#data quality metrics

[garymdm]

Proactive Data Quality Management - Early Detection of Issues

The early detection of data quality issues is vital for maintaining the integrity of information that drives business decisions. By ensuring high-quality data through proactive measures, organizations can enhance operational efficiency, reduce costs, improve customer satisfaction, manage risks effectively, and foster a culture of continuous improvement in their data practices. In this post we…

[shannoneichorn]

As a Christian aerospace engineer, I am all in on this heresy.

The spaceship is a physical item, imbued with the strength of a child's imagination. As such, we're discussing the design intent mechanical strength, rather than the metaphysical strength of Jesus' power.

Now, as a carpenter, Jesus was pretty strong. But as a spaceship (and I'm going to assume it's a reentry vehicle, too, because I doubt this child planned multiple stages), the hull will have to withstand both atmospheric pressure difference* and the stresses (aerodynamic and thermal) of reentry. Even a carpenter shouldn't be able to bang on it and make a dent. (Not allowing Jesus tools here, because the comparison is on his physical strength and we do not introduce physical defects to heat shields by chipping at them.)

*14.7 psid doesn't sound like all that much. Bike tires, after all, run around 60 psi, which is roughly 45 psid. But if you happened to see the world's largest vacuum chamber in the beginning of the first Avengers movie, you might have noticed that the 3-foot-thick aluminum wall was not enough for 15 psid, and it also had a 6-foot-thick (at the bottom) concrete wall to take some of the pressure. (Granted, that vacuum chamber is probably way bigger than the design intent spaceship, which affects the thickness of the walls.)

I think the kid is right and the spaceship is stronger than Jesus. (It's also less forgiving, but the kid can learn about the harsh realities of spaceflight when he's older.)

i’ve started babysitting for a VERY christian family which is great because they pay me a lot of money but as someone who was raised almost completely agnostic it’s kind of insane. the 2 year old keeps asking me to read her stories from the bible. (why are we reading david and goliath to a 2 year old????) the 5 year old told me today that he was going to bring his legos to heaven with him. he also has repeatedly told me that the lego spaceships he builds are stronger than jesus. (not sure what to say to that. do i deny it??? are things allowed to be stronger than jesus??) had to stop myself mid sentence today because i almost told them im not going to heaven which would DEFINITELY have caused several meltdowns. they’re also both completely fascinated by my nose ring

[andmineworlddigitalmarketing]

Beyond Likes and Shares: Measuring the Real Impact of Your Digital Marketing Efforts

In the whirlwind of social media updates and website traffic reports, it’s easy to get caught up in vanity metrics. Likes, shares, and followers can feel like a direct reflection of your digital marketing success. But are they truly telling the whole story? If you’re serious about maximizing your ROI(Return on Investment), it’s time to look beyond the surface and delve into the metrics that truly matter.

The Problem with Vanity Metrics

Vanity metrics, while visually appealing, often lack context. A high number of likes on a post doesn’t necessarily translate to increased sales or brand loyalty. Similarly, a surge in website traffic might be driven by bots or irrelevant visitors. To truly understand the impact of your digital marketing efforts, you need to focus on metrics that align with your business goals.

Key Metrics That Matter

Instead of relying solely on likes and shares, consider tracking these crucial metrics:

Conversion Rate: This metric measures the percentage of visitors who complete a desired action, such as making a purchase, signing up for a newsletter, or filling out a contact form. A high conversion rate indicates that your marketing efforts are effectively driving results.   

Customer Acquisition Cost (CAC): CAC calculates the total cost of acquiring a new customer through your marketing campaigns. By tracking CAC, you can determine the efficiency of your marketing spend and identify areas for optimization.

Customer Lifetime Value (CLTV): CLTV estimates the total revenue a customer will generate throughout their relationship with your business. By understanding CLTV, you can prioritize customer retention and invest in strategies that foster long-term loyalty.

Website Traffic Quality: Instead of focusing solely on the quantity of website traffic, pay attention to the quality. Analyze metrics such as bounce rate, time on page, and pages per session to understand how visitors are engaging with your content.

Return on Ad Spend (ROAS): For paid advertising campaigns, ROAS measures the revenue generated for every dollar spent. This metric helps you assess the profitability of your ad campaigns and make data-driven decisions.

