Systematic Reviews and Meta Analysis

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

Systematic review Q & A

What is a systematic review.

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

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

Is my research topic appropriate for systematic review methods?

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

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

If not a systematic review, then what?

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

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

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

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

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

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

"neoadjuvant chemotherapy" AND systematic[sb]

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

"neoadjuvant chemotherapy" AND systematic[ti]

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

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

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  • Last Updated: Feb 26, 2024 3:17 PM
  • URL: https://guides.library.harvard.edu/meta-analysis
  • Open access
  • Published: 01 August 2019

A step by step guide for conducting a systematic review and meta-analysis with simulation data

  • Gehad Mohamed Tawfik 1 , 2 ,
  • Kadek Agus Surya Dila 2 , 3 ,
  • Muawia Yousif Fadlelmola Mohamed 2 , 4 ,
  • Dao Ngoc Hien Tam 2 , 5 ,
  • Nguyen Dang Kien 2 , 6 ,
  • Ali Mahmoud Ahmed 2 , 7 &
  • Nguyen Tien Huy 8 , 9 , 10  

Tropical Medicine and Health volume  47 , Article number:  46 ( 2019 ) Cite this article

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The massive abundance of studies relating to tropical medicine and health has increased strikingly over the last few decades. In the field of tropical medicine and health, a well-conducted systematic review and meta-analysis (SR/MA) is considered a feasible solution for keeping clinicians abreast of current evidence-based medicine. Understanding of SR/MA steps is of paramount importance for its conduction. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, this methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly conduct a SR/MA, in which all the steps here depicts our experience and expertise combined with the already well-known and accepted international guidance.

We suggest that all steps of SR/MA should be done independently by 2–3 reviewers’ discussion, to ensure data quality and accuracy.

SR/MA steps include the development of research question, forming criteria, search strategy, searching databases, protocol registration, title, abstract, full-text screening, manual searching, extracting data, quality assessment, data checking, statistical analysis, double data checking, and manuscript writing.

Introduction

The amount of studies published in the biomedical literature, especially tropical medicine and health, has increased strikingly over the last few decades. This massive abundance of literature makes clinical medicine increasingly complex, and knowledge from various researches is often needed to inform a particular clinical decision. However, available studies are often heterogeneous with regard to their design, operational quality, and subjects under study and may handle the research question in a different way, which adds to the complexity of evidence and conclusion synthesis [ 1 ].

Systematic review and meta-analyses (SR/MAs) have a high level of evidence as represented by the evidence-based pyramid. Therefore, a well-conducted SR/MA is considered a feasible solution in keeping health clinicians ahead regarding contemporary evidence-based medicine.

Differing from a systematic review, unsystematic narrative review tends to be descriptive, in which the authors select frequently articles based on their point of view which leads to its poor quality. A systematic review, on the other hand, is defined as a review using a systematic method to summarize evidence on questions with a detailed and comprehensive plan of study. Furthermore, despite the increasing guidelines for effectively conducting a systematic review, we found that basic steps often start from framing question, then identifying relevant work which consists of criteria development and search for articles, appraise the quality of included studies, summarize the evidence, and interpret the results [ 2 , 3 ]. However, those simple steps are not easy to be reached in reality. There are many troubles that a researcher could be struggled with which has no detailed indication.

Conducting a SR/MA in tropical medicine and health may be difficult especially for young researchers; therefore, understanding of its essential steps is crucial. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, we recommend a flow diagram (Fig. 1 ) which illustrates a detailed and step-by-step the stages for SR/MA studies. This methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly and succinctly conduct a SR/MA; all the steps here depicts our experience and expertise combined with the already well known and accepted international guidance.

figure 1

Detailed flow diagram guideline for systematic review and meta-analysis steps. Note : Star icon refers to “2–3 reviewers screen independently”

Methods and results

Detailed steps for conducting any systematic review and meta-analysis.

We searched the methods reported in published SR/MA in tropical medicine and other healthcare fields besides the published guidelines like Cochrane guidelines {Higgins, 2011 #7} [ 4 ] to collect the best low-bias method for each step of SR/MA conduction steps. Furthermore, we used guidelines that we apply in studies for all SR/MA steps. We combined these methods in order to conclude and conduct a detailed flow diagram that shows the SR/MA steps how being conducted.

Any SR/MA must follow the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA checklist 2009) (Additional file 5 : Table S1) [ 5 ].

We proposed our methods according to a valid explanatory simulation example choosing the topic of “evaluating safety of Ebola vaccine,” as it is known that Ebola is a very rare tropical disease but fatal. All the explained methods feature the standards followed internationally, with our compiled experience in the conduct of SR beside it, which we think proved some validity. This is a SR under conduct by a couple of researchers teaming in a research group, moreover, as the outbreak of Ebola which took place (2013–2016) in Africa resulted in a significant mortality and morbidity. Furthermore, since there are many published and ongoing trials assessing the safety of Ebola vaccines, we thought this would provide a great opportunity to tackle this hotly debated issue. Moreover, Ebola started to fire again and new fatal outbreak appeared in the Democratic Republic of Congo since August 2018, which caused infection to more than 1000 people according to the World Health Organization, and 629 people have been killed till now. Hence, it is considered the second worst Ebola outbreak, after the first one in West Africa in 2014 , which infected more than 26,000 and killed about 11,300 people along outbreak course.

Research question and objectives

Like other study designs, the research question of SR/MA should be feasible, interesting, novel, ethical, and relevant. Therefore, a clear, logical, and well-defined research question should be formulated. Usually, two common tools are used: PICO or SPIDER. PICO (Population, Intervention, Comparison, Outcome) is used mostly in quantitative evidence synthesis. Authors demonstrated that PICO holds more sensitivity than the more specific SPIDER approach [ 6 ]. SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) was proposed as a method for qualitative and mixed methods search.

We here recommend a combined approach of using either one or both the SPIDER and PICO tools to retrieve a comprehensive search depending on time and resources limitations. When we apply this to our assumed research topic, being of qualitative nature, the use of SPIDER approach is more valid.

PICO is usually used for systematic review and meta-analysis of clinical trial study. For the observational study (without intervention or comparator), in many tropical and epidemiological questions, it is usually enough to use P (Patient) and O (outcome) only to formulate a research question. We must indicate clearly the population (P), then intervention (I) or exposure. Next, it is necessary to compare (C) the indicated intervention with other interventions, i.e., placebo. Finally, we need to clarify which are our relevant outcomes.

To facilitate comprehension, we choose the Ebola virus disease (EVD) as an example. Currently, the vaccine for EVD is being developed and under phase I, II, and III clinical trials; we want to know whether this vaccine is safe and can induce sufficient immunogenicity to the subjects.

An example of a research question for SR/MA based on PICO for this issue is as follows: How is the safety and immunogenicity of Ebola vaccine in human? (P: healthy subjects (human), I: vaccination, C: placebo, O: safety or adverse effects)

Preliminary research and idea validation

We recommend a preliminary search to identify relevant articles, ensure the validity of the proposed idea, avoid duplication of previously addressed questions, and assure that we have enough articles for conducting its analysis. Moreover, themes should focus on relevant and important health-care issues, consider global needs and values, reflect the current science, and be consistent with the adopted review methods. Gaining familiarity with a deep understanding of the study field through relevant videos and discussions is of paramount importance for better retrieval of results. If we ignore this step, our study could be canceled whenever we find out a similar study published before. This means we are wasting our time to deal with a problem that has been tackled for a long time.

To do this, we can start by doing a simple search in PubMed or Google Scholar with search terms Ebola AND vaccine. While doing this step, we identify a systematic review and meta-analysis of determinant factors influencing antibody response from vaccination of Ebola vaccine in non-human primate and human [ 7 ], which is a relevant paper to read to get a deeper insight and identify gaps for better formulation of our research question or purpose. We can still conduct systematic review and meta-analysis of Ebola vaccine because we evaluate safety as a different outcome and different population (only human).

Inclusion and exclusion criteria

Eligibility criteria are based on the PICO approach, study design, and date. Exclusion criteria mostly are unrelated, duplicated, unavailable full texts, or abstract-only papers. These exclusions should be stated in advance to refrain the researcher from bias. The inclusion criteria would be articles with the target patients, investigated interventions, or the comparison between two studied interventions. Briefly, it would be articles which contain information answering our research question. But the most important is that it should be clear and sufficient information, including positive or negative, to answer the question.

For the topic we have chosen, we can make inclusion criteria: (1) any clinical trial evaluating the safety of Ebola vaccine and (2) no restriction regarding country, patient age, race, gender, publication language, and date. Exclusion criteria are as follows: (1) study of Ebola vaccine in non-human subjects or in vitro studies; (2) study with data not reliably extracted, duplicate, or overlapping data; (3) abstract-only papers as preceding papers, conference, editorial, and author response theses and books; (4) articles without available full text available; and (5) case reports, case series, and systematic review studies. The PRISMA flow diagram template that is used in SR/MA studies can be found in Fig. 2 .

figure 2

PRISMA flow diagram of studies’ screening and selection

Search strategy

A standard search strategy is used in PubMed, then later it is modified according to each specific database to get the best relevant results. The basic search strategy is built based on the research question formulation (i.e., PICO or PICOS). Search strategies are constructed to include free-text terms (e.g., in the title and abstract) and any appropriate subject indexing (e.g., MeSH) expected to retrieve eligible studies, with the help of an expert in the review topic field or an information specialist. Additionally, we advise not to use terms for the Outcomes as their inclusion might hinder the database being searched to retrieve eligible studies because the used outcome is not mentioned obviously in the articles.

The improvement of the search term is made while doing a trial search and looking for another relevant term within each concept from retrieved papers. To search for a clinical trial, we can use these descriptors in PubMed: “clinical trial”[Publication Type] OR “clinical trials as topic”[MeSH terms] OR “clinical trial”[All Fields]. After some rounds of trial and refinement of search term, we formulate the final search term for PubMed as follows: (ebola OR ebola virus OR ebola virus disease OR EVD) AND (vaccine OR vaccination OR vaccinated OR immunization) AND (“clinical trial”[Publication Type] OR “clinical trials as topic”[MeSH Terms] OR “clinical trial”[All Fields]). Because the study for this topic is limited, we do not include outcome term (safety and immunogenicity) in the search term to capture more studies.

Search databases, import all results to a library, and exporting to an excel sheet

According to the AMSTAR guidelines, at least two databases have to be searched in the SR/MA [ 8 ], but as you increase the number of searched databases, you get much yield and more accurate and comprehensive results. The ordering of the databases depends mostly on the review questions; being in a study of clinical trials, you will rely mostly on Cochrane, mRCTs, or International Clinical Trials Registry Platform (ICTRP). Here, we propose 12 databases (PubMed, Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, Clinical trials.gov , mRCTs, POPLINE, and SIGLE), which help to cover almost all published articles in tropical medicine and other health-related fields. Among those databases, POPLINE focuses on reproductive health. Researchers should consider to choose relevant database according to the research topic. Some databases do not support the use of Boolean or quotation; otherwise, there are some databases that have special searching way. Therefore, we need to modify the initial search terms for each database to get appreciated results; therefore, manipulation guides for each online database searches are presented in Additional file 5 : Table S2. The detailed search strategy for each database is found in Additional file 5 : Table S3. The search term that we created in PubMed needs customization based on a specific characteristic of the database. An example for Google Scholar advanced search for our topic is as follows:

With all of the words: ebola virus

With at least one of the words: vaccine vaccination vaccinated immunization

Where my words occur: in the title of the article

With all of the words: EVD

Finally, all records are collected into one Endnote library in order to delete duplicates and then to it export into an excel sheet. Using remove duplicating function with two options is mandatory. All references which have (1) the same title and author, and published in the same year, and (2) the same title and author, and published in the same journal, would be deleted. References remaining after this step should be exported to an excel file with essential information for screening. These could be the authors’ names, publication year, journal, DOI, URL link, and abstract.

Protocol writing and registration

Protocol registration at an early stage guarantees transparency in the research process and protects from duplication problems. Besides, it is considered a documented proof of team plan of action, research question, eligibility criteria, intervention/exposure, quality assessment, and pre-analysis plan. It is recommended that researchers send it to the principal investigator (PI) to revise it, then upload it to registry sites. There are many registry sites available for SR/MA like those proposed by Cochrane and Campbell collaborations; however, we recommend registering the protocol into PROSPERO as it is easier. The layout of a protocol template, according to PROSPERO, can be found in Additional file 5 : File S1.

Title and abstract screening

Decisions to select retrieved articles for further assessment are based on eligibility criteria, to minimize the chance of including non-relevant articles. According to the Cochrane guidance, two reviewers are a must to do this step, but as for beginners and junior researchers, this might be tiresome; thus, we propose based on our experience that at least three reviewers should work independently to reduce the chance of error, particularly in teams with a large number of authors to add more scrutiny and ensure proper conduct. Mostly, the quality with three reviewers would be better than two, as two only would have different opinions from each other, so they cannot decide, while the third opinion is crucial. And here are some examples of systematic reviews which we conducted following the same strategy (by a different group of researchers in our research group) and published successfully, and they feature relevant ideas to tropical medicine and disease [ 9 , 10 , 11 ].

In this step, duplications will be removed manually whenever the reviewers find them out. When there is a doubt about an article decision, the team should be inclusive rather than exclusive, until the main leader or PI makes a decision after discussion and consensus. All excluded records should be given exclusion reasons.

Full text downloading and screening

Many search engines provide links for free to access full-text articles. In case not found, we can search in some research websites as ResearchGate, which offer an option of direct full-text request from authors. Additionally, exploring archives of wanted journals, or contacting PI to purchase it if available. Similarly, 2–3 reviewers work independently to decide about included full texts according to eligibility criteria, with reporting exclusion reasons of articles. In case any disagreement has occurred, the final decision has to be made by discussion.

Manual search

One has to exhaust all possibilities to reduce bias by performing an explicit hand-searching for retrieval of reports that may have been dropped from first search [ 12 ]. We apply five methods to make manual searching: searching references from included studies/reviews, contacting authors and experts, and looking at related articles/cited articles in PubMed and Google Scholar.

We describe here three consecutive methods to increase and refine the yield of manual searching: firstly, searching reference lists of included articles; secondly, performing what is known as citation tracking in which the reviewers track all the articles that cite each one of the included articles, and this might involve electronic searching of databases; and thirdly, similar to the citation tracking, we follow all “related to” or “similar” articles. Each of the abovementioned methods can be performed by 2–3 independent reviewers, and all the possible relevant article must undergo further scrutiny against the inclusion criteria, after following the same records yielded from electronic databases, i.e., title/abstract and full-text screening.

We propose an independent reviewing by assigning each member of the teams a “tag” and a distinct method, to compile all the results at the end for comparison of differences and discussion and to maximize the retrieval and minimize the bias. Similarly, the number of included articles has to be stated before addition to the overall included records.

Data extraction and quality assessment

This step entitles data collection from included full-texts in a structured extraction excel sheet, which is previously pilot-tested for extraction using some random studies. We recommend extracting both adjusted and non-adjusted data because it gives the most allowed confounding factor to be used in the analysis by pooling them later [ 13 ]. The process of extraction should be executed by 2–3 independent reviewers. Mostly, the sheet is classified into the study and patient characteristics, outcomes, and quality assessment (QA) tool.

Data presented in graphs should be extracted by software tools such as Web plot digitizer [ 14 ]. Most of the equations that can be used in extraction prior to analysis and estimation of standard deviation (SD) from other variables is found inside Additional file 5 : File S2 with their references as Hozo et al. [ 15 ], Xiang et al. [ 16 ], and Rijkom et al. [ 17 ]. A variety of tools are available for the QA, depending on the design: ROB-2 Cochrane tool for randomized controlled trials [ 18 ] which is presented as Additional file 1 : Figure S1 and Additional file 2 : Figure S2—from a previous published article data—[ 19 ], NIH tool for observational and cross-sectional studies [ 20 ], ROBINS-I tool for non-randomize trials [ 21 ], QUADAS-2 tool for diagnostic studies, QUIPS tool for prognostic studies, CARE tool for case reports, and ToxRtool for in vivo and in vitro studies. We recommend that 2–3 reviewers independently assess the quality of the studies and add to the data extraction form before the inclusion into the analysis to reduce the risk of bias. In the NIH tool for observational studies—cohort and cross-sectional—as in this EBOLA case, to evaluate the risk of bias, reviewers should rate each of the 14 items into dichotomous variables: yes, no, or not applicable. An overall score is calculated by adding all the items scores as yes equals one, while no and NA equals zero. A score will be given for every paper to classify them as poor, fair, or good conducted studies, where a score from 0–5 was considered poor, 6–9 as fair, and 10–14 as good.

In the EBOLA case example above, authors can extract the following information: name of authors, country of patients, year of publication, study design (case report, cohort study, or clinical trial or RCT), sample size, the infected point of time after EBOLA infection, follow-up interval after vaccination time, efficacy, safety, adverse effects after vaccinations, and QA sheet (Additional file 6 : Data S1).

Data checking

Due to the expected human error and bias, we recommend a data checking step, in which every included article is compared with its counterpart in an extraction sheet by evidence photos, to detect mistakes in data. We advise assigning articles to 2–3 independent reviewers, ideally not the ones who performed the extraction of those articles. When resources are limited, each reviewer is assigned a different article than the one he extracted in the previous stage.

Statistical analysis

Investigators use different methods for combining and summarizing findings of included studies. Before analysis, there is an important step called cleaning of data in the extraction sheet, where the analyst organizes extraction sheet data in a form that can be read by analytical software. The analysis consists of 2 types namely qualitative and quantitative analysis. Qualitative analysis mostly describes data in SR studies, while quantitative analysis consists of two main types: MA and network meta-analysis (NMA). Subgroup, sensitivity, cumulative analyses, and meta-regression are appropriate for testing whether the results are consistent or not and investigating the effect of certain confounders on the outcome and finding the best predictors. Publication bias should be assessed to investigate the presence of missing studies which can affect the summary.

To illustrate basic meta-analysis, we provide an imaginary data for the research question about Ebola vaccine safety (in terms of adverse events, 14 days after injection) and immunogenicity (Ebola virus antibodies rise in geometric mean titer, 6 months after injection). Assuming that from searching and data extraction, we decided to do an analysis to evaluate Ebola vaccine “A” safety and immunogenicity. Other Ebola vaccines were not meta-analyzed because of the limited number of studies (instead, it will be included for narrative review). The imaginary data for vaccine safety meta-analysis can be accessed in Additional file 7 : Data S2. To do the meta-analysis, we can use free software, such as RevMan [ 22 ] or R package meta [ 23 ]. In this example, we will use the R package meta. The tutorial of meta package can be accessed through “General Package for Meta-Analysis” tutorial pdf [ 23 ]. The R codes and its guidance for meta-analysis done can be found in Additional file 5 : File S3.

For the analysis, we assume that the study is heterogenous in nature; therefore, we choose a random effect model. We did an analysis on the safety of Ebola vaccine A. From the data table, we can see some adverse events occurring after intramuscular injection of vaccine A to the subject of the study. Suppose that we include six studies that fulfill our inclusion criteria. We can do a meta-analysis for each of the adverse events extracted from the studies, for example, arthralgia, from the results of random effect meta-analysis using the R meta package.

From the results shown in Additional file 3 : Figure S3, we can see that the odds ratio (OR) of arthralgia is 1.06 (0.79; 1.42), p value = 0.71, which means that there is no association between the intramuscular injection of Ebola vaccine A and arthralgia, as the OR is almost one, and besides, the P value is insignificant as it is > 0.05.

In the meta-analysis, we can also visualize the results in a forest plot. It is shown in Fig. 3 an example of a forest plot from the simulated analysis.

figure 3

Random effect model forest plot for comparison of vaccine A versus placebo

From the forest plot, we can see six studies (A to F) and their respective OR (95% CI). The green box represents the effect size (in this case, OR) of each study. The bigger the box means the study weighted more (i.e., bigger sample size). The blue diamond shape represents the pooled OR of the six studies. We can see the blue diamond cross the vertical line OR = 1, which indicates no significance for the association as the diamond almost equalized in both sides. We can confirm this also from the 95% confidence interval that includes one and the p value > 0.05.

