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Contents
  1. 1Kinds of fire data
  2. 2How fire statistics are collected
  3. 3From counts to frequencies and probabilities
  4. 4What fire statistics show
  5. 5Use in fire safety engineering
  6. 6Reliability of protection systems
  7. 7Limits and sources of bias
  8. 8See also
  9. 9Notes
  10. 10References

Fire statistics are numerical data about fires that have occurred, such as how often fires start, what they start in and what harm they cause, together with the summary values calculated from those data. In the SFPE Handbook, Hall defines a statistic as any item of numerical data, or a quantity such as a mean computed from such data. Statistical analysis then condenses large bodies of data into a few summary values from which useful conclusions can be drawn.[1] For fires, these calculations start from event frequencies per unit of time, such as how often certain items catch fire or certain types of occupancy have fires.[2]

Fire statistics matter because fire risk analysis, performance-based design, product evaluation and the reconstruction of major fires all need data on how likely fires are and how severe they become.[3][4] They are also easy to misread. The national estimates for the United States, for example, cover only fires reported to municipal fire departments, and one term can mean different things in different countries.[5] In fire safety engineering, the quantitative approach relies on measurements and statistical analyses, while the qualitative approach, based on expert judgement, is particularly useful where numerical data are scarce.[6]

Kinds of fire data

Hall and Ahrens group the data needed for engineering analysis into a few generic sources: fire incident data and field observations, usage or exposure data, and laboratory test results.[3] Incident data describe fires that have occurred. Field observations describe other events, such as faults in protection systems found during routine inspection and testing.[3][7] Exposure data describe what is at risk, for example the number of portable electric heaters in use or the floor area of offices. They supply the denominators for frequencies such as fires per year per unit.[3] Laboratory tests measure fire size and ignition under planned conditions, but fires and equipment failures are such rare events that measuring their probabilities in the laboratory can be impractical.[3][2]

Incidents, exposure, laboratory tests and field observations provide distinct evidence.
Fire statistics combine several data types. Based on Hall and Ahrens (2002), pp. 5-65–5-67.

In the United States, the National Fire Incident Reporting System (NFIRS) records the occupancy, the material first ignited, the equipment involved, the form of heat that caused ignition and the ignition factor. It also records whether automatic detection and suppression systems were present.[2] Analysts further use the area of origin and the age, location and activity of victims.[8] Fire incident commanders fill in the coded report after the fire. They can usually name the equipment that started a fire but are unlikely to know which part was involved, and brand and model are not coded.[8]

A new product has no fire history, so its likelihood of ignition may have to be estimated from laboratory tests against established products, and subjective judgement will likely be required.[2] Every data collection also has a purpose of its own. Hall and Ahrens therefore advise asking why a collection was set up, who paid for it and whether its sponsors had an interest in a particular outcome.[9]

How fire statistics are collected

NFIRS, administered by the US Fire Administration, is described as the largest and most detailed fire incident database in the world.[2][10] It captures roughly a million fires a year, but only an estimated half of the fires reported to US fire departments. Since 1980, communities in roughly 40 of the 50 states have taken part, although not all of them every year.[2] To scale NFIRS up to national estimates, analysts use the annual survey of the National Fire Protection Association (NFPA), a stratified random sample of roughly one-tenth of all US fire departments. Separate scaling ratios are used for fires, civilian deaths, civilian injuries and direct property damage.[2]

For 1996, for example, the NFPA survey put civilian deaths in residential structure fires at 4,080, against 1,504 reported to NFIRS. This gave a scaling ratio of 2.71 for that measure and that year.[2] Hall and Ahrens judge the sampling uncertainty of NFIRS negligible because of its size, unless a problem involves hundreds of fires a year or fewer. For the NFPA survey they put it at about 10% for deaths and much less for other measures.[8]

NFIRS records and the NFPA survey inform a projection with uncertainty.
National estimates require attention to sample coverage and scaling. Based on Hall and Ahrens (2002), pp. 5-67–5-69.

