Advertisement

Household Fire Protection Practices in Relation to Socio-demographic Characteristics: Evidence from a Swedish National Survey

  • Finn NilsonEmail author
  • Carl Bonander
Open Access
Article
  • 98 Downloads

Abstract

The sociodemographic inequalities in the ownership of residential fire safety equipment, fire prevention practices and fire protection knowledge was studied using an inductive and data-driven approach based on the responses to a national Swedish survey containing individual-level data on several dimensions of home fire safety practices (n = 7507). Cluster analysis was used to summarise home fire safety data and sociodemographic characteristics of the sample were then regressed on the data ordinal regression analysis. The results showed significant correlations between the level of fire protection and a range of factors (sex, age, family composition, income, housing type and country of birth), suggesting a positive effect of socioeconomic success. Further, the results imply that having experienced a residential fire has a positive impact on future fire protection practices, and that higher levels of fire protection interest increases the probability of having a functional smoke detector.

Keywords

Socioeconomic status Multiple correspondence analysis Fire safety Health inequality Risk factors 

1 Introduction

Although large risk reductions in fire-related deaths have been observed in most high-income countries during the last 50–60 years [1], household fires are still a considerable societal problem [2, 3]. Specifically, although fire mortality has decreased from a general perspective, these reductions seem to have been disproportionate in terms of different socio-demographic groups, as well as there being a levelling-off of the decreasing trend. For example, whilst large decreases have been seen amongst adults and children, only minor rate reductions have been observed amongst older adults [4]. Also, in regards to older adults, several studies have suggested that the changing demographics, in which the number of older people are increasing substantially [5] will lead to increases in the number of deaths in countries such as Japan [6] and Spain [7].

In terms of general fire mortality and apart from the well-established differences in risk between different age groups [8, 9], a number of socio-demographic risk factors have been identified. These include being male [9, 10], living alone [10, 11, 12], belonging to an ethnic minority [13, 14, 15], having low educational attainment [13, 16], as well as other deprivation-related factors such as having a low disposable income, receiving social allowance, being unemployed, receiving health-related early retirement pension, etc. [11, 12, 15, 16, 17, 18, 19, 20]. Interestingly, many of these socio-demographic differences have been observed since the 1970s [21, 22]. However, these seem to have become even more pronounced [11, 17]. One hypothetical reason for this is the fundamental cause theory, stating that socio-demographic differences increase when preventative measures exist [23].

In terms of prevention, fire-related deaths can be hindered at five points in the fire process; reduce heat; stop ignition of first object; hinder fire growth; initiate evacuation; and complete evacuation [24]. Starting with the first two steps, i.e. the development of an unwanted fire, previous studies have shown that the risk of fire, regardless of result, is higher amongst socio-demographically “strong” groups (well educated, high income households) compared to the rest of the population [25, 26]. Therefore, it would seem that it is not that vulnerable socio-demographic groups have a higher risk of fire but rather a reduced ability to hinder fire growth and/or evacuate. Previous studies on child injuries in general have found that sociodemographic differences exist in the possession of safety equipment and the perception of safety. Specifically, they have found that safety equipment is significantly less prevalent in the homes of ethnic minorities [27, 28], single-households [29], low income families [29] and families in rented accommodation [30]. Similar socio-demographic differences have been seen with regards to older people and their fire prevention equipment and evacuation preparedness [31]. If a similar pattern exists regarding the possession and knowledge of fire safety equipment in the general population, this could serve as a potential explanation for the socio-demographic differences in mortality and aid in the identification of prevention measures. It could also help clarify the conflicting results between epidemiological studies of the social determinants of residential fires and studies of fire mortality [32].

2 Method and Materials

For this study, cross-sectional data from a national survey, that was sent to a random sample of the Swedish adult population aged 18–79 years in 2005, was used. The purpose of the survey was to investigate the prevalence of residential fires and to obtain information regarding if the household had various types of fire safety equipment, how the equipment was maintained, and if fire safety education had been completed. The questionnaire also included a variety of sociodemographic questions. The survey was delivered by mail and completed in paper form. The questionnaire was developed by The Swedish Civil Contingencies Agency (MSB), and administered, scanned and entered into a dataset by Statistics Sweden. Each respondent received a letter stating the purpose of the survey and that participation is voluntary, and were asked to consent to the collection of complementary register data. Using the Swedish personal identification number (PIN), a unique identifier that is considered highly reliable as the register covers 99.9% of the Swedish population [33], Statistics Sweden linked administrative register data to each respondent. Data concerning income was obtained from the Income and Taxation register, and country of birth from the Total Population Register, both via Statistics Sweden [34]. Non-respondents received up to three reminders. An anonymized data file was sent to MSB upon completion. The final response rate was 62%, yielding a sample size of 7507 individuals. A complete list of variables included in the analysis are found in Table 1.
Table 1

Description of the Variables Used in the Study

Variable

Type

Categories

Role in cluster analysis

Source

Notes

Smoke detector

Categorical

Household has at least one smoke detector (Yes/No)

Active

Survey

 

Functionality testing frequency

Categorical

Once a week;

Once every other month;

Less often;

Other frequency (free text);

Does not text;

Non-response

Active

Survey

Multiple responses allowed (coded as 7 binary variables)

Testing method

Categorical

Test button;

Visual inspection, light;

Testing in an external battery tester;

By (e.g.) lighting a match;

Other method (free text)

Active

Survey

The question reflects the individual or someone else in the household

Fire extinguishing equipment

Categorical

Household has a hand held-fire extinguisher;

Household has a fire blanket;

Individual has practiced using a hand-held fire extinguisher

Active

Survey

Multiple responses allowed (coded as 3 binary variables)

Education

Categorical

Individual has taken part in fire safety education (at least one course)

Active

Survey

 

Information

 

Individual has obtained fire safety information from: A course;

Postal leaflets; Leaflets obtained from elsewhere; Ratio/TV;

The internet; An open house at the fire department;

Has not obtained any fire safety information

Active

Survey

Multiple responses allowed (coded as 8 binary variables)

Evacuation plan

 

Household has considered evacuation routes in case of a fire

Active

Survey

 

Implemented measures

 

Household has implemented fire safety measures as a consequence of a past fire

Active

Survey

 

Age

Integer

 

