{"id":17220,"date":"2026-05-16T18:00:32","date_gmt":"2026-05-16T18:00:32","guid":{"rendered":"https:\/\/punkfish-academy.com\/divers-vs-profiles-the-new-dan-db-analysis\/"},"modified":"2026-05-16T19:07:10","modified_gmt":"2026-05-16T19:07:10","slug":"divers-vs-profiles-the-new-dan-db-analysis","status":"publish","type":"post","link":"https:\/\/punkfish-academy.com\/en\/divers-vs-profiles-the-new-dan-db-analysis\/","title":{"rendered":"Divers vs. Profiles: The New DAN DB Analysis"},"content":{"rendered":"

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A new analysis of dive profiles \u2013 but what can the data truly tell us?<\/h2>\n

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A new paper on DCS risk assessment is currently sparking discussions. It was published in early 2026 by a group of researchers, almost all affiliated with DAN Europe. The data comes from the DAN DSL Database, analyzing 127,957 dives from 5,907 divers<\/strong>. In addition to dive profiles, the data includes information about the divers: gender, height, weight, BMI, cold exposure, fatigue, and more \u2013 an excellent opportunity to gain insights into the many open questions regarding risk factors.
On social media, some of the study’s “findings” are already being actively shared and discussed: Do women really have a 3-4 times higher risk of getting DCS? And is it plausible that lean, rather underweight divers have a higher risk than overweight ones?
The sometimes surprising results could be due to some structural weaknesses in the study.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”"Identification of DCS risk factors in recreational diving…"” url=”https:\/\/journals.viamedica.pl\/international_maritime_health\/article\/view\/108038″ url_new_window=”on” image=”https:\/\/punkfish-academy.com\/wp-content\/uploads\/2026\/05\/Marroni-2026-Titel.png” _builder_version=”4.27.6″ _module_preset=”cd0bdbae-d9d8-4b43-9d1f-98137af1254b” link_option_url=”https:\/\/journals.viamedica.pl\/international_maritime_health\/article\/view\/108038″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n

Marroni, A., Kot, J., Pieri, M., Pelliccia, R., & Balestra, C. (2026). Identification of DCS risk factors in recreational diving: multifactorial model based on the DAN DSL Database 2024. International Maritime Health<\/i>, 77<\/i>(1), 1\u201312. https:\/\/doi.org\/10.5603\/imh.108038<\/div>\n

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It starts with the question of how many dive profiles were actually examined. Perhaps it was 136,793 \u2013 some dives were sorted out due to data ambiguities but reappear later in the analysis. Or even 127,197, which appear in the question of DCS frequency. The total number of dives examined in the paper is unfortunately no more consistent than the rest, and the careless handling of these numbers raises suspicion.
At first glance, however, this still sounds impressive: many dives, real profiles, a large database. Many divers expect relevant insights from the analysis of such data, which is why the study is mentioned more frequently and perceived by more people than would be normal for a niche topic like decompression. And because it comes from DAN, the study immediately receives a vote of confidence \u2013 but does it truly deserve it? <\/p>\n

The study is long and raises many questions \u2013 too many to discuss in one blog post. However, three problems stand out particularly in the paper, and we want to take a closer look at them in three separate posts. <\/p>\n

    \n
  1. Where does the data come from?<\/strong> The entire data collection is not explained, and it appears that voluntarily reported profiles and DCS cases reported to DAN are simply combined. We discuss why this is a problem here: Statistics is hard to get<\/a> <\/li>\n
  2. Dives as independent events<\/strong> \u2013 the almost 130,000 dives are analyzed as if they were independent events, yet they come from only about 6,000 divers who contributed very different amounts. We discuss why this is a problem in this article. <\/li>\n
  3. Confusion with the “DSSG”<\/strong>: The authors invent a new measure for supersaturation that they do not explain \u2013 but it is clearly NOT the normal GF upon reaching the surface that divers are familiar with. This will be covered in another blog post. <\/li>\n<\/ol>\n<\/div>\n

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    Some Divers Count More Than Others<\/h3>\n

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    In this post, we want to take a closer look at an aspect that can lead to distortions in the results: What happens when parts of the data are not independent of each other?<\/p>\n

    The study itself states that individual divers contributed very different numbers of profiles. Some only a single dive, others very many. The median is three, the maximum is 1,432 dives per diver.
    What about median, averages, and so on? What is meant here? The median is the value where half of the data is below it, and half is above it. This means that out of the almost 6,000 divers, 3,000 contributed a maximum of 3 dives \u2013 making a maximum of 9,000 dives, probably less. The other 3,000 each contributed three or more, accounting for at least 120,000 of the profiles. <\/p>\n

    A person who contributes an extreme number of dives has many characteristics, such as BMI and gender, that remain constant. Their data counts more in the analysis than those who contributed only a few dives, and this can distort the picture. Moreover, a person’s diving behavior is usually relatively consistent, and thus the data that would otherwise vary from dive to dive becomes data where this person has a stronger influence on the statistics than others. <\/p>\n

    This is a relevant methodological problem. To understand it more precisely, let’s start with a small model. <\/p>\n

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    When one diver contributes a lot of data<\/h4>\n

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    To understand fundamentally why it matters when relevant factors are weighted differently in the analysis, let’s take a small model. Right upfront: with larger datasets, the effect is far less pronounced \u2013 we want to show where the methodological problem lies, and therefore use an example where it becomes overtly visible. <\/p>\n

    Let’s imagine 100 divers who share a total of 2000 dives. One diver contributes 1000 dives, while the others are distributed among the remaining 99 divers. <\/p>\n

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    The problem is immediately apparent here. Only one diver contributes 50% of the data \u2013 thus determining person-specific characteristics like gender and BMI by 50%, even though they are only one out of a hundred.
    Next, we’ll look at what this means in the analysis when 10 DCS cases are randomly distributed among the shared profiles. <\/p>\n

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    Two people swap places<\/h4>\n

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    From these profiles, we will now construct a small thought experiment. For this, the characteristics of the 100 divers are randomly determined once: <\/p>\n