John Hopkins Hierarchy. ( Source  https://youtu.be/u_-lxyFtlN8 )

Relevant Articles

• An Analysis of References Used for the Orthopaedic In-Training Examination: What are Their Levels of Evidence and Journal Impact Factors?
• Extending An Evidence Hierarchy To Include Topics Other Than Treatment: Revising The Australian 'Levels Of Evidence'
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• Levels Of Evidence: A Comparison Between Top Medical Journals And General Pediatric Journals
• Levels of Evidence in the Clinical Sports Medicine Literature: Are We Getting Better Over Time?
• Levels Of Evidence Ratings In The Urological Literature: An Assessment Of Interobserver Agreement
• A Nurses’ Guide To The Hierarchy Of Research Designs And Evidence
• What Are The Levels Of Evidence On Which We Base Decisions For Surgical Management Of Lower Extremity Bone Tumors?

Relevant Websites

• Determining The Level Of Evidence: Experimental Research Appraisal - Nursing2020
• Evidence-Based Nursing Research Guide: Evidence Levels & Types - DePaul University Library
• Evidence-Based Practice - Levels of Evidence - Nurse.com
• Grading Levels Of Evidence - Clinical Information Access Portal
• The Levels Of Evidence And Their Role In Evidence-Based Medicine - National Library Of Medicine
• Levels Of Evidence In Research - Elsevier
• Levels of Evidence in Research: Examples, Hierachies & Practice - Research.Com
• Nursing - Evidence-Based Practice: Levels of Evidence - Simmons University Library
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Systematic Reviews: Levels of evidence and study design

Levels of evidence.

"Levels of Evidence" tables have been developed which outline and grade the best evidence. However, the review question will determine the choice of study design.

Secondary sources provide analysis, synthesis, interpretation and evaluation of primary works. Secondary sources are not evidence, but rather provide a commentary on and discussion of evidence. e.g. systematic review

Primary sources contain the original data and analysis from research studies. No outside evaluation or interpretation is provided. An example of a primary literature source is a peer-reviewed research article. Other primary sources include preprints, theses, reports and conference proceedings.

Levels of evidence for primary sources fall into the following broad categories of study designs   (listed from highest to lowest):

• Experimental : RTC's (Randomised Control Trials)
• Quasi-experimental studies (Non-randomised control studies, Before-and-after study, Interrupted time series)
• Observational studies (Cohort study, Case-control study, Case series) 

Based on information from Centre for Reviews and Dissemination. (2009). Systematic reviews: CRD's guidance for undertaking reviews in health care. Retrieved from http://www.york.ac.uk/inst/crd/index_guidance.htm

Hierarchy of Evidence Pyramid

"Levels of Evidence" are often represented in as a pyramid, with the highest level of evidence at the top:

Types of Study Design

The following definitions are adapted from the Glossary in " Systematic reviews: CRD's Guidance for Undertaking Reviews in Health Care " , Centre for Reviews and Dissemination, University of York :

• Systematic Review The application of strategies that limit bias in the assembly, critical appraisal, and synthesis of all relevant studies on a specific topic and research question. 
• Meta-analysis A systematic review which uses quantitative methods to summarise the results
• Randomized control clinical trial (RCT) A group of patients is randomised into an experimental group and a control group. These groups are followed up for the variables/outcomes of interest.
• Cohort study Involves the identification of two groups (cohorts) of patients, one which did receive the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.
• Case-control study Involves identifying patients who have the outcome of interest (cases) and control patients without the same outcome, and looking to see if they had the exposure of interest.
• Critically appraised topic A short summary of an article from the literature, created to answer a specific clinical question.

EBM and Study Design

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Nursing-Johns Hopkins Evidence-Based Practice Model

Jhebp model for levels of evidence, jhebp levels of evidence overview.

• Levels I, II and III

Evidence-Based Practice (EBP) uses a rating system to appraise evidence (usually a research study published as a journal article). The level of evidence corresponds to the research study design. Scientific research is considered to be the strongest form of evidence and recommendations from the strongest form of evidence will most likely lead to the best practices. The strength of evidence can vary from study to study based on the methods used and the quality of reporting by the researchers. You will want to seek the highest level of evidence available on your topic (Dang et al., 2022, p. 130).

The Johns Hopkins EBP model uses 3 ratings for the level of scientific research evidence 

• true experimental (level I)
• quasi-experimental (level II)
• nonexperimental (level III) 

The level determination is based on the research meeting the study design requirements  (Dang et al., 2022, p. 146-7).

You will use the Research Appraisal Tool (Appendix E) along with the Evidence Level and Quality Guide (Appendix D) to analyze and  appraise research studies . (Tools linked below.)

N onresearch evidence is covered in Levels IV and V.

• Evidence Level and Quality Guide (Appendix D)
• Research Evidence Appraisal Tool (Appendix E)

Level I Experimental study

randomized controlled trial (RCT)

Systematic review of RCTs, with or without meta-analysis

Level II Quasi-experimental Study

Systematic review of a combination of RCTs and quasi-experimental, or quasi-experimental studies only, with or without meta-analysis.

Level III Non-experimental study

Systematic review of a combination of RCTs, quasi-experimental and non-experimental, or non-experimental studies only, with or without meta-analysis.

Qualitative study or systematic review, with or without meta-analysis

Level IV Opinion of respected authorities and/or nationally recognized expert committees/consensus panels based on scientific evidence.

Clinical practice guidelines

Consensus panels

Level V Based on experiential and non-research evidence.

Literature reviews

Quality improvement, program, or financial evaluation

Case reports

Opinion of nationally recognized expert(s) based on experiential evidence

These flow charts can also help you detemine the level of evidence throigh a series of questions.

Single Quantitative Research Study

Summary/Reviews 

These charts are a part of the Research Evidence Appraisal Tool (Appendix E) document.

Dang, D., Dearholt, S., Bissett, K., Ascenzi, J., & Whalen, M. (2022). Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model and guidelines. 4th ed. Sigma Theta Tau International

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Systematic Review Process: best practices

Levels of evidence.

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Hierarchy of evidence pyramid

The pyramidal shape qualitatively integrates the amount of evidence generally available from each type of study design and the strength of evidence expected from indicated designs.  Study designs in ascending levels of the pyramid generally exhibit increased quality of evidence and reduced risk of bias.

Understand the different levels of evidence

Meta Analysis  - systematic review that uses quantitative methods to synthesize and summarize the results.

Systematic Review  - summary of the medical literature that uses explicit methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate statistical techniques to combine these valid studies.

Randomised Controlled Trial  - Participants are randomly allocated into an experimental group or a control group and followed over time for the variables/outcomes of interest.

Cohort Study  - Involves identification of two groups (cohorts) of patients, one which received the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.

Case Control Study  - study which involves identifying patients who have the outcome of interest (cases) and patients without the same outcome (controls), and looking back to see if they had the exposure of interest.

Case Series  - report on a series of patients with an outcome of interest. No control group is involved.  (Definitions from CEBM)

Scholarly publications

The Joanna Briggs Institute Reviewers’ Manual 2015 Methodology for JBI Scoping Reviews

Clarifying differences between review designs and methods

Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach

A scoping review of scoping reviews: advancing the approach and enhancing the consistency

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Evidence-Based Practice (EBP)

• The EBP Process
• Forming a Clinical Question
• Inclusion & Exclusion Criteria
• Acquiring Evidence
• Appraising the Quality of the Evidence
• Writing a Literature Review
• Finding Psychological Tests & Assessment Instruments

What Is a Literature Review?

A literature review is an integrated analysis of scholarly writings that are related directly to your research question. Put simply, it's  a critical evaluation of what's already been written on a particular topic . It represents the literature that provides background information on your topic and shows a connection between those writings and your research question.

A literature review may be a stand-alone work or the introduction to a larger research paper, depending on the assignment. Rely heavily on the guidelines your instructor has given you.

What a Literature Review Is Not:

• A list or summary of sources
• An annotated bibliography
• A grouping of broad, unrelated sources
• A compilation of everything that has been written on a particular topic
• Literary criticism (think English) or a book review

Why Literature Reviews Are Important

• They explain the background of research on a topic
• They demonstrate why a topic is significant to a subject area
• They discover relationships between research studies/ideas
• They identify major themes, concepts, and researchers on a topic
• They identify critical gaps and points of disagreement
• They discuss further research questions that logically come out of the previous studies

To Learn More about Conducting and Writing a Lit Review . . .

Monash University (in Australia) has created several extremely helpful, interactive tutorials. 

• The Stand-Alone Literature Review, https://www.monash.edu/rlo/assignment-samples/science/stand-alone-literature-review
• Researching for Your Literature Review,  https://guides.lib.monash.edu/researching-for-your-literature-review/home
• Writing a Literature Review,  https://www.monash.edu/rlo/graduate-research-writing/write-the-thesis/writing-a-literature-review

Keep Track of Your Sources!

A citation manager can be helpful way to work with large numbers of citations. See UMSL Libraries' Citing Sources guide for more information. Personally, I highly recommend Zotero —it's free, easy to use, and versatile. If you need help getting started with Zotero or one of the other citation managers, please contact a librarian.

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• Volume 21, Issue 4
• New evidence pyramid
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• M Hassan Murad ,
• Mouaz Alsawas ,
• http://orcid.org/0000-0001-5481-696X Fares Alahdab
• Rochester, Minnesota , USA
• Correspondence to : Dr M Hassan Murad, Evidence-based Practice Center, Mayo Clinic, Rochester, MN 55905, USA; murad.mohammad{at}mayo.edu

https://doi.org/10.1136/ebmed-2016-110401

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• EDUCATION & TRAINING (see Medical Education & Training)
• EPIDEMIOLOGY
• GENERAL MEDICINE (see Internal Medicine)

The first and earliest principle of evidence-based medicine indicated that a hierarchy of evidence exists. Not all evidence is the same. This principle became well known in the early 1990s as practising physicians learnt basic clinical epidemiology skills and started to appraise and apply evidence to their practice. Since evidence was described as a hierarchy, a compelling rationale for a pyramid was made. Evidence-based healthcare practitioners became familiar with this pyramid when reading the literature, applying evidence or teaching students.

Various versions of the evidence pyramid have been described, but all of them focused on showing weaker study designs in the bottom (basic science and case series), followed by case–control and cohort studies in the middle, then randomised controlled trials (RCTs), and at the very top, systematic reviews and meta-analysis. This description is intuitive and likely correct in many instances. The placement of systematic reviews at the top had undergone several alterations in interpretations, but was still thought of as an item in a hierarchy. 1 Most versions of the pyramid clearly represented a hierarchy of internal validity (risk of bias). Some versions incorporated external validity (applicability) in the pyramid by either placing N-1 trials above RCTs (because their results are most applicable to individual patients 2 ) or by separating internal and external validity. 3

Another version (the 6S pyramid) was also developed to describe the sources of evidence that can be used by evidence-based medicine (EBM) practitioners for answering foreground questions, showing a hierarchy ranging from studies, synopses, synthesis, synopses of synthesis, summaries and systems. 4 This hierarchy may imply some sort of increasing validity and applicability although its main purpose is to emphasise that the lower sources of evidence in the hierarchy are least preferred in practice because they require more expertise and time to identify, appraise and apply.

The traditional pyramid was deemed too simplistic at times, thus the importance of leaving room for argument and counterargument for the methodological merit of different designs has been emphasised. 5 Other barriers challenged the placement of systematic reviews and meta-analyses at the top of the pyramid. For instance, heterogeneity (clinical, methodological or statistical) is an inherent limitation of meta-analyses that can be minimised or explained but never eliminated. 6 The methodological intricacies and dilemmas of systematic reviews could potentially result in uncertainty and error. 7 One evaluation of 163 meta-analyses demonstrated that the estimation of treatment outcomes differed substantially depending on the analytical strategy being used. 7 Therefore, we suggest, in this perspective, two visual modifications to the pyramid to illustrate two contemporary methodological principles ( figure 1 ). We provide the rationale and an example for each modification.

