Journal of Reproductive Medicine Gynaecology & Obstetrics Category: Medical Type: Research Article

Maternal Exposure to Emission of Using New Furniture during Pregnancy and Preterm Birth

Jia Sun#1, Rong Yang#2, Hong Xian1, Bin Zhang2, Guang-Hui Dong3, Pam Xaverius1, Hualiang Lin4, Morgan LeBaige1, Tongzhang Zheng5, Zhengmin Qian1* and Aifen Zhou2*
1 Department Of Epidemiology And Biostatistics, College For Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO 63104, United States
2 Department Of Maternal Health Care, Wuhan Children’s Hospital (Wuhan Maternal And Child Healthcare Hospital), Tongji Medical College, Huazhong University Of Science & Technology, 100 Xianggang Road, Wuhan 430030, China
3 Department Of Environmental And Occupational Health, School Of Public Health, Sun Yat-sen University, Guangzhou, China
4 Department Of Epidemiology, School Of Public Health, Sun Yat-sen University, Guangzhou, China
5 Department Of Epidemiology, Brown University School Of Public Health, United States

*Corresponding Author(s):
Zhengmin Qian
Department Of Epidemiology And Biostatistics, College For Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO 63104, United States
Tel:+1 3149778158,
Fax:+1 3149773234
Email:zhengmin.qian@slu.edu
Aifen Zhou
Department Of Maternal Health Care, Wuhan Children’s Hospital (Wuhan Maternal And Child Healthcare Hospital), Tongji Medical College, Huazhong University Of Science & Technology, 100 Xianggang Road, Wuhan 430030, China
Tel:+86 02782433137,
Fax:+86 02782423687
Email:april1972@163.com
# Equally Contributed

Received Date: May 09, 2021
Accepted Date: May 18, 2021
Published Date: May 25, 2021

Abstract

Objectives: To evaluate the association between maternal exposure to emission of using new furniture during Pregnancy and Preterm Birth (PTB). To further assess potential modification effects on this association from maternal prenatal care utilization, working on irregular shifts periconceptionally and multivitamin intake during pregnancy. 

Methods: We used data from a nested case-control study in Wuhan, China, from June 10, 2011 to June 9, 2013. This case-control study was based on a prospective cohort study, which followed all pregnant women in the seven urban districts of Wuhan. Data collection for each birth was completed within 42 days after delivery, through questionnaire survey during a home-visit interview. Binary logistic regression models were built to evaluate the association, adjusting for potential confounders. Potential effect modifiers were assessed by introducing interaction terms in the model and by using stratified analyses. 

Results: 2,393 PTBs and 4,263 controls (full-term births with normal birth weight) were included in the final sample. We observed a significant association between maternal exposure to indoor pollution from new furniture during pregnancy and increased PTB risk, while adjusting for potential confounders (adjusted OR: 1.22, 95% CI: [1.09, 1.35]). Stronger associations were observed when women did not obtain adequate prenatal care, when women had irregular work shifts periconceptionally, and/or when women did not take a multivitamin during pregnancy. However, among all three examined modification effects, only the one from maternal prenatal care utilization was statistically significant. 

Conclusion: Maternal exposure to emission of using new furniture during pregnancy was significantly associated with increased PTB risk in Wuhan, China, from 2011 to 2013. Adequate prenatal care utilization mitigated this association toward the null.

Keywords

Effect modifier; Environmental pollution; Furniture; Prenatal care; Preterm birth; Wuhan

Introduction

The new furniture emits a large amount of toxins, including Organophosphate Esters (OPEs) (flame retardants), Bisphenol A (BPA), Polycyclic Aromatic Hydrocarbons (PAHs), Volatile (VOCs) and Semi Volatile Organ Compounds (SVOCs), and other endocrine disrupting chemicals [1,2]. These toxins may cause adverse birth outcomes, including PTB [1-14]. To date, limited studies have examined the effects of using new furniture on PTB [2-4,11-17]. Furthermore, potential effect modifiers of the association between maternal exposure to indoor pollution from new furniture and PTB were not identified previously in the literature. For example, maternal prenatal care utilization, irregular shift work, and multivitamin intake during pregnancy, have been related to PTB risk [18-20]. They would have important public health implications on preventing PTB if they modified the association between maternal exposure to indoor pollution from new furniture and PTB, because they are modifiable through implementation of new policies or regulations. China has gone through a tremendous economic growth, accompanied with fast urbanization progress [2]. As a result, an increasing number of people have moved to new homes, and numerous pieces of new furniture have been purchased [2]. In Chinese culture, using new furniture represents a good start of the family. New couples and expected parents are more likely to purchase new furniture when they establish their new home [2-4,15-17]. 

Therefore, our study was to investigate the association between maternal exposure to indoor pollution, defined as using new furniture during pregnancy and PTB. We further evaluate the modification effect from maternal prenatal care utilization, irregular work shift, and multivitamin intake during pregnancy. We used the data from a prospective cohort nested case-control study in the city of Wuhan, China, because in their cohort study stage, they followed all pregnant women in the seven districts to minimize selection bias [21]. In addition, we would also minimize impact of residual confounders relating to different prenatal care systems, because Wuhan is unique with its single prenatal care system, thereby increasing our statistical power [21]. Our main hypothesis is that maternal exposure to indoor pollution, defined as using new furniture during pregnancy, was significantly associated with increased PTB risk in Wuhan, China from 2011 to 2013. We further hypothesize that maternal prenatal care utilization, periconceptional irregular work shift, and multivitamin intake during pregnancy modified this association significantly.

Methods

Study location and population 

Wuhan has a single prenatal care system, which is managed and regulated by the Wuhan Medical and Health Center for Women and Children (WMHCWC) [21]. This center is also responsible for electronically collecting and storing all information relating to maternal pregnancy and birth outcomes for all women in the city [21]. During our study period, under the former regulation of “One-Child” policy, the birth rate in Wuhan was approximately 13.7% and most of the deliveries were from nulliparous women [21,22].

