Different effects of PM_{10} exposure on preterm birth by gestational period estimated from time-dependent survival analyses
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DOI: 10.1007/s00420-008-0380-7
- Cite this article as:
- Suh, Y.J., Kim, H., Seo, J.H. et al. Int Arch Occup Environ Health (2009) 82: 613. doi:10.1007/s00420-008-0380-7
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Abstract
Purpose
We conducted this study to determine if the preterm risks due to PM_{10} exposure vary with the exposure periods during pregnancy. This study was also conducted to estimate the different effects of PM_{10} exposure on preterm birth by exposure periods using the extended Cox model with PM_{10} exposure as a time-dependent covariate.
Methods
We studied birth data obtained from the Korea National Statistical office for 374,167 subjects who were delivered between 1998 and 2000 in Seoul, South Korea. We used PM_{10} data that was measured hourly to give 24-h averages at 27 monitoring stations in Seoul. The extended Cox model with time-dependent exposure was used to determine if the risk of preterm delivery could be associated with PM_{10} exposures for each trimester during pregnancy.
Results
Effect of PM10 exposure prior to the 37 weeks of gestational period was stronger on the risk of premature birth than that posterior to the 37 weeks of gestational weeks. This trend was consistent for each trimester; however, the hazard ratios for preterm delivery associated with PM_{10} exposure in the first and third trimester were slightly higher than those of the second trimester.
Conclusions
The risk of preterm birth associated with exposure to PM_{10} differed with the exposure period of the neonates. Therefore, when studying the impact of air pollution exposure during pregnancy, the exposure period during pregnancy should be considered.
Keywords
Preterm birthPM_{10} exposureExtended Cox modelIntroduction
Gestational age, infant birth weight and growth of the fetus may have an effect on perinatal health. In addition, perinatal death, infant mortality, and illnesses in adults can all be caused by preterm birth, which is defined as delivery prior to 37 weeks of pregnancy (Joseph and Kramer 1996). Many studies have shown that environmental factors, including cigarette smoking (Horta et al. 1997; Cnattingius et al. 2006; Raatikainen et al. 2007), environmental tobacco smoke (ETS) (Windham et al. 2000; Kharrazi et al. 2004), organic solvents (Wang et al. 2000; Ha et al. 2002), and alcohol consumption can contribute to preterm delivery (Albertsen et al. 2004). The relationship between birth outcomes and air pollution exposure has also been examined in many epidemiological studies that have been conducted to evaluate factors including total suspended particulates (TSP) (Wang et al. 1997; Bobak and Leon 1999; Bobak 2000; Ha et al. 2001), sulfur dioxide (SO_{2}) (Wang et al. 1997; Bobak and Leon 1999; Bobak 2000; Ha et al. 2001; Maisonet et al. 2001), carbon monoxide (CO) (Ha et al. 2001; Maisonet et al. 2001; Ritz and Yu 1999; Medeiros and Gouveia 2005; Salam et al. 2005), ozone (O_{3}) (Salam et al. 2005; Hansen et al. 2006), nitrogen dioxide (NO_{2}) (Ha et al. 2001; Medeiros and Gouveia 2005), and particulate matter <10 μm (PM_{10}) (Bobak and Leon 1999; Medeiros and Gouveia 2005; Hansen et al. 2006; Dejmek et al. 1999; Ritz et al. 2000; Ha et al. 2003; Rogers and Dunlop 2006; Leem et al. 2006). In a previous study that we conducted in Seoul, South Korea, exposure to air pollution during pregnancy was found to be significantly associated with the occurrence of preterm birth (Ha et al. 2004).
