Ambient temperature and air pollution, and therisk of preterm birth in Tehran, Iran: a time seriesstudy

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Ambient temperature and air pollution, and the risk of preterm birth in Tehran, Iran: a time series study

Mehdi Ranjbaran, Rasool Mohammadi, Mehdi Yaseri, Mehdi Kamari &

Kamran Yazdani

To cite this article: Mehdi Ranjbaran, Rasool Mohammadi, Mehdi Yaseri, Mehdi Kamari &

Kamran Yazdani (2020): Ambient temperature and air pollution, and the risk of preterm birth in Tehran, Iran: a time series study, The Journal of Maternal-Fetal & Neonatal Medicine, DOI:

10.1080/14767058.2020.1731458

To link to this article: https://doi.org/10.1080/14767058.2020.1731458

Published online: 11 Mar 2020.

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ORIGINAL ARTICLE

Ambient temperature and air pollution, and the risk of preterm birth in Tehran, Iran: a time series study

Mehdi Ranjbaranâ , Rasool Mohammadi^b , Mehdi Yaseriâ, Mehdi Kamari^cand Kamran Yazdaniâ

aDepartment of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran;

bDepartment of Epidemiology & Biostatistics, School of Public Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran;^cDeputy of Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran

ABSTRACT

Objective:The aim of the present study was to determine the relationship between temperature and air pollution, and preterm birth in Tehran, Iran.

Methods:In this time series study, the daily data of preterm births, air pollution, and maximum, minimum and mean temperature from March 2015 to March 2018 were used. To evaluate the effect of air pollution and temperature with and without adjustment of their mutual effects on preterm birth in lags (days) 0–21, the Distributed Lag Non-linear Models (DLNM) was used. The relative risk (RR) was estimated for extreme, moderate and mild heat (99th, 95th, 75th percentile) and cold (1st, 5th, 25th percentile) compared with the median, and for each 10-unit increase in PM_2.5, NO₂, and O₃, 5-unit increase in SO₂, and 1-unit increase in CO.

Results:The highest RR was seen in extreme (26.9C) and moderate (24.8C) heat of minimum temperature on lag 0 (RR ¼ 1.17; 1.05–1.31, Adjusted RR ¼ 1.16; 1.04–1.29, RR ¼ 1.15;

1.05–1.26, Adjusted RR¼ 1.14; 1.03–1.25, respectively). In regard of cold, the only significant effect was for maximum temperature on lags 7–9 (RR¼ 1.02; 1.00–1.04). Each 10-unit increase in PM2.5 in Lag 0 (RR¼1.008; 1.001–1.014) and lag 1 (RR¼ 1.004; 1.001–1.007) and in NO2in lag 0 (RR¼1.006; 1.000–1.012) had significant effects.

Conclusion:Maternal exposure to a minimum daily temperature of 26.9 and 24.8C compared to 13.2C increased the risk of preterm birth by 17 and 15% on the same day, respectively. This risk increased by 0.8 and 0.6%, on the same day for each 10-unit increase in PM_2.5 and NO₂, respectively.

ARTICLE HISTORY Received 11 October 2019 Revised 5 February 2020 Accepted 14 February 2020 KEYWORDS

Air pollution; ambient temperature; premature birth

Introduction

Despite improved maternal and neonatal care, preterm birth is still an important health problem worldwide.

According to the World Health Organization (WHO) estimates, 5–18% of the births are preterm in 184 countries and more than 15 million preterm infants are born annually, of whom more than 1 million die due to the consequences of preterm birth [1].

According to the results of a meta-analysis, the prevalence of preterm birth is 9.2% in Iran [2]. However, the etiology of preterm birth is largely unknown [3,4].

On the other hand, there are inconsistent reports of the effect of the temperature (upper and lower spec- tra) and air pollution on pregnancy outcomes, including preterm birth in different parts of the world, and there is growing trend in the relevant studies worldwide [5–12]. In a systematic review, although Zhang et al. found evidence of the effect of high and low

temperature on preterm birth, stillbirth, and low birth weight (LBW), they considered the evidence insuffi- cient and emphasized further research with advanced designs, more accurate measurement of exposure to temperature during pregnancy, and more efficient methods of finding exposure windows [5]. In this regard, only one study was conducted in Sabzevar, Iran, which showed a significant correlation between extreme cold and heat, and preterm birth [8].

The results of the systematic reviews and meta- analyses of the effect of air pollution on preterm birth are also inconsistent [13,14] and more studies are required to confirm the results. In Iran, only one study was conducted in Ahwaz, in this regard [15].