Not used

Register

Respondent data

Annual income (in thousands SEK)

Continuous

 

Not used

Register

Respondent data

Sex

Categorical

Male; Female

Supplementary

Register

Respondent data

Age group

Categorical

18–29 years;

30–49 years;

50–64 years;

65–79 years

Supplementary

Register

Derived from Age into groups

used by the Swedish Civil Contigencies Agency

Marital status

Categorical

Unmarried;

Married;

Divorced; Widowed

Supplementary

Register

Respondent data

Income group

Categorical

Lower, middle and upper tertiles

Supplementary

Register

Derived from Annual income

Family type

Categorical

Children < 18 years living at home;

Single adult household;

Adult household (> 1 adult)

Supplementary

Survey

 

Ethnicity

Categorical

Native Swede;

Other Nordic countries; Other

Supplementary

Register

Based on country of birth of the respondent

Housing type

Categorical

Single-family home;

Multi-family home;

Other

Supplementary

Survey

 

Fire in the past five years

Categorical

Has your household experienced a residential fire in the past 5 years? (Yes/No)

Not used

Survey

 

Smoke detector functionality at survey completion

Categorical

Yes, all are functional; Yes, some are functional; No;

Do not know/could not test

Not used

Survey

The respondent, or someone else in the household, was asked to test functionality before answering the question

2.1 Statistical Analysis

To effectively explore the socio-demographic differences in residential fire protection practices, the different components of the questionnaire relating to these were summarised. Since the variables available were mainly categorical, multiple correspondence analysis with agglomerative hierarchical clustering was used [35], which is a cluster analysis method that allows for the summary of a larger set of categorical (e.g., nominal or ordinal) variables into a smaller number of clusters [36, 37, 38]. The FactoMineR package for R was used for this part of the analysis.

In the cluster analysis, variables entered to contribute to the clustering procedure are called active variables whilst supplementary variables are used to aid in the interpretation of the clusters, even though they do not actively create the clusters. Table 1 details the role of each variable in the cluster analysis. The available variables that capture safety equipment use, education, information and practices were entered as active variables in order to capture clustering around latent factors related to safety attitudes and behaviours. The goal was to identify a set of clusters that clearly show a variation in the degree to which an individual is interested in, or practice, fire-related safety in their home. Categorical respondent and household characteristics were entered as supplementary variables to analyse how these were distributed between different fire safety clusters (Table 1).

After this, the optimal number of clusters (Q) can be selected in two different ways. The first approach is based on subjective input after a graphical analysis of a hierarchical tree plot (or dendogram) and prior theoretical beliefs regarding the principal components in the data. The second approach is data-driven, and applies an algorithm that automatically selects the optimal Q based on the inertia gain for each additional partitioning [35]. Since there were no prior hypothesis regarding the optimal number of clusters, the latter approach was chosen. The identified clusters were then interpreted using multivariate v-tests to study the statistically significant differences to the sample averages (see [39] for details).

To test other hypotheses (where appropriate), Pearson’s χ2-test (to test bivariate differences between groups), ordinal logistic regression (to estimate the effects of multiple variables on categorical outcome variables), and log-binomial regression models (for binary outcome variables) were used. These analyses were conducted in Stata version 15.1.

3 Results

Six different fire protection clusters were identified in the analysis. Of these, the smallest cluster (n = 82) was mainly clustered around a large number of non-responses regarding safety equipment and other key variables. For this reason, this cluster was omitted from further analysis. Quantitative data from the remaining five clusters can be found in Table 2, where they are compared to the sample average on a range of fire safety behaviours and equipment use.
Table 2

Characteristics of the Clusters Compared to the Sample Average on Observed Fire Safety Practices

Cluster

1

2

3

4

5

Sample average

Variable categories

n = 649 (8.6%)

n = 1371 (18.3%)

n = 2740 (32.9%)

n = 1913 (25.5%)

n = 1022 (13.6%)

n = 7507

Smoke detector

Has at least one smoke detector

20.5(−)

99.8(+)

100.0(+)

100.0(+)

99.6(+)

92.5

Functionality testing frequency

Once a week

0.2(−)

0.7(−)

6.1(+)

6.7(+)

5.6

4.7

Once every other month

0.2(−)

0.4(−)

14.2(+)

15.7(+)

14.7(+)

10.8

Less often

0.5(−)

2.8(−)

73.1(+)

56.8(+)

59.1(+)

47.5

Other frequency (free text response)

0.0(−)

18.1(+)

5.4(−)

12.4(+)

8.4

9.4

Does not test

0.8(−)

77.2(+)

0.4(−)

6.3(−)

9.0(−)

17.2

Non-response

98.5(+)

0.7(−)

0.9(−)

2.1(−)

3.2(−)

10.4

Testing method (multiple responses allowed)

Test button

0.0(−)

6.5(−)

77.0(+)

78.0(+)

68.9(+)

56.2

Visual inspection, light

0.9(−)

4.2(−)

26.9(+)

24.1(+)

30.7(+)

20.2

Testing in an external battery tester

0.2(−)

1.4(−)

4.9(+)

4.6

6.2(+)

3.9

By (e.g.) lighting a match

0.0(−)

2.6(−)

10.5(+)

10.7(+)

15.0(+)

8.7

Other method (free text response)

0.0(−)

14.9(+)

0.1(−)

3.5

3.3

4.1

Fire extinguishing equipment

Has a hand-held fire extinguisher at home

23.4(−)

29.6(−)

38.1(−)

55.5(+)

52.9(+)

41.5

Has a fire blanket at home

1.1(−)

2.4(−)

3.4(−)

8.2(+)

7.1(+)

4.7

Has practiced using a hand-held fire extinguisher

46.4(−)

40.6(−)

38.0(−)

95.9(+)

58.5

56.4

Education

Has taken part in fire safety education (at least one course)

37.1(−)

36.4(−)

26.3(−)

98.0(+)

48.9

50.5

Information (multiple responses allowed)

Has obtained fire safety information from…

A course in fire safety

13.3(−)

5.1(−)

0.2(−)

63.3(+)

25.2(+)

21.9

Postal leaflets

10.2(−)

6.4(−)

8.5(−)

21.2(−)

52.6(+)

17.6

Leaflets obtained elsewhere

5.9(−)

