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Getting Started

Rayyan.ai is web-based software that makes it easy to conduct systematic reviews, and other types of reviews, by yourself or with a team of collaborators. The base version of Rayyan is free to use. Premium features, such as the ability to automatically generate PRISMA diagrams, are included with a premium subscription. Free accounts can have up to three active reviews at a time; premium accounts have unlimited active reviews. Rayyan is browser-based online software. Before using Rayyan, check your browser compatibility here: https://rayyan.ai/check

Starting a Systematic Review

To start a systematic review, log in and click  New Review  on the home page. Enter a title, research field, review domain, and optional description. Select  Systematic Review  as the review type and click the  Create  button.

Screenshot of the My Reviews tab showing how to create a new review

When you create a new review, Rayyan will prompt you to upload references. You can import references directly from Mendeley through their interface. For other databases and citation managers, Rayyan has several migration guides with examples linked. To import your references, first export them from your citation manager in a supported file format (e.g. BibTeX). Choose the  Select Files…  button and navigate to the file you just exported. Upload this file to Rayyan.

Screenshot of Rayyan showing the menu for uploading a library

Once you have uploaded your references, they will appear in the middle pane of your review interface. Clicking on a reference shows the details and abstract for that reference. The left panel will populate with keywords from the references that you just uploaded. You can also filter by decision, label, reason, search method, and topic from this pane.

Screenshot of a Rayyan review showing the left panes populated with labels and the center pane populated with reference titles

Adding Collaborators

Rayyan allows researchers to add an unlimited number of reviewers to an active review. To add a reviewer, click  All Reviews , choose your active review, and click Invite . Enter your collaborators’ email addresses and a message.

Screenshot showing the My Reviews tab with the invite button highlighted in red

Collaborators will need their own Rayyan account to participate in your review. Once they log in, the reviews that they have been invited to appear under their  Collaboration Reviews  tab on the home page.

Screenshow showing the Collaboration Reviews tab with the shared review highlighted in red

Removing Duplicates

Clicking the  Detect Duplicates  button at the top will scan the uploaded references for duplicates.

A screenshot of the review page with the Detect Duplicates button highlighted in red

A facet titled  Possible Duplicates  will show on the left pane. Clicking on a set of possible duplicates will bring up more detail in the bottom pane.

A screenshot of the Possible Duplicates facet

After reviewing the set of possible duplicates, you can choose to delete the duplicate(s) or mark them as not duplicates.

A screenshot showing the Delete and Not Duplicate buttons

Premium accounts have a feature called Auto Deduplication, which automatically detects and deletes 100% matches. These deleted matches are still available for users to see under the  Auto Deduplicated  tab. Possible duplicates that are not a 100% match will still need to be screened by a reviewer.

Blinding is turned on by default in collaborative reviews. Collaborators will not be able to see each others’ inclusion/exclusion decisions until blinding is turned off. This can be done on the home page by clicking on the active review. Click the  Blind On/Off  button to toggle this option. Only the review owner can change the blind options.

Screenshot showing the Blind On toggle

Screening Abstracts

Under  My Reviews , click  Show  under your active reviews to start screening. You can view references individually or select multiple references at the same time using the shift key and the up/down arrows. To include references, select the reference(s) and click  Include  or press I on the keyboard. To exclude, click  Exclude  or press E. If you’re not sure whether to include or exclude this reference, click  Maybe  or press the M or U keys. For a full list of keyboard shortcuts in Rayyan, see this link:  https://help.rayyan.ai/hc/en-us/articles/4414328743953-Shortcut-Keys

You and your collaborators can add reason and label tags to your references so that you can remember and share your reasons for inclusion or exclusion of a particular article. The  Reason  dropdown menu comes pre-populated with common reasons for inclusion/exclusion. To add a custom reason, type your reason in the  Reason  box and press enter.

Screenshot showing the Reasons bar

Labels can be added in the same way, by typing into the  Label  box.

Screenshow showing the Labels bar

Computing Ratings

After making 50 decisions, the  Compute Ratings  option becomes available.

Screenshot showing the compute ratings button

Rayyan will rate the likelihood of inclusion for the remaining references based on the decisions you have made so far. This is displayed next to the reference name on a one-to-five-star scale. You can recompute ratings after making more decisions. The more decisions made, the more accurate the ratings should become.

Screenshot showing the main panel with ratings assigned to the references

Review Chat

The review chat allows you to leave messages for your collaborators. You do not have to be online at the same time as your collaborators to leave messages in the chat. Your messages will be visible the next time your collaborators login and view this review.

systematic review software rayyan

Any criterion that populates in the left pane can be used to filter your references. This includes filtering by year, author, journal, type of reference, topic, labels, keywords for inclusion or exclusion, duplicate status, and inclusion.

Selecting multiple filters from the same facet (e.g. “exclusion reasons”) is treated as an “or”– selecting “nonrandomized” and “observational” will include references that include either of those labels.

Screenshot showing a single facet

Selecting multiple filters from difference facets (e.g. “exclusion reasons” and “labels”) is treated as an “and”– selecting “nonrandomized” and “2018” will filter for references that include both of those criteria.

Screenshot showing multiple facets

The “at most” filter can be used to display references based on how many collaborators have screened the reference. Choose “at most 0” to see references that have not been screened by any reviewers.

Screenshot showing the Maximum Collaborator Decisions facet

Resolving Conflicts

Once you have finished screening and want to compare the decisions, turn the blind off by clicking the  Blind On/Off  button at the top of the My Reviews tab on the home page. To resolve conflicts with your collaborators, filter by “conflict” (under the  Inclusion Decisions  facet) to see references where there is disagreement about whether to include or exclude that reference. Once you have reached consensus, one of the collaborators will need to change their decision. After refreshing the page, resolved conflicts will no longer show under the “conflict” filter.

Screenshot showing the conflict filter

Moving to Full-Text Reviews

Once you have finished title or abstract reviews and are ready to move on to full-text reviews, you can continue with your current review or make a new review. Making a new review is often easier, as you will not need to filter out the excluded abstracts.

To start a new full-text review, make a new review from the home page. Return to your abstract review, set the filter to “included”, and click the  Copy  button on the top right of the page.

Screenshot with the Copy button highlighted in red

A new box will appear. Choose “filtered” from the drop-down menu and copy the results to the (currently empty) full-text review. Re-invite collaborators to the new full-text review.

Screenshot showing the copy articles menu

Select your reference(s) and choose “upload PDF full texts”. Choose the PDF files on your disk and map the PDFs to the correct reference. The full text PDFs will now be associated with the references that you chose to include earlier.

Screenshot showing the Upload PDF Full Text button

Once finished with your full-text review, you can choose to export the references. The export link will be sent to you via email. This link will contain the included articles and a .csv log file which lists the actions taken during the review.

Screenshot with the Export button highlighted in red

Rayyan has a help chatbot and human support available for questions. Their Help and FAQ pages can be found here:

Help Center:  https://help.rayyan.ai/hc/en-us

FAQ Page:  https://help.rayyan.ai/hc/en-us/categories/360003671417-FAQ

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Rayyan for Systematic and Scoping Reviews

  • Creating an account
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What is Rayyan?

Rayyan is a free web application designed to facilitate the screening process for researchers working on systematic reviews, scoping reviews and other literature review projects. It enables authors to collaborate on projects to get suggestions for article inclusion and exclusion.

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  • Research Skills Blog

5 software tools to support your systematic review processes

By Dr. Mina Kalantar on 19-Jan-2021 13:01:01

4 software tools to support your systematic review processes | IFIS Publishing

Systematic reviews are a reassessment of scholarly literature to facilitate decision making. This methodical approach of re-evaluating evidence was initially applied in healthcare, to set policies, create guidelines and answer medical questions.

Systematic reviews are large, complex projects and, depending on the purpose, they can be quite expensive to conduct. A team of researchers, data analysts and experts from various fields may collaborate to review and examine incredibly large numbers of research articles for evidence synthesis. Depending on the spectrum, systematic reviews often take at least 6 months, and sometimes upwards of 18 months to complete.

The main principles of transparency and reproducibility require a pragmatic approach in the organisation of the required research activities and detailed documentation of the outcomes. As a result, many software tools have been developed to help researchers with some of the tedious tasks required as part of the systematic review process.

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The first generation of these software tools were produced to accommodate and manage collaborations, but gradually developed to help with screening literature and reporting outcomes. Some of these software packages were initially designed for medical and healthcare studies and have specific protocols and customised steps integrated for various types of systematic reviews. However, some are designed for general processing, and by extending the application of the systematic review approach to other fields, they are being increasingly adopted and used in software engineering, health-related nutrition, agriculture, environmental science, social sciences and education.

Software tools

There are various free and subscription-based tools to help with conducting a systematic review. Many of these tools are designed to assist with the key stages of the process, including title and abstract screening, data synthesis, and critical appraisal. Some are designed to facilitate the entire process of review, including protocol development, reporting of the outcomes and help with fast project completion.

As time goes on, more functions are being integrated into such software tools. Technological advancement has allowed for more sophisticated and user-friendly features, including visual graphics for pattern recognition and linking multiple concepts. The idea is to digitalise the cumbersome parts of the process to increase efficiency, thus allowing researchers to focus their time and efforts on assessing the rigorousness and robustness of the research articles.

This article introduces commonly used systematic review tools that are relevant to food research and related disciplines, which can be used in a similar context to the process in healthcare disciplines.

These reviews are based on IFIS' internal research, thus are unbiased and not affiliated with the companies.

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This online platform is a core component of the Cochrane toolkit, supporting parts of the systematic review process, including title/abstract and full-text screening, documentation, and reporting.

The Covidence platform enables collaboration of the entire systematic reviews team and is suitable for researchers and students at all levels of experience.

From a user perspective, the interface is intuitive, and the citation screening is directed step-by-step through a well-defined workflow. Imports and exports are straightforward, with easy export options to Excel and CVS.

Access is free for Cochrane authors (a single reviewer), and Cochrane provides a free trial to other researchers in healthcare. Universities can also subscribe on an institutional basis.

Rayyan is a free and open access web-based platform funded by the Qatar Foundation, a non-profit organisation supporting education and community development initiative . Rayyan is used to screen and code literature through a systematic review process.

Unlike Covidence, Rayyan does not follow a standard SR workflow and simply helps with citation screening. It is accessible through a mobile application with compatibility for offline screening. The web-based platform is known for its accessible user interface, with easy and clear export options.

Function comparison of 5 software tools to support the systematic review process

Eppi-reviewer.

EPPI-Reviewer is a web-based software programme developed by the Evidence for Policy and Practice Information and Co-ordinating Centre  (EPPI) at the UCL Institute for Education, London .