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The proposed new evidence-based medicine pyramid. (A) The traditional pyramid. (B) Revising the pyramid: (1) lines separating the study designs become wavy (Grading of Recommendations Assessment, Development and Evaluation), (2) systematic reviews are ‘chopped off’ the pyramid. (C) The revised pyramid: systematic reviews are a lens through which evidence is viewed (applied).

Rationale for modification 1

In the early 2000s, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group developed a framework in which the certainty in evidence was based on numerous factors and not solely on study design which challenges the pyramid concept. 8 Study design alone appears to be insufficient on its own as a surrogate for risk of bias. Certain methodological limitations of a study, imprecision, inconsistency and indirectness, were factors independent from study design and can affect the quality of evidence derived from any study design. For example, a meta-analysis of RCTs evaluating intensive glycaemic control in non-critically ill hospitalised patients showed a non-significant reduction in mortality (relative risk of 0.95 (95% CI 0.72 to 1.25) 9 ). Allocation concealment and blinding were not adequate in most trials. The quality of this evidence is rated down due to the methodological imitations of the trials and imprecision (wide CI that includes substantial benefit and harm). Hence, despite the fact of having five RCTs, such evidence should not be rated high in any pyramid. The quality of evidence can also be rated up. For example, we are quite certain about the benefits of hip replacement in a patient with disabling hip osteoarthritis. Although not tested in RCTs, the quality of this evidence is rated up despite the study design (non-randomised observational studies). 10

Rationale for modification 2

Another challenge to the notion of having systematic reviews on the top of the evidence pyramid relates to the framework presented in the Journal of the American Medical Association User's Guide on systematic reviews and meta-analysis. The Guide presented a two-step approach in which the credibility of the process of a systematic review is evaluated first (comprehensive literature search, rigorous study selection process, etc). If the systematic review was deemed sufficiently credible, then a second step takes place in which we evaluate the certainty in evidence based on the GRADE approach. 11 In other words, a meta-analysis of well-conducted RCTs at low risk of bias cannot be equated with a meta-analysis of observational studies at higher risk of bias. For example, a meta-analysis of 112 surgical case series showed that in patients with thoracic aortic transection, the mortality rate was significantly lower in patients who underwent endovascular repair, followed by open repair and non-operative management (9%, 19% and 46%, respectively, p<0.01). Clearly, this meta-analysis should not be on top of the pyramid similar to a meta-analysis of RCTs. After all, the evidence remains consistent of non-randomised studies and likely subject to numerous confounders.

Therefore, the second modification to the pyramid is to remove systematic reviews from the top of the pyramid and use them as a lens through which other types of studies should be seen (ie, appraised and applied). The systematic review (the process of selecting the studies) and meta-analysis (the statistical aggregation that produces a single effect size) are tools to consume and apply the evidence by stakeholders.

Implications and limitations

Changing how systematic reviews and meta-analyses are perceived by stakeholders (patients, clinicians and stakeholders) has important implications. For example, the American Heart Association considers evidence derived from meta-analyses to have a level ‘A’ (ie, warrants the most confidence). Re-evaluation of evidence using GRADE shows that level ‘A’ evidence could have been high, moderate, low or of very low quality. 12 The quality of evidence drives the strength of recommendation, which is one of the last translational steps of research, most proximal to patient care.

One of the limitations of all ‘pyramids’ and depictions of evidence hierarchy relates to the underpinning of such schemas. The construct of internal validity may have varying definitions, or be understood differently among evidence consumers. A limitation of considering systematic review and meta-analyses as tools to consume evidence may undermine their role in new discovery (eg, identifying a new side effect that was not demonstrated in individual studies 13 ).

This pyramid can be also used as a teaching tool. EBM teachers can compare it to the existing pyramids to explain how certainty in the evidence (also called quality of evidence) is evaluated. It can be used to teach how evidence-based practitioners can appraise and apply systematic reviews in practice, and to demonstrate the evolution in EBM thinking and the modern understanding of certainty in evidence.

• Leibovici L
• Agoritsas T ,
• Vandvik P ,
• Neumann I , et al
• ↵ Resources for Evidence-Based Practice: The 6S Pyramid. Secondary Resources for Evidence-Based Practice: The 6S Pyramid Feb 18, 2016 4:58 PM. http://hsl.mcmaster.libguides.com/ebm
• Vandenbroucke JP
• Berlin JA ,
• Dechartres A ,
• Altman DG ,
• Trinquart L , et al
• Guyatt GH ,
• Vist GE , et al
• Coburn JA ,
• Coto-Yglesias F , et al
• Sultan S , et al
• Montori VM ,
• Ioannidis JP , et al
• Altayar O ,
• Bennett M , et al
• Nissen SE ,

Contributors MHM conceived the idea and drafted the manuscript. FA helped draft the manuscript and designed the new pyramid. MA and NA helped draft the manuscript.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Linked Articles

• Editorial Pyramids are guides not rules: the evolution of the evidence pyramid Terrence Shaneyfelt BMJ Evidence-Based Medicine 2016; 21 121-122 Published Online First: 12 Jul 2016. doi: 10.1136/ebmed-2016-110498
• Perspective EBHC pyramid 5.0 for accessing preappraised evidence and guidance Brian S Alper R Brian Haynes BMJ Evidence-Based Medicine 2016; 21 123-125 Published Online First: 20 Jun 2016. doi: 10.1136/ebmed-2016-110447

Read the full text or download the PDF:

AACN Levels of Evidence

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Added to Collection

Level A  — Meta-analysis of quantitative studies or metasynthesis of qualitative studies with results that consistently support a specific action, intervention, or treatment (including systematic review of randomized controlled trials).

Level B  — Well-designed, controlled studies with results that consistently support a specific action, intervention, or treatment.

Level C  — Qualitative studies, descriptive or correlational studies, integrative review, systematic review, or randomized controlled trials with inconsistent results.

Level D  — Peer-reviewed professional and organizational standards with the support of clinical study recommendations.

Level E  — Multiple case reports, theory-based evidence from expert opinions, or peer-reviewed professional organizational standards without clinical studies to support recommendations.

Level M  — Manufacturer’s recommendations only.

(Excerpts from Peterson et al. Choosing the Best Evidence to Guide Clinical Practice: Application of AACN Levels of Evidence. Critical Care Nurse. 2014;34[2]:58-68.)

What is the purpose of levels of evidence (LOEs)?

“The amount and availability of research supporting evidence-based practice can be both useful and overwhelming for critical care clinicians. Therefore, clinicians must critically evaluate research before attempting to put the findings into practice. Evaluation of research generally occurs on 2 levels: rating or grading the evidence by using a formal level-of-evidence system and individually critiquing the quality of the study. Determining the level of evidence is a key component of appraising the evidence.1-3 Levels or hierarchies of evidence are used to evaluate and grade evidence. The purpose of determining the level of evidence and then critiquing the study is to ensure that the evidence is credible (eg, reliable and valid) and appropriate for inclusion into practice.3 Critique questions and checklists are available in most nursing research and evidence-based practice texts to use as a starting point in evaluation.”

How are LOEs determined?

“The most common method used to classify or determine the level of evidence is to rate the evidence according to the methodological rigor or design of the research study.3,4 The rigor of a study refers to the strict precision or exactness of the design. In general, findings from experimental research are considered stronger than findings from nonexperimental studies, and similar findings from more than 1 study are considered stronger than results of single studies. Systematic reviews of randomized controlled trials are considered the highest level of evidence, despite the inability to provide answers to all questions in clinical practice.”4,5

Who developed the AACN LOEs?

“As interest in promoting evidence-based practice has grown, many professional organizations have adopted criteria to evaluate evidence and develop evidence-based guidelines for their members.”1,5 Originally developed in 1995, AACN’s rating scale has been updated in 2008 and 2014 by the Evidence-Based Practice Resources Workgroup (EBPRWG). The 2011-2013 EBPRWG continued the tradition of previous workgroups to move research to the patient bedside.

What are the AACN LOEs and how are they used?

The AACN levels of evidence are structured in an alphabetical hierarchy in which the highest form of evidence is ranked as A and includes meta-analyses and meta-syntheses of the results of controlled trials. Evidence from controlled trials is rated B. Level C, the highest level for nonexperimental studies includes systematic reviews of qualitative, descriptive, or correlational studies. “Levels A, B, and C are all based on research (either experimental or nonexperimental designs) and are considered evidence. Levels D, E, and M are considered recommendations drawn from articles, theory, or manufacturers’ recommendations.”

“Clinicians must critically evaluate research before attempting to implement the findings into practice. The clinical relevance of any research must be evaluated as appropriate for inclusion into practice.”

• Polit DF, Beck CT. Resource Manual for Nursing Research: Generating and Assessing Evidence for Nursing Practice. 9th ed. Philadelphia, PA: Williams & Wilkins; 2012.
• Armola RR, Bourgault AM, Halm MA, et al; 2008-2009 Evidence-Based Practice Resource Work Group of the American Association of Critical-Care Nurses. Upgrading the American Association of Critical-Care Nurses’ evidence-leveling hierarchy. Am J Crit Care. 2009;18(5):405-409.
• Melnyk BM, Fineout-Overholt, E. Evidence-Based Practice in Nursing and Healthcare: A Guide to Best Practice. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2011.
• Gugiu PC, Gugiu MR. A critical appraisal of standard guidelines for grading levels of evidence. Eval Health Prof. 2010;33(3):233-255. doi:10.1177/0163278710373980.
• Evans D. Hierarchy of evidence: a framework for ranking evidence evaluating healthcare interventions. J Clin Nurs. 2003;12(1):77-84.

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Nursing Resources : Table of Evidence

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Example of a Table of Evidence

• Evidence table

Assessing the evidence and Building a Table

• EBM-Assessing the Evidence, Critical Appraisal

One of the most important steps in writing a paper is showing the strength and rationale of the evidence you chosen.  The following document discusses the reasoning, grading and creation of a "Table of Evidence."  While table of evidences can differ, the examples given in this article are a great starting point.

• << Previous: Types of Studies
• Next: Qualitative vs Quantitative >>
• Last Updated: Mar 19, 2024 10:39 AM
• URL: https://researchguides.library.wisc.edu/nursing

SYSTEMATIC REVIEW article

Evaluating the efficacy of mesenchymal stem cells for diabetic neuropathy: a systematic review and meta-analysis of preclinical studies provisionally accepted.

• 1 Department of Plastic Surgery, First Affiliated Hospital of Zhengzhou University, China
• 2 Key Laboratory of Tissue Engineering Research, Shanghai Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, China

The final, formatted version of the article will be published soon.

Diabetic neuropathy affects nearly half of all diabetics and poses a significant threat to public health. Recent preclinical studies suggest that mesenchymal stem cells (MSCs) may represent a promising solution for the treatment of diabetic neuropathy. However, an objective assessment of the preclinical effectiveness of MSCs is still pending. We conducted a comprehensive search of PubMed, Web of Science, Embase, and Cochrane library to identify preclinical studies that investigate the effects of MSCs on diabetic neuropathy up until 15 September 2023. Outcome indicators consisted of motor and sensory nerve conduction velocities, intra-epidermal nerve fiber density, sciatic nerve blood flow, capillary-to-muscle fiber ratio, neurotrophic factors, angiogenic factors and inflammatory cytokines. The literature review and meta-analysis were conducted independently by two researchers. 23 studies that met the inclusion criteria were included in this system review for qualitative and quantitative analysis. Pooled analyses indicated that MSCs exhibited an evident benefit in diabetic neuropathy in terms of motor (SMD=2.16, 95% CI: 1.71 to 2.61) and sensory nerve conduction velocities (SMD=2.93, 95% CI: 1.78 to 4.07), intra-epidermal nerve fiber density (SMD=3.17, 95% CI: 2.28 to 4.07), sciatic nerve blood flow (SMD=2.02, 95% CI: 1.37 to 2.66), and capillary-tomuscle fiber ratio (SMD=2.28, 95% CI: 1.55 to 3.01, P<0.00001). Furthermore, after MSC therapy, the expressions of neurotrophic and angiogenic factors increased significantly in most studies, while the levels of inflammatory cytokines were significantly reduced. The relevance of this review relies on the fact that summarizes an extensive body of work entailing substantial preclinical evidence that supports the efficacy of MSCs in mitigating diabetic neuropathy. While MSCs emerge as a promising potential treatment for diabetic neuropathy, further research is essential to elucidate the underlying mechanisms and the best administration strategy for MSCs.

