Nutritional status, gestational weight gain and associated factors of pregnant women in the city of São Paulo, 2012 to 2020: a cohort study

Nutritional status, gestational weight gain and associated factors of pregnant women in the city of São Paulo, 2012 to 2020: a cohort study | 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 Nutritional status, gestational weight gain and associated factors of pregnant women in the city of São Paulo, 2012 to 2020: a cohort study Fernanda Ferreira CORRÊA, Eliana de Aquino BONILHA Ms, Wesley Pereira da SILVA Mr, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4874735/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 4 You are reading this latest preprint version Abstract Background Gestational weight gain (GWG) is a critical issue related to the type of delivery and postpartum health in newborns and mothers. In Brazil, pregnant women's public health recommends monitoring GWG. Therefore, the objective of this study is to evaluate gestational weight gain and associated health factors of pregnant women monitored at SUS in the city of São Paulo between 2012 and 2020. Methods This is a cohort study of pregnant women seen from 2012 to 2020 in São Paulo, Brazil. The database used was from the Integrated Health Care Management System related to the Live Birth Information System. The variables used were: mother's height, mother's date of birth (used to calculate mother's age), type of pregnancy, gestational weeks, type of delivery, weight at the time of appointment, mother´s race/skin color, number of prenatal consultations, mother's marital status, and mother's education level, initial weight, final weight, initial gestational age, final gestational age, and initial and final BMI. Inclusion criteria considered that pregnant women had a recorded initial weight before 13 weeks and up to 15 days before delivery and a single pregnancy. The final database includes 276.220 pregnant women. Results The frequency of women according to initial BMI was 12.004 (4.4%) underweight, 132.049 (48.3%) normal weight, 78.856 (28.8%) overweight, and 50.660 (18.5%) living with obesity. The population consisted of 59.881 (21.9%), 37.217 (13.6%) and 176.471 (64.5%) women with LWG, AWG and EWG, respectively. Weight gain was associated with initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits. Conclusion The proportion of pregnant women with inadequate weight gain is high, relating initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits. Interventions such as nutritional education should be suggested to help achieve adequate GWG. Nutritional Status Pregnant Women Gestational Weight Gain Nutritional Status Cohort Studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Women’s nutritional status and gestational weight gain (GWG) during pregnancy are critical issues related to the type of delivery and postpartum newborn and mothers’ health [ 1 – 3 ]. Considering nutritional status, body mass index (BMI) is the most monitored parameter used during pregnancy [ 4 ]. Previous studies showed that initial BMI is a strong predictor of GWG and that GWG needs to be adjusted according to the category of women's BMI [ 4 , 5 ]. In Brazil, pregnant women's public health recommends monitoring GWG during the gestational period [ 6 ]. Brazil has a health service called "Sistema Único de Saúde," known as SUS, which provides health-related care to the entire Brazilian population free of charge, including people in social vulnerability[ 7 , 8 ]. The SUS offers prenatal care with several health professionals to monitor and prevent mother and newborn morbidity and mortality [ 8 ]. Pregnant women's GWG reflects maternal physiological adaptations and the growth of the fetus, placenta, and accumulation of amniotic fluid. Thus, GWG is a critical factor to monitor during pregnancy [ 9 ]. Until recently, Atalah et al. [ 10 ] charts combined with the 2009 US Institute of Medicine [ 11 ] were applied to GWG recommendations. The International Fetal and Newborn Growth Consortium for the 21st Century [ 12 ] GWG standards for publication were also used. However, they are limited to women classified as normal weight, and rely on a weight measured between 9 and 14 gestational weeks for GWG calculation, which decreases their utility for monitoring weight gain in the first trimester (the charts start at the 14th gestational week). Kac et al. (2021) [ 13 ] developed new GWG charts according to initial BMI for Brazilian women that were adopted into the Brazilian healthcare system. Kac's charts exclude pregnancies that delivered preterm (< 37 weeks), small ( 95th) for gestational age. Delivered infants with low birth weight (weight 4000 g) were also excluded. Moreover, the authors included only 10 to 40 weeks of pregnancy to ensure our estimates had reasonable statistical precision. Despite the increase in research on GWG, there is a notable scarcity of data on the relationship between the nutritional status of pregnant women in the city of São Paulo. As such, we aimed to evaluate gestational weight gain and associated factors of pregnant women monitored at primary care of SUS in the city of São Paulo between 2012 and 2020. Methods Study type and data acquisition This is a cohort study from 2012 to 2020 that used data derived from the Primary Health Information System (SIGA) of the city of São Paulo. From the SIGA, we obtained data on pregnant women. Moreover, we linked this dataset to the Live Birth Information System (SINASC). From SINASC, we obtained data about pregnancy and childbirth characteristics. From both databases, we extracted raw data (e.g., the mother's weight and height, birth date, type of pregnancy, gestational weeks, type of birth, race/skin color, number of antenatal care visits, marital status, and schooling level). From the raw data, we created new variables, including initial weight, final weight, initial gestational age, final gestational age, initial body mass index (BMI), and final BMI. The raw data and transformations used are described in supplementary material 1 . Inclusion and exclusion criteria Inclusion criteria considered that pregnant women who underwent prenatal care in the City of São Paulo from 2012 to 2020 had a record of initial weight before 13 weeks and up to 15 days before delivery and had a single pregnancy. The exclusion criteria were pregnant women without a weight record 13 weeks from the beginning of pregnancy or up to 15 days before the birth, multiple pregnancies, newborns with congenital anomalies, and birth at below 18 or beyond 40 weeks of gestation. According to Kac et al.[ 13 ], the data included also considered the identification of gestational week and GWG. Incomplete data and extreme data (> six standard deviations) were excluded. This study uses data obtained from health services, and therefore, adequate data curation is essential to avoid mistaken estimates. Health professionals who collect anthropometric data are previously trained for the function; however, typing errors and other technical problems lead to incorrect data that was excluded from the analysis. Ethical aspects This study is part of the research project entitled “Como tornar as intervenções no parto e seus desfechos mais visíveis aos sistemas de informação?” [“How can childbirth interventions and their outcomes be more visible to information systems?”] (project with funding already approved in the Call for Data Science for Maternal and Child Health CNPq/Bill & Melinda Gates Foundation/2020/2022), approved by the Research Ethics Committee of the Municipal Department of Health of São Paulo, under number 4.829.5. Statistical analysis Data distribution was assessed using the Kolmogorov-Smirnov test. Comparisons between groups according to weight gain (Low Weight Gain - LWG vs Adequate Weight Gain - AWG vs Excessive Weight Gain- EWG) were performed using analysis of variance (ANOVA) with Tukey post hoc. In cases of heterogeneous variances, Welch’s correction with Games-Howell post hoc was chosen. Associations between weight gain and descriptive variables were analyzed using the Chi-square test or Fisher's Exact Test. Finally, multinomial logistic regression models were designed, considering gestational weight gain as the outcome (with AWG as a reference). The independent variables were initial BMI (underweight, overweight, obese vs normal weight), education (basic 1, basic 2, high school, incomplete college, complete college vs no education), marital status (single, stable union, divorced and widowed vs married), race/skin color (black, yellow, mixed race, indigenous vs white), number of health service consultations (1 to 3, 4 to 6, 7 or more vs none) and age (younger than 15 years old, 15 to 19 years old, 35 to 49 years old and over 49 years old vs 20 to 34 years old). The alpha error adopted to reject the null hypothesis was 5%. Data are presented as mean, standard deviation, odds ratios and the 95% confidence interval. JAMOVI software was used. Results The initial database contains 352.937 records. Nevertheless, 46.760 women were excluded because they did not meet the inclusion criteria (final gestational age 45 weeks), were multiparous, or whose children had an inborn error. Then, 4.315 were excluded because they presented incorrect values, and 1.029 were excluded because body mass, BMI or height presented extreme values ( than six standard deviations). Furthermore, 24.629 were excluded because their gestational age was 40 weeks, in disagreement with the criteria proposed by Kac et al. Finally, 2635 were excluded because the weight change (variation) did not correspond to the proposal by Kac et al. Figure 1 presents the flowchart of the participants included in the present study. Figure 1 . Flowchart of research participants in the present study. Insert Fig. 1 . Table 1 displays the characteristics of women during the gestational period in the city of São Paulo between 2012 and 2020. Welch's correction was applied for most comparisons of continuous variables. The frequency of women according to BMI was 12.004 (4.4%) underweight, 132.049 (48.3%) normal weight, 78.856 (28.8%) overweight and 50.660 (18.5%) living with obesity. Table 1 Sample characteristics - women in the gestational period between 2012 and 2020 in the city of São Paulo (n = 273.569) Variable LWG AWG EWG p-value Age (years) (mean/SD) 26.7 ± 6.98 25.8 ± 6.86 26.4 ± 6.68 < 0.001 < 15 years old (n; %) 1184 (2.0) 890 (2.4) 2.998 (1.7) 49 years old (n; %) 0 (0) 1 (0.0) 3 (0.0) Body weight (kg) (mean/SD) 68.9 ± 16.50 59.3 ± 8.98 66.2 ± 