Open Access

The relationship between spontaneous abortion and female workers in the semiconductor industry

Annals of Occupational and Environmental MedicineThe official journal of the Korean Society of Occupational and Environmental Medicine201729:49

https://doi.org/10.1186/s40557-017-0204-x

Received: 7 May 2017

Accepted: 21 September 2017

Published: 9 October 2017

Abstract

Background

This study investigated the relationship between job type and the risk for spontaneous abortion to assess the reproductive toxicity of female workers in the semiconductor industry.

Methods

A questionnaire survey was administered to current female workers of two semiconductor manufacturing plants in Korea. We included female workers who became pregnant at least 6 months after the start of their employment with the company. The pregnancy outcomes of 2,242 female workers who experienced 4,037 pregnancies were investigated. Personnel records were used to assign the subjects to one of three groups: fabrication process workers, packaging process workers, and clerical workers. To adjust for within-person correlations between pregnancies, a generalized estimating equation was used. The logistic regression analysis was limited to the first pregnancy after joining the company to satisfy the assumption of independence among pregnancies. Moreover, we stratified the analysis by time period (pregnancy in the years prior to 2008 vs. after 2009) to reflect differences in occupational exposure based on semiconductor production periods.

Results

The risk for spontaneous abortion in female semiconductor workers was not significantly higher for fabrication and packaging process workers than for clerical workers. However, when we stratified by time period, the odds ratio for spontaneous abortion was significantly higher for packaging process workers who became pregnant prior to 2008 when compared with clerical workers (odds ratio: 2.21; 95% confidence interval: 1.01–4.81).

Conclusions

When examining the pregnancies of female semiconductor workers that occurred prior to 2008, packaging process workers showed a significantly higher risk for spontaneous abortions than did clerical workers. The two semiconductor production periods in our study (prior to 2008 vs. after 2009) had different automated processes, chemical exposure levels, and working environments. Thus, the conditions prior to 2008 may have increased the risk for spontaneous abortions in packaging process workers in the semiconductor industry.

Keywords

Semiconductor industry Occupational exposure Spontaneous abortion Women Korea Reproductive toxicity

Background

The semiconductor industry began in the 1960s in England and the United States as a sector of the microelectronics industry; it was fully introduced in Korea during the 1970s [1]. Today, the semiconductor industry in Korea has a world-class production capacity with a high growth rate [2]. The current working population of the Korean semiconductor industry is about 107,000, accounting for 0.7% of the entire working population [3]. The percentage of female workers in this industry is 30.8%, which is higher than in other manufacturing industries [4]. In 2007, a woman working in a Korean semiconductor factory who was responsible for cleaning semiconductors died of leukemia. As a result, discussions on working environments in the semiconductor industry and occupational diseases began taking place. In 2011, the courts ruled that leukemia was related to semiconductor work. Meanwhile, 18 other workers whose jobs involved semiconductor processes were diagnosed with leukemia, aplastic anemia, lymphoma, breast cancer, and ovarian cancer; these conditions were subsequently recognized as occupational diseases by the Korea Workers’ Compensation and Welfare Service (KWCWS) and the courts. Currently, not only cancers but also diseases related to reproductive toxicity, such as infertility, are included in the list of occupational diseases in semiconductor workers by KWCWS [5]. Therefore, efforts to protect and improve the health of women in the semiconductor industry are needed.

In the semiconductor industry, the process of producing semiconductors involves the use of various chemicals and produces various by-products [6]. During this process, female workers might be exposed to chemicals that can affect their health [7]. The semiconductor industry experiences rapid technological advancements and hazardous materials used in the process change frequently [8]. Risk factors for reproductive toxicity in the semiconductor industry include physical factors, such as exposure to ionizing and non-ionizing radiation; ergonomic factors, such as heavy lifting, a work posture that requires standing for long durations; shift work; and work-related stress [9]. Moreover, interactions between these factors might occur. Therefore, when conducting an exposure assessment in the semiconductor industry, it is difficult to limit the exposure to specific risk factors; thus, an assessment of occupational exposure in the semiconductor industry should include production periods [7].

Previous studies on factors that affect the health of workers in the semiconductor industry focused on cancers and reproductive toxicity [10]. Reproductive toxicity (including spontaneous abortion, infertility, menstrual irregularities, low birth weight, preterm delivery, and congenital anomalies) affects women of childbearing age who are exposed to occupational hazards from production work. Spontaneous abortion can reveal occupational risks more quickly than post-delivery pregnancy outcomes, such as congenital anomalies or stillbirth; thus, it is an important criterion for examining reproductive toxicity in the semiconductor industry [11]. A study of female workers in the Korean electronics industries showed that they had a higher risk of spontaneous abortion than the control groups (women who were not exposed to occupational hazards, such as economically inactive women, the female working population as a whole, and female workers employed in the banking industry) [12]. This was the first study to examine reproductive toxicity in female workers who produced semiconductors and circuit boards in the Korean electronics industries. However, because this study examined female workers in the electronics industry, reproductive toxicity in female semiconductor workers could not be accurately assessed. Furthermore, the study used claims data from the National Health Insurance rather than data collected for the study objectives.

Reproductive toxicity in female semiconductor workers is an important issue that has received increasing attention. Thus, our study aimed to investigate the risk for spontaneous abortion in female semiconductor workers to assess reproductive toxicity in the semiconductor industry. We analyzed differences in the risk for spontaneous abortion by job type and year of pregnancy.