Nested case-control study 

Exclusion criteria of our birth cohort were women that were non-permanent residents, not living in the core-districts, without clear home addresses, children born with a birth defect, extreme birth weight (< 500g or > 5000g), extreme gestational ages (< 20 weeks or >46 weeks), and non-singleton births, 95,911 births were left from the original cohort [21]. Data collection was initiated within two weeks of delivery, and all PTBs within the original cohort were included [21]. Controls were selected to match PTBs’ birth date and birth location during sampling [21]. A home-visit interview was conducted for each PTB and its matched control soon after sampling and a comprehensive questionnaire survey was asked to be completed within 42 days after delivery [21]. This data collection was administered by trained nurses and medical staff from WMHCWC [21]. The study protocol and use of data were approved by the Institutional Review Board of Saint Louis University, MO, USA.

Preterm birth data 

We defined our cases as PTB that was a live birth before 37 complete weeks of gestation, according to the World Health Organization [23]. Control was defined as full-term birth (> 37 complete weeks gestation) with normal birth weight (> 2500 g). In the original case-control study, 63.8% of the total sampled births completed the questionnaire [21]. Among them, we identified all PTBs as our cases and included all their controls as our controls.

Exposure and covariates 

Exposure and covariates were measured by information provided in the questionnaire survey. Indoor pollution defined as using new furniture during pregnancy was assessed by asking this question in the questionnaire: “Did you buy or make new furniture when you moved into your most recent residence (Yes/No)?” Answer “yes” to the first question will be considered as maternal exposure to indoor pollution from using new furniture and will be considered as our exposure of interest in this study. Covariates were selected and constructed based on our literature review. Age is defined as maternal age at delivery (< 21, 21-30, or >30) [24]. Pre-pregnancy BMI is defined as maternal BMI measured on the closest date before their current pregnancy [25,26]. Pre-pregnancy BMI was categorized based on the international standard (underweight / normal weight / overweight / obese) [27,28]. Education is defined as the highest maternal educational achievement at the time of delivery (primary school or lower / high school / college or higher / others) [21]. Parity is defined as the number of deliveries that had an infant capable of survival [21]. Living district is defined as maternal living district during their current pregnancy [21]. Self-rated health status is measured by asking this question in the questionnaire: “Would you say that, in general, your health is: poor/fair/good/very good?” Answer “poor” and “fair” was considered as “not good” of self-rated health status, due to low prevalence of the answer of “not good” (< 1%); answer “good” and “very good” was considered as “good” and “very good” of self-rated health status, respectively. Physical activity is defined as duration of maternal daily outdoor sports during pregnancy, in minutes, which was further categorized based on the international standard of recommended duration of physical activity for pregnant women (< 20 minutes/day, 20 -30 minutes/day, or >30 minutes/day; recommendation: 20-30 minutes/day) [29-31]. Prenatal care utilization is defined as maternal capability to initiate prenatal care at the earliest point of pregnancy and to attend as many as possible of the recommended prenatal care visits, which is measured by the Chinese Msinistry of Health (inadequate, intermediate, adequate, or more than adequate) [18]. Periconceptional work shift was measured by asking new mothers their work shifts before and/or during their latest pregnancy and irregular work shift is defined as women who worked on a schedule before and/or during pregnancy that is not a regular day shift [19]. Multivitamin intake during pregnancy is measured by asking new mothers whether they took multivitamins during their latest pregnancy (yes / no).

Statistical analysis 

Distributions of maternal characteristics among cases and controls, including demographics, socioeconomic status, use of new furniture during pregnancy, and main covariates, were described and analyzed. We used Pearson’s Chi-square test and Student’s t-test to analyze categorical variables and continuous variables, respectively [32,33]. 

We used binary logistic regression analyses to evaluate the association between maternal exposure to indoor pollution, defined as using new furniture during pregnancy and PTB risk. In particular, we used univariate regression models to calculate crude Odds Ratios (OR) and 95% Confidence Intervals (95% CI), and multivariate regression models to calculate adjusted ORs and their 95% CIs. Covariates were selected if: (1) they were risk factors of PTB based on our literature review and (2) they could change the point estimate by more than 5% [21,34]. 

We used multivariate regression models to assess our three identified effect modifiers, maternal prenatal care utilization, irregular shift work and multivitamin intake during pregnancy [21]. For each effect modifier, we built one multivariate regression model with all identified potential confounders, this specified effect modifier, and the interaction term of maternal exposure to indoor pollution from new furniture during pregnancy and the specified effect modifier [21]. P-value of the interaction term will be used to indicate the significance of potential modification effect [21,35]. The association between maternal exposure to indoor pollution from new furniture and PTB was further assessed in each subgroup of our study population, which was generated by stratifying our study population according to their exposure status for each one of the three effect modifiers [36].

Sensitivity analysis 

In our data, some participants reported their education levels as others. In our sensitivity analysis, we excluded these participants and thus generated a subgroup of our study population. We tested our final model in this subgroup to assess the robustness of our study results against the decision we made for covariate construction [21,36]. In addition, 8.4% of our study population moved to a new address during their pregnancy [21]. We narrowed our study population to exclude these women who moved during pregnancy. We further tested our final model in this subgroup to assess the robustness of our study results against changing of residual confounders in relation to the woman’s living environment [21]. Two tailed p < 0.05 was set as the significance level for all our analyses. SAS University Edition (SAS Institute Inc. 2018) was used to conduct all our statistical analyses.

Results

Descriptive results 

Our final sample comprised 6,656 births and their general characteristics are shown in table 1. From the 6,656 births, 2,393 were PTBs (cases) and 4,263 were full term births (controls). 45.6% of our pregnant women used new furniture during their pregnancy and most of them were in the PTB group than in the control group. Most of our pregnant women were young, normal weighted, healthy and nulliparous. A higher proportion of women who delivered PTBs were older than 30 years old, as compared to those who delivered full term birth with normal birth weight (controls). The average pre-pregnancy BMI of the PTB group was higher than it was in the control group, with more overweight women in the PTB group than the control group. More than half of our pregnant women had an education level of college or higher, and most of the other half had an education level of high school. In our study population, a higher proportion of women who delivered PTB reported poor health status during their pregnancy, as compared to those who delivered full term birth with normal birth weight. Women in the control group had more time to do exercise during their pregnancies than women in the PTB group. A higher proportion of women in our control group held a regular-shift work job and/or attended adequate prenatal care during their pregnancy than those in our PTB group. A slightly higher proportion of women in our PTB group took multivitamins during their pregnancy than those in our control group. 

