Abstract
Objective
The primary aim of the study was to evaluate the incidence of congenital anomalies (CAs) and their association with maternal and environmental factors among live-born infants in Isfahan Province, Iran.
Methods
This cross-sectional study was conducted from September 2023, to May 2024. A total of 2555 women of reproductive age (15–55 years) who had at least one live birth, residing in Isfahan Province (central Iran), were selected using multistage random sampling from the internet-based health event database of the Ministry of Health, known as SIB. These women were invited to complete an online validated questionnaire, either during a telephone interview or in person. Univariate and multivariable logistic regression analyses were conducted to investigate the relationships between CAs and factors such as maternal age, consanguinity, parity, socioeconomic status, lifestyle, and nutritional characteristics.
Results
A total, 34 live births were classified as CAs, representing 1.33% (95% confidence interval: 0.92–1.85), which is lower than the national estimate of 1.8% and the global estimate of 2–3% for major congenital anomalies, as reported in the World Health Organization’s fact sheet on congenital anomalies. The significant predictors of congenital anomalies were advanced maternal age (odds ratio [OR] = 1.05, 95% CI: 1.00–1.09, p = 0.001), first-degree consanguineous marriage (OR = 5.46, 95% CI: 2.16–9.20, p < 0.001), and higher parity (OR = 6.80, 95% CI:1.85–24.98 for four or more pregnancies, p = 0.002).
Conclusion
These findings highlight the importance of targeted interventions, including expanded genetic counseling, improved prenatal screening for high-risk groups, and the creation of a national CA registry.
Supplementary Information
The online version contains supplementary material available at 10.1007/s44197-026-00533-y.
Keywords: Congenital Anomalies, Prevalence, Parity, Consanguinity, Maternal Age
Introduction
Congenital anomalies (CAs), which include structural or functional abnormalities present at birth, pose a significant global public health issue. These conditions affect about 2–3% of live-born infants and significantly contribute to neonatal illness and death rates. The World Health Organization estimates that 6% of infants worldwide are born with congenital disorders, leading to hundreds of thousands of related fatalities each year. This figure may underestimate the real burden, as it does not include unreported stillbirths and terminated pregnancies [1].
In Iran, the prevalence of congenital anomalies has been reported as 1.8% (with higher rates in boys) in a meta-analysis [2], 2.3% overall in a review of national studies [3], and 1.12% in a community-based study in rural northwest regions [4]. Globally, the three most common congenital anomalies are congenital heart defects, neural tube defects, and Down syndrome [1]. In Iran, the three most commonly reported congenital anomalies are musculoskeletal (27.5%), skin (19.7%), and genitourinary system (15.8%) anomalies [3]. In some areas, such as the northwest of Iran, nervous system anomalies were the most common, representing 24% of cases [4].
Established risk factors in the context include advanced maternal age [5–7], consanguineous marriage [8, 9], environmental exposures such as air pollution and pesticides [4], maternal obesity and diabetes [9], and socioeconomic deprivation [10]. Additional contributors include infections during pregnancy [11], low maternal education [2], and lifestyle factors such as food patterns [12].
While these insights are valuable, the lack of comprehensive, population-based epidemiological data on how clinical, environmental, and demographic factors jointly influence CAs limits the development of evidence-based interventions. Previous Iranian studies have been predominantly descriptive or hospital-based, with limited generalizability [2–4]. To address these gaps, this study adopts a multidisciplinary approach, integrating population-based data with advanced epidemiological analysis and multivariate modeling to elucidate novel patterns of risk interaction in Isfahan Province. The goal of this research is to provide reliable evidence to inform the development of targeted interventions, such as environmental regulations and genetic counseling, the incidence of CAs, and neonatal health outcomes in Iran.
Methods
From September 23, 2023, to May 21, 2024, a descriptive-analytical cross-sectional study was conducted to recruit participants and collect data. Eligible participants included women of reproductive age (15–55 years) who had at least one live birth, provided informed consent, and completed a questionnaire during interviews. This registry was part of the “SIB” system, an integrated health informatics framework designed to encompass the entire population in Iran, including the Isfahan Province.
Based on the lifetime history of anomaly, which stands at 1.12% [4], the initial sample size was calculated to be approximately 420 participants. This estimation was derived from a margin of error (d) of 1%, an alpha level of 5%, and a 10% nonresponse rate.