14.41 < 0.001 Initial BMI (kg/m 2 ) (mean/SD) 26.8 ± 5.99 23.2 ± 3.02 25.7 ± 5.23 < 0.001 Initial BMI < 0.001 Underweight (n; %) 2.922 (4.9) 1.528 (4.1) 7.554 (4.3) Normal weight (n; %) 24.174 (40.4) 26.177 (70.3) 81.698 (46.3) Overweight (n; %) 17.842 (29.8) 9.512 (25.6) 51.502 (29.2) Obesity (n; %) 14.943 (25.0) 0 (0) 35.717 (20.2) Gestational BMI (kg/m 2 ) (n; %) 27.88 ± 5.49 26.46 ± 2.83 30.87 ± 4.91 < 0.001 Final body weight (kg) (n; %) 71.61 ± 15.23 67.57 ± 8.55 79.53 ± 13.77 < 0.001 Δ (Change; kg) (n; %) 2.69 ± 5.07 8.25 ± 1.31 13.31 ± 4.16 < 0.001 Initial gestational age (w) (n; %) 8.63 ± 2.07 8.62 ± 2.07 8.49 ± 2.06 0.020 Final gestational age (w) (n; %) 35.09 ± 6.73 37.34 ± 4.55 38.32 ± 3.60 < 0.001 Type of birth < 0.001 Vaginal delivery (n; %) 38.477 (64.3) 25.980 (69.8) 107.029 (60.6) Cesarean delivery (n; %) 21.404 (35.7) 11.237 (30.2) 69.442 (39.4) Marital status < 0.001 Single (n; %) 31.996 (53.5) 20.909 (56.2) 91.741 (52.0) Married (n; %) 12.683 (21.2) 6.966 (18.7) 39.632 (22.5) Stable union (marriage not officialized by the government) (n; %) 14.347 (24.7) 8.830 (23.7) 42.447 (24.1) Divorced (n; %) 746 (1.2) 454 (1.2) 2.348 (1.3) Widow (n; %) 89 (0.1) 46 (0.1) 244 (0.1) Schooling level < 0.001 Without (n; %) 96 (0.2) 46 (0.1) 173 (0.1) Basic studies 1 (n; %) 1704 (2.8) 864 (2.3) 3.676 (2.1) Basic studies 2 (n; %) 13.865 (23.2) 8.149 (21.9) 34.390 (19.5) High school (n; %) 38.393 (64.1) 24.860 (66.8) 119.706 (67.9) Incomplete college (n; %) 2.531 (4.2) 1.467 (3.9) 8.233 (4.7) Complete college (n; %) 3.280 (5.5) 1.818 (4.9) 10.242 (5.8) Race/skin color < 0.001 White (n; %) 21.495 (35.9) 13.631 (36.6) 64.263 (36.4) Black (n; %) 6.020 (10.1) 3.277 (8.8) 15.897 (9.0) Yellow (n; %) 232 (0.4) 222 (0.6) 783 (0.4) Brown (n; %) 31.958 (53.4) 19.972 (53.7) 95.103 (53.9) Indigenous (n; %) 169 (0.3) 111 (0.3) 413 (0.2) Antenatal care visits (n; %) < 0.001 0 77 (0.1) 31 (0.1) 104 (0.1) 1–3 1.942 (3.2) 623 (1.7) 1.711 (1.0) 4–6 10.292 (17.2) 5.383 (14.5) 16.609 (9.4) ≥ 7 47.538 (79.4) 31.172 (83.8) 157.995 (89.6) Legend : kg: kilograms; kg/m 2 : kilograms per square meter; n = number of observations in absolute values; % proportion of the number of observations; Δ difference between the final and initial (change); w: weeks. Analysis of variance (ANOVA) with Tukey post hoc. In cases of heterogeneous variances, Welch correction with Games-Howell post hoc was chosen. Associations between weight gain and descriptive variables were analyzed using the Chi-square test or Fisher's Exact Test. The alpha error adopted to reject the null hypothesis was 5%. Insert Table 1 Figure 2 presents the frequency of pregnant women according to gestational age at birth and BMI. The sample consisted of 59.881 (21.9%), 37.217 (13.6%) and 176.471 (64.5%) women with LWG, AWG and EWG, respectively. Figure 3 shows the distribution of pregnant women according to gestational age at birth and weight gain during pregnancy according to Kac et al. [ 13 ] criteria. Insert Figs. 2 and 3 . Age Age (W (84.853, 2) = 226; p < 0.001) differed between groups. Women in the AWG group are younger than LWG (MD: -0.96; p < 0.001) and EWG (MD: -0.59; p < 0.001) groups, while the LWG women group are older than the EWG group (MD: 0.37; p < 0.001). Body weight and BMI The initial (W (100.717, 2) = 9613; p < 0.001) and gestational (W (101.257, 2) = 25076; p < 0.001) body weight differed between the groups. For the initial weight, the AWG group showed lower body weight than LWG (MD: -9.61 kg; p < 0.001) and EWG (MD: -6.91 kg; p < 0.001) groups. Still, the LWG group showed higher body weight than the EWG group (MD: 2.70 kg; p < 0.001). For gestational weight, the AWG group showed lower body weight than the LWG (MD: -4.05 kg; p < 0.001) and the EWG (MD: -11.97 kg; p < 0.001) groups. Moreover, the EWG group exhibited higher body weight than LWG (MD: 7.92 kg; p < 0.001). Likewise, the initial (W (104.118, 2) = 10771; p < 0.001) and gestational (W (104.505, 2) = 29002; p < 0.001) BMI differed between the groups. The initial BMI of the AWG group is lower than the LWG (MD: -3.60 kg/m 2 ; p < 0.001) and EWG (MD: -2.47 kg/m 2 ; p < 0.001) groups, while the BMI of the LWG group is higher in relation to the EWG group (MD: 1.13 kg/m 2 ; p < 0.001). For gestational BMI, the difference between the AWG group and the LWG group reduces (MD: -1.41 kg/m 2 ; p < 0.001), while the difference between the AWG and EWG group increases (MD: -4.41 kg/m 2 ; p < 0.001). Correspondingly, the difference between the BMI of the LWG group and the EWG group changes (MD: -3.00 kg/m 2 ; p < 0.001). The body weight change data (final - initial; delta) between the groups also showed statistical differences (W (127.564,118, 2) = 144.979; p < 0.001). The AWG group presented higher body mass change than the LWG group (MD: 5.56 kg; p < 0.001) and lower body mass change compared to the EWG group (MD: -5.06 kg; p < 0.001). Finally, the LWG group presents a smaller weight difference compared to the EWG group (MD: -10.62 kg; p < 0.001). Figure 4 illustrates initial and gestational body weight, BMI, and body mass change ( Δ) for each group. Insert Fig. 4 . Weight gain was associated with initial BMI (X 2 (273,569.6) = 13.395; p < 0.001), type of birth (X 2 (273,569.2) = 1.183; p < 0.001), race/skin color (X 2 (273.569, 10) ; 99.4; p < 0.001), marital status (X 2 (273.569, 8) = 320; p < 0.001), women's age (X 2 (273.568, 2) = 585; p < 0.001) and antenatal care visits (X 2 (273.477, 6) = 4.630; p < 0.001). Table 2 depicts the factors associated with gestational weight gain. Underweight was associated with LWG (OR: 2.02; CI: 95% 1.89–2.15) and EWG (OR: 1.59; 95% CI: 1.49–1.68). Similarly, overweight was associated with LWG (OR: 2.07; 95% CI: 2.01–2.14) and EWG (OR: 1.74; 95% CI: 1.70–1.79). Finally, obesity was also a factor associated with LWG (OR: 7.48e + 6; 95% CI: 7.48e + 6–7.48e + 6) and EWG (OR: 5.16e + 6; 95% CI: 5.16e + 6–5.16e + 6). Table 2 Factors associated with gestational weight gain during pregnancy between 2012 and 2020 in the city of São Paulo (n = 273.569) LWG vs AWG EWG vs AWG Variable OR 95% IC p-value OR 95% IC p-value Mother's initial BMI (kg/m 2 ) 18.5–24.99 1.00 < 18.5 2.02 1.89–2.15 < 0.001 1.58 1.49–1.68 < 0.001 25.0–29.99 2.07 2.01–2.14 < 0.001 1.74 1.70–1.79 30 7.48e + 6 7.48e + 6–7.48e + 6 < 0.001 5.16e + 6 5.16e + 6–5.16e + 6 < 0.001 Mother's age (years) 20–34 1.00 ≤ 15 1.10 1.00–1.20 0.042 1.016 0.93–1.09 0.696 16–19 1.03 0.99–1.07 0.068 1.028 0.99–1.06 0.083 35–49 0.93 0.89–0.97 < 0.001 0.808 0.78–0.83 49 2.53e- 7 2.53e 7 – 2.53e 7 < 0.001 0.674 0.06–6.57 0.734 Mother's race/skin color White 1.00 Asians 0.78 0.64–0.94 0.013 0.86 0.74–1.00 0.063 Indigenous 1.00 0.78–1.29 0.949 0.86 0.69–1.06 0.175 Brown 1.00 0.97–1.03 0.717 1.01 0.99–1.04 0.229 Black 1.06 1.01–1.11 0.011 0.96 0.91–1.00 0.063 Marital status Married 1.00 Divorced 0.88 0.78–1.00 0.061 0.92 0.82–1.02 0.140 Single 0.99 0.96–1.03 0.845 0.91 0.89–0.94 < 0.001 Stable union 0.98 0.94–1.02 0.400 0.95 0.91–0.98 0.006 Widow 1.04 0.72–1.51 0.800 1.02 0.73–1.41 0.894 Schooling level High School 1.00 No education 1.12 0.77–1.62 0.531 0.77 0.55–1.08 0.144 Basic school 1 1.07 0.98–1.17 0.112 0.82 0.76–0.89 < 0.001 Basic school 2 1.08 1.05–1.12 < 0.001 0.91 0.88–0.94 7 1.00 0 1.89 1.23–2.90 0.003 0.75 0.49–1.12 0.166 1–3 2.22 2.02–2.44 < 0.001 0.59 0.54–0.65 < 0.001 4–6 1.34 1.29–1.39 < 0.001 0.65 0.62–0.67 < 0.001 kg: kg/m 2 : kilograms per square meter; n = number of observations in absolute values; % proportion of the number of observations; Multinominal logistic regression analysis. Outcome: LWG vs. AWG and EWG vs. AWG. The alpha error adopted to reject the null hypothesis was 5%. Insert Table 2 Women aged 49 years also decreased the odds of LWG (OR: 2.53e-7; 95% CI: 2.53e-7–2.53e-7). Considering ethnicity, Asian women showed lower odds for LWG (OR: 0.78; 95% CI: 0.64–0.94), while Black women present higher odds for LWG (OR: 1.06; 95% CI: 1.01–1.11). Moreover, a stable union reduced the odds of EWG (OR: 1.06; 95% CI: 1.01–1.11). Considering schooling level, we observed that basic school 2 (OR: 1.08; 95% CI: 1.05–1.12) and college (OR: 1.08; 95% CI: 1.01–1.15) increased the odds for LWG. Moreover, basic school 1 (OR: 0.82; 95% CI: 0.76–0.89), basic school 2 (OR: 0.91; 95% CI: 0.88–0.94) decreased the odds for EWG. In contrast, incomplete college (OR: 1.09; 95% CI: 1.02–1.14) and complete college (OR: 1.08; 95% CI: 1.02–1.14) increased the odds for EWG. Finally, the number of antenatal care visits was associated with GWG. For instance, considering 7 visits or more as reference, none (OR: 1.89; 95% CI: 1.23–2.90), from one to three (OR: 2.22; 95% CI: 2.02–2.44) and from four to six (OR: 1.32; 95% CI: 1.29–1.39) visits increased the odds for LWG. From one to three (OR: 0.59; 95% CI: 10.54–0.65) and from four to six (OR: 0.65; 95% CI: 10.62–0.67) decreased the odds for EWG. Discussion We aimed to evaluate GWG, and associated factors of pregnant women monitored at SUS in the city of São Paulo between 2012 and 2020. Our data revealed that 86.4% of the sample showed inadequate GWG, with 21.9% LGW and 64.5% EGW. The main factors associated with LWG were obesity, overweight, underweight, age ( 49 years), race/skin color (Asian and Afro-descendants) and number of consultations. The factors related to EGW were obesity, overweight and underweight, age (35–49 years), and marital status (single and stable union). The studies mainly use the IOM guidelines to verify the GWG. However, the INTERGROWTH-21st [ 12 ] standards may be more generalizable to women in low- or middle-income countries’ settings than the IOM guidelines [ 4 ]. We applied the recent Kac et al. 13 charts to define GWG to avoid underestimations in the first trimester. The GWG is critical and is considered an essential indicator for monitoring maternal and fetal health. Previous studies suggest that LWG increases the chance of having a small gestational age neonate [ 14 ]. Like our findings, previous studies showed that initial underweight increased the odds of LWG, and initial overweight or obesity increased the odds of EWG [ 15 ]. Contrary to our findings, studies suggest that Asia was categorized as having GWG below the guidelines [ 16 ]. Our findings revealed that Asian women had lower odds of LWG compared to white women. Interestingly, we found that more antenatal care visits were associated with GWG. For instance, compared with seven visits or more, all categories (none, from one to three and from four to six antenatal care visits) increased the odds for LWG. Likewise, from