Temporal trends and maternal factors associated with Congenital Anomalies among live births in São Paulo, Brazil: A population-based study, 2015–2023

Temporal trends and maternal factors associated with Congenital Anomalies among live births in São Paulo, Brazil: A population-based study, 2015–2023 | 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 Temporal trends and maternal factors associated with Congenital Anomalies among live births in São Paulo, Brazil: A population-based study, 2015–2023 Fabio Antonio Venancio, Lucas Moreira dos Santos, Analícia de Camargo Cremonez, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9323088/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Congenital anomalies are a major cause of infant morbidity, mortality, and long-term disability worldwide. Their occurrence is influenced by maternal, demographic, and healthcare-related factors, and temporal patterns may vary according to anomaly group and surveillance quality. In Brazil, population-based analyses remain limited, especially those examining overall prevalence together with system-specific trends. Methods This population-based retrospective study used data from the Brazilian Live Birth Information System (SINASC). All live births registered in São Paulo between 2015 and 2023 were eligible. Annual prevalence rates of congenital anomalies were calculated per 10,000 live births with 95% confidence intervals (95% CI). Maternal, pregnancy, and neonatal characteristics were compared according to congenital anomaly status using Pearson’s chi-square test. Temporal comparisons across epidemiological periods were performed using count regression models with offset for live births, and incidence rate ratios (IRR) were estimated. Poisson models were initially fitted, and negative binomial regression was used when overdispersion was detected. Annual trends by anomaly group were also evaluated, and factors associated with congenital anomalies were assessed using adjusted logistic regression. Results A total of 4,311,399 live births were analyzed, of which 53,113 (1.23%) presented congenital anomalies. Overall prevalence increased from 103.90 per 10,000 live births in 2015 to 143.92 in 2023, the highest rate in the series. Compared with the baseline period (2018–2019), the post-COVID period (2023) showed a significantly higher rate in the negative binomial model (IRR = 1.179; 95% CI 1.028–1.353; p = 0.0188), while no significant differences were found for the Zika or COVID-19 periods. Musculoskeletal anomalies were the most frequent group throughout the series, whereas circulatory anomalies showed a marked increase, from 16.64 to 31.79 per 10,000 live births. Digestive anomalies showed the largest annual increase (IRR = 1.098; 95% CI 1.051–1.149; p < 0.001), followed by circulatory anomalies (IRR = 1.058; 95% CI 1.038–1.079; p < 0.001). Nervous system anomalies decreased over time (IRR = 0.967; 95% CI 0.940–0.995; p = 0.021). Higher odds of congenital anomalies were observed among male newborns, older mothers, adolescent pregnancies, and multiple pregnancies, while prenatal care was associated with lower odds. Conclusions Congenital anomaly prevalence increased in São Paulo between 2015 and 2023, driven mainly by circulatory and digestive system anomalies. These findings highlight the importance of continuous population-based surveillance and system-specific trend analysis. congenital anomalies live births temporal trends maternal factors surveillance São Paulo Brazil Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Congenital anomalies are an important cause of infant morbidity, mortality, and long-term disability worldwide, with substantial impact on health systems and families, particularly in low- and middle-income settings.[ 1 , 2 ] Although global age-standardized incidence rates of congenital birth defects have remained relatively stable, the absolute number of affected births remains high, and temporal patterns vary according to anomaly group, geographic setting, and surveillance quality.[ 1 , 2 ] The interpretation of congenital anomaly trends is complex because observed prevalence may reflect not only true changes in occurrence, but also improvements in prenatal diagnosis, neonatal screening, and case ascertainment in birth registries. Population-based studies have documented substantial increases in prenatal detection over time, especially after the implementation of more detailed ultrasound protocols and advances in fetal imaging.[ 3 , 4 ] In Europe, registry-based analyses have also shown that temporal trends differ considerably across anatomical subgroups, reinforcing the importance of system-specific analyses rather than relying only on overall prevalence estimates.[ 5 ] Maternal and perinatal factors also play a central role in the occurrence of congenital anomalies. Advanced maternal age, adolescent pregnancy, and multiple gestation have all been associated with higher risk, while maternal metabolic conditions such as diabetes and obesity are recognized risk factors for several structural anomalies, particularly congenital heart defects.[ 6 – 10 ] These factors may have changed over time in many populations and could contribute to shifts in congenital anomaly prevalence. In Brazil, studies based on national data have reported prevalence estimates lower than those observed in many high-income countries, suggesting possible underreporting or differences in diagnostic and surveillance capacity.[ 11 ] At the same time, the country has experienced important epidemiological events with direct implications for congenital anomaly patterns. The Zika virus epidemic, particularly during 2015–2016, was strongly associated with an increase in congenital neurological anomalies, especially microcephaly and other manifestations of congenital Zika syndrome.[ 12 – 14 ] These findings highlight the importance of maintaining robust surveillance systems capable of detecting both long-term trends and unusual changes related to emerging public health threats. Despite the relevance of congenital anomalies as a public health problem, temporal analyses in large Brazilian populations remain limited, particularly those examining both overall prevalence and specific anomaly groups. Understanding these patterns is essential for improving surveillance, identifying priority conditions, and informing maternal and child health policies. Therefore, this study aimed to evaluate temporal trends and maternal and perinatal factors associated with congenital anomalies among live births in São Paulo, Brazil, between 2015 and 2023, using population-based registry data. METHODS Study design and setting This was a population-based retrospective study of live births in the state of São Paulo, Brazil, from 2015 to 2023. The study was based on secondary data obtained from the Brazilian Live Birth Information System (Sistema de Informações sobre Nascidos Vivos, SINASC), which is derived from the Live Birth Certificate (Declaração de Nascido Vivo, DNV). São Paulo is the most populous state in Brazil and has a large and diverse population, making it an important setting for monitoring temporal patterns of congenital anomalies. The study period was defined from 2015 to 2023 because, at the time of data extraction, records for 2024 were still incomplete and therefore not suitable for stable population-based estimates. The inclusion of incomplete data could have led to underestimation of prevalence and distortion of temporal comparisons. Data source and study population All live births registered in SINASC for mothers residing in São Paulo between 2015 and 2023 were eligible for inclusion. The study population consisted of all live births with available information on congenital anomaly status. Births with missing information on the main outcome were excluded from analyses requiring classification of congenital anomalies. SINASC contains demographic, maternal, gestational, and neonatal information routinely collected at birth, including maternal age, maternal education, race/skin color, marital status, prenatal care, gestational characteristics, neonatal sex, Apgar scores, and congenital anomalies recorded on the DNV. Outcome definition The main outcome was the presence of congenital anomalies among live births, as recorded in SINASC. For descriptive and multivariable analyses, the outcome was treated as a binary variable (yes/no). For temporal analyses by anomaly group, congenital anomalies were classified according to ICD-10 groups recorded in the birth registry. The groups analyzed were: chromosomal abnormalities, circulatory system, cleft lip and cleft palate, digestive system, eye, ear, face and neck, genital organs, musculoskeletal system, nervous system, respiratory system, urinary system, and others. Study variables Maternal, pregnancy, and neonatal variables were selected based on data availability in SINASC and their epidemiological relevance. The descriptive analysis included prenatal care (yes/no), adolescent pregnancy, maternal age group, maternal education, marital status, maternal race/skin color, sex of the newborn, type of pregnancy, and Apgar scores at 1 and 5 minutes. For the adjusted logistic regression, the following variables were included: maternal education, maternal race/ethnicity, sex of the newborn, maternal age, paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies. Maternal and paternal ages were analyzed as continuous variables in the multivariable model, while the remaining variables were entered as categorical variables. For temporal comparisons by epidemiological period, four periods were defined a priori based on major public health contexts: Zika period (2015–2017), baseline period (2018–2019), COVID-19 period (2020–2022), and post-COVID period (2023). Statistical analysis Absolute and relative frequencies were calculated for maternal, pregnancy, and neonatal characteristics according to the presence of congenital anomalies. Differences between groups were assessed using Pearson’s chi-square test. Annual prevalence rates of congenital anomalies were calculated as the number of cases divided by the total number of live births in each year, expressed per 10,000 live births. Corresponding 95% confidence intervals (95% CI) were estimated assuming a Poisson distribution for the number of cases. To compare rates across predefined epidemiological periods, incidence rate ratios (IRR) and 95% confidence intervals were estimated using count regression models with the logarithm of the number of live births included as an offset. The baseline period (2018–2019) was used as the reference category. Poisson regression models were initially fitted; when overdispersion was detected, negative binomial regression was adopted as the main inferential model. To formally assess annual temporal trends according to ICD-10 anomaly group, separate count regression models were fitted for each anomaly group, using year as the independent variable and the logarithm of live births as an offset. Incidence rate ratios per year (IRR) and 95% confidence intervals were estimated. Poisson models were initially considered, and negative binomial regression was used when overdispersion was identified. An IRR greater than 1 indicated an increasing annual trend, whereas an IRR lower than 1 indicated a decreasing annual trend. Factors associated with congenital anomalies were evaluated using adjusted logistic regression models. Odds ratios (OR), 95% confidence intervals, and p-values were estimated. Variables were included in the multivariable model according to epidemiological relevance and availability in the dataset. The final model included maternal education, maternal race/ethnicity, sex of the newborn, maternal age, paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies. This study used secondary, de-identified data from a population-based information system, with no direct contact with participants. Because the analyses were based on routinely collected administrative data, no individual informed consent was required. All data management, statistical analyses, and graph construction were performed using the R statistical software.