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Demographic Characteristics and Environmental Risk Factors Exposure of Birth Defects in Pregnant Women: A Population-based Study

  • Author Bio: LIN Hong, female, born in 1995, Master's degree majoring in women and children's health;LUO Mi Yang, female, born in 1991, PhD, in epidemiology of chronic diseases.
  • Corresponding author: LIN Hong; LUO Jia You, jiayouluo@csu.edu.cn
  • Received Date: 2018-06-28
    Fund Project:

    the National Natural Science Foundation of China 81172680

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  • [1]

    Ministry of Health of the People's Republic of China. Report on the prevention and control of birth defects in China[R] Beijing: Ministry of Health of the People's Republic of China, 2012; 1-2. (In Chinese)
    [2]

    Foster WG, Evans JA, Little J, et al. Human exposure to environmental contaminants and congenital anomalies:a critical review. Crit Rev Toxicol, 2017; 47, 59-84. doi: 10.1080/10408444.2016.1211090
    [3]

    Huang J, Wu J, Li T, et al. Effect of exposure to trace elements in the soil on the prevalence of neural tube defects in a high-risk area of China. Biomed Environ Sci, 2011; 24, 94-101.
    [4]

    Hoang TT, Marengo LK, Mitchell LE, et al. Original Findings and Updated Meta-Analysis for the Association Between Maternal Diabetes and Risk for Congenital Heart Disease Phenotypes. Am J Epidemiol, 2017; 186, 118-28. doi: 10.1093/aje/kwx033
    [5]

    Zhang J, Jiang L, Ma Y. Survey on preconceptual exposure to risk factors among married women at reproductive age in rural Henan. Chinese Journal of Family Planning, 2011; 19, 611-3. (In Chinese)
    [6]

    Deatsman S, Vasilopoulos T, Rhoton-Vlasak A. Age and Fertility:A Study on Patient Awareness. JBRA Assist Reprod, 2016; 20, 99-106.
    [7]

    Feng Shanwei, Li Ning, et al. Occupational exposure during preconceptional period in women in rural China. Chinese Journal of Reproductive Health, 2015; 2, 102-7. (In Chinese)
    [8]

    Berin E, Sundell M, Karki C, et al. Contraceptive knowledge and attitudes among women seeking induced abortion in Kathmandu, Nepal. Int J Womens Health, 2014; 6, 335-41.
    [9]

    Brender JD, Zhan FB, Suarez L, et al. Linking environmental hazards and birth defects data. Int J Occup Environ Health, 2006; 12, 126-33. doi: 10.1179/oeh.2006.12.2.126
    [10]

    Rong Z, Qian C, Yun L. The analysis of cigarette smoking behaviors and its influencing factors among chinese urban and rural residents. Journal of Nanjing Medical University, 2014; 34, 84-9. (In Chinese)
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Demographic Characteristics and Environmental Risk Factors Exposure of Birth Defects in Pregnant Women: A Population-based Study

    Corresponding author: LIN Hong; 
    Corresponding author: LUO Jia You, jiayouluo@csu.edu.cn
  • 1. Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China
  • 2. Saw Swee Hock School of Public Health, National University of Singapore, 119077 Singapore
  • 3. Xiangya Hospital of Central South University, Changsha 410000, Hunan, China
  • 4. Hunan Provincial Maternal and Child Health Care Hospital, Changsha 410000, Hunan, China
Fund Project:  the National Natural Science Foundation of China 81172680

Abstract: 

  • Worldwide, the incidence of birth defects in low-income countries is 6.42%, while in middle-income and high-income countries it is 5.57% and 4.72%, respectively; approximately 303, 000 newborns die from birth defects each year. In China, the incidence of birth defects is about 5.6%, and around 8.14 million people have congenital disabilities, accounting for 9.6% of total disabled people[1]. Birth defect remains a major clinical and public health challenge because of its high fatality rate and protracted and severe sequela.

