Familial Aggregation of High-Risk Driving Behaviors in Northwestern Iran: A Cross-Sectional Study
Abstract
Although the association between human factors, such as driving traffic risky behavior (DRB) and road traffic crashes (RTCs), have extensively been studied, there is a gap in understanding the role of familial predisposing factors in DRB occurrence. This study in northwestern Iran aimed to elucidate the sociodemographic profile of drivers and assess the familial aggregation (FA) of DRB in first-degree relatives. This cross-sectional study used stratified random sampling to examine the FA of DRB among 541 individuals in Tabriz, Iran, in 2023. The head of household served as a proband and first-degree relatives were included. Data were collected using two standard self-administered questionnaires. The generalized estimating equations with 95% confidence intervals (CIs) assessed the FA of DRB. The overall prevalence of high-risk driving behavior was 46.02%, with significant FA observed between mothers and offspring (OR: 1.97, 95%CI: 1.05–3.69). Fathers or offspring with violation driving behaviors significantly increased the likelihood of similar violations among their offspring or fathers approximately. Offspring’s slip behaviors were significantly associated with these behaviors in their parents and siblings. Moreover, lapse behaviors showed significant FA among siblings. Our findings showed that FA exists in the DRB, particularly in the slip behavior dimension, with aggregation between fathers-offspring, mothers-offspring, and siblings. No FA of DRB was found between spouses. Regardless of the reason for FA, these imply that the family plays a significant role in DRB occurrence, suggesting the potential effectiveness of a family-based prevention program. Screening programs are recommended to identify DRB in relatives referred to a trauma referral hospital to provide targeted preventive interventions.
1. Introduction
Road traffic crashes (RTCs) are a significant global public health challenge [1] and the leading cause of death among individuals aged between 15 and 29, resulting in an estimated annual toll of 1.35 million [2].
Globally, road traffic injuries (RTIs) rank as the 8th leading cause of mortality, accounting for 41.2 million years of healthy life lost, with 90% of disability-adjusted life years occurring in low- and middle-income countries [3, 4]. Apart from the human toll, RTCs present a substantial obstacle to economic growth, particularly in developing countries, where they contribute to approximately 2%-3% of the gross domestic product (GDP) [5]. In Iran, RTCs account for over 50% of unintentional and intentional injury-related deaths, with a road traffic death rate of 20.5 per 100,000 individuals [2]. The economic impact is profound, with RTCs causing a loss equivalent to 6%-7% of the country’s GDP [6]. Car occupants are responsible for 29% of all RTCs mortality. In Iran, drivers and passengers of 4-wheeled cars and light vehicles contribute to 20% and 28% of RTCs deaths, respectively [7]. The driving license in Iran is an identity document issued integrated and solely by the Faraja traffic police organization. The minimum age to obtain a driving license is 18 years. The credit of a driver’s license is 5 years. Upon expiration, it can be renewed for another 10-year period, provided that physical and mental heaths are verified. Individuals over 70 years of age must undergo a health assessment every 5 years to confirm their physical and mental health. In the first year of obtaining a driver’s license, there are legal restrictions for drivers including the following: (1) in the first 3 months of driving, a person with a second or third level license must be accompanied by someone with at least a third level license for 1 year, (2) drivers are not allowed to drive from 24:00 to 05:00 in the morning, (3) drivers are prohibited from traveling on suburban roads, except for intercity highways, within a distance of 25 km from the cities, and (4) it is necessary to install hazard signs in the front and on the left rear of the car [8].
The complexity of RTCs involves interactions among various components of the driving system, encompassing road infrastructure, road users, and the vehicle [9–11]. As evidence shows, 70%–95% of RTCs are associated with human factors, of which behavioral risk factors constitute 60% [12–15].
