1. Background
Any behavior that can increase risk of diseases or discomfort, or directly impair the organs is considered a high-risk behavior (1). Various studies have shown an increased prevalence of high-risk behaviors in different occupational groups (2-8).
Street cleaners and municipal staff are among occupational groups with essential role in keeping towns clean and tidy; yet they are exposed to a variety of high-risk behaviors that may affect their performance. They are also highly exposed to infections such as AIDS and hepatitis (3, 7), which doubles the importance of studying this group.
During young and active ages, committing one high-risk behavior like drug use potentially increases odds of committing others, and possible membership in groups that commit such behaviors (9, 10). Several studies have shown that the potential for committing multiple high-risk behaviors is greater than committing one particular high-risk behavior (11-13), which indicates the importance of the issue. Municipal workers are usually young and active, indicating a greater tendency toward multiple high-risk behaviors.
Previous studies have reported the prevalence of high risk behaviors such as smoking, alcohol use, drug use, tattoo, high-speed driving, unprotected sex, non-use of seat belt, as 6 - 31% in hospital staff (14), 17% - 31% in prisoners (15), 31% - 52% in high-school students (16) and 5% - 51% in adolescents (17, 18).
Some studies have shown that high-risk behaviors are closely related to the way peers commit them and the relationship among peers (5, 18). This point has great relevance because municipal workers have to spend long hours together.
To the best of our knowledge, no comprehensive study of the subject has been conducted in this group, nationally.
2. Objectives
Given the importance of high-risk behaviors in terms of more severe consequences in municipal workers, this study aimed to investigate the prevalence of high-risk behaviors and related factors among this group in Zahedan city, Iran. The results may be used by relevant authorities for planning and identification of the high- risk groups.
3. Patients and Methods
This cross-sectional study was conducted on 671 municipal male workers and staff from different areas of Zahedan city located in south-east of Iran in 2013. Information was obtained through face to face interviews and the participants’ answers were collected in data forms. These forms contained demographic data including age, work history, ethnicity, marital status and education level and history of high-risk behaviors. Explanations were provided about study objectives and assurances about confidentiality, thus the participants entered the study with informed consents. The participants were studied in terms of 5 high-risk behaviors, including tattoo, drug injection or use, needle stick, alcohol use, and self-injury. Any participant with at least one of these behaviors was considered in the high-risk behavior group and accordingly analyzed. Data were analyzed in Stata.12 software using chi-square, exact chi-square and multivariate regression tests. Variables were modeled according to the Hosmer-Lemshow method. P < 0.05 was considered statistically significant.
4. Results
A total of 671 municipal workers and staff were studied, including 297 (44.3%) administrative personnel, 192 (28.6%) street cleaners, and 182 (27.1%) drivers. Mean age of the participants (all male) was 42.6 ± 9.1. Fifty-seven cases (8.5%) reported previous high-risk behaviors (95% confidence interval: 3.8 - 13.4), of whom, 22 (7.4%) were staff, 20 (10.9%) were drivers, and 15 (7.8%) were street cleaners and manual workers. Table 1 presents demographic details and a history of high-risk behaviors in municipal workers and staff in Zahedan.