It provides comprehensive functionalities for coding and screening. Users can create different levels of coding in a code set tool for clustering, screening, and administration of documents. EPPI-Reviewer allows direct search and import from PubMed. The import of search results from other databases is feasible in different formats. It stores, references, identifies and removes duplicates automatically. EPPI-Reviewer allows full-text screening, text mining, meta-analysis and the export of data into different types of reports.

There is no limit for concurrent use of the software and the number of articles being reviewed. Cochrane reviewers can access EPPI reviews using their Cochrane subscription details.

EPPI-Centre has other tools for facilitating the systematic review process, including coding guidelines and data management tools.

CADIMA is a free, online, open access review management tool, developed to facilitate research synthesis and structure documentation of the outcomes.

The Julius Institute and the Collaboration for Environmental Evidence established the software programme to support and guide users through the entire systematic review process, including protocol development, literature searching, study selection, critical appraisal, and documentation of the outcomes. The flexibility in choosing the steps also makes CADIMA suitable for conducting systematic mapping and rapid reviews.

CADIMA was initially developed for research questions in agriculture and environment but it is not limited to these, and as such, can be used for managing review processes in other disciplines. It enables users to export files and work offline.

The software allows for statistical analysis of the collated data using the R statistical software. Unlike EPPI-Reviewer, CADIMA does not have a built-in search engine to allow for searching in literature databases like PubMed.

DistillerSR

DistillerSR is an online software maintained by the Canadian company, Evidence Partners which specialises in literature review automation. DistillerSR provides a collaborative platform for every stage of literature review management. The framework is flexible and can accommodate literature reviews of different sizes. It is configurable to different data curation procedures, workflows and reporting standards. The platform integrates necessary features for screening, quality assessment, data extraction and reporting. The software uses Artificial Learning (AL)-enabled technologies in priority screening. It is to cut the screening process short by reranking the most relevant references nearer to the top. It can also use AL, as a second reviewer, in quality control checks of screened studies by human reviewers. DistillerSR is used to manage systematic reviews in various medical disciplines, surveillance, pharmacovigilance and public health reviews including food and nutrition topics. The software does not support statistical analyses. It provides configurable forms in standard formats for data extraction.

DistillerSR allows direct search and import of references from PubMed. It provides an add on feature called LitConnect which can be set to automatically import newly published references from data providers to keep reviews up to date during their progress.

The systematic review Toolbox is a web-based catalogue of various tools, including software packages which can assist with single or multiple tasks within the evidence synthesis process. Researchers can run a quick search or tailor a more sophisticated search by choosing their approach, budget, discipline, and preferred support features, to find the right tools for their research.

If you enjoyed this blog post, you may also be interested in our recently published blog post addressing the difference between a systematic review and a systematic literature review.

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Rayyan-a web and mobile app for systematic reviews

Affiliations.

  • 1 Qatar Computing Research Institute, HBKU, Doha, Qatar. [email protected].
  • 2 Qatar Computing Research Institute, HBKU, Doha, Qatar.
  • 3 Cochrane Bahrain, Awali, Bahrain.
  • PMID: 27919275
  • PMCID: PMC5139140
  • DOI: 10.1186/s13643-016-0384-4

Background: Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan ( http://rayyan.qcri.org ), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan's users and collected feedback through a built-in feature.

Results: Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The "taster" review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The "suggestions" and "hints," based on the "prediction model," appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users.

Conclusions: Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

Keywords: Automation; Evidence-based medicine; Systematic reviews.

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Rayyan for Systematic Reviews

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What is Rayyan?

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Rayyan is a web-tool designed to help researchers working on systematic reviews, scoping reviews and other knowledge synthesis projects, by dramatically speeding up the process of screening and selecting studies.

From Rayyan Terms of Service

Where is data stored?

"Rayyan relies on cloud services like Heroku and Amazon Web Services to securely store its database, logs and uploaded files, to host and monitor the machines running Rayyan, and to send system emails. This Agreement is bound by the agreements of such services." For more information, visit https://rayyan.ai/terms

What is the data backup policy?

"Automatic daily backups of the entire Rayyan database are stored on Amazon Web Services to ensure data durability, with a 1-month retention policy."

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Rayyan—a web and mobile app for systematic reviews

Mourad ouzzani.

1 Qatar Computing Research Institute, HBKU, Doha, Qatar

Hossam Hammady

Zbys fedorowicz.

2 Cochrane Bahrain, Awali, Bahrain

Ahmed Elmagarmid

Associated data.

Not applicable.

Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making.

We developed Rayyan ( http://rayyan.qcri.org ), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature.

Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan.

As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users.

Conclusions

Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

Randomized controlled trials (RCTs) play a pivotal role in medical research and are widely considered to be the best way of achieving results that can genuinely increase our knowledge about treatment effectiveness [ 1 ]. Although there is an increasing requirement for randomized controlled trials to guide healthcare decision-making, the synthesis of the results of more than one RCT in a systematic review can summarize the effects of their individual outcomes and provide numerical answers about the effectiveness of a particular intervention.

A systematic review is a summary of the medical literature that uses explicit methods to systematically search, critically appraise, and synthesize the data on a specific topic. The need for rigor in the production of systematic reviews has led to the development of a formal process for their conduct. This process has clearly designated steps to identify primary studies and the methods which will be employed to assess their methodological quality, the way in which data will be extracted, and the statistical techniques that will be used in the synthesis and reporting of that data [ 2 ]. Transparency and reproducibility are assured through the documenting of all of the decisions taken to include or exclude studies throughout the review process.

Identification of studies: The overarching aim is to ensure that an exhaustive scrutiny of the literature creates as comprehensive a list as possible of published and unpublished primary studies which are deemed relevant to answering the research question.

The number of citations generated by this search for eligible studies will depend on a variety of factors not least of all those involving some of the inherent aspects of the clinical topic. Thus, a clinical intervention which has been used extensively over a long period of time may be underpinned by a large body of research which in many instances may contain a substantial number of studies some of which may date back in excess of 20 years. Other possible contributory factors will include the comparative “interest” in the topic by clinicians, healthcare policy makers, and the media and may even include the potentially “vested” interest of the pharmaceutical industry.

Although the initial searches for trials for a systematic review may in some cases identify up to, and possibly extend beyond, 1000 citations, this will depend in part on the level of sensitivity and specificity built into the search strategy used to search the individual databases. While it is difficult to generalize what number of references to studies might be expected in an average yield, a minimum of 100 would not be an unreasonable number for many clinical topics.

Identification of potentially eligible studies: One of the most time consuming aspects of conducting a systematic review is the preliminary filtering or sifting through the citations from the searches, particularly if these number in the several hundreds and possibly in the thousands. Systematic review authors use a variety of electronic or manual methods to complete this task, which in any event must be double-checked by a co-author to ensure that all potentially eligible studies and those that require further full-text assessment have been identified. In addition, the tracking of decisions to include or exclude studies and the reporting of these judgments in a PRISMA flow diagram is mandatory for all Cochrane reviews and is now being done increasingly in other systematic reviews as this becomes a more widely accepted prerequisite for manuscript publication [ 3 ]. Moreover, the comprehensive documentation of these decisions by the review authors ensures the transparency, clarity, and traceability of the selection process and ultimately reinforces the robustness of the completed systematic review.

Identification and selection of studies can be challenging and very tedious, and a number of methods are used by review authors to facilitate the process. This can be performed either manually, i.e., by simply “highlighting” them in the printed copy of the search document by the use of different colors of a text marker, or electronically using the text highlighting function in the electronic copy of the search document. Alternative methods include the use of software such as EndNote or Reference Manager, if they are available to the review author. No single method can satisfactorily fulfill all the principal requirements of speed, accuracy, and simplicity in use, and each has its advantages, disadvantages, and adherents.

Interest in the automation of systematic reviews has been driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making as much as engaging with technology to allow review authors to redirect their focus on aspects where they are best at [ 4 ]. An increasing number of projects are underway which focus on the automation of segments of the systematic review process, and although several tools and software have been developed, so far, none of them span the entire process of review production [ 5 ].

Although the challenges faced by developers to automate and integrate the multiple steps in the workflow may seem insurmountable, recent advances in technology have helped overcome some of these hurdles [ 6 ]. However, accuracy and efficiency should not be sacrificed at the expense of speed, but flexibility, aligned with the potential for individual user customizability, should be built into the tool to allow for a range of users to create and use different personal preference-based interfaces. Automation should also target several key areas such as exploring ways of enhancing the user interface and user experience, developing systems which will ensure adequate workflow support, and the fostering of further developments in machine learning and data/text mining.

The process of automation of systematic reviews continues to present a number of additional challenges in that many of the tools have been developed independently as stand-alone software and are often not compatible with other tools [ 5 ]. In some instances, appropriate reliability and functionality testing has not been undertaken, and some tools are no longer being maintained by the developers or are prohibitively expensive to the average user. Moreover, some of the tools currently available require a level of technical skills beyond that of many review authors and also involve a steep learning curve and level of complexity which may necessitate a repetitive learn/re-learn phase if they are not used regularly. All of these challenges show how unsatisfying the existing landscape for systematic review automation is. The developers of Rayyan aim to address these challenges for providing an integrated solution, by working directly with systematic reviewers whilst continuously taking into account users’ feedback.

Rayyan ( http://rayyan.qcri.org ) was developed specifically to expedite the initial screening of abstracts and titles using a process of semi-automation but with a clear objective of incorporating a level of usability which would be compatible with the skillset of a broad cross-section of potential users. The ab initio objectives of the developers of the Rayyan app were to try and circumvent some of the complexities and challenges faced by reviewers with some of the existing tools. While our ultimate goal is to support the entire systematic review process, we initially focus on facilitating abstract/title screening and collaboration in addition to other supporting features around them. Thus, much of the focus of the development was on creating an inbuilt user-definable and partly self-customizable interface which would ensure Rayyan was largely intuitive in use as well as being user-friendly at all skill levels. We present here an exceptional case report of the development process of Rayyan, an app for the rapid exploring and filtering of searches for eligible studies for systematic reviews.

There was a recognition by the developers of the need for a tool which would satisfy the requirements of a broad spectrum of review authors with a diverse range of competencies and skills and specifically one which would permit rapid and reliable exploration and sharing of search results but without being technologically burdensome. Therefore, engagement with an experienced Cochrane systematic review author (ZF) who had worked extensively with a large number of co-authors with mixed levels of experience proved to be pivotal to the development process.

The app underwent pilot testing prior to release and had extensive subsequent evaluation from a wide range of users, with a variety of skill levels and competencies, from across the globe. Sharing of user experiences and a reflexive response by the developers to an evolving “wish list” of requests by users enabled modifications and improvements to be made progressively and in real time, all of which proved to be a highly productive and effective collaboration in the development of Rayyan.