To reduce and control injuries and diseases, it is crucial to concentrate on identifying and reducing modifiable risk factors, which include associated high-risk behaviors [16, 17]. An emerging area of research suggests that familial aggregation (FA) may play a significant role in the perpetuation of traffic risky behaviors. The revelation of FA in risky behaviors related to RTCs holds immense potential for preventing the occurrence or exacerbation of high-risk behaviors [18]. FA studies constitute a foundational step in genetic epidemiology, laying the groundwork for more specialized assessments. These investigations drive into the etiology of unhealthy conditions by examining phenotypes’ biochemical, physical, or behavioral features directly observable or measurable in the laboratory. Within the FA framework, an individual is selected from each family as the proband, with the proband’s positivity or negativity depending on the considered phenotype. The identification of the FA in a phenotype indicates a potential attribution to genetic, environmental, or their interaction. Moreover, such phenotypes become candidates for more detailed studies in genetic epidemiology.
The presence of the FA explains that the intervention programs targeting high-risk behaviors in the community are more effective when focused on families rather than individuals. Moreover, existing FA suggests that establishing a screening system in referral hospitals for traffic crashes can be beneficial. If the injured individual is a driver displaying risky behaviors, this system can also screen family members for risky behaviors, leading to improved road safety and a decrease in traffic crashes [19, 20]. As for theoretical justification, the shared environmental may also align with behavioral learning components, indicating that behavior is largely influenced by interactions with other surrounding factors. On the other hand, FA may also be attributed to the aggregation of certain genes or disorders within the family [21, 22].
To the best of the authors’ knowledge, there was no published evidence of the FA of driving risky behaviors (DRBs). This knowledge gap is significant, as the presence of FA in DRB, regardless of their environmental or genetic origins, has substantial implications for public health intervention. Therefore, this study aimed to elucidate the sociodemographic profile of drivers in northwestern Iran and systematically assess the FA of DRB in first-degree relatives. By unraveling the FA of DRB, this research endeavors to contribute valuable insights for designing targeted interventions, enhancing road safety, and advancing personalized strategies for preventing RTCs.
2. Materials and Methods
2.1. Study Design and Population
The present cross-sectional study was carried out on a cohort of 541 drivers in Tabriz, northwestern Iran, from August to March 2023. The source population was comprised of clients from the Asadabadi family medicine clinic. Asadabadi is a publicly accessible healthcare facility situated to the west of Tabriz that serves as a prominent representation of the general population of Tabriz. This clinic, located within the broader infrastructure of Asadabadi Hospital, offers a wide range of outpatient medical services, encompassing preventive measures, diagnostic procedures, and outpatient treatment for individuals. In addition, the clinic is responsible for imparting education and counseling to the general populace. In this study, participants’ relatives were identified by deeming the heads of households as probands. The initial phase involved selecting probands from a comprehensive client list; if they agreed to participate in the study, their first relatives, including children, spouses, and other eligible household members, were included.
2.2. Sampling and Sample Size
Since the study involved urban and rural communities, the stratified random sampling method was employed, and each rural and urban region of Tabriz was treated as a separate stratum (about 15% of Tabriz residents live in rural areas). Samples were selected proportionally based on the population size within each stratum. The sample size for all relatives was computed through the initial pilot study (n = 250) and an assessment of the likelihood of risky behavior among family members. To ensure robust statistical power, a larger sample size was chosen. Finally, the sample size was calculated at 540, considering the 95% confidence interval (CI), α = 0.05, d = 0.12p, and p = 0.372 (prevalence of DRB in the proband) and incorporating a 20% attrition rate.
2.3. Eligibility Criteria
Participants meeting the following inclusion criteria were eligible for enrollment: individuals aged at least 14 years old, with driving ability and history, having resided in Tabriz for a minimum of 1 year, and lacking any disability associated with driving. Individuals with driving restrictions and mental disabilities were excluded from the study.
2.4. Measurements
Data were collected through the administration of two standard questionnaires. The driving traffic behaviors (DTBs) were assessed through the Persian version of the Manchester driving behavior questionnaire (DBQ). This self-reported tool is utilized to measure three categories of DTBs: violations (11 items), lapses (8 items), and slips (8 items). Violations encompass intentional actions such as speeding, of which the individual is aware. Slips involve actions that do not achieve the planned and intended outcome due to misjudgment and/or attention deficit. Lapses are unintended behaviors resulting from memory deficits, such as missing the highway exit.