Since a cluster sampling method was used, the effect of this approach on the sample size was also calculated. It was assumed that each cluster would include 20 individuals, with an intra-cluster correlation estimated at 0.04. As a result, the design effect (DEFF) was calculated using the formula DEFF = 1 + (m − 1) × ICC, where m is the average cluster size [20] and ICC is the intra-cluster correlation coefficient [13]. Substituting the values, DEFF = 1 + (20 − 1) × 0.04 = 1.76. This resulted in an adjusted sample size of approximately 740 participants (420 × 1.76). To accurately determine prevalence across subgroups, it was essential to replicate the sample size calculation for each subgroup. Therefore, after considering three main subgroups (age, education, and occupation, the required sample size was approximately 2220 participants. The final sample size was 2555, adjusted to accommodate a 15% attrition rate. A multi-stage sampling method was used for the procedure. First, 150 health facilities were selected through systematic random sampling, with selection proportional to each facility’s population size. Then, stratification was applied to ensure an even distribution of the sample across the specified age groups (15–25 years, 26–35 years, and 36–55 years). From the list of women registered in the Ministry of Health’s SIB system, individuals were randomly selected within each of the chosen health facilities. A STROBE-compliant flow diagram showing participant selection and enrolment is provided in Supplementary Figure S1. The outcome variable was whether a woman had given birth to at least one congenital anomaly during her lifetime. Congenital anomalies, as defined by the WHO, are structural or functional abnormalities present at birth [1].
The independent variables were selected a priori based on a comprehensive literature review of established clinical, environmental, and demographic risk factors for congenital anomalies [1–19] and their availability within the SIB database. The independent variables included age at conception, spouse’s age (years), age at marriage, interval from marriage to first pregnancy, job, spouse’s job, education level, household socioeconomic status, number of pregnancies, consanguineous marriage(marriages between first cousins (sharing grandparents)/second-degree or higher include more distant relatives/No), body mass index (BMI), blood type, fasting blood sugar, frequency of fast food consumption (never, monthly, weekly, daily), physical activity level (yes/no), mobile phone usage frequency (less than 2 h/more than 2 h daily), and distance from the Telecommunications Tower to the home. The distance from the participant’s residence to the nearest telecommunications tower was obtained via self-report during the structured interview. Participants were asked to estimate the distance using mobile GPS applications or local landmarks, and responses were dichotomized as < 10 m or ≥ 10 m. SES was assessed using the validated SES questionnaire, and participants were classified into three SES levels (low, middle, high) based on their perceived economic and social standing [13]. A pilot study was conducted with 150 women from non-sampled facilities to assess questionnaire validity and reliability. Internal consistency (Cronbach’s α = 0.82 for sociodemographic; 0.79 for pregnancy sections) and test-retest reliability (Pearson r = 0.87, p < 0.001) confirmed instrument stability; minor refinements were made based on feedback. The validated questionnaire was administered by trained, local-language-fluent interviewers via telephone or in-person interviews. Responses were entered directly into the PORSLINE online platform during interviews, enabling real-time transmission to supervisors for immediate quality control. This process eliminated manual transcription errors and enhanced data accuracy and reliability. The data collected were analyzed using SPSS version 24. In the univariate analysis, the independent-sample Student t-test, Fisher’s exact test, and Chi-square test were used, along with odds ratios. Both univariate and multivariable analyses were performed using Firth’s penalized logistic regression to reduce small-sample bias in odds ratio estimation due to the rare outcome (34 events). In a multivariate analysis, the backward elimination procedure was employed to examine relationships and control for confounding variables in order to select an appropriate model. Before multivariable modeling, the assumptions of binary logistic regression were formally assessed. Linearity of the logit for continuous covariates was examined using the Box-Tidwell test; multicollinearity was evaluated with variance inflation factors (all VIF < 4); influential observations were checked using Cook’s distance; and model fit was confirmed with the Hosmer-Lemeshow goodness-of-fit test (p > 0.05). All assumptions were satisfactorily met. To find confidence intervals for the model parameters, asymptotic standard errors were applied. In all cases, the 95% confidence intervals were calculated, and the cutoff for judging the relationship was a P-value < 5. Where applicable, the results are reported as the mean and standard deviation (mean ± SD).
Results
This descriptive-analytical, cross-sectional study enrolled 2555 women aged 15–55 years (mean age: 33.12 years, SD: 8.38) with at least one live birth from 150 health facilities. Of these, 34 women reported having at least one live-born infant with CAs, resulting in a prevalence rate of 1.33% (95% CI: 0.92–1.85). Univariate and multivariable logistic regression analyses were performed to identify associations between CAs and demographic, reproductive, clinical, and lifestyle factors. The results are summarized in Table 1, which displays crude odds ratios (ORC), adjusted odds ratios (ORA), 95% confidence intervals (CI), and p-values.
Table 1.
