Maternal PM2.5 exposure and preterm birth in Bangladesh: A nationwide cross-sectional analysis

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Pregnant women are among the most vulnerable populations to PM 2.5 exposure worldwide. Although the impacts of maternal PM 2.5 exposure on preterm birth (PTB) have been widely studied globally, epidemiological evidence specific to Bangladesh is lacking. Therefore, we assessed the associations between maternal PM 2.5 exposure and PTB risk across different pregnancy periods in Bangladesh. Methods We used Demographic and Health Survey (DHS) data to conduct this cross-sectional study in Bangladesh. A total of 8,059 births occurring between 2017 and 2022 were included, along with their sociodemographic information. The monthly PM 2.5 concentrations were estimated using satellite-based models linked to each cluster’s global position system (GPS) location. We constructed exposure-specific multivariable logistic regression models to assess the association between maternal PM 2.5 exposure and PTB risk. Results During the birth period (2017–2022), the average PM 2.5 concentration was 67.9 µg/m³. According to the fully adjusted models, a 10 µg/m³ increase in PM 2.5 was significantly associated with PTB risk during the complete pregnancy period (OR = 1.13; 95% CI: 1.04–1.23), preconception (OR = 1.03; 95% CI: 1.00–1.07), and third trimester (OR = 1.05; 95% CI: 1.01–1.09). The association was particularly strong in rural areas (OR = 1.30; 95% CI: 1.17–1.45). The probability curve indicated a nonlinear increase in the PTB probability with increasing PM 2.5 concentration, with a predicted probability of 6% at the lowest exposure level to 14% at concentrations above 100 µg/m³. Conclusion Our study revealed a significant association between maternal PM 2.5 exposure and increased odds of PTB. These findings highlight the need for targeted specific interventions to reduce air pollution to protect vulnerable populations. Air pollution Particulate matter PTB Preterm birth Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Air pollution has emerged as one of the leading environmental risk factors for mortality worldwide, contributing to an estimated 7.9 million deaths in 2023 [ 1 ]. The burden of these deaths remains disproportionately high in low- and middle-income countries (LMICs), where populations experience greater exposure levels and have limited access to healthcare resources. Among various pollutants, fine particulate matter (PM 2.5 ; aerodynamic diameter < 2.5 µm) has been identified as particularly harmful. Numerous studies have demonstrated strong associations between both acute and chronic exposure to PM 2.5 and increased risks of cardiopulmonary diseases [ 2 – 4 ]. Researchers emphasize that no safe threshold exists for PM 2.5 exposure, as adverse effects occur even at very low concentrations [ 5 ]. The public health impacts of outdoor PM 2.5 exposure are well documented across the life course, with increasing evidence that pregnant individuals and developing fetuses are particularly vulnerable [ 6 – 9 ]. In this study, we specifically focused on the effect of PM 2.5 exposure during pregnancy on preterm birth (PTB), defined as a live birth occurring before 37 completed weeks of gestation [ 10 ]. PTB not only contributes significantly to fetal and neonatal morbidity and mortality, but also carries the risk of lifelong complications, including cardiopulmonary diseases and neurodevelopmental impairments [ 11 , 12 ]. According to global estimates, approximately 13.4 million babies were born prematurely in 2020 [ 13 ]. Multiple risk factors are known to be associated with PTB, including socioeconomic status, multiple pregnancies, maternal health behaviors, and chronic conditions [ 14 ]. Among these factors, environmental exposures, particularly outdoor air pollution have emerged as key contributors to adverse birth outcomes [ 12 ]. Recent studies have consistently highlighted both the acute and chronic effects of PM 2.5 air pollution on PTB risk [ 15 – 17 ]. However, much of this evidence originates from high-income countries, with limited data available from LMICs including Bangladesh, where population vulnerabilities are more pronounced [ 18 ]. Bangladesh was ranked as the most polluted country in the world in 2023, with an average PM 2.5 concentration of 79.9 µg/m³ [ 19 ]. This level is more than twice the Bangladesh National Ambient Air Quality Standard (BNAAQS; 35 µg/m³) and approximately fifteen times the World Health Organization (WHO) guideline of 5 µg/m³. In recent years, numerous studies have reported high outdoor PM 2.5 concentrations in urban areas of Bangladesh [ 20 – 22 ]. Evidence also shows that brick kilns, vehicle emissions, road dust, biomass burning, industrial activities, and transboundary pollution are major sources of PM 2.5 in major urban areas of Bangladesh [ 23 , 24 ]. However, there is limited epidemiological evidence on the health impacts of PM 2.5 exposure in Bangladesh. Most existing studies focus on characterizing pollution levels or identifying sources. Studies that address exposure often rely on modeled estimates from urban centers, with limited validation. Although global evidence shows that pregnancy represents a critical window of vulnerability to PM 2.5 exposure, no nationwide epidemiological investigation has examined the association between PM 2.5 exposure and trimester-specific preterm birth in Bangladesh. This gap limits the ability to contextualize global findings within a population experiencing the highest PM 2.5 pollution levels worldwide. Given this knowledge gap, it is crucial to generate local evidence that the reflects health effects of PM 2.5 exposure. Such information is essential for understanding the health burden associated with PM 2.5 and for guiding national air quality management policies. Therefore, we conducted this cross-sectional study in Bangladesh to examine the association between maternal PM 2.5 exposure during pregnancy and PTB, via exposure estimates derived from remote sensing observations. We also assessed potential effect modifiers of these associations. Methods Study population We conducted this study using the nationally representative Demographic and Health Survey (DHS) dataset [ 25 ]. In Bangladesh, DHS has been conducted since 1993, but we used latest available DHS for 2022. The DHS program collects standardized information on demographic, maternal, and child health indicators across LMICs. For our analysis, we used the Kids Recode (KR) file, which includes data on all children born in the five years preceding the survey to interviewed women. The DHS employs a two-stage, stratified cluster sampling design on the basis of the most recent national population census, ensuring that the sample is representative at the national and subnational levels. In the first stage, enumeration areas are selected as primary sampling units (clusters); in the second stage, households within each cluster are systematically selected [ 26 ]. The KR dataset provides detailed information on maternal sociodemographic characteristics, child characteristics, household factors, and other relevant health indicators. Geographic coordinates for each DHS cluster were also used. To protect respondent confidentiality, the DHS applies a standard geospatial displacement procedure, shifting urban cluster coordinates by up to 2 km and rural cluster coordinates by up to 5 km. This displacement method follows established DHS protocols described elsewhere [ 27 ]. The DHS follows rigorous ethical procedures, and all survey protocols were approved by ICF Macro International and the relevant national Institutional Review Board [ 18 ]. Because we used deidentified, publicly available secondary data, no additional ethical approval was required for this analysis. The DHS 2022 dataset included 675 clusters nationwide in Bangladesh. A total of 8,784 observations were documented in the KR file. We first excluded observations with missing birth date information. Because this study focused on live-born children, all records of children who were not alive at the time of the survey were removed. We also excluded twin births to avoid bias related to multiple gestations. Finally, we omitted respondents who had lived in the household for less than one year, as their exposure history may not accurately reflect the current household environment. After applying these criteria, 8,059 respondents remained eligible for analysis, of whom 7,424 were term births and 635 were preterm births (Fig. 1 ). Exposure assessment We retrieved a gridded global PM 2.5 dataset developed by the Atmospheric Composition Analysis Group of Washington University in St. Louis (V6.GL.02) [ 28 ]. The dataset integrates Aerosol Optical Depth (AOD) data from multiple NASA satellite instruments including moderate resolution imaging spectroradiometers (MODIS), multi resolution imaging spectroradiometers (MISR), and sea–viewing wide field–of–view sensor (SeaWIFS) instruments with outputs from the GEOS-Chem chemical transport model. Initial PM 2.5 estimates are refined through a residual Convolutional Neural Network (CNN) and further calibrated against global ground-based observations using Geographically Weighted Regression (GWR). The dataset is provided in NetCDF format at a high spatial resolution of 0.01° × 0.01°, using the WGS84 coordinate reference system. For this analysis, we extracted monthly PM 2.5 estimates using a 2-km buffer for urban clusters and a 5-km buffer for rural clusters from 2016–2022. Maternal PM₂.₅ exposure was assigned across four exposure windows: the complete pregnancy period, preconception (12 weeks before conception), 1st trimester (conception to 13 weeks), 2nd trimester (14–27 weeks), 3rd trimester (28 weeks to birth) [ 29 ]. For each exposure window, we estimated the mean monthly PM 2.5 concentration linked to the respondent’s cluster coordinates. Pregnancy outcomes included term birth (≥ 37 gestational weeks) and preterm birth (< 37 gestational weeks) [ 30 ]. We also divided the PM 2.5 concentration levels into different quantiles for categorizing exposure levels and comparing the risk of preterm birth across exposure categories Statistical analysis We performed multivariable logistic regression models to estimate the association between PM₂.₅ exposure and PTB. On the basis of pregnancy duration and child birth date, we assigned PTB as a binary outcome: 1 for preterm birth (< 37 gestational weeks) and 0 for term birth (≥ 37 gestational weeks). We constructed four separate models with adjustments for potential confounders and effect modifiers. Model 1 (crude model) included only PM 2.5 concentration as the predictor variable. Model 2 was adjusted for maternal sociodemographic characteristics including maternal age (≤ 30, ≥ 31 years), maternal education level (no education, primary, secondary, higher), birth order, place of residence (urban, rural), wealth index (poorest, poorer, middle, richer, richest), and sex of child (male, female). Model 3 was adjusted for health-related characteristics including delivery by caesarean section (yes, no), number of antenatal care visits, place of delivery (home, health facility), perceived distance to health facility (big problem, not a big problem), and number of births in the last five years. Model 4 (fully adjusted model) incorporated all covariates from Models 2 and 3 simultaneously. We conducted stratified analyses by all binary and categorical variables to explore potential effect modifications and assess whether the association between PM₂.