Methods

Study population

A structured questionnaire survey was administered to current female workers of two semiconductor manufacturing plants (A and B plants) by a trained surveyor between April 9 and May 21, 2015. Prior to the survey, researchers explained the objectives and methods of the study. Of 21,969 employees registered in the personnel records, 14,241 (64.8%) participated in the survey and 14,226 (99.9%) consented to the use of the survey data. Of the 6879 female workers who participated in the survey, 2438 women experienced 4552 pregnancies. We included female workers currently working in the semiconductor manufacturing industry who experienced a pregnancy at least 6 months after the start of their employment with the company. We excluded cases in which the timing or outcome of the pregnancy (e.g., nulligravida, current pregnancy) could not be confirmed and cases in which the pregnancy occurred while working somewhere other than the two plants included in this study. Ultimately, 2242 female workers and 4037 pregnancies were analyzed (Fig. 1).
Fig. 1

Selection of study population

This study was approved by the Institutional Review Board of the Korea National Open University (ABN01–201502–11-02). Informed consent was received from the subjects prior to administering the questionnaire survey.

Exposure assessment

Since June 1996, the semiconductor company in this study has electronically recorded personnel data. We classified exposure based on job information contained in these personnel records. Subjects were classified into 18 groups according to their job characteristics based on professional guidance from the company’s department of environmental safety. After determining the intensity and duration of exposure to harmful factors for each group, we estimated the potential risk according to job type. We then categorized the subjects into three groups based on the characteristics of exposure as follows.

First, personnel records were used to divide the subjects into clerical workers (who worked weekdays in the office) and production workers (who worked shifts on the production floor). The production workers were further divided into fabrication (FAB) and packaging (PKG) process workers. Most of the female production workers were operators. To increase the specificity of the workers’ exposure, field engineers and office-based engineers were included in the group of clerical workers.

The semiconductor industry tends to hire workers who are capable of performing specific work and are likely to remain in their jobs. However, some workers may be required to perform more than one job during their employment. In these instances, we determined which position was held the longest, which is the most common method for job classification and best represents the worker’s occupational experience [13]. The last job and the job held longest were the same for all pregnancy cases, and the concordance rate for the first job and the job held longest was 91.5%.

For an assessment of reproductive toxicity, it is important to determine the time window of exposure. Short-term exposure before pregnancy is critical, but long-term exposure can also cause irreversible changes in germ cells [14]. However, it is difficult to independently evaluate the exposure to each chemical because workers are simultaneously exposed to a number of different chemicals during the semiconductor production process [7]. Accordingly, the job held longest was used as the independent variable.

Pregnancy outcomes

We investigated pregnancy outcomes and end-points. Pregnancy outcomes were classified as full-term delivery, preterm delivery, stillbirth, spontaneous abortion, and induced abortion. Spontaneous abortion was defined as a pregnancy being terminated before 20 weeks; in these cases, the gestational age (GA) was assessed in weeks. Full-term and preterm deliveries were defined as delivery at GAs of ≥37 and <37 weeks, respectively, whereas stillbirth was defined as infant death at a GA of ≥20 weeks. Because the end-points of the pregnancies were surveyed in date format (year and month), pregnancy duration was converted to months to estimate the start of pregnancy. Consequently, the GA of spontaneous abortion cases was converted to months. The GAs of full-term delivery, preterm delivery, stillbirth, and induced abortion were assumed to be 10, 9, 5, and 3 months, respectively.

The questionnaire was designed by the researchers for the purpose of this study. The questionnaire included information on demographic characteristics, environmental and occupational characteristics, medical history, mental health, safety and health systems, compensation, reproductive toxicity, and women’s health. Data on the women’s demographic characteristics (age at conception, body mass index [BMI] at interview, educational level at interview, and smoking status at interview) and occupational characteristics (duration of employment at conception, year of conception, study plant employed at conception, and work schedule at conception) were used for analysis in the survey. Smoking status was categorized as current/former or never smokers. Pregnancy-related survey data were used to derive the obstetric history. Gravidity was calculated as the number of pregnancies, including the current pregnancy. The obstetric history was classified according to pregnancy outcome prior to pregnancy, including full-term delivery, preterm delivery, spontaneous abortion, induced abortion, and stillbirth.

Statistical analysis

Demographic and occupational characteristics as well as obstetric history were compared by job type. To compare ages at conception as continuous variables, a Welch’s analysis of variance (ANOVA) was performed. Because the duration of employment did not show a normal distribution, a Kruskal-Wallis test was conducted. For all other categorical variables, the chi-square test was used.

A logistic regression analysis was performed to assess the risk of spontaneous abortion in female semiconductor workers according to their job type. Model 1 was adjusted for the confounding variables of age at conception, BMI, educational level, and smoking status; model 2 was adjusted for all variables in Model 1 plus pregnancy outcome prior to pregnancy; and model 3 was adjusted for all variables in Model 2 plus duration of employment at pregnancy, year of conception, work location, and shift work.

Two different methods were used to compare the risks for spontaneous abortion associated with the occupational exposure of female semiconductor workers. First, the odds ratios (OR) of spontaneous abortion in relation to the job held longest were estimated using log-binomial regression. Generalized estimating equations (GEEs) with exchangeable working correlation structures were used to account for within-person correlations between pregnancies [15]. Second, logistic regression analysis was limited to the first pregnancies of the female workers after they joined the company to satisfy the assumption of independence among pregnancies [16, 17]. Moreover, we stratified the analysis by time period of conception (prior to 2008 vs. after 2009) to analyze the differences in exposure based on semiconductor production periods. To select subjects who had no job changes after joining the company for the sensitivity analysis, the analysis was limited to women whose first job, last job, and job held longest were identical.

SPSS version 23 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis. ORs and 95% confidence intervals (CIs) are presented. A p-value of <0.05 in a two-tailed test was considered to be statistically significant.

