Accident analysis in practice: A review of Human Factors Analysis and Classification System (HFACS) applications in the peer reviewed academic literature

Abstract

The Human Factors Analysis & Classification System (HFACS) is arguably the most popular accident analysis method within Human Factors and Ergonomics. This literature review examines and reports on peer reviewed studies that have applied HFACS to analyse and understand the cause of accidents in a diverse set of domains. Four databases (PubMed, ScienceDirect, Scopus, Web of Science) were searched for articles published up to the date 31 July 2018. A total of 43 HFACS studies were included. The most popular accident contexts were aviation, maritime, and rail. A greater number of contributory factors were found at the lower end of the sociotechnical systems analyzed, including the human operator and operating environment levels. Notably, more than 60% of the studies used HFACS in a modified form to analyse how a network of interacting latent and active factors contributed to the occurrence of an accident.

Full text

Accident analysis in practice: A review of Human Factors
Analysis and Classification System (HFACS) applications in the
peer reviewed academic literature
Adam Hulme*1, Neville A. Stanton2, Guy H. Walker3, Patrick Waterson4, Paul M. Salmon1
1University of the Sunshine Coast, Sippy Downs, Australia
2University of Southampton, Southampton, United Kingdom
3Heriot-Watt University, Scotland, United Kingdom
4Loughborough University, Leicestershire, United Kingdom
The Human Factors Analysis & Classification System (HFACS) is arguably the most popular accident
analysis method within Human Factors and Ergonomics. This literature review examines and reports on
peer reviewed studies that have applied HFACS to analyse and understand the cause of accidents in a
diverse set of domains. Four databases (PubMed, ScienceDirect, Scopus, Web of Science) were searched
for articles published up to the date 31 July 2018. A total of 43 HFACS studies were included. The most
popular accident contexts were aviation, maritime, and rail. A greater number of contributory factors were
found at the lower end of the sociotechnical systems analyzed, including the human operator and operating
environment levels. Notably, more than 60% of the studies used HFACS in a modified form to analyse how
a network of interacting latent and active factors contributed to the occurrence of an accident.
INTRODUCTION
Accident analysis methods are used to reconstruct accident
scenarios to identify contributory factors and support the
development of interventions designed to prevent further
similar occurrences. The Human Factors Analysis and
Classification System (HFACS) is one such method that was
originally developed for use in aviation but has since been
applied in many safety-critical domains. The development of
HFACS can be traced back to Reason’s (1990) theory of latent
and active failures (i.e., the so-called ‘Swiss cheese’ model).
Latent failures include factors such as deficient organizational
management practices, inadequate or missing resources,
supervisory violations, poor equipment design, and
insufficient staff training protocols and procedures.
Conversely, active failures include unsafe acts such as human
errors and violations that that take place closer in time to the
occurrence of an accident.
Although highly popular, Reason’s (1990) model is
theoretical in nature, and prior to HFACS inception, did not
provide a classification system to classify contributory factors.
In response to this, Shappell and Wiegmann (2001) developed
an aviation specific method (i.e., HFACS) which adopted the
Swiss cheese levels along with categories of failure modes
across four levels: (i) unsafe acts; (ii) preconditions for unsafe
acts; (iii) unsafe supervision; and, (iv) organizational
influences. Each of the four levels contain at least three
categories of contributory factors, with a total of 17 original
categories that were later extended to 19 via the addition of
environmental factors (Li and Harris, 2006). When applying
HFACS, analysts classify the human (active) errors and the
related latent failures across levels of the work system. This
process offers a formal structure to facilitate factor
identification and categorization.
HFACS is arguably one of the most commonly applied
accident analysis methods in the last two decades. Given this,
as well as wider discussions around the fitness for purpose of
contemporary accident analysis methods (Salmon et al., 2017),
it is timely to review the scholarly literature from a current
state of the science perspective. Doing so will provide insight
into what contexts HFACS has been applied to, as well as how
this approach has evolved over time. In addition, any
limitations identified will support further methodological
development. Therefore, the aim of this literature review is to
examine and report on peer reviewed studies that have applied
HFACS to analyse and understand the cause of accidents in a
diverse set of domains.
METHODS
This paper formed part of a broader study involving a
comprehensive review of accident analysis methods since
1990 (Hulme et al., 2019). The following methods section has
been amended from its original to reflect the identification,
inclusion, and synthesis of HFACS studies.
Electronic search
Four databases (PubMed, ScienceDirect, Scopus, Web of
Science) were searched for published journal articles up to the
date 31 July 2018. Database limits were imposed on both the
published language and document type to maintain a
manageable and relevant search strategy (i.e., the search was
restricted to include peer reviewed journal articles).
Eligibility criteria
To be eligible for inclusion, studies were required to comply
with the following criteria:
• Analyses involved an application of HFACS. Domain-
specific adaptations to the original terminology and/or
taxonomy was permitted (e.g., HFACS-RR is a modified
version of HFACS for railroad accidents (Reinach and
Viale, 2006);