Different models and methods have been proposed to study the effect of air pollution and temperature on health outcomes. However, since the short-term effects of temperature on pregnancy outcomes may be nonlinear, may not be limited to the exposure day,

CONTACTKamran Yazdani [email protected], [email protected] Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, 16 Azar Ave, Tehran, Iran

ß2020 Informa UK Limited, trading as Taylor & Francis Group https://doi.org/10.1080/14767058.2020.1731458

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it is recommended to use the Distributed Lag Non- linear Models (DLNM) [16]. Considering the need for more studies and lack of evidence in this regard, the aim of the present study was to determine the effect of temperature and air pollution on preterm birth in Tehran, capital of Iran between 2015 and 2018 using the DLNM model.

Materials and methods Data collection

In this time series study, outcome variable was daily number of preterm births occurred from March 2015 to March 2018 whose mothers were permanent Tehran residents. Data were obtained from the Iranian Maternal and Neonatal Network, an important system for monitoring maternal and neonatal health indices, belongs to the Neonatal Health Office, Iranian Ministry of Health. Meteorological data, including the daily minimum, maximum, and mean 24-h temperature and relative humidity were collected from three stations of Iran Meteorological Organization: Mehrabad, Shemiran, and Geophysic [17], and the mean values of these stations were calculated for each variable. The National Oceanic and Atmospheric Administration website was used for missing data, which were 13 days for mean temperature, 14 days for maximum temperature, and 7 days for minimum temperature [18].

The air pollution data, including carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter 2.5 microns (PM2.5) were collected from 21 active stations of Tehran Air Quality Control Company [19,20] and 16 stations of the Department of the Environment. Since there were missing data for some stations, the hourly data of the air pollutants were validated according to the WHO criteria [21]. Based on the WHO [21] and US Environmental Protection Agency (EPA) [22] criteria, the daily data of each pollutant, including the maximum 8-h moving average for O3(ppb), maximum 1-h average for NO2 (ppb), maximum 8-h moving average for CO (ppm), and 24-h average for SO2 (ppb) and PM2.5 (mg/m³) were calculated according to the average values of validated stations.

Statistical analysis

Appropriate descriptive statistics were used to describe the data. For time series analysis, considering the possibility of overdispersion in daily cases of preterm birth, a quasi-Poisson regression model in

combination with the DLNM was used as follows:

YtPoisson ðltÞ:LogðltÞ ¼aþcb ðX, 5, lag, 4Þ þns ðRhÞ þns ðTime, 7yearÞ

þas:factor ðHolidayÞ þas:factor ðDOWÞ

Where Yt represents the number of preterm births on day t,ais the intercept, cb is the“cross-basis” function defined for temperature or air pollution, X is the temperature or air pollution. A b-spline function with five degrees of freedom (df) and a lag with 4 df was define for temperature and a lin function was defined for air pollution, and ns represents a natural cubic spline function [23,24]. A natural cubic spline was also used for relative humidity (Rh). Moreover, a natural cubic spline with 7 df for each year (73) was applied to control long-term trends and seasonality in the outcomes of this study. The Akaike Information Criterion (AIC) was used to select the df. In the above model, DOW indicates the day of the week at time t and Holiday is a binomial variable representing holidays.

The df for X and lag (day) was also selected based on AIC. In the adjusted model for air pollution, to assess the effect of temperature, the air pollutants that had a significant association with preterm birth were entered into the model using the ns function. Also, in the adjusted model for temperature, to investigate the effect of air pollution, the mean daily temperature was entered into the model using the ns function.

We evaluated the effect of different lags (days) including exposure from lag 0 to lag 21 prior to the outcome. The relative risk and 95% confidence interval (RR; 95% CIs) was estimated for extreme, moderate and mild heat (99th, 95th, 75th percentile) and cold (1st, 5th, 25th percentile) corresponding to minimum, maximum, and mean daily temperature compared with their respective median temperature as a reference. For the effect of air pollution, depending on the type of pollutant, the RR and 95%, CIs was calculated for every 1-, 5-, or 10-unit increase. The data were analyzed using DLNM package incorporated in the R soft- ware version 3.5.2 [16,23]. p-value .05 were considered significant.

Ethical considerations

The proposal of the study was approved by the Ethics Committee of the School of Public Health, Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1397.

156). The required authorizations were obtained from the relevant organizations to access exposures and outcomes data that were not available free of charge.

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Moreover, the personal data of the mothers were not included in the data.

