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Get around this paywall in a flash: DOI: 10.1126/science.196.4287.293 URL: http://science.sciencemag.org/content/196/4287/293/tab-pdf PMC (Pubmed Central) ID: PMC4167664 Pubmed ID: 17756097 Title: Ribulose bisphosphate carboxylase: a two-layered, square-shaped molecule of symmetry 422 Citation: Baker, T. S., Eisenberg, D., & Eiserling, F. (1977). Ribulose Bisphosphate Carboxylase: A Two-Layered, Square-Shaped Molecule of Symmetry 422. Science, 196(4287), 293-295. doi:10.1126/science.196.4287.293 or try your favourite citation format (Harvard, Bibtex, etc).

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15 Best Websites to Download Research Papers for Free

Is your thirst for knowledge limited by expensive subscriptions? Explore the best websites to download research papers for free and expand your academic reach.

With paywalls acting like impenetrable fortresses, accessing scholarly articles becomes a herculean task. However, a beacon of hope exists in the form of free-access platforms, quenching our thirst for intellectual wisdom. Let’s set sail on this scholarly journey.

Table of Contents

Today’s champions of academia aren’t just about offering free access, they uphold ethics and copyright respectability. Let’s delve into these repositories that are reshaping the academia world. You can download free research papers from any of the following websites.

Best Websites to Download Research Papers

#1. sci-hub – best for accessing paywalled academic papers.

Despite its contentious standing, Sci-Hub offers an invaluable service to knowledge-seekers. While navigating the tightrope between access and legality, it represents a game-changing force in the world of academic research.

#2. Library Genesis (Libgen) – Best for a Wide Range of Books and Articles

It’s not just a repository, but a vibrant confluence of multiple disciplines and interests, catering to the unique intellectual appetite of each knowledge seeker.

Source: https://libgen.is

#3. Unpaywall – Best for Legal Open Access Versions of Scholarly Articles

What are the benefits of Unpaywall?

#4. Directory of Open Access Journals (DOAJ) – Best for Peer-Reviewed Open Access Journals

Source: https://doaj.org

#5. Open Access Button – Best for Free Versions of Paywalled Articles

What are the benefits of Open Access Button?

#6. Science Open – Best for a Wide Variety of Open Access Scientific Research

Consider Science Open as a bustling town square in the city of scientific knowledge, where scholars from all walks of life gather, discuss, and dissect over 60 million articles. 

You might also like:

#7. CORE – Best for Open Access Content Across Disciplines

With its unparalleled aggregation and comprehensive access, CORE embodies the grand orchestra of global research. It stands as an essential tool in the modern researcher’s toolkit.

#8. ERIC – Best for Education Research

What are the benefits of ERIC?

#9. PaperPanda – Best for Free Access to Research Papers

It’s like having a personal research assistant, guiding you through the maze of scholarly literature.

#10. Citationsy Archives – Best for Research Papers from Numerous Fields

Source: https://citationsy.com

#11. OA.mg – Best for Direct Download Links to Open Access Papers

Source: https://oa.mg

#12. Social Science Research Network (SSRN) – Best for Social Sciences and Humanities Research

SSRN serves as an invaluable resource for researchers in the social sciences and humanities, fostering a community that drives innovation and advancements in these fields.

#13. Project Gutenberg – Best for Free Access to eBooks

Project Gutenberg serves as a testament to the power of literature and the accessibility of knowledge. It enables readers worldwide to embark on intellectual journeys through its extensive collection of free eBooks.

#14. PLOS (Public Library of Science) – Best for Open Access to Scientific and Medical Research

As a leading publisher of open access research, PLOS fosters the dissemination of cutting-edge scientific discoveries to a global audience. 

#15. arXiv.org – Best for Preprints in Science, Mathematics, and Computer Science

In a world where knowledge is king, accessing a research paper shouldn’t feel like an impossible task. Thanks to these free and innovative websites, we can escape the barriers of paywalls and dive into a vast ocean of intellectual wealth. 

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Best Websites To Download Research Papers For Free: Beyond Sci-Hub

Navigating the vast ocean of academic research can be daunting, especially when you’re on a quest for specific research papers without the constraints of paywalls. Fortunately, the digital age has ushered in an era of accessible knowledge, with various platforms offering free downloads of scholarly articles.

In this article, we explore some of the best websites that provide researchers, students, and academicians with free access to a plethora of research papers across diverse fields, ensuring that knowledge remains within everyone’s reach.

Best Websites To Download Research Papers For Free

Google Scholar

As a researcher, you might find Google Scholar to be a repository brimming with academic papers covering a broad span of domains like social sciences, computer science, and humanity, including:

• Journal articles
• Conference papers, and

Unlike other websites to download research papers, Google Scholar provides free access to a vast collection of scholarly literature, making it one of the best websites to download research.

Not every article is available in full PDF format directly; however, Google Scholar often links to other open access resources like DOAJ (Directory of Open Access Journals) and open-access repositories where you can directly download papers.

For instance, if you’re searching for a specific 2023 research paper in mathematics, you can use Google Scholar to locate the paper and check if it’s available for free download either on the platform itself or through links to various open access sources.

In many cases, Google Scholar integrates with tools like Unpaywall and Open Access Button, which are browser extensions that help you find free versions of paywalled articles.

These extensions often redirect you to open-access content, including those on platforms like Sci-Hub and Library Genesis, although it’s crucial to be aware of the legal and ethical implications of using such services.

ResearchGate

ResearchGate is a unique platform that blends social networking with academic research, making it an essential tool for researchers and scientists across various disciplines.

Here, you have access to a digital library of millions of research papers, spanning fields from computer science to social sciences and beyond.

When you’re on ResearchGate, downloading a research paper is relatively straightforward, especially if it’s open access. Many researchers upload the full PDF of their work, providing free access to their peer-reviewed articles.

If the research paper you’re interested in isn’t available for direct download, ResearchGate offers a unique feature: you can request a copy directly from the author.

This approach not only gets you the paper but also potentially opens a line of communication with leading experts in your field.

It’s important to note that ResearchGate isn’t just a repository; it’s a platform for discovery and connection. You can:

• Follow specific researchers
• Join discussions, and
• Receive notifications about new research in your domain.

While it doesn’t have the controversial direct download links like Sci-Hub or Library Genesis, ResearchGate offers a more ethical and legal route to accessing academic papers. 

ScienceOpen

ScienceOpen is a comprehensive repository that hosts a multitude of open-access research articles across various fields, from the social sciences to computer science. 

The process of downloading a research paper on ScienceOpen is remarkably straightforward. Since it’s an open-access platform, most of the papers are available to download as PDFs without any cost.

This means you can access high-quality, peer-reviewed academic research without encountering paywalls that are often a barrier in many other scientific platforms.

For instance, if you’re delving into the latest 2023 scientific papers in mathematics, ScienceOpen can be your go-to source. You can easily search for research papers using:

• Browsing through various open access journals featured on the site.

The direct download feature simplifies access to these papers, making it convenient for you to obtain the research you need.

Directory of Open Access Journals (DOAJ)

The Directory of Open Access Journals (DOAJ) is a digital library is an extensive repository of open-access, peer-reviewed journals, covering a wide array of subjects from humanities to nuclear science.

When you’re navigating DOAJ, you’ll discover that it’s not just a platform to download research papers; it’s a gateway to a world of academic research.

Each journal article listed is freely accessible, meaning you can download these scholarly articles without any cost or subscription.

The process is simple: search for research papers using specific keywords, subjects, or even DOAJ’s advanced search functionality that includes filters like:

• Language, or
• The year of publication.

For example, if you’re delving into the latest developments in scientific research in 2023, DOAJ allows you to refine your search to the most recent publications.

Once you find a relevant research paper, you can easily access the full text in PDF format through a direct download link. This is particularly useful for accessing high-quality, open-access research papers that are not always readily available on other platforms like Sci-Hub or Library Genesis.

PubMed hosts millions of research articles, primarily in the fields of medicine and life sciences, but also encompassing a broad range of scientific research.

When you’re on PubMed, you can search for research papers using:

• Authors, or
• Specific journal names.

While PubMed lists both open-access and subscription-based journal articles, it offers a unique feature for accessing papers for free.

If you’re looking for a particular research paper, say in the domain of computer science or social sciences from 2023, you can directly access its abstract on PubMed. For open access articles, a free full-text link is often available, allowing you to download the research paper in PDF format.

PubMed integrates with tools like Unpaywall and the Open Access Button. These browser extensions help you find open-access versions of the articles you’re interested in, bypassing the paywalls that often restrict access to scholarly literature.

While PubMed itself doesn’t provide direct download links for all articles, its connection with these tools and various open access repositories ensures that you, as a researcher, have greater access to scientific papers.

Sci-Hub (with Caution)

Sci-Hub, often dubbed the ‘Pirate Bay of Science,’ has been a game-changer in the scientific community since its inception by Alexandra Elbakyan in 2011.

It operates as a controversial, yet widely used platform providing free access to millions of research papers and academic articles that are typically locked behind paywalls.

As a researcher, you might find Sci-Hub an intriguing, albeit contentious, tool for accessing scholarly literature.

When you’re looking to download a research paper from Sci-Hub, the process is relatively straightforward. Say you need a journal article on computer science or a groundbreaking study in social sciences from 2023; you just need to have the DOI (Digital Object Identifier) of the paper.

By entering this DOI into Sci-Hub’s search bar, the website bypasses publisher paywalls, offering you direct download links to PDF versions of the articles.

It’s crucial to note that while Sci-Hub provides access to a vast repository of scientific research, its legality is under constant scrutiny. The platform operates via various proxy links and has been the subject of numerous legal battles with publishers and academic institutions.

Nevertheless, Sci-Hub remains a popular go-to for researchers and scientists globally, especially those without access to university libraries or digital archives.

While it opens doors to a wealth of knowledge, users should be aware of the ethical and legal implications of using such a service in their respective countries.

Wrapping Up: You Can Get Free Academic Papers 

The digital landscape offers a wealth of resources for accessing academic research without financial barriers. The platforms we share here provide an invaluable service to the scholarly community, democratising access to knowledge and fostering intellectual growth.

Whether you’re a seasoned researcher or a curious student, these websites bridge the gap between you and the vast world of academic literature, ensuring that the pursuit of knowledge remains an inclusive and equitable journey for all. Remember to consider the legal and ethical aspects when using these resources.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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Download Research Papers for Free: Legal and Ethical Methods

14 Legal Ways to Download Research Papers for Free: The Ultimate Guide

Are you a student, researcher, or curious individual looking to access scholarly articles without breaking the bank? You’re in luck! This comprehensive guide will walk you through various legal and ethical methods to Download Research Papers for Free. We’ll cover everything from open-access databases to contacting authors directly, ensuring you have all the tools to fuel your academic pursuits.

