imiss 2011 · 2018. 2. 22. · nmd 38,4% ihs fairplay 34,1% common 13,1% not reported 40,6% chao...

IMISS 2011International Maritime Incident and Near Miss Reporting Conference

fredag 2. september 2011

Thank you.

MARTINHASSELNAVIGATOR

Hello everyone.My name is Martin, and I am a safety engineer with Safetec Nordic.

UNDERREPORTING

I’m here to talk to you about underreporting of maritime accidents.

INTRODUCTION

So, could you all please stand up, and wave your hands, like we did yesterday?Bo never told you why, so I will. It gets your blood circulating, so you're able to stay awake for another 20 minutes.

A good foundation is essential for most successful endeavors.And accident databases are in many cases the foundation that serves as validation and basis of many types of risk assessments.Especially in the realm of maritime transport and risk associated with typical accidents such as collision and grounding and such.

So are we standing on solid ground?

Or are risk analyses based on statistical data like a house of cards?

In some aspects of life a few holes can be good

but with regards to information and data, holes are bad.

MARINE DATABASES

Flag states

Most countries have their own national accident databases, and there are a few international databases, such as the IMO's GISIS, and the EU's EMCIP, which has been talked about earlier today.

Commercial actors

There's also a few commercial actors that sell maritime transportation data to a wide range of businesses.I'll get back to that shortly.

Accident reporting

What's the point again of all this accident reporting to authorities?

investigate

We want to be able to investigate certain accidents

to learn from our mistakes

improve

improve design and technical equipment

change legislation

save money

and save money.Because accidents cost...

...a lot of money.

Nordic Marine Insurance Statistics Database

Thats why many insurance companies have their own accident databases, such as the NoMIS database, compiled by Cefor.

CEFOR is the “Nordic Association of Marine Insurers” a co-operative that pools their Hull & Machinery claims data in order to achieve greater coverage and accuracy in their statistical analyses of insurance rates. Members voluntarily submit updated claims information electronically each month. The data transfer is standardized, and thus secure, convenient and practical.

Anonymized

The data is anonymized upon submission and entered into the NoMIS database. Which has global coverage of most vessel types and flags. The database’s strength is in its secured input, meaning it can present data with great confidence and accuracy. It also offers SQL advanced search and sorting options, and is complemented with general vessel specifics from Lloyds List. NoMIS’ low level of cause and consequence description, and its anonymous nature makes it unsuitable for individual risk management analyses. However, it still retains value as a statistical tool to see large trends, or from an insurers point of view.

Proprietary and closed

The problem for researchers is that it is strictly proprietary, and access is categorically denied.We were only given anonymized data through their actuary, and no direct access.Our hypothesis was that underreporting would be less prevalent in such a database, where ship operators have a monetary incentive to report accidents.

The Norwegian Maritime Directorate

The Norwegian national maritime authority has a casualty database that is fairly typical in scope and nature for flag state databases.

The NMD’s database covers all Norwegian registered vessels, and all vessels sailing in Norwegian waters. Access to the database requires access to the NMD’s intranet, but a small excerpt is available on their website.

IMOʼs GISIS Casualty database

The casualty module of the IMO’s Global Integrated Shipping Information System is an example of one of the international accident databases.

Excellent framework

GISIS receives accident reports submitted from flag state authorities, but only requires accidents categorized as “very serious” or “serious” to be reported, “unless there are important lessons to be learned.” This database is relatively new, with the biggest potential from a risk management perspective, and has the most amount of data fields.

Largely empty

The only problem is that it is in fact virtually empty.Also, all accident investigation reports submitted from flag state authorities are not made publicly available.

IHS Fairplay Sea-Web

One of the commonly used commercial databases is IHS Fairplay, previously called, and maybe better known as Lloyd’s Register Fairplay Sea-Web.

Sea-Web is the oldest, largest and most comprehensive of all the databases I've studied. It has good records of vessel and owner history and ship particulars, but does not have a satisfactory detail level of accidents in my opinion.

Encyclopedic proportions

The database is continuously updated, but has limited search capabilities. All in all, Sea-Web is the “Encyclopedia Britannica” of marine databases...

Expensive

...but at the same time it's relatively expensive to use.

METHODOLOGY

my research has tried to quantify underreporting

but before I go into the methodology, I want to start by talking a little about historical data and statistics in general.

Somebody once said that there are three types of lies.

DAMNED LIESLIES,AND STATISTICS.

Lies, damned lies and statistics.It may not be that bad...

...but statistics should be used with caution.

Because the future is not a linear continuation of the past.And the events of the last 50 years is not necessarily an indication of events to come in the next 50 years.

Let me give you a few examples:

Dangerous?

Statistics tell us that the majority of fatalities from leisure crafts are men over 40.Does that mean they are more at risk? Is there a causal link?Or is it simply a reflection of the fact that most boat-owners, are men over 40?

Dangerous?

Statistics also tell us that most leisure craft accidents happen in good weather.Does that mean nice weather is dangerous?Or is it simply a reflection of the fact that more people are out at sea when the weather is nice?

That said. A lot of people study for a long time and become good at looking at statistics.

It is possible to do some magic with statistics, if you know what you are doing.

Unfortunately, I'm not a statistician… so I called one and asked what methodology would be best suited to find underreporting in accident data.

He suggested several options, such as Bayesian conditional probability and several versions of capture-recapture methodology.

Whale hello there...

Capture-recapture methodology is most commonly used in epidemiology and for estimating animal population sizes.However, it can also be used to compare data from two sources, to find an estimate of underreporting, and has been used in several articles on road safety and personal injury reporting.

Chao's lower bound estimate

A capture-recapture method called Chao's lower bound estimate seemed like the best tool to give the most precise estimation of underreporting in maritime accident databases.