    Due to the complicated pathogenesis of diseases, many modifiable environmental risk factors related to birth defects have been identified, including heavy metals, solvents, pesticides, maternal illnesses, and maternal smoking, among others[2-4]. During early pregnancy, environmental risk factors can selectively act on the fetus tissues and organs that are in an active stage of development and differentiation, resulting in abnormalities in their morphology or function. Till date, more attention has been paid to the association and possible functioning of environmental risk factors leading to birth defects. However, the distribution of pregnant women exposed to environmental risk factors remains a major concern. There is still a lack of research with large sample sizes concerning exposure to environmental risk factors related to birth defects among pregnant women with different demographic characteristics. Therefore, we conducted this study to explore the association between demographic characteristics and exposure to environmental risk factors, which may help to identify populations at risk for birth defects and provide more effective and targeted prevention strategies for birth defects in China.

    The study population included pregnant women who received prenatal care at medical institutions in Liuyang, Hunan, China between June 2013 and November 2014. Stratified random sampling was performed in this study. According to the urban-rural ratio, population density, and fertility levels, two streets and 11 towns were randomly selected in Liuyang city (four streets and 33 towns in total). Finally, all pregnant women who met the inclusion criteria were enrolled in this study in the chosen street or town. The inclusion criteria were the following: a) 16-20 gestational weeks; b) local resident for more than six months; and c) willing to participate in the survey. Pregnant women with severe physical symptoms and mental disorders who were unable to complete the survey were excluded. All participants provided written informed consent.

    A face-to-face questionnaire survey was conducted with participants in order to gather information about their demographic characteristics and exposure to environmental risk factors, starting from three months before conception to the first trimester of pregnancy. The questionnaire was divided into two parts: part 1, demographic characteristics; and part 2, genetic risk factors and environmental risk factors (25 variables from four categories: physical and chemical risk factors, behavioral and lifestyle risk factors, disease and drug risk factors, and adverse reproductive history) (Supplementary Table S1, available in www.besjournal.com). The questionnaire was designed by experts from the research team and adjusted based on a pilot study.

    Category Variables
    Demographic characteristics Maternal age, ethnicity, place of residence, educational level, occupation, and family per capita annual income.
    Environmental risk factors
    Physical and chemical risk factors X-rays contact, noise pollution, use of microwave oven or induction cooker, and exposure to toxic and hazardous substances, pesticides, fireworks production-related toxic substances, housing renovations, and environmental pollution.
    Behavioral and lifestyle risk factors Cosmetic use, hair dye or perm, pet ownership, intake of pickled or smoked food, alcohol use, active smoking, and passive smoking.
    Disease and drug risk factors History of chronic diseases, respiratory infections, and infectious diseases, and drug intake for the treatment of diseases, contraceptive drugs, and ovulation induction agents.
    Adverse reproductive history History of preterm birth, stillbirth, abortion, and labor induction.
    Genetic risk factors Maternal family history of birth defects and consanguineous marriage, paternal family history of birth defects and consanguineous marriage.

    Table Supplementary Table S1.  Data Collected from Participants

    The definition of each variable was provided to participants, for example: 1) education level was classified into three categories: primary school and below, secondary school, and tertiary/high school; 2) X-rays contact: exposure to X-rays more than once a month or more than three times, or a large dose of contact; 3) passive smoking: no-smokers inhale smoke exhaled by smokers at least one day (> 15 min) in a week; 4) chronic diseases: history of doctor-diagnosed chronic diseases three months before conception, such as diabetes and hypertension, among others; 5) abortion: history of at least one abortion before this pregnancy; 6) maternal family history of birth defects: history of birth defects among relatives of maternal families within three generations; 7) exposure rate: proportion of pregnant women exposed to risk factors within the total number of pregnant women.

    Standardized training was provided for all the researchers to ensure the quality of the interview. This study used dual data entry, and logical checks were performed during data entry. Data were analyzed using SPSS statistical software, version 18.0 (USA). Descriptive statistics were summarized using percentages, means, and standard deviations (SD), where appropriate. Separate logistic regression models were used to assess the association between demographic characteristics and exposure to every environmental risk factor. The significance level was determined at α = 0.05.