Why Access to Research Papers Matters

Before we dive into the methods, let’s quickly address why free access to research papers is so crucial:

• Advancing knowledge: Open access to research promotes the spread of ideas and accelerates scientific progress.
• Equalizing opportunities: Free access levels the playing field for researchers and students worldwide, regardless of their financial resources.
• Encouraging collaboration: When research is freely available, it’s easier for scientists to build upon each other’s work and collaborate across institutions.

Now, let’s explore the various ways you can legally and ethically obtain research papers without spending a dime.

10 Legal Ways to Download Research Papers for Free: The Ultimate Guide

1. leverage open access databases.

Open-access databases are treasure troves of freely available scholarly articles. Here are some of the best options:

• PubMed Central (PMC): A free full-text archive of biomedical and life sciences journal literature.
• Directory of Open Access Journals ( DOAJ ): A community-curated online directory that indexes high-quality, open-access, peer-reviewed journals.
• arXiv: A repository of electronic preprints for physics, mathematics, computer science, and related fields.
• CORE: The world’s largest collection of open-access research papers.

Pro tip: Many of these databases offer email alerts for new papers in your area of interest, helping you stay up-to-date with the latest research.

2. Utilize Academic Search Engines

Specialized academic search engines can help you find both open-access and potentially accessible papers:

• Google Scholar: The most popular academic search engine, with features like cited by and related articles.
• Microsoft Academic: A free public web search engine for academic publications and literature.
• Semantic Scholar: An AI-powered research tool for scientific literature.

These search engines often provide direct links to free full-text versions when available or point you towards institutional repositories.

3. Explore Institutional Repositories

Many universities and research institutions maintain their own repositories of scholarly work produced by their faculty and students. These repositories often make papers freely available to the public. Try searching for “[University Name] repository” to find these goldmines of information.

4. Check Author’s Websites and Social Media

Many researchers maintain personal websites or profiles on academic social networks where they share their work. Try searching for the author’s name followed by their institution or area of expertise. Platforms to check include:

• ResearchGate
• Academia.edu

5. Contact the Authors Directly

If you can’t find a free version of a paper, don’t hesitate to reach out to the authors. Most researchers are happy to share their work and may send you a copy of their paper. Look for the corresponding author’s email address in the paper’s abstract or contact information.

6. Use Browser Extensions

Several browser extensions can help you find free versions of paywalled articles:

• Unpaywall: A legal and simple tool that searches for free versions of scholarly articles.
• Open Access Button: Searches for free, legal copies of research papers.
• Kopernio: Helps you access PDF versions of scientific articles.

7. Take Advantage of Preprint Servers

Preprint servers host early versions of research papers before they undergo peer review. While these papers should be approached with caution, they can be valuable sources of cutting-edge research:

• bioRxiv: For life sciences
• chemRxiv: For chemistry and related fields
• SocArXiv: For social sciences

8. Utilize Your Library’s Resources

Don’t forget about your local library! Many public and university libraries offer:

• Access to academic databases
• Interlibrary loan services
• Remote access to digital resources

Even if you’re not currently a student, some libraries offer cards to community members that include database access.

9. Explore Sci-Hub Alternatives

While Sci-Hub is popular, it operates in a legal grey area. Instead, consider these alternatives:

• Open Access Button: A legal tool that helps you request access to research papers.
• Lazy Scholar: A browser extension that finds free full-text PDF versions of articles.
• Unpaywall: Another legal alternative that finds open-access versions of articles.

10. Stay Informed About Open Access Initiatives

Keep an eye on developments in the open access movement. Initiatives like Plan S are working to make all publicly funded research freely available. Following these developments can help you stay ahead of the curve in accessing free research.

Ethical Considerations and Best Practices

While accessing free research papers, it’s crucial to keep these ethical considerations in mind:

• Respect copyright laws and publisher agreements.
• Use obtained papers for personal research and educational purposes only.
• Properly cite all sources in your work.
• Support open access initiatives when possible.

Accessing research papers for free is not only possible but also increasingly important in our interconnected world. By utilizing the methods outlined in this guide, you can tap into a vast wealth of knowledge without breaking the bank. Remember to always respect copyright laws and support the open access movement to ensure that knowledge remains freely accessible to all.

14 Websites to Download Research Paper for Free – 2024 – Alternative Methods

Collecting and reading relevant research articles to one’s research areas is important for PhD scholars. However, downloading a research paper is one of the most difficult tasks for any research scholar. You must pay for access to high-quality research materials or subscribe to the journal or publication. In this article, ilovephd lists the top 14 websites to download research papers, journals, books, datasets, patents, and conference proceedings for free.

Check the 14 best free websites to download and read research papers listed below:

Sci-Hub is a website link with over 64.5 million academic papers and articles available for direct download. It bypasses publisher paywalls by allowing access through educational institution proxies.  To download papers Sci-Hub  stores papers in its repository, this storage is called Library Genesis (LibGen) or Library Genesis Proxy 2024. It helps researchers to download free articles by simply using the Digital Object Identifier (DOI) of the article.

Visit: Working Sci-Hub Proxy Links – 2024

2. Z-Library

The Z-Library clones Library Genesis, a shadow library project. Z-Library facilitates file sharing of scholarly journal articles, academic texts, and general-interest books (including some copyrighted materials). While most of its books come from Library Genesis, further expanding the collection, users can also directly upload content to the site. This user-contributed content helps to make literature even more widely available. Additionally, individuals can donate to the website’s repository, furthering their mission of free access.

Z-Library claims to have a massive collection, boasting more than 10,139,382 Books books and 84,837,646 Articles articles as of April 25, 2024. According to the project’s page for academic publications (at booksc.org), it aspires to be “the world’s largest e-book library” as well as “the world’s largest scientific papers repository.” Interestingly, Z-Library also describes itself as a donation-based non-profit organization.

Visit Z-Library – You can Download 70,000,000+ scientific articles for free

3. Library Genesis

The Library Genesis aggregator is a community aiming to collect and catalog item descriptions for the most scientific, scientific, and technical directions, as well as file metadata. In addition to the descriptions, the aggregator contains only links to third-party resources hosted by users. All information posted on the website is collected from publicly available public Internet resources and is intended solely for informational purposes.

Visit: libgen.li

4. Unpaywall – Free Research Paper Download

Unpaywall harvests Open Access content from over 50,000 publishers and repositories, and makes it easy to find, track, and use. It is integrated into thousands of library systems, search platforms, and other information products worldwide. If you’re involved in scholarly communication, there’s a good chance you’ve already used Unpaywall data.

Unpaywall is run by OurResearch, a nonprofit dedicated to making scholarships more accessible to everyone. Open is our passion. So it’s only natural our source code is open, too.

Visit: unpaywall.org

5. GetTheResearch.org

GetTheResearch.org is an  Artificial Intelligence(AI)  powered search engine for searching and understanding  scientific articles  for researchers and scientists. It was developed as a part of the  Unpaywall  project. Unpaywall is a database of 23,329,737 free scholarly Open Access(OA) articles from over 50,000 publishers and repositories, and make it easy to find, track, and use.

Visit: Find and Understand 25 Million Peer-Reviewed Research Papers for Free

6. Directory of Open Access Journals (DOAJ)

DOAJ (Directory of Open Access Journals) was launched in 2003 with 300 open-access journals. Today, this independent index contains almost 17,500 peer-reviewed, open-access journals covering all areas of science, technology, medicine, social sciences, arts, and humanities. Open-access journals from all countries and in all languages are accepted for indexing.

DOAJ is financially supported by many libraries, publishers, and other like-minded organizations. Supporting DOAJ demonstrates a firm commitment to open access and the infrastructure that supports it.

Visit: doaj.org

7. Researcher

The Researcher is a free journal-finding mobile application that helps you to read new journal papers every day that are relevant to your research. It is the most popular mobile application used by more than 3 million scientists and researchers to keep themselves updated with the latest academic literature.

Visit: 10 Best Apps for Graduate Students 

8. Science Open

ScienceOpen  is a discovery platform with interactive features for scholars to enhance their research in the open, make an impact, and receive credit for it. It provides context-building services for publishers, to bring researchers closer to the content than ever before. These advanced search and discovery functions, combined with post-publication peer review, recommendation, social sharing, and collection-building features make  ScienceOpen  the only research platform you’ll ever need.

Visit: scienceopen.com

OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author’s works – OA.mg is the place to find it.

Visit: oa.mg

10. Internet Archive Scholar

Internet Archive Scholar (IAS) is a full-text search index that includes over 25 million research articles and other scholarly documents preserved in the Internet Archive. The collection spans from digitized copies of eighteenth-century journals through the latest Open Access conference proceedings and pre-prints crawled from the World Wide Web.

Visit: Sci hub Alternative – Internet Archive Scholar

11. Citationsy Archives

Citationsy was founded in 2017 after the reference manager Cenk was using at the time, RefMe, was shut down. It was immediately obvious that the reason people loved RefMe — a clean interface, speed, no ads, and simplicity of use — did not apply to CiteThisForMe. It turned out to be easier than anticipated to get a rough prototype up.

Visit: citationsy.com

CORE is the world’s largest aggregator of open-access research papers from repositories and journals. It is a not-for-profit service dedicated to the open-access mission. We serve the global network of repositories and journals by increasing the discoverability and reuse of open-access content.

It provides solutions for content management, discovery, and scalable machine access to research. Our services support a wide range of stakeholders, specifically researchers, the general public, academic institutions, developers, funders, and companies from a diverse range of sectors including but not limited to innovators, AI technology companies, digital library solutions, and pharma.

Visit: core.ac.uk

13. Dimensions

The database called “Dimensions” covers millions of research publications connected by more than 1.6 billion citations, supporting grants, datasets, clinical trials, patents, and policy documents.

Dimensions is the most comprehensive research grants database that links grants to millions of resulting publications, clinical trials, and patents. It

provides up-to-the-minute online attention data via Altmetric, showing you how often publications and clinical trials are discussed around the world. 226m Altmetric mentions with 17m links to publications.

Dimensions include datasets from repositories such as Figshare, Dryad, Zenodo, Pangaea, and many more. It hosts millions of patents with links to other citing patents as well as to publications and supporting grants.

Visit: dimensions.ai

14. PaperPanda – Download Research Papers for Free

PaperPanda is a Chrome extension that uses some clever logic and the Panda’s detective skills to find you the research paper PDFs you need. Essentially, when you activate PaperPanda it finds the DOI of the paper from the current page, and then goes and searches for it. It starts by querying various Open Access repositories like OpenAccessButton, OaDoi, SemanticScholar, Core, ArXiV , and the Internet Archive. You can also set your university library’s domain in the settings (this feature is in the works and coming soon). PaperPanda will then automatically search for the paper through your library. You can also set a different custom domain in the settings.

Visit: PaperPanda

I hope this article will help you to know some of the best websites to download research papers and journals for free. By utilizing open-access databases, free search tools, and potentially even your local university library, you can access a wealth of valuable scholarly information without infringing on a copyright. Remember, ethical practices in research paper downloading are important, so always prioritize legal access to materials whenever possible. Happy researching!

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PubMed Central ® (PMC) is a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM)

Discover a digital archive of scholarly articles, spanning centuries of scientific research.