Keywords: Diabetic neuropathy, Neurological Disorder, Stem Cell Therapy, Mesenchymal Stem Cells, Meta-analysis

Received: 04 Dec 2023; Accepted: 17 Apr 2024.

Copyright: © 2024 Li, Yue, Yu, Cheng, Cao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: MD, PhD. Ximei Wang, Department of Plastic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

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• DOI: 10.17116/kurort202410102157

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• Published: 26 November 2023

Impact of industrial robots on environmental pollution: evidence from China

• Yanfang Liu 1  

Scientific Reports volume  13 , Article number:  20769 ( 2023 ) Cite this article

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• Environmental sciences
• Environmental social sciences

The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO 2 , and industrial dust, the impacts generated by industrial robots are exhibiting trends of a “W” shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution.

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Introduction

Since the reform and opening up, China’s rapid economic growth has created a world-renowned “economic growth miracle” 1 . With the rapid economic growth, China’s environmental pollution problem is becoming more and more serious 2 . According to the “ Global Environmental Performance Index Report ” released by Yale University in the United States in 2022, China’s environmental performance index scores 28.4 points, ranking 160th out of 180 participating countries. The aggravation of environmental pollution not only affects residents’ health 3 , but also affects the efficiency of economic operation 4 . According to calculation of the General Administration of Environmental Protection, the World Bank and the Chinese Academy of Sciences, China’s annual losses caused by environmental pollution account for about 10% of GDP. Exploring the factors that affect environmental pollution and seeking ways to reduce environmental pollution are conducive to the development of economy within the scope of environment.

Industrial robots are machines that can be automatically controlled, repeatedly programmed, and multi-purpose 5 . They replace the low-skilled labor force engaged in procedural work 6 , reducing the raw materials required for manual operation. Industrial robots improve the clean technology level and energy efficiency of coal combustion, reducing pollutant emissions in front-end production. Industrial robots also monitor the energy consumption and sewage discharge in the production process in real time. The excessive discharge behavior of enterprises in the production process is regulated, reducing the emission of pollutants in the end treatment. Based on the selection and coding of literature (Appendix A ), this paper uses the meta-analysis method to compare the impacts of multiple factors such as economics, population, technology, and policy on environmental pollution. As shown in Table 1 , compared to other factors, industrial robots demonstrate greater advantages in reducing environmental pollution. There is a lack of research on the relationship between industrial robots and environmental pollution in China. With the advent of artificial intelligence era, China’s industrial robot industry has developed rapidly. According to data released by the International Federation of Robotics (IFR), from 1999 to 2019, China’s industrial robot ownership and installation shows an increasing trend year by year (Fig.  1 ). In 2013 and 2016, China’s industrial robot installation (36,560) and ownership (349,470) exceeds Japan for the first time, becoming the world’s largest country in terms of installation and ownership of industrial robots. Whether the application of industrial robots in China contributes to the reduction of environmental pollution? What is the mechanism of the impact of China’s industrial robots on environmental pollution? Researching this issue is crucial for filling the gaps in existing research and providing a reference for other countries to achieve emission reduction driven by robots.

Industrial robot installations in the world’s top five industrial robot markets from 1999 to 2019.

Based on the above analysis, this paper innovatively incorporates industrial robots and environmental pollution into a unified framework. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the ordinary panel model and mediating effect model to empirically test the impact of industrial robots on China’s environmental pollution and its transmission channels. The panel quantile model is used to empirically analyze the heterogeneous impact of industrial robots on environmental pollution under different environmental pollution levels.

Literature review

A large number of scholars have begun to study the problem of environmental pollution. Its research content mainly includes two aspects: The measurement of environmental pollution and its influencing factors. Regarding the measurement, some scholars have used SO 2 emissions 7 , industrial soot emissions 8 and PM2.5 concentration 9 and other single indicators to measure the degree of environmental pollution. The single indicator cannot fully and scientifically reflect the degree of environmental pollution. To make up for this defect, some scholars have included industrial SO 2 emissions, industrial wastewater discharge and industrial soot emissions into the environmental pollution evaluation system, and used the entropy method to measure environmental pollution level 10 . This method ignores the different characteristics and temporal and spatial trends of different pollutants, which makes the analysis one-sided. Regarding the influencing factors, economic factors such as economic development level 11 , foreign direct investment 12 and income 13 , population factors such as population size 14 and urbanization level 15 , energy consumption 16 all have an impact on environmental pollution. Specifically, economic development and technological innovation can effectively reduce environmental pollution 17 . The expansion of population size can aggravate environmental pollution. Income inequality can reduce environmental pollution, but higher income inequality may aggravate environmental pollution 18 . There are “pollution heaven hypothesis” and “pollution halo hypothesis” between foreign direct investment and environmental pollution 19 . Technological factors also have a non-negligible impact on environmental pollution 20 .

With continuous deepening of research, scholars have begun to focus on the impact of automation technology, especially industrial robot technology, on the environment. Ghobakhloo et al. 21 theoretically analyzed the impact of industrial robots on energy sustainability, contending that the application of industrial robots could foster sustainable development of energy. Using data from multiple countries, a few scholars have empirically analyzed the effect of industrial robots on environmental pollution (Table 2 ). Luan et al. 22 used panel data from 73 countries between 1993 and 2019 to empirically analyze the impact of industrial robots on air pollution, finding that the use of industrial robots intensifies environmental pollution. Using panel data from 66 countries from 1993 to 2018, Wang et al. 23 analyzed the impact of industrial robots on carbon intensity and found that industrial robots can reduce carbon intensity. On the basis of analyzing the overall impact of industrial robots on environmental pollution, some scholars conducted in-depth exploration of its mechanism. Based on data from 72 countries between 1993 and 2019, Chen et al. 5 explored the impact of industrial robots on the ecological footprint, discovering that industrial robots can reduce the ecological footprint through time saving effect, green employment effect and energy upgrading effect. Using panel data from 35 countries between 1993 and 2017, Li et al. 24 empirically examined the carbon emission reduction effect of industrial robots, finding that industrial robots can effectively reduce carbon emissions by increasing green total factor productivity and reducing energy intensity. Although the above studies have successfully estimated the overall impact of industrial robots on environmental pollution and its mechanisms, they have not fully considered the role of technological progress, labor structure and other factors in the relationship between the two. These studies all chose data from multiple countries as research samples and lack research on the relationship between industrial robots and environmental pollution in China, an emerging country.

The above literature provides inspiration for this study, but there are still shortcomings in the following aspects: Firstly, there is a lack of research on the relationship between industrial robots and environmental pollution in emerging countries. There are significant differences between emerging and developed countries in terms of institutional background and the degree of environmental pollution. As a representative emerging country, research on the relationship between industrial robots and environmental pollution in China can provide reliable references for other emerging countries. Secondly, theoretically, the study of the impact of industrial robots on environmental pollution is still in its initial stage. There are few studies that deeply explore its impact mechanism, and there is a lack of analysis of the role of technological progress and labor structure in the relationship between the two.

The innovations of this paper are as follows: (1) In terms of sample selection, this paper selects panel data from 30 provinces in China from 2006 to 2019 as research samples to explore the relationship between industrial robots and environmental pollution in China, providing references for other emerging countries to improve environmental quality using industrial robots. (2) In terms of theory, this paper is not limited to revealing the superficial relationship between industrial robots and environmental pollution. it starts from a new perspective and provides an in-depth analysis of how industrial robots affect environmental pollution through employment skill structure and green technology innovation. This not only enriches research in the fields of industrial robots and the environment, but is also of great significance in guiding the direction of industrial policy and technology research and development.

Theoretical analysis and hypothesis

Industrial robots and environmental pollution.

As shown in Fig.  2 , the impact of industrial robots on environmental pollution is mainly reflected in two aspects: Front-end production and end treatment. In front-end production, industrial robots enable artificial substitution effects 25 . Manual operation is replaced by machine operation, reducing the raw materials needed for manual operation. Through the specific program setting of industrial robots, clean energy is applied to industrial production 26 . The use of traditional fuels such as coal and oil is reduced. In terms of end treatment, the traditional pollutant concentration tester only measures a single type of pollutant. Its data cannot be obtained in time. It is easy to cause pollution incidents. Industrial robots can measure a variety of pollutants, and have the function of remote unmanned operation and warning. It reflects the pollution situation in time, reducing the probability of pollution incidents. The use of robots can upgrade sewage treatment equipment and improve the accuracy of pollution treatment, reducing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 1.

The impact of industrial robots on environmental pollution.

Hypothesis 1

The use of industrial robots can reduce environmental pollution.

Mediating effect of green technology innovation

Industrial robots can affect environmental pollution by promoting green technology innovation. The transmission path of “industrial robots-green technology innovation-environmental pollution” is formed. Industrial robots are the materialization of technological progress in the field of enterprise R&D. Its impact on green technology innovation is mainly manifested in the following two aspects: Firstly, industrial robots classify known knowledge, which helps enterprises to integrate internal and external knowledge 27 . The development of green technology innovation activities of enterprises is promoted. Secondly, enterprises can simulate existing green technologies through industrial robots. The shortcomings of green technology in each link are found. Based on this, enterprises can improve and perfect green technology in a targeted manner. Industrial robots can collect and organize data, which enables enterprises to predict production costs and raw material consumption. Excessive procurement by enterprises can occupy working capital. Inventory backlog leads to warehousing, logistics and other expenses, increasing storage costs 28 . Forecasting the consumption of raw materials allows enterprises to purchase precisely, preventing over-procurement and inventory backlog, thereby reducing the use of working capital and storage costs 29 . The production cost of enterprises is reduced. Enterprises have more funds for green technology research and development.

The continuous innovation of green technology is helpful to solve the problem of environmental pollution. Firstly, green technology innovation helps use resources better 30 , lowers dependence on old energy, and reduces environmental damage. Secondly, green technology innovation promotes the greening of enterprises in manufacturing, sales and after-sales 31 . The emission of pollutants in production process is reduced. Finally, green technology innovation improves the advantages of enterprises in market competition 32 . The production possibility curve expands outward, which encourages enterprises to carry out intensive production. Based on the above analysis, this paper proposes hypothesis 2.

Hypothesis 2

Industrial robots can reduce environmental pollution through green technology innovation.

Mediating effect of employment skill structure

Industrial robots can affect environmental pollution through employment skill structure. The transmission path of “industrial robots-employment skill structure-environmental pollution” is formed. Industrial robots have substitution effect and creation effect on the labor force, improving the employment skill structure. Regarding the substitution effect, enterprises use industrial robots to complete simple and repetitive tasks to improve production efficiency, which crowds out low-skilled labor 6 . Regarding the creation effect, industrial robots create a demand for new job roles that matches automation, such as robot engineers, data analysts, machine repairers, which increases the number of highly skilled labor 33 . The reduction of low-skilled labor and increase of high-skilled labor improve employment skill structure.

High-skilled labor is reflected in the level of education 34 . Its essence is to have a higher level of skills and environmental awareness, which is the key to reducing environmental pollution. Compared with low-skilled labor, high-skilled labor has stronger ability to acquire knowledge and understand skills, which improves the efficiency of cleaning equipment and promotes emission reduction. The interaction and communication between highly skilled labor is also crucial for emission reduction. The excessive wage gap between employees brings high communication costs, which hinders the exchange of knowledge and technology between different employees. The increase in the proportion of high-skilled labor can solve this problem and improve the production efficiency of enterprises 35 . The improvement of production efficiency enables more investment in emission reduction research, decreasing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 3.