The Persian version of the tool is the most commonly used tool in the majority of Iranian studies across different regions in the country. The validity and reliability of the tool have been confirmed by Alavi et al. [23], Parishad et al. [24], and Goudarzi [25] as a valid and reliable tool to assess driving behaviors in Iranian drivers. Based on Iranian psychometrics studies [23, 24], the test–retest reliability for violation, slip, and lapse and overall DTB was found to be 0.92, 0.96, 0.93, and 0.97, respectively. Furthermore, the questionnaire exhibited a Cronbach’s alpha of 0.87. Items are considered on a 6-point Likert scale (five is always and zero is never). A higher DBQ score correlates with an increased likelihood of engaging in risky driving behaviors.
The second questionnaire administered in this study was a short version of the socioeconomic status (SES) questionnaire, which was utilized to assess the drivers’ SES level [26]. The reliability of the SES questionnaire was assessed using Cronbach’s alpha coefficient, yielding a satisfactory value (α > 0.65). Comprising six self-reported items, the questionnaire assessed house cost, education, occupation, health expenditure, salary, and car cost. Each item above received a rating on a spectrum ranging from a minimum 5-point Likert scale to a maximum 7-point Likert scale. The lowest score across all items stands at 1, except for occupation. Consequently, to calculate the total score, it becomes imperative to reverse the score of this item. Furthermore, the items about costs and income were given a weight of 0.25. A higher score on the questionnaire was associated with a higher level of SES.
2.5. Data Management and Statistical Analysis
The STATA software package Version 17 was used to manage and analyze the data. To maintain objectivity and minimize the potential bias, the proband status remained blind for both the individual responsible for entering data into the software and the data analysts. Frequency and percentage were reported for categorical variables. Through the application of the mean clustering method, the probands and their relatives were classified into two groups: those without risky behavior and those with risky behavior.
Multiple logistic regression was used to scrutinize the association between variables and DRB. All variables with a p value of 0.2 or lower in bivariate analysis are incorporated into the regression model. Subsequently, the generalized estimating equations (GEEs) algorithm was applied, incorporating an exchangeable structure and 95% CI. This analytical approach aimed to determine clusters within familial relationships, specifically among parents-offspring, mothers-offspring, fathers-offspring, spousal pairs, and siblings.
In addition, variables with a p value of < 0.05 in the multiple logistic regression underwent adjustment during the assessment of FA. The p value of 0.05 was considered statistically significant [27, 28].
3. Results
Almost 5.4% of the individuals who were invited to participate in the study declined to take part (response rate was 94.6%). Since we had access to enough samples, we continued sampling to achieve the required sample size. Out of 541 participants, 139 subjects were probands. The overall prevalence of high-risk driving behavior was 46.02%, distributed as follows: 36.69% in fathers, 37.76% in mothers, and 52.96% in their offspring. Notably, 38 subjects (38.8%) among 98 spouses and 124 subjects (39.8%) among 304 offspring were linked with probands exhibiting DRB (Figure 1).
As shown in Table 1, the majority of participants among males (52.54%) and females (55.56%) had DRB. Importantly, no statistically significant association between gender and the presence of DRB was identified (p > 0.05). The study encompassed 541 respondents, with 3.88% of the respondents aged less than 18, almost 62% aged between 31% and 60%, and 2.96% aged 60 years. Approximately 76% of those under 18% and 34% of those aged 30–60 exhibited DRB. A substantial proportion (60.40%) of the people in the married group demonstrated an absence of DRB. Moreover, the prevalence of DRB was higher among the very low (55.56%) and low (61.31%) SES group. Regarding the family size, 53% of the participants with five or more family members and 29.41% of the subjects with two members displayed DRB. Approximately 84% of the participants were residents of the urban area, and 45% of them reported engaging in risky driving behavior. About 45% of rural participants had risky driving behaviors. We did not find a statistically significant association between risky driving behavior and the participants’ living areas (p = 0.530).