Characteristics

Total births

No. (%)a

PTBs

No. (%)a

Controls

No. (%)a

p-value

6656 (100.0)

2393 (36.0)

4263 (64.1)

Living district

  Jiangan

1256 (18.9)

496 (20.7)

760 (17.8)

< 0.05

  Jianghan

821 (12.3)

294 (12.3)

527 (12.4)

 

  Qiaokou

933 (14.0)

345 (14.4)

588 (13.8)

 

  Hanyang

997 (15.0)

363 (15.2)

634 (14.9)

 

  Wuchang

1172 (17.6)

395 (16.5)

777 (18.2)

 

  Hongshan

967 (14.5)

327 (13.7)

640 (15.0)

 

  Qingshan

510 (7.7)

173 (7.2)

337 (7.9)

 

Age, years

 Mean (SD)

27.46 (3.64)

27.83 (3.95)

27.24 (3.44)

< 0.05

< 21

118 (1.8)

43 (1.8)

75 (1.8)

< 0.05

  21-30

5408 (81.3)

1842 (77.0)

3566 (83.7)

 

> 30

1130 (17.0)

508 (21.2)

622 (14.6)

 

Pre-pregnancy BMIb

  Mean (SD)

20.64 (2.62)

20.74 (2.69)

20.58 (2.57)

< 0.05

  Underweight (<18.5)

1325 (19.9)

456 (19.1)

869 (20.4)

> 0.05

  Normal weight (18.5-24.9)

4892 (73.5)

1763 (73.7)

3129 (73.4)

 

  Overweight (25-29.9)

405 (6.1)

161 (6.7)

244 (5.7)

 

  Obese (>30)

34 (0.5)

13 (0.5)

21 (0.5)

 

  Education

  Primary school or lower

131 (2.0)

51 (2.1)

80 (1.9)

> 0.05

  High school

3038 (45.6)

1045 (43.7)

1993 (46.8)

 

  College or higher

3460 (52.0)

1284 (53.7)

2176 (51.0)

 

  Others

27 (0.4)

13 (0.5)

14 (0.3)

 

Parity

  0

5537 (83.2)

1956 (81.7)

3581 (84.0)

< 0.05

> 1

1119 (16.8)

437 (18.3)

682 (16.0)

 

Self-rated health status

  Not good

1364 (20.5)

625 (26.1)

739 (15.1)

< 0.05

  Good

2903 (43.6)

1033 (43.2)

1870 (43.9)

 

  Very good

2389 (35.9)

735 (30.7)

1654 (38.8)

 

Physical activityc

Mean (SD), minutes/day

43.39 (24.30)

42.13 (23.64)

44.02 (24.61)

< 0.05

< 20 minutes/day

2033 (30.5)

860 (35.9)

1173 (27.5)

< 0.05

  20 - 30 minutes/day

2612 (39.2)

897 (37.5)

1715 (40.2)

 

> 30 minutes/day

2011 (30.2)

636 (26.6)

1375 (32.3)

 

Prenatal care utilizationd

  Inadequate care

1207 (18.1)

483 (20.2)

724 (17.0)

< 0.05

  Intermediate care

4192 (63.0)

1575 (65.8)

2617 (61.4)

 

  Adequate care

923 (13.9)

256 (10.7)

667 (15.7)

 

  Adequate plus care

334 (5.0)

79 (3.3)

255 (6.0)

 

Periconceptional work shifts

  Regular day shift

2773 (41.7)

968 (40.5)

1805 (42.3)

> 0.05

  Irregular work shift

3255 (48.9)

1212 (50.7)

2043 (47.9)

 

  Not working

628 (9.4)

213 (8.9)

415 (9.7)

 

Multivitamin intake during pregnancy

  Yes

3524 (52.9)

1307 (54.6)

2217 (52.0)

< 0.05

  No

3132 (47.1)

1086 (45.4)

2046 (48.0)

 

Indoor pollution (Using new furniture during pregnancy)

  Yes

3032 (45.6)

1138 (47.6)

1894 (44.4)

< 0.05

  No

3624 (54.5)

1255 (52.4)

2369 (55.6)

 

Table 1: Maternal characteristics and distribution of exposure by cases and controls, Wuhan, China, from June 10, 2011, to June 9, 2013.

a. Numbers may not add to 100% because of rounding

b. BMI was calculated and categorized according to International standards [27,28].

c. Maternal daily physical activity duration of 20 - 30 minutes is recommended by the international standard [29,31].

d. Maternal prenatal care utilization was measured by the Chinese Ministry of Health [18]. 

In our seven core-districts of Wuhan, the proportion of pregnant women who used new furniture during their pregnancy was slightly higher in Hanyang, Wuhchang, Hongshan and Qingshan districts (Table 2). Our pregnant women who used new furniture during pregnancy were older than those who did not. In particular, a higher proportion of women who used new furniture were older than 30 years, as compared to those who did not use. Pre-pregnancy BMI was evenly distributed between women who used new furniture and who did not use new furniture during their pregnancy. In our study population, a higher proportion of women who used new furniture had an education background of college or higher, reported being nulliparous, and in very good health status during their pregnancy, as compared to those who did not purchase new furniture during pregnancy. 