Demographic, Crude, and Multivariate Analysis (Firth’s Penalized Logistic Regression) of the Relationship Between Having CA and SES, Fertility, Clinical Markers, and Various Lifestyle Risk Factors Among Women in Isfahan Province, Iran (N = 2555)
| Variables | Frequency (%) /Mean (SD) | ORC (95% CI) | ORA (95% CI) | P-value* | |||
|---|---|---|---|---|---|---|---|
| Women with CA (34) | Women without CA (2521) | ||||||
| Consanguineous Marriage | No | 14 (41.2) | 1779(70.6) | Ref. | Ref. | 0.001 | |
| Yes: First-degree relatives | 16 (47.1) | 375(14.9) | 5.48 (2.68–11.18) | 4.46 (2.16–9.20) | |||
| Yes: Second-degree or higher relatives | 4 (11.8) | 367(14.6) | 1.49 (0.51–4.31) | 1.47 (0.51–4.28) | |||
| Number of pregnancies | One | 5(14.7) | 937(37.2) | Ref. | Ref. | 0.002 | |
| Two | 11(32.4) | 879(34.9) | 3.79 (1.27–11.31) | 2.62 (0.84–8.16) | |||
| Three | 8(23.5) | 449(17.8) | 4.86 (1.50–15.70) | 2.84 (0.81–9.95) | |||
| Four and above | 10(29.4) | 256(10.2) | 13.46 (4.25–42.64) | 6.80 (1.85–24.98) | |||
| Having Abortion | No | 1929(76.5) | 24(70.6) | Ref. | 0.807 | ||
| Yes | 529(23.5) | 10(29.4) | 0.81 (0.16–1.23) | --- | |||
| Physical Activity (Sporting) | No | 19 (55.9) | 1464(58.1) | Ref. | --- | 0.799 | |
| Yes | 15 (44.1) | 1057(41.9) | 1.09 (0.55–2.16) | --- | |||
| Mobile Phone Use | Less than 2 h | 20 (58.8) | 1348(53.5) | Ref. | --- | 0.490 | |
| More than 2 h | 14 (41.2) | 1173(46.5) | 0.79 (0.40–1.55) | --- | |||
| Women’s Occupational Status | Housewife | 26 (76.5) | 2113(83.8) | Ref. | --- | 0.401 | |
| Employed | 8 (23.5) | 408(16.2) | 1.40 (0.64–3.04) | --- | |||
| Husband’s Occupational Status | Private-owned business | 25 (73.5) | 1821(72,2) | Ref. | --- | 0.111 | |
| Employee | 6 (17.6) | 605(24.0) | 0.73 (0.31–1.73) | --- | |||
| Unemployed and retired | 3 (8.8) | 95(3.8) | 2.90 (0.93–9.02) | --- | |||
| Educational status | Under College | 8 (23.5) | 582(23.1) | Ref. | --- | 0.895 | |
| College | 14 (41.2) | 999(39.6) | 0.97 (0.41–2.27) | --- | |||
| University | 12 (35.3) | 940(37.3) | 0.83 (0.35–1.99) | --- | |||
| Socioeconomic status | Low | 13(38.2) | 909(36.1) | Ref. | --- | 0.451 | |
| Middle | 12(35.3) | 1129(44.8) | 0.74 (0.34–1.61) | ||||
| High | 9(26.5) | 483(19.2) | 1.28 (0.56–2.95) | ||||
| Fast Food Consumption | No | 26(76.5) | 1847(73.3) | Ref. | --- | 0.744 | |
| Yes | 8(23.5) | 674(26.7) | 0.88 (0.40–1.91) | --- | |||
| Blood Type | A | 7 (20.6) | 833 (28.1) | Ref. | --- | 0.605 | |
| B | 5 (14.7) | 556 (18.7) | 1.10 (0.36–3.32) | --- | |||
| AB | 3 (8.8) | 231 (7.8) | 1.68 (0.47–6.03) | --- | |||
| O | 16 (47.1) | 992 (33.4) | 1.85 (0.87–3.91) | --- | |||
| Distance to the Telecommunications Tower and home | Under 10 m | 5 (14.7) | 287(11.4) | Ref. | --- | 0.548 | |
| Upper 10 m | 29 (85.3) | 2232(88.6) | 0.74 (0.28–1.94) | --- | |||
| Age | Under 30 | 5(14.7) | 1014(40.2) | Ref. | --- | 0.004 | |
| 31–40 | 15(44.1) | 1009(40.0) | 2.83 (1.07–7.52) | --- | |||
| Upper 41 | 14(41.2) | 498(19.8) | 5.37 (2.00-14.41) | --- | |||
| Age (mean ± sd) | 38.26 )7.28( | 33.12 )8.38) | 1.08 (1.04–1.12) | 1.05 (1.00-1.09) | 0.001 | ||
| Spouse Age | 42.62(7.12) | 37.27(8.20) | 1.07 (1.03–1.10) | --- | 0.001 | ||
| Marriage Age | 20.11(4.21) | 20.92(6.44) | 0.98 (0.90–1.06) | --- | 0.612 | ||
| Interval from marriage to first pregnancy(months) | 27.85(24.66) | 28.63(27.85) | 1.00 (0.99–1.01) | --- | 0.999 | ||
| BMI | 27.077(6.23) | 26.216(5.38) | 0.98 (0.79–1.20) | --- | 0.812 | ||
| FBS | 90.15(10.34) | 85.77(12.96) | 1.02 (1.00-1.05) | --- | 0.076 | ||