    Initially, a total of 10, 475 pregnant women were surveyed; 312 of these women (3.0%) were excluded because of missing data (> 20%). Data from 10, 163 pregnant women were further analyzed, and their demographic characteristics are shown in Supplementary Table S2, available in www.besjournal.com. In this study, the exposure rate to environmental risk factors was 94.0% and 1.8% concerning genetic risk factors. Within the environmental risk factors group, the highest exposure rate was observed in behavioral and lifestyle risk factors (88.7%), followed by adverse reproductive history (34.8%), physical and chemical risk factors (25.8%), and disease and drug risk factors (24.2%). Further, within these four categories, the most common risk factors found were the use of microwave ovens or induction cookers (9.5%), pickled or smoked food intake (55.9%), respiratory infection (17.5%), and abortion history (32.1%). Only 5.9% of pregnant women had not been exposed to any risk factors, as summarized in Supplementary Tables S3 and S4, available in www.besjournal.com.

    Demographic Characteristics n Percentage (%)
    Age (years)
      18-24 3, 404 33.5
      25-29 4, 551 44.8
      30-34 1, 631 16.0
      35-47 577 5.7
    Educational level
      Below primary/primary 220 2.2
      Secondary 8, 289 81.6
      Tertiary/higher 1, 654 16.3
    Maternal occupation
      Farmer 1, 380 13.6
      Housewife 5, 531 54.4
      Factory worker 583 5.7
      Company employee/self-employed 1, 877 18.5
      Other 501 4.9
      Staff in administrative institutions 291 2.9
    Residential address
      Rural 8, 853 87.1
      Urban 1, 310 12.9
    Family per capita annual income (yuan)
       < 10, 000 714 7.0
      10, 000-14, 999 1, 756 17.3
      15, 000-29, 999 4, 593 45.2
      30, 000-49, 999 2, 267 22.3
    ≥   50, 000 833 8.2

    Table Supplementary Table S2.  Demographic Characteristics of Pregnant Women (n = 10, 163)

    Risk Factors Number of Exposed Women (n = 10, 163) Exposure Rate (%) Order
    Genetic risk factors 181 1.8 -
    Environmental risk factors 9, 558 94.0 -
    Physical and chemical risk factors 2, 619 25.8 3
    Behavioral and lifestyle risk factors 9, 018 88.7 1
    Disease and drug risk factors 2, 459 24.2 4
    Previous history of adverse pregnancy outcomes 3, 533 34.8 2
    Numbers of exposure to risk factors
    0 599 5.9 -
    1 1, 325 13.0 -
    2 1, 992 19.6 -
    3 2, 333 23.0 -
    4 1, 765 17.4 -
    ≥ 5 2, 149 21.1 -
    Note.Pregnant women could have been exposed to more than one risk factor.

    Table Supplementary Table S3.  Analysis of Exposure to Risk Factors among Pregnant Women (n = 10, 163)

    Risk Factors Number of Exposed Women (n = 10, 163) Exposure Rate (%) Order
    Physical and chemical risk factors
      X-rays contact 254 2.5 5
      Noise pollution 768 7.6 2
      Microwave oven or induction cooker use 968 9.5 1
      Toxic and hazardous substances 222 2.2 6
      Pesticides 58 0.6 8
      Fireworks production-related toxic substances 545 5.4 3
      Housing renovations 522 5.1 4
      Environmental pollution 218 2.1 7
    Behavioral and lifestyle risk factors
      Cosmetic use 5, 296 52.1 3
      Hair dye or perm 5, 642 55.5 2
      Pet raising 1, 457 14.3 5
      Pickled or smoked food intake 5, 682 55.9 1
      Alcohol use 162 1.6 6
      Active smoking 56 0.6 7
      Passive smoking 3, 209 31.6 4
    Disease and drug risk factors
      Chronic diseases 568 5.6 2
      Respiratory infections 1, 783 17.5 1
      Infectious diseases 95 0.9 5
      Drugs for disease treatment 172 1.7 3
      Contraceptive drugs 148 1.5 4
      Ovulation induction agents 80 0.8 6
    Previous history of adverse pregnancy
      Preterm birth 61 0.6 4
      Stillbirth 122 1.2 3
      Abortion 3, 263 32.1 1
      Labor induction 317 3.1 2
    Genetic risk factors
      Maternal family history of birth defects 74 0.7 1
      Maternal family history of consanguineous marriage 49 0.5 3
      Paternal family history of birth defects 50 0.5 2
      Paternal family history of consanguineous marriage 39 0.4 4

