Epidemiology of Spontaneous Abortion in Iran: A Comprehensive Analysis of Demographic, Socioeconomic, Fertility, Clinical Characteristics, and Lifestyle Factors

Epidemiology of Spontaneous Abortion in Iran: A Comprehensive Analysis of Demographic, Socioeconomic, Fertility, Clinical Characteristics, and Lifestyle Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiology of Spontaneous Abortion in Iran: A Comprehensive Analysis of Demographic, Socioeconomic, Fertility, Clinical Characteristics, and Lifestyle Factors Mahboubeh Hojati, Shahrokh Izadi, Margan Mansourian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6461180/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aims to investigate the epidemiology of spontaneous abortion (SA) and its associations with various factors, including demographic, socioeconomic, fertility, clinical, and lifestyle variables among women in Isfahan Province, Iran. The objective is to provide evidence to help policymakers design targeted public health interventions to improve reproductive health outcomes. Methods A descriptive-analytical cross-sectional study was conducted from September 2023 to May 2024, recruiting 3,000 women aged 15–55 years through multistage cluster sampling from 150 health facilities in Isfahan. Data on demographic characteristics, socioeconomic status (SES), fertility (e.g., maternal history of stillbirth), clinical markers (e.g., hemoglobin levels), and certain lifestyle risk factors (e.g., mobile phone use, consumption of fast food and canned food) were collected through structured interviews. Chi-square tests, independent samples t-tests, and univariate and multivariable logistic regression analyses were performed. odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to determine significant risk factors for SA. Results Lifetime SA prevalence was 19.3%. Maternal age increased SA odds by 5.6% per year (AOR = 1.056, 95% CI: 1.040–1.072). Middle SES reduced odds by 29.8% (AOR = 0.702, 95% CI: 0.500–0.985) and high SES by 39.0% (AOR = 0.610, 95% CI: 0.415–0.895) compared to low SES. First-degree consanguinity increased odds by 28% (AOR = 1.28, 95% CI: 0.98–1.66). Monthly canned food consumption lowered odds by 22.6% (AOR = 0.774, 95% CI: 0.597–1.004), while weekly fast food intake increased odds by 34.6% (AOR = 1.346, 95% CI: 0.947–1.914). Mobile phone use and clinical markers showed no strong associations. Conclusion Maternal age, socioeconomic status, and consanguinity are key factors in spontaneous abortion. Interventions targeting healthcare access and genetic counseling, alongside further exploration of lifestyle impacts, are warranted. Spontaneous Abortion Demographic Socioeconomic Fertility Clinical Characteristics Lifestyle Iran 1. Introduction In recent decades, a notable decline in fertility rates (defined as the number of births per 1,000 women of reproductive age) has been observed in the industrialized world. This phenomenon is widely recognized as being influenced by significant societal transformations. Nevertheless, it remains to be determined whether this trend is attributable solely to changes in social structures or whether a reduction in fecundity within the population constitutes an additional contributing factor( 1 ). Reproductive health complications, such as abortion, exacerbate the decline in fertility rates. Spontaneous abortion, also known as miscarriage, is defined as the termination of pregnancy occurring before the age of fetal viability, which is generally considered to be before 20 weeks of gestation. This condition is characterized by the natural expulsion of the fetus or embryo, typically weighing less than 500 grams, without any human intervention. The global incidence of miscarriage is estimated to be about 15–20% of pregnancies. The age of fetal viability can vary by country, with some defining it as early as 16 weeks in Norway and as late as 28 weeks in Nigeria. Various factors contribute to miscarriage, including genetic abnormalities, maternal health issues, and environmental factors( 2 , 3 ). Spontaneous abortion, which can occur due to various factors, including chromosomal abnormalities, maternal health issues, and environmental influences, represents a substantial portion of pregnancy losses. It is estimated that approximately 10–20% of clinically recognized pregnancies end in Spontaneous abortion, highlighting the need for comprehensive reproductive health services and support for affected individuals( 4 ). Research has shown that factors such as maternal age, race, low education, low income, not living with a spouse, poor housing, and location ( 5 ), Infections such as Listeria monocytogenes ( 6 ), lack of prenatal care and counseling( 7 ), low maternal socioeconomic status( 8 ), poor nutrition ( 9 ), lifestyle changes ( 10 ), and exposure to particulate matter (PM) ( 11 ). The rank of the first pregnancy, short pregnancy interval, type of cesarean section versus vaginal delivery, family history of Spontaneous abortion, number of previous Spontaneous abortion, and gestation( 8 ) are associated with an increased risk of spontaneous abortion. Spontaneous abortion remains a highly contentious issue on a global scale, characterized by notable variations in prevalence across different regions. This variability is primarily influenced by cultural, religious, and political factors that significantly impact individuals' access to reproductive health services. The field of abortion epidemiology is further complicated by clinical factors, lifestyle choices, and the level of socioeconomic stability. Although extensive research has been conducted on spontaneous abortion worldwide, there exists a notable paucity of data specifically on the Middle East, and particularly Iran. Previous studies, such as those conducted by Zheng et al.( 12 ) in China and Norsker et al. ( 13 ) in Denmark, researchers have examined demographic and socioeconomic risk factors; however, there is a dearth of reports addressing the combined influence of these factors along with fertility, clinical, and lifestyle variables in Iran, where consanguinity and unique sociocultural dynamics are prevalent. This deficiency poses a significant research question: What are the key demographic, socioeconomic, fertility, clinical, and lifestyle factors associated with SA among women in Isfahan Province, Iran? Accordingly, this study intends to conduct a comprehensive investigation of these factors to inform targeted public health interventions and promote reproductive health equity within the region. 2. Methods 2.1. Study Design, Period, Area, and Study Populations A descriptive-analytical cross-sectional study was conducted from September 23, 2023, to May 21, 2024, encompassing both participant recruitment and data collection. This research was conducted in the Isfahan district located in the central region of the Iranian plateau, approximately 450 kilometers south of Tehran, the capital of the country. Eligible women were defined as all women aged 15 to 55 years who accepted the invitation to participate in the study and complete the questionnaire during a brief interview. Participants were selected by systematic random sampling from a list of women in the aforementioned age group who have household records in the comprehensive "SIB" system. The SIB system is a comprehensive, computerized health infrastructure that covers the vast majority of the population in Isfahan Province (and most of the other provinces of Iran). 2.2. Eligibility Criteria 2.2.1. Inclusion criteria Women of childbearing age, filed in the SIB system, (15–55 years) residing in Isfahan Province. 2.2.2. Exclusion criteria Non-Iranian residents. 2.3. Sample Size and Sampling Techniques Based on the lifetime history of abortion, which stands at 18.8%( 14 ) the initial sample size was calculated to be approximately 250 participants. This estimation was derived from a margin of error (d) of 5%, an alpha level of 5%, and a 10% nonresponse rate. Given that a cluster sampling method was employed, the impact of this methodology on the sample size was also calculated. It was assumed that the sample size within each cluster would consist of 20 individuals, and the Intra-Cluster Correlation was estimated to be 0.04. Consequently, the design effect (DEFF) was determined using the formula DEFF = 1 + (m − 1) × ICC, where m is the average cluster size ( 20 ) and ICC is the intra-cluster correlation coefficient ( 15 ). Substituting the values, DEFF = 1 + (20 − 1) × 0.04 = 1.76. This resulted in an adjusted sample size of approximately 440 participants (250 × 1.76). In light of the need to ascertain prevalence among various subgroups, it is essential to replicate the calculated sample size for each subgroup. Therefore, considering seven primary subgroups (age, education, marital status, occupation, income, residency, and consanguinity), which are based on demographic characteristics and socio-economic status, the final sample size was projected to be approximately 3,000 individuals. A multi-stage sampling approach was employed for the sampling procedure. Initially, 150 health facilities were selected using a systematic random sampling technique, proportional to the population size of each facility. Subsequently, to ensure a uniform distribution of the sample across the defined age ranges (15–25 years, 26–35 years, and 36–55 years), stratification was applied. Using the list of women registered in the Ministry of Health’s SIB system, these individuals were chosen through a simple random sampling technique from within each selected health facility. 2.4. Variables The outcome variable is having at least one Spontaneous abortion during a woman’s lifetime. The independent variables were age (at conception ending in abortion), age of spouse(years), job, spouse job, education, household socioeconomic status, reside place, number of pregnancies, history of stillbirth, marital status, BMI, blood type, clinical markers, frequency of consumption of canned food (never/monthly/weekly/daily), frequency of consumption of fast food (never/monthly/weekly/daily), level of physical activity (hour/day), frequency of use of mobile phones (hour/day), and history of exposure to pesticides (one month before and during pregnancy). The data were collected using a computerized checklist via telephone or face-to-face interview by an interviewer adept in the local language and briefed about field methods. 2.5. Operational Definition and Terms Spontaneous abortion, also known as miscarriage, is defined as the loss of a pregnancy before 20 weeks of gestation ( 16 , 17 ). Urban residents: participants who live in the municipal areas. Rural residents: participants who live in rural areas (countryside). Socioeconomic status: Socioeconomic status (SES) encompasses factors such as income, education level, and employment status, which collectively impact individuals' health and social inclusion( 18 ). SES was assessed by asking participants, "How do you evaluate your socioeconomic status?" These self-reported categories were directly used to classify participants into three SES levels (low, middle, high), reflecting their perceived economic and social standing. This subjective approach was chosen to capture the participants’ assessment of their socioeconomic conditions, which may influence health behaviors and access to care, as supported by Adler et al. ( 19 ). 2.6. Data Collection Tool Primary data was collected from women who attended the selected health institutions using structured and pre-tested internet-based computerized checklists. The checklists were pretested on 5% of women before actual data collection in non-sampled health facilities; corrections and modifications were made based on the gaps identified during the interview. The questionnaire was grouped into two categories: sociodemographic characteristics and pregnancy-related factors. 2.7. Data Collection Technique, Data Processing, and Analysis Data were gathered through telephone or face-to-face interviews. The data collection was conducted by a team of 100 clinical midwives and family health experts operating within health institutions, under the district supervisor. The interviewers were briefed about field methods and working with the computerized questionnaire. Continuous oversight was maintained by the principal investigators, who ensured daily verification of the questionnaires for completeness and consistency. Feedback was systematically provided to the data collectors based on the analysis of the completed questionnaires. Given the computerized and internet-based nature of the questionnaire, responses were automatically recorded and exported directly into SPSS version 23 for analysis, eliminating the need for manual data entry. This approach substantially reduced the potential for data entry errors, enhancing the reliability and credibility of the study findings. An exploratory data analysis was done to check potential outliers. The variance inflation factor was used to check for the presence of multicollinearity. Descriptive summaries of the study population were presented using frequencies and proportions. A univariate analysis was conducted to assess crude associations between the lifetime incidence of spontaneous abortion (SA) and independent variables. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Subsequently, all variables were incorporated into a multivariable logistic regression model. This comprehensive model adjusted simultaneously for BMI, Blood Type, FBS, BUN, PLT, and hemoglobin, allowing for the estimation of adjusted ORs and 95% CIs. This approach aimed to delineate the independent effect of each variable on SA while accounting for potential interrelationships among the covariates, as per Rothman et al.'s guidelines ( 20 ). Findings from both the univariate and multivariable analyses are presented. 2.8. Limitation Given that the findings rely solely on participant responses and the retrospective design, this study may have introduced recall bias, potentially impacting the estimated prevalence of spontaneous abortion and the associations between spontaneous abortion and the independent variables. 3. Results 3.1. Sociodemographic and Lifestyle Factors This cross-sectional study enrolled 3,000 women aged 15–55 years (mean: 32.21 years, SD: 8.53) from public health facilities in Isfahan Province, Iran, with a 100% response rate. The lifetime prevalence of spontaneous abortion (SA) among these individuals was observed to be 19.3% (n = 578). The majority of the women in the study were married, comprising 98.5% of the participants, and a significant portion, 81.3%, identified as housewives. Employed women had lower odds of SA (OR C = 0.78, 95% CI: 0.61–1.004) compared to housewives (19.9% prevalence). For paternal occupational status, women with retired husbands had the highest odds of SA (OR C = 2.30, 95% CI: 1.32–4.00), followed by those with unemployed husbands (OR C = 1.41, 95% CI: 0.71–2.8), compared to husbands in private businesses (reference; 18.6% prevalence). Governmental (OR C = 1.08, 95% CI: 0.84–1.4) and nongovernmental employees (OR C = 1.04, 95% CI: 0.76–1.42) showed odds close to unity. Women with postgraduate education had lower odds of SA (OR C = 0.62, 95% CI: 0.49–0.79) compared to those with primary education (23.7% prevalence). Diploma degree holders also showed reduced odds (OR C = 0.79, 95% CI: 0.63–1.001). Middle SES women had 36% lower odds (OR C = 0.64, 95% CI: 0.47–0.86), and high SES women had 43% lower odds (OR C = 0.57, 95% CI: 0.41–0.78) compared to low SES (27.0% prevalence). First-degree consanguineous marriages increased SA odds by 41% (OR C = 1.41, 95% CI: 1.11–1.8) compared to non-consanguineous marriages (18.7% prevalence). Second-degree or higher consanguinity showed no difference (OR C = 1.00, 95% CI: 0.76–1.3). Women with a maternal history of stillbirth had 72% higher odds of SA (OR C = 1.72, 95% CI: 0.84–3.49) compared to those without (19.1% prevalence). In terms of lifestyle Factors, mobile phone use for more than 2 hours/day was associated with slightly lower odds of SA (OR C = 0.86, 95% CI: 0.71–1.03) compared to less than 2 hours (20.3% prevalence). Fast food consumption showed varied effects: monthly intake reduced odds slightly (OR C = 0.82, 95% CI: 0.67–0.99), while daily intake increased odds (OR C = 1.22, 95% CI: 0.51–2.88) compared to never (20.7% prevalence). Weekly fast-food intake had neutral odds (OR C = 1.03, 95% CI: 0.74–1.43). Monthly canned food consumption reduced SA odds by 26% (OR C = 0.74, 95% CI: 0.58–0.94) compared to never (20.3% prevalence), and weekly canned food consumption showed wider Cis, and reduced SA odds by 38% (OR C = 0.62, 95% CI: 0.32–1.19) compared to never, while daily intake increased odds (OR C = 1.20, 95% CI: 0.39–3.72). Physical activity had a minimal impact, with middle (OR C = 1.10, 95% CI: 0.91–1.34) and high levels (OR C = 1.04, 95% CI: 0.75–1.45) showing odds close to low activity (reference). Pesticide exposure showed average exposure increasing odds (OR C = 1.53, 95% CI: 0.86–2.74), while high exposure had lower odds (OR C = 0.84, 95% CI: 0.37–1.91) compared to minimal exposure. Blood type and proximity to telecommunications towers showed no notable differences (OR C s near 1.0). Each year of maternal age increased SA odds by 5% (OR C = 1.05, 95% CI: 1.04–1.06), with SA group mean age at 35.29 years (SD = 8.06) versus 31.47 years (SD = 8.48) for non-SA. Paternal age also increased the odds by 5% per year (OR C = 1.05, 95% CI: 1.03–1.06; SA group mean = 40.14 years, SD = 7.92 vs. 36.66 years, SD = 8.14). Age at marriage (OR C = 1.00, 95% CI: 0.98–1.01) and interval to first pregnancy (OR C = 1.00, 95% CI: 0.99–1.00) showed negligible effects. Clinical markers like FBS (OR C = 1.00, 95% CI: 0.98–1.01), PLT (OR C = 1.00, 95% CI: 0.99–1.00), hemoglobin (OR C = 1.24, 95% CI: 0.76–2.03), and BMI (OR C = 1.01, 95% CI: 0.97–1.06) had minimal impact. BUN showed a slight increase in odds (OR C = 1.04, 95% CI: 0.99–1.09) (Table 1 ). Table 1 Demographic, socioeconomic status (SES), fertility, clinical markers, and some lifestyle risk factors associated with spontaneous abortion among women in Isfahan province, Iran (N = 3,000( Variables Frequency (%) /Mean (SD) OR C (95% CI) P-value $ Women with Spontaneous Abortion (578) Women Without spontaneous abortion. (2422) Total (3000) Marital status Married 568 (19.2) 2388 (80.8) 2956 (98.5) Ref 0.66 $ Divorced 5 (19.2) 21 (80.8) 26 (0.9) 1.61(0.57–4.55) Widow 5 (27.8) 13 (72.2) 18 (0.6) 1.00(0.37–2.66) Women’s Occupational Status Housewife 486 (19.9) 1952 (80.1) 2438 (81.3) Ref 0.053 $ employed 92 (16.4) 470 (83.6) 562 (18.7) 0.78(0.61–1.004) Husband’s Occupational Status Private-owned business 400 (18.6) 1750 (81.4) 2150 (71.7) Ref 0.038 $ Governmental employee 91 (19.9) 366 (80.1) 457 (15.2) 1.08(0.84–1.4) Nongovernmental employee 56 (19.3) 234 (80.7) 290 (9.7) 1.04(0.76–1.42) Unemployed 11 (24.4) 34 (75.6) 45 (1.5) 1.41(0.71–2.8) Retired 20 (34.5) 38 (65.5) 58 (1.9) 2.30(1.32-4.00) * Educational status Under Diploma 157(23.7) 506(76.3) 663 Ref < 0.001 $ Diploma/degree 231 (19.8) 936 (80.2) 1167 (38.9) 0.79(0.63–1.001) Postgraduate Diploma and above 190 (16.2) 980 (83.8) 1170 (39.0) 0.62(0.49–0.79) * Residency Urban 535 (19.3) 2233 (80.7) 2768 (92.4) Ref 0.714 $ Rural 42 (18.3) 187 (81.7) 229 (7.6) 0.937(0.66–1.32) Socioeconomic status Low SES 70 (27.0) 189 (73.0) 259 (8.6) Ref 0.002 $ Middle SES 321 (19.2) 1350 (80.8) 1671 (55.7) 0.64(0.47–0.86) * Haigh SES 187 (17.5) 883 (82.5) 1070 (35.7) 0.57(0.41–0.78) * Consanguineous Marriage No 388 (18.7) 1692 (81.3) 2080 (69.3) Ref < 0.001 $ Yes: First-degree relatives 109 (24.5) 336 (75.5) 445 (14.8) 1.41(1.11–1.8) * Yes: Second-degree or higher relatives 81 (18.7) 353 (81.3) 434 (14.5) 1.00(0.76–1.3) Maternal History of Stillbirth No 567 (19.1) 2395 (80.9) 2962 (98.7) Ref 0.098 $$ Yes 11 (28.9) 27 (71.1) 38 (1.3) 1.72(0.84–3.49) Mobile Phone Use Less than 2 hours 322(20.3) 1261(79.7) 1583(52.7) Ref 0.064 $ More than 2 hours 256(18.1) 1161(81.9) 1417(47.3) 0.86(0.71–1.03) Fast Food Consumption Never 256 (20.7) 982 (79.3) 1238 (41.3) Ref 0.152 $ monthly 259(17.6) 1211(82.4) 1470(49) 0.82(0.67–0.99) weekly 56(21.3) 207(78.7) 263(8.7) 1.03(0.74–1.43) daily 7 (24.1) 22 (75.9) 29 (0.9) 1.22(0.51–2.88) Canned Food Consumption Never 469 (20.3) 1842 (79.7) 2311 (77.0) Ref 0.053 $ monthly 94(15.9) 498(84.1) 592(19.7) 0.74(0.58–0.94) weekly 11(13.8) 69(86.3) 80(2.6) 0.62(0.32–1.19) daily 13(76.5) 4(23.5) 17(0.56) 1.20(0.39–3.72) Physical Activity (Sporting) Low 324 (18.7) 1411 (81.3) 1735 (57.8) Ref 0.598 $ Middle 203 (20.3) 799(79.7) 1002(33.4) 1.10(0.91–1.34) High 51(19.4) 212(80.6) 263(8.76) 1.04(0.75–1.45) Pesticide Exposure A little 555(19.2) 2343(80.8) 2898(96.6) Ref 0.314 $ Average 16 (26.7) 44 (73.3) 60 (2.0) 1.53(0.86–2.74) A lot 7(16.7) 35(83.3) 42(1.4) 0.84(0.37–1.91) Blood Type A 155 (18.5) 685 (81.5) 840 (28.0) Ref 0.938 $ B 109 (19.4) 452 (80.6) 561 (18.7) 1.06(0.81–1.39) AB 48 (20.5) 186 (79.5) 234 (7.8) 1.14(0.79–1.63) O 199 (19.7) 809 (80.3) 1008 (33.6) 1.08(0.86–1.37) Distance to Telecommunications Tower and home Under 10 m 69 (21.1) 258 (78.9) 327 (10.9) Ref 0.376 $ Upper 10 m 509 (19.1) 2162 (80.9) 2671 (89.1) 0.88(0.66–1.16) Age 15–25 83(9.6) 785(90.4) 868(28.9) Ref < 0.001 $ 26–35 202(18.9) 866(81.1) 1068(35.6) 2.20(1.67–2.89) 36–45 229(27.7) 597(72.3) 826(27.5) 3.62(2.76–4.76) 46–55 64(27.1) 172(72.9) 236(7.9) 3.51(2.44–5.07) Age 35.29 (8.058) 31.47 (8.477) 32.21 (8.53) 1.05(1.04–1.06) < 0.001 # Spouse Age 40.14 (7.916) 36.66 (8.138) 37.33(8.20) 1.05(1.03–1.06) < 0.001 # Marriage Age 20.78 (5.767) 20.80 (6.278) 20.79(6.18) 1.00(0.98–1.01) 0.94 # Interval from marriage to first pregnancy 40.46 (34.268) 38.28 (30.827) 31.67(27.6) 1.00(0.99-1.00) 0.14 # FBS 86.17 (14.31) 85.68 (11.93) 85.78 (12.432) 1.00(0.98–1.01) 0.70 # PLT 240000 (57.89) 239000 (56.74) 239000 (56.934) 1.00(0.99-1.00) 0.72 # BUN 12.15 (5.449) 11.15 (4.595) 11.35 (4.788) 1.04(0.99–1.09) 0.09 # Hemoglobin 13.11 (1.100) 12.86 (1.050) 12.92 (1.061) 1.24(0.76–2.03) 0.38 # BMI 25.99 (4.50) 25.58 (5.09) 25.66(4.96) 1.01(0.97–1.06) 0.41 # $ chi-square test $$ Fisher’s Exact test # independent sample t-test 3.2. Multivariable Logistic Regression Analysis Table 2 presents adjusted odds ratios (AORs) from a multivariable logistic regression model, including variables with bivariate associations, adjusted for BMI, blood type, FBS, BUN, PLT, and hemoglobin. Each year of maternal age increased SA odds by 5.6% (AOR = 1.056, 95% CI: 1.040–1.072). The interval from marriage to first pregnancy slightly increased odds by 0.4% per month (AOR = 1.004, 95% CI: 1.000–1.008). Age at marriage had no effect (AOR = 0.993, 95% CI: 0.977–1.009). Divorced women had 59.6% higher odds (AOR = 1.596, 95% CI: 0.533–4.776) compared to married women. Employed women had 22.3% lower odds of SA (AOR = 0.777, 95% CI: 0.579–1.041) compared to housewives. Governmental employee husbands increased odds by 30.7% (AOR = 1.307, 95% CI: 0.984–1.735), while other categories like retired (AOR = 0.984, 95% CI: 0.440–2.201) showed no effect compared to private business owners. Postgraduate education reduced odds by 26.1% (AOR = 0.739, 95% CI: 0.537–1.018), while diploma/degree had no effect (AOR = 0.999, 95% CI: 0.751–1.329) compared to under diploma. Middle SES reduced SA odds by 29.8% (AOR = 0.702, 95% CI: 0.500–0.985), and high SES by 39.0% (AOR = 0.610, 95% CI: 0.415–0.895) compared to low SES. First-degree consanguinity increased odds by 28% (AOR = 1.28, 95% CI: 0.98–1.66), and Maternal history of stillbirth increased odds by 10.7% (AOR = 1.107, 95% CI: 0.519–2.358). Monthly canned food consumption reduced SA odds by 22.6% (AOR = 0.774, 95% CI: 0.597–1.004), while weekly (AOR = 0.600, 95% CI: 0.305–1.178) and daily (AOR = 1.114, 95% CI: 0.336–3.691) showed variable effects. Weekly fast food intake increased odds by 34.6% (AOR = 1.346, 95% CI: 0.947–1.914), and daily by 53.2% (AOR = 1.532, 95% CI: 0.611–3.843), compared to never. Monthly fast food had minimal effect (AOR = 0.931, 95% CI: 0.755–1.148). Mobile phone use over 2 hours/day slightly reduced odds (AOR = 0.908, 95% CI: 0.747–1.104). Physical activity, pesticide exposure, and telecommunications tower proximity showed negligible effects (AORs near 1.0). Table 2 Multivariable Logistic Regression Analysis of Factors Associated with Spontaneous Abortion among Women in Isfahan Province, Iran (2023) (N = 3000) Variable P-value Adjusted OR (AOR) 95% CI for AOR Lower Upper Age (years) .000 1.056 1.040 1.072 Interval from marriage to first pregnancy (months) .031 1.004 1.000 1.008 Marriage Age .402 .993 .977 1.009 Marital status (ref: Married) Divorced .403 1.596 .533 4.776 Women’s Occupational Status (ref: Housewife) Employed .090 .777 .579 1.041 Spouse occupational status (ref: Private) Governmental employee .064 1.307 .984 1.735 Nongovernmental employee .385 1.173 .818 1.681 Unemployed .569 .802 .376 1.713 Retired .969 .984 .440 2.201 Educational Status (ref: Under Diploma) Diploma/degree .995 .999 .751 1.329 Postgraduate Diploma and above .064 .739 .537 1.018 Socioeconomic Status (SES) (ref: Low SES) Middle SES .040 .702 .500 .985 High SES .012 .610 .415 .895 Residency (ref: Urban) Rural .670 .925 .646 1.325 Consanguineous Marriage (ref: No) Yes: First-degree relatives .058 1.28 .98 1.66 Yes: Second-degree or higher relatives .87 .97 .72 1.31 Maternal History of Stillbirth (ref: No) Yes .793 1.107 .519 2.358 Fast Food Consumption (ref: Never) monthly .504 .931 .755 1.148 weekly .098 1.346 .947 1.914 Daily .363 1.532 .611 3.843 Canned Food Consumption (ref: Never) monthly .053 .774 .597 1.004 weekly .138 .600 .305 1.178 Daily .860 1.114 .336 3.691 Mobile Phone Use (ref: Less than 2 hours) More than 2 hours .332 .908 .747 1.104 Physical Activity (Sporting) (ref: low) Middle .546 1.065 .868 1.308 High .654 1.081 .768 1.523 Pesticide Exposure (ref: A Little) Average .080 1.717 .938 3.143 A lot .960 .978 .420 2.282 Distance to Telecommunications Tower and home(Under 10 m) Upper 10 m .653 .935 .697 1.254 Model Adjusted for BMI, Blood Type, FBS, BUN, PLT, and Hemoglobin Discussion This cross-sectional study involving 3,000 women in Isfahan Province, Iran, provides a comprehensive analysis of the demographic, socioeconomic, fertility, clinical characteristics, and lifestyle factors associated with spontaneous abortion (SA), which exhibits a lifetime prevalence of 19.3%. This prevalence aligns with global estimates of 10–20% for clinically recognized pregnancies ending in miscarriage, as reported by the World Health Organization ( 21 ) and corroborated