Results

The mean age during pregnancy was lower for FAB workers (27.7 ± 3.5 years) and PKG workers (27.8 ± 3.5 years) than for clerical workers (29.6 ± 3.1 years). FAB and PKG workers were more likely to be obese (BMI ≥25 kg/m2), have lower educational levels, and be a current or former smokers than were clerical workers. PKG workers had longer durations of employment at conception than FAB and clerical workers. The A plant had a higher percentage of clerical workers than the B plant. Regarding the women’s work schedules, FAB and PKG workers mostly worked rotating shifts, whereas clerical workers mostly worked fixed daytime hours. We found no significant differences in pregnancy outcomes prior to pregnancy or year of conception between the three types of workers (Table 1).
Table 1

Characteristics of 4037 pregnancies of female workers in the semiconductor industry

 

The job held longest

 

FAB (N = 2314)

PKG (N = 1372)

Clerical worker (N = 351)

Total (N = 4037)

P-value

Age at conception (years)

 Mean ± SD

27.7 ± 3.5

27.8 ± 3.5

29.6 ± 3.1

27.8 ± 3.5

<0.001a

 Post hoc testb

b

b

a

  

 ≤29

1747 (75.5)

1023 (74.6)

195 (55.6)

2965 (73.4)

<0.001

 30–34

506 (21.9)

304 (22.2)

141 (40.2)

951 (23.6)

 

 ≥35

61 (2.6)

45 (3.3)

15 (4.3)

121 (3.0)

 

BMI at interview (kg/m2)

 ≤24.9

1911 (82.6)

1104 (80.5)

320 (91.2)

3335 (82.6)

<0.001

 ≥25.0

403 (17.4)

268 (19.5)

31 (8.8)

702 (17.4)

 

Level of education at interview

 High school

1521 (65.7)

1000 (72.9)

81 (23.1)

2602 (64.5)

<0.001

 College/university

793 (34.3)

372 (27.1)

270 (76.9)

1435 (35.5)

 

Smoking status at interview

 Non smoker

1843 (79.6)

1114 (81.2)

341 (97.2)

3298 (81.7)

<0.001

 Smoker

471 (20.4)

258 (18.8)

10 (2.8)

739 (18.3)

 

Pregnancy outcome prior to pregnancy

 Primigravida

1232 (53.2)

744 (54.2)

180 (51.3)

2156 (53.4)

0.358

 Full term delivery

784 (33.9)

426 (31.0)

124 (35.3)

1334 (33.0)

 

 Preterm delivery

32 (1.4)

18 (1.3)

7 (2.0)

57 (1.4)

 

 Spontaneous abortion

200 (8.6)

137 (10.0)

24 (6.8)

361 (8.9)

 

 Induced abortion

52 (2.2)

38 (2.8)

13 (3.7)

103 (2.6)

 

 Stillbirth

14 (0.6)

9 (0.7)

3 (0.9)

26 (0.6)

 

Duration of employment at conception (years)

 Median (IQR)

7.6 (5.3–10.5)

8.0 (5.6–10.9)

7.3 (4.9–10.5)

7.8 (5.3–10.7)

0.009c

 Post hoc testd

a

b

a

  

 ≤4

523 (22.6)

255 (18.6)

94 (26.8)

872 (21.6)

0.004

 5–9

1123 (48.5)

685 (49.9)

154 (43.9)

1962 (48.6)

 

 ≥10

668 (28.9)

432 (31.5)

103 (29.3)

1203 (29.8)

 

Year of conception

 ≥2009

1466 (63.4)

867 (63.2)

212 (60.4)

2545 (63.0)

0.559

 ≤2008

848 (36.6)

505 (36.8)

139 (39.6)

1492 (37.0)

 

Study plant

 A plant

1369 (59.2)

740 (53.9)

274 (78.1)

2383 (59.0)

<0.001

 B plant

945 (40.8)

632 (46.1)

77 (21.9)

1654 (41.0)

 

Work schedule

 Days only

235 (10.2)

125 (9.1)

299 (85.2)

659 (16.3)

<0.001

 Rotating shift

2079 (89.8)

1247 (90.9)

52 (14.8)

3378 (83.7)

 

FAB fabrication process worker, PKG package process worker

Smokers included current and former smokers

SD standard deviation, BMI body mass index, IQR interquartile range

aWelch one-way analysis of variance

bGames-Howell test

cKruskal-Wallis test

dPairwise comparisons

In the regression analysis, the job type and risk for spontaneous abortion were not significantly different in the unadjusted and adjusted regression models. However, age at conception of ≥30 years, a history of smoking, and spontaneous abortion prior to pregnancy were associated with higher odds for spontaneous abortion (Table 2).
Table 2

Crude and adjusted odds ratios of spontaneous abortion by risk factors of female workers employed in the semiconductor industry

 

Pregnancy

SA

Crude

Adjusted

 

Model 1a

Model 2b

Model 3c

 

(N = 4037)

(N = 529)

OR

(95% CI)

OR

(95% CI)

OR

(95% CI)

OR

(95% CI)

The job held longest

 FAB

2314

291 (12.6)

1.15

0.81–1.64

1.09

0.71–1.67

1.08

0.70–1.66

1.08

0.64–1.81

 PKG

1372

199 (14.5)

1.36

0.94–1.96

1.28

0.82–1.99

1.26

0.81–1.97

1.24

0.72–2.12

 Clerical worker

351

39 (11.1)

1.0

 

1.0

 

1.0

 

1.0

 

Age at conception (years)

 ≤29

2965

354 (11.9)

1.0

 

1.0

 

1.0

 

1.0

 

 30–34

951

141 (14.8)

1.28

1.04–1.58

1.35

1.08–1.69

1.34

1.06–1.70

1.34

1.01–1.78

 ≥35

121

34 (28.1)

2.88

1.91–4.35

2.98

1.94–4.59

2.93

1.88–4.55

2.95

1.79–4.85

BMI at interview (kg/m2)

 ≤24.9

3335

421 (12.6)

1.0

 

1.0

 

1.0

 

1.0

 

 ≥25.0

702

108 (15.4)

1.26

1.00–1.58

1.16

0.91–1.48

1.15

0.90–1.48

1.16

0.91–1.48

Level of education at interview

 High school

1435

185 (12.9)

1.0

 

1.0

 

1.0

 

1.0

 