Results

Descriptive statistics

A total of 542,492 births were registered in the city of Tehran during March 2015 to March 2018, with approximately equal numbers per year. Among permanent residents of Tehran, the overall risk of preterm birth was 7.36 cases per 100 live births (37,763/513,110).

The mean daily preterm birth number was 34.5 cases (SD ¼ 9.9; min ¼ 1, Max ¼ 59). Figure 1 presents the daily distribution of temperature and air pollutants. The concentrations of all air pollutants except for ozone were higher in cold seasons. The minimum and maximum daily temperature ranged from7.2 to 28.3C and 1.4 to 42.2C, respectively.

The mean daily temperature was between 4.2 and 36.5C.

Relationship between temperature and preterm birth

Among air pollutants, PM2.5 and NO2, which had significant relationships with preterm birth in separate models, were controlled in the adjusted model.

Figure 2 shows overall trend of the relationship between maximum, minimum, and mean daily temperature and preterm birth during 21 lags (days) in three-dimensional mode.Tables 1–3 also show the RR and 95% CIs of these effects for the 1st, 5th, 25th, 75th, 95th, and 99th percentiles in comparison with the median temperature for lags 0–10. In the unadjusted model, in terms of extreme heat (99th percentile), maximum, minimum, and mean temperature had a significant effect on preterm birth in lag 0 and within the early lags after exposure. The highest RR was related to the min temperature on lag 0 for both 99th (RR ¼ 1.17) and 95th (RR ¼ 1.15) percentiles;

these effects were somewhat lower in the adjusted model. For extreme (first percentile) and moderate (fifth percentile) cold on lag 0 and within early lags of exposure, the RR was below 1 and statistically non- significant. In this regard, the only significant effect (RR¼1.02) was observed for maximum daily temperature for extreme cold on lags 7–9 after exposure (results for lags 11–21 are not presented for heat and cold effects).

Relationship between air pollution and preterm birth

Figure 3 presents the relationship between different air pollutants and preterm birth during 21 lags of exposure with and without adjustment for temperature. Moreover, the RR and 95% CIs for lags 0–3 are presented in Table 4. In the unadjusted model, PM2.5

in lags 0 (RR ¼1.008) and 1 (RR ¼1.006) and NO2 in lag 0 (RR ¼ 1.004) had significant effects on preterm birth; in other words, each 10-unit increase in the concentration of these air pollutants increased the risk of preterm birth by less than 1%. In the adjusted model for mean temperature, only the effect of PM2.5 in lags 0 (RR¼1.007) was significant. The association between other air pollutants and preterm birth was not statistically significant.

Discussion

The main objectives of this study was to determine the association between temperature and air pollution with preterm birth. The results showed that in the lag 0 and lags 1–10, exposure to extreme and moderate heat of minimum, maximum, and mean temperature increased the risk of preterm birth in both adjusted and unadjusted models. A weak effect was observed for extreme cold according to maximum daily temperature in days 7–9 after exposure. Among different air pollutants, PM2.5had a significant effect on preterm birth in lags 0 and 1 in the unadjusted and in lag 0 in the adjusted model. NO2 only had a significant effect in preterm birth in the unadjusted model, while the effects of O3, CO, and SO2 were not significant in either model.

Temperature

Zhang et al. conducted a systematic review of 36 studies with different designs, analytical and measurement methods, and exposure windows, published on the effect of temperature on pregnancy outcomes by November 2016. They found some evidence of the effect of high temperature on preterm birth, stillbirth, and LBW. In addition, some studies have reported the adverse effects of cold on pregnancy outcomes like preterm birth and LBW. In general, although no defin- ite causal relationship has been found for the effect of temperature on pregnancy outcomes, there is stronger evidence for the adverse effect of heat versus cold [5], which is in agreement with the results of the present study.

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Figure 1. Daily distribution of temperature and air pollutants in Tehran during 2015–2018. (A) maximum daily temperature (C), (B) mean daily temperature (C), (C) minimum daily temperature (C), (D) PM2.5 (mg/m³), (E) maximum 1-h average of NO₂(ppb), (F) maximum 8-h moving average of O3 (ppb), (G) mean 24-h average of SO2 (ppb), (H) maximum 8-h moving average of CO (ppm).