by Zheng et al. (2017) in a Chinese cohort ( 12 ). The significant association of maternal age with SA (AOR = 1.04, 95% CI: 1.01–1.06) and a mean difference of 3.82 years between women with and without SA reinforce the well-established link between advanced maternal age and SA. Nybo Andersen et al. (2000) reported a similar age-related increase in SA risk, attributing it to chromosomal trisomies and declining oocyte quality( 22 ), findings echoed by de La Rochebrochard and Thonneau (2002) in a multicenter European study ( 23 ). The crude paternal age effect (mean difference: 3.48 years) attenuated in univariable analysis (OR C = 1.05, 95% CI: 1.03–1.06), suggesting confounding by maternal age, consistent with Kleinhaus et al. (2006), who found paternal age effects diminished after adjustment ( 24 ). SES emerged as a critical determinant, with middle and high SES conferring protective effects. This aligns with Norsker et al. (2012) in the Danish National Birth Cohort, where lower SES was associated with a higher SA risk (HR = 1.15 for low income), likely due to disparities in healthcare access and nutrition ( 13 ). Zheng et al. (2017) similarly found that rural Chinese women with lower income and education faced elevated SA odds, a pattern mirrored in this study( 12 ). The protective effect of SES suggests mediation via improved prenatal care. Educational attainment’s crude association attenuated after adjustment, indicating SES mediation, consistent with Annan et al. (2021)( 25 ). Paternal occupational status suggested socioeconomic stressors, though adjusted effects were non-significant, where economic pressures indirectly influenced outcomes( 26 ). First-degree consanguinity increased SA odds by 28% with a prevalence of 24.5% in affected unions versus 18.7% in non-consanguineous ones. This supports Tadmouri et al. (2009), who reported elevated miscarriage risk in Saudi consanguineous marriages( 27 ). The genetic etiology, likely recessive alleles, aligns with Oniya et al. (2019), who reviewed consanguinity’s reproductive consequences across Arab populations( 28 ). The absence of a second-degree effect (AOR = 0.97) suggests a dose-response tied to genetic proximity, warranting genomic studies to pinpoint causative variants. Clinical markers (FBS, PLT, hemoglobin) showed no association, though BUN’s borderline significance hints at renal involvement, meriting further study as suggested by Simpson (2007)( 29 ). Maternal stillbirth history’s wide CI reflects a 10% increase in SA. The study by Bhattacharya et al. (2008) aligns with our findings, reporting a 15% increased odds of spontaneous abortion (AOR = 1.15, 95% CI: 0.67–1.97) following a stillbirth history ( 30 ). Lifestyle factors yielded mixed results. Mobile phone use hinted at an environmental exposure, possibly electromagnetic fields, though the lack of a dose-response pattern across higher durations suggests confounding or recall bias. There is emerging evidence suggesting a correlation between mobile phone use and the risk of spontaneous abortion, potentially linked to environmental exposure to electromagnetic fields (EMFs). A study indicated that mobile phone use during pregnancy may be associated with early spontaneous abortions, highlighting a possible risk factor for pregnant women( 31 ). Additionally, a systematic review and meta-analysis found that exposure to EMFs significantly increased the risk of miscarriage, with a rate ratio of 1.699, indicating a notable association( 32 ). Another study specifically noted that pregnant women exposed to electromagnetic radiation had a higher incidence of abortion, reinforcing the concern regarding EMF exposure from mobile devices ( 33 ). Fast food intake showed SA prevalence and 54% increased odds of SA (AOR = 1.54, 95% CI: 0.61–3.84). Fast food is often linked to poor diet quality and higher energy density, which can contribute to obesity and other health issues ( 34 ) that can affect pregnancy outcomes( 35 ). Our multivariable model effectively adjusted for confounding factors, but the retrospective design introduces a risk of recall bias. The large sample size (N = 3,000) improves the precision of our findings, but the small size of certain subgroups (e.g., stillbirths: n = 38) limits statistical power, a limitation. Additionally, the absence of temporality highlights the need for prospective cohorts to establish causality. Conclusion Maternal age, socioeconomic status (SES), and first-degree consanguinity are primary determinants of the factors associated with spontaneous abortion (SA). These findings are consistent with global trends while also emphasizing the specific priorities pertinent to regional contexts. Interventions must focus on enhancing healthcare access for low SES populations and providing genetic counseling within consanguineous groups. Furthermore, although the direct relationship between lifestyle factors and the prevalence of abortion warrants additional exploration, existing evidence suggests that dietary habits—including fast food consumption during pregnancy- may influence reproductive health outcomes. Declarations Consent to Participate Declaration All participants in this study provided informed consent before their engagement. The study's objectives, procedures, potential risks, and benefits were communicated to each participant clearly and understandably by trained interviewers fluent in the local language. Participants were informed that their participation was strictly voluntary and that they had the right to withdraw from the study without any consequences. Verbal informed consent was obtained from all participants, as approved by the ethics committee of Isfahan University of Medical Sciences, considering the cultural and logistical context of the study, which included both telephone and in-person interviews. The consent process was meticulously documented in the study records to ensure compliance with ethical standards. Funding Declaration This study received no specific funding from public, commercial, or not-for-profit agencies. Human Ethics Declaration This study was conducted by the Declaration of Helsinki and was approved by the Ethics Committee of Isfahan University of Medical Sciences (Approval No IR.MUI.DHMT.REC.1402.099). All procedures involving human participants were reviewed to ensure ethical compliance, including protecting participants’ confidentiality and securing personal data. Author Contribution The idea of article title is by Sh. I. and M.H. and were responsible for the search and data extraction processes. M.H., Sh. I., and M.M. contributed to the initial drafting of the manuscript. Statistical analyses and data interpretation were performed by M. H. and M. M. The final editing of the manuscript was undertaken by Sh. I. , M. M. All authors involved in this research reviewed and approved the final version of the manuscript. Acknowledgement The present research is based on the results of the research project with the code IR.MUI.DHMT.REC.1402.099, which was approved by the Isfahan University of Medical Sciences. We thank the research deputy of Isfahan University of Medical Sciences for this purpose. Data Availability The datasets used and analyzed during the current study are available from the first author upon reasonable request ( [email protected] ). References Jensen TK, Carlsen E, Jørgensen N, Berthelsen JG, Keiding N, Christensen K, et al. Poor semen quality may contribute to recent decline in fertility rates. Human Reproduction. 2002;17(6):1437-40. Dudukina E, Horváth-Puhó E, Sørensen HT, Ehrenstein V. Association between vaginal bleeding in pregnancy that resulted in delivery and risk of cancer: A Danish registry-based cohort study. Paediatr Perinat Epidemiol. 2024;38(4):330-42. Ogu R, Ojule J. Miscarriage and Maternal Health. In: Abduljabbar HS, editor. Complications of Pregnancy. Rijeka: IntechOpen; 2019. Mohammed AA, Ftnan ZA, Mohammed A. Serum CA-125 for early prediction of miscarriage. population. 2020;2:3. Baruwa OJ, Amoateng AY, Biney E. Induced abortion in Ghana: prevalence and associated factors. Journal of Biosocial Science. 2022;54(2):257-68. Baguiya A, Mehrtash H, Bonet M, Adu-Bonsaffoh K, Compaore R, Bello FA, et al. Abortion-related infections across 11 countries in Sub-Saharan Africa: Prevalence, severity, and management. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2022;156 Suppl 1:36-43. Javinani A, Radmard F, Razavinia FS, Masoumi M. Preconception Obstetrics and Rheumatology Consultation: A Protective Factor Against Spontaneous Abortion in Women With Autoimmune Rheumatic Disorders. Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases. 2022;28(1):e166-e70. Wolomby-Molondo JJ, Calvert C, Seguin R, Qureshi Z, Tuncalp O, Filippi V. The relationship between insecurity and the quality of hospital care provided to women with abortion-related complications in the Democratic Republic of Congo: A cross-sectional analysis. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2022;156 Suppl 1:20-6. Wesselink AK, Willis SK, Laursen ASD, Mikkelsen EM, Wang TR, Trolle E, et al. Protein-rich food intake and risk of spontaneous abortion: a prospective cohort study. European journal of nutrition. 2022. Wesselink AK, Wise LA, Hatch EE, Mikkelsen EM, Savitz DA, Kirwa K, Rothman KJ. A prospective cohort study of seasonal variation in spontaneous abortion. Epidemiology. 2022;33(3):441-8. Zhang Q, Wang N, Hu Y, Creedy DK. Prevalence of stress and depression and associated factors among women seeking a first-trimester induced abortion in China: a cross-sectional study. Reproductive health. 2022;19(1):64. Zheng D, Li C, Wu T, Tang K. Factors associated with spontaneous abortion: a cross-sectional study of Chinese populations. Reproductive health. 2017;14:1-9. Norsker FN, Espenhain L, á Rogvi S, Morgen CS, Andersen PK, Andersen A-MN. Socioeconomic position and the risk of spontaneous abortion: a study within the Danish National Birth Cohort. BMJ open. 2012;2(3):e001077. Salimi Y, Mansournia M, Abdollahpour I, Nedjat S. Lifetime Prevalence of Abortion in 15-50 Year-Old Females in Tehran and Its Predictors; A Population-Based Cross-Sectional Study. Iranian Journal of Epidemiology. 2021;17(3):243-36. Kish L. Survey sampling. new york: John wesley & sons. Am Polit Sci Rev. 1965;59(4):1025. Abdelazim IA, AbuFaza M, Purohit P, Farag H. Miscarriage definitions, causes and management: review of literature. ARC J Gynecol Obstet. 2017;2(3):20-31. Nawawi DR, Ruansa I, Mariana M. Risk Factors of Spontaneous Abortion. Sriwijaya Journal of Medicine. 2022;5(3):137-41. Vukojević M, Zovko A, Talić I, Tanović M, Rešić B, Vrdoljak I, Splavski B. Parental socioeconomic status as a predictor of physical and mental health outcomes in children–literature review. Acta Clinica Croatica. 2017;56(4.):742-8. Adler NE, Epel ES, Castellazzo G, Ickovics JR. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health psychology. 2000;19(6):586. Rothman KJ, Greenland S, Lash TL. Modern epidemiology: Wolters Kluwer Health/Lippincott Williams & Wilkins Philadelphia; 2008. Organization WHo. Abortion Accessed March 24, 2025 [Available from: https://www.who.int/health-topics/abortion#tab=tab_1. Andersen A-MN, Wohlfahrt J, Christens P, Olsen J, Melbye M. Maternal age and fetal loss: population based register linkage study. Bmj. 2000;320(7251):1708-12. de La Rochebrochard E, Thonneau P. Paternal age and maternal age are risk factors for miscarriage; results of a multicentre European study. Human reproduction. 2002;17(6):1649-56. Kleinhaus K, Perrin M, Friedlander Y, Paltiel O, Malaspina D, Harlap S. Paternal age and spontaneous abortion. Obstetrics & Gynecology. 2006;108(2):369-77. Annan RA, Gyimah LA, Apprey C, Asamoah-Boakye O, Aduku LNE, Azanu W, et al. Predictors of adverse birth outcomes among pregnant adolescents in Ashanti Region, Ghana. J Nutr Sci. 2021;10:e67. Maconochie N, Doyle P, Prior S, Simmons R. Risk factors for first trimester miscarriage—results from a UK‐population‐based case–control study. BJOG: An International Journal of Obstetrics & Gynaecology. 2007;114(2):170-86. Tadmouri GO, Nair P, Obeid T, Al Ali MT, Al Khaja N, Hamamy HA. Consanguinity and reproductive health among Arabs. Reproductive health. 2009;6:17. Oniya O, Neves K, Ahmed B, Konje JC. A review of the reproductive consequences of consanguinity. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2019;232:87-96. Simpson JL. Causes of fetal wastage. Clinical obstetrics and gynecology. 2007;50(1):10-30. Bhattacharya S, Townend J, Shetty A, Campbell D, Bhattacharya S. Does miscarriage in an initial pregnancy lead to adverse obstetric and perinatal outcomes in the next continuing pregnancy? Bjog. 2008;115(13):1623-9. Mahmoudabadi FS, Ziaei S, Firoozabadi M, Kazemnejad A. Use of mobile phone during pregnancy and the risk of spontaneous abortion. Journal of Environmental Health Science and Engineering. 2015;13:1-4. Irani M, Aradmehr M, Ghorbani M, Baghani R. Electromagnetic Field Exposure and Abortion in Pregnant Women: A Systematic Review and Meta-Analysis. Malays J Med Sci. 2023;30(5):70-80. Mortazavi SMJ, Mortazavi SA, Paknahad M. Association between electromagnetic field exposure and abortion in pregnant women living in Tehran. Int J Reprod Biomed. 