For heterogeneity, we see that I 2 = 0%, which means no heterogeneity is detected; the study is relatively homogenous (it is rare in the real study). To evaluate publication bias related to the meta-analysis of adverse events of arthralgia, we can use the metabias function from the R meta package (Additional file 4 : Figure S4) and visualization using a funnel plot. The results of publication bias are demonstrated in Fig. 4 . We see that the p value associated with this test is 0.74, indicating symmetry of the funnel plot. We can confirm it by looking at the funnel plot.

figure 4

Publication bias funnel plot for comparison of vaccine A versus placebo

Looking at the funnel plot, the number of studies at the left and right side of the funnel plot is the same; therefore, the plot is symmetry, indicating no publication bias detected.

Sensitivity analysis is a procedure used to discover how different values of an independent variable will influence the significance of a particular dependent variable by removing one study from MA. If all included study p values are < 0.05, hence, removing any study will not change the significant association. It is only performed when there is a significant association, so if the p value of MA done is 0.7—more than one—the sensitivity analysis is not needed for this case study example. If there are 2 studies with p value > 0.05, removing any of the two studies will result in a loss of the significance.

Double data checking

For more assurance on the quality of results, the analyzed data should be rechecked from full-text data by evidence photos, to allow an obvious check for the PI of the study.

Manuscript writing, revision, and submission to a journal

Writing based on four scientific sections: introduction, methods, results, and discussion, mostly with a conclusion. Performing a characteristic table for study and patient characteristics is a mandatory step which can be found as a template in Additional file 5 : Table S3.

After finishing the manuscript writing, characteristics table, and PRISMA flow diagram, the team should send it to the PI to revise it well and reply to his comments and, finally, choose a suitable journal for the manuscript which fits with considerable impact factor and fitting field. We need to pay attention by reading the author guidelines of journals before submitting the manuscript.

The role of evidence-based medicine in biomedical research is rapidly growing. SR/MAs are also increasing in the medical literature. This paper has sought to provide a comprehensive approach to enable reviewers to produce high-quality SR/MAs. We hope that readers could gain general knowledge about how to conduct a SR/MA and have the confidence to perform one, although this kind of study requires complex steps compared to narrative reviews.

Having the basic steps for conduction of MA, there are many advanced steps that are applied for certain specific purposes. One of these steps is meta-regression which is performed to investigate the association of any confounder and the results of the MA. Furthermore, there are other types rather than the standard MA like NMA and MA. In NMA, we investigate the difference between several comparisons when there were not enough data to enable standard meta-analysis. It uses both direct and indirect comparisons to conclude what is the best between the competitors. On the other hand, mega MA or MA of patients tend to summarize the results of independent studies by using its individual subject data. As a more detailed analysis can be done, it is useful in conducting repeated measure analysis and time-to-event analysis. Moreover, it can perform analysis of variance and multiple regression analysis; however, it requires homogenous dataset and it is time-consuming in conduct [ 24 ].

Conclusions

Systematic review/meta-analysis steps include development of research question and its validation, forming criteria, search strategy, searching databases, importing all results to a library and exporting to an excel sheet, protocol writing and registration, title and abstract screening, full-text screening, manual searching, extracting data and assessing its quality, data checking, conducting statistical analysis, double data checking, manuscript writing, revising, and submitting to a journal.

Availability of data and materials

Not applicable.

Abbreviations

Network meta-analysis

Principal investigator

Population, Intervention, Comparison, Outcome

Preferred Reporting Items for Systematic Review and Meta-analysis statement

Quality assessment

Sample, Phenomenon of Interest, Design, Evaluation, Research type

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Acknowledgements

This study was conducted (in part) at the Joint Usage/Research Center on Tropical Disease, Institute of Tropical Medicine, Nagasaki University, Japan.

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Additional files

Additional file 1:.

Figure S1. Risk of bias assessment graph of included randomized controlled trials. (TIF 20 kb)

Additional file 2:

Figure S2. Risk of bias assessment summary. (TIF 69 kb)

Additional file 3:

Figure S3. Arthralgia results of random effect meta-analysis using R meta package. (TIF 20 kb)

Additional file 4:

Figure S4. Arthralgia linear regression test of funnel plot asymmetry using R meta package. (TIF 13 kb)

Additional file 5:

Table S1. PRISMA 2009 Checklist. Table S2. Manipulation guides for online database searches. Table S3. Detailed search strategy for twelve database searches. Table S4. Baseline characteristics of the patients in the included studies. File S1. PROSPERO protocol template file. File S2. Extraction equations that can be used prior to analysis to get missed variables. File S3. R codes and its guidance for meta-analysis done for comparison between EBOLA vaccine A and placebo. (DOCX 49 kb)

Additional file 6:

Data S1. Extraction and quality assessment data sheets for EBOLA case example. (XLSX 1368 kb)

Additional file 7:

Data S2. Imaginary data for EBOLA case example. (XLSX 10 kb)

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Tawfik, G.M., Dila, K.A.S., Mohamed, M.Y.F. et al. A step by step guide for conducting a systematic review and meta-analysis with simulation data. Trop Med Health 47 , 46 (2019). https://doi.org/10.1186/s41182-019-0165-6

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When is conducting a Meta-Analysis appropriate?

Methods and guidance, examples of meta-analyses, supplementary resources.

A meta-analysis is defined by Haidlich (2010) as "quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes , than any individual study contributing to the pooled analysis" (p.29).

According to Grant & Booth (2009) , a meta-analysis is defined as a "technique that statistically combines the results of quantitative studies to provide a more precise effect of the results" (p.94).

Characteristics

  • A meta-analysis can only be conducted after the completion of a systematic review , as the meta-analysis statistically summarizes the findings from the studies synthesized in a particular systematic review. A meta-analysis cannot exist with a pre-existing systematic review . Grant & Booth (2009) state that "although many systematic reviews present their results without statistically combining data [in a meta-analysis], a good systematic review is essential to a meta-analysis of the literature" (p.98).
  • Conducting a meta-analysis requires all studies that will be statistically summarized to be similar - i.e. that population, intervention, and comparison. Grant & Booth (2009) state that "more importantly, it requires that the same measure or outcome be measured in the same way at the same time intervals" (p.98).

When to Use It: According to the Cochrane Handbook , "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).

Conducting meta-analyses can have the following benefits, according to Deeks et al. (2021, section 10.2) :

  • To improve precision. Many studies are too small to provide convincing evidence about intervention effects in isolation. Estimation is usually improved when it is based on more information.
  • To answer questions not posed by the individual studies. Primary studies often involve a specific type of participant and explicitly defined interventions. A selection of studies in which these characteristics differ can allow investigation of the consistency of effect across a wider range of populations and interventions. It may also, if relevant, allow reasons for differences in effect estimates to be investigated.
  • To settle controversies arising from apparently conflicting studies or to generate new hypotheses. Statistical synthesis of findings allows the degree of conflict to be formally assessed, and reasons for different results to be explored and quantified.

The following resource provides further support on conducting a meta-analysis.

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  • Cochrane Handbook for Systematic Reviews of Interventions. Chapter 10: Analysing data and undertaking meta-analyses

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  • PRISMA 2020 checklist

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  • Marioni, R. E., Suderman, M., Chen, B. H., Horvath, S., Bandinelli, S., Morris, T., Beck, S., Ferrucci, L., Pedersen, N. L., Relton, C. L., Deary, I. J., & Hägg, S. (2019). Tracking the epigenetic clock across the human life course: a meta-analysis of longitudinal cohort data .  The journals of gerontology: Series A, Biological sciences and medical sciences ,  74 (1), 57–61. doi: 10.1093/gerona/gly060

Deeks, J.J., Higgins, J.P.T., & Altman, D.G. (Eds.). (2021).  Chapter 10: Analysing data and undertaking meta-analyses . In Higgins, J.P.T., Thomas J., Chandler, J., Cumpston, M., Li, T., Page, M.J., & Welch, V.A. (Eds.),  Cochrane Handbook for Systematic Reviews of Interventions  version 6.2. Cochrane. Available from www.training.cochrane.org/handbook

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The following challenges of conducting meta-analyses in systematic reviews are derived from Grant & Booth (2009) , Haidlich (2010) , and Deeks et al. (2021) .

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Literature Review, Systematic Review and Meta-analysis

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

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

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

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

Literature Review: GO-GN Insights

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

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

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

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Cell-therapy for Parkinson’s disease: a systematic review and meta-analysis

  • Fang Wang 1   na1 ,
  • Zhengwu Sun 2   na1 ,
  • Daoyong Peng 1   na1 ,
  • Shikha Gianchandani 3 ,
  • Weidong Le 4 ,
  • Johannes Boltze 3 &
  • Shen Li   ORCID: orcid.org/0000-0001-6779-9812 5 , 6  

Journal of Translational Medicine volume  21 , Article number:  601 ( 2023 ) Cite this article

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Cell-based strategies focusing on replacement or protection of dopaminergic neurons have been considered as a potential approach to treat Parkinson’s disease (PD) for decades. However, despite promising preclinical results, clinical trials on cell-therapy for PD reported mixed outcomes and a thorough synthesis of these findings is lacking. We performed a systematic review and meta-analysis to evaluate cell-therapy for PD patients.

We systematically identified all clinical trials investigating cell- or tissue-based therapies for PD published before July 2023. Out of those, studies reporting transplantation of homogenous cells (containing one cell type) were included in meta-analysis. The mean difference or standardized mean difference in quantitative neurological scale scores before and after cell-therapy was analyzed to evaluate treatment effects.

The systematic literature search revealed 106 articles. Eleven studies reporting data from 11 independent trials (210 patients) were eligible for meta-analysis. Disease severity and motor function evaluation indicated beneficial effects of homogenous cell-therapy in the ‘off’ state at 3-, 6-, 12-, or 24-month follow-ups, and for motor function even after 36 months. Most of the patients were levodopa responders (61.6–100% in different follow-ups). Cell-therapy was also effective in improving the daily living activities in the ‘off’ state of PD patients. Cells from diverse sources were used and multiple transplantation modes were applied. Autografts did not improve functional outcomes, while allografts exhibited beneficial effects. Encouragingly, both transplantation into basal ganglia and to areas outside the basal ganglia were effective to reduce disease severity. Some trials reported adverse events potentially related to the surgical procedure. One confirmed and four possible cases of graft-induced dyskinesia were reported in two trials included in this meta-analysis.

Conclusions

This meta-analysis provides preliminary evidence for the beneficial effects of homogenous cell-therapy for PD, potentially to the levodopa responders. Allogeneic cells were superior to autologous cells, and the effective transplantation sites are not limited to the basal ganglia.

PROSPERO registration number : CRD42022369760

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease, and no curative therapy is currently available [ 1 ]. Thus, alternative solutions are urgently needed. PD has long been considered to be among the most promising target diseases for cell replacement therapy due to the specific loss of dopaminergic neurons in the substantia nigra [ 2 ], and cell-based therapies for PD has been explored clinically during the past decades. Initial studies mostly focused on transplantation of tissues such as embryonic mesencephalic tissue, adrenal medulla tissue, carotid body tissue, and sympathetic ganglion tissue. A meta-analysis on tissue transplantation demonstrated improved functional outcome [ 3 ]. However, tissue transplantation has several shortcomings including severe graft-induced dyskinesia (GID), substantial outcome heterogeneity, unsurmountable difficulties in quality control, immunogenicity, and ethical restrictions. Therefore, researchers gradually switched to transplantation of homogenous cells (defined as cell populations containing only one cell type that was extracted, isolated, expanded, and characterized). These comprise neural progenitor cells, fetal stem cells, bone marrow mesenchymal stem cells, retinal pigment epithelial cells, or induced pluripotent stem cells. With the advances in regenerative medicine, engineered cells are being tested as well. Lately, implantation of autologous, induced pluripotent stem cell-derived midbrain dopaminergic progenitor cells was reported [ 4 ], which may help to overcome ethical concerns if used properly. Although homogeneous cell transplantation is translationally promising, mixed results were reported from individual trials and no meta-analysis of those results has been conducted so far. A meta-analysis is therefore necessary to provide an overall assessment of the safety and efficacy of cell-therapy approaches in PD. In this study, we systematically reviewed all clinical trials on tissue or cell transplantation for PD and performed a meta-analysis for homogenous cells in treatment of PD.

This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [ 5 ].

Search strategy

We systematically identified all clinical trials investigating cell-therapies for PD indexed in PubMed, Embase, Web of Science, and Cochrane databases before July 2023. The search terms were: (Parkinson disease OR Parkinson’s disease OR Parkinsonian disorders OR Parkinsonism OR Parkinsonisms OR Parkinson OR Parkinsons) AND (cell therapy OR cell therapeutics OR cell treatment OR cell treatments OR transplantation OR implantation), filtering for clinical trials. Only reports in English language were included.

FW and ZWS (review authors) screened studies for initial inclusion based on titles and abstracts. Full text screening for eligibility was performed if an initial decision could not be made. In case FW and ZWS could not reach a consensus, SL was consulted, followed by discussion and joint consensus in all cases. We also screened related reviews, together with reference lists of included publications, to identify other relevant articles [ 2 , 6 , 7 , 8 , 9 ].

Inclusion and exclusion criteria for the systematic review

The inclusion criteria were: (1) recruited patients were diagnosed with idiopathic PD; (2) cell or tissue transplantation; (3) randomized controlled trials (RCTs), open-label studies, cohort studies, case reports, prospective studies, or retrospective studies.

Exclusion criteria were: (1) trials focusing on secondary PD or Parkinsonism-plus syndrome; (2) transplantation of more than one tissue type; (3) reviews and book chapters.

Additional inclusion and exclusion criteria for the meta-analysis

The studies included in the systematic review were further screened for the meta-analysis with the following inclusion criteria: (1) transplantation with homogenous cell populations (containing only one type of cells); (2) using objective methods to evaluate treatment responses such as imaging, biochemical indicators or quantitative scales, including Unified Parkinson Disease Rating Scale (UPDRS), or its part II/III (UPDRSII/UPDRSIII), Hoehn and Yahr (H&Y) Staging Scale, Beck Depression Inventory (BDI), Beck Anxiety Inventory, Mini-mental State Examination (MMSE), Parkinson's Disease Quality of Life Questionnaire, or Schwab and England Scale; (3) quantitative data available before and after cell-therapy.

Exclusion criteria were: (1) missing or incomplete reporting of efficacy endpoints or sample size; (2) transplantation of mixed or uncharacterized cell populations; (3) case reports. The study selection process is presented in Fig.  1 a.

figure 1

a PRISMA flow diagram. b Pie chart of the total number of publications on different types of tissue or cell transplantation between 1982 and 2021. c Numbers of publications on different types of tissue or cell transplantation in each decade. The numbers of articles on embryonic mesencephalic tissue transplantation published in 1982–1991, 1992–2001, 2002–2011, 2012–2021 are 8, 39, 16 and 3, respectively. Articles reporting adrenal medulla transplantation are 12, 4, 0, and 0. Articles reporting sympathetic ganglion transplantation are 0, 3, 1 and 0. Articles on transplantation of other tissue are 0, 0, 2 and 1. Transplantation of homogenous cell populations are 0, 0, 6 and 11, respectively

Data extraction

Data regarding study population, intervention, and outcome were extracted into a standardized form from texts and graphs in each study by the review authors. When only graphic representation was available, values of mean and standard deviation (SD) or standard error (SE) were estimated from high-resolution digital graphs using GetData Graph Digitizer v2.20. Study information including cell source, grafting location, cell dose, sample size, patient age, disease duration, follow-up duration, primary and secondary endpoints, baseline (before transplantation) data, the clinical outcome information, as well as adverse events were collected. Adverse events were defined as an anticipated or unanticipated untoward medical occurrence, unintended disease or injury, or untoward clinical signs (including abnormal laboratory findings) whether or not related to cell transplantation. Neurological function before and after cell transplantation was compared for individual patients to evaluate treatment effects (self-comparison). For RCTs, baseline and outcome data were collected from the treatment groups. SE was converted to SD only when SE was reported.

Outcomes of interest were quantitative neurological scale scores in the ‘on’ or ‘off’ state. The ‘off’ state was defined as a period in which the patients withdrew antiparkinsonian medication for 12 h [ 10 ]. The ‘on’ state was at the time of the patients’ peak response to antiparkinsonian medication [ 10 ].

Risk of bias assessment

FW and ZWS independently assessed the risk of bias at the study level of included RCTs and non-RCTs in accordance with the Cochrane Collaboration Guidelines [ 11 ]. The risk of bias was assessed as ‘low’, ‘moderate’, ‘high’ or ‘incomplete reporting’ across the following domains: randomization; allocation concealment; blinding of therapists (intervention supervisors); blinding of patients; blinding of outcome assessors; handling of incomplete data (use of intention-to-treat analysis); selective reporting; and multivariate adjustment for potential confounders. Discrepancies in the risk of bias assessment were resolved by discussion among review authors and SL.

Statistical analysis

The mean difference (MD) or standardized mean difference (SMD) in quantitative neurological scale scores before and after cell-therapy was analyzed to evaluate the treatment effects. Forest plots were created to depict both the pooled MD or SMD along with their 95% confidence intervals (CI). The statistical significance of the pooled effect size of all studies was judged by a Z-test. A P value < 0.05 was considered statistically significant. We considered only trials that demonstrated clinical homogeneity to be appropriate for meta-analysis. Potential heterogeneity between studies was initially explored through a visual exploration of the forest plots. A test for statistical heterogeneity (a consequence of clinical or methodological diversity, or both, among trials) was then performed using Cochran’s Q-statistic test ( P value < 0.1 indicating significance) and I 2 analysis using the following equation:

in which Q is the Chi 2 statistic and df is its degrees of freedom. This describes the percentage of variability in effect estimates that is due to heterogeneity rather than sampling error (chance). Values greater than 50% are considered to represent substantial heterogeneity. When values were > 70%, we attempted to interpret the variation. If the value was less than 30%, we presented the overall estimate using a fixed-effect model. If there was evidence of heterogeneity ( I 2  > 30%) between trials, we used a random-effect model based on the DerSimonian and Laird method [ 12 ]. A leave-one-out sensitivity analysis was performed by iteratively removing one study at a time to confirm whether the findings were driven by any single study. Potential publication bias was evaluated using funnel plots. Review Manager 5.3 was used to complete all statistical calculations.

Study characteristics and systematic review of the literature

Overview on retrieved records.

The initial search returned 903 records, of which 136 were retrieved for full-text review (Fig.  1 a). One hundred and six articles were included in the systematic review [ 4 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 ]. Eighty-nine articles reported tissue transplantation or transplantation of mixed cell populations, including 66 articles using embryonic mesencephalic tissues (Additional file 9 : Table S1) [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], 16 articles reporting adrenal medulla tissue transplantation (Additional file 10 : Table S2) [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 ], two articles reporting carotid body tissue transplantation [ 95 , 96 ], four sympathetic ganglion tissue transplantation articles [ 97 , 98 , 99 , 100 ], and one adipose-derived stromal vascular fraction cell transplantation (Fig.  1 b) [ 101 ]. Seventeen publications reported transplantation of homogenous cell populations [ 4 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 ]. One hundred and four articles explored treatment efficacy, 63 articles reported safety, 92 articles investigated motor symptoms, and 18 articles examined non-motor symptoms. There were 84 articles using allotransplantation, 5 articles on xenotransplantation, and 17 articles on autotransplantation.

Changes in predominantly used cell material over time

Predominantly used cell sources for PD treatment changed over time (Fig.  1 c). Adrenal medulla tissue transplantation was the most widely studied approach before 1991 (n = 12) and was observed until 2001, but not thereafter. Embryonic mesencephalic tissue transplantation was investigated across all four decades and with most reports published in 1992–2001 (n = 39), gradually decreasing after 2002. Autonomic ganglion tissue transplantation was performed in a few studies between 1992–2011 (n = 3 + 1). Other tissues were investigated by one or two studies only. Treatment with homogenous cell populations became a research focus after 2002 and the most frequently used treatment strategy in the recent decade.