Learn how to find and read articles of interest to you.

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Referencing and Writing Advice Unlocking Knowledge Getting the green light when using plagiarism detection software doesn’t mean you haven’t plagiarised.

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Repurposing weather modification for cloud research showcased by ice crystal growth

Competing Interest: The authors declare no competing interests.

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Fabiola Ramelli, Jan Henneberger, Christopher Fuchs, Anna J Miller, Nadja Omanovic, Robert Spirig, Huiying Zhang, Robert O David, Kevin Ohneiser, Patric Seifert, Ulrike Lohmann, Repurposing weather modification for cloud research showcased by ice crystal growth, PNAS Nexus , Volume 3, Issue 9, September 2024, pgae402, https://doi.org/10.1093/pnasnexus/pgae402

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The representation of cloud processes in models is one of the largest sources of uncertainty in weather forecast and climate projections. While laboratory settings offer controlled conditions for studying cloud processes, they cannot reproduce the full range of conditions and interactions present in natural cloud systems. To bridge this gap, here we leverage weather modification, specifically glaciogenic cloud seeding, to investigate ice growth rates within natural clouds. Seeding experiments were conducted in supercooled stratus clouds (at − 8 to − 5 ∘ C) using an uncrewed aerial vehicle, and the created ice crystals were measured 4–10 min downwind by in situ and ground-based remote sensing instrumentation. We observed substantial variability in ice crystal growth rates within natural clouds, attributed to variations in ice crystal number concentrations and in the supersaturation, which is difficult to reproduce in the laboratory and which implies faster precipitation initiation than previously thought. We found that for the experiments conducted at − 5.2 ∘ C, the ice crystal populations grew nearly linearly during the time interval from 6 to 10 min. Our results demonstrate that the targeted use of weather modification techniques can be employed for fundamental cloud research (e.g. ice growth processes, aerosol–cloud interactions), helping to advance cloud microphysics parameterizations and to improve weather forecasts and climate projections.

The rate at which ice crystals grow in the atmosphere plays a crucial role in precipitation formation and the Earth’s climate system. However, it has been challenging to systematically study ice crystals in natural clouds. Here, we use weather modification to infer the growth rates of ice crystals in a natural cloud environment. We find that the observed ice crystal growth rates have a considerable larger variability than in previously published laboratory studies, which can potentially accelerate precipitation initiation. The methodology proposed herein will advance our understanding in cloud physics and has the potential to revolutionize the parameterization of cloud processes in weather and climate models.

The concentration and size of ice crystals in clouds have a significant influence on precipitation formation ( 1 , 2 ), the radiative energy budget ( 3 ), and cloud lifetime ( 4 ). Despite their important role, fundamental knowledge gaps in ice formation and growth processes exist, owing to complex interactions among the hydrometeors and the wide variety of ice crystal shapes and densities found in nature ( 5 , 6 ). Understanding and quantifying the growth of ice crystals in the atmosphere is crucial for developing physically based parameterizations in weather and climate models ( 7 ).

At temperatures above − 38 ∘ C, a special subset of aerosols known as ice nucleating particles are required for ice crystals to form ( 8 ). Once formed, ice crystals grow by vapor deposition into their preferred ice habit at the given ambient temperature and relative humidity. The vapor attaches to the crystal surface in steps centered on the corners, where the supersaturation is highest, and then spreads as layers along the faces ( 9–11 ). While temperature determines the main growth direction by controlling the relative rates of surface diffusion on the basal and prism faces ( 10 , 12 , 13 ), supersaturation determines the magnitude of the growth rate and the degree of secondary growth features such as hollows ( 9 , 14–17 ). Furthermore, ice crystals can grow at the expense of evaporating cloud droplets when the vapor pressure lies between ice ( ⁠ e i ⁠ ) and water saturation ( ⁠ e s ) (i.e. e i < e s ⁠ ) ( 18 , 19 ), a process referred to as the Wegener–Bergeron–Findeisen (WBF) process ( 20–22 ). The speed of the WBF process strongly depends on whether cloud droplets and ice crystals are uniformly distributed (genuinely mixed) or spatially separated (conditionally mixed) within a cloud volume ( 23 ). As ice crystals grow, they can collide and coalesce with other hydrometeors (e.g. riming, aggregation) or splinter and produce new ice crystals through secondary ice production processes ( 24 ). However, like microphysics in general, ice processes cannot be resolved in weather and climate models and need to be parameterized. Improper representation of ice processes in models introduces uncertainties in both weather forecasts and climate projections ( 7 , 19 , 25 , 26 ).

Microphysical parameterizations are developed based on both theory and observations from laboratory and field studies ( 27 ). These parameterizations consist of simplified rate equations that are tailored to represent a specific microphysical process. However, these process rate parameterizations, such as the vapor diffusional growth rate of ice crystals, are difficult to constrain. The only way to quantify individual process rates under controlled conditions is through laboratory experiments. For example, Ryan et al. ( 28 ) measured the axial and mass growth rates of freely suspended ice crystals in a cloud chamber at temperatures ranging from − 21 ∘ C to − 3 ∘ C for growth times of up to 3 min. Similarly, Takahashi et al. ( 29 ) measured the vapor diffusional growth rate of ice crystals for extended growth periods of up to 30 min in a vertical supercooled cloud tunnel at temperatures ranging between − 23 ∘ C and − 3 ∘ C. Both studies observed the maximum growth rates on the basal face at around − 6 ∘ C (i.e. along c -axis, columnar growth) and on the prism face at − 15 ∘ C (i.e. along a -axis, plate-like growth). While laboratory studies are crucial for studying processes within a well-controlled environment, they have difficulty exploring the full range of atmospheric conditions present in natural clouds due to design limitations (e.g. short residence time) and the constrained setting (e.g. limited variability in temperature and/or supersaturation).

Up to now, it has been challenging to use field observations to constrain microphysical parameterizations, given the simultaneous occurrence of multiple interacting processes in natural clouds and the significant temporal and spatial variability. This necessitates the use of sophisticated sampling strategies that go beyond measuring the current state of the cloud. Possible approaches include cloud measurements along quasi-Lagrangian flight tracks ( 30–32 ) or repeated penetrations in quasi-steady-state clouds such as wave clouds ( 33 , 34 ). These studies were limited to temperatures below − 10 ∘ C, where clouds contain sufficiently high concentrations of naturally occurring ice nucleating particles ( 8 ), to obtain solid statistics. To address this lack of observations, here we use glaciogenic cloud seeding (i.e. injection of artificial ice nucleating particles) to produce ice crystals and initiate subsequent growth processes in supercooled stratus clouds otherwise devoid of ice (see Methods ). By adjusting the distance and time between seeding and measurement, we can quantify the ice crystal growth rates across a range of growth times and temperatures. This approach allows growth rate studies to extend to temperatures above − 10 ∘ C, a temperature range that is particularly important, as it covers crucial precipitation initiation processes (e.g. WBF, seeder–feeder, Hallett-Mossop).

The observations presented here were obtained during the CLOUDLAB measurement campaigns ( 35 ) conducted in the Swiss Plateau, where wintertime stratus clouds were seeded by a burn-in-place flare mounted on an uncrewed aerial vehicle (UAV) to trigger ice formation and subsequent ice growth processes. The seeding-induced microphysical changes were observed 4 to 10 min downwind of the seeding location by ground-based remote sensing instrumentation and in situ instrumentation (e.g. holographic imager HOLIMO) mounted on a tethered balloon system ( 36 ). One seeding experiment included several consecutive seeding missions (conducted within 30 min of each other) with changing distance (2–3 km) between seeding and measurement location. The environmental conditions were assumed to remain approximately constant during this time period due to the persistent nature of stratus clouds ( 37 ). Following this approach, systematic ice growth studies were conducted in this natural quasi-steady-state environment by adjusting the seeding distance (and consequently ice growth times) and measuring the resulting ice crystal sizes. A conceptual overview of the observational setup and experimental approach is shown in Fig. 1 and described in more detail in the Methods section.

Schematic of the experimental setup of CLOUDLAB. Seeding particles (brown plume) are released into a supercooled stratus cloud by a UAV flying tracks perpendicular to the wind. This triggers ice formation and growth via the WBF process, as illustrated by the increase in the ice (white hexagons) and the depletion of the liquid phase (blue circles). The seeding signal is measured downwind by a tethered balloon system and by vertically pointing and scanning cloud radars (blue). The expected radar seeding reflectivities are highlighted by blue–green–yellow colors. The seeding distance (and thus ice growth time) is varied between consecutive seeding missions, while the environmental parameters such as temperature ( T ) and wind speed ( u , from left to right) remain approximately constant, enabling systematic studies on ice growth.

Two seeding experiments conducted at different seeding temperatures are shown in Fig. 2 ( ⁠ T = − 5.2 ∘ C) and Fig. S1 ( ⁠ T = − 7.2 ∘ C). The experimental parameters of the seeding missions are summarized in Table 1 . For both sets of experiments, the seeding material was introduced 2 km and 3 km upwind of the observational site by the UAV, leading to different growth times. The seeding pattern consisted of four 400 m horizontal legs at constant altitude perpendicular to the wind direction within the flare burning time of 5 min.

Temporal evolution of the microphysical properties observed during the seeding missions S5-6.6 and S5-9.3 conducted on 2023 January 24 at a seeding temperature of − 5.2 ∘ C and at seeding distances of 2 and 3 km, respectively. a) Time–height radar reflectivity measured by the vertically pointing W-band cloud radar. The white line shows the height of the tethered balloon, whereas the white stars indicate the height and start time of the seeding missions. The black bars highlight the time periods of expected seeding signal, taking into account the mean wind speed and the flare burning time of 5 min. b) Radar reflectivity measured by the scanning Ka-band cloud radar during a plan position indicator (PPI) scan at 19:31 UTC at an elevation angle of 30° and an azimuth angle ranging between 212° and 302°. c) Timeseries of the cloud droplet number concentration (gray line) and ice crystal number concentration (blue crosses). The background periods (BG-6.6: 18:45:45–18:55:45 UTC, BG-9.3: 19:16:00–19:26:00 UTC) and seeding periods (S5-6.6: 18:57:45–19:01:45 UTC, S5-9.3: 19:28:00–19:33:20 UTC) are highlighted by the horizontal gray bars. d) Violin plots of the liquid water content (LWC, gray) and ice water content (IWC, blue) measured during the background and seeding periods (same periods as depicted in c). Note that no ice crystals were measured during the background periods (i.e. IWC = 0 ⁠ ). e) Timeseries of the ice crystal size distributions in terms of the major axis length. f) Cloud droplet size distribution (gray) and ice crystal size distribution (blue) measured during BG-6.6 (dashed), S5-6.6 (solid), BG-9.3 (dotted), and S5-9.3 (dot-dashed) in terms of the equivalent area diameter. The data presented in c)–f) were obtained from HOLIMO and averaged over 2 s c,d) and 10 s e), respectively.

Experimental parameters of the seeding missions.