Hypothesis 3

Industrial robots can reduce environmental pollution by optimizing employment skills structure.

Model construction and variable selection

Model construction, panel data model.

The panel data model is a significant statistical method, first introduced by Mundlak 36 . Subsequently, numerous scholars have used this model to examine the baseline relationships between core explanatory variables and explained variables 37 . To test the impact of industrial robots on environmental pollution, this paper sets the following panel data model:

In formula ( 1 ), Y it is the explained variable, indicating the degree of environmental pollution in region i in year t . IR it is the core explanatory variable, indicating the installation density of industrial robots in region i in year t . X it is a series of control variables, including economic development level (GDP), urbanization level (URB), industrial structure (EC), government intervention (GOV) and environmental regulation (ER). \(\lambda i\) is the regional factor. \(\varphi t\) is the time factor. \(\varepsilon it\) is the disturbance term.

Mediating effect model

To test the transmission mechanism of industrial robots affecting environmental pollution, this paper sets the following mediating effect model:

In formula ( 2 ), M is the mediating variable, which mainly includes green technology innovation and employment skill structure. Formula ( 2 ) measures the impact of industrial robots on mediating variables. Formula ( 3 ) measures the impact of intermediary variables on environmental pollution. According to the principle of mediating effect 38 , the direct effect \(\theta 1\) , mediating effect \(\beta 1 \times \theta 2\) and total effect \(\alpha 1\) satisfy \(\alpha 1 = \theta 1 + \beta 1 \times \theta 2\) .

Panel quantile model

The panel quantile model was first proposed by Koenke and Bassett 39 . It is mainly used to analyze the impact of core explanatory variables on the explained variables under different quantiles 40 . To empirically test the heterogeneous impact of industrial robots on environmental pollution under different levels of environmental pollution, this paper sets the following panel quantile model:

In formula ( 4 ), \(\tau\) represents the quantile value. \(\gamma 1\) reflects the difference in the impact of industrial robots on environmental pollution at different quantiles. \(\gamma 2\) indicates the different effects of control variables at different quantiles.

Variable selection

Explained variable.

The explained variable is environmental pollution. Considering the timeliness and availability of data, this paper selects industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as indicators of environmental pollution.

Explanatory variable

According to production theory, industrial robots can enhance production efficiency 41 . Efficient production implies reduced energy wastage, which in turn decreases the emission of pollutants. Industrial robots can upgrade pollution control equipment, heightening the precision in pollution treatment and reducing pollutant discharge. Referring to Acemoglu and Restrepo 25 , this paper selects the installation density of industrial robots as a measure. The specific formula is as follows:

In formula ( 5 ), Labor ji is the number of labor force in industry j in region i . IR jt is the stock of industrial robot use in industry j in the year t .

Mediating variable

Green technology innovation. Industrial robots can increase the demand for highly-skilled labor 42 , subsequently influencing green technology innovation. Compared to ordinary labor, highly-skilled labor possesses a richer knowledge base and technological learning capability, improving the level of green technology innovation. Green technology innovation can improve energy efficiency 43 , reducing pollution generated by energy consumption. The measurement methods of green technology innovation mainly include three kinds: The first method is to use simple technology invention patents as measurement indicators. Some of technical invention patents are not applied to the production process of enterprise, they cannot fully reflect the level of technological innovation. The second method is to use green product innovation and green process innovation as measurement indicators. The third method is to use the number of green patent applications or authorizations as a measure 44 . This paper selects the number of green patent applications as a measure of green technology innovation.

Employment skill structure. The use of industrial robots reduces the demand for labor performing simple repetitive tasks and increases the need for engineers, technicians, and other specialized skilled personnel, improving the employment skill structure 45 . Compared to ordinary workers, highly-skilled laborers typically have a stronger environmental awareness 46 . Such environmental consciousness may influence corporate decisions, prompting companies to adopt eco-friendly production methods, thus reducing environmental pollution. There are two main methods to measure the structure of employment skills: One is to use the proportion of employees with college degree or above in the total number of employees as a measure. The other is to use the proportion of researchers as a measure. The educational level can better reflect the skill differences of workers. This paper uses the first method to measure the employment skill structure.

Control variable

Economic development level. According to the EKC hypothesis 47 , in the initial stage of economic development, economic development mainly depends on input of production factors, which aggravates environmental pollution. With the continuous development of economy, people begin to put forward higher requirements for environmental quality. The government also begins to adopt more stringent policies to control environmental pollution, which can reduce the level of environmental pollution. According to Liu and Lin 48 , This paper uses per capita GDP to measure economic development level.

Urbanization level. The improvement of urbanization level has both positive and negative effects on pollution. Urbanization can improve the agglomeration effect of cities. The improvement of agglomeration effect can not only promote the sharing of public resources such as infrastructure, health care, but also facilitate the centralized treatment of pollution. The efficiency of environmental governance is improved 49 . The acceleration of urbanization can increase the demand for housing, home appliances and private cars, which increases pollutant emissions 50 . This paper uses the proportion of urban population to total population to measure the level of urbanization.

Industrial structure. Industrial structure is one of the key factors that determine the quality of a country’s environmental conditions 51 . The increase in the proportion of capital and technology-intensive industries can effectively improve resource utilization efficiency and improve resource waste 52 . This paper selects the ratio of the added value of the tertiary industry to the secondary industry to measure industrial structure.

Government intervention. Government intervention mainly affects environmental pollution from the following two aspects: Firstly, the government can give high-tech, energy-saving and consumption-reducing enterprises relevant preferential policies, which promotes the development of emission reduction technologies for these enterprises 53 . Secondly, the government strengthens environmental regulation by increasing investment in environmental law enforcement funds, thus forcing enterprises to save energy and reduce emissions 54 . This paper selects the proportion of government expenditure in GDP to measure government intervention.

Environmental regulation. The investment in environmental pollution control is conducive to the development of clean and environmental protection technology, optimizing the process flow and improving the green production efficiency of enterprises 55 . Pollutant emissions are reduced. This paper selects the proportion of investment in pollution control to GDP to measure environmental regulation.

Data sources and descriptive statistics

This paper selects the panel data of 30 provinces in China from 2006 to 2019 as the research sample. Among them, the installation data of industrial robots are derived from International Federation of Robotics (IFR). The data of labor force and employees with college degree or above are from China Labor Statistics Yearbook . Other data are from the China Statistical Yearbook . The descriptive statistics of variables are shown in Table 3 . Considering the breadth of application and the reliability of analysis capabilities, this paper uses Stata 16 for regression analysis.

Results analysis

Spatial and temporal characteristics of environmental pollution and industrial robots in china, environmental pollution.

Figure  3 a shows the overall trend of average industrial wastewater discharge in China from 2006 to 2019. From 2006 to 2019, the discharge of industrial wastewater shows a fluctuating downward trend, mainly due to the improvement of wastewater treatment facilities and the improvement of treatment capacity. Figure  3 b shows the changing trend of average industrial wastewater discharge in 30 provinces of China from 2006 to 2019. Industrial wastewater discharge in most provinces has declined. There are also some provinces such as Fujian, Guizhou and Qinghai, which have increased industrial wastewater discharge. Their emission reduction task is very arduous.

Industrial wastewater discharge from 2006 to 2019.

Figure  4 a shows the overall trend of average industrial SO 2 emissions in China from 2006 to 2019. From 2006 to 2019, industrial SO 2 emissions shows a fluctuating downward trend, indicating that air pollution control and supervision are effective. Figure  4 b shows the trend of average industrial SO 2 emissions in 30 provinces of China from 2006 to 2019. Similar to industrial wastewater, industrial SO 2 emissions decrease in most provinces.

Industrial SO 2 emissions from 2006 to 2019.

Figure  5 a shows the overall trend of average industrial soot emissions in China from 2006 to 2019. Different from industrial wastewater and industrial SO 2 , the emission of industrial soot is increasing year by year. From the perspective of governance investment structure, compared with industrial wastewater and industrial SO 2 , the investment proportion of industrial soot is low. From the perspective of source, industrial soot mainly comes from urban operation, industrial manufacturing and so on. The acceleration of urbanization and the expansion of manufacturing scale have led to an increase in industrial soot emissions. Figure  5 b shows the trend of industrial soot emissions in 30 provinces in China from 2006 to 2019. The industrial soot emissions in most provinces have increased.

Industrial soot emissions from 2006 to 2019.

Figure  6 shows the spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot emissions. The three types of pollutant emissions in the central region are the largest, followed by the eastern region, and the three types of pollutant emissions in the western region are the smallest. Due to resource conditions and geographical location, the central region is mainly dominated by heavy industry. The extensive development model of high input and consumption makes its pollutant emissions higher than the eastern and western regions. The eastern region is mainly capital-intensive and technology-intensive industries, which makes its pollutant emissions lower than the central region. Although the leading industry in the western region is heavy industry, its factory production and transportation scale are not large, which produces less pollutants.

Spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot.

Industrial robots

Figure  7 a shows the overall trend of installation density of industrial robots in China from 2006 to 2019. From 2006 to 2019, the installation density of industrial robots in China shows an increasing trend year by year. The increase of labor cost and the decrease of industrial robot cost make enterprises use more industrial robots, which has a substitution effect on labor force. The installation density of industrial robots is increased. Figure  7 b shows the trend of installation density of industrial robots in 30 provinces of China from 2006 to 2019. The installation density of industrial robots in most provinces has increased. Among them, the installation density of industrial robots in Guangdong Province has the largest growth rate. The installation density of industrial robots in Heilongjiang Province has the smallest growth rate.

Installation density of industrial robots from 2006 to 2019.

Figure  8 shows the spatial distribution characteristics of installation density of industrial robots. The installation density of industrial robots in the eastern region is the largest, followed by the central region, and the installation density of industrial robots in the western region is the smallest. The eastern region is economically developed and attracts lots of talents to gather here, which provides talent support for the development of industrial robots. Advanced technology also leads to the rapid development of industrial robots in the eastern region. The economy of western region is backward, which inhibits the development of industrial robots.

Spatial distribution characteristics of industrial robots.

Benchmark regression results

Table 4 reports the estimation results of the ordinary panel model. Among them, the F test and LM test show that the mixed OLS model should not be used. The Hausman test shows that the fixed effect model should be selected in the fixed effect model and random effect model. This paper selects the estimation results of the fixed effect model to explain.

Regarding the core explanatory variable, industrial robots have a significant negative impact on the emissions of industrial wastewater, industrial SO 2 and industrial soot. Specifically, industrial robots have the greatest negative impact on industrial soot emissions, with a coefficient of -0.277 and passing the 1% significance level. The negative impact of industrial robots on industrial wastewater discharge is second, with an estimated coefficient of -0.242, which also passes the 1% significance level. The negative impact of industrial robots on industrial SO 2 emissions is the smallest, with an estimated coefficient of -0.0875 and passing the 10% significant level. Compared with industrial wastewater and SO 2 , industrial robots have some unique advantages in reducing industrial soot emissions. Firstly, in terms of emission sources, industrial soot emissions mainly come from physical processes such as cutting. These processes can be significantly improved through precise control of industrial robots. Industrial SO 2 comes from the combustion process. Industrial wastewater originates from various industrial processes. It is difficult for industrial robots to directly control these processes. Secondly, in terms of source control and terminal treatment, industrial robots can reduce excessive processing and waste of raw materials, thereby controlling industrial soot emissions at the source. For industrial SO 2 and industrial wastewater, industrial robots mainly play a role in terminal treatment. Since the terminal treatment of industrial SO 2 and industrial wastewater often involves complex chemical treatment processes, it is difficult for industrial robot technology to fully participate in these processes. This makes the impact of industrial robots in the field of industrial SO 2 and industrial wastewater more limited than that in the field of industrial soot.