[ 15 ] RESULTS Between 2015 and 2023, a total of 4,311,399 live births were analyzed in São Paulo, Brazil, of which 53,113 (1.23%) presented congenital anomalies. Prenatal care coverage was high overall (99.02%). However, the absence of prenatal care was more frequent among mothers of newborns with congenital anomalies than among those without anomalies (1.14% vs. 0.97%; p = 0.001) Table 1 . Table 1 Maternal, pregnancy, and neonatal characteristics according to the presence of congenital anomalies, São Paulo, Brazil, 2015–2023. Category Congenital Anomalies Total No Yes p-value N % N % N % Prenatal care 0.001 No 33503 0.98 33020 0.97 483 1.14 Yes 3384870 99.02 3342848 97.79 42022 98.86 Adolescent pregnancy 0.007 No 3810414 88.01 3763377 86.92 47037 88.39 Yes 519071 11.99 512891 11.85 6180 11.61 Maternal age < 0.001 10–19 years 519071 11.99 512891 11.85 6180 11.61 20–34 years 3038187 70.17 3003567 69.37 34620 65.05 35–39 years 614107 14.18 605164 13.98 8943 16.80 ≥ 40 years 158034 3.65 154560 3.57 3474 6.53 Unknown 0.00 Maternal education < 0.001 Secondary education 2772665 64.14 2740292 63.39 32373 60.88 Higher education or more 1239193 28.67 1222236 28.27 16957 31.89 Primary education 303166 7.01 299410 6.93 3756 7.06 No schooling 3050 0.07 3000 0.07 50 0.09 Unknown 4848 0.11 4809 0.11 39 0.07 Marital status < 0.001 Married 1791922 41.49 1770020 40.99 21902 49.64 Single 1751958 40.57 1731022 40.08 20936 47.45 Divorced 67481 1.56 66643 1.54 838 1.90 Widowed 6771 0.16 6665 0.15 106 0.24 Separated 20729 0.48 20451 0.47 278 0.63 Unknown 7631 0.18 7573 0.18 58 0.13 Maternal race/skin color < 0.001 White 2492059 58.01 2461980 57.31 30079 56.78 Brown 1514573 35.26 1496116 34.83 18457 34.84 Black 263915 6.14 259855 6.05 4060 7.66 Asian 22111 0.51 21768 0.51 343 0.65 Indigenous 3282 0.08 3245 0.08 37 0.07 Newborn sex < 0.001 Male 2215758 51.18 2185973 50.49 29785 55.97 Female 2113163 48.81 2090294 48.28 22869 42.97 Unknown 564 0.01 1 0 563 1.06 Type of pregnancy < 0.001 Singleton 4218765 97.44 4167564 96.26 51201 96.21 Twin 106846 2.47 104932 2.42 1914 3.60 Triplet or more 2665 0.06 2596 0.06 69 0.13 Unknown 1209 0.03 1176 0.03 33 0.06 Apgar score at 1 minute < 0.001 8–10 2971299 88.96 2941805 88.08 29494 71.65 3–7 342850 10.27 333521 9.99 9329 22.66 0–2 25704 0.77 23362 0.7 2342 5.69 Apgar score at 5 minutes < 0.001 8–10 4184439 98.29 4138504 97.22 45935 89.45 3–7 63462 1.49 59393 1.4 4069 7.92 0–2 9142 0.21 7794 0.18 1348 2.63 Adolescent pregnancy differed significantly between groups ( p = 0.007). Among live births with congenital anomalies, 11.61% occurred among adolescent mothers, compared with 11.85% among those without anomalies. Maternal age distribution was significantly associated with congenital anomalies ( p < 0.001). Among newborns with congenital anomalies, 65.05% were born to mothers aged 20–34 years, 16.80% to mothers aged 35–39 years, 11.61% to mothers aged 10–19 years, and 6.53% to mothers aged ≥ 40 years. Compared with live births without anomalies, the anomaly group showed a higher proportion of advanced maternal age, particularly among mothers aged 35–39 years (16.80% vs. 13.98%) and ≥ 40 years (6.53% vs. 3.57%). Maternal education was also significantly associated with congenital anomalies ( p < 0.001). Among mothers of newborns with congenital anomalies, 60.88% had completed secondary education, 31.89% had higher education, and 7.06% had elementary education. Compared with births without anomalies, higher education was more frequent in the anomaly group (31.89% vs. 28.27%), whereas completed secondary education was less frequent (60.88% vs. 63.39%). Marital status differed significantly according to congenital anomaly status ( p < 0.001). Among newborns with congenital anomalies, 49.64% were born to married mothers and 47.45% to single mothers. Compared with births without anomalies, the anomaly group showed slightly higher proportions of divorced (1.90% vs. 1.54%) and separated mothers (0.63% vs. 0.47%). Regarding maternal race/skin color, significant differences were observed ( p < 0.001). Among births with congenital anomalies, 56.78% occurred among White mothers, 34.84% among Brown mothers, and 7.66% among Black mothers. Compared with births without anomalies, Black mothers were proportionally more represented in the anomaly group (7.66% vs. 6.05%), whereas White mothers were slightly less represented (56.78% vs. 57.31%). Male newborns were more frequent among those with congenital anomalies compared with those without anomalies (55.97% vs. 50.49%; p < 0.001). Singleton pregnancies predominated in both groups, although multiple pregnancies were proportionally more frequent among births with anomalies, including twin pregnancies (3.60% vs. 2.42%) and triplet or higher-order pregnancies (0.13% vs. 0.06%) ( p < 0.001). Neonatal vitality differed markedly according to congenital anomaly status. At 1 minute, Apgar scores of 8–10 were less frequent among newborns with anomalies than among those without anomalies (71.65% vs. 88.08%), whereas scores of 3–7 (22.66% vs. 9.99%) and 0–2 (5.69% vs. 0.70%) were more common in the anomaly group ( p < 0.001). Similarly, at 5 minutes, Apgar scores of 8–10 were less frequent among newborns with anomalies (89.45% vs. 97.22%), while scores of 3–7 (7.92% vs. 1.40%) and 0–2 (2.63% vs. 0.18%) were more frequent ( p < 0.001) The annual rates of congenital anomalies per 10,000 live births in São Paulo between 2015 and 2023 are presented in Table 2 . A total of 53,113 cases were identified during the study period, with yearly counts ranging from 5,373 cases in 2021 to 6,511 cases in 2018. Table 2 Annual number of cases, live births, and rates of congenital anomalies per 10,000 live births in São Paulo, Brazil, 2015–2023 Year Cases Live births Rate per 10,000 95% CI 2015 5,395 519,225 103.90 101.15–106.72 2016 6,253 500,842 124.85 121.77–127.98 2017 6,442 513,800 125.38 122.34–128.48 2018 6,511 510,291 127.59 124.51–130.73 2019 5,730 491,874 116.49 113.50–119.55 2020 5,622 468,965 119.88 116.77–123.06 2021 5,373 449,442 119.55 116.37–122.79 2022 5,625 439,662 127.94 124.62–131.33 2023 6,266 435,384 143.92 140.38–147.53 The lowest rate was observed in 2015 (103.90 per 10,000 live births; 95%CI 101.15–106.72). Rates increased in the following years, reaching 124.85 in 2016 and 125.38 in 2017. In 2018, the rate rose to 127.59 per 10,000 live births, followed by a decrease in 2019 (116.49). During the COVID-19 period, rates remained relatively stable, with 119.88 per 10,000 live births in 2020 and 119.55 in 2021. An increase was observed in 2022 (127.94). The highest rate in the entire series occurred in 2023, reaching 143.92 per 10,000 live births (95%CI 140.38–147.53). The temporal distribution of congenital anomalies by ICD-10 group is presented in Figs. 1 – 3 , with the corresponding annual numerical values shown in Supplementary Table S1 . Overall, musculoskeletal anomalies consistently presented the highest birth prevalence throughout the study period, ranging from 34.67 per 10,000 live births in 2015 to 41.43 in 2023 (Figs. 1 – 3 ; Supplementary Table S1 ). Circulatory system anomalies showed the second highest prevalence and a clear upward trend over time, increasing from 16.64 per 10,000 live births in 2015 to 31.79 in 2023. The total prevalence of congenital anomalies also increased over the study period, rising from 103.90 per 10,000 live births in 2015 to 143.92 in 2023. A particularly notable pattern was observed for circulatory system anomalies, which showed a progressive increase over time, with a more marked rise during the later years of the series (Figs. 1 and 3 ; Supplementary Table S1 ). After relatively high levels already observed between 2016 and 2018, prevalence remained above 21 per 10,000 live births during 2019–2021 and continued to increase thereafter, reaching 26.07 in 2022 and peaking at 31.79 in 2023. This sustained increase contrasts with the more stable patterns observed for most other anomaly groups and represents the most pronounced upward trend among the major congenital anomaly categories. Among the remaining major groups, nervous system anomalies showed an early peak in 2016 (16.41 per 10,000 live births), followed by a decline and subsequent stabilization between 2020 and 2023, when rates ranged from 10.21 to 11.32 per 10,000 live births (Figs. 1 and 2 ; Supplementary Table S1 ). Eye, ear, face and neck anomalies remained relatively stable over the series, varying from 8.38 in 2020 to 11.07 in 2023. Genital organ anomalies also showed limited fluctuation, with rates generally around 8–10 per 10,000 live births across the study period. Digestive system anomalies increased over time, particularly in the final year, rising from 10.19 in 2022 to 16.51 in 2023. The faceted visualization further highlights the temporal patterns of less frequent anomaly groups (Fig. 2 ). Cleft lip and cleft palate remained relatively stable, fluctuating between 5.55 and 7.21 per 10,000 live births. Chromosomal abnormalities varied within a narrow range, from 4.41 to 5.91 per 10,000 live births. Other anomalies showed a slight overall decline, from 4.80 in 2015 to 3.95 in 2023. Respiratory system anomalies remained the least frequent group throughout the series, ranging from 1.17 to 2.21 per 10,000 live births, while urinary system anomalies showed low but slightly increasing rates in the final year, reaching 3.42 per 10,000 live births in 2023 (Supplementary Table S1 ). When the six groups with the highest mean prevalence were plotted together with the total prevalence, the overall pattern became more evident (Fig. 3 ). Musculoskeletal anomalies remained the dominant group across all years, whereas circulatory system anomalies exhibited the most consistent and pronounced increase over time. The trajectory of the total prevalence curve closely followed the combined influence of these trends, with a marked rise observed in 2023. Taken together, these patterns suggest that the recent increase in the overall prevalence of congenital anomalies was driven primarily by increases in circulatory and digestive anomalies, with a smaller contribution from musculoskeletal anomalies. Between 2015 and 2023, the rate of congenital anomalies ranged from 117.94 to 143.92 per 10,000 live births across epidemiological periods. The baseline period (2018–2019) presented a rate of 122.15 per 10,000 live births. During the Zika period (2015–2017), the rate was slightly lower (117.94 per 10,000), whereas the COVID-19 period (2020–2022) showed a rate comparable to baseline (122.38 per 10,000). The highest rate was observed in the post-COVID period (2023), reaching 143.92 per 10,000 live births (Table 3 ). Table 3 Rates of congenital anomalies per 10,000 live births and incidence rate ratios (IRR) according to epidemiological periods, São Paulo, Brazil, 2015–2023 Panel A. Rates per 10,000 live births Period Years Cases (n) Live births (n) Rate per 10,000 Baseline 2018–2019 12,241 1,002,165 122.15 Zika 2015–2017 18,090 1,533,867 117.94 COVID-19 2020–2022 16,620 1,358,069 122.38 Post-COVID 2023 6,266 435,384 143.92 Panel B. Incidence rate ratios (IRR) compared to baseline (2018–2019) Period IRR 95% CI p-value Zika (2015–2017) 0.966 0.944–0.988 0.0027 COVID-19 (2020–2022) 1.002 0.979–1.026 0.872 Post-COVID (2023) 1.178 1.143–1.215 < 0.001 Negative binomial regression (final model due to overdispersion) Period IRR 95% CI p-value Zika (2015–2017) 0.967 0.873–1.072 0.523 COVID-19 (2020–2022) 1.003 0.905–1.112 0.950 Post-COVID (2023) 1.179 1.028–1.353 0.0188 In regression analyses, Poisson models suggested a lower rate during the Zika period (IRR = 0.966; 95%CI 0.944–0.988; p = 0.003) and a substantially higher rate in 2023 (IRR = 1.178; 95%CI 1.143–1.215; p < 0.001) compared with baseline, while no significant difference was observed during the COVID-19 period (IRR = 1.002; 95%CI 0.979–1.026; p = 0.872). However, because overdispersion was detected, negative binomial regression was considered the primary model. In this model, neither the Zika period (IRR = 0.967; 95%CI 0.873–1.072; p = 0.523) nor the COVID-19 period (IRR = 1.003; 95%CI 0.905–1.112; p = 0.950) differed significantly from baseline. In contrast, the post-COVID period remained significantly associated with a higher rate of congenital anomalies (IRR = 1.179; 95%CI 1.028–1.353; p = 0.0188), corresponding to an approximate 18% increase compared to the baseline period. To formally assess temporal trends in congenital anomaly groups, count regression models with an offset for the number of live births were fitted for each group, and the results are presented in Table 4 . Significant increasing trends were observed for digestive system anomalies, circulatory system anomalies, chromosomal abnormalities, and musculoskeletal system anomalies. Digestive system anomalies showed the steepest increase over the study period, with an average annual increase of 9.8% (IRR = 1.098; 95%CI 1.051–1.149; p < 0.001). Circulatory system anomalies increased by 5.8% per year on average (IRR = 1.058; 95%CI 1.038–1.079; p < 0.001). Chromosomal abnormalities showed a smaller but significant annual increase of 2.3% (IRR = 1.023; 95%CI 1.002–1.044; p = 0.030), while musculoskeletal system anomalies increased by 1.3% per year (IRR = 1.013; 95%CI 1.003–1.023; p = 0.013). Table 4 Annual temporal trends in congenital anomaly groups, São Paulo, Brazil, 2015–2023 Congenital anomaly group Model IRR per year 95% CI p-value Digestive system Negative binomial 1.098 1.051–1.149 < 0.001 Circulatory system Negative binomial 1.058 1.038–1.079 < 0.001 Respiratory system Negative binomial 1.033 0.994–1.073 0.093 Chromosomal abnormalities Negative binomial 1.023 1.002–1.044 0.030 Musculoskeletal system Negative binomial 1.013 1.003–1.023 0.013 Genital organs Negative binomial 1.010 0.993–1.027 0.259 Urinary system Negative binomial 1.004 0.976–1.033 0.764 Eye, ear, face and neck Negative binomial 1.002 0.978–1.026 0.884 Cleft lip and cleft palate Negative binomial 0.994 0.974–1.015 0.605 Others Poisson 0.970 0.953–0.988 < 0.001 Nervous system Negative binomial 0.967 0.940–0.995 0.021 In contrast, significant decreasing trends were identified for nervous system anomalies and the group classified as others. Nervous system anomalies decreased by 3.3% per year on average (IRR = 0.967; 95%CI 0.940–0.995; p = 0.021), while anomalies classified as others decreased by 3.0% annually (IRR = 0.970; 95%CI 0.953–0.988; p < 0.001). No statistically significant temporal trends were observed for respiratory system anomalies (IRR = 1.033; 95%CI 0.994–1.073; p = 0.093), genital organ anomalies (IRR = 1.010; 95%CI 0.993–1.027; p = 0.259), urinary system anomalies (IRR = 1.004; 95%CI 0.976–1.033; p = 0.764), eye, ear, face and neck anomalies (IRR = 1.002; 95%CI 