The association of premature delivery with air pollution exposure during pregnancy has been investigated using various statistical approaches that can be classified as community- or individual-based. In studies that use a community-based approach, a time-series analysis based on a Poisson regression model is commonly used to find the increment of preterm delivery due to air pollution exposure (Anderson et al. 2001; Roberts 2004; Roberts 2005; Peng et al. 2005; Sagiv et al. 2005). However, because the population-based approach assumes that the population contains homogeneous characteristics for the entire study period, it is difficult to use individual-based confounders that vary with time. Therefore, the individual-based approach has become more popular. This approach has generally involved the use of a logistic regression model in which the distribution of predictors is compared in women that experienced preterm birth and those who carried their child to term (Bobak 2000; Hansen et al. 2006; Ritz et al. 2000; Ha et al. 2004; Xu et al. 1995; Maroziene and Grazuleviciene 2002; Yang et al. 2002; Yang et al. 2003; Tsai et al. 2003; Woodruff et al. 2003; Liu et al. 2003; Wilhelm and Ritz 2005). For studies in which a quantitative outcome, such as gestational age or fetal growth associated with air pollution exposure is required, a linear regression analysis approach has commonly been performed (Xu et al. 1995; Hansen et al. 2007).
In a previously conducted study, we compared the results obtained when logistic regression and Cox proportional hazards models were used to investigate the relationship between pollution and preterm birth in South Korea (Ha et al. 2004). In the present study, which was conducted to determine if the risk for a preterm birth due to air pollution exposure varied with gestational age, we used the extended Cox model, after accounting for the time-dependent covariate. We then compared the results from (1) the conventional individual-based logistic regression analysis, (2) the conventional regression analysis, (3) the Cox proportional hazards model, and (4) the extended Cox model with time-dependent exposure to determine the usefulness of the time-to-event analysis rather than conventional approaches in the study of preterm delivery due to exposure to air pollution, such as PM_{10}, in pregnant Korean women.
Materials and methods
Population and data collection
Birth data containing information such as the gender and weight of the infant, parental age, education and occupation, parity, birth order, gestational age, marital status, birth date, place of birth and history of delivery were obtained from the Korea National Statistical Office. Only subjects who were delivered between January 1, 1998 and December 31, 2000 in Seoul, South Korea were included in this study. The confounding factors to be controlled in this study were the gender of the infant, maternal age, paternal education, marital status, birth order, maternal occupation, maternal history of stillbirth, and month and day of the week at birth. Subjects who were born before 25 weeks or after 41 weeks of gestation were excluded, as were subjects who were delivered as multiple neonates. Finally 374,167 individuals were included in our analysis. The prevalence of preterm birth (before 37 weeks of gestation) in the subjects was 3.3% (N = 12,153).
Air pollution data
We obtained PM_{10} data, which was measured hourly to give 24-h averages at 27 monitoring stations in Seoul, from the National Institute of Environmental Research. We ruled out exposure data when there was a PM_{10} of ≥200 μg/m^{3} because these data may have been influenced by dust and sand storms in Mongolia or China. The air pollution exposure for each participant, based on the averaged exposure at 27 monitoring stations in Seoul, gestational age and delivery date, was then calculated for the first, second and third trimester. Each trimester was defined as 3 calendar months. Exposure during third trimester corresponded to the averaged exposure for a period from 186 days of gestation to birth. Infants with gestational duration less than 186 days have missing values for the third trimester exposure.