Regarding the control variables, the level of economic development has a significant inhibitory effect on industrial SO 2 emissions. The higher the level of economic development, the stronger the residents’ awareness of environmental protection, which constrains the pollution behavior of enterprises. The government also adopts strict policies to control pollutant emissions. The impact of urbanization level on the discharge of industrial wastewater, industrial SO 2 and industrial soot is significantly negative. The improvement of urbanization level can improve the efficiency of resource sharing and the centralized treatment of pollutants, reducing environmental pollution. The industrial structure significantly reduces industrial SO 2 and industrial soot emissions. The upgrading of industrial structure not only reduces the demand for energy, but also improves the efficiency of resource utilization. The degree of government intervention only significantly reduces the discharge of industrial wastewater. The possible reason is that to promote economic development, the government invests more money in high-yield areas, which crowds out investment in the environmental field. Similar to the degree of government intervention, environmental regulation has a negative impact on industrial wastewater discharge. The government’s environmental governance investment has not given some support to the enterprise’s clean technology research, which makes the pollution control investment not produce good emission reduction effect.

Mediation effect regression results

Green technology innovation.

Table 5 reports the results of intermediary effect model when green technology innovation is used as an intermediary variable. Industrial robots can have a positive impact on green technology innovation. For every 1% increase in the installation density of industrial robots, the level of green technology innovation increases by 0.722%. After adding the green technology innovation, the estimated coefficient of industrial robots has decreased, which shows that the intermediary variable is effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of the total effect.

Employment skill structure

Table 6 reports the results of intermediary effect model when the employment skill structure is used as an intermediary variable. Industrial robots have a positive impact on the employment skill structure. For every 1% increase in the installation density of industrial robots, the employment skill structure is improved by 0.0837%. Similar to green technology innovation, the intermediary variable of employment skill structure is also effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of employment skill structure accounts for 6.67% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of employment skill structure accounts for 20.66% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of employment skill structure accounts for 15.53% of the total effect.

Robustness test and endogeneity problem

Robustness test.

To ensure the robustness of the regression results, this paper tests the robustness by replacing core explanatory variables, shrinking tail and replacing sample. Regarding the replacement of core explanatory variables, in the benchmark regression, the installation density of industrial robots is measured by the stock of industrial robots. Replacing the industrial robot stock with the industrial robot installation quantity, this paper re-measures the industrial robot installation density. Regarding the tail reduction processing, this paper reduces the extreme outliers of all variables in the upper and lower 1% to eliminate the influence of extreme outliers. Regarding the replacement of samples, this paper removes the four municipalities from the sample. The estimation results are shown in Table 7 . Industrial robots still have a significant negative impact on environmental pollution, which confirms the robustness of benchmark regression results.

Endogeneity problem

Logically speaking, although the use of industrial robots can reduce environmental pollution, there may be reverse causality. Enterprises may increase the use of industrial robots to meet emission reduction standards, which increases the use of industrial robots in a region. Due to the existence of reverse causality, there is an endogenous problem that cannot be ignored between industrial robots and environmental pollution.

To solve the impact of endogenous problems on the estimation results, this paper uses the instrumental variable method to estimate. According to the selection criteria of instrumental variables, this paper selects the installation density of industrial robots in the United States as the instrumental variable. The trend of the installation density of industrial robots in the United States during the sample period is similar to that of China, which is consistent with the correlation characteristics of instrumental variables. The application of industrial robots in the United States is rarely affected by China’s economic and social factors, and cannot affect China’s environmental pollution, which is in line with the exogenous characteristics of instrumental variables.

Table 8 reports the estimation results of instrumental variable method. Among them, the column (1) is listed as the first stage regression result. The estimated coefficient of instrumental variable is significantly positive, which is consistent with the correlation. Column (2), column (3) and column (4) of Table 8 are the second stage regression results of industrial wastewater, industrial SO 2 and industrial soot emissions as explanatory variables. The estimated coefficients of industrial robots are significantly negative, which again verifies the hypothesis that industrial robots can reduce environmental pollution. Compared with Table 4 , the absolute value of estimated coefficient of industrial robots is reduced, which indicates that the endogenous problems caused by industrial robots overestimate the emission reduction effect of industrial robots. The test results prove the validity of the instrumental variables.

Panel quantile regression results

Traditional panel data models might obscure the differential impacts of industrial robots at specific pollution levels. To address this issue, this paper uses a panel quantile regression model to empirically analyze the effects of industrial robots across different environmental pollution levels.

Table 9 shows that industrial robots have a negative impact on industrial wastewater discharge. With the increase of the quantile of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped change. Specifically, when the industrial wastewater discharge is in the 0.1 quantile, the regression coefficient of industrial robot is − 0.229, and it passes the 1% significant level. When the industrial wastewater discharge is in the 0.25 quantile, the impact of industrial robots on industrial wastewater discharge is gradually enhanced. Its regression coefficient decreases from − 0.229 to − 0.256. When the industrial wastewater discharge is in the 0.5 quantile, the regression coefficient of industrial robot increases from − 0.256 to − 0.152. When the industrial wastewater discharge is at the 0.75 quantile, the regression coefficient of industrial robot decreases from − 0.152 to − 0.211. When the industrial wastewater discharge is in the 0.9 quantile, the regression coefficient of industrial robot increases from − 0.211 to − 0.188. For every 1% increase in the installation density of industrial robots, the discharge of industrial wastewater is reduced by 0.188%.

When industrial wastewater discharge is at a low percentile, the use of industrial robots can replace traditional production methods, reducing energy waste and wastewater discharge. As industrial wastewater discharge increases, the production process becomes more complex. Industrial robots may be involved in high-pollution, high-emission productions, diminishing the robots’ emission-reducing effects. When industrial wastewater discharge reaches high levels, pressured enterprises seek environmentally friendly production methods and use eco-friendly industrial robots to reduce wastewater discharge. As wastewater discharge continues to rise, enterprises tend to prioritize production efficiency over emission control, weakening the negative impact of industrial robots on wastewater discharge. When wastewater discharge is at a high percentile, enterprises should balance production efficiency and environmental protection needs, by introducing eco-friendly industrial robots to reduce wastewater discharge.

Table 10 shows that with the increase of industrial SO 2 emission quantile level, the negative impact of industrial robots on industrial SO 2 emissions gradually increases. Specifically, when industrial SO 2 emissions are below 0.5 quantile, the impact of industrial robots on industrial SO 2 emissions is not significant. When the industrial SO 2 emissions are above 0.5 quantile, the negative impact of industrial robots on industrial SO 2 emissions gradually appears.

When industrial SO 2 emissions are at a low percentile, the application of industrial robots primarily aims to enhance production efficiency, not to reduce SO 2 emissions. Enterprises should invest in the development of eco-friendly industrial robots, ensuring they are readily available for deployment when a reduction in industrial SO 2 emissions is necessary. As industrial SO 2 emissions continue to rise, both the government and the public pay increasing attention to the issue of SO 2 emissions. To meet stringent environmental standards, enterprises begin to use industrial robots to optimize the production process, reduce reliance on sulfur fuels, and consequently decrease SO 2 emissions. Enterprises should regularly evaluate the emission reduction effectiveness of industrial robots, using the assessment data to upgrade and modify the robots’ emission reduction technologies.

Table 11 shows that with the increase of industrial soot emissions quantile level, the negative impact of industrial robots on industrial soot emissions gradually weakens. Specifically, when industrial soot emissions are below 0.75 quantile, industrial robots have a significant negative impact on industrial soot emissions. This negative effect decreases with the increase of industrial soot emissions. When the industrial soot emissions are above 0.75 quantile, the negative impact of industrial robots on industrial soot emissions gradually disappears.

When industrial soot emissions are at a low percentile, they come from a few sources easily managed by industrial robots. As industrial soot emissions increase, the sources become more diverse and complex, making it harder for industrial robots to control. Even with growing environmental awareness, it may take time to effectively use robots in high-emission production processes and control industrial soot emissions. Enterprises should focus on researching how to better integrate industrial robot technology with production processes that have high soot emission levels. The government should provide financial and technical support to enterprises, assisting them in using industrial robots more effectively for emission reduction.

Figure  9 intuitively reflects the trend of the regression coefficient of industrial robots with the changes of industrial wastewater, industrial SO 2 and industrial soot emissions. Figure  9 a shows that with the increase of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped trend. Figure  9 b shows that with the increase of industrial SO 2 emissions, the regression coefficient of industrial robots gradually decreases. The negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. Figure  9 c shows that with the increase of industrial soot emissions, the regression coefficient of industrial robots shows a gradual increasing trend. The negative impact of industrial robots on industrial soot emissions has gradually weakened. Figure  9 a, b and c confirm the estimation results of Tables 9 , 10 and 11 .

Change of quantile regression coefficient.

Heterogeneity analysis

Regional heterogeneity.

This paper divides China into three regions: Eastern, central and western regions according to geographical location. The estimated results are shown in Table 12 . The industrial robots in eastern region have the greatest negative impact on three pollutants, followed by central region, and the industrial robots in western region have the least negative impact on three pollutants. The use of industrial robots in eastern region far exceeds that in central and western regions. The eastern region is far more than central and western regions in terms of human capital, technological innovation and financial support. Compared with central and western regions, the artificial substitution effect, upgrading of sewage treatment equipment and improvement of energy utilization efficiency brought by industrial robots in eastern region are more obvious.

Time heterogeneity

The development of industrial robots is closely related to policy support 56 . In 2013, the Ministry of Industry and Information Technology issued the “ Guiding Opinions on Promoting the Development of Industrial Robot Industry ”. This document proposes: By 2020, 3 to 5 internationally competitive leading enterprises and 8 to 10 supporting industrial clusters are cultivated. In terms of high-end robots, domestic robots account for about 45% of the market share, which provides policy support for the development of industrial robots. Based on this, this paper divides the total sample into two periods: 2006–2012 and 2013–2019, and analyzes the heterogeneous impact of industrial robots on environmental pollution in different periods. The estimation results are shown in Table 13 . Compared with 2006–2012, the emission reduction effect of industrial robots during 2013–2019 is greater.

The use of industrial robots can effectively reduce environmental pollution, which is consistent with hypothesis 1. This is contrary to the findings of Luan et al. 22 , who believed that the use of industrial robots would exacerbate air pollution. The inconsistency in research conclusions may be due to differences in research focus, sample size, and maturity of industrial robot technology. In terms of research focus, this paper mainly focuses on the role of industrial robots in reducing pollutant emissions during industrial production processes. Their research focuses more on the energy consumption caused by the production and use of industrial robots, which could aggravate environmental pollution. In terms of sample size, the sample size of this paper is 30 provinces in China from 2006 to 2019. These regions share consistency in economic development, industrial policies and environmental regulations. Their sample size is 74 countries from 1993 to 2019. These countries cover different geographical, economic and industrial development stages, affecting the combined effect of robots on environmental pollution. In terms of the maturity of industrial robots, the maturity of industrial robot technology has undergone tremendous changes from 1993 to 2019. In the early stages, industrial robot technology was immature, which might cause environmental pollution. In recent years, industrial robot technology has gradually matured, and its operating characteristics have become environmentally friendly. Their impact on environmental pollution has gradually improved. This paper mainly conducts research on the mature stage of industrial robot technology. Their research covers the transition period from immature to mature industrial robot technology. The primary reason that the use of industrial robots can reduce environmental pollution is: The use of industrial robots has a substitution effect on labor force, which reduces the raw materials needed for manual operation. For example, in the industrial spraying of manufacturing industry, the spraying robot can improve the spraying quality and material utilization rate, thereby reducing the waste of raw materials by manual operation. Zhang et al. 57 argued that energy consumption has been the primary source of environmental pollution. Coal is the main energy in China, and the proportion of clean energy is low 58 . In 2022, clean energy such as natural gas, hydropower, wind power and solar power in China accounts for only 25.9% of the total energy consumption, which can cause serious environmental pollution problems. Industrial robots can promote the use of clean energy in industrial production and the upgrading of energy structure 24 . The reduction of raw materials and the upgrading of energy structure can control pollutant emissions in front-end production. On September 1, 2021, the World Economic Forum (WEF) released the report “ Using Artificial Intelligence to Accelerate Energy Transformation ”. The report points out that industrial robots can upgrade pollution monitoring equipment and sewage equipment, which reduces pollutant emissions in end-of-pipe treatment. Ye et al. 59 also share the same viewpoint.