 College/university

2602

344 (13.2)

1.03

0.85–1.25

1.04

0.84–1.29

1.03

0.84–1.28

1.08

0.87–1.34

Smoking status at interview

 Non smoker

3298

410 (12.4)

1.0

 

1.0

 

1.0

 

1.0

 

 Smoker

739

119 (16.1)

1.35

1.08–1.69

1.44

1.14–1.81

1.42

1.13–1.80

1.37

1.08–1.73

Pregnancy outcome prior to pregnancy

 Primigravida

2156

267 (12.4)

1.0

   

1.0

 

1.0

 

 Full term delivery

1334

166 (12.4)

1.01

0.82–1.24

  

0.95

0.76–1.20

0.92

0.73–1.16

 Preterm delivery

57

6 (10.5)

0.83

0.35–1.96

  

0.87

0.36–2.09

0.84

0.35–2.01

 Spontaneous abortion

361

69 (19.1)

1.67

1.25–2.24

  

1.58

1.16–2.15

1.53

1.12–2.08

 Induced abortion

103

15 (14.6)

1.21

0.69–2.12

  

0.79

0.40–1.56

0.81

0.41–1.59

 Stillbirth

26

6 (23.1)

2.12

0.84–5.33

  

1.63

0.59–4.49

1.70

0.62–4.70

Duration of employment at conception (years)

 ≤4

872

93 (10.7)

1.0

     

1.0

 

 5–9

1962

268 (13.7)

1.33

1.03–1.70

    

1.26

0.97–1.65

 ≥10

1203

168 (14.0)

1.36

1.04–1.78

    

1.08

0.76–1.52

Year of conception

 ≥2009

2545

372 (14.6)

1.0

     

1.0

 

 ≤2008

1492

157 (10.5)

0.69

0.56–0.84

    

0.80

0.64–1.00

Study plant

 A plant

2383

313 (13.1)

1.0

     

1.0

 

 B plant

1654

216 (13.1)

0.99

0.82–1.20

    

1.01

0.82–1.23

Work schedule

 Days only

659

82 (12.4)

1.0

     

1.0

 

 Rotating shift

3378

447 (13.2)

1.07

0.83–1.38

    

0.97

0.69–1.38

FAB fabrication process worker, PKG package process worker

Smokers included current and former smokers

SA spontaneous abortion, OR odds ratio, CI confidence interval

Values displaying significant differences are shown in bold (p-value ≤ 0.05)

aMultiple logistic regression model adjusted for the job held longest, age, BMI, education, smoking

bMultiple logistic regression model adjusted for the job held longest, age, BMI, education, smoking, pregnancy outcome just prior to pregnancy

cMultiple logistic regression model adjusted for the job held longest, age, BMI, education, smoking, pregnancy outcome prior to pregnancy, duration of employment, year of conception, study plant, shift work

When analyses were performed using a GEE and limited to first pregnancies after joining the company, the risks for spontaneous abortion were not significantly different among job types. When the analysis was limited to first pregnancies after joining the company, the OR for spontaneous abortion was higher than that in the GEE analysis (Tables 3 and 4).
Table 3

Odds ratios of spontaneous abortion among generalised estimate equation of all pregnancies

 

Pregnancy

SA

Crude GEE

Adjusted GEEa

(N = 4037)

(N = 529)

OR

(95% CI)

OR

(95% CI)

The job held longest

 FAB

2314

291 (12.6)

1.12

0.81–1.57

1.12

0.79–1.58

 PKG

1372

199 (14.5)

1.28

0.91–1.81

1.26

0.88–1.79

 Clerical worker

351

39 (11.1)

1.0

 

1.0

 

Age at conception (years)

 ≤29

2965

354 (11.9)

1.0

 

1.0

 

 30–34

951

141 (14.8)

1.25

1.03–1.50

1.28

1.02–1.60

 ≥35

121

34 (28.1)

2.41

1.75–3.33

2.48

1.69–3.64

Duration of employment (years)

 ≤4

872

93 (10.7)

1.0

 

1.0

 

 5–9

1962

268 (13.7)

1.29

1.02–1.63

1.22

0.96–1.54

 ≥10

1203

168 (14.0)

1.37

1.06–1.78

1.06

0.78–1.43

Year of conception

 ≥2009

2545

372 (14.6)

1.0

 

1.0

 

 ≤2008

1492

157 (10.5)

0.73

0.60–0.88

0.84

0.68–1.02

FAB fabrication process worker, PKG package process worker

GEE generalized estimating equation, OR odds ratio, CI confidence interval

Values displaying significant differences are shown in bold (p-value ≤ 0.05)

aAdjusted for age, BMI, education, smoking, duration of employment, year of conception, study plant

Table 4

Odds ratios of spontaneous abortion among logistic regression model of first pregnancies

 

Pregnancy

SA

Crude logistic

Adjusted logistica

(N = 2242)

(N = 98)

OR

(95% CI)

OR

(95% CI)

The job held longest

 FAB

1285

54 (4.2)

1.40

0.60–3.31

1.15

0.46–2.85

 PKG

759

38 (5.0)

1.69

0.70–4.05

1.34

0.52–3.41

 Clerical worker

198

6 (3.0)

1.0

 

1.0

 

Age at conception (years)

 ≤29

1886

71 (3.8)

1.0

 

1.0

 

 30–34

326

21 (6.4)

1.76

1.07–2.91

1.31

0.70–2.47

 ≥35

30

6 (20.0)

6.39

2.53–16.12

4.18

1.40–12.43

Duration of employment (years)

 ≤4

664

24 (3.6)

1.0

 

1.0

 

 5–9

1167

48 (4.1)

1.14

0.69–1.89

0.95

0.56–1.58

 ≥10

411

26 (6.3)

1.80

1.02–3.18

1.30

0.63–2.67

Year of conception

 ≥2009

1275

82 (6.4)

1.0

 

1.0

 

