Long-term Air Pollution Exposure and Infertility in Reproductive-aged Women: A Nationwide Cohort Study in Taiwan

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Causes include age-related fertility decline and environmental factors. Air pollution is a potential factor, but large-scale evidence is still lacking. This study examines the impact of several air pollutants on infertility in females aged 15 to 60, hypothesizing that air pollution increases infertility risks. Method We constructed a cohort from Taiwan’s National Health Insurance Research Database (NHIRD) of females aged under 15 or over 60 between July 1, 2003, and December 31, 2013. Concentrations of SO2, CO2, CO, O3, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 were estimated based on insurance registration. We calculated the HRs of exposure at a standard deviation increment for 10 years to determine the dose-response effect between air pollutants and infertility. Result Long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 was associated with increased infertility in women of reproductive age. Each standard deviation increase in exposure to these pollutants indicated a higher incidence of infertility by 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. Conversely, ozone exposure was associated with a 52% reduction in infertility risk. Conclusion This study demonstrates the significant impact of air pollution on female infertility, showing a clear dose-response relationship between exposure to various pollutants and infertility rates. These findings highlight the need for efforts to reduce air pollution and its effects on reproductive health. Further research is needed to understand the mechanisms and inform public health policies. Female infertility Air pollution reproductive health Figures Figure 1 Figure 2 Introduction Female Infertility refers to the incapacity to achieve a clinical pregnancy, impacting more than 186 million individuals globally. It is estimated to affect approximately 1 in 7 couples in developed nations ( 1 ). In summary, infertility affects an estimated 8 to 12% of reproductive-aged couples worldwide ( 1 ). Secondary infertility stands as the prevailing manifestation of female infertility worldwide. This condition pertains to a woman who, having previously experienced a clinical pregnancy, is now unable to achieve another clinical pregnancy ( 2 ). Secondary infertility is most common in regions of the world with high rates of unsafe abortion and poor maternity care, leading to post-abortive and postpartum infections ( 1 ). Numerous factors that could affect the inherent fertility of couples have been acknowledged. These encompass the duration of undesired non-conception, the decline in female fertility associated with age, infertility attributed to medical conditions, sperm quality, the influence of endocrine-disrupting chemicals, and environmental origins ( 3 ). Air pollution emerges as a significant health concern, correlating with heightened rates of both mortality and morbidity ( 4 ). For example, it has been linked to elevated cancer risk, as well as cardiovascular and respiratory disorders in both adults and children ( 6 ). Furthermore, it's associated with negative perinatal outcomes and associated with increased mortality risk following hip fracture surgery in older adults ( 5 ). Several lines of evidence suggest that exposure to environmental contaminants is involved in the pathobiology of adverse reproductive health effects, including decreased semen quality, sub-fertility, reduced fetal growth and pre-term birth ( 6 ). Two systematic reviews indicate a strong link between female infertility and air pollution ( 7 ). Conforti et al. found that in the IVF population, nitrogen dioxide and ozone were associated with reduced live birth rates, while in the general population, particulate matter (PM2.5 and PM10), sulfur dioxide, carbon monoxide, and nitrogen dioxide were linked to reduced fecundability, miscarriage, and stillbirths due to air pollution exposure ( 7 ). Another systematic review analyzed the impact of air pollution on fertility rates, observing a statistically significant reduction in fertility rates associated with higher traffic-related air pollution, particularly in relation to the coarse fraction of particulate matter (PM) ( 8 ). Apart from the aforementioned study, animal research has explored the impact of particulate urban air pollution (PM 2.5) on reproductive health. These studies found that exposure to PM 2.5 is associated with a reduced number of viable fetuses, higher incidence of implantation failures, low birth weight, low placenta weight, changes in estrous cyclicity, decreased ovarian follicles count, and decreased fertility indices ( 9 ). Given the paucity of clinical evidence regarding the impact of air pollutants on female infertility, we embarked on this retrospective study to investigate the potential influence of decade-long exposure to various air pollutants on the likelihood of infertility among Taiwanese women aged 15 to 60. Our hypothesis postulated that extended exposure to air pollution might affect the incidence of female infertility. Specifically, we examined the effects of 11 air pollution compounds: sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), particulate matter with a diameter of less than 10 µm (PM10), particulate matter with a diameter of less than 2.5 µm (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). Through the utilization of a population-based approach and comprehensive air pollutant exposure data, this study endeavors to bridge these significant knowledge gaps, offering insights into the intricate relationship between air pollution and the risk of female infertility. Materials and methods Data source In this study, we retrieved data from the National Health Insurance Research Database (NHIRD) of Taiwan, which was launched by the Taiwan government in 1995 and collected comprehensive health care for 98.29% of the population ( 10 ). The database provided nationwide medical information including gender, date of birth, employment, inpatient and outpatient diagnoses, medical procedures, drug usage, treatment duration, and medical costs of approximately 23 million insured individuals in Taiwan ( 11 ). We identified baseline information by using the Longitudinal Health Insurance Database 2000, a subset of the NHIRD containing 1 million randomly enrolled patients. The Research Ethics Committee approved this study protocol (IRB: 240310). This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant regulations. Due to the anonymity of patient data in NHIRD, patient-informed consent was not required for this study. Study population We only included patients with complete available data, while those with missing, inconsistent, or unknown records of baseline information such as gender and birth year were therefore precluded. The enrollment population was defined as those who were female gender between July 1, 2003, and December 31, 2013. The further criteria for exclusion were detailed as follows, 1) subjects aged under 15 or over 60 at the beginning of the study, 2) those diagnosed with infertility, history of prior surgery of the genital organs, or history of radiotherapy before the beginning of the study, 3) subjects whose survival data were before the beginning of the study, and 4) subjects’ follow-up were under 5 years. Exposure data collection We obtained the cumulative daily average concentration of eleven air pollutants, including sulfur dioxide (SO 2 ), carbon monoxide (CO), ozone (O 3 ), particulate matter < 10 µm in size (PM 10 ), particulate matter < 2.5 µm in size (PM 2.5 ), nitrogen oxides (NO X ), nitrogen monoxide (NO), nitrogen dioxide (NO 2 ), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4), from the measurements recorded by the 76 monitoring stations maintained by the Taiwan Environmental Protection Administration (EPA), Executive Yuan. Data were collected for the period from July 1, 1993, to December 31, 2013. The cumulative daily average concentration of each pollutant was calculated from 10 years prior to the end of follow-up. The data were then integrated with each patient’s residential area postal codes and their residence change considered by insurance registration during the study period. Outcomes and Confounders The development of infertility was the major outcome of interest. We followed every patient until the primary end-point defined by either the development of infertility (withdrawal from the NHI program) or end of the study period (December 31, 2013), with survival time measured in months. Several confounders were introduced and adjusted in this study, including age, urbanization level, insurance amount, CCI score, ambient temperature, season, and lag0-2. Above them, patient data such as age, insurance amount, and CCI score were obtained from the NHIRD database. Specifically, the insurance amount was measured as the average value during the period of air pollutant exposure assessment, while the comorbidities were defined before the survival date. The meteorological factor of ambient temperature was collected from the EPA. The urbanization level was recorded according to the patient’s residence at the beginning of the follow-up period, while the season was defined by date. We also adopted the 3-day moving average of current day and two preceding days concentrations of air pollutants before the primary end-point (lag0-2) for adjusted confounders, based on the largest effect estimate found in previous literature ( 12 ). Statistical analysis To demonstrate patient characteristics associated with air pollution, patients were divided by the exposure concentration of each pollutant into 3 tertiles and were compared using the chi-square chi-squared test or one-way analysis of variance among tertiles, post hoc tests were performed to explore differences when significance was indicated in the one-way analysis of variance. We plotted crude cumulative incidence curves of infertility for participants in three tertiles, with the difference between tertiles assessed by log-rank tests. We conducted Cox regression models to explore the dose-response effect between air pollutants and infertility risk by calculating the HRs of exposure at a standard deviation increment for 10 years. The regression models were adjusted for the abovementioned confounding factors. All tests were 2-sided, and statistically, significance was considered when p < 0.05. Analyses were performed using MetaTrial Research Platform (Biomedica Corp). Results Study population After the inclusion of patients with complete available data (n = 882391), those who were female gender were identified (n = 417251). Patients who met the exclusion criteria (with 132930, 1645, 35825 and 32823 respectively for the four criteria) were further excluded from the analysis. As a result, a total of 232125 participants were included in the analysis, with the selection process depicted in Figure 1. Characteristics and descriptive results Table 1 showed the characteristics of the included cohort (n = 232125), with 929 (12.51%) events of infertility occurring, as well as comorbidities. Characteristics of cohorts with exposure to each pollutant were presented by tertiles and were further detailed in Supplementary Table 1, whereas the means and distributions of 10-year exposure to each pollutant were demonstrated in Table 2. Cumulative incidence of infertility For the outcome of interest, the incidence of infertility, we exhibited the results among tertiles in Table 3, with the p-value of one-way analysis of variance and post-hoc analysis. The cumulative incidence curves revealed notable variations in the intensity and direction of the relationships between each type of air pollution and infertility (Figure 2). The dose-response effect between air pollutants and the incidence of infertility To explore the dose-response effect between the concentrations of air pollutants and the incidence of infertility, we performed Cox regression models and calculated the HRs of exposure at a standard deviation increment for 10 years. The hazard ratios were listed in Table 4, indicating that each increase of an SD in average exposure concentration of SO 2 , CO, PM 10 , PM 2.5 , NO X , NO, NO 2 , THC, NMHC, and CH 4 was associated with a significant increase in the incidence of infertility of 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. However, a significant negative association was observed in O 3 , with a 52% reduction in the infertility rate. Discussion Principal Results This study found that long-term exposure to SO 2 , CO, PM 10 , PM 2.5 , NO X , NO, NO 2 , THC, NMHC, and CH 4 was associated with associated with the incidence of infertility in age-reproductive women. In females, each increase of a standard deviation in average exposure concentration of SO 2 , CO, PM 10 , PM 2.5 , NO X , NO, NO 2 , THC, NMHC, and CH 4 , indicated a higher incidence of infertility of 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. On the contrary, we found a significant reduction of 52% in the hazard ratio with ozone. Comparison with Prior Work Our findings suggested that long-term exposure to SO 2 , CO, PM 10 , PM 2.5 , NO X , NO, NO 2 , THC, NMHC, and CH 4 acted consistently as a risk factor for higher incidence of infertility. Previous literature had discovered significant associations between increased levels of traffic-related air pollution, particularly the coarse fraction of particulate matter, and reduced fertility rates, with chronic exposure being more influential in infertility risk compared to short-term exposure ( 13 ). The same trends were indicated in the infertility risk, with the pollutants of SO 2 , NO 2 . In a polluted area, Legro et al. found variable, cycle-dependent effects of declining air quality on reproductive outcomes after in vitro fertilization, with consistent associations between increased NO 2 and lower live birth rates ( 14 ). Furthermore, there is a suggestion that the likelihood of conception at the first unprotected menstrual cycle may decrease for couples exposed to average SO 2 levels exceeding 40 µg/m³ during the second month before conception ( 15 ). The ovarian reserve is a crucial measure of a woman's reproductive potential, primarily determined by the quantity and quality of oocytes within the ovaries ( 16 ). A groundbreaking big-data approach by Santi et al. examined how environmental factors, particularly rising air pollution, impact anti-mullerian hormone (AMH) serum levels. While genetics play a major role in ovarian reserve at birth, this study suggests that environmental factors can also influence the decline in AMH and ovarian reserve during adulthood ( 17 ). In contrast to our findings, Wu et al. ( 18 ) suggest that extended exposure to the air pollutant SO 2 is linked to lower antral follicle count and a higher risk of poor ovarian response (< 4 oocytes retrieved), respectively, while NO 2 , PM 10 , PM 2.5 , CO, and O 3 show no significant associations. These findings raise concerns about the potential adverse impact of atmospheric SO 2 exposure on women's ovarian reserve. O 3 , a secondary air pollutant formed from the combination of hydrocarbons and nitrogen oxides in sunlight, exhibits potential non-linearities in this process ( 19 ). Legro et al. discovered a connection between O 3 concentration at a patient's address and the likelihood of a live birth ( 14 ). However, when accounting for NO 2 and in vitro fertilization laboratory interactions, this association became insignificant. Moreover, Boulet et al. observed a weak positive correlation between O 3 and implantation or live birth rates. Wu et al. identified higher O 3 exposure in women with normal ovarian reserve (NOR) compared to poor responders ( 18 ). However, multipollutant models showed no significant correlations. Possible mechanism While the precise mechanisms behind air pollution-related female infertility are not yet fully understood, we propose a potential mechanism: maternal exposure to contaminants during the pre- and peri-conceptional periods could lead to abnormalities in oocytes, embryos, and fetuses ( 20 ). This could potentially hinder safe childbirth, impact overall health and mental well-being, and thereby increase vulnerability to and susceptibility to infertility. Air pollutants disrupt both animal and human gametogenesis, reducing reproductive efficacy ( 7 ). These endocrine-disrupting properties could activate the Ras/Erk pathway via interactions with nuclear receptors like estrogen or androgen receptors and specific cytosolic targets, while diesel exhaust particles and PM 2.5 impact ovarian function by disrupting the endocrine system, increasing oxidative stress and inflammation, and activating specific targets that trigger abnormal MAPK signaling ( 21 ). Indeed, in a recent study of 777 men, increased air pollutant concentrations were significantly associated with both the hypomethylation of F3, ICAM-1, and TLR-2, and the hypermethylation of IFN-γ and IL-6 ( 22 ). What's most intriguing is that research has demonstrated that adding antioxidant factors to ovarian stimulation can enhance reproductive outcomes in women with polycystic ovarian syndrome ( 23 ). Regarding ovarian function, a study did find a significant negative association between SO 2 exposure throughout the entire antral follicle development stage and AFC ( 24 ). This suggests that during the follicular vascularization transition stage, SO 2 might be expedited in reaching follicles and granulosa cells, potentially contributing to impaired follicle development, hastened follicle loss, or altered ovarian function. The ovarian toxicity of environmental contaminants, characterized by meiotic disruption in oocyte formation resulting from genetic or epigenetic aberrations, can affect dormant follicles within the primordial pool that undergo growth stimulation and have the potential to develop into the antral stage ( 25 ). Subsequently, these antral candidates are typically chosen to progress to the pre-ovulatory stage, marked by the transition from primary to pre-antral, during which the oocyte within the follicle enlarges, granulosa cells proliferate, and a layer of theca cells forms outside the granulosa cells, serving as a conduit for transporting steroids and growth factors to the granulosa cells to promote follicle vascularization ( 26 ). Additionally, this bloodstream enables toxins to reach follicles, where they can suppress granulosa cell proliferation and growth. Furthermore, SO 2 toxicology in mammalian organ development involves cellular injuries caused by inflammation and oxidative stress, such as lipid peroxidation, DNA damage, protein oxidation, and interference with signal transduction ( 27 ). Up to this point, data indicate that ozone therapy may have a beneficial impact on various medical conditions, including human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS), fibromyalgia, cystitis, and osteomyelitis ( 28 ). Ozone therapy has shown promise as a potential treatment for women with tubal infertility, inflammatory