Lead Generation: How many qualified leads are you generating? Track form submissions, downloads, and other lead-generating actions.

Search Engine Rankings: Where does your website rank for relevant keywords? Tracking your search engine rankings can provide valuable insights into your SEO performance.

Tools and Strategies for Tracking Key Metrics

To effectively track these metrics, you’ll need to utilize the right tools and strategies:

Google Analytics: This powerful tool provides comprehensive insights into website traffic, user behavior, and conversion rates.

Social Media Analytics Platforms: Each social media platform offers its own analytics tools, providing data on engagement, reach, and audience demographics.

CRM Systems: Customer relationship management (CRM) systems help you track customer interactions, manage leads, and measure the effectiveness of your marketing campaigns.

Marketing Automation Software: These tools automate marketing tasks, such as email marketing and social media posting, and provide detailed analytics on campaign performance.

Making Data-Driven Decisions

By focusing on the metrics that matter, you can make informed decisions about your digital marketing strategy. Analyze your data regularly, identify trends, and adjust your approach accordingly. Remember, digital marketing is an ongoing process of optimization and improvement.

Conclusion

Don’t let vanity metrics distract you from your true goals. Focus on measuring the real impact of your digital marketing efforts, and you’ll be well on your way to achieving sustainable growth and success.

[projectchampionz]

STRATEGIES FOR EFFECTIVE PHYSICIAN ENGAGEMENT AND COLLABORATION IN HEALTHCARE ORGANIZATIONS

STRATEGIES FOR EFFECTIVE PHYSICIAN ENGAGEMENT AND COLLABORATION IN HEALTHCARE ORGANIZATIONS 1.1 Introduction Physician engagement and collaboration are critical to the success of healthcare organizations, as they are directly linked to improved patient outcomes, organizational efficiency, and overall healthcare quality. In a rapidly evolving healthcare environment, effective physician engagement…

[vertagedialer]

[mostlysignssomeportents]

The Coprophagic AI crisis

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in TORONTO on Mar 22, then with LAURA POITRAS in NYC on Mar 24, then Anaheim, and more!

A key requirement for being a science fiction writer without losing your mind is the ability to distinguish between science fiction (futuristic thought experiments) and predictions. SF writers who lack this trait come to fancy themselves fortune-tellers who SEE! THE! FUTURE!

The thing is, sf writers cheat. We palm cards in order to set up pulp adventure stories that let us indulge our thought experiments. These palmed cards – say, faster-than-light drives or time-machines – are narrative devices, not scientifically grounded proposals.

Historically, the fact that some people – both writers and readers – couldn't tell the difference wasn't all that important, because people who fell prey to the sf-as-prophecy delusion didn't have the power to re-orient our society around their mistaken beliefs. But with the rise and rise of sf-obsessed tech billionaires who keep trying to invent the torment nexus, sf writers are starting to be more vocal about distinguishing between our made-up funny stories and predictions (AKA "cyberpunk is a warning, not a suggestion"):

https://www.antipope.org/charlie/blog-static/2023/11/dont-create-the-torment-nexus.html

In that spirit, I'd like to point to how one of sf's most frequently palmed cards has become a commonplace of the AI crowd. That sleight of hand is: "add enough compute and the computer will wake up." This is a shopworn cliche of sf, the idea that once a computer matches the human brain for "complexity" or "power" (or some other simple-seeming but profoundly nebulous metric), the computer will become conscious. Think of "Mike" in Heinlein's *The Moon Is a Harsh Mistress":

https://en.wikipedia.org/wiki/The_Moon_Is_a_Harsh_Mistress#Plot

For people inflating the current AI hype bubble, this idea that making the AI "more powerful" will correct its defects is key. Whenever an AI "hallucinates" in a way that seems to disqualify it from the high-value applications that justify the torrent of investment in the field, boosters say, "Sure, the AI isn't good enough…yet. But once we shovel an order of magnitude more training data into the hopper, we'll solve that, because (as everyone knows) making the computer 'more powerful' solves the AI problem":

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

As the lawyers say, this "cites facts not in evidence." But let's stipulate that it's true for a moment. If all we need to make the AI better is more training data, is that something we can count on? Consider the problem of "botshit," Andre Spicer and co's very useful coinage describing "inaccurate or fabricated content" shat out at scale by AIs:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4678265