₅ exposure and PTB varied across population subgroups. To assess the robustness of our findings, we performed sensitivity analyses by systematically adding and removing selected covariates from fully adjusted model. Additional variables tested included source of drinking water, type of toilet facility, district of residence (64 districts of Bangladesh), and maternal literacy. We also examined model stability by removing variables such as delivery location, delivery type, birth order, and place of residence. This approach allowed us to evaluate whether our findings were sensitive to the inclusion or exclusion of covariates with potential but uncertain effects, ensuring that the observed associations were not driven by specific variable selections. We conducted all statistical analyses using R software (Version 4.1.1). We presented the results as odds ratios (ORs) with 95% confidence intervals (CIs) for each 10 µg/m³ increase in PM 2.5 concentration. We determined statistical significance at p < 0.05. We processed spatial data and managed cluster GPS coordinates using ArcGIS Pro 3.5. Results After applying the exclusion criteria, a total of 8,059 participants were included in the study, of whom 635 (7.9%) experienced preterm births between 2017 and 2022. As summarized in Table 1 , approximately 75% of mothers were aged 30 years or younger, whereas 25% were 31 years or older. With respect to education, the majority (52%) had attained secondary education, and 18% had higher education. In terms of socioeconomic status, 24% of the participants were classified within the poorest wealth quintile. A substantial proportion of respondents (68%) resided in rural areas, whereas 32% in urban settings. Most births (55%) were delivered vaginally, while 45% were delivered via caesarean section. Most deliveries (64%) occurred in health facilities, whereas 36% took place at home. Notably, 54% of participants reported that distance to health care posed a major challenge in accessing maternal services. Across exposure quartiles of PM 2.5 concentrations, statistically significant differences (p < 0.05) were observed for education level, wealth index, place of residence, delivery type, preterm birth, and place of delivery, indicating varying demographic and socioeconomic distributions by air pollution exposure level. Table 1 Maternal characteristics of the study population by PM 2.5 quartile between 2017 and 2022 Characteristics Overall First Quartile (28.8–58.8 µg/m 3 ) Second Quartile (58.8–69.4 µg/m 3 ) Third Quartile (69.4–77.9 µg/m 3 ) Fourth Quartile (77.9–105.5 µg/m 3 ) p-value 2 Total Participants N = 8,059 1 N = 2,015 1 N = 2,015 1 N = 2,014 1 N = 2,015 1 Preterm 635 (7.9%) 133 (6.6%) 148 (7.3%) 138 (6.9%) 216 (11%) < 0.001 Maternal Age 0.10 ≤ 30 Years 6,062 (75%) 1,514 (75%) 1,477 (73%) 1,534 (76%) 1,537 (76%) ≥ 31Years 1,997 (25%) 501 (25%) 538 (27%) 480 (24%) 478 (24%) Education < 0.001 No education 511 (6.3%) 170 (8.4%) 131 (6.5%) 112 (5.6%) 98 (4.9%) Primary 1,935 (24%) 565 (28%) 443 (22%) 453 (22%) 474 (24%) Secondary 4,180 (52%) 931 (46%) 1,099 (55%) 1,058 (53%) 1,092 (54%) Higher 1,433 (18%) 349 (17%) 342 (17%) 391 (19%) 351 (17%) Wealth Index < 0.001 Poorest 1,921 (24%) 579 (29%) 517 (26%) 455 (23%) 370 (18%) Poorer 1,607 (20%) 387 (19%) 404 (20%) 416 (21%) 400 (20%) Middle 1,576 (20%) 335 (17%) 421 (21%) 402 (20%) 418 (21%) Richer 1,452 (18%) 335 (17%) 346 (17%) 389 (19%) 382 (19%) Richest 1,503 (19%) 379 (19%) 327 (16%) 352 (17%) 445 (22%) Place of Residence < 0.001 Urban 2,576 (32%) 585 (29%) 573 (28%) 646 (32%) 772 (38%) Rural 5,483 (68%) 1,430 (71%) 1,442 (72%) 1,368 (68%) 1,243 (62%) Delivery Type 0.002 Vaginal 2,683 (55%) 708 (60%) 646 (54%) 635 (52%) 694 (55%) Caesarean 2,189 (45%) 479 (40%) 551 (46%) 582 (48%) 577 (45%) Sex of Child 0.063 Male 4,143 (51%) 1,015 (50%) 1,083 (54%) 1,004 (50%) 1,041 (52%) Female 3,916 (49%) 1,000 (50%) 932 (46%) 1,010 (50%) 974 (48%) Place of Delivery < 0.001 Home 1,770 (36%) 493 (42%) 430 (36%) 416 (34%) 431 (34%) Health care facilities 3,110 (64%) 694 (58%) 768 (64%) 805 (66%) 843 (66%) Distance to health care 0.2 Big problem 3,675 (46%) 950 (47%) 891 (44%) 896 (44%) 938 (47%) Not a Big problem 4,384 (54%) 1,065 (53%) 1,124 (56%) 1,118 (56%) 1,077 (53%) 1 n (%); 2 Chi-squared tests The bubble map (left) displays the spatial distribution of PTB cases, showing that the number of preterm births is relatively greater in countryside areas than in towns and large cities (Fig. 2 ). This is consistent with the dataset composition, where approximately 75% of respondents resided in rural areas. However, the prevalence of PTB was highest in large cities (10.4%), followed by towns (8.8%), and countryside areas (7.4%). Large cities represent divisional headquarters, whereas towns correspond to district-level urban centers. The map further indicated that PTB clusters were more concentrated in the central and southern regions of Bangladesh. The right panel shows the average PM 2.5 concentration across the country from 2016 to 2022, with values ranging from approximately 20 µg/m 3 to over 100 µg/m 3 . During the study period, the mean exposure to PM 2.5 over the birth period was 67.9 µg/m 3 . Table 2 presents the ORs for PTB risk associated with each 10 µg/m³ increase in PM 2.5 exposure across different pregnancy periods, with progressive adjustment for covariates in Models 2 to 4. Among all the models, complete pregnancy exposure had the strongest and most consistent associations. According to the fully adjusted model, each 10 µg/m³ increase in PM 2.5 was associated with a 13% increase in the odds of PTB (OR = 1.13, 95% CI: 1.04–1.23). The magnitude of association decreased slightly after adjustment (OR ranging from 1.20 in Model 1 to 1.13 in Model 4) but remained statistically significant throughout. Third trimester exposure demonstrated the second strongest association, with ORs of approximately 1.05 across all the models (Model 4: OR = 1.05, 95% CI: 1.01–1.09), indicating 5% increased odds of PTB per 10 µg/m³ increase in PM 2.5 . Similarly, the preconception exposure period also reached statistical significance (Model 4: OR = 1.03, 95% CI: 1.00–1.07), with a borderline significance level. In contrast, first trimester exposure showed no significant associations in the adjusted models (Models 2 to 4). Second trimester exposure showed significant associations in Models 1 and 2 but lost statistical significance after full adjustment. Table 2 Odds ratios of PTB associated with 10 µg/m 3 increase in PM 2.5 during different pregnancy trimesters Exposure Period Model 1 Model 2 Model 3 Model 4 OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value Complete pregnancy 1.20 (1.13–1.28) 0.001 1.17 (1.10–1.25) < 0.001 1.14 (1.05–1.24) 0.001 1.13 (1.04–1.23) 0.003 Preconception 1.03 (1.01–1.06) 0.01 1.03 (1.00-1.06) 0.02 1.03 (1.00-1.07) 0.05 1.03 (1.00-1.07) 0.04 First trimester 1.03 (1.00-1.05) 0.05 1.02 (0.99–1.05) 0.12 1.02 (0.98–1.05) 0.33 1.01 (0.97–1.05) 0.45 Second trimester 1.04 (1.02–1.07) 0.001 1.04 (1.01–1.07) 0.008 1.01 (0.97–1.05) 0.42 1.01 (0.97–1.05) 0.53 Third trimester 1.05 (1.02–1.08) 0.001 1.04 (1.01–1.08) 0.004 1.05 (1.01–1.09) 0.01 1.05 (1.01–1.09) 0.01 Figure 3 depicted the predicted probability of preterm birth across the full range of observed PM 2.5 concentrations on the basis of the fully adjusted model. The probability curve indicated a nonlinear increase in PTB, increasing from 6% at the lowest exposure to 14% at concentrations above 100 µg/m³. Notably, the curve demonstrated no evidence of a threshold effect at lower exposure levels. Even at PM 2.5 concentrations below the BNAAQS of 35 µg/m 3 , there remains a substantial predicted probability of PTB, ranging from approximately 6–7%. The widening confidence bonds at higher exposure levels reflect the reduced precision of estimates where fewer observations are available, although the overall trend remains clear throughout the exposure range. Across all the models, higher PM 2.5 exposure was generally associated with increased odds of PTB, with the magnitude and statistical significance varying by exposure period and quartile (Fig. 4 ). For the complete pregnancy period, the fourth quartile presented the strongest and most consistent associations. In all the models, the highest quartile was significantly associated with increased odds of PTB, although the effect size decreased with full covariate adjustment (Model 4: OR = 1.37, 95% CI: 1.02–1.84). In contrast, the second and third quartiles showed no meaningful associations across any model for the complete pregnancy period. During the preconception period, both the third and fourth quartiles exhibited consistent associations across all the models, with nearly identical estimates in the fully adjusted model (OR = 1.29, 95% CI: 1.02–1.63). No statistically significant associations were observed for other exposure periods, particularly in the fully adjusted model. On the basis of the fully adjusted Model 4, we explored the potential modifying effects of individual, household, and health factors on the relationship between PM 2.5 and PTB (Table 3 ). When the interaction terms were tested individually, only place of residence and place of delivery significantly modified the effect of PM 2.5 . According to the stratified analysis, the association was strong and significant in rural areas (OR = 1.30; 95% CI: 1.17–1.45) but not in urban areas (OR = 0.92; 95% CI: 0.82–1.05). Similarly, PM 2.5 exposure increased PTB risk for home births (OR = 1.40; 95% CI: 1.18–1.67), whereas the association was nonsignificant among facility deliveries (OR = 1.04; 95% CI: 0.95–1.15). Furthermore, the association was significant among mothers ≤ 30 years (OR = 1.11; 95% CI: 1.02–1.22) and those with secondary education (OR = 1.15; 95% CI: 1.03–1.30). Socioeconomic gradients were also evident, with the association being significant for the poorest (OR = 1.25; 95% CI: 1.04–1.53) and middle-income groups (OR = 1.25; 95% CI: 1.01–1.55) but not for wealthier groups. The effect was also significant among vaginal deliveries (OR = 1.19; 95% CI: 1.05–1.36), and women reported difficulty accessing healthcare (OR = 1.14; 95% CI: 1.02–1.26). Table 3 ORs and corresponding 95% CIs of PTB risk associated with a 10 µg/m 3 increase in PM 2.5 , stratified by potential effect modifiers N OR (95% CI) p-value Adjusted model 4 1.13 (1.04–1.23) 0.003 Maternal Age ≤ 30 Years 6,062 (75%) 1.11 (1.02–1.22) 0.02 ≥ 31Years 1,997 (25%) 1.18 (0.99–1.4) 0.05 Education No education 511 (6.3%) 1.33 (0.62–3.32 0.48 Primary 1,935 (24%) 1.09 (0.91–1.31) 0.32 Secondary 4,180 (52%) 1.15 (1.03–1.30) 0.01 Higher 1,433 (18%) 1.10 (0.93–1.30) 0.26 Wealth Index Poorest 1,921 (24%) 1.25 (1.04–1.53) 0.02 Poorer 1,607 (20%) 1.11 (0.92–1.34) 0.28 Middle 1,576 (20%) 1.25 (1.01–1.55) 0.04 Richer 1,452 (18%) 1.06 (0.87–1.28) 0.58 Richest 1,503 (19%) 1.09 (0.93–1.27) 0.29 Place of Residence* Urban 2,576 (32%) 0.92 (082-1.05) 0.23 Rural 5,483 (68%) 1.30 (1.17–1.45) 0.001 Delivery Type Vaginal 2,683 (55%) 1.19 (1.05–1.36) 0.006 Caesarean 2,189 (45%) 1.07 (0.96–1.19) 0.22 Sex of Child Male 4,143 (51%) 1.06 (0.95–1.18) 0.29 Female 3,916 (49%) 1.22 (1.08–1.38) 0.001 Place of Delivery* Home 1,770 (36%) 1.40 (1.18–1.67) 0.001 Other 3,110 (64%) 1.04 (0.95–1.15) 0.36 Distance to health care No problem 3,675 (46%) 1.13 (0.98–1.29) 0.08 Big problem 4,384 (54%) 1.14 (1.02–1.26) 0.01 * Effect modified at interaction term We conducted sensitivity analysis to assess the robustness of the fully adjusted model by systematically adding and removing covariates. The primary exposure effect remained remarkably stable across all specifications, demonstrating the model's reliability (Fig. 5 ). We sequentially incorporated four covariates, including source of drinking water, type of toilet facility, literacy, and district (64 districts), and the ORs and CIs remained nearly identical to those of the full model across all four specifications, indicating that these variables did not substantially alter the primary association. We then performed leave-one-out analyses, individually