systematic literature review vs meta analysis

• En español – ExME
• Em português – EME

Systematic reviews vs meta-analysis: what’s the difference?

Posted on 24th July 2023 by Verónica Tanco Tellechea

You may hear the terms ‘systematic review’ and ‘meta-analysis being used interchangeably’. Although they are related, they are distinctly different. Learn more in this blog for beginners.

What is a systematic review?

According to Cochrane (1), a systematic review attempts to identify, appraise and synthesize all the empirical evidence to answer a specific research question. Thus, a systematic review is where you might find the most relevant, adequate, and current information regarding a specific topic. In the levels of evidence pyramid , systematic reviews are only surpassed by meta-analyses. 

To conduct a systematic review, you will need, among other things: 

• A specific research question, usually in the form of a PICO question.
• Pre-specified eligibility criteria, to decide which articles will be included or discarded from the review. 
• To follow a systematic method that will minimize bias.

You can find protocols that will guide you from both Cochrane and the Equator Network , among other places, and if you are a beginner to the topic then have a read of an overview about systematic reviews.

What is a meta-analysis?

A meta-analysis is a quantitative, epidemiological study design used to systematically assess the results of previous research (2) . Usually, they are based on randomized controlled trials, though not always. This means that a meta-analysis is a mathematical tool that allows researchers to mathematically combine outcomes from multiple studies.

When can a meta-analysis be implemented?

There is always the possibility of conducting a meta-analysis, yet, for it to throw the best possible results it should be performed when the studies included in the systematic review are of good quality, similar designs, and have similar outcome measures.

Why are meta-analyses important?

Outcomes from a meta-analysis may provide more precise information regarding the estimate of the effect of what is being studied because it merges outcomes from multiple studies. In a meta-analysis, data from various trials are combined and generate an average result (1), which is portrayed in a forest plot diagram. Moreover, meta-analysis also include a funnel plot diagram to visually detect publication bias.

Conclusions

A systematic review is an article that synthesizes available evidence on a certain topic utilizing a specific research question, pre-specified eligibility criteria for including articles, and a systematic method for its production. Whereas a meta-analysis is a quantitative, epidemiological study design used to assess the results of articles included in a systematic-review. 

Remember: All meta-analyses involve a systematic review, but not all systematic reviews involve a meta-analysis.

If you would like some further reading on this topic, we suggest the following:

The systematic review – a S4BE blog article

Meta-analysis: what, why, and how – a S4BE blog article

The difference between a systematic review and a meta-analysis – a blog article via Covidence

Systematic review vs meta-analysis: what’s the difference? A 5-minute video from Research Masterminds:

• About Cochrane reviews [Internet]. Cochranelibrary.com. [cited 2023 Apr 30]. Available from: https://www.cochranelibrary.com/about/about-cochrane-reviews
• Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29–37.

Verónica Tanco Tellechea

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Subscribe to our newsletter

You will receive our monthly newsletter and free access to Trip Premium.

Related Articles

How to read a funnel plot

This blog introduces you to funnel plots, guiding you through how to read them and what may cause them to look asymmetrical.

Heterogeneity in meta-analysis

When you bring studies together in a meta-analysis, one of the things you need to consider is the variability in your studies – this is called heterogeneity. This blog presents the three types of heterogeneity, considers the different types of outcome data, and delves a little more into dealing with the variations.

Natural killer cells in glioblastoma therapy

As seen in a previous blog from Davide, modern neuroscience often interfaces with other medical specialities. In this blog, he provides a summary of new evidence about the potential of a therapeutic strategy born at the crossroad between neurology, immunology and oncology.

Covidence website will be inaccessible as we upgrading our platform on Monday 23rd August at 10am AEST, / 2am CEST/1am BST (Sunday, 15th August 8pm EDT/5pm PDT) 

The difference between a systematic review and a meta-analysis

• Best Practice

Home | Blog | Best Practice | The difference between a systematic review and a meta-analysis

Covidence explains the difference between systematic review & meta-analysis.

Systematic review and meta-analysis are two terms that you might see used interchangeably. Each term refers to research about research, but there are important differences!

A systematic review is a piece of work that asks a research question and then answers it by summarising the evidence that meets a set of pre-specified criteria. Some systematic reviews present their results using meta-analysis, a statistical method that combines the results of several trials to generate an average result. Meta-analysis adds value because it can produce a more precise estimate of the effect of a treatment than considering each study individually 🎯.

Let’s take a look at a few related questions that you might have about systematic reviews and meta-analysis.

🙋🏽‍♂️ What are the stages of a systematic review?

A systematic review starts with a research question and a protocol or research plan. A review team searches for studies to answer the question using a highly sensitive search strategy. The retrieved studies are then screened for eligibility using the inclusion and exclusion criteria (this is done by at least two people working independently). Next, the reviewers extract the relevant data and assess the quality of the included studies. Finally, the review team synthesises the extracted study data (perhaps using meta-analysis) and presents the results. The process is shown in figure 1.

Covidence helps researchers complete systematic review quickly and easily! It supports reviewers with study selection, data extraction and quality assessment. Data exported from Covidence can be saved in Excel for reliable transfer to your choice of data analysis software or, if you’re writing a Cochrane Review, to RevMan 5.

🙋🏻‍♀️ What does 'systematic' actually mean?

In this context, systematic means that the methods used to search for and analyse the data are

transparent, reproducible and defined before searching begins. This is what differentiates a systematic review from a descriptive review that might be based on, for example, a subset of the literature that the author is familiar with at the time of writing. Systematic reviews strive to be as thorough and rigorous as possible to minimise the bias that would result from cherry-picking studies in a non-systematic way. Systematic reviews sit at the top of the evidence hierarchy because it is widely agreed that studies with rigorous methods are those best able to minimise the risk of bias on the results of the study. This is what makes systematic reviews the most reliable form of evidence (see figure 2). 

🙋🏾‍♂️ Why don't all systematic reviews use meta-analysis?

Meta-analysis can improve the precision of an effect estimate. But it can also be misleading if it is performed with data that are not sufficiently similar, or with data whose methodological quality is poor (for example, because the study participants were not properly randomized). So it’s not always appropriate to use meta-analysis and many systematic reviews do not include them. Reviews that do not contain meta-analysis can still synthesise study data to produce something that has greater value than the sum of its parts.

🙋🏾‍♀️ What does meta-analysis do?

Meta-analysis produces a more precise estimate of treatment effect. There are several types of effect size and the most suitable type is chosen by the review team based on the type of outcomes and interventions under investigation. Typical effect sizes in systematic reviews are the odds ratio, the risk ratio, the weighted mean difference and the standardized mean difference. The results of a meta-analysis are displayed using a forest plot like the one in figure 3.

Some meta-analyses also include subgroup analysis or meta-regression. These techniques are used to explore a factor (for example, the age of the study participant) that might influence the relationship between the treatment and the intervention. Plans to analyse the data using these techniques should be described and justified before looking at the data, ideally at the research plan or protocol stage, to avoid introducing bias. Like meta-analysis, subgroup analysis and meta-regression are advisable only in certain circumstances.

Systematic reviewer pro-tip

  Think carefully before you plan subgroup analysis or meta-regression and always ask a methodologist for advice

🙋🏼‍♀️ What are the other ways to synthesise evidence?

Systematic reviews combine study data in a number of ways to reach an overall understanding of the evidence. Meta-analysis is a type of statistical synthesis. Narrative synthesis combines the findings of multiple studies using words. All systematic reviews, including those that use meta-analysis, are likely to contain an element of narrative synthesis by summarising in words the evidence included in the review. But narrative synthesis doesn’t just describe the included studies: it also seeks to explain the gathered evidence, for example by looking at similarities and differences between the study findings and by exploring possible reasons for those similarities and differences in a systematic way. Narrative synthesis should not be confused with narrative review, which is a term sometimes used for a non-systematic review of the literature (for example in a textbook chapter) where there is no systematic attempt to address issues of bias.

There are many types of systematic review . What they all have in common is the use of transparent and reproducible methods that are defined before the search begins. There is no ‘best’ way to synthesise systematic review evidence, and the most suitable approach will depend on factors such as the nature of the review question, the type of intervention and the outcomes of interest.

Covidence is a web-based tool that saves you time at the screening, selection, data extraction and quality assessment stages of your review. It provides easy collaboration across teams and a clear overview of task status, helping you to efficiently complete your review. Sign up for a free trial today! 😀

1 Effectiveness of psychosocial interventions for reducing parental substance misuse – McGovern, R – 2021 | Cochrane Library https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD012823.pub2/full .  Accessed 25 March 2021

Laura Mellor. Portsmouth, UK

Perhaps you'd also like....

Data Extraction Tip 5: Communicate Regularly

The Covidence Global Scholarship recipients are putting evidence-based research into practice. We caught up with some of the winners to discover the impact of their work and find out more about their experiences.

Data Extraction Tip 4: Extract the Right Amount of Data

Data Extraction Tip 3: Pilot the Template

Better systematic review management, head office, working for an institution or organisation.

Find out why over 350 of the world’s leading institutions are seeing a surge in publications since using Covidence!

Request a consultation with one of our team members and start empowering your researchers:

By using our site you consent to our use of cookies to measure and improve our site’s performance. Please see our Privacy Policy for more information. 

Systematic Reviews and Meta Analysis

• Getting Started
• Guides and Standards
• Review Protocols
• Databases and Sources
• Randomized Controlled Trials
• Controlled Clinical Trials
• Observational Designs
• Tests of Diagnostic Accuracy
• Software and Tools
• Where do I get all those articles?
• Collaborations
• EPI 233/528
• Countway Mediated Search
• Risk of Bias (RoB)

Systematic review Q & A

What is a systematic review.

A systematic review is guided filtering and synthesis of all available evidence addressing a specific, focused research question, generally about a specific intervention or exposure. The use of standardized, systematic methods and pre-selected eligibility criteria reduce the risk of bias in identifying, selecting and analyzing relevant studies. A well-designed systematic review includes clear objectives, pre-selected criteria for identifying eligible studies, an explicit methodology, a thorough and reproducible search of the literature, an assessment of the validity or risk of bias of each included study, and a systematic synthesis, analysis and presentation of the findings of the included studies. A systematic review may include a meta-analysis.

For details about carrying out systematic reviews, see the Guides and Standards section of this guide.

Is my research topic appropriate for systematic review methods?

A systematic review is best deployed to test a specific hypothesis about a healthcare or public health intervention or exposure. By focusing on a single intervention or a few specific interventions for a particular condition, the investigator can ensure a manageable results set. Moreover, examining a single or small set of related interventions, exposures, or outcomes, will simplify the assessment of studies and the synthesis of the findings.

Systematic reviews are poor tools for hypothesis generation: for instance, to determine what interventions have been used to increase the awareness and acceptability of a vaccine or to investigate the ways that predictive analytics have been used in health care management. In the first case, we don't know what interventions to search for and so have to screen all the articles about awareness and acceptability. In the second, there is no agreed on set of methods that make up predictive analytics, and health care management is far too broad. The search will necessarily be incomplete, vague and very large all at the same time. In most cases, reviews without clearly and exactly specified populations, interventions, exposures, and outcomes will produce results sets that quickly outstrip the resources of a small team and offer no consistent way to assess and synthesize findings from the studies that are identified.

If not a systematic review, then what?

You might consider performing a scoping review . This framework allows iterative searching over a reduced number of data sources and no requirement to assess individual studies for risk of bias. The framework includes built-in mechanisms to adjust the analysis as the work progresses and more is learned about the topic. A scoping review won't help you limit the number of records you'll need to screen (broad questions lead to large results sets) but may give you means of dealing with a large set of results.

This tool can help you decide what kind of review is right for your question.

Can my student complete a systematic review during her summer project?

Probably not. Systematic reviews are a lot of work. Including creating the protocol, building and running a quality search, collecting all the papers, evaluating the studies that meet the inclusion criteria and extracting and analyzing the summary data, a well done review can require dozens to hundreds of hours of work that can span several months. Moreover, a systematic review requires subject expertise, statistical support and a librarian to help design and run the search. Be aware that librarians sometimes have queues for their search time. It may take several weeks to complete and run a search. Moreover, all guidelines for carrying out systematic reviews recommend that at least two subject experts screen the studies identified in the search. The first round of screening can consume 1 hour per screener for every 100-200 records. A systematic review is a labor-intensive team effort.

How can I know if my topic has been been reviewed already?

Before starting out on a systematic review, check to see if someone has done it already. In PubMed you can use the systematic review subset to limit to a broad group of papers that is enriched for systematic reviews. You can invoke the subset by selecting if from the Article Types filters to the left of your PubMed results, or you can append AND systematic[sb] to your search. For example:

"neoadjuvant chemotherapy" AND systematic[sb]

The systematic review subset is very noisy, however. To quickly focus on systematic reviews (knowing that you may be missing some), simply search for the word systematic in the title:

"neoadjuvant chemotherapy" AND systematic[ti]

Any PRISMA-compliant systematic review will be captured by this method since including the words "systematic review" in the title is a requirement of the PRISMA checklist. Cochrane systematic reviews do not include 'systematic' in the title, however. It's worth checking the Cochrane Database of Systematic Reviews independently.

You can also search for protocols that will indicate that another group has set out on a similar project. Many investigators will register their protocols in PROSPERO , a registry of review protocols. Other published protocols as well as Cochrane Review protocols appear in the Cochrane Methodology Register, a part of the Cochrane Library .

• Next: Guides and Standards >>
• Last Updated: Feb 26, 2024 3:17 PM
• URL: https://guides.library.harvard.edu/meta-analysis

Understanding the Differences Between a Systematic Review vs Meta Analysis

Automate every stage of your literature review to produce evidence-based research faster and more accurately.

The advent of evidence-based medicine has increased the demand for systematic methods to analyze and synthesize clinical evidence. When it comes to the search for the best available clinical evidence, randomized control trials, systematic reviews, and meta-analysis are considered the “gold standard” [1].

Since both systematic reviews and meta-analyses are secondary research approaches (research of research), sometimes the terms are used interchangeably, but there are vast differences between them.

A systematic review is a review that collects, critically appraises, and synthesizes all the available evidence to answer a specifically formulated research question.

A meta-analysis, on the other hand, is a statistical method that is used to pool results from various independent studies, to generate an overall estimate of the studied phenomenon.

Systematic reviews can sometimes use meta-analysis to synthesize their results, but they are two very distinct techniques. In this article, we will look at the definition of a systematic review , and understand how it is different from a meta-analysis.

What Is A Systematic Review?

In section 1.2.2 of the Cochrane Handbook, titled What is a systematic review?, the following definition can be found, “A systematic review attempts to collate all empirical evidence that fits the pre-specified eligibility criteria in order to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made (Antman 1992, Oxman 1993). The key characteristics of a systematic review are: a clearly stated set of objectives with pre-defined eligibility criteria for the studies; an explicit, reproducible methodology; a systematic search that attempts to identify all the studies that would meet the eligibility criteria; an assessment of the validity of the findings of the included studies, for example through the assessment of the risk of bias; and a systematic presentation, and synthesis, of the characteristics and findings of the included studies”[2].

The evidence collected in a systematic review can be analyzed and synthesized, quantitatively, or qualitatively. The quantitative analysis of empirical evidence can use a meta-analysis as the statistical approach. To know more about how to write a systematic review , you can read our article; previously linked.

What Is Meta-Analysis?

Meta-analysis is a statistical method used to combine the results of individual studies. It uses a quantitative, formal, and epidemiological study design to systematically assess the results of previous studies to derive conclusions about a specific research parameter [3]. It is therefore an approach for systematically combining pertinent qualitative and quantitative study data from several included studies to establish a single conclusion that has significant statistical power.

Typically, the primary studies included in a meta-analysis are randomized controlled trials (RCTs). In a meta-analysis, the main objective is to provide more precise estimates of the effects of a treatment or of a risk factor for a disease, than any of the individual studies included in the pooled analysis. The data is also analyzed for heterogeneity (variation within outcomes), and generalizability (similarities between outcomes) within the individual studies, which facilitates more effective clinical decision making. Examining the heterogeneity of effect estimates within the primary studies is perhaps the most important task in a meta-analysis.

Meta-analyses of observational studies such as cohort studies are frequently performed, but no widely accepted guidance is available at the moment. While these meta-analyses are frequently published in literature, they are considered suboptimal to those involving RCTs.  The main reason is that the observational studies may entail an increased risk of biases and high levels of heterogeneity. Researchers who have to conduct meta-analyses on observational studies ought to carefully consider whether all included studies are able to answer the same clinical question.

Although meta-analysis is a subset of systematic reviews, a systematic review may or may not include a meta-analysis. An advantage of meta-analysis is that it has the ability to be completely objective in evaluating the research parameter. However, not all research areas have enough evidence to allow a meta-analysis. The inclusion of meta-analysis in a systematic review depends on the research question, the intervention to be studied, and the desired outcomes.

• Sur RL, Dahm P. History of evidence-based medicine. Indian journal of urology: IJU: journal of the Urological Society of India. 2011;27(4):487–9.
• Clarke M, Chalmers I. Discussion sections in reports of controlled trials published in general medical journals: islands in search of continents? Jama. 1998;280(3):280–2.
• Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29-37.

3 Reasons to Connect

How to conduct a meta-analysis in eight steps: a practical guide

• Open access
• Published: 30 November 2021
• Volume 72 , pages 1–19, ( 2022 )

Cite this article

You have full access to this open access article

• Christopher Hansen 1 ,
• Holger Steinmetz 2 &
• Jörn Block 3 , 4 , 5  

153k Accesses

50 Citations

158 Altmetric

Explore all metrics

Avoid common mistakes on your manuscript.

1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php .

The macros of David B. Wilson can be downloaded from: http://mason.gmu.edu/~dwilsonb/ .

The macros of Field and Gillet ( 2010 ) can be downloaded from: https://www.discoveringstatistics.com/repository/fieldgillett/how_to_do_a_meta_analysis.html .

The tutorials can be found via: https://www.metafor-project.org/doku.php .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: https://www.erim.eur.nl/research-support/meta-essentials/ .

Aguinis H, Dalton DR, Bosco FA, Pierce CA, Dalton CM (2011a) Meta-analytic choices and judgment calls: Implications for theory building and testing, obtained effect sizes, and scholarly impact. J Manag 37(1):5–38

Google Scholar  

Aguinis H, Gottfredson RK, Joo H (2013) Best-practice recommendations for defining, identifying, and handling outliers. Organ Res Methods 16(2):270–301

Article   Google Scholar  

Aguinis H, Gottfredson RK, Wright TA (2011b) Best-practice recommendations for estimating interaction effects using meta-analysis. J Organ Behav 32(8):1033–1043

Aguinis H, Pierce CA, Bosco FA, Dalton DR, Dalton CM (2011c) Debunking myths and urban legends about meta-analysis. Organ Res Methods 14(2):306–331

Aloe AM (2015) Inaccuracy of regression results in replacing bivariate correlations. Res Synth Methods 6(1):21–27

Anderson RG, Kichkha A (2017) Replication, meta-analysis, and research synthesis in economics. Am Econ Rev 107(5):56–59

Appelbaum M, Cooper H, Kline RB, Mayo-Wilson E, Nezu AM, Rao SM (2018) Journal article reporting standards for quantitative research in psychology: the APA publications and communications BOARD task force report. Am Psychol 73(1):3–25

Aytug ZG, Rothstein HR, Zhou W, Kern MC (2012) Revealed or concealed? Transparency of procedures, decisions, and judgment calls in meta-analyses. Organ Res Methods 15(1):103–133

Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50(4):1088–1101. https://doi.org/10.2307/2533446

Bergh DD, Aguinis H, Heavey C, Ketchen DJ, Boyd BK, Su P, Lau CLL, Joo H (2016) Using meta-analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership-performance relationship. Strateg Manag J 37(3):477–497

Becker BJ (1992) Using results from replicated studies to estimate linear models. J Educ Stat 17(4):341–362

Becker BJ (1995) Corrections to “Using results from replicated studies to estimate linear models.” J Edu Behav Stat 20(1):100–102

Bergmann C, Tsuji S, Piccinini PE, Lewis ML, Braginsky M, Frank MC, Cristia A (2018) Promoting replicability in developmental research through meta-analyses: Insights from language acquisition research. Child Dev 89(6):1996–2009

Bernerth JB, Aguinis H (2016) A critical review and best-practice recommendations for control variable usage. Pers Psychol 69(1):229–283

Bernerth JB, Cole MS, Taylor EC, Walker HJ (2018) Control variables in leadership research: A qualitative and quantitative review. J Manag 44(1):131–160

Bijmolt TH, Pieters RG (2001) Meta-analysis in marketing when studies contain multiple measurements. Mark Lett 12(2):157–169

Block J, Kuckertz A (2018) Seven principles of effective replication studies: Strengthening the evidence base of management research. Manag Rev Quart 68:355–359

Borenstein M (2009) Effect sizes for continuous data. In: Cooper H, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis. Russell Sage Foundation, pp 221–235

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis. John Wiley, Chichester

Book   Google Scholar  

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1(2):97–111

Borenstein M, Hedges L, Higgins J, Rothstein H (2013) Comprehensive meta-analysis (version 3). Biostat, Englewood, NJ

Borenstein M, Higgins JP (2013) Meta-analysis and subgroups. Prev Sci 14(2):134–143

Bosco FA, Steel P, Oswald FL, Uggerslev K, Field JG (2015) Cloud-based meta-analysis to bridge science and practice: Welcome to metaBUS. Person Assess Decis 1(1):3–17

Bosco FA, Uggerslev KL, Steel P (2017) MetaBUS as a vehicle for facilitating meta-analysis. Hum Resour Manag Rev 27(1):237–254

Burgard T, Bošnjak M, Studtrucker R (2021) Community-augmented meta-analyses (CAMAs) in psychology: potentials and current systems. Zeitschrift Für Psychologie 229(1):15–23

Cheung MWL (2015a) Meta-analysis: A structural equation modeling approach. John Wiley & Sons, Chichester

Cheung MWL (2015b) metaSEM: An R package for meta-analysis using structural equation modeling. Front Psychol 5:1521

Cheung MWL (2019) A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychol Rev 29(4):387–396

Cheung MWL, Chan W (2005) Meta-analytic structural equation modeling: a two-stage approach. Psychol Methods 10(1):40–64

Cheung MWL, Vijayakumar R (2016) A guide to conducting a meta-analysis. Neuropsychol Rev 26(2):121–128

Combs JG, Crook TR, Rauch A (2019) Meta-analytic research in management: contemporary approaches unresolved controversies and rising standards. J Manag Stud 56(1):1–18. https://doi.org/10.1111/joms.12427