 ≤2008

967

16 (1.7)

0.24

0.14–0.42

0.32

0.18–0.55

FAB fabrication process worker, PKG package process worker

OR odds ratio, CI confidence interval

Values displaying significant differences are shown in bold (p-value ≤ 0.05)

aAdjusted for age, BMI, education, smoking, duration of employment, year of conception, study plant

Among women who conceived prior to 2008, the risk of spontaneous abortion was significantly higher for PKG workers than for clerical workers (OR 2.21, 95% CI: 1.01–4.81). In contrast, among women who conceived after 2009, there were no differences in the risk for spontaneous abortion according to job type (Table 5). The association between job type and the risk for spontaneous abortion was also analyzed in a sensitivity analysis that was limited to women whose first job, last job, and job held longest were identical; no significant differences in the OR for spontaneous abortion by job type were found in this analysis (Table 6).
Table 5

Odds ratios of spontaneous abortion among generalised estimate equation of all pregnancies stratified by year of conception

Year of conception

The job held longest

Pregnancy

SA

Crude GEE

Adjusted GEEa

(N = 4037)

(N = 529)

OR

(95% CI)

OR

(95% CI)

≤2008

FAB

848

80 (9.4)

1.62

0.75–3.49

1.54

0.71–3.32

PKG

505

69 (13.7)

2.31

1.06–5.02

2.21

1.01–4.81

Clerical worker

139

8 (5.8)

1.0

 

1.0

 

≥2009

FAB

1466

211 (14.4)

0.98

0.68–1.40

1.01

0.69–1.48

PKG

867

130 (15.0)

1.03

0.71–1.49

1.05

0.71–1.55

Clerical worker

212

31 (14.6)

1.0

 

1.0

 

FAB fabrication process worker, PKG package process worker

GEE generalized estimating equation, SA spontaneous abortion, OR odds ratio, CI confidence interval

Values displaying significant differences are shown in bold (p-value ≤ 0.05)

aAdjusted for age, BMI, education, smoking, duration of employment, study plant

Table 6

Odds ratios of spontaneous abortion among generalized estimate equation of all pregnancies, limited to female workers whose first, last, and job held longest were the same

Category

The job matching

Pregnancy

SA

Crude GEE

Adjusted GEEa

(N = 3692)

(N = 489)

OR

(95% CI)

OR

(95% CI)

3 category

FAB

2244

285 (12.7)

1.12

0.76–1.64

1.07

0.71–1.60

PKG

1193

175 (14.7)

1.28

0.86–1.91

1.19

0.79–1.81

Clerical worker

255

29 (11.4)

1.0

 

1.0

 

2 category

Process workerb

3437

460 (13.4)

1.18

0.81–1.72

1.11

0.75–1.65

Clerical worker

255

29 (11.4)

1.0

 

1.0

 

FAB fabrication process worker, PKG package process worker

GEE generalized estimating equation, SA spontaneous abortion, OR odds ratio, CI confidence interval

aAdjusted for age, BMI, education, smoking, duration of employment, year of conception, study plant

bProcess worker included fabrication process worker and package process worker

Discussion

This study investigated the association between the risk for spontaneous abortions and the job type of female semiconductor workers to assess their reproductive health. The risk for spontaneous abortion for female semiconductor workers was not significantly higher for FAB and PKG workers than for clerical workers according to their job held longest. However, when the analysis was stratified for the year of conception, we found significantly higher odds for spontaneous abortions (OR 2.21, 95% CI: 1.01–4.81) in PKG workers than clerical workers when the pregnancy occurred prior to 2008.

In this study, the spontaneous abortion rates for FAB and PKG workers were 12.6% and 14.5%, respectively; these were higher than the rate of 11.1% found in clerical workers. These spontaneous abortion rates for female workers involved in semiconductor production were higher than the spontaneous abortion rate of 11.1% reported for Korean domestic 15- to 44-year-old married women [18]. In 1988, Pastides et al. first reported data on reproductive toxicity in the semiconductor industry using a questionnaire survey. They showed that the risk for spontaneous abortion was higher for diffusion and photolithographic process workers than for non-FAB workers [19]. Later, the University of California conducted two cohort studies with support from the Semiconductor Industry Association in the United States. The first study—a retrospective cohort study using questionnaire data and medical records—showed that FAB workers had higher odds for experiencing a spontaneous abortion than did non-FAB workers (OR 1.43, 95% CI: 0.95–2.09). Women working in masking and etching-related processes, in particular, had a significantly higher risk for spontaneous abortion [20]. In the second study (a prospective cohort study using urine samples that were collected daily for 6 months), FAB workers again had a higher risk for spontaneous abortion than did non-FAB workers (OR 1.25, 95% CI: 0.63–1.76) [21]. An epidemiological study on reproductive toxicity in the semiconductor industry revealed that the risk for spontaneous abortions tended to be higher for FAB workers; in particular, when the analysis was broken down by sub-processes, job types, and level of exposure to hazards, the risk for reproductive toxicity was significantly increased [7].

Other studies reported that the work processes of the semiconductor industry were not associated with an increased risk for spontaneous abortions. In a case-control study including patients diagnosed with intrauterine spontaneous abortions based on pathologic results, FAB workers did not have an increased risk for spontaneous abortions when compared with non-FAB workers (OR 0.87, 95% CI: 0.45–1.60) [22]. A nested case-control study on current and former female semiconductor workers showed an increased risk for spontaneous abortions in FAB workers (OR 0.58, 95% CI: 0.26–1.30) compared with non-FAB workers [23]. These conflicting results might be caused by the relatively low numbers of pregnancies among the FAB workers in the studies (15 and 36, respectively).