| Variable | Categories | Driving risky behaviors in probands and relatives | p value | ||
|---|---|---|---|---|---|
Yes (n = 249) n (%) |
No (n = 292) n (%) |
Total n = 541 |
|||
| Family members | Father (proband) | 51 (36.69) | 88 (63.13) | 139 (25.7) | 0.001 |
| Mother | 37 (37.76) | 61 (62.24) | 98 (18.11) | ||
| Children | 161 (52.96) | 143 (47.04) | 304 (56.19) | ||
| Age group | < 18 | 16 (76.19) | 5 (23.81) | 21 (3.88) | 0.03 |
| 18–30 | 81 (47.65) | 89 (52.35) | 170 (31.42) | ||
| 31–60 | 145 (34.41) | 189 (56.59) | 334 (61.74) | ||
| > 60 | 7 (43.75) | 9 (56.25) | 16 (2.96) | ||
| Gender | Male | 140 (47.46) | 155 (52.54) | 295 (54.53) | 0.46 |
| Female | 109 (44.31) | 137 (55.69) | 246 (45.47) | ||
| Marital status | Single | 137 (50.74) | 133 (49.26) | 270 (49.91) | 0.3a |
| Married | 99 (36.60) | 151 (60.40) | 250 (46.21) | ||
| Divorced | 2 (66.67) | 1 (33.33) | 3 (0.55) | ||
| Widowed | 11 (61.11) | 7 (37.89) | 18 (3.33) | ||
| Education | Illiterate/Primary | 38 (38.38) | 61 (61.62) | 99 (18.30) | 0.3 |
| Secondary | 126 (48.65) | 133 (51.35) | 259 (47.87) | ||
| Under graduate | 79 (47.02) | 89 (52.98) | 168 (31.05) | ||
| Post graduate | 6 (40) | 9 (60) | 15 (2.77) | ||
| Socioeconomic status | Very low | 45 (55.56) | 36 (44.44) | 81 (14.97) | 0.001 |
| Low | 84 (61.31) | 53 (38.69) | 137 (25.32) | ||
| Medium | 74 (42.05) | 102 (57.95) | 176 (32.53) | ||
| High | 40 (16.06) | 89 (68.99) | 129 (23.84) | ||
| Very high | 6 (31.01) | 12 (66.67) | 18 (3.33) | ||
| Family size | 2 members | 5 (29.41) | 12 (70.59) | 17 (3.14) | 0.002a |
| 3 members | 40 (32.79) | 82 (67.21) | 122 (22.55) | ||
| 4members | 95 (47.98) | 103 (52.02) | 198 (36.60) | ||
| ≥ 5 members | 149 (73.03) | 55 (26.96) | 204 (37.70) | ||
| Length of driving during a day (hours) | 2.15 (1.12) | 1.84 (1.04) | 1.98 (1.07) | 0.12b | |
| Duration of driving history (year) | 12.35 (8.85) | 10.75 (8.28) | 11.62 (8.62) | 0.03b | |
| Residency | Urban | 211 (45.96) | 248 (54.04) | 459 (84.84) | 0.53c |
| Rural | 38 (46.34) | 44 (53.66) | 82 (15.16) | ||
- ap obtained from Fisher’s exact test.
- bp obtained from the independent samples t-test.
- cp obtained from the Chi-Square test.
Table 2 demonstrates the association between DRB and related factors using multiple logistic regression. The results suggest that a statistically significant positive association exists between family size and DRB in all dimensions. In other words, the incidence of DRB increases with the expansion of family size. In specific detail, drivers with five or more members of the family had approximately 2.75 times higher DRB (0.93–8.1) than families with two members, as indicated by the total score. Furthermore, a statistically significant negative association emerged between DRB and SES. The likelihood of total DRB, slip, and lapse for individuals driving with a very low SES level was 1.72 (odds ratio [OR]: 1.72 and 95% CI: 1.01–2.92), 1.54 (OR: 1.54 and 95% CI: 0.91–2.63), and 1.67 (OR: 1.67 and 95% CI: 0.98–2.84) times higher than those with a medium SES level, respectively. The driving duration during the day (hours) was positively associated with the presence of DRB in all dimensions. Conversely, DRB in all dimensions significantly decreased with increased driving experience (years).