one to three and four to six antenatal care visits decreased the odds of EWG. In Brazil, the Ministry of Health suggests at least six appointments with doctors, nurses, dentists, and other health professionals. The consequences of inadequate weight gain during pregnancy range from an increased risk of preeclampsia, gestational diabetes, complications during childbirth, postpartum weight retention, and chronic diseases to complications for the newborn, such as higher odds for preterm birth, infant mortality, alterations in the child's body composition, and non-communicable chronic diseases in adulthood [ 17 – 20 ] EWG is the primary nutritional problem to be addressed in prenatal care provided by primary health care services in the city of São Paulo. Living with overweight and obesity increases the odds of EWG. Therefore, avoiding overweight and obesity before pregnancy is essential. Previous studies showed that overweight and obesity during pregnancy are critical factors for health-related problems for mothers and newborns [ 21 , 22 ]. The worldwide incidence and prevalence of overweight and obesity have increased substantially over the past few decades. This fact is consistent with the current situation in Brazil [ 23 ]. Likewise, women are more likely to become obese in the coming years [ 23 ]. Studies show that access to nutritional care in health services contributes to improved diet quality and appropriate GWG for women, especially those who are overweight or obese [ 24 , 25 ]. Hence, encouraging the action of nutritionists and other health professionals who contribute to weight management is essential in health services that operate with women during the gestational period. In our study, LWG, AWG, and EWG women were mainly in high school, being 64.1, 66.8, and 67.9%, respectively. We found that basic school 1 and college increased the odds for LWG, while basic school 1 and basic school 2 decreased the odds for EWG. Likewise, the incomplete college and college increased the odds for EWG. It is believed that poor schooling levels usually have low incomes as well, which contributes to several gestation-related problems [ 26 – 28 ]. Moreover, in Brazil, Black women are subjugated, and racism is still a complex factor. Racism and racial discrimination against Afro-Brazilians remain a major social and political problem in Brazil [ 29 ]. Black mothers may have less access to health services and even less access to healthy food, factors that may favor the inadequate weight gain observed in the country. Previous data showed that experiencing racial discrimination led to an increase in obesity and worsening dietary practices, leading to more significant consumption of ultra-processed food [ 30 ]. One of this study's notable strengths is its novel approach (using Kac et al. guidelines). The information from the SIGA database, which has never been analyzed before, provides a renewed perspective. Furthermore, the absence of previous studies analyzing the nutritional status of pregnant women in the city of São Paulo counts for the originality of this research. The GWG studies are critical because they directly influence the development of public policies. They assist health-related managers in making informed decisions about resource allocation, enhancing prenatal care, monitoring GWG, and providing crucial nutritional guidance for this population. Thus, we strongly advocate for further studies on the dietary patterns of SUS users to identify dietary factors related to GWG. Our study presents some limitations, such as the use of administrative data (data obtained in the work routine of health professionals) rather than research-related data for prenatal monitoring, which can lead to inconsistent measures and records. However, it is known that health professionals in the city of São Paulo's health units were trained in anthropometry, which may minimize measurement and recording errors. Another limitation was the evaluation of pregnant women up to 40 weeks. Kac et al. charts only support pregnant women from 10 to 40 weeks. Conclusions The proportion of women with inadequate weight gain (low and excessive) is high, relating initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits. The main factors associated with LWG were obesity, overweight, underweight, age, race/skin color, and number of antenatal care visits. The factors related to EGW were obesity, overweight and underweight, age, and marital status. Our results emphasized the significance of body weight control both before and during pregnancy. Interventions such as nutritional education should be suggested to help achieve adequate GWG. Abbreviations GWG: gestational weight gain BMI: body mass index LWG: low weight gain AWG: adequate weight gain EWG: excessive weight gain SIGA: Primary Health Information System SINASC: Live Birth Information System ANOVA: analysis of variance Declarations Ethics approval and consent to participate This study is part of the research project entitled “Como tornar as intervenções no parto e seus desfechos mais visíveis aos sistemas de informação?” [“How can childbirth interventions and their outcomes be more visible to information systems?”] (project with funding already approved in the Call for Data Science for Maternal and Child Health CNPq/Bill & Melinda Gates Foundation/2020/2022), approved by the Research Ethics Committee of the Municipal Department of Health of São Paulo, under number 4.829.5. Consent for publication Not Applicable Availability of data and materials The datasets used and/or analysed 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 work was supported, in whole or in part, by the Bill & Melinda Gates Foundation [ID INV-027961]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (445847/2020-4). Authors' contributions FFC and EAB conceived the work and wrote the main manuscript. EAB acquired the data. WPS, TCM, and MVLSQ analyzed and interpreted the data. CSGD conceived the work. All authors revised the manuscript. All authors read and approved the final manuscript. 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Association among pre-pregnancy body mass index, gestational weight gain and neonatal birth weight: a prospective cohort study in China. BMC Pregnancy Childbirth. 2020 Dec 12;20(1):690. Brasil, Ministério da Saúde, Departamento de Atenção Básica. Atenção ao Pré-Natal de Baixo Risco . Brasília ; 2012. Belo KO, Drumond Jr. Funcionamento da atenção primária e acesso à atenção especializada. Dados secundários: processo de construção, análise e triangulação. In: Castro CP de, Campos GW de S, Fernandes JA, editors. Atenção Primária e Atenção Especializada no SUS: análise das redes de cuidado em grandes cidades brasileiras . 1st ed. São Paulo: Hucitec; 2021. p. 24–5. Leal M do C, Esteves-Pereira AP, Viellas EF, Domingues RMSM, Gama SGN da. Prenatal care in the Brazilian public health services. Rev Saude Publica. 2020 Jan 21;54:8. Mardones F, Rosso P, Erazo Á, Farías M. Comparison of Three Gestational Weight Gain Guidelines Under Use in Latin America. Front Pediatr. 2021 Oct 13;9. Atalah E, Castillo C, Castro R, Aldea A. [Proposal of a new standard for the nutritional assessment of pregnant women]. Rev Med Chil. 1997 Dec;125(12):1429–36. Weight Gain During Pregnancy. Washington, D.C.: National Academies Press; 2009. Papageorghiou AT, Kennedy SH, Salomon LJ, Altman DG, Ohuma EO, Stones W, et al. The INTERGROWTH-21st fetal growth standards: toward the global integration of pregnancy and pediatric care. Am J Obstet Gynecol. 2018 Feb;218(2S):S630–40. Kac G, Carilho TR, Rasmussen KM, Reichenheim ME, Farias DR, Hutcheon JA. Gestational weight gain charts: results from the Brazilian Maternal and Child Nutrition Consortium. Am J Clin Nutr. 2021 May;113(5):1351–60. Mustafa HJ, Seif K, Javinani A, Aghajani F, Orlinsky R, Alvarez MV, et al. Gestational weight gain below instead of within the guidelines per class of maternal obesity: a systematic review and meta-analysis of obstetrical and neonatal outcomes. Am J Obstet Gynecol MFM. 2022 Sep;4(5):100682. Suliga E, Rokita W, Adamczyk-Gruszka O, Pazera G, Cieśla E, Głuszek S. Factors associated with gestational weight gain: a cross-sectional survey. BMC Pregnancy Childbirth. 2018 Dec 3;18(1):465. Goldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018 Dec 31;16(1):153. Perumal N, Wang D, Darling AM, Liu E, Wang M, Ahmed T, et al. Suboptimal gestational weight gain and neonatal outcomes in low and middle income countries: individual participant data meta-analysis. BMJ. 2023 Sep 21;e072249. Abubakari A, Asumah MN, Abdulai NZ. Effect of maternal dietary habits and gestational weight gain on birth weight: an analytical cross-sectional study among pregnant women in the Tamale Metropolis. Pan African Medical Journal. 2023;44. Mishra KG, Bhatia V, Nayak R. Maternal Nutrition and Inadequate Gestational Weight Gain in Relation to Birth Weight: Results from a Prospective Cohort Study in India. Clin Nutr Res. 2020;9(3):213. Kac G, Arnold CD, Matias SL, Mridha MK, Dewey KG. Gestational weight gain and newborn anthropometric outcomes in rural Bangladesh. Matern Child Nutr. 2019 Oct 24;15(4). Chan SY. Gestational Weight Gain in Women With Obesity and Consideration of Infant Morbidity and Mortality in Clinical Practice. JAMA Netw Open. 2021 Dec 30;4(12):e2141508. Langley‐Evans SC, Pearce J, Ellis S. Overweight, obesity and excessive weight gain in pregnancy as risk factors for adverse pregnancy outcomes: A narrative review. Journal of Human Nutrition and Dietetics. 2022 Apr 20;35(2):250–64. Estivaleti JM, Guzman-Habinger J, Lobos J, Azeredo CM, Claro R, Ferrari G, et al. Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030. Sci Rep. 2022 Jul 26;12(1):12699. Gama SGN da, Viellas EF, Schilithz AOC, Filha MMT, Carvalho ML de, Gomes KRO, et al. Fatores associados à cesariana entre primíparas adolescentes no Brasil, 2011-2012. Cad Saude Publica. 2014 Aug;30(suppl 1):S117–27. Seabra G, Padilha P de C, de Queiroz JA, Saunders C. Sobrepeso e obesidade pré-gestacionais: prevalência e desfechos associados à gestação. Rev Bras Ginecol Obstet. 2011 Nov;33(11). Wang JW, Wang Q, Wang XQ, Wang M, Cao SS, Wang JN. Association between maternal education level and gestational diabetes mellitus: a meta-analysis. The Journal of Maternal-Fetal & Neonatal Medicine. 2021 Feb 16;34(4):580–7. Cohen AK, Kazi C, Headen I, Rehkopf DH, Hendrick CE, Patil D, et al. Educational Attainment and Gestational Weight Gain among U.S. Mothers. Women’s Health Issues. 