Statistics of this kind describe reported fires only. The US national estimates exclude fires handled by private brigades, federal fire-fighting organisations or the occupants themselves. Fires in buildings with sprinklers or good detection are more likely to be controlled on site, and no system based on fire service reports can capture fires that are never reported.[5] Other countries keep their own records. The United Kingdom has asked fire questions in the British Crime Survey about all fires, not only reported ones, and it records the time before discovery, which NFIRS does not.[10]

Collections of notable fires are a different kind of source. The NFPA Fire Analysis and Research Division keeps a database of significant incidents found through press clippings. It can serve as a representative, even complete, record of large fires, but for other fires it is only anecdotal.[5] Such accounts, like NFPA's reports of fire investigations, show the range of fires that can happen. Hall and Ahrens stress, however, that anecdotes cannot be used to estimate incident rates or probabilities.[5]

From counts to frequencies and probabilities

A count of fires says little until it is related to how much of something is exposed. In a hypothetical example by Meacham, 20 fires a year in toasters as they are switched on is a frequency, not a probability. The probability per switch-on also needs the number of switch-ons, and with 20 million a year it would be one in a million.[11] Exposure can be measured as products in use, establishments, people or floor area. The main challenge is to find exposure data that are statistically representative and structured in the same way as the incident data.[12]

A national fire death rate is built in the same way. Notarianni describes the US residential fire death rate as the national number of residential fire deaths divided by the number of occupied residential units. Approximately 3,500 people die each year in US residential fires, but the rate varies with house type, region and community size.[13] Data on national practices can also mislead, because they are not weighted by the likelihood of a fire. In 1989 a randomly selected US home had a better than 85% chance of having a smoke detector, but among homes that had fires the chance was below 50%.[14]

Tables of fire starts per occupancy follow the same idea, and the International Fire Engineering Guidelines (2005 edition) reproduce values from the UK code of practice BS7974, published by BSI in 2001. These give 0.003 fire starts per year for a dwelling and 0.3 for a hospital. That is roughly 3 starts a year for every 1,000 dwellings against 3 for every 10 hospitals.[15] The guidelines prefer starts per unit of floor area where such data exist, and they caution that the probability of fire starts may have changed with new technologies and materials.[16] Finnish data published in 1998 show why floor area matters. The number of fires per square metre falls as buildings get larger, while the chance of a fire somewhere in the whole building generally rises with floor area.[17]

What fire statistics show

Casualty statistics show where and how people die in fires. Most fatal fire victims die at home. People die in fires because, for example, they are trapped by fire or smoke, are overcome in their sleep or inhale too much toxic smoke.[18] Citing studies published in 1981 and 1989, Gottuk and Lattimer report that over half of all fire fatalities have been attributed to carbon monoxide inhalation.[19] Citing a 1994 study, they add that nearly 75% of deaths from smoke inhalation occur away from the room where the fire started.[20]

Statistics also have blind spots. Purser notes that fire statistics cannot show how often smouldering fires occur, because many of them become flaming fires before they are discovered.[21] UK fire statistics for 1983 nevertheless offer an indication. For victims aged 19 to 49, deaths were 20 times more likely in fires found 30 minutes or more after ignition than in fires found within 5 minutes. Purser considers it likely that such long-burning fires involved smouldering.[22]

Most analyses need the number of fires of a certain type, usually defined by cause, not only the total. Hall and Ahrens list six cause-related characteristics in common use. Two describe the source of heat, or ignition source: the form of heat of ignition and the equipment involved. Two describe the form and type of material first ignited, and the last two are the ignition factor that brought heat and fuel together and the area of origin.[8] The International Fire Engineering Guidelines (2005 edition) note that no quantitative methods exist for predicting the potential for ignition. Fire statistics can instead supply ignition frequencies and the nature of the materials ignited.[23]

Statistics also measure the cost of fire. For more than two decades, the World Fire Statistics Centre has compared the total cost of fire in 15 to 20 countries, with losses also shown as percentages of gross domestic product (GDP). In the 2002 SFPE Handbook, the core costs for the United States typically ran at about 1% of GDP. The US fire death rate relative to population consistently ranked among the highest of the countries studied.[24]

Use in fire safety engineering

Fire risk analysis seeks to answer three questions: what could happen, how bad it would be and how likely it is.[25] Some assessments look back, inferring probabilities from fire loss experience after an event such as the introduction of home smoke alarms. Others look ahead, for example to the effect of requiring sprinklers in homes.[25] The available data differ widely. US homes offer very many documented fires with limited detail, US nuclear power plants far fewer fires with much more detail, and a single building very few fires at all.[26] In a hazard assessment, historical loss data help engineers avoid focusing on unique hazards while overlooking more common but equally important ones.[27]