"Botshit" was coined last December, but the internet is already drowning in it. Desperate people, confronted with an economy modeled on a high-speed game of musical chairs in which the opportunities for a decent livelihood grow ever scarcer, are being scammed into generating mountains of botshit in the hopes of securing the elusive "passive income":

https://pluralistic.net/2024/01/15/passive-income-brainworms/#four-hour-work-week

Botshit can be produced at a scale and velocity that beggars the imagination. Consider that Amazon has had to cap the number of self-published "books" an author can submit to a mere three books per day:

https://www.theguardian.com/books/2023/sep/20/amazon-restricts-authors-from-self-publishing-more-than-three-books-a-day-after-ai-concerns

As the web becomes an anaerobic lagoon for botshit, the quantum of human-generated "content" in any internet core sample is dwindling to homeopathic levels. Even sources considered to be nominally high-quality, from Cnet articles to legal briefs, are contaminated with botshit:

https://theconversation.com/ai-is-creating-fake-legal-cases-and-making-its-way-into-real-courtrooms-with-disastrous-results-225080

Ironically, AI companies are setting themselves up for this problem. Google and Microsoft's full-court press for "AI powered search" imagines a future for the web in which search-engines stop returning links to web-pages, and instead summarize their content. The question is, why the fuck would anyone write the web if the only "person" who can find what they write is an AI's crawler, which ingests the writing for its own training, but has no interest in steering readers to see what you've written? If AI search ever becomes a thing, the open web will become an AI CAFO and search crawlers will increasingly end up imbibing the contents of its manure lagoon.

This problem has been a long time coming. Just over a year ago, Jathan Sadowski coined the term "Habsburg AI" to describe a model trained on the output of another model:

https://twitter.com/jathansadowski/status/1625245803211272194

There's a certain intuitive case for this being a bad idea, akin to feeding cows a slurry made of the diseased brains of other cows:

https://www.cdc.gov/prions/bse/index.html

But "The Curse of Recursion: Training on Generated Data Makes Models Forget," a recent paper, goes beyond the ick factor of AI that is fed on botshit and delves into the mathematical consequences of AI coprophagia:

https://arxiv.org/abs/2305.17493

Co-author Ross Anderson summarizes the finding neatly: "using model-generated content in training causes irreversible defects":

https://www.lightbluetouchpaper.org/2023/06/06/will-gpt-models-choke-on-their-own-exhaust/

Which is all to say: even if you accept the mystical proposition that more training data "solves" the AI problems that constitute total unsuitability for high-value applications that justify the trillions in valuation analysts are touting, that training data is going to be ever-more elusive.

What's more, while the proposition that "more training data will linearly improve the quality of AI predictions" is a mere article of faith, "training an AI on the output of another AI makes it exponentially worse" is a matter of fact.

Name your price for 18 of my DRM-free ebooks and support the Electronic Frontier Foundation with the Humble Cory Doctorow Bundle.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/03/14/14/inhuman-centipede#enshittibottification

Image: Plamenart (modified) https://commons.wikimedia.org/wiki/File:Double_Mobius_Strip.JPG

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

[read-marx-and-lenin]

Do you have any good positive statistics about the succeseses of socialist states? I need some numbers to pull out when people bring up the big scary numbers when trying argue that communism is historically a force for good.

It usually depends on what is being argued in the first place. Statistics are notoriously malleable and easy to take out of context, and whenever you're arguing based on statistics you need to make sure you're first in agreement as to the relevance and reliability of the statistics to the argument at hand.

That said, my two go-to papers when it comes to providing general statistics in defense of socialism when arguing with liberals are "Capitalism, socialism, and the physical quality of life" by Cereseto & Waitzkin, 1986, and "The true extent of global poverty and hunger" by Jason Hickel, 2016. Both are by Western researchers, both are published in Western journals, and both use Western economic data, which provides liberals with little opportunity to dismiss them as fraudulent.

The first paper by Cereseto and Waitzkin directly compared socialist and capitalist nations at similar levels of economic development, showing that socialist nations consistently outperformed capitalist nations when it came to metrics regarding the physical quality of life. That is, socialism is demonstrably superior to capitalism at feeding, sheltering, clothing, and providing medicine for its citizens, and even Western data sources confirmed this.

