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Contents
  1. 1Purpose and main data types
  2. 2Incident reporting and national estimates
  3. 3Ignition sources and fire causes
  4. 4Smoke, injury and consequence data
  5. 5Reliability and effectiveness
  6. 6Unknown data and reporting bias
  7. 7Organisational sources and roles
  8. 8Use and review of statistics
  9. 9See also
  10. 10Notes
  11. 11References

Fire statistics are organised data about fires, their causes, development, consequences and the performance of protective measures. They can describe incident frequency, first items ignited, ignition factors, casualties, damage and system operation. Their usefulness depends on definitions, reporting coverage, exposure data and the question being asked.[1][2]

Statistics support fire risk analysis, design, product evaluation and investigation. They do not replace those essential engineering analyses. A count of incidents cannot by itself establish probability, causation or effectiveness without appropriate exposure and contextual evidence.[1][3]

Fire data come from incident reports, field observations, laboratory tests, inspections, simulations and expert judgement. Each source has different strengths and biases. A larger or more detailed dataset is not automatically representative of the population or problem under study.[4]

Purpose and main data types

Incident data describe fires that have occurred and their recorded characteristics. They can include occupancy, area of origin, equipment involved, first fuel, ignition factor, casualties and the presence of detection or suppression. The available detail depends on the reporting system and its coding.[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.

Usage or exposure data describe the population at risk. Examples include the number of products in use, buildings of a type, occupants or floor area. These data provide the denominators needed to express incident frequencies per unit or per period.[1]

Laboratory data describe controlled fire behaviour and system response. They can vary fuel, ignition source, geometry or protection to examine particular mechanisms. Their relevance to field conditions must still be evaluated rather than assumed.[1][5]

Field observations show how systems and buildings perform in use. They can reveal impairment, degradation, maintenance quality and actual activation. Those observations complement laboratory performance and incident counts.[5]

Fire risk combines these sources to examine likelihood and consequence. The relevant dataset depends on whether the question concerns a product, building, protective system or population. A single source rarely answers every part of that assessment.[1][3]

A frequency is a count of occurrences within a defined period. A rate relates that count to an exposure measure such as building-years, product units or floor area. The distinction is necessary before comparing different populations or years.[1][6]

Probability requires knowledge of relevant opportunities, not just incidents. Watts and Hall illustrate this with fires per appliance use, which also requires the number of times the appliance is used. An annual fire count without use data cannot establish the probability per use.[6]

Exposure can change even when the recorded incident count stays similar. More products, larger buildings or different occupancy can alter the underlying opportunities for fire. Interpretation should therefore include how the exposed population has changed.[4]

Severity measures also require suitable denominators. Deaths per fire, damage per fire and deaths per exposed person describe different quantities. They should not be exchanged when comparing fire consequences across datasets.[7]

Individual and societal risk use different consequence presentations. A location-based individual probability and the frequency of multiple fatalities are not the same measure. Statistics must be aggregated at the level appropriate to the question.[8]

Incident reporting and national estimates

The NFPA chapter uses the US National Fire Incident Reporting System, NFIRS, as a detailed example. It describes data on occupancy, first material ignited, equipment, heat of ignition, ignition factor and protective systems. The discussion is about that US system, not a universal reporting structure.[2]

Hall and Ahrens explain that NFIRS covered an estimated half of fires reported to US fire departments during the period discussed. Participation varied across states and communities, and not every participant submitted data every year. These facts are specific to the handbook's 2002 account.[2]

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.

NFIRS is a sample whose complete reporting universe cannot be inferred from its own records alone. The NFPA survey provides another data source used to project national estimates. Different severity measures can require different scaling ratios because small and severe fires are represented differently.[7]

The chapter gives a historical 1996 example of 4,080 civilian deaths in residential structure fires in the NFPA survey compared with 1,504 in NFIRS. The resulting scaling ratio of 2.71 belongs to that measure and year. It must not be reused as a current multiplier.[7]

Some fires never enter municipal records. Occupants or private brigades may handle small incidents without reporting them, and occupancies with those resources can be underrepresented. Statistics therefore describe reported fires rather than every fire that occurs.[4]

Ignition sources and fire causes

Ignition source classification requires attention to several fields. The NFIRS discussion separates the form of heat, equipment involved, first material ignited, ignition factor and area of origin. A single equipment label does not capture every part of that sequence.[2][7]

Coding systems can group products with different properties or uses. The chapter gives an example of one equipment code covering irons, hair dryers and electric blankets. Those devices may be used in different places and ignite different nearby items.[9]

First-fuel coding can also be ambiguous. A record may identify gasoline rather than the floor covering on which it was spilled, while broad material categories can include several products. Interpretation must consider what the code actually represents.[9]