In Australia, the data sheets that accompany the Fire Safety Verification Method (print version 1.1, 2020) turn incident statistics into characteristic design values, whose use is not mandatory.[28] Their fire growth rates come from an analysis by Holborn and colleagues of fire investigation data from the Greater London area for 1996 to 2000. Of 481 fires in residential buildings, 34% were classed as very slow growing, 50% as slow and 11% as medium.[29]

The same data sheet estimates the share of fires that would become fully developed if no sprinklers were present. For dwellings it adopts about 18%, based on values of 15.5% for Australia, 18.3% for the USA and Canada and 23.2% for New Zealand. Hotels, hospitals and care homes are given 5% and most other buildings 15%.[30] The data sheet explains the low value by the 24-hour staff and monitored detection that such buildings are likely to have. These bring a higher proportion of their fires to the attention of the fire brigade.[30] The accompanying handbook predicts the probability of flashover from the frequency of fires that occupants were unable to manage, taken from fire statistics, together with sprinkler reliability and an analysis of fire brigade intervention.[31]

In New Zealand, the commentary on the first edition of Verification Method C/VM2 (2013) cites New Zealand Fire Service Emergency Incident Statistics for 2005 to 2010. In these, approximately 3% of all structure fires spread to adjacent property, a risk the commentary calls small but not insignificant.[32] In the Netherlands, Van Rossum uses actual outcomes to show that the building regulations (Bbl) were not developed around one quantified goal. Per hour of stay, the chance of dying in a fire is much greater in a residential building than in an assembly building, yet the detection requirements are stricter for assembly buildings.[33]

Reliability of protection systems

Incident data are one of five kinds of source that Hall and Ahrens identify for the reliability of fire protection systems, for which data are very scarce.[5][7] The Australian data sheets define reliability as the probability that a system performs to its specification. Efficacy is the degree to which the system then achieves its design objective, and effectiveness combines the two.[34] For smoke alarms, reliability counts only fires large and close enough to activate an alarm, and effectiveness is efficacy multiplied by reliability.[35] A battery-operated alarm system with limited coverage, a typical reliability of 85% and an efficacy of 93% thus has a typical effectiveness of 79%.[36]

UK Home Office statistics for dwelling fires from 2010/11 to 2017/18, analysed in the same data sheet, show how much definitions matter. Mains-powered smoke alarms failed to operate in 21% of the fires where they were present. Fires whose smoke never reached a detector, or that started outside the covered area, do not count as failures; excluding them left about 4%, a reliability of 96%. For battery-powered alarms the same steps turned 39% into 13%, a reliability of 87%.[37]

For US home fires from 2009 to 2013, an NFPA analysis by Ahrens (2015) found that battery-operated alarms operated in 80% of fires large enough to activate them. Hard-wired alarms with battery backup operated in 95%.[38] Measured as alerting the occupants once an alarm operates, efficacy in the US data was about 97%. Measured as the reduction in deaths, US and UK data gave only 50% to 55%, so the data sheet stresses that the definition must be stated whenever an efficacy is quoted.[39]

Reported fires can make protection look less reliable than it is, because successful systems often keep fires too small to be reported. In the United States, reported home fires consistently show roughly one-third of smoke alarms not operating, against one-fifth in a study of home installations in general.[12] For sprinklers, reported fires suggest failure to operate in one-sixth to one-fifth of the fires large enough to activate them, while real reliability could easily be well over 90%.[12] Inspection and testing data lean the other way, because they tend to come from properties with very advanced maintenance programmes. This is one reason why estimates of sprinkler reliability in Australia are much higher than in the United States.[7] In Australia, Marryatt's 1988 compilation found control in over 99% of fires in sprinklered buildings, but it left out fires in which the sprinklers failed to operate or had no water.[40]

Limits and sources of bias

Missing entries can distort patterns. If the number of fires stayed the same while the share with an unreported cause steadily fell, ignoring the unknowns would make every specific cause appear to increase.[8] Most national estimates therefore allocate unknown entries in the same proportions as the known ones. This assumes that the unknown fires would show the same pattern if their details were known.[8] The Australian data sheets apply the same method, for example to unknown fire sizes in New Zealand data and to unknown reasons why smoke alarms did not operate.[30][41]

Known and unknown incident fields lead to different apparent category shares and uncertainty.
Unknown data and reporting bias must be addressed explicitly. Based on Hall and Ahrens (2002), pp. 5-67–5-70.