0.978–1.026; p = 0.884), or cleft lip and cleft palate (IRR = 0.994; 95%CI 0.974–1.015; p = 0.605). To analyze the factors associated with congenital anomalies, an adjusted logistic regression model was conducted, and the results are presented in Fig. 4 . The analysis included maternal education, race/ethnicity, sex of the newborn, maternal and paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies. Maternal education was not significantly associated with congenital anomalies. Compared with mothers with no schooling, the odds of congenital anomalies were similar among mothers with primary education (OR = 1.04; 95%CI 0.54–2.02; p = 0.903), secondary education (OR = 1.03; 95%CI 0.53–1.99; p = 0.924), and higher education (OR = 1.27; 95%CI 0.66–2.45; p = 0.482). Significant differences were observed according to maternal race/ethnicity. Compared with White mothers, higher odds of congenital anomalies were observed among Asian mothers (OR = 1.19; 95%CI 1.04–1.38; p = 0.015), mothers of mixed race (Pardo) (OR = 1.11; 95%CI 1.07–1.15; p < 0.001), and Black mothers (OR = 1.25; 95%CI 1.18–1.34; p < 0.001). No significant association was observed among Indigenous mothers (OR = 0.63; 95%CI 0.28–1.40; p = 0.257). Male newborns had higher odds of congenital anomalies compared with females (OR = 1.30; 95%CI 1.26–1.33; p < 0.001). Maternal age was positively associated with congenital anomalies, with the odds increasing by approximately 3% for each additional year of maternal age (OR = 1.03; 95%CI 1.03–1.03; p < 0.001). In contrast, paternal age was not significantly associated with congenital anomalies (OR = 1.00; 95%CI 1.00–1.00; p = 0.103). Adolescent pregnancy was associated with higher odds of congenital anomalies (OR = 1.40; 95%CI 1.32–1.49; p < 0.001). Regarding the type of pregnancy, twin pregnancies showed higher odds compared with singleton pregnancies (OR = 1.29; 95%CI 1.19–1.39; p < 0.001), whereas triplet or higher-order pregnancies were not significantly associated (OR = 1.45; 95%CI 0.95–2.21; p = 0.085). Prenatal care was associated with a lower likelihood of congenital anomalies. Mothers who received prenatal care had reduced odds compared with those who did not receive prenatal care (OR = 0.73; 95%CI 0.61–0.87; p < 0.001). Finally, the number of previous pregnancies was not significantly associated with congenital anomalies (OR = 0.99; 95%CI 0.98–1.01; p = 0.352). DISCUSSION This population-based study of 4,311,399 live births in São Paulo, Brazil, between 2015 and 2023 identified 53,113 cases of congenital anomalies (1.23%), with an overall prevalence increase from 103.90 to 143.92 per 10,000 live births. Temporal trends were heterogeneous across anatomical systems, with significant increases in circulatory and digestive system anomalies, a modest increase in musculoskeletal anomalies, and a decline in nervous system anomalies. These findings add important epidemiological evidence on congenital anomaly surveillance in a large middle-income setting and reinforce the value of system-specific analyses in birth defect registries. The 38.5% increase in overall congenital anomaly prevalence observed during the study period is consistent with trends reported by population-based surveillance systems. Analyses from European congenital anomaly registries documented increasing prevalence for several congenital anomaly subgroups over time.[ 5 ] A recent Brazilian national study also reported lower prevalence than that usually observed in high-income settings, suggesting that differences in prenatal diagnosis, case ascertainment, and registry completeness may partly explain international variation.[ 11 ] Global Burden of Disease analyses indicate that, although age-standardized incidence rates of congenital birth defects have remained relatively stable, the absolute number of affected births has increased, particularly in lower-resource settings.[ 1 , 2 ] Several factors may explain the increase observed in São Paulo. First, improvements in surveillance quality and reporting completeness likely contributed, as birth defect registries often demonstrate improved case ascertainment over time with greater familiarity among professionals and maturation of information systems.[ 5 , 11 ] Second, advances in prenatal diagnosis have substantially increased detection. A population-based study from Western Australia documented a 5.5-fold increase in prenatal diagnosis prevalence over time, especially for cardiovascular, urogenital, and chromosomal anomalies.[ 3 ] Similarly, implementation of more detailed first-trimester anatomical ultrasound protocols has increased detection of major fetal anomalies in large population studies.[ 4 ] Third, demographic shifts, particularly increasing maternal age, may have contributed to rising prevalence, given the well-established association between advanced maternal age and both chromosomal and non-chromosomal anomalies.[ 6 , 16 , 7 ] Finally, changes in the prevalence of maternal risk factors, including diabetes and obesity, may also influence temporal trends.[ 8 – 10 ] Overall, the prevalence observed in this study falls within the range reported internationally but is higher than previous Brazilian estimates, suggesting either genuine regional differences or improvements in case ascertainment within the São Paulo birth registry system. One of the most important findings of this study was the significant annual increase in circulatory system anomalies (IRR 1.058, 95% CI 1.038–1.079). This corresponds to an average annual increase of approximately 5.8%. Similar patterns have been reported in international surveillance systems. Analyses from the EUROCAT registries documented increasing prevalence for several severe congenital heart defects over time, including atrioventricular septal defects and tetralogy of Fallot.[ 5 ] Improved prenatal detection through advances in fetal echocardiography likely explains a substantial part of this increase. For example, a nationwide study in the Czech Republic showed that the involvement of pediatric cardiologists in prenatal screening programs significantly improved detection of major congenital heart defects, while overall incidence remained stable.[ 17 ] Similarly, a large systematic review demonstrated that reported birth prevalence of congenital heart disease has increased over time largely due to improvements in diagnostic technologies and screening practices.[ 18 ] Postnatal detection has also improved through widespread implementation of newborn screening programs for critical congenital heart disease using pulse oximetry. In the United States, mandatory newborn screening has been associated with reduced early infant mortality and fewer emergency hospitalizations.[ 19 , 20 ] While these programs improve early identification and outcomes, they may also contribute to increased reported prevalence in surveillance systems. At the same time, true increases in incidence cannot be completely excluded. Maternal metabolic conditions such as diabetes and obesity are well-established risk factors for congenital heart defects.[ 5 , 8 , 9 ] A large meta-analysis including more than 80 million births showed that pre-gestational diabetes was associated with a more than threefold increased risk of congenital heart defects, while gestational diabetes was associated with a moderate but significant increase in risk.[ 8 ] Maternal obesity has also been associated with increased risk of circulatory system malformations.[ 9 ] In addition, a population-based study from Canada identified maternal diabetes, hypertension, and maternal congenital heart disease as significant predictors of congenital heart defects in offspring.[ 21 ] Another consideration is the potential role of maternal infections or inflammatory conditions affecting fetal cardiovascular development. Although the ecological nature of the present study precludes causal inference, the temporal overlap with the COVID-19 pandemic raises hypotheses that deserve further investigation. However, establishing such associations would require individual-level exposure data and more detailed analytical approaches. Digestive system anomalies showed the largest relative increase among all anomaly groups (IRR 1.098, 95% CI 1.051–1.149). This corresponds to an average annual increase of approximately 9.8% and represents a notable finding that warrants careful interpretation. Global analyses have shown that the overall burden of digestive congenital anomalies has declined over recent decades, largely due to improvements in surgical management and survival rather than changes in birth prevalence.[ 22 – 24 ] A systematic review reported birth prevalence estimates for major digestive congenital anomalies ranging from 0.86 to 3.11 per 10,000 births.[ 25 ] In contrast, European surveillance data suggest that fluctuations in digestive anomaly prevalence may partly reflect surveillance artifacts rather than sustained epidemiological changes.[ 5 ] Several explanations may account for the increase observed in São Paulo. Improvements in prenatal detection through second-trimester ultrasound may increase identification of gastrointestinal anomalies such as duodenal atresia, esophageal atresia, and abdominal wall defects. Improved postnatal diagnosis and notification may also contribute, particularly for anomalies that are not immediately apparent at birth. In addition, more complete follow-up and improvements in registry practices may increase case ascertainment. Although true increases in incidence related to environmental or maternal risk factors cannot be excluded, the evidence supporting such associations remains limited.[ 26 ] Musculoskeletal anomalies showed a small but statistically significant increase over time (IRR 1.013, 95% CI 1.003–1.023). Although the relative increase was modest, this group represents the largest share of congenital anomalies in many surveillance systems. Global studies consistently report musculoskeletal anomalies among the most prevalent congenital defects, although they generally contribute less to mortality compared with cardiac or neurological anomalies.[ 27 ] European surveillance data also indicate that trends in musculoskeletal anomalies may be influenced by surveillance artifacts or differences in diagnostic classification across registries.[ 5 ] In São Paulo, the modest increase observed may reflect improved prenatal detection, changes in diagnostic classification, or demographic shifts such as increasing maternal age, rather than a substantial change in underlying risk. Nervous system anomalies showed a significant decreasing trend (IRR 0.967, 95% CI 0.940–0.995). This corresponds to an average annual reduction of approximately 3.3% and is likely strongly influenced by the Zika virus epidemic that affected Brazil during 2015–2016. The Zika epidemic produced a dramatic temporary increase in congenital neurological anomalies, particularly microcephaly and other manifestations of congenital Zika syndrome.[ 28 , 14 , 12 , 29 , 30 , 13 ] Multiple studies documented strong associations between maternal Zika infection and microcephaly, neurological abnormalities, and neuroimaging abnormalities in newborns.[ 14 , 12 , 30 , 13 ] For example, a prospective cohort study conducted in São Paulo State reported that neonatal Zika RT-PCR positivity was associated with a fivefold increased risk of microcephaly.[ 12 ] Thus, the declining trend observed in this study likely reflects the peak of Zika-associated neurological anomalies in the early years of the study period followed by gradual normalization after the epidemic subsided. No significant temporal trends were observed for genital, urinary, eye/ear/face/neck, cleft lip and palate, or respiratory anomalies. This stability is consistent with international surveillance literature showing that many congenital anomaly groups remain relatively stable over time.[ 1 , 2 , 27 ] Stable trends may reflect relatively constant detection practices, unchanged etiological exposures, or well-established prenatal screening protocols. For example, cleft lip and palate have long been targeted in prenatal ultrasound screening programs, which may contribute to stable ascertainment.[ 5 ] The multivariable analysis identified several factors associated with congenital anomalies that are consistent with previous epidemiological literature. Male sex, advanced maternal age, adolescent pregnancy, and multiple pregnancy were all associated with higher odds of congenital anomalies [ 6 , 16 , 7 , 21 ] Multifetal pregnancies have been repeatedly associated with increased risk of structural anomalies.[ 6 , 7 ] Associations with maternal race or ethnicity should be interpreted cautiously because they likely reflect complex interactions between social determinants, environmental exposures, and access to healthcare rather than biological differences.[ 7 ] The protective association observed with prenatal care is consistent with evidence that adequate prenatal care facilitates early detection and management of maternal conditions associated with congenital anomalies, including folic acid supplementation and glycemic control.[ 4 , 7 , 31 ] Maternal metabolic conditions deserve particular attention. Both pre-gestational and gestational diabetes have been associated with increased risk of congenital anomalies overall, especially congenital heart defects.[ 8 , 10 ] Maternal obesity is also associated with higher risk of congenital heart defects and neural tube defects, with evidence of a dose–response relationship with body mass index.