Overview and architecture

Rayyan is built on top of a cloud-based multi-tier service-oriented elastic architecture (Fig. ​ (Fig.1). 1 ). Scalability in Rayyan is underpinned by this cloud-based architecture which allows it to scale accordingly during peak times and as the number of users grows and they create more reviews and upload more citations. Moreover, at times, Rayyan may be actively processing data for tens of users or is just staying idle. The cloud-based architecture enables it to expand or shrink its hardware resources as needed. As a result, it is cost effective in idle times, with no costs incurred for resources not being used, and at the same time horizontally scales out in busy times easily. Part of the resources are only manually scalable, which means that Rayyan administrators will need to upgrade them as needed, for example, in increasing database storage needs, push notifications volume, and email messages volume. Other resources are automatically scalable to support the appropriate traffic in a cost-effective manner without sacrificing performance. This applies to web servers and background job workers.

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Object name is 13643_2016_384_Fig1_HTML.jpg

Rayyan architecture. Rayyan is a fully cloud-based architecture that uses a cloud platform as a service allowing elastic scaling of resources as we get more users and more requests. Rayyan’s workers are distributed using the load balancer to different app servers (Ruby web workers). These workers are elastic; they auto-scale based on traffic to guarantee minimal response time. For longer jobs or the elastic delayed jobs (the worker bees), such as upload parsing, similarity computation, and label predictions, they are handled through a queuing system. All workers have access to the storage layers: Postgres (for permanent storage), Solr (for indexing and searching), and Memcached (for caching results). Other parts of Rayyan, written in Java, are attachable to the jobs using an Apache Thrift service. Real-time notifications, on job completion or chat messages, for example, are delivered using Pusher, while other transactional information are delivered using the Mailchimp Mandrill service. All system activities are logged by Logentries and later backed up on AWS S3, while live instrumentation and monitoring is done by NewRelic

Rayyan itself is written in the popular open-source framework Ruby on Rails [ 7 ], and runs on Heroku [ 8 ] which is a Platform as a Service based on the cloud-hosting Amazon Web Services. It integrates with other cloud services to fulfill the different tiers it requires. Examples of these services are Heroku Postgres [ 9 ] for SQL database management; Logentries [ 10 ] for central logging, tagging, and alerting; NewRelic [ 11 ] for app analytics, health monitoring, and alerting; Pusher [ 12 ] for real-time push notifications; and HireFire [ 13 ] for auto-scaling the app according to load.

Workflow and user experience

After logging into Rayyan, users are presented with a dashboard of all their current reviews (Fig. ​ (Fig.2). 2 ). They can either create a new review or work on an existing one. For each review, they upload one or more citation file obtained from searching different databases. Rayyan supports several standard formats, e.g., RefMan RIS and EndNote. At the outset, Rayyan processes the citation file by extracting different metadata, e.g., title, authors, and computing others, e.g., MeSH terms and language of the article, for each article or study in the citation file. These will then populate the facets in the review workbench (Fig. ​ (Fig.3) 3 ) to help explore and filter the studies. MeSH terms are presented as a word cloud allowing users to quickly grasp the main topics presented in the studies. In addition, users can filter studies based on two predefined lists of keywords that will most likely hint to either include or exclude a study. The user can also modify these two lists by removing and adding keywords, thus giving more flexibility in the labeling and selection of studies. Rayyan was seeded with two lists obtained from the EMBASE project to filter RCTs [ 14 ].

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Rayyan dashboard. The dashboard lists all reviews for this user as well as for each review the progress in terms of decisions made and estimated time spent working on the review for all collaborators

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Rayyan workbench. The workbench shows the different ways users interact with the app

Users can also label their citations and define their individual reasons for exclusion which facilitates the sharing and tracking of these decisions. Citations can be explored through a similarity graph (Fig. ​ (Fig.4) 4 ) in which the citations are represented as nodes in a graph and clustered based on how similar they are (using an edit distance) in terms of title and abstract content as well as common authors. The similarity thresholds can be tuned independently for each attribute, i.e., title, abstract, and authors, as well an overall threshold.

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Similarity graph. Interacting with citations through the similarity graph

Rayyan mobile app

With the mobile app, users can screen reviews they have already uploaded from the web app. The most notable feature is the ability to use the app while offline. Users first download the entire review while online then work on it even in the absence of a network connection, and then, once connected, the app will automatically sync back to the Rayyan servers.

Predicting included and excluded studies

An important feature of the Rayyan app is its ability to learn from users’ decisions to include or exclude studies which can then be used to build a model that would allow suggestions to be offered on studies that are awaiting screening. More specifically, after removing stop words and stemming the remaining words from the title and abstract, Rayyan extracts all the words (unigrams) and pairs of words (bigrams) and previously computed MeSH terms. These are then used as features by a support vector machine (SVM) classifier [ 15 ]. As users label citations to studies as excluded or included, Rayyan calls the SVM classifier which learns the features of these excluded and included citations and builds a model, or classifier, accordingly. The classifier then runs on the citations that await labeling and outputs a score of how close each study matches the include and exclude classes. That score is then turned into a five-star rating that is presented to the user. As the user continues to label more citations, if Rayyan believes it can improve its prediction quality, then it will use these new labeled examples to produce a new model and then run it on the remaining non-labeled citations. This process is repeated until there are no more citations to label or the model cannot be improved any further.

Results and Discussions

Evaluating the prediction algorithm.

To test the quality of Rayyan’s SVM classifier, we used the above features on a collection of systematic reviews from a study published in [ 16 ]. In this study, test collections were built for each of 15 review topics (Table ​ (Table1) 1 ) which had been conducted by the Oregon EPC, Southern California EPC, and Research Triangle Institute/University of North Carolina (RTI/UNC) EPC. For each review, we know all the articles and what was included/excluded. The ratio of included articles ranged from 0.5 to 21.7%, with the largest review containing 3465 studies and the smallest 310.

Statistics about inclusion and exclusion decisions for 15 systematic reviews from [ 16 ]

A twofold cross-validation was used with 50% of the data going to training and 50% to testing. This process was repeated ten times, and the results were averaged. Two metrics were used for the evaluation of the quality of the classifier, AUC and WSS@95. The ROC (receiver operating characteristic) curve is obtained by graphing the true positive rate against the false positive rate as we vary the threshold used by the classifier. AUC refers simply to the area under this curve; 1.0 is a perfect score and 0.5 is equivalent to a random ordering. The work saved over random sampling measured at 0.95 recall (WSS@95), introduced in [ 16 ], refers to the percentage of studies that the reviewers do not have to go through because they have been screened out by the classifier at a recall of 0.95, compared to random sampling. WSS = TN + FN N − ( 1 − Recall ) where TN is the number of true negatives, FN is the number of false negatives, and N is the total number of instances in the dataset. Recall refers to the recall of the positive class (included studies). The results we obtained are AUC=0.87±0.09 and WSS@95=0.49±0.18. The 49% result is important since it shows that Rayyan can help save time using the automatic prediction. While these results illustrate appreciable time savings for the prediction feature, it is important to keep in mind that Rayyan offers much more time savings because of all the facets, filtering features, and visual cues which help expediting the screening process.

Pilot testing Rayyan

Pilot testing entailed the early evaluation of two specific aspects of functionality which had been built into the app. Critically important at the outset and before any further development could be considered was an assessment of how accurately Rayyan performed in a direct comparison with the manual methods which had been used on several Cochrane reviews. Equally significant, at this stage of the development process, was the necessity to provide the developers with an early overview of the potential added benefit of the “prediction” feature.

In December 2013, two Cochrane reviews, which had been authored and published previously by ZF, were used for the initial testing of the app [ 17 , 18 ]. The search results for these two reviews, which were available as MS Word documents, provided references to 273 and 1030 individual studies. As these systematic reviews had already been published, the final selection of studies for inclusion and exclusion had been undertaken previously using “manual” methods (electronic highlight marker in the MS Word doc), and the consolidated results of the selection process were reported in the published Cochrane review. Tracking of the decisions at every stage throughout the selection process, including reasons for exclusion and agreements and disagreements between authors, had been annotated in the MS Word doc, and key details were reported in the PRISMA flow diagram in the published Cochrane review.

The testing phase commenced with the developers (HH/MO) creating separate folders for each of the Cochrane reviews in Rayyan followed by uploading of the corresponding searches for each review. Access (username/password) to the web site as well as an introduction to the functionality of the app was given to the tester (ZF) by the developer (HH). Although the “results” of the selection process were already known to the tester, and thus the experiment was not technically “blinded,” familiarity with the searches and the results at this stage allowed a quick overview of the look and feel of the app and enabled early comments by the tester on the functionality of the app which could then be proactively addressed by the development team.

The first and smaller of the “test” Cochrane systematic reviews (273 records) had been updated more recently, and the new searches and identified studies were already included in the latest version of the published Cochrane review. These additional searches for the update were subsequently uploaded into Rayyan, and the combined searches were subjected to further evaluation with the app but only after pre-testing of the earlier batch of searches. This Cochrane systematic review was used principally as a “taster” to allow the tester to become familiar with the app and to permit exploration of the options available for identifying, selecting, and tagging of the individual references using the Include/Exclude/Undecide “buttons” and to further annotate the reasons for exclusion if appropriate. All ad hoc responses and comments made by the testers and users during the early development phase were transmitted in real time through the “send us a message” function in the app such that these requests could be acted on contemporaneously by the developers and then re-evaluated further by the testers as part of an iterative process.

Testing of the Rayyan app on the second Cochrane review (1030 records) required a few attempts to identify the 11 trials which had been previously selected by the Cochrane review authors using “manual” methods during the process of conducting the systematic review. This part of the testing phase proved to be more substantive in view of the larger number of citations and also because it sought to assess the added value of the prediction features, i.e., “suggestions” and “hints.” These citations were star-rated (1 to 5 stars) based on a near-match similarity in text and wording and were offered to the tester as being potentially eligible studies for further consideration with the expectation that this would help in expediting the selection process.

Testers’ comments

The testers’ initial comments indicated that overall the app was comparatively easy to use, readily navigable, and intuitive with no perceived requirement for a “Help” function. However, this option was discussed as a possible additional feature but which would be subject to the further “independent” and more extensive testing of the Rayyan app by a larger group of users.

A number of key positive features were identified by the tester, as indeed were some areas that would require additional attention at this early stage of the development process. Particular reference was made to the immediate visibility of the selection options of “Undecided/Included/Excluded,” that they were one-click available which allowed for the quick tagging of studies and that these choices were clearly displayed, readily accessible, and immediately responsive on selection. Specific mention was made of the pulldown option under “Reasons” (see Fig. ​ Fig.5) 5 ) which allowed the selection of either one or multiple generic and commonly used reasons why a study was to be excluded, i.e., “wrong population/ wrong publication type/wrong study design” but with the capability of adding other “self-generated” reasons to the existing predefined list. The capability of filtering the references by inclusion decision or by the collaborating author who made the decision provided an instantaneous overview of potential disagreements on study eligibility which could be discussed and resolved subsequently (see Fig. ​ Fig.6). 6 ). The ability to quickly visualize the cumulative totals as studies were either excluded or included, and the word display of tagged studies which could be used as limiters were considered added-value functions. The topic summary word “cloud” was also noted in that it provided a very practical and graphical indication of the total number of studies identified by their keywords and with the number of studies correlating with the font size of the text in the word “cloud.”