Listed are the mission name (consisting of temperature and growth time), CLOUDLAB mission ID, the date and time of the seeding mission, the seeding temperature ( T ), the distance ( d ) between the seeding and measurement location, and the ice crystal growth time ( t ) assuming immediate nucleation after seeding (see Methods).

Here, we provide a comprehensive description of the seeding-induced microphysical changes for the experiment conducted at a temperature of − 5.2 ∘ C (Fig. 2 ), but the findings also remain applicable for the experiment conducted at − 7.2 ∘ C (Fig. S1 ). The natural background cloud on 2023 January 24 was characterized by a radar reflectivity of − 25 dBZ, a cloud droplet concentration of 250–350 cm − 3 ⁠ , a liquid water content of 0.2–0.25 g m −3 , and a low ice water content of <0.001 g m −3 (Fig. 2 a,c,d). During the passage of the seeding plume, the radar observations indicated regions of locally enhanced radar reflectivities of up to 0 dBZ (Fig. 2 a,b), which clearly stand out from the natural background. Although the seeding material was injected at a constant altitude, seeding signatures were observed across a large vertical extent in the cloud radar due to turbulent mixing, updrafts induced from latent heat release, and sedimentation of ice crystals ( 38 ). HOLIMO revealed high concentrations of ice crystals (up to 2,000 L −1 ) with a simultaneous reduction in cloud droplet number concentration during the passage of the seeding plume (Fig. 2 c). During some time periods, the liquid phase was entirely depleted, demonstrating the efficiency of the WBF process in natural clouds. After the passage of the seeding plume, the cloud properties returned to the natural background levels present prior to seeding. Thus, the crucial assumption of constant microphysical and environmental properties in the background between consecutive seeding missions was substantiated by observations. The ice crystals measured during S5-9.3 were consistently larger than the ice crystals measured during S5-6.6, due to the longer growth time (Fig. 2 e,f). This increase in ice crystal size was also visible in the enhanced radar reflectivities during S5-9.3 (Fig. 2 a). Thus, by employing glaciogenic cloud seeding in supercooled stratus clouds, we were able (i) to produce a sufficient number of ice crystals at temperatures above − 10 ∘ C, ensuring robust statistics and (ii) to systematically adjust the growth time (by changing seeding distance) between consecutive seeding missions, resulting in different ice crystal sizes, allowing for comprehensive growth studies in natural clouds.

Given that the shape of ice crystals resulting from growth by vapor deposition is influenced by the environmental conditions ( 6 ), growth rates are typically reported individually for the basal and prism faces ( 28 , 29 ). Figure 3 gives an overview of the ice crystal axial dimensions measured during the seeding experiment at − 5.2 ∘ C for different growth times. Ice crystals with a major axis length of up to 500 μ m and up to 800 μ m were measured during S5-6.6 and S5-9.3, respectively. The major axis length of the ice crystals measured during S5-9.3 was on average 33% larger than the one observed during S5-6.6. A large variability was observed in the major axis length, while the minor axis varied less for both missions (Fig. 3 b,c). The variability among the higher values of the ice crystal size distribution might even be underestimated due to sedimentation of larger ice crystals (Fig. 2 a). The variability in the major axis length at − 5.2 ∘ C is likely linked to varying levels of supersaturation experienced by the ice crystals during their growth, as discussed in the following paragraphs. The ice crystals measured at − 5.2 ∘ C were characterized by a mean aspect ratio of 3.1 ± 1.3 (Fig. 3 a) and a columnar shape (Fig. S3A ). On the other hand, the ice crystals observed during the seeding missions at − 7.2 ∘ C exhibited an aspect ratio of 1.6 ± 0.3 (Fig. S2A ). The ice crystal images showed the occurrence of plates and short columns including a large number of hollow columns indicative of high supersaturation (Fig. S3B ).

Overview of the ice crystal axial dimensions measured during the seeding missions S5-6.6 ( ⁠ N = 17,796 ⁠ ) and S5-9.3 ( ⁠ N = 7,041 ⁠ ). a) 2D histogram of the major and minor axis length of the detected ice crystals. The number of ice crystals observed with a given major and minor axis length during S5-9.3 are shown with the green colormap, while S5-6.6 is shown by the purple contour lines. The dots represent the corresponding median axial dimensions (including all observed ice crystals) and the black lines show contour lines of aspect ratio (defined by major axis length divided by minor axis length). b, c) Normalized probability density function (PDF) of the major and minor axis length. The triangles represent the corresponding time-weighted medians, whereas the numbers indicate the mean values and standard deviations of the PDFs. In the upper-right corner, a schematic of a column’s major and minor axes is displayed, corresponding to growth along the basal and prism faces.

The diffusional growth of ice crystals can be expressed as the growth of the axial dimensions as a function of time. While Ryan et al. ( 41 ) found that the ice crystal growth can be adequately described as a linear function of time (i.e. constant growth rate) at short time scales (<3 min), Takahashi et al. ( 29 ) found a power-law function more suitable for longer time periods (3–30 min). In the following, we evaluate whether the growth of the observed ice crystal populations can be represented by a constant growth rate, i.e. independent of time. At − 5.2 ∘ C, an average linear growth rate of 0.13 ± 0.038 μ m s −1 was observed for the minor axis and 0.50 ± 0.23 μ m s −1 for the major axis (Fig. 4 ), whereas the average linear growth rates at − 7.2 ∘ C were 0.17 ± 0.050 μ m s −1 for the minor axis and 0.28 ± 0.087 μ m s −1 for the major axis (Fig. S4 ). While columnar growth dominated the seeding missions at − 5.2 ∘ C, a simultaneous presence of columnar and plate-like growth patterns (resulting in either short columns or plates) was observed for the missions at − 7.2 ∘ C, in line with previous studies ( 6 , 29 , 41 ). Interestingly, no significant difference was observed for the average growth rates measured during S5-6.6 and S5-9.3 (Fig. 4 ), as indicated by a Kolmogorov–Smirnov test ( ⁠ P ≥ 0.05). Moreover, the growth rates computed from the difference between consecutive seeding missions (i.e. S5-2.7, see Methods section) was in good agreement with the respective seeding missions. These findings suggest that at − 5.2 ∘ C, the observed ice crystal populations grew approximately linearly over time (i.e. constant growth rate) during the time period of 6.6 to 9.2 min. This also aligns with the laboratory study of Knight ( 40 ), who recorded long time series, lasting up to several hours, of individually grown ice crystals at − 5 ∘ C and found nearly constant growth rates over time for needles.

Linear growth rates of ice crystals along the minor and major axes are shown for seeding missions S5-6.6 (purple) and S5-9.3 (green). The left halves of the violin plots illustrate the mean distribution including all data, while the right halves of the violin plots show the mean distribution within stable mixed-phase regions (≤ 50 L −1 , light color) and rapid glaciating regions (≥ 500 L −1 , dark color), with the ice crystal number concentration averaged over a 1 s interval. The colored numbers represent the number of time steps included in the respective half violin plots. Black markers denote the linear growth rates reported in the specified studies (Ryan et al. ( 28 ): T = − 5.0 ∘ C, t = 2.5 min; Takahashi et al. ( 29 ): T = − 5.3 ∘ C, t = 5 min; Castellano et al. ( 39 ): T = − 6.5 ∘ C, t = 4.5 min; Knight ( 40 ): T = − 5.0 ∘ C, t = 40 min). Additionally, the linear growth rate computed from the difference between the seeding missions (S5-2.7) is shown as a black square (see Methods section). A Kolmogorov–Smirnov test was conducted at a significance level of 0.05 to evaluate whether the distribution of growth rates measured during S5-6.6 and S5-9.3 were significantly different; a P -value ≤ 0.05 indicates a significant difference. The test was performed on 500 independent subsamples, each with a sample size of 100. The mean P -values across these subsamples are reported, with rejection rates of 7% for the minor axis and 3% for the major axis.

The average linear growth rates observed in Figs. 4 and S4 were notably lower compared to the linear growth rates documented in earlier laboratory studies ( 28 , 29 , 39 ) and showed a substantial variability. Nevertheless, the largest measured ice crystals exhibited comparable or even higher growth rates than those reported in previous studies. Considering the high ice crystal number concentrations measured within the seeding plume (Figs. 2 c and S1C ), the variability in the observed linear growth rates and the lower averages compared to earlier studies can partly be attributed to the competition among the ice crystals for the limited available water vapor. Indeed, a depletion of the liquid phase was observed during the seeding missions, particularly in time periods with high ice crystal number concentrations, with certain cloud regions experiencing complete glaciation (Figs. 2 c,d and S1C,D ). To account for that, we investigated the growth rates of ice crystal populations for different mixed-phase conditions, specifically exploring the growth rates within stable mixed-phase regions and rapid glaciating regions. Based on the mean ice crystal number concentrations and glaciation times reported in Korolev et al. ( 42 ), our data were divided into two regimes using an ice crystal concentration threshold of below 50 L −1 to define “stable mixed-phase regions” (i.e. vapor unlimited regime) and above 500 L −1 to define “rapid glaciating regions” (i.e. vapor-limited regime). The separation into these regions shows that at − 5.2 ∘ C, the growth rates along the major axis are significantly lower in the rapid glaciating regions (0.38 ± 0.18 μ m s −1 ) compared to the stable mixed-phase regions (0.53 ± 0.24 μ m s −1 ), as indicated by the Kolmogorov–Smirnov test ( ⁠ P ≤ 0.05 ⁠ ). This suggests that the number concentration of ice crystals and thus the competition for the limited available water vapor can significantly reduce the supersaturation and ice crystal growth rates. Indeed, direct numerical simulations conducted by Chen et al. ( 43 ) confirm that fluctuations in the small-scale supersaturation field in the immediate surrounding of ice crystals broaden the ice crystal size distribution, particularly in environments close to ice saturation.

To investigate the impact of the supersaturation on the growth rates of individual ice crystals, we used the aspect ratio at − 5.2 ∘ C as a proxy for the supersaturation, where a high aspect ratio is assumed to be an indicator for high supersaturation experienced by the ice crystals during growth. While fluctuations in the supersaturation do not change the basic habit of ice crystals (e.g. column vs. plate), it influences how fast the preferred axis grows ( 15 ). Indeed, as depicted in Fig. 5 b, ice crystals with a larger aspect ratio were observed at − 5.2 ∘ C when lower ice crystal number concentrations and higher cloud droplet number concentrations were present. These ice crystals likely experienced higher supersaturation during growth, consistent with previous laboratory studies ( 44 , 45 ) that observed faster ice growth in the presence of higher liquid water content. This accelerated growth was attributed to the repeated close proximity of supercooled cloud droplets, which enhanced rapid vapor transport from the cloud droplets to the ice crystals (( 44 ), “vapor flush” effect). As a result, the observed growth rates (1.03 ± 0.16 μ m s −1 , Fig. 5 c) align more closely or even exceed those reported in previous laboratory studies and can be considered representative of conditions in which ice crystal growth is not vapor-limited or in which cloud droplets and ice crystals are uniformly distributed within a cloud volume (( 23 ), genuinely mixed). On the other hand, when considering ice crystals with lower aspect ratio, the ice crystal growth rates were significantly lower (0.24 ± 0.090 μ m s −1 ) and likely limited by water vapor. This case can be important in regions with high ice crystal number concentrations, such as those with strong secondary ice production ( 46 , 47 ) or in mixed-phase regions in which cloud droplets and ice crystals are spatially separated (( 23 ), conditionally mixed).