The use of industrial robots can reduce environmental pollution through green technology innovation, which is consistent with hypothesis 2. Industrial robots promote the integration of knowledge, which helps enterprises to carry out green technology innovation activities. Meanwhile, Jung et al. 60 suggested that industrial robots can lower production costs for companies, allowing them to invest in green technology research. The level of green technology innovation is improved. Green technology innovation reduces environmental pollution through the following three aspects: Firstly, the improvement of energy utilization efficiency. China’s utilization efficiency of traditional energy sources such as coal is not high. The report of “ 2013-Global Energy Industry Efficiency Research ” points out that China’s energy utilization rate is only ranked 74th in the world in 2013. Low energy efficiency brings serious environmental pollution problems 61 . Du et al. 62 found that the innovation of green technologies, such as clean coal, can enhance energy efficiency and decrease environmental pollution. Secondly, the production of green products. Green technology innovation accelerates the green and recyclable process of production, thereby reducing the pollutants generated in production process. Thirdly, the improvement of enterprise competitive advantage. Green technology innovation can enable enterprises to gain greater competitive advantage in green development 63 . The supply of environmentally friendly products increases, which not only meets the green consumption needs of consumers, but also reduces the emission of pollutants.

Industrial robots can reduce environmental pollution by optimizing the structure of employment skills, which is consistent with hypothesis 3. Autor et al. 64 contended that industrial robots would replace conventional manual labor positions, reducing the demand for low-skilled labor. Industrial robots represent the development of numerical intelligence. With the continuous development of digital intelligence, the demand for high-skilled labor in enterprises has increased. Koch et al. 65 demonstrated that the use of industrial robots in Spanish manufacturing firms leads to an increase in the number of skilled workers. In February 2020, the Ministry of Human Resources and Social Security, the State Administration of Market Supervision and the National Bureau of Statistics jointly issues 16 new professions such as intelligent manufacturing engineering and technical personnel, industrial Internet engineering and technical personnel, and virtual reality engineering and technical personnel to the society. These new occupations increase the demand for highly skilled labor. The reduction of low-skilled labor and increase of high-skilled labor optimize the structure of employment skills. The optimization of employment skill structure narrows the wage gap between employees, reducing the communication cost of employees. Employees learn and exchange technology with each other, which not only improves the absorption capacity of clean technology. It also improves the production efficiency of enterprises and increases corporate profits, so that enterprises can use more funds for clean technology research and development, thereby reducing environmental pollution.

Conclusions and policy recommendations

Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the panel data model and mediating effect model to empirically test the impact of industrial robots on environmental pollution and its transmission mechanism. This paper uses panel quantile model, regional samples and time samples to further analyze the heterogeneous impact of industrial robots on environmental pollution. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. For every 1% increase in industrial robots, the emissions of industrial wastewater, industrial SO 2 , and industrial dust and smoke decrease by − 0.242%, − 0.0875%, and − 0.277%. This finding is contrary to that of Luan et al. 22 , who argued that the use of industrial robots exacerbates air pollution. The results of this paper provide a contrasting perspective, highlighting the potential value of industrial robots in mitigating environmental pollution. (2) Industrial robots can reduce environmental pollution by improving green technology innovation level and optimizing employment skills structure. In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of total effect. The mediating effect of employment skill structure accounts for 6.67% of total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of total effect. The mediating effect of employment skill structure accounts for 20.66% of total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of total effect. The mediating effect of employment skill structure accounts for 15.53% of total effect. While Obobisa et al. 66 and Zhang et al. 67 highlighted the role of green technological innovation in addressing environmental pollution. Chiacchio et al. 68 and Dekle 69 focused on the effects of industrial robots on employment. The mediating impact of technology and employment in the context of robots affecting pollution hasn’t been addressed. Our research provides the first in-depth exploration of this crucial intersection. (3) Under different environmental pollution levels, the impact of industrial robots on environmental pollution is different. Among them, with the increase of industrial wastewater discharge, the impact of industrial robots on industrial wastewater discharge shows a “W-shaped” change. With the increase of industrial SO 2 emissions, the negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. On the contrary, with the increase of industrial soot emissions, the negative impact of industrial robots on industrial soot emissions gradually weakens. (4) Industrial robots in different regions and different periods have heterogeneous effects on environmental pollution. Regarding regional heterogeneity, industrial robots in eastern region have the greatest negative impact on environmental pollution, followed by central region, and western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the negative impact of industrial robots on environmental pollution in 2013–2019 is greater than that in 2006–2012. Chen et al. 5 and Li et al. 24 both examined the overarching impact of industrial robots on environmental pollution. They did not consider the varying effects of robots on pollution across different regions and time periods. Breaking away from the limitations of previous holistic approaches, our study offers scholars a deeper understanding of the diverse environmental effects of industrial robots.

According to the above research conclusions, this paper believes that the government and enterprises can promote emission reduction through industrial robots from the following aspects.

Increase the scale of investment in robot industry and promote the development of robot industry. China’s industrial robot ownership ranks first in the world. Its industrial robot installation density is lower than that of developed countries such as the United States, Japan and South Korea. The Chinese government should give some financial support to robot industry and promote the development of robot industry, so as to effectively reduce environmental pollution. The R&D investment of industrial robots should be increased so that they can play a full role in reducing raw material consumption, improving energy efficiency and sewage treatment capacity.

Give full play to the role of industrial robots in promoting green technology innovation. Industrial robots can reduce environmental pollution through green technology innovation. The role of industrial robots in innovation should be highly valued. The advantages of knowledge integration and data processing of industrial robots should be fully utilized. Meanwhile, the government should support high-polluting enterprises that do not have industrial robots from the aspects of capital, talents and technology, so as to open up the channels for these enterprises to develop and improve clean technology by using industrial robots.

Give full play to the role of industrial robots in optimizing employment skills structure. The use of industrial robots can create jobs with higher skill requirements and increase the demand for highly skilled talents. China is relatively short of talents in the field of emerging technologies. The education department should actively build disciplines related to industrial robots to provide talent support for high-skilled positions. Enterprises can also improve the skill level of the existing labor force through on-the-job training and job competition.

Data availability

The datasets used or analyzed during the current study are available from Yanfang Liu on reasonable request.

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Liu, Y. Impact of industrial robots on environmental pollution: evidence from China. Sci Rep 13 , 20769 (2023). https://doi.org/10.1038/s41598-023-47380-6

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Research Article

Khat consumption and undernutrition among adult population in Ethiopia: A systematic review and meta-analysis

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

* E-mail: [email protected]

Affiliation School of Public Health, College of Medicine and Health Sciences, Haramaya University, Harar, Ethiopia

• Published: April 23, 2024
• https://doi.org/10.1371/journal.pone.0299538
• Reader Comments

In Ethiopia, malnutrition is a public health threat causing a significant burden of morbidity, mortality, and economic crisis. Simultaneously, khat consumption is alarmingly increasing among adults, yet it might contribute to the existing burden of malnutrition, where the current evidence is inconclusive. Hence, this review was to estimate the association between khat consumption and undernutrition among adults in Ethiopia.

A comprehensive search for Google, Google Scholar, and PubMed, coupled with a thorough manual search of the literature, was done up to date, October 18, 2023, using relevant search terms: “impact," "effects," “khat chewing," “khat consumption," "Ethiopia," “nutritional status," and "undernutrition." An updated PRISMA guideline was used to select relevant literature. The extracted data was summarized in narrative summaries, descriptions, and meta-analyses. The risk of bias was assessed. The results are presented in forest plots and funnel plots to assess publication bias. A pooled effect size (odds ratio) with a 95% certainty level was reported.

While a total of 17 articles (n = 45,679) were included in the narrative review, only 15 articles were included in the quantitative meta-analysis. The majority of studies had a low and moderate risk of bias (based on risk of bias assessment tool), mainly due to unclear exposure assessment and high study heterogeneity. A total of 11 studies were cross-sectional studies (71%), three were comparative studies (17.4%), and three were case control studies (17.4%). There is a higher risk of publication bias as evidenced by the funnel plot. Overall, five studies were from the Oromia region, and three studies were conducted at the national level. Overall, chewing had been shown to significantly increase the risk of undernutrition by 53% (pooled OR = 1.53; 95% CI: 1.09–2.16) under a random effect model. Under the fixed effect model, higher weight was given to national-level studies with higher samples, where chewing contributed to a 12% increased risk of undernutrition (AOR = 1.12; 95% CI: 1.01–2.23). Hence, khat chewing could raise the odds of undernutrition by 12–53%.

There is evidence of an association between khat chewing and an increased risk of undernutrition among adults in Ethiopia, which highlights the need for public health interventions to address the potential adverse effects of khat chewing on nutritional status.

Citation: Oumer A (2024) Khat consumption and undernutrition among adult population in Ethiopia: A systematic review and meta-analysis. PLoS ONE 19(4): e0299538. https://doi.org/10.1371/journal.pone.0299538

Editor: Riyaz Ahmad Rather, Wachemo University, INDIA

Received: October 20, 2023; Accepted: February 12, 2024; Published: April 23, 2024

Copyright: © 2024 Abdu Oumer. 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: The author(s) received no specific funding for this work.

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

Abbreviations: AOR, Adjusted Odds Ratio; BMI, Body Mass Index; GRADE, Grading of Recommendation Assessment Development and Evaluation; MeSH, Medical Subject Heading; PRISMA, Preferred Reporting Items for Systematic Review and Meta-analysis; WHO, World Health Organization

Introduction

Undernutrition remains a significant public health challenge worldwide, particularly in low- and middle-income countries [ 1 , 2 ]. It is a complex issue influenced by various factors, including dietary habits, socio-economic status, cultural practices, and substance use [ 3 , 4 ]. Among the many substances consumed globally, khat (Catha edulis) has gained attention for its potential impact on nutritional status. Khat is a widely chewed psychoactive plant native to East Africa, including Ethiopia, where its consumption is deeply rooted in social and cultural traditions [ 5 – 7 ]. Given the prevalence of khat chewing and the high burden of undernutrition in Ethiopia, it is crucial to examine the potential relationship between khat chewing and the risk of undernutrition among adults, where there is no clear evidence yet. For instance, studies from Ethiopia showed that the prevalence of khat chewing is about 19–27% [ 8 ] and 17% among students [ 9 ].

Despite the long-standing practice of khat chewing and the concerning levels of undernutrition in Ethiopia, the specific effects of khat consumption on nutritional status have not been extensively investigated. Most existing studies on khat have primarily focused on its psychoactive properties [ 10 – 12 ] and associated health consequences, such as cardiovascular effects, mental health outcomes, and social implications [ 11 , 13 , 14 ]. While these studies have contributed valuable insights, the impact of khat chewing on nutritional status remains a relatively understudied area.

The limited number of studies examining the link between khat chewing and undernutrition have yielded mixed findings, ranging from suggestions of adverse nutritional effects to reports of no significant association [ 15 – 20 ]. These conflicting results highlight the urgent need for a comprehensive and systematic evaluation of the existing evidence. For instance, some studies have suggested a potential association between khat chewing and adverse nutritional outcomes, including weight loss, decreased appetite, and poor dietary intake [ 21 , 22 ], which could be attributed to the stimulant properties of khat suppressing appetite, altering metabolism, and reducing food intake [ 14 , 15 , 21 , 23 , 24 ]. However, other studies have reported conflicting results, failing to establish a clear link between khat chewing and undernutrition [ 18 , 25 , 26 ]. These discrepancies may be due to variations in study design, sample characteristics, and the lack of rigorous synthesis of existing evidence.