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Reasons for exclusion. Users can select or add a reason for exclusion and exclude the study at the same time

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Filtering by exclusion/inclusion decisions by author

Translation of study abstracts prior to assessment of a study for inclusion may be necessary if these have been published in other than the review author’s native language. A unique feature of Rayyan includes the option to be able to forward a link to the specific reference in the app directly to a selected translator who can then translate the portion of text or abstract and respond by pasting the translation directly below the study reference within the app. The ease and benefit of being able to do this directly within and from Rayyan were also highlighted in the initial testing phase. It was also noted in the early testing phase that some of the references to citations were incomplete and in some instances that the detail was substituted by a series of question marks replacing these details. This fault was reviewed by the developers and was considered to be due to errors incurred in formatting when the file was uploaded into Rayyan which could be readily identified and in general did not represent a significant number of references.

Testimonials from users highlighted the ease with which the exploration of searches could be completed, the large amount of time saved, the comparative simplicity and satisfaction in being able to readily share and compare individual authors’ decisions to include or exclude studies.

Additional features incorporated after rollout

The highlighting of text, to enable rapid identification of important keywords, for example, trials and randomized placebo was considered by the developers and added as a “Highlights ON” button. Blinded and independent selection of studies is a critical aspect of the review process, and the option to be able to hide individual authors decisions about included studies was also added by request.

User data after rollout

Rayyan has attracted significant interest from a large and well distributed number of users from around the globe. As of November 2016, there are more than 2000 users originating from more than 60 countries. These users are conducting hundreds of reviews on a total of more than 1.6M citations with the individual reviews ranging in size from tens to more than 38k citations.

Workshops, presentations, and user feedback

Several opportunities arose in 2014/2015 to unveil Rayyan to the global research community, which included workshops at the Cochrane Colloquium in Hyderabad (2014), at Evidence-Live Oxford (2015), and the Cochrane Colloquium Vienna (2015). These expositions allowed for further development and the integration of several novel features based on feedback and suggestions received from attendees. We have also two other conduits through which users can give us feedback, a feature built into the web site and survey that our users can take any time (thus far, 66 respondents). From all of these feedback channels, the strongest feature of the app was its functionality, i.e., in the clear and unambiguous way in which studies could be viewed in context together with the completed selections, and how the “undecided” studies could be fed back into the system and that these were then highlighted as “hint.” From the survey, two important highlights relate to time savings and the most important features in Rayyan. Our users reported a 40% average time savings when using Rayyan compared to others tools, with 37% of the respondents reporting more than 50% time savings. For the second part, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews as the two most important features of Rayyan.

Future development

Based on the pilot study reported here and the different interactions with review authors, plans are underway to add several new features. The ultimate goal is to support most of the review process where machine learning, data/text mining, and information extraction techniques along with good software engineering best practices can provide clearly discernible quality coupled with speed, to facilitate reviewers efforts in the process of creating and updating systematic reviews. Key facets of the planned extensions include the following:

  • Better detection of duplicates and a user-guided process for handling these duplicates.
  • Assessment of risk of bias, with the initial focus on the domain-based criteria defined by Cochrane, to include identifying and extracting of supporting sentences from the full-text articles. Users will be able to validate these automatic judgments and annotate the full text with their own assessments.
  • Automatic extraction of the values or the text related to PICO and other data elements. Again, users will be able to validate the extracted information and annotate the full text to extract more elements.
  • Extending Rayyan API such that other software platforms can use Rayyan’s features by simple REST calls.

Rayyan has been shown to be a very useful app with significant potential to lighten the load of systematic review authors by speeding up the tedious part of the process of selection of studies for inclusion within the review. Experiments on a set of 15 reviews showed that the prediction embedded in Rayyan can reduce the time for screening articles. In addition, our survey showed that our users reported time savings in the order of 40% on average compared to other tools they have been using in the past. Rayyan’s two most important features compared to other competitors are its ability to help in abstract and title screening and the ability to collaborate on the same review. A comprehensive comparison of Rayyan with other systems would require additional studies to be conducted, more especially those which build on several previous reports [ 7 , 19 ]. These have been confirmed by our survey and the many testimonials from our users. Rayyan would benefit from several improvements including a better handling of duplicates, automatic data extraction from full text, automatic risk of bias analysis, and seamless integration with Review Manager (RevMan), the Cochrane software used for preparing and maintaining Cochrane reviews.

Rayyan is available for free at http://rayyan.qcri.org and is fully funded by Qatar Foundation, a non-profit organization in the State of Qatar.

Acknowledgements

This work was fully funded by Qatar Foundation, a non-profit organization in the State of Qatar.

Availability of data and materials

Authors’ contributions.

MO and HH designed and together with ZF drafted the manuscript. ZF provided systematic review content oversight and led the testing mentioned in the manuscript. MO, HH, and AE are the technical leads on the Rayyan project. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Contributor information.

Mourad Ouzzani, Email: aq.gro.fq@inazzuom .

Hossam Hammady, Email: aq.gro.fq@ydammahh .

Zbys Fedorowicz, Email: moc.liamg@zciworodefsybz .

Ahmed Elmagarmid, Email: aq.gro.fq@dimragamlea .

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Systematic Reviews and Meta Analysis

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Software and Tools

  • Where do I get all those articles?
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Covidence is Web-based to for managing the review workflow. Tools for screening records, managing full-text articles, and extracting data make the process much less burdensome. Covidence currently is available for Harvard investigators with a hms.harvard.edu, hsdm.harvard.edu, or hsph.harvard.edu email address. To make use of Harvard's institutional account:

  • If you don't already have a Covidence account, sign up for one at: https://www.covidence.org/signups/new Make sure you use your hms, hsph, or hsdm Harvard email address.
  • Then associate your account with Harvard's institutional access at: https://www.covidence.org/organizations/58RXa/signup Use the same address you used in step 1 and follow the instructions in the resulting email.

Once your account is linked to the Harvard account, you will have access to the full range of Covidence features and can create unlimited reviews. You can do this when logged in to your individual Covidence account by going to your account dashboard page and clicking the 'Start a new review' button. This will take you to a new page where you can select the Harvard account to set up the new review.

Rayyan is an alternative review manager that has a free option. It has ranking and sorting option lacking in Covidence but takes more time to learn. We do not provide support for Rayyan.

Other Review Software Systems

There are a number of tools available to help a team manage the systematic review process. Notable examples include Eppi-Reviewer ,  DistillerSR , and PICO Portal . These are subscription-based services but in some cases offer a trial project. Use the Systematic Review Toolbox to explore more options.

Citation Managers

Citation managers like EndNote or Zotero can be used to collect, manage and de-duplicate bibliographic records and full-text documents but are considerable more painful to use than specialized systematic review applications. Of course, they are handy for writing up your report.

Need more, or looking for alternatives? See the SR Toolbox , a searchable database of tools to support systematic reviews and meta-analysis.

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  • Last Updated: Feb 14, 2024 2:47 PM
  • URL: https://guides.library.harvard.edu/meta-analysis
  • Open access
  • Published: 20 June 2019

Usability and acceptability of four systematic review automation software packages: a mixed method design

  • Gina Cleo   ORCID: orcid.org/0000-0002-0902-4928 1 ,
  • Anna Mae Scott 1 ,
  • Farhana Islam 2 ,
  • Blair Julien 2 &
  • Elaine Beller 1  

Systematic Reviews volume  8 , Article number:  145 ( 2019 ) Cite this article

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New software packages help to improve the efficiency of conducting a systematic review through automation of key steps in the systematic review. The aim of this study was to gather qualitative data on the usability and acceptability of four systematic review automation software packages (Covidence, SRA-Helper for EndNote, Rayyan and RobotAnalyst) for the citation screening step of a systematic review.

We recruited three volunteer systematic reviewers and asked them to use allocated software packages during citation screening. They then completed a 12-item online questionnaire which was tailored to capture data for the software packages used.

All four software packages were reported to be easy or very easy to learn and use. SRA-Helper for EndNote was most favoured by participants for screening citations and Covidence for resolving conflicts. Overall, participants reported that SRA-Helper for EndNote would be their software package of choice, primarily due to its efficiency.

This study identified a number of considerations which systematic reviewers can use as a basis of their decision which software to use when performing the citation screening and dispute resolution steps of a systematic review.

Peer Review reports

Systematic reviews are the foundation of evidence-based practice. Yet, despite advancements in automation of some of the steps of systematic reviews [ 1 , 2 ], conducting a systematic review remains a largely manual process that requires considerable expertise, time and financial resources. Software packages have recently become available to help to improve the efficiency of some of the steps of the systematic review process, including literature searching, de-duplicating of search results, screening citations and resolving conflicts. Each software package has its strengths and limitations. This study aimed to, firstly, assess the usability (ease of use and learnability) and acceptability (sufficient to serve the purpose for which it is intended) of the following, commonly used, systematic review automation software packages: Covidence, SRA-Helper for EndNote, Rayyan and RobotAnalyst, and secondly, to identify key advantages and disadvantages of each, as perceived by the users.

Three volunteer systematic reviewers, who were commencing a systematic review (882 total citation records to screen), were recruited via email invitation from the study authors (GC and EB). The systematic reviewers were asked to use allocated software packages for the title/abstract screening and dispute resolution steps of the systematic review and to complete an online questionnaire which was tailored to capture data for the software package used. One participant was assigned to use and review Covidence and SRA-Helper for EndNote (BJ), and one was assigned to Rayyan and RobotAnalyst (FI). The final reviewer was assigned to use and review Covidence vs SRA-Helper for EndNote vs Rayyan vs RobotAnalyst (AMS). Each software was used to screen 220 or 221 references, for a total of 882 references screened.

Covidence ( www.covidence.org ) is a web-based screening and data extraction tool; it is one of the tools recommended by the Cochrane Collaboration [ 3 ]. Covidence allows authors to import and screen citations and full-text articles, resolve conflicts, extract data using customisable forms and export results in various formats.

SRA-Helper for EndNote ( https://github.com/CREBP/EndNoteHelper ) is a downloadable automation script which works as an add-on to EndNote; it is part of the Systematic Review Accelerator. SRA-Helper for EndNote allows users to map keyboard keys (e.g. 1, 2, 3) to folders (e.g. include, exclude, background). When the user highlights a reference (e.g. Smith 1998), and presses the key (e.g. 1), the reference automatically moves to the relevant folder (e.g. include).

