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A brief introduction of meta‐analyses in clinical practice and research

Xiao‐meng wang.

1 Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou Guangdong, China

Xi‐Ru Zhang

Zhi‐hao li, wen‐fang zhong, associated data.

Data sharing is not applicable to this article because no datasets were generated or analyzed during the current study.

With the explosive growth of medical information, it is almost impossible for healthcare providers to review and evaluate all relevant evidence to make the best clinical decisions. Meta‐analyses, which summarize all existing evidence and quantitatively synthesize individual studies, have become the best available evidence for informing clinical practice. This article introduces the common methods, steps, principles, strengths and limitations of meta‐analyses and aims to help healthcare providers and researchers obtain a basic understanding of meta‐analyses in clinical practice and research.

This article introduces the common methods, principles, steps, strengths and limitations of meta‐analyses and aims to help clinicians and researchers obtain a basic understanding of meta‐analyses in clinical practice and research.

1. INTRODUCTION

With the explosive growth of medical information, it has become almost impossible for healthcare providers to review and evaluate all related evidence to inform their decision making. 1 , 2 Furthermore, the inconsistent and often even conflicting conclusions of different studies can confuse these individuals. Systematic reviews were developed to resolve such situations, which comprehensively and systematically summarize all relevant empirical evidence. 3 Many systematic reviews contain meta‐analysis, which use statistical methods to combine the results of individual studies. 4 Through meta‐analyses, researchers can objectively and quantitatively synthesize results from different studies and increase the statistical strength and precision for estimating effects. 5 In the late 1970s, meta‐analysis began to appear regularly in the medical literature. 6 Subsequently, a plethora of meta‐analyses have emerged and the growth is exponential over time. 7 When conducted properly, a meta‐analysis of medical studies is considered as decisive evidence because it occupies a top level in the hierarchy of evidence. 8

An understanding of the principles, performance, advantages and weaknesses of meta‐analyses is important. Therefore, we aim to provide a basic understanding of meta‐analyses for clinicians and researchers in the present article by introducing the common methods, principles, steps, strengths and limitations of meta‐analyses.

2. COMMON META‐ANALYSIS METHODS

There are many types of meta‐analysis methods (Table  1 ). In this article, we mainly introduce five meta‐analysis methods commonly used in clinical practice.

Meta‐analysis methods

2.1. Aggregated data meta‐analysis

Although more information can be obtained based on individual participant‐level data from original studies, it is usually impossible to obtain these data from all included studies in meta‐analysis because such data may have been corrupted, or the main investigator may no longer be contacted or refuse to release the data. Therefore, by extracting summary results of studies available in published accounts, an aggregate data meta‐analysis (AD‐MA) is the most commonly used of all the quantitative approaches. 9 A study has found that > 95% of published meta‐analyses were AD‐MA. 10 In addition, AD‐MA is the mainstay of systematic reviews conducted by the US Preventive Services Task Force, the Cochrane Collaboration and many professional societies. 9 Moreover, AD‐MA can be completed relatively quickly at a low cost, and the data are relatively easy to obtain. 11 , 12 However, AD‐MA has very limited control over the data. A challenge with AD‐MA is that the association between an individual participant‐level covariate and the effect of the interventions at the study level may not reflect the individual‐level effect modification of that covariate. 13 It is also difficult to extract sufficient compatible data to undertake meaningful subgroup analyses in AD‐MA. 14 Furthermore, AD‐MA is prone to ecological bias, as well as to confounding from variables not included in the model, and may have limited power. 15

2.2. Individual participant data meta‐analysis

An individual participant data meta‐analysis (IPD‐MA) is considered the “gold standard” for meta‐analysis; this type of analysis collects individual participant‐level data from original studies. 15 Compared with AD‐MA, IPD‐MA has many advantages, including improved data quality, a greater variety of analytical types that can be performed and the ability to obtain more reliable results. 16 , 17

It is crucial to maintain clusters of participants within studies in the statistical implementation of an IPD‐MA. Clusters can be retained during the analysis using a one‐step or two‐step approach. 18 In the one‐step approach, the individual participant data from all studies are modeled simultaneously, at the same time as accounting for the clustering of participants within studies. 19 This approach requires a model specific to the type of data being synthesized and an appropriate account of the meta‐analysis assumptions (e.g. fixed or random effects across studies). Cheng et al . 20 proposed using a one‐step IPD‐MA to handle binary rare events and found that this method was superior to traditional methods of inverse variance, the Mantel–Haenszel method and the Yusuf‐Peto method. In the two‐step approach, the individual participant data from each study are analyzed independently for each separate study to produce aggregate data for each study (e.g. a mean treatment effect estimate and its standard error) using a statistical method appropriate for the type of data being analyzed (e.g. a linear regression model might be fitted for continuous responses, or a Cox regression might be applied for time‐to‐event data). The aggregate data are then combined to obtain an summary effect in the second step using a suitable model, such as weighting studies by the inverse of the variance. 21 For example, using a two‐step IPD‐MA, Grams et al . 22 found that apolipoprotein‐L1 kidney‐risk variants were not associated with incident cardiovascular disease or death independent of kidney measures.

Compared to the two‐step approach, the one‐step IPD‐MA is recommended for small meta‐analyses 23 and, conveniently, must only specify one model; however, this requires careful distinction of within‐study and between‐study variability. 24 The two‐step IPD‐MA is more laborious, although it allows the use of traditional, well‐known meta‐analysis techniques in the second step, such as those used by the Cochrane Collaboration (e.g. the Mantel–Haenszel method).

2.3. Cumulative meta‐analysis

Meta‐analyses are traditionally used retrospectively to review existing evidence. However, current evidence often undergoes several updates as new studies become available. Thus, updated data must be continuously obtained to simplify and digest the ever‐expanding literature. Therefore, cumulative meta‐analysis was developed, which adds studies to a meta‐analysis based on a predetermined order and then tracks the magnitude of the mean effect and its variance. 25 A cumulative meta‐analysis can be performed multiple times; not only can it obtain summary results and provide a comparison of the dynamic results, but also it can assess the impact of newly added studies on the overall conclusions. 26 For example, initial observational studies and systematic reviews and meta‐analyses suggested that frozen embryo transfer was better for mothers and babies; however, recent primary studies have begun to challenge these conclusions. 27 Maheshwari et al . 27 therefore conducted a cumulative meta‐analysis to investigate whether these conclusions have remained consistent over time and found that the decreased risks of harmful outcomes associated with pregnancies conceived from frozen embryos have been consistent in terms of direction and magnitude of effect over several years, with an increasing precision around the point estimates. Furthermore, continuously updated cumulative meta‐analyses may avoid unnecessary large‐scale randomized controlled trials (RCTs) and prevent wasted research efforts. 28

2.4. Network meta‐analysis

Although RCTs can directly compare the effectiveness of interventions, most of them compare the effectiveness of an intervention with a placebo, and there is almost no direct comparison between different interventions. 29 , 30 Network meta‐analyses comprise a relatively recent development that combines direct and indirect evidence to compare the effectiveness between different interventions. 31 Evidence obtained from RCTs is considered as direct evidence, whereas evidence obtained through one or more common comparators is considered as indirect evidence. For example, when comparing interventions A and C, direct evidence refers to the estimate of the relative effects between A and C. When no RCTs have directly compared interventions A and C, these interventions can be compared indirectly if both have been compared with B (placebo or some standard treatments) in other studies (forming an A–B–C “loop” of evidence). 32 , 33

A valid network meta‐analysis can correctly combine the relative effects of more than two studies and obtain a consistent estimate of the relative effectiveness of all interventions in one analysis. 34 This meta‐analysis may lead to a greater accuracy of estimating intervention effectiveness and the ability to compare all available interventions to calculate the rank of different interventions. 34 , 35 For example, phosphodiesterase type 5 inhibitors (PDE5‐Is) are the first‐line therapy for erectile dysfunction, although there are limited available studies on the comparative effects of different types of PDE5‐Is. 36 Using a network meta‐analysis, Yuan et al . 36 calculated the absolute effects and the relative rank of different PDE5‐Is to provide an overview of the effectiveness and safety of all PDE5‐Is.

Notably, a network meta‐analysis should satisfy the transitivity assumption, in which there are no systematic differences between the available comparisons other than the interventions being compared 37 ; in other words, the participants could be randomized to any of the interventions in a hypothetical RCT consisting of all the interventions included in the network meta‐analysis.

2.5. Meta‐analysis of diagnostic test accuracy

Sensitivity and specificity are commonly used to assess diagnostic accuracy. However, diagnostic tests in clinical practice are rarely 100% specific or sensitive. 38 It is difficult to obtain accurate estimates of sensitivity and specificity in small diagnostic accuracy studies. 39 , 40 Even in a large sample size study, the number of cases may still be small as a result of the low prevalence. By identifying and synthesizing evidence on the accuracy of tests, the meta‐analysis of diagnostic test accuracy (DTA) provides insight into the ability of medical tests to detect the target diseases 41 ; it also can provide estimates of test performance, allow comparisons of the accuracy of different tests and facilitate the identification of sources of variability. 42 For example, the FilmArray® (Biomerieux, Marcy‐l'Étoile, France) meningitis/encephalitis (ME) panel can detect the most common pathogens in central nervous system infections, although reports of false positives and false negatives are confusing. 43 Based on meta‐analysis of DTA, Tansarli et al . 43 calculated that the sensitivity and specificity of the ME panel were both > 90%, indicating that the ME panel has high diagnostic accuracy.

3. HOW TO PERFORM A META‐ANALYSIS

3.1. frame a question.

Researchers must formulate an appropriate research question at the beginning. A well‐formulated question will guide many aspects of the review process, including determining eligibility criteria, searching for studies, collecting data from included studies, structuring the syntheses and presenting results. 44 There are some tools that may facilitate the construction of research questions, including PICO, as used in clinical practice 45 ; PEO and SPICE, as used for qualitative research questions 46 , 47 ; and SPIDER, as used for mixed‐methods research. 48

3.2. Form the search strategy

It is crucial for researchers to formulate a search strategy in advance that includes inclusion and exclusion criteria, as well as a standardized data extraction form. The definition of inclusion and exclusion criteria depends on established question elements, such as publication dates, research design, population and results. A reasonable inclusion and exclusion criteria will reduce the risk of bias, increase transparency and make the review systematic. Broad criteria may increase the heterogeneity between studies, and narrow criteria may make it difficult to find studies; therefore, a compromise should be found. 49

3.3. Search of the literature databases

To minimize bias and reduce hampered interpretation of outcomes, the search strategy should be as comprehensive as possible, employing multiple databases, such as PubMed, Embase, Cochrane Central Registry of Controlled Trials, Scopus, Web of Science and Google Scholar. 50 , 51 Removing language restrictions and actively searching for non‐English bibliographic databases may also help researchers to perform a comprehensive meta‐analysis. 52

3.4. Select the articles

The selection or rejection of the included articles should be guided by the criteria. 53 Two independent reviewers may screen the included articles, and any disagreements should be resolved by consensus through discussion. First, the titles and abstracts of all relevant searched papers should be read, and inclusion or exclusion criteria applied to determine whether these papers meet. Then, the full texts of the included articles should be reviewed once more to perform the rejection again. Finally, the reference lists of these articles should be searched to widen the research as much as possible. 54

3.5. Data extraction

A pre‐formed standardized data extraction form should be used to extract data of included studies. All data should be carefully converted using uniform standards. Simultaneous extraction by multiple researchers might also make the extracted data more accurate.

3.6. Assess quality of articles

Checklists and scales are often used to assess the quality of articles. For example, the Cochrane Collaboration's tool 55 is usually used to assess the quality of RCTs, whereas the Newcastle Ottawa Scale 56 is one of the most common method to assess the quality of non‐randomized trials. In addition, Quality Assessment of Diagnostic Accuracy Studies 2 57 is often used to evaluate the quality of diagnostic accuracy studies.

3.7. Test for heterogeneity

Several methods have been proposed to detect and quantify heterogeneity, such as Cochran's Q and I 2 values. Cochran's Q test is used to determine whether there is heterogeneity in primary studies or whether the variation observed is due to chance, 58 but it may be underpowered because of the inclusion of a small number of studies or low event rates. 59 Therefore, p < 0.10 (not 0.05) indicates the presence of heterogeneity given the low statistical strength and insensitivity of Cochran's Q test. 60 Another common method for testing heterogeneity is the I 2 value, which describes the percentage of variation across studies that is attributable to heterogeneity rather than chance; this value does not depend on the number of studies. 61 I 2 values of 25%, 50% and 75% are considered to indicate low, moderate and high heterogeneity, respectively. 60

3.8. Estimate the summary effect

Fixed effects and random effects models are commonly used to estimate the summary effect in a meta‐analysis. 62 Fixed effects models, which consider the variability of the results as “random variation”, simply weight individual studies by their precision (inverse of the variance). Conversely, random effects models assume a different underlying effect for each study and consider this an additional source of variation that is randomly distributed. A substantial difference in the summary effect calculated by fixed effects models and random effects models will be observed only if the studies are markedly heterogeneous (heterogeneity p < 0.10) and the random effects model typically provides wider confidence intervals than the fixed effect model. 63 , 64

3.9. Evaluate sources of heterogeneity

Several methods have been proposed to explore the possible reasons for heterogeneity. According to factors such as ethnicity, the number of studies or clinical features, subgroup analyses can be performed that divide the total data into several groups to assess the impact of a potential source of heterogeneity. Sensitivity analysis is a common approach for examining the sources of heterogeneity on a case‐by‐case basis. 65 In sensitivity analysis, one or more studies are excluded at a time and the impact of removing each or several studies is evaluated on the summary results and the between‐study heterogeneity. Sequential and combinatorial algorithms are usually implemented to evaluate the change in between‐study heterogeneity as one or more studies are excluded from the calculations. 66 Moreover, a meta‐regression model can explain heterogeneity based on study‐level covariates. 67

3.10. Assess publication bias

A funnel plot is a scatterplot that is commonly used to assess publication bias. In a funnel plot, the x ‐axis indicates the study effect and the y ‐axis indicates the study precision, such as the standard error or sample size. 68 , 69 If there is no publication bias, the plot will have a symmetrical inverted funnel; conversely, asymmetry indicates the possibility of publication bias.

3.11. Present results

A forest plot is a valid and useful tool for summarizing the results of a meta‐analysis. In a forest plot, the results from each individual study are shown as a blob or square; the confidence interval, usually representing 95% confidence, is shown as a horizontal line that passes through the square; and the summary effect is shown as a diamond. 70

4. PRINCIPLES OF META‐ANALYSIS PERFORMANCE

There are four most important principles of meta‐analysis performance that should be emphasized. First, the search scope of meta‐analysis should be expanded as much as possible to contain all relevant research, and it is important to remove language restrictions and actively search for non‐English bibliographic databases. Second, any meta‐analysis should include studies selected based on strict criteria established in advance. Third, appropriate tools must be selected to evaluate the quality of evidence according to different types of primary studies. Fourth, the most suitable statistical model should be chosen for the meta‐analysis and a weighted mean estimate of the effect size should be calculated. Finally, the possible causes of heterogeneity should be identified and publication bias in the meta‐analysis must be assessed.

5. STRENGTHS OF META‐ANALYSIS

Meta‐analyses have several strengths. First, a major advantage is their ability to improve the precision of effect estimates with considerably increased statistical power, which is particularly important when the power of the primary study is limited as a result of the small sample size. Second, a meta‐analysis has more power to detect small but clinically significant effects and to examine the effectiveness of interventions in demographic or clinical subgroups of participants, which can help researchers identify beneficial (or harmful) effects in specific groups of patients. 71 , 72 Third, meta‐analyses can be used to analyze rare outcomes and outcomes that individual studies were not designed to test (e.g. adverse events). Fourth, meta‐analyses can be used to examine heterogeneity in study results and explore possible sources in case this heterogeneity would lead to bias from “mixing apples and oranges”. 73 Furthermore, meta‐analyses can compare the effectiveness of various interventions, supplement the existing evidence, and then offer a rational and helpful way of addressing a series of practical difficulties that plague healthcare providers and researchers. Lastly, meta‐analyses may resolve disputes caused by apparently conflicting studies, determine whether new studies are necessary for further investigation and generate new hypotheses for future studies. 7 , 74

6. LIMITATIONS OF META‐ANALYSIS

6.1. missing related research.

The primary limitation of a meta‐analysis is missing related research. Even in the ideal case in which all relevant studies are available, a faulty search strategy can miss some of these studies. Small differences in search strategies can produce large differences in the set of studies found. 75 When searching databases, relevant research can be missed as a result of the omission of keywords. The search engine (e.g. PubMed, Google) may also affect the type and number of studies that are found. 76 Moreover, it may be impossible to identify all relevant evidence if the search scope is limited to one or two databases. 51 , 77 Finally, language restrictions and the failure to search non‐English bibliographic databases may also lead to an incomplete meta‐analysis. 52 Comprehensive search strategies for different databases and languages might help solve this issue.

6.2. Publication bias

Publication bias means that positive findings are more likely to be published and then identified through literature searches rather than ambiguous or negative findings. 78 This is an important and key source of bias that is recognized as a potential threat to the validity of results. 79 The real research effect may be exaggerated or even falsely positive if only published articles are included. 80 For example, based on studies registered with the US Food and Drug Administration, Turner et al . 81 reviewed 74 trials of 12 antidepressants to assess publication bias and its influence on apparent efficacy. It was found that antidepressant studies with favorable outcomes were 16 times more likely to be published than those with unfavorable outcomes, and the apparent efficacy of antidepressants increased between 11% and 69% when the non‐published studies were not included in the analysis. 81 Moreover, failing to identify and include non‐English language studies may also increase publication bias. 82 Therefore, all relevant studies should be identified to reduce the impact of publication bias on meta‐analysis.

6.3. Selection bias

Because many of the studies identified are not directly related to the subject of the meta‐analysis, it is crucial for researchers to select which studies to include based on defined criteria. Failing to evaluate, select or reject relevant studies based on stricter criteria regarding the study quality may also increase the possibility of selection bias. Missing or inappropriate quality assessment tools may lead to the inclusion of low‐quality studies. If a meta‐analysis includes low‐quality studies, its results will be biased and incorrect, which is also called “garbage in, garbage out”. 83 Strictly defined criteria for included studies and scoring by at least two researchers might help reduce the possibility of selection bias. 84 , 85

6.4. Unavailability of information

The best‐case scenario for meta‐analyses is the availability of individual participant data. However, most individual research reports only contain summary results, such as the mean, standard deviation, proportions, relative risk and odds ratio. In addition to the possibility of reporting errors, the lack of information can severely limit the types of analyses and conclusions that can be achieved in a meta‐analysis. For example, the unavailability of information from individual studies may preclude the comparison of effects in predetermined subgroups of participants. Therefore, if feasible, the researchers could contact the author of the primary study for individual participant data.

6.5. Heterogeneity

Although the studies included in a meta‐analysis have the same research hypothesis, there is still the potential for several areas of heterogeneity. 86 Heterogeneity may exist in various parts of the studies’ design and conduct, including participant selection, interventions/exposures or outcomes studied, data collection, data analyses and selective reporting of results. 87 Although the difference of the results can be overcome by assessing the heterogeneity of the studies and performing subgroup analyses, 88 the results of the meta‐analysis may become meaningless and even may obscure the real effect if the selected studies are too heterogeneous to be comparable. For example, Nicolucci et al . 89 conducted a review of 150 published randomized trials on the treatment of lung cancer. Their review showed serious methodological drawbacks and concluded that heterogeneity made the meta‐analysis of existing trials unlikely to be constructive. 89 Therefore, combining the data in meta‐analysis for studies with large heterogeneity is not recommended.

6.6. Misleading funnel plot

Funnel plots are appealing because they are a simple technique used to investigate the possibility of publication bias. However, their objective is to detect a complex effect, which can be misleading. For example, the lack of symmetry in a funnel plot can also be caused by heterogeneity. 90 Another problem with funnel plots is the difficulty of interpreting them when few studies are included. Readers may also be misled by the choice of axes or the outcome measure. 91 Therefore, in the absence of a consensus on how the plot should be constructed, asymmetrical funnel plots should be interpreted cautiously. 91

6.7. Inevitable subjectivity

Researchers must make numerous judgments when performing meta‐analyses, 92 which inevitably introduces considerable subjectivity into the meta‐analysis review process. For example, there is often a certain amount of subjectivity when deciding how similar studies should be before it is appropriate to combine them. To minimize subjectivity, at least two researchers should jointly conduct a meta‐analysis and reach a consensus.

The explosion of medical information and differences between individual studies make it almost impossible for healthcare providers to make the best clinical decisions. Meta‐analyses, which summarize all eligible evidence and quantitatively synthesize individual results on a specific clinical question, have become the best available evidence for informing clinical practice and are increasingly important in medical research. This article has described the basic concept, common methods, principles, steps, strengths and limitations of meta‐analyses to help clinicians and investigators better understand meta‐analyses and make clinical decisions based on the best evidence.

AUTHOR CONTRIBUTIONS

CM designed and directed the study. XMW and XRZ had primary responsibility for drafting the manuscript. CM, ZHL, WFZ and PY provided insightful discussions and suggestions. All authors critically reviewed the manuscript for important intellectual content.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflicts of interest.

ACKNOWLEDGEMENTS

This work was supported by the Project Supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019 to CM) and the Construction of High‐level University of Guangdong (G820332010, G618339167 and G618339164 to CM). The funders played no role in the study design or implementation; manuscript preparation, review or approval; or the decision to submit the manuscript for publication.

Wang X‐M, Zhang X‐R, Li Z‐H, Zhong W‐F, Yang P, Mao C. A brief introduction of meta‐analyses in clinical practice and research . J Gene Med . 2021; 23 :e3312. 10.1002/jgm.3312 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Xiao‐Meng Wang and Xi‐Ru Zhang contributed equally to this work.

DATA AVAILABILITY STATEMENT

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• Review Article
• Published: 08 March 2018

Meta-analysis and the science of research synthesis

• Jessica Gurevitch 1 ,
• Julia Koricheva 2 ,
• Shinichi Nakagawa 3 , 4 &
• Gavin Stewart 5  

Nature volume  555 ,  pages 175–182 ( 2018 ) Cite this article

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Meta-analysis is the quantitative, scientific synthesis of research results. Since the term and modern approaches to research synthesis were first introduced in the 1970s, meta-analysis has had a revolutionary effect in many scientific fields, helping to establish evidence-based practice and to resolve seemingly contradictory research outcomes. At the same time, its implementation has engendered criticism and controversy, in some cases general and others specific to particular disciplines. Here we take the opportunity provided by the recent fortieth anniversary of meta-analysis to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.

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Acknowledgements

We dedicate this Review to the memory of Ingram Olkin and William Shadish, founding members of the Society for Research Synthesis Methodology who made tremendous contributions to the development of meta-analysis and research synthesis and to the supervision of generations of students. We thank L. Lagisz for help in preparing the figures. We are grateful to the Center for Open Science and the Laura and John Arnold Foundation for hosting and funding a workshop, which was the origination of this article. S.N. is supported by Australian Research Council Future Fellowship (FT130100268). J.G. acknowledges funding from the US National Science Foundation (ABI 1262402).