2016 Jul;26(4):460–7. Silva LM, Jansen PW, Steegers EA, Jaddoe VW, Arends LR, Tiemeier H, et al. Mother’s educational level and fetal growth: the genesis of health inequalities. Int J Epidemiol. 2010 Oct;39(5):1250–61. Guimarães JMN, Yamada G, Barber S, Caiaffa WT, Friche AA de L, Menezes MC de, et al. Racial Inequities in Self-Rated Health Across Brazilian Cities: Does Residential Segregation Play a Role? Am J Epidemiol. 2022 May 20;191(6):1071–80. Fanton M, Rodrigues YE, Schuch I, de Lima Cunha CM, Pattussi MP, Canuto R. Direct and indirect associations of experience of racial discrimination, dietary patterns and obesity in adults from southern Brazil. Public Health Nutr. 2024 Feb 1;27(1):e60. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial11.docx Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 08 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 07 Aug, 2024 First submitted to journal 07 Aug, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4874735","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337642909,"identity":"f29a3423-45eb-44d9-8124-d69e3547b083","order_by":0,"name":"Fernanda Ferreira CORRÊA","email":"data:image/png;base64,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","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Fernanda","middleName":"Ferreira","lastName":"CORRÊA","suffix":""},{"id":337642911,"identity":"89cf64d1-c516-4186-b3e3-c595af8d6d26","order_by":1,"name":"Eliana de Aquino BONILHA Ms","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Eliana","middleName":"de Aquino BONILHA","lastName":"Ms","suffix":""},{"id":337642913,"identity":"b1ce0d8c-4414-4560-979e-07e5ee241e9d","order_by":2,"name":"Wesley Pereira da SILVA Mr","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"Pereira da SILVA","lastName":"Mr","suffix":""},{"id":337642914,"identity":"afc13824-b1bf-4bdf-a3ed-2c6d67ad4f56","order_by":3,"name":"Tarcisio Cantos de MELO Mr","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Tarcisio","middleName":"Cantos de MELO","lastName":"Mr","suffix":""},{"id":337642915,"identity":"ad88679c-67bc-4372-81d8-706743ffff63","order_by":4,"name":"Marcus V. L. dos Santos","email":"","orcid":"","institution":"Centro Universitário São Camilo","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"V. L. dos","lastName":"Santos","suffix":""},{"id":337642916,"identity":"1010914b-86b8-4b89-9c62-b0daa49d5a58","order_by":5,"name":"Carmen Simone G. DINIZ","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"Simone G.","lastName":"DINIZ","suffix":""}],"badges":[],"createdAt":"2024-08-07 12:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4874735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4874735/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-024-06955-5","type":"published","date":"2024-11-13T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63850994,"identity":"8000f0b8-eb24-44a2-8e81-77462eb14a52","added_by":"auto","created_at":"2024-09-03 04:03:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50393,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of research participants in the present study.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/0a5199fba6cfe44a708d7eed.jpeg"},{"id":63850548,"identity":"392e24c0-226c-47c9-b82a-d295564391df","added_by":"auto","created_at":"2024-09-03 03:55:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36745,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of pregnant women according to gestational age at birth and BMI, São Paulo, 2012 to 2020.\u003c/p\u003e","description":"","filename":"Figure2e3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/35660fdc26a7eed0b86a723b.jpeg"},{"id":63850549,"identity":"3e3d70c3-29a0-438f-b17d-75279f7e281e","added_by":"auto","created_at":"2024-09-03 03:55:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41963,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of pregnant women according to gestational age at birth weight gain during pregnancy according to Kac (2021) criteria, São Paulo, 2012 to 2020.\u003c/p\u003e","description":"","filename":"Figure2e3Copy.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/94cee4850b2135411f285056.jpeg"},{"id":63851363,"identity":"57a883e7-1d0c-43cb-bc64-87e7c1a48ed3","added_by":"auto","created_at":"2024-09-03 04:11:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66807,"visible":true,"origin":"","legend":"\u003cp\u003eInitial and gestational body weight, BMI, and body mass change (\u003cstrong\u003eΔ) \u003c/strong\u003efor each group. PPW: initial body weight; \u0026nbsp;GW: gestational weight; PPBMI: initial body mass index; GBMI: gestational body \u0026nbsp;mass index. Analysis of variance (ANOVA) with Welch correction and \u0026nbsp;Games-Howell post hoc. * different from the PPW of the LWE group; ** different \u0026nbsp;from the PPW of the AWG group; # different from the GW of the LWG group; ## \u0026nbsp;different from the GW group of the AWG. p-value ≤ 0.05.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/424e42f32a4cb197b336015d.jpeg"},{"id":69285374,"identity":"f2b7f70a-7c57-471d-b2f2-7b8d472215f4","added_by":"auto","created_at":"2024-11-18 19:25:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1261173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/fd26a72e-13d0-411c-94d5-8cffdd7e1105.pdf"},{"id":63850551,"identity":"ce7c5d15-4836-499d-afc0-d25b07e72f74","added_by":"auto","created_at":"2024-09-03 03:55:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial11.docx","url":"https://assets-eu.researchsquare.com/files/rs-4874735/v1/b3c3b481e895da934674ff7f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional status, gestational weight gain and associated factors of pregnant women in the city of São Paulo, 2012 to 2020: a cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWomen\u0026rsquo;s nutritional status and gestational weight gain (GWG) during pregnancy are critical issues related to the type of delivery and postpartum newborn and mothers\u0026rsquo; health [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Considering nutritional status, body mass index (BMI) is the most monitored parameter used during pregnancy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous studies showed that initial BMI is a strong predictor of GWG and that GWG needs to be adjusted according to the category of women's BMI [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Brazil, pregnant women's public health recommends monitoring GWG during the gestational period [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Brazil has a health service called \"Sistema \u0026Uacute;nico de Sa\u0026uacute;de,\" known as SUS, which provides health-related care to the entire Brazilian population free of charge, including people in social vulnerability[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The SUS offers prenatal care with several health professionals to monitor and prevent mother and newborn morbidity and mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Pregnant women's GWG reflects maternal physiological adaptations and the growth of the fetus, placenta, and accumulation of amniotic fluid. Thus, GWG is a critical factor to monitor during pregnancy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUntil recently, Atalah et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] charts combined with the 2009 US Institute of Medicine [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] were applied to GWG recommendations. The International Fetal and Newborn Growth Consortium for the 21st Century [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] GWG standards for publication were also used. However, they are limited to women classified as normal weight, and rely on a weight measured between 9 and 14 gestational weeks for GWG calculation, which decreases their utility for monitoring weight gain in the first trimester (the charts start at the 14th gestational week). Kac et al. (2021) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] developed new GWG charts according to initial BMI for Brazilian women that were adopted into the Brazilian healthcare system. Kac's charts exclude pregnancies that delivered preterm (\u0026lt;\u0026thinsp;37 weeks), small (\u0026lt;\u0026thinsp;10th) or large (\u0026gt;\u0026thinsp;95th) for gestational age. Delivered infants with low birth weight (weight\u0026thinsp;\u0026lt;\u0026thinsp;2500 g) or macrosomia (weight\u0026thinsp;\u0026gt;\u0026thinsp;4000 g) were also excluded. Moreover, the authors included only 10 to 40 weeks of pregnancy to ensure our estimates had reasonable statistical precision.\u003c/p\u003e \u003cp\u003eDespite the increase in research on GWG, there is a notable scarcity of data on the relationship between the nutritional status of pregnant women in the city of S\u0026atilde;o Paulo. As such, we aimed to evaluate gestational weight gain and associated factors of pregnant women monitored at primary care of SUS in the city of S\u0026atilde;o Paulo between 2012 and 2020.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy type and data acquisition\u003c/h2\u003e \u003cp\u003eThis is a cohort study from 2012 to 2020 that used data derived from the Primary Health Information System (SIGA) of the city of S\u0026atilde;o Paulo. From the SIGA, we obtained data on pregnant women. Moreover, we linked this dataset to the Live Birth Information System (SINASC). From SINASC, we obtained data about pregnancy and childbirth characteristics.\u003c/p\u003e \u003cp\u003eFrom both databases, we extracted raw data (e.g., the mother's weight and height, birth date, type of pregnancy, gestational weeks, type of birth, race/skin color, number of antenatal care visits, marital status, and schooling level). From the raw data, we created new variables, including initial weight, final weight, initial gestational age, final gestational age, initial body mass index (BMI), and final BMI. The raw data and transformations used are described in \u003cb\u003esupplementary material 1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria considered that pregnant women who underwent prenatal care in the City of S\u0026atilde;o Paulo from 2012 to 2020 had a record of initial weight before 13 weeks and up to 15 days before delivery and had a single pregnancy. The exclusion criteria were pregnant women without a weight record 13 weeks from the beginning of pregnancy or up to 15 days before the birth, multiple pregnancies, newborns with congenital anomalies, and birth at below 18 or beyond 40 weeks of gestation. According to Kac et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the data included also considered the identification of gestational week and GWG. Incomplete data and extreme data (\u0026gt;\u0026thinsp;six standard deviations) were excluded. This study uses data obtained from health services, and therefore, adequate data curation is essential to avoid mistaken estimates. Health professionals who collect anthropometric data are previously trained for the function; however, typing errors and other technical problems lead to incorrect data that was excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEthical aspects\u003c/h2\u003e \u003cp\u003eThis study is part of the research project entitled \u0026ldquo;Como tornar as interven\u0026ccedil;\u0026otilde;es no parto e seus desfechos mais vis\u0026iacute;veis aos sistemas de informa\u0026ccedil;\u0026atilde;o?