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However, in line with our findings, some previous studies with a time series design and DLNM analysis, showed a direct relationship between high temperatures and the risk of preterm birth. In these types of studies, ecological confounding factors like air pollutants, relative humidity, holidays, and weekdays are controlled in the model, and individual variables that do not change in the short term cannot play a

confounding role [6,25]. For example, studies conducted in Flanders, Belgium [4], Valencia, Spain [7], Central Australia [26], Sabzevar, Iran [8] have found similar results, although the strength of association may be different in these studies. On the other hand, some studies have reported rather different results; for example, a study conducted in Shenzhen, China found a negative association between high temperatures Figure 2. Three-dimensional graph of the relationship between minimum, maximum, and mean daily temperature and preterm birth in Tehran (reference: median temperature for maximum temperature: 23.2C, for minimum temperature: 13.2C, for mean temperature: 17.9C). (A) unadjusted for PM2.5 and NO₂, (B) adjusted for PM2.5 and NO₂.

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Table 1. The relationship between heat and cold according to maximum daily temperature and preterm birth in Tehran (reference: median for maximum temperature: 23.2C).

Relative risk (95% confidence interval)

Lag (day)

Unadjusted for air pollution (PM2.5and NO2) Adjusted for air pollution (PM2.5and NO2) 99th percentile

(39.04C)

95th percentile (37.28C)

75th percentile (32.63C) Heat

0 1.12 (1.001.26) 1.09 (0.991.21) 1.03 (0.951.12) 1.11 (0.991.24) 1.08 (0.971.19) 1.02 (0.941.11) 1 1.07 (1.021.13) 1.05 (1.001.11) 1.02 (0.981.06) 1.06 (1.011.12) 1.04 (0.991.09) 1.01 (0.971.05) 2 1.04 (1.001.08) 1.03 (0.991.06) 1.01 (0.981.04) 1.03 (0.990.07) 1.02 (0.991.06) 1.00 (0.981.03) 3 1.02 (0.981.07) 1.02 (0.981.06) 1.00 (0.971.04) 1.02 (0.970.06) 1.01 (0.971.05) 1.00 (0.971.03) 4 1.02 (0.981.06) 1.01 (0.981.05) 1.00 (0.971.03) 1.01 (0.970.06) 1.01 (0.971.05) 1.00 (0.971.03) 5 1.02 (0.9971.06) 1.02 (0.981.05) 1.01 (0.981.03) 1.01 (0.980.05) 1.01 (0.981.04) 1.00 (0.981.03) 6 1.02 (0.9961.05) 1.02 (0.991.05) 1.01 (0.991.03) 1.02 (0.990.05) 1.01 (0.991.04) 1.01 (0.991.03) 7 1.03 (1.001.05) 1.02 (0.9971.05) 1.01 (0.991.03) 1.02 (0.990.05) 1.02 (0.991.04) 1.01 (0.991.03) 8 1.03 (1.001.06) 1.02 (0.9981.05) 1.01 (0.991.03) 1.02 (0.9960.05) 1.02 (0.991.04) 1.01 (0.991.03) 9 1.03 (1.001.06) 1.02 (0.9971.05) 1.01 (0.991.03) 1.02 (0.990.05) 1.02 (0.991.04) 1.01 (0.991.03) 10 1.03 (0.9981.06) 1.02 (0.9951.05) 1.01 (0.991.03) 1.02 (0.990.05) 1.02 (0.991.05) 1.01 (0.991.03) Cold

Lag (day) 1st percentile (3.06C)

1st percentile (3.06C)

25th percentile (13.77C) 0 0.99 (0.891.10) 1.00 (0.911.10) 1.01 (0.951.08) 1.02 (0.911.13) 1.02 (0.931.13) 1.02 (0.961.10) 1 1.00 (0.951.05) 1.01 (0.971.06) 1.01 (0.981.05) 1.01 (0.961.06) 1.02 (0.971.07) 1.02 (0.991.05) 2 1.01 (0.981.04) 1.01 (0.991.05) 1.01 (0.991.04) 1.01 (0.971.04) 1.02 (0.981.05) 1.02 (0.991.04) 3 1.01 (0.981.05) 1.02 (0.981.05) 1.01 (0.991.04) 1.01 (0.971.05) 1.01 (0.981.05) 1.01 (0.991.04) 4 1.02 (0.971.05) 1.02 (0.981.05) 1.01 (0.981.04) 1.01 (0.971.05) 1.01 (0.981.05) 1.01 (0.981.03) 5 1.02 (0.991.05) 1.01 (0.991.04) 1.01 (0.991.03) 1.01 (0.981.05) 1.01 (0.981.04) 1.01 (0.991.03) 6 1.02 (0.991.04) 1.01 (0.991.03) 1.00 (0.991.02) 1.02 (0.991.04) 1.01 (0.991.03) 1.00 (0.991.02) 7 1.02 (1.001.04) 1.01 (0.991.03) 1.00 (0.991.02) 1.02 (1.001.04) 1.01 (0.991.03) 1.00 (0.991.02) 8 1.02 (1.001.04) 1.01 (0.981.02) 0.997 (0.981.01) 1.02 (1.001.04) 1.01 (0.981.02) 0.999 (0.981.01) 9 1.02 (1.001.04) 1.01 (0.991.03) 0.996 (0.981.01) 1.02 (1.001.05) 1.01 (0.981.02) 0.998 (0.981.01) 10 1.02 (0.9991.04) 1.01 (0.991.03) 0.99 (0.981.01) 1.02 (1.001.05) 1.01 (0.981.02) 0.997 (0.981.01)