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Jessica Gurevitch

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Julia Koricheva

Evolution and Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, 2052, New South Wales, Australia

Shinichi Nakagawa

Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, 2010, New South Wales, Australia

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Practical Guide to Meta-analysis

• 1 Stanford-Surgery Policy Improvement, Research and Education (S-SPIRE) Center, Palo Alto, California
• 2 Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill
• 3 Department of Surgery, University of Michigan, Ann Arbor
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Meta-analysis is a systematic approach of synthesizing, combining, and analyzing data from multiple studies (randomized clinical trials 1 or observational studies 2 ) into a single effect estimate to answer a research question. Meta-analysis is especially useful if there is debate around the research question in the literature published to date or the individual published studies are underpowered. Vital to a high-quality meta-analysis is a comprehensive literature search, prespecified hypothesis and aims, reporting of study quality, consideration of heterogeneity and examination of bias. In the hierarchy of evidence, meta-analysis appears above observational studies and randomized clinical trials because it rigorously collates evidence across a larger body of literature; however, meta-analysis is largely dependent on the quality of the primary data.

• Editorial Maximizing the Impact of Surgical Health Services Research JAMA Surgery

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Arya S , Schwartz TA , Ghaferi AA. Practical Guide to Meta-analysis. JAMA Surg. 2020;155(5):430–431. doi:10.1001/jamasurg.2019.4523

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Study Design 101: Meta-Analysis

• Case Report
• Case Control Study
• Cohort Study
• Randomized Controlled Trial
• Practice Guideline
• Systematic Review

Meta-Analysis

• Helpful Formulas
• Finding Specific Study Types

A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power. This conclusion is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results.

Meta-analysis would be used for the following purposes:

• To establish statistical significance with studies that have conflicting results
• To develop a more correct estimate of effect magnitude
• To provide a more complex analysis of harms, safety data, and benefits
• To examine subgroups with individual numbers that are not statistically significant

If the individual studies utilized randomized controlled trials (RCT), combining several selected RCT results would be the highest-level of evidence on the evidence hierarchy, followed by systematic reviews, which analyze all available studies on a topic.

• Greater statistical power
• Confirmatory data analysis
• Greater ability to extrapolate to general population affected
• Considered an evidence-based resource

Disadvantages

• Difficult and time consuming to identify appropriate studies
• Not all studies provide adequate data for inclusion and analysis
• Requires advanced statistical techniques
• Heterogeneity of study populations

Design pitfalls to look out for

The studies pooled for review should be similar in type (i.e. all randomized controlled trials).

Are the studies being reviewed all the same type of study or are they a mixture of different types?

The analysis should include published and unpublished results to avoid publication bias.

Does the meta-analysis include any appropriate relevant studies that may have had negative outcomes?

Fictitious Example

Do individuals who wear sunscreen have fewer cases of melanoma than those who do not wear sunscreen? A MEDLINE search was conducted using the terms melanoma, sunscreening agents, and zinc oxide, resulting in 8 randomized controlled studies, each with between 100 and 120 subjects. All of the studies showed a positive effect between wearing sunscreen and reducing the likelihood of melanoma. The subjects from all eight studies (total: 860 subjects) were pooled and statistically analyzed to determine the effect of the relationship between wearing sunscreen and melanoma. This meta-analysis showed a 50% reduction in melanoma diagnosis among sunscreen-wearers.

Real-life Examples

Goyal, A., Elminawy, M., Kerezoudis, P., Lu, V., Yolcu, Y., Alvi, M., & Bydon, M. (2019). Impact of obesity on outcomes following lumbar spine surgery: A systematic review and meta-analysis. Clinical Neurology and Neurosurgery, 177 , 27-36. https://doi.org/10.1016/j.clineuro.2018.12.012

This meta-analysis was interested in determining whether obesity affects the outcome of spinal surgery. Some previous studies have shown higher perioperative morbidity in patients with obesity while other studies have not shown this effect. This study looked at surgical outcomes including "blood loss, operative time, length of stay, complication and reoperation rates and functional outcomes" between patients with and without obesity. A meta-analysis of 32 studies (23,415 patients) was conducted. There were no significant differences for patients undergoing minimally invasive surgery, but patients with obesity who had open surgery had experienced higher blood loss and longer operative times (not clinically meaningful) as well as higher complication and reoperation rates. Further research is needed to explore this issue in patients with morbid obesity.

Nakamura, A., van Der Waerden, J., Melchior, M., Bolze, C., El-Khoury, F., & Pryor, L. (2019). Physical activity during pregnancy and postpartum depression: Systematic review and meta-analysis. Journal of Affective Disorders, 246 , 29-41. https://doi.org/10.1016/j.jad.2018.12.009

This meta-analysis explored whether physical activity during pregnancy prevents postpartum depression. Seventeen studies were included (93,676 women) and analysis showed a "significant reduction in postpartum depression scores in women who were physically active during their pregnancies when compared with inactive women." Possible limitations or moderators of this effect include intensity and frequency of physical activity, type of physical activity, and timepoint in pregnancy (e.g. trimester).

Related Terms

A document often written by a panel that provides a comprehensive review of all relevant studies on a particular clinical or health-related topic/question.

Publication Bias

A phenomenon in which studies with positive results have a better chance of being published, are published earlier, and are published in journals with higher impact factors. Therefore, conclusions based exclusively on published studies can be misleading.

Now test yourself!

1. A Meta-Analysis pools together the sample populations from different studies, such as Randomized Controlled Trials, into one statistical analysis and treats them as one large sample population with one conclusion.

a) True b) False

2. One potential design pitfall of Meta-Analyses that is important to pay attention to is:

a) Whether it is evidence-based. b) If the authors combined studies with conflicting results. c) If the authors appropriately combined studies so they did not compare apples and oranges. d) If the authors used only quantitative data.

Evidence Pyramid - Navigation

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Principles of Meta-Analysis

• First Online: 11 August 2022

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By some the replication continuum is attributed to the work of Lipsey and Wilson ( 1993 ). However, there is no mention of it. Also, the statement ‘the closer to pure replications your collection of studies, the easier it is to argue comparability’ does not appear in the text of Lipsey and Wilson nor can it be interpreted as a paraphrased statement. This means caution is required when looking for the origins of the replication continuum.

Hendrick ( 1990 ) refers to a working paper written by him in 1974 about the dichotomy ‘strict replication’ and ‘conceptual replication.’

The term Simpson’s paradox was introduced by Blyth ( 1972 ), inspired by Simpson ( 1951 ). However, notions by Pearson et al. ( 1899 , p. 278) and Yule ( 1903 , pp. 132–4) about combining data seem to predate Simpson ( 1951 ).

The other two of the three preceding systematic reviews with meta-analysis were dated fifteen years before this systematic review using the odds ratio for conducting the meta-analysis.

The authors do not use the term ‘grey literature’, which is introduced here for consistency of terminology in the book.

See Section 3.3 for more detail on ontology in the context of research paradigms.

See Dickersin ( 1990 , pp. 1385–1386) for some historical notes with regard to publication bias.

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The Role of Meta-Analysis in Scientific Studies

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

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• Why It Matters
• Reasons for Use

Disadvantages

At a glance.

Psychological researchers can use meta-analysis to review and analyze many studies on the same subject. While it can be a very helpful way to get a “big picture” view of a topic, meta-analysis also has limitations.

A meta-analysis is a type of statistical analysis in which the results of multiple studies are combined and then analyzed. Researchers can perform this type of study when there have been previous studies looking at the same question.

A meta-analysis is a type of statistical analysis where researchers review, combine, and analyze the results of multiple studies (integrated results). Meta-analysis is useful when there have been many previous studies on the same topic or asking the same question.

This article discusses when meta-analysis might be used and why it’s important. It also covers some advantages and disadvantages of using meta-analysis in psychology research.

What Is Meta-Analysis?

A simple definition of meta-analysis in psychology is that it’s a study of past studies on a subject that can give researchers a “big picture” view of the topic. To do a meta-analysis, a researcher reviews the published studies on a topic and then analyzes all the results to look for trends. Meta-analysis is used in  psychology , medicine, and other fields.

New studies from around the world are constantly being published, so the amount of research that’s out there on any given topic can be overwhelming. A meta-analysis is helpful because it's designed to summarize all the research information on a subject. There are a few general principles that a meta-analysis follows:

• It is done systematically.
• It uses certain criteria.
• It contains a pool of results.
• It is based on quantitative analysis (mathematical and statistical techniques to measure, model, and understand aspects of human behavior).

Why Is Meta-Analysis Important?

The data provided by a meta-analysis is bigger-picture than a single study, so it gives psychology researchers a better sense of the magnitude of the effect of whatever it is that is being studied—for example, a treatment. A meta-analysis also makes important conclusions clear and can identify trends that can inform future studies, policy decisions, and patient care.

Reasons Researchers Do Meta-Analysis

In addition to summarizing and analyzing integrated results, a meta-analysis also has other uses. For example, psychology researchers can use a meta-analysis to:

• Evaluate effects in different subsets of participants.
• Create new hypotheses to be studied in future research.
• Overcome the limitations of small sample sizes.
• Establish statistical significance.

Increasing Sample Size

One of the reasons why meta-analyses are used is to overcome a very common problem in research: small  sample sizes .

Even though researchers would often prefer to have a large sample size for a study, it requires more resources, such as funds and personnel, than a small sample size does. When individual studies do not use a large number of subjects, it can be harder to draw reliable and valid conclusions from the findings. 

A meta-analysis helps overcome the issue of small sample sizes because it reviews multiple studies from the same subject area, essentially creating a larger sample size.

Establishing Statistical Significance

Meta-analyses can also help establish  statistical significance  across studies that might otherwise seem to have conflicting results. Statistical significance refers to the probability of the study’s results being due to random chance rather than an important difference. 

When you consider multiple studies at the same time, the statistical significance that is established is much greater than it would be in one study on its own. This is important because statistical significance increases the validity of any observed differences in a study, which, in turn, increases the reliability of the information researchers may glean from the findings.

Benefits of a Meta-Analysis

Meta-analyses offer many advantages over individual studies. Here are just a few benefits of meta-analysis:

• It has greater statistical power and the ability to extrapolate to the broader population.
• It is evidence-based.
• It is more likely to show an effect because smaller studies are combined into one larger study.
• It has better accuracy (because smaller studies are pooled and analyzed).
• It is more efficient (because researchers can collect a large amount of data without spending a lot of time, money, and resources since the bulk of the data collection work has already been completed).

Meta-analysis provides a view of the research that has been done in a particular field, summarizes and integrates the different findings, and provides possible directions for future research.

A meta-analysis also reduces the amount of work required to research a topic for other researchers and policymakers. For example, instead of having to look at the results of many smaller studies, people can get a more accurate view of what might be happening in a population by looking at the results of one meta-analysis.

Although it can be a powerful research tool, meta-analysis does have disadvantages:

• It can be difficult and time-consuming to find all of the appropriate studies to look at.
• It requires complex statistical skills and techniques (which can be intimidating and challenging for researchers who may lack experience with this type of research).
• It may have the effect of halting research on a particular topic (for example, rather than giving directions for future research, a meta-analysis may imply that a specific question has been answered sufficiently and no more research is needed).

Types of Bias in Meta-Analysis

The way researchers do a meta-analysis (procedure) can affect the results. Following certain principles is crucial to making sure they draw valid and reliable conclusions from their work.

Even straying slightly from the protocol can produce biased and misleading results. The three main types of bias that can be a problem in meta-analysis are:

• Publication bias :   When "positive" studies are more likely to be accepted and printed.
• Search bias : When the search for studies produces unintentionally biased results. This includes using an incomplete set of keywords or varying strategies to search databases. Also, the search engine used can be a factor.
• Selection bias : When researchers do not clearly define criteria for choosing from the long list of potential studies to be included in the meta-analysis to make sure they get unbiased results.

Examples of Meta-Analysis in Psychology

It can be helpful to look at how a meta-analysis might be used in psychology to research specific topics. For example, imagine that a small study showed that consuming sugar before an exam was correlated to decreased test performance. Taken alone, such results would imply that students should avoid sugar consumption before taking an exam. However, a meta-analysis that pools data looking at eating behavior and subsequent test results might demonstrate that this previous study was an outlier.

Here are a few examples of meta-analysis that have been published on topics in psychology:

• Massoud Sokouti, Ali Reza Shafiee-Kandjani, Mohsen Sokouti, Babak Sokouti. A meta-analysis of systematic reviews and meta-analyses to evaluate the psychological consequences of COVID-19 .  BMC Psychology . 2023;11(1). doi:10.1186/s40359-023-01313-0
• Pim Cuijpers, Franco P, Markéta Čihařová, et al. Psychological treatment of perinatal depression: a meta-analysis .  Psychological Medicine . 2021;53(6):2596-2608. doi: 10.1017/s0033291721004529
• Xu C, Lucille Lucy Miao, Turner DA, DeRubeis RJ. Urbanicity and depression: A global meta-analysis.  Journal of Affective Disorders . 2023;340:299-311. doi:10.1016/j.jad.2023.08.030
• Pauley D, Pim Cuijpers, Papola D, Miguel C, Eirini Karyotaki. Two decades of digital interventions for anxiety disorders: a systematic review and meta-analysis of treatment effectiveness .  Psychological Medicine . Published online May 28, 2021:1-13. doi:10.1017/s0033291721001999
• Bhattacharya S, Goicoechea C, Heshmati S, Carpenter JK, Hofmann S. E fficacy of cognitive behavioral therapy for anxiety-related disorders: A meta-analysis of recent literature .  Current Psychiatry Reports . 2022;25(1):19-30. doi:10.1007/s11920-022-01402-8

A meta-analysis can be a useful research tool in psychology. In addition to providing an accurate, big-picture view of a specific topic, the studies can also make it easier for policymakers and other decision-makers to see a summary of findings more quickly. Meta-analysis can run into problems with bias and may suggest that more research is needed on a particular topic, but researchers can avoid these pitfalls by following procedures for doing a meta-analysis closely

‌George Washington University. Study design 101: Meta-analysis .

Cochrane Library. Chapter 10: Analysing data and undertaking meta-analyses .

Wilson, LC. American Psychological Association. Introduction to meta-analysis: a guide for the novice .

Paul J, Mojtaba Barari. Meta‐analysis and traditional systematic literature reviews—What, why, when, where, and how?  Psychology & Marketing . 2022;39(6):1099-1115. doi: 10.1002/mar.21657

Maziarz M. Is meta-analysis of RCTs assessing the efficacy of interventions a reliable source of evidence for therapeutic decisions?  Studies in History and Philosophy of Science . 2022;91:159-167. doi: 10.1016/j.shpsa.2021.11.007

Cochrane. When not to use meta-analysis in a review .

Association for Psychological Science. Meta-analysis helps psychologists build knowledge .

Mikolajewicz N, Komarova SV. Meta-analytic methodology for basic research: a practical guide . Front Physiol . 2019;10:203. doi:10.3389/fphys.2019.00203

František Bartoš, Maier M, Shanks DR, Stanley TD, Sladekova M, Eric‐Jan Wagenmakers. Meta-analyses in psychology often overestimate evidence for and size of effects. Royal Society Open Science . 2023;10(7). doi:10.1098/rsos.230224

Walker E, Hernandez AV, Kattan MW. Meta-analysis: Its strengths and limitations . Cleve Clin J Med. 2008;75(6):431-439. doi:10.3949/ccjm.75.6.431

By Kristalyn Salters-Pedneault, PhD  Kristalyn Salters-Pedneault, PhD, is a clinical psychologist and associate professor of psychology at Eastern Connecticut State University.

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• Volume 16, Issue 1

What is meta-analysis?

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• Allison Shorten 1 ,
• Brett Shorten 2
• 1 School of Nursing , Yale University , New Haven, Connecticut , USA
• 2 Informed Health Choices Trust, Wollongong, New South Wales, Australia
• Correspondence to : Dr Allison Shorten Yale University School of Nursing, 100 Church Street South, PO Box 9740, New Haven, CT 06536, USA; allison.shorten{at}yale.edu

https://doi.org/10.1136/eb-2012-101118

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When clinicians begin their search for the best available evidence to inform decision-making, they are usually directed to the top of the ‘evidence pyramid’ to find out whether a systematic review and meta-analysis have been conducted. The Cochrane Library 1 is fast filling with systematic reviews and meta-analyses that aim to answer important clinical questions and provide the most reliable evidence to inform practice and research. So what is meta-analysis and how can it contribute to practice?

The Five-step process

There is debate about the best practice for meta-analysis, however there are five common steps.

Step 1: the research question

A clinical research question is identified and a hypothesis proposed. The likely clinical significance is explained and the study design and analytical plan are justified.

Step 2: systematic review

A systematic review (SR) is specifically designed to address the research question and conducted to identify all studies considered to be both relevant and of sufficiently good quality to warrant inclusion. Often, only studies published in established journals are identified, but identification of ‘unpublished’ data is important to avoid ‘publication bias’ or exclusion of studies with negative findings. 4 Some meta-analyses only consider randomised control trials (RCTs) in the quest for highest quality evidence. Other types of ‘experimental’ and ‘quasi-experimental’ studies may be included if they satisfy the defined inclusion/exclusion criteria.

Step 3: data extraction

Once studies are selected for inclusion in the meta-analysis, summary data or outcomes are extracted from each study. In addition, sample sizes and measures of data variability for both intervention and control groups are required. Depending on the study and the research question, outcome measures could include numerical measures or categorical measures. For example, differences in scores on a questionnaire or differences in a measurement level such as blood pressure would be reported as a numerical mean. However, differences in the likelihood of being in one category versus another (eg, vaginal birth versus cesarean birth) are usually reported in terms of risk measures such as OR or relative risk (RR).

Step 4: standardisation and weighting studies

Having assembled all the necessary data, the fourth step is to calculate appropriate summary measures from each study for further analysis. These measures are usually called Effect Sizes and represent the difference in average scores between intervention and control groups. For example, the difference in change in blood pressure between study participants who used drug X compared with participants who used a placebo. Since units of measurement typically vary across included studies, they usually need to be ‘standardised’ in order to produce comparable estimates of this effect. When different outcome measures are used, such as when researchers use different tests, standardisation is imperative. Standardisation is achieved by taking, for each study, the mean score for the intervention group, subtracting the mean for the control group and dividing this result by the appropriate measure of variability in that data set.

The results of some studies need to carry more weight than others. Larger studies (as measured by sample sizes) are thought to produce more precise effect size estimates than smaller studies. Second, studies with less data variability, for example, smaller SD or narrower CIs are often regarded as ‘better quality’ in study design. A weighting statistic that seeks to incorporate both these factors, known as inverse variance , is commonly used.

Step 5: final estimates of effect

The final stage is to select and apply an appropriate model to compare Effect Sizes across different studies. The most common models used are Fixed Effects and Random Effects models. Fixed Effects models are based on the ‘assumption that every study is evaluating a common treatment effect’. 5 This means that the assumption is that all studies would estimate the same Effect Size were it not for different levels of sample variability across different studies. In contrast, the Random Effects model ‘assumes that the true treatment effects in the individual studies may be different from each other’. 5 and attempts to allow for this additional source of interstudy variation in Effect Sizes . Whether this latter source of variability is likely to be important is often assessed within the meta-analysis by testing for ‘heterogeneity’.

Forest plot

The final estimates from a meta-analysis are often graphically reported in the form of a ‘Forest Plot’.

In the hypothetical Forest Plot shown in figure 1 , for each study, a horizontal line indicates the standardised Effect Size estimate (the rectangular box in the centre of each line) and 95% CI for the risk ratio used. For each of the studies, drug X reduced the risk of death (the risk ratio is less than 1.0). However, the first study was larger than the other two (the size of the boxes represents the relative weights calculated by the meta-analysis). Perhaps, because of this, the estimates for the two smaller studies were not statistically significant (the lines emanating from their boxes include the value of 1). When all the three studies were combined in the meta-analysis, as represented by the diamond, we get a more precise estimate of the effect of the drug, where the diamond represents both the combined risk ratio estimate and the limits of the 95% CI.

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Hypothetical Forest Plot

Relevance to practice and research

Many Evidence Based Nursing commentaries feature recently published systematic review and meta-analysis because they not only bring new insight or strength to recommendations about the most effective healthcare practices but they also identify where future research should be directed to bridge the gaps or limitations in current evidence. The strength of conclusions from meta-analysis largely depends on the quality of the data available for synthesis. This reflects the quality of individual studies and the systematic review. Meta-analysis does not magically resolve the problem of underpowered or poorly designed studies and clinicians can be frustrated to find that even when a meta-analysis has been conducted, all that the researchers can conclude is that the evidence is weak, there is uncertainty about the effects of treatment and that higher quality research is needed to better inform practice. This is still an important finding and can inform our practice and challenge us to fill the evidence gaps with better quality research in the future.

• ↵ The Cochrane Library . http://www.thecochranelibrary.com/view/0/index.html (accessed 23 Oct 2012).
• Davey Smith G
• Davey Smoth G
• Higgins JPT ,

Competing interests None.

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• How it works

Meta-Analysis – Guide with Definition, Steps & Examples

Published by Owen Ingram at April 26th, 2023 , Revised On April 26, 2023

“A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies. “

Meta-analysis and systematic review are the two most authentic strategies in research. When researchers start looking for the best available evidence concerning their research work, they are advised to begin from the top of the evidence pyramid. The evidence available in the form of meta-analysis or systematic reviews addressing important questions is significant in academics because it informs decision-making.

What is Meta-Analysis  

Meta-analysis estimates the absolute effect of individual independent research studies by systematically synthesising or merging the results. Meta-analysis isn’t only about achieving a wider population by combining several smaller studies. It involves systematic methods to evaluate the inconsistencies in participants, variability (also known as heterogeneity), and findings to check how sensitive their findings are to the selected systematic review protocol.   

When Should you Conduct a Meta-Analysis?

Meta-analysis has become a widely-used research method in medical sciences and other fields of work for several reasons. The technique involves summarising the results of independent systematic review studies. 

The Cochrane Handbook explains that “an important step in a systematic review is the thoughtful consideration of whether it is appropriate to combine the numerical results of all, or perhaps some, of the studies. Such a meta-analysis yields an overall statistic (together with its confidence interval) that summarizes the effectiveness of an experimental intervention compared with a comparator intervention” (section 10.2).

A researcher or a practitioner should choose meta-analysis when the following outcomes are desirable. 

For generating new hypotheses or ending controversies resulting from different research studies. Quantifying and evaluating the variable results and identifying the extent of conflict in literature through meta-analysis is possible. 

To find research gaps left unfilled and address questions not posed by individual studies. Primary research studies involve specific types of participants and interventions. A review of these studies with variable characteristics and methodologies can allow the researcher to gauge the consistency of findings across a wider range of participants and interventions. With the help of meta-analysis, the reasons for differences in the effect can also be explored. 

To provide convincing evidence. Estimating the effects with a larger sample size and interventions can provide convincing evidence. Many academic studies are based on a very small dataset, so the estimated intervention effects in isolation are not fully reliable.

Elements of a Meta-Analysis

Deeks et al. (2019), Haidilch (2010), and Grant & Booth (2009) explored the characteristics, strengths, and weaknesses of conducting the meta-analysis. They are briefly explained below. 

Characteristics: 

• A systematic review must be completed before conducting the meta-analysis because it provides a summary of the findings of the individual studies synthesised. 
• You can only conduct a meta-analysis by synthesising studies in a systematic review. 
• The studies selected for statistical analysis for the purpose of meta-analysis should be similar in terms of comparison, intervention, and population. 

Strengths: 

• A meta-analysis takes place after the systematic review. The end product is a comprehensive quantitative analysis that is complicated but reliable. 
• It gives more value and weightage to existing studies that do not hold practical value on their own. 
• Policy-makers and academicians cannot base their decisions on individual research studies. Meta-analysis provides them with a complex and solid analysis of evidence to make informed decisions. 