| Variables | Categories | Total | Violation | Slips | Lapse |
|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||
| Age | ≤ 18 | 4 (1.49–10.96) | 2.6 (1.02–6.6) | 1.86 (0.76–4.51) | 6.11 (2.01–18.5) |
| 19–30 | 1.18 (0.81–1.71) | 1.86 (1.28–2.7) | 1.36 (0.94–1.99) | 1.82 (1.25–2.64) | |
| 30–60 | Reference | Reference | Reference | Reference | |
| ≥ 60 | 1.01 (0.36–2.78) | 0.78 (0.27–2.20) | 0.56 (0.17–1.78) | 0.86 (0.30–2.49) | |
| Family size | 2 members | Reference | Reference | Reference | Reference |
| 3 members | 1.17 (0.38–3.55) | 2.19 (0.59–8.07) | 5.93 (0.75–21.41) | 3.13 (0.85–11.47) | |
| 4 members | 2.21 (0.75–6.51) | 7.17 (1.99–25.79) | 10.84 (1.41–23.43) | 6.20 (1.72–22.02) | |
| ≥ 5 members | 2.75 (0.93–8.1) | 4.75 (1.32–17) | 15.68 (2.04–25.89) | 3.68 (1.02–13.21) | |
| SES ∗ | Very high | 0.68 (0.24–1.92) | 1.2 (0.45–3.16) | 0.45 (0.14–1.43) | 0.94 (0.34–2.54) |
| High | 0.62 (0.38–0.95) | 0.68 (0.43–1.09) | 0.79 (0.49–1.27) | 0.99 (0.62–1.58) | |
| Medium | Reference | Reference | Reference | Reference | |
| Low | 2.18 (1.38–3.44) | 1.96 (1.24–3.09) | 1.23 (0.78–1.94) | 2.20 (1.39–3.47) | |
| Very low | 1.72 (1.01–2.92) | 1.42 (0.84–2.24) | 1.54 (0.91–2.63) | 1.67 (0.98–2.84) | |
| Length of driving during a day (hours) | 1.05 (0.98–1.13) | 1.05 (0.97–1.13) | 1.04 (0.97–1.12) | 1.06 (0.98–1.14) | |
| Duration of driving history (year) | 0.98 (0.95–0.99) | 0.98 (0.96–1) | 0.94 (0.92–0.96) | 0.97 (0.95–0.99) | |
- ∗Socioeconomic status.
Table 3 shows the results of the GEE analysis, presenting adjusted OR (95% CI) for DRB. To estimate FA across different dimensions, the variables deemed significant in the simple logistic regression were adjusted for each dimension.
| Variables | Totala | Violationb | Slipc | Lapsed |
|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Spouses | 1.04 (0.74–1.47) | 0.73 (0.45–1.2) | 0.84 (0.48–1.46) | 0.84 (0.47–1.5) |
| Mothers and offspring | 1.97 (1.05–3.69) | 2.21 (1.18–4.14) | 2.51 (1.44–4.37) | 1.18 (0.65–2.12) |
| Fathers and offspring | 1.69 (0.84–3.29) | 1.76 (1.02–3.04) | 2.23 (1.30–3.81) | 1.06 (0.56–2) |
| Siblings | 1.44 (0.82–2.53) | 1.16 (0.69–1.96) | 2.06 (1.25–3.37) | 1.62 (1.22–2.79) |
- aOR adjusted for age and SES.
- bOR adjusted for age, family size, and SES.
- cOR adjusted for family size.
- dOR adjusted for age, family size, and SES.
A significant finding was found regarding the FA of DRB between the mothers and offspring. Notably, mothers or offspring exhibiting DRB were associated with an approximately 1.97 times higher likelihood of the presence of DRB among their offspring or mothers (OR: 1.97 and 95% CI: 1.05–3.69). However, no significant aggregation was observed between spouses, fathers, and offspring, as well as among siblings, based on the total score of DRB.
Fathers or offspring with violation driving behaviors significantly increased the likelihood of similar violations among their offspring or fathers approximately 1.76 (OR: 1.76 and 95% CI: 1.02–3.04) times. Conversely, although a violation in one of the siblings elevated the likelihood of violations among siblings by 1.16 times (OR: 1.16 and 98% CI: 0.69–1.96), this association was not statistically significant. In addition, no aggregation of risky driving behavior between spouses was identified.