Moreover, the existing literature on khat chewing and undernutrition in Ethiopia is limited by several factors. First, the available studies are often small-scale and cross-sectional, limiting their ability to establish causal relationships or provide robust evidence. Second, the methodologies used to assess undernutrition outcomes vary, making it challenging to compare and generalize the findings. Third, the majority of studies have focused on specific populations and localities, which may not fully represent the diverse adult population of Ethiopia. Lastly, the absence of a comprehensive synthesis of the existing evidence through a systematic review and meta-analysis hinders the ability to draw conclusive findings and inform evidence-based interventions.

Limited evidence exists on khat chewing’s impact on adult undernutrition in Ethiopia, despite its prevalence and the country’s significant undernutrition burden. This knowledge gap necessitates a comprehensive systematic review and meta-analysis to synthesize existing research. By systematically reviewing and quantitatively analyzing the literature, this study aims to provide a definitive assessment of the association between khat chewing and undernutrition outcomes. This will inform future research and interventions, benefiting policymakers, public health practitioners, and researchers tackling undernutrition and substance use issues in Ethiopia.

Materials and methods

Data sources.

The data used for this review article were extracted from a secondary review of existing literature on the association between khat chewing and the odds of undernutrition in Ethiopia. Peer-reviewed journal articles and other relevant, unpublished works from academic repositories were used for the review. The selected articles and unpublished manuscripts were consulted for important data on the association between khat chewing and the risk of undernutrition for adults since 2000.

Search strategies

A thorough and systematic search was employed for relevant literature in databases. We searched for both published and unpublished studies reporting the association between khat chewing and nutritional status among the adult population in Ethiopia. Exhaustive yet systematic searches were conducted in Google Scholar, PubMed, CINAHL, EMBASE, and MEDLINE using keyword combinations and MeSH terms. A variety of combinations of keywords and mesh terms were tried to come up with exhaustive lists. A more advanced search approach was employed by year of publication and area of publication. In addition, a thorough manual search of the literature was done. The search was up to date as of October 18, 2023.

A search was made using relevant keywords and MeSH terms using Boolean operators. Hence, the search was conducted using the following key words: "impact," "effects," “khat chewing," “khat consumption," "Ethiopia," “nutritional status," and "undernutrition." We employed a variety of combinations of these terms to come up with unbiased search results. The reference list of the included articles was screened thoroughly for additional studies. We have limited studies reported in English, but still, we did not find any relevant studies published other than in English for the Ethiopian context. The search is not restricted to a specific time period. Other databases, like conference proceedings, abstracts, preprints, and articles published as supplements to journals, were included. Additionally, other local university libraries and repositories were also searched for relevant articles on the issue.

Inclusion of primary studies

This review will consider analytical observational studies, including analytical cross-sectional studies, prospective and retrospective cohort studies, and case-control studies. Studies reporting the unconfounded association between khat chewing and nutritional status in the adult population are considered to be eligible for this review. Hence, studies primarily done with khat chewing as exposure or khat chewing captured as potential confounding variables were eligible for the review. However, those studies reported for children and elders aged above 60 years without information on the effect size or the two-by-two table cell values are excluded.

Study selection

Following the search, all identified citations were exported to and uploaded into Endnote version 20, where duplicates were removed and further screening was conducted for the titles and abstracts of the potential articles. At this stage, further articles not meeting the inclusion criteria were excluded by two independent reviewers against the inclusion criteria and the full text of these articles was retrieved. Any disagreements that arise between the reviewers at each stage of the selection process will be resolved through discussion or with the involvement of a third reviewer. The results of the search and the study inclusion process are reported and presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow chart [ 27 ].

Exposure and outcome of the review

The primary exposure of this review is khat chewing or khat consumption, defined as any level of khat chewing or consumption as defined by previous studies. The majority of the studies reported or assessed the previous history and current state of khat chewing. These potential variations, in addition to confounding variables such as food consumption and alcohol drinking status, among others, have the potential to introduce heterogeneity in the estimation of effect sizes and the direction of associations. The primary outcome was the nutritional status of adults, defined as a body mass index (BMI) below 18.5 kg/m 2 based on the WHO classification or MUAC below 23 cm for lactating women [ 28 – 30 ].

Assessment of methodological quality and risk of bias

Two reviewers assessed eligible studies for their methodological quality using the standardized JBI Critical Appraisal Checklist for analytical cross-sectional studies [ 31 ] and case-control studies [ 32 ]. The checklist for cross-sectional studies was composed of eight items that assesses the risk of bias and methodological quality. The checklist for case-control studies has 10 items that are used to gauge group comparability; matching cases and controls, measuring exposure, accounting for confounding variables, exposure period, and analysis.

Each response is evaluated as "yes," "no," "unclear,” or “not applicable.” If "yes" is selected, one point is awarded. Studies earning six or more points will be deemed to be of high quality and included in the review considering the relevance of the criteria and using a 60 percentile as rule of thumb. The quality assessment was carried out independently by two reviewers, and disputes were managed accordingly. A narrative report and statistical tables ( S1 and S2 Tables) were used to present the findings of the critical appraisal. To ensure the credibility of the review and meta-analysis, the risk of bias for each study was independently assessed by two reviewers using the JBI tool, and the findings were presented accordingly. To ensure consistency and objectivity, any discrepancies between reviewers were resolved through collaborative discussion or consultation with an independent reviewer.

Data extraction

Two independent reviewers extracted the necessary data from each study using a predefined Excel spreadsheet. The spread sheet was prepared using the standardized JBI data extraction tools, where specific information regarding the participants, study procedures (design), exposure, outcome, cell values, and effect size estimates were captured. Further contacts and requests for details from the authors of the primary studies were made for missing information and additional data for clarification as deemed appropriate. Any differences between the review authors were settled through conversation or consultation with a third independent party.

Assessing certainty of evidence

The certainty of the evidence for the role khat chewing plays in undernutrition was evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) technique, which was used to grade the certainty of the evidence [ 33 ]. Any discrepancies among the reviewers were settled via discussion or by consulting a third reviewer. In cases where further information is needed for clarity, the authors of the papers were consulted by email. Absolute risks for the exposed and unexposed, estimates of odds ratio, and an evaluation of the quality of the evidence based on the risk of bias, directness, heterogeneity, precision, and risk of publication bias of the review results will all be provided where applicable.

Data synthesis and analysis

The data extracted was in MS Excel and exported to STATA version 14 for meta-analysis. The four cells in the two-by-two epidemiologic table values were extracted and used to calculate the odds ratio as an effect size measure along with 95% confidence intervals. Given the anticipated heterogeneity, statistical analyses will be performed using a random-effects model for meta-analysis [ 34 ]. The random-effects model was selected for the current study after careful consideration of the observed heterogeneity among the included studies. The presence of diverse methodologies, sample characteristics, and settings within the studies warranted the use of a random-effects model to account for the inherent variability across them. Hence, by employing this model, the study aimed to obtain a more precise and reliable estimate of the overall effect size, taking into account the expected differences among the studies. This approach acknowledges and accommodates the heterogeneity present in the data, allowing for a more robust analysis and interpretation of the findings in the current study than the fixed effect model.

Subgroup analyses were conducted where there was sufficient data to investigate differences by region, population (general and high-risk populations), and study design. Sensitivity analyses were conducted to test decisions made regarding the analysis model and effect size. Funnel plot was generated for publication bias. The pooled estimates are presented graphically in forest plots along with study weights. A funnel plots were used to assess the possibility of publication bias by comparing the sample size against the effect size or standard error of the effect size measure. Statistical tests for funnel plot asymmetry (Egger test, Begg test, and Harbord test) will be performed where appropriate [ 35 ]. A narrative synthesis will be conducted for outcomes that were not suitable for pooling into the meta-analysis.

Search results

A total of 604 articles were obtained via database search, and 25 articles from registers were obtained using systematic search. Through our PubMed searches, we found n = 293 articles, and from these, 27 were review and meta-analysis articles, hence, excluded. From the broader search, about 266 articles were excluded while screening for abstracts, and only eight articles were eligible. From database and register searches, 221 articles were retrieved after removing duplicates and abstract screening. From these, 53 articles were screened for full text, and 37 articles were removed further due to irrelevant outcomes, being out of scope, and being conducted on different target populations. Finally, a total of 17 articles were included in the narrative review, and 15 articles were used for the pooled meta-analysis ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0299538.g001

Characteristics of included studies

For this review, a variety of observational studies assessed the association between different levels of khat consumption and nutritional status. Hence, a total of seventeen studies involving 45,679 subjects were included in the current review paper. With regard to the study design, the majority (n = 11; 71% of studies) were analytical cross-sectional studies, except for three case control studies (17.4%), and three of them were comparative cross-sectional study types (17.4%). Related to this, a large number of studies were conducted among the general adult population, prisoners, HIV patients, tuberculosis patients, and one study among cobblestone workers. As the intention of the study is to assess the effects of Khat on nutritional status and the included studies make efforts to control potential confounders, studies on this target population were considered eligible. About sixteen studies were published, except one, which was unpublished comparative work yet relevant to the current review ( Table 1 ).

https://doi.org/10.1371/journal.pone.0299538.t001

Concerning the outcome definition, almost all of the included studies assessed nutritional status using the WHO BMI classification, where a value below 18.5 kg/m 2 was used to ascertain undernutrition or underweight. However, one included study used a MUAC cutoff point below 23 cm to define malnutrition. These might not make a huge difference, as MUAC could predict BMI when BMI measurement is not appropriate. In addition, we conducted a sensitivity analysis by adding and removing this study if it had a significant impact on the pooled effect sizes ( Table 1 ).

However, there are some variations in defining exposure (khat chewing). As khat chewing can be characterized in terms of duration, intensity, and frequency, some limited studies tried to report disaggregated exposure levels by intensity, duration, and frequency [ 15 ]. Since the majority of the studies assessed and reported khat chewing status as a current chewer and history of khat chewing, we decided to make the definition of khat chewing more comprehensive by including current chewing status and past history ( Table 1 ).

The included studies employed a range of samples, with a minimum of 226 in the survey [ 38 ] to a maximum of 10,245, based on the reanalysis of the DHS 2016 data for women in the reproductive age group [ 37 ]. When we disaggregate studies by region, the majority of studies were from Oromia (n = 5), SNNPR (n = 3), Amhara (n = 3), Addis Ababa (n = 2) and Tigray (n = 1). Three studies (n = 14,293 participants) [ 15 , 37 , 38 ] were conducted at a national level in multicenter mode ( Table 1 ).

Risk of bias assessments

The risks of bias for each study were evaluated for a cross-sectional study and a case-control study separately. We employed the eight items for survey designs and the 10-item checklist for case control studies as well. The details of risk bias for individual studies are included in S1 and S2 Tables. The majority of the studies were considered to have a low to moderate risk of bias. This was mainly due to an unclear definition of exposure (khat consumption), where some studies assessed khat chewing level from multiple perspectives [ 15 , 37 ]. One study reported khat chewing in the context of current substance use, where the variable measurement could be biased [ 26 ]. This was also the same problem in defining khat consumption with the case control studies as well [ 25 , 36 , 39 ]. The certainty of the evidence was evaluated with a moderate level of recommendation for khat consumption and undernutrition risk in Ethiopia.