Criticisms: 

• The meta-analysis uses studies exploring similar topics. Finding similar studies for the meta-analysis can be challenging.
• When and if biases in the individual studies or those related to reporting and specific research methodologies are involved, the meta-analysis results could be misleading.

Steps of Conducting the Meta-Analysis 

The process of conducting the meta-analysis has remained a topic of debate among researchers and scientists. However, the following 5-step process is widely accepted. 

Step 1: Research Question

The first step in conducting clinical research involves identifying a research question and proposing a hypothesis . The potential clinical significance of the research question is then explained, and the study design and analytical plan are justified.

Step 2: Systematic Review 

The purpose of a systematic review (SR) is to address a research question by identifying all relevant studies that meet the required quality standards for inclusion. While established journals typically serve as the primary source for identified studies, it is important to also consider unpublished data to avoid publication bias or the exclusion of studies with negative results.

While some meta-analyses may limit their focus to randomized controlled trials (RCTs) for the sake of obtaining the highest quality evidence, other experimental and quasi-experimental studies may be included if they meet the specific inclusion/exclusion criteria established for the review.

Step 3: Data Extraction

After selecting studies for the meta-analysis, researchers extract summary data or outcomes, as well as sample sizes and measures of data variability for both intervention and control groups. The choice of outcome measures depends on the research question and the type of study, and may include numerical or categorical measures.

For instance, numerical means may be used to report differences in scores on a questionnaire or changes in a measurement, such as blood pressure. In contrast, risk measures like odds ratios (OR) or relative risks (RR) are typically used to report differences in the probability of belonging to one category or another, such as vaginal birth versus cesarean birth.

Step 4: Standardisation and Weighting Studies

After gathering all the required data, the fourth step involves computing suitable summary measures from each study for further examination. These measures are typically referred to as Effect Sizes and indicate the difference in average scores between the control and intervention groups. For instance, it could be the variation in blood pressure changes between study participants who used drug X and those who used a placebo.

Since the units of measurement often differ across the included studies, standardization is necessary to create comparable effect size estimates. Standardization is accomplished by determining, for each study, the average score for the intervention group, subtracting the average score for the control group, and dividing the result by the relevant measure of variability in that dataset.

In some cases, the results of certain studies must carry more significance than others. Larger studies, as measured by their sample sizes, are deemed to produce more precise estimates of effect size than smaller studies. Additionally, studies with less variability in data, such as smaller standard deviation or narrower confidence intervals, are typically regarded as higher quality in study design. A weighting statistic that aims to incorporate both of these factors, known as inverse variance, is commonly employed.

Step 5: Absolute Effect Estimation

The ultimate step in conducting a meta-analysis is to choose and utilize an appropriate model for comparing Effect Sizes among diverse studies. Two popular models for this purpose are the Fixed Effects and Random Effects models. The Fixed Effects model relies on the premise that each study is evaluating a common treatment effect, implying that all studies would have estimated the same Effect Size if sample variability were equal across all studies.

Conversely, the Random Effects model posits that the true treatment effects in individual studies may vary from each other, and endeavors to consider this additional source of interstudy variation in Effect Sizes. The existence and magnitude of this latter variability is usually evaluated within the meta-analysis through a test for ‘heterogeneity.’

Forest Plot

The results of a meta-analysis are often visually presented using a “Forest Plot”. This type of plot displays, for each study, included in the analysis, a horizontal line that indicates the standardized Effect Size estimate and 95% confidence interval for the risk ratio used. Figure A provides an example of a hypothetical Forest Plot in which drug X reduces the risk of death in all three studies.

However, the first study was larger than the other two, and as a result, the estimates for the smaller studies were not statistically significant. This is indicated by the lines emanating from their boxes, including the value of 1. The size of the boxes represents the relative weights assigned to each study by the meta-analysis. The combined estimate of the drug’s effect, represented by the diamond, provides a more precise estimate of the drug’s effect, with the diamond indicating both the combined risk ratio estimate and the 95% confidence interval limits.

Figure-A: Hypothetical Forest Plot

Relevance to Practice and Research 

  Evidence Based Nursing commentaries often include recently published systematic reviews and meta-analyses, as they can provide new insights and strengthen recommendations for effective healthcare practices. Additionally, they can identify gaps or limitations in current evidence and guide future research directions.

The quality of the data available for synthesis is a critical factor in the strength of conclusions drawn from meta-analyses, and this is influenced by the quality of individual studies and the systematic review itself. However, meta-analysis cannot overcome issues related to underpowered or poorly designed studies.

Therefore, clinicians may still encounter situations where the evidence is weak or uncertain, and where higher-quality research is required to improve clinical decision-making. While such findings can be frustrating, they remain important for informing practice and highlighting the need for further research to fill gaps in the evidence base.

Methods and Assumptions in Meta-Analysis 

Ensuring the credibility of findings is imperative in all types of research, including meta-analyses. To validate the outcomes of a meta-analysis, the researcher must confirm that the research techniques used were accurate in measuring the intended variables. Typically, researchers establish the validity of a meta-analysis by testing the outcomes for homogeneity or the degree of similarity between the results of the combined studies.

Homogeneity is preferred in meta-analyses as it allows the data to be combined without needing adjustments to suit the study’s requirements. To determine homogeneity, researchers assess heterogeneity, the opposite of homogeneity. Two widely used statistical methods for evaluating heterogeneity in research results are Cochran’s-Q and I-Square, also known as I-2 Index.

Difference Between Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are both research methods used to synthesise evidence from multiple studies on a particular topic. However, there are some key differences between the two.

Systematic reviews involve a comprehensive and structured approach to identifying, selecting, and critically appraising all available evidence relevant to a specific research question. This process involves searching multiple databases, screening the identified studies for relevance and quality, and summarizing the findings in a narrative report.

Meta-analysis, on the other hand, involves using statistical methods to combine and analyze the data from multiple studies, with the aim of producing a quantitative summary of the overall effect size. Meta-analysis requires the studies to be similar enough in terms of their design, methodology, and outcome measures to allow for meaningful comparison and analysis.

Therefore, systematic reviews are broader in scope and summarize the findings of all studies on a topic, while meta-analyses are more focused on producing a quantitative estimate of the effect size of an intervention across multiple studies that meet certain criteria. In some cases, a systematic review may be conducted without a meta-analysis if the studies are too diverse or the quality of the data is not sufficient to allow for statistical pooling.

Software Packages For Meta-Analysis

Meta-analysis can be done through software packages, including free and paid options. One of the most commonly used software packages for meta-analysis is RevMan by the Cochrane Collaboration.

Assessing the Quality of Meta-Analysis 

Assessing the quality of a meta-analysis involves evaluating the methods used to conduct the analysis and the quality of the studies included. Here are some key factors to consider:

• Study selection: The studies included in the meta-analysis should be relevant to the research question and meet predetermined criteria for quality.
• Search strategy: The search strategy should be comprehensive and transparent, including databases and search terms used to identify relevant studies.
• Study quality assessment: The quality of included studies should be assessed using appropriate tools, and this assessment should be reported in the meta-analysis.
• Data extraction: The data extraction process should be systematic and clearly reported, including any discrepancies that arose.
• Analysis methods: The meta-analysis should use appropriate statistical methods to combine the results of the included studies, and these methods should be transparently reported.
• Publication bias: The potential for publication bias should be assessed and reported in the meta-analysis, including any efforts to identify and include unpublished studies.
• Interpretation of results: The results should be interpreted in the context of the study limitations and the overall quality of the evidence.
• Sensitivity analysis: Sensitivity analysis should be conducted to evaluate the impact of study quality, inclusion criteria, and other factors on the overall results.

Overall, a high-quality meta-analysis should be transparent in its methods and clearly report the included studies’ limitations and the evidence’s overall quality.

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Examples of Meta-Analysis

• STANLEY T.D. et JARRELL S.B. (1989), « Meta-regression analysis : a quantitative method of literature surveys », Journal of Economics Surveys, vol. 3, n°2, pp. 161-170.
• DATTA D.K., PINCHES G.E. et NARAYANAN V.K. (1992), « Factors influencing wealth creation from mergers and acquisitions : a meta-analysis », Strategic Management Journal, Vol. 13, pp. 67-84.
• GLASS G. (1983), « Synthesising empirical research : Meta-analysis » in S.A. Ward and L.J. Reed (Eds), Knowledge structure and use : Implications for synthesis and interpretation, Philadelphia : Temple University Press.
• WOLF F.M. (1986), Meta-analysis : Quantitative methods for research synthesis, Sage University Paper n°59.
• HUNTER J.E., SCHMIDT F.L. et JACKSON G.B. (1982), « Meta-analysis : cumulating research findings across studies », Beverly Hills, CA : Sage.

Frequently Asked Questions

What is a meta-analysis in research.

Meta-analysis is a statistical method used to combine results from multiple studies on a specific topic. By pooling data from various sources, meta-analysis can provide a more precise estimate of the effect size of a treatment or intervention and identify areas for future research.

Why is meta-analysis important?

Meta-analysis is important because it combines and summarizes results from multiple studies to provide a more precise and reliable estimate of the effect of a treatment or intervention. This helps clinicians and policymakers make evidence-based decisions and identify areas for further research.

What is an example of a meta-analysis?

A meta-analysis of studies evaluating physical exercise’s effect on depression in adults is an example. Researchers gathered data from 49 studies involving a total of 2669 participants. The studies used different types of exercise and measures of depression, which made it difficult to compare the results.

Through meta-analysis, the researchers calculated an overall effect size and determined that exercise was associated with a statistically significant reduction in depression symptoms. The study also identified that moderate-intensity aerobic exercise, performed three to five times per week, was the most effective. The meta-analysis provided a more comprehensive understanding of the impact of exercise on depression than any single study could provide.

What is the definition of meta-analysis in clinical research?

Meta-analysis in clinical research is a statistical technique that combines data from multiple independent studies on a particular topic to generate a summary or “meta” estimate of the effect of a particular intervention or exposure.

This type of analysis allows researchers to synthesise the results of multiple studies, potentially increasing the statistical power and providing more precise estimates of treatment effects. Meta-analyses are commonly used in clinical research to evaluate the effectiveness and safety of medical interventions and to inform clinical practice guidelines.

Is meta-analysis qualitative or quantitative?

Meta-analysis is a quantitative method used to combine and analyze data from multiple studies. It involves the statistical synthesis of results from individual studies to obtain a pooled estimate of the effect size of a particular intervention or treatment. Therefore, meta-analysis is considered a quantitative approach to research synthesis.

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6 Strengths and Weaknesses of Meta-Analyses

• Published: April 2022
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Meta-analysis provides a systematic technique for summarizing results of quantitative research and assessing variability. Yet, the technique has come under scrutiny for its susceptibility to flawed conclusions stemming from problems with questionable research practices, publication bias, selection bias, and noncumulative methods and measurement. After briefly reviewing the history of meta-analysis, this chapter considers the strengths and weaknesses of the technique. It then reviews alternatives to, and supplements for, meta-analysis, including systematic review, bias-correction techniques, and large preregistered studies. These alternatives and supplements aim to address the weaknesses of meta-analysis while preserving its strengths. Many recent critiques of meta-analyses highlight persistent flaws with the technique, but ultimately, there seems to be no substitute for a well-done meta-analytic synthesis.

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Meta-analysis: what, why, and how.

This is an excerpt from a blog originally published on Students 4 Best Evidence

What is a meta-analysis?

Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic.

Meta-analyses play a fundamental role in evidence-based healthcare. Compared to other study designs (such as randomized controlled trials or cohort studies), the meta-analysis comes in at the top of the  evidence-based medicine pyramid.  This is a pyramid which enables us to weigh up the different levels of evidence available to us. As we go up the pyramid, each level of evidence is less subject to bias than the level below it. Therefore, meta-analyses can be seen as the pinnacle of evidence-based medicine (1).

Meta-analyses began to appear as a leading part of research in the late 70s. Since then, they have become a common way for synthesizing evidence and summarizing the results of individual studies (2).

Read the full article here

• Open access
• Published: 24 May 2024

Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns

• Ulugbek Khudayberdievich Mirzaev 1 , 2 ,
• Serge Ouoba 1 , 3 ,
• Zayar Phyo 1 ,
• Chanroth Chhoung 1 ,
• Akuffo Golda Ataa 1 ,
• Aya Sugiyama 1 ,
• Tomoyuki Akita 1 &
• Junko Tanaka 1  

BMC Infectious Diseases volume  24 , Article number:  525 ( 2024 ) Cite this article

206 Accesses

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The burden of hepatitis E in Southeast Asia is substantial, influenced by its distinct socio-economic and environmental factors, as well as variations in healthcare systems. The aim of this study was to assess the pooled seroprevalence of hepatitis E across countries within the Southeast Asian region by the UN division.

The study analyzed 66 papers across PubMed, Web of Science, and Scopus databases, encompassing data from of 44,850 individuals focusing on anti-HEV seroprevalence. The investigation spanned nine countries, excluding Brunei and East Timor due to lack of data. The pooled prevalence of anti-HEV IgG was determined to be 21.03%, with the highest prevalence observed in Myanmar (33.46%) and the lowest in Malaysia (5.93%). IgM prevalence was highest in Indonesia (12.43%) and lowest in Malaysia (0.91%). The study stratified populations into high-risk (farm workers, chronic patients) and low-risk groups (general population, blood donors, pregnant women, hospital patients). It revealed a higher IgG—28.9%, IgM—4.42% prevalence in the former group, while the latter group exhibited figures of 17.86% and 3.15%, respectively, indicating occupational and health-related vulnerabilities to HEV.

A temporal analysis (1987–2023), indicated an upward trend in both IgG and IgM prevalence, suggesting an escalating HEV burden.

These findings contribute to a better understanding of HEV seroprevalence in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Research Question

Investigate the seroprevalence of hepatitis E virus (HEV) in Southeast Asian countries focusing on different patterns, timelines, and population cohorts.

Sporadic Transmission of IgG and IgM Prevalence:

• Pooled anti-HEV IgG prevalence: 21.03%

• Pooled anti-HEV IgM prevalence: 3.49%

Seroprevalence among specific groups:

High-risk group (farm workers and chronic patients):

• anti-HEV IgG: 28.9%

• anti-HEV IgM: 4.42%

Low-risk group (general population, blood donors, pregnant women, hospital patients):

• anti-HEV IgG: 17.86%

• anti-HEV IgM: 3.15%

Temporal Seroprevalence of HEV:

Anti-HEV IgG prevalence increased over decades (1987–1999; 2000–2010; 2011–2023): 12.47%, 18.43%, 29.17% as an anti-HEV IgM prevalence: 1.92%, 2.44%, 5.27%

Provides a comprehensive overview of HEV seroprevalence in Southeast Asia.

Highlights variation in seroprevalence among different population groups.

Reveals increasing trend in HEV seroprevalence over the years.

Distinguishes between sporadic and epidemic cases for a better understanding of transmission dynamics.

Peer Review reports

Introduction

Hepatitis E is a major global health concern caused by the hepatitis E virus (HEV), which is a small, nonenveloped, single-stranded, positive-sense RNA virus belonging to the Paslahepevirus genus in the Hepeviridae family. There are eight genotypes of HEV: HEV-1 and HEV-2 infect only humans, HEV-3, HEV-4, and HEV-7 infect both humans and animals, while HEV-5, HEV-6, and HEV-8 infect only animals [ 1 ].

HEV infections affect millions of people worldwide each year, resulting in a significant number of symptomatic cases and deaths. In 2015, the World Health Organization (WHO) reported approximately 44,000 deaths from hepatitis E, accounting for 3.3% of overall mortality attributed to viral hepatitis [ 2 ]. The primary mode of transmission for hepatitis E is through the fecal–oral route. Outbreaks of the disease are often associated with heavy rainfall and flooding [ 3 , 4 ]. Additionally, sporadic cases can occur due to poor sanitation, vertical transmission, blood transfusion or close contact with infected animals, which serve as hosts for the virus [ 5 ]. Southeast Asia carries a substantial burden of hepatitis E, influenced by its unique socio-economic and environmental factors as well as variations in healthcare systems. Understanding the seroprevalence of hepatitis E in this region is crucial for implementing targeted public health interventions and allocating resources. To achieve the effective control and prevention of HEV, it is required to address the waterborne transmission and considering the specific characteristics of each region. By taking these measures, healthcare authorities can work towards reducing the global impact of hepatitis E on public health. Systematic reviews and meta-analyses on hepatitis E play a crucial role in synthesizing and integrating existing research findings, providing comprehensive insights into the epidemiology, transmission, and burden of the disease, thereby aiding evidence-based decision-making and public health strategies [ 6 , 7 ].

Recent systematic reviews and meta-analysis conducted on hepatitis E have varied in their scope or were limited by a smaller number of source materials [ 8 , 9 ]. The objective of this study was to determine the pooled seroprevalence of hepatitis E in countries within Southeast Asia by aggregating findings from a multitude of primary studies conducted across the region.

To commence this systematic review and meta-analysis, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and used the PRISMA assessment checklist [Supplementary Table  1 ]. The study included pertinent research conducted within the population of Southeast Asian countries, as outlined by the United Nations [ 10 ], and perform a meta-analysis on the seroprevalence of hepatitis E in this specific region.

PICOT assessment

In this systematic review and meta-analysis, the eligible population comprised individuals from the Southeast Asia region, irrespective of age, gender, ethnic characteristics, or specific chronic diseases. However, studies involving populations outside the designated countries, travelers, migrants, animal species studies, and those lacking clear descriptions of the study population were excluded.

Intervention and comparison

Intervention and comparison are not applicable to the prevalence studies.

Anti-HEV antibodies positivity either total antibodies or IgG or IgM among the Southeast Asian countries' population was assessed.

All studies conducted between 1987 and 2023 were included in this meta-analysis.

Search strategy

To conduct the data search, we utilized three databases, namely “PubMed”, “Scopus”, and “Web of Science”. The search terms comprised keywords related to the Hepatitis E virus, such as “Hepatitis E virus” OR “Hepatitis E” OR “HEV” AND names of each country “Brunei”, “Cambodia”, “Timor-Leste” OR “East-Timor”, “Laos” OR “Lao PDR”, “Indonesia”, “Malaysia”, “Myanmar” OR “Burma”, “Philippines”, “Singapore”, “Thailand”, “Vietnam” and “Southeast Asia”.

The search process in the databases finished on May 29 th , 2023, with two members of the study team conducting independent searches. Subsequently, the search results were unified. A grey literature search was performed from June 25 th to 30 th , 2023, by examining the references of review manuscripts and conference materials, along with using specific keywords in the Google Scholar database. Notably, during the gray literature search, additional studies from the Philippines that were initially missing in the first search were identified and included. Moreover, due to the diverse language expertise of the team, studies in Russian and French related to Cambodia and Vietnam were also considered for inclusion.

After applying the inclusion and exclusion criteria, each article selected for this systematic review (SR) was considered relevant. The quality assessment of each article was conducted using specific JBI critical appraisal instruments [ 11 ] [Supplementary Table  2 ].

Sporadic transmission of HEV infection

For the systematic review and meta-analysis of sporadic infection of HEV, we divided the study population into cohorts by countries, by risk of acquiring HEV—low and high risk. The low risk cohort included the general population (apparently healthy individuals, students, some ethnic populations, or individuals included in original studies as “general population”), blood donors, pregnant women, and hospital patients, while pig farmers, those with chronic hepatitis, HIV positive patients, and solid organ transplant patients in the high-risk group.

Lastly, we analyzed data in three decades—1987–1999, 2000–2010, and 2011–2023—to reveal seroprevalence rates over time.

Epidemic outbreaks of HEV infection

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation.

Statistical analysis

A meta-analysis of proportions was conducted using the 'meta' and 'metafor' packages in the R statistical software. To account for small proportions, the Freeman-Tukey double arcsine method was applied to transform the data. The Dersimonian and Laird method, which employs a random-effects model, was utilized for the meta-analysis, and the results were presented in a forest plot. Confidence intervals (CIs) for the proportions of individual studies were computed using the Clopper-Pearson method.

Heterogeneity was evaluated using the Cochran Q test and quantified by the I 2 index. Heterogeneity was considered significant if the p -value of the Cochran Q test was below 0.05.

For the assessment of publication bias, a funnel plot displaying the transformed proportions against the sample size was created. The symmetry of the plot was examined using the Egger test ( p  < 0.1).

The initial search yielded 1641 articles, which covered 9 out of 11 Southeast Asia countries. We couldn't find any information on hepatitis E from Brunei. We excluded a study from East Timor because it focused on the wrong population (US Army troops). The final screening resulted in the selection of 57 relevant studies, and the grey literature search added 9 more papers that met our inclusion criteria (Fig.  1 ). Among 9 papers through a grey literature, two relevant studies from the Philippines [ 12 , 13 ], one each from Indonesia [ 14 ] and Lao PDR [ 15 ], one study covered both Vietnam and Cambodia [ 16 ], one study provided HEV seroepidemiology information for Myanmar, Thailand, and Vietnam [ 17 ], two studies reported in Russian [ 18 , 19 ] (from Vietnam) and one reported in French [ 16 ] (from Vietnam and Cambodia). In total, our analysis included 66 papers from which we extracted data. This involved a total of 44,850 individuals (Table  1 ).

Flowchart of the identification, inclusion, and exclusion of the study. Table under flowchart informing about the studies which were found by the initial search in databases

Sporadic transmission IgG and IgM prevalence in Southeast Asian countries (excluding outbreak settings)

The sporadic cases involving 42,248 participants out of 44,850 participants (the remaining 2,602 people are considered in the “ Epidemic outbreaks ” section) from Southeast Asian countries the pooled prevalence of IgG was found to be 21.03%, while for IgM, it was 3.49% among 34,480 individuals who were tested (Fig. 2 ). Among these countries, Myanmar registered the highest pooled prevalence of IgG at 33.46%, while Malaysia had the lowest at 5.93%. For IgM prevalence, Indonesia had the highest rate at 12.43%, and Malaysia again had the lowest at 0.91% (Table  2 ) [Supplementary Figures  1 and 6 ].

Forest plot of meta-analysis of the prevalence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) in Southeast Asian countries. The plot includes the number of study participants for each country

Seroprevalence among specific groups

High risk of acquiring hev.

The high-risk group, which included farm workers and chronic patients, demonstrated a pooled anti-HEV IgG prevalence of 28.9%, with IgM prevalence at 4.42% [Supplementary Figures  2 and 8 ].

Chronic patients

This group, comprising individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest prevalence of pooled IgG among all cohorts, standing at 29.2%. Additionally, IgM prevalence was 3.9% [Supplementary Figures  2 and 7 ].

Farm workers

Farm workers were divided into several subgroups based on exposure to animals (reservoirs of HEV), including pig or ruminant farmers, slaughterhouse workers, butchers, and meat retailers. Among this group, the highest IgG prevalence was observed at 28.4%, while the pooled IgM level was 6.21% [Supplementary Figures  2 and 7 ].

Low risk of acquiring HEV

The low-risk group, comprising the general population, blood donors, pregnant women, and hospital patients, exhibited anti-HEV IgG and IgM prevalence of 17.86% and 3.15%, respectively. [Supplementary Figures  2 and 9 ].

General population

The general population in Southeast Asian countries, represented by 22,571 individuals, showed a presence of IgG in 21.4% of them. IgM was tested in 10,304 participants, and 2.63% of acute infection cases were identified [Supplementary Figures  2 and 7 ].

