**3. Results**

### **3.1 Descriptive statistics and chi-square test**

Socio-demographic and economic factors that are critical in work-related accidents resulting in injury in Turkey are presented in **Table 1**.

19.2, 14.5, 28.9, 20, and 17.4% of those who experienced a work accident participated in the survey in 2008, 2010, 2012, 2014, and 2016, respectively. In terms of age range, 15.6% of employees who experienced work accidents were between 15 and 24 years old, 30.3% were between 25 and 34 years old, 29.2% were between 35 and 44 years old, 18.7% were between 45 and 54 years old, 4.5% were between 55 and 64 years old, and 1.8% were 65 years and older. In terms of education level, while 5.6% of workers, who had experienced work accidents were illiterate, 44.6% graduated from primary school, 21.4% were secondary school graduates, 20.3% were high school graduates, and 8.1% were university graduates. For occupational groups, while 3.2% of work accident victims were managers, 5.1% belonged to professional occupational groups, 5% were technicians and assistant members of professional occupations, 2.1% are office staff, 12.7% were service/sale staff, 15% were qualified agricultural/forestry/aquaculture workers, 26.9% were artists and related employees, 11.9% were facility-machinery operators/assemblers, and 18.1% were workers in non-qualified jobs.. While 67% of work accident victims had very good health, 26.3% had medium health, and 6.7%


**137**

work accidents.

*Modeling the Factors That Affect Work Accidents with Binary Logistic Regression…*

**Variables Work Accident Experience n (%) P**

**Alcohol use** No 25,862 (76.1) 766 (72.7) 26,628 (76.0) 0.009a

**No Yes**

Yes 1887 (5.6) 85 (8.1) 1972 (5.6)

Yes 8103 (23.9) 288 (27.3) 8391 (24.0)

No 32,078 (94.4) 969 (91.9) 33,047 (94.4) 0.001a

had extremely poor health. In addition, 8.1% of work accident victims had received psycho-social support or were depressed. Finally, 27.3% of work accident victims

*Distribution of factors that affect whether individuals experience work accidents.*

According to the chi-square independence test results in **Table 1**, a significant relationship was found between individuals experiencing work accidents with injury and socio-demographic and economic variables (except marital status and

Variance Inflation Factors (VIF) value, β coefficient, standard error, OR value, and confidence intervals related to the binary logistic regression model are shown in **Table 2**. Before model estimation, the issue of multicollinearity between variables should be investigated. Variables with a VIF value over five caused mid-level multicollinearity, and variables with a VIF value over 10 caused high multicollinearity [24]. As seen in **Table 2**, no variable in the model has a VIF value of five or above. Accordingly, no variable that causes multicollinearity

According to the binary logistic regression analysis, when OR < 1, the estimated factor (according to the reference category) had little effect on the investigated state. When OR > 1, it had an increasing effect compared to the reference category [25]. As a result of the analysis, compared to the individuals surveyed in 2008, the odds ratio of individuals who participated in the survey in 2014 (OR = 0.770; 95% CI = 0.615–0.964) and 2016 (OR = 0.782; 95% CI = 0.617–0.991), was lower. In addition, men (OR = 2.246; 95% CI = 1.822–2.769) had higher odds of having a work accident than women. Considering the age variable, compared to the 15–24 group, the age ranges of 25–34 (OR = 0.795; 95% CI = 0.611–1.035), 35–44 (OR = 0.601; 95% CI = 0.450–0.803), 45–54 (OR = 0.486; 95% CI = 0.355–0.665), 55–64 (OR = 0.300; 95% CI = 0.197–0.458) and 65+ (OR = 0.296; 95% CI = 0.162–0.542) had a lower odds ratio of experiencing

In terms of educational status, it was seen that primary school graduates (OR = 1.714; 95% CI = 1.208–2.434), secondary school graduates (OR = 1.554; 95% CI = 1.087–2.222), and high school graduates (OR = 1.612; 95% CI = 1.160–2.238) had higher odds ratio of work accident than university graduates. When the occupational groups were examined, technicians/assistant professional members

(OR = 2.008; 95% CI = 1.225–3.292), service/sales staff (OR = 1.848; 95% CI = 1.189–2.875), qualified agriculture/forestry/aquaculture workers (OR = 3.031; 95% CI = 1.922–4.781), craftsmen/related workers (OR = 4.270;

*DOI: http://dx.doi.org/10.5772/intechopen.93872*

drank alcohol.