\u0026rdquo; [\u0026ldquo;How can childbirth interventions and their outcomes be more visible to information systems?\u0026rdquo;] (project with funding already approved in the Call for Data Science for Maternal and Child Health CNPq/Bill \u0026amp; Melinda Gates Foundation/2020/2022), approved by the Research Ethics Committee of the Municipal Department of Health of S\u0026atilde;o Paulo, under number 4.829.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData distribution was assessed using the Kolmogorov-Smirnov test. Comparisons between groups according to weight gain (Low Weight Gain - LWG vs Adequate Weight Gain - AWG vs Excessive Weight Gain- EWG) were performed using analysis of variance (ANOVA) with Tukey post hoc. In cases of heterogeneous variances, Welch\u0026rsquo;s correction with Games-Howell post hoc was chosen. Associations between weight gain and descriptive variables were analyzed using the Chi-square test or Fisher's Exact Test. Finally, multinomial logistic regression models were designed, considering gestational weight gain as the outcome (with AWG as a reference). The independent variables were initial BMI (underweight, overweight, obese vs normal weight), education (basic 1, basic 2, high school, incomplete college, complete college vs no education), marital status (single, stable union, divorced and widowed vs married), race/skin color (black, yellow, mixed race, indigenous vs white), number of health service consultations (1 to 3, 4 to 6, 7 or more vs none) and age (younger than 15 years old, 15 to 19 years old, 35 to 49 years old and over 49 years old vs 20 to 34 years old). The alpha error adopted to reject the null hypothesis was 5%. Data are presented as mean, standard deviation, odds ratios and the 95% confidence interval. JAMOVI software was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe initial database contains 352.937 records. Nevertheless, 46.760 women were excluded because they did not meet the inclusion criteria (final gestational age\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026gt;\u0026thinsp;45 weeks), were multiparous, or whose children had an inborn error. Then, 4.315 were excluded because they presented incorrect values, and 1.029 were excluded because body mass, BMI or height presented extreme values (\u0026lt;\u0026thinsp;or \u0026gt;\u0026thinsp;than six standard deviations). Furthermore, 24.629 were excluded because their gestational age was \u0026lt;\u0026thinsp;10 or \u0026gt;\u0026thinsp;40 weeks, in disagreement with the criteria proposed by Kac et al. Finally, 2635 were excluded because the weight change (variation) did not correspond to the proposal by Kac et al. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flowchart of the participants included in the present study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Flowchart of research participants in the present study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the characteristics of women during the gestational period in the city of S\u0026atilde;o Paulo between 2012 and 2020. Welch's correction was applied for most comparisons of continuous variables. The frequency of women according to BMI was 12.004 (4.4%) underweight, 132.049 (48.3%) normal weight, 78.856 (28.8%) overweight and 50.660 (18.5%) living with obesity.\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\u003eSample characteristics - women in the gestational period between 2012 and 2020 in the city of S\u0026atilde;o Paulo (n\u0026thinsp;=\u0026thinsp;273.569)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) (mean/SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;15 years old (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1184 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e890 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.998 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u0026ndash;19 years old (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.239 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.026 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.345 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u0026ndash;34 years old (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.931 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.424 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.974 (69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e35\u0026ndash;49 years old (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.527 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.876 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.150 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;49 years old (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody weight (kg) (mean/SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial BMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e) (mean/SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderweight (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.922 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.528 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.554 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal weight (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.174 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.177 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.698 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.842 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.512 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.502 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObesity (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.943 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.717 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational BMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal body weight (kg) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.61\u0026thinsp;\u0026plusmn;\u0026thinsp;15.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.53\u0026thinsp;\u0026plusmn;\u0026thinsp;13.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΔ (Change; kg) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial gestational age (w) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal gestational age (w) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of birth\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVaginal delivery (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.477 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.980 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.029 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCesarean delivery (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.404 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.237 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.442 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSingle (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.996 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.909 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.741 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.683 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.966 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.632 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStable union (marriage not officialized by the government) (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.347 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.830 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.447 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDivorced (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e746 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.348 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWidow (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSchooling level\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithout (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBasic studies 1 (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1704 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e864 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.676 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBasic studies 2 (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.865 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.149 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.390 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh school (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.393 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.860 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.706 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncomplete college (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.531 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.467 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.233 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplete college (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.280 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.818 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.242 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.495 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.631 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.263 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.020 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.277 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.897 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYellow (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e783 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrown (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.958 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.972 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.103 