Transplantation of allogenic tissues

A total of 297 patients receiving embryonic mesencephalic tissue transplantation were included in this review. These studies investigated different outcomes using a broad range of methods including structural imaging, functional imaging, electrophysiology, biochemical indicators, functional outcome measurements by various scales, and pathological studies by autopsy. Some studies indicated that transplants partially replaced dopaminergic neurons following intra-striatal transplantation, and improved symptoms [ 41 , 46 , 51 , 75 ]. Double-blind, sham-controlled clinical trials did not confirm statistically significant benefits from fetal mesencephalic tissue transplantation but revealed adverse events such as GID [ 20 , 23 ].

Transplantation of autologous tissues

The usage of autologous cells is not limited by ethical considerations and avoids severe immune reactions. Autologous cell or tissue transplantation to supply DA was therefore investigated as a potential treatment for PD patients. These autologous DA-secreting cells or tissues included adrenal medulla and carotid body tissues, and sympathetic neurons. In the pioneering work performed by Backlund and collaborators [ 94 ], autologous adrenal medulla cells were implanted into the striatum of four patients to provide a local catecholamine source, but the beneficial effects were minimal. In the following 10 years, clinical studies on adrenal medulla transplantation of 148 PD patients yielded similar results and several autopsies demonstrated that the transplanted adrenal cells did not survive in the host brain [ 118 ].

The carotid body contains neural crest-derived dopaminergic glomus cells that are similar to the chromaffin cells of the adrenal medulla. These cells function as arterial oxygen sensors and release large amounts of dopamine in response to hypoxia. In addition, glial cell line–derived neurotrophic factor (GDNF) secreted from the carotid body might exert neuroprotective effects for these dopaminergic glomus cells as well as nigrostriatal neurons [ 119 ]. A pilot study and a phase I-II blinded clinical study were performed using bilateral intrastriatal transplantation of autologous carotid body cells in patients with advanced-stage PD (n = 6 and 13, respectively) [ 95 , 96 ]. Functional improvement was seen in five and ten patients, respectively, and no patients developed GID.

Some studies investigated the potential of autologous sympathetic neurons since the ganglion contains not only norepinephrinergic but also dopaminergic cells. Long-term clinical evaluation revealed that unilateral intrastriatal implantation of autologous cervical sympathetic ganglion tissue results in a significant improvement of PD symptoms, particularly akinesia and gait disturbance, and a reduction in the patient’s daily levodopa intake [ 99 ]. Following the development of video-guided endoscopic thoracic surgery, it became possible to safely excise three or more ganglia from the thoracic sympathetic trunk in a minimally invasive manner. This option may augment the amount of available tissue, thereby increasing the number of implantation sites. One study endoscopically excised and re-transplanted thoracic sympathetic ganglia in a total of five PD patients [ 98 ]. These autografts were found to improve the patients’ performance by reducing the time spent in the off phase. However, there have been no further clinical studies using these cells.

One study investigated intranasal administration of autologous adipose-derived stromal vascular fraction cells in two patients [ 101 ]. Both patients exhibited improvements in motor and non-motor functions one and five years after transplantation. There is, however, no clear understanding of the underlying mechanism, and any reported results should be confirmed in future studies.

Meta-analysis on studies investigating transplantation of homogenous cell populations

Seventeen publications reported transplantation of homogenous cell populations and 11 were eligible for this meta-analysis [ 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 ]. Two publications were reporting results from one study [ 112 , 113 ]. Two publications were case reports [ 4 , 117 ]. Three publications did not report the quantitative data necessary for this analysis. Attempts to contact the corresponding authors failed and these studies were therefore excluded [ 114 , 115 , 116 ]. An overview of research protocols and subject characteristics is shown in Table 1 .

Risk of bias analysis

The risk of bias assessment is summarized in Table 2 . Ten studies were non-RCTs that did not describe the processes of random sequence generation or allocation concealment in sufficient detail. They were considered as incomplete regarding the risk of bias reporting when evaluating selection bias. In most of the studies included for meta-analysis, it was neither practical nor possible to blind the participants or therapists. This was considered a low risk of performance bias for the therapists, but a moderate risk for the participants. Those studies reporting a dropout or loss of follow-up rate higher than 20% were believed to have a high level of attrition bias. Studies were rated as high-risk for detection bias when neither employing intention-to-treat principles in the data analysis nor describing dropouts, nor blinding evaluators to treatment. All other bias assessment domains shown in Table 2 were considered to have a low risk of bias.

Effects of homogeneous cell populations in PD

Disease course and disability.

UPDRS (monitoring the disease course and the degree of disability) or UPDRSIII (evaluation of motor function) scores were examined in ‘on’ or ‘off’ state at various post-intervention time points. These follow-ups were different across the nine studies reporting those and varied from 1 to 57 months (last follow-up, Table 1 ). A total of 210 patients were investigated in the included trials. Meta-analysis was performed on the last follow-up across studies, and at intermediate follow-up time points (3-, 6-, 12-, 24-, and ≥ 36-month follow-ups) when those were reported by the respective studies.

The meta-analysis revealed overall better post- versus pre-treatment function although considerable heterogeneity was evident (Additional file 1 : Fig. S1). There was a beneficial effect of homogenous cell-therapy on UPDRS scores in the ‘off’ state at the last follow-up and at 3-, 6-, 12- and 24-month follow-ups, but not at the ≥ 36-month follow-up (Fig.  2 ). However, the latter was only reported by two studies (Fig.  2 ). UPDRS scores showed relative homogeneity in the ‘off’ state at 3-, 6-month and ≥ 36-month follow-up analysis (Fig.  2 ). Moreover, cell treatment improved UPDRS scores in the ‘on’ state at the 12-month follow-up, but not at the last follow-up, or at 24-, ≥ 36-month follow-ups (Additional file 2 : Fig. S2). There was no profound heterogeneity among 12-, 24-, ≥ 36-month follow-ups, but at the last follow-up. This might be explained by different transplantation paradigms. For instance, Brazzini et al. infused bone marrow stem cells intraarterially [ 106 ], while other studies administered cells directly into the basal ganglia. Removing the study of Brazzini et al. (leaving-one-out analysis) reduced the heterogeneity to 16%, but the overall result remained unchanged (95% CI − 8.95 to 19.03). When analyzing the H&Y scale, we revealed the overall positive effects of cell-therapy at the last assessed timepoints in ‘on’ or ‘off’ state (Additional file 3 : Fig. S3). However, there was no change in the levodopa equivalent dose of antiparkinsonian medications after 12 months ( P  = 0.56, I 2  = 0%, 95% CI − 103.43 to 191.50).

figure 2

UPDRS scores pre- versus post-transplantation in the ‘off’ state at last follow-up, or 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups. The number of studies included in each analysis are 6, 3, 3, 5, 3, and 2, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of the squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

Motor symptoms

Seven studies measured the effects of homogenous cell-therapy on motor symptoms [ 102 , 104 , 107 , 108 , 110 , 111 , 112 ]. A random-effect model was used to compare the pre- versus post-treatment UPDRSIII scores in the ‘off’ state at the study last follow-up. The meta-analysis yielded a better outcome after cell treatment, but the heterogeneity was high (Fig.  3 ). This might be related to the design of the study by Lige et al. who did not use a fixed observation time. Analyzing the UPDRSIII scores after cell treatment in the ‘off’ state at 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups revealed positive effects (Fig.  3 , Additional file 4 : Fig. S4). Analyzing UPDRSIII scores in the ‘on’ state revealed beneficial effects of cell treatment at the 6- and 24-month follow-ups compared to baseline status, but neither at the last follow-up nor at 3- or 12-month follow-ups (Fig.  4 ). The inter-study heterogeneity was low at the last follow-up and at 3-, 6-, and 24-month follow-ups (Fig.  4 ). The 12-month follow-ups showed a high heterogeneity. This can be explained by the study of Gross et al. [ 111 ], as its RCT design was different from the other three open-labeled pilot studies. Leaving this study out reduced the I 2 value to 0%.

figure 3

UPDRSIII score pre- versus post-transplantation in the ‘off’ state at last follow-up, or 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups. The number of studies included in each analysis are 7, 4, 3, 5, 4, and 3, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

figure 4

UPDRSIII score pre- versus post-transplantation in the ‘on’ state at last follow-up, or 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups. The number of studies included in each analysis are 4, 3, 2, 4, and 3, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

Non-motor symptom-depression

Three studies examined the effects of homogenous cell-therapy on non-motor symptoms [ 106 , 107 , 109 ]. The Beck Depression Inventory (BDI) was used to evaluate the degree of depression in patients but did not reveal significant differences after cell treatment. There was considerable heterogeneity between studies probably resulting from diverse transplantation modes (using bilateral basal ganglia transplantation [ 107 ], combined intravenous and subcutaneous routes [ 109 ], and intra-arterial transplantation [ 106 ], respectively) (Additional file 5 : Fig. S5).

Activities of daily living (ADL)

ADL were assessed using UPDRSII or the Schwab and England score. Four studies examined the UPDRSII scores in the ‘off’ state at the last follow-up [ 107 , 108 , 110 , 112 ]. A fixed-effect model revealed a better outcome after homogenous cell-therapy. Three studies assessed UPDRSII scores in the ‘on’ state (all used allogeneic cells) but did not report treatment effects. There was no obvious heterogeneity (Fig.  5 ) [ 107 , 110 , 112 ].

figure 5

UPDRSII score pre- versus post-transplantation in the ‘off’ and ‘on’ states at the last follow-up. The number of studies included are 4 and 3, respectively. Fixed-effect models are used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

Patients potentially benefited from cell-therapy

There was no study investigating whether the effects of cell-therapy are influenced by patient sex. All studies included had equivalent male/female ratio and an average disease course of more than 5 years. The average age was between 47.2 and 66.4 years. Six studies included in the meta-analysis clearly stated that the enrolled patients had positive responses to dopaminergic therapy [ 102 , 104 , 107 , 110 , 111 , 112 ]. Five studies did not specify levodopa responsiveness. In the analyses of UPDRS scores in the ‘off’ state, the proportion of levodopa-responsive patients was 100%, 100%, 86.5%, 100% and 100% at 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups and 61.6% at last follow up, respectively (Fig.  2 ). The fraction of levodopa-responsive patients was 76%, 100%, 100%, 100%, 100% at 3-, 6-, 12-, 24-, and ≥ 36-month follow-ups, and 81.4% at last follow-up, in the analyses of UPDRSIII scores in the ‘off’ state, respectively (Fig.  3 ). The patients who were responsive to dopaminergic therapy showed functional improvements on UPDRS, UPDRSIII, and UPDRSII scores in the ‘off’ state at the last follow-up, but not in the ‘on’ state (Fig.  6 ).

figure 6

UPDRS, UPDRSIII and UPDRSII scores pre- versus post-transplantation in the ‘off’ and ‘on’ states at the last follow-up with levodopa responders. The number of studies included are 4, 6, 3, 2, 5, and 3, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

Impact of cell immunogenicity and cell type on outcome

Eight studies used allogeneic, and three studies used autologous cells for transplantation. Allogeneic cells (neural progenitor cells, fetal stem cells, retinal pigment epithelial cells, and bone marrow mesenchymal stem cells) showed beneficial effects on UPDRS, UPDRSIII, and UPDRSII scores in the ‘off’ state at the last follow-up, but not in the ‘on’ state (Additional file 6 : Fig. S6) [ 102 , 103 , 107 , 108 , 110 , 111 , 112 ]. There were considerable heterogeneities in the UPDRSIII score analyses in the ‘off’ state, which might be explained by one study not defining fixed observation time points (last follow-up ranged from 7–57 months)[ 108 ]. Removing this study reduced the I 2 value to 37% but did not change the overall result. When autografts (mesenchymal stem cells that cannot differentiate into neural cells) were used, no beneficial effect was observed on H&Y scores in ‘off’ or ‘on’ state (Additional file 7 : Fig. S7) [ 105 , 106 ]. Even though homogenous cell-therapy in general and allogeneic cells in particular showed positive effects on motor function in the ‘off’ state, autologous cell transplantation did not show such effects.

Several types of cells were transplanted including neural progenitor cells (n = 2), fetal stem cells (n = 1), bone marrow mesenchymal stem cells (n = 4), other bone marrow stem cells (including exact cell type not specified, n = 1), and retinal pigment epithelial cells (n = 3). UPDRS or UPDRSIII assessments in the ‘off’ states at the last follow-up revealed better outcomes after retinal pigment epithelium cell and stem/progenitor cell treatment (Fig.  7 ). The heterogeneity was low in retinal pigment epithelial cell studies. However, the UPDRSIII analysis of stem/progenitor cell-therapy revealed high heterogeneity, potentially due to the study of Lige et al. not defining fixed observation time points [ 108 ]. Removing this study reduced the I 2 value to 21%.

figure 7

UPDRS and UPDRSIII scores pre- versus post-transplantation in the ‘off’ state at the last follow-ups after retinal pigment epithelium cell and stem/progenitor cell treatment. The number of studies included are 2, 3, 4, and 4, respectively. Random-effect models are used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

Transplantation route

Among the 11 studies included in this meta-analysis, six studies performed intraparenchymal transplantation into the basal ganglia (Table 1 ) [ 103 , 107 , 108 , 110 , 111 , 112 ]. Unilateral and bilateral intraparenchymal transplantation was performed in three studies each. One study investigated intravenous infusion of allogeneic bone marrow-derived mesenchymal stem cells [ 102 ]. One study transplanted autologous mesenchymal stem cells through intravenous or tandem (intranasal + intravenous) injections [ 104 ]. One study combined intravenous and subcutaneous transplantation of fetal stem cells [ 109 ]. One study injected autologous bone marrow mesenchymal stem cells via intrathecal and intravenous injection [ 105 ]. One study infused bone marrow stem cells using a superselective intraarterial approach to the posterior region of the circle of Willis [ 106 ]. Basal ganglia transplantation resulted in beneficial effects on both UPDRS and UPDRSIII scores in ‘off’ state at the last follow-ups. Non-basal ganglia transplantation improved UPDRSIII scores. However, the I 2 value for UPDRSIII scores were high for transplantation into basal ganglia, which might again be explained by the study of Lige et al. (Fig.  8 ) [ 108 ].

figure 8

UPDRS and UPDRSIII scores pre- versus post-transplantation in the ‘off’ state at the last follow-ups after basal ganglia and non-basal ganglia transplantation. The number of studies included are 5, 5, and 2, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI, confidence interval (represented by the lines)

The cell doses used for transplantation were between 1 and 10 × 10 6 /kg in the ten studies investigated. In one study, four doses (1, 3, 6, or 10 × 10 6 /kg) of allogeneic bone marrow-derived mesenchymal stem cells were administered intravenously to investigate a potential dose-dependent efficacy [ 102 ]. The results showed that all doses showed effects on motor symptoms in the ‘off’ state. However, the highest dose achieved the maximum absolute improvement at the 52 weeks follow-up and reduced the UPDRS motor and total scores in the ‘off’ state. Therefore, the included studies suggested that cell doses between one to ten million were all effective.

Imaging readouts

Seven included studies applied magnetic resonance (MR) imaging for outcome evaluation [ 102 , 103 , 106 , 107 , 108 , 110 , 111 ], among which four [ 107 , 108 , 110 , 111 ] investigated safety endpoints including inflammatory responses, tumor formation, bleeding, and edema after cell transplantation. Three other studies [ 102 , 103 , 106 ] used MR spectroscopy, MR perfusion, and MR tractography for efficacy evaluation. In one study, MR spectroscopy revealed a significant increase of the mean n - acetylaspartate/creatine ratio in basal ganglia after transplantation [ 106 ]. One study showed MR perfusion increased overall from baseline to 24 weeks post infusion in all basal ganglia structures [ 102 ]. Another study reported a statistically non-significant trend of improvement in fractional anisotropic (FA) values of MR tractography in the genu and the cerebral peduncles steadily over a period of 12 months after transplantation [ 103 ]. Two studies employed positron emission tomography (PET) imaging to evaluate the efficacy [ 107 , 110 ]. The radiopharmaceuticals included FDOPA, DTBZ, and 11 C-β-CFT. FDOPA and DTBZ imaging showed a statistically non-significant trend toward enhanced midbrain dopaminergic activity at one year after grafting in one study [ 107 ]. The other study showed a statistically non-significant trend towards increased dopamine release in 11 C-β-CFT PET imaging during the first 6 months after transplantation [ 110 ]. These studies suggested that cell-therapy partially replaced dopaminergic neurons. Due to the heterogeneity in imaging methodology, the limited number of studies and overall small sample sizes, however, prevented a meaningful meta-analysis of the imaging readouts regarding efficacy.

Adverse events of homogenous cell transplantation

The reports for adverse events of homogenous cell-therapy for PD are listed in Table 3 . No tumor formation or severe immune rejections were observed. Two trials reported GID [ 102 , 111 ]. There were other adverse events including surgical injury and complications, such as phlebitis and hematoma. Psychonosema was noted such as hallucination or disturbance in attention.

Sensitivity analysis

Sensitivity analyses were performed to evaluate the robustness of the estimated pooled effect size for UPDRS, UPDRSIII, UPDRSII scores and non-motor symptoms. The pooled effect was stable for UPDRSIII and UPDRSII in the ‘off’ state and non-motor symptoms, indicating that these results were not driven by any single study. However, when either the study by Brazzini et al. [ 106 ] or the one by Lige et al. [ 108 ] was removed, statistical significance was lost for the pooled effect size of homogenous cell-therapy on H&Y scores in the ‘on’ or ‘off’ state at the last follow-up. On the contrary, when the study of Madrazo et al. [ 107 ] was removed, cell-therapy became beneficial for UPDRS and UPDRSIII scores in the ‘on’ state at the last follow-up. Removing the study of Gross et al. [ 111 ] also resulted in the detection of a cell treatment effect on UPDRSIII scores in the ‘on’ state at the 12-month follow-up.

Publication bias

Funnel plots were plotted for the meta-analysis including more than 5 studies (Additional file 8 : Fig. S8). These plots were symmetrical and evenly distributed, and few effects fell outside the 99% CI, suggesting that the present meta-analyses were not substantially affected by publication bias.

Tissue transplantation

Intracerebral grafting of fetal mesencephalic tissue, which is rich in dopaminergic neuroblasts, was first reported in 1979, ameliorating the symptoms of experimental PD rats [ 120 , 121 ]. Thereafter, about 400 PD patients were grafted with human fetal mesencephalic tissue in the 1980s–1990s. Fetal tissue grafts have survived over two decades in some patients despite ongoing PD pathology [ 122 ]. In addition, several trials showed engraftment of fetal tissue with wide outgrowth and robust innervation of the host striatum by donor-derived DA neurons [ 54 , 56 , 58 , 66 , 123 , 124 ]. However, due to GID, fetal tissue transplantation was abandoned. The overall discouraging results may be partly related to differences between studies in cell sources, preparation, and transplantation paradigms [ 23 , 124 ]. In addition, multiple fetal donors (typically 3–5) were pooled to obtain sufficient numbers of cells for one patient. This may contribute to the heterogeneity of outcomes and may indicate a lack of material for widespread clinical usage. Ethical arguments also limit fetal tissue transplantation. Therefore, transplantation of human fetal mesencephalic tissue is very unlikely to be developed into a routine treatment for PD patients.

Autologous adrenal medulla and carotid body tissues, and sympathetic neurons were explored as PD treatments because these can either secret DA or exert neurotrophic effects, but their precise therapeutic mechanism is uncertain [ 95 , 96 , 97 , 98 , 99 , 100 ]. Tissue transplantation was less investigated in the recent decade. Lately, the concept experienced a renaissance due to advances in regenerative medicine and tissue engineering, using optimized grafting and defined immunosuppression protocols [ 2 ]. Successful in-vitro differentiation of embryonic stem cells [ 125 , 126 , 127 ] or induced pluripotent stem cells [ 4 ] towards a midbrain dopaminergic fate may allow the development of cell-therapies for PD while avoiding many practical and ethical concerns regarding tissue transplantation, although there are still many challenges in translating in-vitro success to in-vivo applications, and potential ethical concerns surrounding embryonic stem cells usage. What remains is the need for cell transplants that can not only functionally integrate but survive in the host brain over long periods.