Codes can also lump different things together. One NFIRS equipment code covers irons, hair dryers and electric blankets, which are used in very different places and tend to ignite different items.[9] The floor covering code includes carpet, tile, rugs, hardwood, vinyl flooring and even stairs.[8] One of the materials most often reported as first ignited in floor covering fires is petrol, which refers to a liquid spilled on the floor rather than to the covering itself. Hall and Ahrens consider it unsound to exclude such fires as unfair tests, because all fires involving a product belong to its fire problem.[9]

Definitions also differ between countries and data systems. In fire incident databases a space heater heats a relatively small area, whereas the US Department of Energy uses the term for all heating systems.[5] Ramachandran notes that a "room" in fire brigade reports is not necessarily a fire compartment. Fires recorded as spreading beyond the room include fires that passed through an open door or window. Such counts therefore do not give a valid estimate of how often the walls, floors and ceilings of a room fail.[42]

Past data may not suit the future. Meacham asks whether fire loss data for cellulosic materials before 1960 could indicate losses involving synthetic materials after 1960.[43] Because fires are rare and people change buildings and their contents over time, Meacham calls predicting future risks from past loss experience challenging at best.[11] The International Fire Engineering Guidelines (2005 edition) likewise warn that data may lose relevance through new technologies, materials, regulations and construction practices.[44] Data sometimes have to be borrowed from abroad: the Australian data sheets quote a Senate committee finding that Australian fire statistics are hard to locate and compare. For smoke alarms they therefore draw mainly on data from the US, the UK and New Zealand.[35]

Fire losses also vary at random from year to year, since several large-loss fires may occur in one year and few or none the next. A US cost-benefit study of residential sprinklers described by Notarianni therefore used mean values for the five years from 1989 to 1993.[13][45] Where systematic data are lacking, estimates from experienced people can be used. Their recollections, however, tend to make reliable systems seem even more reliable and unreliable ones even less reliable.[7]

See also

Notes

  1. ↑ Hall (2002c), p. 1-193.
  2. ↑ 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Hall and Ahrens (2002), p. 5-67.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 Hall and Ahrens (2002), p. 5-65.
  4. ↑ Hall and Ahrens (2002), p. 5-66.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 Hall and Ahrens (2002), p. 5-70.
  6. ↑ Van Rossum (2027), §1.4.
  7. ↑ 7.0 7.1 7.2 7.3 Hall and Ahrens (2002), p. 5-71.
  8. ↑ 8.0 8.1 8.2 8.3 8.4 8.5 8.6 Hall and Ahrens (2002), p. 5-68.
  9. ↑ 9.0 9.1 9.2 Hall and Ahrens (2002), p. 5-69.
  10. ↑ 10.0 10.1 Hall and Ahrens (2002), p. 5-76.
  11. ↑ 11.0 11.1 Meacham (2002), p. 5-159.
  12. ↑ 12.0 12.1 12.2 Hall and Ahrens (2002), p. 5-72.
  13. ↑ 13.0 13.1 Notarianni (2002), p. 5-61.
  14. ↑ Hall (2002b), p. 5-151.
  15. ↑ BSI (2001), as cited in National Research Council of Canada et al. (2005), p. 3.2-2.
  16. ↑ National Research Council of Canada et al. (2005), p. 3.2-2.
  17. ↑ Rahikainen and Keski-Rahkonen (1998), as cited in National Research Council of Canada et al. (2005), p. 3.2-3.
  18. ↑ Van Rossum (2027), §7.
  19. ↑ Anderson et al. (1981) and Harwood and Hall (1989), as cited in Gottuk and Lattimer (2002), p. 2-54.
  20. ↑ Gann et al. (1994), as cited in Gottuk and Lattimer (2002), p. 2-54.
  21. ↑ Purser (2002), p. 2-134.
  22. ↑ Home Office (1983), as cited in Purser (2002), p. 2-134.
  23. ↑ National Research Council of Canada et al. (2005), p. 2.4-4.
  24. ↑ Ramachandran (2002b), pp. 5-79–5-80.
  25. ↑ 25.0 25.1 Watts and Hall (2002), p. 5-2.
  26. ↑ Watts and Hall (2002), p. 5-3.
  27. ↑ Meacham (2002), p. 5-155.
  28. ↑ Australian Building Codes Board (2020a), Data Sheet B2, p. 1.
  29. ↑ Holborn et al. (2004), as cited in Australian Building Codes Board (2020a), Data Sheet B2, pp. 4–7.
  30. ↑ 30.0 30.1 30.2 Australian Building Codes Board (2020a), Data Sheet B2, pp. 6–9.
  31. ↑ Australian Building Codes Board (2020b), p. 92.
  32. ↑ Ministry of Business, Innovation and Employment (2013), p. 23.
  33. ↑ Van Rossum (2027), §1.2.
  34. ↑ Australian Building Codes Board (2020a), Data Sheet C3, p. 87.
  35. ↑ 35.0 35.1 Australian Building Codes Board (2020a), Data Sheet C3, p. 91.
  36. ↑ Australian Building Codes Board (2020a), Data Sheet C3, p. 92.
  37. ↑ Home Office (2018), as cited in Australian Building Codes Board (2020a), Data Sheet C3, pp. 105–109.
  38. ↑ Ahrens (2015), as cited in Australian Building Codes Board (2020a), Data Sheet C3, pp. 109–111.
  39. ↑ Australian Building Codes Board (2020a), Data Sheet C3, p. 111.
  40. ↑ Marryatt (1988), as cited in National Research Council of Canada et al. (2005), pp. 3.7-2–3.7-3.
  41. ↑ Australian Building Codes Board (2020a), Data Sheet C3, pp. 106, 108.
  42. ↑ Ramachandran (2002c), p. 3-388.
  43. ↑ Meacham (2002), p. 5-154.
  44. ↑ National Research Council of Canada et al. (2005), p. 3.1-2.
  45. ↑ Notarianni (2002), p. 5-62.

