A Study of Differences in the Perceived Risk of Attaining a Residential Fire Injury

The unequal distribution of residential fire fatalities is well documented with considerable sociodemographic dimensions amongst victims. Apart from the well-established differences in risk between different age groups [1, 2], several sociodemographic risk factors have been identified. These include being male [2, 3], living alone [3, 4], belonging to an ethnic minority [5‐7], having low educational attainment [5, 8], as well as certain deprivation-related factors such as having a low disposable income, receiving social allowance, being unemployed, receiving health-related early retirement pension, etc. [4, 7‐13].

However, it is also well-known that fatal fires can be hindered at several stages [14], and several studies have shown that interventions such as smoke alarms, education or multi-facetted programs are effective [3, 15‐17], if they are adjusted to different sociodemographic groups [16, 18]. On an aggregate level, however, high-risk groups seem to be less inclined to employ protective measures [19]—according to some [20]—due to their low perceived risk of their situation. In other words, given their low or faulty perception of their individual risk, they refrain from applying preventative measures. If high-risk groups have a faulty perception of their risk, this could explain why sociodemographic groups with high risk of fire mortality to a lesser degree have suitable risk preventative measures implemented in their homes [19] and are to a greater extent reliant on societal protection in the prevention of fire fatalities [21‐24].

Risk perception and fire safety has been approached in several different ways in the fire safety literature. The Protective Action Decision Model (PADM) [25] is a commonly invoked theory in risk perception of fire safety, particularly in helping to understand human behaviour and risk perception in relation to evacuation [26, 27]. An inherent assumption of PADM is that individuals often have erroneous risk perception and that society has a responsibility to correct such misconceptions [25]. However, although several studies have indicated that individuals may not entirely assess risk correctly, [28‐30], especially regarding risks that are rare, if they are promoted in the media or evoke dread [29], this does not mean that individuals are unaware of their risk or unable to determine their own risk. On the contrary, data suggests that individuals tend to have quite accurate perceptions of their mortality risk—at least in relation to relatively common hazards [31‐33]—with the exception of men consistently assessing their risk as lower compared to women despite having a higher objective risk [34].

Why certain risks can be understood correctly and other faultily can in turn be understood through the Protection Motivation Theory (PMT) [35]. The basis of PMT is that fear is constructed through three elements: (1) the noxiousness or severity of an event; (2) the probability of an event occurring if no protective measures are taken or behaviour is adapted; and (3) the efficacy of a response to reduce or eliminate the noxious event [36]. Therefore, as knowledge, control and self-efficacy are crucial ingredients in risk perception, common hazards that originate close to the individual and that are easily dealt with are more often understood correctly, compared to fuzzy and uncontrollable hazards. Residential fires could be viewed as both rare and uncontrollable events as well as common occurrences with smaller residential fires being relatively common [37] although most people will not experience a serious event—not least given the sociodemographic differentiation in risk [38].

As such, it is not clear—neither from the current literature nor the theoretical understanding of risk perception—whether there are sociodemographic differences in fire risk perception. This study will therefore investigate how the perceived risk of being injured in a residential fire varies in the Swedish population and how this perceived risk varies depending upon different sociodemographic factors. Also, the paper will investigate which factors are the most important and if these factors correspond to the known sociodemographic risk factors underlying fire mortality.

As seen in Table 1, several statistically significant differences in perceived risk were seen in terms of sociodemographic groups. Although there was no statistically significant difference between people living alone (single households) or households with more than one person (p = 0.103), in all other topics significant differences are seen. Men perceived their risk to be significantly lower than women, as did younger age groups compared to older adults. Studying the effect size (epsilon2), gender and education stand out, as do age, income and occupation with significantly lower perceived risk amongst men, higher educated, younger, and higher income individuals. Students and employed/business owners perceive their risk as lower compared to those off work.

In terms of multiple comparisons, the Dunn–Bonferroni test found significant differences in perceived risk between respondents born in the Nordic countries and the rest of Europe (adj. p < 0.001) with those born in the Nordic countries perceiving their risk as lower. There was no significant difference between respondents born in Europe (including Nordic countries) and born outside of Europe. Furthermore, the post hoc tests found that perceived risk was significantly different between high income and medium income, H = 800.344, adj. p < 0.001, and between high income and low income, H = 995.024, adj. p < 0.001, with a higher income corresponding to a lower perceived risk. There was no significant difference between low-income respondents and medium-income respondents.