| PLT | 241.67(61.01) | 236.15(64.64) | 0.99 (0.98–1.01) | --- | 0.509 | ||
| BUN | 12.44(4.81) | 11.35(4.94) | 0.92 (0.69–1.21) | --- | 0.540 | ||
|
Adjusted odds ratios are shown only for variables retained in the final multivariable model (backward elimination). OR = odds ratio; CI = confidence interval. *P-values are from the likelihood ratio test in Firth’s penalized logistic regression. Bonferroni-adjusted p-values for the three primary hypotheses (across 20 tests): maternal age p = 0.020, first-degree consanguinity p < 0.020, parity ≥ 4 p = 0.040. Ref. = reference category. | |||||||
Advanced maternal age has been identified as a significant predictor of CAs. Each additional year in maternal age is linked to a 8% increase in the odds of CAs (ORC = 1.08, 95% CI: 1.04–1.12, p = 0.001). Women aged under 30 years (14.7% of CA cases, 40.2% of non-CA cases) served as reference, those aged 31–40 years (44.1% of CA cases, 40.0% of non-CA cases) had an ORC of 2.83 (95% CI: 1.07–7.52), and those over 41 years (41.2% of CA cases, 19.8% of non-CA cases) had an ORC of 5.37 (95% CI: 2.00–14.41), although these associations were not retained in the adjusted model, suggesting confounding effects. First-degree consanguineous marriage significantly increased the odds of CAs by over four times (ORA = 4.46, 95% CI: 2.16–9.20, p < 0.001), with 47.1% of CA cases and 14.9% of non-CA cases reporting first-degree consanguinity (ORC = 4,48, 95% CI: 2.68–11.18). Second-degree or higher consanguinity showed no significant association (ORA = 1.47, 95% CI: 0.51–4.28).
The number of pregnancies was significantly associated with CAs. Compared to women with one pregnancy (14.7% of CA cases; 37.2% of non-CA cases), those with two pregnancies (32.4% of CA cases; 34.9% of non-CA cases) had an ORA of 2.62 (95% CI: 0.84–8.16). Women with three pregnancies (23.5% of CA cases; 17.8% of non-CA cases) had an ORA of 2.84 (95% CI: 0.81–9.95), and women with four or more pregnancies (29.4% of CA cases; 10.2% of non-CA cases) had an ORA of 6.80 (95% CI: 1.85–24.9).
Discussion
This study offers a comprehensive epidemiological analysis of CAs among live-born infants in Isfahan Province, Iran, showing a lifetime prevalence of 1.33% (95% CI: 0.92–1.85), which falls within the lower range of national estimates (11.66–249.4 per 10,000 live births) and global figures of 2–3% [1–4]. The findings highlight advanced maternal age, first-degree consanguinity, and higher parity as key predictors of CAs, aligning with established genetic and demographic risk factors, while emphasizing the limited influence of lifestyle and environmental factors in this context [9, 11].
Advanced maternal age is a key risk factor for congenital anomalies, with each additional year of age associated with a 7% increase in the odds of occurrence. This link has been shown in univariate analyses for women aged 31 to 40 and those 41 and older. The relationship reflects age-related declines in oocyte quality and an increased risk of chromosomal anomalies, as supported by earlier research [7, 14, 15].
First-degree consanguineous marriage was the strongest predictor, increasing the odds of CAs by over fourfold. This finding aligns with regional studies highlighting consanguinity’s role in elevating recessive genetic disorder risks due to increased homozygosity [8, 16–18]. With 47.1% of CAs cases linked to first-degree consanguinity compared to 14.9% in non-CAs cases, this underscores Iran’s high consanguinity rates (approximately 30–50% nationally) as a critical public health concern [8].