• Analyses aimed to understand the cause of accidents
(singular events or aggregated datasets) from the
perspective of both latent and active failures;
• Outcomes included documented accidents ranging from
major events (e.g., multiple fatalities) to relatively minor
incidents and anomalies (e.g., component failures,
personal injury); and,
• Information sources were original peer-reviewed journal
articles published in English.
Data extraction
Extracted study information included: (i) study/date; (ii)
accident context; (iii) data sources/year; (iv) the version of the
method (e.g., HFACS-RR); (v) number of accidents analyzed;
(vi) errors; (vii) categories; and, (ix) relationships among
factors including any unique features of the approach taken
such as the use of additional theories, analyses, or statistical
techniques.
Data organization and interpretation
An overview of the HFACS studies in the review is presented
in Table 1. The non-weighted and weighted mean proportion
of a given HFACS category was computed (Figure 1).
Calculating a weighted mean proportion (Eq. 1) and weighted
standard deviation (Eq. 2) was performed as the number of
accidents analyzed with HFACS varied across studies. Studies
providing information about the frequency of the presence of
each HFACS category, as well as the number of accidents
analyzed, were eligible for a quantitative synthesis:
Where x is the frequency of a HFACS category for a given
study (calculated manually where necessary), and w is the
weighted factor based on the total number of accidents. The
relative quality of the data, as well as the nature and severity
of accidents, were treated as equal given that the purpose was
only to understand where classification efforts have been
concentrated on HFACS.
RESULTS
Study identification
After searching four databases, a total of 690 articles were
identified. After removing 197 duplicates and examining 493
titles and abstracts, a total of 43 HFACS studies were
included. There were 14 studies published between the years
2000 and 2009, and 29 studies published between the years
2010 and 31 July 2018, inclusive (Table 1).
Study overview
Domains
Most studies aimed to understand the human and
organizational factors underpinning aviation (n=15) and
maritime (n=10) accidents. Studies in the mining (n=7), rail
(n=6), construction (n=2), nuclear power (n=2), and industrial
(n=1) work domains were identified. The sources of accident
data and type and severity of accidents varied across studies.
Modifications
In terms of HFACS framework modifications, eight studies
incorporated an additional fifth level above the organizational
level (Reinach and Viale, 2006; Patterson and Shappell, 2010;
Chauvin et al., 2013; Chen et al., 2013; Theophilus et al.,
2017; Verma and Chaudhari, 2017; Yıldırım et al., 2017;
Zhang et al., 2018). This fifth level included the categories of,
for example, ‘outside factors’, ‘external factors’, and
‘regulatory and statutory’ influences. Thirty-four studies used
a traditional four level HFACS framework that included
between 17 and 20 individual categories. One study modified
the HFACS framework to include 28 categories across the
traditional four levels (Batalden and Sydnes, 2014).
Table 1: Authors, date, and context of 43 HFACS studies
ordered by ascending publication date
Study
Context
Accidents
Wiegmann & Shappell (2001)*†‡
Aviation
119
Gaur (2005)*†‡
Aviation
48
Dambier & Hinkelbein (2006)†
Aviation
239
Li & Harris (2006)*‡
Aviation
523
Reinach & Viale (2006)†
Rail
6
Tvaryanas et al. (2006)
Aviation
221
Shappell et al. (2007)*‡
Aviation
1020
Baysari et al. (2008)*†‡
Rail
23
Gibb & Olson (2008)
Aviation
124
Lenne et al. (2008)*‡
Aviation
169
Li et al. (2008)*‡
Aviation
41
Tvaryanas & Thompson (2008)
Aviation
48
Baysari et al. (2009)*†‡
Rail
19
Celik (2009)
Maritime
1
Patterson & Shappell (2010)*‡
Mining
508
Wang et al. (2011)†
Maritime
2
Hale et al. (2012)†
Construction
26
Lenne et al. (2012)*†‡
Mining
263
Chauvin et al. (2013)*‡
Maritime
27
Chen et al. (2013)†
Maritime
1
Hooper & O'Hare (2013)*‡
Aviation
288
Li & Harris (2013)*‡
Aviation
523
Wang et al. (2013)†
Maritime
1
Akhtar & Utne (2014)†
Maritime
93
Akyuz & Celik (2014)†
Maritime
1
Equation 1:
𝑥𝑤=%(𝑥𝑖∗𝑤𝑖)
∑𝑤𝑖
𝑛
𝑖=1
𝑛
𝑖=1
Equation 2:
𝑠𝑤= .𝑛%𝑤𝑖(𝑥𝑖−𝑥𝑤)2
∑𝑤𝑖
𝑛
𝑖=0
𝑛
𝑖=0