Examining the OR of FA regarding slip behaviors demonstrated a significant aggregation among parents-offspring and siblings. Offspring whose fathers and mothers exhibited slip behavior had 2.23 times (OR: 2.23 and 95% CI: 1.30–3.81) and 2.51 times (OR: 2.51 and 95% CI: 1.44–4.37) higher odds of slip behavior, respectively, compared with offspring whose fathers and mothers did not exhibit such behaviors. Moreover, the presence of slip behaviors in one of the siblings increased the odds of slip behaviors among siblings by 2.06 times (OR: 2.06 and 98% CI: 1.25–3.73).
Notably, there was a significant FA of lapse behaviors among siblings. In other words, a lapse in one sibling increased the likelihood of lapses in their brothers or sisters by almost 1.62 times (OR: 1.62 and 95% CI: 1.22–2.79).
4. Discussion
According to the findings, there is a statistically significant FA between mothers and offspring. This indicated that the offspring of mothers engaging in risky behavior are more likely to exhibit similar risky behavior to those whose mothers do not engage in such risky behavior. No significant FA was found among siblings and spouses regarding total DRB. To further interpret the FA of DRB, various aspects of DTB were examined separately.
Results revealed that slip behaviors significantly tend to aggregate between parents-offspring and among siblings. In other words, slip behaviors are prevalent among offspring whose parents (father or mother) exhibit slip behaviors. Moreover, if one sibling exhibits slip behaviors, the likelihood of this behavior increases among other siblings. In theory, shared genes, environments, or both could explain the FA of driving slip behavior. As previously mentioned, slips are unintentional errors often related to attention or transaction execution [29]. Also, unintentional behaviors influenced by psychological processes may have a genetic or hereditary basis [30]. The neurotransmitter serotonin is identified as a potential factor in the genetic transmission of slip behaviors, playing a role in regulating a broad array of behavioral, psychological, and biological functions. The serotonergic function contributes to negative moods, risky health behaviors, and altered biological functions, increasing disease risks in specific individuals and groups. Studies demonstrated the association between serotonin and attention deficit, emphasizing the importance of parental serotonin transmission [31, 32]. The shared environmental component of slip behaviors aligns with behavioral learning theory [19, 20], positing that behavior is influenced by interaction with the external world. Williams et al. also highlight a significant difference in serotonin levels across various SES levels, indicating an interaction between genetic and environmental components of slip behaviors [33, 34].
According to the present study, although lapse behaviors are aggregated between parents and offspring and among siblings, this aggregation is statistically significant only among siblings, suggesting a potential influence of genetics, environmental, and interaction factors. Lapses are memory-associated behaviors linked to three categories: sensory memory, short-term or working memory, and long-term memory. It is the capacity that enables us to connect experiences, learn, and understand our lives [35, 36]. Memory problems may be related to various factors, including a sudden medical event such as traumatic brain injury, reversible factors such as vitamin deficiencies and depression, and underlying inherited conditions like Alzheimer’s diseases [37, 38]. The causes of behavior can be explained depending on which memory part is involved. Previous research indicates that working memory has moderate to high heritability [39, 40], and the KIBRA gene locus is associated with human memory performance [41]. In addition, according to the Hintze et al.’s study, epigenetic modifications are involved in the creation and maintenance of behavioral memory at multiple levels [42]. Environmental stimuli may contribute to behavioral adaptation, as seen in studies linking the same socioeconomic status and environmental triggers to similar memory levels [43–45]. This may be explained by the fact that behavior is a factor that can be learned from the surrounding environment, and the FA of memory-related behaviors may result from genetic diseases or disorders within the family.
The study also revealed that violent driving behavior is more common in offspring whose fathers and mothers exhibit these behaviors. Violations, intentional actions likely known by the person, are influenced by learning from the environment, aligning with behavioral learning theory. This theory states that interactions with the outside world influence all behavior. So, the aggregation of these behaviors in the parent’s offspring may suggest that the offspring will be exposed to high-risk individuals influenced by the formation and display of risky behaviors [19, 20]. DRB in all dimensions increases with longer driving hours (in a day), and long driving hours were found to be a risk factor for RTCs in previous studies [46–48]. However, DRB was negatively associated with increased driving experience. Tavakoli, Sokouni Ravasani, and Ayazi demonstrated that more experienced participants had less risky behavior [49].