2017;15(2):115-6. Rouhani MH, Mirseifinezhad M, Omrani N, Esmaillzadeh A, Azadbakht L. Fast Food Consumption, Quality of Diet, and Obesity among Isfahanian Adolescent Girls. J Obes. 2012;2012:597924. Amini S, Jafarirad S, Mohseni H, Ehsani H, Hejazi L, Feghhi N. Comparison of food intake and body mass index before pregnancy between women with spontaneous abortion and women with successful pregnancy. The Iranian Journal of Obstetrics, Gynecology and Infertility. 2017;20(10):35-42. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6461180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453047698,"identity":"3116223a-4a05-4e15-8962-a1a859db9385","order_by":0,"name":"Mahboubeh Hojati","email":"","orcid":"","institution":"Isfahan University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Mahboubeh","middleName":"","lastName":"Hojati","suffix":""},{"id":453047699,"identity":"e2daf0ce-c731-4fdd-a36a-9ce304b652bc","order_by":1,"name":"Shahrokh Izadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACNiCWSGA4IMfAwEOiFmPitYCABAPDgcQGorXw8R9+eOPBnzvpG46fPfjgA4OdnG4DQYcdM7ZIbHuWu+FMXrLhDIZkY7MDhLQwNphJJDYczt1wIMdMmgfowm0EtTCzf5NI+HM43eD8G2K1sPGYSSSwHU4wuEG0LTw8xUC/HDaceeONseEMAyL8It9/fOPNH38Oy/OdzzF88KHCTo6gFjhQAKs0IFY52LoGUlSPglEwCkbBiAIA/hpDNW51YXcAAAAASUVORK5CYII=","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Shahrokh","middleName":"","lastName":"Izadi","suffix":""},{"id":453047700,"identity":"2e4940d4-b9e1-41d2-ad44-990149783e6d","order_by":2,"name":"Margan Mansourian","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Margan","middleName":"","lastName":"Mansourian","suffix":""}],"badges":[],"createdAt":"2025-04-16 08:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6461180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6461180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85032603,"identity":"ef417666-57ae-4d50-ade6-81df63cfa4e0","added_by":"auto","created_at":"2025-06-20 07:38:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1516954,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6461180/v1/4876385d-3920-43b9-9be1-6e7ddb9a736a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology of Spontaneous Abortion in Iran: A Comprehensive Analysis of Demographic, Socioeconomic, Fertility, Clinical Characteristics, and Lifestyle Factors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent decades, a notable decline in fertility rates (defined as the number of births per 1,000 women of reproductive age) has been observed in the industrialized world. This phenomenon is widely recognized as being influenced by significant societal transformations. Nevertheless, it remains to be determined whether this trend is attributable solely to changes in social structures or whether a reduction in fecundity within the population constitutes an additional contributing factor(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReproductive health complications, such as abortion, exacerbate the decline in fertility rates. Spontaneous abortion, also known as miscarriage, is defined as the termination of pregnancy occurring before the age of fetal viability, which is generally considered to be before 20 weeks of gestation. This condition is characterized by the natural expulsion of the fetus or embryo, typically weighing less than 500 grams, without any human intervention. The global incidence of miscarriage is estimated to be about 15\u0026ndash;20% of pregnancies. The age of fetal viability can vary by country, with some defining it as early as 16 weeks in Norway and as late as 28 weeks in Nigeria. Various factors contribute to miscarriage, including genetic abnormalities, maternal health issues, and environmental factors(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpontaneous abortion, which can occur due to various factors, including chromosomal abnormalities, maternal health issues, and environmental influences, represents a substantial portion of pregnancy losses. It is estimated that approximately 10\u0026ndash;20% of clinically recognized pregnancies end in Spontaneous abortion, highlighting the need for comprehensive reproductive health services and support for affected individuals(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch has shown that factors such as maternal age, race, low education, low income, not living with a spouse, poor housing, and location (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), Infections such as Listeria monocytogenes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), lack of prenatal care and counseling(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), low maternal socioeconomic status(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), poor nutrition (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), lifestyle changes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and exposure to particulate matter (PM) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The rank of the first pregnancy, short pregnancy interval, type of cesarean section versus vaginal delivery, family history of Spontaneous abortion, number of previous Spontaneous abortion, and gestation(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) are associated with an increased risk of spontaneous abortion.\u003c/p\u003e \u003cp\u003eSpontaneous abortion remains a highly contentious issue on a global scale, characterized by notable variations in prevalence across different regions. This variability is primarily influenced by cultural, religious, and political factors that significantly impact individuals' access to reproductive health services. The field of abortion epidemiology is further complicated by clinical factors, lifestyle choices, and the level of socioeconomic stability. Although extensive research has been conducted on spontaneous abortion worldwide, there exists a notable paucity of data specifically on the Middle East, and particularly Iran. Previous studies, such as those conducted by Zheng et al.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) in China and Norsker et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) in Denmark, researchers have examined demographic and socioeconomic risk factors; however, there is a dearth of reports addressing the combined influence of these factors along with fertility, clinical, and lifestyle variables in Iran, where consanguinity and unique sociocultural dynamics are prevalent. This deficiency poses a significant research question: What are the key demographic, socioeconomic, fertility, clinical, and lifestyle factors associated with SA among women in Isfahan Province, Iran?\u003c/p\u003e \u003cp\u003eAccordingly, this study intends to conduct a comprehensive investigation of these factors to inform targeted public health interventions and promote reproductive health equity within the region.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design, Period, Area, and Study Populations\u003c/h2\u003e \u003cp\u003eA descriptive-analytical cross-sectional study was conducted from September 23, 2023, to May 21, 2024, encompassing both participant recruitment and data collection. This research was conducted in the Isfahan district located in the central region of the Iranian plateau, approximately 450 kilometers south of Tehran, the capital of the country. Eligible women were defined as all women aged 15 to 55 years who accepted the invitation to participate in the study and complete the questionnaire during a brief interview. Participants were selected by systematic random sampling from a list of women in the aforementioned age group who have household records in the comprehensive \"SIB\" system. The SIB system is a comprehensive, computerized health infrastructure that covers the vast majority of the population in Isfahan Province (and most of the other provinces of Iran).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Eligibility Criteria\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Inclusion criteria\u003c/h2\u003e \u003cp\u003eWomen of childbearing age, filed in the SIB system, (15\u0026ndash;55 years) residing in Isfahan Province.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Exclusion criteria\u003c/h2\u003e \u003cp\u003eNon-Iranian residents.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Size and Sampling Techniques\u003c/h2\u003e \u003cp\u003eBased on the lifetime history of abortion, which stands at 18.8%(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) the initial sample size was calculated to be approximately 250 participants. This estimation was derived from a margin of error (d) of 5%, an alpha level of 5%, and a 10% nonresponse rate.\u003c/p\u003e \u003cp\u003eGiven that a cluster sampling method was employed, the impact of this methodology on the sample size was also calculated. It was assumed that the sample size within each cluster would consist of 20 individuals, and the Intra-Cluster Correlation was estimated to be 0.04. Consequently, the design effect (DEFF) was determined using the formula DEFF\u0026thinsp;=\u0026thinsp;1 + (m \u0026minus;\u0026thinsp;1) \u0026times; ICC, where m is the average cluster size (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and ICC is the intra-cluster correlation coefficient (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Substituting the values, DEFF\u0026thinsp;=\u0026thinsp;1 + (20\u0026thinsp;\u0026minus;\u0026thinsp;1) \u0026times; 0.04\u0026thinsp;=\u0026thinsp;1.76. This resulted in an adjusted sample size of approximately 440 participants (250 \u0026times; 1.76).\u003c/p\u003e \u003cp\u003eIn light of the need to ascertain prevalence among various subgroups, it is essential to replicate the calculated sample size for each subgroup. Therefore, considering seven primary subgroups (age, education, marital status, occupation, income, residency, and consanguinity), which are based on demographic characteristics and socio-economic status, the final sample size was projected to be approximately 3,000 individuals.\u003c/p\u003e \u003cp\u003eA multi-stage sampling approach was employed for the sampling procedure. Initially, 150 health facilities were selected using a systematic random sampling technique, proportional to the population size of each facility. Subsequently, to ensure a uniform distribution of the sample across the defined age ranges (15\u0026ndash;25 years, 26\u0026ndash;35 years, and 36\u0026ndash;55 years), stratification was applied. Using the list of women registered in the Ministry of Health\u0026rsquo;s SIB system, these individuals were chosen through a simple random sampling technique from within each selected health facility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables\u003c/h2\u003e \u003cp\u003eThe outcome variable is having at least one Spontaneous abortion during a woman\u0026rsquo;s lifetime. The independent variables were age (at conception ending in abortion), age of spouse(years), job, spouse job, education, household socioeconomic status, reside place, number of pregnancies, history of stillbirth, marital status, BMI, blood type, clinical markers, frequency of consumption of canned food (never/monthly/weekly/daily), frequency of consumption of fast food (never/monthly/weekly/daily), level of physical activity (hour/day), frequency of use of mobile phones (hour/day), and history of exposure to pesticides (one month before and during pregnancy). The data were collected using a computerized checklist via telephone or face-to-face interview by an interviewer adept in the local language and briefed about field methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Operational Definition and Terms\u003c/h2\u003e \u003cp\u003eSpontaneous abortion, also known as miscarriage, is defined as the loss of a pregnancy before 20 weeks of gestation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban residents: participants who live in the municipal areas.\u003c/p\u003e \u003cp\u003eRural residents: participants who live in rural areas (countryside).\u003c/p\u003e \u003cp\u003eSocioeconomic status: Socioeconomic status (SES) encompasses factors such as income, education level, and employment status, which collectively impact individuals' health and social inclusion(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). SES was assessed by asking participants, \"How do you evaluate your socioeconomic status?\" These self-reported categories were directly used to classify participants into three SES levels (low, middle, high), reflecting their perceived economic and social standing. This subjective approach was chosen to capture the participants\u0026rsquo; assessment of their socioeconomic conditions, which may influence health behaviors and access to care, as supported by Adler et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data Collection Tool\u003c/h2\u003e \u003cp\u003ePrimary data was collected from women who attended the selected health institutions using structured and pre-tested internet-based computerized checklists. The checklists were pretested on 5% of women before actual data collection in non-sampled health facilities; corrections and modifications were made based on the gaps identified during the interview. The questionnaire was grouped into two categories: sociodemographic characteristics and pregnancy-related factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Data Collection Technique, Data Processing, and Analysis\u003c/h2\u003e \u003cp\u003eData were gathered through telephone or face-to-face interviews. The data collection was conducted by a team of 100 clinical midwives and family health experts operating within health institutions, under the district supervisor. The interviewers were briefed about field methods and working with the computerized questionnaire. Continuous oversight was maintained by the principal investigators, who ensured daily verification of the questionnaires for completeness and consistency. Feedback was systematically provided to the data collectors based on the analysis of the completed questionnaires.