Blood donors

Blood donors, as a selected subgroup of the general population, exhibit differences in health status, age, gender distribution, and representativeness, warranting separate assessment. Among blood donors in Southeast Asian countries, the pooled prevalence of IgG and IgM were found to be 11.77% and 0.83%, respectively [Supplementary Figures  2 and 7 ].

Pregnant women

Pregnant women considered a vulnerable group regarding disease consequences, demonstrated an anti-HEV IgG prevalence of 18.56% among 1,670 individuals included in the study. Furthermore, 1.54% of them tested positive for anti-HEV IgM [Supplementary Figures  2 and 7 ].

Hospital patients

A group of 18,792 patients who visited hospitals with clinical signs of acute infection, jaundice, high temperature, and elevated liver enzymes, showed anti-HEV IgG and IgM prevalence of 16.3% and 4.45%, respectively [Supplementary Figures  2 and 7 ].

Temporal seroprevalence of HEV

Given the studies' long duration, the data was presented by decades: 1987–1999, 2000–2010, and 2011–2023. The prevalence of IgG showed an upward trend over these decades, with rates of 12.47%, 18.43%, and 29.17%. Similarly, for IgM, the prevalence rates were 1.92%, 2.44%, and 5.27% for the first, second, and third decades, respectively (Fig. 3 ).

The prevalence of anti-HEV IgG and IgM in Southeast Asian countries throughout the decades

Evaluating the trend of seroprevalence over decades within the same population and country proved challenging due to the limited availability of research papers. Consequently, we assessed anti-HEV antibody prevalence over decades, considering population cohorts and individual countries.

In Fig.  4 , we can see that all population groups show a consistent increase in the prevalence of both IgG and IgM antibodies over the decades. Figure  5 , we analyze the prevalence of anti-HEV antibodies in different countries over time, except for Indonesia and Malaysia, where we observe an increase in prevalence.

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within population cohorts across Southeast Asian nations divided by decades. The population cohorts delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Blood donors group has the anti-HEV IgM only for the last decade

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within countries of Southeast Asia divided by decades. The countries delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Philippines has the anti-HEV IgG antibodies information only for the first decade. Philippines, Myanmar, Singapore have anti-HEV IgM information only for single decade

Some studies lacked information on the collection time of the samples [ 13 , 19 , 41 , 48 , 59 , 62 , 64 , 82 ]. In these studies, the pooled IgG and IgM prevalence was 26.5% and 4.75%, respectively [Supplementary Figures  3 , 4 , 5 , 10 , 11 , 12 ].

Epidemic outbreaks

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation. The outbreaks occurred between 1987 and 1998 in several Southeast Asian countries, namely Indonesia [ 31 , 33 , 34 ], Vietnam [ 77 ], and Myanmar [ 54 ] [Supplementary Figure  13 ]. These outbreak investigations involved a total of 2,602 individuals, with most participants from Indonesia (2,292 individuals). The studies were mainly conducted using a case–control design. Among the participants, 876 were considered controls, while 1,726 were classified as cases. The pooled prevalence of total anti-HEV immunoglobulins was estimated as 61.6% (95% CI 57.1–66) (Table  2 ).

Assessment of publication bias

We checked for publication bias using a funnel plot and Egger's test. Both the studies on anti-HEV IgG and IgM showed asymmetry with Egger's test indicating a p -value less than 0.001 for both cases (Fig. 6 ).

Funnel plot of anti-HEV IgG ( A ) and anti-HEV IgM prevalence. Double arcsine transformed proportion of individual studies is plotted against the sample size. The distribution of studies in the funnel plot revealed the presence of publication bias

A paper search yielded varying numbers of manuscripts from Southeast Asian countries. The Philippines had the fewest studies, while Thailand had the highest with 15 studies. No data was found for Brunei Darussalam and East Timor or Timor Leste on the human species.

The results of this study provide valuable insights into the seroprevalence of IgG and IgM antibodies against HEV in different populations across Southeast Asian countries. Understanding the prevalence of these antibodies is essential for assessing the burden of HEV infection and identifying high-risk groups.

The extensive analysis of anti-HEV IgG prevalence in this study covered a wide range of population groups in Southeast Asia, including the general population, blood donors, pregnant women, hospital patients, farm workers, and chronic patients. The results unveiled an overall pooled prevalence of 21.03%, indicating significant exposure to the Hepatitis E virus among individuals in the region at some point in their lives. Moreover, a consistent increase in IgG prevalence was observed over the years, with the highest prevalence occurring in the most recent decade (2011–2023). This suggests a progressive rise in HEV exposure within the region.

Upon examining the prevalence data across different decades and population cohorts, a uniform upward trend in HEV antibody prevalence became apparent across all groups. Several factors could be assessed as potential contributors to this trend:

Notably, the expanding population in Southeast Asian nations during this timeframe increased the number of individuals at risk of Hepatitis E infection.

The rapid urbanization, characterized by the migration from rural to urban areas, led to higher population density and conditions conducive to Hepatitis E virus transmission [ 84 ]. Access to clean drinking water and adequate sanitation facilities emerged as critical factors in preventing Hepatitis E. Regions with inadequate infrastructure, particularly in water and sanitation, faced an elevated risk due to contaminated water sources. Climate-related events, such as heavy rainfall and flooding, significantly impacted waterborne diseases like Hepatitis E. The increasing frequency and severity of such events emphasized the importance of considering climate-related factors in assessing prevalence trends [ 85 ]. Consumption of contaminated or undercooked meat, particularly pork, was identified as a source of Hepatitis E transmission. Changes in food consumption habits over time may have contributed to changes in seroprevalence [ 86 ]. Limited access to healthcare facilities in certain areas exacerbated the spread of Hepatitis E. Increased awareness together with advances in medical research and the establishment of robust surveillance systems likely improved the detection and reporting of Hepatitis E cases, contributing to the observed increase in seroprevalence [ 87 , 88 , 89 ]. These multifaceted factors have likely played a collective role in shaping the changing landscape of Hepatitis E seroprevalence in Southeast Asian nations over the past decades. The upward trend emphasizes the importance of continued monitoring, intervention, and public health measures to mitigate the spread of Hepatitis E in the region.

Among specific populations, pregnant women exhibited an IgG prevalence of 18.56%, indicating that a considerable number of pregnant individuals have been exposed to HEV. Pregnant women are particularly vulnerable to the consequences of HEV infection, as it can lead to severe outcomes for both the mother and the foetus.

Hospital patients with clinical signs of acute infection showed an IgG prevalence of 16.3%, suggesting that HEV is still a significant cause of acute hepatitis cases in the hospital setting. Similarly, farm workers, especially those exposed to animals (reservoirs of HEV), had a high prevalence of IgG (28.4%), highlighting the occupational risk associated with zoonotic transmission.

Chronic patients, including individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest pooled IgG prevalence among all cohorts at 29.2%. This finding underscores the importance of monitoring HEV infection in immunocompromised individuals, as they may develop chronic HEV infection, which can lead to severe liver complications.

The prevalence of IgM antibodies, which are indicative of recent or acute HEV infection, was lower overall compared to IgG. The general population showed an IgM prevalence of 2.63% among acute infection cases. Among hospital patients exhibiting clinical signs of acute infection, the prevalence of IgM antibodies indicative of recent or acute HEV infection was higher at 4.45%.

Farm workers, particularly those exposed to animals, demonstrated the highest IgM prevalence at 6.21%. This finding highlights the occupational risk of acquiring acute HEV infection in this population due to direct or indirect contact with infected animals.

The study also identified a high-risk group, consisting of farm workers and chronic patients, with a pooled IgG prevalence of 28.9% and an IgM prevalence of 4.42%. This group is particularly susceptible to HEV infection and requires targeted interventions to reduce transmission and prevent severe outcomes.

Overall, this study provides valuable data on the seroprevalence of HEV antibodies in different populations in Southeast Asian countries. It highlights the importance of continued surveillance and public health interventions to control HEV transmission, especially in vulnerable groups. Understanding the prevalence trends over time can aid in developing effective strategies for the prevention and management of HEV infections in the region. However, further research and studies are warranted to explore the underlying factors contributing to the observed seroprevalence trends and to design targeted interventions to reduce HEV transmission in specific populations. Among the countries of Southeast Asia Myanmar was the most for HEV infection, while Malaysia registered the lowest seroprevalence.

This study has some limitations that we should be aware of. We looked at studies in three languages (English, Russian, and French), but we couldn't find data from two out of the 11 countries. This means we might not have a complete picture of the disease's prevalence in the whole region.

The way we divided the groups based on occupation or status could be questioned. Different criteria might give us different results, so it's something we need to consider. Another challenge is that the study covers a long time from 1989 to 2023 by published research and involves many different countries. This makes it difficult to compare the results because the tests used, and the diagnostic abilities might have changed over time and vary across countries.

Despite these limitations, our study presents a detailed epidemiologic report of combined seroprevalence data for HEV in Southeast Asian countries following the UN division. It gives us a basic understanding of the disease's prevalence in the region and offers some insights into potential risk factors. However, to get a more accurate picture, future research should address these limitations and include data from all countries in the region. Furthermore, certain countries such as Myanmar and the Philippines have not reported HEV prevalence data since 2006 and 2015, respectively. The absence of recent HEV prevalence reports from certain countries raises concerns about the availability of up-to-date epidemiological data for assessing the current status of hepatitis E virus infections in these regions.

Our comprehensive analysis study involving Southeast Asian countries provides significant insights into the seroprevalence of hepatitis E virus (HEV) infection in this region and in various populations. The rates of anti-HEV antibodies observed among different groups, as well as the increasing trend in seroprevalence over decades, emphasize the dynamic nature of HEV transmission in the region. These findings contribute to a better understanding of HEV prevalence across countries, populations, and time periods in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Availability of data and materials

All data generated or analyzed during this study were included in this paper either in the results or supplementary information.

Abbreviations

Hepatitis E Virus

Preferred reporting items for systematic review and meta-analysis

Enzyme-Linked Immunosorbent Essay

Hepatitis E virus Immunoglobulin G

Hepatitis E Virus Immunoglobulin M

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Acknowledgements

The authors would like to thank all researchers of the primary research included in this study.

This work was supported by Project Research Center for Epidemiology and Prevention of Viral Hepatitis and Hepatocellular Carcinoma, Hiroshima University led by Prof. Junko Tanaka (PI).

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Ulugbek Khudayberdievich Mirzaev, Serge Ouoba, Ko Ko, Zayar Phyo, Chanroth Chhoung, Akuffo Golda Ataa, Aya Sugiyama, Tomoyuki Akita & Junko Tanaka

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UM, TA, and JT conceptualized the study. UM and SO contributed to developing the study design and data acquisition. UM, CC, ZP, AG, SO, and JT analysed and interpreted the data. UM, KK, and AS drafted the manuscript. TA, AS, KK, SO, and JT contributed to the intellectual content of the manuscript. All authors read and approved the final manuscript. JT and TA shared the co-correspondence. 

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Mirzaev, U.K., Ouoba, S., Ko, K. et al. Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns. BMC Infect Dis 24 , 525 (2024). https://doi.org/10.1186/s12879-024-09349-2

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A systematic review and meta-analysis of the association between e-cigarette use among non-tobacco users and initiating smoking of combustible cigarettes

• Mimi M. Kim 1 ,
• Isabella Steffensen 1 ,
• Red Thaddeus D. Miguel 1 ,
• Tanja Babic 1 &
• Julien Carlone 1  

Harm Reduction Journal volume  21 , Article number:  99 ( 2024 ) Cite this article

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Introduction

The rapid increase in e-cigarette use over the past decade has triggered an important public health question on the potential association between e-cigarette use and combustible cigarette smoking. Following AMSTAR 2 and PRISMA guidelines, this evidence synthesis sought to identify and characterize any associations between e-cigarette use among individuals not smoking cigarettes and initiation of cigarette smoking.

The protocol was registered on September 24, 2018 (PROSPERO 2018 CRD42018108540). Three databases were queried from January 01, 2007 to April 26, 2023. Search results were screened using the PICOS review method.

Among 55 included studies (40 “good” and 15 “fair”; evidence grade: “high”) that adjusted for gender, age, and race/ethnicity between groups, generally, there was a significant association between non-regular e-cigarette use and initiation of cigarette smoking, further supported by the meta-analytic results (AOR 3.71; 95% CI 2.86–4.81). However, smoking initiation was most often measured as ever/current cigarette smoking. Two studies (quality: 2 “good”) evaluated progression to regular cigarette smoking among individuals with regular use of e-cigarettes, and generally found no significant associations. One study (“good”) evaluated smoking initiation among individuals with regular use of e-cigarettes, finding an increasing probability of ever smoking cigarettes with increased e-cigarette use. Twelve studies (10 “good” and two “fair”) examining progression to regular smoking among individuals with non-regular use of e-cigarettes reported inconsistent findings.

Conclusions

Numerous methodological flaws in the body of literature limit the generalizability of these results to all individuals who are not smoking cigarettes with few studies measuring established/regular use/smoking of e-cigarettes and cigarettes. Further, studies did not control adequately for specific confounding variables representing common liabilities between e-cigarette use and cigarette smoking, nor did they account for sufficient follow-up durations. Collectively, these flaws limit the generalizability of findings to the question of an association between e-cigarette use and cigarette smoking initiation.

Implications

In order to support robust determinations regarding e-cigarette use and the initiation of—or progression to—cigarette smoking, future research should apply measures of e-cigarette and cigarette use in a manner consistent with examining true initiation (i.e., established and/or regular use, as opposed to ever or current use), increase follow-up durations to adequately evaluate progression to regular smoking, and sufficiently account for known or suspected confounding variables that would represent common liabilities between e-cigarette use and cigarette smoking.

Empirical evidence suggests e-cigarette aerosol does not contain most of the approximately 7000 chemicals present in cigarette smoke [ 1 , 2 ]. However, with the decline in cigarette smoking prevalence, there has been a parallel increasing prevalence in electronic cigarette (e-cigarette) use [ 3 , 4 , 5 , 6 ].

The potential association between e-cigarette use and cigarette smoking is an important public health issue [ 7 , 8 , 9 ]. Understanding the individual and population level impact of e-cigarettes requires an objective synthesis of the empirical evidence that informs on the potential association between e-cigarette use and subsequent cigarette smoking and the inherent risks to health presented by e-cigarettes themselves [ 2 ]. Among the public health concerns of the use of e-cigarettes is the question of youth who may transition from e-cigarettes to cigarette smoking [ 2 ]. Hence, an assessment of causality is central to understanding the public health effect of e-cigarettes.

The Common Liability model is an important consideration when assessing causality between e-cigarette and cigarette smoking, particularly among tobacco non-users [ 10 , 11 ]. Specifically, the common liability model posits that risks associated with using different substances can be explained by identifying common predisposing factors that also influence use behaviors [ 10 , 11 ]. According to this model, where risk-taking propensities and psychosocial processes can be factors that link patterns of multiple addictions, common liability can provide a parsimonious explanation of substance use and addiction co-occurrence [ 11 ]. Thus, narrowly focusing on the association between e-cigarette use and subsequent cigarette smoking without consideration of potential common liability factors limits an inference of causality [ 12 ].

The current systematic review and meta-analysis evaluated potential associations between e-cigarette use among tobacco non-users and cigarette smoking initiation, applying a level of methodological rigor not previously reported in other reviews. Based on a general understanding of the available published literature on e-cigarette use and cigarette smoking, a priori outcome measures included: age at initiation of smoking combustible cigarettes; percent who initiated smoking combustible cigarettes; and initiation and progression to regular smoking of combustible cigarettes. Study design was not limited in the inclusion criteria. While previous systematic reviews have examined the relationship between e-cigarette use and the onset of cigarette smoking in youth and young adults [ 3 , 13 , 14 , 15 , 16 , 17 ], as well as in the general population [ 18 , 19 ], this review specifically focused on initiation of and progression to regular cigarette smoking—an outcome measure unique to this systematic review. Furthermore, given the rapid rate of emerging evidence on e-cigarette use, this review provides an important timely evidence synthesis to previous reviews.

The methods and results reported here correspond to a larger systematic review addressing the key research question, “Are there any potential associations between e-cigarette use among non-tobacco users and intention to smoke combustible cigarettes or initiating smoking of combustible cigarettes?” The focus of the findings reported here is the identification and characterization of any potential associations between e-cigarette use among non-tobacco users and the initiation of cigarette smoking.

The review protocol was registered with PROSPERO (The International Prospective Register of Systematic Reviews) on September 24, 2018 (PROSPERO 2018 CRD42018108540; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018108540 ).

This review strictly followed standards of systematic review methodology (“high” overall rating by A MeaSurement Tool to Assess systematic Reviews [AMSTAR] 2) [ 20 ] and reporting (Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA]) [ 21 ].

Terminology

Specific terminology in this review are fully reported in Supplemental Section 1 : Terminology.

Literature search methods

MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica Database), and PsycINFO were the database sources for the literature search. Applying search terms developed using medical subject headings (MeSH) and text words related to the associations between e-cigarette use and cigarette smoking intention and initiation, a full literature search was executed by an information specialist. Search dates were restricted to 2007 onwards due to the mass market introduction of e-cigarettes in the US [ 1 , 2 ] (Supplemental Section 2 : Literature Search Strategy).

The screening process was executed according to the PICOS (Population or participants and conditions of interest, Interventions or exposures, Comparisons or control groups, Outcomes of interest, and Study designs) review method (Supplemental Section 3 : Inclusion/Exclusion Criteria) [ 22 ]. The population of interest—tobacco non-users—without restriction by age. The interventions and controls were individuals using e-cigarettes and non-users, respectively. Outcome measures identified a priori included: age of initiation for cigarette smoking, initiation of cigarette smoking, and initiation and progression to regular cigarette smoking (not included in previously published systematic reviews [ 3 , 13 ]). Given the limited available evidence from randomized controlled trials (RCTs), this review was not limited by study design. The search strategy included: published peer-reviewed literature; theses and dissertations; government and industry documents; clinical trial registries (clinicaltrials.gov); gray literature in Google Scholar; consideration of reference lists across included studies; and content expert consultation. Studies were restricted to English-only publications.

Although the established/regular e-cigarette use provides the strongest evidence measure of sustained use behaviors, this review did not restrict use criteria. Additionally, studies were not restricted to those controlling for specific confounding variables that would represent common liabilities between e-cigarette use and cigarette smoking. The current review focused on studies that adjusted for at least the confounders of age, gender, and race/ethnicity.

Evidence synthesis

Two reviewers independently screened articles based on the inclusion/exclusion criteria at the title/abstract level and then, full-text for studies not excluded based on the title/abstract alone. Data extraction was first conducted by one reviewer and then checked by a second reviewer. Across all levels of review and data extraction, discrepancies were resolved through discussion between the two reviewers and included a third team member when adjudication was necessary. All data were extracted and recorded in the DistillerSR platform (Evidence Partners, Ottawa, Canada) [ 23 ].

Estimates of the difference between individuals using e-cigarettes and individuals who are not using e-cigarettes are presented with the best measures of precision (i.e., 95% confidence intervals [CIs]) and/or statistical significance (i.e., p value) reported in the included studies. Reporting references to “significant” and/or “significantly” are only used to indicate statistical significance (i.e., p  < 0.05 and/or CI excludes 1.0). The DerSimonian–Laird method was used to conduct random-effects meta-analyses where included studies were weighted by the inverse of the sum of within-study variance plus between-study variance [ 24 ]. The Cochran’s Q statistic assessed heterogeneity across pooled studies which was then quantified using the inconsistency index (I 2 ).

Study authors were contacted to obtain missing data. All meta-analytic data were analyzed through Review Manager version 5.3 [ 25 ], in Windows 10 Pro version 22H2.

Sensitivity analyses

Data permitting, sensitivity analyses were planned to include stratification of results (or removal of data inputs) from: studies that did not adjust for meso- and macro-level variables in addition to age, race/ethnicity, and gender; studies that did not define e-cigarette use or regular cigarette smoking; and studies with a questionable definition of e-cigarette use and/or regular cigarette smoking. Additionally, data permitting, stratification by age group, and a sensitivity analysis of age, was planned. A sub-group analysis for the meta-analysis based on the country where the study was implemented, and a sensitivity analysis excluding studies graded as “Fair,” was likewise planned.

Assessment of confounding

This review applied the Socio-Ecological Model as defined by McLeroy et al. [ 26 ] to guide consideration of the interrelationships between individuals and their social (micro-), physical (meso-), and policy (macro-) environments (further detail reported in Supplemental Section 4 : Conceptual Framework).

Evaluation of confounding factors was followed according to Cochrane guidelines for systematic reviews [ 27 ]; specifically, during protocol writing, a list of potential confounding factors was identified a priori based on evidence and expert opinion from members of the research team and external advisors; and during the systematic review process, the variables that individual study authors considered were recorded for additional post hoc consideration.

Outcomes and related psychometrics

Recognizing that not all the outcome measures are equally valid and reliable, this review examined the Contextual Question (CQ): “Have measures used to examine initiation and progression to regular cigarette smoking been psychometrically assessed as reliable and valid?” Specific criteria were applied to assess reliability and validity across the outcome measures [ 28 ] (full reporting in Supplemental Section 5 : Contextual Questions).

Study quality assessment

Two reviewers independently appraised study quality using the Downs and Black checklist. Individual studies were graded as either “excellent,” “good,” “fair,” or “poor” [ 29 ] (Full reporting in Supplemental Section 6 : Study Quality Assessment). A funnel plot was planned to test for the risk of publication bias if 10 or more studies provided estimates pooled in the meta-analysis.

Strength of evidence evaluation

Strength of evidence (SOE) was assessed for studies that controlled for age, gender, and race/ethnicity and those that did not control for key confounders. The overall SOE was graded as “high,” “moderate,” “low,” or “insufficient” using the Agency for Healthcare Research and Quality (AHRQ) Evidence Based Practice (EPC) grading system [ 30 ] (full reporting in Supplemental Section 7 : Strength of Evidence).

Consideration of industry funding bias

The potential impact of funding bias on results and conclusions has been a topic addressed in the evidence base [ 31 , 32 , 33 ]. As indicated in the conflict-of-interest disclosure for this review, and given the recent increase of peer-reviewed systematic reviews and meta-analyses, this topic with potential industry and public health impact may have a heightened importance as a methodological issue. To specifically address any potential concerns of funding bias in this reported evidence synthesis, this review was executed with the highest standards of the systematic review methodology including: a priori protocol registration (PROSPERO 2018 CRD42018108540; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018108540 ); strict adherence to the PICOS throughout the execution of this review; a transparent and replicable search strategy executed by an information specialist with corresponding literature research results (Supplemental Section 8 : Literature Search Output, Studies Reviewed at the Full-Text Level); full reporting of excluded studies including reason for exclusion (Supplemental Section 9 : List of Excluded Studies); full reporting details on quantitative methods; and the expected details, per AMSTAR-2 and PRISMA guidelines, to disseminate a fully transparent and replicable evidence synthesis. Overall, the methodological rigor of this review with fully transparent and replicable reporting can also serve as a measure to minimize publication bias with systematic reviews.