Table 1. Frequency Distribution of Demographic Details and History of High-Risk Behaviors in Municipal Workers and Staff in Zahedan in 2013
| Variables | No. (%) |
|---|---|
| Demographic Details | |
| Age, y | |
| Less than 29 | 45 (6.7) |
| 30 - 39 | 205 (30.6) |
| 40 to 49 | 251 (37.4) |
| Over 50 | 170 (25.3) |
| Work History, y | |
| Less than 9 | 258 (38.5) |
| 10 to 19 | 119 (17.7) |
| Over 20 | 294 (43.8) |
| Ethnicity | |
| Fars | 529 (78.8) |
| Balooch | 142 (21.2) |
| Marital Status | 15 (2.2) |
| Single | 656 (97.8) |
| Married | |
| Education | |
| Illiterate | 29 (4.3) |
| Junior High | 333 (49.6) |
| Senior High | 162 (24.1) |
| University | 147 (29.9) |
| History of High-Risk Behaviors | |
| Tattoo | |
| Yes | 45 (6.7) |
| No | 626 (93.3) |
| Alcohol Use | |
| Yes | 2 (0.3) |
| No | 669 (99.7) |
| Drug Injection or Use | |
| Yes | 1 (0.1) |
| No | 670 (99.9) |
| History of Needle Stick | |
| Yes | 77 (11.5) |
| No | 594 (88.5) |
| Self-Injury | |
| Yes | 8 (1.2) |
| No | 663 (98.8) |
| General History of High-Risk Behavior | |
| Yes | 57 (8.5) |
| No | 614 (91.5) |
In this study, the relationship between high-risk behaviors and every single independent variable was examined in a univariate analysis. Chi-square test showed a significant relationship only between committing high-risk behaviors and ethnicity (P = 0.007), and a greater prevalence of high-risk behaviors was found in the Balooch than in Fars ethnicity. Although the prevalence of high-risk behaviors was greater in married people than in singles and also in drivers than in staff and workers, no significant relationship was found between high-risk behaviors and marital status or type of job (P > 0.05). Moreover, no significant relationship was found between high-risk behaviors and age or work experience (P > 0.05).
To control confounding factors and assess variables together, multivariate logistic regression test was used, which showed a significant relationship between high-risk behaviors and only variables of ethnicity and education (P < 0.05). Although those with a longer work history had greater odds of committing high-risk behaviors than newly employed workers, this variable had no significant relationship with high-risk behaviors, and only improved the fitness and remained in the final model (Table 2).
Table 2. Coefficients of Multivariate Logistic Regression in Assessing Factors Related to High-Risk Behaviors in Municipal Workers and Staff
| Independent Variables | β | SE (β) | OR | 95% CI | P Value |
|---|---|---|---|---|---|
| Ethnicity | |||||
| Fars | 1 | ||||
| Balooch | 0.83 | 0.299 | 2.3 | 1.28 - 4.1 | 0.005 |
| Education | |||||
| Illiterate | 1 | ||||
| Primary School or Junior High | -1.8 | 7.4 | 0.01 | 0.001 - 7.6 | 0.999 |
| Senior High | 0.89 | 0.43 | 2.45 | 1.05 - 5.7 | 0.037 |
| University | 0.88 | 0.47 | 2.42 | 0.96 - 6.1 | 0.06 |
| Work History, y | |||||
| Less than 9 | 1 | ||||
| 10 to 19 | -0.32 | 0.46 | 0.6 | 0.3 - 2.1 | 0.12 |
| Over 20 | 0.43 | 0.54 | 0.15 | 0.15 - 4.7 | 0.08 |
Participants were also studied in terms of body mass index (BMI), and 52 (8.1%) were low weight (BMI < 18.5), 255 (39.5%) were overweight (BMI > 25), and the rest were normal (Table 3). The chi-square test showed that distribution of BMI and obesity was not unrelated to job status in the municipality (P = 0.001), and workers and street cleaners were least obese (15.9%).
Furthermore, only workers reported coming across needles on the streets, of whom, 9 cases (1.3%) had a history of imprisonment, 22 (3.3%) history of blood transfusion, and 24 (3.6%) history of phlebotomy.
Table 3. Frequency Distribution of Obesity in Municipal Workers, Staff and Drivers
| Job Position | Body Mass Index | Total | ||
|---|---|---|---|---|
| ≤ 18.4 | 18.5 - 24.9 | ≥ 25 | ||
| Staff | 13 (4.6) | 132 (46.8) | 137 (48.6) | 282 (100) |
| Street Cleaners | 32 (17.6) | 121 (66.5) | 29 (15.9) | 182 (100) |
| Drivers | 7 (3.9) | 85 (47.0) | 89 (49.2) | 181 (100) |
| Total | 52 (8.1) | 338 (52.4) | 255 (39.5) | 645 (100) |
5. Discussion
The present study showed that the prevalence of high-risk behaviors among municipal workers and staff in south-east Iran was not very high compared to other groups such as prisoners, hospital staff and school adolescents. Previous studies have reported the prevalence of high-risk behaviors such as smoking, alcohol use, drug use, tattoo, high-speed driving, unprotected sex, non-use of seat belt, in hospital staff up to 31% (14), female prisoners up to 31% (15), and adolescents up to 50% (17, 18).