conditions like pelvic inflammatory disease (PID), urovagina and ischemia/reperfusion injury, potentially avoiding expensive assisted reproductive technologies, inhibiting endometrial cell necrosis and reducing inflammation, effectively treating urovagina and leading to successful pregnancies, and offering an alternative to ovary-sparing approaches for ovarian torsion ( 29 ). Other studies have shown the effectiveness of ozone treatments in various medical conditions beyond infertility. Ozone therapy has been demonstrated to significantly improve hemorheological parameters and enhance oxygen unloading in patients with peripheral occlusive arterial disease ( 30 ). Ozone exhibits neuroprotective effects in an in vitro brain ischemia model, pointing towards a potential clinical trial for stroke patients who cannot undergo thrombolytic treatment ( 30 ). In conclusion, ozone treatment is suggested to have clinical potential through its ability to boost nitric oxide synthase expression, regulate endogenous NO levels, and maintain cellular redox balance. This study possesses several notable strengths. Firstly, it is the inaugural investigation to reveal the association between long-term exposure to air pollution and female infertility. Secondly, our analysis encompassed a comprehensive range of pollutants and utilized a significantly large sample, bolstering the statistical robustness of the findings. Thirdly, we conducted stratified analyses considering potential confounding factors such as age, urbanization level, insurance amount, CCI score, ambient temperature, season, and lag0-2, along with a wide spectrum of demographic characteristics. Lastly, the population-based data used in this study is representative of the general population in Taiwan. Our study also has several limitations. Firstly, cigarette consumption, a significant factor in ambient air quality, was not included due to data retrieval issues, which may introduce bias. Secondly, the NHIRD lacks details on disease severity, clinical manifestations, laboratory findings, and causes of death, preventing further analysis or determination of specific causes of death. Thirdly, potential errors in assessing air pollution may exist, particularly when linking data through health insurance registration. Fourthly, biases might be present as death was identified by withdrawal from the NHI program. Although we included and adjusted for several confounders, unmeasured or unknown confounders may have introduced bias. The retrospective nature of this study also presents lower statistical quality and potential bias from unknown confounders and inaccuracies in primary records, underscoring the need for a well-designed prospective, randomized, controlled study to establish a causal relationship. Lastly the NHIRD lacks information on factors such as menopausal status, marital status, male-factor infertility, use of oral contraceptives, and smoking, which could have influenced our findings. Despite these limitations, our study provides robust evidence that long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 is associated with an increased risk of female infertility. Conclusion This study illustrates the considerable influence of air pollution on female infertility, revealing a distinct dose-response correlation between exposure to different pollutants and infertility rates. Prolonged exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 heightened infertility risks, while ozone exposure mitigated them. Our results underscore the importance of vigilance among clinicians and government action to mitigate this risk. They emphasize the necessity for extensive initiatives to curb air pollution and its impact on reproductive health. Further investigation is warranted to elucidate the underlying mechanisms and guide public health policies. Declarations Other Information Funding Statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Disclosure Statement: none CRediT Authorship Contribution Statement: Yu-Chieh Lo: Conceptualization; Investigation; Methodology; Writing—original draft preparation. Yeu-Chai Jang: Conceptualization; Validation; Visualization; Writing – review and editing. Yi-Jie Kuo: Supervision; Validation Shun-Jen, Cheng: Software; Formal analysis Resources; Shu-Han Chuang: Project administration, Resources Cheng-Hsien Chang: Project administration, Resources Yu-Pin Chen: Supervision. All authors have read and agreed to the published version of the manuscript. Attestation Statement: The subjects in this trial have not concomitantly been involved in other randomized trials. Data regarding any of the subjects in the study have not been previously published unless specified. Data will be made available to the editors of the journal for review or query upon request. Data Sharing Statement: All data generated or analyzed during this study are included in this published paper and its Multimedia Appendix files. More detailed data sets are not publicly accessible due to the requirement of obtaining approval from the Taiwan Ministry of Health and Welfare. Researchers interested in obtaining access to this data set may initiate the process by submitting an application form to the Ministry of Health and Welfare. For further guidance and assistance, please reach out to the MOHW staff via email at [email protected] . The address of the Taiwan Ministry of Health and Welfare is as follows: No.488, Sec. 6, Zhongxiao E. Rd., Nangang Dist., Taipei City 115, Taiwan. You can also contact them by phone at +886-2-8590-6848. Capsule: Long-term exposure to multiple air pollutants significantly increases female infertility risks, demonstrating a clear dose-response relationship, while ozone exposure notably reduces it. Declarations of interest: The author declares no funding or any financial and personal relationships with individuals or organizations that could inappropriately influence or bias this work. References Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Human reproduction update. 2015;21(4):411-26. Nachtigall RD. International disparities in access to infertility services. Fertility and sterility. 2006;85(4):871-5. Alviggi C, Guadagni R, Conforti A, Coppola G, Picarelli S, De Rosa P, et al. Association between intrafollicular concentration of benzene and outcome of controlled ovarian stimulation in IVF/ICSI cycles: a pilot study. Journal of ovarian research. 2014;7:1-6. 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Characteristic N (%) Infertility 4981 (2.15) Age, years 15-35 121732 (52.44) 36-60 110393 (47.56) Mean ± SD 35.08 ± 12.45 Urbanization level 1 (highest) 138881 (59.83) 2 71156 (30.65) 3 14280 (6.15) 4 (lowest) 1826 (0.79) unknown 5982 (2.58) Insurance amount, NT$ financially dependent 1343 (0.58) 1-19999 79868 (34.41) 20000-39999 102875 (44.32) >=40000 41027 (17.67) unknown 7012 (3.02) CCI score Mean ± SD 1.10 ± 1.68 Comorbidity Inflammatory disease of the ovary, fallopian tube, pelvic cellular tissue, and peritoneum 35926 (15.48) Inflammatory disease of the uterus 12241 (5.27) Inflammatory disease of the cervix, vagina, and vulva 107803 (46.44) Endometriosis 8828 (3.80) Hypertension 38765 (16.70) Diabetes mellitus 27325 (11.77) Hypertriglyceridemia 286 (0.12) Hypercholesterolemia 29970 (12.91) Coronary artery disease 21789 (9.39) Disorders of eating 302 (0.13) SD, standard deviation; CCI score, Charlson Comorbidity Index score. Table 2. Mean and distribution of air pollutants over the exposure period SO 2 (ppb) CO (ppm) O 3 (ppb) PM 10 (μg/m 3 ) PM 2.5 (μg/m 3 ) NO X (ppb) NO (ppb) NO 2 (ppb) THC (ppm) NMHC (ppm) CH 4 (ppm) Mean 4.14 0.53 28.67 54.19 32.43 25.72 7.54 18.18 2.24 0.29 1.95 SD 1.35 0.11 1.73 8.93 6.05 6.8 3.68 3.48 0.12 0.08 0.07 T 1 3.59 0.48 27.77 47.66 27.67 21.44 4.93 16.55 2.17 0.25 1.93 T 2 4.04 0.59 29.11 56.18 34.95 29.6 8.51 20.49 2.31 0.33 1.97 SD, standard deviation; T 1 , 33.33 percentile; T 2 , 66.66 percentile; ppb, parts per billion; ppm, parts per million; μg/m 3 , microgram/cubic meter; SO 2 , sulfur dioxide; CO, carbon monoxide; O 3 , ozone; PM 10 , particulate matter < 10 μm in size; PM 2.5 , particulate matter < 2.5 μm in size; NO X , nitrogen oxides; NO, nitrogen monoxide; NO 2 , nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH 4 , methane. Table 3. Incidence of infertility among tertiles Pollutants Tertiles of average daily exposure, n (%) P value Total, n (%) T1 (lowest) T2 T3 (highest) SO 2 974/77141 (1.26) 1799/75237 (2.39) 2208/79747 (2.77) <0.001 4981/232125 (2.15) CO 443/77375 (0.57) 1351/77375 (1.75) 3187/77375 (4.12) <0.001 4981/232125 (2.15) O 3 3579/77372 (4.63) 796/77365 (1.03) 606/77388 (0.78) <0.001 4981/232125 (2.15) PM 10 1243/77360 (1.61) 1681/72522 (2.32) 2057/82243 (2.50) <0.001 4981/232125 (2.15) PM 2.5 832/77369 (1.08) 2077/75510 (2.75) 2064/79229 (2.61) <0.001 4973/232108 (2.14) NO X 603/77370 (0.78) 1778/77380 (2.30) 2600/77375 (3.36) <0.001 4981/232125 (2.15) NO 561/77375 (0.73) 1839/77374 (2.38) 2581/77376 (3.34) <0.001 4981/232125 (2.15) NO 2 675/77348 (0.87) 1506/77402 (1.95) 2800/77375 (3.62) <0.001 4981/232125 (2.15) THC 268/76816 (0.35) 1084/76816 (1.41) 3608/76817 (4.70) <0.001 4960/230449 (2.15) NMHC 541/76813 (0.70) 1786/76819 (2.32) 2633/76817 (3.43) <0.001 4960/230449 (2.15) CH 4 335/76816 (0.44) 451/72513 (0.62) 4174/81120 (5.15) <0.001 4960/230449 (2.15) SO 2 , sulfur dioxide; CO, carbon monoxide; O 3 , ozone; PM 10 , particulate matter < 10 μm in size; PM 2.5 , particulate matter < 2.5 μm in size; NO X , nitrogen oxides; NO, nitrogen monoxide; NO 2 , nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH 4 , methane. Table 4. Hazard ratios for incidence of infertility of long-term exposure at an SD increment. Pollutant Adjusted HR (95% CI) P value SD SO 2 1.13 (1.09,1.18) < 0.001 1.13 ppb CO 2.16 (2.09,2.22) < 0.001 0.11 ppm O 3 0.48 (0.47,0.49) < 0.001 1.73 ppb PM 10 1.35 (1.28,1.43) < 0.001 8.93 μg/m 3 PM 2.5 1.77 (1.67,1.88) < 0.001 6.05 μg/m 3 NO X 1.80 (1.73,1.86) < 0.001 6.80 ppb NO 1.66 (1.61,1.72) < 0.001 3.68 ppb NO 2 1.76 (1.69,1.83) < 0.001 3.48 ppb THC 2.16 (2.09,2.22) < 0.001 0.12 ppm NMHC 1.52 (1.47,1.56) < 0.001 0.08 ppm CH 4 2.81 (2.72,2.90) < 0.001 0.07 ppm HR, hazard ratio; CI, confidence interval; SD, standard deviation; SO 2 , sulfur dioxide; CO 2 , carbon dioxide; CO, carbon monoxide; O 3 , ozone; PM 10 , particulate matter < 10 μm in size; PM 2.5 , particulate matter < 2.5 μm in size; NO X , nitrogen oxides; NO, nitrogen monoxide; NO 2 , nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH 4 , methane. Cox regression models were adjusted for age, urbanization level, insurance amount, CCI score, Inflammatory disease of the ovary, fallopian tube, pelvic cellular tissue, and peritoneum, Inflammatory disease of the uterus, Inflammatory disease of the cervix, vagina, and vulva, Endometriosis, Hypertension, Diabetes mellitus, Hypertriglyceridemia, Hypercholesterolemia, Coronary artery disease, ambient temperature, lag0-2, season, and controlled pollutants Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 19 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yeu-Chai","middleName":"","lastName":"Jang","suffix":""},{"id":442121969,"identity":"b9000fd3-d66b-4cd4-a726-4f625b272c69","order_by":2,"name":"Shu-Han Chuang","email":"","orcid":"","institution":"Changhua Christian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu-Han","middleName":"","lastName":"Chuang","suffix":""},{"id":442121971,"identity":"80d313cb-18ab-414f-abb5-23ead857cb66","order_by":3,"name":"Shun-Jen Cheng","email":"","orcid":"","institution":"Wan Fang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shun-Jen","middleName":"","lastName":"Cheng","suffix":""},{"id":442121972,"identity":"7c0fc803-c99b-49a6-b054-c531074dfe5e","order_by":4,"name":"Yi-Jie Kuo","email":"","orcid":"","institution":"Wan Fang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi-Jie","middleName":"","lastName":"Kuo","suffix":""},{"id":442121973,"identity":"0410d68c-676a-427e-931a-0a12ea447387","order_by":5,"name":"Cheng-Hsien Chang","email":"","orcid":"","institution":"Changhua Christian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cheng-Hsien","middleName":"","lastName":"Chang","suffix":""},{"id":442121974,"identity":"c7fa9dcf-a4e9-407f-bbf3-a1f48e6ec2cc","order_by":6,"name":"Yu-Pin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACfvbmww8SKmBcNiK0SPYcSzN4cIaBgQemhYeQFoMbOQaSD9tI05JgYJA47zCDvdgZA4YPZUCGRAIBh515kPAgcdthBh7pHAPGGeeADEJa+I4nHDBI3HYbrIWZtw2kl4AWhgOJDRKJc6Ba/hKjReBEMoNEYgNUCyMxWoCBzGaQcOw/D8/ttIKDPefSeXjuP8CvhZ+9//PDHzVpcuyzkzc++FFmLcfec4CAX6AAHBsHGIiIllEwCkbBKBgFRAAAKNlCmM7XEUUAAAAASUVORK5CYII=","orcid":"","institution":"Wan Fang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu-Pin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-03-19 09:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6260294/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6260294/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24213-x","type":"published","date":"2025-09-30T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80837972,"identity":"64bc46c6-8ab0-4fbe-a50c-2c27bcf3b093","added_by":"auto","created_at":"2025-04-17 15:17:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103712,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of the selection process.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6260294/v1/00d8acbcfe418865d71984ca.jpg"},{"id":80837974,"identity":"b8e67c99-a24f-4e97-9d1e-325e9362881e","added_by":"auto","created_at":"2025-04-17 15:17:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290674,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence curves of infertility among tertiles\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6260294/v1/c00d67241b379805ce18b8c2.png"},{"id":92883909,"identity":"3108c847-c43c-4c04-a5e0-4c168646d176","added_by":"auto","created_at":"2025-10-06 16:10:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6260294/v1/d71a82f6-5c2f-4710-b796-115917516c8c.pdf"},{"id":80838789,"identity":"3b1f9cc2-f789-4813-9779-0058752e2947","added_by":"auto","created_at":"2025-04-17 15:25:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":185259,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6260294/v1/004fe8d3c5bbe3eada1a449e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term Air Pollution Exposure and Infertility in Reproductive-aged Women: A Nationwide Cohort Study in Taiwan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemale Infertility refers to the incapacity to achieve a clinical pregnancy, impacting more than 186\u0026nbsp;million individuals globally. It is estimated to affect approximately 1 in 7 couples in developed nations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In summary, infertility affects an estimated 8 to 12% of reproductive-aged couples worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Secondary infertility stands as the prevailing manifestation of female infertility worldwide. This condition pertains to a woman who, having previously experienced a clinical pregnancy, is now unable to achieve another clinical pregnancy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Secondary infertility is most common in regions of the world with high rates of unsafe abortion and poor maternity care, leading to post-abortive and postpartum infections (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Numerous factors that could affect the inherent fertility of couples have been acknowledged. These encompass the duration of undesired non-conception, the decline in female fertility associated with age, infertility attributed to medical conditions, sperm quality, the influence of endocrine-disrupting chemicals, and environmental origins (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir pollution emerges as a significant health concern, correlating with heightened rates of both mortality and morbidity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For example, it has been linked to elevated cancer risk, as well as cardiovascular and respiratory disorders in both adults and children (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, it's associated with negative perinatal outcomes and associated with increased mortality risk following hip fracture surgery in older adults (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Several lines of evidence suggest that exposure to environmental contaminants is involved in the pathobiology of adverse reproductive health effects, including decreased semen quality, sub-fertility, reduced fetal growth and pre-term birth (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Two systematic reviews indicate a strong link between female infertility and air pollution (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Conforti et al. found that in the IVF population, nitrogen dioxide and ozone were associated with reduced live birth rates, while in the general population, particulate matter (PM2.5 and PM10), sulfur dioxide, carbon monoxide, and nitrogen dioxide were linked to reduced fecundability, miscarriage, and stillbirths due to air pollution exposure (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Another systematic review analyzed the impact of air pollution on fertility rates, observing a statistically significant reduction in fertility rates associated with higher traffic-related air pollution, particularly in relation to the coarse fraction of particulate matter (PM) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Apart from the aforementioned study, animal research has explored the impact of particulate urban air pollution (PM 2.5) on reproductive health. These studies found that exposure to PM 2.5 is associated with a reduced number of viable fetuses, higher incidence of implantation failures, low birth weight, low placenta weight, changes in estrous cyclicity, decreased ovarian follicles count, and decreased fertility indices (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the paucity of clinical evidence regarding the impact of air pollutants on female infertility, we embarked on this retrospective study to investigate the potential influence of decade-long exposure to various air pollutants on the likelihood of infertility among Taiwanese women aged 15 to 60. Our hypothesis postulated that extended exposure to air pollution might affect the incidence of female infertility. Specifically, we examined the effects of 11 air pollution compounds: sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), particulate matter with a diameter of less than 10 \u0026micro;m (PM10), particulate matter with a diameter of less than 2.5 \u0026micro;m (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). Through the utilization of a population-based approach and comprehensive air pollutant exposure data, this study endeavors to bridge these significant knowledge gaps, offering insights into the intricate relationship between air pollution and the risk of female infertility.