Rayyan ( https://rayyan.qcri.org ) is a web-based application which allows multiple authors to create and collaborate on systematic reviews. Throughout the citation screening process, Rayyan offers suggestions for article inclusion based on the authors’ prior selections [ 4 ].

RobotAnalyst ( www.nactem.ac.uk/robotanalyst ) is a web-based application, developed to support the citation screening phase of systematic reviews. RobotAnalyst prioritises references by relevancy predictions and updates the predictive model after the author makes each screening selection.

To identify the advantages and disadvantages of each software package, we used a 12-item questionnaire which included 3 Likert-scale questions and 9 free text questions (Additional file  1 : Table S1). Questions 1–8 focused on the usability and acceptability of each software package; these questions were repeated for each software package being assessed. Questions 9–11 were comparator questions, which assessed the user’s preference for one software tool over another when screening citations and resolving conflicts. We used Qualtrics ( www.qualtrics.com ) to disseminate the questionnaires and collect the data.

The three systematic reviewers answered all of the questions presented in the qualitative questionnaire.

We summed the participants’ quantitative responses for each software package as displayed in Table  1 (lowest possible score = 2 points; highest possible score = 10 points). All four software packages were reported to be easy or very easy to learn and use (Table  1 ). Covidence had the highest rating for general usability, scoring 9 out of a possible 10 points. This may be due to its ‘straightforward process and simple layout’, and it being available online, without the need to download it, ‘making it versatile/accessible when out of office’. SRA-Helper for EndNote was rated fastest for response time (10/10), as ‘it’s not dependent on internet connection’ and RobotAnalyst the slowest (5/10). When combining scores for ease of learning how to use the software, usability and response time, SRA-Helper for EndNote scored highest (28/30).

SRA-Helper for EndNote was most favoured by participants for screening citations and Covidence for resolving conflicts (Table  2 ). Overall, participants reported that in conducting future systematic reviews, SRA-Helper for EndNote would be their software package of choice, primarily due to its efficiency (Table  2 ).

The key advantages considered relevant by systematic reviewers included visibility of already screened/yet to be screened citations (e.g. in a form of a countdown or summary of progress), ability to highlight key terms for inclusion and exclusion, ability to use keyboard shortcuts, fast response time of the software (e.g. between decision to include and the reference moving off the screen and into the ‘included’ folder), ability to add notes or labels to the references, guidance of decision for subsequent references on the basis of prior decisions and ease of learning how to use the tool and intuitiveness of the layout.

The key disadvantages considered relevant by the systematic reviewers included glitches in the software (e.g. crashes), slow response time of the software, inability to see progress (references screened vs those remaining to be screened), inability to change mind once decision to include/exclude was made, requirement to download or install software, inability to highlight included/excluded terms, unreliable predictions and poor layout (e.g. decision buttons too close together). The advantages and disadvantages of using each automation software package to screen citations and resolve conflicts are summarised in Table  3 .

Overall, the systematic reviewers found all four of the software tools easy to learn and use. SRA-Endnote helper was strongly preferred (28/30 points), with RobotAnalyst, Covidence and Rayyan scoring lower but similarly (22, 23 and 24/30, respectively). The strong preference for SRA-Endnote Helper may be explained by its ease of learning to use and very quick response time due to it being a desktop (rather than web-based) tool.

Among the key characteristics considered relevant were display of screening progress, ability to revise decisions, ability to highlight inclusion/exclusion terms, ability to use keyboard shortcuts rather than the mouse, software response time and reliability (i.e. no bugs or crashes). Users, particularly those newer to systematic reviews, also cited the intuitiveness of the layout and ease of learning how to use the tool as important.

It is worth emphasising here that what is considered an advantage or disadvantage will vary by systematic reviewer: one may prefer a web-based tool that is a bit slower in response time but available anywhere without a download or install, whilst another may prefer a downloadable tool that is faster in response time but requires a download or install. However, whilst the individual preferences will vary, our aim here was to identify what those key considerations are—to help systematic reviewers (particularly those new to these tools) to make their own decisions which to use.

Although the sample of systematic reviewers included in the present assessment is small ( n  = 3), it deliberately included both novice systematic reviewers and an experienced systematic reviewer. We are therefore confident that the considerations they raised are likely to be the issues considered relevant by the larger systematic review population. This can only be formally measured with a larger sample in future research.

Ongoing and future developments in the automation of screening—including automated screening and text mining—would help to increase the efficiency and reduce human effort required [ 1 , 2 ].

The results of this qualitative report highlight multiple advantages and disadvantages of automation software packages for screening in systematic reviews. The outcomes show that the SRA-Endnote helper was the preferred software due to the fast response time, user-friendly set up, intuitive layout and the fact that it does not rely on internet connection. This study identified a number of relevant considerations for systematic review software packages, and individual systematic reviewers can take this into consideration whilst performing the citation screening and dispute resolution steps of a systematic review.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

  • Systematic Review Accelerator

Tsafnat G, Glasziou P, Karystianis G, Coiera E. Automated screening of research studies for systematic reviews using study characteristics. Syst Rev. 2018;25:64.

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O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4:5.

Covidence. Cochrane Community. https://community.cochrane.org/help/tools-and-software/covidence . Accessed 3 Jul 2018.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5:210.

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Acknowledgements

Not applicable

GC, FI and BJ received no funding with respect for this study. AMS and EB are supported by a NHMRC grant APP1044904 (Centre for Research Excellence in Minimising Antibiotic Resistance for Acute Respiratory Infection, CREMARA).

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Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Australia

Gina Cleo, Anna Mae Scott & Elaine Beller

Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia

Farhana Islam & Blair Julien

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Contributions

GC developed the interview schedule and analysed the data. AMS, FI and BJ tested the software and completed the questionnaires. EB conceived and was overseeing the study. All authors read, contributed to and approved the final manuscript.

Corresponding author

Correspondence to Gina Cleo .

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As this research involved staff participating by virtue of their professional role, ethics approval was not sought.

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Competing interests.

EndNote Helper was developed by our research centre (Centre for Research in Evidence-Based Practice). The authors declare that they have no other competing interests.

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Additional file

Additional file 1:.

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Cleo, G., Scott, A.M., Islam, F. et al. Usability and acceptability of four systematic review automation software packages: a mixed method design. Syst Rev 8 , 145 (2019). https://doi.org/10.1186/s13643-019-1069-6

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National and regional prevalence of gestational diabetes mellitus in India: a systematic review and Meta-analysis

  • Neha Mantri   ORCID: orcid.org/0000-0002-1709-1274 1 ,
  • Akhil Dhanesh Goel   ORCID: orcid.org/0000-0002-6156-7903 2 ,
  • Mamta Patel   ORCID: orcid.org/0000-0002-4659-3687 1 ,
  • Pritish Baskaran 2 ,
  • Gitashree Dutta 2 ,
  • Manoj Kumar Gupta   ORCID: orcid.org/0000-0002-5367-5795 2 ,
  • Vikas Yadav 3 ,
  • Madhukar Mittal   ORCID: orcid.org/0000-0002-6919-5614 4 ,
  • Shashank Shekhar 5 &
  • Pankaj Bhardwaj   ORCID: orcid.org/0000-0001-9960-3060 6  

BMC Public Health volume  24 , Article number:  527 ( 2024 ) Cite this article

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Gestational diabetes mellitus (GDM) is frequently misdiagnosed during pregnancy. There is an abundance of evidence, but little is known regarding the regional prevalence estimates of GDM in India. This systematic review and meta-analysis aims to provide valuable insights into the national and regional prevalence of GDM among pregnant women in India.

We conducted an initial article search on PubMed, Scopus, Google Scholar, and ShodhGanga searches to identify quantitative research papers (database inception till 15th June,2022). This review included prevalence studies that estimated the occurrence of GDM across different states in India.

Two independent reviewers completed the screening of 2393 articles, resulting in the identification of 110 articles that met the inclusion criteria, which collectively provided 117 prevalence estimates. Using a pooled estimate calculation (with an Inverse square heterogeneity model), the pooled prevalence of GDM in pregnant women was estimated to be 13%, with a 95% confidence interval (CI) ranging from 9 to 16%.. In India, Diabetes in Pregnancy Study of India (DIPSI) was the most common diagnostic criteria used, followed by International Association of Diabetes and Pregnancy Study Groups (IADPSG) and World Health Organization (WHO) 1999. It was observed that the rural population has slightly less prevalence of GDM at 10.0% [6.0–13.0%, I 2 = 96%] when compared to the urban population where the prevalence of GDM was 12.0% [9.0–16.0%, I 2  = 99%].

Conclusions

This review emphasizes the lack of consensus in screening and diagnosing gestational diabetes mellitus (GDM), leading to varied prevalence rates across Indian states. It thoroughly examines the controversies regarding GDM screening by analyzing population characteristics, geographic variations, diagnostic criteria agreement, screening timing, fasting vs. non-fasting approaches, cost-effectiveness, and feasibility, offering valuable recommendations for policy makers. By fostering the implementation of state-wise screening programs, it can contribute to improving maternal and neonatal outcomes and promoting healthier pregnancies across the country.

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Manifestation of glucose intolerance in pregnancy, often, named Gestational Diabetes Mellitus (GDM) is emerging as a major public health problem. The World Health Organization 1999 report provides a fundamental definition which states “Gestational diabetes is a carbohydrate intolerance resulting in hyperglycemia of variable severity with onset or first recognition during pregnancy” [ 1 ]. Nevertheless, there has been substantial debate over how to characterize glucose in pregnancy, which has complicated clinical work and research over the past three decades. Additionally, it may start at the same time as pregnancy, which increases the risk of it going undetected and having adverse maternal and neonatal complications [ 2 , 3 , 4 , 5 , 6 ].

In 2015, the International Diabetic Federation (IDF) reported that 1 in 11 people worldwide have diabetes, with 75% of them residing in low and middle-income countries [ 7 ]. There is a huge variation in the prevalence of GDM globally from 10.1% (Eastern & Southeastern Asia) to 13.61% (Africa) depending on screening strategies, diagnostic criteria, and the background population’s ethnic composition [ 8 , 9 ]. South East Asia region had 6.9 million live births being affected by hyperglycemia in pregnancy; with an estimated prevalence of 24.2% [ 10 ]. India, being the largest populous country in the world, shows the prevalence of GDM in the ranging from 3 to 35% [ 11 , 12 , 13 , 14 , 15 ].

Currently, the Diabetes in Pregnancy Study Group of India advocates for universal screening using a single non-fasting 2-h 75 g OGTT, with 2 h value > 140 mg/dL being diagnostic of GDM [ 16 ]. The International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria are based on the findings of the large-scale Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study and hence popular globally, [ 17 ] but its drawback is argued to be the large number of false-positive cases due to lower fasting cutoffs and hence adding to the burden of GDM [ 18 , 19 ]. In addition, diagnosing the Indian population by international studies can be inconclusive as the HAPO study lacked Indian representativeness in its findings [ 17 ].