    Table Supplementary Table S4.  Exposure Rate Per Risk Factor and Their Ranking

    As seen in Tables 1, 2, and 3, results from logistic regression analysis showed that youngest pregnant women had higher odds of renovating their house (OR = 2.75), using cosmetics (OR = 1.73), dyeing or perming hair (OR = 1.64), and were less likely to have an adverse reproductive history (OR = 0.25). Pregnant women with lower educational levels had increased risk of exposure to noise (OR = 1.91), toxic and hazardous substances (OR = 3.28), and fireworks production-related toxic substances (OR = 13.74), as well as an adverse reproductive history (OR = 1.73). Farmers and housewives had a lower risk of respiratory infections (OR = 0.50; OR = 0.49) and exposure to microwave ovens or induction cookers (OR = 0.60; OR = 0.65). Factory workers had higher exposure risk of fireworks production-related toxic substances (OR = 4.77). Women who were company employees or self-employed had higher exposure risk of cosmetics (OR = 1.41) and lower exposure risk of respiratory infections (OR = 0.56), as well as less risk of labor induction (OR = 0.37). On the other hand, rural pregnant women had higher risk of exposure to fireworks production-related toxic substances (OR = 2.11), environmental pollution (OR = 1.80), passive smoking (OR = 1.46), disease and drug risk factors (OR = 1.35), and lower risk of exposure to microwave ovens or induction cookers (OR = 0.62), and housing renovations (OR = 0.71). Pregnant women with lower income had a lower risk of exposure to cosmetics (OR = 0.76), pickled and smoked food (OR = 0.72), and passive smoking (OR = 0.75).