Excluding awkward incidents to make a product or design appear safer is unsound. Hall and Ahrens argue that those cases belong to the overall fire problem and should be addressed when evaluating probabilities or acceptability. Selective exclusion would bias the initial estimate.[4]

Fire investigation can provide more detail about a particular incident than routine coding allows. That detail can clarify mechanisms and test the meaning of database categories. A detailed case is still not automatically representative of every fire in that category.[4]

Smoke, injury and consequence data

Smoke inhalation and other injury outcomes need clear case definitions and exposure conditions. NFIRS records some victim characteristics, locations and activities, but the coding does not provide every physiological or toxicological detail. Consequence analysis may require other sources and models.[7][10]

Laboratory toxic effects and fire-effluent data can supplement incident records. They can describe species concentrations, exposure conditions and effects that are not routinely measured at a fire scene. Their relevance to real occupants must be assessed carefully.[1][5]

Victim location and activity can help examine exposure to smoke and heat. The number of casualties alone does not identify the route or conditions that caused them. Consequence analysis should therefore connect outcome data with the developing environment and occupant actions.[7][11]

Property-loss data can also be influenced by reporting coverage. Small-loss fires may be underrepresented, which can raise the apparent average loss per recorded fire. Separate scaling and careful review are needed for different consequence measures.[7]

Fatalities and injuries should not be treated as interchangeable counts. They have different consequences, coding and uncertainty. The analysis should state which outcome is used and how unknown or missing records are handled.[7][9]

Reliability and effectiveness

Reliability of fire protection systems concerns whether systems perform when required. Reliability data can come from incident outcomes, inspection, testing, maintenance records and field observations. Each captures a different part of operational performance.[5]

Inspection programmes can reveal failures that do not draw attention, but they may miss transient impairments. They can also overrepresent properties with advanced maintenance and trained staff. Reliability from those programmes may not apply to typical buildings.[12]

Incident-based effectiveness requires a clear outcome definition. ABCB distinguishes smoke-alarm efficacy, meaning achievement of a specified objective when the system performs to specification, from reliability under conditions expected to cause activation. Those are different measures.[13]

ABCB also warns that its general estimate of waking effectiveness spans all times of day and residential occupancies. It should not be interpreted as the ability to wake every occupant type. Age, alcohol or drugs, background noise and alarm characteristics can alter response.[14]

An operating system does not guarantee the desired human outcome. Detection, audible alerting, occupant response and reduced injury are successive possible criteria. Statistics used for one should not be presented as evidence for all of them.[13][15]

Unknown data and reporting bias

Unknown or blank fields can materially change category patterns. If the proportion of unknown causes changes over time, ignoring those records can make every known cause appear to increase. Analysis must address the unknown records rather than treating them as absent events.[7]

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.

Proportional allocation assigns unknown records in the same shares as known records. That provides a transparent method but assumes that unknown and known cases have the same distribution. The assumption may not hold, so its uncertainty should be examined.[7][9]

Coding inconsistencies and factual errors create additional uncertainty. A second source can help in some cases, but it is not necessarily more reliable and is often unavailable. Data cleaning must not erase unusual but genuine fires.[9]

Opportunity samples arise when cases are collected because they are easy to obtain or newsworthy. They can overrepresent severe, urban or unusual events. Such data can show what is possible without establishing how often it occurs.[4]

The purpose of data collection also matters. Hall and Ahrens recommend examining why the system was created, who funded it and which questions it was designed to answer. Those factors can affect definitions, coverage and availability.[4]

Laboratory and simulation studies can investigate conditions that are rare or unsafe in real fires. They allow controlled variation but may simplify geometry, fuel, ventilation or occupant behaviour. Their representativeness must be assessed before applying results to a building.[5]

Evacuation drills are a form of simulation. They can reveal human response and route performance but may avoid dangerous conditions or include trained participants. Drill data should not be treated as exact predictions of every actual emergency.[5][16]

Expert judgement is used where systematic observations are limited. Experience can supply useful estimates, but recollection and halo or horns effects can bias assessments towards higher or lower reliability. Group methods may reduce some bias without eliminating it.[12]

New products create a particular problem because they lack incident history. Laboratory comparisons may be needed, but rare ignition makes direct probability measurement difficult. Subjective estimates and explicit assumptions can remain part of the analysis.[2]

Fire models can supplement missing observations by representing physical processes. Their outputs still depend on scenario selection and input data. A model cannot create representative fire statistics merely by producing a detailed result.[17][5]

Organisational sources and roles

NFPA publishes fire-analysis research and statistical resources in addition to codes and standards. Hall and Ahrens are identified as NFPA fire-analysis specialists in the 2002 handbook. That role is separate from adopting NFPA documents as legal requirements.[1][18]