DeSimone JA, Köhler T, Schoen JL (2019) If it were only that easy: the use of meta-analytic research by organizational scholars. Organ Res Methods 22(4):867–891. https://doi.org/10.1177/1094428118756743

DeSimone JA, Brannick MT, O’Boyle EH, Ryu JW (2020) Recommendations for reviewing meta-analyses in organizational research. Organ Res Methods 56:455–463

Duval S, Tweedie R (2000a) Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56(2):455–463

Duval S, Tweedie R (2000b) A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc 95(449):89–98

Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634

Eisend M (2017) Meta-Analysis in advertising research. J Advert 46(1):21–35

Elliott JH, Synnot A, Turner T, Simmons M, Akl EA, McDonald S, Salanti G, Meerpohl J, MacLehose H, Hilton J, Tovey D, Shemilt I, Thomas J (2017) Living systematic review: 1. Introduction—the why, what, when, and how. J Clin Epidemiol 91:2330. https://doi.org/10.1016/j.jclinepi.2017.08.010

Field AP, Gillett R (2010) How to do a meta-analysis. Br J Math Stat Psychol 63(3):665–694

Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Quart 68:103–106

Fortunato S, Bergstrom CT, Börner K, Evans JA, Helbing D, Milojević S, Petersen AM, Radicchi F, Sinatra R, Uzzi B, Vespignani A (2018) Science of science. Science 359(6379). https://doi.org/10.1126/science.aao0185

Geyer-Klingeberg J, Hang M, Rathgeber A (2020) Meta-analysis in finance research: Opportunities, challenges, and contemporary applications. Int Rev Finan Anal 71:101524

Geyskens I, Krishnan R, Steenkamp JBE, Cunha PV (2009) A review and evaluation of meta-analysis practices in management research. J Manag 35(2):393–419

Glass GV (2015) Meta-analysis at middle age: a personal history. Res Synth Methods 6(3):221–231

Gonzalez-Mulé E, Aguinis H (2018) Advancing theory by assessing boundary conditions with metaregression: a critical review and best-practice recommendations. J Manag 44(6):2246–2273

Gooty J, Banks GC, Loignon AC, Tonidandel S, Williams CE (2021) Meta-analyses as a multi-level model. Organ Res Methods 24(2):389–411. https://doi.org/10.1177/1094428119857471

Grewal D, Puccinelli N, Monroe KB (2018) Meta-analysis: integrating accumulated knowledge. J Acad Mark Sci 46(1):9–30

Gurevitch J, Koricheva J, Nakagawa S, Stewart G (2018) Meta-analysis and the science of research synthesis. Nature 555(7695):175–182

Gusenbauer M, Haddaway NR (2020) Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res Synth Methods 11(2):181–217

Habersang S, Küberling-Jost J, Reihlen M, Seckler C (2019) A process perspective on organizational failure: a qualitative meta-analysis. J Manage Stud 56(1):19–56

Harari MB, Parola HR, Hartwell CJ, Riegelman A (2020) Literature searches in systematic reviews and meta-analyses: A review, evaluation, and recommendations. J Vocat Behav 118:103377

Harrison JS, Banks GC, Pollack JM, O’Boyle EH, Short J (2017) Publication bias in strategic management research. J Manag 43(2):400–425

Havránek T, Stanley TD, Doucouliagos H, Bom P, Geyer-Klingeberg J, Iwasaki I, Reed WR, Rost K, Van Aert RCM (2020) Reporting guidelines for meta-analysis in economics. J Econ Surveys 34(3):469–475

Hedges LV, Olkin I (1985) Statistical methods for meta-analysis. Academic Press, Orlando

Hedges LV, Vevea JL (2005) Selection methods approaches. In: Rothstein HR, Sutton A, Borenstein M (eds) Publication bias in meta-analysis: prevention, assessment, and adjustments. Wiley, Chichester, pp 145–174

Hoon C (2013) Meta-synthesis of qualitative case studies: an approach to theory building. Organ Res Methods 16(4):522–556

Hunter JE, Schmidt FL (1990) Methods of meta-analysis: correcting error and bias in research findings. Sage, Newbury Park

Hunter JE, Schmidt FL (2004) Methods of meta-analysis: correcting error and bias in research findings, 2nd edn. Sage, Thousand Oaks

Hunter JE, Schmidt FL, Jackson GB (1982) Meta-analysis: cumulating research findings across studies. Sage Publications, Beverly Hills

Jak S (2015) Meta-analytic structural equation modelling. Springer, New York, NY

Kepes S, Banks GC, McDaniel M, Whetzel DL (2012) Publication bias in the organizational sciences. Organ Res Methods 15(4):624–662

Kepes S, McDaniel MA, Brannick MT, Banks GC (2013) Meta-analytic reviews in the organizational sciences: Two meta-analytic schools on the way to MARS (the Meta-Analytic Reporting Standards). J Bus Psychol 28(2):123–143

Kraus S, Breier M, Dasí-Rodríguez S (2020) The art of crafting a systematic literature review in entrepreneurship research. Int Entrepreneur Manag J 16(3):1023–1042

Levitt HM (2018) How to conduct a qualitative meta-analysis: tailoring methods to enhance methodological integrity. Psychother Res 28(3):367–378

Levitt HM, Bamberg M, Creswell JW, Frost DM, Josselson R, Suárez-Orozco C (2018) Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: the APA publications and communications board task force report. Am Psychol 73(1):26

Lipsey MW, Wilson DB (2001) Practical meta-analysis. Sage Publications, Inc.

López-López JA, Page MJ, Lipsey MW, Higgins JP (2018) Dealing with effect size multiplicity in systematic reviews and meta-analyses. Res Synth Methods 9(3):336–351

Martín-Martín A, Thelwall M, Orduna-Malea E, López-Cózar ED (2021) Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1):871–906

Merton RK (1968) The Matthew effect in science: the reward and communication systems of science are considered. Science 159(3810):56–63

Moeyaert M, Ugille M, Natasha Beretvas S, Ferron J, Bunuan R, Van den Noortgate W (2017) Methods for dealing with multiple outcomes in meta-analysis: a comparison between averaging effect sizes, robust variance estimation and multilevel meta-analysis. Int J Soc Res Methodol 20(6):559–572

Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine. 6(7):e1000097

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106(1):213–228

Moreau D, Gamble B (2020) Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations. Psychol Methods. https://doi.org/10.1037/met0000351

O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S (2015) Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev 4(1):1–22

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan—a web and mobile app for systematic reviews. Syst Rev 5(1):1–10

Owen E, Li Q (2021) The conditional nature of publication bias: a meta-regression analysis. Polit Sci Res Methods 9(4):867–877

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E,McDonald S,McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372. https://doi.org/10.1136/bmj.n71

Palmer TM, Sterne JAC (eds) (2016) Meta-analysis in stata: an updated collection from the stata journal, 2nd edn. Stata Press, College Station, TX

Pigott TD, Polanin JR (2020) Methodological guidance paper: High-quality meta-analysis in a systematic review. Rev Educ Res 90(1):24–46

Polanin JR, Tanner-Smith EE, Hennessy EA (2016) Estimating the difference between published and unpublished effect sizes: a meta-review. Rev Educ Res 86(1):207–236

Polanin JR, Hennessy EA, Tanner-Smith EE (2017) A review of meta-analysis packages in R. J Edu Behav Stat 42(2):206–242

Polanin JR, Hennessy EA, Tsuji S (2020) Transparency and reproducibility of meta-analyses in psychology: a meta-review. Perspect Psychol Sci 15(4):1026–1041. https://doi.org/10.1177/17456916209064

R Core Team (2021). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .

Rauch A (2020) Opportunities and threats in reviewing entrepreneurship theory and practice. Entrep Theory Pract 44(5):847–860

Rauch A, van Doorn R, Hulsink W (2014) A qualitative approach to evidence–based entrepreneurship: theoretical considerations and an example involving business clusters. Entrep Theory Pract 38(2):333–368

Raudenbush SW (2009) Analyzing effect sizes: Random-effects models. In: Cooper H, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis, 2nd edn. Russell Sage Foundation, New York, NY, pp 295–315

Rosenthal R (1979) The file drawer problem and tolerance for null results. Psychol Bull 86(3):638

Rothstein HR, Sutton AJ, Borenstein M (2005) Publication bias in meta-analysis: prevention, assessment and adjustments. Wiley, Chichester

Roth PL, Le H, Oh I-S, Van Iddekinge CH, Bobko P (2018) Using beta coefficients to impute missing correlations in meta-analysis research: Reasons for caution. J Appl Psychol 103(6):644–658. https://doi.org/10.1037/apl0000293

Rudolph CW, Chang CK, Rauvola RS, Zacher H (2020) Meta-analysis in vocational behavior: a systematic review and recommendations for best practices. J Vocat Behav 118:103397

Schmidt FL (2017) Statistical and measurement pitfalls in the use of meta-regression in meta-analysis. Career Dev Int 22(5):469–476

Schmidt FL, Hunter JE (2015) Methods of meta-analysis: correcting error and bias in research findings. Sage, Thousand Oaks

Schwab A (2015) Why all researchers should report effect sizes and their confidence intervals: Paving the way for meta–analysis and evidence–based management practices. Entrepreneurship Theory Pract 39(4):719–725. https://doi.org/10.1111/etap.12158

Shaw JD, Ertug G (2017) The suitability of simulations and meta-analyses for submissions to Academy of Management Journal. Acad Manag J 60(6):2045–2049

Soderberg CK (2018) Using OSF to share data: A step-by-step guide. Adv Methods Pract Psychol Sci 1(1):115–120

Stanley TD, Doucouliagos H (2010) Picture this: a simple graph that reveals much ado about research. J Econ Surveys 24(1):170–191

Stanley TD, Doucouliagos H (2012) Meta-regression analysis in economics and business. Routledge, London

Stanley TD, Jarrell SB (1989) Meta-regression analysis: a quantitative method of literature surveys. J Econ Surveys 3:54–67

Steel P, Beugelsdijk S, Aguinis H (2021) The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews. J Int Bus Stud 52(1):23–44

Suurmond R, van Rhee H, Hak T (2017) Introduction, comparison, and validation of Meta-Essentials: a free and simple tool for meta-analysis. Res Synth Methods 8(4):537–553

The Cochrane Collaboration (2020). Review Manager (RevMan) [Computer program] (Version 5.4).

Thomas J, Noel-Storr A, Marshall I, Wallace B, McDonald S, Mavergames C, Glasziou P, Shemilt I, Synnot A, Turner T, Elliot J (2017) Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol 91:31–37

Thompson SG, Higgins JP (2002) How should meta-regression analyses be undertaken and interpreted? Stat Med 21(11):1559–1573

Tipton E, Pustejovsky JE, Ahmadi H (2019) A history of meta-regression: technical, conceptual, and practical developments between 1974 and 2018. Res Synth Methods 10(2):161–179

Vevea JL, Woods CM (2005) Publication bias in research synthesis: Sensitivity analysis using a priori weight functions. Psychol Methods 10(4):428–443

Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36(3):1–48

Viechtbauer W, Cheung MWL (2010) Outlier and influence diagnostics for meta-analysis. Res Synth Methods 1(2):112–125

Viswesvaran C, Ones DS (1995) Theory testing: combining psychometric meta-analysis and structural equations modeling. Pers Psychol 48(4):865–885

Wilson SJ, Polanin JR, Lipsey MW (2016) Fitting meta-analytic structural equation models with complex datasets. Res Synth Methods 7(2):121–139. https://doi.org/10.1002/jrsm.1199

Wood JA (2008) Methodology for dealing with duplicate study effects in a meta-analysis. Organ Res Methods 11(1):79–95

Download references

Open Access funding enabled and organized by Projekt DEAL. No funding was received to assist with the preparation of this manuscript.

Author information

Authors and affiliations.

University of Luxembourg, Luxembourg, Luxembourg

Christopher Hansen

Leibniz Institute for Psychology (ZPID), Trier, Germany

Holger Steinmetz

Trier University, Trier, Germany

Erasmus University Rotterdam, Rotterdam, The Netherlands

Wittener Institut Für Familienunternehmen, Universität Witten/Herdecke, Witten, Germany

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jörn Block .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table 1 .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Hansen, C., Steinmetz, H. & Block, J. How to conduct a meta-analysis in eight steps: a practical guide. Manag Rev Q 72 , 1–19 (2022). https://doi.org/10.1007/s11301-021-00247-4

Download citation

Published : 30 November 2021

Issue Date : February 2022

DOI : https://doi.org/10.1007/s11301-021-00247-4

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

• Locations and Hours
• UCLA Library
• Research Guides
• Biomedical Library Guides

Systematic Reviews

• Types of Literature Reviews

What Makes a Systematic Review Different from Other Types of Reviews?

• Planning Your Systematic Review
• Database Searching
• Creating the Search
• Search Filters and Hedges
• Grey Literature
• Managing and Appraising Results
• Further Resources

Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91–108. doi:10.1111/j.1471-1842.2009.00848.x

• << Previous: Home
• Next: Planning Your Systematic Review >>
• Last Updated: Apr 17, 2024 2:02 PM
• URL: https://guides.library.ucla.edu/systematicreviews

Main Navigation Menu

Systematic reviews.

• Getting Started with Systematic Reviews

What is a Systematic Review and Meta-Analysis

Differences between systematic and literature reviews.

• Finding and Evaluating Existing Systematic Reviews
• Steps in a Systematic Review
• Step 1: Developing a Question
• Step 2: Selecting Databases
• Step 3: Grey Literature
• Step 4: Registering a Systematic Review Protocol
• Step 5: Translate Search Strategies
• Step 6: Citation Management Tools
• Step 7: Article Screening
• Other Resources
• Interlibrary Loan (ILL)

A systematic review collects and analyzes all evidence that answers a specific research question. In a systematic review, a question needs to be clearly defined and have inclusion and exclusion criteria. In general, specific and systematic methods selected are intended to minimize bias. This is followed by an extensive search of the literature and a critical analysis of the search results. The reason why a systematic review is conducted is to provide a current evidence-based answer to a specific question that in turn helps to inform decision making. Check out the Centers for Disease Control and Prevention and Cochrane Reviews links to learn more about Systematic Reviews.

A systematic review can be combined with a meta-analysis. A meta-analysis is the use of statistical methods to summarize the results of a systematic review. Not every systematic review contains a meta-analysis. A meta-analysis may not be appropriate if the designs of the studies are too different, if there are concerns about the quality of studies, if the outcomes measured are not sufficiently similar for the result across the studies to be meaningful.

Centers for Disease Control and Prevention. (n.d.).  Systematic Reviews . Retrieved from  https://www.cdc.gov/library/researchguides/sytemsaticreviews.html

Cochrane Library. (n.d.).  About Cochrane Reviews . Retrieved from  https://www.cochranelibrary.com/about/about-cochrane-reviews

Source: Kysh, Lynn (2013): Difference between a systematic review and a literature review. [figshare]. Available at:  https://figshare.com/articles/Difference_between_a_systematic_review_and_a_literature_review/766364

• << Previous: Getting Started with Systematic Reviews
• Next: Finding and Evaluating Existing Systematic Reviews >>
• Last Updated: May 7, 2024 1:42 PM
• URL: https://guides.library.ucmo.edu/systematicreviews

Literature Review, Systematic Review and Meta-analysis

Literature reviews can be a good way to narrow down theoretical interests; refine a research question; understand contemporary debates; and orientate a particular research project. It is very common for PhD theses to contain some element of reviewing the literature around a particular topic. It’s typical to have an entire chapter devoted to reporting the result of this task, identifying gaps in the literature and framing the collection of additional data.

Systematic review is a type of literature review that uses systematic methods to collect secondary data, critically appraise research studies, and synthesise findings. Systematic reviews are designed to provide a comprehensive, exhaustive summary of current theories and/or evidence and published research (Siddaway, Wood & Hedges, 2019) and may be qualitative or qualitative. Relevant studies and literature are identified through a research question, summarised and synthesized into a discrete set of findings or a description of the state-of-the-art. This might result in a ‘literature review’ chapter in a doctoral thesis, but can also be the basis of an entire research project.

Meta-analysis is a specialised type of systematic review which is quantitative and rigorous, often comparing data and results across multiple similar studies. This is a common approach in medical research where several papers might report the results of trials of a particular treatment, for instance. The meta-analysis then statistical techniques to synthesize these into one summary. This can have a high statistical power but care must be taken not to introduce bias in the selection and filtering of evidence.

Whichever type of review is employed, the process is similarly linear. The first step is to frame a question which can guide the review. This is used to identify relevant literature, often through searching subject-specific scientific databases. From these results the most relevant will be identified. Filtering is important here as there will be time constraints that prevent the researcher considering every possible piece of evidence or theoretical viewpoint. Once a concrete evidence base has been identified, the researcher extracts relevant data before reporting the synthesized results in an extended piece of writing.

Literature Review: GO-GN Insights

Sarah Lambert used a systematic review of literature with both qualitative and quantitative phases to investigate the question “How can open education programs be reconceptualised as acts of social justice to improve the access, participation and success of those who are traditionally excluded from higher education knowledge and skills?”

“My PhD research used systematic review, qualitative synthesis, case study and discourse analysis techniques, each was underpinned and made coherent by a consistent critical inquiry methodology and an overarching research question. “Systematic reviews are becoming increasingly popular as a way to collect evidence of what works across multiple contexts and can be said to address some of the weaknesses of case study designs which provide detail about a particular context – but which is often not replicable in other socio-cultural contexts (such as other countries or states.) Publication of systematic reviews that are done according to well defined methods are quite likely to be published in high-ranking journals – my PhD supervisors were keen on this from the outset and I was encouraged along this path. “Previously I had explored social realist authors and a social realist approach to systematic reviews (Pawson on realist reviews) but they did not sufficiently embrace social relations, issues of power, inclusion/exclusion. My supervisors had pushed me to explain what kind of realist review I intended to undertake, and I found out there was a branch of critical realism which was briefly of interest. By getting deeply into theory and trying out ways of combining theory I also feel that I have developed a deeper understanding of conceptual working and the different ways theories can be used at all stagesof research and even how to come up with novel conceptual frameworks.”

Useful references for Systematic Review & Meta-Analysis: Finfgeld-Connett (2014); Lambert (2020); Siddaway, Wood & Hedges (2019)

Research Methods Handbook Copyright © 2020 by Rob Farrow; Francisco Iniesto; Martin Weller; and Rebecca Pitt is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

• Research Process

Systematic Literature Review or Literature Review?

• 3 minute read

Table of Contents

As a researcher, you may be required to conduct a literature review. But what kind of review do you need to complete? Is it a systematic literature review or a standard literature review? In this article, we’ll outline the purpose of a systematic literature review, the difference between literature review and systematic review, and other important aspects of systematic literature reviews.

What is a Systematic Literature Review?

The purpose of systematic literature reviews is simple. Essentially, it is to provide a high-level of a particular research question. This question, in and of itself, is highly focused to match the review of the literature related to the topic at hand. For example, a focused question related to medical or clinical outcomes.

The components of a systematic literature review are quite different from the standard literature review research theses that most of us are used to (more on this below). And because of the specificity of the research question, typically a systematic literature review involves more than one primary author. There’s more work related to a systematic literature review, so it makes sense to divide the work among two or three (or even more) researchers.

Your systematic literature review will follow very clear and defined protocols that are decided on prior to any review. This involves extensive planning, and a deliberately designed search strategy that is in tune with the specific research question. Every aspect of a systematic literature review, including the research protocols, which databases are used, and dates of each search, must be transparent so that other researchers can be assured that the systematic literature review is comprehensive and focused.

Most systematic literature reviews originated in the world of medicine science. Now, they also include any evidence-based research questions. In addition to the focus and transparency of these types of reviews, additional aspects of a quality systematic literature review includes:

• Clear and concise review and summary
• Comprehensive coverage of the topic
• Accessibility and equality of the research reviewed

Systematic Review vs Literature Review

The difference between literature review and systematic review comes back to the initial research question. Whereas the systematic review is very specific and focused, the standard literature review is much more general. The components of a literature review, for example, are similar to any other research paper. That is, it includes an introduction, description of the methods used, a discussion and conclusion, as well as a reference list or bibliography.

A systematic review, however, includes entirely different components that reflect the specificity of its research question, and the requirement for transparency and inclusion. For instance, the systematic review will include:

• Eligibility criteria for included research
• A description of the systematic research search strategy
• An assessment of the validity of reviewed research
• Interpretations of the results of research included in the review

As you can see, contrary to the general overview or summary of a topic, the systematic literature review includes much more detail and work to compile than a standard literature review. Indeed, it can take years to conduct and write a systematic literature review. But the information that practitioners and other researchers can glean from a systematic literature review is, by its very nature, exceptionally valuable.

This is not to diminish the value of the standard literature review. The importance of literature reviews in research writing is discussed in this article . It’s just that the two types of research reviews answer different questions, and, therefore, have different purposes and roles in the world of research and evidence-based writing.

Systematic Literature Review vs Meta Analysis

It would be understandable to think that a systematic literature review is similar to a meta analysis. But, whereas a systematic review can include several research studies to answer a specific question, typically a meta analysis includes a comparison of different studies to suss out any inconsistencies or discrepancies. For more about this topic, check out Systematic Review VS Meta-Analysis article.

Language Editing Plus

With Elsevier’s Language Editing Plus services , you can relax with our complete language review of your systematic literature review or literature review, or any other type of manuscript or scientific presentation. Our editors are PhD or PhD candidates, who are native-English speakers. Language Editing Plus includes checking the logic and flow of your manuscript, reference checks, formatting in accordance to your chosen journal and even a custom cover letter. Our most comprehensive editing package, Language Editing Plus also includes any English-editing needs for up to 180 days.

• Publication Recognition

How to Make a PowerPoint Presentation of Your Research Paper

• Manuscript Preparation

What is and How to Write a Good Hypothesis in Research?

You may also like.

Descriptive Research Design and Its Myriad Uses

Five Common Mistakes to Avoid When Writing a Biomedical Research Paper

Making Technical Writing in Environmental Engineering Accessible

To Err is Not Human: The Dangers of AI-assisted Academic Writing

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

Choosing the Right Research Methodology: A Guide for Researchers

Why is data validation important in research?