3.7(−)

4.1(−)

10.3

31.7(+)

9.5

Newspapers

10.6(−)

3.1(−)

2.0(−)

3.7(−)

79.7(+)

14.1

Radio/TV

13.7(−)

5.0(−)

3.8(−)

8.3(−)

79.8(+)

16.5

The internet

0.9

0.8(−)

0.0(−)

0.9(−)

8.3(+)

1.6

An open house at the fire department

2.8

1.8(−)

0.9(−)

5.8(+)

8.5(+)

3.5

Has not obtained any fire safety information

49.9(+)

51.8(+)

60.6(+)

3.9(−)

0.0(−)

35.1

Other

Has considered evacuation routes in case of a fire

62.3(−)

55.4(−)

72.2(−)

90.6(+)

86.4(+)

74.7

Has implemented fire safety measures as a consequence of a past fire

0.8(−)

1.1

0.7(−)

2.0(+)

2.2(+)

1.3

Notes (+) = significantly greater than the sample average (at the 0.05-level) according to a multivariate v-test, (−) = significantly lower than the sample average. The values in each cell represent the percentage of individuals in the cluster belonging to each variable category unless otherwise stated. The sum of observations from all clusters does not correspond to the sample total due to omission of 82 individuals who formed an uninterpretable, “unknowns” cluster

The results are interpreted in that the clusters represent five distinct levels of safety interest and behaviours related to residential fire safety: (1) Uninterested in fire safety, with negative responses to almost all questions regarding safety equipment in the home; (2) Minimal fire safety, where individuals belonging to this cluster have smoke detectors, but do not test their functionality; (3) Reliance on detection, which is similar to the previous cluster, but with regular testing of the smoke detector’s functionality; (4) Formally educated in fire safety, which is characterised by individuals who are safety conscious, have extinguishing equipment in their home, and have obtained their knowledge through formal fire safety education, and (5) Informally educated in fire safety, which exhibit similar fire safety practices to individuals in the previous cluster, but who have obtained their safety information elsewhere (e.g. through leaflets or newspapers), meaning that compared to cluster 4, knowledge and information has more actively been searched for.

While the rank order of the clusters in terms of fire safety interest is clear, the exact distinction between cluster 1 and 2 and between 4 and 5 is less pronounced. For example, the clusters Uninterested in fire safety and Minimal fire safety, i.e. clusters 1 and 2, mainly differ in whether or not a smoke detector is installed. In Sweden, the owner of a property is responsible for maintaining a reasonable level of fire protection and therefore, if the property is a rental property, the fire protection responsibility is not with the resident, but with the landlord [40], which could serve as an underlying cause for the observed difference in smoke detector use. Unfortunately, this could not be tested using the available data.

Both cluster 4 and 5 exhibit a high level of safety consciousness and therefore rank higher than the other three. As Table 2 suggests, almost all individuals in the formally educated cluster (cluster 4) have obtained formal fire safety training (n = 1875, 98%), while only half of the informally educated cluster (cluster 5) has taken part in such training (n = 500, 48%). To explore this further, the differences in the context in which individuals in the two clusters generally obtained their fire training was studied using Pearson’s χ2-test (Table 3). The results imply that individuals in the formally educated cluster who had received fire training were more likely to have received work-based education compared to the informally educated cluster. Still, having received fire training at work was the most common answer in both groups (77.7 vs. 70.2%, p < 0.001). Individuals in the informally educated cluster were instead more likely than those in the formally educated cluster to have received school-based (13.4 vs. 22.8%, p < 0.001) or military-based education (20.4 vs. 31.0%, p < 0.001). In essence, the formally educated cluster appears more likely to have held jobs where fire training is provided, while individuals in the informally educated cluster are more likely to have actively sought out information on their own (even after obtaining formal fire safety training).
Table 3

Comparison Between the Informally and Formally Educated Clusters in Answers to the Follow-up Question: “In What Context Did You Receive Your Fire Safety Training?” for Individuals Who Reported Having Obtained Formal Fire Safety Training

 

Cluster 5

Cluster 6

    

Answer

n = 1875

n = 500

Percentage point difference

Relative change (%)

χ2(1)

p value

School

13.39

22.8

− 9.4

− 41.3

26.9

0.00

Work

77.71

70.2

7.5

10.7

12.2

0.00

Military training

20.37

31

− 10.6

− 34.3

25.5

0.00

Civil defense training

7.95

7.6

0.4

4.6

0.1

0.80

Fire brigade

20.53

22.8

− 2.3

− 10.0

1.2

0.27

Other

6.61

7

− 0.4

− 5.6

0.9

0.76

Cannot remember

0.11

0.4

− 0.3

− 72.5

2.0

0.16

Notes The data presented above is a subset of the sample that answered yes to having received formal fire safety education. Hence, the cluster sizes (n) do not correspond to the actual cluster size reported in the main tables

3.1 Socio-demographic Differences Between the Clusters

Several statistically significant differences emerged when supplementary, socio-demographic variables were used to characterise the clusters. The quantitative results are presented in Table 4, and an interpretation of the cluster analysis, from a socio-demographic perspective, is presented in Table 5. The socio-demographic variables that are highlighted are those that are over-represented in the clusters compared to the sample average (according to the multivariate v-tests seen in Table 4), and thus represent how the clusters distinguishes themselves from the sample norm.
Table 4

Sociodemographic Characteristics of the Five Clusters

Cluster

1

2

3

4

5

Sample average

Variable

n = 649 (8.6%)

n = 1371 (18.3%)

n = 2740 (32.9%)

n = 1913 (25.5%)

n = 1022 (13.6%)

n = 7507

Continuous

Age (mean, SD)

47.9 (18.2)

45.9 (17.2)

52.0 (16.9)

49.8 (14.5)

52.1 (15.4)

50.1 (16.5)

Annual income, in thousands SEK (mean, SD)

187.1 (175.5)

215.5 (179.6)

212.8 (159.2)

248.3 (152.2)

238.1 (174.0)

223.5 (165.7)

Categorical (percentage of cluster)

Male sex

40.7

36.2(−)

42.6

52.0(+)

46.5

44.2

Age group

18–29 years

21.1(+)

20.1(+)

11.0(−)

9.1(−)

8.2(−)