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndigenous (n; %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntenatal care visits (n; %)\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026ndash;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.942 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e623 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.711 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u0026ndash;6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.292 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.383 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.609 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.538 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.172 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157.995 (89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLegend\u003c/b\u003e: kg: kilograms; kg/m\u003csup\u003e2\u003c/sup\u003e: kilograms per square meter; n\u0026thinsp;=\u0026thinsp;number of observations in absolute values; % proportion of the number of observations; Δ difference between the final and initial (change); w: weeks. Analysis of variance (ANOVA) with Tukey post hoc. In cases of heterogeneous variances, Welch correction with Games-Howell post hoc was chosen. Associations between weight gain and descriptive variables were analyzed using the Chi-square test or Fisher's Exact Test. The alpha error adopted to reject the null hypothesis was 5%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the frequency of pregnant women according to gestational age at birth and BMI. The sample consisted of 59.881 (21.9%), 37.217 (13.6%) and 176.471 (64.5%) women with LWG, AWG and EWG, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the distribution of pregnant women according to gestational age at birth and weight gain during pregnancy according to Kac et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAge\u003c/h2\u003e \u003cp\u003eAge (W\u003csub\u003e(84.853, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;226; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differed between groups. Women in the AWG group are younger than LWG (MD: -0.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and EWG (MD: -0.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) groups, while the LWG women group are older than the EWG group (MD: 0.37; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBody weight and BMI\u003c/h2\u003e \u003cp\u003eThe initial (W\u003csub\u003e(100.717, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9613; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gestational (W\u003csub\u003e(101.257, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;25076; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) body weight differed between the groups. For the initial weight, the AWG group showed lower body weight than LWG (MD: -9.61 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and EWG (MD: -6.91 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) groups. Still, the LWG group showed higher body weight than the EWG group (MD: 2.70 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For gestational weight, the AWG group showed lower body weight than the LWG (MD: -4.05 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the EWG (MD: -11.97 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) groups. Moreover, the EWG group exhibited higher body weight than LWG (MD: 7.92 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eLikewise, the initial (W\u003csub\u003e(104.118, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;10771; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gestational (W\u003csub\u003e(104.505, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;29002; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) BMI differed between the groups. The initial BMI of the AWG group is lower than the LWG (MD: -3.60 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and EWG (MD: -2.47 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) groups, while the BMI of the LWG group is higher in relation to the EWG group (MD: 1.13 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor gestational BMI, the difference between the AWG group and the LWG group reduces (MD: -1.41 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the difference between the AWG and EWG group increases (MD: -4.41 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Correspondingly, the difference between the BMI of the LWG group and the EWG group changes (MD: -3.00 kg/m\u003csup\u003e2\u003c/sup\u003e; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe body weight change data (final - initial; delta) between the groups also showed statistical differences (W\u003csub\u003e(127.564,118, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;144.979; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AWG group presented higher body mass change than the LWG group (MD: 5.56 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower body mass change compared to the EWG group (MD: -5.06 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, the LWG group presents a smaller weight difference compared to the EWG group (MD: -10.62 kg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates initial and gestational body weight, BMI, and body mass change (\u003cb\u003eΔ)\u003c/b\u003e for each group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWeight gain was associated with initial BMI (X\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(273,569.6)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;13.395; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), type of birth (X\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(273,569.2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.183; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), race/skin color (X\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(273.569, 10)\u003c/sub\u003e; 99.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (X\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(273.569, 8)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;320; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), women's age (X\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(273.568, 2)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;585; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and antenatal care visits (X\u003csup\u003e2\u003c/sup\u003e \u003csub\u003e(273.477, 6)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.630; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the factors associated with gestational weight gain. Underweight was associated with LWG (OR: 2.02; CI: 95% 1.89\u0026ndash;2.15) and EWG (OR: 1.59; 95% CI: 1.49\u0026ndash;1.68). Similarly, overweight was associated with LWG (OR: 2.07; 95% CI: 2.01\u0026ndash;2.14) and EWG (OR: 1.74; 95% CI: 1.70\u0026ndash;1.79). Finally, obesity was also a factor associated with LWG (OR: 7.48e\u0026thinsp;+\u0026thinsp;6; 95% CI: 7.48e\u0026thinsp;+\u0026thinsp;6\u0026ndash;7.48e\u0026thinsp;+\u0026thinsp;6) and EWG (OR: 5.16e\u0026thinsp;+\u0026thinsp;6; 95% CI: 5.16e\u0026thinsp;+\u0026thinsp;6\u0026ndash;5.16e\u0026thinsp;+\u0026thinsp;6).\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\u003eFactors associated with gestational weight gain during pregnancy between 2012 and 2020 in the city of S\u0026atilde;o Paulo (n\u0026thinsp;=\u0026thinsp;273.569)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLWG vs AWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eEWG vs AWG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% IC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% IC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's initial BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;24.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026ndash;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0\u0026ndash;29.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u0026ndash;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70\u0026ndash;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.48e\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.48e\u0026thinsp;+\u0026thinsp;6\u0026ndash;7.48e\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16e\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.16e\u0026thinsp;+\u0026thinsp;6\u0026ndash;5.16e\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's age (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53e-\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53e\u003csup\u003e7\u003c/sup\u003e \u0026ndash; 2.53e\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u0026ndash;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndigenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSchooling level\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic school 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic school 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of antenatal care visits\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026ndash;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026ndash;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u0026ndash;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u0026ndash;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ekg: kg/m\u003csup\u003e2\u003c/sup\u003e: kilograms per square meter; n\u0026thinsp;=\u0026thinsp;number of observations in absolute values; % proportion of the number of observations; Multinominal logistic regression analysis. Outcome: LWG vs. AWG and EWG vs. AWG. The alpha error adopted to reject the null hypothesis was 5%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInsert Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWomen aged\u0026thinsp;\u0026lt;\u0026thinsp;15 years showed higher odds for LWG (OR: 1.10; 95% CI: 1.00\u0026ndash;1.20), while those aged 35\u0026ndash;49 years reduced the odds for LWG (OR: 0.93; 95% CI: 0.89\u0026ndash;0.97) and EWG (OR: 0.80; 95% CI: 0.78\u0026ndash;0.83). Age\u0026thinsp;\u0026gt;\u0026thinsp;49 years also decreased the odds of LWG (OR: 2.53e-7; 95% CI: 2.53e-7\u0026ndash;2.53e-7). Considering ethnicity, Asian women showed lower odds for LWG (OR: 0.78; 95% CI: 0.64\u0026ndash;0.94), while Black women present higher odds for LWG (OR: 1.06; 95% CI: 1.01\u0026ndash;1.11). Moreover, a stable union reduced the odds of EWG (OR: 1.06; 95% CI: 1.01\u0026ndash;1.11). Considering schooling level, we observed that basic school 2 (OR: 1.08; 95% CI: 1.05\u0026ndash;1.12) and college (OR: 1.08; 95% CI: 1.01\u0026ndash;1.15) increased the odds for LWG. Moreover, basic school 1 (OR: 0.82; 95% CI: 0.76\u0026ndash;0.89), basic school 2 (OR: 0.91; 95% CI: 0.88\u0026ndash;0.94) decreased the odds for EWG. In contrast, incomplete college (OR: 1.09; 95% CI: 1.02\u0026ndash;1.14) and complete college (OR: 1.08; 95% CI: 1.02\u0026ndash;1.14) increased the odds for EWG. Finally, the number of antenatal care visits was associated with GWG. For instance, considering 7 visits or more as reference, none (OR: 1.89; 95% CI: 1.23\u0026ndash;2.90), from one to three (OR: 2.22; 95% CI: 2.02\u0026ndash;2.44) and from four to six (OR: 1.32; 95% CI: 1.29\u0026ndash;1.39) visits increased the odds for LWG. From one to three (OR: 0.59; 95% CI: 10.54\u0026ndash;0.65) and from four to six (OR: 0.65; 95% CI: 10.62\u0026ndash;0.67) decreased the odds for EWG.