Rwth Stuckey, Department of Epidemiology

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Rwth Stuckey, Department of Epidemiology
Medicine, Faculty of Medicine Nursing Health
Sciences, Monash UniversityAnthony LaMontagne,
Centre for the Study of Health Society, School
of Population Health, University of Melbourne

• OHS Legislative Relevance to Occupational Light
  Vehicle Use in Australia

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Rationale

• focus of OHS on professional drivers and heavy
  vehicles.
• light vehicles are widely used across all
  industries and occupations
• work-related light vehicle injury and exposure
  data is difficult to isolate.
• in 1999-2001 more than 80 of NOHSC OLV user
  fatal claims were not employed as drivers.
• lack of public health surveillance necessitates
  reliance on workers compensation data, a poor
  proxy for the total OHS picture.
• characterising the problem requires working
  backwards from the available fatality and injury
  data to establish some sense of the exposures of
  the much larger total OLV user population.

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Objectives Methods

• To define and characterise exposures and hazards
  for OLV users in Australia.
• To describe OLV user OH other cover across
  work arrangements jurisdictions
• To propose changes to OH regulatory framework to
  better address OLV user OHS issues.
• Mapping an understanding based on fatality,
  injury and vehicle use information
• Literature review, websites searches, white
  papers, reports, conferences, government and
  academic research reports.
• Data sources identified NOHSC, ABS, ATSB etc.
• Different questions asked of the available data

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Characterising the Problem Compensated Fatal
Occupational Vehicle Claims By Vehicle Type
Group, AUS, 1994-2002
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Characterising the Problem Light Vehicle Claims
as a Percentage of all Road Vehicle Claims by
Occupation Australia, 1994-2000
Data Sourced from NOHSC On-line Statistical
database (NOSI) http//NOHSC.info.au.comp/
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Characterising the Problem Business And Private
Use In Australia
Data sourced from ABS Survey of Motor Vehicle
Use. Catalogue No. 9208.0 and ABS Year Book
Australia 2003 Transport, General transport
activity, Road transport activity
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Characterising the Problem Precarious Employment
Trends in Australia, 1998-2002
Graph compiled from data sourced from ABS
publications ABS Aust Social trends 4102.0,
1999 ABS Australian Social Trends 2001. Work -
National summary tables ABS 4102.0.2002 Aust
Social trends 2002, Work Paid work employment
arrangements. Work - National summary tables ABS
Australian Social Trends 2003 Catalogue No. 4102.0
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Characterising the Problem Employment
Arrangement By Occupation, Australia, 1998
Data source ABS Australian Social Trends 2000
Work - Paid Work Employment arrangements in the
late 1990s
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Characterising the Problem

• Changes in work arrangements lead to an
  increasing number of people driving light fleet
  vehicles while working, including
• employees employed specifically to drive
• employees driving salary sacrificed vehicles,
  driving identified work vehicles, casually
  driving pool vehicles,
• own account workers using vehicles between
  workplaces.
• Own account workers driving vehicles designed for
  specific work purposes such as tradesmen using
  vans, and workers using their vehicle as an
  office base

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The OLV Use Policy Framework The Varied
Organisation of Vehicles And Work Arrangements
Across OLV Users
Indicates the one sub-group who have been the
focus of previous research
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The OLV Use Policy Framework Comparison Of
Selected Aspects Of Australian Work And Motor
Accident Insurance Scheme Cover For Workers
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Simplified Flowchart of the Events Necessary to
the Documentation of Company Employed OLVU
Injuries
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Simplified Flowchart of the Events Necessary to
the Incident Documentation of Self Employed or
Own Account OLV User
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Proposed Changes To The OHS Regulatory Framework
To Better Address OHS Issues For OLV Users

• Recognition of vehicles as workplaces and the
  varied work activities of OLV users
• Inclusive and consistent definitions
• Consistency of cover across jurisdictions
• Cover for all OLV users
• Improved data collection and flow of information
  between all data collection sources
• Accurate data to used to inform policy
• How can it be done?