Table 2. The relationship between heat and cold according to minimum daily temperature and preterm birth in Tehran (reference: median for minimum temperature: 13.2C).

Lag (day)

(26.93C)

0 1.17 (1.051.31) 1.15 (1.051.26) 1.10 (1.031.18) 1.16 (1.041.29) 1.14 (1.031.25) 1.09 (1.021.17) 1 1.12 (1.061.18) 1.11 (1.061.15) 1.07 (1.041.11) 1.11 (1.051.17) 1.10 (1.051.15) 1.07 (1.031.11) 2 1.08 (1.041.12) 1.07 (1.041.11) 1.05 (1.031.08) 1.08 (1.041.12) 1.07 (1.041.11) 1.05 (1.031.08) 3 1.06 (1.021.11) 1.06 (1.021.10) 1.04 (1.011.08) 1.06 (1.011.11) 1.06 (1.021.10) 1.04 (1.011.08) 4 1.06 (1.011.10) 1.05 (1.011.09) 1.04 (1.011.07) 1.05 (1.011.10) 1.05 (1.011.09) 1.04 (1.011.07) 5 1.06 (1.021.09) 1.05 (1.021.08) 1.04 (1.021.07) 1.05 (1.021.09) 1.05 (1.021.08) 1.04 (1.021.07) 6 1.06 (1.031.09) 1.05 (1.031.08) 1.04 (1.021.06) 1.05 (1.031.08) 1.05 (1.031.08) 1.04 (1.021.06) 7 1.06 (1.021.09) 1.05 (1.031.08) 1.04 (1.031.06) 1.05 (1.031.08) 1.05 (1.031.07) 1.04 (1.021.06) 8 1.06 (1.031.08) 1.05 (1.031.08) 1.04 (1.021.06) 1.05 (1.031.08) 1.05 (1.031.07) 1.04 (1.021.06) 9 1.06 (1.031.08) 1.05 (1.031.08) 1.04 (1.021.06) 1.05 (1.031.08) 1.05 (1.031.07) 1.04 (1.021.06) 10 1.05 (1.031.08) 1.05 (1.031.08) 1.04 (1.021.06) 1.05 (1.021.08) 1.05 (1.021.07) 1.04 (1.021.06) Cold

25th percentile (5.27C) 0 0.97 (0.871.05) 1.00 (0.931.08) 0.998 (0.951.05) 0.97 (0.891.06) 1.01 (0.931.09) 1.00 (0.951.06) 1 0.98 (0.941.03) 1.01 (0.981.05) 1.01 (0.981.04) 0.98 (0.941.03) 1.01 (0.981.05) 1.01 (0.981.04) 2 0.99 (0.971.03) 1.02 (0.991.05) 1.01 (0.991.03) 0.99 (0.961.02) 1.02 (0.991.05) 1.01 (0.991.03) 3 1.00 (0.971.04) 1.02 (0.991.05) 1.01 (0.991.04) 0.99 (0.961.03) 1.02 (0.981.05) 1.01 (0.991.03) 4 1.00 (0.971.04) 1.02 (0.991.05) 1.01 (0.991.03) 0.99 (0.961.03) 1.01 (0.981.05) 1.01 (0.981.03) 5 0.999 (0.971.03) 1.01 (0.991.04) 1.01 (0.981.02) 0.99 (0.961.02) 1.01 (0.981.03) 1.00 (0.981.02) 6 0.996 (0.971.02) 1.00 (0.981.03) 0.997 (0.981.01) 0.99 (0.971.02) 1.01 (0.991.02) 0.997 (0.981.01) 7 0.99 (0.971.02) 0.999 (0.981.02) 0.99 (0.981.01) 0.99 (0.971.02) 0.999 (0.981.02) 0.99 (0.981.01) 8 0.99 (0.971.01) 0.99 (0.981.01) 0.989 (0.981.00) 0.99 (0.971.01) 0.995 (0.981.02) 0.989 (0.981.00) 9 0.989 (0.971.01) 0.99 (0.971.01) 0.986 (0.981.00) 0.99 (0.971.01) 0.99 (0.971.01) 0.987 (0.971.00) 10 0.988 (0.971.01) 0.988 (0.971.01) 0.98 (0.970.999) 0.99 (0.971.01) 0.99 (0.971.01) 0.985 (0.971.00)