The initial database search (January 1, 2007 to August 31, 2018) yielded 2526 articles, with four additional articles identified through other sources [ 3 , 34 , 35 , 36 ], resulting in 2530 articles. The first updated literature search (January 1, 2018 to August 30, 2019) yielded 1525 articles with 307 duplicate articles due to applied overlapping timeframes between the two searches. This overlapping timeframe conducted searches from the first of the year; therefore, overlapping search timeframes were unavoidable. Additionally, two articles were identified through other sources [ 37 , 38 ], resulting in 1220 unique articled retrieved. A second updated literature search for the timeframe of January 1, 2019 to October 7, 2020 yielded 2211 articles, of which 595 were duplicate articles with the previous database search, resulting in 1616 unique articles retrieved. A third updated search for the January 1, 2020 to November 24, 2021 timeframe yielded 3245 articles, of which 935 were duplicate articles with the previous database search, resulting in 2310 unique articles retrieved. Finally, a fourth updated search for the January 1, 2021 to April 26, 2023 period yielded 3925 articles, of which 1420 were duplicate articles with the previous database search, resulting in 2505 unique articles retrieved.

A cumulative total of 10,175 articles were retrieved from the specified databases, with an additional six additional articles identified from other sources (total: 10,181). Of the 10,181 potentially relevant articles, 9186 were excluded at the title/abstract level, resulting in 995 articles eligible for review at full-text level (Supplemental Section 8 : Literature Search Output, Studies Reviewed at the Full-Text Level). Subsequently, a further 873 articles were excluded (Supplemental Section 9 : List of Excluded Studies), resulting in 122 studies eligible for inclusion in the larger systematic review (Supplemental Section 10 : List of Included Studies). Inter-rater reliability at Level 2 screening was considered substantial or near perfect agreement [ 39 ] across all literature searches with a weighted overall kappa ranging from 0.72 to 0.95 (refer to Fig.  1 for each level of screening).

PRISMA flowchart

Of the 122 studies identified in the systematic review, 99 studies reported on cigarette smoking initiation or progression and were eligible for the qualitative and quantitative evidence. Of these 99 studies, 55 reported results that were adjusted for gender, age, and race/ethnicity between groups. For each included study, data were extracted on: study characteristics (Supplemental Section 11 : Study and Sample Characteristics, Adjusted Studies), demographic and baseline characteristics (Supplemental Section 12 : Demographic and Baseline Characteristics, Adjusted Studies), and study outcomes (Supplemental Section 13 : Evidence Tables, Adjusted Studies). Studies reporting unadjusted results are presented in Supplemental Section 14 (Study and Sample Characteristics, Unadjusted Studies), Supplemental Section 15 (Study and Sample Characteristics, Unadjusted Studies), and Supplemental Section 16 (Evidence Tables, Unadjusted Studies), but are not included in the qualitative or quantitative synthesis of evidence.

The highest number of studies (10 studies) were published in both 2020 [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] and 2018 [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]; followed by 7 studies in each of 2021 [ 60 , 61 , 62 , 63 , 64 , 65 , 66 ], 2019 [ 37 , 67 , 68 , 69 , 70 , 71 , 72 ], and 2017 [ 34 , 36 , 73 , 74 , 75 , 76 , 77 ]; six studies in 2022 [ 78 , 79 , 80 , 81 , 82 , 83 ]; three studies in each of 2023 [ 84 , 85 , 86 ] and 2015 [ 87 , 88 , 89 ]; and two studies in 2016 [ 90 , 91 ]. Studies were predominantly longitudinal in design and were from registered surveys. Of the 55 included studies, 41 were conducted in the US [ 36 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 51 , 52 , 53 , 54 , 56 , 58 , 59 , 62 , 63 , 64 , 65 , 67 , 68 , 69 , 70 , 71 , 72 , 75 , 76 , 77 , 78 , 79 , 81 , 82 , 83 , 86 , 87 , 88 , 89 , 90 , 91 ], five in the UK [ 34 , 37 , 61 , 66 , 85 ], two in Canada [ 60 , 73 ], one study in each of Mexico [ 74 ], Netherlands [ 57 ], Netherlands and Belgium [ 84 ], Romania [ 55 ], South Korea [ 42 ], Switzerland [ 50 ], and Thailand [ 80 ]. In terms of the study population, four studies defined their study population as “adults” [ 40 , 48 , 69 , 72 ], one study stratified their results by youth and adult populations [ 43 ]; three studies defined their participants as 12 years or older [ 59 , 81 , 86 ]. For the remaining 47 studies that defined participants, respondents were categorized as “youth,” “adolescents,” or “young adults” (participants defined as “students” were between grade 6 and college level) [ 34 , 36 , 37 , 41 , 42 , 44 , 45 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 70 , 71 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 82 , 83 , 84 , 85 , 87 , 88 , 89 , 90 , 91 ].

In addition to age, sex, and race/ethnicity, most studies included further adjustments with varying combinations of other micro, meso, and macro covariates. However, none of the studies sufficiently adjusted for potential confounding variables that would represent common liabilities between e-cigarette use and cigarette smoking [ 92 ]—meaning that a bias for those predisposing elements would exist among individuals using e-cigarettes that would likely be unadjusted for in the included studies.

Initiation of cigarette smoking was evaluated by the largest number of included studies (49 adjusted studies) [ 34 , 36 , 37 , 41 , 42 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 66 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ], followed by initiation and progression to regular cigarette smoking (12 adjusted studies) [ 37 , 40 , 45 , 48 , 54 , 61 , 65 , 66 , 69 , 72 , 73 , 86 ]. One adjusted study examined the potential relationship between e-cigarette use and age of initiation for cigarette smoking [ 75 ].

The reliability and validity of each outcome measure were evaluated according to the CQ with a comprehensive but not systematic review of the literature. The objective in doing so was to provide fuller context for the interpretation of findings from the evidence synthesis. All measures were single-item measures related to the initiation and/or progression of cigarette smoking. All three measures of initiation were supported by empirical data regarding their reliability and/or validity, and therefore qualified as “acceptable”—including initiation of cigarette smoking, age of initiation for cigarette smoking, and initiation and progression to regular cigarette smoking (full reporting in Supplemental Section 5 : Contextual Questions).

Quality appraisal for each included study was conducted by two reviewers according to the Downs and Black checklist [ 29 ]. Forty (73%) were rated “good” quality [ 34 , 37 , 40 , 41 , 44 , 45 , 46 , 47 , 48 , 49 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 84 , 86 , 87 , 88 , 90 , 91 ], 15 (27%) were rated “fair,” [ 36 , 42 , 43 , 50 , 63 , 69 , 70 , 71 , 79 , 80 , 81 , 82 , 83 , 85 , 89 ] and no studies were rated “excellent” or “poor” (Supplemental Section 6 : Study Quality Assessment). Publication bias was assessed using funnel plots, and no publication bias was detected.

The overall SOE among the adjusted data regarding the association between e-cigarette use and age of initiation of cigarette smoking was graded “moderate”; the body of evidence specific to e-cigarette use and initiation of cigarette smoking was graded “high”; and the body of evidence specific to initiation and progression to regular cigarette smoking was graded “moderate.” The SOE domain score table and the SOE and CQ ratings summary table for both the adjusted and unadjusted data are presented in Supplemental Section 7 : Strength of Evidence.

Definitions of e-cigarette use by outcome measure

Among the 55 included studies, one evaluated age of cigarette smoking initiation [ 75 ], 42 evaluated initiation of cigarette smoking [ 34 , 36 , 41 , 42 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 55 , 56 , 57 , 58 , 59 , 60 , 62 , 63 , 64 , 67 , 68 , 70 , 71 , 74 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 87 , 88 , 89 , 90 , 91 ], five evaluated progression to regular smoking [ 40 , 45 , 48 , 65 , 69 ], and seven studies evaluated both initiation of cigarette smoking and progression to regular smoking [ 37 , 54 , 61 , 66 , 72 , 73 , 86 ].

Among the 49 studies that examined initiation of cigarette smoking, only one evaluated the association between regular e-cigarette use and initiation of cigarette smoking. Wills et al. [ 77 ] defined e-cigarette use on a frequency scale (1–2 times ever use, 3–4 times ever use, yearly/monthly, and weekly/daily), with the initiation of cigarette smoking defined as having “ever smoked a whole cigarette”. For the remaining studies that examined initiation of cigarette smoking among individuals with non-regular use of e-cigarettes, ever use was the most common measure of both e-cigarette use (39 studies) [ 34 , 37 , 42 , 43 , 44 , 46 , 47 , 49 , 52 , 54 , 55 , 56 , 57 , 59 , 61 , 63 , 64 , 67 , 68 , 70 , 71 , 72 , 74 , 76 , 77 , 78 , 79 , 80 , 81 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 93 ] and cigarette use (33 studies) [ 34 , 37 , 42 , 43 , 44 , 46 , 47 , 49 , 52 , 55 , 56 , 57 , 58 , 61 , 63 , 64 , 66 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 76 , 77 , 78 , 79 , 80 , 84 , 87 , 89 , 91 ], with current or past 30-day use being the second most common measure (16 studies for e-cigarette use [ 36 , 41 , 52 , 53 , 54 , 58 , 60 , 62 , 67 , 72 , 73 , 78 , 79 , 80 , 86 , 91 ] and 23 studies for cigarette use [ 36 , 41 , 43 , 47 , 52 , 53 , 54 , 58 , 59 , 60 , 62 , 63 , 64 , 68 , 74 , 76 , 78 , 79 , 80 , 81 , 85 , 90 , 93 ]). The most commonly evaluated relationship for these two tobacco use behaviors was between ever use of e-cigarettes and ever use of cigarettes (30 studies) [ 34 , 37 , 42 , 43 , 44 , 46 , 47 , 49 , 52 , 55 , 56 , 57 , 61 , 63 , 64 , 66 , 67 , 68 , 70 , 71 , 72 , 74 , 76 , 77 , 78 , 79 , 80 , 84 , 87 , 88 ]. Ever use of e-cigarettes and current use of cigarettes was the second most commonly evaluated relationship (17 studies) [ 43 , 47 , 52 , 54 , 59 , 63 , 64 , 68 , 74 , 76 , 78 , 79 , 80 , 81 , 85 , 90 , 93 ], followed by current use of e-cigarettes and current use of cigarettes (11 studies) [ 36 , 41 , 52 , 53 , 54 , 58 , 60 , 62 , 78 , 79 , 80 ].

Twelve studies examined the association between e-cigarette use and initiation of and progression to regular cigarette smoking [ 37 , 45 , 48 , 54 , 61 , 65 , 66 , 69 , 72 , 73 , 86 , 94 ]. All of these 12 studies evaluated the association between non-regular e-cigarette use and progression to regular cigarette smoking. Additionally, two of the 12 studies also evaluated the association between regular e-cigarette use and progression to regular cigarette smoking [ 40 , 48 ]. Azagba et al. [ 94 ] defined e-cigarette use as either every day (current daily use and having ever used fairly regularly), some day (current use and having ever used fairly regularly), or experimental (current use and never having used fairly regularly), with progression to regular cigarette smoking defined as transitioning from either current non-established to current-established cigarette smoking, current non-established to current daily-established cigarette smoking, or current-established to current daily-established cigarette smoking [ 40 ]. Among individuals with established (having ever used fairly regularly) e-cigarette use, Wei et al. [ 48 ] evaluated transitions from non-current, non-established cigarette smoking to either exclusive current-established cigarette smoking or current-established dual use of cigarettes and e-cigarettes.

For the 12 studies that used definitions of non-regular e-cigarette use, e-cigarette use was defined as follows: current or past-30-day use in two studies [ 45 , 73 ]; e-cigarette experimentation, defined as non-established use (less than 100 times during lifetime) in one study [ 69 ]; and ever use of e-cigarettes in four studies [ 37 , 61 , 65 , 66 ]. Three studies applied multiple definitions of non-regular e-cigarette use: Chaffee et al. [ 54 ] included ever, past 30-day, and former e-cigarette use; Sun et al. [ 86 ] included ever and past 30-day use, while McMillen et al. [ 72 ] included ever and past 30-day e-cigarette use. Two studies that evaluated regular e-cigarette use also evaluated non-regular use defined as experimental use [ 40 , 48 ].

In the one study that evaluated age of initiation of cigarette smoking, e-cigarette use was defined as current use [ 75 ].

Qualitative synthesis of best available evidence

Fifty-five studies adjusted for three main confounders (gender, age, and race/ethnicity) between groups, and were analyzed in the qualitative review and quantitative syntheses reported below. Results for each outcome measure in the qualitative analysis were stratified by regular versus non-regular e-cigarette use.

Adjusted data for age of initiation, initiation of cigarette smoking, and progression to regular smoking are provided in Supplemental Section 13 : Evidence Tables, Adjusted Studies. Unadjusted data for age of initiation of cigarette smoking, initiation of cigarette smoking, and initiation and progression to regular cigarette smoking are provided in Supplemental Section 16 : Evidence Tables, Unadjusted Studies; however, unadjusted data are not included in qualitative analysis.

Age of initiation of cigarette smoking (regular e-cigarette use)

No studies provided adjusted analyses of age of initiation of cigarette smoking among individuals with regular use of e-cigarettes.

Age of initiation of cigarette smoking (non-regular e-cigarette use: 1 study)

One adjusted study was identified that investigated the association between non-regular e-cigarette use and age of initiation of cigarette smoking [ 75 ] (Summary characteristics of this study are provided in Table  1 ). In a cross-sectional analysis, McCabe et al. [ 75 ] reported that the adjusted odds of smoking the first cigarette at an earlier age (Grade 8 or below) were significantly higher among individuals using e-cigarettes (current [past-30-day]) versus individuals who are not using e-cigarettes (adjusted odds ratio [AOR] 4.12, 95% CI 2.56–6.62). Further, the odds of an earlier age of onset of daily cigarette smoking (before 8th grade level) were not significantly different between individuals currently using e-cigarettes (past-30-day) and individuals who are not using e-cigarettes (AOR 1.67, 95% CI 0.385–7.25) [ 75 ].

Initiation of cigarette smoking (regular e-cigarette use: 1 study)

One adjusted study was identified that investigated the association between regular e-cigarette use and odds of initiation of cigarette smoking among individuals not smoking cigarettes at baseline[ 77 ] (Summary characteristics of this study are provided in Table  2 ).

In their study of 1070 individuals who never smoked cigarettes at baseline, Wills et al. [ 77 ] examined the association between e-cigarette use and initiation of cigarette smoking by stratifying the probability of smoking onset by frequency of e-cigarette use at baseline, including a measure of regular (weekly/daily) e-cigarette use. Compared with individuals who are not using e-cigarettes, all individuals who have used e-cigarettes had significantly higher adjusted odds of initiating cigarette smoking: individuals who ever used e-cigarettes (1–2 times): AOR 2.88 (95% CI 1.96–4.22); individuals who ever used e-cigarettes (3–4 times): AOR 2.29 (95% CI 1.35–3.87); weekly/daily users: AOR 4.09 (95% CI 2.43–6.88); and yearly/monthly users: AOR 4.17 (95% CI 2.03–8.57).

Initiation of cigarette smoking (non-regular e-cigarette use: 49 studies)

Forty-nine adjusted studies examined the association between non-regular e-cigarette use and initiation of cigarette smoking among individuals not smoking cigarettes at baseline [ 34 , 36 , 37 , 41 , 42 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 66 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. Summary characteristics of these 49 studies are provided in Table  3 .

As discussed in the search results of the meta-analysis, 12 studies met the inclusion criteria of the meta-analysis [ 34 , 43 , 52 , 56 , 59 , 63 , 66 , 76 , 77 , 80 , 81 , 84 ]. These studies are included in Table  3 , but are not discussed in qualitative synthesis. For a variety of reasons, 37 studies did not meet the criteria to be included in the quantitative synthesis (Supplemental Section 17 : Meta-Analytic Results); however, these studies contained information important to the research question and are described below.

Twenty-four studies—15 prospective cohort studies [ 37 , 46 , 49 , 55 , 58 , 61 , 62 , 68 , 70 , 71 , 73 , 83 , 87 , 89 , 90 ], eight longitudinal panel studies [ 41 , 47 , 50 , 53 , 72 , 82 , 86 , 88 ], and one retrospective cohort study [ 67 ]—all reported statistically significant AORs, showing a higher likelihood of individuals who have used e-cigarettes (non-regular use: ever, ever in the past 12 months, and current) initiating smoking compared with individuals who are not using e-cigarettes. Their AORs ranged from 1.75 (95% CI 1.10–2.77) in a prospective cohort of Grade 9 individuals who never smoked cigarettes at baseline reporting any cigarette use at follow-up (either 6 or 12 months) [ 87 ] to 8.3 (95% CI 1.2–58.6) in a prospective cohort of 16–26 year old non-susceptible individuals who never smoked a cigarette reporting ever cigarette use (at least one puff) at 18-month follow-up [ 89 ].

Four studies calculated the adjusted relative risk (ARR) of individuals who have used e-cigarettes (ever and current [past-30-day]) smoking cigarettes compared with individuals who are not using e-cigarettes [ 36 , 64 , 74 , 78 ]. Lozano et al. [ 74 ] found a statistically significantly higher risk for trying smoking (ARR 1.40, 95% CI 1.22–1.60), however, no significant difference was reported for current smoking (≥ 1 cigarette in the past 30 days; ARR 1.43, 95% CI 0.94–2.16). Miech et al. [ 36 ] also found a statistically significantly higher risk for current smoking (ARR 4.78, 95% CI 1.91–11.96).

Keller-Hamilton et al. [ 64 ] reported that individuals who have used e-cigarettes at baseline were more than twice as likely to report ever (ARR 2.71, 95% CI 1.89–3.87) and current (i.e., past 30 day) smoking (ARR 2.20, 95% CI 1.33–3.64) at follow-up compared to individuals who are not using e-cigarettes. Similar results were reported in a propensity score-matched analysis (ever cigarette use ARR 2.22; 95% CI 0.90–5.47; past 30-day cigarette use ARR 1.25; 95% CI 0.41–3.82). Using data from Waves 1–5 of the PATH study, Harlow et al. [ 78 ] showed that, among baseline never-smokers, ever e-cigarette use at Wave 2 was associated with a higher likelihood of ever smoking at Waves 3, 4, and 5 (ARR 2.7, 95% CI 2.4–3.0). This association was present for all sub-categories of e-cigarette ever-use, namely former use (ARR 2.5, 95% CI 2.2–2.9), current (i.e., past 30-day) use (ARR 3.5, 95% CI 2.9–4.1), use of tobacco-flavored (ARR 2.5, 95% CI 1.8–3.5), and nontobacco-flavored (ARR 2.8, 95% CI 2.5–3.1) e-cigarettes. In a marginal structural model that accounted for time-dependent confounding, ever e-cigarette use was similarly associated with a higher likelihood of ever smoking at follow-up waves (ARR 2.4, 95% CI 2.1–2.7), regardless of the sub-category of ever use (former use ARR 2.2, 95% CI 2.0–2.5; current use ARR 3.1, 95% CI 2.6–3.7), or e-cigarette flavor (tobacco flavored ARR 2.4, 95% CI 1.7–3.3; nontobacco flavored ARR 2.4, 95% CI 2.2–2.7) [ 78 ]. The study also reported that the likelihood of being an individual who currently smoked (i.e., past 30-day) at Waves 3–5 was higher among individuals who have ever used e-cigarettes at baseline (ARR 2.9, 95% CI 2.5–3.3), quit e-cigarette use (ARR 2.6, 95% CI 2.2–3.1), currently used (ARR 3.8, 95% CI 3.1–4.6), used tobacco-flavored (ARR 2.6, 95% CI 1.7–3.9), and non-tobacco-flavored (ARR 3.0, 95% CI 2.6–3.4) e-cigarettes [ 78 ]. Similarly, in the marginal structural model, the likelihood of past 30-day cigarette use at Waves 3–5 was associated with ever (ARR 2.5, 95% CI 2.2–2.9), former (ARR 2.3, 95% CI 1.9–2.7), current (ARR 3.4, 95% CI 2.8–4.2), tobacco-flavored (ARR 2.3, 95% CI 1.5–3.5), and nontobacco-flavored (ARR 2.6, 95% CI 2.2–3.0) [ 78 ] e-cigarette use.

A study by Aleyan et al. [ 60 ] calculated regression coefficients to estimate the association between past 30-day e-cigarette use at Wave 1 and initiation of cigarette smoking at Wave 3. Past-30-day e-cigarette use at Wave 1 was significantly associated with past 30-day cigarette smoking (β = 1.06; SE = 0.28; 95% CI 0.52–1.60; p  < 0.001), and dual use at Wave 3 (β = 1.31; SE = 0.24; 95% CI 0.84–1.79; p  < 0.001). Further, the association between past 30-day e-cigarette use at Wave 1 and cigarette smoking at Wave 3 remained significant after adjustment for having one or more friends who smoked at Wave 1.

Kintz et al. [ 44 ] calculated a phi-coefficient for the relationship between ever use of e-cigs at baseline and subsequent cigarette initiation (self-reported first use) at follow-up, and found that baseline ever e-cigarette use was significantly associated with cigarette smoking initiation at follow-up (phi coefficient = 0.141, p  < 0.001).

Two studies applied a multistate Markov model to evaluate the probability of transitioning to cigarette smoking [ 42 , 85 ]. A study by Kang et al. [ 42 ] applied a multistate Markov model to show that individuals who have ever used e-cigarettes at baseline had a 9.52% (95% CI 6.57–13.85) probability of transitioning to dual e-cigarette and cigarette use, whereas individuals who are not using e-cigarettes at baseline had a 1.39% (95% CI 1.29–1.49) probability of transitioning to exclusive cigarette use. Parnham et al. [ 85 ] examined transition probabilities between e-cigarette use and smoking in UK adolescents and young adults. In an analysis that adjusted for age, wave of data collection, sex, ethnicity, and tertiles of household income, adjusted probability of transition from ever e-cigarette use to smoking ranged from 14% (95% CI 13–16) in Year 1 to 27% (95% CI 25–29) in Year 5, while the probability of transitioning from e-cigarette never use to smoking ranged from 2% (95% CI 2–2) to 10% (95% CI 9–10) [ 85 ].

The study by Loukas et al. [ 79 ] reported hazard ratios for the association between past 30-day and ever e-cigarette use and transition from never to current cigarette smoking. After adjusting for covariates, both past 30-day (HR 2.69, 95% CI 1.95–3.72) and ever (HR 2.16, 95% CI 1.79–2.62) e-cigarette use were associated with a higher likelihood of transition to smoking.

Conner et al. [ 61 ] evaluated cigarette smoking initiation (ever use) among individuals who have used e-cigarettes “early” and “late”, defined as reporting ever e-cigarette use at either Wave 3 (early) or Wave 4 (late), respectively. The authors found that the adjusted odds of individuals using e-cigarettes early, compared to individuals who never used e-cigarettes, initiating cigarette smoking was statistically significant both at Wave 4 (AOR 1.39, 95% CI 1.29–1.50) and at Wave 5 (AOR 3.55, 95% CI 2.82–4.49). Similarly, individuals using e-cigarettes late were significantly more likely to initiate cigarette smoking at Wave 5 compared to individuals who have never used e-cigarettes (AOR 2.87, 95% CI 2.33–3.53) [ 61 ].