Characteristics

Using new furniture during pregnancy

Yes

No. (%)a

No

No. (%)a

p-value

3032 (45.6)

3624 (54.5)

Living district

  Jiangan

495 (16.3)

761 (21.0)

< 0.05

  Jianghan

345 (11.4)

476 (13.1)

 

  Qiaokou

340 (11.2)

593 (16.4)

 

  Hanyang

521 (17.2)

476 (13.1)

 

  Wuchang

552 (18.2)

620 (17.1)

 

  Hongshan

498 (16.4)

469 (12.9)

 

  Qingshan

281 (9.3)

229 (6.3)

 

Age, years

 Mean (SD)

27.82 (3.50)

27.15 (3.73)

< 0.05

< 21

39 (1.3)

79 (2.2)

< 0.05

  21-30

2436 (80.3)

2972 (82.0)

 

> 30

557 (18.4)

573 (15.8)

 

Pre-pregnancy BMIb

  Mean (SD)

20.62 (2.57)

20.65 (2.65)

> 0.05

  Underweight (<18.5)

591 (19.5)

734 (20.3)

> 0.05

  Normal weight (18.5-24.9)

2259 (74.5)

2633 (72.7)

 

  Overweight (25-29.9)

168 (5.5)

237 (6.5)

 

  Obese (>30)

14 (0.5)

20 (0.6)

 

Education

  Primary school or lower

28 (0.9)

103 (2.8)

< 0.05

  High school

1184 (39.1)

1854 (51.2)

 

  College or higher

1807 (59.6)

1653 (45.6)

 

  Others

13 (0.4)

14 (0.4)

 

Parity

  0

2676 (88.3)

2861 (79.0)

< 0.05

> 1

356 (11.7)

763 (21.1)

 

Self-rated health status

  Not good

590 (19.5)

774 (21.4)

< 0.05

  Good

1270 (41.9)

1633 (45.1)

 

  Very good

1172 (38.7)

1217 (33.6)

 

Physical activityc

  Mean (SD), minutes/day

43.38 (23.61)

43.40 (24.90)

> 0.05

< 20 minutes/day

858 (28.3)

1175 (32.4)

< 0.05

  20 - 30 minutes/day

1195 (39.4)

1417 (39.1)

 

> 30 minutes/day

979 (32.3)

1032 (28.5)

 

Prenatal care utilizationd

  Inadequate care

472 (15.6)

735 (20.2)

< 0.05

  Intermediate care

1883 (62.1)

2309 (63.7)

 

  Adequate care

490 (16.2)

433 (12.0)

 

  Adequate plus care

187 (6.2)

147 (4.1)

 

Periconceptional work shifts

  Regular day shift

1338 (44.1)

1435 (39.6)

< 0.05

  Irregular work shift

1380 (45.5)

1875 (51.7)

 

  Not working

314 (10.4)

314 (9.7)

 

Multivitamin intake during pregnancy

  Yes

1756 (57.9)

1768 (48.8)

< 0.05

  No

1276 (42.1)

1856 (51.2)

 

Table 2: Maternal characteristics by exposure to indoor pollution defined as using new furniture, Wuhan, China, from June 10, 2011, to June 9, 2013.

a. Numbers may not add to 100% because of rounding.

b. BMI categorized according to International standards [27,28].

c. Maternal daily physical activity duration of 20 - 30 minutes is recommended by the international standard [29-31].

d. Maternal prenatal care utilization was measured by the Chinese Ministry of Health [18].

Model analyses 

Our results showed the significant association between maternal exposure to indoor pollution, defined as using new furniture during pregnancy and increased risk of PTB (crude OR=1.13, 95% CI: [1.03, 1.25]) (Model 1, Table 3). The effect magnitude of this positive association was increased while adjusting for potential confounders. In particular, the effect magnitude was increased by 1% while adjusting for potential confounders identified previously in the literature, including maternal age, pre-pregnancy BMI, education level and parity (adjusted OR: 1.14, 95% CI: [1.03, 1.26]) (Model 2, Table 3). The largest increase in effect magnitude (8%) was observed after adjusting for additional covariates identified in our study, including maternal living district, self-rated health status, physical activity duration and prenatal care utilization during pregnancy. Finally, 22% increase in the odds of PTB was attributable to maternal exposure to indoor pollution, defined as using new furniture during pregnancy(adjusted OR: 1.22, 95% CI: [1.09, 1.35]) (Model 3, Table 3). This effect estimation did not change significantly in our sensitivity analyses. After excluding women with unclear education background, this association remained significantly positive (adjusted OR: 1.21, 95% CI: [1.09, 1.35]) (Model 4, Table 3). After excluding women who moved during pregnancy, but this association remained significantly positive (adjusted OR: 1.19, 95% CI: [1.07, 1.33]) (Model 5, Table 3). 

Modela

Odds ratio

95% confidence interval

Model 1

1.13

1.03, 1.25

Model 2

1.14

1.03, 1.26

Model 3

1.22

1.09, 1.35

Model 4 (sensitivity analysis)

1.21

1.09, 1.35

Model 5 (sensitivity analysis)

1.19

1.07, 1.33

Table 3: Association between maternal exposure to indoor pollution, defined as using new furniture during pregnancy and PTB, in Wuhan, China, from June 10, 2011, to June 9, 2013. 

a. Model 1 is the crude model from the univariate analysis; Model 2 was adjusted for covariates from previous literature, including maternal age, pre-pregnancy BMI, education level, and parity; Model 3 was adjusted for all covariates from model 2 and also adjusted for covariates from our confounder analysis, including maternal living district, self-rated health status, physical activity duration, and prenatal care utilization, during pregnancy; Model 4 was the Model 3 tested among our study population after excluded mothers with unclear educational background; Model 5 was the Model 3 tested among our study population after excluded mothers who moved during pregnancy.

Modification effects 

A significant interaction existed between maternal prenatal care utilization and maternal exposure to indoor pollution, defined as using new furniture during pregnancy, on their associations with PTB (p < 0.05) (Table 4). Adequate prenatal care attenuated the association between maternal exposure to indoor pollution from new furniture and increased PTB risk. In particular, for women who did not have adequate prenatal care, the adverse effect of indoor pollution from using new furniture on their PTB risk was significant (adjusted OR: 1.19, 95% CI [1.04, 1.36]) (Table 4). Whereas, for women who had adequate prenatal care, this adverse effect of using new furniture during pregnancy on PTB was attenuated and no longer significant (adjusted OR: 1.06, 95% CI [0.78, 1.45]) (Table 4). 