Linear growth rates of ice crystals along the major axis are shown for seeding missions S5-6.6 (purple) and S5-9.3 (green) as a function of aspect ratio. a) The histograms show the number of counts in the respective aspect ratio bin. b) Mean cloud droplet number concentration (CDNC, solid gray lines) and mean ice crystal number concentration (ICNC, dashed blue lines) measured within each aspect ratio bin. c) The boxplots show the linear growth rate along the major axis. As depicted by the black arrow, the aspect ratio is used as a proxy for supersaturation experienced during ice growth, where a low aspect ratio corresponds to low supersaturation (rapid glaciating region), while a high aspect ratio corresponds to high supersaturation (stable mixed-phase region). It is important to note that other factors (e.g. temperature fluctuations) can also influence the aspect ratio.

The observed variability in ice crystal growth rates in natural clouds, and consequently in ice crystal sizes and fall speeds, can potentially accelerate the onset of precipitation initiation through more efficient aggregation ( 48 ) and secondary ice production from ice–ice collisions ( 49 ). Quantifying the variability in ice crystal growth rates and identifying the conditions that influence them has significant implications for predicting the amount and spatial distribution of precipitation, particularly in shallow clouds and in regions with complex terrain ( 50 , 51 ).

In this study, we repurposed weather modification for fundamental cloud research by conducting repeated glaciogenic cloud seeding experiments in persistent supercooled stratus clouds to infer ice crystal growth rates in natural clouds. Seeding material was released from a multirotor UAV and the seeding-induced microphysical changes were measured 4–10 min downwind by ground-based remote sensing and in situ instrumentation on a tethered balloon system. Seeding signatures were detected by increased radar reflectivities, along with elevated ice crystal number concentrations and a simultaneous reduction in the cloud droplet number concentration up to complete glaciation in single cloud patches. This emphasizes the efficiency of the WBF process in producing precipitation-sized particles ( ⁠ ≥ 100 μ m) within a few minutes, highlighting its importance for precipitation initiation.

The seeding distance and consequently the ice growth time were systematically adjusted between consecutive seeding missions, while the environmental conditions remained approximately constant, enabling comprehensive ice growth studies within a quasi-steady-state natural environment. At − 5.2 ∘ C, the seeding experiments revealed columnar growth with an average growth rate of 0.50 ± 0.23 μ m s −1 along the major axis, while a transition to short columns and plates was observed at − 7.2 ∘ C, characterized by an average growth rate of 0.28 ± 0.087 μ m s −1 . Our findings indicate that the ice crystal populations in natural clouds grew nearly linearly over time at − 5.2 ∘ C within the observed time scales (6–10 min). Further investigations over a broader range of time and ambient conditions are required to thoroughly characterize the temporal (in)dependence of growth rates. In-depth knowledge of ice growth processes and the ways they are represented in numerical weather prediction models directly impacts the precipitation forecast skill (e.g. extreme precipitation).

Our observations show large variability in ice crystal growth rates within natural clouds, underscoring their sensitivity to both ice crystal number concentrations and supersaturation. Specifically, in rapid glaciating regions characterized by high ice crystal number concentrations, the linear growth rates at − 5.2 ∘ C (0.38 ± 0.18 μ m s −1 ) were significantly lower compared to those in water vapor unlimited regions (0.53 ± 0.24 μ m s −1 ), as the ice crystals in rapid glaciating regions likely experienced lower supersaturation in their immediate vicinity during growth. This variability in ice crystal growth rates potentially accelerates precipitation initiation, with significant implications for both precipitation forecast and climate simulations. Thus, we recommend that future research focuses on investigating ice crystal growth rates under conditions of fluctuating supersaturation and/or temperature, using well-designed field experiments or innovative laboratory approaches.

This study provides unambiguous evidence that the targeted use of weather modification techniques can be employed to study microphysical processes in natural clouds, specifically enabling the investigation of ice crystal growth rates at temperatures above − 10 ∘ C—a pursuit previously limited in field studies due to the scarcity of naturally occurring ice nucleating particles at these high temperatures. Future work aims to extend this analysis to different environmental and microphysical conditions, leveraging these observations to constrain and validate model simulations. In particular, these comprehensive observations can help to develop more realistic parameterizations for weather and climate models, extending beyond what is achievable in a laboratory setting. Overall, this study shows the versatile use of weather modification, not only for precipitation enhancement and climate intervention but also for advancing our understanding of cloud processes (e.g. ice growth processes, aerosol-cloud interactions), validating remote sensing retrievals, and refining model parameterizations.

CLOUDLAB seeding experiments

In the framework of the CLOUDLAB project ( 35 ), targeted glaciogenic cloud seeding experiments were conducted in supercooled stratus clouds. These experiments were performed in a restricted airspace in the Swiss Plateau near Eriswil, Switzerland (47°04′14″N, 7°52′22″E), which is embedded between the Alps and Jura mountains and often covered by persistent wintertime stratus clouds ( 37 ). The principle behind glaciogenic cloud seeding is based on the concept that natural precipitation is limited by the number of ice crystals and thus ice nucleating particles, and that the injection of additional ice nucleating particles into supercooled clouds can trigger ice and subsequent precipitation formation ( 52 ). In contrast to earlier seeding initiatives primarily aimed at enhancing precipitation ( 38 , 52–54 ), CLOUDLAB employs seeding as a method to further our understanding of the fundamental processes governing ice formation and growth. The seeding material was injected into supercooled stratus clouds from a burn-in-place flare mounted on a UAV which flew horizontal tracks perpendicular to the wind 1–4 km upwind of the observational site ( 55 ). The seeding-induced microphysical changes were observed by an extensive set of ground-based remote sensing and in situ instrumentation downstream of the seeding location. By repeating seeding experiments in this quasi-steady-state environment, with systematic adjustment of one experimental parameter (i.e. ice crystal growth time), we were able to perform laboratory-like studies on ice crystal growth in natural clouds.

Instrumentation

The seeding material was introduced into the cloud by a multirotor UAV (Meteodrone MM-670, Meteomatics AG), which has a propeller heating mechanism to enable flight in supercooled clouds with icing conditions. The UAV was equipped with one burn-in-place flare ( 55 , 56 ), which contains a mixture of silver iodide, silver chloride, ammonium salt, and potassium salt (Zeus MK2, Cloud Seeding Technologies). One seeding flare has a burning time of around 5 min. The flight pattern of the UAV (i.e. autonomous mission) was determined before each seeding mission based on the wind measurements from a radar wind profiler (LAP-3000, Vaisala) installed at the measurement site.

A 94 GHz (W-band) frequency-modulated continuous wave Doppler cloud radar (FMCW-94-DP, RPG, ( 57 )) and a 35 GHz (Ka-band) pulsed, polarimetric Doppler radar (Mira-36, Metek, ( 58 )) were deployed at the observational site to measure the background cloud and seeding signatures. The FMCW-94-DP was operated in vertical pointing mode, whereas the Mira-36 performed Range Height Indicator (RHI) scans perpendicular to the main wind direction or Plan Position Indicator (PPI) scans at a fixed elevation angle downwind of the measurement site. The scans were conducted at a speed of 1°/s.

The tethered balloon system HoloBalloon equipped with the holographic imager HOLIMO ( 36 ) provided co-located in situ measurements of the phase-resolved cloud properties, which complemented the radar observations. HOLIMO images an ensemble of cloud particles in the size range between 6 μ m and 2 mm in a 3D sample volume ( 36 , 59 ). Here, we used a sample volume of 26.95 cm 3 for the ice crystal growth analysis and a smaller sample volume of 11.76 cm 3 for observing the cloud microphysical properties (e.g. Figs. 2 and S1 ) due to instrumental limitations (e.g. sizing small particles). The HOLIMO data were analyzed with a frame rate of 20 Hz during seeding conditions and of 10 Hz during background conditions.

The images captured by HOLIMO were automatically classified into cloud droplets (circular) and ice crystals (noncircular) based on the particle shape using a neural network ( 60 ). As in Ramelli et al. ( 61 ), differentiation between cloud droplets and ice crystals was done for particles larger than 25 μ m in major axis diameter due to resolution limitations (i.e. all particles smaller than 25 μ m in diameter were classified as cloud droplets). After the automated classification, the ice crystals were manually classified into pristine and aggregated ice crystals based on the particle shape. This results in the following uncertainties: cloud droplet number concentrations (5%), ice crystal number concentration for ice crystals smaller than 100 μ m (15%), ice crystal number concentration for ice crystals larger than 100 μ m (5–10%) ( 61 , 62 ). The liquid and ice water contents were derived from the size distributions measured by HOLIMO, with the ice water content calculated using the mass-size relationship from Cotton et al. ( 63 ).

Inferring ice crystal growth rates from seeding experiments

In this study, a linear ice crystal growth rate was derived from the seeding experiments by dividing the observed size dimension by the corresponding growth time. For that, it was essential to precisely characterize both the size and growth time of the measured ice crystals. To size the ice crystals, we analyzed the minor and major axis dimensions of the pristine ice crystals detected by HOLIMO. Aggregated ice crystals were not considered in the growth analysis. The uncertainty in sizing ice crystals was estimated at 20% for ice crystals with a diameter of 25 μ m and 5–10% for ice crystals larger than 100 μ m ( 62 ). An additional uncertainty of 10% is introduced due to uncertainty of the orientation of the ice crystals with respect to the camera plane, resulting in a combined size uncertainty of 15% for ice crystals larger than 100 μ m.

The ice crystal growth time, spanning from nucleation to detection, was estimated by considering observations from multiple data sources; e.g. (i) dividing the seeding distance by the wind speed measured by the wind profiler or (ii) considering the time between the seeding flare ignition and (a) the first appearance of ice crystals in the vertically pointing cloud radar, (b) the first appearance of ice crystals in HOLIMO, and (c) the first appearance of seeding particles in the Portable Optical Particle Spectrometer (POPS, Handix Scientific) mounted on HoloBalloon. Following these approaches, the growth time could be estimated with an uncertainty of 10%. The estimation of the growth time was based on the assumption that most ice crystals nucleated immediately after the release of the seeding material. This is supported by the high ice nucleating activity of the seeding particles at temperatures below − 5 °C previously observed in the laboratory ( 64 ). After the initial nucleation event, the high concentration of ice crystals reduces the ambient saturation ratio, which might then become too low to support further nucleation. The uncertainty associated with the nucleation time can be reduced by comparing observations from consecutive seeding missions where the ice crystals underwent the same nucleation process (as done in Figs. 4 and S4 ). This was achieved by computing an average linear growth rate based on two successive seeding missions by taking the difference in median ice crystal dimensions (e.g. S5-6.6 and S5-9.3) and dividing it by the corresponding difference in growth time (e.g. S5-2.7). The good agreement between the computed linear growth rates from the intermission comparison with the linear growth rates observed in individual missions (Figs. 4 and Fig. S4 ) further supports the assumption that most ice crystals nucleated immediately after seeding. However, we cannot rule out the possibility of further nucleation events downstream of the seeding location. Continued nucleation could explain some of the variability observed in the lower range of growth rates. Overall, considering the uncertainties in both size and growth time, the linear growth rate was inferred with a 20% uncertainty.