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(95th and 99th percentiles) and preterm birth based on the 2005–2011 data [6]. A study in Stockholm based on the data of 1998–2006 showed that when the mean temperature reached the 75th percentile during 4 weeks before birth, the risk of preterm birth increased by 4–5% (reference group: mean annual temperature), while no consistent effect was observed for extreme heat during 4 weeks before birth [27]. A study conducted in Rome, Italy showed that the risk of preterm birth increased by 1.9% for each 1C increase in temperature, which was not statistically significant [28].

Although some previous studies with similar designs and analytical methods found a direct relationship between cold and preterm birth, the results of our study showed no significant association except for a weak effect of maximum daily temperature in lags 7–9 after exposure. For example, studies in Shenzhen, China [6], Sabzevar, Iran [8], Flanders, Belgium [4], Central Australia [23], Brisbane, Australia [29] reported a significant effect of cold on the risk of preterm birth. On the other hand, a study in Rome found no relationship between cold and preterm birth [28]. A study conducted in Stockholm found a weak negative but inconsistent effect of cold during 4 weeks before birth [27].

In a retrospective study of the data of 23 million births in the USA, low temperature was associated with a rela- tively low risk of preterm birth [30].

A possible explanation for these controversies and inconsistencies in the results may be climate diversity of various cities. For example, in the study by Liang et al. in Shenzhen, China, the median temperature was 24.5C and the 1st and 99th percentiles of daily mean temperature were 9 and 30.7C respectively, which were very different from our study [6]. Similarly, in the study performed in Sabzevar, Iran, the extreme high and low temperatures were 11.2 and 45.4C, respectively, which were more extreme than the high and low temperatures in the present study [8]. As an another explanation, the role of development and the socioeconomic status of people living in different cities regarding the use of heating and cooling applian- ces and their behavior toward high and low temperature cannot be neglected [8,30].

As for the possible mechanisms of the effect of temperature, there is a better biological explanation for heat versus cold. During pregnancy, due to changes in the thermoregulation ability of the body [31,32] and weight gain and the resulting decrease in the body surface area to body mass ratio, the Table 3. The relationship between heat and cold according to mean daily temperature and preterm birth in Tehran (reference:

median for mean temperature: 17.9C).

Lag (day)

(33.07C)

0 1.16 (1.031.31) 1.13 (1.021.26) 1.07 (0.981.16) 1.15 (1.021.29) 1.12 (1.011.24) 1.05 (0.971.15) 1 1.10 (1.041.17) 1.09 (1.031.14) 1.05 (1.001.09) 1.09 (1.031.16) 1.08 (1.021.13) 1.04 (0.991.08) 2 1.06 (1.021.11) 1.05 (1.021.09) 1.03 (0.9991.06) 1.05 (1.011.09) 1.05 (1.011.08) 1.03 (0.991.06) 3 1.04 (0.991.09) 1.04 (0.991.08) 1.02 (0.9891.06) 1.04 (0.991.09) 1.03 (0.991.08) 1.02 (0.9871.06) 4 1.04 (0.991.08) 1.04 (0.991.08) 1.02 (0.9891.06) 1.03 (0.991.08) 1.03 (0.991.07) 1.02 (0.9881.06) 5 1.04 (1.001.08) 1.03 (1.001.07) 1.02 (0.9961.05) 1.03 (0.991.07) 1.03 (0.9961.06) 1.02 (0.9951.05) 6 1.04 (1.011.07) 1.04 (1.001.07) 1.02 (1.001.05) 1.03 (1.001.07) 1.03 (1.001.06) 1.02 (1.001.05) 7 1.04 (1.011.07) 1.04 (1.001.06) 1.03 (1.001.05) 1.04 (1.011.07) 1.03 (1.011.06) 1.03 (1.001.05) 8 1.04 (1.011.07) 1.04 (1.001.07) 1.03 (1.001.05) 1.04 (1.011.07) 1.03 (1.011.06) 1.03 (1.001.05) 9 1.04 (1.011.07) 1.04 (1.001.07) 1.03 (1.001.05) 1.04 (1.011.07) 1.03 (1.011.06) 1.03 (1.001.05) 10 1.04 (1.011.07) 1.04 (1.001.07) 1.03 (1.001.05) 1.04 (1.011.07) 1.03 (1.011.06) 1.03 (1.001.05) Cold