Statistical analysis
As a preliminary study, we investigated the relationship between preterm delivery and covariates, such as the gender of the infant, maternal age, paternal education, marital status, birth order, maternal occupation, maternal history of stillbirth and month and day of the week at birth using χ^{2} test or t test. We then evaluated the impact of PM_{10} exposure during each trimester to determine the risk of preterm birth and reduced pregnancy period using conventional statistical approaches, such as multiple logistic regression and multiple linear regression analyses, respectively, after adjusting for confounding factors and a continuous effect of a linear long-term trend. Odds ratios and regression coefficients were estimated for preterm delivery according to the increase of averaged interquartile range (IQR) of PM_{10} concentration levels in each trimester during pregnancy using the fitted logistic regression and regression models, respectively. As an alternative approach, we first performed Cox proportional hazards regression analysis, after adjusting for confounding factors and a continuous effect of long-term trend, on preterm delivery and estimated the hazard ratios for preterm birth according to the increase of averaged IQR of PM_{10} concentration levels in each trimester during pregnancy. We considered preterm delivery as an event and subject with a term birth as censoring in the Cox regression model. We censored the term births after 37 weeks of pregnancy at 38 gestational weeks in the Cox proportional hazards model (not at the date of birth). That is, the time variable of the Cox proportional hazards model was set to 38 for the term births after 37 weeks. Tied data were controlled by Breslow’s approximation. We next analyzed the data using an extended Cox model with time-dependent exposure, which permits nonproportional hazards, managing for a time-dependent covariate of PM_{10} exposure during each trimester by adding a product term on time function (Kleinbaum and Klein 2005). The statistical model for the extended Cox model with time-dependent exposure in this study was as follows: \( h(t|X(t)) = h_{0} (t)\exp [ {\sum\nolimits_{i = 1}^{p} {\beta_{i} X_{i} + \delta_{1} X_{1} g_{1} (t)} } ] \) for gestational age, t. In this equation, h_{0}(t) is a baseline hazard function as all covariates are zero, X_{i} (i = 2, …, p) is the ith time-independent covariate, and X_{1} × g_{1}(t) is a product term where g_{1}(t) is a function of t for X_{1}, which is a time-dependent covariate of PM_{10} exposure during each trimester. We first tried the extended Cox model containing dummy variables for different time periods. As a result, since the outcomes for different time periods showed linearity, we considered g_{1}(t) as a linear indicator function in this study. That is, we considered g_{1}(t) to be 1 if t < 32; 3 if 32 ≤ t < 37; 5 if t = 37; and 7 if t > 37. So, the corresponding hazard ratios per IQR of X_{1} were estimated by \( {\text{HR}} = { \exp }[ {{\text{IQR}}( {\hat{\beta }_{1} + \hat{\delta }_{1} } )} ] \) if t < 32; \( {\text{HR}} = { \exp }[ {{\text{IQR}}( {\hat{\beta }_{1} + 3\hat{\delta }_{1} } )} ] \) if 32 ≤ t < 37; \( {\text{HR}} = { \exp }[ {{\text{IQR}}( {\hat{\beta }_{1} + 5\hat{\delta }_{1} } )} ] \) if t = 37; \( {\text{HR}} = { \exp }[ {{\text{IQR}}( {\hat{\beta }_{1} + 7\hat{\delta }_{1} } )} ] \) if t > 37 where \( \hat{\beta }_{1} \) and \( \hat{\delta }_{1} \) are estimated coefficients for X_{1} and X_{1}g_{1}(t), respectively. All statistical analyses were conducted using SAS (version 8.2, SAS institute, Cary, NC).