Supplementary material is available at PNAS Nexus online.

F.R., J.H., C.F., A.J.M., N.O., R.S., H.Z. and U.L. acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement 101021272 CLOUDLAB). K.O. and P.S. were supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 654109 and from the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) Project 408027490. ROD acknowledges EEARO-NO-2019-0423/IceSafari, contract no. 31/2020, and EU-HORIZON-WIDERA-2021 under grant number 101079385 for financial support.

F.R., J.H, and U.L. designed research; F.R., J.H., C.F., A.J.M., N.O., R.S., and H.Z. performed seeding experiments; R.S., K.O., and P.S. operated cloud radars; F.R., J.H., R.S., and R.O.D. analyzed data; F.R. wrote the article with contributions from all authors.

Data are available at https://doi.org/10.5281/zenodo.13712737 ( 65 ). Analysis and plotting scripts are available at https://doi.org/10.5281/zenodo.13715279 ( 66 ).

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• Lifetime effects and cost-effectiveness of statin therapy for older people in the United Kingdom: a modelling study
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• http://orcid.org/0000-0002-0951-1304 Borislava Mihaylova 1 , 2 ,
• Runguo Wu 2 ,
• Junwen Zhou 1 ,
• Claire Williams 1 ,
• http://orcid.org/0000-0002-4154-1431 Iryna Schlackow 1 ,
• Jonathan Emberson 3 ,
• Christina Reith 3 ,
• Anthony Keech 4 ,
• John Robson 5 ,
• Richard Parnell 6 ,
• Jane Armitage 3 ,
• http://orcid.org/0000-0003-0239-7278 Alastair Gray 1 ,
• John Simes 4 ,
• Colin Baigent 3
• 1 Health Economics Research Centre, Nuffield Department of Population Health , University of Oxford , Oxford , UK
• 2 Health Economics and Policy Research Unit, Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 3 Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health , University of Oxford , Oxford , UK
• 4 NHMRC Clinical Trials Centre , The University of Sydney , Sydney , New South Wales , Australia
• 5 Clinical Effectiveness Group, Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 6 Patient and Public Representative , Havant , UK
• Correspondence to Dr Borislava Mihaylova; boby.mihaylova{at}dph.ox.ac.uk

Background Cardiovascular disease (CVD) risk increases with age. Statins reduce cardiovascular risk but their effects are less certain at older ages. We assessed the long-term effects and cost-effectiveness of statin therapy for older people in the contemporary UK population using a recent meta-analysis of randomised evidence of statin effects in older people and a new validated CVD model.

Methods The performance of the CVD microsimulation model, developed using the Cholesterol Treatment Trialists’ Collaboration (CTTC) and UK Biobank cohort, was assessed among participants ≥70 years old at (re)surveys in UK Biobank and the Whitehall II studies. The model projected participants’ cardiovascular risks, survival, quality-adjusted life years (QALYs) and healthcare costs (2021 UK£) with and without lifetime standard (35%–45% low-density lipoprotein cholesterol reduction) or higher intensity (≥45% reduction) statin therapy. CTTC individual participant data and other meta-analyses informed statins’ effects on cardiovascular risks, incident diabetes, myopathy and rhabdomyolysis. Sensitivity of findings to smaller CVD risk reductions and to hypothetical further adverse effects with statins were assessed.

Results In categories of men and women ≥70 years old without (15,019) and with (5,103) prior CVD, lifetime use of a standard statin increased QALYs by 0.24–0.70 and a higher intensity statin by a further 0.04–0.13 QALYs per person. Statin therapies were cost-effective with an incremental cost per QALY gained below £3502/QALY for standard and below £11778/QALY for higher intensity therapy and with high probability of being cost-effective. In sensitivity analyses, statins remained cost-effective although with larger uncertainty in cost-effectiveness among older people without prior CVD.

Conclusions Based on current evidence for the effects of statin therapy and modelling analysis, statin therapy improved health outcomes cost-effectively for men and women ≥70 years old.

• Health Care Economics and Organizations
• Computer Simulation
• Cardiovascular Diseases
• Outcome Assessment, Health Care

Data availability statement

Data may be obtained from a third party and are not publicly available. The datasets used in the current study may be obtained from third parties (UK Biobank https://www.ukbiobank.ac.uk/ ; Whitehall II study www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/whitehall-ii ) and are not publicly available. Researchers can apply to use the UK Biobank resource and Whitehall II study data.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/heartjnl-2024-324052

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Randomised studies showed that statins reduce the incidence of myocardial infarction and ischaemic stroke by about one quarter for every 1 mmol/L reduction in low-density lipoprotein cholesterol but direct evidence among older people without prior cardiovascular disease (CVD) is limited.

In previous studies, statin therapy has been shown to be cost-effective in older people, but it has been suggested that a small further adverse effect would offset its cardiovascular benefit.

Despite markedly increased CVD risks with advancing age, lower statin use is reported among older people.

WHAT THIS STUDY ADDS

The value of statin therapy was reassessed using a contemporary UK CVD model validated in older people together with the synthesised evidence of statins’ beneficial effects on CVD events and adverse effects on myopathy, rhabdomyolysis and incident diabetes.

The study reported that both standard and higher intensity statin therapies enhanced health outcomes, with higher intensity therapy achieving larger benefits, and were cost-effective in people ≥70 years old in the UK. These findings remained robust in scenarios with smaller CVD risk reductions and further hypothetical adverse effects with statin therapy, though with increased uncertainty among older people without CVD.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

While ongoing statin trials in older people without CVD will add valuable data, particularly in those over the age of 75 years, statin treatment of individuals should not be delayed while awaiting their findings.

Increasing statin uptake and adherence among older people will reduce CVD risks.

Introduction

Statins are widely available generically and a cornerstone in cardiovascular disease (CVD) prevention. High-quality randomised evidence has shown that statins reduce the incidence of myocardial infarction (MI) and ischaemic stroke by about one quarter for every 1 mmol/L reduction in low-density lipoprotein cholesterol (LDL-C). More intensive statin regimens achieve larger reductions in LDL-C and prevent more atherosclerotic cardiovascular events. 1 However, there is less definitive evidence for statin benefit among older patients without CVD history 2 and guidelines stop short of making specific recommendations on initiating statins for primary CVD prevention in older people. 3 4 Despite the growing proportion of older people (people ≥70 years old make up about 30% of those over the age of 40 years in the UK) and the markedly higher cardiovascular risk with increasing age, lower statin use is reported. 5 6

Evidence for treatments’ long-term effects and cost-effectiveness guides healthcare decisions in many countries and healthcare systems, including in the UK. Such evidence ensures that by implementing cost-effective treatments, healthcare systems efficiently use their resources to maximise population health. Previous evidence has indicated that statin therapy is likely to be cost-effective for older people, but the estimates were sensitive to further adverse effects of statins or lower statin effectiveness. 7–9 A recent individual participant data meta-analysis of large statin trials strengthened the evidence for efficacy and safety of statins in older people. 2 Therefore, we set out to reassess the lifetime effects and cost-effectiveness of statin therapy in people ≥70 years old in the contemporary UK population, in categories by prior CVD, sex and LDL-C level, using this evidence 2 and a new UK CVD microsimulation model. 10

Study population

The lifetime effects and cost-effectiveness of statin therapy were assessed in categories of UK adults ≥70 years old in the UK Biobank and the Whitehall II cohort studies. All UK Biobank participants ≥70 years old at recruitment into the study (2006–2010), and those who reached this age by subsequent resurveys, were included in the present study from their earliest eligible attendance. All Whitehall II participants ≥70 years old at phase 9 (2007–2009) in Whitehall II were also included. Information on the derivation of participants’ baseline characteristics is presented in the online supplemental methods . To assess the lifetime effects of statin therapy, a model is required that reliably projects individual participant’s morbidity, mortality, quality of life (QoL) and healthcare costs over their lifetimes without and with statin therapy.

Supplemental material

Cvd microsimulation model.

The CVD microsimulation model has been reported elsewhere. 10 Briefly, the model was developed using the individual participant data of large statin clinical trials, and calibrated using the UK Biobank’s participant data. The model employs a broad range of socio-demographic and clinical characteristics to project annually the first occurrence of MI, stroke, coronary revascularisation, vascular death, incident diabetes, incident cancer and non-vascular death. Participant characteristics and incident events determined health-related QoL 10 and primary care and hospital admission costs 11 in the model. The model was validated in UK Biobank and Whitehall II studies and against national data.

CVD microsimulation model validation in older people

In the present study, the model performance was further assessed among participants ≥70 years old during follow-up in the UK Biobank and Whitehall II studies using their linked electronic hospital admissions, primary care records (UK Biobank only), cancer registrations and death records to identify MIs, strokes, coronary revascularisations (UK Biobank only), incident diabetes (UK Biobank only), cancers and deaths during follow-up.

Effects and costs of statin therapy

The Cholesterol Treatment Trialists’ Collaboration (CTTC) individual participant data meta-analysis of large randomised statin trials informed the relative reductions in the risks of cardiovascular events per 1 mmol/L in LDL-C with statin therapy ( table 1 ) of 24% in MI risk, 16% in stroke, 25% in coronary revascularisation and 12% in cardiovascular death. 2 We assessed the effects of standard (eg, achieving 35%–45% LDL-C reduction: atorvastatin 20 mg/day, rosuvastatin 5–10 mg/day or simvastatin 40–80 mg/day) and higher intensity statin therapy (eg, achieving ≥45% LDL-C reduction: atorvastatin 40–80 mg/day, rosuvastatin 20–40 mg/day) ( online supplemental table 1 ). 12 The reduction in LDL-C achieved with each level of statin intensity was derived using the therapy’s proportional reduction and participant’s untreated LDL-C level (with the effects of any ongoing statin therapy removed). Meta-analyses of statin therapies informed 9% excess odds of new-onset diabetes with standard 13 and further 12% excess odds with higher intensity 14 statin therapy. An overview of cohort studies informed excess rates of myopathy (11 cases per 100 000 treated per year) and rhabdomyolysis (3.4 cases per 100 000 treated per year; 10% case fatality) with statin therapy 15 ; with myopathy and rhabdomyolysis effects on QoL informed from a modelling study. 16 Generic statin medication costs, 17 costs of consultations 18 and blood lipids tests 19 for initiation and monitoring of statin prescribing in the UK National Health Service were included ( table 1 ).