Comparing different occupations, the Dunn’s post-hoc test found that perceived risk was significantly higher amongst those retired compared to the category Other, H = 958.845, adj. p < 0.001. Similarly, students (H = − 1352, adj. p < 0.001,), home workers, those on sick leave or unemployed and retired (H = − 336.186, adj. p 0.006,), and employed or business owners (H = − 889.850, adj. p < 0.001,) all perceived their risk as significantly lower compared to retired individuals. Finally, students experienced their risk as significantly lower compared to home workers, and those on sick leave or unemployed, H = 798.396, adj. p 0.001.

In terms of educational level, the post-hoc test revealed significant differences between all levels of education, with perceived risk decreasing with each level of further education. The largest difference was between high and low educational level, H = 1347.068, compared to high with medium level of education, H = 532.673, and when comparing low and medium education levels, H = 814.395.

Comparing residential types, the perceived risk was significantly lower for people living in an owned apartment compared to those living in a rental house or apartment, H = − 481.312, adj. p < 0.001. Likewise, those living in an owned house, townhouse, or farm perceived their risk as lower compared to those living in an owned apartment, H = 506.325, adj. p < 0.001.

Finally, in terms of socio-geographic categories, significant differences were found between all groups, with an increase in risk perception in each step towards rural areas. The largest difference was found between those living in urban and those living in rural areas, H = 922.629, adj. p < 0.001, where those living in urban areas perceived their risk as lower. Whilst less of a difference, those living in urban areas also perceived their risk as lower compared to those living in “close to urban” areas, H = 364.491, adj. p 0.015. Similarly, those living in “close to urban” areas perceived their risk as lower compared to those living in rural areas, H = 558.138, adj. p < 0.001.

Given the large number of statistically significant results, a stepwise logistic regression was performed, with a total number of nine models. As is clear in Table 2, in which odds ratio with confidence intervals (** = p set to 0.01, * = p set to 0.05) are presented, many of the differences (or lack thereof) seen in Table 1 remain. For instance, the difference between men and women (with women perceiving their risk as higher) remains strong in all nine models, regardless of how many other sociodemographic factors are controlled for, with an odds ratio of 1.9 in the full model, i.e., the likelihood of women reporting higher perceived risks were almost twice as high as for men. Education also remains an important factor. Socio-geography remains significant, where people living in urban areas have a lower risk perception compared to those living in rural areas, even when controlling for other sociodemographic factors. Whether you live alone or not does not appear to be relevant in either our analyses. Being born in another European country, outside of the Nordic region, seems to increase the odds for perceiving a risk of fire injury. However, in contrast to the Kruskal Wallis analysis, we see that being born outside of Europe is less of a relevant factor when controlling for other variables. Finally, in model 4, we see that the odds ratio for the older age group decreases when we add occupation to the model.

For each model, the fit relative to other models is represented by an AIC value. Comparing the AIC values of the different models, it is clear that adding variables will always increase the AIC and the model with the lowest AIC is the model that only includes one variable. This could be interpreted as there are not one or a few variables included in this model that independently strongly out-weigh others. As such, it can be assumed that although there are variables that show significant differences in perceived risk, each variable is not more important than others in terms of the explanatory power. This is further strengthened when looking at the changes in Nagelkerke values between each model, i.e., no individual variable or combination of variable is decidedly more important than the others. This tendency can be confirmed further in the automated stepwise analysis (Table 3), where only one variable was eliminated.

As is clearly seen in this paper, individuals’ perceived risk for sustaining a fire-related injury varies in the Swedish population and is affected by sociodemographic characteristics. Starting with perceived risk and its variation across different sub-groups, several interesting findings exist. First, this study shows that men seem to perceive their risk of being injured in a residential fire to be lower than women, despite being overrepresented in fire fatalities [2, 3]. However, apart from this finding, practically all results show that risk perception is relatively well-aligned with known risk factors. Individuals with known risk factors, such as higher age, lower income and education, being born outside of the Nordic countries, and living in rural areas, all perceive their risk to be higher—a perception that largely mirrors their actual risk in a Swedish setting [2, 13].