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eIn this study, we retrieved data from the National Health Insurance Research Database (NHIRD) of Taiwan, which was launched by the Taiwan government in 1995 and collected comprehensive health care for 98.29% of the population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The database provided nationwide medical information including gender, date of birth, employment, inpatient and outpatient diagnoses, medical procedures, drug usage, treatment duration, and medical costs of approximately 23\u0026nbsp;million insured individuals in Taiwan (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). We identified baseline information by using the Longitudinal Health Insurance Database 2000, a subset of the NHIRD containing 1\u0026nbsp;million randomly enrolled patients. The Research Ethics Committee approved this study protocol (IRB: 240310). This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant regulations. Due to the anonymity of patient data in NHIRD, patient-informed consent was not required for this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eWe only included patients with complete available data, while those with missing, inconsistent, or unknown records of baseline information such as gender and birth year were therefore precluded. The enrollment population was defined as those who were female gender between July 1, 2003, and December 31, 2013. The further criteria for exclusion were detailed as follows, 1) subjects aged under 15 or over 60 at the beginning of the study, 2) those diagnosed with infertility, history of prior surgery of the genital organs, or history of radiotherapy before the beginning of the study, 3) subjects whose survival data were before the beginning of the study, and 4) subjects\u0026rsquo; follow-up were under 5 years.\u003c/p\u003e\n\u003ch3\u003eExposure data collection\u003c/h3\u003e\n\u003cp\u003eWe obtained the cumulative daily average concentration of eleven air pollutants, including sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO), ozone (O\u003csub\u003e3\u003c/sub\u003e), particulate matter\u0026thinsp;\u0026lt;\u0026thinsp;10 \u0026micro;m in size (PM\u003csub\u003e10\u003c/sub\u003e), particulate matter\u0026thinsp;\u0026lt;\u0026thinsp;2.5 \u0026micro;m in size (PM\u003csub\u003e2.5\u003c/sub\u003e), nitrogen oxides (NO\u003csub\u003eX\u003c/sub\u003e), nitrogen monoxide (NO), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4), from the measurements recorded by the 76 monitoring stations maintained by the Taiwan Environmental Protection Administration (EPA), Executive Yuan. Data were collected for the period from July 1, 1993, to December 31, 2013. The cumulative daily average concentration of each pollutant was calculated from 10 years prior to the end of follow-up. The data were then integrated with each patient\u0026rsquo;s residential area postal codes and their residence change considered by insurance registration during the study period.\u003c/p\u003e\n\u003ch3\u003eOutcomes and Confounders\u003c/h3\u003e\n\u003cp\u003eThe development of infertility was the major outcome of interest. We followed every patient until the primary end-point defined by either the development of infertility (withdrawal from the NHI program) or end of the study period (December 31, 2013), with survival time measured in months. Several confounders were introduced and adjusted in this study, including age, urbanization level, insurance amount, CCI score, ambient temperature, season, and lag0-2. Above them, patient data such as age, insurance amount, and CCI score were obtained from the NHIRD database. Specifically, the insurance amount was measured as the average value during the period of air pollutant exposure assessment, while the comorbidities were defined before the survival date. The meteorological factor of ambient temperature was collected from the EPA. The urbanization level was recorded according to the patient\u0026rsquo;s residence at the beginning of the follow-up period, while the season was defined by date. We also adopted the 3-day moving average of current day and two preceding days concentrations of air pollutants before the primary end-point (lag0-2) for adjusted confounders, based on the largest effect estimate found in previous literature (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo demonstrate patient characteristics associated with air pollution, patients were divided by the exposure concentration of each pollutant into 3 tertiles and were compared using the chi-square chi-squared test or one-way analysis of variance among tertiles, post hoc tests were performed to explore differences when significance was indicated in the one-way analysis of variance. We plotted crude cumulative incidence curves of infertility for participants in three tertiles, with the difference between tertiles assessed by log-rank tests. We conducted Cox regression models to explore the dose-response effect between air pollutants and infertility risk by calculating the HRs of exposure at a standard deviation increment for 10 years. The regression models were adjusted for the abovementioned confounding factors. All tests were 2-sided, and statistically, significance was considered when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed using MetaTrial Research Platform (Biomedica Corp).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter the inclusion of patients with complete available data (n = 882391), those who were female gender were identified (n = 417251). Patients who met the exclusion criteria (with 132930, 1645, 35825 and 32823 respectively for the four criteria) were further excluded from the analysis. As a result, a total of 232125 participants were included in the analysis, with the selection process depicted in Figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCharacteristics and descriptive results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 showed the characteristics of the included cohort (n = 232125), with 929 (12.51%) events of infertility occurring, as well as comorbidities. Characteristics of cohorts with exposure to each pollutant were presented by tertiles and were further detailed in Supplementary Table 1, whereas the means and distributions of 10-year exposure to each pollutant were demonstrated in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCumulative incidence of infertility\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For the outcome of interest, the incidence of infertility, we exhibited the results among tertiles in Table 3, with the p-value of one-way analysis of variance and post-hoc analysis. The cumulative incidence curves revealed notable variations in the intensity and direction of the relationships between each type of air pollution and infertility (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe dose-response effect between air pollutants and the incidence of infertility\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the dose-response effect between the concentrations of air pollutants and the incidence of infertility, we performed Cox regression models and calculated the HRs of exposure at a standard deviation increment for 10 years. The hazard ratios were listed in Table 4, indicating that each increase of an SD in average exposure concentration of SO\u003csub\u003e2\u003c/sub\u003e, CO, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, THC, NMHC, and CH\u003csub\u003e4\u003c/sub\u003e was associated with a significant increase in the incidence of infertility of 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. However, a significant negative association was observed in O\u003csub\u003e3\u003c/sub\u003e, with a 52% reduction in the infertility rate.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Results\u003c/h2\u003e \u003cp\u003eThis study found that long-term exposure to SO\u003csub\u003e2\u003c/sub\u003e, CO, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, THC, NMHC, and CH\u003csub\u003e4\u003c/sub\u003e was associated with associated with the incidence of infertility in age-reproductive women. In females, each increase of a standard deviation in average exposure concentration of SO\u003csub\u003e2\u003c/sub\u003e, CO, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, THC, NMHC, and CH\u003csub\u003e4\u003c/sub\u003e, indicated a higher incidence of infertility of 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. On the contrary, we found a significant reduction of 52% in the hazard ratio with ozone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Prior Work\u003c/h2\u003e \u003cp\u003eOur findings suggested that long-term exposure to SO\u003csub\u003e2\u003c/sub\u003e, CO, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003eX\u003c/sub\u003e, NO, NO\u003csub\u003e2\u003c/sub\u003e, THC, NMHC, and CH\u003csub\u003e4\u003c/sub\u003e acted consistently as a risk factor for higher incidence of infertility. Previous literature had discovered significant associations between increased levels of traffic-related air pollution, particularly the coarse fraction of particulate matter, and reduced fertility rates, with chronic exposure being more influential in infertility risk compared to short-term exposure (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The same trends were indicated in the infertility risk, with the pollutants of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e. In a polluted area, Legro et al. found variable, cycle-dependent effects of declining air quality on reproductive outcomes after in vitro fertilization, with consistent associations between increased NO\u003csub\u003e2\u003c/sub\u003e and lower live birth rates (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Furthermore, there is a suggestion that the likelihood of conception at the first unprotected menstrual cycle may decrease for couples exposed to average SO\u003csub\u003e2\u003c/sub\u003e levels exceeding 40 \u0026micro;g/m\u0026sup3; during the second month before conception (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ovarian reserve is a crucial measure of a woman's reproductive potential, primarily determined by the quantity and quality of oocytes within the ovaries (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A groundbreaking big-data approach by Santi et al. examined how environmental factors, particularly rising air pollution, impact anti-mullerian hormone (AMH) serum levels. While genetics play a major role in ovarian reserve at birth, this study suggests that environmental factors can also influence the decline in AMH and ovarian reserve during adulthood (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In contrast to our findings, Wu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) suggest that extended exposure to the air pollutant SO\u003csub\u003e2\u003c/sub\u003e is linked to lower antral follicle count and a higher risk of poor ovarian response (\u0026lt;\u0026thinsp;4 oocytes retrieved), respectively, while NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e show no significant associations. These findings raise concerns about the potential adverse impact of atmospheric SO\u003csub\u003e2\u003c/sub\u003e exposure on women's ovarian reserve.\u003c/p\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e, a secondary air pollutant formed from the combination of hydrocarbons and nitrogen oxides in sunlight, exhibits potential non-linearities in this process (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Legro et al. discovered a connection between O\u003csub\u003e3\u003c/sub\u003e concentration at a patient's address and the likelihood of a live birth (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, when accounting for NO\u003csub\u003e2\u003c/sub\u003e and in vitro fertilization laboratory interactions, this association became insignificant. Moreover, Boulet et al. observed a weak positive correlation between O\u003csub\u003e3\u003c/sub\u003e and implantation or live birth rates. Wu et al. identified higher O\u003csub\u003e3\u003c/sub\u003e exposure in women with normal ovarian reserve (NOR) compared to poor responders (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, multipollutant models showed no significant correlations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePossible mechanism\u003c/h2\u003e \u003cp\u003eWhile the precise mechanisms behind air pollution-related female infertility are not yet fully understood, we propose a potential mechanism: maternal exposure to contaminants during the pre- and peri-conceptional periods could lead to abnormalities in oocytes, embryos, and fetuses (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This could potentially hinder safe childbirth, impact overall health and mental well-being, and thereby increase vulnerability to and susceptibility to infertility. Air pollutants disrupt both animal and human gametogenesis, reducing reproductive efficacy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These endocrine-disrupting properties could activate the Ras/Erk pathway via interactions with nuclear receptors like estrogen or androgen receptors and specific cytosolic targets, while diesel exhaust particles and PM\u003csub\u003e2.5\u003c/sub\u003e impact ovarian function by disrupting the endocrine system, increasing oxidative stress and inflammation, and activating specific targets that trigger abnormal MAPK signaling (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Indeed, in a recent study of 777 men, increased air pollutant concentrations were significantly associated with both the hypomethylation of F3, ICAM-1, and TLR-2, and the hypermethylation of IFN-γ and IL-6 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). What's most intriguing is that research has demonstrated that adding antioxidant factors to ovarian stimulation can enhance reproductive outcomes in women with polycystic ovarian syndrome (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding ovarian function, a study did find a significant negative association between SO\u003csub\u003e2\u003c/sub\u003e exposure throughout the entire antral follicle development stage and AFC (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This suggests that during the follicular vascularization transition stage, SO\u003csub\u003e2\u003c/sub\u003e might be expedited in reaching follicles and granulosa cells, potentially contributing to impaired follicle development, hastened follicle loss, or altered ovarian function. The ovarian toxicity of environmental contaminants, characterized by meiotic disruption in oocyte formation resulting from genetic or epigenetic aberrations, can affect dormant follicles within the primordial pool that undergo growth stimulation and have the potential to develop into the antral stage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Subsequently, these antral candidates are typically chosen to progress to the pre-ovulatory stage, marked by the transition from primary to pre-antral, during which the oocyte within the follicle enlarges, granulosa cells proliferate, and a layer of theca cells forms outside the granulosa cells, serving as a conduit for transporting steroids and growth factors to the granulosa cells to promote follicle vascularization (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Additionally, this bloodstream enables toxins to reach follicles, where they can suppress granulosa cell proliferation and growth. Furthermore, SO\u003csub\u003e2\u003c/sub\u003e toxicology in mammalian organ development involves cellular injuries caused by inflammation and oxidative stress, such as lipid peroxidation, DNA damage, protein oxidation, and interference with signal transduction (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUp to this point, data indicate that ozone therapy may have a beneficial impact on various medical conditions, including human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS), fibromyalgia, cystitis, and osteomyelitis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Ozone therapy has shown promise as a potential treatment for women with tubal infertility, inflammatory conditions like pelvic inflammatory disease (PID), urovagina and ischemia/reperfusion injury, potentially avoiding expensive assisted reproductive technologies, inhibiting endometrial cell necrosis and reducing inflammation, effectively treating urovagina and leading to successful pregnancies, and offering an alternative to ovary-sparing approaches for ovarian torsion (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Other studies have shown the effectiveness of ozone treatments in various medical conditions beyond infertility. Ozone therapy has been demonstrated to significantly improve hemorheological parameters and enhance oxygen unloading in patients with peripheral occlusive arterial disease (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Ozone exhibits neuroprotective effects in an in vitro brain ischemia model, pointing towards a potential clinical trial for stroke patients who cannot undergo thrombolytic treatment (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In conclusion, ozone treatment is suggested to have clinical potential through its ability to boost nitric oxide synthase expression, regulate endogenous NO levels, and maintain cellular redox balance.\u003c/p\u003e \u003cp\u003eThis study possesses several notable strengths. Firstly, it is the inaugural investigation to reveal the association between long-term exposure to air pollution and female infertility. Secondly, our analysis encompassed a comprehensive range of pollutants and utilized a significantly large sample, bolstering the statistical robustness of the findings. Thirdly, we conducted stratified analyses considering potential confounding factors such as age, urbanization level, insurance amount, CCI score, ambient temperature, season, and lag0-2, along with a wide spectrum of demographic characteristics. Lastly, the population-based data used in this study is representative of the general population in Taiwan.