    Demographic Characteristics Physical and Chemical Risk Factors Noise Pollution Microwave Oven or Induction Cooker Use Toxic and Hazardous Substances Fireworks Production Related Toxic Substances Housing Renovations Environmental Pollution
    Age (years)
      18-24 1.02 (0.84-1.25) 1.22 (0.86-1.74) 0.87 (0.64-1.17) 1.18(0.65-2.16) 0.61(0..44-0.84)** 2.75 (1.61-4.69)** 0.80 (0.47-1.38)
      25-29 0.91 (0.74-1.11) 1.18 (0.83-1.67) 1.00 (0.74-1.35) 0.96(0.53-1.75) 0.53(0.39-0.74)** 1, 43 (0.84-2.45) 0.60 (0.35-1.03)
      0-34 1.00 (0.81-1.24) 1.01(0.69-1.49) 0.84 (0.60-1.16) 1.23(0.65-2.32) 0.95 (0.68-1.32) 1.27 (0.71-2.27) 0.82 (0.46-1.48)
      35-47 Reference Reference Reference Reference Reference Reference Reference
    Educational level
      Below primary/primary 1.70 (1.23-2.33)** 1.31 (1.16-3.12)* 0.87 (0.49-1.53) 3.28 (1.32-8.14)* 13.74 (6.90-27.34)** 0.59 (0.25-1.40) 1.14 (0.47-2.74)
      Secondary 1.15 (0.99-1.32) 1.13 (0.90-1.43) l.10 (0.90-1.34) 2.15 (1.24-3.71)** 4.25 (2.34-7.74)** 0.82 (0.64-1.06) 0.84 (0.56-1.27)
      Tertiary/higher Reference Reference Reference Reference Reference Reference Reference
    Maternal occupation
      Farmer 0.87 (0.63-1.19) 0.58 (0.35-0.94)* 0.60 (0.39-0.92)* 1.06(0.30-3.76) 2.41(0.57-10.30) 0.63 (0.37-1.08) 1.58 (0.52-4.79)
      Housewife 0.82 (0.61-1.10) 0.66 (0.42-1.03) 0.65 (0.44-0.96)* 1.05(0.31-3.59) 1.96 (0.46-8.27) 0.71(0.44-1.14) 1.36(0.47-3.95)
      Factory worker 1.18 (0.84-1.65) 0.79 (0.47-1.33) 0.63 (0.39-1.01) 1.87(0.52-6.71) 4.77(1.11-20.46)* 0.68 (0.38-1.24) 1.73 (0.54-5.57)
      Company employee/self-employed 0.97(0.73-1.30) 0.81(0.52-1.25) 1.08 (0.75-1.57) 1.32(0.39-4.49) 0.75 (0.17-3.27) 0.82 (0.52-1.29) 1.46 (0.50-4.23)
      Other 1.18 (0.85-1.64) 0.70 (0.42-1.18) 0.92 (0.59-1.43) 0.87(0.22-3.45) 0.90 (0.18-4.39) 1.27 (0.77-2.11) 2.16(0.70-6.70)
      Staff in administrative institutions Reference Reference Reference Reference Reference Reference Reference
    Residential address
      Rural 0.88 (0.77-1.01) 1.08 (0.85-1.37) 0.62 (0.52-0.75)** 1.70 (0.98-2.94) 2.11 (1.33-3.35)** 0.71(0.56-0.91)** 1.80(1.07-3.04)*
      Urban Reference Reference Reference Reference Reference Reference Reference
    Family per capita annual income (yuan)
       < 10, 000 1.01(0.81-1.27) 0.90 (0.63-1.27) 0.97 (0.69-1.36) 0.80 (0.42-1.55) 1.52 (0.88-2.61) 0.73 (0.49-1.10) 1.04 (0.56-1.94)
      10, 000-14, 999 0.89 (0.74-1.08) 0.67 (0.49-0.90)** 0.96 (0.73-1.28) 0.93(0.54-1.58) 1.76(1.09-2.85)* 0.57 (0.40-0.80)** 0.69 (0.40-1.21)
      15, 000-29, 999 0.79 (0.67-0.94)* 0.70 (0.54-0.91)** 0.85 (0.66-1.08) 0.57 (0.34-0.94)* 1.60(1.01-2.55)* 0.47 (0.35-0.63)** 0.76 (0.47-1.24)
      30, 000-9, 999 0.80 (0.67-0.96)* 0.62 (0.47-0.83)** 0.92 (0.72-1.19) 0.78 (0.46-1.31) 1.12 (0.68-1.85) 0.65 (0.49-0.88)** 0.72 (0.43-1.21)
      ≥50, 000 Reference Reference Reference Reference Reference Reference Reference
    Note.Physical and chemical risk factors on the questionnaire refer to the exposure to any risk factor related to physical and chemical risk factors.*Statistically significant (P < 0.05); **Statistically significant (P < 0.01).

    Table 1.  Association between Demographic Characteristics and Exposure to Physical and Chemical Risk Factors