An important finding in this paper is also that although strong significant differences are seen in relation to several variables, the explanatory power in the different models is low. Whilst this does not entail that the identified significant differences are not relevant, it does raise the question of whether there are other (non-identified) factors that explain differences to a greater degree, or whether the different variables are so intertwined that one cannot separate them from each other? We would argue that the second explanation is more likely, not least when taking the results of the automated forward stepwise regression model into account. Hypothetically, individuals who perceive their risk as high have so many of the risk factors that they cannot be separated from each other. Similar findings regarding multi-risk individuals has been shown in studies on fire mortality when many interlinked different sociodemographic risk factors (rather than a single factor or only a few) increase the risk of perishing in a fire [13].

Regardless, with such similarities between risk perception and actual risk, there are important and interesting ramifications from a prevention perspective. Previous studies have found a significantly lower use of preventative measures or practices amongst ethnic minority families [46‐49], single-households and low income families [50], individuals with a lower educational level [51, 52] as well as those living in socially deprived areas [53, 54], i.e. background factors that are similar to the groups found in this study to have a higher risk perception. The findings in this study therefore clearly question the hypothesis that fire protection is to a greater degree absent in certain groups due to faulty risk perception and that high risk perception leads to improved safety [20]. In fact, fire morbidity risk seems to largely be understood correctly, thereby indicating that high-risk individuals, who perceive their risk as high, are (on an aggregate group level) knowingly refraining from fire prevention practices for other reasons.

These findings therefore indicate that many individuals may be aware of the fact that they require help during a fire. As such it is even more surprising that safety practices are often low in the same groups [19], as the results indicate that individuals’ assessment of their situation may be considerably better than could be assumed. Consequentially, this leads to two conclusions. First, that other factors may hinder individuals from implementing safety precautions despite knowledge of their situation, and second, that it does not seem to be a lack of knowledge or insight that hinders prevention.

Starting with the second point, information regarding fire safety has often been a proposed method in the hope of reducing fire fatalities. Although the effectiveness of information in injury prevention has been questioned severely [55], the results in this study on aggregate risk perception further illustrates the lack of theoretical effectiveness of trying to convince individuals to change behaviour by increasing their fear of the situation [56]. In fact, this study indicates that individuals (on an aggregate level) seem relatively competent in determining their risk profile meaning that societal fear-generating is most likely futile if the purpose from authorities is to increase fire protection measures by increasing fear.

Returning to the first point, several factors could be the cause of a lack of action despite knowledge. For example, financial reasons have been known to hinder older adults in improving their fire safety [57]. However, in clinical trials the distribution of free smoke alarms did not reduce fire-related injuries, predominantly because the smoke alarms were not maintained or installed [58]. In turn, this raises the issue of risk prioritization, i.e., that although individuals are aware of the risk of fire-related injuries, other issues are more pressing in everyday life. Such results have previously been observed in relation to fall-related injuries [59] and may be relevant also for fire safety. Regardless, the results clearly point towards the importance and prioritisation of passive interventions [60] in order to reduce fire-related injuries.

Although the results in this study are important and, we believe, sound, there are some limitations that need to be addressed. First, there are some issues with the data collection. Questionnaires almost always have a certain selection bias in terms of which groups answer. Whilst attempts have been made to minimise this through detailed analysis of non-responses, one must view the results thereafter. Similarly, the data collection is not national. Instead, the survey used a purposeful selection of municipalities that were deemed representative in terms of the Swedish population’s differences in trust. As such, we believe that the results most likely represent the Swedish population although this cannot be entirely certain. Furthermore, some limitations concerning the dependant variable should be mentioned. First, the formulation of the risk perception question is not based on a scientific definition, which could make it harder to compare the findings of the study to previous work. Second, as mentioned previously, the question entails two aspects of fire prevention; both fire and the effect of the fire, which could be problematic. Third, using a single item will always bring an amount of reliability uncertainty, as we cannot know that the respondents understand the question in the same way. Fourth, the use of nonparametric tests limits the interpretation of the results, as no linear relationships can be claimed. Dummy coding the dependant variable for the logistic regression was performed for an easier interpretation of the results, but we understand that the choice to include the neutral option in one of the two categories could be discussed as arbitrary. However, despite the challenges of nonparametric analyses and other limitations mentioned above, we believe that our study shows interesting results worthy of further discussions within the field of fire safety.

This study shows that risk perception varies in the Swedish population depending upon sociodemographic factors. Practically, the results indicate that high-risk groups are relatively aware of their risk profile. These results are important as they indicate that it is not lack of knowledge or risk awareness that is the problem in terms of why high-risk groups are less inclined to implement fire safety practices.