\u003c/p\u003e \u003cp\u003eOur study also has several limitations. Firstly, cigarette consumption, a significant factor in ambient air quality, was not included due to data retrieval issues, which may introduce bias. Secondly, the NHIRD lacks details on disease severity, clinical manifestations, laboratory findings, and causes of death, preventing further analysis or determination of specific causes of death. Thirdly, potential errors in assessing air pollution may exist, particularly when linking data through health insurance registration. Fourthly, biases might be present as death was identified by withdrawal from the NHI program. Although we included and adjusted for several confounders, unmeasured or unknown confounders may have introduced bias. The retrospective nature of this study also presents lower statistical quality and potential bias from unknown confounders and inaccuracies in primary records, underscoring the need for a well-designed prospective, randomized, controlled study to establish a causal relationship. Lastly the NHIRD lacks information on factors such as menopausal status, marital status, male-factor infertility, use of oral contraceptives, and smoking, which could have influenced our findings. Despite these limitations, our study provides robust evidence that long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 is associated with an increased risk of female infertility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study illustrates the considerable influence of air pollution on female infertility, revealing a distinct dose-response correlation between exposure to different pollutants and infertility rates. Prolonged exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 heightened infertility risks, while ozone exposure mitigated them. Our results underscore the importance of vigilance among clinicians and government action to mitigate this risk. They emphasize the necessity for extensive initiatives to curb air pollution and its impact on reproductive health. Further investigation is warranted to elucidate the underlying mechanisms and guide public health policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eOther Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u0026nbsp;\u003c/strong\u003enone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Authorship Contribution Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu-Chieh Lo: Conceptualization; Investigation; Methodology; Writing—original draft preparation.\u003c/p\u003e\n\u003cp\u003eYeu-Chai Jang: Conceptualization; Validation; Visualization; Writing – review and editing.\u003c/p\u003e\n\u003cp\u003eYi-Jie Kuo: Supervision; Validation\u003c/p\u003e\n\u003cp\u003eShun-Jen, Cheng: Software; Formal analysis Resources;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShu-Han Chuang: Project administration, Resources\u003c/p\u003e\n\u003cp\u003eCheng-Hsien Chang: Project administration, Resources\u003c/p\u003e\n\u003cp\u003eYu-Pin Chen: Supervision.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttestation Statement:\u0026nbsp;\u003c/strong\u003eThe subjects in this trial have not concomitantly been involved in other randomized trials. Data regarding any of the subjects in the study have not been previously published unless specified. Data will be made available to the editors of the journal for review or query upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement:\u0026nbsp;\u003c/strong\u003eAll data generated or analyzed during this study are included in this published paper and its Multimedia Appendix files. More detailed data sets are not publicly accessible due to the requirement of obtaining approval from the Taiwan Ministry of Health and Welfare. Researchers interested in obtaining access to this data set may initiate the process by submitting an application form to the Ministry of Health and Welfare. For further guidance and assistance, please reach out to the MOHW staff via email at [email protected]. The address of the Taiwan Ministry of Health and Welfare is as follows: No.488, Sec. 6, Zhongxiao E. Rd., Nangang Dist., Taipei City 115, Taiwan. You can also contact them by phone at +886-2-8590-6848.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCapsule:\u0026nbsp;\u003c/strong\u003eLong-term exposure to multiple air pollutants significantly increases female infertility risks, demonstrating a clear dose-response relationship, while ozone exposure notably reduces it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest:\u003c/strong\u003e The author declares no funding or any financial and personal relationships with individuals or organizations that could inappropriately influence or bias this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Human reproduction update. 2015;21(4):411-26.\u003c/li\u003e\n\u003cli\u003eNachtigall RD. International disparities in access to infertility services. 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Annals of Hematology. 2001;80:745-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline characteristics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"697\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eN (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eInfertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e4981 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 697px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e15-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e121732 (52.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e36-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e110393 (47.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e35.08 \u0026plusmn; 12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 697px;\"\u003e\n \u003cp\u003eUrbanization level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e1 (highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e138881 (59.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e71156 (30.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e14280 (6.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e4 (lowest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e1826 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e5982 (2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 697px;\"\u003e\n \u003cp\u003eInsurance amount, NT$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003efinancially dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e1343 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e1-19999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e79868 (34.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e20000-39999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e102875 (44.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026gt;=40000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e41027 (17.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e7012 (3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 697px;\"\u003e\n \u003cp\u003eCCI score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e1.10 \u0026plusmn; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eInflammatory disease of the ovary, fallopian tube, pelvic cellular tissue, and peritoneum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e35926 (15.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eInflammatory disease of the uterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e12241 (5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eInflammatory disease of the cervix, vagina, and vulva\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e107803 (46.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eEndometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e8828 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e38765 (16.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e27325 (11.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eHypertriglyceridemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e286 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e29970 (12.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e21789 (9.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003eDisorders of eating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 348px;\"\u003e\n \u003cp\u003e302 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 697px;\"\u003e\n \u003cp\u003eSD, standard deviation; CCI score, Charlson Comorbidity Index score.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Mean and distribution of air pollutants over the exposure period\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNO\u003csub\u003eX\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003cp\u003e(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eTHC\u003c/p\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNMHC\u003c/p\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e28.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e54.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e32.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e25.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e27.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e47.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e27.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e21.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e34.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e20.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"width: 643px;\"\u003e\n \u003cp\u003eSD, standard deviation; T\u003csub\u003e1\u003c/sub\u003e, 33.33 percentile; T\u003csub\u003e2\u003c/sub\u003e, 66.66 percentile; ppb, parts per billion; ppm, parts per million; \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e, microgram/cubic meter; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide; CO, carbon monoxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter \u0026lt; 10 \u0026mu;m in size; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter \u0026lt; 2.5 \u0026mu;m in size; NO\u003csub\u003eX\u003c/sub\u003e, nitrogen oxides; NO, nitrogen monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH\u003csub\u003e4\u003c/sub\u003e, methane.