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Questioning risk-based fire and life safety education age priorities
  1. Timothy McNamara
  1. Correspondence to Timothy McNamara, Center for Homeland Defense and Security, Naval Postgraduate School, 1 University Circle, Bldg 220, Rm. 064, Monterey, CA 93943-5155, USA; tim.mcnam{at}gmail.com

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Conventional wisdom assumes that Fire and Life Safety Education (FLSE) programmes should be concentrated on high-risk groups, an approach that prioritises children under 14 years of age and adults aged 65 years and over. Warda et al1 declare it ‘essential to decisively target campaigns to those at highest risk for fire injury’, and identify young children and the elderly as the two age groups requiring this focus due to their higher relative risk of dying in a fire. The US Department of Homeland Security advocates for the same strategy in establishing guidelines for fire prevention grant applications, and UK Fire and Rescue Services similarly allocate FLSE resources by risk.2 ,3 In observing FLSE programmes, Diekman et al4 confirm that young children and older adults have been rightfully recognised as priorities. The result is FLSE campaigns that either teach children youth-oriented lessons such as stop, drop and roll and to never play with matches, or instruct senior citizens in adult-oriented practices such as safe cooking and space heater use, proper smoke alarm maintenance, and responsible smoking habits.

The problem with using risk to determine FLSE age priorities, however, is that there is no justification to support this strategy. Studies have not analysed whether targeting high-risk age groups achieves a greater reduction in injuries or produces a larger increase in the implementation of FLSE practices than alternative age prioritisations. Also missing is a theoretical model or framework to explain why risk is the best way to allocate FLSE among various age groups. The inability to directly validate the current approach is not surprising given that studies of individual FLSE programmes have been criticised as unreliable due to inconsistent design or performance measures, lack of randomness or controls, and inadequate sample size or follow-up time.5 ,6 The …

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