The second more recent paper by Hickel is a scathing critique of post-Soviet liberal narratives regarding the supposed progress made by capitalism as alleviating poverty and hunger worldwide. It demonstrated specifically that the UN's claims regarding progress towards the Millennium Development Goals are based on manipulated statistics, and that the neoliberal economic policies imposed on vulnerable nations by imperialist powers via the World Bank and the IMF have only worsened poverty and hunger. Hickel also shows how the UN and Western liberals have tried to launder China's successes at alleviating poverty into successes of global capitalism despite China's socialist development model being rejected by those same liberal institutions in favor of neoliberal economic policies that have been proven ineffective.

[tom781]

SEO for YouTube: How to Optimize Your Videos for Search

Meet Paul. Paul is a budding YouTuber with a passion for tech reviews and tutorials. He’s been creating content for a while, but his channel isn’t growing as quickly as he’d hoped. Paul’s videos are high-quality, informative, and engaging, yet they’re not reaching a wide audience. The key problem? His videos are not optimized for YouTube’s search algorithm. This is where SEO, or Search Engine Optimization, comes into play.

Understanding YouTube SEO

SEO for YouTube involves optimizing your videos so they rank higher in search results. Higher ranking videos get more views, which can lead to more subscribers and overall channel growth. Here’s how Paul can optimize his videos for YouTube search:

Keyword Research

Paul’s first step is to find the right keywords. Keywords are the terms and phrases that users type into the search bar when looking for videos. Paul uses tools like Google Trends, TubeBuddy, and VidIQ to identify popular keywords related to his content. For instance, if Paul’s video is about the latest iPhone review, he might discover that “iPhone 14 review,” “iPhone 14 unboxing,” and “iPhone 14 vs Samsung Galaxy S22” are popular search terms.

Optimizing Video Titles

Once Paul has his keywords, he needs to incorporate them into his video titles. A good title is clear, concise, and includes the main keyword. For example, instead of titling his video “My Thoughts on the New iPhone,” Paul titles it “iPhone 14 Review: In-Depth Look at Apple’s Latest Smartphone.” This title is more likely to match what users are searching for.

Creating Engaging Thumbnails

Thumbnails are the first thing viewers see. An eye-catching thumbnail can significantly increase click-through rates. Paul creates custom thumbnails that are visually appealing and relevant to the video content. He includes the video title or key phrases in the thumbnail to attract viewers’ attention.

Writing Detailed Descriptions

The video description is another crucial SEO element. Paul writes detailed descriptions for his videos, incorporating his main keyword and related terms naturally. He includes a brief summary of the video, timestamps for different sections, and links to his social media, website, and other relevant videos. This not only helps with SEO but also provides a better viewer experience.

Using Tags Effectively

Tags help YouTube understand the content of a video. Paul uses a mix of broad and specific tags, including his main keyword and variations of it. For his iPhone review video, he might use tags like “iPhone 14,” “iPhone review,” “Apple smartphone review,” and “tech reviews 2023.”

Engaging with Viewers

Engagement metrics like likes, comments, and watch time also influence search rankings. Paul makes an effort to engage with his audience by asking questions in his videos, responding to comments, and encouraging viewers to like and share his videos. The more engagement his videos get, the higher they are likely to rank.

Promoting Videos on Social Media

Paul doesn’t rely solely on YouTube’s search algorithm to drive traffic. He promotes his videos on social media platforms like Twitter, Facebook, and Instagram. By sharing his videos with a broader audience, he increases the chances of getting more views and engagement.

Analyzing and Adjusting

Finally, Paul regularly reviews his analytics to understand what’s working and what’s not. He looks at metrics like watch time, click-through rates, and viewer retention. Based on this data, Paul adjusts his SEO strategy and content approach to continually improve his channel’s performance.

Conclusion

Through consistent effort and strategic optimization, Paul starts to see his videos rank higher in YouTube search results. His channel grows steadily, attracting more viewers and subscribers. By following these SEO practices, Paul not only improves his search rankings but also enhances the overall quality and reach of his content.

For any YouTuber looking to grow their channel, understanding and implementing YouTube SEO is crucial. Just like Paul, you too can optimize your videos and achieve greater success on the platform.