Therapeutic effects of homogenous cell-therapy on PD

To the best of our knowledge, this is the most comprehensive meta-analysis of clinical trials on cell treatments for PD to date. Both cell origin and the site of cell transplantation varied considerably across the studies. Most transplantations (6 out of 11) were performed into basal ganglia uni- or bilaterally. Follow-up time ranged from 1 to 57 months. The key finding from our meta-analysis is that homogenous cell transplantation significantly improves clinical outcomes in PD patients regarding overall disease severity, motor symptoms, and ADL in the ‘off’ state.

The main outcome measurement in our meta-analysis was based on the UPDRS score which is believed to be less susceptible to observer bias than other scores [ 128 ]. Therefore, it is less likely that the clinical improvements observed can be solely attributed to observer bias. Our findings suggest that the investigated cell treatments have a robust effect on the ‘off’ state at the 3-, 6-, 12-, 24-, and even ≥ 36-month follow-ups for motor symptoms. There was indication that the magnitude and duration of functional improvement induced by dopaminergic grafts depend on patient selection, with good preoperative response to L-dopa predicting good response to the graft [ 129 , 130 ]. In this meta-analysis most of the patients included were responsive to dopaminergic therapy, and those patients may also be responsive to cell-therapy. Patients with DA neuron loss restricted to the caudate and putamen are more likely to experience long-term benefits from dopaminergic grafts placed in these areas [ 14 , 25 , 129 , 130 ]. In contrast, long-lasting beneficial outcomes in PD patients with more widespread DA neuron loss are less likely [ 14 ].

Most of the trials in our meta-analysis included PD patients with a good response to L-dopa. This may explain why UPDRS and UPDRSIII at the ‘on’ state did not improve much, as the combination of both treatments would require a significant additional effect that may not be detected with the overall limited numbers of patients investigated. No difference between neurological function pre- and post-transplantation was found in the UPDRS score in the ‘off’ state at ≥ 36 months. Graft function may be compromised by delayed immune reactions, previously characterized by microglial infiltration into the graft [ 23 ]. However, UPDRSIII scores in the ‘off’ state at ≥ 36 months provide preliminary evidence that the cell graft was still functional, but more rigorous RCTs and long-term follow-up studies, especially those ≥ 36 months are needed to confirm this. Those should include tailored assessment of graft functionality, for instance by sophisticated brain imaging.

Only few clinical trials investigating homogenous cell-therapy for PD have focused on the management of non-motor symptoms: four articles investigated cognition [ 102 , 103 , 107 , 109 ], four articles reported depression [ 104 , 106 , 107 , 109 ], one studied anxiety article [ 107 ], and two examined sleep-disorder [ 104 , 109 ]. Although a significant decrease of non-motor symptoms and depression, as well as an improvement in objective parameters of sleep quality, were reported in PD patients after cell treatment in single studies [ 109 ], we could not confirm these findings in our meta-analysis. Several factors could have contributed to this. Firstly, non-motor symptoms may originate from degeneration outside the striatum or in non-dopaminergic systems that may be difficult to target with cell-therapy. Secondly, the cell grafts investigated may simply lack the ability to counter these symptoms. Third and most importantly, the relatively high inter-study heterogeneity regarding cell type and source, transplantation site, and other aspects may just have ‘masked’ minor yet clinically meaningful effects on these endpoints. Therefore, it is crucial to scrutinize non-motor symptoms in future investigations. In summary, the overall positive impact on ADL parameters observed in our meta-analysis may primarily originate from motor symptom improvements. However, overall results should be interpreted with caution as the overall number of available and included studies is relatively low.

Effects of different cell sources and transplantation modes on efficacy

Most of the included studies (n = 8) transplanted allogeneic cells for PD patients and exhibited robust beneficial effects on UPDRS, UPDRSIII, and UPDRSII scores in the ‘off’ state. However, autografts were ineffective in symptoms examined by H&Y score changes in ‘off’ or ‘on’ states as there were not sufficient autograft transplantation studies to be combined to evaluate the UPDRS changes. The three articles evaluating autografts all used bone marrow mesenchymal stem cells [ 104 , 105 , 106 ], which may not differentiate into neural tissue. However, they may exert beneficial immunomodulative and neuroprotective effects. Moreover, six out of the eight articles evaluating allogeneic cells used neural progenitor cells, fetal stem cells, or retinal pigment epithelial cells. Those might be able to differentiate into neuronal cells. Thus, allogeneic cells and autologous cells likely have different mechanisms of action. Therefore, it is rational to speculate that the overall positive effects of homogenous cell-therapy for PD patients in our meta-analysis were mainly due to allogeneic cell transplantation studies and that allogeneic cells may be a better option for PD treatment, particularly, retinal pigment epithelium cell and stem/progenitor cell. Besides, allogeneic cells have some logistical advantages as they can be obtained and prepared in advance and under standardized conditions. They might also be advantageous in inherited PD. However, when using allogeneic cells, the immunological barrier represents a formidable obstacle for the transplanted cells to survive and execute therapeutic effects relying on differentiation and functional integration. Fortunately, with the development of modern immunosuppressants, graft survival and side effects have been greatly improved [ 131 ].

Unexpectedly, we observed that transplantation outside basal ganglia was also effective to improve motor function in PD patients. In these two studies, the intravenously infused bone marrow mesenchymal stem cells were likely to improve PD symptoms through immunomodulatory mechanisms, such as decreasing inflammatory cytokine production, reducing microglial activation and a-synuclein oligomerization [ 102 , 104 ]. This observation may be clinically relevant because such transplantation, in particular systemic cell delivery, may not only be safer and easier to perform, but also less expensive and time-consuming. However, this result is based on a limited number of studies and thus will require confirmation, and the likelihood of immunological consequences is far greater after systematic cell delivery.

Other factors as, for instance, gender, age, and disease courses of the patients may also act as confounding factors. However, due to the lack of available raw data, we were unable to analyze their impact on reported functional outcomes after cell-therapy.

Adverse events of homogenous cell-therapy

No tumor formation or severe immune rejections were reported in the included studies, but one trial reported a case of GID, and another trial reported four cases of possibly GID. Off-state GID was a relatively frequent adverse event after human fetal mesencephalic tissue transplantation. The interpretation of this phenomenon is difficult. Modeling studies suggest that some form of L-DOPA-induced postsynaptic supersensitivity, established before transplantation, may play a role [ 132 , 133 ]. Moreover, small, intracerebral transplants may be more prone to cause GID by forming ‘hot-spots’ of DA release, while the surrounding striatum remains supersensitive [ 133 , 134 ]. Finally, a potential role of excessive serotonin innervation has been discussed [ 8 , 135 , 136 ]. Fetal mesencephalic tissue often used for transplantation also contains serotonergic neurons, and studies on 6-hydroxydopamine-induced PD models suggested that these could exacerbate dyskinesia induced by L-DOPA [ 135 ]. Clinical research suggested that a non-optimal ratio between serotonergic and dopaminergic neurons (or their progenitors) in grafts causes GID [ 136 , 137 , 138 ]. The relatively low incidence of GID in the studies included in our meta-analysis may be related to patient selection, improvement of surgical methods, and higher homogeneity of transplanted cells. Other adverse events such as surgical complications (phlebitis and hematoma) and psychonosema were generally rare. However, two included studies did not provide comprehensive adverse effect reports, which limit the understanding of potential risks associated with the intervention. Due to the inconsistency in reporting of adverse events, we were also unable to compare the safety profiles of different interventions. A thorough and robust safety analysis is imperative for future clinical trials.

Quality of evidence and limitations

Despite the generally encouraging results of our meta-analysis, it is important to keep in mind that most of the included studies were open-label, single-center trials, with outcome data not reported or inadequately described in some studies. Moreover, insufficient information on disease duration in some studies limits the understanding of how the disease stage could affect the treatment outcomes and impact the quality and reliability of the analysis. Although blinding of the participants and therapists was not possible, outcome assessors can be blinded. Nevertheless, a relatively large proportion of studies (n = 5, 41.7%) did not report blinding of outcome assessors. Thus, the results may still contain observer bias.

Another major limitation is that our meta-analysis cannot provide a thorough perspective on how cell-therapies for PD may be improved further. The main reasons are the small number of studies and the overall heterogeneity of cell and tissue types being used. While there is an overall positive effect of cell-based treatments, any kind of optimal approach cannot be identified from this relatively small dataset. Moreover, we can only speculate why systemic cell administration was effective or why overall best effects were obtained with allogeneic cells, and both findings may appear counter-intuitive. A combination of thorough preclinical and clinical research is required to solve these questions. Mechanistic investigations in relevant animal models should identify the most effective cell types and transplantation paradigms while multicenter, large-scale, and double-blinded RCTs are needed to verify the encouraging yet preliminary results of our meta-analysis. Alternative solutions, such as pharmacological therapy and deep brain stimulation, should also be considered in conjunction with cell-based therapies.

According to this meta-analysis, cell therapy was effective for improving disease severity and motor symptoms while also improving ADL in the ‘off’ state of PD patients, especially in levodopa responders. Allogenic cells exerted beneficial effects on these parameters, but autografts did not. Transplantation of cells to areas outside the basal ganglia, including system transplantation of cells, was able to induce therapeutic benefits. Some trials reported adverse events potentially related to the surgical procedure. One confirmed and four possible cases of GID were reported in two trials included in meta-analysis. Therefore, our results suggest modest yet clinically meaningful cell therapy effects in patients with PD although definitive evidence must be provided by future double-blinded large-scale RCTs. These should also monitor the long-term safety of cell-based interventions for PD while the optimal cell population and route of transplantation need to be defined. Cell-therapies in PD are not a stand-alone treatment but must always be considered in combination with established therapies.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

Activities of daily living

Beck Depression Inventory

Confidence interval

Fractional anisotropic

Glial cell line-derived neurotrophic factor

Graft-induced dyskinesia

Hoehn and Yahr Staging Scale

Mean difference

Mini-mental State Examination

Magnetic Resonance

Parkinson’s disease

Positron emission tomography

Randomized controlled trial

Standard deviation

Standard error

Standardized mean difference

Unified Parkinson Disease Rating Scale

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Acknowledgements

This work was supported by National Natural Science Foundation of China (82171319) and Central Committee Healthcare Project (2020YB64).

Author information

Fang Wang, Zhengwu Sun, Daoyong Peng contributed equally to this study.

Authors and Affiliations

Department of Neurology, Central Hospital of Dalian University of Technology, Dalian, China

Fang Wang & Daoyong Peng

Department of Clinical Pharmacy, Central Hospital of Dalian University of Technology, Dalian, China

Zhengwu Sun

School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK

Shikha Gianchandani & Johannes Boltze

Institute of Neurology, Sichuan Academy of Medical Sciences, Sichuan Provincial Hospital, Chengdu, China

Department of Neurology and Psychiatry, Beijing Shijitan Hospital, Capital Medical University, No. 10 Tieyi Road, Beijing, 100038, China

Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China

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FW, ZWS, DYP, and SL had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. JB and SL conceived and designed the study. FW, ZWS, DYP, and SL undertook the statistical analyses. FW, ZWS, and DYP made figures. All authors advised on statistical analyses and visualization. FW and ZWS wrote the first draft of the manuscript. All authors made critical revisions of the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript.

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Correspondence to Shen Li .

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Supplementary Information

Additional file 1: fig. s1.

. UPDRS or UPDRSIII scores pre- versus post-transplantation in ‘on’ or ‘off’ state at the last follow-up. Nine studies are included. Random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 2: Fig. S2

. UPDRS score pre- versus post-transplantation in the ‘on’ state at the last follow-up, or at 12-, 24-, and ≥ 36-month follow-ups. The number of studies included are 4, 3, 2, and 2, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 3: Fig. S3

. H-Y score pre- versus post-transplantation in the ‘on’ or ‘off’ states at the last follow-up. Four studies are included. Random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 4: Fig. S4

. UPDRSIII score pre- versus post-transplantation in the ‘off’ state at 48-month follow-up. Two studies are included. Fixed-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 5: Fig. S5

. Beck Depression inventory score pre- versus post-transplantation in the ‘on’ or ‘off’ states at the last follow-up. Three studies are included. Random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 6: Fig. S6

. UPDRS, UPDRSIII and UPDRSII scores pre- versus post-transplantation in the ‘off’ and ‘on’ states at the last follow-ups after allogeneic cell treatment. The number of studies included are 6, 6, 4, 3, 3, and 3, respectively. If the I 2 value is less than 30%, a fixed-effect model is used. If the I 2 value is greater than 30%, a random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 7: Fig. S7

. H-Y score pre- versus post-transplantation in the ‘on’ or ‘off’ states at the last follow-up after autologous cell treatment. Two studies are included. Random-effect model is used. The sizes of squares represent the weight that each study contributes. The diamond at the bottom represents the overall effect. CI = confidence interval (represented by the lines).

Additional file 8: Fig. S8

. Funnel plots assessing potential publication bias on homogeneous cell transplantation in PD treatment. (a) UPDRS or UPDRSIII scores pre- versus post-transplantation in ‘on’ or ‘off’ state at the last follow-up. (b) UPDRS score pre- versus post-transplantation in the ‘off’ state at last follow-up. (c) UPDRSIII score pre- versus post-transplantation in the ‘off’ state at last follow-up. (d) UPDRSIII scores pre- versus post-transplantation in the ‘off’ states at the last follow-up with levodopa responders. (e) UPDRS scores pre- versus post-transplantation in the ‘off’ states after allogeneic cell treatment. (f) UPDRSIII scores pre- versus post-transplantation in the ‘off’ states at the last follow-ups after allogeneic cell treatment. Each dot represents a single study. The dashed vertical line represents the pooled effect size. The dashed diagonal lines represent 95% confidence limits around the pooled effect size for each standard error on the vertical axis, and are only provided in plots when fixed effect models were used.

Additional file 9: Table S1

. Fetal mesencephalic tissue transplantation: characteristics of the studies and subjects.

Additional file 10: Table S2

. Adrenal medulla transplantation: characteristics of the studies and subjects.

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Wang, F., Sun, Z., Peng, D. et al. Cell-therapy for Parkinson’s disease: a systematic review and meta-analysis. J Transl Med 21 , 601 (2023). https://doi.org/10.1186/s12967-023-04484-x

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Received : 05 April 2023

Accepted : 30 August 2023

Published : 07 September 2023

DOI : https://doi.org/10.1186/s12967-023-04484-x

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6 Strengths and Weaknesses of Meta-Analyses

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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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  • Published: 09 March 2024

Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

  • Konrad Pesudovs   ORCID: orcid.org/0000-0002-6322-9369 1 ,
  • Van Charles Lansingh 2 , 3 , 4 ,
  • John H. Kempen 5 , 6 , 7 ,
  • Ian Tapply 8 ,
  • Arthur G. Fernandes 9 ,
  • Maria V. Cicinelli 10 ,
  • Alessandro Arrigo 11 ,
  • Nicolas Leveziel 12 ,
  • Paul Svitil Briant 13 ,
  • Theo Vos 13 ,
  • Serge Resnikoff 1 , 14 ,
  • Hugh R. Taylor 15 ,
  • Tabassom Sedighi 16 ,
  • Seth Flaxman 17 ,
  • Jaimie Steinmetz 9   na1 ,
  • Rupert Bourne 4 , 12   na1 ,
  • Vision Loss Expert Group of the Global Burden of Disease Study &

the GBD 2019 Blindness and Vision Impairment Collaborators

Eye ( 2024 ) Cite this article

726 Accesses

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Metrics details

  • Epidemiology
  • Lens diseases

To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals.

A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity <6/18, ≥3/60) and blindness (presenting visual acuity <3/60). Estimates are age-standardized using the GBD standard population.

In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%).

Conclusions

The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.

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Introduction

For 2020, the Global Burden of Disease (GBD) Study reported that cataract remained the leading cause of blindness, with approximately 15.2 million cases [95% Uncertainty Interval (UI): 12.7–18.0) that comprised 45% of global blindness [ 1 ]. Cataract also remained the second leading cause of moderate and severe vision impairment (MSVI), with 78.8 million (95% UI: 67.2–91.4) people, which comprised 39% of global MSVI. In spite of global advocacy efforts, such as the VISION 2020 Right to Sight program undertaken by the World Health Organization and International Agency of the Prevention Blindness, and an increase in cataract surgical rates (the number of cataract surgeries per million population) reported across the world, the progress made against cataract has revealed substantial inequality and inequity, with lower-to-middle income countries (LMICs) shouldering the greater burden and having poorer visual outcomes than high-income countries [ 2 , 3 , 4 ].

The majority of cataracts are age-related nuclear cataracts, which typically cause vision loss in the sixth decade or later [ 5 , 6 , 7 ]. Cataracts, part of the spectrum of diabetic eye disease, are also increasing due to a global epidemic of diabetes, with diabetics more likely to develop cataracts and more quickly lose their vision to cataract compared to people without diabetes [ 5 , 8 , 9 ].

Surgery is the only treatment for cataract, during which an artificial intraocular lens replaces the damaged lens. Cataract surgery is extremely efficacious in terms of restoring sight, and multiple studies have demonstrated its cost-effectiveness, which appears to increase over time [ 10 , 11 , 12 ]. In 2015, the International Council of Ophthalmology estimated that globally, there were 14 ophthalmologists performing cataract surgery per million population, but that ranges from less than 1 cataract surgeon per million in low-income countries to as high as 32 in high-income countries, further revealing the global inequity in access to eye care [ 13 ]. Age-related cataract exposes another persistent inequity in universal eye health coverage–– men are 1.7 times more likely to undergo cataract surgery than women, and even in high-income countries, women are more likely to wait longer for surgery and experience poorer outcomes [ 14 ]. This gender inequity is partially due to the fact that women live longer than men, although sociocultural barriers are also at play. Based on the 2015 GBD Vision Loss Expert Group data, if women had the same access to cataract surgery as men, the blindness burden of cataract could decrease by 11%. However, women, in fact, need more access to surgery than men, to address the gender inequity [ 14 ]. Gender differences in cataract burden for 2020 have yet to be analyzed.

With the publication of 2020 GBD vision loss data, there is a need to explore further the global and regional trends in cataract burden since 1990 and better understand the regional and gender inequities of cataract burden. The objective of this article is to provide updated estimates of the global burden of vision loss due to cataract, disaggregated by sex and region, for the period from 2000 to 2020 covered by Global Vision 2020. This is done using the best available ophthalmic epidemiological database, the Global Vision Database which is a comprehensive, continuously updated, online database of ophthalmic epidemiological data curated by the Vision Loss Expert Group (VLEG) [ 15 , 16 , 17 ]. Additionally, we assess progress against the goals set out in ‘Towards universal eye health: global action plan 2014–2019 of the World 60 Health Assembly (2013) [ 18 ]. This Global Action Plan set a target to reduce the prevalence of avoidable blindness by 25% from 2010 to 2019.

The VLEG have maintained, and progressively updated a systematic review of population-based studies of vision impairment and blindness published between Jan 1, 1980, and Oct 1, 2018, including gray literature sources. Data from this systematic review were combined with data from the repository of Rapid Assessment of Avoidable Blindness (RAAB) studies, and data contributed by the GBD obtained from the US National Health and Nutrition Examination survey and the WHO Study on Global Ageing and Adult Health. Detailed methods are published elsewhere [ 17 , 19 ], and briefly described herein.

In total, the systematic review identified 137 studies, and the VLEG extracted data from 70 studies in 2010, and a further 67 studies in 2014–18 [ 16 ]. Studies were primarily national and subnational cross-sectional surveys. The VLEG commissioned the preparation of 5-year age-disaggregated data from the RAAB repository [ 20 ]. Studies were included if they met these criteria: population-representative and visual acuity measured using a test chart that could be mapped to Snellen fractions. Studies using self-reported vision loss were excluded. We used the International Classification of Diseases 11th edition criteria for vision loss, as recommended by the WHO, which categorizes people according to presenting better-eye visual acuity. The classification defines moderate vision loss as better eye visual acuity of 6/60 or better but worse than 6/18, severe vision loss as a visual acuity of 3/60 or better but worse than 6/60, and blindness as visual acuity of worse than 3/60 or less than 10° visual field around central fixation.