\u003c/p\u003e \u003cp\u003eGiven the computerized and internet-based nature of the questionnaire, responses were automatically recorded and exported directly into SPSS version 23 for analysis, eliminating the need for manual data entry. This approach substantially reduced the potential for data entry errors, enhancing the reliability and credibility of the study findings. An exploratory data analysis was done to check potential outliers. The variance inflation factor was used to check for the presence of multicollinearity. Descriptive summaries of the study population were presented using frequencies and proportions.\u003c/p\u003e \u003cp\u003eA univariate analysis was conducted to assess crude associations between the lifetime incidence of spontaneous abortion (SA) and independent variables. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Subsequently, all variables were incorporated into a multivariable logistic regression model. This comprehensive model adjusted simultaneously for BMI, Blood Type, FBS, BUN, PLT, and hemoglobin, allowing for the estimation of adjusted ORs and 95% CIs. This approach aimed to delineate the independent effect of each variable on SA while accounting for potential interrelationships among the covariates, as per Rothman et al.'s guidelines (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Findings from both the univariate and multivariable analyses are presented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Limitation\u003c/h2\u003e \u003cp\u003eGiven that the findings rely solely on participant responses and the retrospective design, this study may have introduced recall bias, potentially impacting the estimated prevalence of spontaneous abortion and the associations between spontaneous abortion and the independent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sociodemographic and Lifestyle Factors\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled 3,000 women aged 15–55 years (mean: 32.21 years, SD: 8.53) from public health facilities in Isfahan Province, Iran, with a 100% response rate. The lifetime prevalence of spontaneous abortion (SA) among these individuals was observed to be 19.3% (n = 578). The majority of the women in the study were married, comprising 98.5% of the participants, and a significant portion, 81.3%, identified as housewives. Employed women had lower odds of SA (OR\u003csub\u003eC\u003c/sub\u003e = 0.78, 95% CI: 0.61–1.004) compared to housewives (19.9% prevalence). For paternal occupational status, women with retired husbands had the highest odds of SA (OR\u003csub\u003eC\u003c/sub\u003e= 2.30, 95% CI: 1.32–4.00), followed by those with unemployed husbands (OR\u003csub\u003eC\u003c/sub\u003e= 1.41, 95% CI: 0.71–2.8), compared to husbands in private businesses (reference; 18.6% prevalence). Governmental (OR\u003csub\u003eC\u003c/sub\u003e= 1.08, 95% CI: 0.84–1.4) and nongovernmental employees (OR\u003csub\u003eC\u003c/sub\u003e = 1.04, 95% CI: 0.76–1.42) showed odds close to unity.\u003c/p\u003e \u003cp\u003eWomen with postgraduate education had lower odds of SA (OR\u003csub\u003eC\u003c/sub\u003e= 0.62, 95% CI: 0.49–0.79) compared to those with primary education (23.7% prevalence). Diploma degree holders also showed reduced odds (OR\u003csub\u003eC\u003c/sub\u003e= 0.79, 95% CI: 0.63–1.001). Middle SES women had 36% lower odds (OR\u003csub\u003eC\u003c/sub\u003e= 0.64, 95% CI: 0.47–0.86), and high SES women had 43% lower odds (OR\u003csub\u003eC\u003c/sub\u003e= 0.57, 95% CI: 0.41–0.78) compared to low SES (27.0% prevalence).\u003c/p\u003e \u003cp\u003eFirst-degree consanguineous marriages increased SA odds by 41% (OR\u003csub\u003eC\u003c/sub\u003e= 1.41, 95% CI: 1.11–1.8) compared to non-consanguineous marriages (18.7% prevalence). Second-degree or higher consanguinity showed no difference (OR\u003csub\u003eC\u003c/sub\u003e= 1.00, 95% CI: 0.76–1.3). Women with a maternal history of stillbirth had 72% higher odds of SA (OR\u003csub\u003eC\u003c/sub\u003e= 1.72, 95% CI: 0.84–3.49) compared to those without (19.1% prevalence).\u003c/p\u003e \u003cp\u003eIn terms of lifestyle Factors, mobile phone use for more than 2 hours/day was associated with slightly lower odds of SA (OR\u003csub\u003eC\u003c/sub\u003e= 0.86, 95% CI: 0.71–1.03) compared to less than 2 hours (20.3% prevalence). Fast food consumption showed varied effects: monthly intake reduced odds slightly (OR\u003csub\u003eC\u003c/sub\u003e= 0.82, 95% CI: 0.67–0.99), while daily intake increased odds (OR\u003csub\u003eC\u003c/sub\u003e= 1.22, 95% CI: 0.51–2.88) compared to never (20.7% prevalence). Weekly fast-food intake had neutral odds (OR\u003csub\u003eC\u003c/sub\u003e= 1.03, 95% CI: 0.74–1.43).\u003c/p\u003e \u003cp\u003eMonthly canned food consumption reduced SA odds by 26% (OR\u003csub\u003eC\u003c/sub\u003e= 0.74, 95% CI: 0.58–0.94) compared to never (20.3% prevalence), and weekly canned food consumption showed wider Cis, and reduced SA odds by 38% (OR\u003csub\u003eC\u003c/sub\u003e= 0.62, 95% CI: 0.32–1.19) compared to never, while daily intake increased odds (OR\u003csub\u003eC\u003c/sub\u003e= 1.20, 95% CI: 0.39–3.72).\u003c/p\u003e \u003cp\u003ePhysical activity had a minimal impact, with middle (OR\u003csub\u003eC\u003c/sub\u003e= 1.10, 95% CI: 0.91–1.34) and high levels (OR\u003csub\u003eC\u003c/sub\u003e= 1.04, 95% CI: 0.75–1.45) showing odds close to low activity (reference). Pesticide exposure showed average exposure increasing odds (OR\u003csub\u003eC\u003c/sub\u003e= 1.53, 95% CI: 0.86–2.74), while high exposure had lower odds (OR\u003csub\u003eC\u003c/sub\u003e= 0.84, 95% CI: 0.37–1.91) compared to minimal exposure.\u003c/p\u003e \u003cp\u003eBlood type and proximity to telecommunications towers showed no notable differences (OR\u003csub\u003eC\u003c/sub\u003es near 1.0).\u003c/p\u003e \u003cp\u003eEach year of maternal age increased SA odds by 5% (OR\u003csub\u003eC\u003c/sub\u003e= 1.05, 95% CI: 1.04–1.06), with SA group mean age at 35.29 years (SD = 8.06) versus 31.47 years (SD = 8.48) for non-SA. Paternal age also increased the odds by 5% per year (OR\u003csub\u003eC\u003c/sub\u003e= 1.05, 95% CI: 1.03–1.06; SA group mean = 40.14 years, SD = 7.92 vs. 36.66 years, SD = 8.14). Age at marriage (OR\u003csub\u003eC\u003c/sub\u003e= 1.00, 95% CI: 0.98–1.01) and interval to first pregnancy (OR\u003csub\u003eC\u003c/sub\u003e= 1.00, 95% CI: 0.99–1.00) showed negligible effects. Clinical markers like FBS (OR\u003csub\u003eC\u003c/sub\u003e= 1.00, 95% CI: 0.98–1.01), PLT (OR\u003csub\u003eC\u003c/sub\u003e= 1.00, 95% CI: 0.99–1.00), hemoglobin (OR\u003csub\u003eC\u003c/sub\u003e= 1.24, 95% CI: 0.76–2.03), and BMI (OR\u003csub\u003eC\u003c/sub\u003e= 1.01, 95% CI: 0.97–1.06) had minimal impact. BUN showed a slight increase in odds (OR\u003csub\u003eC\u003c/sub\u003e= 1.04, 95% CI: 0.99–1.09) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, socioeconomic status (SES), fertility, clinical markers, and some lifestyle risk factors associated with spontaneous abortion among women in Isfahan province, Iran (N = 3,000(\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eFrequency (%) /Mean (SD)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003csub\u003eC\u003c/sub\u003e (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen with Spontaneous Abortion (578)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen Without spontaneous abortion. (2422)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (3000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e568 (19.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2388 (80.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2956 (98.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.66\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (19.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (80.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (0.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61(0.57–4.55)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (0.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.37–2.66)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWomen’s Occupational Status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486 (19.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1952 (80.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2438 (81.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.053\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eemployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (16.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470 (83.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e562 (18.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78(0.61–1.004)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHusband’s Occupational Status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate-owned business\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400 (18.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1750 (81.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2150 (71.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.038\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernmental employee\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (19.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (80.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e457 (15.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(0.84–1.4)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNongovernmental employee\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (19.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234 (80.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290 (9.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.76–1.42)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (75.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (1.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41(0.71–2.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRetired\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20 (34.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e38 (65.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e58 (1.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.30(1.32-4.00)\u003c/b\u003e \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder Diploma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157(23.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e506(76.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma/degree\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231 (19.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e936 (80.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1167 (38.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79(0.63–1.001)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePostgraduate Diploma and above\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e190 (16.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e980 (83.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1170 (39.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.62(0.49–0.79)\u003c/b\u003e \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (19.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2233 (80.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2768 (92.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.714\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (18.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (81.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229 (7.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.937(0.66–1.32)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow SES\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (27.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189 (73.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e259 (8.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.002\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMiddle SES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e321 (19.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1350 (80.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1671 (55.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.64(0.47–0.86)\u003c/b\u003e \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHaigh SES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e187 (17.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e883 (82.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1070 (35.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.57(0.41–0.78)\u003c/b\u003e \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConsanguineous Marriage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e388 (18.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1692 (81.