[ 9 , 32 , 33 ] Taken together, the heterogeneous temporal trends across anatomical systems suggest that the overall increase in congenital anomaly prevalence in São Paulo was primarily driven by increases in circulatory and digestive system anomalies, while other groups remained stable or declined. These findings highlight the importance of analyzing congenital anomalies by specific anatomical groups rather than relying solely on overall prevalence estimates.[ 5 , 1 ] These findings have important implications for clinical practice and public health policy. The increasing prevalence of congenital anomalies, particularly cardiac defects, emphasizes the need for robust prenatal screening programs and specialized fetal assessment.[ 4 , 19 ] Additionally, universal implementation of newborn screening for critical congenital heart disease can substantially reduce infant mortality.[ 19 , 20 ] The results also reinforce the importance of preconception and prenatal interventions targeting modifiable risk factors, including optimal glycemic control in women with diabetes, healthy body weight before pregnancy, and folic acid supplementation.[ 16 , 8 , 10 ] Furthermore, the experience of the Zika epidemic illustrates the importance of maintaining surveillance systems capable of detecting emerging threats to fetal development.[ 28 , 14 , 13 ] This study has several strengths, including its large population-based sample, long time series, detailed classification of congenital anomalies by anatomical system, and the combination of temporal and multivariable analyses. However, several limitations should be considered. The observational design precludes causal inference. Registry-based studies are subject to potential underreporting and misclassification, and improvements in reporting over time may contribute to apparent increases in prevalence. The lack of individual-level exposure data limits evaluation of specific etiological hypotheses, including infections, medications, environmental exposures, and metabolic factors. The absence of data on pregnancy terminations following prenatal diagnosis may affect prevalence estimates. In addition, the analysis included only live births, which may underestimate the true prevalence of severe congenital anomalies. Finally, the study did not include data from 2024 or 2025. At the time of data extraction, registry data for 2024 were still incomplete, and their inclusion could have led to underestimation of prevalence and distortion of temporal comparisons. CONCLUSIONS This large population-based study documented a substantial increase in congenital anomaly prevalence in São Paulo, Brazil, from 2015 to 2023, driven mainly by increases in circulatory and digestive system anomalies. The heterogeneous trends across anatomical systems suggest that improvements in detection and reporting likely contributed to the observed increase, although changes in maternal risk factors may also have played a role. The declining trend in nervous system anomalies is plausibly explained by the temporal pattern of the Zika epidemic. These findings highlight the importance of system-specific congenital anomaly surveillance, continued strengthening of birth defect registries, and public health strategies targeting modifiable maternal risk factors. Declarations Ethics approval and consent to participate This study was conducted using secondary, anonymized data obtained from the Brazilian Live Birth Information System (SINASC), publicly available through the Ministry of Health. According to Brazilian regulations, studies based exclusively on publicly available and de-identified data do not require approval from a Research Ethics Committee or informed consent from participants. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are publicly available from the Brazilian Ministry of Health through the DATASUS platform: https://datasus.saude.gov.br/ The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was partially supported by Centro Universitário de Adamantina, Adamantina, São Paulo, Brazil. Author's contributions Conceptualization, F.A.V.; methodology, F.A.V. and D.C.V.-V.; formal analysis, F.A.V.; investigation, F.A.V., D.C.V.-V., L.M.S., A.C.C., G.M.M., M.E.R.P., M.E.C.L., A.C.B.S., and B.L.P.N.; writing—original draft preparation, F.A.V., D.C.V.-V., and A.C.C.; writing—review and editing, F.A.V. and D.C.V.-V.; visualization, F.A.V., D.C.V.-V., L.M.S., A.C.C., G.M.M., M.E.R.P., M.E.C.L., A.C.B.S., and B.L.P.N.; supervision, F.A.V.; project administration, F.A.V. and D.C.V.-V.; funding acquisition, F.A.V. and D.C.V.-V. All authors have read and agreed to the published version of the manuscript. Acknowledgements Not applicable. References Liu H, Chen K, Wang T, et al. Emerging Trends and Cross-Country Health Inequalities in Congenital Birth Defects: Insights From the GBD 2021 Study. Int J Equity Health. 2025;24(1):50. 10.1186/s12939-025-02412-7 . Kang L, Cao G, Jing W, Liu J, Liu M. 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Regional, and National Differences in the Incidence and Mortality of Digestive Congenital Anomalies From 1990 to 2021, With Projections for Future Trends. Front Public Health. 2025;13:1640700. 10.3389/fpubh.2025.1640700 . Xie X, Pei J, Zhang L, Wu Y. Global Birth Prevalence of Major Congenital Anomalies: A Systematic Review and Meta-Analysis. BMC Public Health. 2025;25(1):449. 10.1186/s12889-025-21642-6 . Cambra K, Ibañez B, Urzelai D, et al. Trends in the Prevalences of Congenital Anomalies and Age at Motherhood in a Southern European Region: A Population-Based Study. BMJ Open. 2014;4(3):e004244. 10.1136/bmjopen-2013-004244 . Yang E, Chen F, Zhang Y, et al. Analyzing the Global Burden of 11 Subtypes of Congenital Birth Defects: Trends, Sociodemographic Correlates, and Outcomes From 1990 to 2021. Ann Med. 2025;57(1):2519686. 10.1080/07853890.2025.2519686 . de Amorin Vilharba BL, Yamamura M, de Azevedo MV, et al. Disease Burden of Congenital Zika Virus Syndrome in Brazil and Its Association With Socioeconomic Data. Sci Rep. 2023;13(1):11882. 10.1038/s41598-023-38553-4 . Baud D, Gubler DJ, Schaub B, Lanteri MC, Musso D. An Update on Zika Virus Infection. Lancet. 2017;390(10107):2099–109. 10.1016/S0140-6736(17)31450-2 . Fontes JMSSA, Miranda-Filho DB, Montarroyos UR, et al. Relative and Attributable Risks of Neurological and Perinatal Adverse Outcomes Among Children With and Without Prenatal Zika Virus Exposure in Northeast Brazil: A Prospective Cohort Study (2015–2018). PLoS Negl Trop Dis. 2025;19(8):e0013344. 10.1371/journal.pntd.0013344 . Novak CM, Graham EM. Obstetric Management, Tests, and Technologies That Impact Childhood Development. Dev Med Child Neurol. 2019;61(9):1002–7. 10.1111/dmcn.14160 . Creanga AA, Catalano PM, Bateman BT. Obesity in Pregnancy. N Engl J Med. 2022;387(3):248–59. 10.1056/NEJMra1801040 . Hedermann G, Hedley PL, Thagaard IN, et al. Maternal Obesity and Metabolic Disorders Associate With Congenital Heart Defects in the Offspring: A Systematic Review. PLoS ONE. 2021;16(5):e0252343. 10.1371/journal.pone.0252343 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-9323088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638252871,"identity":"1486d317-c2b0-4648-b4cc-9875bca1e703","order_by":0,"name":"Fabio Antonio 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2015–2023\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9323088/v1/c98cffe5275e74b5959612db.png"},{"id":109296266,"identity":"905c94e2-b5e4-4bb5-ba9d-fd4f9f9131b9","added_by":"auto","created_at":"2026-05-15 08:46:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2451423,"visible":true,"origin":"","legend":"\u003cp\u003eBirth prevalence of congenital anomalies by ICD-10 group, São Paulo, Brazil, 2015–2023.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9323088/v1/6dea05e2d35b8711fcb09891.png"},{"id":109286266,"identity":"e6f3b318-74b6-49d3-b287-9f9aabfbac0d","added_by":"auto","created_at":"2026-05-15 02:33:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1226876,"visible":true,"origin":"","legend":"\u003cp\u003eBirth prevalence of congenital anomalies per 10,000 live births: top six anomaly groups and total prevalence, São Paulo, Brazil, 2015–2023\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9323088/v1/e5d935448b1269ae2daf0972.png"},{"id":109296556,"identity":"1d707219-20ff-4498-87c6-36a58b82a031","added_by":"auto","created_at":"2026-05-15 08:48:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1186443,"visible":true,"origin":"","legend":"\u003cp\u003eFactors associated with congenital anomalies in live births in the state of São Paulo, Brazil.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9323088/v1/3cb8d808053b9dd58fc0b253.png"},{"id":109297353,"identity":"72c62359-4eaa-4813-9565-d1ee3753fefa","added_by":"auto","created_at":"2026-05-15 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2015–2023","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCongenital anomalies are an important cause of infant morbidity, mortality, and long-term disability worldwide, with substantial impact on health systems and families, particularly in low- and middle-income settings.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Although global age-standardized incidence rates of congenital birth defects have remained relatively stable, the absolute number of affected births remains high, and temporal patterns vary according to anomaly group, geographic setting, and surveillance quality.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe interpretation of congenital anomaly trends is complex because observed prevalence may reflect not only true changes in occurrence, but also improvements in prenatal diagnosis, neonatal screening, and case ascertainment in birth registries. Population-based studies have documented substantial increases in prenatal detection over time, especially after the implementation of more detailed ultrasound protocols and advances in fetal imaging.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In Europe, registry-based analyses have also shown that temporal trends differ considerably across anatomical subgroups, reinforcing the importance of system-specific analyses rather than relying only on overall prevalence estimates.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMaternal and perinatal factors also play a central role in the occurrence of congenital anomalies. Advanced maternal age, adolescent pregnancy, and multiple gestation have all been associated with higher risk, while maternal metabolic conditions such as diabetes and obesity are recognized risk factors for several structural anomalies, particularly congenital heart defects.[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] These factors may have changed over time in many populations and could contribute to shifts in congenital anomaly prevalence.\u003c/p\u003e \u003cp\u003eIn Brazil, studies based on national data have reported prevalence estimates lower than those observed in many high-income countries, suggesting possible underreporting or differences in diagnostic and surveillance capacity.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] At the same time, the country has experienced important epidemiological events with direct implications for congenital anomaly patterns. The Zika virus epidemic, particularly during 2015\u0026ndash;2016, was strongly associated with an increase in congenital neurological anomalies, especially microcephaly and other manifestations of congenital Zika syndrome.[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] These findings highlight the importance of maintaining robust surveillance systems capable of detecting both long-term trends and unusual changes related to emerging public health threats.\u003c/p\u003e \u003cp\u003eDespite the relevance of congenital anomalies as a public health problem, temporal analyses in large Brazilian populations remain limited, particularly those examining both overall prevalence and specific anomaly groups. Understanding these patterns is essential for improving surveillance, identifying priority conditions, and informing maternal and child health policies.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to evaluate temporal trends and maternal and perinatal factors associated with congenital anomalies among live births in S\u0026atilde;o Paulo, Brazil, between 2015 and 2023, using population-based registry data.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis was a population-based retrospective study of live births in the state of S\u0026atilde;o Paulo, Brazil, from 2015 to 2023. The study was based on secondary data obtained from the Brazilian Live Birth Information System (Sistema de Informa\u0026ccedil;\u0026otilde;es sobre Nascidos Vivos, SINASC), which is derived from the Live Birth Certificate (Declara\u0026ccedil;\u0026atilde;o de Nascido Vivo, DNV). S\u0026atilde;o Paulo is the most populous state in Brazil and has a large and diverse population, making it an important setting for monitoring temporal patterns of congenital anomalies.\u003c/p\u003e \u003cp\u003eThe study period was defined from 2015 to 2023 because, at the time of data extraction, records for 2024 were still incomplete and therefore not suitable for stable population-based estimates. The inclusion of incomplete data could have led to underestimation of prevalence and distortion of temporal comparisons.