Data were stratified into datasets including so-called vision-loss envelopes (as per Flaxman et al. [ 16 ]) for all-cause mild, moderate, and severe vision loss, and blindness. Data were input into a mixed-effects meta-regression tool developed by the Institute for Health Metrics and Evaluation (IHME) called MR-BRT (meta regression; Bayesian; regularized; trimmed) [ 21 ]. Presenting vision impairment defined each level of severity. Prevalence data for under-corrected refractive error were extracted where available, and otherwise calculated by subtracting best-corrected vision impairment from presenting vision impairment for each level of severity in studies that reported both measures for a given location, sex, age group, and year. Other causes were quantified as part of the best-corrected estimates of vision impairment at each level of severity. Minimum age for inclusion of data was defined as 20 years for cataract.

We generated location, year, age, and sex-specific estimates of MSVI and blindness using Disease Modeling Meta-Regression (Dismod-MR) 2.1; [ 19 ] the data processing steps are described elsewhere [ 17 ]. In brief, Dismod-MR 2.1 models were run for all vision impairment strata (moderate, severe, blindness) regardless of cause and, separately, for MSVI and blindness for each modeled cause of vision impairment. Then, models of MSVI due to cataract were split into moderate and severe estimates using the ratio of overall prevalence in the all-cause moderate presenting vision impairment and severe presenting vision impairment models. Next, prevalence estimates for cataract were stratified by severity were scaled to the models of all-cause prevalence by severity. This produced final estimates by age, sex, year, and location for cataract vision impairment stratified by severity. We age-standardized our estimates using the GBD standard population [ 22 ]. Data on blindness and MSVI due to AMD were presented by seven super-regions (Southeast Asia/East Asia/Oceania, Central Europe/Eastern Europe/Central Asia, High-income, Latin America and Caribbean, North Africa and Middle East, South Asia, and Sub-Saharan Africa) and globally. Data on other causes of vision impairment and blindness will be presented in separate publications.

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

In 2020, 17.01 million (all ages, 95% uncertainty interval (UI) 14.40–19.93) people were blind due to cataract (Table  1 ). This breaks down by gender as 6.78 million (95% UI 5.73–7.98) men and 10.22 million (95% UI 8.76–11.96) women blind from cataract (Table  2 ). The majority of these are over 50 years of age with 15.17 million (95% UI 12.70–18.00) so affected (Table  1 ). Of these, 5.96 million (95% UI 4.98–7.11) men and 9.22 million (95% UI 7.73–10.88) women are blind from cataract (Table  2 ).

Overall, 83.48 million (95% UI 71.76–96.98) people are estimated to have MSVI from cataract (Table  1 ). Of these 34.59 million (95% UI 29.69–39.95) are men, and 48.89 million (95% UI 42.05–56.06) are women (Table  3 ). Again, the majority are over 50 years of age, 78.79 million (95% UI 67.20–91.40) people, 32.41 million (95% UI 27.55–37.74) men and 46.38 million (95% UI 39.66–53.66) women suffer from MSVI due to cataract (Tables  1 and 3 ).

Cataract caused 39.55% (95% UI: 33.48, 46.34%) of all blindness in 2020 worldwide. Regionally, the highest proportion of cataract-related blindness was found in South Asia (53.20 [95% UI: 45.00, 62.11%]) and Southeast Asia, East Asia, and Oceania (41.82% [95% UI: 35.30, 49.38]) (Table  1 ). The regions with the lowest proportion of all cataract-related blindness of all blind individuals were High Income Countries (16.82% [UI: 13.66, 20.60]), and Central Europe, Eastern Europe, and Central Asia (20.53% [95% UI: 16.50, 25.15]). Cataract caused 28.30% (95% UI: 24.32, 32.54) of all cases with MSVI in 2020 worldwide. Southeast Asia, East Asia, and Oceania (34.00 (29.32-39.00)% [95% UI: 29.32, 39.00]), and South Asia (29.87% [95% UI: 25.64, 34.83]) were regions with the highest percentage of cataract-related MSVI of all visually impaired individuals (Table  1 ).

In 2020, the global age-standardized prevalence of cataract-related blindness in those aged ≥50 years was 0.84% (95% UI: 0.70, 0.99) and for cataract-related MSVI was 1.01% (95% UI: 0.87, 1.15) (Table  1 ). The variation of crude prevalence with age is shown in Fig.  1 . The regions with the highest age-standardized prevalence of cataract-related blindness were South Asia (2.23% [95% UI: 1.89, 2.61]) and Sub-Saharan Africa (1.49% [95% UI: 1.24, 1.78]). The lowest age-standardized prevalence of cataract blindness in 2020 was in the regions of High Income Countries (0.09% [95% UI: 0.07, 0.11]) and Central Europe, Eastern Europe, and Central Asia (0.19% [95% UI: 0.15, 0.23]). The regions with the highest age-standardized prevalence of cataract-related MSVI in 2020 were South Asia (2.15% [95% UI: 1.85, 2.49]), and North Africa and the Middle East (1.33% [95% UI: 1.13, 1.55]). The lowest figures were found in high-income countries (0.35% [95% UI: 0.30, 0.40]) and Central Europe, Eastern Europe, and Central Asia (0.49% [95% UI: 0.41, 0.58]) (Table  1 ). The variation in these results by gender across the regions are shown in Tables  2 and 3 .

figure 1

Crude prevalence of Blindness and MSVI due to cataract in 2020 globally by age.

Between 2000 and 2020, the global percentage change in age-standardized prevalence of cataract-related blindness among adults ≥50 years decreased by 27.54% (95% UI: −27.68, −27.39), among males by −31.78% (95%UI −31.91, −31.64) and by 24.82% in females (95% UI: −24.97, −24.68) (Table  4 ). However, the absolute number of cases (unadjusted for age) increased by 29.72% (95% UI: 29.46, 29.98), in males 25.65% (95% CI 25.39, 25.92) and in females 32.49% (95% CI: 32.23, 32.75). An especially large reduction in the age-standardized prevalence of cataract-related blindness amongst adults aged ≥50 years (both sexes) was found in Southeast Asia, East Asia and Oceania (−42.99% [95% UI: −43.10, −42.88]), North Africa and Middle East (−39.97% [95% UI: −40.13, −39.81]) and South Asia (−36.53% [95% UI: −36.65, −36.41]), with a modest reduction in high-income countries (−6.86% [95% UI: −7.10, −6.62]) (Table  4 ). The greatest percentage increases in absolute number of cases were in Latin America and the Caribbean 71.25% (95% UI 70.86, 71.64) and in high income countries 49.30 (95% UI 48.92, 49.69). Only Central Europe, Eastern Europe, and Central Asia showed a reduction in the caseload (−4.40% [95% UI −4.66, −4.14].

Between 2000 and 2020, the global percentage change in age-standardized prevalence of cataract MSVI among adults ( ≥ 50 years) increased (7.17% [95% UI: 6.98, 7.36]), among males (4.70% [95% UI 4.52, 4.89]) and females (8.94% [95% UI: 8.75, 9.13]) (Table  5 ). However, the absolute number of cases increased by 93.11% (95% UI: 92.75, 93.46), in males 93.69% (95% CI 93.32, 94.05) and in females 92.70% (95% CI: 92.36, 93.04). Sub-Saharan Africa (2.29% [95%UI 2.12, 2.47]) and Southeast Asia, East Asia and Oceania 1.96% [95%UI 1.78, 2.13]) were the only world regions where a substantial increase in the age-standardized prevalence of cataract MSVI was observed with notable decreases in South Asia (-5.53 [95% UI: -5.69, -5.37]) and Latin America and Caribbean (-4.83% [95% UI: -5.01, -4.65]). The increase in the absolute number of cataract MSVI cases was greatest in Southeast Asia, East Asia, and Oceania (115.21% [95%UI 114.83, 115.58]), and least in Central Europe, Eastern Europe, and Central Asia (38.18% [95%UI 37.87, 38.49]) (Table  5 ).

Cataract, the world’s leading cause of blindness, remains one of the greatest opportunities in global health to make impactful and cost-effective contributions. Cataract surgery is safe and highly effective with both higher and lower technology approaches (e.g., phacoemulsification or extracapsular techniques respectively), and can be provided relatively inexpensively [ 23 ]. As a surgical condition, it requires a system able to provide one-at-a-time clinical care, like most causes of blindness and visual impairment. There are various eye service delivery models that can be used to address the cataract burden. However, it makes sense to combine it in a system with other ophthalmic services, ethically addressing other issues that will come to the attention of the service as well as providing a more professional-friendly work environment to retain capable eye care professionals (ophthalmologists, optometrists, eye nurses and others).

As an endemic condition, the ideal approach to the problem is to develop sufficient capacity and health system functionality to make ophthalmic surgery widely available worldwide. Given the relatively low level of infrastructure and consumables required for quality surgery, government health systems are well positioned to address this issue for the economically poorest persons. While funding limitations may constrain their systems’ scale [ 24 ], cataract surgery has considerable economic and quality of life benefits compared to its cost [ 10 ], which can offset the investment. Moreover, several health systems in different locations have demonstrated that self-sustaining services can be provided at costs most patients are willing to pay while also generating surpluses to provide service to the very poor [ 25 , 26 ]. Such “cross-subsidizing” systems have made a large contribution to alleviating cataract blindness in much of the world, although these require a dominant service provider e.g. Aravind Eye Care System in South India. Systems for eye care should contemplate the value of ”patient financial contribution” for cataract surgery as much as possible; offering universal free or highly subsidized surgery may unnecessarily leave that health care financing resource at the table. In addition, surgical campaigns have been used extensively to deal with “backlogs” in cataract blindness; these are ideal for unreached/remote areas where development is unlikely to reach the cataract blind on a reasonable time scale without interfering with the ultimate solution of local capacity development. Our data demonstrate that these sorts of efforts have been fruitful in reducing the per capita levels of cataract blindness over the last 20 years over much of the world. Indeed, the World Health Assembly Global Action Plan target of a 25% reduction from 2010 to 2019 in avoidable vision impairment (WHA 66.3 24/5/2013) was met for cataract blindness (from an age-adjusted prevalence perspective) [ 18 ].

However, the successes have not kept pace with the impact of population growth and aging, with the result that the number of cataract blind is substantially increasing. Cataract also remains the leading cause of blindness despite these improvements and its favorable treatability. Thus, further investment in sustainable health systems able to provide quality cataract surgeries is likely to provide very substantial societal and economic net benefits. Because development is a long-term proposition, sustained commitment will be needed, whether through committed funders (e.g., government or charity programs) or self-sustaining organizations (private non-profit or social enterprise systems, or government systems allowing cost recovery).

While our data demonstrate a notable improvement in blindness (worse than 20/400 visual acuity), we did not see a similar decrease in MSVI (worse than 20/60 to 20/400) which also is associated with substantial disability/economic impact [ 27 , 28 ]. Indeed, MSVI became more prevalent and nearly doubled in the number of cases. This pattern suggests successful targeting of the most severely impaired cases, albeit at the neglect of the less severely impaired. However, MSVI also needs to be targeted to alleviate visual disability and its socioeconomic impacts [ 27 , 28 ]. Indeed, research into willingness to pay for cataract surgery suggests that people in the MSVI range (e.g., younger people otherwise capable of employment) may be more willing to pay for cataract surgery than more severe “blind” persons [ 26 ]. Expansion of the indications for cataract surgery may be needed to accomplish improvements in cataract MSVI also [ 29 ].

The WHO criteria score blindness and visual impairment based on the vision in the better eye. Following this logic, it would seem sensible in an economically constrained environment to focus on operating one eye. However, second eye surgeries also have important benefits to vision, visual ability and well-being [ 30 , 31 ], and has been shown to have very high cost-effectiveness (cost per quality-adjusted life year gained) and a favorable cost-effectiveness in an evidence-based review [ 32 , 33 ]. In addition, second eye surgery provides insurance that vision could continue in the event something happened to the first eye for persons in locations with poor service access. Binocular vision is important for activities requiring depth perception, falls prevention, increases contrast sensitivity and provides better binocular visual acuity than single eye surgery alone [ 31 ]. Because case finding of second eye cataracts and second eye operations have less marginal cost for bilateral cases than first eye cataracts [ 34 ], it is desirable to operate second eyes as well. Persons also may be more willing to pay for a cataract surgery after seeing the result of first eye cataract surgery [ 33 ]. Second eye cataract surgeries generally should be made available to patients in cataract programs, especially if patients are willing to pay some or all of the cost.

While improvements in cataract blindness were observed over the last 20 years, huge disparities in the prevalence remain between low- and high-income regions. South Asia has the highest number of cataract blind and by far the highest prevalence, a significant focus in this super region has the greatest potential for improvement. However, other poor regions (e.g., Sub-Saharan Africa) which are expected to see a growth in the elderly population in coming years and have a very high prevalence of cataract blindness amongst the elderly needing aggressive efforts to develop an eye care system capable of handling the volume of cataract surgery and other eye care services which can be forecast to be needed. Given the very low number of ophthalmologists and other eye care professionals in these areas, the time is now to strengthen and expand both training and systems for eye care delivery [ 34 ].

Our results demonstrated again that women are disproportionately represented amongst the cataract blind and visually impaired, and that the inequity is widening. The extent of this difference varies across the globe, but is generally consistent. The difference might reflect differences in family willingness to pay for male and female surgery [ 35 ]. Differences in acceptance of surgery between males and females could be another explanation. However, acceptance of clinical services tends to be higher among women than men in high income settings. Notably, female survival is generally longer than male survival which might be associated with a higher burden of age-related cataract even if service utilization were equal. Baruwa et al found that five years’ access to free cataract screening and low-cost high quality cataract surgery was associated with equalization in willingness to pay for cataract surgery across males and females [ 36 ]. Improving cataract surgery quality, community knowledge of the benefits of cataract surgery, and reducing barriers to surgical access likely are among the core strategies that need to be implemented in order to overcome the male-female gap in cataract surgery utilization. Without foregoing the promotion of cataract surgery among males, who also need to increase cataract surgery utilization, female surgery promoters and other strategies to increase female use of cataract surgery also could be helpful to reduce the disproportionately higher female cataract blindness and visual impairment burden.

The impact of the COVID-19 pandemic on cataract blindness is unclear at this time. Emerging evidence that service delivery was adversely affected during the emergency phase of the pandemic may drive the cataract burden up [ 37 ]. This may be offset by global decreases in life expectance from the disease and its sequelae [ 37 ]. These impacts may not be visible for several years, but are likely to be overwhelmed by existing trajectories of population growth and ageing.

In summary, as the population grows and ages while coverage of cataract surgery remains incomplete, immense numbers of people remain blind and vision impaired from cataract. These numbers are expected to continue growing markedly as the population increases and ages worldwide, especially in the least developed countries with young but rapidly aging populations and high cataract blindness/MSVI prevalence. While much has been achieved by initiatives to tackle cataract blindness, much more needs to be done to provide cataract surgery to those in need. Programs for delivering cataract to the vision impaired should not only target the blind, but also those with MSVI who also substantially benefit from treatment and appear to be under-targeted. High quality service provision is essential for inciting demand for cataract surgery, and thus is a key issue along with increasing the number of surgeries. Ophthalmologist training, which takes a long time, needs to be developed urgently in areas of insufficient coverage. Eye care systems in which ophthalmologists can operate successfully and other eye care professionals can work successful also are very important. While all regions with substantial numbers of cataract blind need increased services, females especially need to access cataract surgery more. Culturally appropriate efforts to promote female cataract surgery are an important piece of what needs to be done. Globally, immense increases in resource mobilization for treating cataract are required. All sources of healthcare financing need to be tapped to develop sustainable eye care systems able to tackle the cataract problem with high quality surgery.

What was known before

Globally, in 2020, 17.0 million people were blind and nearly 83.5 million were visually impaired by cataract.

What this study adds

The contribution of cataract to blindness and moderate and severe vision impairment (MSVI) by region and the change in this contribution between 2000 and 2020. The change in global age-standardized prevalence of cataract-related blindness and MSVI between 2000 and 2020 and the differences by sex and region.

GBD 2019 Blindness and Vision Impairment Collaborators; Vision Loss Expert Group of the Global Burden of Disease Study. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9:e144–e160.

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Open Access funding enabled and organized by CAUL and its Member Institutions.

Author information

These authors contributed equally: Jaimie Steinmetz, Rupert Bourne.

Authors and Affiliations

University of New South Wales, Sydney, NSW, Australia

Konrad Pesudovs & Serge Resnikoff

Department of Research, Instituto Mexicano de Oftalmología, Queretaro, Mexico

Van Charles Lansingh

Chief Medical Office, Help Me See, Jersey City, NJ, USA

Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA

Van Charles Lansingh & Rupert Bourne

Department of Ophthalmology, Massachusetts Eye and Ear and Harvard Medical School Schepens Eye Research Institute, Boston, MA, USA

John H. Kempen

MCM Eye Unit, MyungSung Christian Medical Center (MCM) Multispecialty Hospital and MyungSung Medical School, Addis Ababa, Ethiopia

Department of Ophthalmology, Faculty of Medicine, Addis Ababa University, Addis Ababa, Ethiopia

Department of Ophthalmology, Cambridge University Hospitals, Cambridge, UK

Universidade Federal de Sao Paulo, Sao Paulo, São Paulo, Brazil

Arthur G. Fernandes & Jaimie Steinmetz

Department of Ophthalmology, San Raffaele Scientific Institute, Milano, Italy

Maria V. Cicinelli

IRCCS San Raffaele Scientific Institute, Vita-Salute University, via Olgettina 60, 20123, Milan, Italy

Alessandro Arrigo

Ophthalmology Department, CHU de Poitiers, Poitiers, France

Nicolas Leveziel & Rupert Bourne

Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

Paul Svitil Briant, Theo Vos, Theo Vos, Katrin Burkart, Kaleb Coberly, Xiaochen Dai, Stephen S. Lim, Tomislav Mestrovic, Ali H. Mokdad, Christopher J. L. Murray & Jaimie D. Steinmetz

Brien Holden Vision Institute, Sydney, NSW, Australia

Serge Resnikoff & Serge Resnikoff

Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia

Hugh R. Taylor

Vision and Eye Research Unit, Postgraduate Medical Institute, Anglia Ruskin University, Cambridge, UK

Tabassom Sedighi

Department of Computer Science, University of Oxford, Oxford, UK

Seth Flaxman & Seth Flaxman

Medicine & Health, University of New South Wales, Sydney, NSW, Australia

  • Konrad Pesudovs

HelpMeSee, Instituto Mexicano de Oftalmologia, Santiago de Querétaro, Mexico

University of Miami, Gables, FL, USA

University of Utah, Salt Lake City, UT, USA

Department of Ophthalmology, Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School, Boston, MA, USA

Eye Unit, MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College, Addis Ababa, Ethiopia

Department of Ophthalmology, Addis Ababa University, Addis Ababa, Ethiopia

Sight for Souls, Bellevue, WA, USA

Addenbrooke’s Hospital, Cambridge, UK

Federal University of Sao Paolo, Sao Paolo/SP, Brazil

Arthur G. Fernandes

University of Calgary, Calgary, AB, Canada

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy

Maria Vittoria Cicinelli

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Milan, Italy

IRCCS San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy

University of Poitiers, Poitiers, France

Nicolas Leveziel

CHU de Poitiers, Poitiers, France

School of Optometry and Vision Sciences, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia

Serge Resnikoff

School of Population and Global Health, University of Melbourne, Carlton, VIC, Australia