12.6

30–49 years

32.2

38.7(+)

33.3(−)

38.8(+)

34.0

35.6

50–64 years

22.5(−)

22.6(−)

26.0(−)

33.6(+)

32.0(+)

27.8

65–79 years

24.2

18.7(−)

29.7(+)

18.5(−)

25.8

24.0

Marital status

Unmarried

37.9(+)

38.0(+)

27.3(−)

30.6

26.4(−)

30.8

Married

38.2(−)

40.7(−)

51.7(+)

50.4(+)

53.7(+)

48.4

Divorced

18.3

17.1

15.3

15.5

14.0

15.8

Widowed

5.5

4.2

5.7(+)

3.5(−)

5.9

5.0

Income group

Lower tertile

47.3(+)

38.4(+)

37.4(+)

21.8(−)

30.1(−)

33.6

Middle tertile

29.0(−)

30.5(−)

33.0

37.0(+)

34.3

33.3

Upper tertile

23.7(−)

31.1

29.6(−)

41.2(+)

35.6

33.1

Family type

Children < 18 years living at home

25.7(−)

32.8(+)

27.9(−)

33.4(+)

30.6

30.3

Single adult household

23.9(+)

16.5

14.8

12.1(−)

12.3(−)

14.9

Adult household (> 1 adult)

46.1(−)

47.7(−)

54.4(+)

52.9

54.5

52.0

Ethnicity

Native Swede

80.7(−)

85.1(−)

85.9(−)

91.2(+)

90.3(+)

87.2

Other Nordic countries

3.5

2.8(−)

4.6

3.6

4.9

8.8

Other

15.7(+)

12.0(+)

9.5

5.3(−)

4.8(−)

8.8

Housing type

Single-family home

39.6(−)

46.7(−)

57.6

70.5(+)

71.3(+)

59.1

Multi-family home

55.2(+)

50.0(+)

39.3

27.0(−)

26.1(−)

37.8

Other

4.3(+)

2.8

2.3

2.0

2.0

2.4

Notes (+) = significantly greater than the sample average (at the 0.05-level) according to a multivariate v-test, (–) = significantly lower than the sample average (tests were not performed for continuous variables). The values in each cell represent the percentage of individuals in the cluster belonging to each variable category unless otherwise stated. The sum of observations from all clusters does not correspond to the sample total due to omission of 82 individuals who formed an uninterpretable, “unknowns” cluster

Table 5

Qualitative Interpretation and Description of Each Cluster in Terms of Sociodemographic Characteristics

Cluster

Qualitative description

Cluster 1—Uninterested in fire safety

The individuals in this cluster are often young (18–29 years), have a low level of income and are more often born outside of Sweden. They often live in a single household in a multi-family house

Cluster 2—Minimal fire safety

The individuals in this cluster are more often young (18–29 years), unmarried and have a low level of income. Women are more prevalent in this cluster. Individuals in this cluster more often live in multi-family houses, are born outside of Sweden and have children

Cluster 3—Reliance on fire detection

The individuals in this cluster are more often older (65 years or above), married or widowed and have a low level of income. They are slightly more often female and born in Scandinavia or Europe

Cluster 4—Formally educated in fire safety

The individuals in this cluster more often live in a single-family home, are more often men, middle-aged (50–64 years) or 30–49 years, born in Sweden, married, have children and have a high or medium level of income

Cluster 5—Informally educated in fire safety

The individuals in this cluster more often live in a single-family home, are more often middle-aged (50–64 years), born in Sweden and are married

As can be seen in Tables 4 and 5, considerable socio-demographic differences exist between the five clusters. As mentioned previously, Uninterested in fire safety and Minimal fire safety merely differed in whether a smoke detector was installed. However, with the addition of supplementary variables, socio-demographic differences appeared between these clusters that could explain the differences in protection. Specifically, although being unmarried was more common in both clusters, in the Minimal fire safety cluster, female respondents were more prevalent compared to the Uninterested in fire safety cluster where men were more common. Gender differences in fire protection has previously been well established [41] and could therefore serve as a partial explanation for the difference. Likewise, in the Minimal fire safety cluster, having children was more common, a factor that has previously been shown to increase worry and risk perception [42], and therefore likely to increase the motivation to protect.

Socio-demographic differences were also observed between the two other similar clusters; Formally educated in fire safety and Informally educated in fire safety. It would seem that differences exist regarding income, age and whether children live at home (Informally educated in fire safety earn more, are older and are less likely to have children living at home). Therefore, although job type is not available in the dataset, given the sociodemographic differences, it may be that the individuals in the informal education group more often have jobs where formal fire training is less likely to be required.

The Reliance on detection cluster differs considerably from other clusters, in that older adults and women are more prevalent in this group. Given the prevalence of testing smoke detectors in various ways, this group seems to be fire safety conscious, while heavily reliant on detection rather than extinguishing or escaping the fire. This could potentially be an artefact of a perceived (or actual) ability to cope with a fire by other means than escape or by the help of the rescue services. Specifically, old age has considerable effects on the physical and cognitive abilities of an individual [43] meaning that evacuation or more complex fire extinguishing can be difficult or impossible. Therefore, an early detection becomes the only reasonable preventative measure for older adults with reduced capabilities.

3.2 Regression Results

Many of the socio-demographic variables presented in Table 4 co-vary (e.g. age and income), and it is therefore also important to consider how each variable independently affects fire safety behaviour. To identify which variables still appeared to modify safety practices, while keeping the others constant, a four-level fire protection scale (from 1 to 4, where 1 low and 4 is high) was coded using the obtained clusters, merging the formally and informally educated clusters into one due to their similarities in exhibited fire safety behaviour. The results from this can be found in Table 6. The robustness of the results was also tested to a three-level version of the scale, merging the Uninterested in fire safety and Minimal fire safety clusters as well. As can be seen, the inferences and effect sizes are largely invariant to coding scheme. They were also robust to using the full range of the clusters in a five-level scale, where switching the rank order of the two educated clusters does not affect the results (available from the authors upon request).
Table 6

Ordinal Logistic Regression Results for Correlations Between Sociodemographic Characteristics and Different Degrees of Fire Safety Behaviour (from Low to High)

 

Outcome

Variable category

Four level fire protection scale (Odds ratio, 95% CI)