Batalden & Sydnes (2014)†
Maritime
94
Daramola (2014)*
Aviation
42
Gong et al. (2014)
Aviation
2
Kim et al. (2014)*†‡
Nuclear
38
Yunxiao & Yangke (2014)*‡
Mining
107
Madigan et al. (2016)*†‡
Rail
74
Wong et al. (2016)*
Construction
52
Akyuz (2017)†
Maritime
1
Al-Wardi (2017)*‡
Aviation
40
Fu et al. (2017)
Mining
1
Theophilus et al. (2017)*‡
O&G
11
Verma & Chaudhari (2017)*‡
Mining
102
Yıldırım et al. (2017)†
Maritime
257
Yoon et al. (2017)
Nuclear
1
Zhan et al. (2017)†
Rail
1
Zhou & Lei (2017)*‡
Rail
407
Mirzaei Aliabadi et al. (2018)
Mining
295
Zhang et al. (2018)
Mining
94
*, indicates 22 studies eligible for a quantitative HFACS categorization
summary as visualized in Figure 1; †, indicates 19 studies included in Table
2; ‡, indicates 20 studies included in Table 2
Table 2 provides a summary of the total accident, error, and
HFACS category frequencies, as well as measures of central
tendency applicable only to studies reporting the necessary
information.
Table 2: Summary of accidents, errors, and HFACS category
frequencies across studies. Errors indicates the total number of
causal/contributory factors identified, whereas categories refer
to the total HFACS categories/causal codes identified
No.
Total
Range
Median
Mean
Accidents
43
5,965
1-1,020
48
139 (203)
Errors
19†
6,938
15-2,868
153
365 (681)
Categories
20‡
15,720
55-2,686
324
786 (868)
†, ‡, includes only those studies indicated in Table 1
Supplementary HFACS analyses
Twenty-seven (62.8%) studies modelled interactions across
errors and/or HFACS categories (Table 3). The aim was to
analyse and understand how a network of interacting latent
and active factors contributed to the occurrence of an accident.
The approaches and techniques to visualize and model
relationships among factors included traditional statistical
modelling (e.g., chi-squared test, Fisher’s exact test, logistic
regression analyses), hierarchical decision-making process
methods (e.g., Analytical Network Process, Fuzzy Analytic
Hierarchy Process), and quantitative probability modelling
(e.g., Bayesian networks).
Table 3: Authors, date, and other analyses reported in 27
HFACS studies ordered by ascending publication date
Study
Other analyses
Li & Harris (2006)
χ2, λ
Tvaryanas et al. (2006)
χ2, Cramer’s V, LR
Lenne et al. (2008)
χ2, FET, LR
Li et al. (2008)
χ2, λ
Tvaryanas & Thompson (2008)
PCA, PM
Celik (2009)
FAHP, PW, FC
Wang et al. (2011)
BN, CPT, FAHP
Lenne et al. (2012)
FET
Chauvin et al. (2013)
χ2, MCA, HC, CTA
Chen et al. (2013)
WBA
Hooper & O'Hare (2013)
χ2, λ, LR
Wang et al. (2013)
BN, CPT, FAHP
Akhtar & Utne (2014)
BN, CPT
Akyuz & Celik (2014)
CM matrix, GCV, NCV
Daramola (2014)
χ2
Gong et al. (2014)
Qualitative, AcciTree
Kim et al. (2014)
χ2
Madigan et al. (2016)
χ2, ASR
Wong et al. (2016)
FET, LCA
Akyuz (2017)
ANP
Theophilus et al. (2017)
χ2, FET, correlations
Verma & Chaudhari (2017)
Fuzzy Reasoning
Yıldırım et al. (2017)
χ2, CA
Zhan et al. (2017)
F-DEMATEL, ANP
Zhou & Lei (2017)
χ2, λ
Mirzaei Aliabadi et al. (2018)
BN, CPT
Zhang et al. (2018)
FET, QM
χ2, chi-squared test; λ, Goodman & Kruskal’s lambda; ANP, Analytical
Network Process; ASR, Adjusted Standardized Residuals; BN, Bayesian
Network; CM, Cognitive Mapping; CPT, Conditional Probability Table;
CTA, Classification Tree Analysis; FAHP, Fuzzy Analytical Hierarchy
Process; FC, Factor Clustering; F-DEMATEL, Fuzzy Decision Making Trail
& Evaluation Laboratory; FET, Fisher Exact Test; GCV, Global Centrality
Value; HC, Hierarchical Clustering; ICAM, Incident Case Analysis Method;
LCA, Latent Class Analysis; LR, Logistic Regression; MCA, Multiple
Correspondence Analysis; NCV, Normal Centrality Value; PCA, Principal
Component Analysis; PW, Priority Weights; WBA, Why-Because Analysis
HFACS general classifications
A total of 22 (51.2%) studies reported the frequency of the
presence of a HFACS category (Figure 1). The weighted mean
proportions and standard deviations of 18 HFACS categories
are based on 4,456 accidents (i.e., 74.7% of the total accidents
analyzed). In terms of the weighted mean proportions, skill-
based error (53.5%), decision error (36.5%), physical
environment (30.6%), violation (27.2%), and inadequate
supervision (25.5%) were the most frequently coded HFACS
categories. The lowest proportions were found for
physiological state (3.4%), supervisory violation (4.9%),
failed to correct a known problem (5.6%), organizational
climate (8.9%), and physical and mental limitation (9.3%).
DISCUSSION
This review has examined and reported on peer reviewed
studies that have applied HFACS to analyse and understand
the cause of accidents in a diverse set of domains. The
findings show that HFACS has progressively found its way
outside of the aviation context and into other domains of
application. Clearly, researchers and practitioners with a bona
fide interest in maritime, rail, and mining recognize the value
and utility of this approach to support accident analysis
efforts. Indeed, HFACS offers a relatively complete taxonomy
that can be readily adapted to meet the needs of a given work
system. Despite its routine adaptation, the lack of a focus on
the wider sociotechnical system beyond the organizational