Vision and Eye Research Institute, Anglia Ruskin University, Cambridge, UK

Tabassom Sedighi & Rupert Bourne

Ufa Eye Research Institute, Ufa, Russia

Mukkharram M. Bikbov

School of Life Course and Population Sciences, King’s College London, London, UK

Tasanee Braithwaite

The Medical Eye Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

University Hospital, Dijon, France

National University of Singapore, Singapore, Singapore

Ching-Yu Cheng

Singapore Eye Research Institute, Singapore, Singapore

University of Michigan, Ann Arbor, MI, USA

Monte A. Del Monte

Kellogg Eye Center, Ann Arbor, MI, USA

Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

Joshua R. Ehrlich

Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA

National Eye Institute, Bethesda, MD, USA

Leon B. Ellwein

Mass Eye and Ear, Harvard Medical School, Boston, MA, USA

David Friedman

Ribeirão Preto Medical School, University of São Paulo, Sao Paulo, Brazil

João M. Furtado

Institute of Ophthalmology UCL & NIHR Biomedical Research Centre, London, UK

Gus Gazzard

Sankara Nethralaya, Medical Research Foundation, 600006, Chennai, India

Ronnie George

Stanford University, Stanford, CA, USA

M. Elizabeth Hartnett

Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany

Jost B. Jonas

Associated Ophthalmologists of Monastir, Monastir, Tunisia

Rim Kahloun

Fattouma Bourguiba University Hospital, University of Monastir, Monastir, Tunisia

Moncef Khairallah

Allen Foster Community Eye Health Research Centre, Gullapalli Pratibha Rao International Centre for Advancement of Rural Eye care, L.V. Prasad Eye Institute, Hyderabad, India

Rohit C. Khanna

Brien Holden Eye Research Centre, L.V. Prasad Eye Institute, Banjara Hills, Hyderabad, India

School of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia

Rohit C. Khanna, Konrad Pesudovs & Serge Resnikoff

University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA

Nova Southeastern University College for Optometry, Fort Lauderdale, FL, USA

Janet Leasher

Ulster University, Coleraine, UK

Julie-Anne Little

Suraj Eye Instate, 559, New colony, Nagpur, India

Vinay Nangia

Institute of Optics and Optometry, University of Social Science, 121 Gdanska str., Lodz, 90-519, Poland

Michal Nowak

Centre for Public Health, Queens University Belfast, Northern Ireland, Belfast, UK

John Hopkins Wilmer Eye Institute, Baltimore, MD, USA

Pradeep Ramulu

1st Department of Ophthamology, Medical School, Aristotle University of Thessaloniki, Ahepa Hospital, Thessaloniki, Greece

Fotis Topouzis

University of Crete Medical School, Giofirakia, Greece

Mitiadis Tsilimbaris

Beijing Institute of Ophthamology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthamology and Visual Sciences Key Laboratory, Beijing, China

Ya Xing Wang

Beijing Institute of Ophthamology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China

Ningli Wang

Chief Medical Office, HelpMeSee, New York, NY, USA

Mexican Institute of Ophthalmology, Queretaro, Mexico

Department of Ophthalmology, Harvard University, Boston, MA, USA

Eye Unit, MyungSung Medical College, Addis Ababa, Ethiopia

Department of Ophthalmology and Visual Sciences, Federal University of São Paulo, Sao Paulo, Brazil

Scientific Institute San Raffaele Hospital, Vita-Salute University, Milan, Italy

Ophthalmology Department, CHU de Poitiers (Poitiers University Hospital), Poitiers, France

Unité 1084, National Institute of Health and Medical Research (INSERM), Poitiers, France

Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA

Theo Vos, Katrin Burkart, Xiaochen Dai, Stephen S. Lim, Awoke Misganaw, Ali H. Mokdad & Christopher J. L. Murray

Tabassom Sedighi, Shahina Pardhan & Rupert Bourne

Department of Mathematics, Imperial College London, London, UK

Seth Flaxman

Department of Clinical Governance and Quality Improvement, Aleta Wondo Hospital, Aleta Wondo, Ethiopia

Yohannes Habtegiorgis Abate

The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran

Mohammad Abdollahi

School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran

School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Mozhan Abdollahi

Pediatrics Nursing Department, Debre Berhan University, Debre Berhan, Ethiopia

Ayele Mamo Abebe

Department of Community Medicine, Babcock University, Ilishan-Remo, Nigeria

Olumide Abiodun

Department of Family and Community Health, University of Health and Allied Sciences, Ho, Ghana

Richard Gyan Aboagye

Department of Adult Health Nursing, Aksum University, Aksum, Ethiopia

Woldu Aberhe Abrha

Department of Nursing, Al Zaytoonah University of Jordan, Amman, Jordan

Hasan Abualruz

Department of Banking and Finance, University of Human Development, Sulaymaniyah, Iraq

Hiwa Abubaker Ali

Clinical Sciences Department, University of Sharjah, Sharjah, United Arab Emirates

Eman Abu-Gharbieh

Department of Therapeutics, United Arab Emirates University, Al Ain, United Arab Emirates

Salahdein Aburuz

College of Pharmacy, University of Jordan, Amman, Jordan

Department of Public Health, Wolkite University, Wolkite, Ethiopia

Tadele Girum Girum Adal

College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia

Mesafint Molla Adane

Centre for Social Research in Health, University of New South Wales, Sydney, NSW, Australia

Isaac Yeboah Addo

Quality and Systems Performance Unit, Cancer Institute NSW, Sydney, NSW, Australia

Faculty of Medicine, Universitas Padjadjaran (Padjadjaran University), Bandung, Indonesia

Qorinah Estiningtyas Sakilah Adnani

Department of Life Sciences, University of Management and Technology, Lahore, Pakistan

Muhammad Sohail Afzal & Muhammad Umair

Department of Biotechnology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Shahin Aghamiri

School of Public Health, University of Technology Sydney, Sydney, NSW, Australia

Bright Opoku Ahinkorah

Department of Medical Biochemistry, Shaqra University, Shaqra, Saudi Arabia

Aqeel Ahmad

Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan

Sajjad Ahmad

Department of Natural Sciences, Labanese American University, Beirut, Lebanon

Department of Epidemiology and Biostatistics, Shahrekord University of Medical Sciences, Shahrekord, Iran

Department of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Ali Ahmadi & Siamak Sabour

Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan

Ayman Ahmed

Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland

Department of Biosciences, COMSATS Institute of Information Technology, Islamabad, Pakistan

Haroon Ahmed

Department of Ophthalmology, University of Leipzig Medical Center, Leipzig, Germany

Ahmad Samir Alfaar

Department of Ophthalmology, Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, Germany

Department of Zoology, Abdul Wali Khan University Mardan, Mardan, Pakistan

Center for Biotechnology and Microbiology, University of SWAT, Swat, Pakistan

Syed Shujait Shujait Ali

Centre for Research in Molecular Medicine, Institute of Molecular Biology and Biotechnology, Lahore, Pakistan

Awais Altaf

Department of Population and Behavioural Sciences, University of Health and Allied Sciences, Ho, Ghana

Hubert Amu & Emmanuel Manu

Department of Medicine, University of Thessaly, Volos, Greece

Sofia Androudi

Department of Ophthalmology, Inselspital, Bern, Switzerland

Rodrigo Anguita

Department of Vitreoretinal, Moorfields Eye Hospital, London, UK

Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, India

Abhishek Anil, Rimple Jeet Kaur & Muhammad Aaqib Shamim

All India Institute of Medical Sciences, Bhubaneswar, India

Abhishek Anil

Regenerative Medicine, Organ Procurement and Transplantation Multi-diciplinary Center, Guilan University of Medical Sciences, Rasht, Iran

Saeid Anvari

School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia

Anayochukwu Edward Anyasodor

Department of Social Sciences, Berekum College of Education, Berekum, Ghana

Francis Appiah

School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran

Jalal Arabloo & Haleh Ayatollahi

College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates

Mosab Arafat

College of Art and Science, Ottawa University, Surprise, AZ, USA

Damelash Areda

School of Life Sciences, Arizona State University, Tempe, AZ, USA

Department of Anatomy, Shiraz University of Medical Sciences, Shiraz, Iran

Reza Arefnezhad

College of Medicine and Health Sciences, Adigrat University, Adigrat, Ethiopia

Brhane Berhe Aregawi

Department of Environmental Health, Mekelle University, Mekelle, Ethiopia

Akeza Awealom Asgedom

University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore, Pakistan

Tahira Ashraf

Department of Immunology, Zanjan University of Medical Sciences, Zanjan, Iran

Seyyed Shamsadin Athari

School of Nursing and Midwifery, Debre Berhan University, Debre Berhan, Ethiopia

Bantalem Tilaye Tilaye Atinafu & Birhan Tsegaw Taye

Faculty of Nursing, Philadelphia University, Amman, Jordan

Maha Moh’d Wahbi Atout

Department of Forensic Medicine, Lumbini Medical College, Palpa, Nepal

Alok Atreya

Department of Health Information Management, Iran University of Medical Sciences, Tehran, Iran

Haleh Ayatollahi

Department of Neurovascular Research, Nested Knowledge, Inc., Saint Paul, MN, USA

Ahmed Y. Azzam

Faculty of Medicine, October 6 University, 6th of October City, Egypt

Department of Nursing, Semnan University of Medical Sciences and Health Services, Semnan, Iran

Hassan Babamohamadi

School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Sara Bagherieh

Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Rishikesh, India

Yogesh Bahurupi

University Institute of Public Health, The University of Lahore, Lahore, Pakistan

Atif Amin Baig & Shumaila Nargus

Institute of Health and Wellbeing (IHW), Federation University Australia, Melbourne, VIC, Australia

Biswajit Banik

Manna Institute, University of New England, Armidale, NSW, Australia

Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA

Mainak Bardhan

Department of Academics, Indian Institute of Public Health, Gurgaon, India

Saurav Basu

Department of Medical Education, University of Nevada, Las Vegas, Las Vegas, NV, USA

Kavita Batra

Department of Surgery, Jimma University, jimma, Ethiopia

Nebiyou Simegnew Bayileyegn

Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran

Fatemeh Bazvand

School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia

Addisu Shunu Beyene

School of Public Health, Haramaya University, Harar, Ethiopia

Addisu Shunu Beyene & Awoke Masrie Asrat Derese

Department of Forensic Chemistry, Government Institute of Forensic Science, Aurangabad, Aurangabad, India

Devidas S. Bhagat

Department of Public Health, North Dakota State University, Fargo, ND, USA

Akshaya Srikanth Bhagavathula

Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, India

Pankaj Bhardwaj

School of Public Health, All India Institute of Medical Sciences, Jodhpur, India

Global Health Neurology Lab, NSW Brain Clot Bank, Sydney, NSW, Australia

Sonu Bhaskar

Department of Neurology and Neurophysiology, South West Sydney Local Heath District and Liverpool Hospital, Sydney, NSW, Australia

Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Jasvinder Singh Bhatti

Epidemiology Department, Ufa Eye Research Institute, Ufa, Russia

Mukharram Bikbov

Department of Ophthalmology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Niloufar Bineshfar

Department of Medicine, Division of Clinical Epidemiology, McGill University, Montreal, QC, Canada

Marina G. Birck

Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada

Faculty of Health Sciences, University of Botswana, Gaborone, Botswana

Veera R. Bitra

Ophthalmology Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK

International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK

Department of Biopharmaceutics and Clinical Pharmacy, The University of Jordan, Amman, Jordan

Yasser Bustanji

Department of Basic Biomedical Sciences, University of Sharjah, Sharjah, United Arab Emirates

School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada

Zahid A. Butt

Al Shifa School of Public Health, Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan

Harvard Business School, Harvard University, Boston, MA, USA

Florentino Luciano Caetano dos Santos

Internal Medicine Department, Hospital Italiano de Buenos Aires (Italian Hospital of Buenos Aires), Buenos Aires, Argentina

Luis Alberto Cámera

Board of Directors, Argentine Society of Medicine, Buenos Aires, Argentina

School of Sciences, University of Minho, Braga, Portugal

Vera L. A. Carneiro

Association of Licensed Optometry Professionals, Linda-a-Velha, Portugal

College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, Australia

Muthia Cenderadewi

Public Health Departement, University of Mataram, Mataram, Indonesia

Department of Anesthesiology and Perioperative Medicine, University of Rochester, Rochester, NY, USA

Eeshwar K. Chandrasekar

Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Vijay Kumar Chattu

Saveetha Dental College, Saveetha University, Chennai, India

Macquarie Medical School, Macquarie University, Sydney, NSW, Australia

Nitin Chitranshi & Yuyi You

Chitkara College of Pharmacy, Chitkara Univeristy, Rajpura, Punjab, India

Hitesh Chopra

Center for Biomedicine and Community Health, VNU-International School, Hanoi, Viet Nam

Dinh-Toi Chu

University Hospital Center of Porto, University of Porto, Porto, Portugal

João M. Coelho

Therapeutic and Diagnostic Technologies, Cooperativa de Ensino Superior Politécnico e Universitário (Polytechnic and University Higher Education Cooperative), Gandra, Portugal

Natália Cruz-Martins

Institute for Research and Innovation in Health, University of Porto, Porto, Portugal

Department of Addiction Medicine, Haukland University Hospital, Bergen, Norway

Omid Dadras

Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

Ingram School of Engineering, Texas State University, San Marcos, TX, USA

Subasish Das

Ophthalmology Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania

Ana Maria Dascalu

Ophthalmology Department, Emergency University Hospital Bucharest, Bucuresti, Romania

Department of Radiology, Tabriz University of Medical Sciences, Tabriz, Iran

Mohsen Dashti

Iran University of Medical Sciences, Tehran, Iran

Maedeh Dastmardi & Maryam Moradi

Tehran University of Medical Science, Tehran University of Medical Sciences, Tehran, Iran

Maedeh Dastmardi

USAID-JSI, Jimma University, Addis Ababa, Ethiopia

Berecha Hundessa Demessa

Department of Nursing, Arba Minch University, Arba Minch, Ethiopia

Biniyam Demisse, Abera M. Mersha & Bereket Beyene Shashamo

Department of Biomedical Sciences, Jimma University, Jimma, Ethiopia

Diriba Dereje

St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool, UK

Nikolaos Dervenis

Department of Ophthalmology, Aristotle University of Thessaloniki, Thessaloniki, Greece

Department of Community Medicine, Chettinad Hospital and Research Institute, Chennai, India

Vinoth Gnana Chellaiyan Devanbu

Department of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam

Thanh Chi Do

Department of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Viet Nam

Thao Huynh Phuong Do

Epidemiology and Data Analysis Laboratory, University Center FMABC, Santo André, Brazil

Francisco Winter dos Santos Figueiredo

Department of Conservative Dentistry with Endodontics, Medical University of Silesia, Katowice, Poland

Arkadiusz Marian Dziedzic

School of Health Sciences, Universiti Sains Malaysia (University of Science Malaysia), Kubang Kerian, Malaysia

Hisham Atan Edinur

Advanced Nursing Department, Universitas Airlangga, Surabaya, Indonesia

Ferry Efendi

Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, GA, USA

Institute for Health Care Policy and Innovation, University of Michigan, Ann Arbor, MI, USA

Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria

Michael Ekholuenetale, Adeniyi Francis Fagbamigbe & Kayode Raphael Fowobaje

Faculty of Public Health, University of Ibadan, Ibadan, Nigeria

Michael Ekholuenetale

Department of Biological Sciences, University of Medical Sciences, Ondo, Ondo, Nigeria

Temitope Cyrus Ekundayo

Biomedical Informatics and Medical Statistics Department, Alexandria University, Alexandria, Egypt

Iman El Sayed

Faculty of Medicine, University of Tripoli, Tripoli, Libya

Muhammed Elhadi

Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran

Mohammad Hassan Emamian

Department of Ophthalmology, University of California Los Angeles, Los Angeles, CA, USA

Mehdi Emamverdi

Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA

Azin Etemadimanesh

Research Centre for Healthcare and Community, Coventry University, Coventry, UK

Adeniyi Francis Fagbamigbe

Department of Oral Biology, The University of Lahore, Lahore, Pakistan

Ayesha Fahim

School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Hossein Farrokhpour & Soheil Mohammadi

Endocrinology and Metabolism Research Institute, Non-Communicable Diseases Research Center (NCDRC), Tehran, Iran

Hossein Farrokhpour

Department of Environmental Health Engineering, Isfahan University of Medical Sciences, Isfahan, Iran

Ali Fatehizadeh

Department of Social Medicine and Epidemiology, Guilan University of Medical Sciences, Rasht, Iran

Alireza Feizkhah

University Eye Clinic, University of Genoa, Genoa, Italy

Lorenzo Ferro Desideri

Department of Nursing, Wollega University, Nekemte, Ethiopia

Getahun Fetensa

Institute of Public Health, Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, Germany

Florian Fischer

Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran

Ali Forouhari, Alireza Peyman & Mohsen Pourazizi

Emergency Department, Isfahan University of Medical Sciences, Isfahan, Iran

Ali Forouhari

Department of Biotechnological and Applied Clinical Sciences (DISCAB), Multiple Sclerosis Research Center, L’Aquila, Italy

Matteo Foschi

Department of Neuroscience, Multiple Sclerosis Research Center, Ravenna, Italy

Child Survival Unit, Centre for African Newborn Health and Nutrition, Ibadan, Nigeria

Kayode Raphael Fowobaje

Department of Community Medicine, Datta Meghe Institute of Medical Sciences, Wardha, India

Abhay Motiramji Gaidhane

Department of Community Medicine, ESIC Medical College & Hospital, Hyderabad, India

Aravind P. Gandhi

Department of Midwifery, Adigrat University, Adigrat, Ethiopia

Miglas W. W. Gebregergis

Department of Environmental Health, Wollo University, Dessie, Ethiopia

Mesfin Gebrehiwot

Department of Health, Policy Research Institute, Mekelle, Ethiopia

Brhane Gebremariam

Simon Fraser University, Mekelle, Ethiopia

Department of Public Health, Jimma University, Jimma, Ethiopia

Urge Gerema

Department of Ophthalmology, Tehran University of Medical Sciences, Tehran, Iran

Fariba Ghassemi & Alireza Mahmoudi

Department of Radiology, Mayo Clinic, Rochester, MN, USA

Sherief Ghozy

Health Systems and Policy Research Department, Indian Institute of Public Health, Gandhinagar, India

Mahaveer Golechha

Department of Genetics, Sana Institute of Higher Education, Sari, Iran

Pouya Goleij

Universal Scientific Education and Research Network (USERN), Kermanshah University of Medical Sciences, Kermanshah, Iran

Postgraduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Bárbara Niegia Garcia Goulart

Department of Epidemiology and Biostatistics, Anhui Medicla University, Hefei, China

Shi-Yang Guan

Department of Anesthesia, Madda Walabu University, Goba, Ethiopia

Zewdie Gudisa

Toxicology Department, Shriram Institute for Industrial Research, Delhi, India

Sapna Gupta

School of Medicine, Deakin University, Geelong, VIC, Australia

Veer Bala Gupta

Faculty of Medicine Health and Human Sciences, Macquarie University, Sydney, NSW, Australia

Vivek Kumar Gupta

Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

Arvin Haj-Mirzaian

Obesity Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Aram Halimi

Department of Ophthalmology, Thomas Jefferson University, Philadelphia, PA, USA

Shahin Hallaj

Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA

School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates

Samer Hamidi

Department of Nursing, Arak University of Medical Sciences, Arak, Iran

Mehdi Harorani

Department of Ophthalmology, Iran University of Medical Sciences, Karaj, Iran

Hamidreza Hasani

Department of Public Health, Madda Walabu University, Goba, Ethiopia

Demisu Zenbaba Heyi

School of Dentistry, Hanoi Medical University, Hanoi, Viet Nam

Nguyen Quoc Hoan

Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India

Ramesh Holla

Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea

Sung Hwi Hong

Research Department, Electronic Medical Records for the Developing World, York, UK

Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam

Mehdi Hosseinzadeh

Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq

Department of Psychology, Tsinghua University, Beijing, China

Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA

John J. Huang

School of Biotechnology, Tan Tao University, Long An, Viet Nam

Hong-Han Huynh

Department of Health Promotion and Education, University of Ibadan, Ibadan, Nigeria