[frankenstinegirlz]

A statistical case for socialism

We’ve often heard the claim that “socialism killed millions.” This phrase is repeated so frequently that it's accepted by many without question. But when we take a serious look at the data, a different picture begins to emerge, one that challenges the dominant narrative. In fact, socialism has repeatedly shown its ability to improve the material well-being of ordinary people.

An important study, Economic Development, Political-Economic System, and the Physical Quality of Life by Shirley Ceresto and Howard Waitzkin, offers compelling evidence in support of this. The researchers found that socialist countries generally produced better outcomes in crucial quality of life metrics. Take infant mortality, for instance. In low-income capitalist countries, there were about 131 infant deaths per 1,000 infants. In contrast, low-income socialist countries saw nearly half that rate—just 71 deaths per 1,000. That difference isn’t just a number—it represents thousands of children’s lives.

And it doesn't stop there. Socialist countries also tended to outperform their capitalist counterparts in areas like calorie intake, life expectancy, and literacy. These aren’t minor details—they are fundamental measures of human well-being. The overall conclusion of the study was clear: socialist systems often deliver better results when it comes to basic human needs.

Let’s also consider a modern example: Cuba. Despite decades of harsh economic sanctions and political pressure from the United States, Cuba continues to outperform many wealthier nations in key health metrics. According to data from cubaplatform.org, 

Cuba has roughly seven doctors per 1,000 people, one of the highest ratios in the world. It even surpasses countries like the United States and Canada. That’s not just impressive, it’s a powerful testament to what a society can achieve when it prioritizes public welfare over profit.

Of course, no system is perfect, but if we truly care about human life, dignity, and well-being, we owe it to ourselves to question the narratives we’ve been fed. The data tells a story that can’t be ignored: socialism, in many cases, has worked—and worked well.

[garymdm]

Beyond Validation: How the Best Data Quality Rules are Actually Business Rules

Data quality is the lifeblood of good decision-making. Inaccurate or incomplete data can lead to missed opportunities, wasted resources, and even regulatory fines. But how do we ensure our data quality rules are effective? Conventional Data Quality MetricsData Quality Rules are Business RulesFraming the Data Quality ConversationThe Power of Business-Driven RulesMoving from Validation to Business…

[dailycupofcreativitea]

Man I work with tumour data for a variety of cancer types and ovarian cancer data is by far the most fucked up

It's just all over the place and destroys our metrics, we have to keep giving it conditional passes (like "yes this fails all our quality checks but...it's ovarian cancer so we kind of have to lower our standards")

Every time we see something crazy the geneticist goes "...is this ovarian?" and we check and it is and then we all go "ah everything makes sense now" By far the cleanest and most predictable data I've worked with is pancreatic cancer (despite it being one of the most aggressive cancers)

[luxe-pauvre]

What we hardly talk about is how we’ve reorganized not just industrial activity but any activity to be capturable by computer, a radical expansion of what can be mined. Friendship is ground zero for the metrics of the inner world, the first unquantifiable shorn into data points: Friendster testimonials, the MySpace Top 8, friending. Likewise, the search for romance has been refigured by dating apps that sell paid-for rankings and paid access to “quality” matches. Or, if there’s an off-duty pursuit you love—giving tarot readings, polishing beach rocks—it’s a great compliment to say: “You should do that for money.” Join the passion economy, give the market final say on the value of your delights. Even engaging with art—say, encountering some uncanny reflection of yourself in a novel, or having a transformative epiphany from listening, on repeat, to the way that singer’s voice breaks over the bridge—can be spat out as a figure, on Goodreads or your Spotify year in review. And those ascetics who disavow all socials? They are still caught in the network. Acts of pure leisure—photographing a sidewalk cat with a camera app or watching a video on how to make a curry—are transmuted into data to grade how well the app or the creators’ deliverables are delivering. If we’re not being tallied, we affect the tally of others. We are all data workers. Twenty years ago, anti-capitalist activists campaigned against ads posted in public bathroom stalls: too invasive, there needs to be a limit to capital’s reach. Now, ads by the toilet are quaint. Clocking out is obsolete when, in the deep quiet of our minds, we lack the pay grade to determine worth.

Thea Lim, The Collapse of Self-Worth in the Digital Age

[starpains]

A deep dive into AO3 stats

I was feeling a bit bored today, so I decided to create something awesome for myself!