Three level fire protection scale (Odds ratio, 95% CI)

Male sex

1.34 (1.21, 1.47)

1.36 (1.24, 1.50)

Age group

18–29 years

1 (reference)

1 (reference)

30–49 years

1.52 (1.30, 1.78)

1.55 (1.3, 1.85)

50–64 years

1.97 (1.65, 2.35)

2.03 (1.71, 2.43)

65–79 years

1.58 (1.33, 1.89)

1.63 (1.37, 1.95)

Marital status

Unmarried

1 (reference)

1 (reference)

Married

0.98 (0.87, 1.1)

0.99 (0.86, 1.14)

Divorced

0.86 (0.75, 0.99)

0.87 (0.74, 1.02)

Widowed

1.13 (0.89, 1.43)

1.14 (0.9, 1.44)

Income group

Lower tertile

1 (reference)

1 (reference)

Middle tertile

1.35 (1.2, 1.52)

1.32 (1.18, 1.49)

Upper tertile

1.3 (1.15, 1.46)

1.26 (1.12, 1.42)

Family type

Children under 18 living at home

1 (reference)

1 (reference)

Single person household (one adult)

0.7 (0.58, 0.83)

0.71 (0.6, 0.85)

Adult only household (more than one adult)

0.9 (0.80, 1.02)

0.9 (0.8, 1.01)

Ethnicity

Native Swede

1 (reference)

1 (reference)

Other Nordic countries

1.12 (0.9, 1.38)

1.13 (0.91, 1.4)

Other

0.61 (0.52, 0.71)

0.61 (0.52, 0.71)

Housing type

Single-family home

1 (reference)

1 (reference)

Multi-family home

0.52 (0.47, 0.57)

0.51 (0.46, 0.56)

Other

0.55 (0.42, 0.73)

0.57 (0.43, 0.74)

Diagnostics

Log-likelihood

− 9013.5

− 7751.6

Likelihood ratio test, χ2(17)

654.0***

641.2***

n

7425

7425

Notes The four level scale is coded as follows: (1) Uninterested in fire safety, (2) Minimal fire safety, (3) Reliance on detection, and (4) Formally educated in fire safety + Informally educated in fire safety. The three level scale merges (1) and (2) into one category. The odds ratios (OR) can be interpreted as the change in odds for a belonging to a higher level on the fire protection scale associated with a change in predictor category compared to its reference value (indicated by “reference” in the table), keeping all other variables in the model constant

***p < 0.001

Running ordinal logistic regression models on these scales shows that men score higher on the fire safety scale than women, and that young respondents score significantly lower than older respondents. Marital status does not appear to affect these behaviours when adjusted for the other covariates. Rather, it appears that family type is the dominant variable, where single adult households score much lower than households with children or adult-only households with more than one adult. Individuals with lower income are on average less likely to exhibit fire safety behaviours than respondents in the middle- or high-income groups, and immigrants from non-Scandinavian countries also score significantly lower than native Swedes or immigrants from other Scandinavian countries. Finally, respondents living in single family homes tend to score higher than those living in multi-family homes (Table 6).

3.3 Correlation with Fires in the Past 5 Years

While the survey was not designed to test the causal effects of different safety behaviours (which would require an experimental or quasi-experimental setting), correlations were tested with self-reported residential fires in the past five years using a log-binomial regression model (residential fires reported in the sample = 273). For this, the four-level fire safety scale derived above was used (the inferences were invariant to using the alternative scales). The results produced a positive coefficient, which if taken at face value would suggest that higher fire safety scores are associated with a higher risk of fires (Risk Ratio [RR] 1.17, 95% confidence interval [CI]: 1.02–1.32). However, since the questions regarding safety practices refer to the individual’s current state, while the residential fire question encompasses a five-year span, this could be an artefact of reverse causality. This notion is supported by the fact that omitting individuals who reported having changed their fire safety practices due to a past fire (n = 100) from the model yields a non-significant coefficient (RR 1.02, 95% CI: 0.88–1.19). The differences in past fire prevalence by cluster, and the effect of removing the individuals who have changed their safety practices since, are shown in the left panel of Fig. 1. This result is consistent with previous research on individual disaster preparedness and fires [44], but cannot explain a large part of the variation in fire safety behaviours due to the low prevalence of residential fires in the sample (2 percent).
Figure 1

Prevalence of fires in the past 5 years (left panel) and prevalence of functioning smoke detectors at the time of the survey (right panel). The subsets are coded as follows: (1) left panel: individuals who reported having changed their practices as a consequence of a previous fire were removed (2) right panel: individuals who reported not being able to test their smoke detector functionality at the time of the survey were removed

3.4 Correlation with Smoke Detector Functionality

During the survey, respondents were also asked to check the functionality of their smoke detector and report the results. In total, 82.1 percent of the sample reported having at least one functional smoke detector in their home. Testing the correlation between the four-level fire safety scale and functionality in the same manner as above, the probability of having a functional smoke detector increases, on average, by 19 percent for each step in the scale (RR 1.19, 95% CI: 1.18–1.20). As can be seen in Fig. 1, the cluster that does not frequently test the functionality (Minimal fire safety) clearly has a lower probability of having a functional smoke detector as compared to those that do, despite the fact that they are just as likely to have a smoke detector in their home (Table 2). Removing the individuals who reported not being able to test their alarms functionality at the time of the survey, these differences were smaller, but still remained statistically significant (RR 1.14, 95% CI: 1.13–1.16). In fact, even when ignoring the Uninterested in fire safety cluster while accounting for ability to test, the prevalence of functional smoke detectors is still significantly greater in the three clusters that regularly perform functionality tests compared to the Minimal fire safety cluster (89.7 vs. 96.1%, χ2(1) = 81.6, p < .001).

4 Discussion

The aim of this study was to investigate socio-demographic differences in the ownership of residential fire safety equipment, fire prevention practices and knowledge of fire prevention. The results show clearly that, in Sweden, considerable differences exist in household fire protection practices between different socio-demographic groups. These results are consistent with previous studies that have found a significantly lower use of preventative measures or practices amongst ethnic minority families [27, 28, 45, 46], single-households and low income families [29], individuals with a lower educational level [47, 48] as well as those living in socially deprived areas [49, 50], thereby indicating that the level of protection is a highly plausible cause of the socio-demographic differentiation in fire-related mortality.