To solve the inconsistencies in diagnosis and management of GDM, a technical and operational guideline has been developed under the aegis of the Maternal Health Division, Ministry of Health and Family Welfare, Government of India in February 2018 [ 20 ]. However, subsequent studies have shown high variability in the prevalence, from rates as low as 0% in Manipur to 42% in Lucknow, Uttar Pradesh [ 21 , 22 ]. A variety of factors may contribute to this variability, including differences in the genetics and population across India, as well as differences in screening practices.

A pan India prospective study (2021) conducted by FOGSI and DIPSI shows about one-third of the pregnant women are diagnosed with GDM during the first trimester and over a quarter of them have a history of fetal loss in the previous pregnancies [ 23 ]. Hence, GDM is a topic of considerable controversy when it comes to its screening, diagnosis and its cost-effectiveness.

With this aim, we conducted a systematic review to estimate the national and regional prevalence of GDM in pregnant women to foster the implementation of programs state-wise effectively. This analysis aims to investigate how various factors, such as different screening criteria, geographical locations (urban versus rural areas), techniques used for blood collection, and the timing of screening during pregnancy (early versus late), might influence the observed prevalence of GDM in pregnant women in India.

Methodology

Study protocol.

This Systematic Review and Meta-Analysis is written in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [ 24 ] and is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (Ref.no. CRD42022335011).

Search strategy

We framed our research question using the PICO(S)(T) methodology (Population-pregnant women; Intervention-nil; Comparison-nil; Outcome-GDM; Study design-cross-sectional in India).

We performed searches in PubMed and Scopus using selected keywords. These results were supplemented by relevant studies from Google Scholar and ShodhGanga—Indian thesis repository ( https://shodhganga.inflibnet.ac.in/ ). The last day fir performing the search was 15th June 2022. No date or language restrictions were imposed. The cross-references of the included studies were explored for additional studies. Keywords were identified by iterative discussion among reviewers, and a search query was developed separately for each database. The controlled descriptors (such as MeSH terms) and Boolean operators were used to develop a robust search strategy. (See Additional file 1 : Search Strategy).

Eligibility criteria

The studies reporting the prevalence of GDM in pregnant women in India were included.

Inclusion criteria

Community or hospital-based studies.

Original published articles and short communications.

Studies providing the prevalence of GDM

Studies conducted in India

Type of studies: cross-sectional studies.

Exclusion criteria

Overviews, editorials, other review papers, or method protocols without results

Molecular or genetic studies, animal studies, Invitro studies.

Studies that did not differentiate between GDM and type 1 and/or type 2 diabetes

Studies that reported risk factors, associated biomarkers, or outcomes of GDM without reference to GDM prevalence

Studies which have not reported screening methods

Experimental studies

Three authors independently examined search results for inclusion. Disagreements, if any, were settled by consensus with a fourth author.

Study selection

A reviewer independently conducted searches on all information sources from various databases and uploaded to Rayyan QCRI online software [ 25 ]. Rayyan QCRI helped in ensuring a systematic and comprehensive search and selection process. A fourth reviewer managed Rayyan QCRI software, who identified and removed the duplicate citations. Three authors independently screen titles and abstracts with turned “blind” function on. The discrepancies between the three reviewers were discussed with a fourth author for making a consensus to select the articles. Full-text copies of all selected studies were obtained to find more details. We documented the reasons for the exclusion of studies explored as full text. The study inclusion process is presented using the PRISMA flowchart. The reference management software Mendeley Desktop ( https://www.mendeley.com ) for Windows was used to store, organize, cite, and manage all the included articles.

Data extraction

After selecting eligible studies, we obtained full-text articles for all included studies. Two reviewers independently performed data extraction of relevant information. Data were extracted regarding author, year of publication, study location, site (hospital- or community-based or data-based), study type, trimester, sample size, diagnostic criteria, and prevalence of GDM. We recorded investigators’ definitions of GDM and screening and diagnostic criteria used for GDM.

When a study reports the prevalence of GDM in the same population using multiple diagnostic criteria, the most recent and up-to-date criteria was selected in the following sequence-.IADPSG/ WHO 2013 > DIPSI> WHO 1999 > ADA > NICE> Carpenter and Coustan > NDDG> O’Sullivan and Mahan’s Criteria as framed after the iterative discussion.

Bias reporting

The methodological quality of the studies was analyzed independently by two investigators using the AXIS tool which critically appraises study design and reporting quality as well as the risk of bias in cross-sectional studies. We assessed bias using the AXIS Tool for Prevalence Studies in our systematic review [ 26 ]. The AXIS tool has 20 components assessing the quality of the studies with special focus on the presented methods and results based on a combination of evidence, epidemiological processes, experience of the researchers and Delphi participants. The components included in this checklist are addressing study objective, design, sample size, sample population, sample frame, selection process, non-responders, risk factors and outcome measured, appropriateness of statistical methods, consistency of results, discussion justified, limitation of the study, ethical approval and any conflict of interest or funding received.

Data synthesis and analysis

The prevalence of GDM from different studies were pooled together using the Inverse variance heterogeneity method. Heterogeneity was assessed using I 2 Statistics. High heterogeneity in the study was analyzed using sub-group analysis and sensitivity analysis. MetaXL software was used for data synthesis [ 27 ]. Publication bias was determined using DoI plot and LFK index.

On searching PubMed ( n  = 1883), Scopus ( n  = 345), Google Scholar ( n  = 92), and ShodhGanga—reservoir of Indian theses ( n  = 73), a total of 2393 articles were identified related to GDM (see Fig. 1 : PRISMA flowchart) Thus, the full texts of 140 articles were assessed for eligibility. During this process, a total of 13 authors were contacted for full-text via email, out of which ( n  = 11) responded. Remaining 2 articles were included based on only abstract and in data extraction sheet, missing data were reported. Thus, a final 117 articles were included in the systematic review and meta-analysis for the analysis. (See Table 1 : Data Extraction Sheet).

figure 1

PRISMA Flowchart

A total of 13 studies were found to report the data in separate studies which was part of a large study. The studies by Punnose J et al. 2018 [ 28 ] and Punnose J et al. 2021 [ 29 ] and Agarwal MM et al. 2018 [ 30 ] was conducted in the same population ( n  = 36,530) during the time period January 2006–December 2016 and was also reported in multiple publication. Thus, data from these studies were considered as one data and the study with the longest time period (Punnose J et al. 2018) was included in the review. Similarly, a study was conducted in the South Indian pregnant women ( n  = 304) during July 2011 to August 2012 by Nayak PK et al. 2013 [ 31 ] and Mitra S et al. 2014 [ 32 ] and was reported as separate studies. Thus, we included the Mitra S et al. 2014 with the complete data for the analysis. Similarly, a project “Women in India with GDM Strategy (WINGS)” was carried out in Tamil Nadu between January 2013 and December 2015 in Pregnant women ( n  = 1459) and were reported as two separate studies by Bhavdharini et al. (2016 and 2017). We considered them as one data and included Bhavdharini et al. 2016 in our study.

Likewise, studies, namely, Rajput R et al. 2012, Tripathi R et al. 2012, Kumar CN et al., C R et al. 2014, Bhattacharya et al. 2002, Balaji V et al. 2006, Balaji V et al. 2012, and Seshiah V et a 2007, were reported as separate studies using data from a large study and hence, were excluded from the analysis.

Five studies were added using suffix (A, B and C) as they reported the prevalence of GDM using different sub-sets of population, but were otherwise reported in the same study. Taneja et al. 2020 in Punjab used the same criteria of screening GDM in women at different gestational age (26 to 28 weeks and after 34 weeks) [ 33 ]. These were considered as 2 separate studies and labelled as Taneja (A) and Taneja (B) respectively. Similarly, a study was conducted by Siddique et al. using ADA criteria in Saket, Muzzaffarpur and Bhilai area on different subset of population [ 34 ]. These studies were also considered as three different studies and labelled as Siddique (A), (B) and (C) respectively. Also, a community based study was conducted in urban, semi-urban and rural area of Chennai city on a different sub-set of population [ 35 ]. These were considered as three different studies and labelled as Seshiah V et al. 2009 (A), (B) and (C) respectively.

A total of 19 articles utilized a combination of criteria to estimate the prevalence of GDM [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ].

The variation in diagnostic criteria during estimation of Glucose in pregnant women pose a challenge in data extraction. Thus, the most recent and up-to-date criteria was selected in the following sequence-IADPSG/ WHO 2013 > DIPSI>WHO 1999 > ADA > NICE> Carpenter and Coustan > NDDG> O’Sullivan and Mahan’s Criteria as framed after the iterative discussion by the subject experts.

Diagnostic criteria

A variety of diagnostic criteria were used in a total of 117 studies included in the review. (See Table 2 : Different GDM Screening criteria).

DIPSI (29 prevalence estimates) [ 23 ] was the most common diagnostic criteria used, followed by IADPSG / WHO 2013 (38 prevalence estimates) [ 53 ], WHO 1999 (24 prevalence estimates) [ 54 ], and ADA (11 prevalence estimates) [ 55 ]. Other criteria used were Carpenter and Coustan Criteria (6 prevalence estimates) [ 56 ], NDDG (1 prevalence estimate) [ 57 ], NICE (1 prevalence estimate) [ 58 ], and O’Sullivan and Mahan’s criteria (1 prevalence estimate) [ 59 ]. There was no clear description of study criteria used in 6 studies [ 33 , 60 , 61 , 62 , 63 ].

Capillary versus venous blood

A total of 6 prevalence estimates used capillary blood glucose (CBG) or glucometer measurements rather than venous plasma glucose (VPG) [ 30 , 64 , 65 , 66 , 67 , 68 ]. Three studies use capillary blood followed by venous blood glucose estimation [ 12 , 48 , 69 ]. In 3 studies, a comparative assessment of capillary and venous blood glucose estimation was done on the prevalence of the GDM in the pregnant women [ 70 , 71 , 72 ].

Two-step versus one-step procedure

A total of 93 studies ( n  = 93) uses one-step procedure to estimate the prevalence of GDM, whereas, only 19 studies ( n  = 19) used two-step procedure for the diagnosis of the GDM in the study population. There was no clear description of study criteria used in 5 studies.

Risk of Bias

We assessed the Risk of Bias using the AXIS tool [ 26 ]. Overall, 117 studies were included in the Risk of Bias assessment using the AXIS tool. A horizontal bar graph showing the Risk of bias tool result for each component is given in Fig. 2 Risk of Bias.

figure 2

Risk of Bias Assessment

Majority of the study components revealed a low risk of bias namely, objective of the study, appropriateness of the study design, study population defined, appropriateness of sample frame, risk factors measured according to the objectives and with the appropriate study tool, accuracy of choice of statistical methods, measures of replicability of the study, description of the basic data, results internally consistent, all results presented and justification of discussion and conclusion.