Some of you might already know that I work in data analytics, and I’m not sure if everyone realizes this, but you can actually connect MS Excel Power Query to AO3 to sync your stats and perform in-depth analysis—far beyond what AO3’s built-in stats offer.

My stats aren’t exactly impressive since I only joined the fandom six months ago, but here’s a glimpse of the kind of insights you can generate with this setup:

I analyzed reader engagement with my fics by calculating the percentage of people who comment, leave kudos, or bookmark each fic relative to the total number of hits it received. This gave me three measures of engagement: Comment Threads per Hit (CTpH), Kudos per Hit (KpH), and Bookmarks per Hit (BpH).

To avoid skewed results caused by differences in scale across these measures, I standardized them. I did this by dividing each value by the maximum value in its column, which normalized the data and made the metrics comparable. These standardized values were then combined to calculate an Average Engagement per Hit (AVGpH) for each fic. This single metric allowed me to rank my fics from best to worst based on overall engagement per reader.

Here’s the surprising part: my best-performing fic in terms of engagement isn’t my most kudosed one (ranked 4th), nor is it the most commented (ranked 2nd), or the most bookmarked (ranked 8th). This highlights that pure volume of kudos, comments, or bookmarks doesn’t always reflect the quality of reader engagement relative to the fic’s visibility (hits). Instead, it’s the balance across all three engagement types that determines which fic truly resonates most with readers.

I also analyzed my stats per word to see how the length of my fics influences engagement. While it’s still early to draw definitive conclusions—especially since my longest fic is still a work in progress (I’ll definitely revisit the stats once it’s finished!)—there are already some interesting patterns emerging.

By breaking things down into metrics like Comments per Word (CTpW), Kudos per Word (KpW), Bookmarks per Word (BpW), and Hits per Word (HpW), I could compare how readers interact with fics of different lengths. I combined these into an Average Engagement per Word (AVGpW) to rank my fics by their engagement efficiency.

Key Takeaways

Shorter fics tend to have higher engagement per word. Some of the shorter fics perform exceptionally well, with high Hits per Word and strong kudos and bookmarks per word. On the other hand, longer works show diluted engagement per word, likely because their length makes them a bigger commitment for readers.

Balanced fics perform the best overall. Fics with solid kudos, bookmarks, and hits per word are ranked as the most engaging across the board.

Length affects engagement differently. Shorter or mid-length fics often generate more impact per word, likely because they’re easier for readers to consume quickly. Longer works, while attracting more total engagement, tend to have lower per-word metrics, likely due to their scale.

My top-ranked fic in terms of engagement per word is, interestingly, the one with the most kudos. However, it also has the fewest comments! It’s ranked 7th for bookmarks and has the lowest number of hits among all my fics.

I’m curious to see how these trends shift once my longest fic is complete—it’s a factor that could change these results significantly. For now, though, this analysis highlights how length can influence the way readers engage with your work!

[covid-safer-hotties]

Also preserved in our archive

By Dr. Sanchari Sinha Dutta, Ph.D.

A study published in the journal Communications Medicine reports the prevalence and consequences of post-coronavirus disease 2019 (COVID-19) symptoms in children and young individuals up to 24 months post-infection.

Background A significant proportion of COVID-19 patients consistently experience a range of health complications even after months or years of the initial infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This condition is medically termed as long-COVID.

The Long COVID in Children and Young People (CLoCk) study has been designed to explore long-COVID symptoms in children and adolescents aged under 18 years. The study has reported findings on long-COVID in 20,202 children and adolescents living in England up to 12 months after their initial SARS-CoV-2 infection.

In the current study, scientists have analyzed the CLoCk study data to report long-COVID symptoms and their consequences in children and adolescents for up to 24 months post-SARS-CoV-2 infection. This extended follow-up is crucial for understanding the persistence of symptoms over time and their potential impact on quality of life.

Study Design The study included a total of 12,632 children and adolescents from the CLoCk study who were 11 to 17 years old at the time of their initial SARS-CoV-2 testing (between September 2020 and March 2021).

The participants were categorized into four groups according to their infection status over the period of 24 months. The first group included participants who never tested positive for SARS-CoV-2. The second group included those who were initially test-negative but subsequently tested positive. The third group included those who were initially test-positive but did not have reinfection later on. The fourth group included those who were initially test-positive and also developed reinfection later on.