The results in this study also show that there seems to be a certain “socio-demographic maturity” in the probability of belonging to a high fire protection cluster that takes the form of an inverted u-curve across the lifespan, as shown in Fig. 2. Specifically, younger individuals living in single households with low income tend to exhibit low levels of fire protection. The level of protection then increases with sociodemographic development, to peak during middle-age when individuals have higher income and live in single-family homes with children and to then decrease again with old age, a pattern also seen in a UK government study [51]. Whether this is true from an individual perspective, i.e. that the level of protection varies throughout an individual’s life, cannot be tested without access to longitudinal data, although previous studies have shown that adding a child to a household greatly increases the probability of the household having an existing fire escape plan and the probability decreases with old age [52, 53]. This is particularly interesting given the fact that the curve in Fig. 2 does not mimic cross-sectional evidence of changes in positive attitudes towards risk-taking across the life span, which are consistently negative in most risk-taking domains [54]. Likewise, given that experiences of fires or similar emergency situations have been shown to increase precautionary behaviour [44] and that logically more older people would have experienced emergencies, it could be expected that a linear, increasing, fire protection curve could be seen.
Figure 2

The log-odds of having a high level of fire protection (clusters 4 and 5) by age. The curve was produced non-parametrically using a lowess smoother

Hypothetically, the regression in protective behaviours in old age compared to middle-age may be indicative of a change in the ability to perform active protective behaviours rather than an effect of changes in attitudes and perceptions of fire risks. If this is the case, i.e. that the reduced protection amongst older adults is the result of physical and mental aspects rather than attitude or risk perception, the interventions required to increase the resilience towards residential fires will likely differ between younger and older age groups as well as requiring more innovative solutions for older adults [55].

With regards to the groups with low levels of protection, a number of studies have shown effective interventions such as smoke alarm installations, education or multi-facetted programs [10, 56, 57, 58]. Also, a recent Cochrane review found little evidence that effective interventions to promote home fire safety practices differed in effectiveness by social group [57] meaning that it would seem that the socio-demographic differences in fire protection are not carved in stone. For the oldest age groups, given that it would seem as traditional preventative efforts are somewhat abandoned with increased age as a result of decreased physical and mental capabilities, other prevention efforts with different approaches need to be developed. As highlighted by both Jennings [59] and Corcoran et al. [60] in their respective theoretical models, differences in fire risk and fire protection are most likely the results of complex interactions of individual, societal and structural factors. For older adults this may be particularly important, especially in regards to societal factors such as loneliness, social exclusion and financial difficulties. Such aspects have been highlighted as important to include in prevention programmes [55] given that they have also been shown to increase risk behaviour [61, 62]. Therefore, whilst holistic, multi-facetted programs are required for all groups with low levels of prevention, it would seem unreasonable to suggest that the same interventions are suitable for all groups.

4.1 Limitations

Firstly, data was used from a previously conducted survey and therefore no influence was had on the definition and scope of the variables collected. However, the survey captured many important aspects of residential fire safety behaviours and thus sufficiently served the purposes of this study. Still, since the procedure surrounding the creation and interpretation of the clusters, and the subsequent fire safety scales, is inductive and data-driven, it should be noted that the results could be affected by the addition of more variables relating to fire safety (e.g. explicit questions regarding safety attitudes, knowledge tests, and the presence of passive interventions such as sprinkler systems). Another issue with the data is that some of the safety questions were answered on the behalf of the household, whilst the register data was linked to the respondent, which may introduce some bias into the observed correlations between the affected variables (e.g., age and smoke detector functionality testing). Secondly, the survey response rates might be non-randomly conditional on sociodemographic factors in a manner that is correlated with fire safety practices. If true, this could affect the external validity of the study in the sense that, for instance, respondents with low socioeconomic status are not necessarily representative of non-respondents from the same strata. Thirdly, while we hope that the results are generalisable to other contexts, they may not be comparable to countries in which cultures, fire protection laws and socioeconomic conditions differ greatly from that of Sweden.

5 Conclusion

Considerable socio-demographic differences exist in the level of residential fire protection. This study suggests that socio-demographic factors associated with fire protection are similar to those associated with fire mortality but not with the risk of fire regardless of outcome. Therefore, from a preventative perspective, it would seem important to focus on increasing the fire protection capabilities amongst individuals with lower socio-demographic levels. In particular, in terms of access to information, training and extinguishing equipment.

Notes

Acknowledgements

Open access funding provided by Karlstad University. The authors gratefully thank The Swedish Fire Research Board for financial support (Project Number 301-151). The funding source had no role in the design of the study, the analysis and interpretation of the data or the writing of, nor the decision to publish, the manuscript.

