Crew injuries in container vessel accidents

ABSTRACT

This paper investigates determinants of the probability of non-fatal and fatal crew injuries in container vessel accidents. The determinants are deduced from an estimated probit regression equation based upon 2001–2008 U.S. Coast Guard container vessel accident data. The estimation results suggest that a crew member is: (1) less likely to have a non-fatal injury in containership and ro-ro container vessel accidents if the vessel has a steel hull and the vessel accident occurs at nighttime, (2) more likely to have a fatal injury if fire is involved in containership and ro-ro container vessel accidents, and (3) less likely to have a fatal injury in containership and ro-ro container vessel accidents if the vessel is U.S. flagged, steel hulled, and powered by a diesel engine. These results are critical in developing new policies in reducing non-fatal and fatal crew injuries in container vessel accidents.

KEYWORDS:

• Vessel accident
• crew injury
• probability
• containership¹
• ro-ro container vessel²

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Containerships are designed to carry cargoes loaded in truck-size containers, generally twenty-foot equivalent units (TEUs) and forth-foot equivalent units (FEUs).

2. Roll-on/roll-off container vessels are designed to carry containers, general cargo, and wheeled cargo (trucks, trailers, cars, etc.).

3. For further discussion of human error in shipping, see (Goss 1994; Millar 1980).

4. Serious injury is defined as ‘an injury which is sustained by a person, resulting in incapacitation where the person is unable to function normally for more than 72 hours, commencing within 7 days from the date when the injury was suffered’ (IMO 2008).

5. Ship classification societies such as Lloyd’s Register, Bureau Veritas, and Det Norske Veritas, for example, inspect ships to: ensure their seaworthiness, verify national flag requirements, and investigate their adherence to international safety rules. For further discussion of classification societies, see (Boisson 1994; Miller 1998).

6. Countries establish Port State Control (PSC) Authorities to inspect vessels of all flags that enter their ports. The Port State Control Officer inspects and verifies the conditions of ships and their equipment and the competency of on-board crew. For further discussion of PSC, see (Payoyo 1994; Talley 2002, 2005).

7. Casualty rates (vessel tonnage loss as a percentage of fleet tonnage) of flag states have been investigated by Alderton and Winchester (2002). The investigation utilized data of 121 flag states that had suffered casualties to their vessels for the period 1997–1999 and were recorded in the Lloyd’s casualty database. The investigation revealed that: (1) Flags of Convenience (FOCs) have a worse casualty record than both second/international registers and national flags and (2) newer and faster growing FOCs are more likely to have poorer safety records than established FOCs.

8. Container barge accidents are not considered in this paper, since limited data are available.

9. Vessel accidents occurring in U.S. waters are investigated by the U.S. Coast Guard. Data obtained in the investigation of a given vessel accident is recorded on a standard U.S. Coast Guard vessel accident form. The types of information recorded are those found in this paper. The MISLE data are created by compiling the vessel accident data from the U.S. Coast Guard vessel accident forms. This paper uses a subset of the MISLE data, that is, data for only container vessel accidents. Since the same type of vessel accident information is collected for container, tanker, and bulk freight vessel accidents in the MISLE data, it can be expected that other studies using the MISLE data (but for different types of vessels) report similar determinants of crew injuries.

10. For further discussion of the probit regression statistical model, see (Greene 2012; Stock and Watson 2007).

11. This derivation is found in Greene (2012) and was used to compute the marginal probabilities found in Tables 4 and 5.