Segun Emmanuel Ibitoye

Faculty of Medicine, University of Belgrade, Belgrade, Serbia

Irena M. Ilic

Institute of Health Research, University of Health and Allied Sciences, Ho, Ghana

Mustapha Immurana

Department of Pharmacy, University of Asia Pacific, Dhaka, Bangladesh

Md. Rabiul Islam

Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC, Australia

Sheikh Mohammed Shariful Islam

Sydney Medical School, University of Sydney, Sydney, NSW, Australia

School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa

Chidozie C. D. Iwu

Research and Development Unit, Biomedical Research Networking Center for Mental Health Network (CiberSAM), Sant Boi de Llobregat, Spain

Louis Jacob

Faculty of Medicine, University of Versailles Saint-Quentin-en-Yvelines, Montigny-le-Bretonneux, France

Department of Clinical Pharmacy, University of Science Malaysia, Penang, Malaysia

Ammar Abdulrahman Jairoun

Department of Health and Safety, United Arab Emirates University, Dubai, United Arab Emirates

Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India

Manthan Dilipkumar Janodia

Department of Biochemistry, Government Medical College, Mysuru, India

Shubha Jayaram

National Health System Resource Centre, Ministry of Health & Family Welfare, New Delhi, India

Har Ashish Jindal

Zoonoses Research Center, Islamic Azad University, Karaj, Iran

Mohammad Jokar

Department of Clinical Sciences, Jahrom University of Medical Sciences, Jahrom, Iran

Department of Community Medicine, Manipal Academy of Higher Education, Mangalore, India

Nitin Joseph, Nithin Kumar & Rohith Motappa

Department of Economics, National Open University, Benin City, Nigeria

Charity Ehimwenma Joshua

Department of Oral and Maxillofacial Pathology, Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, India

Vidya Kadashetti

Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

Laleh R. Kalankesh

Institute for Prevention of Non-communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran

Rohollah Kalhor

Health Services Management Department, Qazvin University of Medical Sciences, Qazvin, Iran

Manipal Institute of Management, Manipal Academy of Higher Education, Manipal, India

Sagarika Kamath

Save Sight Institute, University of Sydney, Sydney, NSW, Australia

Himal Kandel, Stephanie Louise Watson & Yuyi You

Sydney Eye Hospital, South Eastern Sydney Local Health District, Sydney, NSW, Australia

Himal Kandel

The Hansjörg Wyss Department of Plastic and Reconstructive Surgery, Nab’a Al-Hayat Foundation for Medical Sciences and Health Care, New York, NY, USA

Rami S. Kantar

Department of Cleft Lip and Palate Surgery, Global Smile Foundation, Norwood, MA, USA

School of Health Professions and Human Services, Hofstra University, Hempstead, NY, USA

Ibraheem M. Karaye

Department of Anesthesiology, Montefiore Medical Center, Bronx, NY, USA

Eye Research Center, Iran University of Medical Sciences, Tehran, Iran

Hengameh Kasraei

Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Department of Ophthalmology, Yenepoya Medical College, Mangalore, India

Soujanya Kaup

Department of ENT, Dr. B. R. Ambedkar State Institute of Medical Sciences (AIMS), Mohali, India

Navjot Kaur

International Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, Nigeria

Gbenga A. Kayode

Julius Centre for Health Sciences and Primary Care, Utrecht University, Utrecht, Netherlands

Department of Public Health, Jordan University of Science and Technology, Irbid, Jordan

Yousef Saleh Khader

Amity Institute of Forensic Sciences, Amity University, Noida, India

Himanshu Khajuria & Biswa Prakash Nayak

Department of Biophysics and Biochemistry, Baku State University, Baku, Azerbaijan

Rovshan Khalilov

Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan

Global Consortium for Public Health Research, Datta Meghe Institute of Higher Education and Research, Wardha, India

Mahalaqua Nazli Khatib

School of Health Sciences, Kristiania University College, Oslo, Norway

Department of International Health and Sustainable Development, Tulane University, New Orleans, LA, USA

Independent Consultant, Jakarta, Indonesia

Soewarta Kosen

San Juan de Dios Sanitary Park, Barcelona, Spain

Ai Koyanagi

Department of Anthropology, Panjab University, Chandigarh, India

Kewal Krishan

Department of Health Research, Almaty, Kazakhstan

Mukhtar Kulimbet

Atchabarov Scientific Research Institute of Fundamental and Aplied Medicine, Kazakh National Medical University, Almaty, Kazakhstan

Faculty of Health and Life Sciences, Coventry University, Coventry, UK

Om P. Kurmi

Department of Medicine, McMaster University, Hamilton, ON, Canada

Department of Health Policy and Strategy, Foundation for People-centric Health Systems, New Delhi, India

Chandrakant Lahariya

SD Gupta School of Public Health, Indian Institute of Health Management Research University, Jaipur, India

Department of Surgery, Washington University in St. Louis, Saint Louis, MO, USA

Unit of Genetics and Public Health, Institute of Medical Sciences, Las Tablas, Panama

Iván Landires

Ministry of Health, Herrera, Panama

College of Optometry, Nova Southeastern University, Fort Lauderdale, FL, USA

Janet L. Leasher

Department of Medical Humanities and Social Medicine, Ajou University School of Medicine, Suwon, South Korea

Medial Research Collaborating Center, Ajou University Medical Center, Suwon, South Korea

Department of Precision Medicine, Sungkyunkwan University, Suwon-si, South Korea

Seung Won Lee

Department of Internal Medicine, University of Texas, Galveston, TX, USA

Wei-Chen Lee

School of Biomedical Sciences, Coleraine, UK

Department of Community Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Karaikal, India

Preetam Bhalchandra Mahajan

School of Pharmacy, University of the West Indies, St. Augustine, Trinidad and Tobago

Sandeep B. Maharaj

Fellow, Planetary Health Alliance, Boston, MA, USA

Department of Food Hygiene and Safety, Qazvin University of Medical Sciences, Qazvin, Iran

Razzagh Mahmoudi

Department of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana, India

Kashish Malhotra

Department of Clinical Pharmacy, Jouf University, Sakaka, Saudi Arabia

Tauqeer Hussain Mallhi

Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Vahid Mansouri

Department of Public Health, Management and Science University, Shah Alam, Malaysia

Roy Rillera Marzo

Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Subang Jaya, Malaysia

Department GF Ingrassia, University of Catania, Catania, Italy

Andrea Maugeri

Department of Ophthalmology, Princess of Wales Hospital, Wales, UK

Colm McAlinden

School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK

Department of Epidemiology and Biostatistics, Wollo University, Dessie, Ethiopia

Wondwosen Mebratu

Department of Research, Performance Monitoring and Accountability 2020-Ethiopia, Addis Ababa, Ethiopia

Department of Public Health, Arba Minch University, Arba Minch, Ethiopia

Tesfahun Mekene Meto

Eye Center, Wuhan University, Wuhan, China

University Centre Varazdin, University North, Varazdin, Croatia

Tomislav Mestrovic

International Ph.D. Program in Medicine, Taipei Medical University, Taipei, Taiwan

Le Huu Nhat Minh

Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan

National Data Management Center for Health, Ethiopian Public Health Institute, Addis Ababa, Ethiopia

Awoke Misganaw

Department of Biomedical Sciences, Mercer University School of Medicine, Macon, GA, USA

Manish Mishra

Department of Surgical Oncology, All India Institute of Medical Sciences, Jodhpur, India

Sanjeev Misra

Molecular Biology Unit, Sirius Training and Research Centre, Khartoum, Sudan

Nouh Saad Mohamed

Bio-Statistical and Molecular Biology Department, Sirius Training and Research Centre, Khartoum, Sudan

Department of Clinical Pharmacy and Pharmacy Practice, Ahmadu Bello University, Zaria, Nigeria

Mustapha Mohammed

School of Pharmaceutical Sciences, Universiti Sains Malaysia (University of Science Malaysia), Penang, Malaysia

Department of Pharmacology, Abadan School of Medical Sciences, Abadan, Iran

Hoda Mojiri-forushani

Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran

Hossein Molavi Vardanjani

School of Health & Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, Australia

Mohammad Ali Moni

Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran

Fateme Montazeri, Parsa Mousavi & Mohammad-Mahdi Rashidi

School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Fateme Montazeri

Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece

Admir Mulita

College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia

Ganesh R. Naik

Department of Engineering, Western Sydney University, Sydney, NSW, Australia

Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA

Gurudatta Naik

Department of Dental Public Health, King Abdulaziz University, Jeddah, Saudi Arabia

Zuhair S. Natto

Department of Health Policy and Oral Epidemiology, Harvard University, Boston, MA, USA

Independent Consultant, Tehran, Iran

Mohammad Negaresh

Department of Internal Medicine, Ardabil University of Medical Science, Ardabil, Iran

Department of Medical Laboratory Sciences, Adigrat University, Adigrat, Ethiopia

Hadush Negash

Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA

Dang H. Nguyen

Department of Medical Engineering, University of South Florida, Tampa, FL, USA

Department of Surgery, Danang Family Hospital, Danang, Viet Nam

Phat Tuan Nguyen

Department of General Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam

Van Thanh Nguyen

International Islamic University Islamabad, Islamabad, Pakistan

Robina Khan Niazi

School of Pharmacy, University of the Western Cape, Cape Town, South Africa

Osaretin Christabel Okonji

Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada

Andrew T. Olagunju

Department of Psychiatry, University of Lagos, Lagos, Nigeria

Department of Nursing Science, Bowen University, Iwo, Nigeria

Matthew Idowu Olatubi

Department of Pharmacotherapy and Pharmaceutical Care, Medical University of Warsaw, Warsaw, Poland

Michal Ordak

School of Medicine, Western Sydney University, Campbelltown, NSW, Australia

Uchechukwu Levi Osuagwu

Department of Optometry and Vision Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa

Laboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Dolgoprudny, Russia

Nikita Otstavnov

Department of Medicine, University of Ibadan, Ibadan, Nigeria

Mayowa O. Owolabi

Department of Medicine, University College Hospital, Ibadan, Ibadan, Nigeria

Department of Forensic Medicine and Toxicology, Kasturba Medical College, Mangalore, Mangalore, India

Jagadish Rao Padubidri

Research Department, Nepal Health Research Council, Kathmandu, Nepal

Ashok Pandey

Research Department, Public Health Research Society Nepal, Kathmandu, Nepal

Department of Ophthalmology, Nottingham University Hospitals, QMC Campus, Nottingham, UK

Georgios D. Panos

Division of Ophthalmology & Visual Sciences, University of Nottingham, Nottingham, UK

Yonsei University College of Medicine, Seodaemun-gu, South Korea

Seoyeon Park

Global Health Governance Programme, University of Edinburgh, Edinburgh, UK

School of Dentistry, University of Leeds, Leeds, UK

Department of Genetics, Yale University, New Haven, CT, USA

Shrikant Pawar

Centre for Primary Health Care and Equity, University of New South Wales, Kensington, NSW, Australia

Prince Peprah

Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania

Ionela-Roxana Petcu

School of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam

Hoang Tran Pham

College of Health Sciences, VinUniversity, Hanoi, Viet Nam

Nguyen Khoi Quan

Department of Health Sciences, Cihan University Sulaimaniya, Sulaymaniyah, Iraq

Fakher Rahim

Cihan University Sulaimaniya Research Center (CUSRC), Sulaymaniyah, Iraq

Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran

Vafa Rahimi-Movaghar

Manipal TATA Medical College, Manipal Academy of Higher Education, Manipal, India

Mohammad Hifz Ur Rahman

Department of Community Medicine, Employees’ State Insurance Model Hospital, Chennai, India

Sathish Rajaa

Department of Radiology, Stanford University, Stanford, CA, USA

Shakthi Kumaran Ramasamy

Department of Community Medicine, Mahatma Gandhi Medical College and Research Institute, Puducherry, India

Premkumar Ramasubramani

School of Humanities and Social Sciences, Indian Institute of Technology Mandi, Mandi, India

Shubham Ranjan

Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mohammad-Mahdi Rashidi

Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Gorakhpur, Gorakhpur, India

Rama Shankar Rath

Department of Health Behaviour, Environment, and Social Medicine, Gadjah Mada University, Yogyakarta, Indonesia

Annisa Utami Rauf

Department of Primary Care and Public Health, Imperial College London, London, UK

Salman Rawaf

Academic Public Health England, Public Health England, London, UK

Department of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Amirmasoud Rayati Damavandi

Department Biological Sciences, King Abdulaziz University, Jeddah, Egypt

Elrashdy Moustafa Mohamed Redwan

Department of Protein Research, Research and Academic Institution, Alexandria, Egypt

Department of Labour, Directorate of Factories, Government of West Bengal, Kolkata, India

Priyanka Roy

Department of Biostatistics and Epidemiology, International Institute for Population Sciences, Mumbai, India

Koushik Roy Pramanik

Faculty of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

Zahra Saadatian

Infectious Diseases Research Center, Gonabad University of Medical Sciences, gonabad, Iran

Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates

Basema Saddik

Multidisciplinary Laboratory Foundation University School of Health Sciences (FUSH), Foundation University, Islamabad, Pakistan

International Center of Medical Sciences Research (ICMSR), Islamabad, Pakistan

Ophthalmic Epidemiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Faculty of Medicine, Bioscience and Nursing, MAHSA University, Selangor, Malaysia

Sher Zaman Safi

Interdisciplinary Research Centre in Biomedical Materials (IRCBM), COMSATS Institute of Information Technology, Lahore, Pakistan

Research Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, Iran

Amene Saghazadeh

Sharjah Institute of Medical Sciences, University of Sharjah, Sharjah, United Arab Emirates

Fatemeh Saheb Sharif-Askari

Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Amirhossein Sahebkar

Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Multiple Sclerosis Research Center, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Ali Sahraian

Ludwig Maximilian University of Munich, Munich, Germany

Joseph W. Sakshaug

Institute for Employment Research, Nuremberg, Germany

College of Medicine, University of Sharjah, Sharjah, United Arab Emirates

Mohamed A. Saleh

Faculty of Pharmacy, Mansoura University, Mansoura, Egypt

Department of Neurology, Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, Germany

Sara Samadzadeh

Department of Neurology, University of Southern Denmark, Odense, Denmark

School of Public Health, Taipei Medical University, Taipei, Taiwan

Yoseph Leonardo Samodra

Department of Anatomy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates

Vijaya Paul Samuel

Department of Entomology, Ain Shams University, Cairo, Egypt

Abdallah M. Samy

Medical Ain Shams Research Institute (MASRI), Ain Shams University, Cairo, Egypt

Department of Pharmacology and Research, All India Institute of Medical Sciences, Jodhpur, India

Aswini Saravanan

Indira Gandhi Medical College and Research Institute, Puducherry, India

Faculty of Dentistry, AIMST University, Bedong, Malaysia

Siddharthan Selvaraj

Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Farbod Semnani

Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Sabyasachi Senapati

Department of Medicine and Surgery, Government Doon Medical College, Dehradun, India

Yashendra Sethi

Endocrinology and Metabolism Research Center (EMRC), Tehran University of Medical Sciences, Tehran, Iran

Seyed Arsalan Seyedi

National Heart, Lung, and Blood Institute, National Institute of Health, Rockville, MD, USA

Allen Seylani

Public Health Division, An-Najah National University, Nablus, Palestine

Amira A. Shaheen

Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore, Lahore, Pakistan

Samiah Shahid

Research Centre for Health Sciences (RCHS), The University of Lahore, Lahore, Pakistan

Department of Clinical Sciences, Al-Quds University, Ajman, United Arab Emirates

Moyad Jamal Shahwan

Independent Consultant, Karachi, Pakistan

Masood Ali Shaikh

Department of Pathology and Laboratory Medicine, Northwell Health, New York City, NY, USA

Sunder Sham

Amity Institute of Public Health, Amity University, Noida, India

Mohammed Shannawaz

Department of Ophthalmology, Harvard Medical School, Boston, MA, USA

Maryam Shayan

Ophthalmic Research Center (ORC), Shahid Beheshti University of Medical Sciences, Tehran, Iran

Department of Veterinary Public Health and Preventive Medicine, Usmanu Danfodiyo University, Sokoto, Sokoto, Nigeria

Aminu Shittu

Center for Environmental and Respiratory Health Research, University of Oulu, Oulu, Finland

National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan

Department of Public Health Dentistry, Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, India

K. M. Shivakumar

Department of Medical-Surgical Nursing, Mazandaran University of Medical Sciences, Sari, Iran

Seyed Afshin Shorofi

Department of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia

Department of Pediatrics and Child Health Nursing, Dilla University, Dilla, Ethiopia

Migbar Mekonnen Sibhat

Unit of Basic Medical Sciences, University of Khartoum, Khartoum, Sudan

Emmanuel Edwar Siddig

Department of Medical Microbiology and Infectious Diseases, Erasmus University, Rotterdam, Netherlands

Family, Health Promotion, and Life Course Department, Pan American Health Organization, Bogota, Colombia

Juan Carlos Silva

School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

Jasvinder A. Singh

Department of Medicine Service, US Department of Veterans Affairs (VA), Birmingham, AL, USA

Department of Radiodiagnosis, All India Institute of Medical Sciences, Bathinda, India

Paramdeep Singh

Department of Ophthalmology, Hywel Dda University Health Board, Llanelli, UK

Eirini Skiadaresi

Department of Nursing, Dire Dawa University, Dire Dawa, Ethiopia

Yonatan Solomon

Directive Board, Associação de Profissionais Licenciados de Optometria (Association of Licensed Optometry Professionals), Linda-a-Velha, Portugal

Raúl A. R. C. Sousa

Division of Community Medicine, International Medical University, Kuala Lumpur, Malaysia

Chandrashekhar T. Sreeramareddy

Federal Research Institute for Health Organization and Informatics of the Ministry of Health (FRIHOI) , Moscow, Russia

Vladimir I. Starodubov

Soonchunhyang University, Vision Research Foundation, Cheonan-si, South Korea

Mohana Devi Subramaniam

Nursing Professional Education Study Program, University Halu Oleo, Kendari, Indonesia

Sri Susanty

Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran

Seyyed Mohammad Tabatabaei

Clinial Research Development Unit, Mashhad University of Medical Sciences, Mashhad, Iran

Department of Clinical Pharmacy, Mekelle University, Mekelle, Ethiopia

Gebrehiwot Teklay

Pediatric Intensive Care Unit, King Saud University, Riyadh, Saudi Arabia

Mohamad-Hani Temsah

Outpatient Department, Wollega University, Bedele town, Ethiopia

Dufera Rikitu Terefa

Department of Public Health, Wollega University, Nekemte, Ethiopia

Faculty of Public Health, Universitas Sam Ratulangi, Manado, Indonesia

Jansje Henny Vera Ticoalu

Department of Public Health, Arba Minch College of Health Sciences, Arba Minch, Ethiopia

Temesgen Mohammed Toma

Department of Medicine, University of Crete, Heraklion, Greece

Aristidis Tsatsakis

Department of Nursing, Aksum University, Aksum, Ethiopia

Guesh Mebrahtom Tsegay

Department of Physiology, East Carolina University, Greenville, NC, USA

Munkhtuya Tumurkhuu

Department of Epidemiology and Biostatistics, Haramaya University, Harar, Ethiopia

Biruk Shalmeno Tusa

Department of Pharmacology, All India Institute of Medical Sciences, Deoghar, India

Sree Sudha Ty

Department of Public Health, East Carolina University, Greenville, NC, USA

Chukwudi S. Ubah

College of Public Health, Temple University, Philadelphia, PA, USA

Medical Genomics Research Department, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia

Muhammad Umair

Division of Surgery and Interventional Science, University College London, London, UK

Tungki Pratama Umar

Urmia University of Medical Sciences, Urmia, Iran

Rohollah Valizadeh

Department of Cardiovascular Sciences, Katholieke Universiteit Leuven (University of Leuven), Leuven, Belgium

Jef Van den Eynde

Department of Public Health, Debre Markos University, Debre Markos, Ethiopia

Tewodros Eshete Wonde

Department of Ophthalmology Research, Queen Mamohato Memorial Hospital, Maseru, Lesotho