Writing a good review article

Input your search keywords and press Enter.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 02 June 2024

Point prevalence of evidence-based antimicrobial use among hospitalized patients in sub-Saharan Africa: a systematic review and meta-analysis

• Minyahil Tadesse Boltena   ORCID: orcid.org/0000-0002-5081-1480 1 , 2 ,
• Mirkuzie Wolde 1 , 3 ,
• Belachew Hailu 2 ,
• Ziad El-Khatib 4 ,
• Veronika Steck 5 ,
• Selam Woldegerima 6 ,
• Yibeltal Siraneh 1 &
• Sudhakar Morankar 1  

Scientific Reports volume  14 , Article number:  12652 ( 2024 ) Cite this article

Metrics details

• Health care
• Medical research

Excessive and improper use of antibiotics causes antimicrobial resistance which is a major threat to global health security. Hospitals in sub-Saharan Africa (SSA) has the highest prevalence of antibiotic use. This systematic review and meta-analysis aimed to determine the pooled point prevalence (PPP) of evidence-based antimicrobial use among hospitalized patients in SSA. Literature was retrieved from CINAHL, EMBASE, Google Scholar, PubMed, Scopus, and Web of Science databases. Meta-analysis was conducted using STATA version 17. Forest plots using the random-effect model were used to present the findings. The heterogeneity and publication bias were assessed using the I 2  statistics and Egger’s test. The protocol was registered in PROSPERO with code CRD42023404075. The review was conducted according to PRISMA guidelines. A total of 26, 272 study participants reported by twenty-eight studies published from 10 countries in SSA were included. The pooled point prevalence of antimicrobial use in SSA were 64%. The pooled estimate of hospital wards with the highest antibiotic use were intensive care unit (89%). The pooled prevalence of the most common clinical indication for antibiotic use were community acquired infection (41%). The pooled point prevalence of antimicrobial use among hospitalized patients were higher in SSA. Higher use of antibiotics was recorded in intensive care units. Community acquired infection were most common clinical case among hospitalized patients. Health systems in SSA must design innovative digital health interventions to optimize clinicians adhere to evidence-based prescribing guidelines and improve antimicrobial stewardship.

Similar content being viewed by others

Pharmacist-led antimicrobial stewardship programme in a small hospital without infectious diseases physicians

RETRACTED ARTICLE: Pharmacist-driven antimicrobial stewardship program in a long-term care facility by assessment of appropriateness

Secular trend analysis of antibiotic utilisation in some hospitals in Southern Sichuan from 2010 to 2020

Introduction.

Global antibiotic consumption rates surged by 46%, indicating that the defined daily dose (DDD) per 1000 population per day rose from 9.8 to 14.3 between 2000 and 2018 1 . In low- and middle-income countries (LMICs), antibiotic usage increased by 76% and is projected to continue rising by 2030 2 . Hospitals in SSA have a higher prevalence of antibiotic usage (50%), including the use of broad-spectrum cephalosporins and penicillin 3 .

With improving economies and enhanced access to pharmaceuticals, many of LMICs now revealed antibiotic consumption rates comparable to or even surpassing those of high-income countries 4 . Sub-Saharan African countries are experiencing a similar trend in antibiotic consumption, which could be exacerbated by the region’s exceptionally high infectious disease burden 5 . This sharp rise in antibiotic usage with or without prescription, has become a pressing public health concern due to its strong association with the development of antimicrobial resistance in low resource clinical context 6 , 7 .

The misuse and overuse of antibiotics have led to increased rates of antimicrobial resistance, higher levels of morbidity and mortality, and escalated healthcare costs in low-income countries 8 , 9 . To address this issue, evaluating antibiotic prescribing patterns among patients in healthcare facilities is essential in identifying opportunities for antimicrobial stewardship to promote appropriate antibiotic use 10 , 11 .

Point prevalence studies have proven to be reliable and valid methods for measuring antibiotic use among hospitalized patients 12 . They provide crucial insights into the current state of antibiotic use within healthcare settings, aiding in the identification of patterns and deviations from recommended practices 13 . This data can inform targeted interventions to improve guideline adherence, optimize antibiotic selection, dosing, and duration, and reduce inappropriate prescriptions 14 , 15 . By promoting evidence-based clinical decisions, these studies contribute to the prevention of antibiotic overuse, the emergence of antimicrobial resistance, and the enhancement of patient outcomes, thus serving as a vital tool in advancing the quality and effectiveness of real-world healthcare practices 16 , 17 .

In sub-Saharan Africa, several point prevalence studies have reported a high rate of antibiotic use among hospitalized patients, along with inappropriate usage in healthcare facilities 18 . However, there is limited regional-level data available to describe the point prevalence of antibiotic use among hospitalized patients in SSA 19 . Understanding the epidemiology of antibiotic use in this context and assessing the quality of antibiotic prescribing are critical steps in designing effective antimicrobial stewardship interventions aimed at encouraging the rational use of antibiotics and improving clinical outcomes for patients 20 . Therefore, this systematic review and meta-analysis aimed to determine the pooled point prevalence of antibiotic use among hospitalized patients in sub-Saharan Africa.

Search strategy and selection of studies

The search strategy aimed to find both published and unpublished literature. Initially, a preliminary search was conducted on the Google Scholar to identify indexed full texts or metadata of scholarly literature on the topic. We adapted key terms as needed for each database, utilizing a combination of MeSH terms and text words, employing Boolean operators “AND” and “OR” for searches in databases like CINAHL, PubMed, EMBASE, Scopus, and Web of Science ( Appendix I ). Additionally, we examined the reference lists of selected studies for potential additional sources. No restrictions were imposed based on language or publication year. After the search, all identified citations were organized and imported into EndNote version 15.0, with duplicates removed. Two independent reviewers (MTB and BH) screened titles and abstracts, and a third reviewer (ZEK) cross-checked them against the inclusion and exclusion criteria. Relevant studies meeting the criteria were obtained in full, along with their citation details. Studies reporting the point prevalence of antibiotic use among hospitalized patients in SSA, which were published from 2013 to 2023 were eligible for inclusion. Excluded were systematic reviews, Studies having participants sampled inappropriately and the setting not described in detail studies, data analysis not conducted with sufficient coverage of the identified sample, and literature from high-income countries. Two independent reviewers (MTB and BH) assessed the full text of selected citations against the inclusion criteria, with a third reviewer (LWT) conducting a double-check. Reasons for excluding studies failing to meet the inclusion criteria upon full text review were documented. Any disagreements between reviewers at each stage of the study selection process were resolved through discussion or by consulting a third reviewer. The PRISMA checklist ( Appendix II ) and flow chart was used to describe the matching pages in the manuscript with the number of articles identified, included, and excluded with justifications. The results of the search were fully reported in the final systematic review and presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram (Fig.  1 ) 21 .

PRISMA flow diagram of included studies: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 .

Operational definitions

Point prevalence survey of antimicrobial use.

Is a structured assessment done in healthcare settings to determine the percentage of patients receiving antimicrobial treatment at a particular moment 22 . Its goal is to assess the appropriateness of antimicrobial use, including choice, dosage, and duration, to enhance antimicrobial stewardship practices and combat antimicrobial resistance, ensuring effective and sustainable use of these essential medications 23 , 24 .

Evidence-based antimicrobial stewardship practice

Refers to healthcare professionals utilizing scientific evidence, clinical guidelines, and patient data to guide decisions on selecting, dosing, and timing antimicrobial treatment. Its objective is to enhance patient outcomes by reducing antimicrobial resistance and adverse effects, ensuring optimal treatment effectiveness 25 , 26 , 27 , 28 .

Data extraction

The data were extracted from included studies using the data extraction tool prepared by MTB. The tool includes variables such as the name of the author, publication year, study design, data collection period, sample size, study area, and the point prevalence of antimicrobial use. The data extraction tool contains information on the indication for antibiotic use; prevalence of antibiotic use in different wards, classes of antibiotics used, types of antibiotics used, and AWaRe classification. BH extracted the data, and LWT and MTB cross-checked the extracted data for its validity and cleanness. Authors of papers were contacted to request missing or additional data.

Data quality and risk of bias assessment

Eligible studies were critically appraised by two independent reviewers (MTB and BH). Full texts screening including the methodological quality assessment were examined using the JBI’s critical appraisal instrument for prevalence studies 29 . Studies that fulfill at least seven out of the nine domains of the JBI criteria questions were eligible for meta-analysis. The results of the critical appraisal were reported in narrative form and a table. A lower risk of bias (94%) observed after assessment ( Appendix III ). Studies with inadequate sample size, inappropriate sampling frame and poor data analysis were excluded. Articles were reviewed using titles, abstracts, and full text screening.

Data analysis

Included studies were pooled in a statistical meta-analysis using STATA version 17.0. Effect sizes were expressed as a proportion with 95% confidence intervals around the summary estimate. Heterogeneity was assessed using the standard chi-square I 2 test. A random-effects model was used. As pooled proportions from individual cross-sectional design point-prevalence studies are prone to variance instability and can violate the assumption of normality. Therefore, to address this, we did the double arcsine transformation method to stabilize variances, ensuring our meta-analysis results to be more reliable 30 . Sensitivity analyses were conducted to test decisions made regarding the included studies. Visual examination of funnel plot asymmetry ( Appendix IV ) and Egger’s regression tests were used to check for publication bias 31 . A Forest plot with 95% CI was computed to estimate the pooled point prevalence of evidence-based antimicrobial use among hospitalized patients in SSA.

Protocol registration

The review protocol has been registered in PROSPERO with protocol registration number CRD42023404075.

Ethical approval

Not applicable. Unlike primary studies, systematic reviews do not include the collection of deeply personal, sensitive, and confidential information from the study participants. Systematic reviews involve the use of publicly accessible data as evidence and are not required to seek an institutional ethics approval before commencement.

A total of 2260 articles were obtained from CINAHL, EMBASE, Google Scholar, PubMed, Scopus, and Web of Science databases. Following the removal of 605 duplicates, at the title/abstract screening phase (n = 2016) and during the full-article screening (n = 212) articles were excluded. Accordingly, 32 studies were eligible for quality assessment. Finally, 28 studies were included in this meta-analysis (Fig.  1 ).

Study characteristics

The total sample size of this systematic review was 26, 272, ranging from 113 in Malawi 32 to 4, 407 in South Africa 33 . Nine studies were reported from Nigeria 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 . Six articles were published from Ghana 43 , 44 , 45 , 46 , 47 , 48 . Four studies were reported from Kenya 49 , 50 , 51 , 52 . Equally two studies were reported from South Africa 33 , 53 and Tanzania 54 , 55 . Bennin 56 , Botswana 57 , Ethiopia 58 , Malawi 32 , and Uganda 59 reported only one study respectively (Table 1 ).

Antibiotic use by wards among hospitalized patients in sub-Saharan Africa

The use of antibiotics from highest to lowest were surgical (5764), medical (5440), intensive care (4676), obstetrics and gynecology (2410), neonatal (830), oncology (207), and orthopedic (30) wards respectively (Table 2 ).

Most commonly used antibiotics among hospitalized patients in sub-Saharan Africa

Ceftriaxone 32 , 33 , 34 , 37 , 39 , 40 , 41 , 45 , 46 , 47 , 52 , 54 , 55 , 60 , 61 , metronidazole 32 , 34 , 37 , 39 , 40 , 42 , 43 , 44 , 46 , 47 , 52 , 54 , 55 , 59 , gentamicin 33 , 34 , 37 , 39 , 46 , 47 , 52 , 54 , 55 , 59 , ampicillin 33 , 38 , 46 , 54 , 55 , 60 , and cefuroxime 37 , 40 , 42 , 44 , 45 , 46 were the most commonly used antibiotics (Table 3 ). Six studies equally reported ciprofloxacin 32 , 34 , 37 , 39 , 44 , 46 and amoxicillin-clavulanate 33 , 34 , 39 , 42 , 61 , 62 . Only three studies reported ampicillin-cloxacillin combination 39 , 54 , 59 and amoxicillin 32 , 38 , 46 as antibiotics used in hospitals in SSA (Table 3 ).

WHO AWARE classification of antibiotics used by hospitalized patients in sub-Saharan Africa

Only five studies reported antibiotics used based on the WHO’s access, watch, and reserve (AWaRe) classification 33 , 37 , 49 , 53 , 59 (Table 4 ). The most commonly used antibiotics were the access group and ranged between 46.3 and 97.9% 33 , 37 , 49 , 53 , 59 , followed by the watch and reserve group that accounted for 1.8–53.5% 33 , 37 , 49 , 53 , 59 , and 0.0–5.0% 33 , 37 , 49 , 53 , 59 respectively (Table 4 ).

Indications for antibiotic prescription among hospitalized patients in SSA

Community-acquired infection ranged from 27.7 to 61%, surgical antibiotic prophylaxis ranged from 14.6 to 45.3%, hospital-acquired infections ranged from 1.2 to 40.3%, and, medical prophylaxis ranged from 0.5 to 29.1% were the most common clinical indications (Table 5 ). Antibiotic prescription for 938 inpatients were done for unknown clinical indications (Table 5 ).

Pooled point prevalence of evidence-based use of antibiotics in SSA

The pooled point prevalence of evidence-based use of antimicrobials were 64.15% (95%CI: 58.31–69.79%) (Fig.  2 ).

The pooled point prevalence of evidence-based use of antibiotics among hospitalized patients in sub-Saharan Africa.

The pooled prevalence of evidence-based antibiotic use in different wards in hospitals of SSA

Only seven studies from four countries reported the use of antibiotics in intensive care units 41 , 49 , 50 , 51 , 52 , 55 , 58 , ranging from 179 (66.5%) to 1565 (85.9%) (Table 3 ). The pooled point prevalence of antibiotics use in ICU were 87.90% (95% CI: 77.93–95.19%) (Fig.  3 ).

The pooled point prevalence of evidence-based use of antibiotics in intensive care units in hospitals of sub-Saharan Africa.

The uptake of antimicrobials in medical wards ranged from 63 (19.6%) to 236 (73.5%) as reported by thirteen studies 34 , 36 , 37 , 41 , 43 , 49 , 50 , 51 , 52 , 54 , 55 , 58 , 61 from five countries (Table 3 ). The pooled prevalence of use of antibiotics in medical wards were 54.01% (95% CI: 47.24–60.71%) (Fig.  4 ).

The pooled point prevalence of evidence-based use of antibiotics in medical wards in hospitals of sub-Saharan Africa.

Antibiotic use in obstetrics and gynecology wards ranges from 22 (6.9%) to 234 (72.9%)The pooled prevalence of antibiotics use in obstetrics and gynecology wards obtained from data extracted from eight studies published from Ethiopia 58 , Ghana 45 , Kenya 49 , 50 , 51 , 52 , and Nigeria 34 , 37 (Table 3 ), were 45.70% (95% CI: 33.04–58.64) (Fig.  5 ).

The pooled point prevalence of evidence-based use of antibiotics in obstetrics and gynecology wards in hospitals of sub-Saharan Africa.

Five counties from hospitals in sub-Saharan Africa, including Ethiopia 58 , Ghana 61 , Kenya 49 , 50 , 51 , 52 , Nigeria 34 , 37 , 41 , and Tanzania 54 , 55 , produced twelve articles that revealed the antimicrobials uptake in surgical wards with the lowest 74 (23%) to the highest 781 (82.4%) (Table 3 ). The pooled prevalence of antibiotics use in surgical wards were 57.74% (95% CI: 48.64–66.58) (Fig.  6 ).

The pooled point prevalence of evidence-based use of antibiotics in surgical wards in hospitals of sub-Saharan Africa.

The pooled prevalence of clinical indications for evidence-based antibiotic use in SSA

Twenty studies from seven countries in SSA such as, Botswana 57 , Ethiopia 58 , Nigeria 35 , 37 , 39 , 40 , 41 , 42 , 63 , Ghana 43 , 46 , 47 , 48 , 61 , Kenya 49 , 50 , 52 , Tanzania 54 , 55 , and Uganda 59 , reported that community- and hospital acquired infections were the most common clinical indications for antibiotics use (Table 5 ). The pooled prevalence of community- and hospital acquired infections for point of care antibiotics use were 40.99% (95% CI: 35.28–46.82%) (Fig.  7 ) and 11.15% (95% CI: 6.02–17.56%) (Fig.  8 ) respectively.

The pooled prevalence of evidence-based use of antibiotics for community acquired infections in hospitals of sub-Saharan Africa.

The pooled prevalence of evidence-based use of antibiotics for hospital acquired infections in hospitals of sub-Saharan Africa.

Seven countries including Botswana 57 , Ethiopia 58 , Nigeria 34 , 35 , 37 , 39 , 40 , 41 , Ghana 45 , 47 , 61 , 64 , 65 , Kenya 49 , 50 , 52 , Tanzania 54 , 66 , Malawi 32 , and Uganda 59 conducted eighteen studies which reported medical and surgical prophylaxis were the second most common clinical indications for evidence-based uptake of antimicrobials (Table 5 ). The pooled prevalence of medical—and surgical prophylaxis for antibiotics use were 11.86% (95% CI: 8.02–16.33%) (Fig.  9 ) and 28.54% (95% CI: 25.29–31.91%) (Fig.  10 ) respectively.

The pooled prevalence of evidence-based use of antibiotics for medical prophylaxis in hospitals of sub-Saharan Africa.

The pooled prevalence of evidence-based use of antibiotics for surgical prophylaxis in hospitals of sub-Saharan Africa.

The pooled prevalence of the use of antibiotics at point of care for unknown clinical indications reported from 15 articles conducted in five countries Ethiopia 58 , Ghana 46 , 47 , 48 , 62 , 64 , Kenya 49 , 50 , Nigeria 34 , 35 , 37 , 39 , 40 , 41 , and Tanzania 54 (Table 5 ) were 7.67% (95% CI: 4.55–11.33%) (Fig.  11 ).

The pooled prevalence of evidence-based use of antibiotics for unknown clinical indications in hospitals of sub-Saharan Africa.

Visual funnel plots asymmetry examination and Egger’s regression tests revealed that there was no publication bias 67 .

This systematic review and meta-analysis aimed to determine the pooled point prevalence of evidence-based antimicrobial use among hospitalized patients in sub-Saharan Africa. A total of 26, 272 patients admitted to twenty-eight hospitals of ten countries in SSA were included. The pooled point prevalence of antimicrobial use at point of care was 64%. The finding of this study is higher than the antibiotic use in hospitals of Middle East (28.3%) 68 and Europe (30.5%) 69 . This could be attributed to misuse and overuse of antibiotics 70 , 71 , poor infection and disease prevention and control 72 , and, water, sanitation and hygiene practice in health-care facilities 73 , and poor surveillance of antimicrobial resistance in SSA 74 , 75 . The pooled point prevalence of antibiotic use in intensive care unit of hospitals in SSA were 89%. This finding is higher than a point prevalence of use of antimicrobials in ICUs in the United States 62.2%  76 and Poland 59.6% 77 .

The uses of antimicrobials at point of care in surgical and medical wards were 58% and 54% in SSA. The overuse or inappropriate use of antimicrobials at the point of care in medical and surgical wards can lead to antibiotic resistance 8 , which can make infections harder to treat. Moreover, unnecessary antimicrobial use can disrupt the balance of the microbiome, leading to complications like Clostridium difficile infections 78 . The pooled estimate of antibiotics used by inpatients admitted to obstetrics and gynecology wards of the hospitals in SSA were 46%. The finding of this study was higher than the antibiotic consumption in obstetrics and gynecology departments of Peruvian hospital 31% 79 . Higher antibiotic use in obstetrics and gynecology wards in SSA can be attributed to factors such as a higher prevalence of surgical procedures 80 , which often require prophylactic antibiotics to prevent post-operative infections 81 . Additionally, cases of infections related to childbirth, such as postpartum infections or complications following gynecological procedures, may necessitate antibiotic treatment in SSA 82 , 83 .

The pooled prevalence of community and hospital acquired infections in SSA were 41% and 11.15% respectively. The pooled estimate of this review was higher than a study in East Africa that reported 34% CAI 84 . This could be due to non-standardized antibiotic use in SSA. Our review result revealed that HAI in SSA were lower than the finding from LMICs 17.9% 85 .

The misuse of antibiotics in both community and hospital-acquired infections has far-reaching consequences 86 . In the community, inappropriate antibiotic use contributes to the development of antibiotic-resistant bacteria, rendering infections harder to treat and increasing healthcare costs 87 , 88 . Patients may experience treatment failures, longer hospital stays, and increased mortality rates 89 . Moreover, the continued misuse of antibiotics fuels the global crisis of antibiotic resistance, jeopardizing the effectiveness of these essential drugs for future generations 90 , 91 . In hospital settings, similar consequences are exacerbated by the potential for widespread outbreaks of antibiotic-resistant infections among vulnerable patients 92 . The resulting challenges in managing infections can strain healthcare systems, diminish the success of medical interventions, and underscore the critical need for stringent antibiotic stewardship practices to preserve the efficacy of antibiotics.

The pooled prevalence of the most common clinical indications for antibiotic use in hospitals of SSA were community acquired infection (40.99%), surgical prophylaxis (28.54%), medical prophylaxis (11.86%), and hospital acquired infection (11.15%).

This study revealed that the pooled prevalence of HAI (11.15%) is lower than the global estimate (14%) 93 . This could be attributed to inadequate infection control measures 94 , limited resources 95 , overcrowding 96 , and a higher burden of infectious diseases 97 . Poor sanitation and healthcare infrastructure can contribute to the increased risk of infections within healthcare facilities in SSA 98 .

According to this study, the pooled estimate of surgical prophylaxis is higher than Europe (16.8%) 99 and the global surgical antibiotic prophylaxis at point of care (22.8%) 17 . The surgical prophylaxis in SSA is lower than a study reported in Myanmar (34.3%) 100 . Higher surgical antibiotic prophylaxis may be attributed to surgeon’s overuse of antibiotics to mitigate infection risks in environments with higher prevalence of surgical site infections and limited access to post-operative care in SSA 101 , 102 , 103 . Surgeons may also lack awareness of appropriate guidelines, and patients may expect antibiotics due to a perception of their effectiveness 103 .

The pooled point prevalence of medical prophylaxis in this study is lower than European region (24.9%) 69 and Indonesia (47.1%) 104 . A lower point prevalence of medical prophylaxis in SSA suggests limited access and utilization of preventative medical interventions 105 . This may be indicative of healthcare system challenges, resource constraints, or insufficient awareness and education 106 , 107 . It can result in a higher disease burden, increased healthcare costs, and potentially poorer clinical and public health outcomes for the population 10 , 108 .

This review indicated that the pooled prevalence of community acquired infection is higher than a study conducted in the Middle East (16.8%) 68 . Community acquired infection in SSA according to this study were lower than Northern Ireland (66.2%) 109 . Higher prevalence of CAI could be due to lack of essential medical supplies, suboptimal sterilization procedures, and inadequate training in infection control 110 , 111 . High patient-to-nurse ratios and frequent patient turnover can further hinder the implementation of rigorous infection prevention measures, increasing the risk of infections spreading within healthcare settings 112 , 113 .