excluding delivery location, delivery type, birth order number, and place of residence. The effect estimates showed minimal deviation from Model 4, although removing place of residence resulted in a modest increase in the effect. Nevertheless, the direction and statistical significance of the primary association remained unchanged. Discussions In this study, we observed that maternal PM 2.5 during pregnancy was an independent risk factor for PTB among Bangladeshi women, even after we adjusted for sociodemographic and structural factors. Our findings add to the growing body of evidence indicating that exposure to PM 2.5 during pregnancy significantly affects birth outcomes. To the best of our knowledge, this is the first trimester specific, individual-level study in Bangladesh to quantify effect estimates at the national level. We found that higher PM 2.5 exposure throughout the entire pregnancy period was associated with significantly increased odds of PTB. Additionally, place of residence and place of delivery emerged as potential effect modifiers. Our findings provide scientific evidence to bridge the existing knowledge gap between air pollution and adverse birth outcomes and underscore the need for targeted public health policies to protect vulnerable populations, particularly pregnant women in Bangladesh. Our fully adjusted model revealed that each 10 µg/m³ increase in the PM 2.5 concentration was significantly associated with a 13%, 5%, and 3% increase in the odds of PTB for exposures during the entire pregnancy period, the third trimester, and the preconception window, respectively. However, no significant associations were observed for the first and second trimesters. These findings are consistent with those of previous studies conducted in both developing and developed countries. For example, a study involving 15 African countries using DHS data reported that an interquartile range (IQR) increase of 33.9 µg/m³ in the PM 2.5 concentration was significantly associated with PTB (OR = 1.08; 95% CI: 1.01–1.16) [ 18 ]. Similarly, a geospatial, population-based cohort study in the United States revealed that increased PM 2.5 levels (> 15 µg/m³) were linked to higher PTB risk during third trimester (OR = 1.28; 95% CI: 1.20–1.37) and complete pregnancy period (OR = 1.19; 95% CI: 1.09–0.30) [ 17 ]. Using satellite-based PM 2.5 estimates, a Chinese study also found that an IQR increase of 33.6 µg/m³ was associated with increased PTB risk (OR = 1.12; 95% CI: 1.05–1.20) [ 31 ]. However, some studies have reported no associations between PM2.5 exposure and PTB [ 32 , 33 ]. Several factors may explain these discrepancies including exposure assessment differences, variability in PM 2.5 composition or confounder adjustment. Our findings are further supported by the probability estimation curve for PTB risk, which demonstrated that PTB occurred across the entire distribution of PM 2.5 exposure, including at concentrations currently deemed acceptable under the annual BNAAQS (35 µg/m³). In addition to the continuous exposure model, we conducted a quartile based categorical analysis to further examine the association between PM 2.5 exposure and PTB. We observed that the highest exposure category (fourth quartile: 77.9–105.5 µg/m³) was significantly associated with increased odds of PTB across all models compared with the lowest quartile (28.8–58.8 µg/m³) during entire pregnancy window. Similarly, during the preconception period, strong associations between PM 2.5 and PTB risk were consistently observed in the third and fourth quartiles across both crude and adjusted models. In contrast, the first, second, and third trimesters did not show convincing or consistent associations. Similar studies in Thailand and China reported higher risk of PTB at PM 2.5 concentrations exceeding 37.5 µg/m³ and 36.5 µg/m³, with adjusted ORs of 2.46 (95% CI: 2.13–2.85) and 2.54 (95% CI: 1.42–4.55), respectively [ 34 , 35 ]. However, a study in Boston reported a steady increase in PTB risk even at low PM 2.5 levels (< 12 µg/m³) [ 36 ]. This discrepancy may reflect differences in ambient PM 2.5 exposure ranges between populations, where our study region experienced substantially higher pollution levels, potentially exceeding the biological threshold required to trigger oxidative stress, systemic inflammation, and placental vascular dysfunction mechanisms known to contribute to PTB [ 37 ]. The interaction analysis indicated that place of residence and place of delivery significantly modified the association between PM 2.5 exposure and PTB. The association was stronger in rural areas than in urban settings, and women with home deliveries had higher odds of PTB risk than those delivered at health facilities. These patterns likely reflect disparities in healthcare access and quality, as rural populations often experience reduced availability and utilization of antenatal and emergency obstetric care. Studies from India and Iran similarly revealed that PM 2.5 exposure during pregnancy poses a higher risk in rural areas, likely because of combined ambient and indoor pollution and limited access to healthcare [ 38 , 39 ]. In Bangladesh, this risk may be further amplified by the widespread use of solid fuels for cooking, mainly shrubs or grasses, crop residues, and wood which increase household air pollution exposure [ 40 ]. Household combustion of solid fuels increases PTB risk by approximately 30%, whereas a meta-analysis found that biomass, coal, and kerosene use were associated with higher PTB risk (OR = 1.27; 95% CI: 1.19–1.36) [ 41 , 42 ]. These findings highlight the importance of addressing both environmental and structural health determinants to mitigate air pollution related adverse birth outcomes in resource limited settings. Our study did not establish a causal relationship or specific biological pathways linking PM 2.5 exposure to PTB risk. However, several plausible toxicological mechanisms have been proposed. PM 2.5 particles can penetrate deep into the lungs and enter systemic circulation, where they may induce oxidative stress, coagulation disturbances, and placental inflammation, thereby affecting early fetal development [ 43 , 44 ]. PM 2.5 exposure had also been associated with placental dysfunction through impaired oxygen transport, disrupted hormonal regulation, and restricted intrauterine growth [ 45 , 46 ]. Collectively, these mechanisms may compromise fetal growth and contribute to PTB risk. Further in vivo and in vitro studies encompassing the full course of pregnancy are needed to confirm and clarify these associations [ 18 ]. There are several notable strengths in our study. First, the sampling distribution covered both rural and urban populations across the country, which enhances the generalizability of our findings. Furthermore, we utilized fine scale PM 2.5 concentration data for each cluster, providing a more accurate linkage between exposure and participants. This approach can also support future researchers in conducting fine scale exposure assessments, especially given the limited ground based monitoring available in Bangladesh. Moreover, our analysis incorporated both sociodemographic and healthcare related covariates, allowing for more comprehensive adjustment of potential confounders and yielding more reliable estimates. Most importantly, to our knowledge, this is the first nationwide study in Bangladesh to investigate the association between air pollution and PTB. However, we have encountered several unavoidable limitations. In terms of the data, we were not able to consider other air pollutants in the sensitivity analyses. In addition, several important covariates such as maternal smoking status and type of cooking fuels were unavailable in this version of the DHS dataset for Bangladesh. The birth information in the DHS dataset was collected based on maternal recall and birth certificate, which may have introduced recall and information bias. Moreover, potential residual confounding due to unmeasured or misclassified variables cannot be ruled out. As the outcome is relatively common, ORs from logistic regression may slightly overestimate the true risks. Therefore, future studies should employ models that account for complex survey design, and incorporate multiple air pollutants and additional covariates to better capture the multifactorial nature of PTB risk. Conclusions Our study revealed a significant positive association between exposure to PM 2.5 air pollution and PTB risk in Bangladesh. In addition, the association varied by place of residence, with stronger effects observed in rural populations. In addition, place of delivery emerged as an important factor, as home births were significantly associated with higher odds of PTB risk than at facility deliveries. Overall, our study provides comprehensive evidence of the adverse effects of air pollution on PTB in Bangladesh, identifying air pollution as a potentially modifiable risk factor for reducing PTB risk. The current findings can serve as a foundation for future research, and further epidemiological studies particularly longitudinal cohort and case-control designs are warranted to establish more robust causal inferences. Abbreviations AOD : Aerosol Optical Depth BNAAQS : Bangladesh National Ambient Air Quality Standard CI : Confidence interval CNN : Convolutional Neural Network DHS : Demographic and Health Survey GPS : Global Position System GWR : Geographically Weighted Regression IQR : Interquartile range KR : Kids Recode LMICs : Low and Middle Income Countries MISR : Multi Resolution Imaging Spectroradiometers MODIS : Moderate Resolution Imaging Spectroradiometers OR : Odds ratio PM 2.5 : Fine particulate matter (aerodynamic diameter < 2.5) PTB : preterm birth WHO : World Health Organization Declarations Clinical trial number Not Applicable Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Availability of data The data used in this study were obtained from the Demographic and Health Surveys (DHS) Program, a publicly available but restricted-access data repository. Access to the Bangladesh DHS datasets was granted upon request following registration and approval of the research project titled “Impact of air pollution on pregnancy outcome” through the DHS Program website. The datasets include nationally representative survey data for Bangladesh, along with associated GPS cluster datasets, and were provided in SPSS (.sav) formats. Due to DHS data use agreements and confidentiality requirements, the datasets cannot be publicly shared by the authors. Researchers may obtain access to the same data by registering and submitting a data request through the DHS Program at: https://www.dhsprogram.com/data/. Approval is subject to DHS review and terms of use. Competing interests The authors of this manuscript have no conflicts of interest or competing interests (financial or nonfinancial) to report. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or nonprofit sectors. Author contributions The study design, analysis and draft manuscript and editing were prepared by: Abdullah Al Nayeem. Data processing, variable selection and partial editing were performed by Sabiha Sultana. Acknowledgement The authors gratefully acknowledge the support of the DHS Program for providing access to the data used in this study. References IHME. New Report Shows Nearly 9 of 10 Global. Air Pollution Deaths are From Noncommunicable Diseases | Institute for Health Metrics and Evaluation. https://www.healthdata.org/news-events/newsroom/news-releases/new-report-shows-nearly-9-10-global-air-pollution-deaths-are . Accessed 12 Nov 2025. Lu Y, Lin S, Fatmi Z, Malashock D, Hussain MM, Siddique A, et al. Assessing the association between fine particulate matter (PM2.5) constituents and cardiovascular diseases in a mega-city of Pakistan. Environ Pollut. 