Higher parity was also significantly linked to CAs, with women having four or more pregnancies showing 6.80 times higher odds. This may reflect cumulative reproductive stressors or undetected genetic factors heightened by multiple pregnancies [10, 19]. However, this interpretation should be approached with caution due to the wide confidence interval and the limited number of events observed. The association was less strong for three pregnancies, suggesting a potential threshold effect. These findings highlight the importance of targeted prenatal counseling for multiparous women, especially in regions with declining fertility rates like Iran.
Notably, clinical markers such as FBS showed a borderline association in univariate analysis, consistent with evidence linking maternal diabetes to CAs risk [20, 21]. However, its lack of significance in the multivariable model suggests that other factors, such as consanguinity, may influence it. Similarly, BMI, lifestyle factors (e.g., fast-food consumption, mobile phone use, physical activity), and socioeconomic status showed no significant associations, contrasting with prior studies linking environmental exposures and socioeconomic deprivation to CAs [4, 10, 12]. This discrepancy may reflect the study’s focus on live births, possibly underestimating the effects of exposures linked to stillbirths or terminated pregnancies, or the limited variation in environmental exposures within Isfahan’s urban setting.
The study’s strengths include its large sample size (N = 2555), a 100% response rate, and robust multistage sampling using the SIB system, which ensures representativeness. The SIB registry provides near-complete population coverage in Isfahan Province; therefore, selection bias is expected to be minimal. The use of validated questionnaires and real-time data entry via the PORSLINE platform improved data quality.
The study’s limitations include its cross-sectional design, which precludes the establishment of causal relationships, as well as the reliance on maternal self-reports, which may introduce recall bias. Furthermore, the outcome—defined as the presence of at least one live-born infant with a congenital anomaly—was derived from maternal self-reporting, making it potentially susceptible to recall bias, especially concerning older births. While the questionnaire underwent pilot testing and demonstrated commendable reliability, the lack of medical-record verification for all cases presents a limitation that could result in some degree of outcome misclassification.
Residual confounding cannot be entirely discounted due to the unavailability of critical risk factor data, such as periconceptional folic acid supplementation, maternal infections, tobacco use, alcohol consumption, and pre-existing chronic conditions (including diabetes, hypertension, and thyroid disorders). These omissions may have influenced the observed relationships with maternal age and parity. Furthermore, the absence of specific CA subtype data limits detail, and unmeasured confounders (e.g., genetic mutations, specific environmental toxins) may have affected results. Additionally, the study’s focus on live births might underestimate the overall CAs burden, as stillbirths and terminations were excluded.
These findings have important implications for public health policy in Iran. The strong link with first-degree consanguinity emphasizes the need for expanded genetic counseling and premarital screening programs, especially in high-risk communities. The influence of advanced maternal age and higher parity highlights the importance of targeted prenatal screening for older and multiparous women. Future research should use longitudinal designs, include genetic sequencing, and examine spatial clustering of CAs to identify environmental hotspots. National CAs registries are essential for improving data accuracy and guiding evidence-based policies and interventions.
Conclusion
This study, conducted in Isfahan Province, Iran, estimated a CAs prevalence of 1.33% among live-born infants, aligning with the lower end of national estimates. Advanced maternal age, first-degree consanguineous marriage, and higher parity were identified as significant risk factors, emphasizing the importance of genetic and reproductive influences in the development of CAs. Non-significant associations were observed for lifestyle factors (e.g., fast-food consumption, mobile phone use) and environmental exposures (e.g., proximity to telecommunications towers), indicating a limited role in this population.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to thank all participants who took part in this study and the staff of the Health Center of Isfahan University of Medical Sciences for their support during data collection.
Author contributions
Sh.I. and M.H. conceptualized the study, designed the questionnaire, and collected data. M.M. performed the statistical analysis. Sh.I. interpreted the results and supervised the study. M.H. drafted the manuscript and contributed to data interpretation. All authors reviewed and approved the final manuscript.
Funding
This study was conducted as part of the first author’s PhD thesis at Isfahan University of Medical Sciences, Isfahan, Iran. No specific funding was received for this research.
Data Availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Declarations
Consent for Publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Ethics Approval
This study was conducted in accordance with 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).
Consent to Participate
Informed verbal consent was obtained from all individual participants included in the study. The use of verbal consent was approved by the Ethics Committee of Isfahan University of Medical Sciences, considering the cultural context and the inclusion of telephone interviews. Participants were informed of the study objectives, procedures, the voluntary nature of participation, and their right to withdraw at any time without consequences.
Conflict of interest
The authors declare that they have no conflicts of interest.
Footnotes
Publisher’s Note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.