Antibiotic use for unknown clinical indications in SSA hospitals may occur due to inadequate training on antibiotic stewardship and a lack of access to timely microbiological testing 3 , 114 . Clinicians may resort to broad-spectrum antibiotics as a precautionary measure in the absence of specific diagnostic information, contributing to antibiotic misuse and resistance 114 .

The pooled point prevalence of antimicrobial use among hospitalized patients were higher in SSA. Higher use of antibiotics in intensive care unit, surgical, medical, and obstetrics and gynecology wards of hospital in SSA were recorded. Community acquired infection, surgical and medica prophylaxis, and hospital acquired infection were clinical indications reported to have the highest to lowest pooled point prevalence of antibiotics used. Health systems in SSA must design innovative interventions to optimize clinicians adhere to evidence-based prescribing guidelines and improve antimicrobial stewardship.

Implications for evidence-informed policy and clinical practice

A higher pooled point prevalence of antimicrobial use in sub-Saharan Africa implies a need for immediate policy and clinical practice interventions. Policymakers should prioritize allocation of scarce resources for antimicrobial stewardship programs and infection control measures. Innovative intervention must be in place to optimize clinicians adhere to evidence-based prescribing guidelines to combat antimicrobial resistance, reduce adverse effects, and improve patient outcomes.

Health systems in sub-Saharan Africa must emphasize the importance of leveraging clinical decision support digital health interventions to augment evidence-based antimicrobial stewardship. This evidence synthesis informs the policy decision makers to encourage the implementation of such tools to guide clinicians in evidence-based antimicrobial prescribing, reducing inappropriate use, combating resistance, and improving patient care in the context of resource constrained health system. Clinicians can benefit from real-time patient information, aiding in evidence-based prescribing and infection control efforts, significantly improving patient care. Collaboration between policymakers, clinicians, and healthcare facilities is crucial to mitigate the impact of these issues on public health.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

Antimicrobial resistance

The Armauer Hansen Research Institute

Defined daily dose

The Joanna Briggs Institute

Low- and Middle-Income Countries

Preferred reporting items for systematic reviews and meta-analyses

International prospective registry of systematic reviews

Sustainable development goal

• Sub-Saharan Africa

The World Health Organization

Browne, A. J. et al. Global antibiotic consumption and usage in humans, 2000–18: A spatial modelling study. Lancet Planet. Health 5 , e893–e904 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Klein, E. Y. et al. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. Proc. Natl. Acad. Sci. U. S. A. 115 , E3463-e3470 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Siachalinga, L. et al. Current antibiotic use among hospitals in the sub-Saharan Africa region; findings and implications. Infect. Drug Resist. 2023 , 2179–2190 (2023).

Article   Google Scholar  

Versporten, A. et al. Antimicrobial consumption and resistance in adult hospital inpatients in 53 countries: Results of an internet-based global point prevalence survey. Lancet Glob. Health 6 , e619–e629 (2018).

Article   PubMed   Google Scholar  

Belachew, S. A., Hall, L. & Selvey, L. A. Non-prescription dispensing of antibiotic agents among community drug retail outlets in Sub-Saharan African countries: A systematic review and meta-analysis. Antimicrob. Resist. Infect. Control 10 , 13 (2021).

Bell, B. G., Schellevis, F., Stobberingh, E., Goossens, H. & Pringle, M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect. Dis. 14 , 1–25 (2014).

Organization, W. H. WHO Report on Surveillance of Antibiotic Consumption: 2016–2018 Early Implementation (Springer, 2018).

Ayukekbong, J. A., Ntemgwa, M. & Atabe, A. N. The threat of antimicrobial resistance in developing countries: Causes and control strategies. Antimicrob. Resist. Infect. Control 6 , 47 (2017).

Founou, R. C., Founou, L. L. & Essack, S. Y. Clinical and economic impact of antibiotic resistance in developing countries: A systematic review and meta-analysis. PloS one 12 , e0189621 (2017).

Godman, B. et al. Strategies to improve antimicrobial utilization with a special focus on developing countries. Life 11 , 528 (2021).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Majumder, M. A. A. et al. Antimicrobial stewardship: Fighting antimicrobial resistance and protecting global public health. Infect. Drug Resist. 2020 , 4713–4738 (2020).

Sartelli, M. et al. Ten golden rules for optimal antibiotic use in hospital settings: The WARNING call to action. World J. Emerg. Surg. 18 , 50 (2023).

Katyali, D., Kawau, G., Blomberg, B. & Manyahi, J. Antibiotic use at a tertiary hospital in Tanzania: Findings from a point prevalence survey. Antimicrob. Resist. Infect. Control 12 , 112 (2023).

Levy Hara, G. et al. Point prevalence survey of antibiotic use in hospitals in Latin American countries. J. Antimicrob. Chemother. 77 , 807–815 (2022).

Moulin, E. et al. Point prevalence study of antibiotic appropriateness and possibility of early discharge from hospital among patients treated with antibiotics in a Swiss University Hospital. Antimicrob. Resist. Infect. Control 11 , 66 (2022).

Charani, E. et al. Optimising antimicrobial use in humans–review of current evidence and an interdisciplinary consensus on key priorities for research. Lancet Reg. Health-Europe 2021 , 7 (2021).

Google Scholar  

Porto, A. M., Goossens, H., Versporten, A., Costa, S. F. & Group, B. G. P. W. Global point prevalence survey of antimicrobial consumption in Brazilian hospitals. J. Hospit. Infect. 104 , 165–171 (2020).

Article   CAS   Google Scholar  

Bahta, M. et al. Dispensing of antibiotics without prescription and associated factors in drug retail outlets of Eritrea: A simulated client method. PLoS One 15 , e0228013 (2020).

Ayalew, M. B. Self-medication practice in Ethiopia: A systematic review. Patient Preference Adherence 2017 , 401–413 (2017).

Acam, J., Kuodi, P., Medhin, G. & Makonnen, E. Antimicrobial prescription patterns in East Africa: A systematic review. Syst. Rev. 12 , 18 (2023).

Page, M. J. et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int. J. Surg. 88 , 105906 (2021).

Zingg, W. et al. Antimicrobial use in acute care hospitals: National point prevalence survey on healthcare-associated infections and antimicrobial use, Switzerland, 2017. Eurosurveillance 24 , 1900015 (2019).

PubMed   PubMed Central   Google Scholar  

Vandael, E. et al. Point prevalence survey of antimicrobial use and healthcare-associated infections in Belgian acute care hospitals: Results of the Global-PPS and ECDC-PPS 2017. Antimicrob. Resist. Infect. Control 9 , 1–13 (2020).

Gugliotta, C. et al. Prevalence study on health-care associated infections and on the use of antimicrobials carried out with the light protocol of the European Centre for Disease Prevention and Control. Ann. Ig 32 , 357–367 (2020).

CAS   PubMed   Google Scholar  

Glasziou, P., Dartnell, J., Biezen, R., Morgan, M. & Manski-Nankervis, J. A. Antibiotic stewardship: A review of successful, evidence-based primary care strategies. Aust. J. Gen. Pract. 51 , 15–20 (2022).

Magin, P., Davey, A. & Davis, J. Evidence-based strategies for better antibiotic prescribing. Austral. J. Gener. Practition. 51 , 21–24 (2022).

Borek, A. J. et al. Development of an intervention to support the implementation of evidence-based strategies for optimising antibiotic prescribing in general practice. Implement. Sci. Commun. 2 , 1–16 (2021).

Mar, C. D., Hoffmann, T. & Bakhit, M. How can general practitioners reduce antibiotic prescribing in collaboration with their patients?. Austral. J. Gener. Pract. 51 , 25–30 (2022).

Migliavaca, C. B., Stein, C., Colpani, V., Munn, Z. & Falavigna, M. Quality assessment of prevalence studies: A systematic review. J. Clin. Epidemiol. 127 , 59–68 (2020).

Barker, T. H. et al. Conducting proportional meta-analysis in different types of systematic reviews: A guide for synthesisers of evidence. BMC Med. Res. Methodol. 21 , 189 (2021).

Iddagoda, M. T. & Flicker, L. Clinical systematic reviews—a brief overview. BMC Med. Res. Methodol. 23 , 226 (2023).

Bunduki, G. K., Feasey, N., Henrion, M. Y., Noah, P. & Musaya, J. Healthcare-associated infections and antimicrobial use in surgical wards of a large urban central hospital in Blantyre, Malawi: A point prevalence survey. Infect. Prevent. Practice 3 , 100163 (2021).

Skosana, P. et al. A point prevalence survey of antimicrobial utilisation patterns and quality indices amongst hospitals in South Africa; findings and implications. Expert Rev. Anti-infect. Therapy 19 , 1353–1366 (2021).

Abubakar, U. Antibiotic use among hospitalized patients in northern Nigeria: A multicenter point-prevalence survey. BMC Infect. Dis. 20 , 86 (2020).

Umeokonkwo, C. D. et al. Point prevalence survey of antimicrobial prescription in a tertiary hospital in South East Nigeria: A call for improved antibiotic stewardship. J. Glob. Antimicrob. Resist. 17 , 291–295 (2019).

Manga, M. et al. Empirical antibiotherapy as a potential driver of antibiotic resistance: Observations from a point prevalence survey of antibiotic consumption and resistance in Gombe, Nigeria. Afr. J. Clin. Exp. Microbiol. 22 , 273–278 (2021).

Aboderin, A. O. et al. Antimicrobial use among hospitalized patients: A multi-center, point prevalence survey across public healthcare facilities, Osun State, Nigeria. Germs 11 , 523 (2021).

Chijioke, A. N. et al. Prevalence of antimicrobial use in major hospitals in Owerri, Nigeria. EC Microbiol. 3 , 522–527 (2016).

Fowotade, A. et al. Point prevalence survey of antimicrobial prescribing in a Nigerian Hospital: Findings and implications on antimicrobial resistance. West Afr. J. Med. 37 , 216–220 (2020).

Nnadozie, U. U. et al. Patterns of antimicrobial use in a specialized surgical hospital in Southeast Nigeria: Need for a standardized protocol of antimicrobial use in the tropics. Niger. J. Med. 30 , 187–191 (2021).

Oduyebo, O. et al. A point prevalence survey of antimicrobial prescribing in four Nigerian Tertiary Hospitals. Ann. Trop. Pathol. 8 , 42 (2017).

Ogunleye, O. O. et al. A multicentre point prevalence study of antibiotics utilization in hospitalized patients in an urban secondary and a tertiary healthcare facilities in Nigeria: Findings and implications. Expert Rev. Anti-infect. Therapy 20 , 297–306 (2022).

Labi, A.-K. et al. Antibiotic use in a tertiary healthcare facility in Ghana: A point prevalence survey. Antimicrob. Resist. Infect. Control 7 , 1–9 (2018).

Labi, A.-K. et al. Antimicrobial use in hospitalized patients: A multicentre point prevalence survey across seven hospitals in Ghana. JAC-Antimicrob. Resist. 3 , 087 (2021).

Labi, A.-K. et al. Antibiotic prescribing in paediatric inpatients in Ghana: A multi-centre point prevalence survey. BMC Pediatr. 18 , 1–9 (2018).

Amponsah, O. K. O. et al. Point prevalence survey of antibiotic consumption across three hospitals in Ghana. JAC Antimicrob. Resist. 3 , 008 (2021).

Bediako-Bowan, A. A. et al. Antibiotic use in surgical units of selected hospitals in Ghana: A multi-centre point prevalence survey. BMC Public Health 19 , 1–10 (2019).

Ankrah, D. et al. Point prevalence survey of antimicrobial utilization in Ghana’s premier hospital: Implications for antimicrobial stewardship. Antibiot. Basel 2021 , 10 (2021).

Kamita, M. et al. Point prevalence survey to assess antibiotic prescribing pattern among hospitalized patients in a county referral hospital in Kenya. Front. Antibiot. 1 , 993271 (2022).

Okoth, C. et al. Point prevalence survey of antibiotic use and resistance at a referral hospital in Kenya: Findings and implications. Hosp. Pract. 1995 (46), 128–136 (2018).

Omulo, S. et al. Point-prevalence survey of antibiotic use at three public referral hospitals in Kenya. Plos one 17 , e0270048 (2022).

Momanyi, L. et al. Antibiotic prescribing patterns at a leading referral hospital in Kenya: A point prevalence survey. J. Res. Pharm. Pract. 8 , 149–154 (2019).

Skosana, P. et al. A national, multicentre, web-based point prevalence survey of antimicrobial use and quality indices among hospitalised paediatric patients across South Africa. J. Glob. Antimicrob. Resist. 29 , 542–550 (2022).

Article   CAS   PubMed   Google Scholar  

Horumpende, P. G. et al. Point prevalence survey of antimicrobial use in three hospitals in North-Eastern Tanzania. Antimicrob. Resist. Infect. Control 9 , 1–6 (2020).

Seni, J. et al. Antimicrobial use across six referral hospitals in Tanzania: A point prevalence survey. BMJ Open 10 , e042819 (2020).

Ahoyo, T. A. et al. Prevalence of nosocomial infections and anti-infective therapy in Benin: Results of the first nationwide survey in 2012. Antimicrob. Resist. Infect. Control 3 , 17 (2014).

Anand Paramadhas, B. D. et al. Point prevalence study of antimicrobial use among hospitals across Botswana; findings and implications. Expert Rev. Anti Infect. Ther. 17 , 535–546 (2019).

Fentie, A. M. et al. Multicentre point-prevalence survey of antibiotic use and healthcare-associated infections in Ethiopian hospitals. BMJ Open 12 , e054541 (2022).

Kiggundu, R. et al. Point prevalence survey of antibiotic use across 13 hospitals in Uganda. Antibiotics 11 , 199 (2022).

Kiggundu, R. et al. Point prevalence survey of antibiotic use across 13 hospitals in Uganda. Antibiot. Basel 2022 , 11 (2022).

Labi, A. K. et al. Antimicrobial use in hospitalized patients: A multicentre point prevalence survey across seven hospitals in Ghana. JAC Antimicrob. Resist. 3 , 087 (2021).

Labi, A. K. et al. Antibiotic use in a tertiary healthcare facility in Ghana: A point prevalence survey. Antimicrob. Resist. Infect. Control 7 , 15 (2018).

Abubakar, U. Point-prevalence survey of hospital acquired infections in three acute care hospitals in Northern Nigeria. Antimicrob. Resist. Infect. Control 9 , 63 (2020).

Labi, A. K. et al. Antibiotic prescribing in paediatric inpatients in Ghana: A multi-centre point prevalence survey. BMC Pediatr. 18 , 391 (2018).

Amponsah, O. K. O. et al. Point prevalence survey of antibiotic consumption across three hospitals in Ghana. JAC-Antimicrob. Resist. 3 , 008 (2021).

Seni, J. et al. Multicentre evaluation of significant bacteriuria among pregnant women in the cascade of referral healthcare system in North-western Tanzania: Bacterial pathogens, antimicrobial resistance profiles and predictors. J. Glob. Antimicrob. Resist. 17 , 173–179 (2019).

Lin, L. & Chu, H. Quantifying publication bias in meta-analysis. Biometrics 74 , 785–794 (2018).

Article   MathSciNet   PubMed   Google Scholar  

Alothman, A. et al. Prevalence of infections and antimicrobial use in the acute-care hospital setting in the Middle East: Results from the first point-prevalence survey in the region. Int. J. Infect. Dis. 101 , 249–258 (2020).

Plachouras, D. et al. Antimicrobial use in European acute care hospitals: Results from the second point prevalence survey (PPS) of healthcare-associated infections and antimicrobial use, 2016 to 2017. Euro Surveill 2018 , 23 (2018).

Mallah, N., Orsini, N., Figueiras, A. & Takkouche, B. Income level and antibiotic misuse: A systematic review and dose–response meta-analysis. Eur. J. Health Econ. 23 , 1015–1035 (2022).

Mallah, N., Orsini, N., Figueiras, A. & Takkouche, B. Education level and misuse of antibiotics in the general population: A systematic review and dose–response meta-analysis. Antimicrob. Resist. Infect. Control 11 , 24 (2022).

Barrera-Cancedda, A. E., Riman, K. A., Shinnick, J. E. & Buttenheim, A. M. Implementation strategies for infection prevention and control promotion for nurses in Sub-Saharan Africa: A systematic review. Implement. Sci. 14 , 111 (2019).

Bouzid, M., Cumming, O. & Hunter, P. R. What is the impact of water sanitation and hygiene in healthcare facilities on care seeking behaviour and patient satisfaction? A systematic review of the evidence from low-income and middle-income countries. BMJ Glob. Health 3 , e000648 (2018).

Moyo, P. et al. Prevention of antimicrobial resistance in sub-Saharan Africa: What has worked? What still needs to be done?. J. Infect. Public Health 16 , 632–639 (2023).

Kariuki, S., Kering, K., Wairimu, C., Onsare, R. & Mbae, C. Antimicrobial resistance rates and surveillance in sub-saharan africa: Where are we now?. Infect. Drug Resist. 15 , 3589–3609 (2022).

Magill, S. S. et al. Antimicrobial Use in US Hospitals: Comparison of results from emerging infections program prevalence surveys, 2015 and 2011. Clin. Infect. Dis. 72 , 1784–1792 (2020).

Article   ADS   Google Scholar  

Trejnowska, E. et al. Surveillance of antibiotic prescribing in intensive care units in Poland. Can. J. Infect. Dis. Med. Microbiol. 2018 , 14 (2018).

Patangia, D. V., Anthony Ryan, C., Dempsey, E., Paul Ross, R. & Stanton, C. Impact of antibiotics on the human microbiome and consequences for host health. Microbiol. Open 11 , e1260 (2022).

Arteaga-Livias, K. et al. Compliance with antibiotic prophylaxis in obstetric and gynecological surgeries in two peruvian hospitals. Antibiotics 12 , 808 (2023).

Eddy, K. E. et al. Factors affecting the use of antibiotics and antiseptics to prevent maternal infection at birth: A global mixed-methods systematic review. Plos one 17 , e0272982 (2022).

Vippadapu, P. et al. Choice of antimicrobials in surgical prophylaxis - overuse and surgical site infection outcomes from a tertiary-level care hospital. Front. Pharmacol. 2022 , 13 (2022).

Ngonzi, J. et al. Incidence of postpartum infection, outcomes and associated risk factors at Mbarara regional referral hospital in Uganda. BMC Pregn. Childbirth 18 , 270 (2018).

Abdel Jalil, M. H. et al. Surgical site infections following caesarean operations at a Jordanian teaching hospital: Frequency and implicated factors. Sci. Rep. 7 , 12210 (2017).

Beletew, B., Bimerew, M., Mengesha, A., Wudu, M. & Azmeraw, M. Prevalence of pneumonia and its associated factors among under-five children in East Africa: A systematic review and meta-analysis. BMC Pediatr. 20 , 254 (2020).

Murni, I. K. et al. Risk factors for healthcare-associated infection among children in a low-and middle-income country. BMC Infect. Dis. 22 , 406 (2022).

Dadgostar, P. Antimicrobial resistance: Implications and costs. Infect. Drug Resist. 12 , 3903–3910 (2019).

Prestinaci, F., Pezzotti, P. & Pantosti, A. Antimicrobial resistance: A global multifaceted phenomenon. Pathogens Glob. Health 109 , 309–318 (2015).

Erku, D. A., Mekuria, A. B. & Belachew, S. A. Inappropriate use of antibiotics among communities of Gondar town, Ethiopia: A threat to the development of antimicrobial resistance. Antimicrob. Resist. Infect. Control 6 , 112 (2017).

Chinemerem Nwobodo, D. et al. Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace. J. Clin. Lab. Anal. 36 , e24655 (2022).

Aslam, B. et al. Antibiotic resistance: A rundown of a global crisis. Infect. Drug rResist. 2018 , 1645–1658 (2018).

Adebisi, Y. A. Balancing the risks and benefits of antibiotic use in a globalized world: The ethics of antimicrobial resistance. Globaliz. Health 19 , 27 (2023).

Sukhum, K. V. et al. Antibiotic-resistant organisms establish reservoirs in new hospital built environments and are related to patient blood infection isolates. Commun. Med. 2 , 62 (2022).

Raoofi, S. et al. Global prevalence of nosocomial infection: A systematic review and meta-analysis. PLoS One 18 , e0274248 (2023).

Lowe, H. et al. Challenges and opportunities for infection prevention and control in hospitals in conflict-affected settings: A qualitative study. Conflict Health 15 , 94 (2021).

Spencer, S. A. et al. A health systems approach to critical care delivery in low-resource settings: A narrative review. Intens. Care Med. 49 , 772–784 (2023).

Improta, G. et al. A case study to investigate the impact of overcrowding indices in emergency departments. BMC Emerg. Med. 22 , 143 (2022).

Daw, M. A., Mahamat, M. H., Wareg, S. E., El-Bouzedi, A. H. & Ahmed, M. O. Epidemiological manifestations and impact of healthcare-associated infections in Libyan national hospitals. Antimicrob. Resist. Infect. Control 12 , 122 (2023).

Taye, Z. W., Abebil, Y. A., Akalu, T. Y., Tessema, G. M. & Taye, E. B. Incidence and determinants of nosocomial infection among hospital admitted adult chronic disease patients in University of Gondar Comprehensive Specialized Hospital, North-West Ethiopia, 2016–2020. Front. Public Health 11 , 1087407 (2023).

Zarb, P. et al. The European Centre for Disease Prevention and Control (ECDC) pilot point prevalence survey of healthcare-associated infections and antimicrobial use. Eurosurveillance 17 , 20316 (2012).

Oo, W. T. et al. Point-prevalence surveys of antimicrobial consumption and resistance at a paediatric and an adult tertiary referral hospital in Yangon, Myanmar. Infect. Prevent. Pract. 4 , 100197 (2022).

Mehtar, S. et al. Implementation of surgical site infection surveillance in low- and middle-income countries: A position statement for the International Society for Infectious Diseases. Int. J. Infect. Dis. 100 , 123–131 (2020).

Sefah, I. A. et al. Appropriateness of surgical antimicrobial prophylaxis in a teaching hospital in Ghana: Findings and implications. JAC-Antimicrob. Resist. 2022 , 4 (2022).

Mwita, J. C. et al. Key issues surrounding appropriate antibiotic use for prevention of surgical site infections in low-and middle-income countries: A narrative review and the implications. Int. J. Gener. Med. 2017 , 515–530 (2021).