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe aimed to evaluate GWG, and associated factors of pregnant women monitored at SUS in the city of S\u0026atilde;o Paulo between 2012 and 2020. Our data revealed that 86.4% of the sample showed inadequate GWG, with 21.9% LGW and 64.5% EGW. The main factors associated with LWG were obesity, overweight, underweight, age (\u0026lt;\u0026thinsp;15 years, 35\u0026ndash;49 years and \u0026gt;\u0026thinsp;49 years), race/skin color (Asian and Afro-descendants) and number of consultations. The factors related to EGW were obesity, overweight and underweight, age (35\u0026ndash;49 years), and marital status (single and stable union).\u003c/p\u003e \u003cp\u003eThe studies mainly use the IOM guidelines to verify the GWG. However, the INTERGROWTH-21st [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] standards may be more generalizable to women in low- or middle-income countries\u0026rsquo; settings than the IOM guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We applied the recent Kac et al. \u003csup\u003e13\u003c/sup\u003echarts to define GWG to avoid underestimations in the first trimester. The GWG is critical and is considered an essential indicator for monitoring maternal and fetal health. Previous studies suggest that LWG increases the chance of having a small gestational age neonate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLike our findings, previous studies showed that initial underweight increased the odds of LWG, and initial overweight or obesity increased the odds of EWG [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Contrary to our findings, studies suggest that Asia was categorized as having GWG below the guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our findings revealed that Asian women had lower odds of LWG compared to white women.\u003c/p\u003e \u003cp\u003eInterestingly, we found that more antenatal care visits were associated with GWG. For instance, compared with seven visits or more, all categories (none, from one to three and from four to six antenatal care visits) increased the odds for LWG. Likewise, from one to three and four to six antenatal care visits decreased the odds of EWG.\u003c/p\u003e \u003cp\u003eIn Brazil, the Ministry of Health suggests at least six appointments with doctors, nurses, dentists, and other health professionals. The consequences of inadequate weight gain during pregnancy range from an increased risk of preeclampsia, gestational diabetes, complications during childbirth, postpartum weight retention, and chronic diseases to complications for the newborn, such as higher odds for preterm birth, infant mortality, alterations in the child's body composition, and non-communicable chronic diseases in adulthood [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEWG is the primary nutritional problem to be addressed in prenatal care provided by primary health care services in the city of S\u0026atilde;o Paulo. Living with overweight and obesity increases the odds of EWG. Therefore, avoiding overweight and obesity before pregnancy is essential. Previous studies showed that overweight and obesity during pregnancy are critical factors for health-related problems for mothers and newborns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe worldwide incidence and prevalence of overweight and obesity have increased substantially over the past few decades. This fact is consistent with the current situation in Brazil [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Likewise, women are more likely to become obese in the coming years [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies show that access to nutritional care in health services contributes to improved diet quality and appropriate GWG for women, especially those who are overweight or obese [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hence, encouraging the action of nutritionists and other health professionals who contribute to weight management is essential in health services that operate with women during the gestational period.\u003c/p\u003e \u003cp\u003eIn our study, LWG, AWG, and EWG women were mainly in high school, being 64.1, 66.8, and 67.9%, respectively. We found that basic school 1 and college increased the odds for LWG, while basic school 1 and basic school 2 decreased the odds for EWG. Likewise, the incomplete college and college increased the odds for EWG. It is believed that poor schooling levels usually have low incomes as well, which contributes to several gestation-related problems [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, in Brazil, Black women are subjugated, and racism is still a complex factor. Racism and racial discrimination against Afro-Brazilians remain a major social and political problem in Brazil [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Black mothers may have less access to health services and even less access to healthy food, factors that may favor the inadequate weight gain observed in the country. Previous data showed that experiencing racial discrimination led to an increase in obesity and worsening dietary practices, leading to more significant consumption of ultra-processed food [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of this study's notable strengths is its novel approach (using Kac et al. guidelines). The information from the SIGA database, which has never been analyzed before, provides a renewed perspective. Furthermore, the absence of previous studies analyzing the nutritional status of pregnant women in the city of S\u0026atilde;o Paulo counts for the originality of this research. The GWG studies are critical because they directly influence the development of public policies. They assist health-related managers in making informed decisions about resource allocation, enhancing prenatal care, monitoring GWG, and providing crucial nutritional guidance for this population. Thus, we strongly advocate for further studies on the dietary patterns of SUS users to identify dietary factors related to GWG.\u003c/p\u003e \u003cp\u003eOur study presents some limitations, such as the use of administrative data (data obtained in the work routine of health professionals) rather than research-related data for prenatal monitoring, which can lead to inconsistent measures and records. However, it is known that health professionals in the city of S\u0026atilde;o Paulo's health units were trained in anthropometry, which may minimize measurement and recording errors. Another limitation was the evaluation of pregnant women up to 40 weeks. Kac et al. charts only support pregnant women from 10 to 40 weeks.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe proportion of women with inadequate weight gain (low and excessive) is high, relating initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits. The main factors associated with LWG were obesity, overweight, underweight, age, race/skin color, and number of antenatal care visits. The factors related to EGW were obesity, overweight and underweight, age, and marital status. Our results emphasized the significance of body weight control both before and during pregnancy. Interventions such as nutritional education should be suggested to help achieve adequate GWG.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGWG:\u0026nbsp;gestational\u0026nbsp;weight gain\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eLWG: low weight gain\u003c/p\u003e\n\u003cp\u003eAWG: adequate weight gain\u003c/p\u003e\n\u003cp\u003eEWG: excessive weight gain\u003c/p\u003e\n\u003cp\u003eSIGA: Primary Health Information System\u003c/p\u003e\n\u003cp\u003eSINASC: Live Birth Information System\u003c/p\u003e\n\u003cp\u003eANOVA: analysis of variance\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 is part of the research project entitled \u0026ldquo;Como tornar as interven\u0026ccedil;\u0026otilde;es no parto e seus desfechos mais vis\u0026iacute;veis aos sistemas de informa\u0026ccedil;\u0026atilde;o?\u0026rdquo; [\u0026ldquo;How can childbirth interventions and their outcomes be more visible to information systems?\u0026rdquo;] (project with funding already approved in the Call for Data Science for Maternal and Child Health CNPq/Bill \u0026amp; Melinda Gates Foundation/2020/2022), approved by the Research Ethics Committee of the Municipal Department of Health of S\u0026atilde;o Paulo, under number 4.829.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed 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 work was supported, in whole or in part, by the Bill \u0026amp; Melinda Gates Foundation [ID INV-027961]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.\u003c/p\u003e\n\u003cp\u003eConselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) (445847/2020-4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFFC and EAB conceived the work and wrote the main manuscript. EAB acquired the data.\u003c/p\u003e\n\u003cp\u003eWPS, TCM, and MVLSQ analyzed and interpreted the data. CSGD conceived the work. All\u003c/p\u003e\n\u003cp\u003eauthors revised the manuscript. All authors read and approved the final 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\n\u003cli\u003eBarker DJ, Osmond C, Golding J, Kuh D, Wadsworth ME. Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. BMJ. 1989 Mar 4;298(6673):564\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eYang Z, Lai J. Gestational weight gain velocity during each trimester is critical for both maternal health and birth outcomes in China. Matern Child Nutr. 2024 Feb 28; \u003c/li\u003e\n\u003cli\u003eZadik Z. Maternal nutrition, fetal weight, body composition and disease in later life. J Endocrinol Invest. 