Source 1 Source 2Common

Best case

Just to be absolutely certain, I also decided to find a "de facto" level of underreporting.This was done by assuming perfect reporting. Meaning all accidents would be reported to one of my sources, or both.

Not reported

Source 2Source 1 Common

Chao's lower bound estimates the amount of underreporting based on input from the two sources and their overlap.For those of you interested in math, I can tell you that Chao estimates the unknown quantity, shown in grey, like this:

To make sure we compared apples to apples and oranges to oranges, a semi-manual filtration process was performed.To ensure conformity of the data, and to remove double entries and other data abnormalities.

Together with some colleagues from the Norwegian University of Science and Technology we studied a selection of national and international databases, shown here, looking for underreporting.The two sources for each study was the flag state national maritime authority, and IHS Fairplay.

RESULTS

So what did we find?

NMD38,4%

IHS Fairplay34,1%

Common13,1%

Not reported40,6% Chao

The Norwegian national accident database was estimated to have just over a third of all occurred accidents in their records.Indicating a level of underreporting of 40%. And keep in mind that our methodology gives us a lower bound estimate.And if you look at the underreporting from the NMD's point of view, it is over 60%!

Not reported35% Best

The best case scenario still indicated a level of underreporting of 35 %.

Anyone better?

So how did some other countries perform compared to Norway?

61 %67 %

56 %61 %

37 %41 %

UnreportedReported Reporting performance

Turns out they did even worse, except for Sweden. The average level of underreporting across the sample space, is just over 50%.

ANALYSIS

So what does this mean?

COLLISIONANALYSIS

For engineers like myself, that perform risk assessments such as collision risk analyses, on a weekly basis

for clients from

big oil companies and offshore operators

it’s important to discover potential errors that may weaken the foundation of our work.

Statistical data may be missing key parts of the whole picture. And it’s important to be aware of this.

Faulty input will skew the results and give a wrong picture of the risks involved.

Oh no...

But even with the discovery of such high levels of underreporting, all is not lost.First of all, we try to avoid using statistical data, or use a selection of data that has been quality assured.

But when a task requires the use of old technology, or analyses are forced to resort to statistical data for example to find a baseline accident rate,being aware of possible errors, such as underreporting, enables us to correct for this, and making a stable foundation for subsequent assessments.

CONCLUSION

What should be done with the issue of underreporting?

The road ahead

Future studies should focus their investigation on the causes behind underreporting.

How can more than half of maritime accidents go unreported to national authorities?

In today’s world of global and instant media coverage, it is hard to believe how this can be.

Well, I used to be a navigator in the Norwegian Navy, and I have firsthand experience with maritime accidents.It can sometimes be hard to see that a ship is in fact not sailing, but is drifting without any engine power.Or that it is not at anchor, but has grounded. A lot of small accidents, and near-misses can easily avoid our attention.

Incentives

Should we invent better incentives to make ship operators and crewmembers more willing to report incidents?Interviews with operators and crewmembers indicate that they currently have little or no interest in reporting accidents. (As mentioned by Mr Lappalainen yesterday.)It only leads to delays, paperwork, loss of rating or image and has little perceived value for the crew.

Monitoring

Or should we enforce stricter monitoring and inspection?Mandatory reporting of a wider set of incidents, near-misses, under penalty of law?

Big brother

With few incentives to encourage detailed and comprehensive reporting from the end users, incentives could rather be given to the overseers.Class societies could enforce a “big brother” mentality including shipyards, agents, port authorities, pilots, coast guard, and other actors in frequent contact with merchant shipping.Such a routine would have to be global and mandatory, to avoid ports-of-convenience or shipyards-of-convenience.

Further studies should investigate causality and links between accident data, and investigate the underlying reasons to the amounts of underreporting that has been uncovered.

Correction factors

In the meantime, anyone using statistical data for risk assessments should check the data for underreporting, and use correction factors to ensure more correct and conservative calculations.

So to conclude: the reasons behind underreporting is complex at best, but the tools to check a data set for underreporting are simple.

Build your tower on solid ground, by acknowledging the reality of underreporting, and estimating the level of reporting performance.A simple correction factor could then be applied to make sure you continue in the right direction.

Thank you!fredag 2. september 2011

Questions?fredag 2. september 2011

Are there any questions?

Martin Hassel+47 920 11 745mha@safetec.no

http://www.sciencedirect.com/science/article/pii/S0001457511001461

PREDICT - PREVENT - PREPARE

imiss 2011 · 2018. 2. 22. · nmd 38,4% ihs fairplay 34,1% common 13,1% not reported 40,6% chao...

Documents

e tiy s imiss h e s i h^dout i...

e16...

noema tutorials: ii. hls...

461&3*/5&/%&/5 4 - hawaii doe forms/state... · [5.0 34,1...

annual report and financial statements 2016 - pwc … ·...

the san francisco call (san francisco) 1898-03-02 [p...

museum routes in italy in 2018 · musei civici - palazzo...

· pdf filedimensions acc asme b 16.1 class 125 dn mm inch 7...

reduced flange 38999 jam nut receptacles€¦ · 11 35,2...

mixtures of veterinary medicinal compounds in manured...

welcome to the 2012 annual meeting - dbag-website ·...

+domain...

mpca rsb gb - calor · general the mpca-rsb series...

invi.'n fa daiik s m s u ra s ra edsupp indf imiss ion or...

super high power mono perc module 350 w ~ 375 w · nominali...

-case study- - milkomates.pdf · parameters % samples that...

integrated management effectiveness tool (imet) & the ... 1...

menú basal mayo (may) www. colectividadesmaestrohomemade...

mooers. when you...

the formula of the future cow - proagria · antibiotics use...