2019;252:1412–22. https://doi.org/10.1016/j.envpol.2019.06.078 . Zhang Y, He M, Wu S, Zhu Y, Wang S, Shima M, et al. 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Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses. Spat Demogr. 2016;4:155–73. https://doi.org/10.1007/s40980-015-0014-0 . Shen S, Li C, van Donkelaar A, Jacobs N, Wang C, Martin RV. Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS EST Air. 2024;1:332–45. https://doi.org/10.1021/acsestair.3c00054 . Wu J, Ren C, Delfino RJ, Chung J, Wilhelm M, Ritz B. Association between local traffic-generated air pollution and preeclampsia and preterm delivery in the south coast air basin of California. Environ Health Perspect. 2009;117:1773–9. https://doi.org/10.1289/ehp.0800334 . Li Q, Wang Y-Y, Guo Y, Zhou H, Wang X, Wang Q, et al. Effect of airborne particulate matter of 2.5 µm or less on preterm birth: A national birth cohort study in China. Environ Int. 2018;121(Pt):1128–36. https://doi.org/10.1016/j.envint.2018.10.025 . Cai J, Zhao Y, Kan J, Chen R, Martin R, van Donkelaar A, et al. Prenatal Exposure to Specific PM2.5 Chemical Constituents and Preterm Birth in China: A Nationwide Cohort Study. Environ Sci Technol. 2020;54:14494–501. https://doi.org/10.1021/acs.est.0c02373 . Cassidy-Bushrow AE, Burmeister C, Lamerato L, Lemke LD, Mathieu M, O’Leary BF, et al. Prenatal airshed pollutants and preterm birth in an observational birth cohort study in Detroit, Michigan, USA. Environ Res. 2020;189:109845. https://doi.org/10.1016/j.envres.2020.109845 . Stieb DM, Chen L, Beckerman BS, Jerrett M, Crouse DL, Omariba DWR, et al. Associations of Pregnancy Outcomes and PM2.5 in a National Canadian Study. Environ Health Perspect. 2016;124:243–9. https://doi.org/10.1289/ehp.1408995 . Thaichana P, Sripan P, Rerkasem A, Tongsong T, Sangsawang S, Kawichai S, et al. Association of Maternal PM2.5 Exposure with Preterm Birth and Low Birth Weight: A Large-Scale Cohort Study in Northern Thailand (2016–2022). Toxics. 2025;13:304. https://doi.org/10.3390/toxics13040304 . Fleischer NL, Merialdi M, van Donkelaar A, Vadillo-Ortega F, Martin RV, Betran AP, et al. Outdoor Air Pollution, Preterm Birth, and Low Birth Weight: Analysis of the World Health Organization Global Survey on Maternal and Perinatal Health. Environ Health Perspect. 2014;122:425–30. https://doi.org/10.1289/ehp.1306837 . Nachman RM, Mao G, Zhang X, Hong X, Chen Z, Soria CS, et al. Intrauterine Inflammation and Maternal Exposure to Ambient PM2.5 during Preconception and Specific Periods of Pregnancy: The Boston Birth Cohort. Environ Health Perspect. 2016;124:1608–15. https://doi.org/10.1289/EHP243 . Vadillo-Ortega F, Osornio-Vargas A, Buxton MA, Sánchez BN, Rojas-Bracho L, Viveros-Alcaráz M, et al. AIR POLLUTION, INFLAMMATION AND PRETERM BIRTH: A POTENTIAL MECHANISTIC LINK. Med Hypotheses. 2014;82:219–24. https://doi.org/10.1016/j.mehy.2013.11.042 . Jana A, Pramanik M, Maiti A, Chattopadhyay A, Abed Al Ahad M. In-utero exposure to PM2.5 and adverse birth outcomes in India: Geostatistical modelling using remote sensing and demographic health survey data 2019–21. PLOS Glob Public Health. 2025;5:e0003798. https://doi.org/10.1371/journal.pgph.0003798 . Mehrnoush V, Ranjbar A, Banihashemi F, Darsareh F, Shekari M, Shirzadfardjahromi M. Urban-rural differences in the pregnancy-related adverse outcome. Gynecol Obstet Clin Med. 2023;3:51–5. https://doi.org/10.1016/j.gocm.2022.12.001 . Shupler M, Hystad P, Birch A, Miller-Lionberg D, Jeronimo M, Arku RE, et al. Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study. Lancet Planet Health. 2020;4:e451–62. https://doi.org/10.1016/S2542-5196(20)30197-2 . Amegah AK, Quansah R, Jaakkola JJK. Household Air Pollution from Solid Fuel Use and Risk of Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis of the Empirical Evidence. PLoS ONE. 2014;9:e113920. https://doi.org/10.1371/journal.pone.0113920 . Luo M, Liu T, Ma C, Fang J, Zhao Z, Wen Y, et al. Household polluting cooking fuels and adverse birth outcomes: An updated systematic review and meta-analysis. Front Public Health. 2023;11. https://doi.org/10.3389/fpubh.2023.978556 . Balogun HA, Rantala AK, Antikainen H, Siddika N, Amegah AK, Ryti NRI, et al. Effects of Air Pollution on the Risk of Low Birth Weight in a Cold Climate. Appl Sci. 2020;10:6399. https://doi.org/10.3390/app10186399 . Boyle AK, Rinaldi SF, Norman JE, Stock SJ. Preterm birth: Inflammation, fetal injury and treatment strategies. J Reprod Immunol. 2017;119:62–6. https://doi.org/10.1016/j.jri.2016.11.008 . Nääv Å, Erlandsson L, Isaxon C, Åsander Frostner E, Ehinger J, Sporre MK, et al. Urban PM2.5 Induces Cellular Toxicity, Hormone Dysregulation, Oxidative Damage, Inflammation, and Mitochondrial Interference in the HRT8 Trophoblast Cell Line. Front Endocrinol. 2020;11. https://doi.org/10.3389/fendo.2020.00075 . Kannan S, Misra DP, Dvonch JT, Krishnakumar A. Exposures to Airborne Particulate Matter and Adverse Perinatal Outcomes: A Biologically Plausible Mechanistic Framework for Exploring Potential Effect Modification by Nutrition. Environ Health Perspect. 2006;114:1636–42. https://doi.org/10.1289/ehp.9081 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 31 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Editor invited by journal 05 Jan, 2026 Submission checks completed at journal 01 Jan, 2026 First submitted to journal 01 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8483529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581403647,"identity":"a09a6e02-c431-4fb7-a10a-e67ae18b3f80","order_by":0,"name":"Abdullah Al Nayeem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYHAD/ocPPgApNnbitfAwG84AaWEmQQubNA+IJqTFnP2M4YOPOQxy5hK5h41tfm2T52NmYPwAFMEJLHtyjA1nbmMwtpyRl/g4t++2YRszA7MkUAQnMDiQYybNu40hccOZA8bGuT23GYFa2Jh58Wk5/8b8999tDPVALWbSlj237QlruZFjxsy4jSHB4HiPmTTDj9uJRGh5VizZu03CcMPxtmTD3obbyW3MjM34/XI+eeOHn9ts5A0OMx988OPPbdv57c0HP3zEo4WBgcMASEhA2IxtYLIBn3ogYH+AxPlDQPEoGAWjYBSMSAAA7yVRQ8m1TzwAAAAASUVORK5CYII=","orcid":"","institution":"Stamford University Bangladesh","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"Al","lastName":"Nayeem","suffix":""},{"id":581403648,"identity":"5d20a9e1-65ff-4467-979a-a3080f1942de","order_by":1,"name":"Sabiha Sultana Suzana","email":"","orcid":"","institution":"Stamford University Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Sabiha","middleName":"Sultana","lastName":"Suzana","suffix":""}],"badges":[],"createdAt":"2025-12-30 17:54:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8483529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8483529/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101751660,"identity":"f022b5f9-e0b3-45f0-bb8c-b5aa843a9481","added_by":"auto","created_at":"2026-02-03 10:22:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82849,"visible":true,"origin":"","legend":"\u003cp\u003eSelection process for birth records from the DHS dataset\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/b08fa63910f616d238742414.jpeg"},{"id":101442886,"identity":"b4eb0255-3700-4147-8d36-62f83d08e30c","added_by":"auto","created_at":"2026-01-29 17:31:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206106,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PTBs by cluster (left) and average PM\u003csub\u003e2.5\u003c/sub\u003e concentration (right) in\u0026nbsp; - Bangladesh (2016 - 2022).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/af7bed0f9faa0fb71b910efe.jpeg"},{"id":101442888,"identity":"58d50719-33fd-42c9-886c-5566b6bc318f","added_by":"auto","created_at":"2026-01-29 17:31:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58666,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted probability of increasing PTB with increasing PM\u003csub\u003e2.5 \u003c/sub\u003econcentration based on the fully adjusted model. The vertical black dotted line indicates the annual standard for PM\u003csub\u003e2.5\u003c/sub\u003e in Bangladesh.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/4b30fadaf57b7b0cf2d31bfe.png"},{"id":101442889,"identity":"776531b5-0498-4a93-9d77-e9f1a84f30c2","added_by":"auto","created_at":"2026-01-29 17:31:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82237,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between PM\u003csub\u003e2.5\u003c/sub\u003e quartiles and PTB risk across different pregnancy periods in four different models\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/e58adbfc42f29585664d50d0.png"},{"id":101442885,"identity":"a2a2536e-7023-4679-8014-e03c8c6ee89e","added_by":"auto","created_at":"2026-01-29 17:31:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71150,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis for fully adjusted model 4\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/f01e17008106d96f3edb5b12.png"},{"id":101754879,"identity":"af3c8b28-4312-41bd-bd9f-e486b2ecfe5a","added_by":"auto","created_at":"2026-02-03 10:47:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1593183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8483529/v1/e1c12696-0912-48c3-8ebf-a4d29ce5a3aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMaternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure and preterm birth in Bangladesh: A nationwide cross-sectional analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAir pollution has emerged as one of the leading environmental risk factors for mortality worldwide, contributing to an estimated 7.9\u0026nbsp;million deaths in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The burden of these deaths remains disproportionately high in low- and middle-income countries (LMICs), where populations experience greater exposure levels and have limited access to healthcare resources. Among various pollutants, fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e; aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5 \u0026micro;m) has been identified as particularly harmful. Numerous studies have demonstrated strong associations between both acute and chronic exposure to PM\u003csub\u003e2.5\u003c/sub\u003e and increased risks of cardiopulmonary diseases [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Researchers emphasize that no safe threshold exists for PM\u003csub\u003e2.5\u003c/sub\u003e exposure, as adverse effects occur even at very low concentrations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The public health impacts of outdoor PM\u003csub\u003e2.5\u003c/sub\u003e exposure are well documented across the life course, with increasing evidence that pregnant individuals and developing fetuses are particularly vulnerable [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we specifically focused on the effect of PM\u003csub\u003e2.5\u003c/sub\u003e exposure during pregnancy on preterm birth (PTB), defined as a live birth occurring before 37 completed weeks of gestation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PTB not only contributes significantly to fetal and neonatal morbidity and mortality, but also carries the risk of lifelong complications, including cardiopulmonary diseases and neurodevelopmental impairments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. According to global estimates, approximately 13.4\u0026nbsp;million babies were born prematurely in 2020 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Multiple risk factors are known to be associated with PTB, including socioeconomic status, multiple pregnancies, maternal health behaviors, and chronic conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among these factors, environmental exposures, particularly outdoor air pollution have emerged as key contributors to adverse birth outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have consistently highlighted both the acute and chronic effects of PM\u003csub\u003e2.5\u003c/sub\u003e air pollution on PTB risk [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, much