 

Adjusted

Subgroup

 

OR

95% CI

p-value

Prenatal care utilizationa,b

Inadequate

1.19

(1.04, 1.36)

< 0.05e

Adequate

1.06

(0.78, 1.45)

 

Maternal work shiftc

Irregular

1.24

(1.03, 1.48)

> 0.1f

Regular

1.18

(0.97, 1.43)

 

Multivitamin intake during pregnancyd

Yes

1.13

(0.98, 1.31)

> 0.1g

No

1.26

(1.08, 1.47)

 

Table 4: The association between maternal exposure to indoor pollution defined as using new furniture during pregnancy and PTB, stratified by potential effect modifiers, in Wuhan, China, from June 10, 2011, to June 9, 2013.

OR: odds ratio; 95% CI: 95% confidence interval.

a. Maternal prenatal care utilization was measured by the Chinese Ministry of Health [18].

b. Adjusted for maternal age, pre-pregnancy BMI, education level, parity, living district, self-rated health status, physical activity duration, and prenatal care utilization, during pregnancy.

c. Adjusted for maternal age, pre-pregnancy BMI, education level, parity, self-rated health status, prenatal care utilization, home decoration, and periconceptional work shift, during pregnancy.

d. Adjusted for maternal age, pre-pregnancy BMI, education level, parity, self-rated health status, physical activity duration, prenatal care utilization, and multivitamin intake, during pregnancy.

e. P value for the interaction term for the two groups of maternal prenatal care utilization (inadequate/adequate).

f. P value for the interaction term for the two groups of maternal periconceptional work shift (irregular work shift/regular work shift).

g. P value for the interaction term for the two groups of maternal multivitamin intake (multivitamin intake during pregnancy/no multivitamin intake during pregnancy).

We also observed an interaction between maternal work shift and maternal exposure to indoor pollution from using new furniture during pregnancy (Table 4), on their associations with PTB risk. In particular, for women who had an irregular-shift work periconceptionally, their exposure to indoor pollution from using new furniture during pregnancy was significantly associated with increased PTB risk (adjusted OR: 1.24, 95% CI [1.03, 1.48]). This adverse effect of using new furniture during pregnancy on PTB risk was attenuated and became non-significant for women who had a regular-shift job periconceptionally (adjusted OR: 1.18, 95% CI [0.97, 1.43]). However, this interaction is not significant in our study (p > 0.1) (Table 4). 

Another interaction was observed between maternal multivitamin intake and maternal exposure to indoor pollution from using new furniture during pregnancy on their associations with PTB risk (Table 4). For women who did not take multivitamins during their pregnancy, the adverse effect of indoor pollution from using new furniture on their PTB risk was significant (adjusted OR: 1.26, 95% CI [1.08, 1.47]). For women who took multivitamins during pregnancy, this adverse effect of indoor pollution from using new furniture on PTB was attenuated and no longer significant (adjusted OR: 1.13, 95% CI [0.98, 1.31]). However, this interaction was not significant in our study (p > 0.1) (Table 4).

Discussion

In this population-based prospective cohort nested case-control study, we found that maternal exposure to indoor pollution from using new furniture during pregnancy was significantly associated with increased PTB risk, thus confirming our main hypothesis. We further identified one effect modifier for this association, maternal prenatal care utilization during pregnancy. In particular, adequate prenatal care utilization reduced the adverse effect of indoor pollution from using new furniture during pregnancy on PTB risk, thus confirming one of our sub-hypotheses. 

The potential mechanism by which exposure to new furniture during pregnancy contributes to PTB could be that new furniture inside a home emits a significantly higher amount of chemicals than older furniture, such as PAHs and VOCs [1-4,15-17]. Perinatal use of new furniture has been linked to inflammatory medical conditions in both parents and their offspring, such as allergic diseases/symptoms, asthma and pneumonia [2-4,15-17]. It is plausible to hypothesize that new furniture may activate maternal inflammatory responses, leading to uterine activation and then PTB [2-4,15-17]. For example, PAHs and VOCs are two major pollutants from both soil vapor intrusion and new furniture emissions [1,32,33]. While no direct evidence can be obtained on the associations between exposures to these two pollutants due to using new furniture, observational studies of soil vapor intrusion did report their associations with adverse birth outcomes, including PTB [1,37,38]. Similarly, both PAHs and VOCs also exist in the emissions from household renovation [1,39]. Two studies from China reported a significant association between exposure to household renovation and increased PTB risk [1,39]. During our model construction, we controlled for maternal exposure to home decoration, which did not increase our effect estimation by more than 1%, so we did not include it as a potential confounder in our final model. This non-significant change of our effect estimation by controlling for maternal exposure to home decoration might indicate that some pollutants emitted from new furniture were different from those emitted from decoration materials, but they still have adverse effects on PTB risk. 

Evidence from observational studies of this adverse effect of maternal exposure to new furniture on PTB has been lacking in the field. We were able to identify only one related study in China, which was a retrospective cohort study in Changsha, China in 2019 [3]. However, they reported a non-significant association between maternal exposure to indoor pollution from new furniture and PTB (adjusted OR: 1.15, 95% CI [0.58, 2.26]) [3]. This inconsistency might be caused by residual confounders that both studies were not be able to control for, such as furniture sources [3]. Despite of this limitation of both studies, our study is in a better position to evaluate this association between maternal exposure to indoor pollution from new furniture and PTB risk, because the previous study was retrospective in nature, with data collection started a couple of years after delivery, so their study participants may not be able to recall whether they purchased new furniture when they were pregnant a couple of years earlier, leaving their study results prone to recall bias [3]. Our study also has a much larger sample size, which is 6,656 births, with 46% of our pregnant women having been exposed to new furniture during pregnancy, while the earlier study’s sample was 3,509 preschool children, with 12% of the participants’ mothers having been exposed to new furniture during pregnancy [3], which might be the reason that they had a much larger 95% CI for effect estimations than ours. In addition, their study might be subject to selection bias, because they only recruited participants from 36 kindergartens in the city [3]. The previous study may be lacking data for children who moved out of the city after birth and children who did not go to those 36 kindergartens. Our study recruited all pregnancies in the city, minimizing this selection bias [21]. 