Karmila, A. et al. The prevalence and factors associated with prophylactic antibiotic use during delivery: A hospital-based retrospective study in Palembang, Indonesia. Antibiot. Basel 2021 , 10 (2021).

Alemu, A. Y. et al. Healthcare-associated infection and its determinants in Ethiopia: A systematic review and meta-analysis. PLoS One 15 , e0241073 (2020).

Ayed, H. B. et al. Prevalence and risk factors of health care–associated infections in a limited resources country: A cross-sectional study. Am. J. Infect. Control 47 , 945–950 (2019).

Saleem, Z. et al. Antibiotic utilization patterns for different wound types among surgical patients: Findings and implications. Antibiotics 12 , 678 (2023).

Ataiyero, Y., Dyson, J. & Graham, M. Barriers to hand hygiene practices among health care workers in sub-Saharan African countries: A narrative review. Am. J. Infect. Control 47 , 565–573 (2019).

Alahmadi, Y. et al. Point-prevalence surveys of antibiotic use and HAIs. Hospital Pharm. Europe Pharm. Pract. 84 , 27–29 (2016).

Fraser, J. L., Mwatondo, A., Alimi, Y. H., Varma, J. K. & Vilas, V. J. D. R. Healthcare-associated outbreaks of bacterial infections in Africa, 2009–2018: A review. Int. J. Infect. Dis. 103 , 469–477 (2021).

Abubakar, U., Amir, O. & Rodríguez-Baño, J. Healthcare-associated infections in Africa: A systematic review and meta-analysis of point prevalence studies. J. Pharm. Policy Pract. 15 , 99 (2022).

Gidey, K., Gidey, M. T., Hailu, B. Y., Gebreamlak, Z. B. & Niriayo, Y. L. Clinical and economic burden of healthcare-associated infections: A prospective cohort study. Plos one 18 , e0282141 (2023).

Igunma, A. & Adebudo, O. Healthcare-associated infections and control strategies. Niger. J. Med. Dental Educ. 5 , 81–87 (2023).

Sono, T. M. et al. Current rates of purchasing of antibiotics without a prescription across sub-saharan Africa; rationale and potential programmes to reduce inappropriate dispensing and resistance. Expert Rev. Anti-infective Therapy 21 , 1025–1055 (2023).

Download references

Acknowledgements

We would like to acknowledge the Ethiopian Evidence Based Health Care and Development Centre, A JBI Centre of Excellence, and the Armauer Hansen Research Institute for proving the training on comprehensive systematic review, meta-analysis, and access to databases.

Author information

Authors and affiliations.

Ethiopian Evidence Based Health Care Centre: A Joanna Briggs Institute’s Center of Excellence, Faculty of Public Health, Institute of Health, Jimma University, Jimma, Ethiopia

Minyahil Tadesse Boltena, Mirkuzie Wolde, Yibeltal Siraneh & Sudhakar Morankar

Armauer Hansen Research Institute, Ministry of Health, Addis Ababa, Ethiopia

Minyahil Tadesse Boltena & Belachew Hailu

University of Technology Sydney, Sydney, Australia

Mirkuzie Wolde

Global Public Health Department, Karolinska Institute, Solna, Sweden

Ziad El-Khatib

Department of Pharmacology and Therapeutics, Faculty of Life Sciences, McGill University, Montreal, Canada

Veronika Steck

Nuffield Department of Population Health, University of Oxford, Oxford, UK

Selam Woldegerima

You can also search for this author in PubMed   Google Scholar

Contributions

MTB and SM was involved in a principal role in the conception of ideas, developing methodologies, and writing the article. MTB, SM, MW, SWG, VS, YS, BH and ZEK were involved in the analysis, interpretation and writing. All authors involved in proofreading and writing. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Minyahil Tadesse Boltena .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Boltena, M.T., Wolde, M., Hailu, B. et al. Point prevalence of evidence-based antimicrobial use among hospitalized patients in sub-Saharan Africa: a systematic review and meta-analysis. Sci Rep 14 , 12652 (2024). https://doi.org/10.1038/s41598-024-62651-6

Download citation

Received : 06 January 2024

Accepted : 20 May 2024

Published : 02 June 2024

DOI : https://doi.org/10.1038/s41598-024-62651-6

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Antibiotic prescribing
• Antimicrobial use
• Evidence-based healthcare
• Hospitalized patients
• Point prevalence survey

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

SYSTEMATIC REVIEW article

Comparison of regional vs. general anesthesia on the risk of dementia: a systematic review and meta-analysis.

• 1 Department of Anesthesiology, Chi Mei Medical Center, Liouying, Tainan City, Taiwan
• 2 Department of Emergency Medicine, E-Da Dachang Hospital, I-Shou University, Kaohsiung City, Taiwan
• 3 School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung City, Taiwan
• 4 School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
• 5 Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan
• 6 Department of Chinese Medicine, Chi Mei Medical Center, Tainan City, Taiwan
• 7 Department of Medical Imaging, Chi Mei Medical Center, Tainan City, Taiwan
• 8 Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan City, Taiwan

Background: Dementia is a gradual and ongoing cognitive decline due to damage to nerve cells in the brain. This meta-analysis aimed to assess the potential relationship between regional anesthesia (RA) and the risk of dementia.

Methods: Electronic databases including Embase, Medline, Google Scholar, and Cochrane Library were searched for studies investigating the association between RA and dementia risk from inception to March 2022. The primary outcome was the risk of dementia in patients who underwent RA (RA group) and those who received general anesthesia (GA group). Secondary outcomes included identifying other potential risk factors for dementia and comparing dementia risk between individuals receiving RA and those not receiving surgery/anesthesia (placebo group).

Results: Eight cohort studies published between 2014 and 2023 were included in this analysis. A meta-analysis of the available data demonstrated no differences in baseline characteristics and morbidities (i.e., age, male proportion, hypertension, diabetes, depression, and severe comorbidities) between the RA and GA groups (all p  > 0.05). Initial analysis revealed that the risk of dementia was higher in the GA group than in the RA group (HR = 1.81, 95% CI = 1.29–2.55, p  = 0.007, I 2 = 99%, five studies). However, when a study featuring a relatively younger population was excluded from the sensitivity analysis, the results showed a similar risk of dementia (HR, 1.17; p  = 0.13) between the GA and RA groups. The pooled results revealed no difference in dementia risk between the RA and placebo groups (HR = 1.2, 95% CI = 0.69–2.07, p  = 0.52, I 2 = 68%, three studies). Sensitivity analysis revealed that the evidence was not stable, suggesting that limited datasets precluded strong conclusions on this outcome. Anxiety, stroke history, hypertension, diabetes, hyperlipidemia, and diabetes are potential predictors of dementia.

Conclusion: Our results emphasize that, while RA could be protective against dementia risk compared to GA, the association between the type of anesthesia and dementia risk might vary among different age groups. Owing to the significant prevalence of dementia among older people and their surgical needs, further investigations are warranted to clarify the association between dementia risk and regional anesthesia.

Systematic review registration : https://www.crd.york.ac.uk/prospero/ , CRD42023411324.

1 Introduction

Dementia, which is a gradual and ongoing decline in cognition due to damage to nerve cells in the brain ( 1 ), is a progressive and irreversible disease that affects a person’s ability to think, reason, communicate, and perform daily activities ( 2 ). The global incidence of dementia rises sharply with age from 5 to 7% in individuals above the age of 60–20% in those over the age of 85 years ( 3 ). As the disease progresses, patients with dementia may require assistance with daily activities and may eventually require 24-h care in a long-term care facility ( 4 ). In addition to the impact on the health and well-being on an individual level, dementia can have a significant influence on medical resources ( 5 ). Those with dementia often require frequent medical attention, including hospitalizations, emergency department visits, and specialist consultations ( 6 – 8 ), imposing a major public health burden worldwide. Efforts are needed to improve early detection and diagnosis as well as avoidance of related risk factors.

As the global population ages, the demand for surgical procedures is expected to increase significantly ( 9 ). In particular, orthopedic procedures, such as hip and knee replacements, are expected to rise due to aging and the corresponding increase in the prevalence of obesity ( 10 – 12 ). Given that the incidence of dementia tends to rise with advancing age ( 3 ), early recognition and mitigation of surgery-related risk factors for dementia in the aged population are critical. Taking into account the results of a prior meta-analysis that reported a potential association between the exposure of general anesthesia (GA) and an increased likelihood of developing dementia ( 13 ), the use of alternative anesthetic strategies may be preferred for older individuals undergoing surgery. On the other hand, compared to GA, controversy still exists over the correlation of regional anesthesia (RA) with a decreased risk of dementia in surgical populations ( 14 , 15 ). Due to the lack of pooled evidence regarding the beneficial effect of RA against the risk of dementia, the objective of this meta-analysis was to assess the potential relationship between RA exposure and the risk of dementia compared with the use of GA.

We registered the protocol of the current meta-analysis on PROSPERO (registration no: CRD42023411324). The report of this study followed the PRISMA criteria.

2.1 Data sources and search strategy

Two independent investigators conducted a search in MEDLINE, EMBASE, Cochrane Library, and Google Scholar for articles focusing on the association of RA with the long-term risk of dementia from inception to March 26, 2023. The search terms included: (“Subarachnoid block” or “Intrathecal anesthesia” or “Spinal block” or “Epidural block” or “Epidural anesthesia” or “spinal anesthesia” or “Epidural nerve block” or “Epidural spinal anesthesia” or “Lumbar epidural anesthesia” or “Lumbar epidural block” or “regional anesthesia” or “Extradural Anesthesia” or “epidural anesthesia” or “neuraxial anesthesia”) and (“General anesthesia” or “Inhalational anesthesia” or “Gas anesthesia” or “Inhalation anesthesia” or “Volatile anesthesia” or “Vapor anesthesia” or “Gaseous anesthesia” or “Inhalation general anesthesia” or “total intravenous anesthesia” or “propofol” or TIVA) and (dementia or Alzheimer’s disease). To facilitate a comprehensive search, both controlled vocabulary and synonyms were used with no restrictions on language and publication date. The search syntax for one database (i.e., MEDLINE) was summarized in Supplementary Table 1 . The investigators also scanned the reference lists of the related articles to ensure the identification of all relevant studies.

2.2 Eligibility criteria for study inclusion

The following inclusion criteria were employed: (a) Population: adults (i.e., 18 years of age or above) without prior history of dementia undergoing surgery regardless of its type and duration; (b) Exposure: patients receiving surgery under RA served as the exposure group; (c) Control: patients subjected to surgery under GA or those did not undergo surgery/anesthesia served as control groups; (d) Outcomes: availability of data on the risk of dementia [i.e., hazard ratio (HR)]; and (e) Type of study: randomized controlled studies or cohort studies (e.g., case–control studies or population-based studies).

The exclusion criteria were (1) studies that focused on patients undergoing surgery under peripheral nerve block; (2) studies that had no information on events or the risk of dementia; (3) surgical procedures involving a combination of RA and GA; and (4) articles presented as reviews, conference abstracts, letters, case reports, or non-peer-reviewed articles.

2.3 Data extraction

Information relevant to the current study, namely the author, publication year, patient characteristics (e.g., age), sample size, study design, risk of dementia, severity of comorbidities, maximum follow-up time, and country of publication, was extracted. Two authors independently retrieved the data using a specific data extraction sheet. A discussion was held to resolve all disagreements. Whenever there was any uncertainty or missing information, the corresponding authors of the studies were contacted for clarification.

2.4 Outcomes and definitions

The main objective of this meta-analysis was to examine the likelihood of dementia in patients who underwent RA (i.e., RA group) vs. those who received GA (i.e., GA group). The secondary outcomes included identifying other potential risk factors for dementia and comparing dementia risk between individuals who received RA and those who did not undergo surgery/anesthesia (i.e., placebo group). The definition of RA for this meta-analysis encompassed spinal or epidural anesthesia with or without the use of adjunct agents such as local anesthesia and sedation, while GA involved the use of volatile or intravenous agents for anesthesia maintenance. Patients who had a Charlson comorbidity score or American Society of Anesthesiologists (ASA) physical status classification system score of 3 or higher were regarded as having severe comorbidities.

2.5 Risk of bias assessment and certainty of evidence

The quality of articles was assessed using the Newcastle-Ottawa Scale (NOS) that consists of eight items grouped under three categories: selection, comparability, and outcome. The selection category includes three items that examine the representativeness of the exposed and unexposed cohorts, the selection of controls, and the assessment of exposure. The comparability category has one item that evaluates the comparability of the cohorts. The outcome category consists of four items that assess the measurement of outcomes, the length and adequacy of follow-up, as well as the ascertainment of exposure. Each item is scored based on predefined criteria, with a higher score indicating a lower risk of bias. The maximum score for the NOS is nine stars. Studies with a score of seven or more stars are considered to have a “low-risk” of bias.

Two authors utilized the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method ( 16 ) to evaluate the certainty of evidence for the study outcomes. For any difference in opinion between the two authors, a third author was invited to resolve the discrepancy through arbitration.

2.6 Statistical analysis

The data were analyzed using the random-effects model to determine the pooled odds ratio (OR) and hazard ratio (HR). Additionally, the 95% confidence interval was calculated and reported for each outcome. To check for heterogeneity, I 2 statistics values ≥50% were considered to represent substantial heterogeneity. The reliability of the primary and secondary outcomes was checked by conducting a sensitivity analysis using a leave-one-out approach. Potential publication bias was identified for outcomes that were reported in 10 or more studies through a visual analysis of a funnel plot. The statistical analyses were conducted using either the Review Manager (RevMan) or comprehensive Meta-Analysis (CMA) V3 software (Biostat, Englewood, NJ, United States). A probability value ( p ) of less than 0.05 was considered to be of statistical significance.

3.1 Study selection

Figure 1 shows the process of comprehensive search for relevant studies. We conducted searches in multiple databases, including Medline, Embase, Cochrane library, and the Google scholar, resulting in the identification of 314 potentially eligible studies. After removing duplicates ( n  = 34) and conducting title and abstract screening, 17 reports were assessed for eligibility. After further exclusion of nine studies based on full-text review due to not performing RA ( n  = 5), not reporting relevant outcomes ( n  = 3), and being a conference abstract ( n  = 1), eight cohort studies published between 2014 and 2023 were included in the final review ( 14 , 15 , 17 – 22 ).

Figure 1 . Selection process of studies based on inclusion and exclusion criteria. RA, Regional anesthesia.

The characteristics of studies including age, gender distribution, sample size, study design, maximum follow-up duration, and country are summarized in Table 1 . The populations in the eligible studies included individuals aged 50 years and above ( 14 ), 55 years and above ( 19 ), 58 years and above ( 15 ), 65 years and above ( 17 ), 66 years and older ( 22 ), female patients aged ≥30 years undergoing hysterectomy ( 18 ), patients who received elective hip fracture surgery ( 20 ), and patients who underwent major elective surgery ( 21 ). The sample sizes varied widely, ranging from 877 to 280,308. The smallest sample size was noted in a case–control study from Sweden (i.e., 877) ( 15 ), while a population-based study from Taiwan had the largest sample size (i.e., 280,308) ( 18 ). The proportion of males in each study varied between zero and 49.5%. Regarding study design, there were five population-based studies ( 14 , 18 , 20 – 22 ), one cohort study ( 19 ), one case–control study ( 15 ), and one prospective study ( 17 ). The maximum follow-up period ranged from 5 to 20 years. The studies were conducted in various countries, including the United States ( 17 ), Taiwan ( 14 , 18 , 20 , 21 ), Korea ( 19 ), and Canada ( 22 ).

Table 1 . Characteristics of included studies ( n  = 8).

A summary of study qualities is presented in Table 1 , which indicated a variation based on NOS. Four studies ( 19 – 22 ) were assigned a score of nine while two studies ( 14 , 18 ) were assigned a score of 7. In contrast, two others ( 15 , 17 ) received a relatively low score of 6, suggesting possible limitations in terms of study quality.

3.2 Outcomes

3.2.1 baseline characteristics between ra and ga groups.

To examine potential differences in baseline characteristics, we initially analyzed the age, incidence of male proportion, hypertension, diabetes, depression, and severity of comorbidities between the RA and GA groups. Some studies in our meta-analysis were designed to compare GA with placebo groups or RA with placebo groups. Consequently, not all studies provided the specific data required for a direct comparison between the GA and RA groups in terms of patient characteristics, such as age and sex. As shown in Figure 2 , there were no differences in age (MD: 0.15 years, 95% CI: −0.15 to 0.45, p  = 0.33, I 2 = 81%) ( Figure 2A ), incidence of male proportion (OR: 1.01, 95% CI: 0.95–1.08, p  = 0.67, I 2 = 80%) ( Figure 2B ), and hypertension (OR: 1.0, 95% CI: 0.92–1.09, p  = 0.98, I 2 = 82%) ( Figure 2C ) between the two groups. Similarly, the incidences of diabetes (OR: 0.98, 95% CI: 0.93–1.05, p  = 0.59, I 2 = 68%) ( Figure 3A ), depression (OR: 1.0, 95% CI: 0.96–1.03, p  = 0.87, I 2 = 36%) ( Figure 3B ), and severe comorbidities (OR: 1.02, 95% CI: 0.97–1.06, p  = 0.45, I 2 = 46%) ( Figure 3C ) were comparable between the two groups.

Figure 2 . Forest plot showing the difference in (A) age, (B) male proportion, and (C) incidence of hypertension between individuals undergoing regional anesthesia (RA group) and general anesthesia (GA group). M-H, Mantel–Haenszel; IV, Inverse variance; and CI, Confidence interval.

Figure 3 . Forest plot comparing the incidence of (A) diabetes, (B) depression, and (C) severe comorbidities between individuals receiving regional anesthesia (RA group) and general anesthesia (GA group). M-H, Mantel–Haenszel; CI, Confidence interval.

3.2.2 Difference in risk of dementia between RA and GA groups

Five studies provided information for comparing the risk of dementia between patients receiving RA and those undergoing GA. Meta-analysis revealed a higher risk of dementia after GA exposure than that following RA exposure (HR: 1.81, 95% CI: 1.29–2.55, p  = 0.007, I 2 = 99%) ( Figure 4 ) ( 14 , 18 , 20 – 22 ). When one study ( 21 ) featuring a relatively younger population was excluded from the sensitivity analysis, the results showed a notable change. The comparison between GA and regional RA exposure demonstrated a similar risk of dementia (HR, 1.17; 95% CI: 0.95–1.43, p  = 0.13; I 2 = 96%) after this exclusion. This adjusted outcome implies that in an older population, RA exposure may not offer a protective effect against dementia.

Figure 4 . Forest plot showing the difference in risk of dementia between patients receiving regional anesthesia (RA) and those undergoing general anesthesia (GA). SE, Standard error; IV, Inverse variance; and CI, Confidence interval.

We did not examine the funnel plot as only seven datasets were available.

3.2.3 Risk of dementia between RA and placebo groups

Three studies provided the dementia risk in RA and placebo groups (i.e., those receiving no anesthesia/surgery). Meta-analysis revealed no difference in dementia risk between RA and placebo groups (HR: 1.2, 95% CI: 0.69–2.07, p  = 0.52, I 2 = 68%) ( Figure 5 ) ( 15 , 17 , 19 ). However, when one study was removed from the pooled evidence, a higher risk of dementia was noted in the RA group, indicating questionable robustness of the pooled evidence ( 17 ).

Figure 5 . Forest plot showing the risk of dementia between regional anesthesia (RA) and placebo groups. SE, Standard error; IV, Inverse variance; and CI, Confidence interval.

3.2.4 Other risk factors for dementia

Other factors that contributed to dementia based on the current study are shown in Figures 6 , 7 . Anxiety, history of stroke, hypertension, diabetes, and hyperlipidemia were identified as significant risk factors for dementia. In contrast, the likelihood of developing dementia was not associated with male gender, or the presence of head injuries, obesity, and hearing impairment.

Figure 6 . Associations of hypertension, diabetes mellitus (DM), male gender, hyperlipidemia, and head injury with the risk of dementia. SE, Standard error; IV, Inverse variance; and CI, Confidence interval.

Figure 7 . Correlations of obesity, anxiety, stroke history, depression, and hearing impairment with the risk of dementia. SE, Standard error; IV, Inverse variance; and CI, Confidence interval.

3.3 Certainty of evidence

The level of certainty of evidence for various outcomes is presented in Supplementary Table 2 . Due to the inherent limitations of observational studies, the certainty of evidence for five outcomes, namely anxiety, history of stroke, diabetes, hyperlipidemia, and male gender, was deemed to be low. Similarly, for six other outcomes, including risk of dementia between RA and GA groups, risk of dementia between RA and placebo groups, hypertension, head injuries, obesity, and hearing impairment, the certainty of evidence was considered to be very low.

4 Discussion

An objective of this meta-analysis was to synthesize available evidence from previous studies to provide a better understanding of the potential association between RA exposure and dementia risk. Our findings showed a higher risk of dementia associated with GA exposure compared to RA exposure. As the pooled data did not reveal any noteworthy difference in age, male proportion, incidence of hypertension, diabetes, and depression, as well as the severity of comorbidities between patients undergoing RA versus those receiving GA, the bias of our results may be minimized. In addition, a lack of difference in dementia risk between RA and placebo groups further supported an absence of correlation between surgery under RA and the risk of dementia. Besides GA, additional risk factors for dementia were identified as hypertension, diabetes, hyperlipidemia, anxiety, history of stroke, and depression, while male gender, history of head injury, obesity, and hearing impairment were not associated with dementia risk.

Focusing on the impact of GA on short-term cognitive dysfunction, although postoperative delirium can occur in up to 28% of older person subjected to major surgery under general anesthesia ( 23 ), the link between GA and cognitive dysfunction after surgery has not been well established. For instance, one retrospective study involving older person aged over 60 who underwent major non-cardiac surgery reported no significant difference in the incidence of postoperative cognitive dysfunction (POCD) between patients undergoing GA (i.e., 14.3%) and those receiving RA (i.e., 13.9%) at the three-month follow-up ( 24 ). A recent randomized clinical trial comprising 950 older adults who underwent surgical repair for hip fracture also found that RA without sedation did not significantly reduce the incidence of postoperative delirium in comparison with GA (6.2 vs. 5.1%, respectively) ( 25 ). Consistently, a meta-analysis comprising eight studies involving 3,555 older person aged over 65 years who underwent hip-fracture surgery revealed no statistically significant difference in the prevalence of postoperative delirium (POD) or postoperative cognitive dysfunction (POCD) between those receiving RA and those undergoing GA at 24 h, 3 days, and 7 days after surgery ( 26 ), indicating a potentially important role of surgery or other provoking factors (e.g., postoperative pain) ( 27 , 28 ) rather than the choice of anesthesia in the development of short-term postoperative cognitive dysfunction.