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData source and study population\u003c/h3\u003e\n\u003cp\u003eAll live births registered in SINASC for mothers residing in S\u0026atilde;o Paulo between 2015 and 2023 were eligible for inclusion. The study population consisted of all live births with available information on congenital anomaly status. Births with missing information on the main outcome were excluded from analyses requiring classification of congenital anomalies.\u003c/p\u003e \u003cp\u003eSINASC contains demographic, maternal, gestational, and neonatal information routinely collected at birth, including maternal age, maternal education, race/skin color, marital status, prenatal care, gestational characteristics, neonatal sex, Apgar scores, and congenital anomalies recorded on the DNV.\u003c/p\u003e\n\u003ch3\u003eOutcome definition\u003c/h3\u003e\n\u003cp\u003eThe main outcome was the presence of congenital anomalies among live births, as recorded in SINASC. For descriptive and multivariable analyses, the outcome was treated as a binary variable (yes/no). For temporal analyses by anomaly group, congenital anomalies were classified according to ICD-10 groups recorded in the birth registry. The groups analyzed were: chromosomal abnormalities, circulatory system, cleft lip and cleft palate, digestive system, eye, ear, face and neck, genital organs, musculoskeletal system, nervous system, respiratory system, urinary system, and others.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eMaternal, pregnancy, and neonatal variables were selected based on data availability in SINASC and their epidemiological relevance. The descriptive analysis included prenatal care (yes/no), adolescent pregnancy, maternal age group, maternal education, marital status, maternal race/skin color, sex of the newborn, type of pregnancy, and Apgar scores at 1 and 5 minutes.\u003c/p\u003e \u003cp\u003eFor the adjusted logistic regression, the following variables were included: maternal education, maternal race/ethnicity, sex of the newborn, maternal age, paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies. Maternal and paternal ages were analyzed as continuous variables in the multivariable model, while the remaining variables were entered as categorical variables.\u003c/p\u003e \u003cp\u003eFor temporal comparisons by epidemiological period, four periods were defined a priori based on major public health contexts: Zika period (2015\u0026ndash;2017), baseline period (2018\u0026ndash;2019), COVID-19 period (2020\u0026ndash;2022), and post-COVID period (2023).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAbsolute and relative frequencies were calculated for maternal, pregnancy, and neonatal characteristics according to the presence of congenital anomalies. Differences between groups were assessed using Pearson\u0026rsquo;s chi-square test.\u003c/p\u003e \u003cp\u003eAnnual prevalence rates of congenital anomalies were calculated as the number of cases divided by the total number of live births in each year, expressed per 10,000 live births. Corresponding 95% confidence intervals (95% CI) were estimated assuming a Poisson distribution for the number of cases.\u003c/p\u003e \u003cp\u003eTo compare rates across predefined epidemiological periods, incidence rate ratios (IRR) and 95% confidence intervals were estimated using count regression models with the logarithm of the number of live births included as an offset. The baseline period (2018\u0026ndash;2019) was used as the reference category. Poisson regression models were initially fitted; when overdispersion was detected, negative binomial regression was adopted as the main inferential model.\u003c/p\u003e \u003cp\u003eTo formally assess annual temporal trends according to ICD-10 anomaly group, separate count regression models were fitted for each anomaly group, using year as the independent variable and the logarithm of live births as an offset. Incidence rate ratios per year (IRR) and 95% confidence intervals were estimated. Poisson models were initially considered, and negative binomial regression was used when overdispersion was identified. An IRR greater than 1 indicated an increasing annual trend, whereas an IRR lower than 1 indicated a decreasing annual trend.\u003c/p\u003e \u003cp\u003eFactors associated with congenital anomalies were evaluated using adjusted logistic regression models. Odds ratios (OR), 95% confidence intervals, and p-values were estimated. Variables were included in the multivariable model according to epidemiological relevance and availability in the dataset. The final model included maternal education, maternal race/ethnicity, sex of the newborn, maternal age, paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies.\u003c/p\u003e \u003cp\u003eThis study used secondary, de-identified data from a population-based information system, with no direct contact with participants. Because the analyses were based on routinely collected administrative data, no individual informed consent was required.\u003c/p\u003e \u003cp\u003eAll data management, statistical analyses, and graph construction were performed using the R statistical software.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eBetween 2015 and 2023, a total of 4,311,399 live births were analyzed in S\u0026atilde;o Paulo, Brazil, of which 53,113 (1.23%) presented congenital anomalies. Prenatal care coverage was high overall (99.02%). However, the absence of prenatal care was more frequent among mothers of newborns with congenital anomalies than among those without anomalies (1.14% vs. 0.97%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaternal, pregnancy, and neonatal characteristics according to the presence of congenital anomalies, S\u0026atilde;o Paulo, Brazil, 2015\u0026ndash;2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eCongenital Anomalies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrenatal care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3384870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3342848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdolescent pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3810414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3763377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e512891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e512891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3038187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3003567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e614107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e605164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2772665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2740292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1239193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1222236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e303166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1791922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1770020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1751958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1731022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal race/skin color\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2492059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2461980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1514573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1496116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e259855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNewborn sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2215758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2185973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2113163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2090294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4218765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4167564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriplet or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApgar score at 1 minute\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2971299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2941805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e333521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApgar score at 5 minutes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4184439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4138504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdolescent pregnancy differed significantly between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Among live births with congenital anomalies, 11.61% occurred among adolescent mothers, compared with 11.85% among those without anomalies.\u003c/p\u003e \u003cp\u003eMaternal age distribution was significantly associated with congenital anomalies (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among newborns with congenital anomalies, 65.05% were born to mothers aged 20\u0026ndash;34 years, 16.80% to mothers aged 35\u0026ndash;39 years, 11.61% to mothers aged 10\u0026ndash;19 years, and 6.53% to mothers aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years. Compared with live births without anomalies, the anomaly group showed a higher proportion of advanced maternal age, particularly among mothers aged 35\u0026ndash;39 years (16.80% vs. 13.98%) and \u0026ge;\u0026thinsp;40 years (6.53% vs. 3.57%).\u003c/p\u003e \u003cp\u003eMaternal education was also significantly associated with congenital anomalies (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among mothers of newborns with congenital anomalies, 60.88% had completed secondary education, 31.89% had higher education, and 7.06% had elementary education. Compared with births without anomalies, higher education was more frequent in the anomaly group (31.89% vs. 28.27%), whereas completed secondary education was less frequent (60.88% vs. 63.39%).\u003c/p\u003e \u003cp\u003eMarital status differed significantly according to congenital anomaly status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among newborns with congenital anomalies, 49.64% were born to married mothers and 47.45% to single mothers. Compared with births without anomalies, the anomaly group showed slightly higher proportions of divorced (1.90% vs. 1.54%) and separated mothers (0.63% vs. 0.47%).\u003c/p\u003e \u003cp\u003eRegarding maternal race/skin color, significant differences were observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among births with congenital anomalies, 56.78% occurred among White mothers, 34.84% among Brown mothers, and 7.66% among Black mothers. Compared with births without anomalies, Black mothers were proportionally more represented in the anomaly group (7.66% vs. 6.05%), whereas White mothers were slightly less represented (56.78% vs. 57.31%).\u003c/p\u003e \u003cp\u003eMale newborns were more frequent among those with congenital anomalies compared with those without anomalies (55.97% vs. 50.49%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Singleton pregnancies predominated in both groups, although multiple pregnancies were proportionally more frequent among births with anomalies, including twin pregnancies (3.60% vs. 2.42%) and triplet or higher-order pregnancies (0.13% vs. 0.06%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eNeonatal vitality differed markedly according to congenital anomaly status. At 1 minute, Apgar scores of 8\u0026ndash;10 were less frequent among newborns with anomalies than among those without anomalies (71.65% vs. 88.08%), whereas scores of 3\u0026ndash;7 (22.66% vs. 9.99%) and 0\u0026ndash;2 (5.69% vs. 0.70%) were more common in the anomaly group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, at 5 minutes, Apgar scores of 8\u0026ndash;10 were less frequent among newborns with anomalies (89.45% vs. 97.22%), while scores of 3\u0026ndash;7 (7.92% vs. 1.40%) and 0\u0026ndash;2 (2.63% vs. 0.18%) were more frequent (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003eThe annual rates of congenital anomalies per 10,000 live births in S\u0026atilde;o Paulo between 2015 and 2023 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of 53,113 cases were identified during the study period, with yearly counts ranging from 5,373 cases in 2021 to 6,511 cases in 2018.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual number of cases, live births, and rates of congenital anomalies per 10,000 live births in S\u0026atilde;o Paulo, Brazil, 2015\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLive births\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRate per 10,000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.15\u0026ndash;106.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500,842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121.77\u0026ndash;127.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e513,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122.34\u0026ndash;128.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.51\u0026ndash;130.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e491,874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113.50\u0026ndash;119.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468,965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116.77\u0026ndash;123.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e449,442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116.37\u0026ndash;122.