of this evidence originates from high-income countries, with limited data available from LMICs including Bangladesh, where population vulnerabilities are more pronounced [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBangladesh was ranked as the most polluted country in the world in 2023, with an average PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 79.9 \u0026micro;g/m\u0026sup3; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This level is more than twice the Bangladesh National Ambient Air Quality Standard (BNAAQS; 35 \u0026micro;g/m\u0026sup3;) and approximately fifteen times the World Health Organization (WHO) guideline of 5 \u0026micro;g/m\u0026sup3;. In recent years, numerous studies have reported high outdoor PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in urban areas of Bangladesh [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Evidence also shows that brick kilns, vehicle emissions, road dust, biomass burning, industrial activities, and transboundary pollution are major sources of PM\u003csub\u003e2.5\u003c/sub\u003e in major urban areas of Bangladesh [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, there is limited epidemiological evidence on the health impacts of PM\u003csub\u003e2.5\u003c/sub\u003e exposure in Bangladesh. Most existing studies focus on characterizing pollution levels or identifying sources. Studies that address exposure often rely on modeled estimates from urban centers, with limited validation. Although global evidence shows that pregnancy represents a critical window of vulnerability to PM\u003csub\u003e2.5\u003c/sub\u003e exposure, no nationwide epidemiological investigation has examined the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and trimester-specific preterm birth in Bangladesh. This gap limits the ability to contextualize global findings within a population experiencing the highest PM\u003csub\u003e2.5\u003c/sub\u003e pollution levels worldwide.\u003c/p\u003e \u003cp\u003eGiven this knowledge gap, it is crucial to generate local evidence that the reflects health effects of PM\u003csub\u003e2.5\u003c/sub\u003e exposure. Such information is essential for understanding the health burden associated with PM\u003csub\u003e2.5\u003c/sub\u003e and for guiding national air quality management policies. Therefore, we conducted this cross-sectional study in Bangladesh to examine the association between maternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure during pregnancy and PTB, via exposure estimates derived from remote sensing observations. We also assessed potential effect modifiers of these associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe conducted this study using the nationally representative Demographic and Health Survey (DHS) dataset [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In Bangladesh, DHS has been conducted since 1993, but we used latest available DHS for 2022. The DHS program collects standardized information on demographic, maternal, and child health indicators across LMICs. For our analysis, we used the Kids Recode (KR) file, which includes data on all children born in the five years preceding the survey to interviewed women. The DHS employs a two-stage, stratified cluster sampling design on the basis of the most recent national population census, ensuring that the sample is representative at the national and subnational levels. In the first stage, enumeration areas are selected as primary sampling units (clusters); in the second stage, households within each cluster are systematically selected [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The KR dataset provides detailed information on maternal sociodemographic characteristics, child characteristics, household factors, and other relevant health indicators. Geographic coordinates for each DHS cluster were also used. To protect respondent confidentiality, the DHS applies a standard geospatial displacement procedure, shifting urban cluster coordinates by up to 2 km and rural cluster coordinates by up to 5 km. This displacement method follows established DHS protocols described elsewhere [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The DHS follows rigorous ethical procedures, and all survey protocols were approved by ICF Macro International and the relevant national Institutional Review Board [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Because we used deidentified, publicly available secondary data, no additional ethical approval was required for this analysis.\u003c/p\u003e \u003cp\u003eThe DHS 2022 dataset included 675 clusters nationwide in Bangladesh. A total of 8,784 observations were documented in the KR file. We first excluded observations with missing birth date information. Because this study focused on live-born children, all records of children who were not alive at the time of the survey were removed. We also excluded twin births to avoid bias related to multiple gestations. Finally, we omitted respondents who had lived in the household for less than one year, as their exposure history may not accurately reflect the current household environment. After applying these criteria, 8,059 respondents remained eligible for analysis, of whom 7,424 were term births and 635 were preterm births (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure assessment\u003c/h3\u003e\n\u003cp\u003eWe retrieved a gridded global PM\u003csub\u003e2.5\u003c/sub\u003e dataset developed by the Atmospheric Composition Analysis Group of Washington University in St. Louis (V6.GL.02) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The dataset integrates Aerosol Optical Depth (AOD) data from multiple NASA satellite instruments including moderate resolution imaging spectroradiometers (MODIS), multi resolution imaging spectroradiometers (MISR), and sea\u0026ndash;viewing wide field\u0026ndash;of\u0026ndash;view sensor (SeaWIFS) instruments with outputs from the GEOS-Chem chemical transport model. Initial PM\u003csub\u003e2.5\u003c/sub\u003e estimates are refined through a residual Convolutional Neural Network (CNN) and further calibrated against global ground-based observations using Geographically Weighted Regression (GWR). The dataset is provided in NetCDF format at a high spatial resolution of 0.01\u0026deg; \u0026times; 0.01\u0026deg;, using the WGS84 coordinate reference system. For this analysis, we extracted monthly PM\u003csub\u003e2.5\u003c/sub\u003e estimates using a 2-km buffer for urban clusters and a 5-km buffer for rural clusters from 2016\u0026ndash;2022. Maternal PM₂.₅ exposure was assigned across four exposure windows: the complete pregnancy period, preconception (12 weeks before conception), 1st trimester (conception to 13 weeks), 2nd trimester (14\u0026ndash;27 weeks), 3rd trimester (28 weeks to birth) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For each exposure window, we estimated the mean monthly PM\u003csub\u003e2.5\u003c/sub\u003e concentration linked to the respondent\u0026rsquo;s cluster coordinates. Pregnancy outcomes included term birth (\u0026ge;\u0026thinsp;37 gestational weeks) and preterm birth (\u0026lt;\u0026thinsp;37 gestational weeks) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We also divided the PM\u003csub\u003e2.5\u003c/sub\u003e concentration levels into different quantiles for categorizing exposure levels and comparing the risk of preterm birth across exposure categories\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe performed multivariable logistic regression models to estimate the association between PM₂.₅ exposure and PTB. On the basis of pregnancy duration and child birth date, we assigned PTB as a binary outcome: 1 for preterm birth (\u0026lt;\u0026thinsp;37 gestational weeks) and 0 for term birth (\u0026ge;\u0026thinsp;37 gestational weeks). We constructed four separate models with adjustments for potential confounders and effect modifiers. Model 1 (crude model) included only PM\u003csub\u003e2.5\u003c/sub\u003e concentration as the predictor variable. Model 2 was adjusted for maternal sociodemographic characteristics including maternal age (\u0026le;\u0026thinsp;30, \u0026ge;\u0026thinsp;31 years), maternal education level (no education, primary, secondary, higher), birth order, place of residence (urban, rural), wealth index (poorest, poorer, middle, richer, richest), and sex of child (male, female). Model 3 was adjusted for health-related characteristics including delivery by caesarean section (yes, no), number of antenatal care visits, place of delivery (home, health facility), perceived distance to health facility (big problem, not a big problem), and number of births in the last five years. Model 4 (fully adjusted model) incorporated all covariates from Models 2 and 3 simultaneously. We conducted stratified analyses by all binary and categorical variables to explore potential effect modifications and assess whether the association between PM₂.₅ exposure and PTB varied across population subgroups.\u003c/p\u003e \u003cp\u003eTo assess the robustness of our findings, we performed sensitivity analyses by systematically adding and removing selected covariates from fully adjusted model. Additional variables tested included source of drinking water, type of toilet facility, district of residence (64 districts of Bangladesh), and maternal literacy. We also examined model stability by removing variables such as delivery location, delivery type, birth order, and place of residence. This approach allowed us to evaluate whether our findings were sensitive to the inclusion or exclusion of covariates with potential but uncertain effects, ensuring that the observed associations were not driven by specific variable selections.\u003c/p\u003e \u003cp\u003eWe conducted all statistical analyses using R software (Version 4.1.1). We presented the results as odds ratios (ORs) with 95% confidence intervals (CIs) for each 10 \u0026micro;g/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration. We determined statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We processed spatial data and managed cluster GPS coordinates using ArcGIS Pro 3.5.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter applying the exclusion criteria, a total of 8,059 participants were included in the study, of whom 635 (7.9%) experienced preterm births between 2017 and 2022. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, approximately 75% of mothers were aged 30 years or younger, whereas 25% were 31 years or older. With respect to education, the majority (52%) had attained secondary education, and 18% had higher education. In terms of socioeconomic status, 24% of the participants were classified within the poorest wealth quintile. A substantial proportion of respondents (68%) resided in rural areas, whereas 32% in urban settings. Most births (55%) were delivered vaginally, while 45% were delivered via caesarean section. Most deliveries (64%) occurred in health facilities, whereas 36% took place at home. Notably, 54% of participants reported that distance to health care posed a major challenge in accessing maternal services. Across exposure quartiles of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed for education level, wealth index, place of residence, delivery type, preterm birth, and place of delivery, indicating varying demographic and socioeconomic distributions by air pollution exposure level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaternal characteristics of the study population by PM\u003csub\u003e2.5\u003c/sub\u003e quartile between 2017 and 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFirst Quartile\u003c/p\u003e \u003cp\u003e(28.8\u0026ndash;58.8 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecond Quartile \u003c/p\u003e \u003cp\u003e(58.8\u0026ndash;69.4 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThird Quartile\u003c/p\u003e \u003cp\u003e(69.4\u0026ndash;77.9\u003c/p\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFourth Quartile \u003c/p\u003e \u003cp\u003e(77.9\u0026ndash;105.5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8,059\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,015\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,015\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,014\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,015\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreterm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e635 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,062 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,514 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,477 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,534 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,537 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;31Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,997 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e501 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e478 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,935 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e453 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e474 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,180 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e931 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,099 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,058 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,092 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,433 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e351 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,921 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e517 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e455 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e370 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,607 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e416 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e400 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,576 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e402 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e418 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,452 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e346 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e389 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e382 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,503 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e352 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e445 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,576 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e646 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e772 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,483 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,430 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,442 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,368 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,243 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDelivery Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,683 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e708 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e646 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e635 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e694 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,189 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e479 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e582 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e577 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of Child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,143 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,015 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,083 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,004 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,041 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,916 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,000 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,010 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e974 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,770 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e416 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e431 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth care facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,110 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e694 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e768 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e805 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e843 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to health care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,675 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e950 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e891 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e896 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e938 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a Big problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,384 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,065 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,124 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,118 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,077 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003e n (%); \u003csup\u003e2\u003c/sup\u003e Chi-squared tests\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe bubble map (left) displays the spatial distribution of PTB cases, showing that the number of preterm births is relatively greater in countryside areas than in towns and large cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is consistent with the dataset composition, where approximately 75% of respondents resided in rural areas. However, the prevalence of PTB was highest in large cities (10.4%), followed by towns (8.8%), and countryside areas (7.4%). Large cities represent divisional headquarters, whereas towns correspond to district-level urban centers. The map further indicated that PTB clusters were more concentrated in the central and southern regions of Bangladesh. The right panel shows the average PM\u003csub\u003e2.5\u003c/sub\u003e concentration across the country from 2016 to 2022, with values ranging from approximately 20 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e to over 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. During the study period, the mean exposure to PM\u003csub\u003e2.5\u003c/sub\u003e over the birth period was 67.9 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the ORs for PTB risk associated with each 10 \u0026micro;g/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e exposure across different pregnancy periods, with progressive adjustment for covariates in Models 2 to 4. Among all the models, complete pregnancy exposure had the strongest and most consistent associations. According to the fully adjusted model, each 10 \u0026micro;g/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e was associated with a 13% increase in the odds of PTB (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI: 1.04\u0026ndash;1.23). The magnitude of association decreased slightly after adjustment (OR ranging from 1.20 in Model 1 to 1.13 in Model 4) but remained statistically significant throughout. Third trimester exposure demonstrated the second strongest association, with ORs of approximately 1.05 across all the models (Model 4: OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.01\u0026ndash;1.09), indicating 5% increased odds of PTB per 10 \u0026micro;g/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e. Similarly, the preconception exposure period also reached statistical significance (Model 4: OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.00\u0026ndash;1.07), with a borderline significance level. In contrast, first trimester exposure showed no significant associations in the adjusted models (Models 2 to 4). Second trimester exposure showed significant associations in Models 1 and 2 but lost statistical significance after full adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratios of PTB associated with 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e during different pregnancy trimesters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e(1.13\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.10\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(1.05\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003cp\u003e(1.04\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreconception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(1.00-1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(1.00-1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(1.00-1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst trimester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(1.00-1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.98\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond trimester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e(1.02\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird trimester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(1.02\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.01\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(1.01\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicted the predicted probability of preterm birth across the full range of observed PM\u003csub\u003e2.5\u003c/sub\u003e concentrations on the basis of the fully adjusted model. The probability curve indicated a nonlinear increase in PTB, increasing from 6% at the lowest exposure to 14% at concentrations above 100 \u0026micro;g/m\u0026sup3;. Notably, the curve demonstrated no evidence of a threshold effect at lower exposure levels. Even at PM\u003csub\u003e2.5\u003c/sub\u003e concentrations below the BNAAQS of 35 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, there remains a substantial predicted probability of PTB, ranging from approximately 6\u0026ndash;7%. The widening confidence bonds at higher exposure levels reflect the reduced precision of estimates where fewer observations are available, although the overall trend remains clear throughout the exposure range.