    Demographic Characteristics Behavioral/Lifestyle Risk Factors Cosmetic Use Hair Dye or Perm Pet Ownership Pickled/Smoked Food Intake Passive Smoking
    Age (years)
      18-24 1.40(1.08-1.81)* 1.73 (1.44-2.08)** 1.64(1.37-1.97) 0.91(0.71-1.16) 1.04 (0.87-1.25) 1.11 (0.92-1.35)
      25-29 1.38 (1.07-1.77)* 1.69 (1.41-2.03)** 1.48 (1.24-1.77)** 0.86 (0.68-1.10) 0.99 (0.83-1.18) 1.06 (0.88-1.29)
      30-34 1.37(1.04-1.82)* 1.39 (1_15-1.70)** 1.29 (1.06-1.56)* 0.90 (0.69-1.17) 0.96(0.79-1.17) 1.07 (0.87-1.32)
      35-47 Reference Reference Reference Reference Reference Reference
    Educational level
      Below primary/primary 1.03 (0.65-1.63) 0.57 (0.42-0.770** 0.71 (0.53-0.36)* 1.10 (0.73-1.67) 1.02 (0.76-1.37) 1.18 (G.87-1.62)
      Secondary 1.02 (0.83-1.25) 0.97 (0.86-1.10) 1.03 (0.91-1.17) 1.04 (0.87-1.24) 1.11 (0.98-1.25) 0.96 (0.84-1.10)
      Tertiary/higher Reference Reference Reference Reference Reference Reference
    Maternal occupation
      Farmer 1.46 (0.92-2.34) 1.17 (0.88-1.55) 1.09 (0.82-1.45) 0.94 (0.63-1.41) 0.84 (0.63-1.12) 0.99 (0.73-1.34)
      Housewife 0.90 (0.58-1.39) 0.92 (0.70-1.20) 0.77 (0.59-0.99)* 0.88 (0.60-1.29) 0.77 (0.59-1.01) 1.09 (0.82-1.46)
      Factory worker 1.27(0.76-2.12) 1.24 (0.91-1.68) 1.30 (0.96-1.78) 0.97(0.63-1.50) 0.86 (0.63-1.17) 0.93 (0.67-1.30)
      Company employee/self-ennployed 1.19 (0.77-1.85) 1.41 (1.08-1.84)* 1.30 (0.99-1.69) 0.87 (0.59-1.28) 0.92 (0.70-1.19) 1.01 (0.76-1.34)
      Other 1.20(0.72-2.00) 1.04 (0.77-1.41) 1.11(0.82-1.51) 1.08 (0.70-1.66) 0.77 (0.57-1.04) 1.41(1.02-1.95)*
      Staff in administrative institutions Reference Reference Reference Reference Reference Reference
    Residential address
      Rural 0.85 (0.69-1.06) 0.69(0.61~0.73)** 0.96(0.84-1.09) 2.05 (1.55-2.54)** 1.07 (0.95-1.22) 1.46(1.27-1.69)**
      Urban Reference Reference Reference Reference Reference Reference
    Family per capita annual income (yuan)
      < 10, 000 0.54 (0.38-0.75)** 0.76(0.62-0.94)* 0.86 (0.70-1.07) 0.85 (0.64-1.13) 0.72 (0.59-0.89)** 0.75 (0.60-0.94)*
      10, 000-14, 999 0.64 (0.47-0.86)** 0.69(0.58-0.82)** 0.65 (0.54-0.770)** 0.78 (0.62-0.99)* 0.80 (0.67-0.95)* 1.08 (0.90-1.30)
      15, 000-29, 999 0.68 (0.52-0.90)** 0.73 (0.62-0.85)** 0.87 (0.74-1.02) 0.75 (0.60-0.92)* 0.83 (0.71-0.98)* 0.90 (0.76-1.06)
      30, 000-49, 999 0.88(0.65-1.18) 0.93 (0.79-1.10) 0.96(0.81-1.13) 0.83 (0.66-1.03) 1.05 (0.89-1.23) 0.82 (0.69-0.97)*
      ≥50, 000 Reference Reference Reference Reference Reference Reference
    Note.Behavioral/lifestyle risk factors on the questionnaire refer to the exposure to any risk factor related to behavioral and lifestyle risk factors.*Statistically significant (P < 0.05); **Statistically significant (P < 0.01).

    Table 2.  Association between Demographic Characteristics and Exposure to Behavioral and Lifestyle Risk Factors