Chaffee et al. [ 54 ] calculated the AORs for initiating smoking in three different groups of individuals who have used e-cigarettes (versus individuals who have not used e-cigarettes) and found the following: a non-significant AOR of 1.57 (95% CI 0.99–2.49) for individuals who have ever used e-cigarettes and have smoked at least 100 + cigarettes; a non-significant AOR of 1.69 (95% CI 0.93–3.05) for individuals who have used e-cigarettes in the past-30-days who have smoked at least 100 cigarettes; a non-significant AOR for individuals who have smoked at least 100 cigarettes but have quit e-cigarette use (AOR 1.55, 95% CI 0.94–2.56); a non-significant AOR for individuals who have ever used e-cigarettes and smoked a cigarette in the past 30 days (AOR 1.32, 95% CI 0.99–1.76); a significant AOR for individuals who have used e-cigarettes in the past 30 days and smoked a cigarette in the past 30 days (AOR 1.64, 95% CI 1.12–2.41); and, a non-significant AOR for individuals who quit e-cigarette use and smoked in the past 30 days (AOR 1.20, 95% CI 0.86–1.68) [ 54 ].

In additional to their overall analysis, Owotomo et al. [ 46 ] reported AORs for cigarette smoking initiation among subgroups of adolescents according to their baseline cigarette smoking intentions. Overall, the authors found ever e-cigarette use to be significantly associated with ever cigarette smoking (AOR 2.58, 95% CI 1.73–3.85). The association remained significant in a subgroup analysis of adolescents with no baseline intention to smoke (AOR 4.62, 95% CI 2.87–7.42); however, among the subgroup of adolescents with baseline cigarette smoking intentions, the association between ever e-cigarette use and cigarette smoking initiation was nonsignificant (AOR 1.57, 95% CI 0.94–2.63). The AOR for the interaction between smoking intention and ever e-cigarette use with regards to smoking initiation was statistically significant (AOR 0.34, 95% CI 0.18–0.64), suggesting the association between e cigarette use and ever cigarette smoking was dependent on previous smoking intention status.

Three of the 37 studies not included in the meta-analysis evaluated initiation of cigarette smoking and either susceptibility or propensity to smoke cigarettes among individuals using e-cigarettes versus individuals who are not using e-cigarettes [ 52 , 57 , 91 ]. Barrington-Trimis et al. [ 52 ] evaluated the association between susceptibility and initiation of cigarette smoking in either individuals who have ever used e-cigarettes or individuals who are not using e-cigarettes and found a statistically significant difference between the two groups. The authors found that among individuals who are not using e-cigarettes, susceptibility to cigarette use was associated with over three times the odds of subsequent initiation of cigarette smoking compared with non-susceptible individuals who are not using e-cigarettes (AOR 3.47, 95% CI 2.38–5.07); however, only a small, non-statistically significant association was observed between susceptible and non-susceptible individuals who have ever used e-cigarettes and initiation of cigarette smoking (AOR 1.57, 95% CI 0.80–3.05) [ 52 ]. Thus, susceptibility only statistically significantly affected the subsequent initiation of cigarette smoking in individuals who are not using e-cigarettes ( p interaction  = 0.04).

Findings from a 2016 study by Wills et al. indicated that the effect of e-cigarette for cigarette smoking onset decreased as propensity increased—the AOR for smoking onset for individuals currently using e-cigarettes (past-30-day) versus individuals who are not using e-cigarettes was 2.23 (95% CI 1.57–3.17) for those in the bottom 10th percentile for propensity to smoke, and 1.32 (95% CI 1.19–1.47) for those in the top 10th percentile for propensity to smoke [ 91 ].

In a 2018 study, Treur et al. provided AORs for low-propensity- and high-propensity-to-smoke groups for ever e-cigarette versus individuals who are not using e-cigarettes, both with and without nicotine [ 57 ]. The investigators found that, for e-cigarettes containing nicotine, the AOR for initiating conventional smoking was 7.80 (95% CI 1.90–32.04) in the low-propensity-to-smoke group, and 2.89 (95% CI 1.47–5.68) in the high-propensity-to-smoke group; for e-cigarettes containing no nicotine, the AOR for initiating conventional smoking was 6.07 (95% CI 2.18–16.90) in the low-propensity-to-smoke group, and 3.30 (95% CI 2.33–4.67) in the high-propensity-to-smoke group.

Treur et al. also compared the effects of e-cigarette use with nicotine and e-cigarette use without nicotine in individuals using e-cigarettes versus individuals who have never used e-cigarettes [ 57 ]. The study reported an AOR for initiation of 5.36 (95% CI 2.73–10.52) for individuals who ever used e-cigarettes without nicotine compared with individuals who are not using e-cigarettes, and an AOR of 11.90 (95% CI 3.36–42.11) for individuals who ever used e-cigarettes with nicotine compared with individuals not using e-cigarettes.

Three studies evaluated initiation in susceptible subgroups [ 34 , 68 , 90 ], two of which were included in the meta-analysis for initiation of cigarette smoking [ 34 , 90 ]. The association between ever e-cigarette use and susceptibility to smoking was evaluated in a 2016 prospective cohort study by Barrington-Trimis et al. [ 90 ]. The study found that ever e-cigarette use had less of an effect in individuals classified as being susceptible to smoking, as demonstrated by a lower odds of initiation of cigarette smoking in that group (AOR 2.12, 95% CI 0.79–5.74), compared with individuals using e-cigarettes initially classified as non-susceptible to smoking (AOR 9.69, 95% CI 4.02–23.4) ( p interaction  = 0.025) [ 90 ]. Interestingly, the effect of e-cigarette use in the susceptible group on initiation of cigarette smoking was not statistically significant.

Berry et al. [ 68 ] reported similar outcomes, both in terms of ever and current cigarette use. In terms of ever cigarette use, the authors demonstrated lower odds of initiation among individuals who had used e-cigarettes in the past versus individuals who are not using e-cigarettes (AOR 3.51, 95% CI 2.52–4.89) among individuals classified as intermediate/high risk for smoking, compared with those classified as low risk (AOR 8.57, 95% CI 3.87–18.97). Similarly, in terms of current cigarette use, odds of initiation were lower among individuals classified as intermediate/high risk (AOR 2.16, 95% CI 1.23–3.79) compared with those classified as low risk (AOR 10.36, 95% CI 3.11–34.54). In both cases, this indicates that e-cigarette use had less of an effect on initiation among those individuals considered intermediate/high risk.

Best et al. [ 34 ], also included in the meta-analysis, found that there was an interaction between susceptibility to smoking and ever e-cigarette use with regards to initiation of cigarette smoking (AOR for e-cigarette use and susceptibility interaction of 0.42, 95% CI 0.19–0.94). In other words, there would be greater interaction between e-cigarette use and non-susceptible populations compared with susceptible populations in terms of initiation of cigarette smoking. It is worth noting that although Best et al. refer in their study to susceptibility and not the intent, the questions that respondents answered, i.e., “Do you think you will smoke cigarettes or hand-rolled cigarettes at any time during the next year” and “If one of your friends offered you a cigarette or hand-rolled cigarettes (roll-ups), would you smoke it?” were questions that measured intent.

Lastly, one study by Barrington-Trimis et al. [ 51 , 52 ] investigating initiation of cigarette smoking, with analyses of switching and dual-use, found that the adjusted odds of reporting dual use (at follow-up) among individuals who had ever used e-cigarettes exclusively at baseline (versus individuals who had never used e-cigarettes at baseline) were higher than the odds of reporting switching from baseline exclusive e-cigarette use to exclusive cigarette smoking at follow-up (AOR 7.16, 95% CI 4.47–11.5 vs. AOR 2.67, 95% CI 1.53–4.65, respectively). In another analysis, the authors also found that the odds of reporting dual use among current (past 30-day) e-cigarette users (versus non-current users) were similarly higher than the odds of reporting switching from exclusive e-cigarette use to exclusive cigarette smoking (AOR 8.86, 95% CI 5.08–15.4 vs. AOR 3.84, 95% CI 1.80–8.19, respectively [ 52 ].

Initiation of and progression to regular cigarette smoking (regular e-cigarette use: 2 studies)

Two adjusted studies were identified that provided adjusted analyses of initiation of and progression to regular cigarette smoking in individuals with regular use of e-cigarettes [ 40 , 48 ]. Summary characteristics of these two studies are provided in Table  4 .

Azagba et al. [ 40 ] defined regular e-cigarette use as either every day or someday use. In terms of the transition from experimental to some-day cigarette smoking, no significant association was found between individuals using e-cigarettes every day and individuals who have never used e-cigarettes (AOR 1.31, 95% CI 0.20–8.58), nor between individuals using e-cigarettes some day and individuals who have never used e-cigarettes (AOR 0.48, 95% CI 0.13–1.78). Similarly, no significant associations were found between individuals using e-cigarettes every day and individuals who have never used e-cigarettes (AOR 0.58, 95% CI 0.09–3.93) and individuals using e-cigarettes some day and individuals who have never used e-cigarettes (AOR 1.14, 95% CI 0.42–3.05) in terms of the transition from experimental to daily cigarette smoking. Likewise, in terms of the transition from some-day to daily cigarette smoking, no significant association was found between individuals using e-cigarettes every day and individuals who have never used e-cigarettes (AOR 1.89, 95% CI 0.98–3.66), nor between individuals using e-cigarettes some day and individuals who have never used e-cigarettes (AOR 1.41, 95% CI 0.84–2.39).

Wei et al. [ 48 ] evaluated transitions from non-current, non-established cigarette smoking to either exclusive current-established cigarette smoking or current-established dual use of cigarettes and e-cigarettes, among baseline individuals using e-cigarettes exclusively. The authors found that individuals who have established e-cigarette use were significantly less likely to transition to exclusive current-established cigarette smoking than individuals who have non-established e-cigarette use (AOR 0.13, 95% CI 0.02–0.87); however, no significant association was found between e-cigarette use (established versus non-established) and transitioning to dual use of cigarettes and e-cigarettes (AOR 0.53, 95% CI 0.05–6.25).

Initiation of and progression to regular cigarette smoking (non-regular e-cigarette use: 11 studies)

Eleven adjusted studies examined the potential association between e-cigarette use and initiation and progression to regular cigarette smoking among individuals with non-regular use of e-cigarettes [ 37 , 40 , 45 , 54 , 61 , 65 , 66 , 69 , 72 , 73 , 86 ]. Study characteristics for the 11 included studies are presented in Table  5 .

Sun et al. [ 86 ] used data from Waves 3–5 of the PATH study to investigate the association between e-cigarette use and the progression into regular cigarette smoking—defined as past 12-month use at Wave 4 with established use and at least 20 days use in the past 30 days at Wave 5. The authors show that the association between ever e-cigarette user and progression into regular smoking is non-significant with baseline e-cigarette ever-users having a lower risk of progressing into established regular smoking 0.13% (95% CI − 0.31 to 0.58) versus 0.17% (95% CI − 0.30 to 0.64) for baseline e-cigarette never-users (ARD − 0.03, 95% CI − 0.33 to 0.27; AOR 0.80, 95% CI 0.10–6.49). Similarly, e-cigarette current use was not associated with progression into established regular smoking as evidenced by the absolute risk of 0.47% (95% CI − 1.46 to 2.39) for individuals currently using e-cigs versus 0.15% (95% CI − 0.27 to 0.58) for e-cig non-users (ARD 0.31, 95% CI − 1.36 to 1.99; AOR 3.14, 95% CI 0.13–74.96) [ 86 ].

In addition to applying measures of regular e-cigarette use described previously, Azagba et al. [ 40 ] also applied a non-regular definition of experimental e-cigarette use. Consistent with their findings from their analyses of regular e-cigarette use, no significant associations were found between experimental and e-cigarette never-users in terms of: transitioning from experimental to someday cigarette smoking (AOR 0.98, 95% CI 0.44–2.20); transitioning from experimental to daily cigarette smoking (AOR 0.59, 95% CI 0.26–1.31); and transitioning from some day to daily cigarette smoking (AOR 1.03, 95% CI 0.61–1.75) [ 40 ].

A longitudinal panel study by McMillen et al. [ 72 ] reported inconsistent findings, depending on the measure of e-cigarette use applied. When evaluating ever e-cigarette use (versus e-cigarette non-use), no significant association with progression to current established cigarette smoking was found (AOR 2.5, 95% CI 0.6–10.9); however, current e-cigarette users were found to be significantly more likely to progress to current established cigarette smoking compared to individuals who are not using e-cigarettes (AOR 8.0, 95% CI 2.8–22.7). Another longitudinal panel study by Pierce et al. [ 65 ] evaluated rate of progression to daily cigarette smoking at Wave 4 among ever (but not daily) tobacco product users at Wave 3 of the PATH survey. The authors found that the adjusted risk difference between individuals who have ever used e-cigarettes versus e-cigarette never-users for progression to daily cigarette smoking was 7% (95% CI 6–9%) higher for individuals using e-cigarettes, although statistical significance was not assessed [ 65 ].

Findings from a prospective cohort study by Chaffee et al. [ 54 ] suggested the AOR of progressing to regular smoking (i.e., smoked ≥ 100 cigarettes and smoked in the past 30 days) was statistically significantly higher in individuals who have ever used e-cigarettes compared with individuals who are not using e-cigarettes (AOR 1.80, 95% CI 1.04–3.12); however, no such association was shown for past-30-day e-cigarette users (AOR 1.76, 95% CI 0.92–3.37). A second prospective cohort study by Hammond et al. [ 73 ] reported that progression to regular cigarette smoking was statistically significantly higher in past-30-day e-cigarette users compared with individuals who are not using e-cigarettes (AOR 1.79, 95% CI 1.41–2.28), while findings from a third prospective cohort study by Conner et al. [ 37 ] suggested statistically significantly higher odds of progressing to regular smoking (≥ 1 cigarette per week) at 2 years among individuals who have ever used e-cigarettes compared with individuals who are not using e-cigarettes (AOR 1.27, 95% CI 1.17–1.39). A fourth prospective cohort study, also by Conner et al. [ 61 ], reported statistically significantly higher odds of regular smoking (defined as smoking at least 1 cigarette per week) at Wave 5 among adolescents who first reported e-cigarette use at 13–14 years old (i.e., early users; AOR 1.25, 95% CI 1.16–1.34), and those who first reported e-cigarette use at 14–15 years (i.e., late users; AOR 1.12, 95% CI 1.08–1.16). The final prospective cohort study by Staff et al. [ 66 ] reported that the adjusted odds of reporting frequent smoking by age 17 were significantly higher for individuals using e-cigarettes compared with individuals who are not using e-cigarettes at baseline (AOR 2.91, 95% CI 1.56–5.4). The odds of frequent smoking remained significantly higher for individuals using e-cigarettes when the samples were matched on risk factors using propensity score matching.

Osibogun et al. [ 45 ] evaluated progression to regular cigarette smoking at both 1 and 2 years from baseline, finding that progression at 1 year was significantly associated with e-cigarette use (AOR 5.0, 95% CI 1.9–12.8). However, progression at 2 years was not significantly associated with e-cigarette use (AOR 3.4, 95% CI 1.0–11.5) [ 45 ].

The one cross-sectional study by Friedman et al. [ 69 ] reported statistically significantly lower odds of current established (≥ 100-lifetime cigarettes and past-30-day use) (AOR 0.22 95% CI 0.10–0.50) or daily (AOR 0.22 95% CI 0.06–0.77) cigarette use among individuals who experimented exclusively with e-cigarettes (experimenting before the age of 18 years) compared with individuals who did not experiment with e-cigarettes. Findings from this study also suggested statistically significantly higher odds of reporting current established cigarette smoking among individuals who first experimented with e-cigarettes and then with cigarettes, compared with individuals who did not experiment with e-cigarettes (AOR 1.89 95% CI 1.09–3.27); however, no significant difference in the odds of daily smoking was shown (AOR 0.73 95% CI NR).

Quantitative synthesis of best available evidence

Meta-analyses were performed by calculating pooled ORs from studies presenting AORs on initiation of cigarette smoking among naïve (individuals who never smoked cigarettes) cigarette smokers who either ever used or never used e-cigarettes. A meta-analysis evaluating the association between regular e-cigarette use and initiation of cigarette smoking was not possible, given that only one study reported adjusted outcomes for this association. Additionally, a meta-analysis evaluating e-cigarette use and initiation and progression to regular smoking was not possible, due to differences in definitions of e-cigarette use and/or outcome measures between studies (full results in Supplemental Section 17 : Meta-Analytic Results; all relevant code is publicly available [DOI: https://doi.org/10.5281/zenodo.10927677 ]).

Twelve studies met all the inclusion criteria and were included in the meta-analysis for initiation of cigarette smoking [ 34 , 43 , 52 , 56 , 59 , 63 , 66 , 76 , 77 , 80 , 81 , 84 ]. All 12 studies included individuals who never smoked cigarettes who were evaluated for initiation of cigarette smoking (minimum inclusion criteria = 1 puff). The studies compared an e-cigarette use group (regardless of frequency, volume, and duration) to a control group of e-cigarette never-users. The results from each study controlled for age, gender, race/ethnicity, and other covariates. All studies were longitudinal in design and had a combined analytic sample of 57,730 respondents.

For the 12 studies, the AORs ranged from 1.35 to 7.41. Pooling their results, the overall OR was 3.71 (95% CI 2.86–4.81). The test for the overall effect of the model was noted to be statistically significant ( p  < 0.00001). Heterogeneity tests revealed an I 2 of 76% and a X 2 of 45.18 ( p  < 0.00001) (Fig.  2 ). An assessment of publication bias—via the development of a funnel plot—was generally symmetrical, suggesting an absence of publication bias (Fig.  3 ).

Meta-analysis of odds of initiation of cigarette smoking among individuals who never smoked cigarettes who used e-cigarettes

Funnel plot for publication bias

Additionally, a sensitivity analysis excluding studies with a “Fair” quality rating was conducted—resulting in the exclusion of four studies [ 43 , 63 , 80 , 81 ]. Results of the eight studies with “Good” rating, presented a pooled OR of 3.96 (95% CI 3.10–5.07), with an I 2 of 60% and a X 2 of 17.64 ( p  < 0.00001) (Fig.  4 ).

Sensitivity analysis of odds of initiation of cigarette smoking among individuals who never smoked cigarettes who used e-cigarettes—excluding studies rated as “fair” quality

A sub-group analysis was conducted based on the country where the study was implemented (US-based or outside the US). The sub-group analysis stratified the results of eight studies conducted in the US [ 43 , 52 , 56 , 59 , 63 , 76 , 77 , 81 ] and four studies conducted outside of the US [ 34 , 66 , 80 , 84 ]. In the eight US studies, AORs ranged from 1.35 to 7.41, and the pooled overall OR was 3.63 (95% CI 2.54–5.18). The test for overall effect revealed that the results were significant ( p  < 0.00001), while heterogeneity was noted with I 2 of 79% and X 2 of 32.77 ( p  < 0.0001). In the studies outside the US the AORs ranged from 2.42 to 5.09 and the pooled OR was 3.94 (95% CI 2.62–5.95), with a significant test for overall effect ( p  < 0.00001), and I 2 of 70% and X 2 of 9.96 ( p  < 0.00001). The test for subgroup difference presented an I 2 of 0% and X 2 of 0.09 ( p  = 0.76) (Fig.  5 ).

Sub-group meta-analysis of odds of initiation of cigarette smoking among individuals who never smoked cigarettes who used e-cigarettes from studies conducted in the US and outside the US

As with the main analysis, a sensitivity analysis of the subgroup analysis based on country was performed, excluding studies graded as “Fair” quality, which resulting in the exclusion of three US-based studies [ 43 , 63 , 81 ], and one study from outside the US [ 80 ]. Pooled results from the remaining five US studies revealed a statistically significant pooled OR of 4.01 (95% CI 2.95–5.47; p  < 0.00001) with an I 2 of 47% and a X 2 of 7.54 ( p  = 0.11). In the remaining studies outside the US the AORs ranged from 2.42 to 5.09 and the pooled OR was 3.83 (95% CI 2.29–5.07), with a significant test for overall effect ( p  < 0.00001), and I 2 of 80% and X 2 of 9.94 ( p  < 0.00001). The test for subgroup difference presented an I 2 of 0% and X 2 of 0.02 ( p  = 0.88) (Fig.  6 ).

Sub-group meta-analysis of odds of initiation of cigarette smoking among individuals who never smoked cigarettes who used e-cigarettes from studies conducted in the US and outside the US—excluding studies rated as “fair” quality

The current systematic review identified a number of “good” quality studies (according to the Downs and Black quality metrics [ 29 ]) that evaluated the association between e-cigarette use and initiation of cigarette smoking, and initiation of and progression to regular cigarette smoking. Over half of the included studies controlled for age, gender, and race/ethnicity and reported adjusted results to provide a higher level of evidence. This review focused on such studies in the quantitative and qualitative synthesis of results.

A meta-analysis of 12 studies evaluating initiation of cigarette smoking indicated an increased odds (3.7 times higher) for individuals who have ever used e-cigarettes compared with individuals who are not using e-cigarettes and no indication of publication bias among the studies was observed [ 34 , 43 , 51 , 56 , 59 , 63 , 66 , 76 , 77 , 80 , 81 , 84 ]. These findings are consistent with previously-conducted meta-analyses, all of which reported increased odds of initiation associated with e-cigarettes: O’Brien et al. [ 16 ] reported 4.06 times higher odds among teenagers; Soneji et al. [ 13 ] reported 3.5 times higher odds among a study population of adolescents and young adults; Chan et al. [ 14 ] and Khouja et al. [ 15 ] both reported 2.9 times higher odds, in populations of youth and youth-young adults, respectively; and Baenziger et al. [ 18 ] and Adermark et al. [ 19 ] reported 3.2 and 3.3 times higher odds, respectively, in samples from the general population.

Only one study, also included in the meta-analysis, reported on initiation of cigarette smoking in individuals with regular use of e-cigarettes, providing outcome data for initiation of cigarette smoking based on the frequency of e-cigarette use at baseline (from 1–2 uses/day to everyday use) [ 77 ]. Wills et al. [ 77 ] found an upward trend for the probability of initiation of cigarette smoking and increased e-cigarette use. Thirty-seven adjusted studies not included in the meta-analysis showed a similar trend, with a higher probability or incidence of initiation of cigarette smoking in the e-cigarette user group [ 36 , 37 , 41 , 42 , 44 , 46 , 47 , 49 , 50 , 52 , 53 , 54 , 55 , 57 , 58 , 60 , 61 , 62 , 64 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 78 , 79 , 82 , 83 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. These studies had similar definitions for e-cigarette use, with any or ever use at baseline, any e-cigarette use in the past 12 months, or any use in the past 30 days. All but one of these studies defined cigarette use as any cigarette use at follow-up, while the remaining study evaluated regular smoking, although definition of regular smoking was not provided.

Six studies compared initiation of cigarette smoking with e-cigarette use between study groups that were susceptible or not susceptible to cigarette smoking [ 34 , 52 , 57 , 68 , 90 , 91 ]. E-cigarette use was either not associated with an increase in smoking initiation in individuals using e-cigarettes susceptible to cigarette smoking [ 52 , 90 ], or the effect of e-cigarette use on initiation of cigarette smoking was less in individuals using e-cigarettes susceptible to cigarette smoking [ 34 , 57 , 68 , 91 ].

The limited data from one study evaluating e-cigarettes with or without nicotine pointed to a higher probability of initiating cigarette smoking with nicotine-containing e-cigarettes [ 57 ]. With regards to “switching” or “dual-use” following initiation of cigarette smoking, two studies found that the odds of reporting dual use among exclusive e-cigarette ever users (versus never users) were higher than the odds of reporting switching from exclusive e-cigarette use to exclusive current cigarette smoking [ 52 , 80 ]. In both studies, analyses of current (past 30-day) e-cigarette users reported similarly higher odds of dual-use compared with switching.