Results
Distribution of study population characteristics in women who experienced preterm delivery and normal controls in Seoul, 1998–2000
N (%) | P value^{a} | Prevalence (%) | ||
---|---|---|---|---|
Preterm | Control | |||
Infant’s gender | <0.0001 | |||
Male | 6,851 (56.4) | 187,933 (51.9) | 3.5 | |
Female | 5,302 (43.6) | 174,081 (48.1) | 3.0 | |
Maternal age (mean ± SD, years) | 29.1 ± 4.1 | 28.3 ± 3.6 | <0.0001 | |
Paternal education (years) | 0.0003 | |||
≤12 | 5,614 (46.2) | 161,164 (44.5) | 3.4 | |
>12 | 6,539 (53.8) | 200,850 (55.5) | 3.2 | |
Marital status | <0.0001 | |||
Single | 95 (0.8) | 1,712 (0.5) | 5.3 | |
Married | 12,058 (99.2) | 360,302 (99.5) | 3.2 | |
Birth order | <0.0001 | |||
First | 6,283 (51.7) | 195,437 (54.0) | 3.1 | |
Second | 4,913 (40.4) | 142,615 (39.4) | 3.3 | |
Third | 878 (7.2) | 22,412 (6.2) | 3.8 | |
Fourth | 66 (0.5) | 1,375 (0.4) | 4.6 | |
Greater than or equal to fifth | 13 (0.1) | 175 (0.1) | 6.9 | |
Maternal occupation during pregnancy | 0.568 | |||
Yes | 507 (4.2) | 14,726 (4.1) | 3.3 | |
No | 11,646 (95.8) | 347,288 (95.9) | 3.2 | |
Maternal history of stillbirth | <0.0001 | |||
Yes | 169 (1.4) | 2,129 (0.6) | 7.4 | |
No | 11,984 (98.6) | 359,885 (99.4) | 3.2 | |
Month of birth | <0.0001 | |||
January | 1,186 (9.8) | 34,467 (9.5) | 3.3 | |
February | 1,046 (8.6) | 31,963 (8.8) | 3.2 | |
March | 992 (8.2) | 34,135 (9.4) | 2.8 | |
April | 873 (7.2) | 30,879 (8.5) | 2.8 | |
May | 928 (7.6) | 29,842 (8.2) | 3.0 | |
June | 1,004 (8.3) | 27,158 (7.5) | 3.6 | |
July | 1,002 (8.2) | 27,951 (7.7) | 3.5 | |
August | 1,044 (8.6) | 28,085 (7.8) | 3.6 | |
September | 997 (8.2) | 29,999 (8.3) | 3.2 | |
October | 971 (8.0) | 30,460 (8.4) | 3.1 | |
November | 987 (8.1) | 28,867 (8.0) | 3.3 | |
December | 1,123 (9.2) | 28,208 (7.8) | 3.8 | |
Day of the week at birth | <0.0001 | |||
Sunday | 1,245 (10.2) | 33,437 (9.2) | 3.6 | |
Monday | 1,882 (15.5) | 58,593 (16.2) | 3.1 | |
Tuesday | 1,802 (14.8) | 56,459 (15.6) | 3.1 | |
Wednesday | 1,843 (15.2) | 54,791 (15.1) | 3.3 | |
Thursday | 1,809 (14.9) | 55,554 (15.4) | 3.2 | |
Friday | 1,858 (15.3) | 57,932 (16.0) | 3.1 | |
Saturday | 1,714 (14.1) | 45,248 (12.5) | 3.7 | |
Total | 12,153 | 374,167 | 3.3 |
Distribution of PM_{10} concentration levels (μg/m^{3}) according to pregnancy period of exposure in Seoul, 1998–2000
Period of PM_{10} exposure | Mean ± SD | Percentile | IQR | |||||
---|---|---|---|---|---|---|---|---|
Min | 25th | 50th (median) | 75th | 90th | Max | |||
First trimester | 65.74 ± 11.06 | 43.78 | 57.88 | 65.19 | 73.25 | 77.48 | 94.11 | 15.37 |
Second trimester | 65.81 ± 12.05 | 42.57 | 56.50 | 66.05 | 73.78 | 80.77 | 96.22 | 17.27 |
Third trimester | 65.99 ± 11.93 | 42.57 | 57.04 | 66.49 | 73.99 | 79.30 | 96.22 | 16.95 |
Odds ratios for developing preterm delivery according to PM_{10} exposure in each trimester during pregnancy in a logistic regression model
Period of PM_{10} exposure | OR^{a} (95% CI) | Gestational age (weeks)^{b} | OR^{c} (95% CI) |
---|---|---|---|
First trimester | 0.91 (0.87–0.96) | <32 | 0.81 (0.70–0.93) |
32–36 | 0.93 (0.88–0.98) | ||
≥37 | Reference | ||
Second trimester | 0.97 (0.91–1.02) | <32 | 1.00 (0.85–1.17) |
32–36 | 0.96 (0.91–1.02) | ||
≥37 | Reference | ||
Third trimester | 1.09 (1.03–1.15) | <32 | 1.36 (1.16–1.59) |
32–36 | 1.05 (0.99–1.12) | ||
≥37 | Reference |
Regression parameter estimates for gestational age (weeks) according to PM_{10} exposure in each trimester during pregnancy in a linear regression model