In the current study, only few workers and staff reported previous high-risk behaviors, but even this exceeded expectations, and requires planning and education for further reduction. Low prevalence of high-risk behaviors in this group may be attributed to participants’ inaccurate answers, leading to low estimates. Since committing one high-risk behavior paves the way for committing others (19, 20), even low prevalence in this group may turn high in the future. Some participants had committed multiple high-risk behaviors concurrently. Therefore, although a low prevalence of high-risk behaviors in this group is a positive point, preventive actions should be taken to prevent recurrence of high-risk behaviors in this group.
The highest prevalence of high-risk behaviors among study participants pertained to tattoo, which was more observed in younger workers. Thus, educating younger workers and staff seems particularly important. Contrary to expectation of higher prevalence of high-risk behaviors in workers, municipal drivers showed a higher prevalence compared to other groups. Therefore, preventive programs should focus more on drivers. As the religion may decrease risky behaviors, thus periodically religion classes could prevent high-risk behaviors among all occupational groups such as municipal workers and drivers (21).
Furthermore, only variables of education and ethnicity affected high-risk behavior in municipal workers and staff, and the Balooch committed more high-risk behaviors. Given the higher prevalence of high-risk behaviors among more educated people, there should be no concern about high-risk behaviors in less-educated ones. What is important is changing people’s attitude toward this issue, especially at the recruitment stage.
Considering that work history remained in the final model, and that the odds of committing high-risk behaviors are greater in those with shorter work history, it can be inferred that in recent years, high-risk behaviors are generally more prevalent, and more committed by youths, as was emerged in this occupational group.
The study also showed that most participants were overweight. Although obesity is not a behavior, it may be induced by high-risk dietary behaviors, such as consuming high-fat foods and lack of exercise. Data suggest that obesity was less prevalent in workers and street cleaners than in staff and drivers. Although these people may have had a good weight before employment, their physical activity along with proper diet seems to make them healthy individuals. On the other hand, considering a high prevalence of obesity among staff and drivers, they must be advised to exercise more to prevent excess weight. The increasing rate of obesity prevalence is related to changes in the life style, especially dietary habits and inadequate daily activity in both rural and urban regions (22, 23). As the consumption of unhealthy outdoor foods and overeating among municipal workers is low; so, it seems that the physical activity is a main factor to decrease obesity among them.
Additionally, 11.5% of the participants amongst workers reported exposure to needle sticks and needles in the streets. In a study conducted in Tehran (24), this was reported 16%. Thus, municipal workers in Zahedan were less exposed to this problem, which may be due to the type of garbage and how they work. In their study, Alavian et al. (24) also found that workers such as street cleaners are exposed to risk of direct contact with uncovered needles and needle sticks, and reported the prevalence of exposure to needle sticks in municipal street cleaners 16%, which is 81 times higher than in other municipal occupations. In a study by Turnberg and Frost (25), 21% of workers reported previous needle injury.
To sum up, the prevalence of high-risk behaviors in municipal workers and staff of Zahedan was not high, and had no relationship with the type of job they did. Given a higher prevalence of high-risk behaviors among more educated workers, there should be no concern about high-risk behaviors in less educated ones. Considering their position, municipal street cleaners enjoy more appropriate weights than other job groups in this organization, which is an advantage of this job. In conclusion, the ethnicity and educational level revealed as important predictors of high-risk behaviors among municipal workers. However, it must be mentioned that beyond the demographic variables, the high-risk behaviors should be also considered psychologically and socially in different groups in societies (26).