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Statin treatment effects and statin treatment costs

Cost-effectiveness of statin therapy

We employed the model to project event risks and survival and summarise life years, quality-adjusted life years (QALYs) and primary and hospital care costs over individuals’ remaining lifetimes (ie, death or 110 years of age) without and with statin therapy and to assess the cost-effectiveness of different statin therapies in categories of older individuals.

Base-case analysis

In our base-case analysis, we assessed the cost-effectiveness of lifetime statin therapy from the perspective of the UK National Health Service under a number of key assumptions based on current evidence. First, the reductions in individuals’ LDL-C levels with a particular statin therapy were assumed to correspond to the average proportional reduction achieved with the therapy. Second, we assumed that the relative effects of a particular statin therapy on event risks were independent of duration of therapy or individual person characteristics including age (ie, the overall effects reported in meta-analyses were employed). Third, disease events were assumed not to differ in severity or otherwise, irrespective of statin treatment status. Finally, statin therapy was assumed not to affect the risks of cancer or other non-vascular events, 20 nor confer any discomfort or disutility beyond the adverse events specified above.

Assessment of uncertainty

We ran 500 microsimulations per individual for each set of parameters. We summarised the parameter uncertainty, including uncertainty in effects of statin therapy on vascular and non-vascular events, all event risk equations, QoL and healthcare cost equations in the decision-analytic model using 1000 sets of parameter values, derived using a bootstrap approach, employing sampling with replacement from respective populations. 21 Values for treatment effects were sampled from lognormal distributions corresponding to the natural logarithm of relative risk reductions with statin therapy.

We report life years and QALYs gained, the additional statin and other healthcare costs (2020/2021 UK£) and the incremental costs per QALY with standard and higher-intensity statin therapies. We discounted future QALYs and costs at 3.5% per year in the summary measures for cost-effectiveness. 22 We present cost-effectiveness acceptability curves for willingness-to-pay values from £0-£40K/QALY.

Sensitivity and scenario analyses

The following parameters were varied. First, in view of the higher uncertainty in the effects of statin therapy in older people, in scenario analyses, we applied relative risk reductions in cardiovascular endpoints per 1 mmol/L LDL-C, informed from data only among: (1) people >75 years old at randomisation and (2) people >75 years old and without prior CVD at randomisation in the individual participant data meta-analysis. 2 Second, to explore sensitivity to possible double counting of statin effects in the model through its direct effect on vascular death risk and indirect effects through MI and stroke risks, we studied the impact of smaller direct relative risk reduction in cardiovascular death with statin therapy (ie, 7% instead of 12% per 1 mmol/L in LDL-C reduction). Third, to assess sensitivity to variation in major non-vascular disease risk, we ran scenario analyses with a small detrimental or beneficial statin effect on incident cancer, informed by the 95% CI limits reported in a meta-analysis of randomised statin trials. 20 Fourth, in acknowledgement of substantial rates of statin discontinuation and reinitiation, a scenario analysis assessed statin cost-effectiveness using estimated real-world compliance with statin derived from routine UK data, 23 with statin effects and costs discontinued with therapy discontinuation. Fifth, to acknowledge the uncertainty concerning any further QoL disutility from taking a daily statin pill, we included analyses with yearly disutility equal to 0.001, 0.002 or 0.005. Sixth, we present scenarios with doubled risk of non-vascular death; with lower general QoL; and both together to assess sensitivity to further reduced potential in older people to benefit from preventive treatment. We also present scenario analyses with only healthcare costs for CVD and incident diabetes included; with higher costs of statin therapy and with 1.5% discount rate for costs and outcomes.

Further details are provided in the online supplemental methods .

Patient and public involvement

Three members of the public were involved in the study management and steering groups. Study methods and results were also discussed in separate sessions with our lay members who helped us refine the study methodology and approach to presenting study findings.

The baseline characteristics of participants ≥70 years old in the UK Biobank and Whitehall II studies in categories by prior CVD are presented in table 2 and online supplemental table 2 . There were 15 019 (52% men; mean age 72.5 years) participants without CVD and 5103 (66% men; mean age 72.9 years) with history of CVD. Among participants without and with prior CVD, 29% and 58%, respectively, were prescribed a statin at baseline and the derived untreated mean LDL-C levels were 4.2 mmol/L (SD 0.78 mmol/L) and 4.3mmol/L (SD 0.98 mmol/L), respectively.

Baseline characteristics of UK Biobank and Whitehall II participants 70 years and older

In model validation, the cumulative event rates predicted by the CVD microsimulation model, using the baseline characteristics of participants ≥70 years old, corresponded mostly well to the observed rates of cardiovascular and non-vascular events in categories of participants by prior CVD, respectively, though higher MI risks, but not cardiovascular death risks, were predicted among participants with prior CVD in UK Biobank but not in Whitehall II study ( figure 1 ).

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CVD microsimulation model validation among UK Biobank and Whitehall II participants 70 years and older.In the Whitehall II study, no linked data for CRV and diabetes were available and, therefore, no model validation performed for CRV and diabetes. CRV, coronary revascularisation; CVD, cardiovascular disease; MI, myocardial infarction; NVD, nonvascular death; VD, vascular disease.

In participant categories by sex, prior CVD and LDL-C level, standard statin therapy was projected to increase individual survival (undiscounted) by 0.37 to 1.05 life years (0.24 to 0.7 QALYs), and higher intensity statin therapy by a further 0.08 to 0.21 life years (0.04 to 0.13 QALYs) ( figure 2A , online supplemental tables 3 and 4 ). Across these categories, the incremental cost per QALY gained for standard statin therapy compared with no statin ranged from £116 to £3502 and that for higher intensity compared with standard statin from £2213 to £11 778 per QALY ( figure 2B ). The analyses of parameter uncertainty indicated that at £20 000/QALY willingness to pay threshold, higher intensity statin therapy had a very high probability of being cost-effective across all categories of men and women ≥70 years old ( figure 3 ). The probability that statin therapy was cost-effective for people ≥70 years old remained high even at a cost-effectiveness threshold of £5K/QALY. However, at this lower threshold, the standard statin therapy had the highest probability of being cost-effective among women with a pretreatment LDL-C lower than 4.1 mmol/L and among men with a pretreatment LDL-C lower than 3.4 mmol/L ( figure 3 ).

Life years and QALYs gained (A) and cost-effectiveness (B) of lifetime statin therapy in categories by prior cardiovascular disease, sex and pre-treatment LDL cholesterol level. Incremental Cost-Effectiveness Ratio (ICER) is the ratio of the incremental costs divided by the incremental QALYs with costs and QALYs discounted at 3.5% per year. CVD, cardiovascular disease; LDL, low density lipoprotein; QALY, quality-adjusted life years.

Probability that lifetime statin therapy is cost-effective in categories by prior cardiovascular disease, sex and pre-treatment LDL cholesterol level. The probability that the treatment scenario provides the highest QALYs gain at the particular threshold of cost-effectiveness plotted. CVD, cardiovascular disease; LDL-C, low-density lipoprotein cholesterol; QALY, quality-adjusted life years.

These cost-effectiveness results remained robust in a wide range of sensitivity analyses ( figure 4 , online supplemental table 5 ) with higher sensitivity noted for a higher intensity statin at a five times higher price. In particular, although reduced gains in QALYs were projected, standard statin therapy remained cost-effective in people ≥70 years old if relative risk reductions after age 75 were equal to those reported in the subgroup of participants >75 years old, or indeed in the subgroup of participants >75 years old without CVD at randomisation, in the CTTC meta-analysis ( figures 4 and 5 and online supplemental figure 1 ). Higher intensity statin therapy remained cost-effective among older people with pretreatment cholesterol levels 3.4 mmol/L or higher. In these scenario analyses with lower CVD risk reductions with statin therapy, the probability of standard or higher intensity statin therapy being cost-effective remained higher than no statin therapy in all categories of older people but was substantially reduced among older women with lower LDL-C levels.

Sensitivity analyses of cost-effectiveness of statin therapy for people 70 years or older. (A) Incremental cost (£) per QALY gained (standard statin vs no statin). (B) Incremental cost (£) per QALY gained (higher intensity vs standard statin). See online supplemental methods table 7 for description of sensitivity analyses. The * on the horizontal axes represent the base-case analysis. CVD, cardiovascular disease; LDL, low-density lipoprotein; NVD, nonvascular death; QALY, quality-adjusted life year; QoL, quality of life.

Life years and QALYs gained and cost-effectiveness of lifetime statin therapy in older people: scenario analyses with CVD reductions with statin therapy in people>75 years old informed from effects of statin therapy among participants>75 years old (Scenario 1) or >75 years old without CVD (Scenario 2) from Cholesterol Treatment Trialists’ collaborative meta-analysis. Statin effects up to age 75 as in base-case analysis; statin effect thereafter as per respective scenario analysis. CVD, cardiovascular disease; ICER, Incremental Cost-Effectiveness Ratio with costs and QALYs discounted at 3.5% per year; LDL, low-density lipoprotein; QALY, quality-adjusted life year.

This assessment of the lifetime effects and cost-effectiveness of statin therapy in people ≥70 years old in the UK used contemporary patient data, a validated CVD microsimulation model and a meta-analysis of the effects of statin treatment across age categories. It concluded that lifetime statin treatment increased quality-of-life-adjusted survival in older men and women and, at UK cost of generic statins, was highly cost-effective for all, irrespective of their CVD history or LDL-C level. Higher intensity statin therapy was the strategy likely to bring the highest health benefits cost-effectively, although standard statin regimens would achieve most of these benefits. These findings remained robust in sensitivity analyses with smaller cardiovascular risk reductions with statin therapy, though smaller benefits were projected and standard statin therapy became the preferred option for older people with LDL-C levels <3.4 mmol/L.

In this analysis, we used the overall relative risk reductions in cardiovascular events per 1 mmol/L LDL-C reduction with statin therapy given the similar relative risk reductions across age categories in the individual participant meta-analysis of statin trials. 2 The meta-analysis, however, noted trends towards smaller proportional reductions in major coronary events and vascular deaths in older people. Data were particularly limited among participants >75 years old without prior CVD, where there was no direct evidence for statistically significant cardiovascular risk reductions with statin therapy. In the present report, two scenario analyses assessed the sensitivity of findings to the size of statin effects using relative risk reductions in cardiovascular events in the meta-analysis (1) among participants >75 years old, and (2) among participants >75 years old without prior CVD at randomisation. 2 In both scenarios, despite smaller net health benefits, statin therapy remained cost-effective although with larger uncertainty.

We previously reported that statin therapy, at generic prices, is highly cost-effective in UK across patients 40–70 years old irrespective of their sex, age, CVD risk and LDL-C level. 21 Here, we extend this work to older people and indicate that, although the gains in QALYs are smaller, the additional costs are also lower, and the incremental cost per QALY remains highly attractive. Moreover, with a substantially higher CVD risk (99% of ≥70 years old UK Biobank participants without prior CVD had estimated 10 year CVD risk ≥10%; and 88% had 10-year CVD risk ≥15%, data not shown), the level of risk is irrelevant in guiding statin treatment decisions in older people.