In previous studies, exposure classification of the subjects consisted mainly of FAB versus non-FAB workers or different FAB sub-processes or hazards [7]. Workers may be exposed to a number of substances associated with reproductive toxicity during the FAB process in the semiconductor industry, including carbon monoxide, N,N-dimethylacetamide, 2-ethoxyethanol, ethylbenzene, ethylene glycol, 2-methoxy-1-propanol, 2-methoxy-1-propylacetate, nitrous oxide, and xylene. During the PKG process, workers may be exposed to antimony trioxide, N,N-dimethylformamide, ethylbenzene, ethylene oxide, methanol, phenol, trichloroethylene, and xylene (Additional file 1: Table S1) [24]. Previous studies have shown that ethylene glycol ether, isopropyl alcohol, xylene, and butyl acetate are significantly associated with an increased risk for spontaneous abortions in FAB workers. Among the FAB sub-processes, photolithography, diffusion, masking, dopant application, thin film application, and etching are known to increase the risk for spontaneous abortions [7].

The PKG process consists of wafer dicing, where the wafer is cut into chips; die bonding, where the chips are connected to the circuit board; wire bonding, where the chips are connected to the circuit board with a gold wire; molding, where the chips are wrapped with resin; and testing, where electrical and thermal stress are applied to the chips. During molding, which is performed to protect the semiconductor chips from the environment, an epoxy molding compound (EMC) is used under high-temperature conditions (180 °C). Although the EMC is a high-molecular-weight polymer, volatile organic compounds (VOCs; in particular, benzene and methylisobutylketone) and formaldehyde can be generated under heat. In the FAB and PKG processes, general ventilation is implemented based on particle number concentration, but mixing of indoor air occurs between the sub-processes. Therefore, VOCs and formaldehyde that are PKG-generated by-products can affect other nearby sub-processes [6]. Moreover, the testing process can include ionizing radiation exposure. Because semiconductor equipment is densely arranged, continued exposure to extremely low frequency magnetic fields (ELF-MF) may occur [25]. In a previous study on the association between exposure to petrochemicals as an occupational hazard and the risk for spontaneous abortions, the risk for spontaneous abortion was significantly increased by exposure to benzene, gasoline, and hydrogen sulfide [26]. In a systematic review using a meta-analysis, women who were exposed to formaldehyde showed a significantly increased risk for spontaneous abortions [27]. Future studies on the relationship between reproductive toxicity and exposure to VOCs, formaldehyde, ionizing radiation, and ELF-MF in the semiconductor industry are needed.

The weight of the wafer increases along with its diameter, which makes manual handling of wafers difficult. To overcome this issue, many processes have become automated and older processes, chemicals, and operational methods that had been used to produce smaller wafers are being phased out [28, 29]. Therefore, differences in occupational exposure based on the semiconductor production period must be considered. In the late 2000s, semiconductor production infrastructure changed from a wafer diameter of 8 in. to one of 12 in. [30]. It is likely that the level of exposure to occupational hazards differed depending on the semiconductor production period; consequently, the risk for spontaneous abortions would be higher during the PKG process used in the past. In this study, when female semiconductor workers became pregnant prior to 2008, the risk for spontaneous abortions in PKG workers was significantly higher than that in clerical workers. When female workers who entered the company before 2008 experienced repetitive pregnancies, the age and gravidity of pregnancy prior to 2008 was lower than those for pregnancies after 2009. This may result in an increase in the average age of pregnancy after 2009 (Additional file 1: Table S2). The average age of the first pregnancy for women who entered the company after 2009 was also higher than for women who began their employment prior to 2008 (Additional file 1: Table S3). Thus, the age at which female workers begin to experience a pregnancy is now higher than in the past. Therefore, pregnancies that occurred after 2009 are more likely to be at high risk for spontaneous abortions.

There is a critical period of reproductive toxicity determined by occupational hazards. Reproductive toxicity can manifest as various features depending on which hazard the worker was exposed to and when. Therefore, it is critical to identify an adequate exposure assessment period for determining reproductive toxicity in women. Maternal exposure in the few months before conception is important for infertility, and teratogenic effects may arise during the organogenesis phase during the first 3–8 weeks of pregnancy. Long-term exposure may cause irreversible changes to the DNA of germ cells, and certain substances may persist and concentrate in body fat [14]. Reproductive toxicity can be the result of chronic or acute exposure. In the assessment of the cumulative effects of hazards, we found no differences in the risk for spontaneous abortions according to the duration of employment. In this study, the time window of occupational exposure was assumed to be 6 months or longer [17, 31].

When comparing the first job and the job held longest, we found few cases in which the employee changed from a clerical worker to a production worker (Additional file 1: Table S4). When the classification of exposure was selected as the job held longest, there may be misclassification bias due to the inclusion of some production workers as clerical workers, which might underestimate the risk of spontaneous abortion for PKG workers. The median duration of employment at pregnancy was 7.8 years (interquartile range: 5.3–10.7). Female workers were likely to become pregnant after working at the company for some time. Therefore, this study classified the exposure according to the job held longest.

The risk factors for spontaneous abortion have been shown to include the mother’s age of ≥35 years, a history of spontaneous abortions, gravidity, smoking, BMI, and educational level [3237]. In this study, the risk for spontaneous abortion was significantly higher for mothers who were ≥35 years old, had a history of spontaneous abortions, and were current and former smokers. However, BMI and educational level were not significantly associated with the risk for spontaneous abortions. In this study, BMI and educational level were assessed at the time of the survey and not during pregnancy. Therefore, misclassification bias might have affected the results.

The limitations in studying the health impact of working in the semiconductor industry include the healthy worker and infertile worker effects [7, 12, 23]. The healthy worker effect refers to the concept that healthy people have a higher probability of being hired and of remaining at their workplace, whereas those who are not healthy are more likely to quit their jobs. The infertile worker effect refers to the phenomenon of infertile women or those without children remaining at work or female workers leaving their jobs during their pregnancy. These effects can be compensated for by selecting an external control group with similar forms of employment or an internal control group with a lower occupational exposure level, rather than selecting from the general population. In this study, the healthy and infertile worker effects were minimized by adjusting for age at the time of pregnancy and work period; moreover, we selected clerical workers who were employed in the semiconductor industry as the control group [38].