    Demographic Characteristics Disease/Drug Risk Factors Chronic Disease Respiratory Infection Adverse ReproductiveHistory Preterm Birth Stillbirth Abortion Labor Induction
    Age (years)
      18-24 1.31 (1.05-1.63)* 0.84 (0.59-1.21) 1.36(1.06-1.75)* 0.25 (0.21-0.30)** 0.28(0.11-0.73)** 0.29 (0.15-0.57)** 0.25 (0.21-0.31)** 0.33 (0.21-0.51)**
      25-29 1.24 (0.99-1.54) 0.83 (0.58-1.18) 1.26(0.98-1.62) 0.40 (0.34-0.48)** 0.46 (0.20-1.09) 0.54 (0.30-0.97)* 0.40 (0.34-0.48)** 0.59 (0.40-0.87)**
      30-34 1.25 (0.99-1.58) 0.95 (0.65-1.40) 1.33(1.01-1.73)* 0.65 (0.54-0.79)** 0.86 (0.35-2.09) 0.70(0.37-1.33) 0.64 (0.52-0.77)** 1.02(0.67-1.54)
      35-47 Reference Reference Reference Reference Reference Reference Reference Reference
    Educational level
      Below primary/primary 1.51(1.08-2.10)* 1.32 (0.74-2.36) 1.29 (0.87-1.90) 1.73(1.27-2.36)** 1.14 (0.12-10.88) 3.66 (1.05-12.83)* 1.52 (1.11-2.09)** 3.21 (1.46-7.08)**
      Secondary 1.12 (0.97-1.30) 1.10 (0.83-1.46) 1.17(0.99-1.38) 1.60(1.39-1.83)** 2.38 (0.83-6.82) 2.94 (1.26-6.85)* 1.49(1.30-1.71)** 2.78 (1.63-4.73)**
      Tertiary/higher Reference Reference Reference Reference Reference Reference Reference Reference
    Maternal occupation
      Farmer 0.58 (0.43-0.79)** 1.27 (0.65-2.46) 0.50 (0.36-0.70)** 0.96(0.70-1.30) 0.38 (0.07-2.13) 1.00 (0.21-4.83) 0.95 (0.70-1.31) 1.05(0.43-2.53)
      Housewife 0.54 (0.40-0.71)** 1.09 (0.58-2.07) 0.49 (0.36-0.67)** 0.92 (0.69-1.23) 0.42 (0.08-2.09) 0.83 (0.1S-3.85) 0.97 (0.73-1.31) 0.55(0.23-1.31)
      Factory worker 0.73 (0.53-1.01) 0.86 (0.41-1.78) 0.75(0.53-1.07) 0.97 (0.69-1.35) 0.11 (3.01-1.35) 1.17 (0.23-5.91) 1.02 (0.73-1.43) 0.59(0.23-1.54)
      Company Employee/self-employed 0.63 (0.47-0.83)** 1.04 (0.55-1.96) 0.56 (0.41-0.76)** 0.91 (0.68-1.21) 0.43 (3.09-2.21) 0.67 (0.14-3.17) 0.97 (0.72-1.30) 037 (0.15-0.90)*
      Others 0.55(0.39-0.77)** 1.01 (0.49-2.09) 0.50 (0.35-0.71)** 1.07 (0.77-1.49) 0.60 (0.09-3.97) 0.79 (0.14-4.36) 1.16(0.83-1.62) 0.72(0.27-1.90)
      Staff in administrative institutions Reference Reference Reference Reference Reference Reference Reference Reference
    Residential address
      Rural 1.35(1.16-1.58)** 1.50(1.10-2.06)* 1.34(1.12-1.59)** 1.08 (0.94-1.23) 1.27 (0.52-3.11) 2.00 (0.90-4.43) 1.05 (0.91-1.20) 1.36(0.87-2.12)
      Urban Reference Reference Reference Reference Reference Reference Reference
    Family per capita annual income (yuan)
      <10,000 1.03 (0.82-1.29) 1.62(1.04-2.51)* 0.82 (0.63-1.06) 1.05 (0.84-1.31) 4.40 (0.90-21.49) 0.85 (0.38-1.89) 0.98 (0.78-1.23) 1.17(0.65-2.09)
      10,000-14,999 0.73 (0.60-0.88)** 1.10 (0.73-1.64) 0.70(0.56-0.87)** 0.93 (0.77-1.12) 2.19 (0.46-10.36) 0.48 (0.23-0.99)* 0.99 (0.82-1.20) 0.87 (0.52-1.46)
      15,000-29,999 0.75(0.63-0.90)** 1.11 (0.77-1.60) 0.73 (0.60-0.89)** 1.00 (0.85-1.18) 2.27 (0.52-9.90) 0.49 (0.26-0.94)* 1.02 (0.86-1.21) 1.01(0.63-1.62)
      30,000-49,999 0.86(0.71-1.03) 1.28 (0.88-1.87) 0.86 (0.71-1.05) 1.05 (0.88-1.25) 2.95 (0.67-13.10) 0.87 (0.45-1.67) 1.07 (0.89-1.28) 1.24 (0.75-2.03)
      ≥50,000 Reference Reference Reference Reference Reference Reference Reference Reference
    Note.Disease and drug risk factors on the questionnaire refer to the exposure to any risk factor related to disease and drug risk factors.Adverse reproductive history on the questionnaire refers to the exposure to any risk factor related to previous adverse pregnancy. Statistically significant (P < 0.05);*Statistically significant (P < 0.01).