Our study has several limitations. The measurement of our covariates was based on self-report data, so might be subjected to self-reporting bias. We did not have information on the duration, frequency, and strength of maternal exposure to indoor pollution from new furniture during pregnancy. However, during our model construction, we controlled for home ventilation, number of windows and doors of the living space, and area per family member of the living space, and they changed our effect estimation less than 5%. This indicates that these differences on the exposure duration, frequency, and strength of maternal exposure to indoor pollution from new furniture among our study population may not be significant enough to change our effect estimation, so we did not include them in our final model. Our study may be subject to misclassification bias, because we do not have data on the exact toxic chemicals emitted from new furniture and we did not know if the used furniture that our pregnant women had during their pregnancy also emitted those chemicals. This could lead to overestimation of our effect estimation if our pregnant women who used new furniture were also exposed to the same toxic chemicals from their used furniture, and lead to underestimation if our pregnant women who bought their new furniture shortly before becoming pregnant and did not report themselves as using new furniture during current pregnancy. We also did not have data on maternal exposure to new furniture during pregnancy at an environment outside their home, such as the work place, which could lead to underestimation of our effect estimation. For the modification effect of maternal periconceptional shift work and the modification effect of postconceptional multivitamin intake, we could not control for the variety of irregular work shifts and/or different doses and types of multivitamins taken by pregnant women. Lacking such data may be the reason that these two modification effects were not significant in our study. In addition, there might be residual confounders that we were unable to control for [40,41]. For example, we lacked information on maternal dampness-related exposures and ambient air pollution exposures during pregnancy, which were reported as potential confounders of the association between maternal household renovation and PTB [1,3,21,39,42]. Therefore, we suggest further study to include information on these potential confounders. 

Our study also offers important strengths. We collected data on other indoor pollutants, such as cooking fuel, heating fuel, second-hand smoking, home decoration, contact with pets, contact with deformity toxin, proximity to pollution sites, proximity to traffic on a main road, exposure to certain noises, and time spent outdoors during pregnancy. All have been considered previously in the literature as potential confounders of the association between maternal exposure to indoor pollution of new furniture and/or home decoration and PTB risk [1,3,39] (Supplementary table S1). However, in our data analysis, we did not observe significant changes in our effect estimation by controlling for these covariates (< 5%), so we did not include them in our final model. Similarly, we also examined the potential confounding effect from maternal chronic health conditions before and/or during pregnancy [1,3,39], and we did not observe a change of effect estimation by more than 5%. We also identified potential confounders that other studies did not control for, such as maternal self-rated health condition, prenatal care utilization and physical activity duration during pregnancy. These potential confounders could have indicated that maternal self-perception of health and behaviors during pregnancy might influence the association between maternal exposure to indoor pollution from new furniture and PTB risk, though no significant relationship was found in our study. 

In light of our finding on the association between maternal exposure to indoor pollution from new furniture and increased PTB risk, we suggest that manufacturers of furniture avoid using chemicals that pose reproductive health hazards and inform their customers and advertisers of the chemical compositions of their furniture. We also suggest the government improves regulations on availability and utilization of hazardous chemicals on furniture and publicize the information of potential health hazards of chemicals found in furniture. Our finding of the modification effect of maternal prenatal care utilization on the association between maternal exposure to indoor pollution from new furniture and increased PTB risk sheds light on the potential mitigating tool for pregnant women to protect themselves from possible PTB risk attributed to exposure to new furniture. We thus recommend pregnant women attend adequate prenatal care, meaning that they should initiate their first prenatal care as early as possible and attend all recommended prenatal care visits on time [18,43].

Conclusion

We found a significant association between maternal exposure to indoor pollution, defined as using new furniture, during pregnancy and increased PTB risk, after adjusting for potential confounders. Adequate maternal prenatal care utilization mitigated this association significantly in our study site, Wuhan, China. Maternal periconceptional irregular shift work and no use of multivitamins during pregnancy strengthened this association, but these two modification effects were not statistically significant in our study population.

Acknowledgment

Our study was supported by the Health Effects Institute under the agreement of our contract (HEI Agreement: #4791-RFA09-2/10–5). We also appreciated the support from Saint Louis University, Wuhan Medical and Health Center for Women and Children, and Wuhan Environmental Monitoring Center, on the implementation of our study protocol and data collection process.

Author Commitments

Jia Sun contributes to data analysis and manuscript writing. Rong Yang contributes to protocol development and manuscript editing. Jia Sun and Rong Yang contributed equally to this work. Hong Xian contributes to methodology and manuscript editing. Bin Zhang contributes to methodology and data collection. Guang-Hui Dong contributes to manuscript editing. Pamela K. Xaverius contributes to conceptualization and methodology. Hualiang Lin contributes to methodology. Morgan LeBaige contributes to manuscript editing. Tongzhang Zheng contributes to data collection and management. Zhengmin Qian contributes to protocol development, data management, conceptualization, methodology, validation, investigation, manuscript editing and funding acquisition. Aifen Zhou contributes to methodology, validation, conceptualization, funding acquisition and manuscript editing. Zheng Min Qian and Ai Fen Zhou contributed equally as corresponding authors. All emails and questions should be directed to Professor Aifen Zhou at april1972@163.com.

Competing Interest Declaration

Authors of this work declare that they have no conflict of interest.