This reassessment of statins’ value in the contemporary older UK population confirms findings of earlier cost-effectiveness studies 8 9 and reaffirms that, despite substantial reductions in CVD incidence and mortality over the last decades, statins remain a cornerstone in CVD prevention in this population. Our findings differ from an earlier study of cost-effectiveness of statin therapy for the primary prevention of CVD in people ≥75 years old, which reported that, although statin treatment was highly cost-effective, even a small hypothetical increase in a geriatric-specific adverse effect (ie, reducing disability-adjusted life years by 0.003–0.004) would offset its cardiovascular benefit. 7 In our study, the known small excesses of myopathy, rhabdomyolysis and incident diabetes with statin treatment were explicitly integrated, and our findings remained robust to hypothetical further statin-associated reductions in QoL up to 0.005 QALY/ year and to lower statin efficacy, suggesting that the value of statin therapy for older people is more certain than implied. It is important to also underline that high-quality randomised evidence indicate that the vast majority of adverse effects reported on statin therapy were also reported in the absence of statin therapy, 24 25 indicating serious misattribution of adverse effects in observational and uncontrolled studies.

Our results indicate that older people are likely to cost-effectively benefit from statin treatment. Statin treatment rates in our ≥70 years old cohort (29% among people without CVD to 58% among people with prior CVD) were similar to statin treatment rates reported by the Health Survey for England. 26 Hence, from the 9.1 million adults ≥70 years old in UK, 27 a third of them with prior CVD, 26 just over 40%, or less than 4 million, are receiving statin treatment. While further evidence for statins effects in older people will be helpful, the robustness of the findings to variations in key parameters suggests that delaying statin treatment in the millions of older people while awaiting new evidence is unjustifiable.

Our study has a number of strengths. We used a contemporary UK CVD model, developed using a large and rich population biobank with demonstrable ability to predict cardiovascular and mortality risks in older people. We used the baseline characteristics of more than 20 000 people ≥70 years old to evaluate lifetime benefits and cost-effectiveness of statin therapy. A further strength of our analysis is the use of synthesised randomised evidence for the effects of statin therapy by age that allowed us to study the robustness of our findings to somewhat smaller reductions in cardiovascular risks in older people. Finally, the reported excesses in myopathy, rhabdomyolysis and incident diabetes with standard and higher intensity statin therapy were integrated allowing the net effects of treatment to be fully assessed.

The study has some limitations. First, the majority of our data is among people aged 70 to early 80s. Our findings, however, were very similar in participants 70–75 and ≥75 years old (results not shown), which suggest that they are generalisable to much older people. Second, our model and results are based on population cohorts, in which the healthy volunteer effect may limit generalisability. To address this limitation, the model used a broad range of socioeconomic, lifestyle and clinical characteristics that allow generalisations to populations with different distributions of these characteristics. Moreover, statin therapy remained cost-effective in scenario analyses with substantially higher risk of non-vascular death and lower QoL. Third, a small excess in milder muscle symptoms was recently reported with statin treatment across randomised studies with excess confined to the first year of treatment. 28 The sensitivity analyses suggest that this adverse effect is unlikely to materially alter statin’s cost-effectiveness. Fourth, two ongoing large statin trials, scheduled to complete in 2026, will add valuable further data to the direct evidence of effects of statin therapy in people aged ≥75 years without atherosclerotic CVD. 29 30 Fifth, missing baseline data were imputed using a single imputation. Moreover, while the model performance was good for most participant categories, endpoints and across the two datasets, there were some deviations. Therefore, it is possible that the uncertainty may be larger than reported by the model. However, the consistency of cost-effectiveness results across categories of participants and across a broad range of sensitivity analyses for key parameters indicate that our general findings are robust.

In conclusion, this study reports that statin therapy is highly likely to be cost-effective in older people, although there was greater uncertainty among older people without CVD in scenario analysis with substantially smaller CVD risk reductions with statin therapy. While further randomised evidence will be helpful, the robustness of these findings indicates that older people are likely to benefit cost-effectively from statin therapy and should be considered for treatment.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This work used data of participants in research studies (UK Biobank, Whitehall II) who have consented to collection and use of their data for research. Ethics committee approval was not required for this secondary research study. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This research has been conducted using data from Cholesterol Treatment Trialists’ Collaboration https://www.cttcollaboration.org/ , UK Biobank Resource under Application Number 56757 www.ukbiobank.ac.uk , and Whitehall II study www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/whitehall-ii . We thank all the participants, staff and other contributors to these resources. Project Oversight Group: Colin Baigent, Alison Gater, Borislava Mihaylova, Stephen Morris, Paul Roderick (Chair), Natalie Rowland, Peter Sever, Liam Smeeth. We also thank further members of the public with whom we discussed the project and emerging results.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

BM and RW are joint first authors.

Correction notice This article has been corrected since it was first published. Missing panel and axes titles have been added to Figure 1.

Collaborators Cholesterol Treatment Trialists’ Collaborators: CTT secretariat: J Armitage, C Baigent, E Barnes, L Blackwell, R Collins, K Davies, J Emberson, J Fulcher, H Halls, WG Herrington, L Holland, A Keech, A Kirby, B Mihaylova, R O’Connell, D Preiss, C Reith, J Simes, K Wilson. CTT Collaborating trialists: A to Z trial (phase Z): M Blazing, E Braunwald, J de Lemos, S Murphy; TR Pedersen, M Pfeffer, H White, S Wiviott; AFCAPS/TEXCAPS (AirForce/Texas Coronary Atherosclerosis Prevention Study) M Clearfield, JR Downs, A Gotto Jr, S Weis; ALERT (Assessment of Lescol in Renal Transplantation) B Fellström, H Holdaas (deceased), A Jardine, TR Pedersen; ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) D Gordon, B Davis; C Furberg, R Grimm, S Pressel, JL Probstfield, M Rahman, L Simpson; ALLIANCE (Aggressive Lipid-Lowering Initiation Abates New Cardiac Events) M Koren; ASCOT (Anglo-Scandinavian Cardiac Outcomes Trial) B Dahlöf, A Gupta, N Poulter, P Sever, H Wedel; ASPEN (Atorvastatin Study for the Prevention of Coronary Heart Disease Endpoints in Non-Insulin Dependent Diabetes Mellitus) RH Knopp (deceased); AURORA (A study to evaluate the Use of Rosuvastatin in subjects On Regular haemodialysis: an Assessment of survival and cardiovascular events) S Cobbe, B Fellström, H Holdaas (deceased), A Jardine, R Schmieder, F Zannad; CARDS (Collaborative Atorvastatin Diabetes Study) DJ Betteridge (deceased), HM Colhoun, PN Durrington, J Fuller (deceased), GA Hitman, A Neil; CARE (Cholesterol And Recurrent Events Study) E Braunwald, B Davis, CM Hawkins, L Moyé, M Pfeffer, F Sacks; CORONA (Controlled Rosuvastatin Multinational Trial in Heart Failure) J Kjekshus, H Wedel, J Wikstrand; 4D (Die Deutsche Diabetes Dialyse Studie): C Wanner, V Krane; GISSI (Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico) Heart Failure and Prevention trials: MG Franzosi, R Latini, D Lucci, A Maggioni;, R Marchioli, EB Nicolis, L Tavazzi, G Tognoni; HOPE-3: J Bosch, E Lonn, S Yusuf; HPS (Heart Protection Study): J Armitage, L Bowman, R Collins, A Keech, M Landray, S Parish, R Peto, P Sleight (deceased); IDEAL (Incremental Decrease in Endpoints through Aggressive Lipid-lowering) JJP Kastelein, TR Pedersen; JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin) R Glynn, A Gotto Jr, JJP Kastelein, W Koenig, J MacFadyen, PM Ridker; LIPID (Long-term Intervention with Pravastatin in Ischaemic Disease) A Keech, S MacMahon, I Marschner, A Tonkin, J Shaw (deceased), J Simes, H White; LIPS (Lescol Intervention Prevention Study) PW Serruys; Post-CABG (Post-Coronary Artery Bypass Graft Study) G Knatterud (deceased); PROSPER (Prospective Study of Pravastatin in the Elderly at Risk) GJ Blauw, S Cobbe, I Ford, P Macfarlane, C Packard, N Sattar, J Shepherd (deceased), S Trompet; PROVE-IT (Pravastatin or Atorvastatin Evaluation and Infection Therapy) E Braunwald, CP Cannon, S Murphy; SEARCH (Study of Effectiveness of Additional Reductions in Cholesterol and Homocysteine): R Collins, J Armitage, L Bowman, R Bulbulia, R Haynes, S Parish, R Peto, P Sleight (deceased); SPARCL (Stroke Prevention by Aggressive Reduction in Cholesterol Levels): P Amarenco, KM Welch; (4S Scandinavian Simvastatin Survival Study) J Kjekshus, TR Pedersen, L Wilhelmsen; TNT (Treating to New Targets) P Barter, A Gotto Jr, J LaRosa, JJP Kastelein, J Shepherd (deceased); WOSCOPS (West of Scotland Coronary Prevention Study) S Cobbe, I Ford, S Kean, P Macfarlane, C Packard, M Roberston, N Sattar, J Shepherd (deceased), R Young, Other CTT Members: H Arashi, R Clarke, M Flather, S Goto, U Goldbourt, J Hopewell, GK Hovingh, G Kitas, C Newman, MS Sabatine, GG Schwartz, L Smeeth, J Tobert, J Varigos, J Yamamguchi.

Contributors BM and CB conceived the study. BM, IS, JE, CR, JR, AG, JA, CB secured funding. All authors contributed to study design. BM, RW, JZ, CW, IS performed the analyses. BM drafted the paper with support from RW. All authors provided comments on the paper. BM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding This study was funded by the UK NIHR Health Technology Assessment (HTA) Programme (17/140/02). Further support from the British Heart Foundation (PG/18/16/33570 and CH/1996001/9454), the UK Medical Research Council (MC_UU_00017/4), the National Institute for Health Research Barts Biomedical Research Centre (NIHR203330) and NHMRC, Australia is acknowledged. The study was designed and analysed independently of all funders and the views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care or any other funder. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Competing interests AK reports research support from Abbott, Amgen, ASPEN, Bayer, Mylan, Novartis, Sanofi, Viatris; speaker fees from Novartis; and is a Data Safety Monitoring Board member for Kowa. JR reports funding from North East London Integrated Care Service. JA reports receiving a grant to their research institution from Novartis for the ORION 4 trial of inclisiran. JS reports receiving grants for his institution from Amgen, Bayer, BMS, MSD, Pfizer and Roche; consulting fees from FivepHusion, and is a chair (unpaid) of STAREE DSMB. CB reports research grants from Boehringer Ingelheim and Health Data Research UK and is a chair (unpaid) of a Data Safety Monitoring Board for Merck. All other authors declare no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer-reviewed.

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