*a p < .01 b p < .05.*

**Table 1.**

**Psycho-social support/ depression**

work schedule).

**3.2 Model estimation**

between variables in the model exists.

*Modeling the Factors That Affect Work Accidents with Binary Logistic Regression… DOI: http://dx.doi.org/10.5772/intechopen.93872*


**Table 1.**

*Occupational Wellbeing*

**Level of education**

Did not finish school/illiterate

Professional occupation group

Service employee and sale representative

> Qualified agricultural worker

Equipment and machinery operator

Non-qualified job worker

**Variables Work Accident Experience n (%) P**

**Survey year** 2008 5271 (15.5) 202 (19.2) 5473 (15.6) 0.033b

**Gender** Female 9885 (29.1) 159 (15.1) 10,044 (28.7) 0.000a

**Age** 15–24 3757 (11.1) 164 (15.6) 3921 (11.2) 0.000a

**No Yes**

 5085 (15.0) 153 (14.5) 5238 (15.0) 10,131 (29.8) 305 (28.9) 10,436 (29.8) 7204 (21.2) 211 (20.0) 7415 (21.2) 6274 (18.5) 183 (17.4) 6457 (18.4)

Male 24,080 (70.9) 895 (84.9) 24,975 (71.3)

25-34 9478 (27.9) 319 (30.3) 9797 (28.0) 35–44 9978 (29.4) 308 (29.2) 10,286 (29.4) 45–54 6913 (20.4) 197 (18.7) 7110 (20.3) 55–64 2790 (8.2) 47 (4.5) 2837 (8.1) 65+ 1049 (3.1) 19 (1.8) 1068 (3.0)

Primary school 11,985 (35.3) 470 (44.6) 12,455 (35.6) Secondary school 5104 (15.0) 226 (21.4) 5330 (15.2) High school 7093 (20.9) 214 (20.3) 7307 (20.9) University 7600 (22.4) 85 (8.1) 7685 (21.9)

Married 25,925 (76.3) 788 (74.8) 26,713 (76.3)

Full time 31,887 (93.9) 999 (94.8) 32,886 (93.9)

Technician 2502 (7.4) 53 (5.0) 2555 (7.3) Office worker 1980 (5.8) 22 (2.1) 2002 (5.7)

Artist 4659 (13.7) 283 (26.9) 4942 (14.1)

Medium 7276 (21.4) 277 (26.3) 7553 (21.6) Very Poor 1529 (4.5) 71 (6.7) 1600 (4.6)

4815 (14.2) 54 (5.1) 4869 (13.9)

5456 (16.1) 134 (12.7) 5590 (16.0)

5164 (15.2) 158 (15.0) 5322 (15.2)

2807 (8.3) 125 (11.9) 2932 (8.4)

4123 (12.1) 191 (18.1) 4314 (12.3)

Very Good 25,153 (74.1) 706 (67.0) 25,859 (73.9) 0.000a

**Marital status** Single 8040 (23.7) 266 (25.2) 8306 (23.7) 0.239

**Work schedule** Part-time 2078 (6.1) 55 (5.2) 2133 (6.1) 0.229

**Occupation** Manager 2459 (7.2) 34 (3.2) 2493 (7.1) 0.000a

2183 (6.4) 59 (5.6) 2242 (6.4) 0.000a

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**General Health**

*Distribution of factors that affect whether individuals experience work accidents.*

had extremely poor health. In addition, 8.1% of work accident victims had received psycho-social support or were depressed. Finally, 27.3% of work accident victims drank alcohol.

According to the chi-square independence test results in **Table 1**, a significant relationship was found between individuals experiencing work accidents with injury and socio-demographic and economic variables (except marital status and work schedule).