NFIRS and the NFPA survey serve different functions in the cited US method. NFIRS supplies detailed incident records, while the NFPA survey provides a structured basis for national projection. Their strengths are used together rather than treated as interchangeable datasets.[2][7]

Other databases may contain detailed fire-investigation or insurance records, but access and representativeness can be limited. Proprietary data may not be available, and older records may not exist in accessible digital form. Data availability must be confirmed before planning an analysis.[4]

Statistical definitions can vary between countries and collection systems. Similar terms can cover different equipment or events, making direct comparison unsafe. Definitions and coding guidance should be reviewed before merging sources.[4]

Fire risk assessment must document these choices of source, definition and scope. The report should make clear which figures are observed, projected, modelled or judged. This allows a reviewer to distinguish statistical evidence from the assumptions used to interpret it.[1][19]

Use and review of statistics

A useful analysis starts with the decision and the population to which it applies. It then identifies the required incident, exposure and consequence data. Collecting a convenient dataset first can lead to detailed but irrelevant results.[1][4]

Each statistic should retain its source year and collection conditions. The NFIRS examples in the 2002 handbook are not current national estimates. Likewise, an effectiveness value from a particular occupancy or study should not be treated as universal.[7][14]

Sensitivity checks can show how unknown allocation, sampling assumptions and model inputs affect the result. Where those changes alter a decision, the uncertainty should be reported rather than hidden by one preferred estimate. This is part of responsible statistical interpretation.[9][17]

Data should be revisited when the building or population changes. Altered fuel, occupancy, detection or maintenance can make old evidence less relevant. A previous statistical conclusion is not a permanent property of the system.[20][5]

The aim is not to find a single impressive number. It is to assemble representative evidence that answers a defined question while exposing uncertainty. Good fire statistics make both the observed patterns and their limitations available for engineering decisions.[1][4]

See also

Notes

  1. ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 Hall and Ahrens (2002), pp. 5-65–5-67.
  2. ↑ 2.0 2.1 2.2 2.3 2.4 2.5 2.6 Hall and Ahrens (2002), pp. 5-67–5-69.
  3. ↑ 3.0 3.1 Watts and Hall (2002b), pp. 5-1–5-3.
  4. ↑ 4.00 4.01 4.02 4.03 4.04 4.05 4.06 4.07 4.08 4.09 4.10 Hall and Ahrens (2002), pp. 5-69–5-72.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 Hall and Ahrens (2002), pp. 5-71–5-73.
  6. ↑ 6.0 6.1 Watts and Hall (2002a), p. 5-159.
  7. ↑ 7.00 7.01 7.02 7.03 7.04 7.05 7.06 7.07 7.08 7.09 7.10 7.11 Hall and Ahrens (2002), pp. 5-67–5-69.
  8. ↑ Barry (2002), pp. 5-210–5-211.
  9. ↑ 9.0 9.1 9.2 9.3 9.4 9.5 Hall and Ahrens (2002), pp. 5-68–5-70.
  10. ↑ Watts and Hall (2002a), pp. 5-154–5-156.
  11. ↑ Proulx (2002), pp. 3-344–3-345.
  12. ↑ 12.0 12.1 Hall and Ahrens (2002), pp. 5-71–5-72.
  13. ↑ 13.0 13.1 Australian Building Codes Board (2020), pp. 88–90.
  14. ↑ 14.0 14.1 Australian Building Codes Board (2020), pp. 89–90.
  15. ↑ Proulx (2002), p. 3-350.
  16. ↑ Proulx (2002), pp. 3-348–3-350.
  17. ↑ 17.0 17.1 Notarianni (2002), pp. 5-43–5-44.
  18. ↑ DiNenno (2002), pp. iii–iv.
  19. ↑ Watts and Hall (2002a), p. 5-160.
  20. ↑ Australian Building Codes Board et al. (2005), p. 0.3-8.

References

  • Australian Building Codes Board. (2020). Fire safety verification method data sheets (Version 1.1). Author.
  • Australian Building Codes Board, National Research Council of Canada, International Code Council, & Department of Building and Housing New Zealand. (2005). International fire engineering guidelines (2005 ed.). Australian Building Codes Board.
  • Barry, T. F. (2002). Quantitative risk assessment in chemical process industries. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • DiNenno, P. J. (Ed.). (2002). SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • Hall, J. R., Jr., & Ahrens, M. J. (2002). Data for engineering analysis. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • Notarianni, K. A. (2002). Uncertainty. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • Proulx, G. (2002). Movement of people: The evacuation timing. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • Watts, J. M., Jr., & Hall, J. R., Jr. (2002a). Building fire risk analysis. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
  • Watts, J. M., Jr., & Hall, J. R., Jr. (2002b). Introduction to fire risk analysis. In P. J. DiNenno (Ed.), SFPE handbook of fire protection engineering (3rd ed.). National Fire Protection Association.
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