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2080 (69.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYes: First-degree relatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e109 (24.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e336 (75.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e445 (14.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.41(1.11–1.8)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes: Second-degree or higher relatives\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (18.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353 (81.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e434 (14.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.76–1.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal History of Stillbirth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567 (19.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2395 (80.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2962 (98.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.098\u003csup\u003e$$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (71.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (1.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72(0.84–3.49)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMobile Phone Use\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 2 hours\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322(20.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1261(79.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1583(52.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.064\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 2 hours\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256(18.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1161(81.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1417(47.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86(0.71–1.03)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFast Food Consumption\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (20.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e982 (79.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1238 (41.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.152\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259(17.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1211(82.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1470(49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82(0.67–0.99)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweekly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(21.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207(78.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e263(8.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(0.74–1.43)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edaily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (24.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (75.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (0.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(0.51–2.88)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCanned Food Consumption\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (20.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1842 (79.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2311 (77.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.053\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003emonthly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e94(15.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e498(84.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e592(19.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.74(0.58–0.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweekly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(86.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80(2.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62(0.32–1.19)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edaily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(76.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(23.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(0.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20(0.39–3.72)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePhysical Activity (Sporting)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324 (18.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1411 (81.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1735 (57.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.598\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (20.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799(79.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1002(33.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(0.91–1.34)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(19.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212(80.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e263(8.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.75–1.45)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePesticide Exposure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA little\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e555(19.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2343(80.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2898(96.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.314\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (26.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (73.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (2.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53(0.86–2.74)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA lot\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(16.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(83.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(1.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84(0.37–1.91)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBlood Type\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (18.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e685 (81.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e840 (28.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.938\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (19.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452 (80.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e561 (18.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(0.81–1.39)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (20.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186 (79.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234 (7.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14(0.79–1.63)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199 (19.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e809 (80.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1008 (33.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(0.86–1.37)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDistance to Telecommunications Tower and home\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder 10 m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (21.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (78.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327 (10.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.376\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper 10 m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509 (19.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2162 (80.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2671 (89.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.66–1.16)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15–25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(9.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e785(90.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e868(28.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e26–35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e202(18.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e866(81.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1068(35.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.20(1.67–2.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e36–45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e229(27.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e597(72.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e826(27.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.62(2.76–4.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e46–55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e64(27.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e172(72.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e236(7.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.51(2.44–5.07)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e35.29 (8.058)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e31.47 (8.477)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e32.21 (8.53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.05(1.04–1.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpouse Age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e40.14 (7.916)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e36.66 (8.138)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e37.33(8.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.05(1.03–1.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarriage Age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.78 (5.767)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.80 (6.278)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.79(6.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.98–1.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInterval from marriage to first pregnancy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.46 (34.268)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.28 (30.827)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.67(27.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.99-1.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.17 (14.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.68 (11.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.78 (12.432)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.98–1.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240000 (57.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239000 (56.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e239000 (56.934)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.99-1.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.15 (5.449)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.15 (4.595)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.35 (4.788)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.99–1.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.11 (1.100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.86 (1.050)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.92 (1.061)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24(0.76–2.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.99 (4.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.58 (5.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.66(4.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.97–1.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e$\u003c/sup\u003e chi-square test\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e$$\u003c/sup\u003eFisher’s Exact test\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e#\u003c/sup\u003e independent sample t-test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Multivariable Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents adjusted odds ratios (AORs) from a multivariable logistic regression model, including variables with bivariate associations, adjusted for BMI, blood type, FBS, BUN, PLT, and hemoglobin. Each year of maternal age increased SA odds by 5.6% (AOR = 1.056, 95% CI: 1.040–1.072). The interval from marriage to first pregnancy slightly increased odds by 0.4% per month (AOR = 1.004, 95% CI: 1.000–1.008). Age at marriage had no effect (AOR = 0.993, 95% CI: 0.977–1.009). Divorced women had 59.6% higher odds (AOR = 1.596, 95% CI: 0.533–4.776) compared to married women.\u003c/p\u003e \u003cp\u003eEmployed women had 22.3% lower odds of SA (AOR = 0.777, 95% CI: 0.579–1.041) compared to housewives. Governmental employee husbands increased odds by 30.7% (AOR = 1.307, 95% CI: 0.984–1.735), while other categories like retired (AOR = 0.984, 95% CI: 0.440–2.201) showed no effect compared to private business owners. Postgraduate education reduced odds by 26.1% (AOR = 0.739, 95% CI: 0.537–1.018), while diploma/degree had no effect (AOR = 0.999, 95% CI: 0.751–1.329) compared to under diploma.\u003c/p\u003e \u003cp\u003eMiddle SES reduced SA odds by 29.8% (AOR = 0.702, 95% CI: 0.500–0.985), and high SES by 39.0% (AOR = 0.610, 95% CI: 0.415–0.895) compared to low SES. First-degree consanguinity increased odds by 28% (AOR = 1.28, 95% CI: 0.98–1.66), and Maternal history of stillbirth increased odds by 10.7% (AOR = 1.107, 95% CI: 0.519–2.358).