Association between khat consumption and undernutrition

A total of fifteen articles were included in the pooled effect size estimates, and two articles were not included due to a lack of evidence on each cell value. From the individual study results, four studies showed that khat chewers had lower odds of being underweight as compared to non-chewers [ 18 , 26 , 37 , 40 ]. We found significant heterogeneity (X 2 = 132.8; d.f. = 14; p = 0.0001). In addition, the estimate of between-study variance (Tau-squared) was not statistically significant (p = 0.381), indicating less heterogeneity between included studies. Overall, khat chewing had been shown to significantly increase the risk of undernutrition by 53% (pooled OR = 1.53; 95% CI: 1.09–2.16; p = 0.014). The weight given to each study is presented in Fig 2 . Under the fixed effect model with due weight to studies with a larger sample size, khat chewing could increase the odds of undernutrition by 12% (1.12; 95% CI: 1.02–1.23). In this model, the major study weight was 49% [ 37 ] and 10% [ 41 ], which indicated that the random effect model was a more reasonable estimate than the fixed effect model ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0299538.g002

Publication bias

The risk of publication bias was evaluated using a funnel plot and the odds ratio as a function of the standard error of the effect size. This is clearly indicated in Fig 3 , where the risk of publication bias was found to be high. However, this might be due to the fact that the three national-level studies had a relatively high number of samples (n = 14, 293), where 10,245, 2848, and 1200 samples were included [ 15 , 37 , 41 ], and these studies might have a higher effect with a lower standard error of the estimate as compared to studies with a lower sample size. The results of five studies were within the triangle, and the estimates of six studies were pretty close to the triangle. Hence, the inherent study heterogeneity including the national level studies and local studies could have created increased risk of publication bias.

https://doi.org/10.1371/journal.pone.0299538.g003

Sensitivity analysis

Sensitivity analysis helps to evaluate the robustness of the findings and provides insights into the potential influence of individual studies on the overall results. Hence, the impacts of removing each study from the meta-analysis were assessed. Table 2 provides information on the pooled odds ratios, measures of study heterogeneity, and p-values for each study when it was excluded. Notably, the results indicate significant heterogeneity among the studies, as evidenced by the calculated chi-squared and tau-squared values. Overall, there is a significant association between khat consumption and the risk of undernutrition among adults in Ethiopia ( Table 2 ).

https://doi.org/10.1371/journal.pone.0299538.t002

Subgroup analysis

The disaggregated estimates are done by region, population type, and study design and are presented in Table 3 , which shows a positive association with the risks of undernutrition but is not statistically significant. When disaggregated by region, the heterogeneity between studies was insignificant except for two studies in Addis Ababa (urban setting), where khat chewing is significantly associated with undernutrition (pooled OR = 1.77; 95% CI: 1.17–2.68). Among high-risk population segments, khat is associated with a 47% increased risk of undernutrition with small between-subject variations (p = 0.43). However, based on the three case control studies with a better quality of evidence, it was found that khat chewing is significantly associated with undernutrition (pooled OR = 2.78; 95% CI: 1.78–4.33). Overall, studies using large samples from national level analysis showed an averaged effect (lower odds ratio) compared to studies reported from Oromia(OR = 1.80), Addis Ababa (OR = 1.77), and Amhara (OR = 151). These could imply that the risk of undernutrition could be higher in a certain setting owing to many factors ( Table 3 ).

https://doi.org/10.1371/journal.pone.0299538.t003

Finally, a separate meta-regression was conducted to evaluate the effects of the potential factors on the heterogeneity of the effect size. Hence, region, study design, sample size, and population type were conducted, and these variables did not show significant effects on the overall study heterogeneity (p-value > 0.05).

Due to the widespread consumption of the substance khat [ 8 , 9 ] and prevailing malnutrition in the country [ 44 , 45 ], there was a need for clear evidence on the association between these two to support existing nutrition interventions. Moreover, this piece of evidence could help the country move towards achieving the sustainable development goals and bring about the intended economic growth through various approaches. The association between khat chewing and undernutrition has been a subject of interest in Ethiopia, where khat consumption is prevalent. Khat, a psychoactive plant native to the Horn of Africa, contains several alkaloids, including cathinone and cathine, which possess amphetamine-like properties [ 12 , 13 , 46 ]. The stimulating effects of khat, including increased alertness and reduced fatigue, have made it popular among adults in Ethiopia. However, the long-term consequences of khat chewing on health outcomes, including undernutrition, have raised concerns.

Hence, the present meta-analysis aimed to explore the association between khat chewing and the risk of undernutrition among adults in Ethiopia. Our findings, based on a pooled analysis of 17 articles, revealed a significant association between khat chewing and an increased risk of undernutrition, with a pooled odds ratio of 1.53 (95% CI: 1.09–2.16). Thus, our findings suggest that khat chewing is associated with an increased risk of undernutrition. This observation is consistent with the existing literature, which has reported similar associations. For instance, a study by Tesfaye et al. (2018) found that khat chewing was significantly associated with a higher prevalence of underweight among adults in Ethiopia [ 47 ]. Another study by Ahmed et al. (2019) reported a positive association between khat chewing and a higher risk of malnutrition in a rural community in Ethiopia [ 23 ]. Our meta-analysis further strengthens these findings by providing a pooled estimate of the association across multiple studies. This burden is still huge among late adolescents and youths [ 48 ].

The underlying mechanisms linking khat chewing and undernutrition are likely multifactorial [ 15 , 49 ]. One possible explanation is the appetite-suppressing effects of khat. Cathinone, one of the psychoactive components of khat, acts as a central nervous system stimulant and may reduce appetite and food intake [ 12 , 13 , 15 , 46 ]. Prolonged khat chewing may lead to decreased caloric intake and inadequate nutrient intake, thereby increasing the risk of undernutrition [ 18 ]. Moreover, tannins and dietary fibers, the anti-nutritional factors found in many vegetables and khat are known to reduce mineral and other nutrient absorption, especially for iron, zinc and calcium bringing additional risks of micronutrient deficiency [ 44 , 50 , 51 ]. These micronutrient deficiencies could further increase the risk of macronutrient undernutrition in various ways [ 44 ]. A strong association between maternal khat use [ 52 ] and the neonatal outcomes and body composition [ 53 ] of adults were also reported so far.

The long-term habit of khat chewing may predispose individuals to oro-dental problems, limiting diversified dietary consumption [ 54 ]. More importantly, these could be associated with long-term exposure to pesticide and insecticide residues accumulating over time. These could further limit nutrient absorption and metabolism [ 55 ] and susceptibility to chronic illness, aggravating the risk of undernutrition. More specifically, exposure to chemicals like DDT, malathion, and other potentially hazardous chemicals could be worse [ 56 , 57 ]. Additionally, those who chew khat usually have a habit of using other substances like alcohol, shisha , cigarette, and others, further limiting food intake and predisposing to malnutrition [ 15 , 58 , 59 ].

Studies have indicated that habitual khat chewing is associated with a lower diversified diet, reduced appetite, and decreased meal frequency and amount, which may lead to negative nutritional outcomes, particularly among individuals engaged in physically demanding activities [ 60 , 61 ]. However, the impact of khat on nutrition and health is complex, with potential positive and negative consequences depending on individual factors and contextual circumstances. Prolonged khat chewing may also be linked to increased consumption of high-calorie soft drinks and sedentary behavior, potentially contributing to the growing issues of obesity and non-communicable diseases in urban areas [ 62 ].

On the contrary, some of the previous studies showed an inverse association between khat consumption and the risk of undernutrition [ 25 , 26 ]. These studies were mainly conducted among diseased individuals (tuberculosis, HIV, and psychiatric disorders), where khat consumption is restricted due to various medical indications. Hence, those with advanced illnesses tend to reduce or avoid khat consumption frequency, intensity, and duration compared to those with stable clinical conditions. Due to this and other potential situations, the risk could be higher among non-khat-chewers. Furthermore, as indicated before, khat chewing with longer hours of stay in a sedentary lifestyle could limit physical activity and may increase the risk of obesity on the reverse. However, khat chewing on any of the above premises, is not beneficial in promoting optimal nutrition and economic development.

More importantly, khat chewing may also contribute to undernutrition indirectly through its social and economic impact. Khat chewing is often associated with social gatherings and has been reported to divert financial resources away from basic necessities, including food [ 63 ]. This diversion of resources may result in reduced food security and inadequate access to a balanced diet, ultimately leading to undernutrition. This could further limit economic productivity and working hours, limiting economic growth, food security, and self-sufficiency for better nutrition and health [ 6 , 64 ].

The disaggregated estimates showed heterogeneity among studies by setting, population and study design. Among high-risk population segments, khat is associated with a 47% increased risk of undernutrition with small between-subject variations (p = 0.43). However, based on the three case control studies with a better quality of evidence, it was found that khat chewing is significantly associated with undernutrition. Overall, studies using large samples from national level analysis showed an averaged effect (lower odds ratio) compared to studies reported from Oromia, Addis Ababa, and Amhara regions. These could imply that the risk of undernutrition could be higher in a certain setting oning to many factors. Furthermore, the negative impacts of khat chewing might concentrate in certain regions and populations that could be associated with the food security, feeding practice and other concomitant substance uses.

It is important to acknowledge some limitations of the current study. Firstly, the included studies were observational in nature, which limits our ability to establish causality and the association could be further confounded by many unmeasured factors. Secondly, there was heterogeneity among the studies in terms of study design, sample size, population characteristics, and the risk of bias among the included studies. However, we performed a random-effects model to account for this heterogeneity. Finally, the studies included in this meta-analysis were conducted in Ethiopia, which may limit the generalizability of our findings to other populations yet very informative for the study setting.

In conclusion, the current meta-analysis provides evidence of an association between khat chewing and an increased risk of undernutrition among adults in Ethiopia. These findings highlight the need for public health interventions to address the potential adverse effects of khat chewing on nutritional status. Efforts should focus on raising awareness about the potential health consequences of khat chewing, improving access to nutritious food, and promoting healthier alternatives to cope with fatigue and stress. Thus, it is imperative to consider khat chewing as relevant risk factors in addressing undernutrition and its consequences in Ethiopia. Further rigorous research shall be conducted in a more controlled and well-designed manner to empirically illustrate the role of khat chewing for undernutrition and overnutrition.

Khat chewing poses a significant risk for undernutrition, especially among women and vulnerable groups like HIV, tuberculosis, and psychiatric patients. This risk likely increases with higher intensity and prolonged duration of chewing, hence, high-intensity and prolonged khat chewing may significantly exacerbate the risk of undernutrition. Multifaceted agricultural, social, and economic interventions are required to halt the existing khat consumption, especially among the vulnerable segments of the population like HIV, tuberculosis, and psychiatric patients. Additionally, khat chewing’s link to overnutrition and non-communicable diseases might be associated with increased physical inactivity, high-calorie, and processed consumption. Further research on the association between khat chewing and adult undernutrition in Ethiopia should prioritize longitudinal studies to establish causality and investigate specific mechanisms. Additionally, exploring the potential long-term effects of pesticide residues and examining the link between khat chewing and overnutrition/non-communicable diseases will provide valuable insights for targeted interventions and policies to improve nutritional health outcomes. Hence, public health interventions are needed to address the negative effects of khat chewing on nutrition, including raising awareness, promoting healthier alternatives, and implementing multifaceted agricultural, social, and economic interventions at a scale.

Supporting information

S1 checklist. prisma 2020 checklist..

https://doi.org/10.1371/journal.pone.0299538.s001

S1 Table. The JBI parameters for inclusion of cross-sectional study articles on the association between khat chewing and undernutrition among adults in Ethiopia.

https://doi.org/10.1371/journal.pone.0299538.s002

S2 Table. The JBI parameters for inclusion of case control study articles on the association between khat chewing and undernutrition among adults in Ethiopia.

https://doi.org/10.1371/journal.pone.0299538.s003

Acknowledgments

Declarations.

The author is very grateful to the authors of the primary studies for their valuable contribution in the field. In addition, I am very grateful to the independent reviewers (not to be mentioned in name) for his time and valuable contribution in extracting data from each study.

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Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr.

Patient Safety and Quality: An Evidence-Based Handbook for Nurses.

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From: Chapter 7, The Evidence for Evidence-Based Practice Implementation

• Cite this Page Titler MG. The Evidence for Evidence-Based Practice Implementation. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Evidence Table, [Evidence-Based Practice in Nursing].
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