When studying reproductive toxicity in semiconductor processes, it is necessary to define pregnancy. This study confirmed pregnancies and pregnancy outcomes based on data from the questionnaire survey. Because a spontaneous abortion is a traumatic event, the subjects might have found it difficult to respond to questions about spontaneous abortion. The possibility of such a recall bias occurring in only one group among the production and clerical worker groups is very low; this can underestimate the effects of occupational hazards on the risk for spontaneous abortions by non-differential misclassification bias [23].

A surveillance system of reproductive health is needed to assess and monitor the risks, working conditions, and hazardous materials to which future childbearing-age female workers may be exposed. Moreover, individuals who were employed during periods when working conditions were poor and not managed properly should be followed up to further assess reproductive toxicity according to semiconductor production periods.

Conclusions

In this study, we administered a reproductive health-related questionnaire survey to current female workers in the semiconductor industry. We found that female PKG workers who worked in the semiconductor industry prior to 2008 had an increased risk for spontaneous abortions. In previous studies investigating reproductive toxicity, the focus was placed on FAB and its sub-processes; however, our data indicate that an assessment of reproductive toxicity associated with PKG processes is needed. These results could be used as a basis for future maternity protection policies.

Abbreviations

ANOVA: 

Analysis of variance

CI: 

Confidence interval

ELF-MF: 

Low-frequency magnetic field

EMC: 

Epoxy molding compound

FAB: 

Fabrication

GA: 

Gestational age

GEE: 

Generalized estimating equation

KWCWS: 

Korea workers’ compensation and welfare service

OR: 

Odds ratio

PKG: 

Packaging

VOC: 

Volatile organic compound

Declarations

Acknowledgements

This study was supported by the verification committee of industrial health.

Funding

Not applicable.

Availability of data and materials

Authors do not wish to share data. The dataset generated by the verification committee of industrial health. The dataset were used under IRB approval for the current study and so are not publicly available.

Authors’ contributions

HJG and SCR designed the research. SYL and YDK conceived the idea for the study. JBR, SYE and HRL participated in research process and collected data. HCK performed statistical analysis. HCK interpreted the data and drafted the manuscript. MJP and SCR devised and supervised the entire process. JBR critically revised the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of the Korea National Open University (ABN01–201502–11-02), and informed consent was received from the subjects prior to administering the questionnaire survey.

Consent for publication

Consent for publications were obtained from individual person.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Occupational and Environmental Medicine, Dankook University College of Medicine
(2)
Department of Preventive Medicine, Dankook University College of Medicine
(3)
Department of Occupational and Environmental Medicine, College of Medicine, Kyung Hee University
(4)
Department of Obstetrics and Gynecology, Dankook University College of Medicine
(5)
Department of Preventive Medicine and Medical Research Institute, Chungbuk National University College of Medicine
(6)
Department of Occupational and Environmental Medicine, College of Medicine, Seoul St. Mary’s Hospital, Catholic University of Korea