\u003c/p\u003e \u003cp\u003eMonthly canned food consumption reduced SA odds by 22.6% (AOR = 0.774, 95% CI: 0.597–1.004), while weekly (AOR = 0.600, 95% CI: 0.305–1.178) and daily (AOR = 1.114, 95% CI: 0.336–3.691) showed variable effects. Weekly fast food intake increased odds by 34.6% (AOR = 1.346, 95% CI: 0.947–1.914), and daily by 53.2% (AOR = 1.532, 95% CI: 0.611–3.843), compared to never. Monthly fast food had minimal effect (AOR = 0.931, 95% CI: 0.755–1.148). Mobile phone use over 2 hours/day slightly reduced odds (AOR = 0.908, 95% CI: 0.747–1.104). Physical activity, pesticide exposure, and telecommunications tower proximity showed negligible effects (AORs near 1.0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Analysis of Factors Associated with Spontaneous Abortion among Women in Isfahan Province, Iran (2023) (N = 3000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted OR (AOR)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI for AOR\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterval from marriage to first pregnancy (months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage Age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.402\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.977\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMarital status (ref: Married)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.403\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.533\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.776\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWomen’s Occupational Status (ref: Housewife)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.777\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.579\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSpouse occupational status (ref: Private)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernmental employee\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNongovernmental employee\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.173\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.818\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.681\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.569\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.802\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.969\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.440\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.201\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eEducational Status (ref: Under Diploma)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma/degree\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.995\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.751\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate Diploma and above\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic Status (SES) (ref: Low SES)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMiddle SES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.702\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.985\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh SES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.610\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.415\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.895\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eResidency (ref: Urban)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.670\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.646\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.325\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eConsanguineous Marriage (ref: No)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes: First-degree relatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes: Second-degree or higher relatives\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaternal History of Stillbirth (ref: No)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.793\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.519\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.358\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFast Food Consumption (ref: Never)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonthly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.504\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.755\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweekly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.346\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.947\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.914\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.363\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.532\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.611\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.843\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCanned Food Consumption (ref: Never)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonthly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.774\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.597\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweekly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.138\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.600\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.305\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.860\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.336\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.691\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMobile Phone Use (ref: Less than 2 hours)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 2 hours\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.332\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.908\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.747\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.104\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePhysical Activity (Sporting) (ref: low)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.546\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.868\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.308\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.654\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.768\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.523\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePesticide Exposure (ref: A Little)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.938\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.143\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA lot\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.978\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.420\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.282\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDistance to Telecommunications Tower and home(Under 10 m)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper 10 m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.697\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.254\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eModel Adjusted for BMI, Blood Type, FBS, BUN, PLT, and Hemoglobin\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study involving 3,000 women in Isfahan Province, Iran, provides a comprehensive analysis of the demographic, socioeconomic, fertility, clinical characteristics, and lifestyle factors associated with spontaneous abortion (SA), which exhibits a lifetime prevalence of 19.3%. This prevalence aligns with global estimates of 10–20% for clinically recognized pregnancies ending in miscarriage, as reported by the World Health Organization (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and corroborated by Zheng et al. (2017) in a Chinese cohort (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The significant association of maternal age with SA (AOR = 1.04, 95% CI: 1.01–1.06) and a mean difference of 3.82 years between women with and without SA reinforce the well-established link between advanced maternal age and SA. Nybo Andersen et al. (2000) reported a similar age-related increase in SA risk, attributing it to chromosomal trisomies and declining oocyte quality(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), findings echoed by de La Rochebrochard and Thonneau (2002) in a multicenter European study (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The crude paternal age effect (mean difference: 3.48 years) attenuated in univariable analysis (OR\u003csub\u003eC\u003c/sub\u003e = 1.05, 95% CI: 1.03–1.06), suggesting confounding by maternal age, consistent with Kleinhaus et al. (2006), who found paternal age effects diminished after adjustment (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). SES emerged as a critical determinant, with middle and high SES conferring protective effects. This aligns with Norsker et al. (2012) in the Danish National Birth Cohort, where lower SES was associated with a higher SA risk (HR = 1.15 for low income), likely due to disparities in healthcare access and nutrition (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Zheng et al. (2017) similarly found that rural Chinese women with lower income and education faced elevated SA odds, a pattern mirrored in this study(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The protective effect of SES suggests mediation via improved prenatal care. Educational attainment’s crude association attenuated after adjustment, indicating SES mediation, consistent with Annan et al. (2021)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Paternal occupational status suggested socioeconomic stressors, though adjusted effects were non-significant, where economic pressures indirectly influenced outcomes(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirst-degree consanguinity increased SA odds by 28% with a prevalence of 24.5% in affected unions versus 18.7% in non-consanguineous ones. This supports Tadmouri et al. (2009), who reported elevated miscarriage risk in Saudi consanguineous marriages(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The genetic etiology, likely recessive alleles, aligns with Oniya et al. (2019), who reviewed consanguinity’s reproductive consequences across Arab populations(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The absence of a second-degree effect (AOR = 0.97) suggests a dose-response tied to genetic proximity, warranting genomic studies to pinpoint causative variants.\u003c/p\u003e\u003cp\u003eClinical markers (FBS, PLT, hemoglobin) showed no association, though BUN’s borderline significance hints at renal involvement, meriting further study as suggested by Simpson (2007)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Maternal stillbirth history’s wide CI reflects a 10% increase in SA. The study by Bhattacharya et al. (2008) aligns with our findings, reporting a 15% increased odds of spontaneous abortion (AOR = 1.15, 95% CI: 0.67–1.97) following a stillbirth history (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLifestyle factors yielded mixed results. Mobile phone use hinted at an environmental exposure, possibly electromagnetic fields, though the lack of a dose-response pattern across higher durations suggests confounding or recall bias. There is emerging evidence suggesting a correlation between mobile phone use and the risk of spontaneous abortion, potentially linked to environmental exposure to electromagnetic fields (EMFs). A study indicated that mobile phone use during pregnancy may be associated with early spontaneous abortions, highlighting a possible risk factor for pregnant women(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Additionally, a systematic review and meta-analysis found that exposure to EMFs significantly increased the risk of miscarriage, with a rate ratio of 1.699, indicating a notable association(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Another study specifically noted that pregnant women exposed to electromagnetic radiation had a higher incidence of abortion, reinforcing the concern regarding EMF exposure from mobile devices (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFast food intake showed SA prevalence and 54% increased odds of SA (AOR = 1.54, 95% CI: 0.61–3.84). Fast food is often linked to poor diet quality and higher energy density, which can contribute to obesity and other health issues (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) that can affect pregnancy outcomes(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur multivariable model effectively adjusted for confounding factors, but the retrospective design introduces a risk of recall bias. The large sample size (N = 3,000) improves the precision of our findings, but the small size of certain subgroups (e.g., stillbirths: n = 38) limits statistical power, a limitation. Additionally, the absence of temporality highlights the need for prospective cohorts to establish causality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMaternal age, socioeconomic status (SES), and first-degree consanguinity are primary determinants of the factors associated with spontaneous abortion (SA). These findings are consistent with global trends while also emphasizing the specific priorities pertinent to regional contexts. Interventions must focus on enhancing healthcare access for low SES populations and providing genetic counseling within consanguineous groups. Furthermore, although the direct relationship between lifestyle factors and the prevalence of abortion warrants additional exploration, existing evidence suggests that dietary habits—including fast food consumption during pregnancy- may influence reproductive health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e \u003c/p\u003e \u003cp\u003eAll participants in this study provided informed consent before their engagement. The study's objectives, procedures, potential risks, and benefits were communicated to each participant clearly and understandably by trained interviewers fluent in the local language. Participants were informed that their participation was strictly voluntary and that they had the right to withdraw from the study without any consequences. Verbal informed consent was obtained from all participants, as approved by the ethics committee of Isfahan University of Medical Sciences, considering the cultural and logistical context of the study, which included both telephone and in-person interviews. The consent process was meticulously documented in the study records to ensure compliance with ethical standards.