Guadie Sharew Wondimagegn

Ophthalmology Unit, Bahir Dar University, Bahirdar, Ethiopia

School of Public Health, Zhejiang University, Zhejiang, China

Department of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

China Center for Health Development Studies, Peking University, Beijing, China

Center for the Study of Aging and Human Development, Duke University, Durham, NC, USA

The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA

Iman Yazdani Nia

Department of Health Management, Süleyman Demirel Üniversitesi (Süleyman Demirel University), Isparta, Türkiye

Department of Pharmacology, Bahir Dar University, Bahir Dar, Ethiopia

Yazachew Yismaw

Pharmacy Department, Alkan Health Science, Business and Technology College, Bahir Dar, Ethiopia

Department of Pediatrics, Kyung Hee University, Seoul, South Korea

Dong Keon Yon

Department of Neuropsychopharmacology, National Center of Neurology and Psychiatry, Kodaira, Japan

Naohiro Yonemoto

Department of Public Health, Juntendo University, Tokyo, Japan

Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China

Chuanhua Yu

Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA

Mikhail Sergeevich Zastrozhin

Addictology Department, Russian Medical Academy of Continuous Professional Education, Moscow, Russia

College of Traditional Chinese Medicine, Hebei University, Baoding, China

Hanqing Zhao

Department of Ophthalmology, Iran University of Medical Sciences, Tehran, Iran

Makan Ziafati

Department of Biochemistry and Pharmacogenomics, Medical University of Warsaw, Warsaw, Poland

Magdalena Zielińska

Department of Anatomy, Addis Ababa University, Addis Ababa, Ethiopia

Yossef Teshome Zikarg

Department of Nursing, Yasuj University of Medical Sciences, Yasuj, Iran

Mohammad Zoladl

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a meta analysis literature review

Correction to: Comparative Efficacy, Durability and Safety of Faricimab in the Treatment of Diabetic Macular Edema: A Systematic Literature Review and Network Meta-Analysis

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  • 1 Clarostat Consulting Ltd, Bollington, UK.
  • 2 F. Hoffmann-La Roche Ltd, Grenzacherstrasse, Basel, Switzerland.
  • 3 F. Hoffmann-La Roche Ltd, Grenzacherstrasse, Basel, Switzerland. [email protected].
  • 4 Pepose Vision Institute, Chesterfield, MO, USA.
  • PMID: 38480662
  • DOI: 10.1007/s12325-024-02831-y

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Tooth abnormalities associated with non-syndromic cleft lip and palate: systematic review and meta-analysis

  • Published: 21 June 2022
  • Volume 26 , pages 5089–5103, ( 2022 )

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  • Gabriela Fonseca-Souza   ORCID: orcid.org/0000-0002-8040-8553 1 ,
  • Luiza Becker de Oliveira   ORCID: orcid.org/0000-0002-7924-8409 1 ,
  • Letícia Maira Wambier   ORCID: orcid.org/0000-0002-9696-0406 2 ,
  • Rafaela Scariot   ORCID: orcid.org/0000-0002-4911-6413 1 &
  • Juliana Feltrin-Souza   ORCID: orcid.org/0000-0001-9969-3721 1  

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To evaluate the association between orofacial clefts (OFC) and tooth abnormalities (TA).

We searched PubMed, Scopus, Web of Science, Cochrane Library, LILACS, and BBO, and in the gray literature and selected observational studies that evaluated the association between TA and OFC. The risk of bias was analyzed using the Newcastle–Ottawa Scale. A random-effects meta-analysis was performed comparing the presence and absence of OFC, cleft type—cleft palate (CP) and cleft lip with or without palate (CL/P)—and cleft laterality—unilateral and bilateral. The certainty of evidence was evaluated using the GRADE approach.

A total of 99 studies were included in the qualitative analysis, and 37 were included in the meta-analysis. Only four studies were classified as low risk of bias. Significant associations were observed between the presence of OFC and tooth agenesis (OR = 19.46; 95%CI = 4.99–75.96), supernumerary teeth (OR = 4.04; 95%CI = 1.26–12.99), developmental defects of enamel (OR = 3.15; 95%CI = 1.28–7.80), microdontia (OR = 15.57; 95%CI = 1.06–228.51), and taurodontism (OR = 1.74; 95%CI = 1.74–2.86). Individuals with CP had a lower frequency of supernumerary teeth (OR = 0.22; 95%CI = 0.08–0.64), peg-shaped tooth (OR = 0.31; 95%CI = 0.12–0.80), and morphological TA (OR = 0.13; 95%CI = 0.04–0.45) than individuals with CL/P. No TA was significantly associated with cleft laterality ( p  > 0.05). The quality of the evidence was very low in all analyses.

Individuals with OFC had a higher frequency of TA than those without OFC. Individuals with CP had a lower frequency of TA than individuals with CL/P. No TA was associated to cleft laterality.

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Help to identify the treatment needs of individuals affected by OFC, improving the services provided to this population.

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This work was supported by the Brazilian Agency for Higher Education Personnel Coordination and Improvement (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [CAPES]).

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Fonseca-Souza, G., de Oliveira, L.B., Wambier, L.M. et al. Tooth abnormalities associated with non-syndromic cleft lip and palate: systematic review and meta-analysis. Clin Oral Invest 26 , 5089–5103 (2022). https://doi.org/10.1007/s00784-022-04540-8

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  • Indian J Dermatol
  • v.59(2); Mar-Apr 2014

Understanding and Evaluating Systematic Reviews and Meta-analyses

Michael bigby.

From the Department of Dermatology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA

A systematic review is a summary of existing evidence that answers a specific clinical question, contains a thorough, unbiased search of the relevant literature, explicit criteria for assessing studies and structured presentation of the results. A systematic review that incorporates quantitative pooling of similar studies to produce an overall summary of treatment effects is a meta-analysis. A systematic review should have clear, focused clinical objectives containing four elements expressed through the acronym PICO (Patient, group of patients, or problem, an Intervention, a Comparison intervention and specific Outcomes). Explicit and thorough search of the literature is a pre-requisite of any good systematic review. Reviews should have pre-defined explicit criteria for what studies would be included and the analysis should include only those studies that fit the inclusion criteria. The quality (risk of bias) of the primary studies should be critically appraised. Particularly the role of publication and language bias should be acknowledged and addressed by the review, whenever possible. Structured reporting of the results with quantitative pooling of the data must be attempted, whenever appropriate. The review should include interpretation of the data, including implications for clinical practice and further research. Overall, the current quality of reporting of systematic reviews remains highly variable.

Introduction

A systematic review is a summary of existing evidence that answers a specific clinical question, contains a thorough, unbiased search of the relevant literature, explicit criteria for assessing studies and structured presentation of the results. A systematic review can be distinguished from a narrative review because it will have explicitly stated objectives (the focused clinical question), materials (the relevant medical literature) and methods (the way in which studies are assessed and summarized).[ 1 , 2 ] A systematic review that incorporates quantitative pooling of similar studies to produce an overall summary of treatment effects is a meta-analysis.[ 1 , 2 ] Meta-analysis may allow recognition of important treatment effects by combining the results of small trials that individually might lack the power to consistently demonstrate differences among treatments.[ 1 ]

With over 200 speciality dermatology journals being published, the amount of data published just in the dermatologic literature exceeds our ability to read it.[ 3 ] Therefore, keeping up with the literature by reading journals is an impossible task. Systematic reviews provide a solution to handle information overload for practicing physicians.

Criteria for reporting systematic reviews have been developed by a consensus panel first published as Quality of Reporting of Meta-analyses (QUOROM) and later refined as Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA).[ 4 , 5 ] This detailed, 27-item checklist contains items that should be included and reported in high quality systematic reviews and meta-analyses. The methods for understanding and appraising systematic reviews and meta-analyses presented in this paper are a subset of the PRISMA criteria.

The items that are the essential features of a systematic review include having clear objectives, explicit criteria for study selection, an assessment of the quality of included studies, criteria for which studies can be combined, appropriate analysis and presentation of results and practical conclusions that are based on the evidence evaluated [ Table 1 ]. Meta-analysis is only appropriate if the included studies are conceptually similar. Meta-analyses should only be conducted after a systematic review.[ 1 , 6 ]

Criteria for evaluating a systematic review or the meta-analysis

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A Systematic Review Should Have Clear, Focused Clinical Objectives

A focused clinical question for a systematic review should contain the same four elements used to formulate well-built clinical questions for individual studies, namely a Patient, group of patients, or problem, an Intervention, a Comparison intervention and specific Outcomes.[ 7 ] These features can be remembered by the acronym PICO. The interventions and comparison interventions should be adequately described so that what was done can be reproduced in future studies and in practice. For diseases with established effective treatments, comparisons of new treatments or regimens to established treatments provide the most useful information. The outcomes reported should be those that are most relevant to physicians and patients.[ 1 ]

Explicit and Thorough Search of the Literature

A key question to ask of a systematic review is: “Is it unlikely that important, relevant studies were missed?” A sound systematic review can be performed only if most or all of the available data are examined. An explicit and thorough search of the literature should be performed. It should include searching several electronic bibliographic databases including the Cochrane Controlled Trials Registry, which is part of the Cochrane Library, Medline, Embase and Literatura Latino Americana em Ciências da Saúde. Bibliographies of retrieved studies, review articles and textbooks should be examined for studies fitting inclusion criteria. There should be no language restrictions. Additional sources of data include scrutiny of citation lists in retrieved articles, hand-searching for conference reports, prospective trial registers (e.g., clinical trials.gov for the USA and clinical trialsregister.eu for the European union) and contacting key researchers, authors and drug companies.[ 1 , 8 ]

Reviews should have Pre-defined Explicit Criteria for what Studies would be Included and the Analysis should Include Only those Studies that Fit the Inclusion Criteria

The overwhelming majority of systematic reviews involve therapy. Randomized, controlled clinical trials should therefore be used for systematic reviews of therapy if they are available, because they are generally less susceptible to selection and information bias in comparison with other study designs.[ 1 , 9 ]

Systematic reviews of diagnostic studies and harmful effects of interventions are increasingly being performed and published. Ideally, diagnostic studies included in systematic reviews should be cohort studies of representative populations. The studies should include a criterion (gold) standard test used to establish a diagnosis that is applied uniformly and blinded to the results of the test(s) being studied.[ 1 , 9 ]

Randomized controlled trials can be included in systematic reviews of studies of adverse effects of interventions if the events are common. For rare adverse effects, case-control studies, post-marketing surveillance studies and case reports are more appropriate.[ 1 , 9 ]

The Quality (Risk of Bias) of the Primary Studies should be Critically Appraised

The risk of bias of included therapeutic trials is assessed using the criteria that are used to evaluate individual randomized controlled clinical trials. The quality criteria commonly used include concealed, random allocation; groups similar in terms of known prognostic factors; equal treatment of groups; blinding of patients, researchers and analyzers of the data to treatment allocation and accounting for all patients entered into the trial when analyzing the results (intention-to-treat design).[ 1 ] Absence of these items has been demonstrated to increase the risk of bias of systematic reviews and to exaggerate the treatment effects in individual studies.[ 10 ]

Structured Reporting of the Results with Quantitative Pooling of the Data, if Appropriate

Systematic reviews that contain studies that have results that are similar in magnitude and direction provide results that are most likely to be true and useful. It may be impossible to draw firm conclusions from systematic reviews in which studies have results of widely different magnitude and direction.[ 1 , 9 ]

Meta-analysis should only be performed to synthesize results from different trials if the trials have conceptual homogeneity.[ 1 , 6 , 9 ] The trials must involve similar patient populations, have used similar treatments and have measured results in a similar fashion at a similar point in time.

Once conceptual homogeneity is established and the decision to combine results is made, there are two main statistical methods by which results are combined: random-effects models (e.g., DerSimonian and Laird) and fixed-effects models (e.g., Peto or Mantel-Haenszel).[ 11 ] Random-effects models assume that the results of the different studies may come from different populations with varying responses to treatment. Fixed-effects models assume that each trial represents a random sample of a single population with a single response to treatment [ Figure 1 ]. In general, random-effects models are more conservative (i.e., random-effects models are less likely to show statistically significant results than fixed-effects models). When the combined studies have statistical homogeneity (i.e., when the studies are reasonably similar in direction, magnitude and variability), random-effects and fixed-effects models give similar results.

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Fixed-effects models (a) assume that each trial represents a random sample (colored curves) of a single population with a single response to treatment. Random-effects models (b) assume that the different trials’ results (colored curves) may come from different populations with varying responses to treatment.

The point estimates and confidence intervals of the individual trials and the synthesis of all trials in meta-analysis are typically displayed graphically in a forest plot [ Figure 2 ].[ 12 ] Results are most commonly expressed as the odds ratio (OR) of the treatment effect (i.e., the odds of achieving a good outcome in the treated group divided by the odds of achieving a good result in the control group) but can be expressed as risk differences (i.e., difference in response rate) or relative risk (probability of achieving a good outcome in the treated group divided by the probability in the control group). An OR of 1 (null) indicates no difference between treatment and control and is usually represented by a vertical line passing through 1 on the x-axis. An OR of greater or less than 1 implies that the treatment is superior or inferior to the control respectively.

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Annotated results of a meta-analysis of six studies, using random effects models reported as odd ratios using MIX version 1.7 (Bax L, Yu LM, Ikeda N, Tsuruta H, Moons KGM. Development and validation of MIX: comprehensive free software for meta-analysis of causal research data. BMC Med Res Methodol http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1626481/ ). The central graph is a typical Forest Plot

The point estimate of individual trials is indicated by a square whose size is proportional to the size of the trial (i.e., number of patients analyzed). The precision of the trial is represented by the 95% confidence interval that appears in Forest Plots as the brackets surrounding point estimate. If the 95% confidence interval (brackets) does not cross null (OR of 1), then the individual trial is statistically significant at the P = 0.05 level.[ 12 ] The summary value for all trials is shown graphically as a parallelogram whose size is proportional to the total number of patients analyzed from all trials. The lateral tips of the parallelogram represent the 95% confidence interval and if they do not cross null (OR of 1), then the summary value of the meta-analysis is statistically significant at the P = 0.05 level. ORs can be converted to risk differences and numbers needed to treat (NNTs) if the event rate in the control group is known [ Table 2 ].[ 13 , 14 ]

Deriving numbers needed to treat from a treatment's odds ratio and the observed or expected event rates of untreated groups or individuals

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The difference in response rate and its reciprocal, the NNT, are the most easily understood measures of the magnitude of the treatment effect.[ 1 , 9 ] The NNT represents the number of patients one would need to treat in order to achieve one additional cure. Whereas the interpretation of NNT might be straightforward within one trial, interpretation of NNT requires some caution within a systematic review, as this statistic is highly sensitive to baseline event rates.[ 1 ]

For example, if a treatment A is 30% more effective than treatment B for clearing psoriasis and 50% of people on treatment B are cleared with therapy, then 65% will clear with treatment A. These results correspond to a rate difference of 15% (65-50) and an NNT of 7 (1/0.15). This difference sounds quite worthwhile clinically. However if the baseline clearance rate for treatment B in another trial or setting is only 30%, the rate difference will be only 9% and the NNT now becomes 11 and if the baseline clearance rate is 10%, then the NNT for treatment A will be 33, which is perhaps less worthwhile.[ 1 ]

Therefore, NNT summary measures within a systematic review should be interpreted with caution because “control” or baseline event rates usually differ considerably between studies.[ 1 , 15 ] Instead, a range of NNTs for a range of plausible control event rates that occur in different clinical settings should be given, along with their 95% confidence intervals.[ 1 , 16 ]

The data used in a meta-analysis can be tested for statistical heterogeneity. Methods to tests for statistical heterogeneity include the χ 2 and I.[ 2 , 11 , 17 ] Tests for statistical heterogeneity are typically of low power and hence detecting statistical homogeneity does not mean clinical homogeneity. When there is evidence of heterogeneity, reasons for heterogeneity between studies – such as different disease subgroups, intervention dosage, or study quality – should be sought.[ 11 , 17 ] Detecting the source of heterogeneity generally requires sub-group analysis, which is only possible when data from many or large trials are available.[ 1 , 9 ]

In some systematic reviews in which a large number of trials have been performed, it is possible to evaluate whether certain subgroups (e.g. children versus adults) are more likely to benefit than others. Subgroup analysis is rarely possible in dermatology, because few trials are available. Subgroup analyses should always be pre-specified in a systematic review protocol in order to avoid spurious post hoc claims.[ 1 , 9 ]

The Importance of Publication Bias

Publication bias is the tendency that studies that show positive effects are more likely to be published and are easier to find.[ 1 , 18 ] It results from allowing factors other than the quality of the study to influence its acceptability for publication. Factors such as the sample size, the direction and statistical significance of findings, or the investigators’ perception of whether the findings are “interesting,” are related to the likelihood of publication.[ 1 , 19 , 20 ] Negative studies with small sample size are less likely to be published.[ 1 , 19 , 20 ] Studies published are often dominated by the pharmaceutical company sponsored trials of new, expensive treatments often compared with the placebo.

For many diseases, the studies published are dominated by drug company-sponsored trials of new, expensive treatments. Such studies are almost always “positive.”[ 1 , 21 , 22 ] This bias in publication can result in data-driven systematic reviews that draw more attention to those medicines. Systematic reviews that have been sponsored directly or indirectly by industry are also prone to bias through over-inclusion of unpublished “positive” studies that are kept “on file” by that company and by not including or not finishing registered trials whose results are negative.[ 1 , 23 ] The creation of study registers (e.g. http://clinicaltrials.gov ) and advance publication of research designs have been proposed as ways to prevent publication bias.[ 1 , 24 , 25 ] Many dermatology journals now require all their published trials to have been registered beforehand, but this policy is not well policed.[ 1 ]

Language bias is the tendency for studies that are “positive” to be published in an English-language journal and be more quickly found than inconclusive or negative studies.[ 1 , 26 ] A thorough systematic review should therefore not restrict itself to journals published in English.[ 1 ]

Publication bias can be detected by using a simple graphic test (funnel plot), by calculating the fail-safe N, Begg's rank correlation method, Egger regression method and others.[ 1 , 9 , 11 , 27 , 28 ] These techniques are of limited value when less than 10 randomized controlled trials are included. Testing for publication bias is often not possible in systematic reviews of skin diseases, due to the limited number and sizes of trials.[ 1 , 9 ]

Question-driven systematic reviews answer the clinical questions of most concern to practitioners. In many cases, studies that are of most relevance to doctors and patients have not been done in the field of dermatology, due to inadequate sources of independent funding.[ 1 , 9 ]

The Quality of Reporting of Systematic Reviews

The quality of reporting of systematic reviews is highly variable.[ 1 ] One cross-sectional study of 300 systematic reviews published in Medline showed that over 90% were reported in specialty journals. Funding sources were not reported in 40% of reviews. Only two-thirds reported the range of years that the literature was searched for trials. Around a third of reviews failed to provide a quality assessment of the included studies and only half of the reviews included the term “systematic review” or “meta-analysis” in the title.[ 1 , 29 ]

The Review should Include Interpretation of the Data, Including Implications for Clinical Practice and Further Research

The conclusions in the discussion section of a systematic review should closely reflect the data that have been presented within that review. Clinical recommendations can be made when conclusive evidence is found, analyzed and presented. The authors should make it clear which of the treatment recommendations are based on the review data and which reflect their own judgments.[ 1 , 9 ]

Many reviews in dermatology, however, find little evidence to address the questions posed. The review may still be of value even if it lacks conclusive evidence, especially if the question addressed is an important one.[ 1 , 30 ] For example, the systematic review may provide the authors with the opportunity to call for primary research in an area and to make recommendations on study design and outcomes that might help future researchers.[ 1 , 31 ]

Source of Support: Nil

Conflict of Interest: Nil.

IMAGES

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    Objective To evaluate the association between orofacial clefts (OFC) and tooth abnormalities (TA). Methods We searched PubMed, Scopus, Web of Science, Cochrane Library, LILACS, and BBO, and in the gray literature and selected observational studies that evaluated the association between TA and OFC. The risk of bias was analyzed using the Newcastle-Ottawa Scale. A random-effects meta-analysis ...

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