References

  1. Liu W, Huang C, Li B, Zhao Z, Yang X, et al. (2019) Household renovation before and during pregnancy in relation to preterm birth and low birthweight in China. Indoor Air 29: 202-214.
  2. Deng Q, Lu C, Ou C, Chen L, Yuan H (2016) Preconceptional, prenatal and postnatal exposure to outdoor and indoor environmental factors on allergic diseases/symptoms in preschool children. Chemosphere 152: 459-467.
  3. Lu C, Cao L, Norbäck D, Li Y, Chen J, et al. (2019) Combined effects of traffic air pollution and home environmental factors on preterm birth in China. Ecotoxicol Environ Saf 184: 109639.
  4. Norbäck D, Lu C, Zhang Y, Li B, Zhao Z, et al. (2018) Lifetime-ever pneumonia among pre-school children across China - Associations with pre-natal and post-natal early life environmental factors. Environ Res 167: 418-427.
  5. Encarnação T, Pais AA, Campos MG, Burrows HD (2019) Endocrine disrupting chemicals: Impact on human health, wildlife and the environment. Sci Prog 102: 3-42.
  6. Keimowitz AR, Strunsky N, Wovkulich K (2016) Organophosphate flame retardants in household dust before and after introduction of new furniture. Chemosphere 148: 467-472.
  7. McKenna ST, Birtles R, Dickens K, Walker RG, Spearpoint MJ, et al. (2018) Flame retardants in UK furniture increase smoke toxicity more than they reduce fire growth rate. Chemosphere 196: 429-439.
  8. Muscogiuri G, Colao A (2017) Phtalates: New cardiovascular health disruptors? Arch Toxicol 91: 1513-1517.
  9. Birks L, Casas M, Garcia AM, Alexander J, Barros H, et al. (2016) Occupational Exposure to Endocrine-Disrupting Chemicals and Birth Weight and Length of Gestation: A European Meta-Analysis. Environ Health Perspect 124: 1785-1793.
  10. Hoffman K, Stapleton HM, Lorenzo A, Butt CM, Adair L, et al. (2018) Prenatal exposure to organophosphates and associations with birthweight and gestational length. Environ Int 116: 248-254.
  11. Bao S, Xia W, Xu S, Li Y, Lu B, et al. (2020) Multiple metal exposure and platelet counts during pregnancy: A repeated measure study. Environ Int 136: 105491.
  12. Hu J, Zhao H, Braun JM, Zheng T, Zhang B, et al. (2019) Associations of Trimester-Specific Exposure to Bisphenols with Size at Birth: A Chinese Prenatal Cohort Study. Environ Health Perspect 127: 107001.
  13. Qiu L, Xu S, Zhou A, Liu W, Liao J, et al. (2020) Association between changes in gestational blood pressure and vanadium exposure in China. Environ Toxicol Pharmacol 79: 103424.
  14. Huang S, Xia W, Li Y, Zhang B, Zhou A, et al. (2017) Association between maternal urinary chromium and premature rupture of membranes in the Healthy Baby Cohort study in China. Environ Pollut 230: 53-60.
  15. Deng Q, Lu C, Jiang W, Zhao J, Deng L, et al. (2017) Association of outdoor air pollution and indoor renovation with early childhood ear infection in China. Chemosphere 169: 288-296.
  16. Zhang J, Sun C, Liu W, Zou Z, Zhang Y, et al. (2018) Associations of household renovation materials and periods with childhood asthma, in China: A retrospective cohort study. Environ Int 113: 240-248.
  17. Norbäck D, Zhang X, Fan Q, Zhang Z, Zhang Y, et al. (2019) Home environment and health: Domestic risk factors for rhinitis, throat symptoms and non-respiratory symptoms among adults across China. Sci Total Environ 681: 320-330.
  18. Zhang B, Yang R, Liang S-W, Wang J, Chang JJ, et al. (2017) Association between prenatal care utilization and risk of preterm birth among Chinese women. J Huazhong Univ Sci Technolog Med Sci 37: 605-611.
  19. van Melick MJ, van Beukering MD, Mol BW, Frings-Dresen MH, Hulshof CT (2014) Shift work, long working hours and preterm birth: a systematic review and meta-analysis. Int Arch Occup Environ Health 87: 835-849.
  20. Wolf HT, Hegaard HK, Huusom LD, Pinborg AB (2017) Multivitamin use and adverse birth outcomes in high-income countries: A systematic review and meta-analysis. Am J Obstet Gynecol 217: 404.
  21. Qian Z, Zhang B, Liang S, Wang J, Yang S, et al. (2016) Ambient Air Pollution and Adverse Pregnancy Outcomes in Wuhan, China. Res Rep Health Eff Inst: 1-65.
  22. Demographic Wg (2019) Demographic Distribution. Demographic Wg.
  23. WHO (2018) Preterm birth. WHO, Geneva, Switzerland.
  24. Yang S, Yang R, Liang S, Wang J, Weaver NL, et al. (2017) Symptoms of anxiety and depression during pregnancy and their association with low birth weight in Chinese women: A nested case control study. Arch Womens Ment Health 20: 283-290.
  25. Chen Y, Liu Y, Zhang Y, Hu R, Qian Z, et al. (2018) Gestational Weight Gain per Pre-Pregnancy Body Mass Index and Birth Weight in Twin Pregnancies: A Cohort Study in Wuhan, China. Sci Rep 8: 12496.
  26. Xiong C, Zhou A, Cao Z, Zhang Y, Qiu L, et al. (2016) Association of pre-pregnancy body mass index, gestational weight gain with cesarean section in term deliveries of China. Sci Rep 6: 37168.
  27. Ardern CI, Janssen I, Ross R, Katzmarzyk PT (2004) Development of health-related waist circumference thresholds within BMI categories. Obes Res 12: 1094-1103.
  28. CDC (2020) About adult BMI. CDC, Georgia, USA.
  29. Ferrari N, Graf C (2017) [Recommendations for Physical Activity During and After Pregnancy]. Gesundheitswesen 79: 36-39.
  30. Zhang Y, Dong S, Zuo J, Hu X, Zhang H, et al. (2014) Physical activity level of urban pregnant women in Tianjin, China: A cross-sectional study. PLoS One 9: 109624.
  31. Cai M, Zhang B, Yang R, Zheng T, Dong G, et al. (2021) Association between maternal outdoor physical exercise and the risk of preterm birth: a case-control study in Wuhan, China. BMC Pregnancy Childbirth 21: 206.
  32. Hao Y, Wang Y, Xi L, Li G, Zhao F, et al. (2016) A Nested Case-Control Study of Association between Metabolome and Hypertension Risk. Biomed Res Int 2016: 7646979.
  33. Zheng J, Zhou Y, Li S, Zhang P, Zhou T, et al. (2017) Effects and Mechanisms of Fruit and Vegetable Juices on Cardiovascular Diseases. Int J Mol Sci 18: 555.
  34. Ritz B, Wilhelm M, Hoggatt KJ, Ghosh JK (2007) Ambient air pollution and preterm birth in the environment and pregnancy outcomes study at the University of California, Los Angeles. Am J Epidemiol 166: 1045-1052.
  35. Erten YT, Sahinkaya T, Dinc E, Kilinc BE, Bayraktar B, et al. (2016) The effects of compression garments and electrostimulation on athletes' muscle soreness and recovery. J Exerc Rehabil 12: 308-313.
  36. Liao Y, Sun J, Qian Z, Mei S, Li Y, et al. (2020) Modification by seasonal influenza and season on the association between ambient air pollution and child respiratory diseases in Shenzhen, China. Atmospheric Environment 234: 117621.
  37. Suter MA, Aagaard KM, Coarfa C, Robertson M, Zhou G, et al. (2019) Association between elevated placental polycyclic aromatic hydrocarbons (PAHs) and PAH-DNA adducts from Superfund sites in Harris County, and increased risk of preterm birth (PTB). Biochem Biophys Res Commun 516: 344-349.
  38. Forand SP, Lewis-Michl EL, Gomez MI (2012) Adverse birth outcomes and maternal exposure to trichloroethylene and tetrachloroethylene through soil vapor intrusion in New York State. Environ Health Perspect 120: 616-621.
  39. Liu W, Huang C, Cai J, Wang X, Zou Z, et al. (2018) Household environmental exposures during gestation and birth outcomes: A cross-sectional study in Shanghai, China. Sci Total Environ 615: 1110-1118.
  40. Yang R, Liang S, Flick LH, Yang S, Hu K, et al. ()2016 Relationship Between Common Mental Disorder Symptoms During Pregnancy and Preterm Birth Among Chinese Women in Wuhan. Matern Child Health J 20: 2121-9.
  41. Yang R, Mei H, Zheng T, Fu Q, Zhang Y, et al. (2020) Pregnant women with COVID-19 and risk of adverse birth outcomes and maternal-fetal vertical transmission: A population-based cohort study in Wuhan, China. BMC Med 18: 330.
  42. Tan Y, Yang R, Zhao J, Cao Z, Chen Y, et al. (2017) The Associations Between Air Pollution and Adverse Pregnancy Outcomes in China. Adv Exp Med Biol 1017: 181-214.
  43. Weisman CS, Hillemeier MM, Chase GA, Dyer A-M, Baker SA, et al. (2006) Preconceptional health: Risks of adverse pregnancy outcomes by reproductive life stage in the Central Pennsylvania Women's Health Study (CePAWHS). Womens Health Issues 16: 216-224.