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study received no specific funding from public, commercial, or not-for-profit agencies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHuman Ethics Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003e This study was conducted by the Declaration of Helsinki and was approved by the Ethics Committee of Isfahan University of Medical Sciences (Approval No IR.MUI.DHMT.REC.1402.099). All procedures involving human participants were reviewed to ensure ethical compliance, including protecting participants\u0026rsquo; confidentiality and securing personal data.\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe idea of article title is by Sh. I. and M.H. and were responsible for the search and data extraction processes. M.H., Sh. I., and M.M. contributed to the initial drafting of the manuscript. Statistical analyses and data interpretation were performed by M. H. and M. M. The final editing of the manuscript was undertaken by Sh. I. , M. M. All authors involved in this research reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe present research is based on the results of the research project with the code IR.MUI.DHMT.REC.1402.099, which was approved by the Isfahan University of Medical Sciences. We thank the research deputy of Isfahan University of Medical Sciences for this purpose.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the first author upon reasonable request ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJensen TK, Carlsen E, J\u0026oslash;rgensen N, Berthelsen JG, Keiding N, Christensen K, et al. Poor semen quality may contribute to recent decline in fertility rates. Human Reproduction. 2002;17(6):1437-40.\u003c/li\u003e\n\u003cli\u003eDudukina E, Horv\u0026aacute;th-Puh\u0026oacute; E, S\u0026oslash;rensen HT, Ehrenstein V. Association between vaginal bleeding in pregnancy that resulted in delivery and risk of cancer: A Danish registry-based cohort study. Paediatr Perinat Epidemiol. 2024;38(4):330-42.\u003c/li\u003e\n\u003cli\u003eOgu R, Ojule J. Miscarriage and Maternal Health. In: Abduljabbar HS, editor. Complications of Pregnancy. Rijeka: IntechOpen; 2019.\u003c/li\u003e\n\u003cli\u003eMohammed AA, Ftnan ZA, Mohammed A. Serum CA-125 for early prediction of miscarriage. population. 2020;2:3.\u003c/li\u003e\n\u003cli\u003eBaruwa OJ, Amoateng AY, Biney E. Induced abortion in Ghana: prevalence and associated factors. Journal of Biosocial Science. 2022;54(2):257-68.\u003c/li\u003e\n\u003cli\u003eBaguiya A, Mehrtash H, Bonet M, Adu-Bonsaffoh K, Compaore R, Bello FA, et al. Abortion-related infections across 11 countries in Sub-Saharan Africa: Prevalence, severity, and management. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2022;156 Suppl 1:36-43.\u003c/li\u003e\n\u003cli\u003eJavinani A, Radmard F, Razavinia FS, Masoumi M. Preconception Obstetrics and Rheumatology Consultation: A Protective Factor Against Spontaneous Abortion in Women With Autoimmune Rheumatic Disorders. Journal of clinical rheumatology : practical reports on rheumatic \u0026amp; musculoskeletal diseases. 2022;28(1):e166-e70.\u003c/li\u003e\n\u003cli\u003eWolomby-Molondo JJ, Calvert C, Seguin R, Qureshi Z, Tuncalp O, Filippi V. The relationship between insecurity and the quality of hospital care provided to women with abortion-related complications in the Democratic Republic of Congo: A cross-sectional analysis. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2022;156 Suppl 1:20-6.\u003c/li\u003e\n\u003cli\u003eWesselink AK, Willis SK, Laursen ASD, Mikkelsen EM, Wang TR, Trolle E, et al. Protein-rich food intake and risk of spontaneous abortion: a prospective cohort study. European journal of nutrition. 2022.\u003c/li\u003e\n\u003cli\u003eWesselink AK, Wise LA, Hatch EE, Mikkelsen EM, Savitz DA, Kirwa K, Rothman KJ. A prospective cohort study of seasonal variation in spontaneous abortion. Epidemiology. 2022;33(3):441-8.\u003c/li\u003e\n\u003cli\u003eZhang Q, Wang N, Hu Y, Creedy DK. Prevalence of stress and depression and associated factors among women seeking a first-trimester induced abortion in China: a cross-sectional study. Reproductive health. 2022;19(1):64.\u003c/li\u003e\n\u003cli\u003eZheng D, Li C, Wu T, Tang K. Factors associated with spontaneous abortion: a cross-sectional study of Chinese populations. Reproductive health. 2017;14:1-9.\u003c/li\u003e\n\u003cli\u003eNorsker FN, Espenhain L, \u0026aacute; Rogvi S, Morgen CS, Andersen PK, Andersen A-MN. Socioeconomic position and the risk of spontaneous abortion: a study within the Danish National Birth Cohort. BMJ open. 2012;2(3):e001077.\u003c/li\u003e\n\u003cli\u003eSalimi Y, Mansournia M, Abdollahpour I, Nedjat S. Lifetime Prevalence of Abortion in 15-50 Year-Old Females in Tehran and Its Predictors; A Population-Based Cross-Sectional Study. Iranian Journal of Epidemiology. 2021;17(3):243-36.\u003c/li\u003e\n\u003cli\u003eKish L. Survey sampling. new york: John wesley \u0026amp; sons. Am Polit Sci Rev. 1965;59(4):1025.\u003c/li\u003e\n\u003cli\u003eAbdelazim IA, AbuFaza M, Purohit P, Farag H. Miscarriage definitions, causes and management: review of literature. ARC J Gynecol Obstet. 2017;2(3):20-31.\u003c/li\u003e\n\u003cli\u003eNawawi DR, Ruansa I, Mariana M. Risk Factors of Spontaneous Abortion. Sriwijaya Journal of Medicine. 2022;5(3):137-41.\u003c/li\u003e\n\u003cli\u003eVukojević M, Zovko A, Talić I, Tanović M, Re\u0026scaron;ić B, Vrdoljak I, Splavski B. Parental socioeconomic status as a predictor of physical and mental health outcomes in children\u0026ndash;literature review. Acta Clinica Croatica. 2017;56(4.):742-8.\u003c/li\u003e\n\u003cli\u003eAdler NE, Epel ES, Castellazzo G, Ickovics JR. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health psychology. 2000;19(6):586.\u003c/li\u003e\n\u003cli\u003eRothman KJ, Greenland S, Lash TL. Modern epidemiology: Wolters Kluwer Health/Lippincott Williams \u0026amp; Wilkins Philadelphia; 2008.\u003c/li\u003e\n\u003cli\u003eOrganization WHo. Abortion Accessed March 24, 2025 [Available from: https://www.who.int/health-topics/abortion#tab=tab_1.\u003c/li\u003e\n\u003cli\u003eAndersen A-MN, Wohlfahrt J, Christens P, Olsen J, Melbye M. Maternal age and fetal loss: population based register linkage study. Bmj. 2000;320(7251):1708-12.\u003c/li\u003e\n\u003cli\u003ede La Rochebrochard E, Thonneau P. Paternal age and maternal age are risk factors for miscarriage; results of a multicentre European study. Human reproduction. 2002;17(6):1649-56.\u003c/li\u003e\n\u003cli\u003eKleinhaus K, Perrin M, Friedlander Y, Paltiel O, Malaspina D, Harlap S. Paternal age and spontaneous abortion. Obstetrics \u0026amp; Gynecology. 2006;108(2):369-77.\u003c/li\u003e\n\u003cli\u003eAnnan RA, Gyimah LA, Apprey C, Asamoah-Boakye O, Aduku LNE, Azanu W, et al. Predictors of adverse birth outcomes among pregnant adolescents in Ashanti Region, Ghana. J Nutr Sci. 2021;10:e67.\u003c/li\u003e\n\u003cli\u003eMaconochie N, Doyle P, Prior S, Simmons R. Risk factors for first trimester miscarriage\u0026mdash;results from a UK‐population‐based case\u0026ndash;control study. BJOG: An International Journal of Obstetrics \u0026amp; Gynaecology. 2007;114(2):170-86.\u003c/li\u003e\n\u003cli\u003eTadmouri GO, Nair P, Obeid T, Al Ali MT, Al Khaja N, Hamamy HA. Consanguinity and reproductive health among Arabs. Reproductive health. 2009;6:17.\u003c/li\u003e\n\u003cli\u003eOniya O, Neves K, Ahmed B, Konje JC. A review of the reproductive consequences of consanguinity. European Journal of Obstetrics \u0026amp; Gynecology and Reproductive Biology. 2019;232:87-96.\u003c/li\u003e\n\u003cli\u003eSimpson JL. Causes of fetal wastage. Clinical obstetrics and gynecology. 2007;50(1):10-30.\u003c/li\u003e\n\u003cli\u003eBhattacharya S, Townend J, Shetty A, Campbell D, Bhattacharya S. Does miscarriage in an initial pregnancy lead to adverse obstetric and perinatal outcomes in the next continuing pregnancy? Bjog. 2008;115(13):1623-9.\u003c/li\u003e\n\u003cli\u003eMahmoudabadi FS, Ziaei S, Firoozabadi M, Kazemnejad A. Use of mobile phone during pregnancy and the risk of spontaneous abortion. Journal of Environmental Health Science and Engineering. 2015;13:1-4.\u003c/li\u003e\n\u003cli\u003eIrani M, Aradmehr M, Ghorbani M, Baghani R. Electromagnetic Field Exposure and Abortion in Pregnant Women: A Systematic Review and Meta-Analysis. Malays J Med Sci. 2023;30(5):70-80.\u003c/li\u003e\n\u003cli\u003eMortazavi SMJ, Mortazavi SA, Paknahad M. Association between electromagnetic field exposure and abortion in pregnant women living in Tehran. Int J Reprod Biomed. 2017;15(2):115-6.\u003c/li\u003e\n\u003cli\u003eRouhani MH, Mirseifinezhad M, Omrani N, Esmaillzadeh A, Azadbakht L. Fast Food Consumption, Quality of Diet, and Obesity among Isfahanian Adolescent Girls. J Obes. 2012;2012:597924.\u003c/li\u003e\n\u003cli\u003eAmini S, Jafarirad S, Mohseni H, Ehsani H, Hejazi L, Feghhi N. Comparison of food intake and body mass index before pregnancy between women with spontaneous abortion and women with successful pregnancy. The Iranian Journal of Obstetrics, Gynecology and Infertility. 2017;20(10):35-42.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spontaneous Abortion, Demographic, Socioeconomic, Fertility, Clinical Characteristics, Lifestyle, Iran","lastPublishedDoi":"10.21203/rs.3.rs-6461180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6461180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the epidemiology of spontaneous abortion (SA) and its associations with various factors, including demographic, socioeconomic, fertility, clinical, and lifestyle variables among women in Isfahan Province, Iran. The objective is to provide evidence to help policymakers design targeted public health interventions to improve reproductive health outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA descriptive-analytical cross-sectional study was conducted from September 2023 to May 2024, recruiting 3,000 women aged 15\u0026ndash;55 years through multistage cluster sampling from 150 health facilities in Isfahan. Data on demographic characteristics, socioeconomic status (SES), fertility (e.g., maternal history of stillbirth), clinical markers (e.g., hemoglobin levels), and certain lifestyle risk factors (e.g., mobile phone use, consumption of fast food and canned food) were collected through structured interviews. Chi-square tests, independent samples t-tests, and univariate and multivariable logistic regression analyses were performed. odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to determine significant risk factors for SA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLifetime SA prevalence was 19.3%. Maternal age increased SA odds by 5.6% per year (AOR\u0026thinsp;=\u0026thinsp;1.056, 95% CI: 1.040\u0026ndash;1.072). Middle SES reduced odds by 29.8% (AOR\u0026thinsp;=\u0026thinsp;0.702, 95% CI: 0.500\u0026ndash;0.985) and high SES by 39.0% (AOR\u0026thinsp;=\u0026thinsp;0.610, 95% CI: 0.415\u0026ndash;0.895) compared to low SES. First-degree consanguinity increased odds by 28% (AOR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 0.98\u0026ndash;1.66). Monthly canned food consumption lowered odds by 22.6% (AOR\u0026thinsp;=\u0026thinsp;0.774, 95% CI: 0.597\u0026ndash;1.004), while weekly fast food intake increased odds by 34.6% (AOR\u0026thinsp;=\u0026thinsp;1.346, 95% CI: 0.947\u0026ndash;1.914). Mobile phone use and clinical markers showed no strong associations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMaternal age, socioeconomic status, and consanguinity are key factors in spontaneous abortion. Interventions targeting healthcare access and genetic counseling, alongside further exploration of lifestyle impacts, are warranted.\u003c/p\u003e","manuscriptTitle":"Epidemiology of Spontaneous Abortion in Iran: A Comprehensive Analysis of Demographic, Socioeconomic, Fertility, Clinical Characteristics, and Lifestyle Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 10:33:49","doi":"10.21203/rs.3.rs-6461180/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eff3d031-9b78-4e63-a51b-41024f913faf","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-20T07:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 10:33:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6461180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6461180","identity":"rs-6461180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}