25th percentile (9.14C) 0 0.97 (0.881.07) 0.99 (0.911.08) 1.00 (0.941.07) 0.989 (0.891.09) 1.01 (0.921.11) 1.01 (0.951.08) 1 0.989 (0.941.04) 1.01 (0.961.05) 1.01 (0.981.04) 0.996 (0.951.04) 1.01 (0.971.06) 1.02 (0.981.05) 2 1.00 (0.971.04) 1.02 (0.991.05) 1.01 (0.991.04) 1.00 (0.971.03) 1.01 (0.991.05) 1.02 (0.991.04) 3 1.01 (0.981.05) 1.02 (0.991.05) 1.01 (0.991.04) 1.01 (0.971.05) 1.02 (0.981.05) 1.01 (0.991.04) 4 1.02 (0.981.05) 1.02 (0.981.05) 1.01 (0.991.04) 1.01 (0.971.05) 1.01 (0.981.05) 1.01 (0.981.04) 5 1.02 (0.981.04) 1.01 (0.9871.04) 1.01 (0.991.03) 1.01 (0.981.04) 1.01 (0.981.04) 1.01 (0.981.03) 6 1.01 (0.991.04) 1.01 (0.9881.03) 1.00 (0.991.02) 1.01 (0.981.04) 1.01 (0.991.03) 1.00 (0.991.02) 7 1.01 (0.991.03) 1.01 (0.9871.03) 0.997 (0.981.01) 1.01 (0.991.03) 1.01 (0.991.03) 0.998 (0.981.01) 8 1.01 (0.991.03) 1.00 (0.981.02) 0.99 (0.981.01) 1.01 (0.991.03) 1.00 (0.991.02) 0.995 (0.981.01) 9 1.01 (0.9871.03) 1.00 (0.981.02) 0.99 (0.981.01) 1.01 (0.991.03) 1.00 (0.981.02) 0.99 (0.981.01) 10 1.01 (0.9851.03) 0.99 (0.9771.02) 0.989 (0.971.01) 1.01 (0.991.03) 1.00 (0.981.02) 0.99 (0.981.01)

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Figure 3. The relationship between air pollution and preterm birth in Tehran. (A: unadjusted for mean temperature, B: adjusted for mean temperature.) The centerline represents Relative Risk (RR) and the dashed line indicates the 95% confidence interval. Air pollutants; maximum 8-h moving average of O₃(ppb), maximum 1-h average of NO₂(ppb), maximum 8-h moving average of CO (ppm), and mean 24-h average of SO₂(ppb) and PM2.5 (mg/m³).

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pregnant women’s ability to lose heat is compromised, making them more susceptible to heat injury compared to cold injury [30]. As a result, the mothers become dehydrated upon heat exposure [6] and the fetal blood circulation reduces, leading to uterus con- tractions [3,6,33]. Moreover, heat stress can result in the secretion of some hormones like cortisol and oxy- tocin that induce labor [6,33,34].

In this study, adjusting the effect of air pollution had a very weak effect on the relationship between temperature and preterm birth. Several previous studies also found that entering air pollution variables into the model had little impact on the effect of temperature [7,8,28].

Air pollution

Each 10mg/m³increase in PM2.5 in lag 0 (RR ¼1.008) and lag 1 (RR¼ 1.004) and 10 ppb increase in NO2 in lag 0 (RR ¼ 1.006) had significant weak effects on

preterm birth. Although this effects may seem to be weak, it can be concluded that when for example the concentration of PM2.5 increases from a “good level” of 0–12mg/m³[35] to the 99th percentile in our study, i.e. about 80mg/m³, the risk of preterm birth increases by about 6% (1.008⁷¼1.06).

Several systematic reviews and meta-analyses have examined the effect of air pollution on different pregnancy outcomes. The results of a systematic review and meta-analysis by Li et al. showed an increased risk of preterm birth upon mothers’exposure to PM2.5dur- ing pregnancy; however, the authors believed that further studies were required regarding the duration of exposure [13]. In a meta-analysis by Zhu et al., for each 10mg/m³increase in PM2.5during pregnancy, the pooled risk of preterm birth increased significantly by 10% [36]. Yuan et al. conducted a systematic review of cohort studies and concluded that exposure to PM2.5

before birth had undesirable effects on pregnancy outcomes, including preterm birth [37]. Shah and Figure 3. Continued.