Supplemental Table

Characteristics

Total births

No. (%)a

PTBs

No. (%)

Controls

No. (%)

p-value

 

6656 (100)

2393 (36.0)

4263 (64.1)

 

Home decoration

  Yes

2797 (42.0)

1041 (43.5)

1756 (41.2)

> 0.05

  No

3859 (58.0)

1352 (56.5)

2507 (58.8)

 

Cooking fuel in the residence

  Natural gas

3882 (58.3)

1418 (59.3)

2464 (57.8)

> 0.05

  Liquefied petroleum gas

2500 (37.6)

872 (36.4)

1628 (38.2)

 

  Electricity

192 (2.9)

66 (2.8)

126 (3.0)

 

  Coal

63 (1.0)

28 (1.1)

35 (0.8)

 

  Wood

3 (0.1)

1 (0.0)

2 (0.1)

 

  Other

16 (0.2)

8 (0.3)

8 (0.2)

 

Heating fuel in the residence

  Electricity

4423 (66.5)

1591 (66.5)

2832 (66.4)

> 0.05

  Gas

180 (2.7)

59 (2.5)

121 (2.8)

 

  Liquefied petroleum gas

13 (0.2)

7 (0.3)

6 (0.1)

 

  Coal

26 (0.4)

13 (0.5)

13 (0.3)

 

  Wood

2 (0.0)

1 (0.0)

1 (0.0)

 

  Others

43 (0.7)

14 (0.6)

29 (0.7)

 

  No heating

1969 (29.6)

708 (29.6)

1261 (29.6)

 

Maternal second-hand smoking

  Yes

3836 (57.6)

1426 (59.6)

2410 (56.5)

< 0.05

  No

2820 (42.4)

967 (40.4)

1853 (43.5)

 

Living near pollution site

  Yes

456 (6.9)

154 (6.4)

302 (7.1)

> 0.05

  No

6200 (93.2)

2239 (93.6)

3961 (92.9)

 

Contact with pets

  Never

5915 (88.9)

2114 (88.3)

3801 (89.2)

> 0.05

  Sometimes

630 (9.5)

236 (9.7)

394 (9.2)

 

  Always

111 (1.7)

43 (1.8)

68 (1.6)

 

Maternal chronic health conditions

  Yes

1110 (18.9)

459 (19.2)

651 (15.3)

< 0.05

  No

5546 (83.3)

1934 (80.8)

3612 (84.7)

 

Exposure to deformity toxin

  Yes

201 (3.0)

87 (3.6)

114 (2.7)

< 0.05

  No

6455 (97.0)

2306 (96.4)

4149 (97.3)

 

Living near traffic main road

  Yes

4296 (64.5)

1592 (66.5)

2704 (63.4)

< 0.05

  No

2360 (35.5)

801 (33.5)

1559 (36.6)

 

Noise exposure during pregnancy

  Yes

2985 (44.9)

1086 (45.4)

1899 (44.6)

> 0.05

  No

3671 (55.2)

1307 (54.6)

2364 (55.5)

 

Stay outdoor, hours/day

  Means (SD)

1.91 (1.08)

1.84 (1.06)

1.95 (1.08)

< 0.05

Table S1: Distribution of other maternal exposures during pregnancy, by case and control, Wuhan, China, from June 10, 2011, to June 9, 2013.

Citation: Sun J, Yang R, Xian H, Zhang B, Dong G-H, et al. (2021) Maternal Exposure to Emission of Using new Furniture during Pregnancy and Preterm Birth. J Reprod Med Gynecol Obstet 6: 075.

Copyright: © 2021  Jia Sun#, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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