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e439,662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.62\u0026ndash;131.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e435,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140.38\u0026ndash;147.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe lowest rate was observed in 2015 (103.90 per 10,000 live births; 95%CI 101.15\u0026ndash;106.72). Rates increased in the following years, reaching 124.85 in 2016 and 125.38 in 2017. In 2018, the rate rose to 127.59 per 10,000 live births, followed by a decrease in 2019 (116.49).\u003c/p\u003e \u003cp\u003eDuring the COVID-19 period, rates remained relatively stable, with 119.88 per 10,000 live births in 2020 and 119.55 in 2021. An increase was observed in 2022 (127.94). The highest rate in the entire series occurred in 2023, reaching 143.92 per 10,000 live births (95%CI 140.38\u0026ndash;147.53).\u003c/p\u003e \u003cp\u003eThe temporal distribution of congenital anomalies by ICD-10 group is presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with the corresponding annual numerical values shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Overall, musculoskeletal anomalies consistently presented the highest birth prevalence throughout the study period, ranging from 34.67 per 10,000 live births in 2015 to 41.43 in 2023 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Circulatory system anomalies showed the second highest prevalence and a clear upward trend over time, increasing from 16.64 per 10,000 live births in 2015 to 31.79 in 2023. The total prevalence of congenital anomalies also increased over the study period, rising from 103.90 per 10,000 live births in 2015 to 143.92 in 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA particularly notable pattern was observed for circulatory system anomalies, which showed a progressive increase over time, with a more marked rise during the later years of the series (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). After relatively high levels already observed between 2016 and 2018, prevalence remained above 21 per 10,000 live births during 2019\u0026ndash;2021 and continued to increase thereafter, reaching 26.07 in 2022 and peaking at 31.79 in 2023. This sustained increase contrasts with the more stable patterns observed for most other anomaly groups and represents the most pronounced upward trend among the major congenital anomaly categories.\u003c/p\u003e \u003cp\u003eAmong the remaining major groups, nervous system anomalies showed an early peak in 2016 (16.41 per 10,000 live births), followed by a decline and subsequent stabilization between 2020 and 2023, when rates ranged from 10.21 to 11.32 per 10,000 live births (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Eye, ear, face and neck anomalies remained relatively stable over the series, varying from 8.38 in 2020 to 11.07 in 2023. Genital organ anomalies also showed limited fluctuation, with rates generally around 8\u0026ndash;10 per 10,000 live births across the study period. Digestive system anomalies increased over time, particularly in the final year, rising from 10.19 in 2022 to 16.51 in 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe faceted visualization further highlights the temporal patterns of less frequent anomaly groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cleft lip and cleft palate remained relatively stable, fluctuating between 5.55 and 7.21 per 10,000 live births. Chromosomal abnormalities varied within a narrow range, from 4.41 to 5.91 per 10,000 live births. Other anomalies showed a slight overall decline, from 4.80 in 2015 to 3.95 in 2023. Respiratory system anomalies remained the least frequent group throughout the series, ranging from 1.17 to 2.21 per 10,000 live births, while urinary system anomalies showed low but slightly increasing rates in the final year, reaching 3.42 per 10,000 live births in 2023 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the six groups with the highest mean prevalence were plotted together with the total prevalence, the overall pattern became more evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Musculoskeletal anomalies remained the dominant group across all years, whereas circulatory system anomalies exhibited the most consistent and pronounced increase over time. The trajectory of the total prevalence curve closely followed the combined influence of these trends, with a marked rise observed in 2023. Taken together, these patterns suggest that the recent increase in the overall prevalence of congenital anomalies was driven primarily by increases in circulatory and digestive anomalies, with a smaller contribution from musculoskeletal anomalies.\u003c/p\u003e \u003cp\u003eBetween 2015 and 2023, the rate of congenital anomalies ranged from 117.94 to 143.92 per 10,000 live births across epidemiological periods. The baseline period (2018\u0026ndash;2019) presented a rate of 122.15 per 10,000 live births. During the Zika period (2015\u0026ndash;2017), the rate was slightly lower (117.94 per 10,000), whereas the COVID-19 period (2020\u0026ndash;2022) showed a rate comparable to baseline (122.38 per 10,000). The highest rate was observed in the post-COVID period (2023), reaching 143.92 per 10,000 live births (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRates of congenital anomalies per 10,000 live births and incidence rate ratios (IRR) according to epidemiological periods, S\u0026atilde;o Paulo, Brazil, 2015\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePanel A. Rates per 10,000 live births\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLive births (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRate per 10,000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,002,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZika\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,533,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,358,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-COVID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B. Incidence rate ratios (IRR) compared to baseline (2018\u0026ndash;2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\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\u003eZika (2015\u0026ndash;2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.944\u0026ndash;0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0027\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\u003eCOVID-19 (2020\u0026ndash;2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.979\u0026ndash;1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\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\u003ePost-COVID (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.143\u0026ndash;1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNegative binomial regression (final model due to overdispersion)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\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\u003eZika (2015\u0026ndash;2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u0026ndash;1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.523\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\u003eCOVID-19 (2020\u0026ndash;2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u0026ndash;1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950\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\u003ePost-COVID (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.028\u0026ndash;1.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn regression analyses, Poisson models suggested a lower rate during the Zika period (IRR\u0026thinsp;=\u0026thinsp;0.966; 95%CI 0.944\u0026ndash;0.988; p\u0026thinsp;=\u0026thinsp;0.003) and a substantially higher rate in 2023 (IRR\u0026thinsp;=\u0026thinsp;1.178; 95%CI 1.143\u0026ndash;1.215; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with baseline, while no significant difference was observed during the COVID-19 period (IRR\u0026thinsp;=\u0026thinsp;1.002; 95%CI 0.979\u0026ndash;1.026; p\u0026thinsp;=\u0026thinsp;0.872). However, because overdispersion was detected, negative binomial regression was considered the primary model. In this model, neither the Zika period (IRR\u0026thinsp;=\u0026thinsp;0.967; 95%CI 0.873\u0026ndash;1.072; p\u0026thinsp;=\u0026thinsp;0.523) nor the COVID-19 period (IRR\u0026thinsp;=\u0026thinsp;1.003; 95%CI 0.905\u0026ndash;1.112; p\u0026thinsp;=\u0026thinsp;0.950) differed significantly from baseline. In contrast, the post-COVID period remained significantly associated with a higher rate of congenital anomalies (IRR\u0026thinsp;=\u0026thinsp;1.179; 95%CI 1.028\u0026ndash;1.353; p\u0026thinsp;=\u0026thinsp;0.0188), corresponding to an approximate 18% increase compared to the baseline period.\u003c/p\u003e \u003cp\u003eTo formally assess temporal trends in congenital anomaly groups, count regression models with an offset for the number of live births were fitted for each group, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant increasing trends were observed for digestive system anomalies, circulatory system anomalies, chromosomal abnormalities, and musculoskeletal system anomalies. Digestive system anomalies showed the steepest increase over the study period, with an average annual increase of 9.8% (IRR\u0026thinsp;=\u0026thinsp;1.098; 95%CI 1.051\u0026ndash;1.149; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Circulatory system anomalies increased by 5.8% per year on average (IRR\u0026thinsp;=\u0026thinsp;1.058; 95%CI 1.038\u0026ndash;1.079; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chromosomal abnormalities showed a smaller but significant annual increase of 2.3% (IRR\u0026thinsp;=\u0026thinsp;1.023; 95%CI 1.002\u0026ndash;1.044; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), while musculoskeletal system anomalies increased by 1.3% per year (IRR\u0026thinsp;=\u0026thinsp;1.013; 95%CI 1.003\u0026ndash;1.023; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual temporal trends in congenital anomaly groups, S\u0026atilde;o Paulo, Brazil, 2015\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongenital anomaly group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRR per year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.051\u0026ndash;1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirculatory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.038\u0026ndash;1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u0026ndash;1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosomal abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002\u0026ndash;1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.003\u0026ndash;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenital organs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u0026ndash;1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u0026ndash;1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye, ear, face and neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u0026ndash;1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCleft lip and cleft palate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.974\u0026ndash;1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoisson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u0026ndash;0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative binomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u0026ndash;0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, significant decreasing trends were identified for nervous system anomalies and the group classified as others. Nervous system anomalies decreased by 3.3% per year on average (IRR\u0026thinsp;=\u0026thinsp;0.967; 95%CI 0.940\u0026ndash;0.995; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), while anomalies classified as others decreased by 3.0% annually (IRR\u0026thinsp;=\u0026thinsp;0.970; 95%CI 0.953\u0026ndash;0.988; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eNo statistically significant temporal trends were observed for respiratory system anomalies (IRR\u0026thinsp;=\u0026thinsp;1.033; 95%CI 0.994\u0026ndash;1.073; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093), genital organ anomalies (IRR\u0026thinsp;=\u0026thinsp;1.010; 95%CI 0.993\u0026ndash;1.027; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.259), urinary system anomalies (IRR\u0026thinsp;=\u0026thinsp;1.004; 95%CI 0.976\u0026ndash;1.033; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.764), eye, ear, face and neck anomalies (IRR\u0026thinsp;=\u0026thinsp;1.002; 95%CI 0.978\u0026ndash;1.026; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.884), or cleft lip and cleft palate (IRR\u0026thinsp;=\u0026thinsp;0.994; 95%CI 0.974\u0026ndash;1.015; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.605).\u003c/p\u003e \u003cp\u003eTo analyze the factors associated with congenital anomalies, an adjusted logistic regression model was conducted, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis included maternal education, race/ethnicity, sex of the newborn, maternal and paternal age, adolescent pregnancy, type of pregnancy, prenatal care, and previous pregnancies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMaternal education was not significantly associated with congenital anomalies. Compared with mothers with no schooling, the odds of congenital anomalies were similar among mothers with primary education (OR\u0026thinsp;=\u0026thinsp;1.04; 95%CI 0.54\u0026ndash;2.02; p\u0026thinsp;=\u0026thinsp;0.903), secondary education (OR\u0026thinsp;=\u0026thinsp;1.03; 95%CI 0.53\u0026ndash;1.99; p\u0026thinsp;=\u0026thinsp;0.924), and higher education (OR\u0026thinsp;=\u0026thinsp;1.27; 95%CI 0.66\u0026ndash;2.45; p\u0026thinsp;=\u0026thinsp;0.482).