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Balkhair analyzed 41 studies that met the inclusion criteria. According to the results, exposure to SO2 and PM2.5 were associated with an increased risk of preterm birth, while the evidence for the effects of NO2, CO, and O3was not convincing [14].

In a study that had a similar design and analysis with our study, Dastoorpoor et al. found a significant association between each 10-unit increase in NO2and CO and preterm birth in lag 0 in Ahwaz, Iran.

Moreover, they reported a significant relationship between preterm birth and each 10-unit increase in CO in lag 1, PM10 in lags 10, 11, and 12, and NO in lags 3, 4, 10, 11, 12, and 13 [15].

It seems that PM2.5 is an important air pollutant that has received much attention in primary and secondary studies and the available evidence supports its adverse effects on pregnancy outcomes. There are controversial reports of the effects of other air pollutants, which could be secondary to several reasons like different designs of the studies, exposure time, concentrations of pollutants, analytical approach, and demographic characteristics of study populations.

As for a possible mechanism of action, PM2.5due to their small size can pass the pulmonary barrier, enter the circulation, and find their way to different organs [14,38], resulting in oxidative inflammation in lungs and other organs like the placenta, which increases the risk of preterm birth. The phagocytic system has no effect on PM2.5due to its small size [14].

Limitations and strengths

Having data from relevant national organizations was an element of strength, but since the data were at the aggregate level, as with other similar studies might not indicate the real exposure of people, because mothers might reduce their exposure by using air con- ditioning equipment that could affect the actual exposure. This study was the first research into the effects of ambient temperature and air pollution on pregnancy outcomes in a large sample size in the most populated city of Iran, Tehran, as the capital and one of the polluted cities in the world. In addition, the air pollution data were first validated using methods recommended by the WHO before use.

Conclusion

High temperature (heat) had a significant effect on preterm birth in both unadjusted and adjusted for PM2.5 and NO2, as exposure to a minimum daily temperature of 26.9C (extreme heat) and 24.8C Table4.TherelationshipbetweenairpollutionandpretermbirthinTehran. ModelLag(day)

Relativerisk(95%confidenceinterval) 1ppmincreaseinCO10mg/m3increaseinPM2.510ppbincreaseinNO210ppbincreaseinO35ppbincreaseinSO2 Unadjustedformeantemperature01.008(1.0011.014)1.006(1.0001.012)0.990(0.9771.003)1.013(0.9961.032)1.011(0.9991.023) 11.004(1.0011.007)1.003(0.9991.006)0.993(0.9851.001)1.007(0.9961.018)1.006(0.9991.013) 21.001(0.9991.004)1.001(0.9981.003)0.996(0.9891.002)1.002(0.9941.010)1.002(0.9971.007) 30.999(0.9971.002)0.999(0.9971.002)0.998(0.9921.004)0.999(0.9911.007)0.999(0.9951.005) Adjustedformeantemperature01.007(1.0001.013)1.004(0.9991.010)0.990(0.9781.003)1.012(0.9931.030)1.007(0.9951.019) 11.003(0.9991.001)1.001(0.9981.004)0.993(0.9851.001)1.004(0.9921.016)1.002(0.9951.010) 21.001(0.9971.003)0.999(0.9971.002)0.996(0.9891.002)0.999(0.9911.009)0.999(0.9931.005) 30.999(0.9961.002)0.998(0.9961.001)0.998(0.9921.004)0.997(0.9981.006)0.997(0.9921.003)

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(moderate heat) compared to 13.2C (median) increased the risk of preterm birth by 17 and 15% on the same day, respectively. As for air pollution, each 10mg/m³ increase in the 24-h average of PM2.5 and 10 ppb increase in the maximum 1-h average of NO2

in lag 0 increased the risk of preterm birth by 0.8 and 0.6%, respectively. Other air pollutants had no significant effects on preterm birth.

Acknowledgements

The authors wish to thank the personnel of the Office of Neonatasl Health, Iranian Ministry of Health and Medical Education, especially Dr. Abbas Habibelahi and Dr.

Mohammad Heidarzadeh for their assistance in collecting the primary data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This study was parts of a PhD thesis supported by Tehran University of Medical Sciences [grant no: 240/700].

ORCID

Mehdi Ranjbaran http://orcid.org/0000-0002-0313-3373 Rasool Mohammadi http://orcid.org/0000-0002-4507-5079 Kamran Yazdani http://orcid.org/0000-0001-6666-1272

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