Participants reported long-COVID symptoms and their consequences, which were examined at 3, 6, 12, and 24 months after the initial SARS-CoV-2 infection. To operationalize long-COVID in children, the study used the Delphi research definition, focusing on persistent symptoms and associated difficulties in daily functioning.

Important Observations All study participants reported experiencing some symptoms 24 months after their initial infection. The most frequently reported symptoms were tiredness, trouble sleeping, shortness of breath, and headaches.

A variation in symptom prevalence was observed between the study groups. While participants who never tested positive exhibited the lowest prevalence of symptoms, the highest prevalence was observed among participants who initially tested positive and later developed reinfection.

The study groups also observed a variation in the total number of reported symptoms. While 35% of participants who initially tested positive and subsequently developed reinfection reported no symptoms, 46% of participants who never tested positive for SARS-CoV-2 reported the same experience. However, even among the never-positive group, 14% experienced five or more symptoms, highlighting the non-specific nature of many symptoms reported.

Among participants who reported experiencing more than five symptoms, about 14% were from the never-positive group, and 21% were from the initial test-positive and subsequent reinfection group.

Despite significant variation in symptoms, only a slight variation in self-rated health, symptom severity, and symptom impact was observed between the study groups at a 24-month timepoint. This finding raises questions about whether self-perceived health metrics can fully capture the burden of long-COVID in children.

Considering the demographic characteristics of participants, the study found that long-COVID is more common among older participants, female participants, as well as socioeconomically deprived participants.

Participants who fulfilled the long-COVID Delphi research definition exhibited more difficulties, worse quality of life, and more tiredness than those who did not meet the long-COVID Delphi research definition.

Only 7.2% of the participants fulfilled the long-COVID Delphi research definition at 3-, 6-, 12-, and 24-month time points. These participants reported an average of five symptoms at 3 months, five at 6 months, six at 12 months, and five at 24 months post-infection. This consistent subgroup reflects a more severe and persistent burden of symptoms, emphasizing the need for targeted support.

Considering vaccination status, the study found no apparent trend in the number of reported symptoms, health status, quality of life, and symptom impact or severity between vaccinated and unvaccinated participants at 24 months.

Study Significance The study finds that a considerable proportion of children and adolescents (aged 11 to 17) consistently experience, on average, five symptoms over the period of 24 months post-SARS-CoV-2 infection, irrespective of their infection status during this period.

While the most commonly reported symptoms are tiredness, trouble sleeping, shortness of breath, and headaches, the participants less frequently report abdominal pain, concentration difficulties, and muscle pain. Although reported by a minority, these less frequent symptoms can still significantly affect daily activities and warrant further attention.

The study used the long-COVID Delphi research definition to analyze symptoms, which, in contrast to the World Health Organization (WHO) definition, does not require symptoms to have arisen within the first three months of infection. It is the only definition currently being used for children and adolescents and is considered to be more powerful in capturing long-COVID symptoms, particularly for those who remained asymptomatic or unaware of having an infection during the acute SARS-CoV-2 infection phase.

Crucially, the study emphasizes that many of the reported symptoms are common in adolescents irrespective of their SARS-CoV-2 infection status, suggesting a potential overlap between long-COVID and general adolescent health issues.

Notably, the study could not find any significant variation in self-rated health, symptom severity, or symptom impact among children and adolescents with varying infection and vaccination status. Furthermore, the symptoms reported by participants are non-specific and often commonly reported in adolescents, even before the COVID-19 pandemic.

Considering the findings, scientists highlight the need for further studies to understand the pathophysiology, develop diagnostic tests, and identify effective interventions for long-COVID management in children and adolescents. In particular, longitudinal studies are essential to clarify the natural history of symptoms and their impact over time.

Journal reference: Stephenson, T., Pinto Pereira, S. M., Nugawela, M. D., Dalrymple, E., Harnden, A., Whittaker, E., Heyman, I., Ford, T., Segal, T., Chalder, T., Ladhani, S. N., McOwat, K., Simmons, R., Xu, L., & Shafran, R. (2024). A 24-month National Cohort Study examining long-term effects of COVID-19 in children and young people. Communications Medicine, 4(1), 1-12. DOI:10.1038/s43856-024-00657-x, www.nature.com/articles/s43856-024-00657-x