\u003c/p\u003e \u003cp\u003eSignificant differences were observed according to maternal race/ethnicity. Compared with White mothers, higher odds of congenital anomalies were observed among Asian mothers (OR\u0026thinsp;=\u0026thinsp;1.19; 95%CI 1.04\u0026ndash;1.38; p\u0026thinsp;=\u0026thinsp;0.015), mothers of mixed race (Pardo) (OR\u0026thinsp;=\u0026thinsp;1.11; 95%CI 1.07\u0026ndash;1.15; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Black mothers (OR\u0026thinsp;=\u0026thinsp;1.25; 95%CI 1.18\u0026ndash;1.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant association was observed among Indigenous mothers (OR\u0026thinsp;=\u0026thinsp;0.63; 95%CI 0.28\u0026ndash;1.40; p\u0026thinsp;=\u0026thinsp;0.257).\u003c/p\u003e \u003cp\u003eMale newborns had higher odds of congenital anomalies compared with females (OR\u0026thinsp;=\u0026thinsp;1.30; 95%CI 1.26\u0026ndash;1.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Maternal age was positively associated with congenital anomalies, with the odds increasing by approximately 3% for each additional year of maternal age (OR\u0026thinsp;=\u0026thinsp;1.03; 95%CI 1.03\u0026ndash;1.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, paternal age was not significantly associated with congenital anomalies (OR\u0026thinsp;=\u0026thinsp;1.00; 95%CI 1.00\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.103).\u003c/p\u003e \u003cp\u003eAdolescent pregnancy was associated with higher odds of congenital anomalies (OR\u0026thinsp;=\u0026thinsp;1.40; 95%CI 1.32\u0026ndash;1.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding the type of pregnancy, twin pregnancies showed higher odds compared with singleton pregnancies (OR\u0026thinsp;=\u0026thinsp;1.29; 95%CI 1.19\u0026ndash;1.39; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas triplet or higher-order pregnancies were not significantly associated (OR\u0026thinsp;=\u0026thinsp;1.45; 95%CI 0.95\u0026ndash;2.21; p\u0026thinsp;=\u0026thinsp;0.085).\u003c/p\u003e \u003cp\u003ePrenatal care was associated with a lower likelihood of congenital anomalies. Mothers who received prenatal care had reduced odds compared with those who did not receive prenatal care (OR\u0026thinsp;=\u0026thinsp;0.73; 95%CI 0.61\u0026ndash;0.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, the number of previous pregnancies was not significantly associated with congenital anomalies (OR\u0026thinsp;=\u0026thinsp;0.99; 95%CI 0.98\u0026ndash;1.01; p\u0026thinsp;=\u0026thinsp;0.352).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis population-based study of 4,311,399 live births in S\u0026atilde;o Paulo, Brazil, between 2015 and 2023 identified 53,113 cases of congenital anomalies (1.23%), with an overall prevalence increase from 103.90 to 143.92 per 10,000 live births. Temporal trends were heterogeneous across anatomical systems, with significant increases in circulatory and digestive system anomalies, a modest increase in musculoskeletal anomalies, and a decline in nervous system anomalies. These findings add important epidemiological evidence on congenital anomaly surveillance in a large middle-income setting and reinforce the value of system-specific analyses in birth defect registries.\u003c/p\u003e \u003cp\u003eThe 38.5% increase in overall congenital anomaly prevalence observed during the study period is consistent with trends reported by population-based surveillance systems. Analyses from European congenital anomaly registries documented increasing prevalence for several congenital anomaly subgroups over time.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] A recent Brazilian national study also reported lower prevalence than that usually observed in high-income settings, suggesting that differences in prenatal diagnosis, case ascertainment, and registry completeness may partly explain international variation.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Global Burden of Disease analyses indicate that, although age-standardized incidence rates of congenital birth defects have remained relatively stable, the absolute number of affected births has increased, particularly in lower-resource settings.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral factors may explain the increase observed in S\u0026atilde;o Paulo. First, improvements in surveillance quality and reporting completeness likely contributed, as birth defect registries often demonstrate improved case ascertainment over time with greater familiarity among professionals and maturation of information systems.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Second, advances in prenatal diagnosis have substantially increased detection. A population-based study from Western Australia documented a 5.5-fold increase in prenatal diagnosis prevalence over time, especially for cardiovascular, urogenital, and chromosomal anomalies.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Similarly, implementation of more detailed first-trimester anatomical ultrasound protocols has increased detection of major fetal anomalies in large population studies.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Third, demographic shifts, particularly increasing maternal age, may have contributed to rising prevalence, given the well-established association between advanced maternal age and both chromosomal and non-chromosomal anomalies.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Finally, changes in the prevalence of maternal risk factors, including diabetes and obesity, may also influence temporal trends.[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOverall, the prevalence observed in this study falls within the range reported internationally but is higher than previous Brazilian estimates, suggesting either genuine regional differences or improvements in case ascertainment within the S\u0026atilde;o Paulo birth registry system.\u003c/p\u003e \u003cp\u003eOne of the most important findings of this study was the significant annual increase in circulatory system anomalies (IRR 1.058, 95% CI 1.038\u0026ndash;1.079). This corresponds to an average annual increase of approximately 5.8%. Similar patterns have been reported in international surveillance systems. Analyses from the EUROCAT registries documented increasing prevalence for several severe congenital heart defects over time, including atrioventricular septal defects and tetralogy of Fallot.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eImproved prenatal detection through advances in fetal echocardiography likely explains a substantial part of this increase. For example, a nationwide study in the Czech Republic showed that the involvement of pediatric cardiologists in prenatal screening programs significantly improved detection of major congenital heart defects, while overall incidence remained stable.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Similarly, a large systematic review demonstrated that reported birth prevalence of congenital heart disease has increased over time largely due to improvements in diagnostic technologies and screening practices.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePostnatal detection has also improved through widespread implementation of newborn screening programs for critical congenital heart disease using pulse oximetry. In the United States, mandatory newborn screening has been associated with reduced early infant mortality and fewer emergency hospitalizations.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] While these programs improve early identification and outcomes, they may also contribute to increased reported prevalence in surveillance systems.\u003c/p\u003e \u003cp\u003eAt the same time, true increases in incidence cannot be completely excluded. Maternal metabolic conditions such as diabetes and obesity are well-established risk factors for congenital heart defects.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] A large meta-analysis including more than 80\u0026nbsp;million births showed that pre-gestational diabetes was associated with a more than threefold increased risk of congenital heart defects, while gestational diabetes was associated with a moderate but significant increase in risk.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Maternal obesity has also been associated with increased risk of circulatory system malformations.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] In addition, a population-based study from Canada identified maternal diabetes, hypertension, and maternal congenital heart disease as significant predictors of congenital heart defects in offspring.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAnother consideration is the potential role of maternal infections or inflammatory conditions affecting fetal cardiovascular development. Although the ecological nature of the present study precludes causal inference, the temporal overlap with the COVID-19 pandemic raises hypotheses that deserve further investigation. However, establishing such associations would require individual-level exposure data and more detailed analytical approaches.\u003c/p\u003e \u003cp\u003eDigestive system anomalies showed the largest relative increase among all anomaly groups (IRR 1.098, 95% CI 1.051\u0026ndash;1.149). This corresponds to an average annual increase of approximately 9.8% and represents a notable finding that warrants careful interpretation.\u003c/p\u003e \u003cp\u003eGlobal analyses have shown that the overall burden of digestive congenital anomalies has declined over recent decades, largely due to improvements in surgical management and survival rather than changes in birth prevalence.[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] A systematic review reported birth prevalence estimates for major digestive congenital anomalies ranging from 0.86 to 3.11 per 10,000 births.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In contrast, European surveillance data suggest that fluctuations in digestive anomaly prevalence may partly reflect surveillance artifacts rather than sustained epidemiological changes.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral explanations may account for the increase observed in S\u0026atilde;o Paulo. Improvements in prenatal detection through second-trimester ultrasound may increase identification of gastrointestinal anomalies such as duodenal atresia, esophageal atresia, and abdominal wall defects. Improved postnatal diagnosis and notification may also contribute, particularly for anomalies that are not immediately apparent at birth. In addition, more complete follow-up and improvements in registry practices may increase case ascertainment. Although true increases in incidence related to environmental or maternal risk factors cannot be excluded, the evidence supporting such associations remains limited.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMusculoskeletal anomalies showed a small but statistically significant increase over time (IRR 1.013, 95% CI 1.003\u0026ndash;1.023). Although the relative increase was modest, this group represents the largest share of congenital anomalies in many surveillance systems.\u003c/p\u003e \u003cp\u003eGlobal studies consistently report musculoskeletal anomalies among the most prevalent congenital defects, although they generally contribute less to mortality compared with cardiac or neurological anomalies.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] European surveillance data also indicate that trends in musculoskeletal anomalies may be influenced by surveillance artifacts or differences in diagnostic classification across registries.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn S\u0026atilde;o Paulo, the modest increase observed may reflect improved prenatal detection, changes in diagnostic classification, or demographic shifts such as increasing maternal age, rather than a substantial change in underlying risk.\u003c/p\u003e \u003cp\u003eNervous system anomalies showed a significant decreasing trend (IRR 0.967, 95% CI 0.940\u0026ndash;0.995). This corresponds to an average annual reduction of approximately 3.3% and is likely strongly influenced by the Zika virus epidemic that affected Brazil during 2015\u0026ndash;2016.\u003c/p\u003e \u003cp\u003eThe Zika epidemic produced a dramatic temporary increase in congenital neurological anomalies, particularly microcephaly and other manifestations of congenital Zika syndrome.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Multiple studies documented strong associations between maternal Zika infection and microcephaly, neurological abnormalities, and neuroimaging abnormalities in newborns.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] For example, a prospective cohort study conducted in S\u0026atilde;o Paulo State reported that neonatal Zika RT-PCR positivity was associated with a fivefold increased risk of microcephaly.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThus, the declining trend observed in this study likely reflects the peak of Zika-associated neurological anomalies in the early years of the study period followed by gradual normalization after the epidemic subsided.