Several experimental studies have demonstrated a potential association between an exposure to inhalation agents and the neuropathogenesis of Alzheimer’s Disease (AD) as reflected by an increased production and aggregation of β-amyloid peptides (Aβ) as well as elevated levels of cerebrospinal fluid tau protein ( 29 – 32 ). Regarding the correlation of GA with long-term dementia risk, early evidence from clinical studies did not find a link between GA and dementia risk. For instance, there was no significant correlation between prior exposure to GA and the development of AD in a meta-analysis published in 2011 that examined 15 studies involving 1,752 cases and 5,261 controls ( 33 ). However, a subsequent population-based study including 135,873 patients showed a dose-dependent relationship between GA and dementia risk ( 14 ), suggesting that early evidence may be inconclusive. A relatively recent meta-analysis, which involved 23 studies and 412,253 participants over the age of 60 or 65 years without a pre-existing diagnosis of dementia or AD, revealed a significant positive correlation between exposure to GA and the incidence of AD despite significant heterogeneity ( 13 ). Therefore, even though a link between GA and short-term cognitive dysfunction has not been established, it appears that GA might be a potential risk factor for dementia.

Current interest in the prevention of dementia focuses on the identification of potentially modifiable risk factors ( 5 , 34 ). Previous pooled evidence supported no impact of anesthetic techniques (i.e., RA vs. GA) on short-term cognitive dysfunction ( 26 ), while the influence of different anesthetic approaches on long-term dementia risk remained to be clarified. Compared with RA, our meta-analysis demonstrated a higher risk of dementia associated with exposure to GA. However, sensitivity analysis indicated that among older individuals, exposure to RA might not provide a protective effect against dementia. This discovery is significant, highlighting that older patients should employ preventive strategies irrespective of the type of anesthesia used. To the best of our knowledge, our meta-analysis is the first to address a potential beneficial effect of RA on the long-term dementia risk compared to GA. In addition, there was no significant difference in dementia risk between individuals receiving RA and the placebo group (i.e., those who did not receive surgery or anesthesia), supporting that RA may be protective against long-term dementia associated with surgery-related factors. As there is no curative treatment for patients with dementia, preventive measures are of critical importance ( 35 ). Dementia has become an increasingly costly and burdensome condition as populations age. Indeed, it is predicted that the number of patients with AD will increase to approximately 13.8 million by the year 2050 ( 36 ). Our findings showed that RA may be a favorable alternative to GA for older adults or those at high risk of dementia.

It is important to note that for some comparisons, including the analysis of dementia risk in the RA group versus placebo, only a limited number of studies were available. Although pooling results from three studies for this outcome suggested a comparable dementia risk with RA compared to placebo, sensitivity analysis revealed a contradictory finding. This highlights the unreliability of drawing conclusions from such a small number of studies. The limited datasets available for certain analyses are a major limitation of our work, which precludes strong conclusions from those particular comparisons. Larger, high-quality studies are needed to more definitively determine the impact of RA on dementia risk relative to both GA and no anesthesia exposure.

The development of dementia is influenced by a variety of factors, including environmental factors (e.g., air pollution), genetics (e.g., apolipoprotein E gene), and comorbidities (e.g., hypertension or diabetes) ( 37 – 41 ). In the current meta-analysis, anxiety, history of stroke, hypertension, diabetes, hyperlipidemia, and diabetes were identified as dementia risk factors. In contrast, male gender, head injuries, obesity, and hearing impairment did not increase the likelihood of developing dementia. Existing literature suggests a possible gender-related difference in dementia risk. Specifically, a previous study revealed that women exhibit a higher incidence of AD compared to men after the age of 85 ( 42 ). In contrast, another investigation reported an overall lower incidence of vascular dementia in women than that in men ( 43 ). However, it is worth noting that some studies conducted in the Italian and Spanish populations did not observe a significant association between gender and dementia risk ( 44 , 45 ). In concert with the finding of those studies, the absence of a significant influence of gender on dementia risk in our study may be due to the enrollment of individuals from different ethnic backgrounds as well as the inclusion of both AD and vascular dementia in our analysis.

The results of the current meta-analysis may have several limitations that may restrict the extrapolation of our findings. First, the scarcity of existing studies may impede the strength of evidence. In addition, the relatively small sample sizes in some of the studies available for specific analyses may obscure the significance of the findings as shown in the impaired robustness of our findings on sensitivity analysis. Second, given that age is a risk factor for dementia, the inclusion of individuals with a wide range of age may impact our results. Besides, the wide variation in follow-up duration from between 3 and 7 years to as long as 20 years may influence our outcomes. Third, despite our inclusion of studies recruiting individuals of different ethnicities (i.e., United States, Taiwan, Korea, and Sweden), the relatively small number of studies still limits the extrapolation of our findings to the global population. Fourth, the meta-analytical nature of the current study precluded the exclusion of confounders that may affect the outcomes (e.g., selection bias in patient allocation). Finally, variations in the techniques of anesthesia (e.g., intravenous vs. inhalational anesthesia for GA; epidural vs. spinal anesthesia for RA) as well as the type’s dementia (e.g., Alzheimer’s disease vs. vascular dementia) could introduce heterogeneity into the current meta-analysis.

5 Conclusion

Our results showed an increased risk of developing dementia in individuals exposed to general anesthesia than those receiving regional anesthesia, but no significant difference in the risk of dementia between those subjected to RA and those in the placebo group (i.e., those did not receive surgery and anesthesia). Furthermore, hypertension, diabetes, hyperlipidemia, anxiety, a history of stroke, and depression were identified as risk factors for dementia, while there was no significant association of male gender, history of head injury, obesity, and hearing impairment with the risk of dementia. Further investigations are necessary to elucidate the impact of regional anesthesia on dementia risk, taking into account the high prevalence of dementia as well as the increasing need for surgery among the older person.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.

Author contributions

I-WC: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing. C-KS: Data curation, Methodology, Supervision, Writing – original draft, Writing – review & editing. J-YC: Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. H-TC: Project administration, Resources, Validation, Writing – original draft, Writing – review & editing. K-ML: Writing – original draft, Writing – review & editing, Data curation, Methodology. K-CH: Data curation, Writing – original draft, Writing – review & editing, Conceptualization, Investigation, Software. C-CK: Conceptualization, Investigation, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by Chi Mei Medical Center, Tainan, Taiwan, grant number CMOR11203.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1362461/full#supplementary-material

1. Whalley, LJ, Dick, FD, and McNeill, G. A life-course approach to the aetiology of late-onset dementias. Lancet Neurol . (2006) 5:87–96. doi: 10.1016/S1474-4422(05)70286-6

PubMed Abstract | Crossref Full Text | Google Scholar

2. Arvanitakis, Z, Shah, RC, and Bennett, DA. Diagnosis and management of dementia. JAMA . (2019) 322:1589–99. doi: 10.1001/jama.2019.4782

3. LoGiudice, D, and Watson, R. Dementia in older people: an update. Intern Med J . (2014) 44:1066–73. doi: 10.1111/imj.12572

Crossref Full Text | Google Scholar

4. Ribbe, MW, Ljunggren, G, Steel, K, Topinková, E, Hawes, C, Ikegami, N, et al. Nursing homes in 10 nations: a comparison between countries and settings. Age Ageing . (1997) 26:3–12. doi: 10.1093/ageing/26.suppl_2.3

5. Livingston, G, Huntley, J, Sommerlad, A, Ames, D, Ballard, C, Banerjee, S, et al. Dementia prevention, intervention, and care: 2020 report of the lancet commission. Lancet . (2020) 396:413–46. doi: 10.1016/S0140-6736(20)30367-6

6. Wimo, A, Jönsson, L, Gustavsson, A, McDaid, D, Ersek, K, Georges, J, et al. The economic impact of dementia in Europe in 2008—cost estimates from the Eurocode project. Int J Geriatr Psychiatry . (2011) 26:825–32. doi: 10.1002/gps.2610

7. Williamson, LE, Evans, CJ, Cripps, RL, Leniz, J, Yorganci, E, and Sleeman, KE. Factors associated with emergency department visits by people with dementia near the end of life: a systematic review. J Am Med Dir Assoc . (2021) 22:2046–2055.e35. doi: 10.1016/j.jamda.2021.06.012

8. Benner, M, Steiner, V, and Pierce, LL. Family caregivers’ reports of hospitalizations and emergency department visits in community-dwelling individuals with dementia. Dementia . (2018) 17:585–95. doi: 10.1177/1471301216653537

9. Etzioni, DA, Liu, JH, Maggard, MA, and Ko, CY. The aging population and its impact on the surgery workforce. Ann Surg . (2003) 238:170–7. doi: 10.1097/01.SLA.0000081085.98792.3d

10. Harms, S, Larson, R, Sahmoun, A, and Beal, J. Obesity increases the likelihood of total joint replacement surgery among younger adults. Int Orthop . (2007) 31:23–6. doi: 10.1007/s00264-006-0130-y

11. Wells, V, Hearn, T, McCaul, K, Anderton, S, Wigg, A, and Graves, S. Changing incidence of primary total hip arthroplasty and total knee arthroplasty for primary osteoarthritis. J Arthroplast . (2002) 17:267–73. doi: 10.1054/arth.2002.30414

12. Pilz, V, Hanstein, T, and Skripitz, R. Projections of primary hip arthroplasty in Germany until 2040. Acta Orthop . (2018) 89:308–13. doi: 10.1080/17453674.2018.1446463

13. Lee, JJ, Choi, GJ, Kang, H, Baek, CW, Jung, YH, Shin, HY, et al. Relationship between surgery under general anesthesia and the development of dementia: a systematic review and meta-analysis. Biomed Res Int . (2020) 2020:1–22. doi: 10.1155/2020/3234013

14. Chen, PL, Yang, CW, Tseng, YK, Sun, WZ, Wang, JL, Wang, SJ, et al. Risk of dementia after anaesthesia and surgery. Br J Psychiatry . (2014) 204:188–93. doi: 10.1192/bjp.bp.112.119610

15. Strand, AK, Nyqvist, F, Ekdahl, A, Wingren, G, and Eintrei, C. Is there a relationship between anaesthesia and dementia? Acta Anaesthesiol Scand . (2019) 63:440–7. doi: 10.1111/aas.13302

16. Guyatt, GH, Oxman, AD, Vist, GE, Kunz, R, Falck-Ytter, Y, Alonso-Coello, P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ . (2008) 336:924–6. doi: 10.1136/bmj.39489.470347.AD

17. Aiello Bowles, EJ, Larson, EB, Pong, RP, Walker, RL, Anderson, ML, Yu, O, et al. Anesthesia exposure and risk of dementia and Alzheimer's disease: a prospective study. J Am Geriatr Soc . (2016) 64:602–7. doi: 10.1111/jgs.14024

18. Chen, YC, Oyang, YJ, Lin, TY, and Sun, WZ. Risk assessment of dementia after hysterectomy: analysis of 14-year data from the National Health Insurance Research Database in Taiwan. J Chin Med Assoc . (2020) 83:394–9. doi: 10.1097/JCMA.0000000000000286

19. Kwon, Y-S, Lee, J-J, Lee, S-H, Kim, C, Yu, H, Sohn, J-H, et al. Risk of dementia in patients who underwent surgery under neuraxial anesthesia: a nationwide cohort study. J Personal Med . (2021) 11:1386. doi: 10.3390/jpm11121386

20. Sun, M, Chen, WM, Wu, SY, and Zhang, J. Dementia risk amongst older adults with hip fracture receiving general anaesthesia or regional anaesthesia: a propensity-score-matched population-based cohort study. Br J Anaesth . (2023) 130:305–13. doi: 10.1016/j.bja.2022.11.014

21. Sun, M, Chen, WM, Wu, SY, and Zhang, J. Dementia risk after major elective surgery based on the route of anaesthesia: a propensity score-matched population-based cohort study. EClinicalMedicine . (2023) 55:101727. doi: 10.1016/j.eclinm.2022.101727

22. Velkers, C, Berger, M, Gill, SS, Eckenhoff, R, Stuart, H, Whitehead, M, et al. Association between exposure to general versus regional anesthesia and risk of dementia in older adults. J Am Geriatr Soc . (2021) 69:58–67. doi: 10.1111/jgs.16834

23. Evered, LA, Chan, MT, Han, R, Chu, MH, Cheng, BP, Scott, DA, et al. Anaesthetic depth and delirium after major surgery: a randomised clinical trial. Br J Anaesth . (2021) 127:704–12. doi: 10.1016/j.bja.2021.07.021

24. Rasmussen, LS, Johnson, T, Kuipers, HM, Kristensen, D, Siersma, VD, Vila, P, et al. Does anaesthesia cause postoperative cognitive dysfunction? A randomised study of regional versus general anaesthesia in 438 elderly patients. Acta Anaesthesiol Scand . (2003) 47:260–6. doi: 10.1034/j.1399-6576.2003.00057.x

25. Li, T, Li, J, Yuan, L, Wu, J, Jiang, C, Daniels, J, et al. Effect of regional vs general anesthesia on incidence of postoperative delirium in older patients undergoing hip fracture surgery: the RAGA randomized trial. JAMA . (2022) 327:50–8. doi: 10.1001/jama.2021.22647

26. Bhushan, S, Huang, X, Duan, Y, and Xiao, Z. The impact of regional versus general anesthesia on postoperative neurocognitive outcomes in elderly patients undergoing hip fracture surgery: a systematic review and meta-analysis. Int J Surg . (2022) 105:106854. doi: 10.1016/j.ijsu.2022.106854

27. Lynch, EP, Lazor, MA, Gellis, JE, Orav, J, Goldman, L, and Marcantonio, ER. The impact of postoperative pain on the development of postoperative delirium. Anesth Analg . (1998) 86:781–5.

PubMed Abstract | Google Scholar

28. Vaurio, LE, Sands, LP, Wang, Y, Mullen, EA, and Leung, JM. Postoperative delirium: the importance of pain and pain management. Anesth Analg . (2006) 102:1267–73. doi: 10.1213/01.ane.0000199156.59226.af

29. Dong, Y, Zhang, G, Zhang, B, Moir, RD, Xia, W, Marcantonio, ER, et al. The common inhalational anesthetic sevoflurane induces apoptosis and increases beta-amyloid protein levels. Arch Neurol . (2009) 66:620–31. doi: 10.1001/archneurol.2009.48

30. Xie, Z, Dong, Y, Maeda, U, Alfille, P, Culley, DJ, Crosby, G, et al. The common inhalation anesthetic isoflurane induces apoptosis and increases amyloid beta protein levels. Anesthesiology . (2006) 104:988–94. doi: 10.1097/00000542-200605000-00015

31. Eckenhoff, RG, Johansson, JS, Wei, H, Carnini, A, Kang, B, Wei, W, et al. Inhaled anesthetic enhancement of amyloid-beta oligomerization and cytotoxicity. Anesthesiology . (2004) 101:703–9. doi: 10.1097/00000542-200409000-00019

32. Perucho, J, Rubio, I, Casarejos, MJ, Gomez, A, Rodriguez-Navarro, JA, Solano, RM, et al. Anesthesia with isoflurane increases amyloid pathology in mice models of Alzheimer's disease. J Alzheimer's Dis . (2010) 19:1245–57. doi: 10.3233/JAD-2010-1318

33. Seitz, DP, Shah, PS, Herrmann, N, Beyene, J, and Siddiqui, N. Exposure to general anesthesia and risk of Alzheimer's disease: a systematic review and meta-analysis. BMC Geriatr . (2011) 11:83. doi: 10.1186/1471-2318-11-83

34. Barnes, DE, and Yaffe, K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol . (2011) 10:819–28. doi: 10.1016/S1474-4422(11)70072-2

35. Sabayan, B, and Sorond, F. Reducing risk of dementia in older age. JAMA . (2017) 317:2028. doi: 10.1001/jama.2017.2247

36. Hebert, LE, Weuve, J, Scherr, PA, and Evans, DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology . (2013) 80:1778–83. doi: 10.1212/WNL.0b013e31828726f5

37. Alzheimer's Association. 2023 Alzheimer's disease facts and figures. Alzheimers Dement . (2023) 19:1598–1695. doi: 10.1002/alz.13016

38. Chen, Q, Wang, T, Kang, D, and Chen, L. Protective effect of apolipoprotein E epsilon 3 on sporadic Alzheimer's disease in the Chinese population: a meta-analysis. Sci Rep . (2022) 12:13620. doi: 10.1038/s41598-022-18033-x

39. Chen, J-H, Lin, K-P, and Chen, Y-C. Risk factors for dementia. J Formos Med Assoc . (2009) 108:754–64. doi: 10.1016/S0929-6646(09)60402-2

40. Abolhasani, E, Hachinski, V, Ghazaleh, N, Azarpazhooh, MR, Mokhber, N, and Martin, J. Air pollution and incidence of dementia: a systematic review and meta-analysis. Neurology . (2023) 100:e242–54. doi: 10.1212/WNL.0000000000201419

41. Agarwal, R, and Tripathi, CB. Association of apolipoprotein E genetic variation in Alzheimer's disease in Indian population: a meta-analysis. Am J Alzheimers Dis Other Dement . (2014) 29:575–82. doi: 10.1177/1533317514531443

42. Andersen, K, Launer, LJ, Dewey, ME, Letenneur, L, Ott, A, Copeland, JR, et al. Gender differences in the incidence of AD and vascular dementia: the EURODEM studies. EURODEM incidence research group. Neurology . (1999) 53:1992–7. doi: 10.1212/WNL.53.9.1992

43. Ruitenberg, A, Ott, A, van Swieten, JC, Hofman, A, and Breteler, MM. Incidence of dementia: does gender make a difference? Neurobiol Aging . (2001) 22:575–80. doi: 10.1016/S0197-4580(01)00231-7

44. López-Pousa, S, Vilalta-Franch, J, Llinàs-Regla, J, Garre-Olmo, J, and Román, GC. Incidence of dementia in a rural community in Spain: the Girona cohort study. Neuroepidemiology . (2004) 23:170–7. doi: 10.1159/000078502

45. Ravaglia, G, Forti, P, Maioli, F, Martelli, M, Servadei, L, Brunetti, N, et al. Incidence and etiology of dementia in a large elderly Italian population. Neurology . (2005) 64:1525–30. doi: 10.1212/01.WNL.0000160107.02316.BF

Keywords: cognitive function, general anesthesia, regional anesthesia, dementia, risk factors

Citation: Chen I-W, Sun C-K, Chen J-Y, Chen H-T, Lan K-M, Hung K-C and Ko C-C (2024) Comparison of regional vs. general anesthesia on the risk of dementia: a systematic review and meta-analysis. Front. Public Health . 12:1362461. doi: 10.3389/fpubh.2024.1362461

Received: 28 December 2023; Accepted: 20 May 2024; Published: 03 June 2024.

Reviewed by:

Copyright © 2024 Chen, Sun, Chen, Chen, Lan, Hung and Ko. 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) and the copyright owner(s) 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: Kuo-Chuan Hung, [email protected] ; Ching-Chung Ko, [email protected]

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• J Family Med Prim Care
• v.2(1); Jan-Mar 2013

Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare

S. gopalakrishnan.

Department of Community Medicine, SRM Medical College, Hospital and Research Centre, Kattankulathur, Tamil Nadu, India

P. Ganeshkumar

Healthcare decisions for individual patients and for public health policies should be informed by the best available research evidence. The practice of evidence-based medicine is the integration of individual clinical expertise with the best available external clinical evidence from systematic research and patient's values and expectations. Primary care physicians need evidence for both clinical practice and for public health decision making. The evidence comes from good reviews which is a state-of-the-art synthesis of current evidence on a given research question. Given the explosion of medical literature, and the fact that time is always scarce, review articles play a vital role in decision making in evidence-based medical practice. Given that most clinicians and public health professionals do not have the time to track down all the original articles, critically read them, and obtain the evidence they need for their questions, systematic reviews and clinical practice guidelines may be their best source of evidence. Systematic reviews aim to identify, evaluate, and summarize the findings of all relevant individual studies over a health-related issue, thereby making the available evidence more accessible to decision makers. The objective of this article is to introduce the primary care physicians about the concept of systematic reviews and meta-analysis, outlining why they are important, describing their methods and terminologies used, and thereby helping them with the skills to recognize and understand a reliable review which will be helpful for their day-to-day clinical practice and research activities.

Introduction

Evidence-based healthcare is the integration of best research evidence with clinical expertise and patient values. Green denotes, “Using evidence from reliable research, to inform healthcare decisions, has the potential to ensure best practice and reduce variations in healthcare delivery.” However, incorporating research into practice is time consuming, and so we need methods of facilitating easy access to evidence for busy clinicians.[ 1 ] Ganeshkumar et al . mentioned that nearly half of the private practitioners in India were consulting more than 4 h per day in a locality,[ 2 ] which explains the difficulty of them in spending time in searching evidence during consultation. Ideally, clinical decision making ought to be based on the latest evidence available. However, to keep abreast with the continuously increasing number of publications in health research, a primary healthcare professional would need to read an insurmountable number of articles every day, covered in more than 13 million references and over 4800 biomedical and health journals in Medline alone. With the view to address this challenge, the systematic review method was developed. Systematic reviews aim to inform and facilitate this process through research synthesis of multiple studies, enabling increased and efficient access to evidence.[ 1 , 3 , 4 ]

Systematic reviews and meta-analyses have become increasingly important in healthcare settings. Clinicians read them to keep up-to-date with their field and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research and some healthcare journals are moving in this direction.[ 5 ]

This article is intended to provide an easy guide to understand the concept of systematic reviews and meta-analysis, which has been prepared with the aim of capacity building for general practitioners and other primary healthcare professionals in research methodology and day-to-day clinical practice.

The purpose of this article is to introduce readers to:

• The two approaches of evaluating all the available evidence on an issue i.e., systematic reviews and meta-analysis,
• Discuss the steps in doing a systematic review,
• Introduce the terms used in systematic reviews and meta-analysis,
• Interpret results of a meta-analysis, and
• The advantages and disadvantages of systematic review and meta-analysis.

Application

What is the effect of antiviral treatment in dengue fever? Most often a primary care physician needs to know convincing answers to questions like this in a primary care setting.

To find out the solutions or answers to a clinical question like this, one has to refer textbooks, ask a colleague, or search electronic database for reports of clinical trials. Doctors need reliable information on such problems and on the effectiveness of large number of therapeutic interventions, but the information sources are too many, i.e., nearly 20,000 journals publishing 2 million articles per year with unclear or confusing results. Because no study, regardless of its type, should be interpreted in isolation, a systematic review is generally the best form of evidence.[ 6 ] So, the preferred method is a good summary of research reports, i.e., systematic reviews and meta-analysis, which will give evidence-based answers to clinical situations.