There was no clear description of response rate bias in 48 studies. Also, there was no description of Ethical consent in 22 studies. Only 9 studies reported funding, but there was no clarity of 28 studies on their funding sources keeping them in unclear risk of bias.

A high risk of bias was revealed in the sample size justification in 57 studies. Further, the results from 90 studies lacks generalizability to the general population marking them with high risk of bias. There was no description about non-responders and their information in 87 studies revealing the high risk of bias. Many studies ( n  = 63) which did not discuss their limitations were categorized as having high risk of bias.

Prevalence estimates of GDM in pregnant women in India

The final 117 studies were used for prevalence estimates of GDM in pregnant population in India. A total of 106 studies were conducted in a hospital-based setting and 11 were community-based studies.

We found a pooled estimate (with an Inverse square heterogeneity model) of the prevalence of overall GDM in pregnant women was 13% [95% CI, 9–16%, n  = 117 studies] with the heterogeneity of the studies high at 99% which restricts the generalizability of the findings ( Fig. 3 Forest Plot depicting the pooled prevalence of GDM in India) The possible reasons could be studies varied widely in population type, geography, as well as the diagnostic method used. (Table 3 Sub group Analysis) The publication date of the studies ranged from 1989 to 2022.

Geographical Zones

figure 3

Forest Plot depicting the pooled prevalence of GDM in India

India has a union of 28 states and 8 Union territories, divided as “North,” “South,” “East,” “Central” or “West” based on the Inter-state council secretariat classification of geographic regions of India [ 73 ]. Therefore, region-wise subgroup analysis was also conducted to get estimates of the prevalence of GDM. North region includes Haryana, Himachal Pradesh, Punjab, Delhi, Chandigarh, Uttarakhand, Jammu and Kashmir and Ladakh. States like Gujarat, Rajasthan, Maharashtra, Goa, Daman and Diu and Dadara and Nagar Haveli comprises West Region of India. South India includes Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana, Andaman and Nicobar Islands, Lakshadweep and Puducherry. East and North-eastern states are Bihar, Jharkhand, Odisha, West Bengal, Arunachal Pradesh, Sikkim, Mizoram, Assam, Meghalaya, Manipur, Nagaland and Tripura. Central Zone of India includes Chhattisgarh, Uttar Pradesh and Madhya Pradesh.

The prevalence of GDM varies across the 5 zones of India. The highest prevalence of GDM was found in North region followed by South India. Areas of low prevalence include West, Central and Eastern zone of India. One of the confounding factors behind low prevalence could be lesser studies conducted in these zones to estimate the prevalence. (Fig. 4 Map of India showing the prevalence of GDM in 5 different zones of India).

figure 4

Map of India showing prevalence of GDM in 5 different zones of India

The pooled prevalence of GDM in North Zone was found to be 16.1% [95% CI, 9.9–22.7, I 2  = 98.9%, n  = 31 studies]. The maximum weightage (36.53) was by a study from Punnose J et al. conducted in 2018 [ 28 ].

Similarly, the pooled prevalence of GDM in West Zone was found to be 7% [95% CI, 3.3–11.2, I 2  = 98.9%, n  = 17 studies]. The maximum weightage (50.24) was by a study from Naik RR et al. 2019 [ 74 ].

In Central Zone , the pooled prevalence of GDM was found to be 12.0% [95% CI, 4.3–21.1, I 2  = 99.29%, n  = 13 studies]. A study by Jain R et al. conducted in 2016 has a maximum weightage of 66.55 [ 75 ].

The pooled prevalence of GDM in South Zone was 12.6% [95% CI, 7.8–17.8, I 2  = 98.38%, n  = 47 studies]. The maximum weightage was held with study by Jeeyasalan L et al. conducted in 2016 [ 63 ].

In East and North-eastern Region , the pooled prevalence of 11.5% was found. [95% CI, 5.3–18.4, I 2  = 97.34%, n  = 9 studies]. The maximum weightage (27.27) by a study done by Hussain et al. in 2020.) [ 76 ].

Urban versus Rural Studies

A total of 92 studies were conducted in urban areas, 8 studies in semi-urban areas and 17 studies in rural areas. The pooled prevalence in the rural population was 10.0% [6.0–13.0%, I 2 = 96%, n  = 10 studies], whereas, the pooled prevalence of 12.0% [9.0–16.0%, I 2  = 99%, n  = 88 studies] was found in the urban population. A study conducted by Seshiah V et al. in 2009 included the study participants from urban, semi-urban and rural areas of Tamil Nadu [ 35 ].

Diagnostic and Screening criteria

With the subgroup-analysis using diagnostic criteria, the prevalence of GDM using WHO 1999 criteria was 12.0% (9.0–16.0%), I 2 = 97% studies, n  = 57 studies] which was slightly less than the prevalence of GDM with DIPSI criteria [ 23 ] 13.0% [3.0–24.0%, I 2 = 99%, n  = 29 studies] The IADPSG/ WHO 2013 criteria detected a higher prevalence of GDM as 17.0% [12.0–22.0%, I 2  = 99%, n  = 38 studies], while, ADA criteria pooled a lower prevalence of 7.0 [4.0–10.0%, I 2  = 86%, n  = 11 studies]. There was prevalence range of 13.0% [3.0–24.0%, I 2  = 99%, n = 9 studies] was using other criteria like C&C criteria, NICE, NDDG and O′ Sullivan Criteria.

Small study effects

We evaluated the small study effects like publication bias using the DOI plot and LFK index. There was no asymmetry in the National pooled estimate [LFK index = − 0.67] and Zonal estimate except for the North zone and West zone. (See Fig. 5 : DOI Plot for Publication bias).

figure 5

DOI plot for publication bias

Plethora of studies discussing the GDM prevalence in India are published, but there is a scarcity of studies discussing the regional estimates of GDM prevalence in India. A systematic review and meta-analysis conducted by Katherine T Li et al. quantitatively examined the prevalence of GDM across India based on 64 studies up to the year 2016 and explored the prevalence of GDM which ranged 0 to 41.9% [ 77 ].

This systematic review and meta-analysis included 110 studies reporting the prevalence of GDM ranging from 9 to 16% in pregnant women in India. We found a pooled estimate (with an Inverse square heterogeneity model) of the prevalence of overall GDM in pregnant women was 13% [95% CI, 9–16%] with the heterogeneity of the studies high at I 2  = 99%. The possible reasons behind this heterogeneity could be studies varying widely in population type, geography, study duration and the diagnostic method used. Our study also highlighted the discrepancy in prevalence estimates due to different screening criteria, gestational age of screening, capillary versus venous blood estimation and one-step versus two-step procedure used for diagnosing GDM.

Which diagnostic criteria is suitable for Indian pregnant women?

The most commonly used criteria were DIPSI followed by IADPSG/WHO 2013 and WHO 1999. With descriptive analysis, we found that the WHO 1999 criteria detected a high prevalence of GDM as compared to IADPSG and DIPSI which almost detected the pooled prevalence of 12–13%.

Das Mukhopadhyay et al. did not find any significant difference between the prevalence rates of GDM among DIPSI and IADPSG criteria [ 52 ]. But he concluded that DIPSI being simple in execution and patient friendly is close to the international consensus. In a study by Singh et al. in 2021, it was observed that DIPSI was only 37.1% sensitive as compared to IADPSG criteria [ 51 ]. Contrary to these findings, Seshiah et al. found a high concordance between DIPSI and IADPSG criteria [ 78 ]. The low sensitivity of DIPSI has been reported by studies such as Mohan et al.2014 [ 41 ]. and Herath et al. [ 79 ]. Sensitivity of DIPSI is quite low, hence to be used as screening and diagnostic tool at the same time is still questionable. This is the dire requirement of our country to have a better sensitive method for diagnosing GDM so that patients do not escape diagnosis (false-negatives cases) detected by DIPSI which later on crunch out the health system.

Indeed, in 2013, the WHO embraced the IADPSG criteria, replacing the earlier 1999 criteria. The DIPSI criteria were formulated based on the 2-hour post-glucose (PG) values of the WHO 1999 criteria, primarily focusing on the simplicity of assessing the 2-hour PG value independently. It’s important to note that the Fasting Plasma Glucose (FPG) parameter from the WHO 1999 criteria is considered outdated now, indicative of diabetes [ 53 ].

Further, IADPSG recommendation necessarily requires estimation of plasma glucose in three blood samples after administrating 75 g oral glucose load. Pregnant women resent this procedure, as they are pricked three times and feel too much of blood is drawn. Whereas, DIPSI criterion requires one blood sample drawn at 2-h for estimating the plasma glucose Future studies should compare the outcomes of the GDM cases diagnosed by different criteria as this would provide the final answer as to which criteria is more suitable for Indians.

Does sensitivity and Specifity of the diagnostic test matters?

A study by Mohan V et al. in 2014 compared the IADPSG, DIPSI and WHO 1999 criteria shows that the non-fasting OGTT has poor sensitivity compared to both the WHO 1999 criteria (27.7%) and the IADPSG criteria (22.6%) [ 41 ]. Thus, the current DIPSI guidelines of doing a single-step non-fasting OGTT using the 2-h VPG cut point of 140 mg/dl (7.8 mmol/l) to diagnose GDM would miss 72.3% of women with GDM diagnosed by the WHO 1999 criteria and 77.4% of women with GDM diagnosed by the IADPSG criteria. Similarly, a study by Tripathi R et al. 2017, a two-hour 75 g non-fasting DIPSI test was done and followed by OGTT [ 40 ]. Using OGTT as per the WHO 2013 /IADPSG criteria as gold standard, the sensitivity of 75 g non-fasting test was low. With this low sensitivity, about one quarter of women with GDM were missed. Missing such a large number is not acceptable for a diagnostic test, especially as GDM is associated with both maternal and perinatal complications. On contrary, in the study population, Seshiah V 2012, utilized both DIPSI and IADPSG criteria to ascertain the prevalence of GDM, which were 13.4 and 14.6% respectively [ 43 ].

Which is appropriate- early screening or risk-based screening?

There is a debate regarding the timing of screening for GDM, whether it should be done during the first prenatal visit or during the recommended period of 24–28 weeks of gestation. On the question of when to screen for GDM, a descriptive analysis by Li et al. 2018 showed that a substantial percentage of patients (11.4–60% of GDM cases) develop GDM in the first trimester, but that a similarly large percentage of patients (16–40% of GDM cases) are missed at the first visit [ 77 ]. Conducting the screening at later stages of pregnancy is linked to increased risks of maternal and perinatal morbidity and mortality. Many studies on GDM also suggest that early screening and dietary control of GDM can promote the curtailment of maternal and perinatal morbidities [ 80 , 81 ]. Additionally, Raets et al. demonstrated that there is need for clear guidelines and criteria concerning early screening for GDM [ 82 ]. In line with the Flemish consensus of 2019 on screening for GDM, this review recommend to universally screen for diabetes in early pregnancy [ 83 ].