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross all the models, higher PM\u003csub\u003e2.5\u003c/sub\u003e exposure was generally associated with increased odds of PTB, with the magnitude and statistical significance varying by exposure period and quartile (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For the complete pregnancy period, the fourth quartile presented the strongest and most consistent associations. In all the models, the highest quartile was significantly associated with increased odds of PTB, although the effect size decreased with full covariate adjustment (Model 4: OR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.02\u0026ndash;1.84). In contrast, the second and third quartiles showed no meaningful associations across any model for the complete pregnancy period. During the preconception period, both the third and fourth quartiles exhibited consistent associations across all the models, with nearly identical estimates in the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.02\u0026ndash;1.63). No statistically significant associations were observed for other exposure periods, particularly in the fully adjusted model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the basis of the fully adjusted Model 4, we explored the potential modifying effects of individual, household, and health factors on the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and PTB (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When the interaction terms were tested individually, only place of residence and place of delivery significantly modified the effect of PM\u003csub\u003e2.5\u003c/sub\u003e. According to the stratified analysis, the association was strong and significant in rural areas (OR\u0026thinsp;=\u0026thinsp;1.30; 95% CI: 1.17\u0026ndash;1.45) but not in urban areas (OR\u0026thinsp;=\u0026thinsp;0.92; 95% CI: 0.82\u0026ndash;1.05). Similarly, PM\u003csub\u003e2.5\u003c/sub\u003e exposure increased PTB risk for home births (OR\u0026thinsp;=\u0026thinsp;1.40; 95% CI: 1.18\u0026ndash;1.67), whereas the association was nonsignificant among facility deliveries (OR\u0026thinsp;=\u0026thinsp;1.04; 95% CI: 0.95\u0026ndash;1.15). Furthermore, the association was significant among mothers\u0026thinsp;\u0026le;\u0026thinsp;30 years (OR\u0026thinsp;=\u0026thinsp;1.11; 95% CI: 1.02\u0026ndash;1.22) and those with secondary education (OR\u0026thinsp;=\u0026thinsp;1.15; 95% CI: 1.03\u0026ndash;1.30). Socioeconomic gradients were also evident, with the association being significant for the poorest (OR\u0026thinsp;=\u0026thinsp;1.25; 95% CI: 1.04\u0026ndash;1.53) and middle-income groups (OR\u0026thinsp;=\u0026thinsp;1.25; 95% CI: 1.01\u0026ndash;1.55) but not for wealthier groups. The effect was also significant among vaginal deliveries (OR\u0026thinsp;=\u0026thinsp;1.19; 95% CI: 1.05\u0026ndash;1.36), and women reported difficulty accessing healthcare (OR\u0026thinsp;=\u0026thinsp;1.14; 95% CI: 1.02\u0026ndash;1.26).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eORs and corresponding 95% CIs of PTB risk associated with a 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e, stratified by potential effect modifiers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted model 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (1.04\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,062 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.02\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;31Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,997 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.99\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33 (0.62\u0026ndash;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,935 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.91\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,180 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.03\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,433 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.93\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,921 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.04\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,607 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.92\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,576 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.01\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,452 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.87\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,503 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.93\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Residence*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,576 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (082-1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,483 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.17\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDelivery Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,683 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.05\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,189 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.96\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of Child\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,143 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.95\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,916 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.08\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of Delivery*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,770 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.18\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,110 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.95\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to health care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,675 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.98\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,384 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.02\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Effect modified at interaction term\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe conducted sensitivity analysis to assess the robustness of the fully adjusted model by systematically adding and removing covariates. The primary exposure effect remained remarkably stable across all specifications, demonstrating the model's reliability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We sequentially incorporated four covariates, including source of drinking water, type of toilet facility, literacy, and district (64 districts), and the ORs and CIs remained nearly identical to those of the full model across all four specifications, indicating that these variables did not substantially alter the primary association. We then performed leave-one-out analyses, individually excluding delivery location, delivery type, birth order number, and place of residence. The effect estimates showed minimal deviation from Model 4, although removing place of residence resulted in a modest increase in the effect. Nevertheless, the direction and statistical significance of the primary association remained unchanged.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eIn this study, we observed that maternal PM\u003csub\u003e2.5\u003c/sub\u003e during pregnancy was an independent risk factor for PTB among Bangladeshi women, even after we adjusted for sociodemographic and structural factors. Our findings add to the growing body of evidence indicating that exposure to PM\u003csub\u003e2.5\u003c/sub\u003e during pregnancy significantly affects birth outcomes. To the best of our knowledge, this is the first trimester specific, individual-level study in Bangladesh to quantify effect estimates at the national level. We found that higher PM\u003csub\u003e2.5\u003c/sub\u003e exposure throughout the entire pregnancy period was associated with significantly increased odds of PTB. Additionally, place of residence and place of delivery emerged as potential effect modifiers. Our findings provide scientific evidence to bridge the existing knowledge gap between air pollution and adverse birth outcomes and underscore the need for targeted public health policies to protect vulnerable populations, particularly pregnant women in Bangladesh.\u003c/p\u003e \u003cp\u003eOur fully adjusted model revealed that each 10 \u0026micro;g/m\u0026sup3; increase in the PM\u003csub\u003e2.5\u003c/sub\u003e concentration was significantly associated with a 13%, 5%, and 3% increase in the odds of PTB for exposures during the entire pregnancy period, the third trimester, and the preconception window, respectively. However, no significant associations were observed for the first and second trimesters. These findings are consistent with those of previous studies conducted in both developing and developed countries. For example, a study involving 15 African countries using DHS data reported that an interquartile range (IQR) increase of 33.9 \u0026micro;g/m\u0026sup3; in the PM\u003csub\u003e2.5\u003c/sub\u003e concentration was significantly associated with PTB (OR\u0026thinsp;=\u0026thinsp;1.08; 95% CI: 1.01\u0026ndash;1.16) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, a geospatial, population-based cohort study in the United States revealed that increased PM\u003csub\u003e2.5\u003c/sub\u003e levels (\u0026gt;\u0026thinsp;15 \u0026micro;g/m\u0026sup3;) were linked to higher PTB risk during third trimester (OR\u0026thinsp;=\u0026thinsp;1.28; 95% CI: 1.20\u0026ndash;1.37) and complete pregnancy period (OR\u0026thinsp;=\u0026thinsp;1.19; 95% CI: 1.09\u0026ndash;0.30) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using satellite-based PM\u003csub\u003e2.5\u003c/sub\u003e estimates, a Chinese study also found that an IQR increase of 33.6 \u0026micro;g/m\u0026sup3; was associated with increased PTB risk (OR\u0026thinsp;=\u0026thinsp;1.12; 95% CI: 1.05\u0026ndash;1.20) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, some studies have reported no associations between PM2.5 exposure and PTB [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Several factors may explain these discrepancies including exposure assessment differences, variability in PM\u003csub\u003e2.5\u003c/sub\u003e composition or confounder adjustment. Our findings are further supported by the probability estimation curve for PTB risk, which demonstrated that PTB occurred across the entire distribution of PM\u003csub\u003e2.5\u003c/sub\u003e exposure, including at concentrations currently deemed acceptable under the annual BNAAQS (35 \u0026micro;g/m\u0026sup3;).\u003c/p\u003e \u003cp\u003eIn addition to the continuous exposure model, we conducted a quartile based categorical analysis to further examine the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and PTB. We observed that the highest exposure category (fourth quartile: 77.9\u0026ndash;105.5 \u0026micro;g/m\u0026sup3;) was significantly associated with increased odds of PTB across all models compared with the lowest quartile (28.8\u0026ndash;58.8 \u0026micro;g/m\u0026sup3;) during entire pregnancy window. Similarly, during the preconception period, strong associations between PM\u003csub\u003e2.5\u003c/sub\u003e and PTB risk were consistently observed in the third and fourth quartiles across both crude and adjusted models. In contrast, the first, second, and third trimesters did not show convincing or consistent associations. Similar studies in Thailand and China reported higher risk of PTB at PM\u003csub\u003e2.5\u003c/sub\u003e concentrations exceeding 37.5 \u0026micro;g/m\u0026sup3; and 36.5 \u0026micro;g/m\u0026sup3;, with adjusted ORs of 2.46 (95% CI: 2.13\u0026ndash;2.85) and 2.54 (95% CI: 1.42\u0026ndash;4.55), respectively [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, a study in Boston reported a steady increase in PTB risk even at low PM\u003csub\u003e2.5\u003c/sub\u003e levels (\u0026lt;\u0026thinsp;12 \u0026micro;g/m\u0026sup3;) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This discrepancy may reflect differences in ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure ranges between populations, where our study region experienced substantially higher pollution levels, potentially exceeding the biological threshold required to trigger oxidative stress, systemic inflammation, and placental vascular dysfunction mechanisms known to contribute to PTB [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interaction analysis indicated that place of residence and place of delivery significantly modified the association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and PTB. The association was stronger in rural areas than in urban settings, and women with home deliveries had higher odds of PTB risk than those delivered at health facilities. These patterns likely reflect disparities in healthcare access and quality, as rural populations often experience reduced availability and utilization of antenatal and emergency obstetric care. Studies from India and Iran similarly revealed that PM\u003csub\u003e2.5\u003c/sub\u003e exposure during pregnancy poses a higher risk in rural areas, likely because of combined ambient and indoor pollution and limited access to healthcare [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In Bangladesh, this risk may be further amplified by the widespread use of solid fuels for cooking, mainly shrubs or grasses, crop residues, and wood which increase household air pollution exposure [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Household combustion of solid fuels increases PTB risk by approximately 30%, whereas a meta-analysis found that biomass, coal, and kerosene use were associated with higher PTB risk (OR\u0026thinsp;=\u0026thinsp;1.27; 95% CI: 1.19\u0026ndash;1.36) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings highlight the importance of addressing both environmental and structural health determinants to mitigate air pollution related adverse birth outcomes in resource limited settings.