There are two fundamental categories of research: Primary research and secondary research. Primary research is collecting data directly from patients or population, while secondary research is the analysis of data already collected through primary research. A review is an article that summarizes a number of primary studies and may draw conclusions on the topic of interest which can be traditional (unsystematic) or systematic.

Terminologies

Systematic review.

A systematic review is a summary of the medical literature that uses explicit and reproducible methods to systematically search, critically appraise, and synthesize on a specific issue. It synthesizes the results of multiple primary studies related to each other by using strategies that reduce biases and random errors.[ 7 ] To this end, systematic reviews may or may not include a statistical synthesis called meta-analysis, depending on whether the studies are similar enough so that combining their results is meaningful.[ 8 ] Systematic reviews are often called overviews.

The evidence-based practitioner, David Sackett, defines the following terminologies.[ 3 ]

• Review: The general term for all attempts to synthesize the results and conclusions of two or more publications on a given topic.
• Overview: When a review strives to comprehensively identify and track down all the literature on a given topic (also called “systematic literature review”).
• Meta-analysis: A specific statistical strategy for assembling the results of several studies into a single estimate.

Systematic reviews adhere to a strict scientific design based on explicit, pre-specified, and reproducible methods. Because of this, when carried out well, they provide reliable estimates about the effects of interventions so that conclusions are defensible. Systematic reviews can also demonstrate where knowledge is lacking. This can then be used to guide future research. Systematic reviews are usually carried out in the areas of clinical tests (diagnostic, screening, and prognostic), public health interventions, adverse (harm) effects, economic (cost) evaluations, and how and why interventions work.[ 9 ]

Cochrane reviews

Cochrane reviews are systematic reviews undertaken by members of the Cochrane Collaboration which is an international not-for-profit organization that aims to help people to make well-informed decisions about healthcare by preparing, maintaining, and promoting the accessibility of systematic reviews of the effects of healthcare interventions.

Cochrane Primary Health Care Field is a systematic review of primary healthcare research on prevention, treatment, rehabilitation, and diagnostic test accuracy. The overall aim and mission of the Primary Health Care Field is to promote the quality, quantity, dissemination, accessibility, applicability, and impact of Cochrane systematic reviews relevant to people who work in primary care and to ensure proper representation in the interests of primary care clinicians and consumers in Cochrane reviews and review groups, and in other entities. This field would serve to coordinate and promote the mission of the Cochrane Collaboration within the primary healthcare disciplines, as well as ensuring that primary care perspectives are adequately represented within the Collaboration.[ 10 ]

Meta-analysis

A meta-analysis is the combination of data from several independent primary studies that address the same question to produce a single estimate like the effect of treatment or risk factor. It is the statistical analysis of a large collection of analysis and results from individual studies for the purpose of integrating the findings.[ 11 ] The term meta-analysis has been used to denote the full range of quantitative methods for research reviews.[ 12 ] Meta-analyses are studies of studies.[ 13 ] Meta-analysis provides a logical framework to a research review where similar measures from comparable studies are listed systematically and the available effect measures are combined wherever possible.[ 14 ]

The fundamental rationale of meta-analysis is that it reduces the quantity of data by summarizing data from multiple resources and helps to plan research as well as to frame guidelines. It also helps to make efficient use of existing data, ensuring generalizability, helping to check consistency of relationships, explaining data inconsistency, and quantifies the data. It helps to improve the precision in estimating the risk by using explicit methods.

Therefore, “systematic review” will refer to the entire process of collecting, reviewing, and presenting all available evidence, while the term “meta-analysis” will refer to the statistical technique involved in extracting and combining data to produce a summary result.[ 15 ]

Steps in doing systematic reviews/meta-analysis

Following are the six fundamental essential steps while doing systematic review and meta-analysis.[ 16 ]

Define the question

This is the most important part of systematic reviews/meta-analysis. The research question for the systematic reviews may be related to a major public health problem or a controversial clinical situation which requires acceptable intervention as a possible solution to the present healthcare need of the community. This step is most important since the remaining steps will be based on this.

Reviewing the literature

This can be done by going through scientific resources such as electronic database, controlled clinical trials registers, other biomedical databases, non-English literatures, “gray literatures” (thesis, internal reports, non–peer-reviewed journals, pharmaceutical industry files), references listed in primary sources, raw data from published trials and other unpublished sources known to experts in the field. Among the available electronic scientific database, the popular ones are PUBMED, MEDLINE, and EMBASE.

Sift the studies to select relevant ones

To select the relevant studies from the searches, we need to sift through the studies thus identified. The first sift is pre-screening, i.e., to decide which studies to retrieve in full, and the second sift is selection which is to look again at these studies and decide which are to be included in the review. The next step is selecting the eligible studies based on similar study designs, year of publication, language, choice among multiple articles, sample size or follow-up issues, similarity of exposure, and or treatment and completeness of information.

It is necessary to ensure that the sifting includes all relevant studies like the unpublished studies (desk drawer problem), studies which came with negative conclusions or were published in non-English journals, and studies with small sample size.

Assess the quality of studies

The steps undertaken in evaluating the study quality are early definition of study quality and criteria, setting up a good scoring system, developing a standard form for assessment, calculating quality for each study, and finally using this for sensitivity analysis.

For example, the quality of a randomized controlled trial can be assessed by finding out the answers to the following questions:

• Was the assignment to the treatment groups really random?
• Was the treatment allocation concealed?
• Were the groups similar at baseline in terms of prognostic factors?
• Were the eligibility criteria specified?
• Were the assessors, the care provider, and the patient blinded?
• Were the point estimates and measure of variability presented for the primary outcome measure?
• Did the analyses include intention-to-treat analysis?

Calculate the outcome measures of each study and combine them

We need a standard measure of outcome which can be applied to each study on the basis of its effect size. Based on their type of outcome, following are the measures of outcome: Studies with binary outcomes (cured/not cured) have odds ratio, risk ratio; studies with continuous outcomes (blood pressure) have means, difference in means, standardized difference in means (effect sizes); and survival or time-to-event data have hazard ratios.

Combining studies

Homogeneity of different studies can be estimated at a glance from a forest plot (explained below). For example, if the lower confidence interval of every trial is below the upper of all the others, i.e., the lines all overlap to some extent, then the trials are homogeneous. If some lines do not overlap at all, these trials may be said to be heterogeneous.

The definitive test for assessing the heterogeneity of studies is a variant of Chi-square test (Mantel–Haenszel test). The final step is calculating the common estimate and its confidence interval with the original data or with the summary statistics from all the studies. The best estimate of treatment effect can be derived from the weighted summary statistics of all studies which will be based on weighting to sample size, standard errors, and other summary statistics. Log scale is used to combine the data to estimate the weighting.

Interpret results: Graph

The results of a meta-analysis are usually presented as a graph called forest plot because the typical forest plots appear as forest of lines. It provides a simple visual presentation of individual studies that went into the meta-analysis at a glance. It shows the variation between the studies and an estimate of the overall result of all the studies together.

Forest plot

Meta-analysis graphs can principally be divided into six columns [ Figure 1 ]. Individual study results are displayed in rows. The first column (“study”) lists the individual study IDs included in the meta-analysis; usually the first author and year are displayed. The second column relates to the intervention groups and the third column to the control groups. The fourth column visually displays the study results. The line in the middle is called “the line of no effect.” The weight (in %) in the fifth column indicates the weighting or influence of the study on the overall results of the meta-analysis of all included studies. The higher the percentage weight, the bigger the box, the more influence the study has on the overall results. The sixth column gives the numerical results for each study (e.g., odds ratio or relative risk and 95% confidence interval), which are identical to the graphical display in the fourth column. The diamond in the last row of the graph illustrates the overall result of the meta-analysis.[ 4 ]

Interpretation of meta-analysis[ 4 ]

Thus, the horizontal lines represent individual studies. Length of line is the confidence interval (usually 95%), squares on the line represent effect size (risk ratio) for the study, with area of the square being the study size (proportional to weight given) and position as point estimate (relative risk) of the study.[ 7 ]

For example, the forest plot of the effectiveness of dexamethasone compared with placebo in preventing the recurrence of acute severe migraine headache in adults is shown in Figure 2 .[ 17 ]

Forest plot of the effectiveness of dexamethasone compared with placebo in preventing the recurrence of acute severe migraine headache in adults[ 17 ]

The overall effect is shown as diamond where the position toward the center represents pooled point estimate, the width represents estimated 95% confidence interval for all studies, and the black plain line vertically in the middle of plot is the “line of no effect” (e.g., relative risk = 1).

Therefore, when examining the results of a systematic reviews/meta-analysis, the following questions should be kept in mind:

• Heterogeneity among studies may make any pooled estimate meaningless.
• The quality of a meta-analysis cannot be any better than the quality of the studies it is summarizing.
• An incomplete search of the literature can bias the findings of a meta-analysis.
• Make sure that the meta-analysis quantifies the size of the effect in units that you can understand.

Subgroup analysis and sensitivity analysis

Subgroup analysis looks at the results of different subgroups of trials, e.g., by considering trials on adults and children separately. This should be planned at the protocol stage itself which is based on good scientific reasoning and is to be kept to a minimum.

Sensitivity analysis is used to determine how results of a systematic review/meta-analysis change by fiddling with data, for example, what is the implication if the exclusion criteria or excluded unpublished studies or weightings are assigned differently. Thus, after the analysis, if changing makes little or no difference to the overall results, the reviewer's conclusions are robust. If the key findings disappear, then the conclusions need to be expressed more cautiously.

Advantages of Systematic Reviews

Systematic reviews have specific advantages because of using explicit methods which limit bias, draw reliable and accurate conclusions, easily deliver required information to healthcare providers, researchers, and policymakers, help to reduce the time delay in the research discoveries to implementation, improve the generalizability and consistency of results, generation of new hypotheses about subgroups of the study population, and overall they increase precision of the results.[ 18 ]

Limitations in Systematic Reviews/Meta-analysis

As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers’ ability to assess the strengths and weaknesses of those reviews.[ 5 ]

Even though systematic review and meta-analysis are considered the best evidence for getting a definitive answer to a research question, there are certain inherent flaws associated with it, such as the location and selection of studies, heterogeneity, loss of information on important outcomes, inappropriate subgroup analyses, conflict with new experimental data, and duplication of publication.

Publication Bias

Publication bias results in it being easier to find studies with a “positive” result.[ 19 ] This occurs particularly due to inappropriate sifting of the studies where there is always a tendency towards the studies with positive (significant) outcomes. This effect occurs more commonly in systematic reviews/meta-analysis which need to be eliminated.

The quality of reporting of systematic reviews is still not optimal. In a recent review of 300 systematic reviews, few authors reported assessing possible publication bias even though there is overwhelming evidence both for its existence and its impact on the results of systematic reviews. Even when the possibility of publication bias is assessed, there is no guarantee that systematic reviewers have assessed or interpreted it appropriately.[ 20 ]

To overcome certain limitations mentioned above, the Cochrane reviews are currently reported in a format where at the end of every review, findings are summarized in the author's point of view and also give an overall picture of the outcome by means of plain language summary. This is found to be much helpful to understand the existing evidence about the topic more easily by the reader.

A systematic review is an overview of primary studies which contains an explicit statement of objectives, materials, and methods, and has been conducted according to explicit and reproducible methodology. A meta-analysis is a mathematical synthesis of the results of two or more primary studies that addressed the same hypothesis in the same way. Although meta-analysis can increase the precision of a result, it is important to ensure that the methods used for the reviews were valid and reliable.

High-quality systematic reviews and meta-analyses take great care to find all relevant studies, critically assess each study, synthesize the findings from individual studies in an unbiased manner, and present balanced important summary of findings with due consideration of any flaws in the evidence. Systematic review and meta-analysis is a way of summarizing research evidence, which is generally the best form of evidence, and hence positioned at the top of the hierarchy of evidence.

Systematic reviews can be very useful decision-making tools for primary care/family physicians. They objectively summarize large amounts of information, identifying gaps in medical research, and identifying beneficial or harmful interventions which will be useful for clinicians, researchers, and even for public and policymakers.

Source of Support: Nil

Conflict of Interest: None declared.

• Open access
• Published: 24 May 2024

Rates of bronchopulmonary dysplasia in very low birth weight neonates: a systematic review and meta-analysis

• Alvaro Moreira 1 ,
• Michelle Noronha 1   na1 ,
• Jooby Joy 2   na1 ,
• Noah Bierwirth 1 ,
• Aina Tarriela 1 ,
• Aliha Naqvi 1 ,
• Sarah Zoretic 3 ,
• Maxwell Jones 1 ,
• Ali Marotta 1 ,
• Taylor Valadie 1 ,
• Jonathan Brick 1 ,
• Caitlyn Winter 1 ,
• Melissa Porter 1 ,
• Isabelle Decker 1 ,
• Matteo Bruschettini 4 &
• Sunil K. Ahuja 5 , 6 , 7 , 8 , 9 , 10  

Respiratory Research volume  25 , Article number:  219 ( 2024 ) Cite this article

372 Accesses

2 Altmetric

Metrics details

Large-scale estimates of bronchopulmonary dysplasia (BPD) are warranted for adequate prevention and treatment. However, systematic approaches to ascertain rates of BPD are lacking.

To conduct a systematic review and meta-analysis to assess the prevalence of BPD in very low birth weight (≤ 1,500 g) or very low gestational age (< 32 weeks) neonates.

Data sources

A search of MEDLINE from January 1990 until September 2019 using search terms related to BPD and prevalence was performed.

Study selection

Randomized controlled trials and observational studies evaluating rates of BPD in very low birth weight or very low gestational age infants were eligible. Included studies defined BPD as positive pressure ventilation or oxygen requirement at 28 days (BPD28) or at 36 weeks postmenstrual age (BPD36).

Data extraction and synthesis

Two reviewers independently conducted all stages of the review. Random-effects meta-analysis was used to calculate the pooled prevalence. Subgroup analyses included gestational age group, birth weight group, setting, study period, continent, and gross domestic product. Sensitivity analyses were performed to reduce study heterogeneity.

Main outcomes and measures

Prevalence of BPD defined as BPD28, BPD36, and by subgroups.

A total of 105 articles or databases and 780,936 patients were included in this review. The pooled prevalence was 35% (95% CI, 28-42%) for BPD28 ( n  = 26 datasets, 132,247 neonates), and 21% (95% CI, 19-24%) for BPD36 ( n  = 70 studies, 672,769 neonates). In subgroup meta-analyses, birth weight category, gestational age category, and continent were strong drivers of the pooled prevalence of BPD.

Conclusions and relevance

This study provides a global estimation of BPD prevalence in very low birth weight/low gestation neonates.

Introduction

Bronchopulmonary dysplasia (BPD), characterized as an arrest of lung growth and development, is an important cause of morbidity and mortality in very preterm newborns [ 1 ]. While interventions in neonatal care have led to survival of smaller and younger neonates, therapies for BPD are still limited [ 2 ]. Therefore, there is an urgent need for early prediction of BPD and implementation of strategies and therapies that can attenuate disease progression. To accomplish such endeavors, we must first ascertain large-scale estimates of BPD and its global impact over time. In doing so, the effect of interventions and progress towards reducing rates of BPD can be more readily measured. Valid and consistent estimates of the prevalence of BPD around the globe are largely lacking.

A previous study estimated global rates of BPD; however, the definition of BPD was not determined a priori and the estimation was reported as a set of ranges per country as opposed to a pooled rate [ 3 ]. Challenges to estimating comprehensive rates of BPD include the varying definitions (e.g., 28 day versus 36 week assessment [ 4 , 5 ]), as well as the heterogeneous inclusion criteria of preterm neonates in studies (e.g., gestational-based inclusion compared to birth weight-based parameters or a combination of both). To overcome these barriers, we sought to conduct a systematic review and meta-analysis that would: (i) estimate global trends in the prevalence of BPD, (ii) examine temporal changes in BPD rates, and (iii) stratify BPD rates according to definition, birth weight, gestational age, setting, continent, and gross domestic product (GDP).

We conducted a systematic review and meta-analysis according to recommendations from the Cochrane Handbook for Systematic Reviews of Interventions and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [ 6 ]. A protocol of this review was not registered.

Search strategy

Two investigators (A.M. and M.N.) systematically searched MEDLINE from January 1990 to September 30th, 2019. Search terms included ( bronchopulmonary dysplasia OR chronic lung disease) AND a list of each country. Articles were filtered to include children between the age range of birth and 1 month post-term, no limits were placed on language, and refined to remove review articles. Furthermore, review of references from included studies was performed to supplement our initial search. The full search strategy is presented in eMethods 1 in the Supplement. Lastly, we reviewed all the population-based articles from a systematic review by Siffel et al. [ 3 ] wherein they examined global rates of BPD. To enhance the comprehensiveness of our investigation, we integrated national registries from countries that were publicly available documenting outcomes related to BPD.

Two groups of investigators (group 1: A.T. and M.N.; group 2: A.N. and A.M) independently reviewed the titles and abstracts of all citations to determine suitability for inclusion. This was followed by independent review of the full-text articles to confirm eligibility. A third author (S.Z.) resolved any disagreements. Studies were included if they were international or national level (e.g., population-based) studies reporting rates of BPD from 1990 to 2019. The search was initiated from 1990, as this marks the time when surfactant therapy became increasingly standard of care in neonatal centers [ 7 ]. The end date was chosen as 2019 to exclude publications using the newest definition for BPD [ 8 ]. We included data for all neonates at risk for BPD with confirmed diagnosis occurring in the hospital or prior to discharge. Studies with inclusion criteria of male and female neonates with a birth weight of less than or equal to 1,500 g or a gestational age of less than 32 weeks were included. Due to limited availability of granular patient-level data in the included studies, mortality rates for each study were collected. Case reports, editorials, and commentaries were excluded.

Data extraction

Two sets of authors (A.T. and M.N.; A.N. and A.M.) independently collected study details. Two authors (J.J. and S.Z.) independently verified the accuracy of collated information. Inconsistencies were discussed among a panel of at least four investigators. Study specifics included country, BPD definition, BPD rates, total number of neonates in the study, years of observation, inclusion criteria, and study design. Articles and standardized data collection sheets were maintained in Google Drive folders. GetData Graph Digitizer version 2.26.0.20 was used to collect values from figures when mortality data was not described in the article text.

Risk of bias

The risk of bias was judged in a binary fashion (e.g., yes = 1 or no = 0). We assessed the risk of bias for observational studies according to the Newcastle-Ottawa Quality Assessment Scale in three dimensions, selection, comparability, and outcome. The score for observational studies ranged from 0 to 8, representing bias risk for each article. Studies were defined as having a high risk of bias if the total score was five or lower, moderate risk of bias if the score was between five and six, and low bias if the total summed to greater than seven. We assessed the risk of bias for controlled studies according to the Cochrane Risk of Bias Tool using seven dimensions, selection bias (including random sequence generation and allocation concealment), reporting bias, other bias, performance bias, detection bias, and attrition bias. The score for randomized controlled studies ranged from 0 to 7, representing bias risk for each article.

Definitions and outcomes

A priori , BPD was defined by two categories: (i) BPD28- supplemental oxygen or positive pressure ventilation at 28 days of life, and (ii) BPD36- supplemental oxygen or positive pressure ventilation at 36 weeks postmenstrual age. The pooled prevalence of BPD is presented as forest plots for BPD28 and BPD36. If the study stratified patient numbers by both definitions, we included both to each pooled rate. When articles overlapped in time period for a particular country, the articles with more comprehensive data were selected for inclusion. Prespecified subgroup analyses included birth weight categories, gestational age, years, setting, continent, and gross domestic product (GDP). Precisely, gestational age was divided into extremely low gestational age (ELGA) (≤ 28 weeks) vs. very low gestational age (VLGA) (< 32 weeks), while study setting was stratified into international or national. Study years were binned into three decades: 1990–1999, 2000–2009, 2010–2019. This approach was used to explore temporal changes in BPD. The year 1990 was used as the time of inception as the late 1980s and early 1990s is when clinical trials for surfactant use demonstrated efficacy in the care of preterm neonates with respiratory distress syndrome. Birth weight was sorted into extremely low birth weight (ELBW) ( ≤  1,000 g), very low birth weight (VLBW) (≤ 1,500 g), and modifications of these terms (e.g., 501–750 g, 751–1000 g, 1001–1250 g, and 1251–1500 g). To clarify, the subgroup analysis by birth weight of 1000 g was conducted by categorizing studies based on the specified birth weight ranges. Specifically, studies were included in this subgroup analysis if they reported data on all infants falling within the designated birth weight range of interest and not average birthweight reported for a cohort.

Statistical analysis

The primary outcome was expressed using direct proportions (PR) with a 95% confidence interval (CI) following Freeman-Tukey double arc-sine transformation of the raw data [ 9 ]. Expecting high heterogeneity, defined as an I 2 #x2009;> 50%, all analyses used a DerSimonian–Laird estimate with a random-effects meta-analysis model. The presence of publication bias was evaluated qualitatively using funnel plots and quantitatively conducing Egger’s linear regression test. At least ten studies were needed to perform subgroup analyses. All statistical analyses were performed using R version 4.1.0.

Identification of Eligible studies

Our search yielded 4582 records, of which 2318 were reviewed in full. After applying the eligibility criteria, a total of 42 were included in this review. We also identified three publicly available national datasets: Australian and New Zealand Neonatal Network, Canadian Neonatal Network, and Neonatal Research Network of Japan Database. Meta-analyses were performed on all studies and databases, moving forward now referred to as datasets. In sum, a combination of 74 datasets comprised the analysis for BPD28 and BPD26 as well as their subgroup analyses. The flow diagram of selected articles is shown in Fig.  1 .

Life satisfaction scores at age 30. 12 = highest possible score. 3 = Lowest possible score

Figure 1 PRISMA flowchart of literature identification and study selection.

Study characteristics

Table  1 provides detailed characteristics of the included articles. All the chosen articles were based on cohort investigations and on the two predetermined BPD definitions: BPD28 and BPD36. The most commonly used definition for BPD was BPD36. Thirty countries were represented in the studies, and the countries that produced the most data were Australia and New Zealand ( n  = 24/70 datasets, 34.3%). A total of 672,769 patients were included in this review. Twenty-six out of the 70 datasets (37.1%) in BPD36 were published from 2010 onwards.