Therefore, the review findings indicates an early screening with an OGTT test at 24 weeks coupled with diet counselling and postpartum testing in pregnant women can improve perinatal outcomes [ 75 ]. However, this may not be a logistically feasible or cost-effective strategy for all patients, and screening may need to be risk-stratified in Low or Middle Income Country (LMIC).

How should pregnant women come for GDM screening- fasting or non-fasting?

In their study, Supraneni et al. conducted a comparative analysis of the diagnostic effectiveness of different fasting plasma glucose levels and the one-hour 75 g OGTT in diagnosing GDM [ 84 ]. The study found that fasting plasma glucose levels above 92 mg/dL exhibited better diagnostic effectiveness, but there was no significant difference when compared to the results obtained from the one-hour 75 g OGTT in distinguishing between pregnant women with and without GDM.

Additionally, the researchers observed that utilizing the International Association of Diabetes and Pregnancy Study Groups (IADPSG) cutoffs for fasting and one-hour 75 g OGTT demonstrated good diagnostic properties in the study population. By implementing an exit strategy based on a positive result at either the fasting or one-hour mark, it was estimated that the need for further testing could potentially be reduced in approximately one in five pregnant women. However, accessing antenatal care in a fasting state posed challenges in rural settings, as highlighted in a 2014 study by Mohan et al. [ 41 ]. On the other hand, the DIPSI (Diabetes in Pregnancy Study Group India) guidelines suggest that the GDM test can be conducted at any time during pregnancy, regardless of food intake [ 85 ]. Nevertheless, the DIPSI approach faces difficulties in effectively screening pregnant women for GDM due to low sensitivity and underdiagnosis [ 86 ].

Based on the findings of the review, it is clear that a significant need exists for well-designed and unified programs aimed at effectively managing GDM cases. Implementing such programs would be instrumental in reducing the escalating burden of diabetes in India.

Capillary versus venous blood – does it affect estimation?

There is contradictory evidence reporting varying results and conclusions regarding the accuracy and agreement between blood glucose estimation using venous plasma glucose (VPG) and capillary blood glucose (CBG) methods for diagnosing GDM.

The study by Balaji V in 2012 involving a significant number of cohorts indicated that the Accu-Chek glucometer, a CBG measurement device, provided accurate results that aligned well with laboratory measurements of VPG [ 72 ]. Similarly, another study reported that CBG values provided the closest approximation to VPG values in healthy individuals without diabetes or GDM [ 66 ]. On the other hand, Jadhav DS conducted a hospital-based clinical study in 2017 comparing VPG and CBG estimation using a glucometer based on the DIPSI criteria found a satisfactory level of agreement between the two methods with equal sensitivity. Additionally, the CBG estimation by glucometer demonstrated a small number of false positive cases due to its high specificity (99.46%) [ 70 ].

Indeed, it is interesting to note that in some studies, the capillary blood glucose (CBG) and venous plasma glucose (VPG) values were found to be similar at 1 hour (9.9 mmol/L vs. 9.6 mmol/L) and 2 hours (7.9 mmol/L vs. 7.7 mmol/L) after the glucose load [ 87 ]. These findings suggest a fair agreement between CBG and VPG measurements during the 2-hour OGTT test for (GDM.

However, it is worth mentioning that other studies have reported a slight difference between VPG and CBG values, ranging from 0.28 to 0.5 mmol/L (5–9 mg/dL) specifically at the 2-hour mark, although the difference is relatively small [ 88 ]. These discrepancies in findings may be attributed to several factors, including the specific population under study, the glucose measurement methods used, and the performance characteristics of the glucose measurement devices employed [ 89 ]. The accuracy and agreement between CBG and VPG measurements can vary across different studies and settings.

A recent study by VidyaM Sree et al. demonstrated an excellent diagnostic accuracy (99.77%) of CBG estimation using a one-step OGTT based on DIPSI criteria for GDM in an Indian population. This study highlighted the feasibility and reliability of capillary blood estimation for GDM screening, particularly in countries with limited resources [ 71 ].

This review led to the conclusion that capillary blood estimation is a feasible and reliable method for screening GDM In countries with limited resources as this approach requires less technically trained manpower and equipment. It is important for further research to explore and address these differences in order to establish standardized guidelines and protocols for the diagnosis and management of GDM, particularly in terms of blood glucose estimation methods.

Cost-effectiveness and feasibility- what should be preferred?

The prevalence of GDM varies across different states in India, highlighting the country’s diversity. Even if a universally applicable, feasible, diagnostically accurate, and cost-effective test for GDM is discovered, the gravity of the problem remains consistent.

Supraneni et al. discovered in his study that the IADPSG criteria have good specificity, positive likelihood ratio and post-test probabilities for GDM in their study population [ 87 ]. However, the cost involved for performing IADPSG recommended procedure is high, as this procedure requires three blood tests compared to one blood test of DIPSI.

“DIPSI as one-step screening and diagnostic procedure for assessing GDM in pregnant women which is less time-consuming, economical and feasible” as stated by Mounika E et al. in her study conducted in south Indian Population [ 47 ]. But, the large extent of false negatives is a major limitation of DIPSI test which cannot be overlooked. Swaroop N et al. used one-step DIPSI criteria in his study and found it to be effective but larger studies are required to further validate its importance [ 90 ].

Thus, this review suggests that ideally, and whenever feasible, a single-step 75-g OGTT using the IADPSG criteria should be done in the fasting state as this is the accepted criteria worldwide and would help to bring about international standardization. However, in countries with less resources, DIPSI criteria may be used as a backup option in certain situations where it would be cost-effective without compromising the clinical equipoise: (a) inaccessible areas where pregnant females are not able to visit healthcare facility in fasting state in morning (b) epidemiological studies where fasting sample is unavailable (c) where OGTT is not feasible in some pregnant females due to certain specified reason.

Strength of the review

Our review raises a valid point regarding the challenges of implementing a universal screening program for GDM in India. We have taken into account unpublished literature from the Indian database ShodhGanga to gather comprehensive information about the current scenario of GDM in different zones of India. We have made efforts to contact authors to obtain full-text articles or any necessary information for our analysis, ensuring maximum data inclusion.

The review highlights the need for policymakers to reach a consensus on a universal screening test for diagnosing GDM in pregnant women, considering various key factors. These factors include the variation in diagnostic criteria, such as fasting or non-fasting, one-step or two-step approaches, and the use of capillary or venous blood estimation. Additionally, the review considers the sensitivity and specificity of the diagnostic test, the cost-effectiveness of the screening method, and its feasibility in real-world settings.

We also conducted an analysis to assess publication bias. However, since we have included prevalence studies, the results can be generalized to the population regardless of any bias. Furthermore, we performed a sub-group analysis to provide an overview of the current pooled prevalence of GDM in different geographic zones of India.

The authors suggest that implementing a uniform approach nationwide may not be practical. Instead, they propose adopting a more focused and region-specific strategy to maximize resources and efficiently detect and address cases of GDM.

Overall, our review aims to provide evidence-based insights and encourage policymakers to develop consensus guidelines for GDM screening in India. By considering the diverse factors and conducting thorough analyses, we hope to contribute to the formulation of effective strategies for GDM diagnosis and management across the country.

Limitations

Although we comprehensively searched four databases, we may have included a few more databases to include more GDM-related studies. Further, analyzing the risk factors involved in the prevalence of GDM was not in the scope of our review. Further, some studies did not provide detailed information about their population type, their GDM screening methods, trimester or the distribution between multiple different screening methods that were used. It is imperative to acknowledge the absence of a standardized screening strategy, which introduces a significant limitation to our analysis. Furthermore, we recognize the potential influence of evolving diagnostic criteria on variations in GDM prevalence. To address this concern, it would be beneficial to incorporate a comparative analysis of GDM prevalence across different regions, focusing on studies that employ consistent diagnostic criteria such as DIPSI or IADPSG (WHO 2013). Additionally, we acknowledge that differences in prevalence may be attributed to assessments conducted in distinct time periods. As a means to enhance the comprehensiveness of our review, we highlight the importance of exploring studies that specifically examine trends in GDM within a given population in India over time.

This review emphasizes the growing concern of GDM as a public health issue, particularly in resource-constrained settings like India, where the prevalence of GDM varies significantly among states. Numerous studies conducted in India have revealed poor agreement among existing diagnostic criteria for GDM. To enable prompt diagnosis and enhance the management of GDM in India, it is crucial to incorporate a diagnostic tool that is feasible, cost-effective, and reliable. Such a tool should seamlessly integrate with the existing public healthcare system and benefit the target population. Large-scale population-based studies are necessary to address the conflicts in GDM diagnosis and provide evidence-based criteria that are applicable to the Indian population. By tailoring the screening program based on regional variations, healthcare authorities can better allocate resources and implement interventions focused on areas with higher GDM prevalence or other risk factors.

Availability of data and materials

Available from the corresponding author on reasonable request.

Abbreviations

Gestational Diabetes Mellitus

Diabetes in Pregnancy Study group of India

International Association of Diabetes and Pregnancy Study Group

Hyperglycemia and Adverse pregnancy outcomes

Federation of Obstetric and Gynecological Societies of India

Low-or-Middle Income Country

Oral Glucose Challenge Test

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Bhavadharini B, Mahalakshmi MM, Anjana RM, Maheswari K, Uma R, Deepa M, et al. Prevalence of gestational diabetes mellitus in urban and rural Tamil Nadu using IADPSG and WHO 1999 criteria (WINGS 6). Clin Diabetes Endocrinol. 2016:2. https://doi.org/10.1186/s40842-016-0028-6 .

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NM designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. ADG designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. MP, PB and GS screened titles and abstracts. MKG and SS conducted a full-text review; assessed the quality of each study and reviewed the manuscript. VY screened titles and abstracts and reviewed the manuscript. PB designed the study; interpreted the data and reviewed the manuscript.MM reviewed the manuscript and provided inputs and read the final manuscript. All authors read and approved the final manuscript.

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Mantri, N., Goel, A.D., Patel, M. et al. National and regional prevalence of gestational diabetes mellitus in India: a systematic review and Meta-analysis. BMC Public Health 24 , 527 (2024). https://doi.org/10.1186/s12889-024-18024-9

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DOI : https://doi.org/10.1186/s12889-024-18024-9

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  • Gestational diabetes mellitus
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    Background Gestational diabetes mellitus (GDM) is frequently misdiagnosed during pregnancy. There is an abundance of evidence, but little is known regarding the regional prevalence estimates of GDM in India. This systematic review and meta-analysis aims to provide valuable insights into the national and regional prevalence of GDM among pregnant women in India. Methods We conducted an initial ...

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