References

  • Australian Building Codes Board. (2020a). Data sheets: Fire Safety Verification Method (Handbook annex, Print version 1.1).
  • Australian Building Codes Board. (2020b). Handbook: Fire Safety Verification Method (Print version 1.1).
  • Gottuk, D. T., & Lattimer, B. Y. (2002). Effect of combustion conditions on species production. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 2-54–2-82). National Fire Protection Association; Society of Fire Protection Engineers.
  • Hall, J. R., Jr. (2002b). Product fire risk. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-143–5-152). National Fire Protection Association; Society of Fire Protection Engineers.
  • Hall, J. R., Jr. (2002c). Statistics. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 1-193–1-202). National Fire Protection Association; Society of Fire Protection Engineers.
  • Hall, J. R., Jr., & Ahrens, M. J. (2002). Data for engineering analysis. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-65–5-78). National Fire Protection Association; Society of Fire Protection Engineers.
  • Meacham, B. J. (2002). Building fire risk analysis. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-153–5-175). National Fire Protection Association; Society of Fire Protection Engineers.
  • Ministry of Business, Innovation and Employment. (2013). Commentary for Building Code clauses C1–C6 and Verification Method C/VM2.
  • National Research Council of Canada, International Code Council, Department of Building and Housing, & Australian Building Codes Board. (2005). International fire engineering guidelines (2005 ed.). Australian Building Codes Board.
  • Notarianni, K. A. (2002). Uncertainty. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-40–5-64). National Fire Protection Association; Society of Fire Protection Engineers.
  • Purser, D. A. (2002). Toxicity assessment of combustion products. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 2-83–2-171). National Fire Protection Association; Society of Fire Protection Engineers.
  • Ramachandran, G. (2002b). Measuring fire consequences in economic terms (J. R. Hall Jr., Rev.). In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-79–5-92). National Fire Protection Association; Society of Fire Protection Engineers.
  • Ramachandran, G. (2002c). Stochastic models of fire growth. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 3-381–3-401). National Fire Protection Association; Society of Fire Protection Engineers.
  • Van Rossum, E. (2027). Introduction to Fire Safety Engineering. Fire Safety Academy.
  • Watts, J. M., Jr., & Hall, J. R., Jr. (2002). Introduction to fire risk analysis. In P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D. Walton, R. L. P. Custer, J. R. Hall Jr., & J. M. Watts Jr. (Eds.), SFPE handbook of fire protection engineering (3rd ed., pp. 5-1–5-7). National Fire Protection Association; Society of Fire Protection Engineers.
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