Twelve adjusted studies evaluated initiation of and progression to regular cigarette smoking for individuals using e-cigarettes versus individuals who are not using e-cigarettes [ 37 , 40 , 45 , 48 , 54 , 61 , 65 , 66 , 69 , 72 , 73 , 86 ], two of which applied measures of regular e-cigarette use [ 40 , 48 ]. Both studies generally found no significant associations between regular e-cigarette use and progression to regular cigarette smoking; however, one result suggested that established e-cigarette users were significantly less likely to transition to exclusive cigarette smoking than non-established e-cigarette users [ 48 ]. In terms of studies applying definitions of non-regular e-cigarette use, based on the variability in the results, and variations in the definition of a “regular” cigarette smoker, the current data regarding initiation of and progression to regular cigarette smoking does not support drawing conclusions. This is illustrated in the study by Friedman et al. [ 69 ], which reported statistically lower odds of both current established and daily cigarette use among individuals who experimented exclusively with e-cigarettes(non-established use prior to the age of 18 years old) compared with individuals who did not experiment with e-cigarettes. Conversely, this study also found statistically significantly higher odds of current established cigarette use among individuals who experimented with e-cigarettes first, then with cigarettes, compared with individuals who did not experiment with e-cigarettes; however, no significant difference in the odds of daily smoking was found between e-cigarette-then-cigarette experimenters compared with individuals who did not experiment with e-cigarettes.

Finally, only one adjusted study evaluated age of initiation of cigarette smoking [ 75 ]. Notably, although McCabe et al. [ 75 ] reported a significantly lower age among current e-cigarette users, age of regular (daily) cigarette smoking was not significantly different between current and non-current e-cigarette users.

The current systematic review exhibited three major strengths. Firstly, its comprehensive search methodology yielded a large number of studies for review. Secondly, the current review had a clearly defined PICOS, which assured the identification of the strongest evidence relevant to the research question. Thirdly, guidelines for this review ensured that only demographically adjusted and methodologically consistent studies were included in the quantitative syntheses. Finally, the strict adherence to AMSTAR-2 and PRISMA guidelines ensured the transparency and replicability of this review while minimizing any risk of various forms of bias (e.g. individual study design; industry sponsorship) to provide an unbiased and comprehensive synthesis of this evidence base. Collectively, these strengths support the robustness of this review in terms of comprehensiveness and methodological rigor.

Although the meta-analysis indicated a higher odds for initiation of cigarette smoking among individuals using e-cigarettes—a result generally supported by the studies included in the qualitative synthesis—interpretation of the results is limited for many critical reasons. Specifically, the definition of e-cigarette use was not restricted to regular use. While doing so would have provided the strongest evidence on potential associations with the initiation of cigarette smoking, such a restriction would have yielded too few studies. Instead, the review was broadened to include any measure of e-cigarette use, with most studies measuring ever or current (past-30-day) use. Also, few studies examined initiation and/or progression to regular cigarette smoking, instead applying definitions of cigarette smoking that were more consistent with temporary experimentation and not true initiation, such as ever or current (past-30-day) smoking. Further, included studies were not restricted by specific confounding variables representing common liabilities between e-cigarette use and cigarette smoking, as this would have critically reduced the number of included studies in this review. The common-liability model considers the sequencing of drug use initiation, addiction, and addiction severity and posits that there are common sources of variation in the risk for specific addictions [ 11 ]. This model is critical for consideration given the empirical mixed signals that support or contradict the gateway hypothesis. However, the limited number of studies controlling for confounding variables related to common liability highlights the need for more robust studies to effectively measure the causal relationship between e-cigarette use and the initiation of cigarette smoking.

The majority of studies looked at how an e-cigarette-using population, individuals who never smoked cigarettes at baseline, developed cigarette smoking practices at follow-up. Though this information is indeed fundamental, it is equally important to understand the concepts of switching and dual-use. There are two possible trajectories that lead to an outcome of cigarette smoking among individuals using e-cigarettes. Between the baseline and follow-up surveys, (1) the e-cigarette user could begin cigarette smoking simultaneous with his/her e-cigarette use (dual use); or, (2) the e-cigarette user could eventually stop using the e-cigarette and after some time start smoking cigarettes (switchers). Information regarding whether individuals switched or dual used was limited, with only one adjusted study presenting specific data regarding single or dual use [ 52 ].

Further, 49 of 55 included studies reported on “youth”, “adolescent” and/or “young adult” populations, limiting the utility of the conclusions, as studies in youth and/or young adults are not necessarily translatable to older adults. Indeed, there is evidence that cigarette and e-cigarette smoking behaviors differ in different age groups. For example, one study determined that young adults (18–29 years of age) were more likely to be occasional smokers and reported lower daily consumption compared with older individuals who smoke cigarettes (30 years of age or older) [ 95 ]. Moreover, different age groups may vary in terms of their perceptions of and willingness to take risks, views of smoking addiction, perception of relative cigarette and e-cigarette health risks and/or benefits, and responses to behavioral interventions [ 96 ], which may differentially influence smoking behaviors and inherently, smoking cessation.

Finally, the duration of follow-up for the available studies was generally limited with most studies limited to 12 months in duration. This introduces the potentially limitation to observe whether cigarette smoking behavior actually persisted after initiation, i.e., true initiation and not simply temporary experimentation [ 2 ]. This may explain why so few of the included studies evaluated progression to regular cigarette smoking.

In conclusion, more robust studies are required to determine whether there is an association between e-cigarette use and initiation of cigarette smoking and progression to regular smoking. Based on findings from this review, the available studies neither sufficiently measure e-cigarette use—or cigarette smoking—in a manner consistent with examining causality, nor sufficiently account for known or suspected confounding variables to support robust determinations regarding e-cigarette use and cigarette smoking behaviors. Thus, the utility of the evidence base for policymakers, healthcare providers, and researchers is limited.

Availability of data and materials

All data and materials considered in this review are publicly available.

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Acknowledgements

The authors would like to acknowledge Thera-Business (Ontario CANADA) for providing their systematic review expertise to all study activities across all levels of the updated review process. The authors would also like to thank Dr. Geoffrey Curtin, a retired employee of RAI Services Company, for his scientific contributions during the conceptualization of this review.

All study activities were executed by providers external to RAI Services Company (Thera-Business), who were financially compensated for services according to contractual terms with RAI Services Company. RAI Services Company is a wholly owned subsidiary of Reynolds American Inc., whose operating companies manufacture and market tobacco products. The conception, analysis, and writing for this manuscript was a collaboration between Thera-Business and RAI Services Company.

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MMK conceived the study. MMK, IS, RDM, TB, and JC collected and analysed project data. MMK, IS, and RDM defined the study design, selection of measures, interpretation of data, and co-wrote the manuscript. All authors have read and approved the final article. The corresponding author attests that the listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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Kim, M.M., Steffensen, I., Miguel, R.T.D. et al. A systematic review and meta-analysis of the association between e-cigarette use among non-tobacco users and initiating smoking of combustible cigarettes. Harm Reduct J 21 , 99 (2024). https://doi.org/10.1186/s12954-024-01013-x

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Turnover intention and its associated factors among nurses in Ethiopia: a systematic review and meta-analysis

• Eshetu Elfios 1 ,
• Israel Asale 1 ,
• Merid Merkine 1 ,
• Temesgen Geta 1 ,
• Kidist Ashager 1 ,
• Getachew Nigussie 1 ,
• Ayele Agena 1 ,
• Bizuayehu Atinafu 1 ,
• Eskindir Israel 2 &
• Teketel Tesfaye 3  

BMC Health Services Research volume  24 , Article number:  662 ( 2024 ) Cite this article

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Nurses turnover intention, representing the extent to which nurses express a desire to leave their current positions, is a critical global public health challenge. This issue significantly affects the healthcare workforce, contributing to disruptions in healthcare delivery and organizational stability. In Ethiopia, a country facing its own unique set of healthcare challenges, understanding and mitigating nursing turnover are of paramount importance. Hence, the objectives of this systematic review and meta-analysis were to determine the pooled proportion ofturnover intention among nurses and to identify factors associated to it in Ethiopia.

A comprehensive search carried out for studies with full document and written in English language through an electronic web-based search strategy from databases including PubMed, CINAHL, Cochrane Library, Embase, Google Scholar and Ethiopian University Repository online. Checklist from the Joanna Briggs Institute (JBI) was used to assess the studies’ quality. STATA version 17 software was used for statistical analyses. Meta-analysis was done using a random-effects method. Heterogeneity between the primary studies was assessed by Cochran Q and I-square tests. Subgroup and sensitivity analyses were carried out to clarify the source of heterogeneity.

This systematic review and meta-analysis incorporated 8 articles, involving 3033 nurses in the analysis. The pooled proportion of turnover intention among nurses in Ethiopia was 53.35% (95% CI (41.64, 65.05%)), with significant heterogeneity between studies (I 2  = 97.9, P  = 0.001). Significant association of turnover intention among nurses was found with autonomous decision-making (OR: 0.28, CI: 0.14, 0.70) and promotion/development (OR: 0.67, C.I: 0.46, 0.89).

Conclusion and recommendation

Our meta-analysis on turnover intention among Ethiopian nurses highlights a significant challenge, with a pooled proportion of 53.35%. Regional variations, such as the highest turnover in Addis Ababa and the lowest in Sidama, underscore the need for tailored interventions. The findings reveal a strong link between turnover intention and factors like autonomous decision-making and promotion/development. Recommendations for stakeholders and concerned bodies involve formulating targeted retention strategies, addressing regional variations, collaborating for nurse welfare advocacy, prioritizing career advancement, reviewing policies for nurse retention improvement.

Peer Review reports

Turnover intention pertaining to employment, often referred to as the intention to leave, is characterized by an employee’s contemplation of voluntarily transitioning to a different job or company [ 1 ]. Nurse turnover intention, representing the extent to which nurses express a desire to leave their current positions, is a critical global public health challenge. This issue significantly affects the healthcare workforce, contributing to disruptions in healthcare delivery and organizational stability [ 2 ].

The global shortage of healthcare professionals, including nurses, is an ongoing challenge that significantly impacts the capacity of healthcare systems to provide quality services [ 3 ]. Nurses, as frontline healthcare providers, play a central role in patient care, making their retention crucial for maintaining the functionality and effectiveness of healthcare delivery. However, the phenomenon of turnover intention, reflecting a nurse’s contemplation of leaving their profession, poses a serious threat to workforce stability [ 4 ].

Studies conducted globally shows that high turnover rates among nurses in several regions, with notable figures reported in Alexandria (68%), China (63.88%), and Jordan (60.9%) [ 5 , 6 , 7 ]. In contrast, Israel has a remarkably low turnover rate of9% [ 8 ], while Brazil reports 21.1% [ 9 ], and Saudi hospitals26% [ 10 ]. These diverse turnover rates highlight the global nature of the nurse turnover phenomenon, indicating varying degrees of workforce mobility in different regions.

The magnitude and severity of turnover intention among nurses worldwide underscore the urgency of addressing this issue. High turnover rates not only disrupt healthcare services but also result in a loss of valuable skills and expertise within the nursing workforce. This, in turn, compromises the continuity and quality of patient care, with potential implications for patient outcomes and overall health service delivery [ 11 ]. Extensive research conducted worldwide has identified a range of factors contributing to turnover intention among nurses [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. These factors encompass both individual and organizational aspects, such as high workload, inadequate support, limited career advancement opportunities, job satisfaction, conflict, payment or reward, burnout sense of belongingness to their work environment. The complex interplay of these factors makes addressing turnover intention a multifaceted challenge that requires targeted interventions.

In Ethiopia, a country facing its own unique set of healthcare challenges, understanding and mitigating nursing turnover are of paramount importance. The healthcare system in Ethiopia grapples with issues like resource constraints, infrastructural limitations, and disparities in healthcare access [ 18 ]. Consequently, the factors influencing nursing turnover in Ethiopia may differ from those in other regions. Previous studies conducted in the Ethiopian context have started to unravel some of these factors, emphasizing the need for a more comprehensive examination [ 18 , 19 ].

Although many cross-sectional studies have been conducted on turnover intention among nurses in Ethiopia, the results exhibit variations. The reported turnover intention rates range from a minimum of 30.6% to a maximum of 80.6%. In light of these disparities, this systematic review and meta-analysis was undertaken to ascertain the aggregated prevalence of turnover intention among nurses in Ethiopia. By systematically analyzing findings from various studies, we aimed to provide a nuanced understanding of the factors influencing turnover intention specific to the Ethiopian healthcare context. Therefore, this systematic review and meta-analysis aimed to answer the following research questions.

What is the pooled prevalence of turnover intention among nurses in Ethiopia?

What are the factors associated with turnover intention among nurses in Ethiopia?

The primary objective of this review was to assess the pooled proportion of turnover intention among nurses in Ethiopia. The secondary objective was identifying the factors associated to turnover intention among nurses in Ethiopia.

Study design and search strategy

A comprehensive systematic review and meta-analysis was conducted, examining observational studies on turnover intention among nurses in Ethiopia. The procedure for this systematic review and meta-analysis was developed in accordance with the Preferred Reporting Items for Systematic review and Meta-analysis Protocols (PRISMA-P) statement [ 20 ]. PRISMA-2015 statement was used to report the findings [ 21 , 22 ]. This systematic review and meta-analysis were registered on PROSPERO with the registration number of CRD42024499119.

We conducted systematic and an extensive search across multiple databases, including PubMed, CINAHL, Cochrane Library, Embase, Google Scholar and Ethiopian University Repository online to identify studies reporting turnover intention among nurses in Ethiopia. We reviewed the database available at http://www.library.ucsf.edu and the Cochrane Library to ensure that the intended task had not been previously undertaken, preventing any duplication. Furthermore, we screened the reference lists to retrieve relevant articles. The process involved utilizing EndNote (version X8) software for downloading, organizing, reviewing, and citing articles. Additionally, a manual search for cross-references was performed to discover any relevant studies not captured through the initial database search. The search employed a comprehensive set of the following search terms:“prevalence”, “turnover intention”, “intention to leave”, “attrition”, “employee attrition”, “nursing staff turnover”, “Ethiopian nurses”, “nurses”, and “Ethiopia”. These terms were combined using Boolean operators (AND, OR) to conduct a thorough and systematic search across the specified databases.

Eligibility criteria

Inclusion criteria.

The established inclusion criteria for this meta-analysis and systematic review are as follows to guide the selection of articles for inclusion in this review.

Population: Nurses working in Ethiopia.

Study period: studies conducted or published until 23November 2023.

Study design: All observational study designs, such as cross-sectional, longitudinal, and cohort studies, were considered.

Setting: Only studies conducted in Ethiopia were included.

Outcome; turnover intention.

Study: All studies, whether published or unpublished, in the form of journal articles, master’s theses, and dissertations, were included up to the final date of data analysis.

Language: This study exclusively considered studies in the English language.

Exclusion criteria

Excluded were studies lacking full text or Studies with a Newcastle–Ottawa Quality Assessment Scale (NOS) score of 6 or less. Studies failing to provide information on turnover intention among nurses or studies for which necessary details could not be obtained were excluded. Three authors (E.E., T.G., K.A) independently assessed the eligibility of retrieved studies, other two authors (E.I & M.M) input sought for consensus on potential in- or exclusion.

Quality assessment and data extraction

Two authors (E.E, A.A, G.N) independently conducted a critical appraisal of the included studies. Joanna Briggs Institute (JBI) checklists of prevalence study was used to assess the quality of the studies. Studies with a Newcastle–Ottawa Quality Assessment Scale (NOS) score of seven or more were considered acceptable [ 23 ]. The tool has nine parameters, which have yes, no, unclear, and not applicable options [ 24 ]. Two reviewers (I.A, B.A) were involved when necessary, during the critical appraisal process. Accordingly, all studies were included in our review. ( Table  1 ) Questions to evaluate the methodological quality of studies on turnover intention among nurses and its associated factors in Ethiopia are the followings:

Q1 = was the sample frame appropriate to address the target population?

Q2. Were study participants sampled appropriately.

Q3. Was the sample size adequate?

Q4. Were the study subjects and the setting described in detail?

Q5. Was the data analysis conducted with sufficient coverage of the identified sample?

Q6. Were the valid methods used for the identification of the condition?

Q7. Was the condition measured in a standard, reliable way for all participants?

Q8. Was there appropriate statistical analysis?

Q9. Was the response rate adequate, and if not, was the low response rate.

managed appropriately?

Data was extracted and recorded in a Microsoft Excel as guided by the Joanna Briggs Institute (JBI) data extraction form for observational studies. Three authors (E.E, M.G, T.T) independently conducted data extraction. Recorded data included the first author’s last name, publication year, study setting or country, region, study design, study period, sample size, response rate, population, type of management, proportion of turnover intention, and associated factors. Discrepancies in data extraction were resolved through discussion between extractors.

Data processing and analysis

Data analysis procedures involved importing the extracted data into STATA 14 statistical software for conducting a pooled proportion of turnover intention among nurses. To evaluate potential publication bias and small study effects, both funnel plots and Egger’s test were employed [ 25 , 26 ]. We used statistical tests such as the I statistic to quantify heterogeneity and explore potential sources of variability. Additionally, subgroup analyses were conducted to investigate the impact of specific study characteristics on the overall results. I 2 values of 0%, 25%, 50%, and 75% were interpreted as indicating no, low, medium, and high heterogeneity, respectively [ 27 ].

To assess publication bias, we employed several methods, including funnel plots and Egger’s test. These techniques allowed us to visually inspect asymmetry in the distribution of study results and statistically evaluate the presence of publication bias. Furthermore, we conducted sensitivity analyses to assess the robustness of our findings to potential publication bias and other sources of bias.

Utilizing a random-effects method, a meta-analysis was performed to assess turnover intention among nurses, employing this method to account for observed variability [ 28 ]. Subgroup analyses were conducted to compare the pooled magnitude of turnover intention among nurses and associated factors across different regions. The results of the pooled prevalence were visually presented in a forest plot format with a 95% confidence interval.

Study selection

After conducting the initial comprehensive search concerning turnover intention among nurses through Medline, Cochran Library, Web of Science, Embase, Ajol, Google Scholar, and other sources, a total of 1343 articles were retrieved. Of which 575 were removed due to duplication. Five hundred ninety-three articles were removed from the remaining 768 articles by title and abstract. Following theses, 44 articles which cannot be retrieved were removed. Finally, from the remaining 131 articles, 8 articles with a total 3033 nurses were included in the systematic review and meta-analysis (Fig.  1 ).

PRISMA flow diagram of the selection process of studies on turnover intention among nurses in Ethiopia, 2024

Study characteristics

All included 8 studies had a cross-sectional design and of which, 2 were from Tigray region, 2 were from Addis Ababa(Capital), 1 from south region, 1 from Amhara region, 1 from Sidama region, and 1 was multiregional and Nationwide. The prevalence of turnover intention among nurses ‘ranges from 30.6 to 80.6%. Table  2 .

Pooled prevalence of turnover intention among nurses in Ethiopia

Our comprehensive meta-analysis revealed a notable turnover intention rate of 53.35% (95% CI: 41.64, 65.05%) among Ethiopian nurses, accompanied by substantial heterogeneity between studies (I 2  = 97.9, P  = 0.000) as depicted in Fig.  2 . Given the observed variability, we employed a random-effects model to analyze the data, ensuring a robust adjustment for the significant heterogeneity across the included studies.

Forest plot showing the pooled proportion of turnover intention among nurses in Ethiopia, 2024

Subgroup analysis of turnover intention among nurses in Ethiopia

To address the observed heterogeneity, we conducted a subgroup analysis based on regions. The results of the subgroup analysis highlighted considerable variations, with the highest level of turnover intention identified in Addis Ababa at 69.10% (95% CI: 46.47, 91.74%) and substantial heterogeneity (I 2  = 98.1%). Conversely, the Sidama region exhibited the lowest level of turnover intention among nurses at 30.6% (95% CI: 25.18, 36.02%), accompanied by considerable heterogeneity (I 2  = 100.0%) ( Fig.  3 ).

Subgroup analysis of systematic review and meta-analysis by region of turnover intention among nurses in Ethiopia, 2024

Publication bias of turnover intention among nurses in Ethiopia

The Egger’s test result ( p  = 0.64) is not statistically significant, indicating no evidence of publication bias in the meta-analysis (Table  3 ). Additionally, the symmetrical distribution of included studies in the funnel plot (Fig.  4 ) confirms the absence of publication bias across studies.

Funnel plot of systematic review and meta-analysis on turnover intention among nurses in Ethiopia, 2024

Sensitivity analysis

The leave-out-one sensitivity analysis served as a meticulous evaluation of the influence of individual studies on the comprehensive pooled prevalence of turnover intention within the context of Ethiopian nurses. In this systematic process, each study was methodically excluded from the analysis one at a time. The outcomes of this meticulous examination indicated that the exclusion of any particular study did not lead to a noteworthy or statistically significant alteration in the overall pooled estimate of turnover intention among nurses in Ethiopia. The findings are visually represented in Fig.  5 , illustrating the stability and robustness of the overall pooled estimate even with the removal of specific studies from the analysis.

Sensitivity analysis of pooled prevalence for each study being removed at a time for systematic review and meta-analysis of turnover intention among nurses in Ethiopia

Factors associated with turnover intention among nurses in Ethiopia

In our meta-analysis, we comprehensively reviewed and conducted a meta-analysis on the determinants of turnover intention among nurses in Ethiopia by examining eight relevant studies [ 6 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. We identified a significant association between turnover intention with autonomous decision-making (OR: 0.28, CI: 0.14, 0.70) (Fig.  6 ) and promotion/development (OR: 0.67, CI: 0.46, 0.89) (Fig.  7 ). In both instances, the odds ratios suggest a negative association, signifying that increased levels of autonomous decision-making and promotion/development were linked to reduced odds of turnover intention.

Forest plot of the association between autonomous decision making with turnover intention among nurses in Ethiopia2024

Forest plot of the association between promotion/developpment with turnover intention among nurses in Ethiopia, 2024

In our comprehensive meta-analysis exploring turnover intention among nurses in Ethiopia, our findings revealed a pooled proportion of turnover intention at 53.35%. This significant proportion warrants a comparative analysis with turnover rates reported in other global regions. Distinct variations emerge when compared with turnover rates in Alexandria (68%), China (63.88%), and Jordan (60.9%) [ 5 , 6 , 7 ]. This comparison highlights that the multifaceted nature of turnover intention, influenced by diverse contextual, cultural, and organizational factors. Conversely, Ethiopia’s turnover rate among nurses contrasts with substantially lower figures reported in Israel (9%) [ 8 ], Brazil (21.1%) [ 9 ], and Saudi hospitals (26%) [ 10 ]. Challenges such as work overload, economic constraints, limited promotional opportunities, lack of recognition, and low job rewards are more prevalent among nurses in Ethiopia, contributing to higher turnover intention compared to their counterparts [ 7 , 29 , 36 ].

The highest turnover intention was observed in Addis Ababa, while Sidama region displayed the lowest turnover intention among nurses, These differences highlight the complexity of turnover intention among Ethiopian nurses, showing the importance of specific interventions in each region to address unique factors and improve nurses’ retention.

Our systematic review and meta-analysis in the Ethiopian nursing context revealed a significant inverse association between turnover intention and autonomous decision-making. The odd of turnover intention is approximately reduced by 72% in employees with autonomous decision-making compared to those without autonomous decision-making. This finding was supported by other similar studies conducted in South Africa, Tanzania, Kenya, and Turkey [ 37 , 38 , 39 , 40 ].