\u003c/p\u003e \u003cp\u003eOur study did not establish a causal relationship or specific biological pathways linking PM\u003csub\u003e2.5\u003c/sub\u003e exposure to PTB risk. However, several plausible toxicological mechanisms have been proposed. PM\u003csub\u003e2.5\u003c/sub\u003e particles can penetrate deep into the lungs and enter systemic circulation, where they may induce oxidative stress, coagulation disturbances, and placental inflammation, thereby affecting early fetal development [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. PM\u003csub\u003e2.5\u003c/sub\u003e exposure had also been associated with placental dysfunction through impaired oxygen transport, disrupted hormonal regulation, and restricted intrauterine growth [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Collectively, these mechanisms may compromise fetal growth and contribute to PTB risk. Further in vivo and in vitro studies encompassing the full course of pregnancy are needed to confirm and clarify these associations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several notable strengths in our study. First, the sampling distribution covered both rural and urban populations across the country, which enhances the generalizability of our findings. Furthermore, we utilized fine scale PM\u003csub\u003e2.5\u003c/sub\u003e concentration data for each cluster, providing a more accurate linkage between exposure and participants. This approach can also support future researchers in conducting fine scale exposure assessments, especially given the limited ground based monitoring available in Bangladesh. Moreover, our analysis incorporated both sociodemographic and healthcare related covariates, allowing for more comprehensive adjustment of potential confounders and yielding more reliable estimates. Most importantly, to our knowledge, this is the first nationwide study in Bangladesh to investigate the association between air pollution and PTB.\u003c/p\u003e \u003cp\u003eHowever, we have encountered several unavoidable limitations. In terms of the data, we were not able to consider other air pollutants in the sensitivity analyses. In addition, several important covariates such as maternal smoking status and type of cooking fuels were unavailable in this version of the DHS dataset for Bangladesh. The birth information in the DHS dataset was collected based on maternal recall and birth certificate, which may have introduced recall and information bias. Moreover, potential residual confounding due to unmeasured or misclassified variables cannot be ruled out. As the outcome is relatively common, ORs from logistic regression may slightly overestimate the true risks. Therefore, future studies should employ models that account for complex survey design, and incorporate multiple air pollutants and additional covariates to better capture the multifactorial nature of PTB risk.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed a significant positive association between exposure to PM\u003csub\u003e2.5\u003c/sub\u003e air pollution and PTB risk in Bangladesh. In addition, the association varied by place of residence, with stronger effects observed in rural populations. In addition, place of delivery emerged as an important factor, as home births were significantly associated with higher odds of PTB risk than at facility deliveries. Overall, our study provides comprehensive evidence of the adverse effects of air pollution on PTB in Bangladesh, identifying air pollution as a potentially modifiable risk factor for reducing PTB risk. The current findings can serve as a foundation for future research, and further epidemiological studies particularly longitudinal cohort and case-control designs are warranted to establish more robust causal inferences.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAOD\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAerosol Optical Depth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBNAAQS\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBangladesh National Ambient Air Quality Standard\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCNN\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConvolutional Neural Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDHS\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGPS\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Position System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGWR\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeographically Weighted Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKR\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKids Recode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMICs\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow and Middle Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMISR\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMulti Resolution Imaging Spectroradiometers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMODIS\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModerate Resolution Imaging Spectroradiometers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFine particulate matter (aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePTB\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epreterm birth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e :\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the Demographic and Health Surveys (DHS) Program, a publicly available but restricted-access data repository. Access to the Bangladesh DHS datasets was granted upon request following registration and approval of the research project titled \u003cem\u003e\u0026ldquo;Impact of air pollution on pregnancy outcome\u0026rdquo;\u003c/em\u003e through the DHS Program website. The datasets include nationally representative survey data for Bangladesh, along with associated GPS cluster datasets, and were provided in SPSS (.sav) formats. Due to DHS data use agreements and confidentiality requirements, the datasets cannot be publicly shared by the authors. Researchers may obtain access to the same data by registering and submitting a data request through the DHS Program at: https://www.dhsprogram.com/data/. Approval is subject to DHS review and terms of use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript have no conflicts of interest or competing interests (financial or nonfinancial) to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or nonprofit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design, analysis and draft manuscript and editing were prepared by: Abdullah Al Nayeem. Data processing, variable selection and partial editing were performed by Sabiha Sultana.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support of the DHS Program for providing access to the data used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIHME. New Report Shows Nearly 9 of 10 Global. 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Environ Health Perspect. 2006;114:1636\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1289/ehp.9081\u003c/span\u003e\u003cspan address=\"10.1289/ehp.9081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, Particulate matter, PTB, Preterm birth, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-8483529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8483529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExposure to fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) can affect fetal health via maternal pathways during pregnancy. Pregnant women are among the most vulnerable populations to PM\u003csub\u003e2.5\u003c/sub\u003e exposure worldwide. Although the impacts of maternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure on preterm birth (PTB) have been widely studied globally, epidemiological evidence specific to Bangladesh is lacking. Therefore, we assessed the associations between maternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure and PTB risk across different pregnancy periods in Bangladesh.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used Demographic and Health Survey (DHS) data to conduct this cross-sectional study in Bangladesh. A total of 8,059 births occurring between 2017 and 2022 were included, along with their sociodemographic information. The monthly PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated using satellite-based models linked to each cluster\u0026rsquo;s global position system (GPS) location. We constructed exposure-specific multivariable logistic regression models to assess the association between maternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure and PTB risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the birth period (2017\u0026ndash;2022), the average PM\u003csub\u003e2.5\u003c/sub\u003e concentration was 67.9 \u0026micro;g/m\u0026sup3;. According to the fully adjusted models, a 10 \u0026micro;g/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e was significantly associated with PTB risk during the complete pregnancy period (OR\u0026thinsp;=\u0026thinsp;1.13; 95% CI: 1.04\u0026ndash;1.23), preconception (OR\u0026thinsp;=\u0026thinsp;1.03; 95% CI: 1.00\u0026ndash;1.07), and third trimester (OR\u0026thinsp;=\u0026thinsp;1.05; 95% CI: 1.01\u0026ndash;1.09). The association was particularly strong in rural areas (OR\u0026thinsp;=\u0026thinsp;1.30; 95% CI: 1.17\u0026ndash;1.45). The probability curve indicated a nonlinear increase in the PTB probability with increasing PM\u003csub\u003e2.5\u003c/sub\u003e concentration, with a predicted probability of 6% at the lowest exposure level to 14% at concentrations above 100 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study revealed a significant association between maternal PM\u003csub\u003e2.5\u003c/sub\u003e exposure and increased odds of PTB. These findings highlight the need for targeted specific interventions to reduce air pollution to protect vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Maternal PM2.5 exposure and preterm birth in Bangladesh: A nationwide cross-sectional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 17:31:24","doi":"10.21203/rs.3.rs-8483529/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"50478415285597069950786667345360060899","date":"2026-01-31T06:08:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T09:13:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T11:39:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T17:00:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-01T19:10:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-01-01T19:05:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96694b86-b6eb-4634-aa09-ded2968ce2a6","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T17:31:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 17:31:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8483529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8483529","identity":"rs-8483529","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}