Period of PM_{10} exposure | Beta^{a} (95% CI) | Gestational age (weeks)^{b} | Beta^{c} (95% CI) |
---|---|---|---|
First trimester | 0.04 (0.03–0.06) | <32 | −2.40 (−2.42 to −2.39) |
32–36 | −1.02 (−1.03 to −1.01) | ||
37 | −0.54 (−0.54 to −0.53) | ||
>37 | 0.08 (0.08–0.09) | ||
Second trimester | 0.02 (0.01–0.04) | <32 | −2.40 (−2.42 to −2.39) |
32–36 | −1.02 (−1.03 to −1.01) | ||
37 | −0.54 (−0.54 to −0.53) | ||
>37 | 0.08 (0.07–0.09) | ||
Third trimester | −0.05 (−0.06 to −0.04) | <32 | −2.44 (−2.46 to −2.43) |
32–36 | −1.06 (−1.07 to −1.05) | ||
37 | −0.57 (−0.58 to −0.56) | ||
>37 | 0.04 (0.03–0.05) |
Hazard ratios for developing preterm delivery according to PM_{10} exposure in each trimester during pregnancy
Period of PM_{10} exposure | Cox proportional hazards model | Test for nonproportionality | Gestational age (weeks)^{b} | Extended Cox model with time-dependent exposure | |
---|---|---|---|---|---|
HR^{a} (95% CI) | χ^{2} statistic (df) | P value | HR^{c} (95% CI) | ||
First trimester | 1.00 (0.99–1.01) | 29.85 (1) | <0.0001 | <32 | 1.23 (1.14–1.33) |
32–36 | 1.15 (1.09–1.21) | ||||
37 | 1.07 (1.04–1.10) | ||||
>37 | 1.00 (0.99–1.00) | ||||
Second trimester | 1.00 (0.99–1.01) | 7.66 (1) | 0.006 | <32 | 1.10 (1.03–1.18) |
32–36 | 1.07 (1.02–1.12) | ||||
37 | 1.03 (1.01–1.06) | ||||
>37 | 1.00 (0.99–1.01) | ||||
Third trimester | 1.00 (0.99–1.01) | 26.48 (1) | <0.0001 | <32 | 1.21 (1.12–1.29) |
32–36 | 1.13 (1.08–1.19) | ||||
37 | 1.07 (1.04–1.09) | ||||
>37 | 1.00 (0.99–1.01) |
Discussion
Exposure to adverse environmental agents is a well-known cause of preterm delivery, and many studies have shown that exposure to air pollution, such as PM_{10,} leads to a greater risk of preterm delivery in each month or trimester during pregnancy. However, the period during pregnancy in which the effect of exposure is most significant has varied based on the location in which the study was conducted and the population used in the study. In a cohort study of 97,518 neonates in California, USA, Ritz et al. (2000) reported that the risk of preterm delivery after exposure to a 50 μg/m^{3} increase in PM_{10} levels rose 13% during the first month of pregnancy and 18% during the 6th week before delivery using logistic regression analysis. Hansen et al. (2006) conducted a retrospective cohort study of 28,200 newborns in Brisbane, Australia, and found that the OR for preterm delivery attributed to a 4.5 μg/m^{3} increase in PM_{10} exposure during the first trimester was 1.15 (95% CI = 1.06–1.25) using logistic regression analysis. In a study conducted on 52,113 neonates between 2001 and 2002 in Incheon, South Korea, Leem et al. (2006) found that the relative risk for preterm delivery was 1.27 times greater for women exposed to a high level of PM_{10} exposure (64.6–106.4 μg/m^{3}) than for those exposed to a low PM_{10} (27.0–45.9 μg/m^{3}) during the first trimester (95% CI = 1.04–1.56) using logistic regression analysis. In a previously conducted study of 382,100 newborn babies born in Seoul, South Korea, we detected a significant relationship between PM_{10} concentrations during pregnancy and preterm birth by logistic regression, generalized additive logistic regression and the proportional hazard model with the risk of preterm delivery after a 42.5 μg/m^{3} increase in PM_{10} exposure during the seventh month of pregnancy being 1.07 (95% CI = 1.01–1.14) when logistic regression model was used (Ha et al. 2004).