References

  1. 1.
    Administration USF (2011) Fire death rate trends: an international perspective. Emmitsburg: U.S. Department of Homeland SecurityGoogle Scholar
  2. 2.
    Haagsma JA, Graetz N, Bolliger I, Naghavi M, Higashi H, Mullany EC et al. (2016) The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Inj Prev 22(1):3–18CrossRefGoogle Scholar
  3. 3.
    Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al. (2012) Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859):2197–223CrossRefGoogle Scholar
  4. 4.
    Jonsson A, Runefors M, Särdqvist S, Nilson F (2016) Fire-related mortality in Sweden: temporal trends 1952 to 2013. Fire Technol. 52(6):1697–707CrossRefGoogle Scholar
  5. 5.
    Oecd. Emerging risks in the 21st Century—an Agenda for Action. Paris: OECD2003 Contract No.: ReportGoogle Scholar
  6. 6.
    Sekizawa A (2015) Challenges in fire safety in a society facing a rapidly aging population. Fire Prot Eng. 2015(1). https://www.sfpe.org/page/FPE_2015_Q1_3
  7. 7.
    Fernández-Vigil M, Echeverría Trueba B (2019) Elderly at home: a case for the systematic collection and analysis of fire statistics in Spain. Fire Technol.  https://doi.org/10.1007/s10694-019-00852-6 CrossRefGoogle Scholar
  8. 8.
    Hasofer AM, Thomas I (2006) Analysis of fatalities and injuries in building fire statistics. Fire Saf J 41(1):2–14CrossRefGoogle Scholar
  9. 9.
    Jonsson A, Bonander C, Nilson F, Huss F (2017) The state of the residential fire fatality problem in Sweden: epidemiology, risk factors, and event typologies. J Saf Res 62:89–100. http://dx.doi.org/10.1016/j.jsr.2017.06.008.CrossRefGoogle Scholar
  10. 10.
    Marshall SW, Runyan CW, Bangdiwala SI, Linzer MA, Sacks JJ, Butts JD (1998) Fatal residential fires: who dies and who survives? JAMA 279(20):1633–1637.CrossRefGoogle Scholar
  11. 11.
    Jonsson A, Jaldell H (2018) PA 07-4-2351 identifying sociodemographic risk factors associated with residential fire fatalities: a matched case-control study. BMJ Publishing Group Ltd, LondonGoogle Scholar
  12. 12.
    Holborn PG, Nolan PF, Golt J (2003) An analysis of fatal unintentional dwelling fires investigated by London Fire Brigade between 1996 and 2000. Fire Saf J 38(1):1–42CrossRefGoogle Scholar
  13. 13.
    Jennings CR (1999) Socioeconomic characteristics and their relationship to fire incidence: a review of the literature. Fire Technol 35(1):7–34CrossRefGoogle Scholar
  14. 14.
    Chandler SE, Chapman A, Hollington SJ (1984) Fire incidence, housing and social conditions—the urban situation in Britain. Fire Prev (172):15–20.Google Scholar
  15. 15.
    Istre GR, McCoy MA, Osborn L, Barnard JJ, Bolton A (2001) Deaths and injuries from house fires. N Engl J Med 344(25):1911–1916CrossRefGoogle Scholar
  16. 16.
    Duncanson M, Woodward A, Reid P (2002) Socioeconomic deprivation and fatal unintentional domestic fire incidents in New Zealand 1993–1998. Fire Saf J 37(2):165–179CrossRefGoogle Scholar
  17. 17.
    Xiong L, Bruck D, Ball M (2015) Comparative investigation of ‘survival’ and fatality factors in accidental residential fires. Fire Saf J 73:37–47.CrossRefGoogle Scholar
  18. 18.
    Ballard JE, Koepsell TD, Rivara F (1992) Association of smoking and alcohol drinking with residential fire injuries. Am J Epidemiol 135(1):26–34CrossRefGoogle Scholar
  19. 19.
    Mulvaney C, Kendrick D, Towner E, Brussoni M, Hayes M, Powell J et al. (2009) Fatal and non-fatal fire injuries in England 1995–2004: time trends and inequalities by age, sex and area deprivation. J Public Health (Oxf Engl) 31(1):154–161.  https://doi.org/10.1093/pubmed/fdn103 CrossRefGoogle Scholar
  20. 20.
    Chhetri P, Corcoran J, Stimson RJ, Inbakaran R (2010) Modelling potential socio-economic determinants of building fires in south east Queensland. Geogr Res 48(1):75–85CrossRefGoogle Scholar
  21. 21.
    Berl WG, Halpin BM (1978) Human fatalities from unwanted fires. US Department of Commerce, National Institute of Standards and Technology, GaithersburgGoogle Scholar
  22. 22.
    Jonsson A (2018) Dödsbränder i Sverige (fire-related deaths in Sweden). Sweden: Karlstad University, KarlstadGoogle Scholar
  23. 23.
    Mackenbach JP, Looman CW, Artnik B, Bopp M, Deboosere P, Dibben C et al. (2017) ‘Fundamental causes’ of inequalities in mortality: an empirical test of the theory in 20 European populations. Sociol Health Illn. 39(7):1117–1133.CrossRefGoogle Scholar
  24. 24.
    Runefors M, Johansson N, Van Hees P (2016) How could the fire fatalities have been prevented? An analysis of 144 cases during 2011–2014 in Sweden: an analysis. J Fire Sci 34(6):515–527CrossRefGoogle Scholar
  25. 25.
    Nilson F, Bonander C, Jonsson A (2015) Differences in determinants amongst individuals reporting residential fires in Sweden: results from a cross-sectional study. Fire Technol 51(3):615–626CrossRefGoogle Scholar
  26. 26.
    Greene MA (2012) Comparison of the characteristics of fire and non-fire households in the 2004–2005 survey of fire department-attended and unattended fires. Inj Prev J Int Soc Child Adolesc Inj Prev 18(3):170–175.  https://doi.org/10.1136/injuryprev-2011-040009 CrossRefGoogle Scholar
  27. 27.
    Hapgood R, Kendrick D, Marsh P (2000) How well do socio-demographic characteristics explain variation in childhood safety practices? J Public Health 22(3):307–311CrossRefGoogle Scholar
  28. 28.
    Mulvaney C, Kendrick D (2004) Engagement in safety practices to prevent home injuries in preschool children among white and non-white ethnic minority families. Inj Prev 10(6):375–378CrossRefGoogle Scholar
  29. 29.
    Kendrick D (1994) Children’s safety in the home: parents’ possession and perceptions of the importance of safety equipment. Public Health 108(1):21–55CrossRefGoogle Scholar
  30. 30.
    DiGuiseppi C, Roberts I, Speirs N (1999) Smoke alarm installation and function in inner London council housing. Arch Dis Child 81(5):400–403CrossRefGoogle Scholar
  31. 31.
    Zhang G, Lee AH, Lee HC, Clinton M (2006) Fire safety among the elderly in Western Australia. Fire Saf J 41(1):57–61CrossRefGoogle Scholar
  32. 32.
    Turner SL, Johnson RD, Weightman AL, Rodgers SE, Arthur G, Bailey R, et al. (2017) Risk factors associated with unintentional house fire incidents, injuries and deaths in high-income countries: a systematic review. Inj Prev.  https://doi.org/10.1136/injuryprev-2016-042174 CrossRefGoogle Scholar