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Incidence of infertility among tertiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 56px;\"\u003e\n \u003cp\u003eTertiles of average daily exposure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eTotal, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eT1 (lowest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eT3 (highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e974/77141 (1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1799/75237 (2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2208/79747 (2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e443/77375 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1351/77375 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3187/77375 (4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3579/77372 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e796/77365 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e606/77388 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1243/77360 (1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1681/72522 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2057/82243 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e832/77369 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2077/75510 (2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2064/79229 (2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4973/232108 (2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNO\u003csub\u003eX\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e603/77370 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1778/77380 (2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2600/77375 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e561/77375 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1839/77374 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2581/77376 (3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e675/77348 (0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1506/77402 (1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2800/77375 (3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4981/232125 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eTHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e268/76816 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1084/76816 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e3608/76817 (4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4960/230449 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNMHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e541/76813 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e1786/76819 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2633/76817 (3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4960/230449 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e335/76816 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e451/72513 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4174/81120 (5.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4960/230449 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide; CO, carbon monoxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter \u0026lt; 10 \u0026mu;m in size; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter \u0026lt; 2.5 \u0026mu;m in size; NO\u003csub\u003eX\u003c/sub\u003e, nitrogen oxides; NO, nitrogen monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH\u003csub\u003e4\u003c/sub\u003e, methane.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. Hazard ratios for incidence of infertility of long-term exposure at an SD increment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePollutant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.13 (1.09,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.13 ppb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2.16 (2.09,2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.11 ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.48 (0.47,0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.73 ppb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.35 (1.28,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.93 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.77 (1.67,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6.05 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNO\u003csub\u003eX\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.80 (1.73,1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6.80 ppb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.66 (1.61,1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.68 ppb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.76 (1.69,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.48 ppb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eTHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2.16 (2.09,2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.12 ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eNMHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.52 (1.47,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.08 ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2.81 (2.72,2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.07 ppm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHR, hazard ratio; CI, confidence interval; SD, standard deviation;\u0026nbsp;SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide; CO\u003csub\u003e2\u003c/sub\u003e, carbon dioxide; CO, carbon monoxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter \u0026lt; 10 \u0026mu;m in size; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter \u0026lt; 2.5 \u0026mu;m in size; NO\u003csub\u003eX\u003c/sub\u003e, nitrogen oxides; NO, nitrogen monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; THC, total hydrocarbons; NMHC, nonmethane hydrocarbons; CH\u003csub\u003e4\u003c/sub\u003e, methane.\u003c/p\u003e\n \u003cp\u003eCox regression models were adjusted for age, urbanization level, insurance amount, CCI score, Inflammatory disease of the ovary, fallopian tube, pelvic cellular tissue, and peritoneum, Inflammatory disease of the uterus, Inflammatory disease of the cervix, vagina, and vulva, Endometriosis, Hypertension, Diabetes mellitus, Hypertriglyceridemia, Hypercholesterolemia, Coronary artery disease, ambient temperature, lag0-2, season, and controlled pollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Female infertility, Air pollution, reproductive health","lastPublishedDoi":"10.21203/rs.3.rs-6260294/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6260294/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfertility affects over 186 million people globally, with about 1 in 7 couples in developed nations experiencing it. Causes include age-related fertility decline and environmental factors. Air pollution is a potential factor, but large-scale evidence is still lacking. This study examines the impact of several air pollutants on infertility in females aged 15 to 60, hypothesizing that air pollution increases infertility risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe constructed a cohort from Taiwan’s National Health Insurance Research Database (NHIRD) of females aged under 15 or over 60 between July 1, 2003, and December 31, 2013. Concentrations of SO2, CO2, CO, O3, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 were estimated based on insurance registration. We calculated the HRs of exposure at a standard deviation increment for 10 years to determine the dose-response effect between air pollutants and infertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLong-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 was associated with increased infertility in women of reproductive age. Each standard deviation increase in exposure to these pollutants indicated a higher incidence of infertility by 13%, 116%, 35%, 77%, 80%, 66%, 76%, 116%, 52%, and 181%, respectively. Conversely, ozone exposure was associated with a 52% reduction in infertility risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates the significant impact of air pollution on female infertility, showing a clear dose-response relationship between exposure to various pollutants and infertility rates. These findings highlight the need for efforts to reduce air pollution and its effects on reproductive health. Further research is needed to understand the mechanisms and inform public health policies.\u003c/p\u003e","manuscriptTitle":"Long-term Air Pollution Exposure and Infertility in Reproductive-aged Women: A Nationwide Cohort Study in Taiwan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 15:17:36","doi":"10.21203/rs.3.rs-6260294/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T05:50:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T15:59:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T19:16:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307656018533914285899790301280425307725","date":"2025-04-01T08:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218504175491134536401229762160887443080","date":"2025-03-30T05:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46829439507039707321228288507579787913","date":"2025-03-30T05:28:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187631445288708170132923042369543560621","date":"2025-03-29T08:31:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T02:53:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-27T04:10:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T04:09:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-19T09:51:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6e39c0e-8051-4a1a-84b6-905e5f39f9bd","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:04:53+00:00","versionOfRecord":{"articleIdentity":"rs-6260294","link":"https://doi.org/10.1186/s12889-025-24213-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-09-30 15:57:10","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2025-04-17 15:17:36","video":"","vorDoi":"10.1186/s12889-025-24213-x","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24213-x","workflowStages":[]},"version":"v1","identity":"rs-6260294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6260294","identity":"rs-6260294","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}