The result showed that the family size is significantly associated with DRB. Similar to this result, Schwartz et al. showed that the risk of RTCs has increased among children with three or more siblings [50]. The study did not find a significant association between sex and risky driving behavior. This might be due to the sampling methods of the current study in which the unit for sampling was households rather than individuals in the family. Therefore, DRB was mainly influenced by the whole family rather than its individual members (i.e., by gender and age).
This is the first study to demonstrate that FA of DRB. The current study has several limitations. First, the self-evaluation nature of the questionnaires poses a constraint as it relies on participants’ subjective assessments, minimizing the use of trained data collectors in the assessment process. This may introduce bias or inaccuracies in reporting. Second, we could not gather information on the main supervisor (the individual who taught the respondent how to drive) and its impact on the results. However, the driving license tests conducted by the Faraja traffic police organization in Iran involve two stages: theoretical and practical courses at one of the integrated driving training centers. Trainers at these centers, acting as the main supervisors, complete the same courses annually. The main supervisor delivers the necessary training based on the sources recommended by the Faraja traffic police organization. Although there appear to be no significant differences among the main supervisors within the Iranian driving license system, it is advisable to conduct a study examining the differences between main supervisors in countries with varying driver’s license systems. Third, the study faced challenges in assessing the FA of DRB among family marriages and nonfamily marriages due to the small percentage of family marriages in the sample. The inability to distinguish between these groups limits a comprehensive understanding of how family dynamics may contribute to DRB. Another limitation is that the findings of the present study may not be applicable to communities with different cultures, given the impact of culture and environment on behavior.
It is recommended to conduct a longitudinal study with larger sample size to evaluate FA in DRB, allowing for identification by the gender of offspring and type of marriage. Also, this study can help determine and control of the effects of various confounding variables that cannot be captured in cross-sectional studies such as the duration of exposure to different environmental conditions. Furthermore, twin studies, segregation, and linkage analysis are suggested to differentiate between environmental and genetic influences on the impact of FA in DRB.
5. Conclusions
The current study highlights the presence of FA in various dimensions of DRB, with the slip behavior dimension showing the highest likelihood of FA. This suggests that certain risky behaviors related to slips tend to cluster within families. It appears the aggregation of DRB between father-offspring, mother-offspring, and siblings are the key components of the FA of this behavior. In other words, the family significantly impacts the formation or development of its members’ DRB. Regardless of the underlying causes of FA, this study’s findings will help to implement preventive interventions. Family-based intervention programs are likely to be more effective in preventing or modifying high-risk behaviors within the community, rather than individual interventions. Furthermore, it is possible to establish a screening system in referral hospitals for traffic crashes. In this system, if the injured person is a driver with DRBs, their family members may also undergo screening for those behaviors. If the screening reveals that the individual has risky driving behavior, the necessary theoretical and practical training courses should be provided to reduce these behaviors and reduce the occurrence of traffic accidents. For individuals with negative screening results, appropriate training should be offered on risky driving behaviors to raise their awareness. If the screening reveals that the individual has risky driving behavior, the necessary theoretical and practical training courses should be provided to reduce these behaviors and reduce the occurrence of traffic accidents. For individuals with negative screening results, appropriate training should be offered on risky driving behaviors to raise their awareness.
Ethics Statement
The study protocol was approved by the ethics committee at Tabriz University of Medical Sciences (Ref no. IR.TBZMED.REC. 1400.1169). The study was carried out in accordance with the ethical standards of the 1964 Helsinki Declaration. All methods were carried out in accordance with relevant guidelines and regulations.
Consent
Written informed consent was obtained.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding
The authors would like to acknowledge the Research Deputy of the Tabriz University of Medical Sciences for approving and supporting this research protocol (Grant No. 69209).
Acknowledgments
This study was based on data from Elham Davtalab Esmaeili’s Ph.D. thesis. The authors would like to acknowledge the Research Deputy of the Tabriz University of Medical Sciences for approving and supporting this research protocol (Grant no. 69209). Moreover, the authors are thankful to the Asadabadi Hospital of Tabriz University of Medical Sciences (TUMS) staff and all the participants who helped us conduct the study.
Open Research
Data Availability Statement
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