Figure 1: Non-weighted and weighted mean proportions of 18 HFACS categories across 22 studies (aviation n=10, rail n=4, mining n=4, maritime n=1, construction
n=1, nuclear power n=1, industrial n=1). AMS, Adverse Mental State; APS, Adverse Physiological State; CRM, Crew Resource Management; DEr, Decision Error;
FCP, Failed to Correct a Known Problem; ISu, Inadequate Supervision; OCl, Organizational Climate; OPr, Organizational Process; ORM, Organizational Resource
Management; PEn, Physical Environment; PEr, Perceptual Error; PIO, Planned Inappropriate Operation; PML, Physical-Mental Limitation; PRe, Personal Readiness;
SEr, Skill-based Error; SVi, Supervisory Violation; TEn, Technological Environment; Vi, Violation
level has equally resulted in the extension of HFACS to
account for ‘external’ and ‘outside’ factors. This is a limitation
of the traditional HFACS approach, especially when other
accident analysis methods (e.g., AcciMap (Rasmussen, 1997)
and STAMP (Leveson, 2004)), include the government,
legislative, and regulatory levels of a system.
Another aspect to HFACS that has been absent since its
formalization is the modelling of pathways and relationships
between contributory factors. Notably, over 60% of the studies
applied some form of quantitative or statistical technique to
better understand the relationships and/or strength to which
higher level organizational determinants influenced factors at
the lower end of a system. For example, Li and Harris (2006)
used relatively basic asymmetric measures of association to
quantify the relationships between latent and active failures.
Since then, the use of more sophisticated forms of statistical
and probability modelling (e.g., regression analyses, FAHP
algorithms including priority weighting procedures, BN
modelling) have become more common. In short, HFACS is
being adapted and improved upon to better understand
accident etiology. Many of the modifications ostensibly
represent attempts to bring the HFACS method up to date with
contemporary models of accident causation which place
emphasis on the interactions between decisions and actions
across entire work systems.
A final observation is the identification of a greater
number of contributory factors at the sharp end of the work
systems relative to the number of factors identified at the
supervisory, organizational, and external levels. For example,
in the studies reviewed, many contributory factors were
identified at the ‘unsafe acts’ and ‘preconditions for unsafe
acts’ levels, including skill-based errors, decision errors,
violations, and factors related to the physical environment.
This may be a function of the information and data available
to analysts rather than a consistent feature of accident
causation. The limited number of factors identified at higher
system levels suggests that HFACS analyses and the resulting
prevention interventions could be overlooking the potential
benefit of going upstream where arguably some of the greatest
differences could be made. A disproportionate focus on factors
at the sharp end is not necessarily consistent with the HFACS
philosophy which also emphasizes the role of organizational
factors and latent determinants.
CONCLUSION
Whilst HFACS can help analysts to identify both latent and
active factors underpinning accidents, modifications to the
method have been introduced, and there has been more of a
focus on the identification of skill-based and decision errors
relative to other categories. It is concluded that there may be a
need to revisit the method to incorporate a structured approach
for examining the relationships between contributory factors.
This will support consistent applications of the method that
can be compared across different domains and datasets.
ACKNOWLEDGEMENTS
This work was supported by an Australian Research Council
(ARC) Discovery Project (DP180100806).
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