\u003c/p\u003e \u003cp\u003eNo significant temporal trends were observed for genital, urinary, eye/ear/face/neck, cleft lip and palate, or respiratory anomalies. This stability is consistent with international surveillance literature showing that many congenital anomaly groups remain relatively stable over time.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eStable trends may reflect relatively constant detection practices, unchanged etiological exposures, or well-established prenatal screening protocols. For example, cleft lip and palate have long been targeted in prenatal ultrasound screening programs, which may contribute to stable ascertainment.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe multivariable analysis identified several factors associated with congenital anomalies that are consistent with previous epidemiological literature. Male sex, advanced maternal age, adolescent pregnancy, and multiple pregnancy were all associated with higher odds of congenital anomalies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Multifetal pregnancies have been repeatedly associated with increased risk of structural anomalies.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAssociations with maternal race or ethnicity should be interpreted cautiously because they likely reflect complex interactions between social determinants, environmental exposures, and access to healthcare rather than biological differences.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe protective association observed with prenatal care is consistent with evidence that adequate prenatal care facilitates early detection and management of maternal conditions associated with congenital anomalies, including folic acid supplementation and glycemic control.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMaternal metabolic conditions deserve particular attention. Both pre-gestational and gestational diabetes have been associated with increased risk of congenital anomalies overall, especially congenital heart defects.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Maternal obesity is also associated with higher risk of congenital heart defects and neural tube defects, with evidence of a dose\u0026ndash;response relationship with body mass index.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTaken together, the heterogeneous temporal trends across anatomical systems suggest that the overall increase in congenital anomaly prevalence in S\u0026atilde;o Paulo was primarily driven by increases in circulatory and digestive system anomalies, while other groups remained stable or declined. These findings highlight the importance of analyzing congenital anomalies by specific anatomical groups rather than relying solely on overall prevalence estimates.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThese findings have important implications for clinical practice and public health policy. The increasing prevalence of congenital anomalies, particularly cardiac defects, emphasizes the need for robust prenatal screening programs and specialized fetal assessment.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Additionally, universal implementation of newborn screening for critical congenital heart disease can substantially reduce infant mortality.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe results also reinforce the importance of preconception and prenatal interventions targeting modifiable risk factors, including optimal glycemic control in women with diabetes, healthy body weight before pregnancy, and folic acid supplementation.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Furthermore, the experience of the Zika epidemic illustrates the importance of maintaining surveillance systems capable of detecting emerging threats to fetal development.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study has several strengths, including its large population-based sample, long time series, detailed classification of congenital anomalies by anatomical system, and the combination of temporal and multivariable analyses.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. The observational design precludes causal inference. Registry-based studies are subject to potential underreporting and misclassification, and improvements in reporting over time may contribute to apparent increases in prevalence. The lack of individual-level exposure data limits evaluation of specific etiological hypotheses, including infections, medications, environmental exposures, and metabolic factors. The absence of data on pregnancy terminations following prenatal diagnosis may affect prevalence estimates. In addition, the analysis included only live births, which may underestimate the true prevalence of severe congenital anomalies.\u003c/p\u003e \u003cp\u003eFinally, the study did not include data from 2024 or 2025. At the time of data extraction, registry data for 2024 were still incomplete, and their inclusion could have led to underestimation of prevalence and distortion of temporal comparisons.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis large population-based study documented a substantial increase in congenital anomaly prevalence in S\u0026atilde;o Paulo, Brazil, from 2015 to 2023, driven mainly by increases in circulatory and digestive system anomalies. The heterogeneous trends across anatomical systems suggest that improvements in detection and reporting likely contributed to the observed increase, although changes in maternal risk factors may also have played a role. The declining trend in nervous system anomalies is plausibly explained by the temporal pattern of the Zika epidemic. These findings highlight the importance of system-specific congenital anomaly surveillance, continued strengthening of birth defect registries, and public health strategies targeting modifiable maternal risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using secondary, anonymized data obtained from the Brazilian Live Birth Information System (SINASC), publicly available through the Ministry of Health. According to Brazilian regulations, studies based exclusively on publicly available and de-identified data do not require approval from a Research Ethics Committee or informed consent from participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the Brazilian Ministry of Health through the DATASUS platform: https://datasus.saude.gov.br/\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially supported by Centro Universit\u0026aacute;rio de Adamantina, Adamantina, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, F.A.V.; methodology, F.A.V. and D.C.V.-V.; formal analysis, F.A.V.; investigation, F.A.V., D.C.V.-V., L.M.S., A.C.C., G.M.M., M.E.R.P., M.E.C.L., A.C.B.S., and B.L.P.N.; writing\u0026mdash;original draft preparation, F.A.V., D.C.V.-V., and A.C.C.; writing\u0026mdash;review and editing, F.A.V. and D.C.V.-V.; visualization, F.A.V., D.C.V.-V., L.M.S., A.C.C., G.M.M., M.E.R.P., M.E.C.L., A.C.B.S., and B.L.P.N.; supervision, F.A.V.; project administration, F.A.V. and D.C.V.-V.; funding acquisition, F.A.V. and D.C.V.-V. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu H, Chen K, Wang T, et al. Emerging Trends and Cross-Country Health Inequalities in Congenital Birth Defects: Insights From the GBD 2021 Study. Int J Equity Health. 2025;24(1):50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12939-025-02412-7\u003c/span\u003e\u003cspan address=\"10.1186/s12939-025-02412-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang L, Cao G, Jing W, Liu J, Liu M. Global, Regional, and National Incidence and Mortality of Congenital Birth Defects From 1990 to 2019. 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PLoS ONE. 2021;16(5):e0252343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0252343\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0252343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"congenital anomalies, live births, temporal trends, maternal factors, surveillance, São Paulo, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-9323088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9323088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCongenital anomalies are a major cause of infant morbidity, mortality, and long-term disability worldwide. Their occurrence is influenced by maternal, demographic, and healthcare-related factors, and temporal patterns may vary according to anomaly group and surveillance quality. In Brazil, population-based analyses remain limited, especially those examining overall prevalence together with system-specific trends.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis population-based retrospective study used data from the Brazilian Live Birth Information System (SINASC). All live births registered in S\u0026atilde;o Paulo between 2015 and 2023 were eligible. Annual prevalence rates of congenital anomalies were calculated per 10,000 live births with 95% confidence intervals (95% CI). Maternal, pregnancy, and neonatal characteristics were compared according to congenital anomaly status using Pearson\u0026rsquo;s chi-square test. Temporal comparisons across epidemiological periods were performed using count regression models with offset for live births, and incidence rate ratios (IRR) were estimated. Poisson models were initially fitted, and negative binomial regression was used when overdispersion was detected. Annual trends by anomaly group were also evaluated, and factors associated with congenital anomalies were assessed using adjusted logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 4,311,399 live births were analyzed, of which 53,113 (1.23%) presented congenital anomalies. Overall prevalence increased from 103.90 per 10,000 live births in 2015 to 143.92 in 2023, the highest rate in the series. Compared with the baseline period (2018\u0026ndash;2019), the post-COVID period (2023) showed a significantly higher rate in the negative binomial model (IRR\u0026thinsp;=\u0026thinsp;1.179; 95% CI 1.028\u0026ndash;1.353; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0188), while no significant differences were found for the Zika or COVID-19 periods. Musculoskeletal anomalies were the most frequent group throughout the series, whereas circulatory anomalies showed a marked increase, from 16.64 to 31.79 per 10,000 live births. Digestive anomalies showed the largest annual increase (IRR\u0026thinsp;=\u0026thinsp;1.098; 95% CI 1.051\u0026ndash;1.149; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by circulatory anomalies (IRR\u0026thinsp;=\u0026thinsp;1.058; 95% CI 1.038\u0026ndash;1.079; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Nervous system anomalies decreased over time (IRR\u0026thinsp;=\u0026thinsp;0.967; 95% CI 0.940\u0026ndash;0.995; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Higher odds of congenital anomalies were observed among male newborns, older mothers, adolescent pregnancies, and multiple pregnancies, while prenatal care was associated with lower odds.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCongenital anomaly prevalence increased in S\u0026atilde;o Paulo between 2015 and 2023, driven mainly by circulatory and digestive system anomalies. These findings highlight the importance of continuous population-based surveillance and system-specific trend analysis.\u003c/p\u003e","manuscriptTitle":"Temporal trends and maternal factors associated with Congenital Anomalies among live births in São Paulo, Brazil: A population-based study, 2015–2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 02:33:08","doi":"10.21203/rs.3.rs-9323088/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T14:21:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:34:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T01:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208647964588329834451240032517360092006","date":"2026-05-06T20:33:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3894771008415070450099875119873888065","date":"2026-05-05T22:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179690944707020357748797675044966327022","date":"2026-05-05T19:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T19:26:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T06:06:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T04:53:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T04:52:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-04-04T21:40:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f20b42b-0afe-4972-a855-572a9aa04ed8","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T14:21:56+00:00","index":46,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:34:59+00:00","index":45,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T01:11:38+00:00","index":44,"fulltext":""},{"type":"reviewerAgreed","content":"208647964588329834451240032517360092006","date":"2026-05-06T20:33:21+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"3894771008415070450099875119873888065","date":"2026-05-05T22:12:12+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"179690944707020357748797675044966327022","date":"2026-05-05T19:30:17+00:00","index":33,"fulltext":""},{"type":"reviewersInvited","content":"15","date":"2026-05-05T19:26:22+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T02:33:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 02:33:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9323088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9323088","identity":"rs-9323088","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}