    Table 3.  Association between Demographic Characteristics and Exposure to Disease/Drug Risk Factors and Adverse Reproductive History

    Previous research has shown that around 25% of birth defects are caused by genetic factors, and around 10% are due to environmental factors, while the remaining 65% birth defects may be caused by the combined effect of genetic and environmental factors or other unknown reasons. In this study, we found that the rate of exposure to environmental risk factors was much higher than that of genetic risk factors, which is in agreement with Zhang's[5] findings. Because environmental risk factors, which are different from genetic risk factors, can be controlled through interventions, we should monitor the exposure to environmental risk factors among pregnant women, and health education to reduce or avoid exposure to environmental risk factors should be prioritized.

    In this study, we found that younger pregnant women had a greater probability of using cosmetics and dyeing or perming hair, which may be related to their concern regarding beauty and fashion. On the other hand, older pregnant women were more likely to have an adverse reproductive history. This is possibly due to the increased number of gravidity and parity times, decreased ovarian and uterine function, or increased probability of chromosomal abnormalities among older pregnant women[6].

    Pregnant women with different educational levels suffered from exposure to different environmental risk factors. Feng's study[7] reported that a lower educational level was associated with occupational risk factors, such as exposure to pesticides and toxic chemicals, which was also supported by our study. A possible reason that may explain this finding is that pregnant women with low education may have limited awareness of self-protection, as a consequence of which they may not take adequate protective measures. We also observed that pregnant women with lower educational levels had an increased risk of adverse reproductive history. This may be due to reproductive health knowledge being less accessible to them, which may result in an increased number of adverse pregnancy outcomes[8]. This finding indicates that health education regarding personal protection and reproductive health is essential for pregnant women with low education.

    It is important to notice that pregnant women working as staff in administrative institutions were more likely to be exposed to microwave ovens or induction cookers, while factory workers had higher exposure risk of fireworks production-related toxic substances; these findings indicate that pregnant women with different occupations have differential exposure to environmental risk factors according to their occupation, which required us to pay more attention to these differences. In addition, rural pregnant women had a higher risk of exposure to fireworks production-related toxic substances and environmental pollution. In contrast to our findings, a study[9] carried out in Texas reported that pregnant women living in urban environments were more likely to be exposed to hazardous waste sites and industrial facilities. Therefore, we should analyze different living environments to correctly identify the risk factors that pregnant women are more exposed to, and take targeted prevention and intervention measures. We also found that rural pregnant women had a relatively high exposure risk of passive smoking. A possible explanation for this finding could be that the smoking rate may be higher among Chinese rural residents than in residents in urban areas[10], as tobacco control interventions and regulations have been widely implemented in public places of Chinese urban areas. Therefore, more strategies targeted at passive smoking among pregnant women in rural areas should be implemented.

    This study has several limitations. First, exposure to environmental risk factors in this study was self-reported, thus, recall bias might be present. Second, quantitative data related to environmental risk factors exposure were not collected in this study. Third, the study was only conducted in the province of Hunan and, therefore, does not represent the total pregnant population in China.

    Despite these limitations, this is a large population-based study carried out in China to explore the association between demographic characteristics and exposure to environmental risk factors. We found that the exposure rate to environmental risk factors was much higher than that of genetic risk factors; additionally, there were differences regarding exposure to environmental risk factors among pregnant women according to different demographic characteristics. These results can provide a scientific basis to identify at-risk populations who are more likely to be exposed to risk factors that might cause birth defects, which bear importance both in health education among pregnant women and primary prevention of birth defects.

    We wish to express our gratitude to all participants for their cooperation in this study.

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