2003 Sep 18;26(9):941\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Hortelano JA, Cavero-Redondo I, \u0026Aacute;lvarez-Bueno C, Garrido-Miguel M, Soriano-Cano A, Mart\u0026iacute;nez-Vizca\u0026iacute;no V. Monitoring gestational weight gain and prepregnancy BMI using the 2009 IOM guidelines in the global population: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2020 Dec 27;20(1):649. \u003c/li\u003e\n\u003cli\u003eWang Y, Ma H, Feng Y, Zhan Y, Wu S, Cai S, et al. Association among pre-pregnancy body mass index, gestational weight gain and neonatal birth weight: a prospective cohort study in China. BMC Pregnancy Childbirth. 2020 Dec 12;20(1):690. \u003c/li\u003e\n\u003cli\u003eBrasil, Minist\u0026eacute;rio da Sa\u0026uacute;de, Departamento de Aten\u0026ccedil;\u0026atilde;o B\u0026aacute;sica. Aten\u0026ccedil;\u0026atilde;o ao Pr\u0026eacute;-Natal de Baixo Risco . Bras\u0026iacute;lia ; 2012. \u003c/li\u003e\n\u003cli\u003eBelo KO, Drumond Jr. Funcionamento da aten\u0026ccedil;\u0026atilde;o prim\u0026aacute;ria e acesso \u0026agrave; aten\u0026ccedil;\u0026atilde;o especializada. Dados secund\u0026aacute;rios: processo de constru\u0026ccedil;\u0026atilde;o, an\u0026aacute;lise e triangula\u0026ccedil;\u0026atilde;o. In: Castro CP de, Campos GW de S, Fernandes JA, editors. Aten\u0026ccedil;\u0026atilde;o Prim\u0026aacute;ria e Aten\u0026ccedil;\u0026atilde;o Especializada no SUS: an\u0026aacute;lise das redes de cuidado em grandes cidades brasileiras . 1st ed. S\u0026atilde;o Paulo: Hucitec; 2021. p. 24\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eLeal M do C, Esteves-Pereira AP, Viellas EF, Domingues RMSM, Gama SGN da. Prenatal care in the Brazilian public health services. Rev Saude Publica. 2020 Jan 21;54:8. \u003c/li\u003e\n\u003cli\u003eMardones F, Rosso P, Erazo \u0026Aacute;, Far\u0026iacute;as M. Comparison of Three Gestational Weight Gain Guidelines Under Use in Latin America. Front Pediatr. 2021 Oct 13;9. \u003c/li\u003e\n\u003cli\u003eAtalah E, Castillo C, Castro R, Aldea A. [Proposal of a new standard for the nutritional assessment of pregnant women]. Rev Med Chil. 1997 Dec;125(12):1429\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eWeight Gain During Pregnancy. Washington, D.C.: National Academies Press; 2009. \u003c/li\u003e\n\u003cli\u003ePapageorghiou AT, Kennedy SH, Salomon LJ, Altman DG, Ohuma EO, Stones W, et al. The INTERGROWTH-21st fetal growth standards: toward the global integration of pregnancy and pediatric care. Am J Obstet Gynecol. 2018 Feb;218(2S):S630\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eKac G, Carilho TR, Rasmussen KM, Reichenheim ME, Farias DR, Hutcheon JA. Gestational weight gain charts: results from the Brazilian Maternal and Child Nutrition Consortium. Am J Clin Nutr. 2021 May;113(5):1351\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eMustafa HJ, Seif K, Javinani A, Aghajani F, Orlinsky R, Alvarez MV, et al. Gestational weight gain below instead of within the guidelines per class of maternal obesity: a systematic review and meta-analysis of obstetrical and neonatal outcomes. Am J Obstet Gynecol MFM. 2022 Sep;4(5):100682. \u003c/li\u003e\n\u003cli\u003eSuliga E, Rokita W, Adamczyk-Gruszka O, Pazera G, Cieśla E, Głuszek S. Factors associated with gestational weight gain: a cross-sectional survey. BMC Pregnancy Childbirth. 2018 Dec 3;18(1):465. \u003c/li\u003e\n\u003cli\u003eGoldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018 Dec 31;16(1):153. \u003c/li\u003e\n\u003cli\u003ePerumal N, Wang D, Darling AM, Liu E, Wang M, Ahmed T, et al. Suboptimal gestational weight gain and neonatal outcomes in low and middle income countries: individual participant data meta-analysis. BMJ. 2023 Sep 21;e072249. \u003c/li\u003e\n\u003cli\u003eAbubakari A, Asumah MN, Abdulai NZ. Effect of maternal dietary habits and gestational weight gain on birth weight: an analytical cross-sectional study among pregnant women in the Tamale Metropolis. Pan African Medical Journal. 2023;44. \u003c/li\u003e\n\u003cli\u003eMishra KG, Bhatia V, Nayak R. Maternal Nutrition and Inadequate Gestational Weight Gain in Relation to Birth Weight: Results from a Prospective Cohort Study in India. Clin Nutr Res. 2020;9(3):213. \u003c/li\u003e\n\u003cli\u003eKac G, Arnold CD, Matias SL, Mridha MK, Dewey KG. Gestational weight gain and newborn anthropometric outcomes in rural \u0026lt;scp\u0026gt;Bangladesh\u0026lt;/scp\u0026gt;. Matern Child Nutr. 2019 Oct 24;15(4). \u003c/li\u003e\n\u003cli\u003eChan SY. Gestational Weight Gain in Women With Obesity and Consideration of Infant Morbidity and Mortality in Clinical Practice. JAMA Netw Open. 2021 Dec 30;4(12):e2141508. \u003c/li\u003e\n\u003cli\u003eLangley‐Evans SC, Pearce J, Ellis S. Overweight, obesity and excessive weight gain in pregnancy as risk factors for adverse pregnancy outcomes: A narrative review. Journal of Human Nutrition and Dietetics. 2022 Apr 20;35(2):250\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eEstivaleti JM, Guzman-Habinger J, Lobos J, Azeredo CM, Claro R, Ferrari G, et al. Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030. Sci Rep. 2022 Jul 26;12(1):12699. \u003c/li\u003e\n\u003cli\u003eGama SGN da, Viellas EF, Schilithz AOC, Filha MMT, Carvalho ML de, Gomes KRO, et al. Fatores associados \u0026agrave; cesariana entre prim\u0026iacute;paras adolescentes no Brasil, 2011-2012. Cad Saude Publica. 2014 Aug;30(suppl 1):S117\u0026ndash;27. \u003c/li\u003e\n\u003cli\u003eSeabra G, Padilha P de C, de Queiroz JA, Saunders C. Sobrepeso e obesidade pr\u0026eacute;-gestacionais: preval\u0026ecirc;ncia e desfechos associados \u0026agrave; gesta\u0026ccedil;\u0026atilde;o. Rev Bras Ginecol Obstet. 2011 Nov;33(11). \u003c/li\u003e\n\u003cli\u003eWang JW, Wang Q, Wang XQ, Wang M, Cao SS, Wang JN. Association between maternal education level and gestational diabetes mellitus: a meta-analysis. The Journal of Maternal-Fetal \u0026amp; Neonatal Medicine. 2021 Feb 16;34(4):580\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eCohen AK, Kazi C, Headen I, Rehkopf DH, Hendrick CE, Patil D, et al. Educational Attainment and Gestational Weight Gain among U.S. Mothers. Women\u0026rsquo;s Health Issues. 2016 Jul;26(4):460\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eSilva LM, Jansen PW, Steegers EA, Jaddoe VW, Arends LR, Tiemeier H, et al. Mother\u0026rsquo;s educational level and fetal growth: the genesis of health inequalities. Int J Epidemiol. 2010 Oct;39(5):1250\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eGuimar\u0026atilde;es JMN, Yamada G, Barber S, Caiaffa WT, Friche AA de L, Menezes MC de, et al. Racial Inequities in Self-Rated Health Across Brazilian Cities: Does Residential Segregation Play a Role? Am J Epidemiol. 2022 May 20;191(6):1071\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eFanton M, Rodrigues YE, Schuch I, de Lima Cunha CM, Pattussi MP, Canuto R. Direct and indirect associations of experience of racial discrimination, dietary patterns and obesity in adults from southern Brazil. Public Health Nutr. 2024 Feb 1;27(1):e60. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"Nutritional Status, Pregnant Women, Gestational Weight Gain, Nutritional Status, Cohort Studies","lastPublishedDoi":"10.21203/rs.3.rs-4874735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4874735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGestational weight gain (GWG) is a critical issue related to the type of delivery and postpartum health in newborns and mothers. In Brazil, pregnant women's public health recommends monitoring GWG. Therefore, the objective of this study is to evaluate gestational weight gain and associated health factors of pregnant women monitored at SUS in the city of S\u0026atilde;o Paulo between 2012 and 2020.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is a cohort study of pregnant women seen from 2012 to 2020 in S\u0026atilde;o Paulo, Brazil. The database used was from the Integrated Health Care Management System related to the Live Birth Information System. The variables used were: mother's height, mother's date of birth (used to calculate mother's age), type of pregnancy, gestational weeks, type of delivery, weight at the time of appointment, mother\u0026acute;s race/skin color, number of prenatal consultations, mother's marital status, and mother's education level, initial weight, final weight, initial gestational age, final gestational age, and initial and final BMI. Inclusion criteria considered that pregnant women had a recorded initial weight before 13 weeks and up to 15 days before delivery and a single pregnancy. The final database includes 276.220 pregnant women.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe frequency of women according to initial BMI was 12.004 (4.4%) underweight, 132.049 (48.3%) normal weight, 78.856 (28.8%) overweight, and 50.660 (18.5%) living with obesity. The population consisted of 59.881 (21.9%), 37.217 (13.6%) and 176.471 (64.5%) women with LWG, AWG and EWG, respectively. Weight gain was associated with initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe proportion of pregnant women with inadequate weight gain is high, relating initial BMI, type of birth, race/skin color, marital status, women's age and antenatal care visits. Interventions such as nutritional education should be suggested to help achieve adequate GWG.\u003c/p\u003e","manuscriptTitle":"Nutritional status, gestational weight gain and associated factors of pregnant women in the city of São Paulo, 2012 to 2020: a cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 03:55:41","doi":"10.21203/rs.3.rs-4874735/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-08T10:39:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T00:20:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-08T00:19:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-08-07T12:26:34+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":"68603049-3ad7-4395-9780-587162fafe4f","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:19:28+00:00","versionOfRecord":{"articleIdentity":"rs-4874735","link":"https://doi.org/10.1186/s12884-024-06955-5","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2024-11-13 15:57:02","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-09-03 03:55:41","video":"","vorDoi":"10.1186/s12884-024-06955-5","vorDoiUrl":"https://doi.org/10.1186/s12884-024-06955-5","workflowStages":[]},"version":"v1","identity":"rs-4874735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4874735","identity":"rs-4874735","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}