References

  1. Park JY, Yang JS. History of development in Korea semiconductor industry. Korea Semiconductor Industry Association. http://www.ksia.or.kr/renewal/semi/semi1.jsp (2012). Accessed 1 May 2017.
  2. Oh YS, Lee IJ, Park SG, Park YM, Jang DH. Key indicators of major industries. Korea Institute for Industrial Economics & Trade. http://www.kiet.re.kr/kiet_web/main/ (2016). Accessed 1 May 2017.
  3. Workforce predictions for the second half of 2016. Korea Employment Information Service. https://www.keis.or.kr/user/extra/main/2102/publication/publicationList/jsp/LayOutPage.do?categoryIdx=131&pubIdx=2341&onlyList=N (2016). Accessed 1 May 2017.
  4. Employment trends brief. Korea Employment Information Service. https://www.keis.or.kr/user/extra/main/2107/publication/publicationList/jsp/LayOutPage.do?categoryIdx=126&pubIdx=2481&spage=1&onlyList=N (2016). Accessed 1 May 2017.
  5. Korea Workers’ Compensation and Welfare Service, samsung semiconductor workers 'infertility', 'brain tumor' first occupational disease approval. Supporters for the Health and Rights of People in the Semiconductor Industry. http://cafe354.daum.net/_c21_/home?grpid=1C6KM (2017). Accessed 1 May 2017.
  6. Park SH, Shin JA, Park HD. Exposure possibility to by-products during the processes of semiconductor manufacture. J Korean Soc Occup Environ Hyg. 2012;22:52–9.Google Scholar
  7. Park DU, Lee KM. Critical review of retrospective exposure assessment methods used to associate the reproductive and cancer risks of wafer fabrication workers. J Korean Soc Occup Environ Hyg. 2012;22:9–19.Google Scholar
  8. Kong JO. Working environment and experiences of diseases in semiconductor industry. J Korean Soc Occup Environ Hyg. 2012;22:32–41.Google Scholar
  9. Figa-Talamanca I. Occupational risk factors and reproductive health of women. Occup Med (Lond). 2006;56:521–31.View ArticleGoogle Scholar
  10. Kim MH, Kim H, Paek D. The health impacts of semiconductor production: an epidemiologic review. Int J Occup Environ Health. 2014;20:95–114.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Park JS, Na MC, Paek DM, Moon OR. Industry of employment and spontaneous abortion of female workers. J Prev Med Public Health. 1994;27:242–57.Google Scholar
  12. Kim I, Kim MH, Lim S. Reproductive hazards still persist in the microelectronics industry: increased risk of spontaneous abortion and menstrual aberration among female workers in the microelectronics industry in South Korea. PLoS One. 2015;10:e0123679.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Checkoway H, Pearce N, Kriebel D. Research methods in occupational epidemiology: Oxford University Press; 2004.View ArticleGoogle Scholar
  14. Burdorf A, Figa-Talamanca I, Jensen TK, Thulstrup AM. Effects of occupational exposure on the reproductive system: core evidence and practical implications. Occup Med (Lond). 2006;56:516–20.View ArticleGoogle Scholar
  15. Gaskins AJ, Rich-Edwards JW, Hauser R, Williams PL, Gillman MW, Ginsburg ES, et al. Maternal prepregnancy folate intake and risk of spontaneous abortion and stillbirth. Obstet Gynecol. 2014;124:23–31.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Small CM, Cheslack-Postava K, Terrell M, Blanck HM, Tolbert P, Rubin C, et al. Risk of spontaneous abortion among women exposed to polybrominated biphenyls. Environ Res. 2007;105:247–55.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Settimi L, Spinelli A, Lauria L, Miceli G, Pupp N, Angotzi G, et al. Spontaneous abortion and maternal work in greenhouses. Am J Ind Med. 2008;51:290–5.View ArticlePubMedGoogle Scholar
  18. Kim SG. The 2012 National Survey on Fertility, Family Health & Welfare in Korea. Korea Institute for Health and Social Affairs. http://www.kihasa.re.kr (2012). Accessed 1 May 2017.
  19. Pastides H, Calabrese EJ, Hosmer DW Jr, Harris DR Jr. Spontaneous abortion and general illness symptoms among semiconductor manufacturers. J Occup Med. 1988;30:543–51.PubMedGoogle Scholar
  20. Beaumont JJ, Swan SH, Hammond SK, Samuels SJ, Green RS, Hallock MF, et al. Historical cohort investigation of spontaneous abortion in the Semiconductor Health Study: epidemiologic methods and analyses of risk in fabrication overall and in fabrication work groups. Am J Ind Med. 1995;28:735–50.View ArticlePubMedGoogle Scholar
  21. Eskenazi B, Gold EB, Lasley BL, Samuels SJ, Hammond SK, Wight S, et al. Prospective monitoring of early fetal loss and clinical spontaneous abortion among female semiconductor workers. Am J Ind Med. 1995;28:833–46.View ArticlePubMedGoogle Scholar
  22. Shusterman D, Windham GC, Fenster L. Employment in electronics manufacturing and risk of spontaneous abortion. J Occup Med. 1993;35:381–6.PubMedGoogle Scholar
  23. Elliott RC, Jones JR, McElvenny DM, Pennington MJ, Northage C, Clegg TA, et al. Spontaneous abortion in the British semiconductor industry: An HSE investigation. Health and Safety Executive. Am J Ind Med. 1999;36:557–72.View ArticlePubMedGoogle Scholar
  24. Guide to health management for semiconductor workers. Occupational Safety & Health Research Institute. http://oshri.kosha.or.kr/main (2012). Accessed 1 May 2017.
  25. Park SH, Chung EK. The study on exposure characteristics of working environment and risk factors of workers employed in the semiconductor manufacturing workplace. Park, J. S. http://oshri.kosha.or.kr (2012). Accessed 1 May 2017.
  26. Xu X, Cho SI, Sammel M, You L, Cui S, Huang Y, et al. Association of petrochemical exposure with spontaneous abortion. Occup Environ Med. 1998;55:31–6.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Duong A, Steinmaus C, McHale CM, Vaughan CP, Zhang L. Reproductive and developmental toxicity of formaldehyde: a systematic review. Mutat Res. 2011;728:118–38.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Stellman JM. Encyclopaedia of occupational health & safety. International Labour Organization http://www.iloencyclopaedia.org/part-xiii-12343/microelectronics-and-semiconductors/83/silicon-semiconductor-manufacturing (2011). Accessed 1 May 2017.
  29. Park DU. Review article : review of hazardous agent level in wafer fabrication operation focusing on exposure to chemicals and radiation. J Korean Soc Occup Environ Hyg. 2016;26:1–10.View ArticleGoogle Scholar
  30. Son BH, Jeong DD. A study on the analysis and evaluation of the innovation case at Hynix semiconductor. Korea Institute of Science Technology Evaluation and Planning. http://www.kistep.re.kr/index.jsp (2007). Accessed 1 May 2017.
  31. Shirangi A, Fritschi L, Holman CD. Maternal occupational exposures and risk of spontaneous abortion in veterinary practice. Occup Environ Med. 2008;65:719–25.View ArticlePubMedGoogle Scholar
  32. Jung SJ, Park SK, Shin A, Lee SA, Choi JY, Hong YC, et al. Body mass index at age 18-20 and later risk of spontaneous abortion in the Health Examinees Study (HEXA). BMC Pregnancy Childbirth. 2015;15:228.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Rasch V. Cigarette, alcohol, and caffeine consumption: risk factors for spontaneous abortion. Acta Obstet Gynecol Scand. 2003;82:182–8.View ArticlePubMedGoogle Scholar
  34. Zheng D, Li C, Wu T, Tang K. Factors associated with spontaneous abortion: a cross-sectional study of Chinese populations. Reprod Health. 2017;14:33.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Regan L, Braude PR, Trembath PL. Influence of past reproductive performance on risk of spontaneous abortion. BMJ. 1989;299:541–5.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Nybo AA, Wohlfahrt J, Christens P, Olsen J, Melbye M. Is maternal age an independent risk factor for fetal loss? West J Med. 2000;173:331.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Osborn JF, Cattaruzza MS, Spinelli A. Risk of spontaneous abortion in Italy, 1978-1995, and the effect of maternal age, gravidity, marital status, and education. Am J Epidemiol. 2000;151:98–105.View ArticlePubMedGoogle Scholar
  38. Lee KM, Chun JB, Park DG, Lee WJ. Methods to minimize or adjust for healthy worker effect in occupational epidemiology. J Environ Health Sci. 2011;37:342–7.Google Scholar

Copyright

© The Author(s). 2017

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