  33. 33.
    Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A (2009) The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 24(11):659–667.  https://doi.org/10.1007/s10654-009-9350-y CrossRefGoogle Scholar
  34. 34.
    Statistics S (2019) Finding statistics. Statistics Sweden, StockholmGoogle Scholar
  35. 35.
    Husson F, Lê S, Pagès J (2010) Exploratory multivariate analysis by example using R. CRC Press, Boca RatonzbMATHCrossRefGoogle Scholar
  36. 36.
    Jolliffe I (2002) Principal component analysis. Wiley, HobokenzbMATHGoogle Scholar
  37. 37.
    Sourial N, Wolfson C, Zhu B, Quail J, Fletcher J, Karunananthan S et al. (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. J Clin Epidemiol 63(6):638–646CrossRefGoogle Scholar
  38. 38.
    Di Franco G (2016) Multiple correspondence analysis: one only or several techniques? Qual Quant 50(3):1299–1315CrossRefGoogle Scholar
  39. 39.
    Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25(1):1–18.CrossRefGoogle Scholar
  40. 40.
    SFS (2003:778) Lag om skydd mot olyckor [Civil Protection Act]. The Swedish Government, StockholmGoogle Scholar
  41. 41.
    Gustafson PE (1998) Gender differences in risk perception: theoretical and methodological erspectives. Risk Anal 18(6):805–811CrossRefGoogle Scholar
  42. 42.
    Sjöberg L (1998) Worry and risk perception. Risk Anal 18(1):85–93CrossRefGoogle Scholar
  43. 43.
    Lexell J, Taylor CC, Sjöström M (1988) What is the cause of the ageing atrophy? Total number, size and proportion of different fiber types studied in whole vastus lateralis muscle from 15-to 83-year-old men. J Neurol Sci 84(2–3):275–294CrossRefGoogle Scholar
  44. 44.
    Stumpf K, Knuth D, Kietzmann D, Schmidt S (2017) Adoption of fire prevention measures-predictors in a representative German sample. Saf Sci 94:94–102CrossRefGoogle Scholar
  45. 45.
    Tannous WK, Agho K (2019) Domestic fire emergency escape plans among the aged in NSW, Australia: the impact of a fire safety home visit program. BMC Public Health 19(1):872CrossRefGoogle Scholar
  46. 46.
    Vaughan E, Anderson C, Agran P, Winn D (2004) Cultural differences in young children’s vulnerability to injuries: a risk and protection perspective. Health Psychol 23(3):289CrossRefGoogle Scholar
  47. 47.
    Tannous WK, Whybro M, Lewis C, Ollerenshaw M, Watson G, Broomhall S et al. (2016) Using a cluster randomized controlled trial to determine the effects of intervention of battery and hardwired smoke alarms in New South Wales, Australia: home fire safety checks pilot program. J Saf Res 56:23–27CrossRefGoogle Scholar
  48. 48.
    Sidman EA, Grossman DC, Mueller BA (2011) Comprehensive smoke alarm coverage in lower economic status homes: alarm presence, functionality, and placement. J Commun Health 36(4):525–533CrossRefGoogle Scholar
  49. 49.
    Durand MA, Green J, Edwards P, Milton S, Lutchmun S (2012) Perceptions of tap water temperatures, scald risk and prevention among parents and older people in social housing: a qualitative study. Burns 38(4):585–590CrossRefGoogle Scholar
  50. 50.
    Roberts H, Curtis K, Liabo K, Rowland D, DiGuiseppi C, Roberts I (2004) Putting public health evidence into practice: increasing the prevalence of working smoke alarms in disadvantaged inner city housing. J Epidemiol Community Health 58(4):280–285.CrossRefGoogle Scholar
  51. 51.
    Smith R, Wright M, Solanki A (2008) Analysis of fire and rescue service performance and outcomes with reference to population socio-demographics—Fire Research Series 9/2008. Department for Communities and Local Government: London, UKGoogle Scholar
  52. 52.
    Yang J, Peek-Asa C, Allareddy V, Zwerling C, Lundell J (2006) Perceived risk of home fire and escape plans in rural households. Am J Prev Med 30(1):7–12CrossRefGoogle Scholar
  53. 53.
    Tannous W, Agho K (2018) Factors associated with home fire escape plans in New South Wales: multinomial analysis of high-risk individuals and New South Wales population. Int J Environ Res Public Health 15(11):2353CrossRefGoogle Scholar
  54. 54.
    Rolison JJ, Hanoch Y, Wood S, Liu P-J (2013) Risk-taking differences across the adult life span: a question of age and domain. J Gerontol Ser B Psychol Sci Soc Sci 69(6):870–880CrossRefGoogle Scholar
  55. 55.
    Harpur A, Boyce K, McConnel N (2014) An investigation into the circumstances surrounding elderly dwelling fire fatalities and the barriers to implementing fire safety strategies among this group. Fire Saf Sci 11:1144–1159CrossRefGoogle Scholar
  56. 56.
    Ta VM, Frattaroli S, Bergen G, Gielen AC (2006) Evaluated community fire safety interventions in the United States: a review of current literature. J Community Health 31(3):176CrossRefGoogle Scholar
  57. 57.
    Kendrick D, Young B, Mason-Jones AJ, Ilyas N, Achana FA, Cooper NJ et al. (2013) Home safety education and provision of safety equipment for injury prevention. Evid-Based Child Health Cochrane Rev J 8(3):761–939CrossRefGoogle Scholar
  58. 58.
    Warda L, Tenenbein M, Moffatt ME (1999) House fire injury prevention update. Part II. A review of the effectiveness of preventive interventions. Inj Prev 5(3):217–225CrossRefGoogle Scholar
  59. 59.
    Jennings CR (1997) Urban residential fires: an empirical analysis of building stock and socioeconomic characteristics for Memphis, TennesseeGoogle Scholar
  60. 60.
    Corcoran J, Higgs G, Rohde D, Chhetri P (2011) Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. J Geogr Syst 13(2):193–226CrossRefGoogle Scholar
  61. 61.
    Kharicha K, Iliffe S, Harari D, Swift C, Gillmann G, Stuck AE (2007) Health risk appraisal in older people 1: are older people living alone an ‘at-risk’group? Br J Gen Pract 57(537):271–276Google Scholar
  62. 62.
    Rosén M, Hanning M, Wall S. (1990) Changing smoking habits in Sweden: towards better health, but not for all. Int J Epidemiol 19(2):316–322CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Division of Risk and Environmental Studies, Department of Environmental and Life Sciences, Centre for Public SafetyKarlstad UniversityKarlstadSweden
  2. 2.Health Metrics Unit, The Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden

Personalised recommendations