We used the extended Cox model with time-dependent PM_{10} exposure to confirm that the estimated hazard ratio for preterm birth to PM_{10} exposure during each trimester varied with the gestational period of the neonates in this study. The results obtained using the extended Cox model with time-dependent exposure were similar to those obtained when the multiple regression model was used when the interaction of PM_{10} exposure with gestational period was considered. The hazards for preterm birth associated with PM_{10} exposure were higher with shorter gestational periods of neonates, and fetuses were affected more by PM_{10} exposure with shorter gestational periods. This trend was consistent in each trimester; however, the hazard ratios for preterm delivery by PM_{10} exposure in the first and third trimester were slightly higher than those in the second trimester. Therefore, the risks for preterm delivery or reduced gestational age attributable to PM_{10} exposure during pregnancy according to the exposure period during pregnancy need to be further investigated. We note that the PM_{10} effects on the births after 37 weeks of gestation, which were as considered as censored data in the Cox model, can be statistically interpreted as the effects of exposure duration. But the PM_{10} effects on the births after 37 weeks of gestation are not meaningful as an epidemiological estimate of the preterm risk.
Survival analysis involves a statistical data analysis for which the outcome variable of concern is time until an event takes place. Preterm birth is characteristic of a time that corresponds to the gestational age until preterm birth occurs, therefore, it is reasonable to study preterm delivery using survival analysis. In O’Neill et al. (2003), they confirmed that survival analysis has an advantage over logistic regression analysis by accounting for time dependent exposure and outcome. They performed a sensitivity analysis to prove that the HR is better estimate than the OR in their study of urinary tract infection and preterm delivery (O’Neill et al. 2003). In this study, we used the extended Cox model after accounting for the time-dependent covariate to show that the effect of PM_{10} exposure differs with gestational period, and we found that this time-to-event analysis is more effective than conventional approaches in the study of preterm delivery. Platt et al. (2004) verified the effectiveness of the use of the extended Cox model with time-varying covariates for the study of fetal and infant death. In this study, we found that the effect of PM_{10} exposure on preterm delivery changes according to gestational period. Therefore, the environmental exposures that lead to an increased risk of preterm delivery in each trimester during pregnancy should be studied by different gestational periods.
It should be noted that our study had several limitations, including a lack of individual risk factors for preterm delivery, a low concentration of air pollutants in residential areas. We did not evaluate an accurate distance between each participant’s home address and the closest monitoring station due to a lack of exact Geographic Information System (GIS) data. Also, we could not take Caesarian section into account in the model, since the Birth data obtained from the Korea National Statistical Office had no information about a way of delivery. However, this study also showed several interesting features and strengths. For example, we found that PM_{10} exposure had a greater impact on fetuses with a shorter gestational age, which is reasonable considering the immaturity of the defense mechanisms to toxic insults of exogenous chemicals; however, this has rarely been shown in other studies on preterm birth. Our study also included a larger sample of Korean women than other studies.
In conclusion, the risk for preterm birth attributable to PM_{10} exposure differed by gestational periods. In studies evaluating the association of air pollution exposure during pregnancy to the risk for preterm delivery, exposure period during pregnancy should be considered.
Acknowledgments
This study was supported by a Seoul Research & Business Development grant (#2005-1547-1), Republic of Korea.