Pooled and stratified prevalence of BPD

The pooled prevalence for BPD28 calculated from 27 datasets and 132,424 neonates was 35% (95% CI, 0.28–0.42) using random effects meta-analysis (Fig. 2). For BPD36 ( n  = 70 studies, 672,769 neonates), the pooled prevalence was 21% (95% CI, 0.19–0.24) (Fig. 3). Table  2 depicts the prevalence of BPD28 and BPD36 according to gestational age, birth weight, study period, continent, setting, and GDP (subgroup analysis).

Figure 2 Pooled prevalence for BPD28. Forest plot demonstrating pooled prevalence for BPD28 and 95% CI with a random-effects meta-analysis model.

Figure 3 Pooled prevalence for BPD36. Forest plot demonstrating pooled prevalence for BPD36 and 95% CI with a random-effects meta-analysis model.

Subgroup analysis for BPD28

When stratified by birth weight, the highest rates of BPD28 were found in infants with lower birth weights: <1000 g (ELBW). For instance, infants in the lowest birth weight stratum (< 500 g) had a BPD28 prevalence of 99% (95% CI, 0.97-1.00), while those in the second-lowest birth weight stratum (501–750 g) had a BPD28 prevalence of 87% (95% CI, 0.75–0.96). The BPD28 prevalence was lowest (16%; 95% CI, 0.11–0.22) in infants with the highest birth weights (1251–1500 g). The prevalence of BPD28 was higher in ELGA versus VLGA neonates (90% vs. 29%). The subgroup analysis of BPD28 by setting showed a higher rate in the national compared to multinational studies, as well as Oceania compared to other continents. Overall, no differences were observed in BPD28 prevalence when stratified by year or GDP.

Subgroup analysis for BPD36

The subgroup analysis for prevalence of BPD36 stratified by birth weight was very similar to the BPD28 analysis, in which an upward trend in the prevalence of BPD36 was associated with lower birth weights. For example, the highest prevalence of BPD36 was noted in neonates with a birth weight of less than 1000 g (ELBW). Further stratification of the ELBW neonates revealed BPD36 prevalence rates of 71% (95% CI, 0.51–0.87) and 60% (95% CI, 0.51–0.68) in neonates with birth weights of < 500 g and 501–750 g, respectively. Again, the lowest prevalence of BPD36, 10% (95% CI, 0.07–0.13), was seen in the highest (1251–1500 g) birth weight stratum.

Similar to the findings using the BPD28 definition, prevalence of BPD36 was higher in ELGA neonates (43% n  = 358,636, versus 12% n  = 126,368). Prevalence of BPD36 was also higher in national studies. Lastly, BPD36 prevalence again differed when stratified by continent. The highest prevalence was seen in North America at 329% (95% CI, 0.25–0.33). Rates of BPD36 were similar across GDP strata and year.

Sensitivity analysis and mortality rate

We conducted sensitivity analysis on the prevalence of BPD28 and BPD36 to reduce heterogeneity, defined as an I 2  ≥ 50%. After keeping only 4 studies, the prevalence of BPD28 was 32% (95% CI, 0.31–0.32; I 2  = 0%, eResults 1 ). For BPD36, 10 studies remained after filtering for high heterogeneity. The resulting rate of BPD36 was 25% (95% CI, 0.25–0.26; I 2  = 49%, eResults 2 ). The table in eResults 3 shows the varying range of mortality rates for each of the studies (range of 0–23.9% with an average rate of 8.1%).

Risk of bias and publication bias

Forty-two studies were evaluated by the Newcastle-Ottawa Quality Assessment Scale and one study by the Cochrane Risk of Bias Tool. Thirty (74%) of the observational studies had a moderate bias (total score ranging from 5 to 6) ( eTable 1 ). The domain that had the most bias pertained to questions regarding follow-up outcomes. Nine studies (21%) had low risk of bias (total score between 7 and 8). The single randomized controlled trial had a risk of bias score of five out of seven. Publication bias was low for BPD28 and BPD36. Plots can be viewed in eFigures 1, 2 .

Bronchopulmonary dysplasia remains the most common morbidity of prematurity and carries a significant disease burden [ 10 ]. Throughout the published literature, BPD displays itself as a disease with significant heterogeneity [ 11 , 12 , 13 , 14 ]. This is found not only within different “types” of BPD but also within the definition itself; as published data defines it as oxygen at 28 days, 36 weeks or other combinations of factors [ 15 ]. Therefore, it is essential to have accurate information for prediction, analysis and treatment. We performed this systematic review and meta-analysis to determine large-scale rates of bronchopulmonary dysplasia, with a subgroup analysis according to two major definitions. To our knowledge this is the largest and most comprehensive study describing BPD prevalence to date.

Our study expands on the 2019 study by Siffel et al. [ 3 ] to provide a more complete review of available data. We discovered, reviewed and analyzed data over a 41-year period (versus 11 years), with inclusion of a higher number of studies across more regions. As an additional contrast, we defined BPD (oxygen at 28 days or 36 weeks) and manually extracted data for combined analysis. This allowed us to use pooled data to compare subgroups and pursue further statistical analyses. We were therefore able to provide a more accurate prevalence for each provided outcome, rather than reporting outcomes as a set of ranges from individual studies.

As anticipated, the foremost risk factor for developing BPD was found to be low birth weight, particularly with a weight below 750 g. This trend was evident across both individual subgroup analyses and combined evaluations. Additionally, our observations revealed discrepancies in BPD rates among different gestational age groups, notably between ELGA and VLGA infants. These findings align with existing literature that underscores an inverse association between BPD rates and gestational age/birthweight, further affirming the current understanding in the field [ 8 , 16 ].

We also compared BPD rates across three decades (1990–1999, 2000–2010 and 2010–2020), which showed no difference between the groups across the definitions of BPD. This is found throughout the literature and highlights the difficulty in preventing and treating this disease. Medical advancements in the care of preterm neonates have led to higher survival, especially in the most industrialized nations [ 17 , 18 ]. This coincides with the survival of more infants with BPD and accounts for much of the similarity of the prevalence across decades. While our study focuses on reporting BPD rates in decade cohorts, it’s essential to acknowledge the limitations inherent in utilizing these broader definitions of BPD. We recognize that the clinical landscape of BPD management may have evolved over the past 30 years, potentially leading to improvements not fully captured by the BPD28 and BPD36 definitions. Our exclusion of studies using the newer BPD definition by Jensen et al. was indeed mentioned in the methods section, but we acknowledge the importance of reiterating this point here for clarity.

While the incidence of BPD exhibits considerable variation among different countries, current evidence indicates minimal disparities in its prevalence across major continents. Numerous studies have explored BPD incidence and associated risk factors in various regions spanning North America, Europe, Asia, and Australia, generally yielding comparable rates. For instance, research by Jain et al. found no significant divergence in BPD incidence among preterm infants across North America, Europe, and Australia [ 19 ]. In contrast, our investigation suggests notable differences in BPD rates among regions or continents, particularly with lower rates observed in Europe and South America. However, it’s noteworthy that South America’s data pool was limited to just 1–2 studies. These findings imply that the risk factors and underlying pathophysiology of BPD may not uniformly align across geographical regions, underscoring the imperative for further investigation to elucidate these distinctions. This prompts consideration as to whether disparities in clinical practices might potentially justify these findings.

The Neonatal Research Network (NRN) in the United States has compiled large retrospective analyses of care practice and patient outcomes among extremely premature infants. They have demonstrated that rates of antenatal steroids and surfactant administration have increased, delivery room intubation has decreased [ 7 ]. However, the rates of bronchopulmonary dysplasia (BPD36) in their study ranged from 32 to 45%, which is notably higher than the 21% observed in this study. This difference could be attributed to the varying gestational ages included in the studies, as the NRN’s research comprised newborns between 22 and 28 weeks. In comparison, the Chinese Neonatal Network’s cohort of 8,148 preterm neonates had a BPD36 rate of 29.2%, which is higher than our study’s results, again differences most likely due to their inclusion of neonates 31 weeks and younger whereas our study included neonates of ≤ 32 weeks [ 20 ].

The prevalence of BPD varied depending on the study setting, with national cohorts demonstrating the highest rates for both definitions of BPD. These estimates may be more reliable, as they offer a broader representation across multiple institutions, reducing the impact of outliers and the unique management practices of individual hospitals on the results. Furthermore, many of these national studies employed inclusion criteria that targeted younger gestational ages, further enhancing their robustness. Despite the thought that GDP may have an impact on BPD rates, subgroup analyses based on quartiles of a nation’s GDP showed no differences. One possible explanation for this finding is that other factors beyond GDP, such as access to healthcare and neonatal resources, may play a more significant role.

Limitations

Despite conducting an extensive data search employing multiple reviewers and diverse search methods, there remains a possibility that certain available studies may have been overlooked. Our findings reveal considerable heterogeneity across all examined outcomes, with many I2 values approaching 1. Despite efforts to minimize this through meticulous data extraction and analysis, the persistence of heterogeneity underscores the importance of cautiously applying the results to specific disease populations. For example, Bonamy et al. reported low BPD rates as it exclusively classified the condition in individuals with the severe form of the disease. In an attempt to mitigate the observed heterogeneity, we conducted a sensitivity analysis, which yielded rates comparable to those obtained in the initial analysis characterized by high heterogeneity.

Another constraint stems from the limited granularity of the original datasets, owing to the diverse definitions of BPD and the myriad ways in which data can be presented. This limitation restricts our ability to conduct more sophisticated statistical analyses and may lead to unequal weighting of studies where data accessibility varies. Additionally, there is a notable disparity in the amount of data available for some regions, notably North America, Oceania, and Europe, compared to other global populations. It would have been ideal to gather data as comprehensive as that publicly available from Australia and New Zealand, Canada, and Japan. Moreover, handling mortality data was a significant challenge in our analysis. We encountered variations among studies, where some solely included survivors while others reported mortality rates without adjusting them in their BPD rates. Some observed rates may have been exceptionally low, especially if their mortality rates were high. We were unable to solely include survivors due to variations in study methodologies, with some studies including only survivors while others encompassed all patients in their denominator for BPD, regardless of neonatal mortality. Adapting our analysis to account for this disparity without access to patient-level data limited our analyses. To address this limitation, we included mortality rates in the supplementary materials. This allows for transparency regarding the impact of mortality on our findings and provides additional context for interpreting the results. While we hypothesized differences in pathophysiology as a possible cause for national differences, it is essential to acknowledge other potential factors that may influence BPD rates, such as variations in reporting practices, gestational age and birth weight distributions, and early mortality rates. These factors could contribute to the observed regional differences in BPD rates and warrant further investigation. Also, differences in the sophistication of medical treatment across regions impacts survival and eventual diagnosis of BPD, all of which affect overall outcomes and generalizability.

Conclusions

To conclude, this large systematic review and meta-analysis shows that despite advancements, the prevalence of bronchopulmonary dysplasia has remained consistent through decades and is a significant burden across populations. The data generated from this study could serve as baseline rates for future research and could help guide the development of bundled care strategies aimed at decreasing BPD rates [ 21 ]. Ultimately, a greater understanding of modifiable factors that contribute to BPD development is critical to improving outcomes and reducing the burden of this disease.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information.

Abbreviations

bronchopulmonary dysplasia

gross domestic product

extremely low gestational age

very low gestational age

extremely low birth weight

very low birth weight

confidence interval

Neonatal Research Network

Jobe AH. The new bronchopulmonary dysplasia. Curr Opin Pediatr. 2011. https://doi.org/10.1097/MOP.0b013e3283423e6b .

Article   PubMed   PubMed Central   Google Scholar  

Doyle LW, Carse E, Adams A-M, Ranganathan S, Opie G, Cheong JLY. Ventilation in extremely Preterm infants and respiratory function at 8 years. N Engl J Med. 2017;377(4):329–37. https://doi.org/10.1056/nejmoa1700827 .

Article   PubMed   Google Scholar  

Siffel C, Kistler KD, Lewis JFM, Sarda SP. Global prevalence of bronchopulmonary dysplasia among extremely preterm infants: a systematic literature review. J Matern Neonatal Med. 2021;34(11):1721–31. https://doi.org/10.1080/14767058.2019.1646240 .

Article   Google Scholar  

Jobe AH, Bancalari E. NICHD / NHLBI / ORD workshop Summary. Am J Respir Crit Care Med. 2001;163:1723–9.

Article   CAS   PubMed   Google Scholar  

Higgins RD, Jobe AH, Koso-Thomas M, et al. Bronchopulmonary dysplasia: executive summary of a workshop. J Pediatr. 2018;197:300–8. https://doi.org/10.1016/j.jpeds.2018.01.043 .

Moher D, Liberati A, Tetzlaff J, Altman DG. Academia and Clinic annals of Internal Medicine Preferred reporting items for systematic reviews and Meta-analyses. Ann Intern Med. 2009;151(4):264–9.

Stoll BJ, Hansen NI, Bell EF, et al. Trends in Care practices, Morbidity, and mortality of extremely Preterm neonates, 1993–2012. JAMA. 2015;314(10):1039–51. https://doi.org/10.1001/jama.2015.10244 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bancalari E, Jain D. Bronchopulmonary dysplasia: 50 years after the original description. Neonatology. 2019;115(4):384–91. https://doi.org/10.1159/000497422 .

Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence p = Ì ^ i) E- -. 2013;67(11):974–8.

Thébaud B, Goss KN, Laughon M, et al. Bronchopulmonary dysplasia. Nat Rev Dis Prim. 2019;5(1). https://doi.org/10.1038/s41572-019-0127-7 .

Gibbs K, Jensen EA, Alexiou S, Munson D, Zhang H. Ventilation strategies in severe bronchopulmonary dysplasia. Neoreviews. 2020;21(4):e226–37. https://doi.org/10.1542/NEO.21-4-E226 .

Cochrane database Syst Rev . 2021;10(10):CD001146. doi:10.1002/14651858.CD001146.pub6.

Wu KY, Jensen EA, White AM, et al. Characterization of Disease phenotype in very preterm infants with severe bronchopulmonary dysplasia. Am J Respir Crit Care Med. 2020;201(11):1398–406. https://doi.org/10.1164/RCCM.201907-1342OC .

Bamat NA, Zhang H, McKenna KJ, Morris H, Stoller JZ, Gibbs K. The clinical evaluation of severe bronchopulmonary dysplasia. Neoreviews. 2020;21(7):e442–53. https://doi.org/10.1542/NEO.21-7-E442 .

Ibrahim J, Bhandari V. The definition of bronchopulmonary dysplasia: an evolving dilemma. Pediatr Res. 2018;84(5):586–8. https://doi.org/10.1038/s41390-018-0167-9 .

Jensen EA, Schmidt B. Epidemiology of bronchopulmonary dysplasia. Birth Defects Res Part Clin Mol Teratol. 2014;100(3):145–57. https://doi.org/10.1002/BDRA.23235 .

Article   CAS   Google Scholar  

Bell EF, Hintz SR, Hansen NI, et al. Mortality, In-Hospital morbidity, Care practices, and 2-Year outcomes for extremely Preterm infants in the US, 2013–2018. JAMA. 2022;327(3):248–63. https://doi.org/10.1001/jama.2021.23580 .

Twilhaar ES, Wade RM, De Kieviet JF, Van Goudoever JB, Van Elburg RM, Oosterlaan J. Cognitive outcomes of children born extremely or very Preterm since the 1990s and Associated Risk factors: a Meta-analysis and Meta-regression. JAMA Pediatr. 2018;172(4):361–7. https://doi.org/10.1001/JAMAPEDIATRICS.2017.5323 .

Jain D, Bancalari E. Bronchopulmonary dysplasia: clinical perspective. Birth Defects Res Clin Mol Teratol. 2014;100(3):134–44. https://doi.org/10.1002/bdra.23229 .

Cao Y, Jiang S, Sun J, et al. Assessment of neonatal Intensive Care Unit practices, Morbidity, and Mortality among very Preterm infants in China. JAMA Netw Open. 2021;4(8):e2118904. https://doi.org/10.1001/jamanetworkopen.2021.18904 . Published 2021 Aug 2.

Villosis MFB, Barseghyan K, Ambat MT, Rezaie KK, Braun D. Rates of Bronchopulmonary Dysplasia following implementation of a Novel Prevention Bundle. JAMA Netw Open. 2021;4(6):e2114140. https://doi.org/10.1001/jamanetworkopen.2021.14140 . Published 2021 Jun 1.

Download references

Acknowledgements

Not applicable.

AM reports a grant from the Parker B. Francis Foundation, National Institutes of Health (NIH) Eunice Kennedy Shriver National Institute of Child Health and Human Development K23HD101701 and a research grant from NIH National Heart, Lung, and Blood Institute 2R25-HL126140, outside the submitted work. All other authors declare no competing interests.

Author information

Michelle Noronha and Jooby Joy co-1st authors.

Authors and Affiliations

Department of Pediatrics, Division of Neonatology, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229-3900, USA

Alvaro Moreira, Michelle Noronha, Noah Bierwirth, Aina Tarriela, Aliha Naqvi, Maxwell Jones, Ali Marotta, Taylor Valadie, Jonathan Brick, Caitlyn Winter, Melissa Porter & Isabelle Decker

University of Texas Rio Grande Valley School of Medicine, Edinburg, TX, USA

Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA

Sarah Zoretic

Department of Pediatrics, Lund University, Lund, Sweden

Matteo Bruschettini

Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, TX, USA

Sunil K. Ahuja

Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, TX, USA

The Foundation for Advancing Veterans’ Health Research, South Texas Veterans Health Care System, San Antonio, TX, USA

Department of Microbiology, Immunology & Molecular Genetics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

You can also search for this author in PubMed   Google Scholar

Contributions

AM collected data and was a major contributor in writing the manuscript, assessed risk of bias, and statistical analysis. MN collected data, assessed risk of bias and was a major contributor in writing the manuscript. JJ collected data and was a major contributor in writing the manuscript. NB was a major contributor in writing the manuscript. AT collected data and assessed risk of bias. AN collected data and assessed risk of bias. SZ verified all data collection. MJ collected data. AM collected data.TV reviewed and critiqued manuscript writing. JB reviewed and critiqued manuscript writing. CW was a major contributor in writing the manuscript and reviewed and critiqued manuscript writing. MP collected data and critiqued manuscript writing. ID reviewed and critiqued manuscript writing. MB verified risk of bias and reviewed and critiqued manuscript writing. SA oversaw the project and reviewed and critiqued manuscript writing. All authors approved final version of manuscript.

Corresponding author

Correspondence to Alvaro Moreira .

Ethics declarations

Ethics approval and consent to participate, consent for publication, role of sponsor.

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.

Originality of content

All information and materials in the manuscript are original.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, supplementary material 3, supplementary material 4, supplementary material 5, supplementary material 6, supplementary material 7, supplementary material 8, supplementary material 9, supplementary material 10, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Moreira, A., Noronha, M., Joy, J. et al. Rates of bronchopulmonary dysplasia in very low birth weight neonates: a systematic review and meta-analysis. Respir Res 25 , 219 (2024). https://doi.org/10.1186/s12931-024-02850-x

Download citation

Received : 21 December 2023

Accepted : 14 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1186/s12931-024-02850-x

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Bronchopulmonary dysplasia
• Chronic lung disease
• Meta-analysis

Respiratory Research

ISSN: 1465-993X

• General enquiries: [email protected]

Prognostic Value of Stress Perfusion Cardiac MRI in Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Effects of the Scanner, Stress Agent, and Analysis Technique

Affiliation.

• 1 From the Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Q.F.); Department of Radiology, Cambridge Biomedical Campus, University of Cambridge, Box 219, Level 5, Cambridge CB2 0QQ, England (Q.F., J.R.W.M.); Departments of Radiology (Q.F., J.R.W.M., S.A.) and Cardiology (S.P.H., G.A.), Royal Papworth Hospital, Cambridge, England; and School of Medicine & Population Health and INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, England (S.A.).
• PMID: 38814186
• DOI: 10.1148/ryct.230382

Purpose To perform a systematic review and meta-analysis to assess the prognostic value of stress perfusion cardiac MRI in predicting cardiovascular outcomes. Materials and Methods A systematic literature search from the inception of PubMed, Embase, Web of Science, and China National Knowledge Infrastructure until January 2023 was performed for articles that reported the prognosis of stress perfusion cardiac MRI in predicting cardiovascular outcomes. The quality of included studies was assessed using the Quality in Prognosis Studies tool. Reported hazard ratios (HRs) of univariable regression analyses with 95% CIs were pooled. Comparisons were performed across different analysis techniques (qualitative, semiquantitative, and fully quantitative), magnetic field strengths (1.5 T vs 3 T), and stress agents (dobutamine, adenosine, and dipyridamole). Results Thirty-eight studies with 58 774 patients with a mean follow-up time of 53 months were included. There were 1.9 all-cause deaths and 3.5 major adverse cardiovascular events (MACE) per 100 patient-years. Stress-inducible ischemia was associated with a higher risk of all-cause mortality (HR: 2.55 [95% CI: 1.89, 3.43]) and MACE (HR: 3.90 [95% CI: 2.69, 5.66]). For MACE, pooled HRs of qualitative, semiquantitative, and fully quantitative methods were 4.56 (95% CI: 2.88, 7.22), 3.22 (95% CI: 1.60, 6.48), and 1.78 (95% CI: 1.39, 2.28), respectively. For all-cause mortality, there was no evidence of a difference between qualitative and fully quantitative methods ( P = .79). Abnormal stress perfusion cardiac MRI findings remained prognostic when subgrouped based on underlying disease, stress agent, and field strength, with HRs of 3.54, 2.20, and 3.38, respectively, for all-cause mortality and 3.98, 3.56, and 4.21, respectively, for MACE. There was no evidence of subgroup differences in prognosis between field strengths or stress agents. There was significant heterogeneity in effect size for MACE outcomes in the subgroups assessing qualitative versus quantitative stress perfusion analysis, underlying disease, and field strength. Conclusion Stress perfusion cardiac MRI is valuable for predicting cardiovascular outcomes, regardless of the analysis method, stress agent, or magnetic field strength used. Keywords: MR-Perfusion, MRI, Cardiac, Meta-Analysis, Stress Perfusion, Cardiac MR, Cardiovascular Disease, Prognosis, Quantitative © RSNA, 2024 Supplemental material is available for this article.

Keywords: Cardiac; Cardiac MR; Cardiovascular Disease; MR-Perfusion; MRI; Meta-Analysis; Prognosis; Quantitative; Stress Perfusion.

Publication types

• Systematic Review
• Meta-Analysis
• Cardiovascular Diseases* / diagnostic imaging
• Exercise Test / methods
• Magnetic Resonance Imaging / methods
• Myocardial Perfusion Imaging / methods