The significant association of turnover intention with promotion/development in our study underscores the crucial role of career advancement opportunities in alleviating turnover intention among nurses. Specifically, our analysis revealed that individuals with promotion/development had approximately 33% lower odds of turnover intention compared to those without such opportunities. These results emphasize the pivotal influence of organizational support in shaping the professional environment for nurses, providing substantive insights for the formulation of evidence-based strategies targeted at enhancing workforce retention. This finding is in line with former researches conducted in Taiwan, Philippines and Italy [ 41 , 42 , 43 ].

Our meta-analysis on turnover intention among Ethiopian nurses reveals a considerable challenge, with a pooled proportion of 53.35%. Regional variations highlight the necessity for region-specific strategies, with Addis Ababa displaying the highest turnover intention and Sidama region the lowest. A significant inverse association was found between turnover intention with autonomous decision-making and promotion/development. These insights support the formulation of evidence-based strategies and policies to enhance nurse retention, contributing to the overall stability of the Ethiopian healthcare system.

Recommendations

Federal ministry of health (fmoh).

The FMoH should consider the regional variations in turnover intention and formulate targeted retention strategies. Investment in professional development opportunities and initiatives to enhance autonomy can be integral components of these strategies.

Ethiopian nurses association (ENA)

ENA plays a pivotal role in advocating for the welfare of nurses. The association is encouraged to collaborate with healthcare institutions to promote autonomy, create mentorship programs, and advocate for improved working conditions to mitigate turnover intention.

Healthcare institutions

Hospitals and healthcare facilities should prioritize the provision of career advancement opportunities and recognize the value of professional autonomy in retaining nursing staff. Tailored interventions based on regional variations should be considered.

Policy makers

Policymakers should review existing healthcare policies to identify areas for improvement in nurse retention. Policy changes that address challenges such as work overload, limited promotional opportunities, and economic constraints can positively impact turnover rates.

Future research initiatives

Further research exploring the specific factors contributing to turnover intention in different regions of Ethiopia is recommended. Understanding the nuanced challenges faced by nurses in various settings will inform the development of more targeted interventions.

Strength and limitations

Our systematic review and meta-analysis on nurse turnover intention in Ethiopia present several strengths. The comprehensive inclusion of diverse studies provides a holistic view of the issue, enhancing the generalizability of our findings. The use of a random-effects model accounts for potential heterogeneity, ensuring a more robust and reliable synthesis of data.

However, limitations should be acknowledged. The heterogeneity observed across studies, despite the use of a random-effects model, may impact the precision of the pooled estimate. These considerations should be taken into account when interpreting and applying the results of our analysis.

Data availability

Data set used on this analysis will available from corresponding author upon reasonable request.

Abbreviations

Ethiopian Nurses Association

Federal Ministry of Health

Joanna Briggs Institute

Preferred Reporting Items for Systematic review and Meta-analysis Protocols

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School of Nursing, College of Health Science and Medicine, Wolaita Sodo University, Wolaita Sodo, Ethiopia

Eshetu Elfios, Israel Asale, Merid Merkine, Temesgen Geta, Kidist Ashager, Getachew Nigussie, Ayele Agena & Bizuayehu Atinafu

Department of Midwifery, College of Health Science and Medicine, Wolaita Sodo University, Wolaita Sodo, Ethiopia

Eskindir Israel

Department of Midwifery, College of Health Science and Medicine, Wachamo University, Hossana, Ethiopia

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E.E. conceptualized the study, designed the research, performed statistical analysis, and led the manuscript writing. I.A, T.G, M.M contributed to the study design and provided critical revisions. K.A., G.N, B.A., E.I., and T.T. participated in data extraction and quality assessment. M.M. and T.G. K.A. and G.N. contributed to the literature review. I.A, A.A. and B.A. assisted in data interpretation. E.I. and T.T. provided critical revisions to the manuscript. All authors read and approved the final version.

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Correspondence to Eshetu Elfios .

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Elfios, E., Asale, I., Merkine, M. et al. Turnover intention and its associated factors among nurses in Ethiopia: a systematic review and meta-analysis. BMC Health Serv Res 24 , 662 (2024). https://doi.org/10.1186/s12913-024-11122-9

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Vegetables and Fruits

• Vegetables and fruits are an important part of a healthy diet, and variety is as important as quantity.
• No single fruit or vegetable provides all of the nutrients you need to be healthy. Eat plenty every day.

A diet rich in vegetables and fruits can lower blood pressure, reduce the risk of heart disease and stroke, prevent some types of cancer, lower risk of eye and digestive problems, and have a positive effect upon blood sugar, which can help keep appetite in check. Eating non-starchy vegetables and fruits like apples, pears, and green leafy vegetables may even promote weight loss. [1] Their low glycemic loads prevent blood sugar spikes that can increase hunger.

At least nine different families of fruits and vegetables exist, each with potentially hundreds of different plant compounds that are beneficial to health. Eat a variety of types and colors of produce in order to give your body the mix of nutrients it needs. This not only ensures a greater diversity of beneficial plant chemicals but also creates eye-appealing meals.

Tips to eat more vegetables and fruits each day

• Keep fruit where you can see it . Place several ready-to-eat washed whole fruits in a bowl or store chopped colorful fruits in a glass bowl in the refrigerator to tempt a sweet tooth.
• Explore the produce aisle and choose something new . Variety and color are key to a healthy diet. On most days, try to get at least one serving from each of the following categories: dark green leafy vegetables; yellow or orange fruits and vegetables; red fruits and vegetables; legumes (beans) and peas; and citrus fruits.
• Skip the potatoes . Choose other vegetables that are packed with different nutrients and more slowly digested  carbohydrates .
• Make it a meal . Try cooking new  recipes that include more vegetables. Salads, soups, and stir-fries are just a few ideas for increasing the number of tasty vegetables in your meals.

5 common questions about fruits and vegetables.

Vegetables, fruits, and disease, cardiovascular disease.

There is compelling evidence that a diet rich in fruits and vegetables can lower the risk of heart disease and stroke.

• A meta-analysis of cohort studies following 469,551 participants found that a higher intake of fruits and vegetables is associated with a reduced risk of death from cardiovascular disease, with an average reduction in risk of 4% for each additional serving per day of fruit and vegetables. [2]
• The largest and longest study to date, done as part of the Harvard-based Nurses’ Health Study and Health Professionals Follow-up Study, included almost 110,000 men and women whose health and dietary habits were followed for 14 years.
• The higher the average daily intake of fruits and vegetables, the lower the chances of developing cardiovascular disease. Compared with those in the lowest category of fruit and vegetable intake (less than 1.5 servings a day), those who averaged 8 or more servings a day were 30% less likely to have had a heart attack or stroke. [3]
• Although all fruits and vegetables likely contributed to this benefit, green leafy vegetables, such as lettuce, spinach, Swiss chard, and mustard greens, were most strongly associated with decreased risk of cardiovascular disease. Cruciferous vegetables such as broccoli, cauliflower, cabbage, Brussels sprouts , bok choy, and kale ; and citrus fruits such as oranges, lemons, limes, and grapefruit (and their juices) also made important contributions. [3]
• When researchers combined findings from the Harvard studies with several other long-term studies in the U.S. and Europe, and looked at coronary heart disease and stroke separately, they found a similar protective effect: Individuals who ate more than 5 servings of fruits and vegetables per day had roughly a 20% lower risk of coronary heart disease [4] and stroke, [5] compared with individuals who ate less than 3 servings per day.

Blood pressure

• The  Dietary Approaches to Stop Hypertension (DASH) study [6] examined the effect on blood pressure of a diet that was rich in fruits, vegetables, and low-fat dairy products and that restricted the amount of saturated and total fat. The researchers found that people with high blood pressure who followed this diet reduced their systolic blood pressure (the upper number of a blood pressure reading) by about 11 mm Hg and their diastolic blood pressure (the lower number) by almost 6 mm Hg—as much as medications can achieve.
• A randomized trial known as the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart) showed that this fruit and vegetable-rich diet lowered blood pressure even more when some of the carbohydrate was replaced with healthy unsaturated fat or protein. [7]
• In 2014 a meta-analysis of clinical trials and observational studies found that consumption of a vegetarian diet was associated with lower blood pressure. [8]

Numerous early studies revealed what appeared to be a strong link between eating fruits and vegetables and protection against cancer . Unlike case-control studies, cohort studies , which follow large groups of initially healthy individuals for years, generally provide more reliable information than case-control studies because they don’t rely on information from the past. And, in general, data from cohort studies have not consistently shown that a diet rich in fruits and vegetables prevents cancer.

• For example, over a 14-year period in the Nurses’ Health Study and the Health Professionals Follow-up Study, men and women with the highest intake of fruits and vegetables (8+ servings a day) were just as likely to have developed cancer as those who ate the fewest daily servings (under 1.5). [3]
• A meta-analysis of cohort studies found that a higher fruit and vegetable intake did not decrease the risk of deaths from cancer. [2]

A more likely possibility is that some types of fruits and vegetables may protect against certain cancers.

• A study by Farvid and colleagues followed a Nurses’ Health Study II cohort of 90,476 premenopausal women for 22 years and found that those who ate the most fruit during adolescence (about 3 servings a day) compared with those who ate the lowest intakes (0.5 servings a day) had a 25% lower risk of developing breast cancer. There was a significant reduction in breast cancer in women who had eaten higher intakes of apples, bananas , grapes, and corn during adolescence, and oranges and kale during early adulthood. No protection was found from drinking fruit juices at younger ages. [9]
• Farvid and colleagues followed 90, 534 premenopausal women from the Nurses’ Health Study II over 20 years and found that higher fiber intakes during adolescence and early adulthood were associated with a reduced risk of breast cancer later in life. When comparing the highest and lowest fiber intakes from fruits and vegetables, women with the highest fruit fiber intake had a 12% reduced risk of breast cancer; those with the highest vegetable fiber intake had an 11% reduced risk. [10]
• After following 182,145 women in the Nurses’ Health Study I and II for 30 years, Farvid’s team also found that women who ate more than 5.5 servings of fruits and vegetables each day (especially cruciferous and yellow/orange vegetables) had an 11% lower risk of breast cancer than those who ate 2.5 or fewer servings. Vegetable intake was strongly associated with a 15% lower risk of estrogen-receptor-negative tumors for every two additional servings of vegetables eaten daily. A higher intake of fruits and vegetables was associated with a lower risk of other aggressive tumors including HER2-enriched and basal-like tumors. [11]
• A report by the World Cancer Research Fund and the American Institute for Cancer Research suggests that non-starchy vegetables—such as lettuce and other leafy greens, broccoli, bok choy, cabbage, as well as garlic, onions, and the like—and fruits “probably” protect against several types of cancers, including those of the mouth, throat, voice box, esophagus, and stomach. Fruit probably also protects against lung cancer. [12]

Specific components of fruits and vegetables may also be protective against cancer. For example:

• A line of research stemming from a finding from the Health Professionals Follow-up Study suggests that tomatoes may help protect men against prostate cancer, especially aggressive forms of it. [12] One of the pigments that give tomatoes their red hue—lycopene—could be involved in this protective effect. Although several studies other than the Health Professionals Study have also demonstrated a link between tomatoes or lycopene and prostate cancer, others have not or have found only a weak connection. [14]
• Taken as a whole, however, these studies suggest that increased consumption of tomato-based products (especially cooked tomato products) and other lycopene-containing foods may reduce the occurrence of prostate cancer. [12] Lycopene is one of several carotenoids (compounds that the body can turn into vitamin A) found in brightly colored fruits and vegetables, and research suggests that foods containing carotenoids may protect against lung, mouth, and throat cancer. [12] But more research is needed to understand the exact relationship between fruits and vegetables, carotenoids, and cancer.

Some research looks specifically at whether individual fruits are associated with risk of type 2 diabetes. While there isn’t an abundance of research into this area yet, preliminary results are compelling.

• A study of over 66,000 women in the Nurses’ Health Study, 85,104 women from the Nurses’ Health Study II, and 36,173 men from the Health Professionals Follow-up Study—who were free of major chronic diseases—found that greater consumption of whole fruits—especially blueberries, grapes, and apples—was associated with a lower risk of type 2 diabetes. Another important finding was that greater consumption of fruit juice was associated with a higher risk of type 2 diabetes. [15]
• Additionally a study of over 70,000 female nurses aged 38-63 years, who were free of cardiovascular disease, cancer, and diabetes, showed that consumption of green leafy vegetables and fruit was associated with a lower risk of diabetes. While not conclusive, research also indicated that consumption of fruit juices may be associated with an increased risk among women. (16)
• A study of over 2,300 Finnish men showed that vegetables and fruits, especially berries, may reduce the risk of type 2 diabetes. [17]

Data from the Nurses’ Health Studies and the Health Professional’s Follow-up Study show that women and men who increased their intakes of fruits and vegetables over a 24-year period were more likely to have lost weight than those who ate the same amount or those who decreased their intake. Berries, apples, pears, soy, and cauliflower were associated with weight loss while starchier vegetables like potatoes, corn, and peas were linked with weight gain. [1] However, keep in mind that adding more produce into the diet won’t necessarily help with weight loss unless it replaces another food, such as refined carbohydrates of white bread and crackers.

Gastrointestinal health

Fruits and vegetables contain indigestible fiber, which absorbs water and expands as it passes through the digestive system. This can calm symptoms of an irritable bowel and, by triggering regular bowel movements, can relieve or prevent constipation. [18] The bulking and softening action of insoluble fiber also decreases pressure inside the intestinal tract and may help prevent diverticulosis. [19]

Eating fruits and vegetables can also keep your eyes healthy, and may help prevent two common aging-related eye diseases—cataracts and macular degeneration—which afflict millions of Americans over age 65. [20-23] Lutein and zeaxanthin, in particular, seem to reduce risk of cataracts. [24]

• Bertoia ML, Mukamal KJ, Cahill LE, Hou T, Ludwig DS, Mozaffarian D, Willett WC, Hu FB, Rimm EB. Changes in intake of fruits and vegetables and weight change in United States men and women followed for up to 24 years: analysis from three prospective cohort studies. PLoS medicine . 2015 Sep 22;12(9):e1001878.
• Wang X, Ouyang Y, Liu J, Zhu M, Zhao G, Bao W, Hu FB. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ . 2014 Jul 29;349:g4490.
• Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, Colditz GA, Rosner B, Spiegelman D, Willett WC. Fruit and vegetable intake and risk of major chronic disease. Journal of the National Cancer Institute . 2004 Nov 3;96(21):1577-84.
• He FJ, Nowson CA, Lucas M, MacGregor GA. Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: meta-analysis of cohort studies. Journal of human hypertension . 2007 Sep;21(9):717.
• He FJ, Nowson CA, MacGregor GA. Fruit and vegetable consumption and stroke: meta-analysis of cohort studies. The Lancet . 2006 Jan 28;367(9507):320-6.
• Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, Bray GA, Vogt TM, Cutler JA, Windhauser MM, Lin PH. A clinical trial of the effects of dietary patterns on blood pressure. New England Journal of Medicine . 1997 Apr 17;336(16):1117-24.
• Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, Conlin PR, Erlinger TP, Rosner BA, Laranjo NM, Charleston J. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. JAMA . 2005 Nov 16;294(19):2455-64.
• Yokoyama Y, Nishimura K, Barnard ND, Takegami M, Watanabe M, Sekikawa A, Okamura T, Miyamoto Y. Vegetarian diets and blood pressure: a meta-analysis. JAMA internal medicine. 2014 Apr 1;174(4):577-87.
• Farvid MS, Chen WY, Michels KB, Cho E, Willett WC, Eliassen AH. Fruit and vegetable consumption in adolescence and early adulthood and risk of breast cancer: population based cohort study. BMJ . 2016 May 11;353:i2343.
• Farvid MS, Eliassen AH, Cho E, Liao X, Chen WY, Willett WC. Dietary fiber intake in young adults and breast cancer risk. Pediatrics . 2016 Mar 1;137(3):e20151226.
• Farvid MS, Chen WY, Rosner BA, Tamimi RM, Willett WC, Eliassen AH. Fruit and vegetable consumption and breast cancer incidence: Repeated measures over 30 years of follow‐up. International journal of cancer . 2018 Jul 6.
• Wiseman M. The Second World Cancer Research Fund/American Institute for Cancer Research Expert Report. Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective: Nutrition Society and BAPEN Medical Symposium on ‘Nutrition support in cancer therapy’. Proceedings of the Nutrition Society . 2008 Aug;67(3):253-6.
• Giovannucci E, Liu Y, Platz EA, Stampfer MJ, Willett WC. Risk factors for prostate cancer incidence and progression in the health professionals follow‐up study. International journal of cancer . 2007 Oct 1;121(7):1571-8.
• Kavanaugh CJ, Trumbo PR, Ellwood KC. The US Food and Drug Administration’s evidence-based review for qualified health claims: tomatoes, lycopene, and cancer. Journal of the National Cancer Institute . 2007 Jul 18;99(14):1074-85.
• Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, van Dam RM, Sun Q. Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ . 2013 Aug 29;347:f5001.
• Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care . 2008 Apr 3.
• Mursu J, Virtanen JK, Tuomainen TP, Nurmi T, Voutilainen S. Intake of fruit, berries, and vegetables and risk of type 2 diabetes in Finnish men: the Kuopio Ischaemic Heart Disease Risk Factor Study–. The American journal of clinical nutrition . 2013 Nov 20;99(2):328-33.
• Lembo A, Camilleri M. Chronic constipation. New England Journal of Medicine . 2003 Oct 2;349(14):1360-8.
• Aldoori WH, Giovannucci EL, Rockett HR, Sampson L, Rimm EB, Willett AW. A prospective study of dietary fiber types and symptomatic diverticular disease in men. The Journal of nutrition . 1998 Oct 1;128(4):714-9.
• Brown L, Rimm EB, Seddon JM, Giovannucci EL, Chasan-Taber L, Spiegelman D, Willett WC, Hankinson SE. A prospective study of carotenoid intake and risk of cataract extraction in US men–. The American journal of clinical nutrition . 1999 Oct 1;70(4):517-24.
• Christen WG, Liu S, Schaumberg DA, Buring JE. Fruit and vegetable intake and the risk of cataract in women–. The American journal of clinical nutrition . 2005 Jun 1;81(6):1417-22.
• Moeller SM, Taylor A, Tucker KL, McCullough ML, Chylack Jr LT, Hankinson SE, Willett WC, Jacques PF. Overall adherence to the dietary guidelines for Americans is associated with reduced prevalence of early age-related nuclear lens opacities in women. The Journal of nutrition . 2004 Jul 1;134(7):1812-9.
• Cho E, Seddon JM, Rosner B, Willett WC, Hankinson SE. Prospective study of intake of fruits, vegetables, vitamins, and carotenoidsand risk of age-related maculopathy. Archives of Ophthalmology . 2004 Jun 1;122(6):883-92.
• Christen WG, Liu S, Glynn RJ, Gaziano JM, Buring JE. Dietary carotenoids, vitamins C and E, and risk of cataract in women: a prospective study. Archives of Ophthalmology . 2008 Jan 1;126(1):102-9.

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Huge Meta-Analysis Shows Plant-Based Diets Are Full of Health Benefits

The new work looked at 49 reviews spanning more than 20 years of diet and health research.

People opt to eat a vegetarian or vegan diet for all sorts of reasons. Often ethical, moral, religious or environmental considerations are key factors. But a new, massive analysis hammers home that there are also plenty of selfish reasons to avoid eating meat and animal products. Plant-based diets are correlated with a slew of health benefits, from reduced cancer and heart disease risk to lower inflammation, according to a large “umbrella” review study published May 15 in the journal PLOS One.

Review studies assess the results from multiple primary studies at once. Meta-analyses are a type of review that synthesizes results from more than one past study in a statistically rigorous way. Umbrella reviews take all of that one step further: grouping and assessing past reviews and meta-anaylses into a single sweeping study to cover as much of the available research as possible. They’re a particularly rigorous scientific approach.

The new work looked at 49 reviews spanning more than 20 years of diet and health research. It only included studies of vegetarians (including those who eat eggs and dairy) and vegans (those who abstain from all animal-based products) — not pescatarians or those who otherwise reduce or restrict their meat consumption. The scientists also focused their analysis on studies of generally healthy people, and excluded research on populations with diagnosed illnesses or special dietary requirements (like professional athletes). The resulting umbrella review indicates some strong trends across the field.

Across past review studies, they found that both vegetarians and vegans have significantly better metabolic health and are less likely to develop certain types of cancer and heart disease. Specifically, the researchers noted that total cholesterol levels, LDL (i.e. “bad”) cholesterol levels, glycemic control, lipid profile, blood pressure, and body weight were all significantly healthier among the non-meat-eaters in multiple past studies. Further, C-reactive protein, a measure of systemic inflammation, was significantly lower among vegetarians and vegans. Plus, vegetarians and vegans had significantly lower risk of heart disease caused by blocked blood vessels and a lower risk of mortality from cardiovascular disease, according to the new research. Finally, the researchers found a lower risk of gastrointestinal and prostate cancers among the vegetarians and vegans, compared with omnivores, and a lower risk of mortality from those diseases.

The review suggests multiple possible contributing factors for all of those benefits. For one, the authors note that veg-heads engage in healthier lifestyles overall , exercising more and smoking less. Essential, health-promoting nutrients like fiber and complex carbohydrates are abundant in plant-based foods. Some studies also suggest that plant foods include protective bioactive compounds that actively reduce cancer risk like vitamins and antioxidants. Other research has indicated that avoiding meat also reduces consumption of unhealthy fats, sugars, and processed foods that we know are detrimental. And some of the findings could come down to weight. Since vegetarians and vegans generally had lower body mass index, that could have knock-on health benefits specific to reduced body fat. Regardless of the exact reasons, the upsides are clear.

Though, there are a few caveats. The umbrella analysis didn’t find significant evidence of every examined health benefit. For example, avoiding meat and animal products doesn’t seem to reduce the risk of gestational diabetes or hypertension during pregnancy. And some outcomes are still up for debate, such as the impact of vegetarianism or veganism on pancreatic cancer or HDL (AKA “good”) cholesterol.

Plus, the review notes there are confounding factors that could be influencing the results. The data reviewed was heterogeneous, varying in type and quality– so some of the studies might be stronger reflections of real-world trends than others. And people in different parts of the world may experience different outcomes from adopting a meat-free diet due to regional dietary variation and food availability, so observed effects may differ by location, which the authors didn’t control for in their analysis.

Crucially, herbivore diets can vary in makeup and healthfulness, just as much as omnivore diets can. “Even vegetarian or vegan diets that emphasize consumption of unhealthy plant foods, such as fruit juices, refined grains, potato chips, and even sodas might have detrimental effects on the body,” said study co-author Davide Gori, a biomedical scientist at the University of Bologna in Italy, to Everyday Health . And there are some well-established risks of a vegan diet, like the potential for developing B-12 deficiency and other nutritional shortfalls.

Because of these risks and limitations, the study authors don’t offer a blanket recommendation that everyone suddenly stop eating meat. “Specific patient needs should be considered,” and more research is needed, before suggesting vegetarian and vegan diets “on a large scale,” the authors write.

Yet altogether, the new review is compelling evidence that going meat-free can contribute to a longer and healthier life. In that way, vegetarians and vegans may be killing two birds with one stone, metaphorically speaking: saving animals and preserving their own well-being at the same time.